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The helminth Strongyloides stercoralis , which is transmitted through soil , infects 30–100 million people worldwide . S . stercoralis reproduces sexually outside the host as well as asexually within the host , which causes a life-long infection . To understand the population structure and transmission patterns of this parasite , we re-sequenced the genomes of 33 individual S . stercoralis nematodes collected in Myanmar ( prevalent region ) and Japan ( non-prevalent region ) . We utilised a method combining whole genome amplification and next-generation sequencing techniques to detect 298 , 202 variant positions ( 0 . 6% of the genome ) compared with the reference genome . Phylogenetic analyses of SNP data revealed an unambiguous geographical separation and sub-populations that correlated with the host geographical origin , particularly for the Myanmar samples . The relatively higher heterozygosity in the genomes of the Japanese samples can possibly be explained by the independent evolution of two haplotypes of diploid genomes through asexual reproduction during the auto-infection cycle , suggesting that analysing heterozygosity is useful and necessary to infer infection history and geographical prevalence .
The helminth Strongyloides stercoralis , which is one of the most common and globally distributed human pathogens of clinical importance , infects 30–100 million people worldwide [1 , 2] . This parasite most often resides in areas with tropical or subtropical climates and less frequently in areas with a temperate climate . It occurs infrequently in societies where faecal contamination of soil or water is rare , and therefore , new infections are very rare in countries with developed economies [3] . However , infection can persist for life unless effective treatment eliminates all adult parasites and migrating auto-infective larvae . Therefore , carriers are present in developed countries , representing a potential risk of horizontal transmission among humans [4] . Strongyloides stercoralis is also a natural parasite of dogs [5] . Strongyloides stercoralis is the only medically important nematode that can multiply in the host via an auto-infection cycle to reach critical levels and cause death [1 , 6 , 7] . The complex life cycle includes sexual and asexual reproduction . Infection with S . stercoralis begins when the infective third-stage larvae ( iL3 ) in soil attach to and penetrate the human skin . After reaching the lung through the bloodstream , the parasites ascend to the trachea , and are swallowed to settle in the small intestine ( their final destination ) where the parasitic adults produce eggs through parthenogenesis . The larvae passed in the host faeces develop via either the homogonic route into iL3 forms or the heterogonic route into free-living adult stages that reproduce sexually outside the host . Although most eggs/larvae of the parasite are excreted from the host with faeces , homogonic larval development may occur inside the small intestine giving rise to auto-infective L3 which penetrate the intestinal wall and invade the tissues , ultimately entering the lung and returning to the small intestine to complete development to the parasitic female . In this circumstance , termed auto-infection , repeated generations of development may take place within a single host . [5] . Although strongyloidiasis is usually an indolent disease in immunocompetent hosts , it can cause a hyperinfective syndrome ( disseminated strongyloidiasis ) in immunocompromised hosts through the reproductive capacity of the parasite inside the host . Disseminated strongyloidiasis , if untreated , is associated with mortality rates of approximately 90% [8] . Despite its great medical importance , the threadworm S . stercoralis , is one of the most overlooked helminths [1] . The parasite's complex life cycle has long been considered a major impediment to attempts to control strongyloidiasis . Recently , the genome of S . stercoralis was sequenced and compared with other species of Strongyloides [9] . This comparative genomic study illuminates the use of genome-wide analysis to identify genes related to parasitism , to investigate diversity and population structures , and to determine the transmission route of S . stercoralis . Here , we aimed to determine the intra-species genomic variations of S . stercoralis present in Japan and Myanmar , which differ in socioeconomic status , history of infection and prevalence of this nematode .
The Ethics Committees of the University of the Ryukyus and the University of Medicine-1 Yangon approved this study . Participants , who were informed of the study's aims and procedures , provided written informed consent . All individuals infected with S . stercoralis were treated with ivermectin . Faecal samples were collected in 2014 ( Table 1 ) in Okinawa , Japan , representing an area where S . stercoralis is non-prevalent and where S . stercoralis has not been endemic for at least the last 50 years [10] , and Htantabin , Myanmar as a prevalent area where new infections frequently occur . In Okinawa , Japan , faecal tests were performed for inpatients in one hospital and residents of two elderly nursing homes located in the southern part of Okinawa . For Myanmar samples , a community survey was conducted in three different villages of Htantabin area . Faeces were incubated on 2% ( w/v ) agar plates at 25°C for 2–4 days . This culture condition would allow a portion of parasites to undergo a complete free-living generation involving a sexual cross although worms may mate with their genetically identical siblings in the culture . Individual nematodes ( iL3 ) that crawled out of the faeces were transferred to 0 . 2 ml tubes containing 10 μl of worm lysis solution ( 9 μl Direct PCR [Viagen] , 0 . 5 μl of 20 mg/ml Proteinase K [Qiagen] and 0 . 5 μl of 1 M dithiothreitol [Wako] ) . The lysates were incubated at 60°C for 1 h and then at 95°C for 10 min . To identify nematodes , the 18S ribosomal RNA gene was amplified using 0 . 1 μl of worm lysate with the primers 988F and 1912R [11] , and the amplicons were sequenced using an ABI 3130 sequencer ( Applied Biosystems ) with the BigDye Terminator v3 . 1 kit . Worm lysates were immediately used for further analysis or stored at −30°C . Genomic DNA was amplified from 1 μl of worm lysate using an Illustra GenomiPhi V2 kit ( GE Healthcare ) according to the manufacturer’s protocol . Amplified products were quality-checked using 1% agarose gel electrophoresis , purified using a QIAamp DNA Mini Kit ( Qiagen ) and quantified using Qubit ( Life Technologies ) . Libraries were constructed using a Nextera DNA Sample Prep Kit ( Illumina ) with 100 ng of amplified DNA according to the manufacturer’s protocol . The libraries were sequenced using an Illumina MiSeq with a v3 Reagent kit ( 600 cycles ) according to the manufacturer’s recommended protocol ( https://icom . illumina . com/ ) to produce 300-bp paired-end reads to obtain ~3G base data . Non-WGA reads of the genome reference strain ( SSTP ) were obtained from NCBI SRA under accession number ERR066168 , randomly sampled and used as a reference to evaluate WGA reads . We used Trimmomatic [12] to eliminate adapter contamination from the reads and achieve a minimum quality score = 15 ( SLIDINGWINDOW:4:15 ) before mapping against the S . stercoralis reference genome ( ver . 2 . 0 . 4 ) [9] using SMALT v0 . 7 . 4 ( https://www . sanger . ac . uk/resources/software/smalt/ ) with options–x ( each mate is mapped independently ) and–y 0 . 8 ( mapping to the region of highest similarity in the reference genome at a similarity threshold > 80% ) . Duplicate reads were marked using the Picard tool ( ver . 1 . 95 ) , and indels were realigned with GATK ( version ver . 3 . 3 . 0 ) [13] using the IndelRealigner . Variants were then called using GATK HaplotypeCaller . Variants were annotated using GATK and ANOVA ( ver . 2014-11-12 ) . Depth of coverage was calculated by counting mapped reads per site using GATK DepthOfCoverage [13] . Analysis of population genetics , including calculating nucleotide diversity ( π ) and inbreeding coefficient ( FIN ) , were performed using vcftools ( v0 . 1 . 12b ) [14] . Mean of per-site nucleotide diversities between two genomes were reported as a pair-wise genome distance . Analysis of molecular variance ( AMOVA ) was conducted with R Poppr package [15] . Other statistical analyses were performed using R ( ver 3 . 1 . 1 ) and in-house python scripts . In the previous study , using C . elegans as a model , we found WGA variant calls with low coverage data tends to call heterozygous loci homozygous [16] . To avoid this bias toward calling homozygous sites , we excluded relatively low coverage samples comprising < 70% of genomic regions with 15× depth ( nematodes designated MyHTB122-6 , Rk5-6 , Rk6-4 , Rk7-5 , Rk8-3 and Rk8-8 ) from the heterozygosity-related analyses . Principal component analysis ( PCA ) was performed using R ( ver 3 . 1 . 1 ) implemented with SNPRelate package [17] . Bi-allelic SNPs were extracted from full variant information of all the samples and used for PCA analyses . The mitochondrial genomes of Rk4-1 nematodes were reconstructed from the Illumina reads using MITObim ver 1 . 6 [18] . In the first step , Illumina reads were mapped to the S . stercoralis reference sequence ( Genbank accession No . NC_028624 ) to generate a seed for the second step . In the second step , gaps and ambiguous regions in the seed were replaced by iterative mapping that was repeated until all gaps were closed , and the number of reads remained constant . Reconstructed mitochondrial sequences were refined by correcting bases using ICORN2 [19] , and the assembly was used to represent the Japanese nematode reference mitochondrial genome . Nucleotide sequences of SNP positions in scaffolds > 30 kb , which accounted for 96% of the total genome assembly , were extracted from the vcf files and were used to construct phylogenetic networks based on similarity/dissimilarity with the Neighbor Net method of SplitsTree4 [20] . Computational phasing of the diploid genotypic data was performed using SHAPEIT2 with its default parameters [21] . Phased sequence data from all samples were used to create a separate Maximum Likelihood tree using FastTree ( ver 2 . 1 . 8 ) for each scaffold > 30 kb [22] . To generate a mitochondrial-based phylogeny , reads from each nematode sample were mapped to the Japanese parasite's reference sequence ( see above ) using SMALT v0 . 7 . 4 , and SNPs were called using GATK [13] . The nucleotide sequences of the SNPs were extracted and used to generate Maximum Likelihood trees using FastTree ( ver 2 . 1 . 8 ) [22] . All sequence data were submitted to the DDBJ Sequence Read Archive ( DRA ) under project accession number PRJDB5112 .
We re-sequenced the genomes of 33 S . stercoralis nematodes collected in Myanmar ( prevalent region , nine from three patients ) and Japan ( non-prevalent region , 24 from six patients ) [10] ( Table 1 ) . We applied the WGA method [16] using the Illumina MiSeq to sequence the whole genome of a single nematode . We obtained 300-bp paired-end reads to > 20× coverage ( > 3 Gb ) for each nematode and mapped them to the S . stercoralis reference genome . The mapping ratios of each sample to the reference genome ranged from 77 . 46% to 96 . 96% , and the ratios for reads mapped in the correct orientation and distance ( ‘proper paired’ reads ) ranged from 48 . 94% to 62 . 72% ( S1 Table ) . In contrast , the mapping ratios of non-WGA reference reads were 90 . 95% with 71 . 79% proper pairs ( S1 Table , S1 Fig ) . Although amplification bias depending on genome locations were observed in the WGA samples ( S2 Fig ) , > 10× coverage was achieved for > 80% of the genomic locations , and the median coverage values ranged from 20 to 50 for most samples ( S1 Table , S1 Fig ) . We detected 298 , 202 variant positions , which accounted for 0 . 6% of the total genome , among the 33 samples when compared with the reference . Most variants were SNPs ( 231 , 583 positions ) , and small inserts or deletions ( indels ) were present at 67 , 655 positions ( S2 Table ) . The number of variant positions in individual nematodes ( including homozygous and heterozygous sites compared with the reference ) ranged from 137 , 439–146 , 259 and 135 , 583–157 , 900 of the Myanmar and Japanese samples , respectively ( S2 Table ) . Comparisons with reference gene models revealed that 27 . 7% of the variants were located in intergenic regions , followed by 27 . 3% , 15 . 2% , 12 . 8% and 9 . 9% in exonic , upstream , downstream and intronic regions , respectively ( Fig 1A ) . There were higher frequencies of variant positions in intergenic regions compared with those of the individual nucleotides in the total genome and lower frequencies of variant positions in exonic regions ( Fig 1A ) . In the exon variations , similar numbers of synonymous and non-synonymous SNPs were detected in 34 , 551 and 34 , 960 positions , respectively ( Fig 1B ) . Frameshift indels and stop mutations were less frequent ( 5 , 932 , 1 , 237 , 1 , 158 and 119 for frameshift , non-frameshift , stop-gain and stop-loss , respectively ) ( Fig 1B ) . The distribution of SNPs along the four longest scaffolds is shown in S3A Fig and the distribution of numbers of SNPs by 10-kb window for scaffolds bigger than 100 kb are shown in S3B Fig . Variants were unevenly distributed along the genome with numbers of variant positions in 10-kb window ranging from 3 to 922 ( median = 31 ) , suggesting that they represented ‘hotspots’ . Further , the hotspot regions did not correspond to regions of high coverage mapping ( S2 Fig and S3 Fig ) ( Pearson’s r = -0 . 01 ) , suggesting that the variant call was not significantly influenced by WGA amplification bias . No significant differences in SNP distribution between the two countries were observed ( high correlation coefficient between SNP numbers in 10-kb window of the two countries; Pearson’s r = 0 . 78 , p < 2 . 2e-16 ) . Principal component analysis ( PCA ) of SNPs compared with the reference strain unambiguously separated the Japanese and Myanmar samples from the reference strain by the first PC , which account for 40 . 1% of the variance . Japanese and Myanmar samples were separated by the second PC ( 14 . 1% of variance ) ( Fig 2A ) . Fig 2B shows the PCA results without the reference . The Myanmar and Japanese samples were separated by PC1 ( 28 . 4% ) . PC2 ( 10 . 3% ) grouped the Myanmar samples according to their host origins , although the separation in the Japanese samples was not unambiguous . Pair-wise distances ( π ) of samples originated from different countries ( Japan vs . Myanmar ) were generally higher than those within populations ( Fig 3 ) . In the Myanmar samples , pair-wise distances between hosts were higher compared to those within hosts , although such differences were not observed in the Japanese samples ( Fig 3 ) . Because the parasitic adult stage of Strongyloides is mitotically parthenogenetic , multiple larval progeny of such adults will be , in theory , genetically identical . Although within-host samples showed high similarity to each other ( π values < 7 . 5e-04 ) both in Japan and Myanmar , they still exhibited some differences from each other . Because of possibility of errors in WGA or sequencing process and difficulty in heterozygous SNP call , it is difficult to conclude that they are genetically different or identical progeny . Simulated experiments using proved progeny of single adults will be useful to answer this question . Analysis of molecular variance ( AMOVA ) showed 23 . 7% of variance was associated with differences between populations and 6 . 3% with differences between hosts , whereas more than 100% of the variance was attributed to variation within samples ( S3 Table ) . Although the negative phi-statistics and variance values observed in AMOVA ( S3 Table ) may reflect problems with sample size and analytical strategy , these results suggest a close relationship among the Japanese samples independent of host origin and high heterozygosity within the individual genomes . Next , we constructed phylogenetic networks according to the SNPs , which support the PCA results ( Fig 4 ) . The tree contained two main clades , comprising Myanmar or Japanese samples . All samples in the Myanmar clade from the same host clustered together and were clearly distinct from those of other hosts . Most Japanese samples sub-clustered according to host origin , although the separations were not as clear as those of the Myanmar samples . Further , we found some Japanese samples ( Rk5-6 , Rk7-5 , Rk8-3 and Rk8-8 ) , which have lower coverage ( S1 Table ) , were placed at positions distant from those of other worms of the same host origin . This is likely because of failure to call heterozygous SNP in low coverage samples [16] . We therefore removed these four samples ( Rk5-6 , Rk7-5 , Rk8-3 and Rk8-8 ) and those having lower coverage than the four samples ( based on % of genome regions with 15× coverage; MyHTB122-6 and Rk6-4 ) from further analyses . Two samples from host Rk9 ( Rk9-3 and Rk9-11 ) , which had higher coverage ( S1 Table ) , occupied positions more distant from the other samples as well as a sample from host Rk9 ( Rk9-6 ) ( Fig 4A ) . Next , we used the computationally-phased sequence dataset for the Japanese samples to construct phylogenetic trees for each scaffold ( > 100 kb ) . The two haplotypes in a genome , shown as A and B haplotypes in S4A Fig , separated into distinct clusters for most of samples . This result suggests that the haplotypes in the diploid genomes of most samples evolved independently . Haplotypes of samples Rk8-7 , Rk9-3 and Rk9-11 exhibited distinct haplotype organisations in each-scaffold tree ( shown in black colour in S4A Fig ) . In Myanmar samples segregation of two haplotypes was not clear compared to the Japanese samples and individual scaffold trees showed various patterns ( S4B Fig ) , suggesting past occurrences of chromosome exchange and/or recombination between Myanmar samples . The mitochondrial tree exhibited a similar topology to the nuclear tree ( Fig 4B ) . The Japanese samples were placed into one clade , clearly separated from Myanmar samples with a high support value . Within the Japanese samples , those from host Rk9 clustered with those from host Rk8 and occupied the basal position of the other Japanese samples . As observed in the phylogenies of nuclear genomes , the samples from hosts Rk4 , Rk5 , Rk6 and Rk7 were closely related , but were unambiguously sub-grouped according to host origin . Samples from host Rk9 ( Rk9-3 , Rk9-6 and Rk9-11 ) , which clustered separately in the nuclear tree , grouped together in the mitochondrial tree ( Fig 4B ) . Interestingly , mitochondrial genome sequences of worms from the same host origin were not perfectly identical ( especially in worms from host Rk4 ) although differences were very small and this may be due to sequencing errors . Strongyloides stercoralis employs distinct modes of reproduction as follows: asexual parthenogenetic reproduction by parasitic females inside the host and sexual reproduction by free-living adults outside the host . Asexual reproduction may promote increased heterozygosity because of the absence of recombination and segregation in diploids ( known as Mullers’s ratchet or Meselson effect ) [23 , 24] . We therefore compared the heterozygosities ( πt ) of samples from Japan , where the parasites likely persist longer in the host through asexual auto-infection because no new infections are suggested to be unlikely to have occurred in Japan in the last 50 years [10] and our Japanese samples were collected from elderly people ( Table 1 ) , and samples from Myanmar to represent frequent new infections by larvae that arose through sexual reproduction . As expected , most Japanese samples ( i . e . all except Rk9-11 , Rk9-3 and Rk8-7 ) comprised higher heterozygosities ( πt = 0 . 0015–0 . 0017 in scaffolds > 8 kb ) compared with Myanmar samples ( 0 . 0011–0 . 0013 ) ( S2 Table ) , and this difference was significant ( P < 1 . 8e -5 , Welch t-test , df = 23 ) . Intra-genome heterozygosity does not seem to be highly associated with read depth ( S5 Fig ) , and the excess of heterozygosity in the Japanese samples were consistently observed in the genome ( S6 Fig ) . These results suggest that excess of heterozygosity in the Japanese samples is likely to be true , excluding the possibility of false calls due to contaminations or other uncertain factors . The negative inbreeding coefficients ( FIN ) observed in such Japanese samples ( −0 . 36 to −0 . 22 ) may represent repeated parthenogenetic reproduction of the nematodes in their hosts ( S2 Table ) . Exceptions were Rk9-11 , Rk9-3 and Rk8-7 , which comprised fewer heterozygosities ( 0 . 0009 to0 . 0013 ) and higher FIN values ( −0 . 09 to 0 . 28 ) . The Japanese samples deviated significantly from Hardy-Weinberg equilibrium at 34 . 4% of loci , with 99 . 3% in heterozygous excess , compared with 0 . 8% of the loci in Myanmar samples , none of which were in heterozygous excess ( S4 Table ) , suggesting more frequent asexual reproduction ( insufficient sexual reproduction ) has been used by Japanese worms than Myanmar ones . This point was discussed in [25] with observation of deviation from Hardy-Weinberg equilibrium in some populations of rat Strongyloides ( S . ratti ) and also reviewed in [26] . Next , we compared the heterozygosities of the scaffolds assigned to autosomes and sex chromosome [9] of individual samples ( Fig 5 ) . Two main groups were observed as follows: 1 . Myanmar samples with values ranging from 0 . 001–0 . 0015 in the sex and autosomal scaffolds , 2 . The majority of Japanese samples had with higher heterozygosities compared with those of Myanmar samples in the sex and autosomal scaffolds . The exceptions Rk9-11 , Rk9-3 and Rk8-7 were positioned separately from those shown in the plot . The autosomal heterozygosities of Rk9-3 were lower but had values similar to those of the sex chromosomes of the other Japanese samples , whereas the heterozygosities of the sex chromosomes of Rk8-7 were low and had a value consistent with that of the autosomes of the Japanese samples . The values of both the sex chromosomes and autosomes of Rk9-11 were low . In contrast , the numbers of homozygous SNP sites in these three samples ( S7 Fig ) were greater than other Japanese samples on the sex chromosome of Rk8-7 , the autosomes of Rk9-3 and both types of chromosomes of Rk9-11 ( with an increase of approximately 50% of autosomes compared with Rk9-3 ) . Together , these results suggest that samples Rk8-7 , Rk9-3 and Rk9-11 arose through recent sexual crossing between very closely-related individuals and acquired more homozygous chromosome pairs in sex chromosomes and autosomes . These findings likely explain the positions of Rk9-3 and Rk9-11 in the network tree , which were distant from Rk9-6 ( Fig 4 ) .
A major weakness of research on parasitic helminth genomes is the inability to obtain sufficient quantities of DNA because at present , none of these parasites can be cultured through its entire life cycle outside of a living host . Nevertheless , the WGA technique may solve this problem by producing high yields of whole genomic DNA from a single parasite [16] . Here we used the WGA technique combined with the NGS technology to re-sequence the entire genomes of individual S . stercoralis to acquire a better understanding the population structure of this medically important human pathogen . To the best of our knowledge , this study represents the first genome-wide approach to estimate the genotypic variations in S . stercoralis populations . We show here that WGA detects variants with sensitivity comparable with those of normal variant detection methods , although WGA requires more data ( coverage ) to correctly call heterozygous positions , likely because of amplification bias . Here , our analysis of nematodes collected in Japan and Myanmar detected approximately 0 . 3 million variant positions , representing 0 . 6% of the genome , by comparison to the reference strain isolated from a dog in the United States . Although the reference and samples in this study were originally isolated from different hosts , this level of diversity represents as low as the diversity of C . elegans ( ~0 . 05% ) [27] compared with other nematodes such as Pristionchus pacificus ( ~2% ) [28] and Bursaphelenchus xylophilus ( ~4% ) [29] . This may be explained by the relatively recent divergence of S . stercoralis from a common ancestor of S . stercolaris and the sister species , stronger selective pressure on the obligate parasite compared with free-living organisms such as P . pacificus , or facultative parasites such as B . xylophilus or both . Additionally , the unique mode of reproduction of this species may have affected the diversity level . S . stercoralis is distributed worldwide in areas with warm climates , and it will be interesting to analyse the diversity of S . stercoralis isolated in Africa , South America and Australia to study their global diversity . The data from such an analysis may illuminate the origin and migration routes of S . stercoralis and allow comparison of these attributes in populations of the parasite in humans and dogs as gene flow of parasites are generally determined by host movement [30] . Besides the human strongyloidiasis situations in the two countries ( Japan and Myanmar ) , situations of Strongyloides infection in dogs are also likely to differ between the two countries . Strongyloides infection rate in dogs was reported to be as low as 0 . 4% in Okinawa , Japan [31] . Although we can’t find any reports about Myanmar canine strongyloidiasis , infection rate in Myanmar is possibly very high as reported in other Southeast Asian countries [32 , 33] . Therefore , a genome-wide investigation of their population structures would be of interest to see if a similar intra-genome heterozygosity trend can be observed as in human Strongyloides and to identify if there are interspecies transmissions between dogs and humans . The phylogenetic relationships inferred from nuclear and mitochondrial SNPs were basically similar to each other . However , the relationships of Japanese samples observed in the nuclear trees were more complicated and therefore difficult to interpret . We found this is likely not only because the Japanese samples originated from a small gene pool but is also potentially explained by independent evolution of two haplotypes of the diploid genomes through asexual reproduction . This suggests that analyses of heterozygosity ( e . g . by phasing ) are useful and necessary to gain a better understanding of the structures of populations of S . stercoralis . Because S . stercoralis has not been endemic in Japan for decades [10] , the Japanese samples collected from elderly hosts aged 58–104 years ( Table 1 ) may have been maintained only by auto-infection cycles for a long time . The higher heterozygosity of Japanese compared with Myanmar samples is thus possibly explained by an accumulation of heterozygous positions during the auto-infection cycle [10] . The exceptions Rk9-3 , Rk9-11 and Rk8-7 , which have reduced heterozygosity in sex or autosomal scaffolds or both are likely explained by recent cross events between two very closely related individuals , possibly during their isolation from a faecal culture . This , in turn , provides robust evidence that parthenogenesis of the parasitic female is mitotic ( non-meiotic ) and that free-living adults exchange chromosomes outside the host . Further , positive FIN ( inbreeding efficiency ) values of the Myanmar samples suggest that new infections occur in the prevalent regions by infective larvae produced through sexual reproduction between closely related individuals . It has been suggested that new infections are unlikely to have occurred in Japan in the last 50 years [10] . Assuming that the genomic mutation rate of S . stercoralis is the same as that of C . elegans ( 9*10−9/site/generation ) [34] and the minimum S . stercoralis generation time is 8 days , 50 years of asexually cycling within a human host can cause approximately 1 , 900 heterozygous sites to accumulate in the 86-M base diploid genome . Although this value is high , it is only ~20% of the number of differences observed between samples isolated in Japan and Myanmar ( S2 Table ) . These values suggest that the frequency of sexual reproduction , which can reduce heterozygosity , is also an important factor for determining the number of heterozygous sites in the nematode genome . The analysis of heterozygosity can therefore serve to help draw inferences about the history of infections and the prevalence of parasites in a specific area . | Strongyloides stercoralis , one of the most neglected helminths causes strongyloidiasis mainly in tropical and subtropical regions worldwide . The parasite’s complex lifecycle includes sexual and asexual reproduction outside and inside the host , respectively . The parasite can also asexually complete a life cycle within the host's body , which is called autoinfection causing life-long infection . In order to investigate the population structure and transmission patterns of this parasite we sequenced individual nematodes isolated from human faeces in Japan and Myanmar , where the parasite is present at low and high frequencies , respectively . Whole genome sequencing of small parasites is generally difficult because the amount of DNA is limiting . However , we overcame this problem by combining whole genome amplification with next-generation sequencing . Sequence comparisons revealed 0 . 6% of the genome is variable among samples , and the variants showed clear separation by the location of their origin . We found that heterozygosity within the genomes was higher in Japan , which is likely explained by the predominance of asexual reproduction through auto-infection , suggesting that analyses of heterozygosity are required to better understand the history of a population . | [
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] | 2016 | Genome-Wide Analyses of Individual Strongyloides stercoralis (Nematoda: Rhabditoidea) Provide Insights into Population Structure and Reproductive Life Cycles |
Control of Aedes aegypti , the mosquito vector of dengue , chikungunya and yellow fever , is a challenging task . Pyrethroid insecticides have emerged as a preferred choice for vector control but are threatened by the emergence of resistance . The present study reports a focus of pyrethroid resistance and presence of two kdr mutations—F1534C and a novel mutation T1520I , in Ae . aegypti from Delhi , India . Insecticide susceptibility status of adult-female Ae . aegypti against DDT ( 4% ) , deltamethrin ( 0 . 05% ) and permethrin ( 0 . 75% ) was determined using WHO's standard insecticide susceptibility kit , which revealed resistance to DDT , deltamethrin and permethrin with corrected mortalities of 35% , 72% and 76% respectively . Mosquitoes were screened for the presence of kdr mutations including those reported earlier ( I1011V/M , V1016G/I , F1534C , D1794Y and S989P ) , which revealed the presence of F1534C and a novel mutation T1520I . Highly specific PCR-RFLP assays were developed for genotyping of these two mutations . Genotyping using allele specific PCR and new PCR-RFLP assays revealed a high frequency of F1534C ( 0 . 41–0 . 79 ) and low frequency of novel mutation T1520I ( 0 . 13 ) . The latter was observed to be tightly linked with F1534C and possibly serve as a compensatory mutation . A positive association of F1534C mutation with DDT and deltamethrin resistance in Ae . aegypti was established . However , F1534C-kdr did not show significant protection against permethrin . The Aedes aegypti population of Delhi is resistant to DDT , deltamethrin and permethrin . Two kdr mutations , F1534C and a novel mutation T1520I , were identified in this population . This is the first report of kdr mutations being present in the Indian Ae . aegypti population . Highly specific PCR-RFLP assays were developed for discrimination of alleles at both kdr loci . A positive association of F1534C mutation with DDT and deltamethrin resistance was confirmed .
Aedes aegypti is globally distributed throughout the tropics and subtropics and highly adapted to humans and urban environments . It acts as a primary vector for various arboviral infections including yellow fever virus , dengue virus ( DENV ) and chikungunya virus ( CHIKV ) [1]–[5] . Dengue has recently become a major health problem around the world with more than 120 countries endemic for dengue [6] and has been ranked as the most important mosquito borne viral disease [7] . Recent estimates by the World Health Organization ( WHO ) suggests that 50–100 million dengue infections occur worldwide every year and over 40% of the world's population is now at risk of the disease [8] . A study based on a cartographic approach estimated 90 million apparent dengue infections globally in year 2010 with India accounting for 34% ( 32 million ) infections [9] . Chikungunya is another important arboviral infection spread by Ae . aegypti , prevalent in Africa , Southeast Asia and India [10] . In India it re-emerged in 2006 after a gap of 32 years [10] . Since there is no specific vaccine or drug available for the treatment of dengue and chikungunya , vector control and personal protection are the only options to reduce the spread of these arboviral infections . Vector control strategies employed for Aedes control in India are mainly anti-larval measures , source reduction and use of adulticides ( pyrethrum space spray and malathion-fogging ) during a disease outbreak . Pyrethroids are widely used for personal protection in the form of repellents and insecticide treated materials [11] , which provides effective protection against day biting Aedes . It has also been shown that window curtains and domestic water container covers treated with insecticide may reduce densities of dengue vectors to low levels and potentially affect dengue transmission [12] . In addition pyrethroids have been recommended by WHO for space spraying for Aedes control [13] due to rapid knockdown effect and less mammalian toxicity . However , the use of pyrethroids is being challenged by the rapid emergence of resistance , which needs to be monitored periodically to manage effective programmes to avoid or delay resistance in vector species . Key to this is , understanding of the mechanisms of resistance so that informed decisions can be made to select appropriate insecticides for effective control of target vector species . One of the mechanisms of resistance in insects against DDT and pyrethroids is knockdown resistance ( kdr ) which is conferred by mutation ( s ) in the target site , the voltage gated sodium channel ( VGSC ) . Several kdr mutations have been reported in many insects of agricultural and medical importance including Ae . aegypti . In Ae . Aegypti , eleven non-synonymous mutations at nine different loci have been reported [14]–[17] , amongst which mutations at three loci , i . e . , Iso1011 ( I→M/V ) and Val1016 ( V→G/I ) in domain II and F1534 ( F→C ) in domain III are most commonly reported as contributing to pyrethroid resistance [14]–[22] . The most common kdr-mutations L1014F/S reported in many insects of agricultural and medical importance is not yet found in Ae . aegypti possibly due to codon constraint [23] . Although widespread in Southeast Asia and Latin America , the presence of kdr mutations has yet to be established in India . Here we report the presence of two kdr mutations , F1534C and a novel mutation T1520I , in an Indian Ae . aegypti population .
Aedes immature ( larvae and pupae ) were collected from the water holding containers in domestic and peri-domestic areas in Delhi and were reared to adults . The collection sites and dates of collections are shown in supplemental items S1 Table and S1 Fig . Ground mixtures of dog biscuits and fish food in a ratio of 3∶1 were provided as food for larvae . Emerged adults were identified morphologically and supplied with 10% glucose solution soaked in cotton pads . Two-to four-days old adult Ae . aegypti female mosquitoes were subjected to insecticide susceptibility testing using the WHO's standard insecticide susceptibility test kit . Up to twenty-five mosquitoes in each replicate were exposed to 4% DDT , 0 . 05% deltamethrin and 0 . 75% permethrin impregnated paper ( supplied by WHO collaborative centre , Vector Control Research , Universiti Sains , Malaysia ) alongside appropriate controls for one hour and subsequently transferred to recovery tubes lined with untreated paper . During recovery , mosquitoes were provided access to cotton soaked in 10% glucose and mortalities were recorded after 24 hours . All the bioassays were carried out at 27±1°C and 70±10% relative humidity . Percent mortalities were calculated using Abbott's formula [24] . Dead and alive mosquitoes after recovery was transferred to individual microfuge tubes and stored at −20°C . DNA was isolated from individual mosquitoes following Livak et al . ( 1984 ) [25] . Allele specific PCR assays were employed for genotyping of kdr mutations I1011V/M , V1016G/I and F1534C following Saavedra et al . ( 2007 ) [16] and Yanola et al . ( 2011 ) [26] . For genotyping of D1794Y , PCR-RFLP was carried out as described by Chang et al . ( 2012 ) [27] . In the absence of established PCR-based assays for mutation S989P , direct sequencing was carried out using primers IIP_F and IIS6_R [26] . Dead as well as surviving mosquitoes of some batches of insecticide bioassay tests were genotyped for F1534C mutation to study the association of this kdr mutation with insecticide resistance . DNA sequencing was performed to validate the PCR-based genotyping used for various kdr alleles and also to check for the presence of any novel mutation . Three regions of VGSC were amplified and sequenced: ( i ) partial domain II ( P to S6 ) using primers IIP_F and IIS6_R [26] , ( ii ) partial domain III ( S4–S6 ) using primers Ge-IIIS6_F and IIIS6R [26] , and ( iii ) partial domain IV ( S5–S6 ) using primers 5380F1 and 5380R1 [27] . PCR products were amplified , purified using QIaquick PCR purification kit ( Qiagen Inc ) and subjected to cycle sequencing reaction using BigDye Terminator v3 . 0 . The termination products were run in Applied Biosystems 3730×l DNA Analyzer . Some of the sequencing reactions were performed at Macrogen Inc ( South Korea ) . Sequencing chromatograms were edited using FinchTV ver 1 . 5 . 0 ( Geospiza , Inc . ) . The PCR product of one sample , which was suspected to have an indel in the intron , was cloned in pGEM-T vector system using vendor's protocol and five clones were sequenced . Sequences were aligned using ClustalW implemented in Mega5 [28] . For development of PCR-RFLP assays for detection of kdr alleles at two loci ( F1534 and T1520 ) in domain III-S6 , DNA sequences spanning 200 bp upstream to F1534 and 200 bp downstream to T1520 were checked for 1534C- and 1520I-specific restriction sites using an online tool available at http://insilico . ehu . es/restriction/two_seq . Two unique restriction enzymes SsiI and BsaBI were selected which were specific to1534C ( TTC>TGC ) and 1520I ( ACC>ATC ) sequences respectively . The intron region was excluded when designing PCR-RFLP due to the existence of indel in the intron upstream of T1520 as revealed by sequencing of cloned PCR product . Two primers flanking these two loci , i . e . , AekdrF ( 5′-TGGGAAAGCAGCCGATTC-3′ ) and AekdrR ( 5′-CCTCCGTCATGAACATTTCC-3′ ) were designed with expected amplicon size of 171 bp . The expected sizes of cleaved product for the 1520I allele were 143 and 28 bp when digested with BsaBI , and 103 and 68 bp for 1534C when digested with SSiI . The diagnostic criterion for 1520I allele was taken as the presence of 143 bp band only ( resolution of 28 bp cleaved product can not be resolved on agarose gel ) , whereas presence of 103 and 68 bp bands were considered as diagnostic criteria for 1534C allele . Uncut product of 171 bp was considered the wild allele . For PCR-RFLP , amplification was carried out in 15 µl of reaction mixture containing 1× buffer , 200 µM of each dNTP , 0 . 25 µM of primers AekdrF and AekdrR and 0 . 5 unit of Taq DNA polymerase . The PCR conditions were initial denaturation at 95°C for 3 min followed by 35 cycles each of denaturation at 95°C for 15 s , 50°C for 15 s and extension at 72°C for 30 s and a final extension at 72°C for 7 min . The PCR product was subjected to two separate restriction digestion reactions , one with BsaBI and another with SSiI . Each restriction digestion reaction mixture ( 20 µl ) contained 5 µl of PCR product , 2 units of restriction enzyme and 1× buffer , which was incubated for four hours or overnight at 65°C for BsaBI and 37°C for SSiI . The cleaved product was run on 2 . 5% agarose gel containing ethidium bromide and visualized with a gel documentation system ( Figs . 1 and 2 ) . Representative samples of PCR-RFLP genotyped samples were sequenced for partial domain III to validate PCR-RFLP result where primers AekdrF and AekdrR were used for amplification of PCR product and only AekdrR was used for sequencing reaction . DNA sequences with read length of 200 bp or more have been deposited in GenBank ( accession numbers: KM677247–KM677334 ) . The association of the kdr mutations with resistance phenotype was tested using Fishers' Exact test and Odds Ratio estimation using dominant , recessive and additive models . Hardy-Weinberg equilibrium test was performed using Chi-square or Fisher's Exact test . Analysis of linkage disequilibrium and the Hill and Robertson coefficient r2 were calculated for alleles using CubeX software ( http://www . oege . org/software/cubex ) [29] .
The result of insecticide susceptibility tests carried out on Ae . aegypti against DDT , deltamethrin and permethrin are shown in Table 1 . The result shows high resistance against DDT ( 30 . 2–48 . 1% mortality ) and moderate level of resistance to pyrethroids ( deltamethrin: 64 . 4–74 . 3%; permethrin: 66 . 8–82 . 3% mortalities ) in all sites . Results of allele specific PCR genotyping for the F1534C mutation are shown in Table 1 . The allelic frequency of the 1534C mutant is high in all the three sites ranging from 41—69% . Of the1180 samples genotyped , a total of 34 sample representing FF ( n = 9 ) , FC ( n = 11 ) and CC ( n = 14 ) were sequenced for partial domain III to validate allele specific PCR results . Two samples showed discrepancies where homozygous CC turned out to be FC after sequencing . Sequencing of samples also revealed the presence of a novel mutation C>T on the second codon of T1520 residue ( ACC ) leading to T→I amino acid substitution . Among 34 samples sequenced for partial domain III , eleven were with FC/TT , two with FC/TI , eleven with CC/TT , one with CC/II and the remaining nine were with FF/TT . A total of 166 samples were genotyped for I1011M/V and V1016G/I . Though some samples were observed to be positive for mutations ( genotypes IM = 7 , MM = 1 , IV = 2 for I1011 locus , and VI = 6 for V1016 locus ) by allele specific PCR , sequencing of 29 samples representing all genotypes ( all mutants and 13 wild genotypes ) did not confirm the presence of any of them . Sequencing of partial domain II also did not identify S989P-kdr mutation in any sample . The genotyping for I1011 and V1016 was therefore discontinued assuming that allele specific PCR is not specific and I1011M/V or V1016G/I mutations are absent in the study population . For genotyping of D1794Y , a total of 66 mosquitoes were genotyped using PCR-RFLP and five samples through DNA sequencing , but all turned out to be the reference genotype . The distribution of different F1534 is shown in Table 2 . The proportions of dead and live mosquitoes after exposure to insecticides for each genotype are shown in Fig . 3 . Odds Ratio ( OR ) estimates at 95% confidential intervals ( CI ) and Fisher's exact test using different models ( dominant , recessive and additive ) for dead and live mosquitoes in each treatment group are presented in Table 3 . It was observed that F1534C-kdr conferred greater protection against DDT with all models and highest protection was shown using the recessive model ( OR = 16 . 0 , 95% CI: 5 . 6–45 . 4; p = 0 . 000 ) . Lower protection was shown against deltamethrin when fitted with recessive ( OR = 2 . 0 , 95% CI: 1 . 06–3 . 75; p<0 . 05 ) or additive ( OR = 1 . 85 , 95% CI: 1 . 84–2 . 89; p<0 . 01 ) models . However , F1534C-kdr did not show significant protection against permethrin . Genotyping of F1534 and T1520 alleles were performed on 203 mosquitoes , which revealed a high frequency of the F1534C mutation ( 0 . 79 ) and a very low frequency of the T1520I mutation ( 0 . 13 ) . Genotyping results showing association of T1520 and F1534 alleles are shown in Table 3 . It was observed that T1520I mutation was found in individuals having the 1534C allele only , but never with wild type F1534 . This data infers that 1520I is linked to 1534C . Linkage disequilibrium ( LD ) analysis revealed perfect disequilibrium ( D′ = 1 . 0 , χ2 = 8 . 02 ) though r2 was low ( 0 . 04 ) due to a relatively low frequency of allele 1520I as compared to 1534C , where all individuals with 1520I allele showed association with 1534C , but not all 1534C are associated with 1520I . The present data revealed the presence of three haplotypes with haplotype frequencies fTF = 0 . 21 , fTC = 0 . 66 and fIC = 0 . 13 . However , fIF was absent . Among the samples genotyped using the new PCR-RFLP , a portion of domain III was sequenced for 20 samples ( two sample of TT/CC , five samples of TI/FC , eleven samples of TI/CC and two samples of II/CC ) . Genotyping results agreed with DNA sequencing results .
Vector control is the only option for suppression of Ae . aegypti-borne dengue and chikungunya infections in the absence of vaccine or drugs . Several pyrethroids have been recommended by WHO for use in space spray against Aedes [13] . Though in India pyrethroids are being extensively used in malaria control programme , their use in urban areas is limited to space spraying of pyrethrum and fogging with malathion . However , pyrethroid-based household anti-mosquito gadgets ( liquid vaporizer , mats , coils ) are extensively used as personal protectants against mosquito nuisance . Use of these devices may be contributing to resistance in Ae . aegypti in Delhi . Insecticide susceptibility tests carried out in India by several authors prior to the year 2014 did not reveal pyrethroid resistance , though they were found to be resistant to DDT [30]–[33] . Only in one case , 2% survival was recorded in Aedes aegypti on exposure to diagnostic concentration of deltamethrin in a strain from Jharkhand , India [30] . Very recently , for the first time in India , resistance to pyrethroids has been reported from Assam state [34] . In the absence of baseline insecticide susceptibility data for Indian Ae . aegypti or universally acceptable discriminating dose for Ae . aegypti , we used 4% DDT , 0 . 05% deltamethrin and 0 . 75% permethrin papers for bioassays , the most frequently cited doses in recent publications [30]–[31] , [34]–[40] to facilitate easy comparison . Previous published data from Delhi showed that Ae . aegypti was 100% susceptible to even lower doses of insecticides , i . e . , 0 . 025% deltamethrin and 0 . 25% permethrin [31] . Less than 80% mortalities of mosquitoes at higher doses ( which are 2-fold and 3-fold respectively ) confirm resistance against these insecticides . The extensive use of insecticides for vector control has raised concern over the development of insecticide resistance and adverse effects on the environment and human health [41] . Genes conferring insecticide resistance have been spreading in vector populations , particularly in vectors of pathogens causing malaria and dengue [42] . The fact that dispersal of Aedes may be more rapid than other mosquitoes due to transportability of dried , but viable eggs through containers , a single resistance mechanism can spread rapidly . Knockdown resistance ( kdr ) is one of the mechanisms of DDT and pyrethroid resistance in insects . It is conferred by amino acid substitution ( s ) in the target site ( VGSC ) resulting in reduced sensitivity of the target site . A number of mutations have been reported in the VGSC of Ae . aegypti across Latin America and Southeast Asia amongst which V1016G/I , I1011M/V and F1534C [14] , [18] , [21] , [22] , [15] , [43] are known to confer resistance . F1534C has been reported from Latin America [44] , [22] , [45] and Southeast Asia [15] , [43] , [46] , and shown to confer resistance against DDT and pyrethroids . In our study we provide evidence that this mutation confers a high level of protection against DDT and relatively low protection against deltamethrin . However , we failed to show significant protection against permethrin . Our failure to establish association of F1534C with permethrin resistance is contrary to findings by Harris et al . ( 2010 ) [22] and Yanola et al . , ( 2011 ) [15] . Our result is also contradictory to the findings of Du et al . , ( 2013 ) [47] who were able to demonstrate that F1534C reduced the channel sensitivity to permethrin but not against deltamethrin when expressed in Xenopus oocyte . Failure to establish association of F1534C with permethrin resistance is surprising and needs to be further investigated . It may be possible that the dose of permethrin ( 0 . 75% ) used for discrimination of kdr-resistant mosquito in the Indian population is too high that might have killed kdr-resistant mosquitoes . Another possible reason for such discrepancy may be due to the presence of some other linked mutations . For example , F1534C has shown to be strongly associated with permethrin in Grand Cayman [22] where another mutation V1016I co-existed . It is possible that protection against permethrin in Grand Cayman may be due to the combined effect of F1534C and V1016I . It is interesting to explore such association because such linkage has been shown in Brazil , where V1016I was always associated with F1534C [44] . To know the exact role of any particular kdr mutation , one should perform such association studies using laboratory lines of mosquitoes characterized for complete VGSC sequence . In this study we explored a novel mutation T1520I in an Indian Ae . aegypti population . Its potential role in resistance to insecticides is yet to be ascertained . However , since this mutation has always been found in association with F1534C mutation ( D′ = 1 ) , it may be a compensatory mutation to reduce the fitness cost from possible deleterious effects of F1534C mutation , though it has been shown through laboratory experiments that Ae . aegypti homozygous for F1534C does not have reduced fitness [48] . However , its additive effect on protection against DDT and pyrethroids cannot be ruled out . We also observed that 1520I is associated with 1534C , but not vice versa explaining the low r2 ( 0 . 039 ) , which reflects a very low frequency of T1520I as compared to F1534C . Whether haplotype 1534C/1520I is under positive selection remains to be established . Interestingly a similar linkage association is found in the Brazilian population where 1016I is associated with 1534C , but not vice versa [44] . These different associations in different geographical locations indicate that the most likely association of T1520I in India and V1016I in Brazil with F1534C are under positive selection . Linss et al . 2014 [44] , noted a progressive increase of the NaVR2 haplotype ( double mutant , F1534C with V1016I ) from year 2002 through 2012 and concluded that it is likely to be the most favourably selected allele . Linkage of kdr mutations is very common in Ae . aegypti . Co-segregation has been shown between 1016G with D1794Y [17] , 1016G with 989P [49] and 1016G with 1534C [22] , [46] . Whether positive selection of such linkage associations is due to additive role in protection against insecticides or due to compensatory advantage , is worth investigating . Since novel mutation T1520I is tightly linked to F1534C and homozygotes are found in very low frequency , the exact role of this novel mutation could not be established . Our result shows that F1534 genotypes show significant deviation from Hardy-Weinberg equilibrium in all populations ( p<0 . 0001 ) except in South Delhi-II . Initially we thought that this might be due to discrepancy in allele specific PCR genotyping , which often fails to prevent non-specific annealing during PCR extension . However , when we carried out genotyping using highly specific PCR-RFLP method , there was no change in HWE parameter for F1534 alleles . Surprisingly , T1520 genotypes in the same group of mosquitoes were in perfect HWE ( p = 0 . 99 ) . The possible explanation for such a deviation may be the presence of heterogeneous populations or gene duplication . Further studies are required to resolve this issue . Knockdown resistance , which is known to confer cross-resistance to DDT and pyrethroids cannot be monitored through routine insecticide susceptibility tests and requires a sensitive and reliable molecular method of detection . PCR-based methods , allele specific PCR in particular , are most widely used method for this purpose . The specificity of allele specific PCRs which are based on single nucleotide polymorphism is often compromised due to the fact that single base mismatch often does not prevent extension [50] and leads to non-specific amplification [26] , [51] . Yanola et al . , ( 2011 ) reported an overestimation of 1534C frequency by 1 . 8% while using allele specific PCR [26] . In the present study , non-specific amplification was evident in the allele specific PCRs we employed for identification of various kdr alleles located at different loci . We therefore opted to develop a PCR-RFLP method for the identification of F1534 and T1520 alleles , which is presumed to be specific owing to the fact that restriction enzymes are highly specific . The sequencing of representative samples of PCR-RFLP genotyped samples ( n = 20 ) showed 100% specificity of the assay . An additional advantage of the PCR-RFLP over allele specific PCR was that a single PCR amplicon could be used for detection of four alleles present at two loci since two mutations T1520I and F1534C are in proximity , whereas for allele specific PCR assays normally two PCR reactions are required to be performed for detecting four alleles at two loci . The emergence of pyrethroid resistance in Indian Ae . Aegypti associated with the presence of the F1534C-kdr mutation is a threat to the success of pyrethroid-based Aedes control and necessitates countrywide monitoring of insecticide resistance and mapping the distribution of F1534C and T1520I mutations . Though F1534C mutation is shown to be associated with DDT and pyrethroids , the role of novel mutation T1520I still remains to be investigated . | Dengue and chikungunya are the two important human arboviral infections in India transmitted mainly by Aedes aegypti . In absence of any specific drug or vaccine for these infections , vector control and personal protection are the only control options available . The success of insecticide-based vector control heavily relies upon the knowledge of the status of insecticide resistance in vector populations and the underlying mechanisms of insecticide resistance , especially in the presence of cross-resistance . Knockdown resistance ( kdr ) is one of the mechanisms of resistance that confers cross-resistance to DDT and pyrethroids . Currently , pyrethroids are the only insecticide class recommended for use in long-lasting insecticide nets ( LLIN ) and have proven superior to all other insecticides used in vector control programme , due to low mammalian toxicity , low residual activity in nature and rapid knockdown action . The present study was undertaken to determine the susceptibility status of Ae . aegypti against DDT and pyrethroids , and identification of kdr mutations . Though the presence of kdr mutations in Ae . aegypti has been reported in many countries , such a report is not available from India . This study for the first time reports the presence of two kdr mutations , F1534C and a novel mutation T1520I , in an Indian Ae . aegypti population . | [
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] | 2015 | Pyrethroid-Resistance and Presence of Two Knockdown Resistance (kdr) Mutations, F1534C and a Novel Mutation T1520I, in Indian Aedes aegypti |
Chagas disease has historically been hyperendemic in the Bolivian Department of Cochabamba . In the early 2000s , an extensive vector control program was implemented; 1 . 34 million dwelling inspections were conducted to ascertain infestation ( 2000–2001/2003–2011 ) , with blanket insecticide spraying in 2003–2005 and subsequent survey-spraying cycles targeting residual infestation foci . Here , we assess the effects of this program on dwelling infestation rates ( DIRs ) . Program records were used to calculate annual , municipality-level aggregate DIRs ( 39 municipalities ) ; very high values in 2000–2001 ( median: 0 . 77–0 . 69 ) dropped to ∼0 . 03 from 2004 on . A linear mixed model ( with municipality as a random factor ) suggested that infestation odds decreased , on average , by ∼28% ( 95% confidence interval [CI95] 6–44% ) with each 10-fold increase in control effort . A second , better-fitting mixed model including year as an ordinal predictor disclosed large DIR reductions in 2001–2003 ( odds ratio [OR] 0 . 11 , CI95 0 . 06–0 . 19 ) and 2003–2004 ( OR 0 . 22 , CI95 0 . 14–0 . 34 ) . Except for a moderate decrease in 2005–2006 , no significant changes were detected afterwards . In both models , municipality-level DIRs correlated positively with previous-year DIRs and with the extent of municipal territory originally covered by montane dry forests . Insecticide-spraying campaigns had very strong , long-lasting effects on DIRs in Cochabamba . However , post-intervention surveys consistently detected infestation in ∼3% of dwellings , underscoring the need for continuous surveillance; higher DIRs were recorded in the capital city and , more generally , in municipalities dominated by montane dry forest – an eco-region where wild Triatoma infestans are widespread . Traditional strategies combining insecticide spraying and longitudinal surveillance are thus confirmed as very effective means for area-wide Chagas disease vector control; they will be particularly beneficial in highly-endemic settings , but should also be implemented or maintained in other parts of Latin America where domestic infestation by triatomines is still commonplace .
Chagas disease , caused by infection with the parasite Trypanosoma cruzi , is among the most serious public health problems in Bolivia [1]–[9] . In particular , the disease has historically been hyperendemic in some areas of the country in which Triatoma infestans , the main vector of human Chagas disease , is frequently found infesting houses . The Department of Cochabamba is one such area [5] , [7]–[9] . Domestic T . infestans populations were accidentally introduced in most of their past range across South America , allowing for their elimination through area-wide control campaigns based on the spraying of houses and peridomestic structures with residual insecticides [10] . However , it has become progressively clear that wild populations of this highly efficient vector species are widespread both in the inter-Andean valleys of central- and south-eastern Bolivia , including Cochabamba , and across the semi-arid Gran Chaco [11]–[21] . These wild populations may act as the sources of re-infesting vectors in their natural ranges , and this might hamper long-term efforts to keep dwellings vector-free [10] , [22] . Based on the decades-long , successful experience of domestic Chagas disease vector control through pyrethroid insecticide spraying in the Southern Cone countries of South America ( e . g . , [1] , [5] ) and elsewhere ( e . g . , [22]–[25] ) , Bolivia launched an ambitious Chagas Disease Control Program ( CDCP ) in the early 2000s [1] , [26] , [27] . Here we assess the long-term effects of the CDCP on the frequency of dwelling infestation by triatomine bugs ( primarily T . infestans ) in the Department of Cochabamba . Specifically , we aimed at quantifying how dwelling infestation rates varied with increasing control effort as well as from one year to the next over an 11-year period including a pre-intervention phase and a seven-year follow-up phase . Additionally , we asked whether the widespread occurrence of wild T . infestans foci in the region , and particularly in certain eco-regions , could compromise vector control efforts to any serious degree , thus gauging the need for continuous entomological surveillance [16] , [22] , [28] .
N . E . obtained written permission to use CDCP data from the head of the Epidemiology Unit of the Cochabamba Department Health Service ( document CITE/SEDES/ACE/016/09 ) . All data on dwellings and individuals were anonymized . Cochabamba is one of the nine political Departments of Bolivia; 2010 demographic estimates indicate that about 1 . 9 million people live in the Department , ∼35–40% of them in rural localities; the municipality of Cercado , which includes the capital city , Cochabamba , has ∼620 , 000 inhabitants ( Instituto Nacional de Estadística de Bolivia , INE [www . ine . gob . bo] ) . Poverty affects ∼35% of the population [29]; hence , official census data indicate that , in 2001 , nearly 35% of houses had earthen floors and only about 40% had brick/cement walls ( INE ) . In Bolivia as a whole , ∼45% of dwellings are still substandard ( ∼72% for the lowest-income quintile of the population; see http://sedlac . econo . unlp . edu . ar/eng/statistics-detalle . php ? idE=39 ) , and low-quality housing is known to favor infestation by triatomine bugs [30] . In Cochabamba , T . infestans is highly dominant , but T . sordida may also infest houses and bugs identified as T . guasayana or Panstrongylus megistus are sporadically collected ( see below and refs . [30] , [31] ) . As for other parts of Bolivia [1]–[4] , [6] , the prevalence of human infection by T . cruzi in Cochabamba used to be among the highest worldwide , with published reports suggesting mean values ∼20% – but reaching up to ∼70% or more among adults in some communities [7]–[9] , [31] , [32] . Dwelling infestation by T . infestans is the key determinant of this epidemiological scenario , with T . sordida probably playing no significant role in transmission [33] . Therefore , vector control aimed at domestic T . infestans populations is a crucial component of the Bolivian CDCP [1] , [10] , [26] , [27] , [31] . Our analyses cover 39 of the 45 municipalities of Cochabamba ( Figure 1 ) . These municipalities lie within the ‘at-risk area’ specifically targeted by the CDCP; for each of them , at least 5 years of infestation survey data were available for assessing intervention results over the period of interest ( 2000–2011; see Table S1 ) . The study municipalities are mostly located on the southern and western ( Andean ) parts of the Department , spanning three major eco-regions ( sensu Olson et al . [34]; Figure 1 ) , and include the temperate , montane dry forest valleys where wild T . infestans foci are widespread [16] , [18] . Insecticide spraying was the central tactic of the Cochabamba CDCP . Synthetic pyrethroids ( mainly alpha-cypermethrin 20% , 25 mg a . i . /m2 ) were applied by trained CDCP staff in all dwellings of at-risk localities following standard procedures [35]; in Cochabamba , 205 localities were considered at high , 647 at moderate , and 2024 at low risk within the 39 at-risk municipalities ( CDCP data ) . The intervention proceeded in three main phases , with logistic constraints resulting in some variation in the timing and coverage of control actions across municipalities . Briefly , baseline infestation surveys ( mainly 1999–2001 ) were followed by blanket insecticide spraying over two or three rounds ( mainly during 2001–2005 ) ; finally , infestation surveys and spraying were targeted at dwellings reporting residual/re-emerging infestation foci , whether by dweller notification or by active bug searches by CDCP staff . Bug searches and spraying were scheduled at different times depending on the implementation and results of previous phases [35] . Due to financial constraints , the CDCP did not conduct any activities in 2002 . Overall , a median ∼62% of target houses ( i . e . , those in at-risk municipalities ) were searched for bugs each year ( inter-quartile range 17–100% ) ; much lower values in Cercado ( median 2 . 7% , inter-quartile range 0 . 99–9 . 5% ) likely reflect the fact that only some periurban neighborhoods were considered at risk within the capital city , although an estimate of 59 . 0% and 65 . 3% of target houses were investigated in 2003 and 2004 , respectively ( Table S1 ) . The Cochabamba CDCP provided municipality-level data on dwelling infestation ( numbers of dwellings surveyed and found infested ) and control activities ( houses sprayed and amount of insecticide used ) for each year ( see Table 1 ) , as well as on triatomine catches ( 2007–2010; Table S2 ) . Demographic and social-economic data were retrieved from the Bolivian INE ( www . ine . gob . bo ) and the United Nations Development Program [29] . Eco-region data ( Table 2 , Figure 1 ) were derived from digital maps available from the World Wildlife Fund ( http://worldwildlife . org ) .
The initial surveys revealed extremely high infestation rates , with mean municipality-level values above 70% of investigated dwellings ( Table 1 , Figure 2 ) . The extreme case was Sicaya , where 539 out of 562 dwellings surveyed in 2001 ( i . e . , 95 . 9% ) were infested; four further municipalities had infestation rates above 90% , with a total of 4436 dwellings infested out of 4842 investigated . Average infestation rates began to decline by 2003 , when the effects of the CDCP were becoming evident in some municipalities: two of them still recorded infestation rates >80% , while seven were already below 3% ( Figures 2 and S1 ) . In contrast , dwelling infestation rates were overall strikingly reduced by 2004 , with median values about one order of magnitude lower than those recorded in 2000–2001 ( Table 1 , Figure S1 ) . With the exception of a few municipalities , such low values were sustained over the rest of the assessment period ( Figure 2 ) with a relatively modest investment in insecticides , particularly from 2007 on ( Table 1 ) . It is however important to note that observed dwelling infestation rarely reached zero in any particular municipality and year; when none of the surveyed dwellings were found to be infested in a given municipality , this was seldom consistent across different annual assessments ( Table S3 ) . In most cases , annual mean municipality-level infestation values remained fairly constantly at about 2–4% after 2004 ( Table 1 ) . In Cercado ( which includes the capital city , Cochabamba ) , dwelling infestation rates were in the range of 11–33% during 2003–2010 , with the exceptions of 2004 , with a reported rate of 1 . 45% potentially due to a typing error , and 2011 , when a 6 . 95% rate was reported . Figures 1C and 2 illustrate the spatial patterns of infestation across the assessment period; they indicate that residual infestation , albeit geographically widespread , tended to be more common in the southern municipalities of the Department , as well as in Cercado and in some south-western municipalities – that is , in areas where baseline risk was also higher . In addition , our eco-regional appraisal suggests that municipalities with a higher percentage of territory corresponding to montane dry forest had overall higher infestation rates than those dominated by either highland grasslands ( the Andean Puna ) or moist tropical forests ( the Bolivian Yungas ) ( Table 2 , Figure 1C , D ) . Moreover , only two out of 27 municipalities originally dominated by montane dry forest , Santiváñez and Tacachi , reported zero infestation – and they did so in just one year each , coinciding with small bug-search efforts ( Tables S1 and S3 ) . Finally , the vast majority of the 7321 triatomines collected during entomological surveys carried out between 2007 and 2010 ( the period for which data were available ) were identified as T . infestans , with annual percentages typically ∼93–96% . In 2009 , when only 428 vectors were collected , 73 . 1% of specimens were T . infestans ( CI95 68 . 7–77 . 1% ) and 25 . 7% T . sordida; in the rest of years , T . sordida represented just ∼5% of catches , with fairly constant values suggesting little intervention effects on this latter species ( see Table S2 ) . Other species ( T . guasayana and P . megistus ) were very rare , with just 16 specimens collected over the four-year period assessed ( Table S2 ) . Therefore , infestation figures discussed in this paper refer primarily to T . infestans . The linear mixed model in Table 3 suggests that , on average , a ∼28% ( CI95 6–44% ) reduction of infestation odds was achieved across the study period for each 10-fold increase in control effort – represented by a fixed term measuring the ( log10 ) amount of insecticide used per census inhabitant in each municipality during the previous year . In addition , average dwelling infestation rates correlated positively with rates ascertained the previous year ( Table 3 ) . The model also suggests that infestation odds rose by a factor of ∼3 . 5 ( CI95 1 . 64–7 . 30 ) for each 10-fold increase in the proportion of municipal territory originally corresponding to montane dry forests . The model estimates a strong negative effect of the HDI covariate ( slope coefficient −2 . 16 ) , but with a relatively large SE ( 1 . 03 ) . This suggests that infestation odds were lower in municipalities with higher HDI ( odds ratio [OR] 0 . 12 ) , yet uncertainty about this estimate is substantial ( CI95 0 . 01 to 0 . 93 ) . Table 3 also shows that the municipality random effect explained nearly 30% of the total variance ( an estimate of intra-class correlation [42] ) after controlling for the effects of covariates . Diagnostic plots showed no trends , with normally distributed residuals ( details not shown ) . In our second model , the ‘intervention effort’ covariate was replaced by an ordinal ‘year’ predictor so that year-to-year changes in infestation could be quantified ( Table 4 ) . This model suggests that infestation odds decreased by nearly 90% in 2003 compared to 2001 ( OR 0 . 11; CI95 0 . 06–0 . 19 ) and by nearly 80% in 2004 compared to 2003 ( OR 0 . 22; CI95 0 . 14–0 . 34 ) . Infestation remained largely stable afterwards , with all adjusted coefficients effectively indistinguishable from zero except for a moderate but significant decrease in 2006 compared to 2005 ( OR 0 . 62; CI95 0 . 43–0 . 89 ) . Effect-size estimates for other covariates were similar to those derived from our first model , again suggesting temporal dependence of infestation and higher risk in municipalities within the montane dry forest eco-region ( Tables 3 and 4 ) . The slope coefficient estimate for the HDI covariate was again negative but even more imprecise than in the previous model , with the CI95 including zero . Finally , this model estimated intra-class correlation as 58 . 3% of the total variance ( Table 4 ) ; again , diagnostic plots showed no obvious trends , albeit the distribution of residuals slightly departed from normality ( details not shown ) . We note that , while more complex in structure , this second model had much lower AICc and BIC scores than the first , simpler specification ( ΔAICc = 98 . 4 , ΔBIC = 69 . 3; Tables 3 and 4 ) , suggesting that the ‘year-ordinal’ covariate helps explain variation in infestation rates substantially better than the whole-period averaged effect of intervention effort .
Insecticide-based control of dwelling-infesting vector populations remains the core tool for primary Chagas disease prevention [1] , [5] , [10] , [28] , [62] . The impressive achievements of the coordinated , international Initiative undertaken in the early 1990s across the Southern Cone countries of South America firmly established this view as a major public health dogma [1] , [5] , [10] , [27] , [56] , [61] . This success was later replicated with the effective control , and likely elimination , of accidentally-introduced Rhodnius prolixus populations from Central America and southern Mexico [24] , [25] , [44]–[46] . However , and ironically , some of the most problematic territories , where the disease is highly endemic and its principal vector , T . infestans , is a widespread native pest , did not implement large-scale control programs until the late 1990s . This was the case of the Department of Cochabamba . Unfortunately , no systematic control measures are currently in place in some areas of the Gran Chaco where T . infestans is also the main vector [22] , [62] . In parts of Mexico , Colombia , Venezuela , Ecuador , or Peru , important vector species such as T . infestans , T . dimidiata or R . prolixus are still commonly found infesting dwellings [62] . Highly coordinated vector control campaigns such as those described here and elsewhere ( e . g . , [23]–[25] , [43]–[46] ) are urgently needed in all these countries and territories . Our appraisal demonstrates that ‘classical’ area-wide vector control campaigns have a crucial role to play in the endemic settings where resource-limited communities endure the highest risk of Chagas disease . Yet , by showing that residual dwelling infestation is relatively common despite intensive and highly effective control efforts , our findings also underscore the need for fully operational , long-term entomological-epidemiological surveillance systems [28] . This will require judicious , far-reaching public health policies capable of galvanizing sustained ( and sustainable ) preventive action [10] , [22] , [28] , [62] . In Cochabamba , the relatively high rates of residual infestation in the municipality of Cercado , which includes the densely populated capital city , are particularly worrying; determining the relative importance of control failures ( e . g . , due to operational constraints or insecticide resistance ) and true re-infestation of successfully-treated dwellings by wild vectors should be given high priority . | Chagas disease is among the most serious public health problems in Latin America; the highest prevalence of infection by its causative agent , the parasite Trypanosoma cruzi , has historically been recorded in some parts of Bolivia . In the early 2000s , a massive insecticide-spraying program was set up to control dwelling infestation by the blood-sucking bugs that transmit the disease . Here we provide a detailed assessment of the effects of this program in the Department of Cochabamba , one of the most highly-endemic settings worldwide . Our analyses show that municipality-level dwelling infestation rates plummeted from over 70–80% in 2001–2003 to about 2–3% in 2004–2011 . This residual infestation was higher in the capital city and , more generally , in municipalities where montane dry forests dominate – probably because wild populations of the main vector , Triatoma infestans , are common in that eco-region . Despite the impressive early achievements of the program , with about 0 . 5 million people protected from contagion , sustained disease control will require fully operational long-term surveillance systems . | [
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] | 2014 | Chagas Disease Vector Control in a Hyperendemic Setting: The First 11 Years of Intervention in Cochabamba, Bolivia |
In long-term potentiation ( LTP ) , one of the most studied types of neural plasticity , synaptic strength is persistently increased in response to stimulation . Although a number of different proteins have been implicated in the sub-cellular molecular processes underlying induction and maintenance of LTP , the precise mechanisms remain unknown . A particular challenge is to demonstrate that a proposed molecular mechanism can provide the level of stability needed to maintain memories for months or longer , in spite of the fact that many of the participating molecules have much shorter life spans . Here we present a computational model that combines simulations of several biochemical reactions that have been suggested in the LTP literature and show that the resulting system does exhibit the required stability . At the core of the model are two interlinked feedback loops of molecular reactions , one involving the atypical protein kinase PKMζ and its messenger RNA , the other involving PKMζ and GluA2-containing AMPA receptors . We demonstrate that robust bistability–stable equilibria both in the synapse’s potentiated and unpotentiated states–can arise from a set of simple molecular reactions . The model is able to account for a wide range of empirical results , including induction and maintenance of late-phase LTP , cellular memory reconsolidation and the effects of different pharmaceutical interventions .
In his address to the Royal Society in 1894 , Santiago Ramon y Cajal hypothesized that the brain stores information by adjusting the strengths of associations between neurons , as well as by growing new connections [1] . In the years since , the existence of both of these mechanisms , now known as synaptic plasticity and synaptogenesis , respectively , has been well established , and there is ample evidence that synaptic plasticity plays an important role in learning and memory [2–4] . Neurons communicate by transmitting signals across chemical synapses , where presynaptic axon terminals connect to postsynaptic neurons , most often on their dendrites . When a nerve impulse ( action potential ) arrives at the axon terminal , neurotransmitter molecules are released into the synaptic cleft , a narrow gap between the two neurons , where they activate receptors in the membrane of the postsynaptic neuron . This sets in motion a series of biochemical events in the postsynaptic neuron , the details of which depend on the type of receptor , among other factors . Synaptic strength depends both on the amount of transmitter that is released by the arrival of a nerve impulse at the axon terminal and on the number and sensitivity of the receptors . It may thus be regulated on either the pre- or postsynaptic side , and mechanisms of synaptic plasticity have been shown to operate in both compartments [3] . Plasticity may either strengthen or weaken a synapse , and the effect may be short-lived or long-lasting . Short-term synaptic plasticity , lasting from milliseconds to minutes , is primarily due to presynaptic mechanisms that adjust the amount of transmitter release , whereas postsynaptic modifications that adjust the number and sensitivity of receptors are important for long-term plasticity [4] . In particular , this is true of long-term potentiation ( LTP ) , a type of persistent strengthening of synapses in response to stimulation [5 , 6] , which has been studied extensively in the CA3-CA1 synapses of the rodent hippocampus [4] and is known to depend on an increase in the number of receptors inserted in the postsynaptic membrane [7] . There are at least two forms of LTP: Moderately strong stimulation induces early-phase LTP ( E-LTP ) , which persists for at most a few hours . When the stimulation is stronger , E-LTP may be followed by late-phase LTP ( L-LTP ) , which can last for days , months or longer [7 , 8] and is believed to be an important mechanism for the storage of long-term memories [9 , 10] . The establishment of L-LTP , known as synaptic or cellular memory consolidation , is a process that takes less than an hour [11 , 12] and requires synthesis of new protein . This has been demonstrated by showing that infusion of protein-synthesis-inhibiting drugs such as anisomycin can prevent establishment of L-LTP [12–15] . On the behavioral level , protein synthesis inhibition ( PSI ) has been shown to impair the formation of long-term memory , consistent with the notion of L-LTP as a memory mechanism [16] . Once long-term memory is established , it is in general no longer vulnerable to infusion of a protein synthesis inhibitor [16] . However , memory retrieval can induce a state of transient instability , during which the memory is again susceptible to protein synthesis inhibition [17–19] . This susceptibility of memory to post-retrieval PSI infusion has been shown to correlate with instability of L-LTP at the neural level [20 , 21] , providing further evidence of the importance of LTP as a mechanism of long-term memory . The synaptic destabilization that is triggered by memory retrieval is followed by a period of restabilization which has similarities with the initial synaptic consolidation that follows memory acquisition . It has therefore become known as memory reconsolidation [19] , more specifically synaptic ( or cellular ) reconsolidation , to avoid confusion with the related but distinct phenomenon systems reconsolidation , a temporary dependence on the hippocampus for restabilization of a memory after reactivation ( retrieval ) . For reviews of reconsolidation research , see [22–24] . For a computational model of systems consolidation and reconsolidation , see [25] . In this report , we focus on L-LTP induction and maintenance at glutamatergic synapses , the most abundant type of synapse in the vertebrate nervous system [26 , 27] . Glutamatergic synapses contain several kinds of receptors that are activated by the neurotransmitter glutamate . Of particular interest for LTP are the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor ( AMPA receptor or AMPAR ) , which mediates synaptic transmission [28] , and the N-methyl-D-aspartate receptor ( NMDA receptor or NMDAR ) , which is involved with regulatory functions including the regulation of synaptic strength [29 , 30] . AMPARs are ion channels that open when activated by the neurotransmitter glutamate . The opening of the channel allows positively charged ions , mainly sodium and potassium , to flow through the cell membrane [31] . This causes a partial depolarization of the membrane , which at rest is polarized by a net negative charge inside the cell . The partial depolarization is known as an excitatory postsynaptic potential , or EPSP , and the amplitude of the EPSP produced by a single action potential arriving at a synapse is a measure of synaptic strength . Among other factors , the EPSP amplitude depends on the number of AMPARs inserted in the postsynaptic density ( PSD ) , the area of cell membrane that constitutes the receiving side of the synapse [31] . Thus mechanisms that control the trafficking of AMPARs into and out of the PSD play an important part in the regulation of synaptic strength . AMPARs are heterotetramers , i . e . they consist of four non-identical subunits . The subunits are of four different kinds , named GluA1 , GluA2 , GluA3 and GluA4 , and AMPARs can be made up of different combinations of these [32] . GluA2 is of particular interest here , because L-LTP is associated with an increase in the number of GluA2-containing AMPARs inserted in the PSD [20 , 33 , 34] . AMPA receptors are not permanently inserted in the PSD , but are constantly being recycled . Certain proteins transport AMPARs into the PSD from pools maintained in adjacent areas , while others remove them ( a process known as internalization or endocytosis ) and either recycle them to stand-by pools or mark them for degradation [35 , 36] . Many proteins have been implicated in the induction and maintenance of LTP , including CaMKII , PKA , MAPK and several isoforms of PKC ( for a review , see [7] ) . An atypical isoform of PKC , protein kinase Mζ ( PKMζ ) , is believed to play an important role for L-LTP . The level of PKMζ has been shown to increase as the result of NMDA receptor stimulation [37 , 38] , consistent with its proposed role in L-LTP induction . Inhibition of PKMζ activity results in disruption of established L-LTP [39–41] , and perfusion of PKMζ into a neuron can induce L-LTP [39] . PKMζ activity is believed to increase the number of inserted GluA2-containing AMPARs at the synapse both by facilitating the trafficking of these receptors into the PSD and by inhibiting their removal [42] . GluA2-containing AMPARs are held at extrasynaptic pools by the protein PICK1 which binds to the GluA2 subunit [34] . PKMζ facilitates interaction between the trafficking protein NSF and the GluA2 subunit , which results in its release from PICK1 , freeing the AMPARs to diffuse laterally into the PSD [34] . Furthermore , once GluA2-containing AMPARs are inserted in the PSD membrane , PKMζ prevents their removal by inhibiting the interaction between the protein BRAG2 and the GluA2 subunit [43] , an interaction that plays a key part in endocytosis of GluA2-containing AMPARs [42 , 44] . While GluA2-containing AMPARs are important for the stabilization of L-LTP , there is evidence that GluA2-lacking AMPARs play an important role in the induction of early-phase LTP ( E-LTP ) , and also in reconsolidation . Several studies have shown that GluA2-lacking AMPARs are initially inserted at the time of memory acquisition or LTP induction , and then gradually replaced by GluA2-containing AMPARs during consolidation [45–47] . Hong et al . [20] showed that memory reactivation triggers an abrupt replacement of GluA2-containing AMPARs by GluA2-lacking AMPARs . This is followed by a gradual reversal , i . e . the GluA2-containing AMPARs are restored and the number of GluA2-lacking AMPARs declines , as the potentiated state of the synapse is restabilized [20] . Because the temporary removal of GluA2-containing AMPARs is compensated for by an increase in GluA2-lacking AMPARs , the synaptic strength remains more or less constant during the period of instability [20] . Rao-Ruiz et al . [21] reported similar results , although they observed a brief period of reduced synaptic strength between the GluA2-containing AMPAR removal and GluA2-lacking AMPAR insertion . Taken together , these results suggest that the stabilization of LTP , both initially during consolidation , and after reactivation-induced destabilization , requires insertion of GluA2-containing AMPARs , and that PKMζ plays an important role in maintaining the GluA2-containing AMPARs at the synapse . An important question is how L-LTP , which can last for months or longer [8] , can be maintained by a protein like PKMζ , with a half-life that probably does not exceed several hours or at most a few days [48–51] . A proposed answer to this question involves local translation of messenger RNA ( mRNA ) in or near dendritic spines . Most synapses are formed at dendritic spine heads , with one synapse per spine [52] . It has been shown that PKMζ mRNA is transported from the cell body to dendrites [53 , 54] , but the mRNA in its basal state is translationally repressed by molecules that bind to it , or to the complex of proteins required to initiate translation [50 , 53 , 55] . There is evidence that PKMζ catalyzes reactions that lift this translational block [49 , 56] , possibly through inhibition of the PIN1 protein [42] , resulting in a positive feedback loop [49] . By promoting its own synthesis in this manner , PKMζ may be able to remain at an increased level , and thus maintain L-LTP , for a long time , perhaps indefinitely . It has also been suggested that the increased amount of inserted GluA2-containing AMPARs at a potentiated synapse captures the PKMζ molecules and keeps them from dissipating away from the synaptic compartment [42] . This hypothesis is supported by several studies that show that blocking endocytosis of GluA2-containing AMPARs can prevent depotentiation under protocols that otherwise cause disruption of L-LTP [21 , 33 , 57] . Together with PKMζ’s inhibiting effect on AMPAR endocytosis this constitutes a second feedback loop , a reciprocal relationship in which PKMζ and GluA2-containing AMPARs prevent each other’s removal from the synapse . As we shall see , the interaction between these two feedback loops plays a central role in our explanation of synaptic bistability , that is that synapses have two stable equilibrium states , unpotentiated and potentiated . Transient stimuli can cause a synapse to transition between these two states , but in the absence of such signals it tends to remain in one state or the other . The notion that L-LTP is an important neural correlate of long-term memory ( LTM ) has been supported experimentally by demonstrating that pharmacological interventions that block L-LTP induction also interfere with the establishment of LTM [58] , and that interventions that disrupt established L-LTP also impair consolidated memories [59] . Here we consider three types of pharmaceuticals that have been shown to produce significant results with respect to both L-LTP induction and maintenance , and to related behavior-level memory phenomena . The findings described above suggest a model of L-LTP maintenance with two connected feedback loops: ( 1 ) PKMζ maintains its own mRNA in a translatable state and translation of the mRNA in turn replenishes PKMζ . ( 2 ) PKMζ maintains GluA2-containing AMPARs at the synapse , and these in turn keep PKMζ molecules from dissipating away from the synaptic compartment . Below we describe a computational model that incorporates these relationships and investigate its ability to account for results reported in the empirical literature .
Systems of chemical reactions can be modeled either by deterministic methods based on ordinary differential equations ( ODEs ) or by stochastic simulation . When the numbers of molecules are small , stochastic simulation is the better choice , because random fluctuations then have significant effects that are not captured by deterministic methods [69] . In particular , random fluctuations can cause a small system to spontaneously transition from one steady state to another; the resulting impact on system stability can be studied in a stochastic simulation , but not in a deterministic model [70] , because the latter only accounts for average reaction rates over a large number of molecules . The molecules of interest for our simulation are present in small numbers in a dendritic spine head , e . g . fewer than a hundred PKMζ molecules ( see S1 Text ) and at most ca 150 AMPARs [71 , 72] . This is well below the size of system that can be realistically simulated by deterministic methods [70 , 73] . We therefore base our simulation on the Gillespie algorithm [74] , a well-established and widely used approach to discrete and stochastic simulation of reaction systems [69 , 70 , 73] . The model consists of four inter-dependent pairs of processes ( see Fig 1 ) : The model is implemented as a C++ program and all simulations were executed on an Intel i5-2400 computer running the Debian Linux 8 . 4 operating system . Our computational model simulates the regulation of PKMζ concentration at the postsynaptic density and its role in the induction and maintenance of L-LTP . The goal for the model is to simulate the empirical results described in the introduction and summarized in Table 3 below . Most of the cited results are from studies of Schaffer collateral synapses on CA1 pyramidal neurons in the rat or mouse hippocampus , a few refer to unspecified hippocampal regions or amygdala of rat or mouse .
We model the result of strong NMDAR stimulation as a rapid increase of the population of active E1 enzyme molecules . This causes the translational repression of PKMζ mRNA to be lifted ( reactions 35–37 ) and synthesis of PKMζ to start ( reaction 7 ) . Fig 2 shows a trace of the number of PKMζ molecules , active PKMζ mRNA molecules and GluA2-containing AMPARs inserted in the PSD during a single simulation run . The model has two stable states: an unpotentiated state in which there are very few active mRNA molecules , PKMζ molecules and inserted GluA2-containing AMPARs , and a potentiated state with significantly higher levels of each of these molecules . The brief spike of E1 enzyme lifts the translational repression of enough PKMζ mRNA molecules to trigger a transition to the potentiated state . Although the molecule numbers fluctuate in the potentiated state , it is in fact very stable: No spontaneous depotentiation events are observed even when the model is allowed to run for a full year of simulated time . Fig 3 shows mean molecule counts for 100 simulations of L-LTP induction . It takes the model between 30 and 60 minutes of simulated time to complete the switch to its upper ( potentiated ) steady state in which there is a high number of inserted GluA2-containing AMPARs . This is consistent with the observed duration of the cellular consolidation window [16 , 58] . Simulated PSI infusion prevents NMDAR stimulation from inducing L-LTP , ( Fig 4 ) . Although the spike of activated E1 enzyme releases the translational block of mRNA , resulting in a high level of activated PKMζ mRNA ( RA in the model ) , translation is prevented by the protein synthesis inhibitor , and PKMζ synthesis is not initiated [9 , 37] . When the E1 enzyme returns to its inactive form the mRNA becomes repressed again , and the model remains in its unpotentiated state . Like the potentiated state , the unpotentiated state is very stable: No spontaneous potentiation events are observed even when running the model for a year of simulated time . By introducing a variable delay between stimulation and PSI infusion , we can study the model’s consolidation window , the time interval after induction during which PSI prevents establishment of L-LTP . As shown in Fig 5 , when the delay before PSI infusion is 20 minutes or less , the model consistently settles in the lower ( unpotentiated ) steady state with zero or very few inserted GluA2-containing AMPARs . When the delay is 50 minutes or more , the model settles in the upper ( potentiated ) state where the number of inserted GluA2-containing AMPARs fluctuates between ca 60 and 100 ( cf . Fig 2 ) . With intermediate delays , the probability of settling in the upper state gradually increases with increasing delay . The model’s consolidation window is thus in the range 30 to 45 minutes , consistent with empirical results [11 , 12] . Fig 5 illustrates the model's bistable character: It settles either in the unpotentiated or potentiated state , never in the region with intermediate numbers of inserted GluA2-containing AMPARs . See also Fig 3 and Fig 4 . ZIP application during stimulation and the first 10 minutes thereafter after does not prevent L-LTP induction , ( Fig 6 ) . Presence of ZIP during the first ten minutes after stimulation does not prevent L-LTP induction [65] . The stimulation lifts the translational block and PKMζ production gets started . Even though PKMζ’s enzymatic activity is inhibited , the mRNA stays activated long enough to ride out the ZIP activity . When the ZIP is washed out , PKMζ becomes active and drives the synapse into its potentiated state . L-LTP can be induced by diffusion of PKMζ into a neuron [39 , 41] . We simulate infusion by rapidly increasing the number of PKMζ molecules in the synaptic compartment to 100 . This causes the model to settle into its potentiated state , ( Fig 7 ) . The same level of PKMζ infusion that induces L-LTP in the previous experiment ( 100 molecules ) fails to do so in the presence of PSI ( Fig 8 ) . Although the PKMζ infusion initially causes a temporary increase in the number of inserted GluA2-containing AMPARs , the PSI prevents replenishment to compensate for PKMζ degradation and dissipation and the model returns to its unpotentiated state . This result , though plausible , has not been demonstrated in a published experiment . It thus constitutes a prediction of the model . Fonseca et al . [12] demonstrated that suppressing protein synthesis for 100 minutes by bath application of anisomycin did not disrupt established L-LTP . Fig 9 shows the results of simulating this experiment in our model . The interruption of protein synthesis causes the number of PKMζ molecules to drop , which in turn leads to a transient decline in the number of inserted GluA2-containing AMPARs , but the system recovers when the PSI is removed . If the model is correct , then the transient decrease in the number of GluA2-containing AMPARs may be detectable as a reduced EPSP current after PSI application . However , it is possible that the temporary removal of GluA2-containing AMPARs is compensated for by insertion of GluA2-lacking AMPARs , similarly to what has been shown to happen during retrieval-induced destabilization [45] , in which case the synaptic strength would be maintained . If this is the case , then it may instead be possible to detect a transient increase in rectification index , because GluA2-lacking AMPARs , but not GluA2-contaning ones , are characterized by a slight inward rectification [20 , 45] . Our model thus predicts that one or the other of these two effects ( EPSP reduction or rectification ) should be detectable after PSI application during L-LTP maintenance . The effect of memory reactivation is simulated as a brief spike in the amount of active E2 enzyme ( Fig 10 ) . This results in rapid endocytosis of the inserted GluA2-containing AMPARs [20 , 21] and release of the bound PKMζ molecules which then start to dissipate . However , due to continued synthesis , the PKMζ level is kept from dropping below threshold and the model settles back into the potentiated steady state [18 , 79] . Although the population of inserted GluA2-containing AMPARs is almost completely depleted after reactivation , the levels of PKMζ and active PKMζ mRNA stay well above their depotentiation thresholds and the model reliably recovers from post-reactivation instability ( reconsolidation ) , unless challenged by simulated pharmacological interventions ( see below ) . As mentioned earlier , Hong et al . demonstrated this abrupt decrease of inserted GluA2-containing AMPARs after memory retrieval , as well as a corresponding transient increase of GluA2-lacking AMPARs , which maintained the synaptic strength during the labile period [20] . Simulation of PSI infusion simultaneously with reactivation , or shortly thereafter , causes disruption of L-LTP ( Fig 11 ) . In the absence of new protein synthesis , the PKMζ level drops below threshold and the model settles into its unpotentiated state [18 , 79] . By varying the delay between reactivation and PSI infusion , we can establish the model’s reconsolidation window , the time interval after reactivation during which L-LTP is vulnerable to PSI . As shown in Fig 12 , if PSI infusion is applied 15 minutes or less after reactivation , then the model reliably switches to its lower ( unpotentiated ) steady state with few inserted GluA2-containing AMPARs , but with a delay of 30 minutes or more , L-LTP disruption does not result: the model remains in its potentiated state where the number of inserted GluA2-containing AMPARs fluctuates in the 60–100 range . The model’s reconsolidation window is thus in the range 20 to 30 minutes , consistent with empirical results [18 , 84] . When the GluA23Y peptide is infused together with PSI after reactivation , it prevents the disruption of L-LTP that PSI otherwise causes [57 , 63] . As before , reactivation triggers activation of the E2 enzyme , but here the GluA23Y peptide blocks its endocytotic effect . As a result , the GluA2-containing AMPARs remain inserted and although the PSI stops synthesis of new PKMζ , the existing population of PKMζ molecules , bound to the inserted GluA2-containing AMPARs , declines at a slow enough rate to maintain the synapse in its potentiated state while the PSI wears off ( Fig 13 ) . Infusion of ZIP during L-LTP maintenance causes rapid depotentiation [39–41] . ZIP inhibits PKMζ enzymatic activity , including both the catalysis of its own synthesis and the maintenance of an increased level of inserted GluA2-containing AMPARs in the PSD . The result is rapid removal of GluA2-containing AMPARs and depletion of PKMζ , and the synapse quickly settles into its unpotentiated state ( Fig 14 ) . The minimum duration of ZIP application needed to reliably disrupt L-LTP in the model is around 30 minutes . When the GluA23Y peptide is infused together with ZIP during L-LTP maintenance , the disruptive effect of ZIP is blocked [33] . As before , ZIP inhibits PKMζ’s catalysis of its own synthesis as well as its facilitation of AMPAR trafficking into the PSD and its blocking effect on BRAG2-induced endocytosis of GluA2-containing AMPAR . But in this case , even though BRAG2 remains active , the presence of GluA23Y prevents it from inducing endocytosis of the inserted GluA2-containing AMPARs . As a result , the GluA2-containing AMPARs remain in the PSD and continue to maintain the PKMζ molecules at the synapse . The number of PKMζ molecules declines only slowly and the potentiation is able to survive through the 12-hour period of ZIP activity ( Fig 15 ) .
Clopath et al . [89] describe a mathematical model of synaptic tagging and capture ( STC ) [90] , wherein mechanisms of tag-setting and triggering of protein synthesis interact with a bistable process that maintains potentiation . Although the authors suggest that one of the model’s parameters may represent the level of PKMζ activity , the mechanisms of the process are unspecified , and the model therefore cannot account for the results targeted by our model: the effects of PSI , ZIP and GluA23Y in the contexts of L-LTP induction and maintenance , or of memory reactivation . A simple model by Ogasawara and Kawato [86] simulates L-LTP induction and maintenance as well as reconsolidation based on the interactions of only three molecules: PKMζ , PKMζ mRNA and F-Actin . It is , however , not able to account for most of the results addressed in this paper . A paper by Zhang et al . [88] features a dual-loop model of LTP that exhibits windows of susceptibility to PSI after induction and reactivation as well as vulnerability to a kinase inhibitor in the maintenance phase . The relationship between the kinase and AMPA receptors is not modeled , and thus the ability of an endocytosis blocker like GluA23Y to rescue L-LTP is not accounted for . Also , the kinase modeled in [88] is unnamed but characterized by auto-activation rather than persistent activity , and should therefore probably not be interpreted as PKMζ . Smolen et al . [87] model synaptic tagging and capture , including “cross-tagging” between LTP and LTD . As in our model , synaptic stability is based on PKMζ’s ability to catalyze its own synthesis . Unlike our model , [87] does not account for the effects of protein synthesis inhibition , kinase inhibition , reactivation or the ability of endocytosis blocking to rescue L-LTP . A paper by Jalil et al . [85] models PKMζ regulation at the synapse , with a focus on compensatory interactions between PKMζ and a second atypical PKC isoform , PKCι/λ . Bistability is achieved by combining the PKMζ auto-catalytic synthesis feedback loop with auto-phosphorylation . The model predicts the differential effects of ZIP and PSI at L-LTP induction and maintenance , but does not account for L-LTP rescue by AMPAR endocytosis blocking , nor for reconsolidation . Our model represents a subset of the mechanisms believed to be involved in LTP induction and maintenance [3 , 91] . Some processes not included in our model are: The processes that we have modeled thus form a subset of a more complex machinery . Nevertheless , it is interesting to note that this relatively simple model is able to account for many of the empirical findings regarding the role of PKMζ in L-LTP induction and maintenance , and to exhibit the degree of stability required for a neural mechanism to support long-lasting memories . | The brain stores memories by adjusting the strengths of connections between neurons , a phenomenon known as synaptic plasticity . Different types of plasticity mechanisms have either a strengthening or a weakening effect and produce synaptic modifications that last from milliseconds to months or more . One of the most studied forms of plasticity , long-term potentiation , is a persistent increase of synaptic strength that results from stimulation and is believed to play an important role in both short-term and long-term memory . Researchers have identified many proteins and other molecules involved in long-term potentiation and formulated different hypotheses about the biochemical processes underlying its induction and maintenance . A growing number of studies support an important role for the protein PKMζ ( protein kinase M Zeta ) in long-term potentiation . To investigate the explanatory power of this hypothesis , we built a computational model of the proposed biochemical reactions that involve this protein and ran simulations of a number of experiments that have been reported in the literature . We find that our model is able to explain a wide range of empirical results and thus provide insights into the molecular mechanisms of memory . | [
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] | 2018 | Coupled feedback loops maintain synaptic long-term potentiation: A computational model of PKMzeta synthesis and AMPA receptor trafficking |
The human immunodeficiency virus type 1 ( HIV-1 ) envelope glycoprotein gp120 is a vaccine immunogen that can signal via several cell surface receptors . To investigate whether receptor biology could influence immune responses to gp120 , we studied its interaction with human , monocyte-derived dendritic cells ( MDDCs ) in vitro . Gp120 from the HIV-1 strain JR-FL induced IL-10 expression in MDDCs from 62% of donors , via a mannose C-type lectin receptor ( s ) ( MCLR ) . Gp120 from the strain LAI was also an IL-10 inducer , but gp120 from the strain KNH1144 was not . The mannose-binding protein cyanovirin-N , the 2G12 mAb to a mannose-dependent gp120 epitope , and MCLR-specific mAbs inhibited IL-10 expression , as did enzymatic removal of gp120 mannose moieties , whereas inhibitors of signaling via CD4 , CCR5 , or CXCR4 were ineffective . Gp120-stimulated IL-10 production correlated with DC-SIGN expression on the cells , and involved the ERK signaling pathway . Gp120-treated MDDCs also responded poorly to maturation stimuli by up-regulating activation markers inefficiently and stimulating allogeneic T cell proliferation only weakly . These adverse reactions to gp120 were MCLR-dependent but independent of IL-10 production . Since such mechanisms might suppress immune responses to Env-containing vaccines , demannosylation may be a way to improve the immunogenicity of gp120 or gp140 proteins .
One approach to a vaccine against HIV-1 is the use of the viral envelope glycoproteins ( Env ) as immunogens to induce neutralizing antibodies ( NAbs ) [1–3] . Usually , the Env glycoproteins are presented as adjuvanted , soluble proteins after production in vitro as recombinant proteins , but they can also be expressed in vivo from delivery systems based on DNA or live recombinant viruses ( e . g . , poxvirus or adenovirus vectors ) [4] . Different configurations of Env glycoproteins have been studied as vaccine antigens , initially the surface glycoprotein gp120; more recently , soluble oligomeric gp140 proteins based broadly on the native gp120-gp41 complex [1–3] . Irrespective of how HIV-1 Env glycoproteins have been presented and in whatever configuration , the induction of broadly active NAbs has proven problematic [1] . One generally accepted problem is the evolution of the native Env complex into a configuration that limits the exposure of the few neutralization sites that are present . The potential solution is to further understand the structure of the complex , then to engineer antigens that are better able to present relevant NAb epitopes to the immune system; attempts to do this are in progress in many laboratories worldwide [1] . Here , however , we focus on what we consider to be another factor hindering NAb induction: the limited immunogenicity of HIV-1 Env proteins in general . Although antibody responses to HIV-1 Env can clearly be induced in infected or vaccinated humans and animals , these proteins are not particularly immunogenic . Thus , gp120 or gp140 proteins are typically administered at 100–500 μg per dose , and the binding antibody titers raised against them can be highly variable; some humans and animals respond fairly well , others only poorly [5–9] . Anti-Env antibody titers also decay rather rapidly ( half-lives are typically in the range 30–50 d ) and frequent boosting is required to maintain them . Few directly comparative studies have ever been performed , but the limited information available supports the contention that Env is an unusually problematic immunogen compared to most other vaccine antigens [10] ( S . Plotkin and B . Graham , personal communication ) . The immune responses to HIV-1 Env vaccine antigens are TH2-polarized to an extent that is unusual even for a soluble protein [11 , 12] . The same TH2 bias can also be observed during HIV-1 infection , although this is a much more complex and controversial situation [13–15] . The nature of the immune response to gp120 may be attributable to the fundamental properties of this unusual protein . One feature that distinguishes gp120 from many other vaccine immunogens is its biological activity; gp120 can bind to several cell surface receptors: CD4 , CCR5 , CXCR4 , and several mannose C-type lectin receptors ( MCLR ) including but not limited to DC-SIGN [2] . In vitro , one consequence of gp120 binding to such receptors is the transduction of intracellular signals that can have many different , but generally adverse , effects on the various target cells . Although the gp120 concentrations used to elicit such signals ( μg/ml range ) are usually grossly in excess of what could be present in serum during HIV-1 infection [16] , they are compatible with what is used for immunization ( several hundred μg of protein delivered in a few ml into a localized tissue site ) [5–9] . We therefore considered it possible that gp120 immunization could trigger signals affecting how an immune response develops . For example , one cellular response to gp120 in vitro is the induction of IL-10 , an anti-inflammatory cytokine [17–24] . Here , we have studied what happens when gp120 interacts with human monocyte-derived dendritic cells ( MDDCs ) in vitro . We show that a consequence of JR-FL gp120 binding to these cells from ∼50% of donors is the induction of IL-10 . Moreover , gp120-treated MDDCs impair the proliferation of co-cultured CD4+ T cells and reduce their expression of IL-12 . These responses are also a consequence of the mannose-dependent interaction of gp120 with an MCLR , although they are not obligatorily linked to IL-10 expression . The various outcomes of gp120-MCLR interactions are prevented by enzymatic removal of gp120 mannoses , a method that may improve the immunogenicity of HIV-1 Env proteins and some other vaccine-relevant immunogens .
We investigated how gp120 affected MDDC maturation and cytokine secretion , and MDDC-T cell interactions in view of the key role dendritic cells ( DCs ) play in antigen capture , processing , and presentation . The preparation and properties of the MDDCs are described under Supporting Information ( Figure S1 ) . We were particularly interested to ascertain whether gp120 induced IL-10 expression in MDDCs , in view of the immunosuppressive effects of IL-10 and its role in TH2-polarization of responses to gp120 in immunized mice [11] , and the induction of IL-10 by gp120 in human monocyte/macrophages in vitro [17 , 18 , 20 , 22 , 24] . We therefore used MDDCs that were immature at the start of the experiment ( iMDDCs ) , to enable us to monitor the subsequent maturation process . However , in some studies , we investigated the effects of gp120 on MDDC that were simultaneously induced to mature by other stimuli , notably lipopolysaccharide ( LPS ) . iMDDCs from a day-6 culture were washed thoroughly to prevent further stimulation with IL-4 and GM-CSF , then incubated for two further days with or without CHO-cell expressed , JR-FL ( R5 ) gp120 ( the 3 μg/ml; 25 nM concentration was based on titrations in pilot studies; see below ) . In the absence of any stimulus , the cells produced little IL-10 ( mean 11 ± 2 . 5 pg/ml at 24 h , n = 71 and 28 ± 3 . 8 pg/ml at 48 h , n = 52 ) and no detectable IL-12p70 over a 48-h period starting on day 6 . The addition of JR-FL gp120 triggered significant IL-10 secretion from MDDCs from a subset of the 71 blood donors ( Figure 1A ) . Thus , 24 h later , IL-10 production was increased by >5-fold in MDDCs from 62% ( 44/71 ) donors , with the median increase being 8 . 5-fold ( median control value: 7 . 5 pg/ml; median + gp120 , 64 pg/ml ) . Similar responses were observed at 48 h ( median control value: 17 pg/ml; + gp120 , 98 pg/ml ) . The IL-10 increases triggered by gp120 were significant at both 24 h and 48 h ( Mann-Whitney U test , one tail , p < 0 . 0001 ) . However , MDDCs from 38% of the donors did not respond to gp120 ( IL-10 increases of <5-fold ) . Although a subset was unresponsive to gp120 , day-6 iMDDCs from every donor reacted to the classic TNIL + LPS ( +CD40L when IL-12p70 was analyzed ) maturation stimulus by producing high levels of both IL-10 ( mean 1 , 639 ± 665 pg/ml , n = 71 ) and IL-12p70 ( mean 235 ± 56 pg/ml , n = 12 ) over a 48-h period ( Figure 1A and unpublished data ) . The median fold-increase in IL-10 production in response to TNIL + LPS after 24 h was 149-fold , 17 . 5 times greater than the median response to gp120 . The IL-10 responses to TNIL + LPS and to gp120 did not correlate ( at 24 h , n = 71 , r2 = 0 . 0007 and at 48 h , n = 12 , r2 = 0 . 006 , respectively ) . The time courses of the IL-10 responses to JR-FL gp120 , at both the mRNA and protein levels , and to TNIL + LPS at the mRNA level , are shown as Figure S2 . The donor-dependent variation in the IL-10 response to gp120 could be explained by genetic or epigenetic factors . As a first step to determining which applied , we performed experiments on MDDCs from 11 repeat donors , at two time points , 1–3 months apart . An IL-10 response to gp120 was observed in MDDCs from four of the 11 donors at both time points , whereas there was no response at either time point from cells of the other seven donors ( Figure 1B ) . The consistency of the response pattern is more suggestive of a genetic or a constant epigenetic determinant than of a variable epigenetic factor such as , for example , an inter-current infection . IL-10 secretion by MDDCs from responsive donors was dependent on the concentration and the identity of the gp120 protein used ( Figure 1C ) . The optimal response to JR-FL gp120 occurred at 3 μg/ml , whereas the dose-response curve for LAI gp120 was slightly different , IL-10 secretion being greatest at 10 μg/ml , the highest concentration tested . However , when MDDCs from the same donors were exposed to gp120 from the subtype A virus KNH1144 , there was no IL-10 response ( Figure 1C ) . Furthermore , when MDDCs from five donors were tested comparatively , the same three that responded to JR-FL gp120 also did so to LAI gp120 , and to similar extents , whereas MDDCs from all five donors responded to TNIL + LPS by producing high levels of IL-10 ( Figure 1D ) . Hence both viral and host genetics may influence whether MDDCs produce IL-10 after exposure to gp120 . We determined the viability of iMDDCs and mature MDDCs ( mMDDCs ) exposed for 48 h to the three different gp120s by staining with 7 amino-actinomycin D . Spontaneous cell death in cultures from different donors varied from 2%–3% in iMDDCs and 3%–12% in mMDDCs . The additional death of iMDDCS or mMDDCS measured in the presence of up to 10 μg/ml of JR-FL gp120 , with or without demannosylation ( see below ) , was <7%; for LAI gp120 , it was <5% at up to 20 μg/ml; for KNH1144 gp120 it was <5% at up to 10 μg/ml . The JR-FL , LAI , and KNH1144 proteins used in Figure 1C were all manufactured under good manufacturing process conditions and were essentially LPS-free . We also tested several additional gp120 proteins of different genotypes and expressed in different cell types ( including insect cells ) that we obtained from commercial sources and academic collaborators . In general , the degree of LPS contamination in these preparations was too high for the results to be interpretable , since LPS is itself a highly efficient inducer of IL-10 from MDDCs ( Figure 1 ) . To determine which gp120 receptors on iMDDCs were responsible for activating IL-10 expression , we incubated either gp120 or the cells with ligands that should block known gp120-receptor interactions ( Figures 2A , S3 , and S4 ) . Neither the b12 mAb to the CD4-binding site on gp120 nor sCD4 inhibited IL-10 production , implying that a gp120-CD4 interaction was not responsible . The small-molecule CCR5 antagonist AD101 was not inhibitory , ruling out signals transduced via gp120-CCR5 binding ( Figure S4 ) . The CXCR4 antagonist AMD3100 was inactive against IL-10 induction by gp120 from the X4 virus , LAI , so CXCR4 is also uninvolved ( unpublished data ) . As expected , AMD3100 did not inhibit the IL-10 response to JR-FL gp120 , or AD101 the response to LAI gp120 ( Figure S4 and unpublished data ) . In contrast , when gp120 was pre-treated with either mAb 2G12 or CV-N , IL-10 induction was strongly inhibited ( Figure 2A ) . Both 2G12 and CV-N bind to mannose moieties on gp120 N-linked glycans [25–27] , implicating an interaction between gp120 and an MCLR ( s ) as the critical trigger for IL-10 induction . We also tested whether soluble mannans antagonized gp120-dependent IL-10 expression , but found that mannans themselves strongly activated an IL-10 response ( Figure 2A ) . However , combining gp120 with mannans did not further elevate IL-10 levels , suggesting that both of these mannose-containing ligands bind to , and saturate , the same MCLR ( s ) . To explore which MCLR ( s ) might be involved , we used mAbs specific to DC-SIGN and the mannose receptor ( MR ) . The anti-DC-SIGN mAb AZN-D1 completely blocks the binding of mannosylated gp120 to DC-SIGN in an ELISA ( Figure 2B ) . Two mAbs to DC-SIGN , including AZN-D1 and mAb Clone 19 to the MR , can each reduce the binding of gp120 to a subset of tonsillar B cells [28] . When the anti-DC-SIGN and anti-MR mAbs AZN-D1 and Clone 15–2 were each pre-incubated with iMDDCs , AZN-D1 partially ( ∼50% ) reduced gp120-mediated IL-10 induction whereas Clone 15–2 was not inhibitory; adding the two mAbs together completely abolished the IL-10 response ( Figure 2A ) . An anti-CD4 mAb was not inhibitory by itself at 24 h and did not affect the actions of the anti-MCLR mAbs when combined with them , although it did cause partial ( 45% ± SD 29% ) inhibition at 48 h ( Figure 2A and unpublished data ) . Other than mannan , the various mAbs and ligands described above did not induce IL-10 expression or block LPS-induced IL-10 expression ( Figures 2A , S4 , and unpublished data ) . The same concentrations ( 40 μg/ml ) of different murine isotype control antibodies , alone and in combination , were also without effect ( Figure S4 and unpublished data ) . The above experiments imply that MCLRs , particularly but probably not only DC-SIGN , are the gp120 receptors that trigger the IL-10 response . If so , the high mannose residues on gp120 glycans are likely to be the cognate ligands . To prove this , the mannose moieties were removed from gp120 by enzymatic digestion with α- ( 1 , 2 , 3 , 6 ) -mannosidase [25] . Reducing SDS-PAGE gel analysis showed the demannosylated JR-FL gp120 ( D-gp120 ) was slightly smaller than mock-treated gp120 ( M-gp120; processed without the enzyme ) and had lost its 2G12 epitope ( Figure 2C; compare lanes marked + and − ) . The successful removal of mannose was verified by showing that D-gp120 failed to bind either 2G12 or DC-SIGN-Fc in ELISAs , in contrast to M-gp120 ( Figure 2B ) . However , both mAb b12 and CD4-IgG2 bound efficiently to structures associated with the CD4-binding site on D-gp120 ( Figure 2B ) , showing that JR-FL gp120 was efficiently demannosylated without impairing its overall conformation [25 , 27] . Hence we could now directly assess the role of the high mannose glycans in the IL-10 response . M-gp120 induced substantial IL-10 production ( 150–300 pg/ml ) from MDDCs from five different donors , whereas D-gp120 had no such effect . Influenza virus HA did not stimulate IL-10 production , whereas TNIL + LPS activated a strong response ( Figure 2D ) . An interaction between the mannose moieties on gp120 and an MCLR ( s ) can therefore trigger IL-10 production from MDDCs from a significant proportion of human donors . The lack of effect of HA , which does not bind to DC-SIGN , compared to gp120 is consistent with the outcome of comparative immunization studies with these two viral receptor-binding glycoproteins in mice [11] . The blocking effect of the anti-DC-SIGN and anti-MR mAb combination implicated these MCLRs as likely mediators of the IL-10 response to gp120 ( Figure 2A ) . Because this response is donor-dependent ( Figure 1A ) , we measured DC-SIGN and MR expression on day-6 iMDDCs ( the time of addition of gp120 ) from nine donors , as well as the expression of CD80 , CD83 , and CD86 . DC-SIGN levels varied by 18-fold among these nine donors , MR by 3 . 3-fold ( in studies of other sets of donors , we have found that the expression levels of both these receptors can vary by about an order of magnitude; unpublished data ) . The IL-10 response to JR-FL gp120 correlated with the level of DC-SIGN expression on day 6 ( n = 9 , r2 = 0 . 52 , p = 0 . 028 ) but not with MR expression ( n = 9 , r2 = 0 . 19 , p = 0 . 25 ) . Moreover , there were no correlations between IL-10 production and the expression of CD80 ( n = 9 , r2 = 0 . 052 , p = 0 . 59 ) , CD83 ( n = 9 , r2 = 0 . 33 , p = 0 . 14 ) , or CD86 ( n = 9 , r2 = 0 . 27 , p = 0 . 19 ) . Thus , of the five correlations with IL-10 production that we performed , the only substantial one was with DC-SIGN expression . We next sought to identify which signaling pathway ( s ) was involved in the upregulation of IL-10 expression after the gp120-MCLR interaction . We focused on the ERK1/2 and p38 MAP kinase pathways [29] , because ERK1/2 phosphorylation and activation promotes IL-10 production and inhibits IL-12 production by DC [30] , whereas inhibition of ERK1/2 activation has the opposite effects [31] . Conversely , p38 mediates the induction of IL-12p70 expression by LPS [29] . Furthermore , DC-SIGN ligation by antibodies can lead to ERK1/2 phosphorylation [32] . We observed that TNIL + LPS strongly activated the phosphorylation of both ERK1/2 and p38 within 5 min , effects that persisted for 30–60 min ( Figure 3A and unpublished data ) . A lesser , but still significant , level of ERK1/2 activation occurred after 5–10 min in MDDCs treated with M-gp120 , ERK1/2 phosphorylation levels then declining back to baseline after 30 min . However , there was no detectable phosphorylation of p38 in response to M-gp120 at any time point ( Figure 3A and unpublished data ) . D-gp120 , in contrast , failed to activate the phosphorylation of either ERK1/2 or p38 ( Figure 3A ) , implying that the gp120-MCLR interaction leads to the specific , albeit transient , activation of the ERK1/2 signaling pathway . In the same experiment , M-gp120 , but not D-gp120 , induced modest levels of IL-10 production , whereas TNIL + LPS + CD40L activated a much greater IL-10 response ( Figure 3B ) , observations in proportion to the levels of ERK1/2 activation induced by the different stimuli ( Figure 3A ) . We used signaling inhibitors to determine whether there is a link between ERK1/2 activation and IL-10 production . MDDCs were treated with 5 μM U0126 ( an ERK1/2 inhibitor ) or 10 μM SB 203580 ( a p38 MAP kinase inhibitor ) for 1–2 h , then IL-10 and IL-12p70 production in response to JR-FL M-gp120 or TNIL + LPS + CD40L were measured 24 h later . U0126 inhibited ERK1/2 phosphorylation by ∼60% ( Figure 3A ) , and the same compound reduced the IL-10 responses to both M-gp120 ( by 70% ) and TNIL + LPS + CD40L ( by ∼90% ) ( Figure 3B ) . SB 203580 had a negligible effect on M-gp120-stimulated IL-10 production but did inhibit the IL-10 response to TNIL + LPS by 70% ( Figure 3B ) . In view of the reciprocal effects of the ERK1/2 pathway on IL-10 and IL-12 production by DCs [30 , 31] , we also measured the IL-12p70 responses to M-gp120 and to TNIL + LPS . M-gp120 triggered a very slight increase in IL-12p70 expression . In contrast , TNIL + LPS activated a substantial IL-12p70 response that was completely blocked by SB 203580 but potentiated ( 4 . 7-fold ) by U0126 , a pattern consistent with a previous report [31] . Neither inhibitor , by itself , activated IL-10 or IL-12p70 production ( Figure 3B ) . Together , the use of signaling inhibitors implies that ERK1/2 activation is required for IL-10 production by MDDCs in response to either M-gp120 or TNIL + LPS . We used immunophenotypic analyses to investigate whether gp120 affects iMDDC maturation . Neither M-gp120 nor D-gp120 induced iMDDCs to mature in the absence of TNIL + LPS + CD40L; the cell-surface expression of no maturation marker changed by more than 1 . 5-fold ( unpublished data ) . However , expression of CD80 was reduced by 3-fold , CD83 by 7-fold , and CD86 by 2-fold when iMDDCs were incubated with M-gp120 together with TNIL + LPS + CD40L , compared with when the cells were matured with TNIL + LPS + CD40L alone . DC-SIGN expression was 2- to 3-fold greater on MDDCs treated with TNIL + LPS + CD40L plus M-gp120 than on cells receiving only TNIL + LPS + CD40L , but MR expression was unchanged . D-gp120 did not mimic the effects of M-gp120 on the expression of CD80 , CD83 , CD86 , and DC-SIGN , implicating an MCLR ( s ) as a mediator of these effects of gp120 ( Figure 4 ) . Furthermore , gp120 impaired the maturation of iMDDCs from both IL-10-responding and non-responding donors . The reduced expression of CD80 , CD83 , CD86 , and the increased expression of DC-SIGN did not correlate with IL-10 secretion 48 h after gp120 addition among the 15 donors tested , of which nine were IL-10 responders , six non-responders . There were no correlations: r2 = 0 . 05 for CD80 fold-decrease versus IL-10; r2 = 0 . 02 for CD83 fold-decrease versus IL-10; r2 = 0 . 00004 for the fold-increase in DC-SIGN expression versus IL-10 . The reduction in CD86 expression also did not correlate with IL-10 , r2 = 0 . 00003 . The interaction of gp120 with an MCLR ( s ) therefore partially blocks the TNIL + LPS + CD40L-induced maturation of iMDDCs that normally leads to increases in CD80 , CD83 , and CD86 expression and a reduction in DC-SIGN expression . These events occur irrespective of whether the gp120-treated cells produce IL-10 . We next explored whether the effects of gp120 on MDDC maturation ( and cytokine production , see Supporting Information ) would affect their ability to stimulate the proliferation of allogeneic T cells . To do this , M-gp120 or D-gp120 ( JR-FL ) was added to iMDDCs simultaneously with TNIL + LPS ( i . e . , on day 6 from the start of the MDDC culture ) . Influenza virus HA was used as a control antigen , also given simultaneously with TNIL + LPS . After the iMDDCs had been cultured with the various stimuli for 48 h , the cells were washed to remove any free gp120 or HA , then negatively selected for CD8 , CD14 , CD16 , CD19 , CD36 , CD56 , CD123 , TCRγ/δ , and CD235α . CFSE-labeled , allogeneic CD4+ T cells were then added ( the ratio of 1/10 was optimized for detection of T cell proliferation ) for a 5-d co-culture ( i . e . , from days 8–13 from the start of the MDDC culture ) . T cell proliferation was measured as the proportion of CFSE-negative cells . Flow-cytometric histograms supporting the data presented below are shown in Figure S5 . MDDCs treated with gp120 in the absence of TNIL + LPS did not stimulate T cell proliferation ( Figure 5A ) . However , exposing the TNIL + LPS-stimulated MDDCs to M-gp120 for 24–48 h reduced their ability to stimulate T cell proliferation by ∼65% . D-gp120 was less inhibitory , the ∼30% decrease being little different from the ∼20% decrease seen with the HA control antigen . M-gp120 depressed proliferation significantly more than did D-gp120 ( one-tailed Mann-Whitney U test , n = 15 , p < 0 . 0001 ) . MDDCs from all 15 donors tested ( ten IL-10 responders , five non-responders ) behaved similarly in the T cell proliferation assay; the relative proliferation of CD4+ T cells in co-cultures with M-gp120 + TNIL + LPS-treated MDDCs varied in a narrow range ( 60%–85% reduction in proliferation ) over a broad range of IL-10 responses ( 0–420 pg/ml ) ( Figure 5B ) . Overall , there was no correlation between IL-10 levels in the cultures of the gp120-treated , TNIL + LPS-stimulated MDDCs on day 8 and the inhibition of subsequent T cell proliferation ( % CFSE dilution versus IL-10 , r2 = 0 . 0008 ) . The lack of effect of IL-10 is not surprising because both cytokines and any stimulus for their continued secretion are washed out of the MDDC cultures before the T cells are added . However , the principal point is that , as with maturation marker expression , the MDDC phenotype is adversely affected by gp120 treatment , whether or not the cells have an immediate IL-10 response . We also found that JR-FL and KNH1144 gp120s each caused a ∼70% reduction in the capacity of LPS + TNIL-stimulated MDDCs from four donors to induce T cell proliferation ( similarly to what is shown for JR-FL gp120 in Figure 5A , unpublished data ) . Since KNH1144 gp120 does not induce IL-10 secretion from MDDCs ( Figure 1C ) , this experiment corroborates the finding that the anti-proliferative effect is independent of IL-10 from MDDCs ( Figure 5B ) . It also confirms that KNH1144 gp120 is biologically active in the MDDC system despite its inability to induce IL-10 expression . We measured the concentrations of both IL-10 and IL-12p70 in the various MDDC-T-cell co-cultures on day 13 ( Figure 5C ) . IL-10 concentrations varied by <5-fold overall , the co-cultures with MDDCs exposed to M-gp120 + TNIL + LPS containing the highest level ( 280 ± 45 pg/ml ) . The IL-10 response to M-gp120 + TNIL + LPS was significantly higher than to D-gp120 + TNIL + LPS ( one-tailed Mann-Whitney U test , n = 5 , p = 0 . 028 ) . IL-12p70 concentrations varied much more substantially . They were very low ( <10 pg/ml ) in co-cultures containing MDDCs treated with M-gp120 , D-gp120 , or HA in the absence of TNIL + LPS . When TNIL + LPS was used to mature the MDDCs , IL-12p70 concentrations reached 200 ± 22 pg/ml . The inclusion of D-gp120 or HA caused a modest ( ∼2-fold ) reduction , but when M-gp120 was used only baseline levels of IL-12p70 were produced ( 7 . 2 ± 1 . 7 pg/ml ) . The IL-12p70 response to M-gp120 + TNIL + LPS was significantly lower than to D-gp120 + TNIL + LPS ( one-tailed Mann-Whitney U test , n = 5 , p = 0 . 0040 ) . Thus , compared to the use of TNIL + LPS alone , exposure of the MDDCs also to M-gp120 caused a 76-fold increase in the IL-10/IL-12p70 ratio in the co-cultures , whereas the use of D-gp120 and HA caused only 2 . 4- and 1 . 3-fold increases , respectively . Moreover , the pattern of IL-12p70 responses in the various co-cultures ( Figure 5C , lower panel ) was similar to the pattern of T cell proliferation in the same cultures ( Figure 5A ) . IL-4 was also measured , the concentrations ranging from 5–15 pg/ml in the different co-cultures , with no obvious pattern of response detectable ( unpublished data ) . In conclusion , MDDCs matured in the presence of gp120 are functionally impaired , irrespective of whether they secrete IL-10 soon after gp120 binds to MCLRs .
We show here that exposure to HIV-1 gp120s can impair the maturation of human iMDDCs , triggering cells from some donors to secrete IL-10 , a cytokine generally associated with immunosuppressive responses [23] . Irrespective of whether they secrete IL-10 , the gp120-treated MDDCs mature inefficiently in response to conventional stimuli , and their abilities to stimulate the proliferation of T cells in co-cultures are impaired . The latter defect could be due to their reduced expression of CD80 , CD83 , and CD86 and hence a weakening of the co-stimulatory interactions with T cells that drive the latter's proliferation . The reduction in IL-12p70 levels ( and a substantial increase in the IL-10/IL-12p70 ratio ) in the co-cultures may also be relevant [33] . These various effects are a consequence of an interaction between the mannose components of gp120 glycans and an MCLR ( s ) , in that the enzymatic removal of mannoses from gp120 reduced or prevented their occurrence . We also probed the IL-10 response to gp120 using various blocking ligands . Thus , CV-N and the 2G12 mAb bind to gp120 mannoses , and each inhibited IL-10 induction , whereas inhibitors of gp120 binding to CD4 , CCR5 , or CXCR4 were ineffective . Furthermore , mannan , another MCLR ligand , activated IL-10 expression . Also relevant is that gp120 induces IL-10 expression in immunized mice [11]: Gp120 cannot bind to murine CD4 , CCR5 , or CXCR4 , or to the murine MCLR with the greatest sequence similarity to human DC-SIGN [34] . However , five murine DC-SIGN homologues have been described [35] , so it is possible that some of them do bind gp120 . The influenza HA Env protein does not induce IL-10 expression either in the immunized mice or in our own in vitro experiments; HA binds the MR [36] but not DC-SIGN or DC-SIGNR [37] . Several different MCLRs are known or potential binding sites for gp120 on DC , including DC-SIGN , langerin , and the MR [38] . We found that mAbs to DC-SIGN and the MR together completely ablated the IL-10 response to gp120 , while the anti-DC-SIGN mAb was partially inhibitory by itself . Furthermore , there was a correlation between the extent of IL-10 production and the level of DC-SIGN expression on the MDDCs . Together , these observations strongly suggest a role for DC-SIGN binding in the IL-10 response to gp120 but other MCLRs , particularly the MR , also seem likely to be involved . M-gp120 , but not its demannosylated derivative , activated ERK1/2 phosphorylation , and the ERK1/2 inhibitor U0126 inhibited the IL-10 response to M-gp120 . These findings imply that the gp120-MCLR interaction triggers the ERK1/2 signaling pathway and that this is necessary for activation of IL-10 expression . Whether the same pathway mediates the other MDDC responses to gp120 remains to be determined . This conclusion is consistent with earlier reports on the role of the ERK1/2/MAP kinase pathways in the IL-10 response when DCs are activated by other stimuli , including TLR ligands and DC-SIGN-specific antibodies [30–32] . The binding of pathogens , including HIV-1 , to DC-SIGN has also been shown to activate the Raf-1-acetylation-dependent signaling pathway [39] . The gp120-treated MDDC from about half the 71 donors we studied secreted elevated amounts of IL-10 , and the response pattern was consistent when 11 donors were re-tested a month later . Hence , genetic or other invariable factors and not , for example , an inter-current infection seem most likely to determine whether a donor's MDDCs respond to gp120 in this way , or not . Complex host genetic factors influence IL-10 gene regulation [23 , 40–42] , suggesting one area for further study . The genetics of MCLR expression might also be relevant; different MCLRs might be involved to different extents on MDDCs from different donors . DC-SIGN expression varies considerably in rectal tissue samples from different individuals and has been associated with local increases in the IL-10/IL-12 ratio [33] . Nonetheless , the IL-10 response to gp120 is only one marker for the adverse effect of this ligand on MDDCs; whether or not a donor's cells secreted IL-10 in response to gp120 , they were functionally impaired , matured poorly , and were unable to efficiently stimulate T cell proliferation . We also observed that both the concentration and the identity of the gp120 protein influenced the IL-10 response . Two of the three tested gp120s ( JR-FL and LAI ) triggered IL-10 release from MDDCs of responsive donors , whereas gp120 from KNH1144 did not . We do not yet know why KNH1144 differs from the other two gp120s in this regard . The most likely explanation is that there are subtle differences between the gp120s in exactly how they interact with one or more MCLR , and that the IL-10 response is particularly sensitive to a specific , but as yet uncharacterized , facet of these interactions . Differences in how diverse HIV-1 virions and gp120 proteins interact with DC-SIGN have been reported , although the variations in gp120 structure that affect the interaction have still to be fully defined [43] ( M . Jansson , personal communication ) . A sequence alignment of the JR-FL , LAI , and KNH1144 gp120 proteins , with emphasis on the positions of N-linked glycans , suggests a number of potentially relevant differences ( Figure S6 ) . Since understanding the molecular basis for the lack of IL-10 induction might help in the design of new Env-based immunogens , mutagenesis studies that focus on the N-linked glycans of JR-FL and KNH1144 could be informative , as might the use of additional gp120s that vary in sequence and that are expressed in cell types that lead to differences in glycosylation patterns . It is important , however , that any such reagents be highly purified free of the LPS contaminants that are common in most commercial gp120 preparations and in some others made under non-GMP conditions . The same constraints apply to the use of inactivated HIV-1 virions . We have not yet tested virions , as the focus of the present study is on soluble Env proteins that are ( or were ) vaccine candidates . Moreover , virions contain several TLR activators that might induce different cytokine responses that complicate any analysis of the effects of the Env component [44–46] . Several earlier studies have shown that gp120 and inactivated HIV-1 virions can have complex effects on MDDCs and their interactions with T cells and on cytokine secretion by both cell types in vitro . Thus , compared to LPS , R5 , and X4 gp120s both stimulated much less IL-12 production from MDDCs , but without IL-10 release [19] . Just as we have observed , gp120-treatment impaired MDDC maturation in response to classical stimuli , reducing their ability to stimulate T cells , but unlike our results , CD80 , CD83 , and CD86 were up-regulated on the gp120-treated cells [19] . In another study , exposure of MDDCs to X4 gp120 up-regulated CD80 and CD86 and down-regulated MR , with increased secretion of IL-10 , IL-12 , IL-18 , and TNF-α [47] . Various surface markers were also up-regulated on HIV-1-infected MDDCs , associated with an inability of the cells to secrete IL-12 in response to CD40L [48] . The receptor interactions of gp120 most responsible for its various biological effects were not determined in these various studies . Gp120 is also known to stimulate IL-10 release from monocyte/macrophages in vitro [17 , 18 , 20 , 22 , 24] . There is one report that MDDCs undergoing continued stimulation with GM-CSF and IL-4 did not secrete IL-10 in response to gp120 , although differences in the experimental conditions are probably responsible , and the donor- and gp120-dependent variation we now describe may also be relevant [49] . Our observations are consistent with a mounting body of evidence on the biological effects of ligating MCLRs on DCs . Thus , HIV-1 BaL and a specific DC-SIGN mAb have recently been shown to activate Rho-GTPase-dependent signals via DC-SIGN that favor the formation of DC-T-cell synapses and HIV-1 infection of the T cells [50] . The same signaling events also induced the ATF3 transcription factor that suppressed TLR-response genes , attenuating the LPS responses of the cells by reducing IL-12p70 secretion and down-modulating CD86 and HLA-DR . Thus , as we observed with gp120 , the anti-DC-SIGN mAb induced a semi-immature state in the MDDCs , which failed to stimulate T cell proliferation effectively [50] . Indeed , the binding of an antibody to DC-SIGN was previously found to activate ERK-1/2 but not p38 [32] , similar to what we have observed with gp120 . Cross-linking the MR via a specific mAb can have a broadly similar effect on the MDDC phenotype [51] . It will be worth studying whether the downstream signals activated by the MCLR MAbs are also triggered by gp120 . Of further note is that an allergenic glycoprotein from peanuts also induces ERK1/2 signaling in MDDCs via DC-SIGN , but up-regulates MHC and co-stimulatory molecules and thereby increases the ability of the MDDCs to activate T cell proliferation [52] . Thus , there may be considerable subtleties to how different glycoproteins and mAbs bind to DC-SIGN and other MCLRs on the MDDC surface , and the intracellular consequences of these interactions . Most mAbs to DC-SIGN or the MR do not induce transmembrane signals , but some do [50 , 51]; likewise , some gp120s induce IL-10 expression , others do not . One relevant point may relate to how an MCLR ligand is mannosylated: adding O-linked mannoses to ovalbumin increases lympho-proliferation in mixed BMDC-T-cell cultures while N-linked mannoses have the opposite effect , and mannosylated ovalbumin impaired IL-12p70 secretion [53] . Most mannose residues on gp120 are N-linked [54] , but the relative amounts of N- and O-linked moieties could vary between strains and influence the overall signaling patterns that are activated . Other pathogens also use mannose moieties to suppress immune responses , again via binding to MCLRs . For example , the M . Tb cell wall component ManLAM binds to DC-SIGN at a similar site to gp120′s , induces IL-10 production , impairs DC maturation , and suppresses the host immune response to this pathogen [55 , 56] . Some lactobacilli do much the same , although without the involvement of mannose residues [57] . Although DC-SIGN , and MCLRs in general , are important sentinels for the presence of pathogens , some organisms may be able to subvert at least some of the natural functions of these receptors for their own purposes [58] . DC-SIGN , in particular , may be considered as an unconventional PRR ( pattern recognition receptor ) that drives TH2 and Treg responses [32 , 58] . Silencing SOCS-1 in DC has been shown to reduce the suppressive effect of gp120 on the production of pro-inflammatory cytokines in vitro [59] . Mice immunized with gp120-pulsed , SOCS-1-silenced DC produced higher and more sustained titers of anti-gp120 antibodies , and TH1-polarized cellular responses to gp120 [59] . Conversely , over-expressing SOCS-3 in murine DC increased IL-10 expression , and SOCS-3-transduced DC primed a TH2-dominant response when co-cultured with CD4+ T cells in vitro [60] . Perhaps these observations are linked mechanistically to ours ? Caution must always be taken when extrapolating from cell culture systems to the more complex environment of tissues in vivo where the DC phenotype differs from the MDDCs we have used here and where gp120 concentrations are hard to estimate [16 , 61] . We note , however , that DCs and T cells isolated from HIV-1-infected persons can have aberrant phenotypes that are broadly similar to those of the gp120-exposed MDDCs that we have studied in vitro [62] . In particular , elevated numbers of tolerogenic semi-mature DCs , and FOXP3+ CD4+ regulatory T cells , have been observed in lymph nodes of HIV-1-infected people [63] . Moreover , high levels of IL-10 , accompanied by a reduction in IL-12 , can be found in plasma during primary HIV-1 infection [64] . IL-10 can have a substantial effect on the course of viral infections [65] . Thus , blocking IL-10 signaling by antibodies to its receptor promotes the clearance of lymphocytic choriomenigitis virus and prevents the establishment of a persistent infection [66 , 67] . Perhaps similar events are involved in persistent infection by HIV-1 ? Thus it is possible that , during primary infection , env-gene products could help suppress the development of anti-HIV-1 immune responses at this critical time , particularly as virion-associated gp120 is more efficient than free gp120 at inducing various signaling events [68] . If so , the retention of high mannose moieties on the Env complex would be yet another defense HIV-1 uses in its battle with host immunity . We previously noted that the presence of mannoses on Env is paradoxical because they might facilitate virion clearance from the blood [25]: Counter-functions would justify their retention . Our observations could help understand the outcome of immunizing with Env-based antigens , and perhaps why different individuals respond to these vaccines with Ab titers that can vary over a several-log range [5–9] . When milligram amounts of gp120 are delivered in a bolus into tissues , local concentrations are likely to be high enough to affect the performance of various immune system cells , including DCs , during the earliest , formative stages of the immune response [16] . In a comparative DNA and protein immunization study in mice , the antibody and cytokine responses to gp120 were strongly TH2-polarized , whereas responses to HA were TH1-biased . Furthermore , the TH2 bias of the anti-gp120 response did not occur in IL-10 knock-out mice [11] . Although T-helper phenotypes are more complex in humans than mice , the responses to gp120 , during infection and after vaccination , do appear to be TH2-biased [12–15] . Including Env in multi-component HIV/SIV vaccines can sometimes be deleterious to protection [69 , 70] . Also , immunizing horses with insect cell–expressed Env proteins ( which are enriched for high-mannose moieties ) from Equine Infectious Anemia Virus ( EIAV ) enhanced post-immunization infection with EIAV , whereas EIAV Env proteins expressed in mammalian cells induced protective responses [71–73] . Insect cell–expressed gp120 proteins were also comparatively poor immunogens in mice , because of a limited ability to induce T-helper responses [9] . Any vaccine-related , adverse influences of the high-mannose moieties on gp120 glycans could be overcome by treating gp120 with a mannosidase enzyme . We are now investigating whether this strategy improves the immunogenicity of HIV-1 Env proteins . Deleting a subset of N-linked glycans altered the IgG isotype profile of the antibody response to the HCV E1 protein in immunized mice and improved its immunogenicity overall [74] . Of course , raising higher titers of antibodies and/or reducing the rate of decay of the antibody response to HIV-1 Env will achieve little if those antibodies are non-neutralizing . Our hope , however , is that a general increase in the immunogenicity of Env proteins could facilitate the development of otherwise sub-threshold NAb responses , and/or enable lower amounts of Env trimers to be used . Combining the mannose-removal technique with other strategies intended to increase the immunogenicity of NAb epitopes should also be possible . Several other vaccine antigens that are considered to be problematic from the immunogenicity perspective , such as RSV F , RSV G , CMV gB , and Ebola GP , are also highly glycosylated and/or can bind to MCLRs ( S . Plotkin and B . Graham , personal communication ) [75–77] . Whether these proteins might also contain high-mannose moieties or other carbohydrate structures that can interact with MCLRs and that could be removed enzymatically is worth considering .
Recombinant , CHO-cell expressed monomeric gp120s from HIV-1 JR-FL , LAI , and KNH1144 were manufactured at Progenics , as previously described , under GMP conditions [78] . The concentration of the gp120 stocks was 1 mg/ml , with Endotoxin contamination <3 EU/ml . Gp120 was added to target cells at 3 μg/ml ( 25 nM ) , except when otherwise specified . Insect cell–expressed influenza hemagglutinin ( HA ) protein ( 100 μg/ml ) was purchased from Protein Sciences Corporation and used at 3 μg/ml ( Endotoxin <10 EU/ml , no fungal or bacterial contamination ) . LPS from Salmonella Typhimurium ( 1 mg , Sigma ) was used at 100 ng/ml . Recombinant soluble CD40L ( 50 μg , Bristol-Myers Squibb ) with an Endotoxin level of < 0 . 1 ng per μg ( 1 EU/μg ) was used at 1 ng/ml; TNF-α and IL-1β ( R&D Systems ) at 25 ng/ml and 10 ng/ml , respectively . iMDDCs were incubated for 1 h at 37 °C with various agents before gp120 was added . The anti-DC-SIGN mAb AZN-D1 ( Beckman Coulter ) , the isotype control mouse IgG1 ( Clone 2T8-2F5 , Beckman Coulter ) , the anti-mannose receptor ( MR; CD206 ) mAb Clone 15–2 ( Cell Sciences ) , and the isotype control mouse IgG1 , κ ( Clone MOPC-21 , BD Pharmingen ) were each used at 40 μg/ml , alone or in combination . The CCR5 inhibitor AD101 ( from J . Strizki , Schering Plough Research Institute ) [79] and the CXCR4 inhibitor AMD3100 ( from G . Bridger , AnorMed Incorporated ) [80] were each used at 10 μM . Mannan ( Sigma ) was added at 30 μg/ml . Alternatively , gp120 was mixed with sCD4 ( Progenics ) [81] , mAb b12 ( from D . Burton , Scripps ) [82] , mAb 2G12 ( from H . Katinger , University of Vienna ) [83] , each at 25 μg/ml , or with cyanovirin-N ( CV-N; from R . Shattock , St . George's , London ) [26] at 5 μg/ml for 1 h at room temperature on a roller before addition to the cells . The mannose residues were removed from JR-FL gp120 to make demannosylated gp120 ( D-gp120 ) as follows [25] . Aliquots of gp120 ( 120 μg ) were incubated for 16–18 h at 37 °C with no enzyme ( mock treatment; M-gp120 ) or with α- ( 1 , 2 , 3 , 6 ) -mannosidase ( Jack Bean , GKX-5010; 25 Units/mg , 0 . 14 Units/μg gp120; from ProZyme Incorporated ) in a final volume of 1 . 2 ml , in the presence of protease inhibitors ( Roche ) . A control incubation of enzyme-only ( no gp120 ) was also performed . The samples were desalted into half-strength PBS ( 1/2 PBS ) using PD-10 desalting columns ( GE Healthcare ) and concentrated to 1 ml using Vivaspin 30k MWCO 6 ml spin concentrators ( Vivascience ) . After addition of 1 volume of 1/2 PBS , each sample was processed using the Endofree Red 5/1 Endotoxin removal kit ( Profos AG ) . The final volumes of the D-gp120 and M-gp120 preparations after endotoxin removal were ∼2 ml , with endotoxin levels <8–20 EU/mg and gp120 concentrations 60 μg/ml . SDS-PAGE and western blot analyses were performed using mAbs 2G12 and CA13 ( ARP3119 ) . gp120 proteins were captured onto ELISA wells via sheep antibody D3724 to the gp120 C-terminus , and mAb or CD4-IgG2 binding was assessed essentially as described previously [84] . For DC-SIGN binding to the captured gp120 , the standard procedure was adapted as follows: The plates were washed three times with TSM ( 20 mM Tris , 150 mM NaCl , 1 mM CaCl2 , 2 mM MgCl2 ) , followed by incubation with TSM/1% BSA for 30 min . After three washes with TSM , DC-SIGN-Fc ( a gift from T . Geijtenbeek [85] ) in TSM was added for 2 h , with or without a prior incubation for 15 min with EGTA ( 10 mM ) or mAb AZN-D1 ( 10 μg/ml ) . The plates were washed five times with TSM/0 . 05% Tween , then bound DC-SIGN-Fc was detected with peroxidase-labeled goat anti-human Fc ( 1:3 , 000 ) in TSM/0 . 05% Tween using standard conditions . Peripheral blood mononuclear cells ( PBMC ) were isolated from buffy coats ( New York Blood Center or Research Blood Components ) by Ficoll density gradient centrifugation . Monocytes were isolated to high purity ( >98% ) by magnetic cell sorting with anti-CD14-coated beads according to the manufacturer's recommendations ( Miltenyi Biotec ) . The percentage of CD14+ monocytes among the cells sorted from PBMC was determined by flow cytometry and always exceeded 98% . The CD14− fraction was frozen and used as the source of T cells for MDDC-T cell co-cultures . The monocytes were subsequently cultured for 6–8 d in complete culture medium ( RPMI 1640 , GIBCO/Invitrogen ) containing 1 mM sodium pyruvate , 0 . 1 mM nonessential amino acids , 2 mM L-glutamine , 25 mM HEPES , 100 U/ml penicillin , 100 μg/ml Streptomycin ( all obtained from GIBCO/Invitrogen ) , and supplemented with 5% Human AB serum ( Sigma ) ( R-5 ) , 1 , 000 U/ml GM-CSF ( Leukine , Sargramostim ) , and 1 , 000 U/ml of recombinant human IL-4 ( R&D Systems ) at 37 °C in an atmosphere containing 5% CO2 . Every 2 d , 400 μl of medium were gently removed from each well and replaced by 500 μl of fresh medium containing the appropriate cytokines . iMDDCs were either used without maturation or were differentiated for 24 h or 48 h with TNIL + LPS ± CD40L , a mixture of inflammatory cytokines: 25 ng/ml of TNF-α and 10 ng/ml of IL-1β ( TNIL ) , and LPS ( 10 ng/ml or 100 ng/ml ) ± CD40L ( 1 μg/ml ) . Because elevated CD83 expression on MDDCs ( a response to TNF-α ) is necessary but not sufficient for IL-12 responses [86] , CD40L , a strong inducer of IL-12 , was included in all experiments in which IL-12p70 was measured . The flow-cytometric analysis of maturation markers is described in Supporting Information . iMDDC were incubated with and without gp120 ( 3 μg/ml ) for various times at 37 °C , and analyzed for the expression of IL-10 mRNA by reverse transcriptase ( RT ) -PCR . Total RNA was extracted from 1 × 106 iMDDCs by using the Absolutely RNA Miniprep Kit ( Stratagene ) according to the manufacturer's manual . The isolated total RNA ( 2 μl ) was used for synthesis of cDNA using the Super Script III First-Strand Synthesis System for RT-PCR ( Invitrogen ) . Human IL-10 and β-actin transcripts were amplified using the following primers: IL-10 forward 5′-ATGCCCCAAGCTGAGAACCAAGACCCA-3′ and reverse 5′-TCTCAAGGGGCTGG GTCAGCTATCCCA-3′ . The PCR product is 352 bp and was verified by sequencing . The β-actin primers used were: forward 5′- TCCTGTGGCATCCACGAAACT-3′ and reverse 5′-GAAGCATTTGCGGTGGACGA T-3′ . Their amplification product of 315 bp was also verified by sequencing . The annealing temperature for gradient PCR detection of IL-10 transcripts was optimized so as to avoid cross-reaction with IL-4 , IL-6 , IL-12p35 , and IL-12p40 . Purified monocytes were cultured in RPMI 1640 supplemented with 5% human AB serum , 1 , 000 U/ml GM-CSF , and 1 , 000 U/ml IL-4 for 6 d in order to produce iMDDCs , then washed thoroughly . The cells were aliquoted at various densities from 5 × 105 to 1 × 106 cells/ml into 24-well plates , and then stimulated as described in Results . Cytokine IL-10 and IL-12p70 concentrations in cell-free culture supernatants were measured by ELISA using OptEIA kits from BD Pharmingen , as per the manufacturer's protocol . The detection sensitivity for each cytokine was 4 pg/ml . Chemokine CCL17/TARC , CCL22/MDC , CCL19/MIP-3β , and CXCL10/IP10 were measured by ELISA assays using DuoSet ELISA kits from R&D Systems . For analysis of MAPK signaling pathways , day-6 iMDDCs were collected , washed three times with warm PBS , and then cultured in a serum-free medium for at least 24 h before additional stimuli . The cells were then incubated in the presence or absence of gp120 or TNIL + LPS for various times . Where indicated , an MEK inhibitor ( U0126 , 5 μM ) or a p38 inhibitor ( SB 203580 , 10 μM ) was added to the cultures 1–2 h before gp120 or TNIL + LPS . The cells were harvested and washed twice with cold PBS , then centrifuged into a pellet , and resuspended in 300 μl of lysis buffer ( 1% Nonidet P-40 , 0 . 1% SDS , 0 . 5% sodium deoxycholate in PBS ) containing PMSE ( 100 μg/ml ) and a protease inhibitor mixture ( 500 μg/ml ) ( Roche Diagnostics ) . In some experiments , the supernatants were also collected and stored at −80 °C for later analysis of cytokine content . The total protein concentration of the cell pellets was measured using the bicinchoninic acid assay ( Pierce ) . Samples containing 30 μg of total protein were heated at 100 °C for 5 min in the presence of DTT , then the following assay kits were used according to the manufacturer's instructions ( Calbiochem ) : p38[TOTAL] ELISA kit; P38[pTpY180/182] ELISA kit; ERK1/2[TOTAL] ELISA kit; ERK1/2 [pTpY185/187] ELISA kit . Allogeneic CD4+ T cells were obtained by negative selection with magnetic beads and washed twice with PBS ( see Supporting Information ) ; the cells were then incubated with 2 . 5 μM carboxy-fluorescein diacetate , succinimidyl ester ( CFSE ) ( derived from a 5-mM CFSE stock; Molecular Probes ) for 15 min at room temperature , with gentle agitation every 2–3 min [87] . The reaction was quenched by the addition of an equal volume of RPMI 1640 containing 10% human AB serum followed by incubation for 5 min . The cells were then washed with PBS three times and resuspended at 2 ×106 cells/ml in complete culture medium before use in experiments . For the mixed T lymphocyte reaction assay , CFSE-labeled or unlabeled allogeneic CD4+ T cells were co-cultured with differentially treated MDDCs at a 1/10 ratio for 5 d . ( In preliminary experiments , the DC:T cell ratio was varied over the range 1/10−2 to 1/102 in 10-fold increments , for both iMDDCs and mMDDCs , the optimal ratio for detecting T cell proliferation after 5 d of co-culture being 1/10 . ) Proliferation of the CFSE-labeled naïve T cells was analyzed by flow cytometry [87] . Supernatants were collected from the co-cultures of MDDCs with unlabeled allogeneic CD4+ T-cells on day 5 , for measurement of cytokine levels by ELISA . IL-10 measurements were subjected to the D'Agostino and Spearman omnibus normality test . The data were not uniformly normal . Hence , differences between groups were analyzed by one-tailed Mann-Whitney U test . The α level was set to 0 . 05 . Correlations rather than regression analyses were performed since we analyzed measured variables ( IL-10 secretion , cell surface antigen expression , and cell proliferation ) . The derivation and phenotypic characterization of the iMDDCs and mMDDCs , as well as the purification of CD4+ T cells , are described in Text S1 . We also show the time course of IL-10 induction and Ab controls for the blocking of gp120-induced IL-10 secretion . Furthermore , we describe the effects of mAbs to DC-SIGN and MR on the expression of MDDC maturation markers , and provide examples of flow cytometric histograms illustrating inhibition of T cell proliferation . The cytokine and chemokine responses of gp120-treated MDDC are discussed . | Dendritic cells ( DCs ) initiate immune responses to pathogens or vaccine antigens . The HIV-1 gp120 envelope glycoprotein is an antigen that is a focus of vaccine design strategies . We have studied how gp120 proteins interact with DCs in cell culture . Certain gp120s stimulate DCs from some , but not all , human donors to produce IL-10 , a cytokine that is generally immunosuppressive . In addition , whether or not the DCs produce IL-10 , their ability to mature properly when activated is impaired by gp120—the gp120-treated DCs have a reduced ability to stimulate T cell growth when the two cell types are cultured together . These various effects of gp120 are caused by its binding to cell surface receptors of the mannose C-type lectin receptor family , including ( but probably not exclusively ) one called DC-SIGN . Gp120 binds to these receptors via mannose residues that are present on some of the glycan structures that overlay much of its protein surface . Removing the mannoses by digesting gp120 with a suitable enzyme prevents IL-10 induction and impairment of DC maturation , as does the use of inhibitors of the binding of gp120 to DC-SIGN and similar receptors . This work could help with the design of better HIV-1 vaccines . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [
"viruses",
"virology"
] | 2007 | HIV-1 gp120 Mannoses Induce Immunosuppressive Responses from Dendritic Cells |
Urinary tract infections are the second most common infectious disease in humans and are predominantly caused by uropathogenic E . coli ( UPEC ) . A majority of UPEC isolates express the type 1 pilus adhesin , FimH , and cell culture and murine studies demonstrate that FimH is involved in invasion and apoptosis of urothelial cells . FimH initiates bladder pathology by binding to the uroplakin receptor complex , but the subsequent events mediating pathogenesis have not been fully characterized . We report a hitherto undiscovered signaling role for the UPIIIa protein , the only major uroplakin with a potential cytoplasmic signaling domain , in bacterial invasion and apoptosis . In response to FimH adhesin binding , the UPIIIa cytoplasmic tail undergoes phosphorylation on a specific threonine residue by casein kinase II , followed by an elevation of intracellular calcium . Pharmacological inhibition of these signaling events abrogates bacterial invasion and urothelial apoptosis in vitro and in vivo . Our studies suggest that bacteria-induced UPIIIa signaling is a critical mediator of bladder responses to insult by uropathogenic E . coli .
Urinary tract infections ( UTIs ) are the second most common infectious disease in humans , following respiratory tract infections . Approximately 90% of community-acquired UTIs are caused by uropathogenic E . coli ( UPEC ) [1] . The type 1 pilus is the most common UPEC virulence factor; present in over 90% of clinical UPEC isolates and is required to establish cystitis [2] , [3] , [4] . Type 1 pili mediate attachment to host cells by virtue of the adhesin protein FimH that occupies the pilus tip [5] . FimH possesses a lectin activity specific for mannosylated proteins that maintains bacterial attachment to the urothelium during urine voiding [6] . Following attachment type-1 pili promote bacterial invasion of urothelial cells , thereby contributing to the formation of intracellular bacterial communities ( IBCs ) [7] , [8] . Host cell actin reorganization , PI3-kinase activation , and host protein tyrosine phosphorylation have all been associated with this invasion process in cell culture models [7] . Recent studies employing both cell culture and murine UTI models suggest that UPEC also commandeer the constitutive endocytic/exocytic machinery of urothelial cells early during infection , where bacteria reside in Rab27b/CD63-positive fusiform vesicles [9] . Invasive bacteria can then exploit the cAMP-regulated exocytic process to re-enter the bladder lumen during bladder distension . Urothelium responds to UPEC insult by secreting inflammatory cytokines and chemokines . IL-6 and IL-8 are detectable in UTI patient urine , and murine UTI studies show that the recruitment of neutrophils mediates bacterial clearance by phagocytosis ( reviewed in [10] ) . Superficial urothelial cells also undergo rapid apoptosis and are exfoliated into the lumen of the bladder in murine UTI studies , presumably as a host defense mechanism that contributes to bacterial clearance by purging tissue-associated bacteria during voiding [10] . This urothelial apoptotic process is dependent upon the bacterial expression of type 1 pili since its FimH activates classical extrinsic and intrinsic apoptotic cascades [11] , [12] , [13] . Despite our increased understanding of FimH-induced UPEC invasion and urothelial apoptosis , the signal transducer and downstream second messenger that mediate these two critical events is currently unknown . The bladder urothelium is a stratified epithelium with a superficial layer of “umbrella” cells that are characterized by a highly specialized apical plasma membrane , the asymmetric unit membrane ( AUM ) . This unique membrane structure is comprised mainly of four integral membrane proteins , the uroplakins ( UPs ) Ia , Ib , II and IIIa , [14] , [15] , [16] , [17] , [18] . The AUM is a component of the permeability barrier that protects underlying tissues from noxious components of urine because UPIIIa knockout mice exhibit both altered AUM structure and defective barrier function [19] . In addition to their roles in AUM structure , UPIa plays an important role in UPEC pathogenesis by serving as the receptor for FimH [20] , [21] , [22] . UPII and UPIIIa are type-1 transmembrane proteins that undergo obligatory heterodimerization with UPIa and UPIb , respectively , during transport to the apical cell surface [23] . Of the four major uroplakins , UPIIIa alone is predicted to have an appreciable cytoplasmic domain that may function as a signal transducer [17] . Activation of host signal transduction cascades by bacterial attachment is a well-recognized consequence of host-pathogen interactions [24] , and immediate urothelial signaling events are associated with UPEC invasion and urothelial cell apoptosis [25] , [26] . Recent evidence suggests a possible signaling function for human uroplakins because xUPIII contributes to Xenopus sperm-egg fusion [27] . The xUPIII cytoplasmic tail was shown to undergo phosphorylation on a tyrosine residue and result in Src kinase-dependent egg activation [27] , [28] , [29] . This raises the possibility that mammalian UPIIIa also plays a signaling role in the bladder , in addition to its participation in forming the AUM permeability barrier . We hypothesized that human UPIIIa transduces urothelial signals that mediate UPEC pathogenesis . Here , we report a signaling role for the UPIIIa in bacterial invasion of urothelial cells and in UPEC-induced urothelial cell apoptosis . FimH induces phosphorylation of threonine-244g on the UPIIIa cytoplasmic tail by casein kinase II , followed by an increase in intracellular calcium concentration . Pharmacologic inhibition of these events abrogates bacterial invasion and apoptosis both in vitro and in vivo . Our studies suggest that bacteria-induced UPIIIa signaling is a critical mediator of the pathogenic cascade induced in the host cell and identify a novel therapeutic target for intervention in UTI pathogenesis .
In bladder urothelial cells , uroplakins are expressed at the cell surface and interact with bacterial FimH during UPEC pathogenesis . We examined the expression of uroplakins in cultures of PD07i cells , an immortalized normal human urothelial cell line that is competent to undergo TNF-induced apoptosis [30] , [31] . We sought to confirm that uroplakins are expressed in PD07i cells at the cell surface and that purified FimCH , recombinant FimH in complex with the FimC chaperone [32] , interacts with surface-expressed uroplakin receptors . For this purpose we derived affinity-purified uroplakin antibodies that specifically recognize uroplakin proteins of human origin ( Figure 1A , see Figure S1A for optimization of double staining immunofluorescence using these antisera ) . Under differentiation-inducing conditions that induce robust uroplakin expression [33] , these antibodies detected punctate cell surface expression of human UP1a , UP1b , UPII and UPIIIa in PD07i cells ( Figures 1B–1E ) . UPIa/UPII and UPIb/UPIIIa were shown to co-localize on the PDO7i apical cell surface ( Figures 1F–1K ) . Following treatment of these cells with biotinylated FimCH ( see Figure S1B ) we observed distinct co-localization of FimCH with all the individual uroplakin subunits , suggesting that FimCH binds to uroplakin complexes containing all four uroplakins ( Figure 2 , panels A–L ) . These results indicate that PDO7i cells express all four major uroplakin subunits in complexes that are fully capable of interacting with FimH to mediate binding and potential downstream signaling events . The co-localization of FimCH to UPIIIa on the PDO7i cell surface suggests that FimH binding to the uroplakin receptor complex , which contains UPIIIa , could potentially lead to signaling events mediated by the UPIIIa cytoplasmic domain . Signal transduction cascades in many systems are associated with the phosphorylation of amino acid residues in the cytoplasmic domain of transmembrane receptors [34] . To determine whether UPIIIa functions as a signal transducer in urothelial cells , we examined UPIIIa phosphorylation upon FimCH binding in cultures of PD07i cells . PD07i cultures were treated with BSA as a control protein or with purified FimCH . After immunoprecipitation of cell lysates with an anti-UPIIIa antibody , immunoblots were probed with an anti-phosphothreonine antibody followed by an anti-UPIIIa antibody to determine loading ( Figure 3A ) . Treatment with FimCH increased the level of phosphorylated UPIIIa protein relative to total UPIIIa . In contrast , probing of immunoblots with an anti-phosphotyrosine antibody did not reveal any UPIIIa phosphorylation in response to FimCH ( data not shown ) . UPIIIa phosphorylation was also detected in response to exposure of PD07i cultures to UPEC strain NU14 followed by probing with an anti-phosphothreonine antibody ( Figure S2 ) . These results indicate that FimCH binding leads to UPIIIa threonine phosphorylation and are consistent with a possible role for UPIIIa as a transducer of UPEC-induced signals . Intracellular calcium ( [Ca2+]i ) elevation is a common signaling mechanism downstream of many receptors . Since others and we have previously described [Ca2+]i elevation during infection with UPEC in culture [11] , [25] , we examined whether FimCH binding contributes to an elevation of the intracellular calcium level in urothelial cells . FimCH induced a transient [Ca2+]i increase ( Figure 3B ) within approximately 40 seconds of treatment of PD07i cultures . The FimCH-induced [Ca2+]i increase was attenuated in minimal calcium medium , but the increase was largely restored by the addition of calcium to the assay medium ( Figure 3C , compare with 3B ) . We next examined whether the [Ca2+]i response was dependent on FimCH binding to mannosylated proteins on urothelial cells . Inhibition of FimCH-induced [Ca2+]i elevation was achieved upon pre-incubation of FimCH with α-D-mannopyranoside but not D-glucose ( Figure 3D ) , thereby establishing the specificity of the FimCH-induced effects . To identify whether the calcium mobilized by FimCH was derived from an intracellular or extracellular pool , FimCH-induced [Ca2+]i increase was measured in the presence pharmacological inhibitors selective for specific [Ca2+] pools . Calcium elevation was significantly inhibited by BAPTA and EGTA , intra- and extracellular chelators of calcium , respectively , suggesting a role for both types of calcium stores in FimCH-induced calcium elevation ( Figure 3E; P<0 . 01 ) . An inhibitor of the inositol-triphosphate ( IP3 ) -gated intracellular calcium channel , 2-aminoethoxydiphenyl borate ( 2-APB ) , also significantly inhibited the calcium transient ( P<0 . 05 ) , while the L-type calcium channel blocker nifidipine ( Ndp ) had no effect ( Figure 3E ) . These results suggest that IP3-gated calcium stores contribute to FimCH-induced calcium elevation . Extracellular calcium also contributes to FimCH-induced transients , but the precise mechanism of calcium influx is unclear . To examine the role of UPIIIa in mediating calcium elevation , we used RNA interference to create a urothelial cell line with reduced expression of UPIIIa mRNA and protein ( PD07siUPIII ) [35] . Under differentiation-inducing conditions that induce uroplakin expression , UPIIIa mRNA induction in PD07siUPIII cultures was reduced to less than 1% of the parental PD07i cultures [35] . PD07siUPIII cells exhibited significantly lower FimCH-induced calcium elevation relative to a control PD07i cell line that stably expresses siRNAs against a control protein ( Figure 3F; P<0 . 05 ) . These data suggest that UPIIIa is involved in mediating FimH-induced calcium elevation in urothelial cells . Taken together , these findings suggest that the events initiated upon FimCH binding of urothelial cells include signal transduction through UPIIIa by phosphorylation of UPIIIa threonine ( s ) and elevation of the intracellular second messenger calcium . The UPIIIa cytoplasmic domain consists of ∼52 amino acids that harbor multiple potential phosphorylation sites ( Figure 4A ) . We examined the involvement of threonine-244 ( T244 ) , and serine-282 ( S282 ) , residues predicted to be part of casein kinase II ( CK2 ) and protein kinase C ( PKC ) phosphorylation motifs , in FimCH-induced calcium elevation . We also examined the involvement of tyrosine-266 ( Y266 ) , a potential phosphorylation site homologous to tyrosine-249 previously implicated in UPIII signaling in Xenopus oocytes . Specific mutations in a UPIIIa cDNA were generated that converted the potential phosphorylation sites to either glutamic acid or alanine , to mimic constitutive phosphorylation or block phosphorylation , respectively . Similarly , Y266 was mutated to phenylalanine to block phosphorylation . We utilized COS-7 cells for heterologous expression of the mutated UPIIIa variants since they lack endogenous UPIIIa expression [36] . Recombinant adenoviruses encoding the UPIIIa proteins were used to co-infect COS-7 cells along with an adenovirus encoding UPIb which , in culture , mainly harbor high mannose glycan [19] , [23] that should therefore be able to serve as FimCH receptor . Immmunofluorescence studies were used to confirm heterologous surface uroplakin expression with recombinant adenoviruses . An affinity-purified antibody to UPIIIa , P3 , was used in this experiment ( see Figure S3 for specificity ) . Although infection of COS-7 cells with recombinant adenoviruses encoding either UPIIIa alone did not result in detectable uroplakin expression , co-infection of COS-7 cells with UPIb and UPIIIa viruses resulted in a significant surface UPIIIa expression detectable using either AU1 or P3 antibodies ( Figure 4B iv–viii ) [19] , [23] . Like wild type UPIIIa , infection of COS-7 cells with recombinant adenoviruses encoding UPIIIa site-directed mutants also resulted in detectable UPIIIa surface staining when co-expressed with UPIb ( Figure S4 ) . We next examined the ability of wild-type and mutated UPIIIa variants to mediate [Ca2+]i elevation in response to FimCH . Following exposure to FimCH , cultures co-infected with wild type UPIIIa and UPIb viruses exhibited increased [Ca2+]i , whereas [Ca2+]i elevation was not observed in cultures co-infected with UPIb and LacZ viruses , ( Figures 4C and 4D ) . The kinetics of FimCH-induced [Ca2+]i increase in COS-7 cells were slightly delayed and sustained compared to stimulation of PD07i cultures ( compare Figures 4D and 3B ) , perhaps due to the expression of additional uroplakins in PD07i cultures or other urothelial-specific factors absent in the COS-7 heterologous expression system . Nonetheless , finding UPIIIa-associated , FimCH-induced [Ca2+]i increases demonstrates the utility of COS-7 cells for uroplakin structure/function studies . Expression of the phosphorylation-deficient T244A UPIIIa variant was associated with a diminished FimCH-induced calcium elevation in COS-7 cells ( Figure 4D-i , p<0 . 05 ) , but the S282A and the Y266F variants retained responsiveness to FimCH ( Figure 4D-ii and -iii ) , suggesting a specific requirement for T244 phosphorylation as a mediator UPIIIa signaling . The S282A variant ( Figure 4D-ii ) had an unexpected stimulatory effect on [Ca2+]i elevation in response to FimCH while the constitutively phosphorylated T244E mutation reversed the inhibitory effect observed with the alanine replacement ( Figure 4C and 4D ) . These findings suggest that FimCH-induced phosphorylation of UPIIIa on T244 is important for mediating [Ca2+]i elevation in urothelial cells . Because T244 of the UPIIIa C-terminus resides within a predicted casein kinase II ( CK2 ) phosphorylation motif , we examined the potential role of CK2 in UPIIIa signaling . We performed an in vitro kinase assay with recombinant CK2 and a fusion protein of the C-terminal 52 amino acids of UPIIIa with glutathione-S-transferase ( UP3C-GST ) as substrate . UP3C-GST underwent dose-dependent phosphorylation that was completely blocked by a pharmacological inhibitor of CK2 , 4 , 5 , 6 , 7-tetrabromobenzotriazole ( TBB ) , ( Figure 5A ) . Since UP3C-GST serves as a CK2 substrate in cell-free system , we next examined the potential role of CK2 in FimCH-induced calcium elevation . While the vehicle had no effect on [Ca2+]i elevation in PD07i cultures stimulated with FimCH , TBB significantly abrogated FimCH-induced [Ca2+]i elevation ( Figure 5B; P<0 . 01 ) . To confirm the role of CK2 in modulation of FimH-induced signaling , we also examined the effects of TBB on uroplakin signaling in COS-7 cells expressing UPIIIa variants . TBB significantly reduced the [Ca2+]i elevation induced by FimCH treatment of COS-7 cultures expressing wild type UPIIIa ( co-transfected with UPIb; Figure 5C; P<0 . 05 ) , and TBB significantly enhanced the calcium response in cultures expressing the T244A variant . To confirm the role for CK2 in FimH-induced calcium elevation , we utilized RNA interference to knockdown expression of CK2 in PDO7i cells . CK2 mRNA expression in PDO7i cultures transfected with CK2 siRNA was decreased by 75% compared with non-specific siRNA transfection ( Figure S6 , p<0 . 01 ) . We then found that FimCH induced a significantly attenuated [Ca2+]i elevation in CK2-silenced cells compared with the controls ( Figure 5D ) . These results indicate that CK2 phosphorylates UPIIIa and mediates the FimCH-induced [Ca2+]i elevation in urothelial cells . Previous reports demonstrate that UPEC strains invade urothelial cells in vitro and in vivo , and FimH is required for these processes [7] , [13] , [37] . We examined UPEC invasion of PD07i urothelial cells by the archetypal cystitis strain NU14 [38] . Intracellular NU14 were distinguishable from extracellular NU14 by fluorescence microscopy , confirming that PD07i cultures are useful for examining UPEC invasion of urothelial cells ( Figure 6A ) . Consistent with previous reports , the FimH-deficient isogenic mutant of NU14-1 [38] was severely defective in both adherence and invasion of PD07i cells relative to wild-type NU14 ( Figures 6B and 6C ) . To investigate the role of UPIIIa signaling in UPEC invasion , we utilized 5637 cells , a human carcinoma-derived urothelial cell line , to over-express wild-type and mutated UPIIIa . 5637 cells have been previously shown to support UPEC invasion through α3 and β1 integrins [39] . COS-7 was not utilized for these invasion studies because preliminary experiments showed that COS-7 cells supported high levels of basal NU14 invasion ( data not shown ) . Since the 5637 cell line has constitutive expression of uroplakin proteins including the UPIIIa and its partner UPIb , adenoviral mediated overexpression of the wild-type or mutant UPIIIa alone was used to study their effects on UPEC invasion . Bacterial adherence to cells expressing the wild-type UPIIIa and the UPIIIa T244E variant were similar ( data not shown ) . Wild type UPIIIa expression in 5637 cells significantly increased NU14 invasion relative to control cultures infected with control virus ( Figure 6D; P<0 . 05 ) . In contrast , expression of the UPIIIa T244E variant resulted in a much lower NU14 invasion than the wild type UPIIIa ( P<0 . 05 ) , consistent with a role for T244 in bacterial invasion . To examine the role of CK2 in UPEC invasion , we quantified NU14 invasion in PD07i cultures in the presence of the CK2 inhibitor TBB . TBB had no effect on NU14 adherence to PDO7i cells ( Figure 6E ) but significantly decreased NU14 invasion ( Figure 6F; P<0 . 01 ) , yielding results comparable to treatment with the PI3-kinase inhibitor wortmannin , a known inhibitor of NU14 invasion [7] . To confirm the role of CK2 in NU14 invasion of urothelial cells , we used RNA interference to knockdown the expression of CK2 . Knockdown of CK2 expression had no effect on the adherence of NU14 to PDO7i ( Figure 6G ) but significantly inhibited NU14 invasion when compared with a non-specific siRNA control ( Figure 6H; p<0 . 05 ) . These results indicate a role for CK2 in NU14 invasion of urothelial cells . To determine the role of CK2 in UPEC invasion in vivo , we modified a murine UTI model [40] . Female C57BL/6 mice were instilled via transurethral catheter with 10 µl of NU14 bacterial suspensions at a concentration of 1010 CFU/ml plus either TBB or vehicle . After 2 hours , bladders were removed , opened , and incubated with gentamicin to kill extracellular bacteria before plating the tissue homogenates . Invasive NU14 levels were significantly reduced in the TBB-treated bladders relative to bladders of mice treated with vehicle alone ( Figure 6I; P<0 . 05 ) . These data suggest that CK2 is an important mediator of UPEC pathogenesis at the level of urothelial invasion . Since FimH is known to induce urothelial cell death [11] , [12] , we examined the potential involvement of UPIIIa signaling in apoptosis . We first confirmed the role of FimH by demonstrating that NU14 induces a higher level of caspase activation , than the FimH-deficient strain NU14-1 , in a PD07i cell line expressing a luciferase control plasmid [30] ( Figure 7A; P<0 . 05 ) . We next examined the role of UPIIIa by examining caspase activation in PD07siUPIII , an UPIIIa-deficient cell line . PD07siUPIII showed diminished caspase activation in response to NU14 when compared with control PD07i cultures , thereby confirming our previous observation that FimH-mediated urothelial cell death correlates with UPIIIa expression [35] . We also examined annexin V staining as an early marker of urothelial apoptosis [12] , [30] . We found that annexin V staining was significantly more prevalent in control cultures than PD07siUPIII cultures ( Figure 7B; P<0 . 05 ) . Similarly , parent PD07i cultures treated with FimCH in the presence of the CK2 inhibitor TBB exhibited reduced annexin V staining , whereas FimCH treatment in the presence of vehicle were stained in significantly larger numbers ( Figure 7C ) . We also examined FimCH-induced apoptosis following bacterial infection of organotypic raft cultures that recapitulate the urothelial differentiation program [31] . Organotypic cultures of PD07i cells exhibited FimH-dependent apoptosis , as indicated by TUNEL staining in NU14-treated cultures that was absent in NU14-1-treated cultures ( Figure 7D ) . However , PD07siUPIII organotypic cultures did not undergo FimH-dependent apoptosis . Together , these data suggest that FimH-induced urothelial apoptosis is mediated by both UPIIIa and CK2 signaling . We next examined the role of CK2 in UPEC-induced urothelial apoptosis in vivo by infecting mice with NU14 in the presence or absence of TBB and then detecting apoptosis by TUNEL staining [13] . TUNEL staining of bladder sections revealed foci of cells in the superficial layer of the bladder of control animals instilled with an NU14 suspension , but this was inhibited by TBB ( Figure 7E , panels i and ii ) . To determine whether UPIIIa signaling affected urothelial responses other than those involved in bacterial invasion and apoptosis , we examined TLR-dependent urothelial inflammatory responses in vitro following UPEC infection [41] . UPEC infection as well as treatment with the pro-inflammatory cytokine IL-1β induced similar level of CXCL-8 secretion in both PD07siTNFR2 and PD07siUPIIIa cultures ( Figure S5 ) . These results suggest that UPIIIa signaling is not required for urothelial inflammatory responses . In addition , in vivo murine UTI infection with NU14 elicited similar levels of bladder neutrophil influx in mice instilled with vehicle or the CK2 inhibitor TBB ( Figure 7E , panels iii and iv ) . Taken together , our data suggest that UPIIIa and CK2 specifically mediate two key events in UTI pathogenesis , UPEC invasion of urothelial cells and urothelial apoptosis .
Using monospecific antibodies for individual uroplakins , we showed that UPIa and UPIb co-localized with UPII and UPIIIa , respectively , in cultured human PD07i urothelial cells ( Figure 1 ) . This result is consistent with our previous finding that individual uroplakins preferentially form UPIa/UPII and UPIb/UPIIIa heterodimers , and this step is a prerequisite for uroplakin complex export from the endoplasmic reticulum [23] , [43] , [44] . Uroplakins residing outside the afore-mentioned , preferred heterodimers were much less colocalized ( data not shown ) . The data reported here are also consistent with our previous reports: uroplakins in cultured bovine urothelial cells remain as heterodimers , whereas in normal urothelium in vivo these uroplakin heterodimers proceed to form heterotetramers , six of which then form the hexagonal 16-nm particle [43] . The fact that uroplakin staining appeared punctate suggests that uroplakins accumulated preferentially on apical microvilli of PD07i cells ( Figures 1 and 2 ) , similar to our previous observations in cultured bovine urothelial cells [45] . Taken together , our results establish that human urothelial PD07i cells express all four major uroplakins primarily as heterodimers . Previous studies indicate that UPIa is the main UPEC receptor in normal urothelium , yet data presented here suggest that UPIb may serve as an alternative FimH receptor in cultured urothelial cells ( Figure 2 ) . Of the four major uroplakins present in mouse urothelial plaques , we previously showed that UPIa is the only uroplakin bearing large amounts of high mannose glycan [21] . Consistent with this result , type 1-piliated E . coli or recombinant FimCH preferentially bound UPIa , and similar results were obtained with human uroplakins , suggesting that UPIa is the main UPEC receptor in vivo [20] , [21] , [22] . UPIb and UPIIIa harbor mainly complex sugars [17] , [18] , [21] . UPII is unlikely to function as a UPEC receptor , for proteolytic processing of pro-UPII that harbors three N-glycosylation sites eliminates those sites from mature UPII [46] . Somewhat unexpectedly , we found here that FimCH co-localized with all four uroplakins ( Figure 2 ) , suggesting that FimH may interact with either uroplakin Ia/II or Ib/IIIa heterodimers on PD07i cells . However , this is consistent with previous observations that heterologous expression of UPIa/UPII and UPIb/UPIIIa pairs in COS-1 cells resulted in both UPIa and UPIb bearing high mannose glycans [43] . Together these findings suggest that uroplakins exist mainly as heterodimers in culture models , and both UPIa/UPII and UPIb/UPIIIa pairs are functional FimH receptors . Therefore , while culture models are convenient for identifying molecular mechanisms of FimH-induced UPIIIa signals , we also validate these findings in vivo , where UPIa is the major FimH-receptor ( Figures 6 and 7 ) . Sperm-egg fusion in Xenopus provided the first evidence for a signaling role for uroplakins with phosphorylation of the cytoplasmic tail of xUPIII on Y249 [27] . In contrast to these results , our data implicate phosphorylation of T244 as a key early event induced by FimH binding in culture . Indeed , we found that mutation of hUPIIIa Y266 , the homologous position to xUPIII Y249 , did not influence FimH-induced calcium elevation , suggesting that signaling initiated by modification of hUPIIIa Y266 is not involved in urothelial responses to UPEC . This difference in phosphorylation site utilization may represent ligand-specific , species-specific , and/or tissue-specific signaling . We do not however rule out the possibility that yet-unidentified physiological ligands induce human UPIIIa phosphorylation on tyrosine residues using conserved mechanisms comparable to Xenopus . On the contrary , we speculate that the presence of multiple putative phosphorylation sites on the cytoplasmic tail of UPIIIa mediate responses to diverse stimuli and involvement in multiple biologic processes , such as fusiform vesicle exocytosis that expands the AUM surface area during bladder filling [47] . Our findings that UPIb was sufficient for UPIIIa-mediated responses induced by FimH in COS-7 cells ( Figures 4 and 5 ) is consistent with the conserved function of complexes containing tetraspanin proteins . Tetraspanins typically function as transmembrane linkers in a complex with other proteins that then transduce signals [48] . Thus in the case of uroplakin complexes , tetraspanins function as the ligand binding module ( UPIa/UPIb , and UPIII tranduces FimH-induced signals . The precise mechanism by which FimH-uroplakin interactions transduce signals across the highly impermeable urothelial apical membrane remains unknown . However , Kong and colleagues have recently used cryo-electron microscopy to show that FimH binding induces gross conformational changes in the entire uroplakin receptor complex , including movement of the transmembrane helices to potentially induce structural changes in the uroplakin cytoplasmic tails ( Xiang-Peng Kong , New York University School of Medicine , personal communication ) . These independent results bolster our hypothesis that FimH-induced changes in the transmembrane helices of the uroplakin receptor complex mediate the transmembrane signal that triggers bacterial invasion and host cell responses . The finding that UPIIIa signaling initiates [Ca2+]i elevation has important implications for UPEC pathogenesis . FimH-mediated calcium elevation occurs as a result of calcium release from intracellular stores and by influx from extracellular sources , and calcium elevation promotes global responses critical to UPEC pathogenesis including cytokine stimulation , membrane trafficking and apoptosis [25] , [49] . The findings here extend our earlier observations that sustained calcium elevation is associated with UPEC-induced apoptosis [11] and suggest a role for calcium signaling in both immediate early host responses and subsequent events . Our finding that UPIIIa initiates urothelial calcium also presents a testable hypothesis for the mechanism underlying the modulation of calcium-dependent exocytosis of fusiform vesicles during bladder stretch [47] . Approximately 25% of UTI patients suffer recurrent infections , and invasion of urothelial cells in culture and in mice promotes the establishment of a drug-resistant UPEC niche that may underlie recurrent UTIs in humans [37] . The factors required for UPEC invasion in vitro include expression of type-1 pili , intracellular signaling events mediated by PI3-kinase , and activation of Rho-GTPase [50] . In this study , we examined the role of the UPIIIa signaling in UPEC invasion in vitro and in mice utilizing the CK2 inhibitor TBB , because mutagenesis studies implicated a putative CK2 site at T244 of the UPIIIa cytoplasmic tail . TBB inhibition of UPEC invasion suggests that FimH-mediated mechanisms of invasion in the bladder also rely on UPIIIa and CK2 . The UPIIIa variant T244E , designed to mimic constitutively phosphorylated UPIIIa , inhibited invasion , while the T244A has no such effect . Thus , the phosphorylation status of T244 has implications for UPEC invasion . The absence of correlation between calcium signaling with the T244 mutants and UPEC invasion may result from distinct kinetics . Calcium signaling is an early event occurring within minutes while invasion is measured within hours . These results suggest that UPIIIa-induced signals may interact with other signals induced by UPEC binding to modulate UPEC invasion of urothelial cells . For example , recent studies demonstrate that UPEC adherence induces cAMP increases via TLR4 signaling , and this increase hinders UPEC invasion [51] . The salutary effects of TBB on UPEC invasion in vivo suggest CK2 is a novel therapeutic target for intervention in UTI at the level of inhibiting UPEC invasion . It has been postulated that urothelial apoptosis is a host defense mechanism against UPEC insult , because inhibiting apoptosis in a murine UTI model was detrimental to bacterial clearance from the bladder [13] . We previously showed that UPEC-induced apoptosis is mediated by type 1 pili and occurs through activation of intrinsic and extrinsic cell death pathways [11] , [12] . In this study we demonstrate that FimH-induced apoptosis is dependent upon the expression of UPIIIa and is inhibited by abrogation of signals downstream of UPIIIa . We have also recently shown that increasing levels of UPIII expression in urothelial cells renders cells more susceptible to FimCH-dependent apoptosis [35] . Interestingly , UPIIIa expression or signaling does not appear to be required for urothelial inflammatory responses , suggesting that UPEC-induced signals emanating from UPIIIa are separate and unique from simultaneous inflammatory signals mediated by TLRs . The potential interactions between simultaneous UPEC activation of both pro-apoptotic and pro-inflammatory pathways is intriguing but uncharacterized . We propose a model of UPEC pathogenesis that combines the known responses to UPEC with the UPIIIa signaling described in this study ( Figure 8 ) . The host response is characterized by events in distinct kinetic classes representing immediate early signaling responses , early innate responses , and culminating in late events . Our findings identify FimH-induced UPIIIa signaling mediated by CK2 and subsequent [Ca2+]i elevation as the immediate host responses . These membrane-proximal signals activate two seemingly opposed urothelial outcomes – urothelial invasion by UPEC and UPEC-induced apoptosis . Bacterial invasion occurs through activation of the host cytoskeleton and utilization of conserved endocytic pathways . The demonstration of a role for the endocytic/exocytic machinery of urothelial fusiform vesicles in UPEC invasion leads us to speculate that UPEC-mediated UPIIIa signaling is a bacterial pathogenesis mechanism initiated to activate the endocytic machinery in urothelial cells , thereby gaining access to an intracellular sanctuary [9] . Apoptosis , triggered by an as-yet-unknown signaling intermediate , rids the host of infection and may be considered part of a robust innate response . In the event of a successful innate response , the urothelial cell undergoes apoptosis and is eliminated in the urine . We postulate that initiation of apoptosis may require a threshold of UPIIIa signaling that varies from cell-to-cell depending on levels of UPIIIa expression or expression of other cellular factors . This scenario is consistent with heterogeneous bladder lesions induced by UPEC and the recent finding that urothelial cells may derive from multiple progenitor populations [52] . In the absence of this robust clearance mechanism , invasive bacteria are likely to establish stable reservoirs . Successful establishment of this immune-resistant niche may also require pro-survival TLR signals that shift the equilibrium away from apoptosis and promote cell survival . Irrespective of the precise mechanisms , the balance between pro-survival and pro-apoptotic mechanisms may determine UPEC pathogenesis in the urinary bladder . In summary , two critical pathogenic results of UPEC-urothelial interactions , bacterial invasion and host cell apoptosis , involve UPIIIa and are associated with CK2 and calcium signaling . This first description of human UPIIIa as a signal transducer raises important questions regarding the normal physiological function of this highly expressed protein in the mammalian urinary bladder . Determining how UPIIIa-mediated signals interact with the recently characterized role of TLR4-induced cAMP modulation will illuminate key aspects of both urothelial biology and host-pathogen interactions in UTIs . Finally , identifying calcium and CK2 as immediate early host responses to UPEC offers novel therapeutic targets for intervention in UTIs .
Female C57BL/6 mice were obtained as specific-pathogen-free from Jackson Laboratories ( Bar Harbor ) and housed in Northwestern University's Center for Comparative Medicine . After a 1-week acclimatization , 6- to 10-week old mice were anesthetized with isoflurane and inoculated by transurethral catheter with 108 CFU E coli in 10 µl to minimize reflux to the kidneys [53] . All experiments were conducted using protocols approved by the Animal Care and Use Committee of Northwestern . NU14 was obtained from the urine of a cystitis patient , and NU14-1 lacks functional type 1 pili [38] . Bacteria were cultured in static Luria broth at 37°C to promote type 1 pilus expression [54] that was confirmed by mannose-sensitive haemagglutination [55] , [56] . For in vitro infections , bacteria were centrifuged and washed once in cold PBS . Bacteria were resuspended in culture medium at the appropriate multiplicity of infection ( MOI ) . Bacterial preparation for in vivo studies was performed as previously described [40] . FimCH , a stabilized form of FimH in complex with the FimC chaperonin , was a kind gift from Dr . Hultgren . Briefly , 1 mg of FimH/C in 1 ml 50 mM Na2CO3-NaHCO3 ( pH 9 . 0 ) was mixed with 50 ml sulfo-NHS-biotin ( 1 mg/ml; Cat# 21217 , Pierce Chemical , Rockford , IL ) freshly dissolved in water . The mixture was left on ice for 2 hours , and the buffer was changed to Tris-buffered saline ( TBS ) ( 150 mM NaCl , 50 mM Tris-HCl , pH 7 . 5 ) through ultrafiltration ( Centricon , 10 k-Da cutoff , Millipore , Bedford , MA ) to remove the free biotin [22] . PD07i cells are an immortalized human urothelial cell line previously established by infection of normal human urothelial cells ( obtained by dissociation of pediatric bladder ) with a retrovirus encoding E6E7 of HPV type 16 [30] . PD07i cells were maintained in EpiLife medium ( Invitrogen ) . PD07i cells were used to establish a stable cell line in which UPIIIa expression was blocked using short hairpin RNA ( shRNA ) specific for UPIIIa and designated as PD07isiUPIII ( Clone V2HS_95009 , OpenBiosystems ) . As a control for the retroviral vector used in these studies , PD07i cells expressing shRNA targeting TNF receptor 2 designated PD07isiTNFR2 were also employed to demonstrate specificity ( clone V2HS_94072 , OpenBiosystems ) [30] . Silencing of UPIIIa gene expression was confirmed by real-time analysis , and expression was reduced approximately 99% ( Thumbikat et al . , manuscript in review ) . COS-7 cells and 5637 cells were used for heterologous expression of uroplakins and were cultured in DMEM containing 10% fetal bovine serum . Urothelial biomimetics cultures were generated by organotypic raft culture as previously described [31] . PDO7i cells were suspended ( 1×105 cells/ml ) in a complete bladder urothelium culture medium ( FAD medium ) : 1∶1 mixture of DME and Ham's FI2 medium , containing 10% FCS , hydrocortisone ( 0 . 5 mg/ml ) , cholera toxin ( 5 ng/ml ) , insulin ( 5 ug/ml ) , epidermal growth factor ( 15 ng/ml ) [33] . 2×105 cells were seeded into upper chamber of a 24-mm Transwell with 3 ml of FAD medium in the lower chamber . Culture medium was changed regularly every 3 days ( 3 ml in the basolateral chamber and 2 ml in the apical chamber ) . Cells were fixed at post-confluence day-5 with fresh prepared 4% paraformaldehyde in PBS pH 7 . 4 at 25°C for 15 min before immunostaining . For single uroplakin immunostaining , cells were fixed and incubated at 4°C overnight with anti-UPIa , Ib , II , and IIIa antisera 1∶200 diluted in 1% fish skin gelatin in PBS ( pH 7 . 4 ) . The primary antibodies were detected with an Alexa Fluor 488- conjugated donkey anti—-rabbit IgG ( Cat# A-21206 , Invitrogen , Carlsbad , CA ) in PBS ( pH 7 . 4 ) with 1% FSG fish skin gelatin . Cells were also incubated with 1 ug/ml propidium iodide ( Cat # 537059 , Calbiochem , San Diego , CA ) at 25°C for 5 min for nuclear staining . For uroplakin double staining , monovalent Fab fragment of rhodamine-conjugated donkey anti-rabbit IgG ( Cat# 711-297-003 , Jackson Immunoresearch lab , West Grove , PA ) was used for detecting and blocking of double labeling of rabbit anti-UPIa , and Ib antisera according to http://www . jacksonimmuno . com/technical/fab-blok . asp . Fixed cells were first stained with rabbit anti-uroplakins Ia , or Ib antisera , respectively , followed by detecting and blocking with a monovalent Fab fragment of rhodamine-conjugated donkey anti-rabbit IgG , then the samples were briefly fixed for 15 min with 4% paraformaldehyde in PBS ( pH 7 . 4 ) , and after 15 min neutralized using 100 mM NH4Cl in PBS ( pH 7 . 4 ) , samples were double stained with anti-UPII or UPIIIa rabbit antisera , and finally detected using the Alexa Fluor 488 conjugated donkey anti-rabbit IgG . Fixed cells were incubated for 1 hr at 25°C in a TBS+ buffer ( 150 mM NaCl , 1 mM CaCl2 , 1 mM MgCl2 , 50 mM Tris-Cl pH 7 . 4 ) with 1% fish skin gelatin , 10 ug/ml of biotinylated FimH/C , and rabbit anti-uroplakin Ia , Ib , II , and IIIa antisera ( 1∶200 dilution ) . The biotinylated FimH/C and anti-uroplakins antibodies were detected using FITC-conjugated streptavidin and Alexa Fluor 594-conjugated donkey anti-rabbit IgG ( Cat# A21207 , Invitrogen , Carlsbad , CA ) , respectively . 0 . 1 ug of bovine or human AUM proteins or 10 ng of biotinylated FimH/C were resolved electrophoretically by 17% SDS-PAGE and transferred onto a nitrocellulose membrane ( Cat# 162-0112 , Bio-Rad , Hercules , CA ) . The individual uroplakins were incubate with rabbit antisera against UPIa , Ib , II , and IIIa , and followed by of 1 mg/ml horseradish peroxidase ( HRP ) -labeled goat anti-rabbit IgG ( Cat# A8275 , Sigma , St . Louis , MO ) . The biotinylated FimH/C was detected directly by HRP-labeled streptavidin ( Cat# S5512 , Sigma , St . Louis , MO ) . PDO7i cells ( 5×106 ) were incubated with 10 µg/ml FimCH for 15-30 minutes in culture medium followed by lysis in modified RIPA buffer . Lysates were precleared with protein A sepharose beads ( Santa Cruz Biotechnology ) for 90 minutes , followed by addition of anti-human UPIIIa polyclonal antibody ( rabbit antibody raised against the human UPIIIa peptide QTLWSDPIRTNQL; Invitrogen ) and immunoprecipitation of the complex with protein A sepharose . Eluted proteins were fractionated on 4–15% SDS-polyacrylamide gels and transferred to Immobilon-P membrane ( Millipore ) . Blots were probed with anti-phosphotyrosine or anti-phosphothreonine antibodies ( Cell Signaling Technologies , P-Tyr-100 monoclonal and P-Thr-polyclonal respectively ) and imaged by chemiluminescence . Intracellular calcium ( [Ca2+]i ) was quantified in fura-2/AM-loaded PDO7i or COS 7 cells exposed to 10 µg/ml FimCH by video fluorescence imaging [57] . Briefly , cells grown on chambered coverslips were rinsed in modified Hank's balanced salts ( HBSS ) and incubated for 30 minutes in 5 µM fura-2/AM . The cells were then washed in modified HBSS and monitored at 520 nm after excitation at 340 nm ( bound Ca2+ ) and 380 nm ( free Ca2+ ) using a 20× water immersion lens . Fluorescence was analyzed from at least 30 cells in each experiment using MetaFluor software ( Universal Imaging Corporation ) from regions of interest after correction for system background , shading errors , and autofluorescence of unloaded cells . [Ca2+]i was calculated by the ratio method [58] . Inhibitors 2-aminoethoxydiphenyl borate , nifidipine and CK2 inhibitor 1 ( TBB ) were from Calbiochem , and wortmannin , EGTA and BAPTA-AM were from Sigma . For inhibitor experiments , cells were pre-incubated with drugs or vehicle for 30 minutes at 25°C prior to FimCH exposure . [Ca2+]i elevation was calculated by subtracting the baseline [Ca2+]i from the maximal calcium value . The human UPIb and UPIIIa cDNA clones were kind gifts from Dr . Jennifer Southgate . Individual uroplakin cDNAs were cloned into the multiple-cloning site of pDONR followed by transfer into the pAd/CMV/V5-DEST adenoviral vector with Gateway technology ( Invitrogen ) . The resulting plasmids were linearized by digestion with PmeI and transfected into HEK 293 cells . Recombinant adenoviruses were titered and stored at −70°C until use . UPIIIa variant adenoviruses were generated ( T244A , T244E , S282A , S282E , and Y266F ) by site-directed mutagenesis of the wild type pAd/CMV/UP3 sequence . COS-7 cells and 5637 cells were infected with uroplakin adenoviruses for 4 h ( MOI = 100 ) and incubated for 20 hours before experiments . Anti-AUM antiserum was affinity purified to yield a UPIII-specific “P3” fraction as previously described [59] . Briefly , UPIIIa peptide ( S87 to K101 ) was immobilized on nitrocellulose . Following blocking of the filter with BSA , the filter was incubated with anti-AUM for 1 h at room temperature , and bound antibodies were then eluted with 50 mM diethylamine ( pH 11 . 5 ) and immediately neutralized with 1M Tris-HCl ( pH 7 . 4 ) . The DNA sequence encoding the C-terminal 52 amino acids of UP3 ( UP3C ) was cloned into the Sgf I and Pme I sites of the glutathione-S-transferase ( GST ) vector pFN2A ( Promega ) . UP3C-GST fusion protein was purified from E . coli strain BL21 using standard techniques . Casein kinase II-mediated phosphorylation of the C-terminal domain of uroplakin IIIa was performed using standard protocols [60] . Briefly , kinase reactions were performed in 20 mM Tris-HCl pH7 . 5 , 150 mM KCl , 5 mM MgCl2 , and 500 µM DTT containing 20 µCi 32P-ATP ( 3000 mCi/mmol ) , cold ATP to 5 µM , and 50 U recombinant human recombinant CK2 ( Calbiochem 218701 ) . Some reactions also included 10 µM CK2 inhibitor TBB ( Calbiochem 21708 ) . Reactions were incubated at 30°C for 10 min before termination with SDS loading dye and electrophoresis through 10% polyacrylamide . PD07i cells were transfected overnight using Lipofectamine 2000 ( Invitrogen ) with a total of 200 pmol/well ( 6-well plates ) or 40 pmol/well ( 24-well plates and chambered coverslips ) of combination of equal amounts of each dsRNA duplex from the CSNK2A1 Validated Stealth™ RNAi Duopak ( Invitrogen ) or the equivalent amount of Stealth™ RNAi Negative Control Med GC duplex as a negative control . The following day , the transfection media was removed and fresh culture media was added to the cells . Forty-eight hours following transfection , the cells were used to determine intracellular invasion efficiency , caspase 3/7 activation following infection with NU14 or [Ca2+]I elevation following FimCH treatment . PD07i cells were infected with NU14 ( MOI 10 ) and centrifuged twice at 600×g for 2 . 5 min . Infected cultures were incubated at 37°C for 2 h . To measure adherence , cells were washed 4 times with PBS and incubated with 0 . 05% trypsin/0 . 1%Triton X-100 for 10 minutes to lyse cells . Lysates were harvested , plated on LB-agar containing appropriate selection , and colonies were counted to quantify bound bacteria . To measure invasion , cells were infected and washed as above before incubating in 100 µg/ml gentamicin for 30 minutes at 37°C and plating lysates as described above . For inhibitors , cells were infected ( MOI 100 ) in the presence of inhibitor or vehicle and incubated at 37°C 1 h and then changing to inhibitor-free media for 1 h . Mice were infected via transurethral catheter with 10 µl of bacterial suspension containing 108 CFU [40] containing 10 µM TBB or vehicle . After 2 h , animals were sacrificed and opened bladders were incubated in PBS containing 100 µg/ml gentamicin for 30 minutes at 37°C before rinsing and plating tissue homogenates . To detect intracellular UPEC , PD07i cells were seeded into 4-well chambered slides and grown to confluence . Cells were infected at an MOI 100 with NU14/pcomGFP [61] and incubated at 37°C 2 h . To stain extracellular bacteria , cells were incubated with biotinylated anti-E . coli antibody ( Abcam ) in Epilife/1% BSA at 37°C . Cells were washed and fixed in 1% PFA then blocked in PBS/1% BSA before incubating with Streptavidin-AlexaFluor 594 ( Invitrogen ) . Each step occurred for 30 minutes at 25°C and slides were washed three times with PBS between each step . Images were acquired with OpenLab ( Improvision ) using a 63× HCX PlanApo objective on a Leica DM-IRE2 microscope outfitted with an OCRA2 camera ( Hamamatsu ) , and fluorescent channels were merged with DIC imaging; curves demarking the nucleus and cell margins were added to aid interpretation . For detection of uroplakin expression , COS-7 cells were infected with recombinant adenoviruses encoding human uroplakins ( MOI 100 ) . The following day , cells were fixed for 15 min . at 25°C with freshly prepared 4% paraformaldehyde in PBS pH 7 . 4 . Cells were then rinsed and quenched with 10 mM glycine in PBS pH 7 . 4 at 25°C for 10 min . The cells were then incubated with 3% bovine serum albumin in PBS pH 7 . 4 with affinity purified rabbit anti-UPIII ( P3 fraction ) and AU1 ( mAb ) at 25°C for 1 h , rinsed three times with PBS pH7 . 4 at 25°C , and incubated 1 h with Alexa Fluor 594 goat anti—rabbit IgG ( Invitrogen A-11037 ) and Alexa Fluor 488 goat anti—mouse IgG ( Invitrogen A-11029 ) . Images were collected with a Zeiss Axioskop 2 fluorescent microscopy by AxioVision 4 . 5 software . Apoptosis in cultured cells was assayed using the Annexin-V-FLUOS kit ( Roche Diagnostics ) as previously described [30] . Annexin-positive cells were quantified by examining independent fields in 3 separate wells of a 12-well plate . For each field , brightfield and fluorescent images were captured . Total cells ( brightfield ) and apoptotic cells ( fluorescent ) were quantified manually in each image . Caspase 3/7 activity in urothelial cell cultures was determined using the Apo-One Homogeneous Caspase 3/7 Assay ( Promega ) . Briefly , PD07siLuciferase and PD07siUPIII cultures were grown in 15 cm plates and treated with 15 ml of media containing either NU14 or NU14-1 for 3 hours ( MOI 500 ) . Dishes were washed , harvested by scraping , and resuspended in 500 µl of the manufacturer's recommended hypotonic lysis buffer . Induction of caspase 3/7 activity in each homogenate was normalized both to the levels of untreated control samples of each cell line and to total protein concentration as determined by BCA assay ( Pierce Chemical ) . Apoptosis in bladder sections and urothelial biomemetics was detected using the TUNEL reaction ( Roche ) as previously described [30] . Data were analyzed using Prism version 4 . 0 ( GraphPad ) and presented as mean±SEM . The statistical significance of differences between groups was calculated using Student's two-tailed t test or Mann-Whitney test for two groups or one-way ANOVA with Dunnett's post-test comparison . P<0 . 05 was considered significant . | Urinary tract infections ( UTI ) are the second most common infectious disease in humans and are predominantly caused by uropathogenic E . coli ( UPEC ) . In vitro and in vivo studies have demonstrated that UPEC induce several responses in the bladder , including inflammation , rapid onset of bladder cell death , and bacterial invasion of bladder cells . This last event , invasion , is now also thought to underlie recurrent UTI . Although members of the highly-expressed “uroplakin” proteins serve as bladder receptors for UPEC binding , it was unclear how UPEC binding to uroplakin receptors caused signals within bladder cells that mediate rapid cell death and bacterial invasion . Here , we show that another uroplakin , uroplakin III , which is associated with the receptor transduces signals within the cell in response to UPEC binding . UPEC causes elevated calcium within bladder cells , and this elevation requires phosphorylation of uroplakin III by a specific kinase . Blocking these events blocks both bladder cell death and bacterial invasion of bladder cells by UPEC . Thus , uroplakin III is the mediator of key events in UTI pathogenesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"infectious",
"diseases/urological",
"infections",
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"infections",
"cell",
"biology/cell",
"signaling",
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"diseases/bacterial",
"infections",
"microbiology/cellular",
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"pathogenesis"
] | 2009 | Bacteria-Induced Uroplakin Signaling Mediates Bladder Response to Infection |
HMG-box proteins , including Sox/SRY ( Sox ) and TCF/LEF1 ( TCF ) family members , bind DNA via their HMG-box . This binding , however , is relatively weak and both Sox and TCF factors employ distinct mechanisms for enhancing their affinity and specificity for DNA . Here we report that Capicua ( CIC ) , an HMG-box transcriptional repressor involved in Ras/MAPK signaling and cancer progression , employs an additional distinct mode of DNA binding that enables selective recognition of its targets . We find that , contrary to previous assumptions , the HMG-box of CIC does not bind DNA alone but instead requires a distant motif ( referred to as C1 ) present at the C-terminus of all CIC proteins . The HMG-box and C1 domains are both necessary for binding specific TGAATGAA-like sites , do not function via dimerization , and are active in the absence of cofactors , suggesting that they form a bipartite structure for sequence-specific binding to DNA . We demonstrate that this binding mechanism operates throughout Drosophila development and in human cells , ensuring specific regulation of multiple CIC targets . It thus appears that HMG-box proteins generally depend on auxiliary DNA binding mechanisms for regulating their appropriate genomic targets , but that each sub-family has evolved unique strategies for this purpose . Finally , the key role of C1 in DNA binding also explains the fact that this domain is a hotspot for inactivating mutations in oligodendroglioma and other tumors , while being preserved in oncogenic CIC-DUX4 fusion chimeras associated to Ewing-like sarcomas .
HMG-box factors are abundant nuclear proteins with highly diverse functions in the cell . They contain one or more HMG-box domains that bind the minor groove of DNA , bending the duplex away from the interaction site . Proteins with tandem HMG-box domains usually function as architectural and chromatin factors and do not exhibit DNA sequence specificity . In contrast , proteins with a single HMG-box domain , including Sox/SRY ( Sox ) and TCF/LEF1 ( TCF ) transcription factors , function as developmental regulators and bind specific AT-rich motifs in enhancers and promoters ( reviewed in refs . [1–3] ) . In most cases , however , this binding is not sufficient for appropriate target selection . For example , Sox proteins rarely act on their own and are often assisted by partner factors that bind next to the Sox sites , thereby stabilizing the complex and providing the specificity needed for in vivo function [3] . Once tethered to DNA , HMG-box proteins can exert their transcriptional effects through additional interactions with co-activators or co-repressors . The HMG-box protein Capicua ( CIC ) is a highly conserved transcriptional repressor distantly related to Sox and TCF factors [4] . Studies in Drosophila and mammals have shown that CIC controls multiple developmental decisions acting downstream of Receptor Tyrosine Kinase ( RTK ) signaling . In general , CIC represses RTK-responsive genes by binding to octameric TGAATGAA-like motifs in their promoters and enhancers , and this repression is relieved upon RTK-induced downregulation of CIC . In Drosophila , this mechanism controls anteroposterior and dorsoventral body patterning , intestinal stem cell proliferation , wing development , and other processes , providing a direct link between RTK activation and transcriptional derepression of CIC targets [5–14] . In mammals , CIC is similarly regulated by RTK signaling and controls essential processes such lung alveolarization and liver homeostasis [15–18] . Moreover , CIC has been implicated in distinct human pathologies including spinocerebellar ataxia type 1 [16 , 19] and various forms of cancer , particularly oligodendroglioma ( OD ) [20–23] . In cancer , CIC behaves mainly as a tumor and metastasis suppressor that is inactivated by somatic mutations [22–30] , but it can also exert oncogenic effects resulting in Ewing-like sarcomas [31] ( Fig 1 ) . This latter role originates from chromosomal translocations where CIC becomes fused to a fragment of the DUX4 transcription factor [31–36] . CIC-DUX4 chimeras contain a nearly complete CIC sequence followed by the C-terminal portion of DUX4 , which converts CIC into an activator and causes upregulation of CIC targets such as ETV/PEA3 family genes [15 , 17 , 31] . Nevertheless , the mechanisms underlying CIC activity in normal and pathological processes are not well understood . One unresolved question concerns the role of a conserved domain present at the C-terminus of all CIC proteins . This domain ( referred to as C1 ) does not resemble other known domains and appears to be functionally important . Thus , transgenic assays indicate that C1 is required for CIC repressor activity in early Drosophila embryos [9] . Also , systematic sequencing of human tumors has revealed multiple missense mutations mapping to the C1 sequence , arguing that C1 is essential for CIC function in suppressing growth and metastasis [22–27 , 30] . However , how C1 contributes to CIC function remains unknown . In this work , we set out to investigate the mechanism of C1 action . Unexpectedly , we find that C1 plays a conserved essential role in CIC DNA binding activity . We show that neither the HMG-box nor the C1 domain is capable of binding to DNA separately , but instead function together to mediate efficient DNA binding in both Drosophila and human cells . Thus , CIC employs a new mode of DNA binding that distinguishes it from Sox and TCF proteins , which lack the C1 domain and employ other mechanisms for enhancing their target specificity . Furthermore , our results explain the distinct patterns of human CIC mutations in OD and Ewing-like sarcomas , since the C1 domain should be required for the DNA binding activities of both CIC and CIC-DUX4 in those pathologies , respectively .
The C1 domain is highly conserved in all CIC orthologs across metazoans . The conservation spans 40–45 amino acids with a highly invariable core of 11 residues at the C-terminal end ( Fig 2A and 2B ) . Since CIC contains several conserved motifs that exert context-dependent functions [37] , we tested the requirements of C1 in different Drosophila tissues . To this end , we used CRISPR-Cas9 to generate new cic alleles specifically disrupting the C1-coding sequence ( S1 Fig ) . Among the isolated mutants , we selected an allele ( designated cic4 ) that removes four amino acids in the resulting protein , including three highly conserved residues in the C1 core ( Fig 2B ) . cic4 homozygous flies are semilethal and show a range of developmental defects . During early embryogenesis , maternal CIC protein normally establishes the presumptive trunk and abdominal regions of the embryo by restricting tailless ( tll ) and huckebein ( hkb ) expression to the embryonic poles . At the poles , CIC is downregulated by Torso RTK signaling , thereby enabling localized induction of tll and hkb by broadly distributed activators [5 , 9 , 11 , 12] ( reviewed in 4 ) . In cic mutant embryos , tll expands towards the center of the embryo , which then causes repression of central patterning genes such as knirps ( kni ) and loss of central body regions . Consistent with a loss of maternal CIC function , cic4 females are fully sterile and lay embryos that lack all central thoracic and abdominal segmented regions ( Fig 2C and 2D ) . Indeed , such embryos show clear derepression of tll and loss of the central kni stripe at the blastoderm stage ( Fig 2E and 2F ) , indicating a failure of CIC-mediated repression . This effect is not caused by reduced CIC protein expression or stability , since cic4 embryos exhibit normal levels of CIC accumulation in blastoderm nuclei ( Fig 2G and 2H ) , implying that the CIC4 mutant is functionally defective . A comparison with other cic mutations indicates that cic4 represents a strong hypomorphic allele ( S2 Fig ) . Next , we assayed the effects of cic4 in the follicular epithelium of the ovary . In this tissue , CIC organizes the future dorsoventral ( DV ) axis of the embryo by repressing mirror ( mirr ) , thereby restricting its expression to dorsal positions . In cic mutant backgrounds , mirr becomes derepressed towards ventral regions and this leads to inappropriate repression of pipe , a gene that is critical for induction of ventral embryonic fates [6 , 8 , 38–40] . Consequently , the resulting progeny show a strongly dorsalized phenotype and loss of ventral patterning markers . We find that ovaries from cic4 females show derepressed mirr expression that is similar to that seen in strong cic mutant conditions ( Fig 2I and 2J ) [6 , 8] . In addition , embryos laid by cic4 females lack expression of twist ( twi ) , a target of the maternal DV system that is normally activated in ventral positions ( Fig 2K and 2L ) . Thus , C1 is also required for CIC repressor activity in the follicle cells . Also , cic4 flies consistently show abnormal wings with extra vein tissue ( Fig 2M and 2N ) . This phenotype reflects insufficient CIC activity during wing development , where CIC represses vein-promoting genes downstream of EGFR signaling [7 , 12] ( see also below ) . Finally , cic4 males are sterile and exhibit a severe genitalia rotation phenotype present in other strong cic mutants backgrounds [7] ( Fig 2O and 2P ) . Thus , although we have not examined the requirement of C1 for all CIC functions in Drosophila , our results suggest that C1 mediates a key general aspect of CIC activity in this organism . Since C1 is important for CIC repressor function , we initially hypothesized that C1 might function as a repressor module that interacts with co-repressor factors . As a first test of this idea , we reasoned that replacing the HMG-box region of CIC with a heterologous DNA binding domain should produce a chimeric protein capable of repressing transcription in a C1-dependent manner . For this , we adopted an assay involving the basic-helix-loop-helix ( bHLH ) region of Hairy and the Sex-lethal ( Sxl ) gene [41 , 42] . We made a cic expression construct in which the HMG-box-coding region was replaced by the bHLH-coding region of Hairy and expressed this chimera , CIC ( bHLH ) ( Fig 3A ) , in transgenic embryos under the control of cic genomic sequences . The CIC ( bHLH ) product was clearly detectable in nuclei of central and subterminal regions of the embryo ( Fig 3B ) , whereas a CIC derivative lacking the HMG-box is mainly cytoplasmic [9] , implying that the bHLH region targets CIC to the nucleus . One target recognized by the bHLH domain of Hairy is Sxl , a sex-determining gene activated exclusively in female embryos ( Fig 3C ) . Hairy does not normally regulate Sxl , but it can do so when expressed earlier than usual ( i . e . before or at early stage 5 ) , by mimicking the activity of the Hairy-family repressor Deadpan [41 , 43] . Thus , premature Hairy expression causes inappropriate repression of Sxl and leads to female lethality . Accordingly , we find that early CIC ( bHLH ) expression driven by the maternal cic promoter causes extensive repression of Sxl except in polar regions of the embryo , where CIC ( bHLH ) nuclear levels and activity are lower in response to Torso signaling ( Fig 3D ) . Consistent with this repression effect , CIC ( bHLH ) -expressing females show a clear ‘daugtherless’ phenotype as >95% of their progeny are males . We then tested a CIC ( bHLH ) derivative lacking the C1 domain ( Fig 3A , Materials and methods ) . Surprisingly , this construct behaves similarly to intact CIC ( bHLH ) , causing evident repression of Sxl and lethality of the female progeny ( Fig 3E ) . Thus , targeting CIC to the Sxl regulatory sequences via the Hairy bHLH domain renders CIC-mediated repression independent of C1 . In concordance , we recently found that fusing C1 directly to a bHLH-containing fragment of Hairy does not lead to repression in the Sxl assay [37] , whereas similar fusions with well-characterized repressor domains do [42 , 44–46] . Thus , although we cannot rule out that C1 could have an intrinsic repressor activity in other contexts , the fact that C1 is required for CIC but not CIC ( bHLH ) function points to a role of C1 in HMG-box-mediated DNA binding . Next , we tested the function of C1 in human cultured cells . To this end , we made a series of GFP-tagged human CIC derivatives carrying mutations in the HMG-box and C1 domains ( Fig 4A ) . These constructs had similar levels of expression and were all detected in the nucleus , implying that tagging and mutagenesis did not differentially affect their stability or subcellular localization ( Fig 4B; S3 Fig ) . Then , we assayed the repressor activities of the different constructs using a luciferase reporter under the control of a synthetic promoter carrying CIC binding sites ( CBSs ) derived from the ETV5 promoter [15 , 17 , 31] ( see Fig 4 legend ) . This reporter , ETV5p , was significantly repressed upon cotransfection of GFP-CIC ( WT ) , whereas GFP-CIC constructs carrying either recurrent OD mutations affecting the HMG-box ( R215W ) or C1 ( R1515L ) domains [22 , 26 , 30] , or a complete C1 deletion ( ΔC1 ) , showed reduced repressor activities relative to the intact control ( Fig 4C ) . This indicates that the HMG-box and C1 domains are both required for CIC repressor activity in mammalian cells . We then tested association of the above CIC mutants to endogenous ETV/PEA3 gene promoters by chromatin immunoprecipitation ( ChIP ) . As expected , the R215W mutation caused a reduction in ChIP signals relative to the control CIC protein ( Fig 4D ) . Notably , both C1 mutations also diminished CIC promoter occupancy at all three ETV/PEA3 gene analyzed . The reduction was more pronounced for the full C1 deletion , but the effect of the R1515L mutation was also clearly significant . These results indicate a requirement of both the HMG-box and C1 domains for binding of CIC to its target genes . Having established that C1 mediates CIC association with endogenous targets , we hypothesized that it might contribute directly to DNA binding . To test this idea , we performed EMSA experiments comparing the binding activities of various Drosophila and human CIC constructs . As shown in Fig 5A , these constructs carried intact or mutated HMG-box and C1 domains either alone or combined together in the same polypeptide . These proteins were expressed in vitro or in bacteria , and incubated with probes derived from Drosophila and human CIC targets . Unexpectedly , the Drosophila HMG-box alone ( construct 1 ) was unable to bind DNA , whereas an HMG-C1 construct containing both domains next to each other ( construct 2 ) showed clear , specific binding to a probe from the hkb gene containing two CBSs [12] . Similarly , neither the HMG-box nor the C1 domains alone ( constructs 1 and 3 ) bound to a probe from pointed ( pnt ) [14] , nor did they bind this probe when combined in the same reaction ( Fig 5A and 5B ) . In contrast , this probe was readily bound by a His-tagged HMG-C1 construct purified from bacteria , but not by the equivalent construct bearing the cic4 lesion ( constructs 4 and 5 ) . Likewise , a human HMG-C1 construct efficiently bound to a probe from the ETV4 gene [15 , 17] , whereas recurrent OD mutations mapping to the HMG-box ( R201W and R215W ) or C1 ( R1515L ) domains greatly reduced this binding ( constructs 6–9 ) . We also used human HMG-C1 constructs to test the effects of flexible versus rigid linkers separating the HMG-box and C1 domains ( constructs 10–12; see Fig 5 legend ) ; both linkers permitted effective binding , ruling out a major effect of the sequences connecting the HMG-box and C1 elements . In contrast , placing the HMG-box and C1 domains in reverse order ( construct 13 ) abolished DNA binding ( Fig 5B ) , indicating that this configuration imposes steric constraints on binding . Thus , the HMG-box and C1 domains function together as an obligate , conformationally oriented module for site-specific binding to DNA . As indicated above , the C1 sequence contains a highly conserved core of 11 residues flanked by a somewhat more variable amino-terminal extension . To further test the requirements of these sequences in DNA binding , we assayed three mutations of discrete sub-motifs within C1 ( constructs 15–17 ) . All three mutations prevented binding of Drosophila HMG-C1 to the hkb probe ( Fig 5B ) , indicating that these sub-motifs ( or a full , correctly folded C1 domain ) are important for function . Finally , we asked if , by analogy to other Sox factors [47–49] , the HMG-C1 module binds DNA as a homodimer . To this end , we assayed the binding activities of two HMG-C1 constructs of different size ( constructs 2 and 14 ) using the pnt probe , which contains a single CBS . As expected from their relative molecular masses ( approximately 24 and 53 kD , respectively ) , each of these constructs individually produced protein-DNA complexes of different mobility . Similarly , a binding reaction containing both proteins resulted in the same complexes and no intermediate complex was observed ( Fig 5B ) , indicating that the proteins did not oligomerize . These results strongly suggest that C1 does not mediate dimerization and the HMG-C1 module binds DNA as a monomer , although we cannot formally exclude that other CIC sequences may facilitate oligomerization during DNA binding in vivo . The role of C1 in DNA binding is reminiscent of the mechanism employed by certain TCF factors in DNA recognition . Thus , the TCF orthologs from Drosophila and C . elegans , and some vertebrate TCF isoforms , contain , in addition to the HMG-box , a zinc binding domain known as C-clamp which functions in DNA binding . The C-clamp acts by binding so-called ‘Helper sites’ ( 5’-RCCGCCR-3’ ) located at short distance ( usually <10 bp away ) from the sequence recognized by the TCF HMG-box , thereby augmenting the DNA binding strength and specificity of TCF towards its targets [50–56] . Therefore , although the C1 and C-clamp domains are not related in sequence , we considered the possibility that C1 might also recognize a specific conserved motif adjacent to the consensus CBSs . To this end , we first compared the sequences flanking bona fide CBSs present in three D . melanogaster genes , their D . virilis orthologs , and three mouse promoters . As shown in Fig 6A , this analysis reveals several conserved motifs in the vicinity of CIC octamers from orthologous Drosophila genes , but not across non-orthologous genes . This suggests that those motifs correspond to orthologous sites for other transcription factors in the selected enhancers or promoters . Similarly , the mouse CBSs are flanked by a short A/T-rich extension , but this motif is not well conserved in the Drosophila sequences . This indicates that CIC sites are not surrounded by a particular motif serving as a ‘helper site’ for CIC DNA binding . To directly test the influence in DNA binding of sequences flanking functional CIC octamers , we performed EMSA experiments using probes corresponding to CIC sites present in the Drosophila tll , hkb and intermediate neuroblasts defective ( ind ) genes , and in human ETV5 . These probes span 30–32 bp and do not share significant similarity outside the CIC octamers ( Fig 6B ) . Nevertheless , they were similarly bound by the corresponding Drosophila and human HMG-C1 minimal proteins , indicating that the CIC octamer is the main determinant for DNA recognition in this assay ( Fig 6C and 6D ) . The human protein also bound efficiently a synthetic probe containing a CIC octamer flanked by random sequences ( CBS syn ) . Finally , we tested the binding of human HMG-C1 to an 18-bp probe carrying a CBS derived from ETV5 flanked by only 5 bp on either side ( Fig 6B ) . This probe was bound with similar affinity to that observed using longer probes , and the binding was reduced by a mutation in the CBS ( Fig 6D ) , indicating that a single , isolated CIC octamer is sufficient for effective binding of CIC to DNA . Finally , we have re-examined if CBSs are sufficient for DNA recognition by CIC in vivo . We selected a 12-bp motif containing a CBS from the hkb enhancer and inserted two copies of this sequence in a reporter construct driven by the bottleneck ( bnk ) promoter , which is ubiquitously active in early embryos . These insertions were introduced without disrupting conserved elements in the bnk promoter , thus preserving its regulation by the Zelda activator and other factors ( Fig 6E ) [57] . As shown in Fig 6F and 6G , whereas a control bnk-lacZ reporter directs uniform expression in the early embryo , the reporter containing CBSs is expressed only in polar regions , indicating that it is effectively regulated by endogenous CIC . This result supports our conclusion that CIC binds its target sites without any requirement or modulation by specific flanking sequences . The above results provide a plausible mechanistic explanation for the main pattern of oncogenic CIC-DUX4 chimeras ( which usually include the C1 domain , as shown in Fig 1 ) , since C1 should promote CIC-DUX4 activity by enhancing its binding to DNA . Pursuing this idea , we have established a Drosophila assay of CIC-DUX4 activity in which to test the requirement of C1 . We made a construct encoding Drosophila CIC fused to the C-terminal portion of human DUX4 and expressed this chimera in the developing wing ( Fig 7A ) . This tissue is highly sensitive to changes in CIC activity , which normally acts to promote intervein cell fate except in the presumptive veins where it is inhibited by EGFR signaling ( Fig 7B ) . Thus , loss of CIC function produces extra vein material ( Fig 2N; S2 Fig ) , whereas overexpression of CIC suppresses vein formation ( Fig 7D ) . We find that targeted expression of CIC-DUX4 in the primordial wing blade ( see Materials and methods ) causes severe defects including reduced wing size , ectopic venation and blistered wings due to loss of adhesion between the two wing surfaces ( Fig 7E and 7F ) . This phenotype is markedly different to that caused by overexpression of intact CIC and actually resembles the loss of CIC function ( see S2 Fig ) , consistent with CIC-DUX4 mediating transcriptional activation instead of repression . To test this further , we assessed CIC-DUX4 activity in the wing imaginal disc using a synthetic reporter , CUASC-lacZ , containing CBSs linked to GAL4 binding sites ( Fig 7I ) . In discs expressing GAL4 protein in the wing pouch , the reporter is activated only in presumptive vein stripes since it is repressed by CIC in intervein regions ( Fig 7J ) [12] . In contrast , this pattern appears markedly broadened in CIC-DUX4-expressing discs ( Fig 7K ) , as expected if CIC-DUX4 activates the reporter and overrides the repressor activity of endogenous CIC . We then evaluated the contribution of the C1 domain to CIC-DUX4 activity in this assay . Using CRISPR-Cas9 , we edited the CIC-DUX4-expressing transgene and isolated two mutations deleting either 2 or 11 residues within the C1 domain of CIC-DUX4 ( Fig 7A ) . Both mutations strongly suppressed the phenotypes produced by CIC-DUX4 , with the 11-residue deletion showing almost complete restoration of the wild-type vein pattern ( Fig 7G and 7H ) . This mutant also showed significantly restricted expression of the CUASC-lacZ reporter ( Fig 7M ) . Thus , the C1 domain is required for the opposing activities of CIC and CIC-DUX4 proteins in the Drosophila wing , which is consistent with its role in DNA binding rather than transcriptional repression per se .
HMG-box proteins play critical roles in development and disease by regulating the expression of specific target genes . For both Sox and TCF factors , this control depends on the HMG-box as well as on other DNA-binding and dimerization motifs that cooperate in regulating the correct genomic targets . For instance , Sox proteins typically associate with partner factors that interact with specific DNA sequences close to the Sox sites . Similarly , several TCF isoforms contain a C-clamp domain that recognizes GC-rich motifs adjacent to TCF sites , thereby enhancing the affinity and specificity of TCF binding to its targets . It is believed that such combinatorial modes of DNA recognition are essential for proper developmental regulation by both protein families ( see Fig 8 ) . In this work , we have identified a distinct mode of DNA binding by CIC , which depends on its conserved C1 domain . Compared to the above examples , the C1 motif is unique in that it is located at long distance from the HMG-box , does not display detectable DNA-binding activity on its own , does not mediate dimerization , and is not involved in recognizing auxiliary motifs next to CIC octameric sites . Instead , our results indicate that C1 cooperates with the HMG-box to recognize discrete octameric sites both in vitro and in vivo . Since mutations in the C1 domain do not completely abolish the activity of CIC in flies or in human cells ( S2 Fig; Fig 4 ) , we favor the view that C1 acts by potentiating the binding of the HMG-box to its specific sites . Several mechanistic models could account for C1 function . For example , C1 , like many DNA binding sequences , contains several conserved basic residues in its core , which might establish direct , low-affinity contacts with DNA . Alternatively , the C1 domain could interact with the HMG-box or modulate its folding during DNA recognition . Future high-resolution structural analyses of the HMG-box-C1 module bound to DNA should elucidate the molecular basis of C1 function . Regardless of the precise molecular mechanism , our results reveal a unique mode of DNA binding that distinguishes CIC from Sox and TCF factors ( Fig 8 ) . Thus , the HMG-box-C1 module mediates robust and specific binding to its conserved octameric sites independently of partner factors and auxiliary target sequences . Indeed , CIC recognizes their octameric sites even when those sites are relocated to heterologous or synthetic enhancers ( Fig 6G; see also refs . [11 , 12 , 19 , 31] ) , and our current work demonstrates efficient binding of the HMG-box-C1 polypeptide to an isolated CIC octamer in vitro . It thus appears that HMG-box proteins share a general principle of augmenting their target specificity through modular or cooperative DNA binding , but each individual HMG-box family relies on unique domains and mechanisms for this activity . Furthermore , the distinct binding modes of Sox and CIC proteins give rise to different logics of transcriptional control . Thus , the ‘partner mechanism’ of Sox proteins is highly versatile and leads to either transcriptional activation or repression depending on the partner protein as well as on the promoter context . In contrast , in all cases studied so far , CIC proteins function as dedicated repressors , and Drosophila CIC has been shown to contain an intrinsic repressor motif [37] . Finally , our results imply that the two main subgroups of CIC amino acid substitutions in OD and other tumors , which map to the HMG-box and C1 domains ( Fig 1A ) , cause related defects in DNA binding . This would then lead to derepression of CIC targets such as ETV/PEA3 genes , which encode ETS transcription factors extensively implicated in tumorigenesis , as well as genes encoding feedback inhibitors of RTK signaling like Sprouty and Spred [23 , 29 , 30] . Moreover , our findings help explain the main pattern of oncogenic translocations resulting in CIC-DUX4 sarcomas ( Fig 1B ) : it is not incidental that C1 is preserved in most CIC-DUX4 chimeras , since C1 should be required for effective CIC-DUX4 DNA binding and subsequent aberrant activation of ETV genes and other targets . This is supported by our analyses ( Fig 7 ) showing that an intact C1 domain is required for the activity of a CIC-DUX4 chimera in the Drosophila wing .
The cic4 allele was generated by CRISPR-Cas9-mediated editing . Briefly , a custom gRNA expression construct targeting the C1 coding sequence was prepared in vector pCDF3 [58] and inserted at the attP40 landing site via phiC31-mediated integration [59] ( see S1 Fig for details of the gRNA sequence ) . Transgenic gRNA males were crossed to nanos-cas9 females to obtain founder males , which were then crossed to females carrying the TM3 balancer for recovery of mutant alleles . Induced mutations were characterized by sequencing PCR fragments amplified from candidate flies . A similar scheme using the same gRNA insertion was employed to isolate mutations in the UAS-CIC-DUX4 transgene . Other alleles and chromosomal rearrangements employed were: cicQ474X [10] , cic1 [5] , Df ( 3R ) ED6027 ( see FlyBase ) , and the mirrP2 enhancer trap ( mirr-lacZ; ref . [60] ) . Transgenic flies expressing CIC derivatives were obtained by P-element transformation . Expression of CIC-DUX4 derivatives was achieved using the GAL4-UAS system and the driver line C5 [61] . All crosses were performed at 25°C , unless otherwise noted . Embryos were fixed in 4% formaldehyde-PBS-heptane using standard procedures . Ovaries and wing discs were dissected in PBS and fixed with 4% formaldehyde-PBS . In situ hybridizations were performed using digoxigenin-UTP ( kni , twi and Sxl ) or biotin-UTP ( tll ) labeled antisense RNA probes , followed by incubation with fluorochrome-conjugated anti-digoxigenin or anti-biotin antibodies for FISH analysis , or with secondary antibodies coupled to alkaline phosphatase ( AP ) for histochemical detection . Drosophila CIC was detected using either a guinea pig polyclonal antibody raised against the C-terminal region of the protein [14] , or a rabbit polyclonal recognizing the HMG-box and C-terminal regions . Lac-Z and HA-tagged proteins were detected using monoclonal antibodies 40-1a ( Developmental Studies Hybridoma Bank ) and 12CA5 ( Roche ) , respectively . Immunofluorescence signals were visualized with species-specific secondary antibodies labeled with different fluorochromes ( Molecular Probes ) . Fluorescent and AP-stained samples were mounted in Fluoramount and Permount , respectively . Cuticle preparations were mounted in 1:1 Hoyer’s medium/lactic acid and cleared overnight at 60°C . Wings were rinsed in isopropanol and mounted in Euparal . The reference sequences used for the Drosophila and human CIC proteins are NP_524992 . 1 and NP_055940 . 3 , respectively . The CIC ( bHLH ) and CIC ( bHLH ) ΔC1 constructs were made using a genomic cic-HA rescue transgene in the pCaSpeR4 vector [9 , 62] , by replacing an EagI fragment encoding amino acids 384–583 of CIC ( including the HMG-box ) with a fragment encoding residues 25–150 of Hairy ( containing the bHLH domain ) . CIC ( bHLH ) ΔC1 carries , in addition , a deletion of the region coding for the C1 domain ( residues 1308–1356 ) . The CIC-DUX4 transgene encodes most of Drosophila CIC protein ( residues 1–1380 ) fused to amino acids 325–424 of DUX4 ( thus mirroring the chimera described in ref . 31 ) , and was assembled in pUAST . The constructs used in the EMSA experiments express the following CIC amino-acid fragments: 478–572 ( Dm CIC HMG ) , 478–572 fused to 1288–1378 ( Dm CIC HMG-C1 ) , 1288–1378 ( Dm CIC C1 ) , 188–288 fused to 1451–1527 ( Hs CIC HMG-C1 ) , 188–280 fused to 1457–1527 ( Hs CIC ( HMG-C1 ) min ) , 1457–1527 fused to 188–280 ( Hs CIC C1-HMG ) , and 475–598 fused to 1044–1378 ( Dm CICmini-DNt ) . Hs CIC HMGR201W-C1 , Hs CIC HMGR215W-C1 and Hs CIC HMG-C1R1515L are mutant derivatives of Hs CIC HMG-C1 . Hs CIC HMG-Flex-C1 and Hs CIC HMG-Rig-C1 are identical to Hs CIC ( HMG-C1 ) min except in that they contain flexible ( Flex ) and rigid ( Rig ) linkers separating the HMG-box and C1 domains [63 , 64] . Dm CIC HMG-C1mut1-3 are derivatives of Dm CIC HMG-C1 . All these constructs were subcloned into pET-17b for in vitro expression under the control of the T7 promoter . His-tagged constructs were expressed in bacteria using the pET-29b vector . Dm CIC HMG-C1-His and Dm CIC HMG-C1ΔRQKL-His are derivatives of Dm CIC HMG-C1; Hs CIC HMG-C1-His is based on Hs CIC HMG-C1 . GFP-tagged human CIC constructs were assembled in pcDNA5/FRT/TO [15] . The R215W and R1515L mutations were introduced using the QuikChange site directed mutagenesis kit ( Agilent ) following the manufacturer's guidelines . The C1 deletion ( spanning residues 1464–1519 ) was generated using a recombinant PCR-based approach . Unless indicated otherwise , all plasmids were stably introduced into Flp-In T-REx 293 cells ( Invitrogen ) following instructions from the manufacturer . For Western blot analysis , cells were lysed in a buffer containing 75 mM NaCl , 50 mM Tris-HCl , pH 8 , and 0 . 5% Triton X-100 , supplemented with PMSF and protein inhibitor cocktail Complete Mini ( Roche ) . 50 μg of total protein extract was resolved by SDS-PAGE , transferred to nitrocellulose membranes and probed with antibodies against GFP ( Abcam , ab290 ) and GAPDH ( Sigma Aldrich , G8795 ) . To analyze nuclear or cytoplasmic localization of the different CIC constructs , we transiently transfected a plasmid encoding GFP ( pEFGP-C2 ) as a control or plasmids encoding WT [15] or mutated GFP-CIC constructs into 293T cells . 48h after transfection , cells were fixed with 4% formaldehyde and permeabilized with 0 . 5% Triton X-100 . GFP expression was detected using polyclonal anti-GFP antibodies ( Abcam ab290 , 1:1000 ) followed by counterstaining with Hoechst 33342 . Images were acquired with a Leica TCS SP5 confocal microscope . Luciferase assays were performed in a Glomax luminometer ( Promega ) according to the manufacturer's guidelines . Briefly , we transfected the pGL3proERM-338/-329 tandem reporter vector [31] along with empty pcDNA5/FRT/TO vector or pcDNA5/FRT/TO plasmids expressing wild-type or mutant GFP-tagged human CIC derivatives into 293T cells using jetPRIME reagent ( Polyplus-transfection ) . Cells were lysed after 48 h and assayed for luciferase activity . A Renilla luciferase-expressing vector was used for normalization . ChIP assays were performed as described [65] . Briefly , 2x107 Flp-In T-REx 293 cells stably transfected with pcDNA5/FRT/TO alone or pcDNA5/FRT/TO expressing either wild-type or mutated ( R215W , R1515L or C1 domain deletion ) GFP-tagged human CIC cDNAs were cross-linked for 15 min at room temperature . After washing , cells were sonicated at high intensity during 30 cycles , with 30 s ON and 30 s OFF per cycle ( Bioruptor Plus , Diagenode ) , followed by centrifugation for 15 min at 14 , 000 rpm at 15°C . For each condition , 200 μg of lysate was incubated overnight with 2 μl of anti-GFP antibody ( Abcam , ab290 ) and immunoprecipitated by incubation with 20 μl of protein A/G beads during 1 h at 4°C in a rotating platform . After reverse crosslinking , DNA fragments were recovered by phenol/chloroform extraction and qRT-PCR was carried out in a 7500 Fast Real-Time PCR System ( Applied Biosystems ) using Power SYBR green PCR Mastermix ( Applied Biosystems ) with the following primers: ETV1 promoter , 5-caaccacgtgaccaagaag-3 and 5-GCGCTCCGCTAGGAGATT-3; ETV4 promoter , 5-cttctctctttttctctcggttc-3 and 5-CCAATCAGAATGTAGGGGTTG-3; ETV5 promoter , 5-aagtgcttcactgactcagctaa-3 and 5-CATTGGCCAATCAGCACA-3 . As a negative control we used a region of the CDK1 promoter without known CBSs , amplified with primers 5-ggccttcaacgtatgaattagc-3 and 5-AGTTGGTATTGCACATAAGTCT-3 . EMSA experiments were performed using CIC protein fragments synthesized with the TNT T7 Quick Coupled Transcription/Translation system ( Promega ) . For expression of His-tagged proteins , bacterial cultures were induced for 2 h with 1 mM IPTG and proteins purified using the Proteus IMAC Mini Sample kit . DNA probes were synthesized as complementary oligonucleotides leaving 5’ GG overhangs , or amplified by PCR with primers carrying NotI restriction sites , subcloned , and released by NotI digestion . Probes were then end-labeled using α-32P-dCTP and Klenow Fragment , exo- ( Thermo Scientific ) . The sequences of wild-type and mutant probes are shown in S1 Table . Binding reactions were carried out in a total volume of 20 μl containing 60 mM Hepes pH 7 . 9 , 20 mM Tris-HCl pH 7 . 9 , 300 mM KCl , 5 mM EDTA , 5 mM DTT , 12% glycerol , 1 μg poly ( dI-dC ) , 1 μg BSA , ~1 ng of DNA probe , and 1 μl of programmed or non-programmed ( control ) TNT lysate ( or ~1 ng of bacterially expressed His-tagged protein ) . After incubation for 20 min on ice , protein-DNA complexes were separated on 5% non-denaturing polyacrylamide gels run in 0 . 5X TBE at 4°C , and detected by autoradiography . | Transcription factors bind specific sites in the genome via discrete protein domains that recognize their target DNA sequences . One such domain is the HMG-box , which is found in many chromatin and transcriptional regulators across species . Two salient groups of HMG-box proteins are the Sox/SRY and TCF/LEF1 factors , which are involved in multiple developmental and signaling processes . Extensive genetic and molecular studies have shown , however , that both groups of proteins do not simply bind DNA through their HMG-box , but rely either on additional protein domains or associated factors for targeting their correct sites in the genome . In this work , we have focused on another HMG-box protein , Capicua ( CIC ) , which has recently emerged as an important mediator of Ras/MAPK signaling in both Drosophila and mammals . We find that the HMG-box of CIC does not bind DNA alone and instead requires a separate conserved motif ( C1 ) present in all CIC proteins . The C1 domain is restricted to CIC proteins and exhibits several properties that distinguish it from Sox and TCF domains involved in DNA binding . Thus , CIC proteins represent a separate sub-family of HMG-box factors that have evolved an independent mechanism for enhancing the DNA-binding capabilities of their HMG-box . Notably , our results also explain distinct patterns of human CIC mutations that either inactivate CIC tumor suppressor function or produce oncogenic fusions between CIC and the DUX4 activator factor . | [
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] | 2017 | A new mode of DNA binding distinguishes Capicua from other HMG-box factors and explains its mutation patterns in cancer |
In this study the function of the two isoforms of creatine kinase ( CK; EC 2 . 7 . 3 . 2 ) in myocardium is investigated . The ‘phosphocreatine shuttle’ hypothesis states that mitochondrial and cytosolic CK plays a pivotal role in the transport of high-energy phosphate ( HEP ) groups from mitochondria to myofibrils in contracting muscle . Temporal buffering of changes in ATP and ADP is another potential role of CK . With a mathematical model , we analyzed energy transport and damping of high peaks of ATP hydrolysis during the cardiac cycle . The analysis was based on multiscale data measured at the level of isolated enzymes , isolated mitochondria and on dynamic response times of oxidative phosphorylation measured at the whole heart level . Using ‘sloppy modeling’ ensemble simulations , we derived confidence intervals for predictions of the contributions by phosphocreatine ( PCr ) and ATP to the transfer of HEP from mitochondria to sites of ATP hydrolysis . Our calculations indicate that only 15±8% ( mean±SD ) of transcytosolic energy transport is carried by PCr , contradicting the PCr shuttle hypothesis . We also predicted temporal buffering capabilities of the CK isoforms protecting against high peaks of ATP hydrolysis ( 3750 µM*s−1 ) in myofibrils . CK inhibition by 98% in silico leads to an increase in amplitude of mitochondrial ATP synthesis pulsation from 215±23 to 566±31 µM*s−1 , while amplitudes of oscillations in cytosolic ADP concentration double from 77±11 to 146±1 µM . Our findings indicate that CK acts as a large bandwidth high-capacity temporal energy buffer maintaining cellular ATP homeostasis and reducing oscillations in mitochondrial metabolism . However , the contribution of CK to the transport of high-energy phosphate groups appears limited . Mitochondrial CK activity lowers cytosolic inorganic phosphate levels while cytosolic CK has the opposite effect .
It is well established that creatine kinase ( CK ) catalyzes the reversible transfer of phosphate from ATP to creatine ( Cr ) : ( 1 ) However , how this biochemical function plays a role in cell functioning has been the subject of intense controversy [1] . It is remarkable that two distinct isoforms of CK are expressed in muscle cells , one in the mitochondrial inner membrane space ( IMS ) and one in the cytosol where the contractile elements are located . This led to the idea of the ‘phosphocreatine shuttle’ , proposed by Bessman and Geiger [2]: PCr formation from adenine nucleotide and creatine in the IMS is catalyzed by the mitochondrial isoform of CK , Mi-CK , located in the IMS . PCr may then proceed to the cytosol , which constitutes a mechanism of facilitated diffusion of high-energy phosphate ( HEP ) groups . Retransfer of HEP to adenine nucleotide to energize myofibrillar contraction is done by the muscular isoform of CK , MM-CK , located in the cytosol ( see Figure 1 ) . Transfer of HEP was argued to be accomplished either by direct diffusion of ATP through the mitochondrial outer membrane ( MOM ) and cytosol or indirectly via the ‘phosphocreatine shuttle’ . The phosphocreatine shuttle hypothesis has led to extensive scientific debates on the role of CK , e . g . [1] , [3] , [4] . Besides the energy transfer function , the creatine kinase system was thought to be responsible for ( i ) temporal energy buffering by maintaining an adequate ATP/ADP ratio during interruption of energy supply [5] or during changing energy demand [3] , [6] and ( ii ) for regulation of oxidative phosphorylation [7] . The CK system , transporting creatine instead of ADP from the cytosol to the mitochondria , is a potential key regulator of oxidative phosphorylation . CK inhibition experiments on rabbit hearts [8] , [9] and CK knockout experiments in mice [10] suggest that the creatine kinase system affects the dynamic adaptation of oxidative phosphorylation to energy demand . Mathematical modeling has proven helpful to understand the CK system: several existing models account for a compartmentalized energy metabolism system in myocytes under various conditions [6] , [11]–[16] . The main differences between the model analyzed here and other models described in the literature are addressed in the Discussion . We build on a previously published computational model for the dynamic adaptation of oxidative phosphorylation to changing workloads which captures the key elements responsible for buffering and transport of HEP between IMS and cytosol [17] , [18] . The model incorporates synthesis of ATP from ADP by oxidative phosphorylation in the mitochondria and ATP consumption in the cytosol , the reversible transfer of phosphate groups from ATP to creatine by CK enzyme reactions and metabolite diffusion between IMS and cytosol through the MOM ( see Figure 1 ) . The model's dynamic behavior is affected by 22 free parameters for enzyme kinetics and membrane permeability , which had been determined experimentally and were collected from the scientific literature . In recent work we investigated the sensitivity of the predictions of this CK model with respect to possible error in the parameters using a simplified ensemble approach and found that even a modest error on each model parameter results in a broad range of possible predictions [19] . However , models containing many molecular kinetic parameters , all known with little accuracy , can yield useful predictions as long as the correlation of these inaccuracies is taken into account . Brown et al . showed , using a computational model of nerve growth factor signaling , that viable model predictions can be achieved in spite of a high degree of uncertainty in all kinetic parameters [20] , [21] . The approach identifies so-called ‘sloppy’ combinations of parameters , which , when jointly altered , do not significantly change the outcome of a model simulation , meaning that multiple combinations of parameters describe experimental data equally well . Gutenkunst et al . investigated a variety of metabolic and signaling networks and found these spectra of correlated parameter sensitivities to be universal in Systems Biology models [22] . To use the information from these hidden correlations between parameters , a Bayesian ensemble of distinct parameter sets which agree with experimental data , can be sampled with Markov-Chain Monte Carlo ( MCMC ) methods . The likelihood of a parameter combination being included in the ensemble is proportional to the parameter combination's likelihood to predict the experimental input data set . Starting point for the walk through parameter space is the parameter set obtained from a least-squares parameter fit to experimental data . The resulting ensemble of parameter sets , constrained by the experimental data , allows a quantification of uncertainty not only of parameter values , but also delineates the uncertainty of model predictions for new experimental interventions . Below we demonstrate that combining molecular kinetic data , organellar data and whole organ response data with a sloppy modeling approach is feasible and fruitful . We assembled a set of prior knowledge data on kinetic parameters of the CK enzymes and made use of measurements on the oxidative capacity and kinetics of isolated mitochondria and on metabolite transport across membranes and cytosol . These data at the molecular and organellar level were combined with experimental data on the response of the whole heart: for jumps to multiple heart rate levels the response time of the increase in oxygen uptake in the heart was measured . Based on model analysis of the oxygen transport system , the response time of oxygen uptake at the level of the mitochondria could be calculated from the whole heart level uptake [9] . These response times for wild type CK levels and during CK inhibition played an important role as input data for the MCMC analysis . Based on these data from multiple levels in the system , we predict the contribution of PCr to HEP transport and the buffering capacity of the system toward the high-frequency high-amplitude pulsations of ATP hydrolysis during the cardiac cycle . As a consequence , we determined that the functional role of the CK system in energy transport is limited and that high pulses in ATP hydrolysis are buffered by CK at order 100 millisecond time scales; both functions are presently not directly accessible to experimental measurement . Surprisingly , we also find that the mitochondrial CK isoform plays a role in regulating the cytosolic inorganic phosphate level .
Model parameters were estimated simultaneously to fit the tmito values for all conditions using a least-squares optimization procedure . Different optimization algorithms ( downhill simplex algorithm , Powell's method , Levenberg-Marquardt ) gave similar quality of the fit . Initial and optimized parameter values can be found in Table 1 . Figure 2 shows all tmito values predicted by the model before and after parameter optimization for all conditions . After fitting , the model correctly predicts a quicker energy supply-demand signaling when CK is inhibited by 98% , causing weaker ADP/ATP buffering by CK . In the optimization procedure , the maximum velocities of the Mi-CK and the MM-CK enzyme were decreased by 12 and 36% , respectively , from their initial literature values . These literature enzyme activities for MM-CK and Mi-CK had been taken from the same experimental model , but without inhibition of glycolysis by IAA [8] . The experimental data used in the present analysis was measured in the presence of IAA which was found to impede CK activity by 20% [9] . The drop in estimated CK activity is therefore plausible . Other parameters which are altered significantly by the optimization are the apparent Michaelis constant for inorganic phosphate in the mitochondrion , Kpi , which drops from 800 to 347 µM , and the apparent KM for ADP , Kadp , which increases from 25 to 36 µM . Both parameters occur in the model equation determining the rate of oxidative phosphorylation , which may explain the inverse variation . There exist in vitro measurements of Kpi that are lower than the initial value used in this analysis [18]: Stoner & Sirak for instance measured Kpi to be 360 µM [23] which is close to our optimized value . Likewise , reported values for Kadp vary between 20 and 30 µM [24] , [25] , corroborating the values obtained by the fit . Starting from the optimized parameter set ( see Table 1 ) , we sampled the parameter space to generate an ensemble of 658 independent parameter sets using the Metropolis-Hastings algorithm . The parameter set yielding the lowest cost in the complete ensemble was this optimized parameter set . The distributions of all parameters in the ensemble are shown in Figure 3 . The nine kinetic parameters which had known error values ( see Table 1 ) show a mean value in the ensemble close to the measured value and a standard deviation close to their reported measurement error from the literature , which was to be expected given the prior information in the cost function . However , the parameters for which there was no standard error value available from the literature in general gave a standard deviation in the ensemble which was smaller than the default assigned large standard error ( see Table 1 ) . We tested the effect of different assumptions on the default prior standard deviations on posterior parameter distributions and ensemble predictions , reported in Text S1 which shows that the conclusions reported here are not changed by larger or smaller values on the default prior . The mean value of PSmom , AdN in the ensemble is 31 . 7 s−1 , which is larger than the optimized value of 13 . 3 s−1 found previously [18] . The distribution of PSmom , AdN shows substantial skewing with a minimum value of 7 . 4 s−1 , and a rather sharp exclusion of small values which give slow response times of the system . Based on experiments in isolated permeabilized cardiomyocytes , Sepp et al . ( [26] ) estimated a value for MOM permeability to adenine nucleotides of 1833 nmol/min/mg protein per mM concentration difference . Converting this value expressed per mg tissue protein , assuming 150 mg protein per gram wet weight , this corresponds to PSmom , AdN = 7 . 45±1 . 89 s−1 . This is virtually the same as the minimum estimated in our ensemble analysis . The contribution of PCr to intracellular HEP transfer , Rdiff , PCr , is quantified by the ratio of PCr diffusion ( Jdiff , PCr ) to the total phosphate group diffusion through the MOM: ( 2 ) An ensemble of simulations based on the parameter ensemble described above allows evaluation of the confidence region for the model prediction . In the ensemble , Rdiff , PCr is on average 0 . 17±0 . 09 ( mean±SD ) at heart rate 160 bpm and 0 . 15±0 . 08 at 220 bpm in the case of active CK . Figure 4 shows the 95% confidence interval between upper and lower bound of the ensemble prediction for Rdiff , PCr for IAA and IA conditions in steady state at heart rate 220 bpm . The small oscillations during CK inhibition are due to the 2% residual activity of CK . The upper bound of the 95% confidence interval remains below 0 . 44 during the cardiac cycle for all simulated conditions . Rdiff , PCr decreases during the peaks in ATP hydrolysis and even becomes negative for the lowest trajectories in the ensemble , which indicates that PCr diffuses back to the mitochondria at the end of systole ( Figure 4 ) . The simulations show for these cases that ADP diffuses into the IMS during the peaks of ATP hydrolysis , stimulating a reversal of the mitochondrial CK reaction to produce ATP from PCr , exactly as happens in the cytosol . For these lowest trajectories in the ensemble the CK activity per unit volume of the intermembrane space is higher than the CK activity per unit volume of the cytosol , causing the PCr to go down more steeply in the intermembrane space . This causes the cytosolic PCr concentration to exceed the PCr concentration in the IMS , and a negative gradient forces PCr to diffuse back into the IMS . However , when averaged over the cardiac cycle , Rdiff , PCr is always positive , indicating net flux of PCr from the mitochondria to the cytosol , and for the vast majority of the ensemble PCr diffusion flux never becomes negative during the entire cardiac cycle . Simulations suggested that the relative importance of the PCr shuttle becomes less with higher ATP hydrolysis at heart rates of 160 , 190 and 220 bpm . We tested this hypothesis by predicting Rdiff , PCr for a range of heart rates from 60 to 300 bpm . The ensemble simulations reveal that Rdiff , PCr continuously drops for increasing heart rates for all sampled parameter combinations ( see Figure 5A ) . The predicted decline in Rdiff , PCr and increase in Pi concentration agrees with results of a recent study on perfused rat hearts [27] . Increased energy demand induces an increased ATP gradient between both compartments . At 160 bpm , the average difference between the ATP concentration in IMS and cytosol is 18 . 6 µmol*l−1 , at 220 bpm it becomes 22 . 3 µmol*l−1 for the optimal parameter set . The increased ATP gradient across the MOM induces direct ATP transport instead of facilitated transport via PCr . In order to demonstrate the dependence of shuttle utilization on the membrane conductance for adenine nucleotides , we predicted Rdiff , PCr as a function of PSmom , AdN for the ensemble . The predicted range shown in Figure 5B indicates that only for very small ATP permeability , PCr contribution becomes high . Even for the minimum value for PSmom , AdN in the ensemble ( 7 . 35 s−1 ) , the entire 95% confidence interval of Rdiff , PCr remains below 0 . 5 . Low MOM permeability to adenine nucleotides causes high-energy phosphate group transport via PCr , and that PSmom , AdN is never lower than 7 . 35 s−1 therefore argues against a predominant phosphocreatine transport . Also when the value PSmom , AdN = 7 . 45 s−1 estimated from Sepp et al . ( [26] ) , see above , is incorporated as prior knowledge , the analysis still yields similar predictions of Rdiff , PCr , which stays with 95% confidence between 0 . 16 and 0 . 46 at heart rate 220 bpm . It might be argued that the Kia value of the mitochondrial CK should be set to 290 µM with oxidative phosphorylation active ( [28] ) to reflect functional coupling of CK to the adenine nucleotide translocator ( ANT ) . Optimization based on this Kia value gives as result that on average 18% of the high-energy phosphate flux at a heart rate of 220 beats/min is transported in the form of PCr , the rest as ATP . The parameter values for Vmax , Mi , f calculated from rat heart mitochondria is 1609±113 µM/s in [28] and Vmax , ATPsyn is 2960 µM/s which is about twice the value measured in the rabbit heart study analyzed here . When using the rat heart parameters combined with Kia = 290 µM , the contribution of PCr to high-energy phosphate transport is estimated to be 25% . Further analysis of a model which incorporates a microcompartment which functionally couples the mitochondrial creatine kinase to the adenine nucleotide translocator ( [6] ) shows that it is difficult to explain the response time and molecular kinetic parameters simultaneously with this model . The results of this analysis can be found in Text S2 . The conclusion that the contribution of PCr to high-energy phosphate transport is relatively modest appears to be robust , because the contribution was estimated to be 15–17% in the ensemble study with rabbit heart parameters , see above , and does not become substantially higher in analyses with other parameter sets . The results described above indicate that direct ATP transport is predominant in working heart muscle . Given that PCr energy shuttling is of limited importance , we investigated another potential function of CK , i . e . temporal energy buffering . When ATP consumption by the myofibrils exceeds mitochondrial ATP production during muscle contraction , ATP homeostasis can be maintained by PCr [4] . Ensemble predictions for Rdiff , PCr , concentrations of cytosolic ADP and Pi and ATP synthesis rate at relative CK activity of 2 , 100 , and 300% of wild type levels are shown in Figure 6 . Note that Mi-CK and MM-CK activities are both changed by the same factor in this set of simulations . Even at 3-fold increased CK activity , Rdiff , PCr does not increase dramatically ( Figure 6F ) . However , oscillations of cytosolic ADP concentrations are significantly affected by the CK activity . The amplitude of the ADP oscillation is 77±11 µM at normal CK levels and becomes 146±1 µM if CK is inhibited by 98% , as is the case for IA treated perfused hearts ( Figure 6K , J ) . At threefold increased CK activity this becomes 36±22 µM ( Figure 6L ) . In simulations of a hypothetical case with 10000-fold increase of enzyme activity , oscillations of adenine nucleotide concentrations are almost fully damped to an amplitude of 2 . 6±0 . 2 µM ( data not shown ) . The time course of mitochondrial ATP production oscillates with amplitudes of 566±31 , 215±23 and 91±14 µM*s−1 for 2 , 100 and 300% relative CK activity , respectively ( Figure 6G–I ) . The pulsatility of ATP and ADP concentrations and of ATP synthesis is synchronized to ATP hydrolysis in the myofibrils . The confidence regions for these trajectories are relatively narrow . By blocking CK by 98% , the average concentrations of ADP in the IMS increases to 64±9 µM from 56±9 µM at normal CK levels . In contrast to ADP , the amplitude of oscillations of cytosolic inorganic phosphate stays relatively constant at different CK activities at about 145 µM . This reflects that Pi is not directly buffered by CK . Surprisingly , average levels of cytosolic inorganic phosphate drop with CK activity . The average Pi concentration at 2% CK activity is 1618±97 µM and becomes 1416±80 µM for wild-type CK activity ( Figure 6M , N ) . For all parameter sets in the ensemble the Pi concentration declines when CK activity is increased . Transport of HEP by PCr from mitochondria to cytosol partially takes place via the circuit formed by both CK isoforms , but was predicted to be quantitatively not very important . On the other hand , temporal buffering of the systolic ATP hydrolysis burst needs only the MM-CK activity in the cytosol , which is much higher than the Mi-CK activity ( see Table 1 ) . It was therefore still unclear what the function of the mitochondrial CK isoform is . In order to further elucidate the effect of the compartmentalized CK system on metabolism , we performed ensemble predictions with individual inhibition of both CK isoforms one by one . In Figure 7 , we show the 95% confidence intervals of predicted metabolite concentrations and reaction fluxes . The amplitude of oscillations in mitochondrial ATP synthesis is predicted to rise from 215±23 µM*s−1 at baseline CK activity to 278±33 with 98% Mi-CK inhibition , compared to 375±21 µM when MM-CK is inhibited by 98% ( Figure 7I–K ) . Thus , despite its low activity , Mi-CK still has a small but clear effect on the ATP synthesis oscillation amplitude . Inhibition of Mi-CK has a larger effect when MM-CK is already inhibited ( amplitude 565±31 µM*s−1 , Figure 7L ) . The damping of ADP oscillation is highly affected by MM-CK but not by Mi-CK: 98% inhibition of Mi-CK leads to an increase in the amplitude of systolic ADP oscilation from 77±11 to 83±11 µM ( Figure 7M , N ) , whereas MM-CK inhibition doubles the amplitude to 146±1 µM ( Figure 7O ) . Predictions of Rdiff , PCr illustrate that both Mi-CK and MM-CK are required for a functioning phosphocreatine shuttle . PCr diffusion averaged over the cardiac cycle makes a very small contribution to total HEP delivered from the mitochondria when either Mi-CK or MM-CK is inhibited by 98% . With 98% inhibited Mi-CK activity , Rdiff , PCr is even slightly below zero during diastole with low ATP hydrolysis , meaning that PCr is transported from cytosol to IMS ( Figure 7F ) . Note that this situation is reversed with respect to normal Mi-CK and MM-CK activity where PCr diffusion is always positive during diastole and occasionally becomes negative during ATP hydrolysis peaks . For normal CK activity the explanation for reversed PCr diffusion during ATP hydrolysis ( Figure 7E ) was that the CK activity per unit volume is higher in the IMS than in the cytosol . During Mi-CK inhibition this is of course no longer the case and systolic PCr consumption in the cytosol leads to PCr diffusion from the IMS , explaining the reversal of PCr transport during systole . In contrast , with MM-CK inhibited , ATP is buffered by Mi-CK in the IMS and PCr diffuses to the IMS at the end of the ATP hydrolysis peaks . This explains why Rdiff , PCr goes more negative during ATP hydrolysis peaks with MM-CK inhibition and its oscillation is stronger than for normal Mi-CK and MM-CK activity ( Figure 7E , G ) . When inhibiting Mi-CK activity , our model predicts an increase in the amplitude of [ADP] oscillation in the IMS from 57±8 to 71±8 µM . Mi-CK therefore has a damping effect on oscillations of ADP concentrations in the IMS , which contributes to the damping of mitochondrial ATP synthesis . The concentration of cytosolic Pi is predicted to be lowered by mitochondrial creatine kinase activity . Blocking Mi-CK leads to a Pi increase by about 18% from 1416±80 to 1670±167 µM ( Figure 7Q , R ) . If Mi-CK is inhibited by 100% , the steady state Pi concentration becomes 1678±173 µM ( data not shown ) . MM-CK inhibition decreases the Pi concentration; a combination of Mi-CK and MM-CK inhibition leads to a slightly higher Pi level compared to the wildtype ( Figure 7S , T ) .
The relative importance of the different roles of the CK system in myocytes is still hotly debated [4] . The present study was designed to investigate the function of CK in cardiomyocytes under varying workloads . In particular we sought to elucidate whether the phosphocreatine shuttle is the major pathway for HEP transfer from mitochondria to energy consuming myofibrils as stated in the phosphocreatine shuttle hypothesis or whether CK has other metabolic functions , e . g . the damping of swings in ATP and ADP concentrations and oxidative phosphorylation . Various computational studies of cardiac energy metabolism have been published based on models which contained the creatine kinase reaction , ATP hydrolysis and synthesis . The model analyzed in the present study is a subset of the model of Vendelin et al . ( [6] ) and was described previously [17] , [18] . The diffusion gradients in the cytosol which had been shown to be very small ( [6] ) were replaced by a simple diffusion conductance . The adenine nucleotide translocator and phosphate carrier in the mitochondrial inner membrane and oxidative phosphorylation ( OxPhos ) reactions in the mitochondria in the model of Vendelin et al . were replaced by a Michaelis-Menten equation describing OxPhos flux as a function of ADP and Pi in the intermembrane space [18] . The model was further modified in order to prevent thermodynamically infeasible loops by introducing constraints on the equilibrium of the CK reactions in IMS and cytosol [19] . Some models in the literature implement substrate channeling between ANT and Mi-CK by a microcompartment which is located inside the intermembrane space [6] , [11] , [29] . The performance of those models is discussed below . Other models exist for myocardial energy metabolism which do not consider the role of two creatine kinase isoforms connected via facilitated diffusion . For instance , Beard et al . integrated a detailed model of oxidative phosphorylation [14] with a model of spatially distributed oxygen transport and HEP metabolism to investigate the regulation of oxidative phosphorylation at different cardiac workloads [5] and HEP buffering in hearts at high workloads , acute ischemia and reactive hyperemic recovery . In the present study we predicted the functions of the CK enzyme isoforms based on the following strategy . A set of experimental data from multiple scales was assembled . We based the analysis on our model which had been shown to contain the key elements of the CK system [17] , [18] . The experimental data set allowed to estimate all parameters of this model . In order to set confidence regions for the predictions of CK function , the experimental errors for the data were taken explicitly into account . This was possible by generating an ensemble of model parameter sets . The probability of a set of parameters being part of the ensemble was determined based on the probability of the predicted experimental data set given the parameters . This approach was termed sloppy modeling [21] . Brown et al . [20] and Gutenkunst et al . [22] applied it to time series of protein activity levels measured during dynamic responses of a system as a whole . The surprising finding in their studies was that responses of the system as a whole were predictable with acceptable confidence regions even if all parameters of the model were known with very poor accuracy . This is possible because the correlation between parameters is well defined by the behavior of the system as a whole . A novel aspect in the present study is that we combined data taken from different integration levels in the system: kinetic parameters determined on enzymes in isolation , enzyme activity levels measured in tissue homogenates , organellar capacity levels measured on isolated mitochondria and dynamic response times determined on the heart as a whole . The whole organ response times were very important because they sensitively depend on the permeability of adenine nucleotides through the outer mitochondrial membrane , one of the organellar level parameters . This MOM permeability could not be determined experimentally with any degree of accuracy in isolated mitochondria . Combining some strategically important data from the whole system level with molecular parameters appears sufficient to predict system properties with acceptable confidence regions ( Figures 4–7 ) . Many of the experiments that are invoked to support high degrees of functional coupling between CK and ANT have been done in isolated mitochondria or in isolated myocytes and muscle fibres that were permeabilized . These were often studied at temperatures substantially below the physiological level . An important aspect of our analysis is that we try to estimate the functional roles of CK in the intact heart . To that end we combine the kinetic data from the molecular level with data obtained in isolated perfused hearts . It is important to realize that these hearts were intact , with contractility and cell membranes fully functional . Our model analysis explains the experimental data without invoking direct coupling of CK and ANT . However , the limited permeability of the mitochondrial outer membrane to adenine nucleotides , estimated from the response time in the intact heart , results in a certain degree of dynamic compartmentation of the adenine nucleotides . This approach helps to define the functional roles of CK in the intact heart at physiological temperatures . If CK-ANT direct coupling is the only way that ADP is delivered to the ANT , then the experiments with 98% inhibition of CK cannot be explained . It would then also be hard to explain that Mi-CK knockout animals still have substantial cardiac contractile function [30] . Future CK-ANT interaction models need to address such experimental data sets with CK inhibition and also explain the phosphate-labeling data of Erickson-Viitanen et al . [31] Our findings suggest that the principal role of the CK system in heart muscle is to serve as a temporal energy buffer for ATP and ADP at the 100 millisecond time scale . CK's role in supporting transport of high energy phosphate groups seems of limited importance . If oxygen supply is interrupted , PCr will also buffer ATP and ADP for several seconds [5] . Temporal energy buffering therefore has a relatively large bandwidth . Joubert et al . experimentally investigated the role of the CK shuttle by 31P NMR magnetization transfer protocols in vivo and proposed the hypothesis of a versatile role of PCr on intracellular energy transport depending on cardiac activity [32] , [33] . Partial inhibition of ATP synthesis led to a decrease of indirect energy transport via PCr . This decrease is predicted by our model ( data not shown ) . Some computational models on compartmentalized energy transfer in muscle , as for instance in Vendelin et al . ( [13] ) , assume restricted diffusion of adenosine nucleotides to an extent where energy transport via PCr becomes essential . However , a large restriction of adenine nucleotide permeation of the cytosol and MOM is not compatible with the relatively fast responses of oxidative phosphorylation to cytosolic workload steps [18] . The conductance parameter PSmom , AdN in our model reflects not only the permeation of the MOM proper but in series with that also diffusion in myofibrils and cytosol . The inverse of PSmom , AdN in our model is therefore the sum of the inverse of permeability-surface products ( PS ) for the MOM proper and cytosol , respectively [18] . The present Monte-Carlo ensemble approach indicates that PSmom , AdN lies within a range from 7 . 4 to 115 s−1 ( see Figure 3 ) . Based on the transverse diffusion coefficient of 52 µm2/s for ATP in the myofibrillar space measured with fluorescently labeled ATP [34] , the PS calculated for the cytosol is 216 . 7 sec−1 [18] . Given an ensemble mean PSmom , AdN of 31 . 7 s−1 ( see Table 1 ) we predict that about 15% of the total resistance to diffusion can be attributed to the cytosol . Note that the fluorescently labeled ATP has a higher molecular mass than ATP . The true diffusion coefficient of ATP is probably higher and the contribution of the cytosolic space to diffusional resistance is therefore probably overestimated in this calculation . The contribution of PCr to HEP transport predicted in the present study ( Figure 4 ) is compatible with measured response times of the system ( Figure 2 ) . It has been suggested that in cardiomyocytes the density of mitochondria and their vicinity to myofibrils is sufficient to ensure energy transport via adenosine nucleotides [3] . The prediction by our model that CK-facilitated transport of PCr is not obligatory for HEP transport is in line with the observation that CK knockout has relatively mild effects on cardiac function [10] , [30] , [35] . Activation of oxidative phosphorylation has been proposed to be strongly dependent on substrate channeling of ATP and ADP between the tightly coupled enzymes Mi-CK and ANT , meaning that ATP exported from the mitochondrial matrix via ANT is immediately available as a substrate for Mi-CK . The resulting ADP is then channeled back to stimulate oxidative phosphorylation in the mitochondrial matrix . However , the hypothesis of functional coupling is still debated [1] and other studies seem to contradict it [36] . In order to investigate the effect of functional coupling between the ANT and Mi-CK we implemented and analyzed the model of Vendelin et al . ( [6] ) , where the reactions are coupled by a microcompartment ( see Text S2 ) . The model , which contains constants which phenomenologically reflect the functional coupling of Mi-CK to the ANT is considered to give a good and computationally effective representation of the functional coupling between Mi-CK and oxidative phosphorylation [29] . It appeared to be rather difficult to fit the model of Vendelin et al . to the given experimental data of mitochondrial delay times ( tmito ) when measurements on molecular kinetic parameters are taken into account in the cost function . Especially at low workloads , a quicker response to a step in ATP consumption rate after CK inhibition could not be predicted with this model . Even when all parameters from the model of Vendelin et al . were variable during the optimization procedure , the quality of the fit is far from optimal despite the fact that the model of Vendelin et al . has about three times as many parameters as our present model . We therefore do not consider the microcompartment explicitly in our present study . The present results suggest that most of the delay of the activation of oxidative phosphorylation after temporal changes in ATP hydrolysis is caused by the delay of changes in phosphate metabolite levels outside the inner mitochondrial membrane . To investigate whether processes inside the mitochondria delay the response further , we tested a model of the mitochondrial matrix including metabolite transport across the inner mitochondrial membrane with instananeous step changes in ADP or Pi and also with ADP and Pi simultaneously outside the inner mitochondrial membrane . This corresponds to the model applied in Text S2 with all processes outside the inner mitochondrial membrane removed and the ADP and Pi concentrations outside the inner mitochondrial membrane set as forcing function . After a 20% increase in ADP concentration , ATP synthesis in the mitochondria reached a steady higher level within one second . The response time , calculated as for tmito , was 0 . 4 s . For a step in Pi the response was even faster with a negative value for the response time of −0 . 3 s because the response showed an overshoot . For a simultaneous change in ADP and Pi the mitochondrial response was essentially complete within half a second , with a response time of 0 . 08 s . When extramitochondrial ADP is changing , both mitochondrial oxygen consumption and ATP efflux via the ANT reacted even faster than the ATP synthase reaction . The fast response of mitochondrial metabolism predicted by the model is in agreement with spectroscopic measurements of the oxidation state of the electron carrier cytochrome b which was oxidized with a half-time of 70 milliseconds after a step in extramitochondrial ADP concentration at 26°C , and presumably much faster at the physiological temperature [37] . In studies on isolated rabbit cardiac muscle mitochondria the direct contribution of mitochondrial ATP to PCr formation by Mi-CK is low [31] . It was shown with radioactively labeled phosphate groups that if the concentration of ATP in the environment of the mitochondria is larger than 0 . 2 mM , less than 6% of PCr synthesis uses ATP synthesized immediately beforehand in the mitochondrial matrix . This is incompatible with a model where a major part of PCr is synthesized from ATP directly transferred to creatine kinase via a very small compartment with limited exchange with its environment . By in silico analysis , we inferred distinct roles for the mitochondrial and myofibrillar creatine kinase enzymes . MM-CK is mainly responsible for damping large swings in metabolite concentrations and large oscillations in the rate of oxidative phosphorylation which would otherwise be caused by the large peaks of ATP hydrolysis during the cardiac cycle . Mi-CK restricts high concentrations of inorganic phosphate , which is surprising considering that inorganic phosphate is not handled directly by CK . Despite its low activity , Mi-CK also decreases oscillations of ATP synthesis , mainly due to the effect of Mi-CK on ADP oscillations in the intermembrane space . The effect of the CK isoforms on the buffering of ADP oscillations and the prevention of high concentrations of inorganic phosphate may play a role in the prevention of formation of reactive oxygen species ( ROS ) . ROS production highly depends on the mitochondrial membrane potential , which is increased at low ADP levels [38] , [39] . The electric membrane potential in mitochondria can also be altered by inorganic phosphate , leading to enhanced ROS release [40] . Low ADP concentrations during diastole are prevented by MM-CK according to our predictions ( see Figure 7 ) . A protective role of Mi-CK against oxygen radical formation by preventing high inorganic phosphate concentrations is also predicted by our model . A function of Mi-CK to prevent oxygen radical formation has been found experimentally in isolated brain mitochondria [38] . The energy buffering role of the CK system has been linked to the prevention of oxidative stress in neurons [41] , [42] . Creatine supplements to nutrition have also been shown to have a neuroprotective effect in models of Huntington's disease [43] , [44] . The effects of creatine as a nutritional supplement in health and disease have recently been reviewed by Wallimann et al . [45] . In conclusion , we showed that by using a relatively small ‘skeleton’ model we were able to explain the dynamic adaptation of cardiac energy metabolism to changing workloads and to discern different functions of distinct CK isoenzymes . The sloppy modeling approach enables to make useful predictions of CK system behavior despite limited experimental input data and limited knowledge of kinetic parameters . The concept of sloppy modeling can also be used to find optimal experimental designs to further test the model [46] . We also demonstrated that combining a computational model analysis with experimental data on the level of cellular organelles and isolated enzymes and with the response of the heart as a whole provides a powerful combination that gives valuable insights in the functional roles of CK , such as regulation of oxidative phosphorylation , energy transport , inorganic phosphate levels and buffering of peaks of ATP hydrolysis at the 100 millisecond time scale .
For our analysis , we employed a previously published computational model [18] . It is available in various formats and can be found in the BioModels database [47] as well as in the CellML model repository [48] . The model incorporates the key elements of the CK system with ATP synthesis in the mitochondria and pulsatile ATP hydrolysis in the cytosol ( see Figure 1 ) . The input of the model is a forcing function of cytosolic ATP usage catalyzed by myosin-ATPase and ion pumps . The model contains ten ordinary differential equations ( ODEs ) describing the rate of change of each metabolite concentration ( ADP , ATP , PCr , Cr , Pi ) in two compartments over time . These equations were extensively described previously [18] . Model dynamics depend on 22 kinetic parameters retrieved from the literature which are listed in Table 1 . In general the kinetic constants retrieved from the literature have relatively modest standard errors . However , for the permeability of the MOM to ATP and ADP ( assumed to be equal in the model analysis; cf . [6] ) , reported values differed from 0 . 16 [6] to 85 µm*s−1 in the model of Beard [14] based on measurements of Lee et al . [49] . This large variation is possibly due to mitochondrial isolation or cell membrane permeabilization procedures . The mitochondrial outer membrane permeability-surface product parameter PSmom , AdN influences the response time for dynamic adaptation of oxidative phosphorylation strongly . Therefore the dynamic measurements of venous oxygen outflow in the heart as a whole in response to an increase of heart rate allow estimating the mitochondrial membrane permeability at the organellar level . The whole heart measurements were corrected for oxygen transport delay to reflect events at the level of the mitochondria ( see below ) . The mitochondrial response time tmito is defined as the generalized time constant of the time-course of oxygen consumption ( defined to be equivalent to the first central statistical moment of the impulse response function in case the system is linear ) , previously described in [18] , [50]-[52] . From a model simulation , tmito is calculated as follows: ( 3 ) Where JATPhyd , basis and JATPhyd , test are the values for the ATP hydrolysis rates for the two electrically paced heart rates at baseline and test level , averaged over the cardiac cycle; JATPsyn denotes the time course of ATP synthesis in the mitochondrion . tstep is the time point when the heart rate is increased and tend is the time point of the final oxygen measurement . Note that the average JATPsyn in the steady state before and at the end of a test challenge equals JATPhyd , basis and JATPhyd , test , respectively . In order to correspond with the experimental conditions in [9] , tend was set to 60 seconds with tstep = 0 seconds; an initial run for 40 seconds before the heart rate step ensures that ATP synthesis has adapted to ATP hydrolysis and is found to be in steady state at this stage . In order to investigate the damping capabilities of the modeled system , ATP hydrolysis is simulated as a pulsatile function representing the alternating nature of energy demand in systole and diastole as described in [18] . Figure 8 shows the dynamic response of mitochondrial ATP production in response to a step in heart rate and ATP hydrolysis . Almost all models in systems biology contain parameters that cannot be determined precisely . It is common practice to estimate missing parameter values by a parameter fit to experimental data . After the fit , one can make model predictions and analyze the underlying biological processes . This , however , is dangerous because a range of parameter combinations may agree with the available data equally well , potentially leading to deviating model predictions of new experimental situations . Directions in parameter space where parameter changes do change the simulation outcome very little were termed ‘sloppy’ by Brown et al . , whereas directions where small changes in parameter values affect the dynamic behavior of the system strongly were termed ‘stiff’ [21] . Sloppy parameter sensitivity spectra have been identified for numerous biological models by the analysis of the eigenvectors and eigenvalues of a sensitivity matrix calculated from the chi-square cost function [22] . Sloppy models exhibit a characteristic pattern with the logarithms of eigenvalues approximately uniformly distributed over a large range . A sensitivity analysis of the CK model revealed the presence of both stiff and sloppy parameter combinations and a ‘sloppy’ sensitivity spectrum [53] . Since our model shows sloppy parameter sensitivities and is based on data subject to experimental variation , drawing predictions from an ensemble of parameter sets is preferable to merely relying on one parameter set fit to experimental data . According to the sloppy modeling paradigm ( [21] , [22] ) , the probability of a set of model parameters to be included in the ensemble is proportional to its likelihood to describe given experiment data multiplied by the likelihood of prior experimental information about the parameter values themselves . The sampling process is thus based on Bayesian inference of a posterior distribution of parameter sets , where is the likelihood of the data given a parameter set , is the prior probability of the parameter set based on experimental prior knowledge on single parameter values and the posterior is the probability of a parameter set to describe the given experimental data . The construction of the ensemble with a Markov-Chain Monte Carlo ( MCMC ) method was done with the Metropolis-Hastings algorithm [54] . The Sloppy cell software environment , used for the analysis , was adapted to process all operators which were in the SBML file describing the model . The modified version is provided in Dataset S1 . To speed up convergence , Sloppy Cell takes larger steps along sloppy directions and smaller steps along stiff directions in parameter space; this ‘importance sampling’ is described in [20] , [21] . Measured values of molecular model parameters and their provenance , extracted from the scientific literature , are listed in Table 1 . For nine of the 22 parameters reliable standard measurement errors could be found . In addition to the direct measurements on molecular parameters , we employ tmito values from a study by Harrison et al . where the effects of inhibiting creatine kinase and different sizes of electrically paced heart rate jumps in rabbit hearts were investigated [9] . Isolated hearts were perfused with Tyrode's solution containing among others glucose and pyruvate to provide substrates for energy metabolism . In our dataset we include two experimental conditions where hearts were exposed to either ( i ) iodoacetic acid ( IAA ) to block glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) or ( ii ) iodacetamide ( IA ) to inhibit both CK and GAPDH . In order to provide a sufficient amount of reducing equivalents to fuel aerobic respiration despite the inhibition of glycolysis , the buffer also contained pyruvate . Adenosine was added to the Tyrode buffer to ensure that oxygen supply is non-limiting when oxygen consumption is recorded . The whole heart measurements were corrected for the O2 transport time in the coronary vessels based on a model of oxygen transport by convection in blood vessels and diffusion through tissue . The tmito therefore reflects the response time at the level of the mitochondria ( cf . [9] and references cited there ) . The mean response time was also corrected for a small deviation from an ideal step in beat-to-beat ATP hydrolysis measured as an initial overshoot in rate-pressure product [50] . For each condition , steps in heart rate were imposed from 135 to 160 , 190 and 220 beats per minute , respectively , using electrical pacing . Note that glycolysis is always inactive when the dynamic response is measured , which corresponds to the absence of glycolysis in the computational model . This approach made it possible to isolate the contribution of the CK system from the contribution of glycolysis , which removes substantial complexity from the model analysis . A step in ATP hydrolysis from 486 . 5 to 627 . 6 µmol*l−1 cell water*s−1 corresponds to a step in the electrically paced heart rate from 135 to 220 bpm , as was estimated from measurements of myocardial oxygen consumption [18] . From these values , we linearly interpolated hydrolysis rates of 531 . 4 and 579 . 5 µmol*l−1 cell water*s−1 for heart rates 160 and 190 bpm , respectively . To simulate CK inhibition by IA the model parameters for the maximum velocities of both enzyme reactions were set to 2 . 3% of their original values , corresponding to the CK activity measured for the inhibited hearts . Note that the enzyme activities , the mitochondrial capacities and the whole organ dynamic response times were all measured in the same experimental model by the same laboratory . Model parameters are fitted to experimental data using a modified Levenberg-Marquardt least-squares procedure in logarithmic parameter space , which is part of the SloppyCell modeling environment . For our model and data we calculate the cost for a given parameter set as follows: ( 4 ) with yc being the model prediction of tmito ( Eq . 3 ) as a function of the parameter value θ and dc the measured value for condition c with standard error . The first term of the cost function takes into account the experimental data on the whole heart level , whereas the second term represents prior experimental information about parameter values found in the literature or measured in conjunction with the modeled experiments . The prior cost , which gives a penalty for a parameter θi for drifting to far from its measured value θi* , is calculated as in [54]: ( 5 ) Note how the prior is used to enter experimentally measured information on parameters measured at the molecular level in the second term of Eq . 4 , while the first term contributes measured information on the whole system response . The deviations of the predicted response times from their measured values are penalized relative to their measured standard errors and the deviation of the molecular parameters from measured values are penalized relative to their reported standard errors . Values for molecular parameters reported in the literature are usually given as mean and standard error . However , in the sloppy modeling framework , it is preferable to choose a normal distribution in log space [20] , [22] , [54] . A Gaussian distribution of logarithmic parameters has been proposed to be biologically plausible [55] . This forms a convenient way to deal with dimensionless positive quantities as parameter values [56] . In order to calculate the σ value for a parameter θ in log space from its reported standard error ( considering the span of a 95% confidence region ) , we set the value as follows: ( 6 ) where SE is the absolute standard error of parameter θi . If the standard error is small relative to the mean of the parameter , the shapes of the prior distributions become approximately normal ( see Figure 3 ) . Since standard errors for only nine of all 22 system parameters could be found , we chose the default value for the remaining parameters to be at the maximum of all values for parameters with known error . This maximum was the error of the parameter for the binary dissociation constant for creatine from Mi-CK ( Kib , Mi , and see Table 1 ) . In order to investigate the effect of altered default prior standard deviation on posterior parameter distributions and ensemble predictions , we performed several additional ensemble simulations with lower and higher default values . Results of these simulations can be found in Text S1 . The parameter describing MOM conductance for adenine nucleotides , PSmom , AdN , could not be reliably determined by experiments on the organellar level and was therefore not constrained by a prior . A first estimate of parameter values was determined by a least-squares fit to the data , using the cost function of equation 4 . This initial best parameter estimate resulting from the optimization is used as the starting point for a walk through the parameter space using the Metropolis-Hastings algorithm . Starting the random walk from the optimized set of parameters made the algorithm converge more quickly to the posterior distribution . We use the algorithm's implementation in SloppyCell to sample parameter sets with probability density proportional to . All scripts to reproduce the presented calculations can be found in Dataset S2 . To ensure that the members of the ensemble are statistically independent , we ‘prune’ the ensemble by taking only every nth sample , where n is the maximum correlation time of all parameters . The correlation time of a parameter is defined as the time constant of its autocorrelation function . For our model , taking 50000 steps in the random walk is sufficient to obtain more than 600 independent parameter sets . The independent parameter sets in the ensemble provide the final estimate of the parameters , not only characterized by a mean but also by a standard deviation which reflects the spread of the estimation . Calculations were executed in parallel on a ClusterVision parallel machine with 16 nodes of four 3GHz processors with 4GB RAM . For computational performance reasons , we calculated model simulations for parameter estimation and ensemble sampling with an ATP hydrolysis rate averaged over the cardiac cycle rather than the pulsatile pattern shown in Figure 8 . This reduced the time needed for calculations tremendously , making it feasible to do the ensemble calculations in several hours . However , to investigate the damping characteristics of the system , we use a pulsatile forcing function of ATP hydrolysis ( see Figure 8A ) [18] . To assess the differences in metabolite levels and fluxes caused by replacing the pulsatile function with a time-averaged continuous function , 1000 parameter sets were randomly drawn from all parameter sets tried in the Monte-Carlo random walk , to compare the values of model results between pulsatile and nonpulsatile simulations . The variables most affected by the pulsatile approximation are Rdiff , PCr and tmito . The difference between pulsatile vs . nonpulsatile simulations of all 1000 parameter sets is 7 . 6±4 . 3 and 6 . 8±1 . 5% ( mean±SD ) , respectively . tmito values from nonpulsatile simulations are always slightly smaller than values from a pulsatile simulation , but their deviation is smaller than the standard error of the experimental tmito data . The difference between pulsatile and non-pulsatile model results for other variables is below 4 . 5% of their average values in a nonpulsatile setting . | Creatine kinase ( CK ) has several functions in cellular energy metabolism . It catalyzes the reversible transfer of high-energy phosphate from ATP to creatine , facilitating storage of energy in the form of phosphocreatine . In muscle cells , this extra energy buffer plays a pivotal role in maintaining ATP homeostasis . Another proposed function of CK is the transport of energy from ATP producing to ATP consuming sites via a shuttle mechanism involving a mitochondrial and a myofibrillar isoform of CK . The extent to which this ‘phosphocreatine shuttle’ mechanism is used in muscle and other tissues is hotly debated . We use a computational model of the CK system which can predict energy transport and buffering of high demand peaks to estimate the relative importance of both roles in heart muscle . We validate the model with multiscale data on the level of enzyme kinetic constants and with dynamic oxygen consumption measurements in rabbit hearts . Since model predictions can be strongly affected by changes in parameter values , we employ ‘sloppy’ ensemble modeling which allows to set confidence regions for predictions . Our results indicate that the main function of CK in heart muscle lies more in temporal energy buffering of high peaks in ATP consumption during cardiac contraction than in energy transportation . | [
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] | 2011 | Analyzing the Functional Properties of the Creatine Kinase System with Multiscale ‘Sloppy’ Modeling |
New safe and effective treatments for Chagas disease ( CD ) are urgently needed . Current chemotherapy options for CD have significant limitations , including failure to uniformly achieve parasitological cure or prevent the chronic phase of CD , and safety and tolerability concerns . Fexinidazole , a 2-subsituted 5-nitroimidazole drug candidate rediscovered following extensive compound mining by the Drugs for Neglected Diseases initiative and currently in Phase I clinical study for the treatment of human African trypanosomiasis , was evaluated in experimental models of acute and chronic CD caused by different strains of Trypanosoma cruzi . We investigated the in vivo activity of fexinidazole against T . cruzi , using mice as hosts . The T . cruzi strains used in the study were previously characterized in murine models as susceptible ( CL strain ) , partially resistant ( Y strain ) , and resistant ( Colombian and VL-10 strains ) to the drugs currently in clinical use , benznidazole and nifurtimox . Our results demonstrated that fexinidazole was effective in suppressing parasitemia and preventing death in infected animals for all strains tested . In addition , assessment of definitive parasite clearance ( cure ) through parasitological , PCR , and serological methods showed cure rates of 80 . 0% against CL and Y strains , 88 . 9% against VL-10 strain , and 77 . 8% against Colombian strain among animals treated during acute phase , and 70% ( VL-10 strain ) in those treated in chronic phase . Benznidazole had a similar effect against susceptible and partially resistant T . cruzi strains . Fexinidazole treatment was also shown to reduce myocarditis in all animals infected with VL-10 or Colombian resistant T . cruzi strains , although parasite eradication was not achieved in all treated animals at the tested doses . Fexinidazole is an effective oral treatment of acute and chronic experimental CD caused by benznidazole-susceptible , partially resistant , and resistant T . cruzi . These findings illustrate the potential of fexinidazole as a drug candidate for the treatment of human CD .
One century after its discovery , American trypanosomiasis , or Chagas disease , remains a serious health problem in Latin America , where it affects 8–10 million people with 100 million at risk of acquiring the disease [1] . Chemotherapy , together with vector and transfusion control , is one of the most important elements in the control of Chagas disease , since no vaccine is yet available to prevent infection . Treatment is dependent solely on two drugs , benznidazole and nifurtimox , which have a number of drawbacks including toxicity , drug resistance , and insufficient effectiveness against chronic disease . Nevertheless , as of today , these drugs are the only available therapeutic options in endemic and non-endemic areas . New potential treatment options include inhibitors of the sterol biosynthesis pathway , in particular C14-α-demethylase inhibitors such as posaconazole and ravuconazole , which represent promising new drugs candidates [2] , [3] . Despite the superior in vitro potency and in vivo efficacy of these novel azole derivatives against T . cruzi , and the absence of cross-resistance with currently available drugs , response to drug treatment varies among the different strains of the parasite [3]–[6] . Also , key disadvantages of currently marketed novel azole derivatives , such as posaconazole , are the complexity and cost of manufacturing these compounds , which will need to be addressed to facilitate access to resource-limited populations that constitute the vast majority of patients with Chagas disease [7] . Therefore , the continued search for new trypanocidal treatments that could be effective for Chagas disease remains a priority for Chagas disease control . Nitroimidazoles are a well-known class of pharmacologically active compounds , among which several have shown good activity against trypanosomes [8] . While concerns over mutagenicity have mitigated their potential as drug candidates , several members of this family including metronidazole [9] are widely used as antibiotics , indicating that it is possible to select compounds with acceptable activity/toxicity profile in this class . Today , two non-mutagenic novel nitroimidazole-oxazine compounds ( PA-824 and OPC-67683 ) are in clinical development for tuberculosis [10] , while the 2-substituded 5-nitroimidazole fexinidazole is in clinical development for human African trypanosomiasis ( also known as sleeping sickness ) [11] . Fexinidazole ( previously known as Hoe 239 ) had been in preclinical development as a broad-spectrum antiprotozoal drug by Hoechst in the 1970s–1980s , but its clinical development was not pursued at the time . The molecule was “rediscovered” and selected for development by the Drugs for Neglected Diseases initiative ( DNDi ) as a new drug candidate for sleeping sickness [11] , following a systematic review and profiling of more than 700 nitroheterocyclic compounds ( mostly nitroimidazoles ) from diverse sources , which included assessments of antiparasitic activity and mutagenic potential . Fexinidazole underwent extensive regulatory toxicology studies , including safety pharmacology ( respiratory , cardiovascular , and general behavior ) and 4 weeks of repeated-dose toxicokinetics studies in rat and dogs . Overall , fexinidazole was found to be well tolerated , with no specific toxicity or other concerns [11] . During 2010–2011 , DNDi carried out two Phase I clinical trials assessing the safety and pharmacokinetics of fexinidazole in human volunteers given in single and multiple doses . A phase II/III clinical safety and efficacy study in sleeping sickness patients is slated to begin in mid-2012 . Fexinidazole has previously been described as effective and superior to benznidazole or nifurtimox in one acute murine infection model with the T . cruzi Brazil 32 strain [12] , but the methodologies used to establish cure are no longer considered the most accurate . Here were evaluated the in vivo activity of fexinidazole in mice infected with a panel of T . cruzi strains with differing levels of benznidazole susceptibility and looking at both acute and chronic infection , and using state of the art methods to establish cure .
The Trypanosoma ( Schizotrypanum ) cruzi strains Y ( DTU II ) , CL ( DTU VI ) , VL-10 ( DTU II ) , and Colombian ( DTU I ) [13] were used in this study . Y strain , partially resistant to benznidazole , was used as the standard strain because it induces high parasitemia and 100% mortality , which is generally observed at days 10 to 19 post-infection . CL strain is highly sensitive to benznidazole , and VL-10 and Colombian strains are highly resistant to benznidazole . Fexinidazole ( 1H-imidazole , 1-methyl-2- ( ( 4- ( methylthio ) phenoxy ) methyl ) -5-nitroimidazole ) ( Figure 1 ) was administrated orally in suspension containing methyl cellulose 0 . 5% w/v , with 5% v/v of polysorbate 80 ( Tween 80 ) . Benznidazole ( 2-nitroimidazole- ( N-benzil-2-nitzo-1-imidazoleacetamide; Rochagan , Roche ) ( Figure 1 ) was used as the reference treatment in this study and was administered orally in a water suspension with 4% methyl cellulose . Cyclophosphamide ( N , N-bis ( 2-chloroethyl ) -1 , 3 , 2-oxazaphosphinan-2-amine 2-oxide; Genuxal , Asta Medica Oncologica ) was diluted in ultrapure water and administered intraperitoneally . The treatment consisted of three cycles of 50 mg of cyclophosphamide/kg of body weight for four consecutive days with intervals of three days between each cycle . Female Swiss mice from the Animal Facility at Ouro Preto Federal University ( UFOP ) , Minas Gerais State , Brazil were used in this study . Animals were fed with commercial food and water was available ad libitum . Swiss mice ( 18–20 g ) were inoculated intraperitoneally with 5 . 0×103 bloodstream trypomastigotes of the Y , CL , VL-10 , and Colombian T . cruzi strains . Mice infected with T . cruzi Y strain ( 6 animals/group ) were treated with doses of 100 , 200 , and 300 mg/kg of body weight ( mpk ) of fexinidazole per day . The drugs were administered orally on day 4 post-infection for 7 consecutive days . Drug efficacy was assessed based on three parameters: parasite clearance , time of absence of parasitemia , and mortality . All parameters were compared to those observed in benznidazole treatment using a standardized therapeutic scheme of 100 mpk per day [14] . The first set of experiments was designed to determine the efficacy of fexinidazole to induce parasitological cure in mice infected with Y strain ( partially resistant to benznidazole ) . Groups of 10 mice infected with Y strain were treated with 4 different doses of fexinidazole ( 50 , 100 , 200 , and 300 mpk of drug per day ) . The drug was administered at the time of parasitemia detection , which occurred at day 4 post-inoculation for 20 consecutive days . Parasitological cure was determined as described below , and the results were compared to those achieved with treatment with benznidazole [14] . A group of 10 animals infected with the parasite but receiving no treatment was used as control . The second set of experiment was designed to determine the efficacy of fexinidazole to induce parasitological cure in mice infected with strains of different level of resistance to benznidazole: CL , VL-10 , and Colombian . Because of the high benznidazole resistance of VL-10 and Colombian strains , the dose of fexinidazole used was the dose inducing the highest level of parasitological cure in animals infected with Y strain . The drug was administered at the time of parasitemia detection which occurred at day's 7–8 post-inoculation for 20 consecutive days . Parasitological cure was determinate as described below , and results were compared to those treated with benznidazole and with untreated control groups , infected or uninfected with parasites . Additional experiments were performed in a murine model of chronic phase of the disease to confirm the therapeutic efficacy of fexinidazole as observed in the first and second screenings . In this model , animals ( 10 per group ) were infected with the VL-10 strain , and fexinidazole was administered on day 120 post-infection for 20 consecutive days . According to Coura et al . [15] , two to three months after Chagas infection , the acute phase of the disease is followed by a long chronic period that is initially asymptomatic . This concept was validated by a group of experts during the Applied Research Meeting on Chagas disease , held in Araxá ( Minas Gerais ) , Brazil [16] . Based on this understanding , we chose to start the chronic-phase treatment 120 days after infection . Before treatment , parasitic infection was confirmed by fresh blood examination ( FBE ) during the acute phase and by detection of anti-T . cruzi IgG antibodies in blood . Mice that were uninfected and infected but untreated acted as controls . Parasitological cure was determined following the methodology standardized by Caldas et al . [17] based on a battery of three independent tests: FBE before and after cyclophosphamide immunosuppression ( CyI ) , blood culture , and PCR assays performed on blood samples from mice with negative parasitemia . Animals showing negative results in the three tests were considered as cured ( Figure 2 ) . For blood culture , animals that remained negative by FBE 30 and 180 days after treatment were bled from the orbital venous sinus and 400 µL of blood added in tubes containing 3 mL of LIT medium . The tubes were incubated at 28°C for 120 days and examined monthly for parasite detection . For PCR assay , following the same procedure as for blood culture , 200 µL of collected blood was used for DNA extraction . DNA extraction and PCR were performed according to Gomes et al . [18] with some modifications . The primers used for the parasite minicircle amplification were: S35 5′- AAATAATGTACGGG ( T/G ) GAGATGCATGA-3′ and S36 5′-GGGTTCGATTGGGGTTGGTGT-3′ [19] . Thirty-five cycles of amplifications were carried out in a Research Programmable Thermal Controller ( MiniCycler ) . The cycles consisted of a hold of 5 min at 95°C followed by 35 cycles of 1 min at 95°C for denaturation , 1 min at 65°C for primer annealing , and 1 min at 72°C for primer extension . Five microliters of the PCR product were analyzed by electrophoresis on a 6% polyacrylamide gel and visualized by silver staining . Positive and negative blood samples and reagent controls were processed in parallel in each assay , and all experiments were conducted under controlled conditions . To avoid contamination , DNA extraction , mixing , and electrophoresis were performed in separate , delineated areas . To confirm the absence of inhibition factors , an internal control corresponding to a segment of the murine TNF-α gene was amplified [20] . Blood from treated mice was collected from the orbital venous sinus ( 500 µL ) at 180 days after treatment . T . cruzi specific antibodies were detected by the technique described by Voller et al . [21] . Enzyme-linked immunosorbent assay plates were coated with T . cruzi antigen prepared from alkaline extraction from Y strain at exponential growth in LIT medium . Anti-mouse IgG-peroxidase conjugated antibody ( Sigma Chemical Co . ) was used . The mean absorbance for 10 negative control samples plus two standard deviations were used as the cut-off to discriminate positive and negative results . For histopathological analysis of myocardial tissues , mice were grouped into four categories – infected-treated-cured , infected-treated-not cured , infected-untreated ( control ) , and uninfected ( control ) – and analyzed according to T . cruzi strain . Animals were sacrificed 180 days after treatment and heart tissues fixed with 10% formalin and embedded in paraffin . Blocks were cut into 4-µm sections and stained by hematoxylin–eosin ( H&E ) for inflammation assessment . Twenty fields from H&E slides were randomly chosen at 40× magnification for a total of 1 . 49×106 µm2 analyzed myocardium area . Images were captured using a Leica DM 5000 B microcamera ( Leica Application Suite , model 2 . 4 . 0R1 ) and processed with Leica Qwin V3 image analyzer software . The inflammatory process was evaluated by the correlation index among the number of cells observed in myocardium muscle from infected-treated-cured , infected-treated-not cured , infected-untreated , and uninfected animals [22] . All procedures and experimental protocols were conducted in accordance with COBEA ( Brazilian School of Animal Experimentation ) guidelines for the use of animals in research and approved by the Ethics Committee in Animal Research at UFOP ( Protocol number 2009/17 ) . Histological and serological data were analyzed by nonparametric Tukey's Multiple Comparison Test . Difference was considered significant if the P value was less than or equal to 0 . 05 .
To assess which dose-range of fexinidazole is able to suppress parasitaemia , animals infected with Y strain were treated with 100 , 200 , and 300 mpk of fexinidazole per day for 7 days . All doses tested resulted in a rapid suppression of the parasitaemia , similar to the standard benznidazole ( Table 1 , Figure 3 ) . The 7-days treatment time however did not completely eliminate the parasites , and recrudescence of the parasitaemia occurred in all animals , yet slower in fexinidazole treated animals compared to benznidazole . While untreated animal all succumbed to the infection , all treatments were effective in preventing death ( one animal died in the lowest fexinidazole dose ) . Based on these results , we evaluated the capacity of fexinidazole to induce parasitological cure in animals infected with Y strain over longer-term treatment . Infected animals were treated daily for 20 consecutive days using 50 , 100 , 200 , and 300 mpk of fexinidazole , compared with 100 mpk of benznidazole . The time required to achieve parasite suppression by fexinidazole was dose-dependent , with the highest dose comparable to the standard benznidazole treatment ( Table 2 ) . When looking at the definitive clearance of parasitaemia through the combination of different methods , 80% of mice treated with fexinidazole 300 mpk were cured at the end of a 6-months follow up period versus 50% for benznidazole 100 mpk , fexinidazole 100 mpk and 200 mpk . These data suggest that a higher cure rate can be achieved in animals infected with T . cruzi Y strain with fexinidazole treatment , be it at a higher dose , than with the standard benznidazole dose of 100 mpk . All treatment regimens were effective in preventing death . We also assessed whether the highest dose of fexinidazole would also be more effective than standard benznidazole in treating mice infected with the benznidazole-susceptible CL strain , and the benznidazole-resistant VL-10 and Colombian strains of T . cruzi ( Table 3 ) . For the CL-strain , both the time to achieve parasitaemia suppression and the final cure rate were similar . While benznidazole could not suppress parasitaemia fully for the VL-10 strain , and cured none of the VL-10-infected animals , fexinidazole induced a rapid suppression of parasitaemia ( Figure 3A ) , and cured 88 . 9% of the mice ( Table 3 ) . And while both benznidazole and fexinidazole rapidly suppressed parasitaemia in all animals infected with the Colombian strain , only fexinidazole could clear the parasitaemia resulting in a cure rate of 78% ( Table 3 ) . In the benznidazole-treated group , parasitaemia reappeared after the end of treatment starting on day 8 and persisted in all animals up to day 30 post-treatment ( data not shown ) . Comparative analysis of specific T . cruzi antibodies detected in blood samples collected from mice inoculated with CL , VL-10 , or Colombian T . cruzi strains , after 6 months post-treatment were performed . For these analyses , animals were classified based on results of parasitological and PCR evaluations: +fexinidazole or +benznidazole ( mice treated with fexinidazole or benznidazole with positive parasitological and PCR results [parasitaemia present] ) ; −fexinidazole or −benznidazole ( mice treated with fexinidazole or benznidazole with negative parasitological and PCR results [no parasitaemia] ) ; IC ( infected and untreated controls ) ; NIC ( uninfected control mice ) . The −fexinidazole and −benznidazole animals had IgG antibody levels significantly lower than that of IC animals , and similar to healthy mice . In contrast , +fexinidazole or +benznidazole animals had antibody levels similar to that of IC mice ( Figure 4B ) . In order to evaluate the efficacy of early fexinidazole treatment in preventing the development of subsequent chronic myocardial lesions in mice infected with VL-10 and Colombian T . cruzi strain , quantitative analysis of inflammation of heart tissue in fexinidazole and benznidazole-treated mice was performed at 6 months post-treatment , and compared to heart tissue from untreated infected mice and health mice . In mice infected with VL-10 strain , early fexinidazole treatment was shown to prevent or lessen the typical inflammation of heart tissue associated with the chronic phase of experimental Chagas disease , as illustrated by similar levels of inflammatory cells observed in the heart tissue of fexinidazole-treated and healthy animals ( Figure 4C ) . In contrast , untreated mice and benznidazole-treated mice presented a higher number of inflammatory cells in their heart tissue ( Figure 4C ) . In mice infected with Colombian strain , treatment with either fexinidazole or benznidazole was shown to reduce the number of inflammatory cells in chronic phase of experimental Chagas disease , as illustrated by the significantly higher number of inflammatory cells in the hearts of untreated infected animals compared with fexinidazole- or benznidazole-treated animals ( Figure 4D ) . Additionally , parasite detection was associated with inflammation intensity . Animals negative for T . cruzi ( −fexinidazole ) showed lower inflammation intensity in heart tissue than those positive of T . cruzi in parasitological or/and molecular tests . Also , no differences were observed in number of inflammatory cells between healthy and −fexinidazole treated mice ( Figure 4D ) . Based on the findings from treatment during acute-phase experimental Chagas disease , animals infected with VL-10 were treated during the chronic phase ( 120 days after inoculation ) using the same treatment scheme . CyI treatment induced parasitemia reactivation in 20% ( 2/10 ) of animals treated with fexinidazole or benznidazole , and 60% ( 6/10 ) of untreated infected mice ( Table 4 ) . However , considering the results of blood culture and PCR assays performed at 30 and 180 days after treatment , therapeutic failure was detected in 30% ( 3/10 ) and 70% ( 7/10 ) of mice treated with fexinidazole and benznidazole , respectively ( Table 4 ) . The parasite or its kDNA was detected in 90% ( 9/10 ) of IC mice ( Table 4 ) . Six months after treatment of the chronic infection , animals that had been successfully treated with either benznidazole or fexinidazole ( i . e . no more parasites detected ) had reduced levels of specific anti-T . cruzi IgG antibody levels , comparable to healthy animals . In contrast , the antibody levels remained high and comparable to untreated controls in animals that were treated with either benznidazole or fexinidazole , but without clearing the parasitaemia ( Figure 5A ) . Interestingly , fexinidazole treatment performed during the chronic phase of the infection significantly reduced the cardiac inflammation ( Figures 5B and C ) , both in cured animals and in animals that hadn't fully cleared their parasitemia . In contrast , among the benznidazole-treated animals , a reduction of cardiac inflammation was detected only in those mice that cleared the parasitemia . Benznidazole-treated animals that did not clear their infection had similar levels of inflammation as untreated animals ( Figure 5B and C ) . This study confirms that fexinidazole is effective in curing experimental Chagas infection models , both in the acute and chronic stage , and including infections with benznidazole-resistant T . cruzi strains . At higher but well-tolerated doses , fexinidazole treatment achieves better cure rates and prevention of cardiac inflammation than the current standard treatment of benznidazole .
Current specific chemotherapy options for Chagas disease – benznidazole and nifurtimox – have limitations such as lack of effectiveness in achieving parasitological cure or in preventing the chronic phase of the disease . Fexinidazole , 5-nitroimidazole compound , was originally selected as a broad-spectrum antiprotozoal agent and has recently been rediscovered as a potential drug candidate for treatment of human African trypanosomiasis [11] , [12] , [23] . Previous studies had shown that fexinidazole has curative activity superior to benznidazole and nifurtimox during acute T . cruzi infection with the Brazil 32 strain in a murine model [12] . However , the methodologies used in this 1983 study for detecting parasites in the blood , including subinoculation in healthy mice , are no longer considered adequate to establish cure . We therefore re-evaluated the efficacy of fexinidazole in experimental Chagas disease using more sensitive state of the art methodologies ( including blood culture , PCR and immunosuppression to reactivate lingering parasites ) , as well as using a range of T . cruzi strains with variable levels of resistance to benznidazole . The rapid in vivo activity of fexinidazole conferring complete protection against death to infected mice when it is used in a short-term ( 7 days ) treatment is in line with the activity originally reported by Raether et al . [12] . In our study , we additionally demonstrate that the anti-T . cruzi activity of fexinidazole was dose-dependent , and that a 20 days treatment with 100 or 200 mpk fexinidazole generated 50% definitive cure in mice infected with the benznidazole-susceptible Y strain , comparable to treatment with the standard dose of 100 mpk benznidazole . Moreover , a cure rate of 80% could be achieved when using 300 mpk of fexinidazole . Additionally , fexinidazole ( 300 mpk ) and benznidazole ( 100 mpk ) have similar activity against the susceptible benznidazole T . cruzi CL strain , but fexinidazole was highly effective in curing mice infected with the benznidazole-resistant VL-10 and Colombian strains . Early fexinidazole treatment effectively prevented or reduced the development of myocarditis associated with chronic Chagas disease in mice infected with the benznidazole-resistant VL-10 and Colombian T . cruzi strains . Notably , when treated during the chronic phase of a T . cruzi VL-10 infection , fexinidazole but not benznidazole was also able to reduce myocarditis in all treated animals . This remarkable in vivo anti-T . cruzi activity of fexinidazole may be result of a combination of potent antiparasitic activity and advantageous pharmacokinetics properties . According Torreele et al . [11] , fexinidazole is well absorbed after oral administration and broadly distributed to all organs and tissues in animals . This has implications for in vivo efficacy , considering that after parasitemia control , parasites may slowly replicate in various tissues . Differing patterns of heart lesions were detected in benznidazole-treated animals inoculated with VL-10 and Colombian T . cruzi strains . A reduction in myocarditis in Colombian infected animals was observed , but this was not seen in those infected with VL-10 strain . During benznidazole treatment , animals infected with Colombian strain had negative fresh blood examination ( data not shown ) , while those infected with VL-10 strain had positive parasitemia results . These results suggest that parasite load plays an important role in the pathogenesis of the chronic chagasic cardiomyopathy . This idea is corroborated by observations of Bustamante et al . [24] that re-exposure to the parasite through repeated infections aggravates heart dysfunction . Additionally , Caldas et al . [25] suggested that the efficacy of treatment in preventing chronic cardiac lesions is T . cruzi strain-dependent and probably related to the level of drug resistance to the parasite stock . Specific anti-T . cruzi IgG levels detected 6 months after fexinidazole treatment were similar in non-infected control animals and definitively cured animals , and significantly lower than in non-treated infected mice or treated mice that remained parasitemic . The correlation between parasite detection and increased IgG levels is corroborated by a study by Garcia et al . [26] , which demonstrated that in sera of benznidazole-treated mice , lower levels of antibodies specific to T . cruzi antigens are observed . According to the authors , reduction of antibody levels is possibly correlated with reduction of tissue damage , since antibodies against T . cruzi antigen may exert effects on cross-reactive epitopes on cardiac receptors , modulating the intensity of the heart tissue lesions . This study confirms a strong correlation between specific anti-T . cruzi IgG levels and intensity or cardiac inflammation , along with the capacity of fexinidazole treatment to reduce both . It is important to note that fexinidazole was well tolerated by the T . cruzi-infected animals , and no adverse events were observed during the treatment period . These results are in agreement with others [11] , [12] who demonstrated that fexinidazole was generally well tolerated after a single oral dose or repeated dosing , even at relatively high doses . Our safety findings agree with previous toxicologic studies that showed no effects on blood pressure , heart rate , and electrocardiogram intervals in dogs after single oral dose up to 1000 mg/kg . At the same dose in rats , no effects were observed on behavior characteristics or on respiratory parameters [11] . The No Observed Adverse Event Level ( NOAEL ) in 4-weeks repeated dose toxicokinetics studies in dogs and rats was set at 200 mg/kg/day [11] . In our study , fexinidazole blood levels were not measured , but others have shown that fexinidazole acts as a prodrug , which is oxidized in vivo to the more therapeutically relevant sulfoxide and sulfone metabolites [11] , [27] . The sulfoxide metabolite reaches considerably higher levels in mouse blood than the parent compound , up to 40 , 000 ng/mL at 2 hours following oral dosing . Fexinidazole sulfone , the final metabolite to appear in the blood , continues to accumulate over 8 hours , reaching a concentration of 55 , 000 ng/mL . In mice , rats , and dogs , the half-life of fexinidazole after oral treatment ranges from 1 to 3 hours , while the half-life of the sulfoxide ranges from 2 to 7 hours and sulfone up to 24 h after dosing [11] . As earlier studies had shown mutagenic activity in the Ames test [12] , the genotoxic profile of fexinidazole and its active metabolites was recently re-assessed in detail [28] . Fexinidazole is mutagenic in the classic Ames test; however , mutagenicity is either attenuated or lost in Ames Salmonella strains that lack one or more nitroreductase ( s ) [28] . It is known that these enzymes can nitroreduce compounds with low redox potentials such as fexinidazole , whereas their mammalian cell counterparts cannot . In order to specifically detect mammalian genetic toxicity , fexinidazole was tested in a panel of complementary assays , including an in vitro micronucleus test in human lymphocytes , an in vivo bone marrow micronucleus test in mice and an ex vivo unscheduled DNA synthesis in rats , and all were negative . Thus , it can be concluded that fexinidazole does not pose a genotoxic hazard to patients [10] , [28] . Taken together , these results show that treatment with 300 mpk fexinidazole was able to produce high levels of parasitological cure in mice infected with benznidazole-susceptible , partially resistant , and resistant T . cruzi strains in acute and chronic phases of the experimental Chagas disease , and this is an improvement compared to the current standard treatment with benznidazole . This potent intrinsic and broad anti-T . cruzi activity , taken together with the fact that it is already in clinical development for another parasitic disease , suggests that fexinidazole is a promising drug candidate for human Chagas disease chemotherapy . Safety profile will need to be confirmed on further preclinical testing adapted to the longer treatment duration and Chagas disease presentation . | This study describes the in vivo activity of fexinidazole against Trypanosoma cruzi , the protozoan parasite causing Chagas disease , using mice infected with parasite strains with varying susceptibility to benznidazole , the standard treatment for Chagas . Fexinidazole and benznidazole were shown to have similar activity against benznidazolesusceptible and partially resistant T . cruzi strains ( CL and Y ) , but fexinidazole had potent activity against benznidazole-resistant strains ( VL-10 and Colombian ) . Fexinidazole treatment resulted in parasitological cure during acute disease phase in 88 . 9% of mice infected with the VL-10 strain and 78% with Colombian strain; benznidazole treatment did not result in cure in animals infected with these strains . Fexinidazole treatment was also shown to reduce myocarditis in all VL-10- and Colombian-infected animals , although parasite eradication was not achieved in all treated animals . These data demonstrate that it is possible to achieved better cure rates with fexinidazole in these experimental infection models than what is achieved with the standard benznidazole at the doses tested in this animal study benznidazole treatment regimen . | [
"Abstract",
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"Materials",
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] | [
"medicine",
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] | 2012 | Fexinidazole: A Potential New Drug Candidate for Chagas Disease |
Survival of Borrelia burgdorferi in ticks and mammals is facilitated , at least in part , by the selective expression of lipoproteins . Outer surface protein ( Osp ) A participates in spirochete adherence to the tick gut . As ospB is expressed on a bicistronic operon with ospA , we have now investigated the role of OspB by generating an OspB-deficient B . burgdorferi and examining its phenotype throughout the spirochete life cycle . Similar to wild-type isolates , the OspB-deficient B . burgdorferi were able to readily infect and persist in mice . OspB-deficient B . burgdorferi were capable of migrating to the feeding ticks but had an impaired ability to adhere to the tick gut and survive within the vector . Furthermore , the OspB-deficient B . burgdorferi bound poorly to tick gut extracts . The complementation of the OspB-deficient spirochete in trans , with a wild-type copy of ospB gene , restored its ability to bind tick gut . Taken together , these data suggest that OspB has an important role within Ixodes scapularis and that B . burgdorferi relies upon multiple genes to efficiently persist in ticks .
Lyme disease is the most common tick-borne disease in the United States [1] . The causative organism , Borrelia burgdorferi , is a microaerophilic spirochete that contains a 910-kb linear chromosome and at least 21 linear and circular plasmids [2] . B . burgdorferi is composed of several genospecies known collectively as B . burgdorferi sensu lato ( s . l . ) of which B . burgdorferi sensu stricto , Borrelia garinii , and Borrelia afzelii are responsible for most cases of Lyme borrelliosis worldwide [3 , 4] . B . burgdorferi s . l . is maintained in an enzootic cycle that primarily involves Ixodes ticks and a large range of transmission-competent vertebrate hosts [1 , 3–6] . Ticks of the Ixodes ricinus species complex , including Ixodes scapularis and Ixodes pacificus in eastern and western North America , respectively [3] , and I . ricinus and Ixodes persulcatus in Europe and Eurasia [4] , respectively , are competent vectors for the transmission of B . burgdorferi s . l . during engorgement on a reservoir host [1 , 3–6] . Studies reported so far tend to show similar means of transmission and modes of pathogenesis of the B . burgdorferi s . l . in this group of ticks [3–8] . After entry into the ticks , B . burgdorferi s . l . replicates and persists within the gut , then during a subsequent blood meal , migrates through the vector and is transmitted to a new host [9] . In humans , B . burgdorferi s . l . initially establishes a localized infection in the skin at the site of the tick bite known as erythema migrans , then disseminates via the blood stream and can chronically infect distant organs , resulting in arthritis , carditis , and neurological disease [1 , 10] . Laboratory mice can be infected with B . burgdorferi s . l . and serve as a reliable model for the study of Lyme borreliosis [11] . Variation in the synthesis of outer surface proteins ( Osps ) is a primary strategy by which B . burgdorferi evades the host immune system and adapts to various host microenvironments , such as those in a mammal or a tick vector [12–15] . Numerous studies have shown that B . burgdorferi selectively expresses specific Osps in distinct phases of its life cycle and in specific tissue locations . For example , the expression of B . burgdorferi OspA and OspB is immediately turned on when the spirochetes enter and reside within the arthropod vector . However , during transmission from the arthropod vector to a vertebrate host , B . burgdorferi downregulate OspA and OspB expression and upregulate the expression of proteins such as OspC , DbpA , and BBK32 [16–21] . This selective and temporal gene expression of OspA and OspB in ticks suggests that these two proteins may function during early spirochete colonization and persistence within the tick vector . Indeed , a recent study showing that OspA mediates spirochete adherence within the tick gut by binding to the I . scapularis TROSPA protein [22] supports this contention and indicates how stage-specific gene expression contributes to the maintenance of the natural cycle of the spirochete . The genes ospA and ospB are highly conserved among B . burgdorferi isolates in the United States [23 , 24] . They are encoded on the linear plasmid ( lp ) 54 and are generally expressed by a common promoter [25 , 26] . Both OspA and OspB are surface-exposed lipoproteins that are closely related in terms of sequence and structure [2 , 27 , 28] . Since the discovery of B . burgdorferi as the Lyme disease agent , OspA has been a subject of intensive investigation [29] . In contrast , less is known about the role of OspB in the life cycle of B . burgdorferi . Previous studies have identified an ospB escape mutant in a clonal population of infectious B . burgdorferi with a single base change in the consensus ribosomal binding sequence and a single nucleotide deletion in the open reading frame of ospB gene [30] . These changes in the ospB gene resulted in reduced expression and truncation of this protein that diminished the penetration capability and infectivity of the spirochete in the human umbilical vein endothelium cells [30] . Several reports have indicated that OspB is present on the surface of B . burgdorferi within unfed ticks [31–33] and that OspB antibody inhibits B . burgdorferi colonization in I . scapularis gut [34] . Targeted gene disruption of ospAB operon inhibited B . burgdorferi colonization and persistence in the tick gut [35] . While this study highlighted an important role of OspA in spirochete-tick interaction in vivo , the independent role of OspB in the life cycle of spirochetes remains unclear . Progress in the methodologies for genetic manipulation of virulent B . burgdorferi allows researchers to study the importance of specific B . burgdorferi genes that are required throughout the spirochete life cycle [36] . In the current study , we have analyzed the functional role of OspB during spirochete-tick interactions by generating an OspB-deficient B . burgdorferi , genetically complementing the mutant , and performing in vivo studies in ticks and mice .
To understand the function of B . burgdorferi OspB , an isogenic OspB-deficient mutant was generated from infectious B . burgdorferi B31 clone 5A11 by replacing a 554-base pair ( bp ) internal fragment of the ospB gene with a Borrelia-adapted kanamycin resistance cassette , kanAn , through homologous recombination ( Figure 1 ) . The construct used to inactivate the ospB gene , pXLF11303 , is schematically shown in Figure 1A . After electroporation of clone 5A11 with pXLF11303 , three kanamycin-resistant B . burgdorferi transformants were obtained , of which two were confirmed to be OspB-deficient by immunoblot ( unpublished data ) . The plasmid content of these two mutants was analyzed and compared to that of the wild-type parental clone using an array-based assay [37] , which showed that one of the mutants lost cp9 and the other lost lp28–1 . Because the loss of cp9 has been shown to have little effect on B . burgdorferi infectivity in mice [38] , we chose the ospB mutant lacking cp9 for further studies . To verify the desired genomic arrangements in the ospB mutant , a series of PCRs were performed ( Figure 1B ) . The two PCRs using primer combinations N21/N27 and N28/N17 verified the left and the right junctions of the replacement in the ospB mutant . PCR with primer combination N21/N17 generated different sized DNA fragments from the wild-type strain and the ospB mutant , which is consistent with the replacement of the 554-bp internal fragment of the ospB gene with the 1 , 248-bp kanAn cassette . Collectively , these PCR results show that a double-crossover event had occurred in the mutant , resulting in the inactivation of the ospB gene . RT-PCR and immunoblot further showed that the ospB mutant lacked ospB mRNA and was OspB-deficient ( Figure 1C and 1D ) . The ospB mutant had a similar total protein profile as well as comparable OspA mRNA and protein levels to that of the wild-type isolate ( Figure 1C and 1D ) . To evaluate whether the loss of OspB expression affected the pathogenicity of the spirochete , we examined both the wild-type and the ospB mutant spirochetes in the murine model of Lyme borreliosis [39] . Groups of three C3H/HeN mice were challenged intradermally with in vitro-grown spirochetes , either the mutant or the wild-type B . burgdorferi , at a dose of 105 spirochetes/mouse ( see Materials and Methods for details ) . At 25 d post-inoculation , mice were sacrificed and spirochete infection was assessed by serology and in vitro culturing of the bladder and the spleen . The results of three independent experiments using a total of 18 mice , nine infected with wild-type strain and the other nine infected with the mutant strain , indicated that all mice seroconverted and were culture-positive for spirochetes ( unpublished data ) . The ospB mutant spirochetes recovered from murine tissues remained OspB-deficient , indicating that no reversion had occurred ( Figure 1B , 1C , and 1D ) . Mice infected with the wild-type or the ospB mutant B . burgdorferi had similar spirochete burdens in various tissues , including the bladder , heart , joints , and skin ( p > 0 . 05 , Figure 2A ) . Joint swelling and inflammation were similar in both groups of mice ( p > 0 . 05 , Figure 2B ) . Taken together , these data indicate that at the dose of 105 spirochetes/mouse the ospB mutant is fully infectious and pathogenic in mice . To determine whether the lack of OspB expression influenced the arthropod phase of the B . burgdorferi life cycle , uninfected nymphs were allowed to engorge on mice infected with either the wild-type or the OspB-deficient B . burgdorferi . Infection in mice was confirmed by positive flaB PCR from an ear punch biopsy . Three independent experiments were carried out with a total of 18 mice ( nine infected with wild-type and the other nine infected with the mutant strain ) and a total of 900 naïve nymphs ( 50 nymphs/mice ) . Five nymphs were removed from the murine skin at various time points during feeding , in order to examine the kinetics of spirochete migration , and the remaining nymphs were allowed to feed to repletion . Nymphs were subjected to Q-RT-PCR analyses to determine the spirochete burden ( see Materials and Methods for details ) . At 8 , 24 , and 48 h during feeding , the viable spirochete burden in the ticks fed on the wild-type or the ospB mutant B . burgdorferi-infected mice were comparable ( p > 0 . 05 , Figure 3A ) . From 72 h ( during feeding ) onward , the ospB mutant spirochete levels were dramatically reduced in ticks compared with controls ( p < 0 . 03 , Figure 3A ) . RT-PCR for flaB and ospA transcripts yielded similar results ( Figure 3B ) . The observed reduction of spirochetes in ticks fed on mice infected with the OspB-deficient B . burgdorferi was further evaluated by immunofluorescence analysis ( IFA ) of a subset of the nymphs that were collected at various time points . Tick gut luminal contents ( including the blood meal ) as well as gut tissues washed free of luminal contents were prepared and subjected to IFA ( see Materials and Methods for details ) . At 72 h of tick feeding , a striking number of wild-type spirochetes were detected in both blood meal ( ∼13 spirochetes/microscopic field ) and gut ( ∼8 spirochetes/microscopic field ) tissue samples ( Figure 3C and 3D ) . In contrast , the spirochete number was drastically reduced ( ∼4-fold in blood meal and ∼16-fold in gut tissue ) in nymphs fed on the ospB mutant-infected mice ( See 72-h panel in Figure 3C and 3D ) . Of note , the number of OspB-deficient spirochetes adhering to the tick gut was significantly less ( ∼7-fold , p < 0 . 0001 ) in comparison to the number of the OspB-deficient spirochetes in the blood meal sample ( Figure 3D ) . IFA of ticks from the 48-h post-feeding phase further corroborated these findings ( Figure 3C and 3D ) . Furthermore , a subset of fully engorged nymphs was allowed to molt to the adult stage to determine whether the diminished capacity of ospB mutant to colonize the tick gut was also reflected in the adult stage . The wild-type spirochetes were readily detected in both luminal content and gut sample ( Figure 3C and 3D ) . In contrast , ospB mutant spirochetes were not detected in the gut sample and out of 15 microscopic field observations only one ospB mutant spirochete was seen in the luminal content sample ( p < 0 . 0001 , Figure 3C and 3D ) . A quantitative RT-PCR analysis also corroborated the results ( p < 0 . 02 , Figure 3A ) . Thus , these observations collectively show that the ospB mutant spirochetes and the wild-type B . burgdorferi are acquired by ticks at the same rate ( note results at nymphal stage 8 , 24 , and 48 h during feeding ) , but the ospB mutant spirochetes are not able to persist within the luminal content or fully adhere to the tick gut . To further address whether the loss of OspB expression results in a defect of B . burgdorferi colonization and survival in the ticks , we constructed a strain of the mutant complemented in trans with a wild-type copy of the ospB gene . The promoter of the ospAB operon was fused to the ospB gene and cloned into the shuttle vector pKFSS1 [40] , resulting in the construct designated as pFGN1 ( Figure 1A ) . Electroporation of the ospB mutant with pFGN1 resulted in three positive clones that grew in the BSK-H media supplemented with streptomycin and kanamycin , of which one clone had lost both lp25 and lp28–1 plasmids and the other two clones showed identical endogenous plasmid profiles as its parental isolate ( unpublished data ) . Furthermore , to determine whether these two clones harbored pFGN1 and were indeed from the ospB mutant , total DNA of these complemented clones was examined by PCR amplification . The three PCRs using primer combinations N21/N27 , N28/N17 , and N21/N17 confirmed the inactivated ospB locus ( representative result shown in Figure 1B ) . PCR with primer combination N23/N86 generated different sized DNA fragments from the wild-type and the complemented strains , which confirmed the presence of PospAB-ospB gene fusion , and PCR amplification of the internal sequences of the aadA gene with primer combinations N83/N84 further confirmed the presence of pFGN1 in the transcomplemented strains ( representative result shown in 1E ) . Collectively , these PCR results revealed the expected amplicons with both complemented clones in all the PCR reactions in comparison to the wild-type and the ospB mutant . We chose one of these two clones , designated as the OspB complemented strain ( ospB−/pFGN1 ) for further analysis . OspB expression was detected at both the mRNA and the protein levels in the OspB complemented strain ( Figure 1C and 1D ) . Furthermore , to confirm that the OspB complemented strain contained the pFGN1 plasmid , whole-cell lysates of kanamycin- and streptomycin-resistant cells were used to transform Escherichia coli DH5α-competent cells . Plasmid was then rescued from these E . coli transformants , and restrictive digestions were performed to verify the recovery of pFGN1 ( Figure 1F ) . Immunoblot with mAb B22J was also performed to confirm the presence of OspB protein in these transformed E . coli cells ( unpublished data ) . Experimentally infected nymphs were prepared by microinjection of cultured spirochetes into the rectal aperture of uninfected nymphs as previously described [35] . Three independent experiments were carried out with a total of nine C3H/HeN naïve mice ( three mice for feeding nymphs microinjected with wild-type , three for ospB mutant , and three for OspB complemented strain ) and a total of 135 nymphs ( 15 nymphs/mice ) . Eight nymphs were forcibly removed during feeding ( 48 h ) and analyzed by IFA and Q-RT-PCR . The results of the confocal microscopy revealed that , in contrast to the ospB mutant , the OspB complemented strain readily colonized the tick gut tissue ( p < 0 . 0001 ) , albeit to a lower level than the wild-type isolate ( Figure 4A and 4B ) . No significant difference was seen in the blood meal samples of the nymphs infected with the wild-type , the ospB mutant , or the OspB complemented strain ( Figure 4A and 4B ) . Quantitative RT-PCR analysis of cDNA samples also supported the microscopic observations that showed a significant higher level in the persistence of the OspB complemented strain in comparison to the ospB mutant ( p < 0 . 001 , Figure 4C ) . To further support the role of OspB in the attachment of B . burgdorferi to tick gut tissue , we performed an in vitro binding assay to the tick gut extract ( TGE ) prepared separately from flat-nymphal ticks and fed-nymphal ticks with the wild-type , the ospB mutant , and the OspB complemented strains ( See Materials and Methods for details ) . The results revealed that the binding of the ospB mutant to TGE from flat nymphs and fed nymphs is significantly reduced by ∼40% and 60% , respectively , in comparison to the wild-type B . burgdorferi ( p < 0 . 0001 , Figure 4D ) . In contrast , the OspB complemented strain showed a significant increase in the binding to both TGE in comparison to the ospB mutant ( p < 0 . 0001 ) and was comparable to the wild-type spirochetes ( Figure 4D ) . Taken together , these data from Figure 4 show that genetic complementation of the ospB mutant with a wild-type copy of the ospB gene restores the defects seen in the colonization and survival of the ospB mutant inside the ticks and can restore B . burgdorferi binding to the TGE .
B . burgdorferi present an amazing variety of Osps that enable them to invade , colonize , and persist in environmental niches such as those inside vertebrates or ticks [23 , 26 , 41] . OspA , OspB , OspC , and DbpA are several of the major lipoproteins of B . burgdorferi that are differentially expressed in response to the varying environmental conditions [23 , 26 , 41] . B . burgdorferi upregulates OspA and OspB upon entry into ticks , and OspA contributes to the colonization of spirochetes within the vector gut [22] . Since ospA and ospB are cotranscribed [25 , 26] and colocalized on the bacterial surface [42] , we speculated that OspB might also function for B . burgdorferi within ticks . To determine the precise role of OspB in the life cycle of B . burgdorferi , we have generated an OspB-deficient isogenic isolate of B . burgdorferi . Our data show that OspB facilitates the colonization and survival of B . burgdorferi within ticks . While significant research has focused on the biological role of OspA in spirochete life cycle , relatively little information is available on the role of OspB in the life cycle of B . burgdorferi . Our in vivo studies with the OspB-deficient B . burgdorferi show that OspB is essential for the colonization and persistence of B . burgdorferi in ticks . During tick feeding , the ospB mutant and the wild-type B . burgdorferi enter the ticks from infected mice at the same rate . However , after feeding , the ospB mutant spirochetes are unable to persist within the blood meal or fully adhere to the tick gut , which also leads to a significant reduction in the number of spirochetes in the molted adult ticks ( Figure 3 ) . The binding of residual OspB-deficient spirochetes to the tick gut could be attributed to OspA , as the level of the OspA is unaltered in the OspB-deficient spirochetes ( Figure 1 ) . The luminal face of the gut epithelium is covered by a dense array of glycoproteins that may act as “receptor-buffet” for many pathogens [43] . Some of these glycoproteins are involved in general tissue structure and digestion [43 , 44] and some are involved in innate immunity [45 , 46] . Our surprising finding that the reduced ability of the ospB mutant to attach to the gut epithelium and its subsequent clearance in the gut may suggest that adherence to tick gut cells also is critical for some as yet unknown aspect ( s ) of spirochete viability . Our in vivo analysis showed that in contrast to the ospB mutant , the OspB complemented strain readily colonized tick gut tissue and showed a drastic increase in its persistence within ticks , which was comparable to the wild-type isolate ( Figure 4 ) . Furthermore , our in vitro binding assays with the TGE also supported the in vivo analysis , indicating that in contrast to the ospB mutant , the transcomplemented strain binds with a greater affinity to the TGE . In addition , the difference between the wild-type and the ospB mutant spirochetes in binding to the fed TGE is significantly higher in comparison to the unfed TGE , suggesting that the levels of expression of putative OspB gut receptor proteins/glycolipids might increase during feeding . Furthermore , our in vitro binding data correlated with a previous study [34] showing that the B . burgdorferi N40 OspB protein binds significantly to the TGE . Overall , our studies solidify a great body of experimentation implicating an important role of OspB in the attachment of B . burgdorferi to the tick gut . Yang and co-workers recently examined the role of the ospAB locus in the infectious life cycle of B . burgdorferi [35] . This was accomplished by the generation of an ospAB double mutant from B . burgdorferi strain BbAH130 ( infectious clone recovered after plating B . burgdorferi strain 297 ) , and it was found that disruption of both the ospA and ospB genes had no observable effect on the ability of spirochetes to establish infection in mice , whereas the locus is critically essential for colonization of the tick gut [35] . Spirochetes deficient for both OspA and OspB entered ticks but were unable to persist within ticks for a long time [35] . Furthermore , complementation of the ospAB double mutant with both OspA and OspB expression restores the ability of B . burgdorferi to colonize the gut [35] . On the other hand , complementation with the ospA gene alone could only partially restore ( 50%–60% in comparison to the wild-type ) the colonization defect of the ospAB mutant [35] , suggesting that OspB expression is also required for the complete restoration of the defect . A comparison of our data with the prior study [35] indicated that in contrast to the ospAB double mutant complemented with the ospA gene alone , the ospB mutant analyzed in our study was significantly impaired in its persistence in the tick gut . These variations could have been the results of ( i ) the different B . burgdorferi strains used in the studies ( B31 5A11 versus 297 BbAH130 ) and ( ii ) the relative OspA expression in the ospAB double mutant complemented with the ospA gene ( on a circular plasmid ) compared to the ospB mutant analyzed in our study . Overall , our studies in conjunction with the previous studies [35] show that ( i ) absence of OspB alone could result in severe impairment in colonization and persistence , and ( ii ) absence of both OspA and OspB could lead to the complete impairment in colonization and persistence of B . burgdorferi in ticks . In an evolutionary perspective , the conservation of ospB in the genome of B . burgdorferi is the result of positive selection pressure [23 , 24] , and thus OspB must be of intrinsic value to the organism . Our studies suggest that the function of OspB and OspA are codependent . OspA and OspB share approximately 50% identity and 62% similarity in their amino acid sequences [2] . The crystal structures of OspA and the C-terminal region of OspB have been determined [27 , 28] . Comparison of the crystal structure of OspA and C-terminal region of OspB shows that these two molecules are quite similar [27 , 28] . The C-terminal region of OspB adopts the same fold as is observed for the C-terminal half of OspA [28] . Li and co-workers have identified that the C-terminal barrel domain in OspA is a trio of partially buried charged residues: Arg 139 from beta-strand 10 , Glu-160 from beta-strand 12 , and Lys-189 from beta-strand 15 [27] . The barrel domain of the OspA/B fold features a prominent cavity , in which the first two residues are strictly conserved in both OspA and OspB; position 189 is nearly always Lys in OspA and Arg in OspB [27 , 28] . Studies from Li et al . ( 1997 ) and Becker et al . ( 2005 ) have proposed that the cavity in the OspA/B barrel domain might be a ligand binding site for a small peptide , linear saccharide , or an exposed protein loop [27 , 28] . Furthermore , the mapping of amino acid sequences required for OspA binding to the tick gut showed that the residues 85–103 and 229–247 are important [47] . The percent similarity ( identity ) for the two amino acid stretches are 63 ( 68 ) and 84 ( 79 ) in OspB amino acid sequence at positions 110–128 and 252–270 , respectively [34 , 47] . Given the structural and amino acid sequence conservation of OspA and OspB , it is possible that both lipoproteins recognize either the same target or closely related targets . It has recently been shown that OspB antibodies prevent B . burgdorferi colonization of I . scapularis gut [34] . Because of the high structural similarities between OspA and OspB , it could be reasoned that the OspB antibodies may bind to several epitopes of OspB on the B . burgdorferi surface , and steric hindrance might then interfere with OspB binding to the tick gut; or it is possible that steric hindrance by OspB antibodies also affected OspA-mediated binding of spirochetes to the tick gut [34] . Thus , our studies in conjunction with the previous reported study [34] raises interesting questions regarding the potential of antibody binding interfering with spirochete adherence in ticks . In the in vitro-grown B . burgdorferi cultures , the expression of OspB is lower than OspA ( Figure 1D ) . Since OspA is also involved in the attachment of spirochetes to the tick gut and since we have found that OspB-deficient spirochetes are unable to attach to the tick gut , it is possible that disruption of the OspB resulted in the interference of the OspA-mediated attachment to TROSPA . Three scenarios may be envisioned that may elucidate the possible involvement of OspB in the OspA-TROSPA interactions . Firstly , OspB may directly associate with either OspA or TROSPA and may form a complex structure that is required for the tight attachment of B . burgdorferi to the tick gut . Secondly , OspB may bind to its own receptor within the gut and this interaction might be required for TROSPA to interact with OspA . Finally , OspA and OspB might bind to separate TROSPA molecules on the gut epithelium and both these interactions might be required for the tight attachment of B . burgdorferi to the tick gut . With any of these three models , our finding that OspB-deficient spirochetes were unable to colonize or persist in tick gut is significant because it suggests a possible synergistic interaction between OspA , OspB , and TROSPA . In summary , these data suggest that OspB plays a critical role for B . burgdorferi adherence and persistence in ticks . These studies are not only important in understanding significant roles of spirochete ligands ( such as OspB ) in spirochete colonization and survival at arthropod-pathogen interface , but they also enhance our knowledge in the development of new therapeutic strategies , such as new transmission blocking vaccines that may be useful to combat B . burgdorferi infection .
B . burgdorferi isolate B31 infectious clone 5A11 that lacks lp5 and contains all other 20 plasmids [38 , 48] was used throughout , and will be referred to herein as the wild-type B . burgdorferi . This B . burgdorferi isolate , its ospB deletion mutant , and the OspB complemented mutant ( ospB−/pFGN1 ) were cultivated in vitro at 33 °C in Barbour-Stoenner-Kelly complete media ( BSK-H , Sigma , http://www . sigmaaldrich . com ) . Where necessary , antibiotics kanamycin and streptomycin were added to a final concentration of 350 μg/ml and 50 μg/ml , respectively . Spirochetes from in vitro cultures were needle-inoculated ( intradermally ) at a dose of 105 spirochetes/mouse and were recovered from cultures of ear punch biopsies at 2 wk after inoculation . E . coli strain DH5α ( Invitrogen , http://www . invitrogen . com ) was used as a cloning host . Unfed I . scapularis nymphs were obtained from a colony of I . scapularis ticks maintained by Dr . Durland Fish and Dr . Fred S . Kantor at Yale University . The Borrelia-adapted kanamycin resistance cassette , kanAn , driven by the flaB promoter , was excised from pTAkanAn [49] by EcoRI digestion and cloned into the EcoRI site of pBluescript ( Stratagene , http://www . stratagene . com ) . The resulting construct , designated pXLF10601 , contains two multi-cloning sites flanking the kanAn cassette , which allows efficient cloning of the 5′ and the 3′ arms required for homologous recombination . PCR primers N33/N34 and N35/N36 ( Table 1 ) for the construction of mutant spirochetes were designed to amplify the 5′ arm ( bp 8883–10257 of lp54 ) and the 3′ arm ( bp 10812–12015 of lp54 ) , respectively . The oligonucleotides were designed based on the sequenced genome of B . burgdorferi B31 isolate M1 [2] . The restriction sites designed in the primers allow directional cloning of the 5′ arm into the SacI-BamHI site and the 3′ arm into the NheI-KpnI site of pXLF10601 to generate the construct pXLF11303 ( Figure 1A ) . To generate an ospB mutant , 20 μg of pXLF11303 was electroporated into B . burgdorferi B31 strain and the mixture was incubated at 33 °C overnight in 20 ml of BSK-H medium . After an 18–24 h recovery period , 20 ml of BSK-H medium containing 700 μg/ml kanamycin was added ( final concentration of kanamycin , 350 μg/ml ) and 200 μl aliquots were dispensed into two 96-well plates . After 2–3 wk of incubation at 33 °C in a CO2 incubator , the wells that contained kanamycin-resistant ( KanR ) clones were identified by the color change of the culture medium . The presence of viable spirochetes in these wells was subsequently verified by dark-field microscopy . The homologous recombination between the sequences of pXLF11303 and of the native ospAB operon results in the excision of the ospB gene with the integration of the ΔospB::Kan fragment ( Figure 1A ) . The ospB mutant was confirmed by PCR with primers N21/N27 , N28/N17 , and N21/N17 ( Table 1 and Figure 1B ) ; RT-PCR ( with N16/N11 primers ) ; and Western blot analysis with monoclonal antibody B22J [50] ( Figure 1C and 1D ) . The plasmid profile of the ospB mutants was examined by PCR and array hybridization as previously described [37 , 38] . DNA fragments carrying the wild-type copy of promoter sequences of ospAB operon ( PospAB ) and the ospB gene were PCR-amplified from the B . burgdorferi genomic DNA using primers N29/N30 and N31/N32 , respectively ( Table 1 ) . The PCR products were subsequently cloned into the vector pKFSS1 [40] at the XbaI-PstI site , resulting in pFGN1 ( Figure 1A ) . 20 μg of pFGN1 DNA was electroporated into competent cells of the ospB mutant and the mixture was incubated at 33 °C overnight in 20 ml of BSK-H medium . After an 18–24 h recovery period , 20 ml of BSK-H medium containing 100 μg/ml streptomycin and 700 μg/ml kanamycin was added ( final concentration of streptomycin and kanamycin was 50 μg/ml and 350 μg/ml , respectively ) and 200 μl-aliquots were distributed into two 96-well plates . After 2–3 wk of incubation , streptomycin- and kanamycin-resistant clones were selected and analyzed by PCR with primers N23/86 and N83/N84 to detect the PospAB-ospB and the aadA fragments , respectively . RT-PCR with N16/N11 primers and immunoblot with mAb B22J were also performed to confirm the presence of ospB transcripts and OspB protein , respectively ( Figure 1C and 1D ) . Plasmid content of the complemented clones was analyzed by PCR as previously described [38 , 51] . 105 spirochetes from an in vitro-grown culture were needle-inoculated into the groups of three 3-wk-old C3H/HeN mice . 2 wk later , ear punch biopsies were taken and DNA was extracted for PCR with primers N18 and N19 ( flaB gene primers , Table 1 ) to confirm the infectivity . After 3 wk of infection , tibiotarsal and knee joints were evaluated for swelling [39] . Mice were sacrificed after the tick feeding and evaluation of joint swelling , and tissues such as skin , heart , bladder , and joints were harvested for DNA extraction . In parallel , for recovery of B . burgdorferi from mice , a part of bladder and spleen was inoculated into BSK-H medium and cultivated at 33 °C . After a 3-wk inoculation of B . burgdorferi into mice , nymphal I . scapularis ticks ( 50 ticks/mice ) were allowed to feed on B . burgdorferi-infected mice . Partially fed nymphs ( five nymphs/time point ) were removed from the skin of mice at various time points during feeding ( 8 h , 24 h , 48 h , and 72 h ) . Remaining nymphs were allowed to feed to repletion and were collected at 24 h , 48 h , and 72 h following up to 7 d post-feeding ( corresponding to 96 h , 120 h , 144 h , and 10 d , respectively , from the feeding start time point ) . Three to five guts from each group of nymphs were microscopically dissected in 20 μl phosphate-buffered saline ( PBS ) and washed to remove luminal contents and unbound bacteria . In addition , luminal contents ( including the blood ) from the gut were separately isolated from the fed ticks and subjected to immunofluorescence confocal microscopy as described [35 , 52] . The spirochete burden in ticks was further analyzed by quantitative RT-PCR . Nymphal ticks were allowed to feed to repletion on B . burgdorferi-infected mice . Engorged nymphs were then collected and transferred into 10-ml glass vials with vented lid and stored at 22 °C incubator with a relative humidity of 97% and 16 h:8 h light:dark photoperiod . Engorged nymphs were allowed to molt to the adult stage for 6–8 wk . Five to seven guts from adults were microscopically dissected , and guts and luminal contents were subjected to immunofluorescence confocal microscopy as described [35 , 52] . Spirochete burden in adult ticks was further evaluated by quantitative RT-PCR analysis . The washed gut and luminal contents were subjected to immunofluorescence confocal microscopy as described [35 , 52] . Briefly , gut and luminal content samples were blocked with PBS containing 0 . 05% Tween 20 and 5% goat serum for 1 h at 37 °C and then incubated for 1 h at 37 °C with FITC-labeled anti-Borrelia antibody ( KPL , http://www . kpl . com ) . Samples were subsequently stained with propidium iodide ( 20 μg/ml ) for 3 min at 37 °C followed by mounting with SlowFade-Antifade kit ( Invitrogen ) . The tissues were viewed using a Zeiss LSM 510 scanning laser confocal microscope equipped with an argon/krypton laser ( Zeiss , http://www . zeiss . com ) . Microinjection of B . burgdorferi into the ticks was performed as described [35] . Briefly , B . burgdorferi isolates ( wild-type , ospB mutant , and OspB complemented mutant ) were cultivated under normal conditions in BSK-H medium with or without antibiotics . Log-phase cultures were centrifuged and concentrated in a fresh BSK-H media to a density of 109 spirochetes per ml . 1 μl of the re-suspended cultures was then loaded into a 1-mm diameter glass capillary needle ( World Precision Instruments , http://www . wpiinc . com ) and approximately 1 nl ( 103 spirochetes ) was injected into each tick via the rectal aperture using femtojet microinjector system ( Eppendorf AG , http://www . eppendorf . com ) as described [22 , 35] . 15 microinjected ticks were reared in a humid chamber for 12 h and fed on naïve mice . Five to seven partially fed ticks were detached during feeding at 48 h . The spirochete burden in ticks was further analyzed by confocal microscopy and quantitative RT-PCR . For RT-PCR analysis of B . burgdorferi grown in vitro , total RNA was isolated from B . burgdorferi B31 and its derived mutants using Nucleospin RNAII kit ( BD Biosciences , Clontech , http://www . clontech . com ) and converted to cDNA using iScript cDNA synthesis kit ( Bio-Rad , http://www . bio-rad . com ) . RT-PCR for the ospB , ospA , and flaB genes was performed using the primer combinations N16/N11 , N21/N22 , and N12/N13 , respectively ( Table 1 ) . For the RT-PCR analysis of B . burgdorferi RNA in ticks , RNA from the fed-tick extracts ( three nymphs/group ) was extracted and converted to cDNA . The cDNA was used as a template for the amplification of flaB using primers N18 and N19 and for tick β-actin , using primers N3 and N20 . For the quantification of the spirochete burden in mice and ticks , quantitative PCR ( Q-PCR ) and quantitative RT-PCR ( Q-RT-PCR ) analysis was performed , respectively , as described [22 , 35] . The 225-bp flaB amplicons were quantified using the oligonucleotides N18 and N19 and iQ-SYBR Green Supermix ( Bio-Rad ) . The reaction conditions are initial 95 °C for 10 min followed by 94 °C for 30 s , 60 °C for 1 min , 72 °C for 1 min for 35 cycles . As an internal control and to normalize the amount of template , tick actin amplicons were quantified using N3/N20 ( for tick samples ) , and mouse β-actin amplicons were quantified using the oligonucleotides N25 and N26 ( for mice samples ) . Standard curves for flaB , tick actin , and mouse β-actin were prepared using 10-fold serial dilutions of known quantities ( 4 ng–0 . 4 fg for pCR2 . 1-flaB; 5 ng–0 . 5 fg for tick β-actin; 5 ng–0 . 5 fg for mouse β-actin ) . Guts from flat-nymphal ticks ( 40 ticks ) and fed-nymphal ticks ( 25 ticks ) were dissected in PBS and homogenized on ice using Kontes micro homogenizer ( VWR Scientific Products , http://www . vwrsp . com ) . Total protein concentrations in the TGE were determined using the Bio-Rad Protein Assay Kit ( Bio-Rad Laboratories ) . 100 μl of TGE ( 5 μg/ml ) in PBS was used to coat the wells of 96-well plates ( Nunc , http://www . nuncbrand . com ) . Control wells were coated with fetal bovine serum ( FBS ) ( 10 μg/ml ) . The coated wells were incubated for 4 h at 33 °C with 107 spirochetes per well in PBS-Tween 20 supplemented with 5% FBS . Unbound spirochetes were washed away with PBS-Tween , followed by incubation for 1 h at 33 °C with FITC-labeled anti-Borrelia antibody ( KPL ) . Binding was detected using anti-FITC IgG-horseradish peroxidase ( Amersham , http://www . gehealthcare . com ) as a secondary reagent and TMB microwell peroxidase substrate ( KPL ) was used for color development . The reactions were stopped after 15 min incubation using TMB stop solution ( KPL ) and optical density ( OD ) was read at 450 nm . For sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) analysis , 10 ml of actively growing spirochetes ( 107 cells/ml ) were centrifuged and re-suspended in 25 μl Laemmli Sample buffer ( Bio-Rad ) . Samples were incubated in boiling water for 5 min and loaded onto 12% SDS-PAGE gels . Gels were stained with Simply Blue SafeStain ( Invitrogen ) and processed according to the manufacturer's instruction . Immunoblotting analysis was performed with the protein extracts from 107 spirochetes and processed as described [52] . Monoclonal antibodies directed against OspB ( mAb B22J ) , OspA ( C3 . 78 ) , and FlaB ( H9729 ) were reported previously [22 , 50] .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for B . burgdorferi B31 isolate M1 are NC_001318 for the chromosomal genome sequence and NC_001857 for the lp54 sequence . | Lyme disease is the most common vector-borne disease in North America and Europe . The causative agent Borrelia burgdorferi is a bacterium that is maintained in an enzoonotic cycle between Ixodes ticks and a large range of mammals . Accidental encounters of infected Ixodes ticks with humans results in the transmission of B . burgdorferi and subsequent Lyme disease . Given that global control efforts have met with limited success , the need for developing novel interventions to combat this infection has become all the more vital . A better understanding of how B . burgdorferi interacts with its vector might lead to new ideas for combating the Lyme disease . B . burgdorferi upregulates outer surface protein ( Osp ) A and B during entry into ticks , and OspA contributes to the colonization of bacterium within the vector gut . We now demonstrate that OspB also facilitates the colonization and survival of B . burgdorferi in ticks . This work provides the basis for future studies as to how this protein facilitates interaction of B . burgdorferi to the tick gut and thus ultimately a basis for the development of novel strategies to interrupt the spirochete life cycle . | [
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] | 2007 | Outer Surface Protein B Is Critical for Borrelia burgdorferi Adherence and Survival within Ixodes Ticks |
Beneficial microbial symbionts serve important functions within their hosts , including dietary supplementation and maintenance of immune system homeostasis . Little is known about the mechanisms that enable these bacteria to induce specific host phenotypes during development and into adulthood . Here we used the tsetse fly , Glossina morsitans , and its obligate mutualist , Wigglesworthia glossinidia , to investigate the co-evolutionary adaptations that influence the development of host physiological processes . Wigglesworthia is maternally transmitted to tsetse's intrauterine larvae through milk gland secretions . We can produce flies that lack Wigglesworthia ( GmmWgm− ) yet retain their other symbiotic microbes . Such offspring give rise to adults that exhibit a largely normal phenotype , with the exception being that they are reproductively sterile . Our results indicate that when reared under normal environmental conditions GmmWgm− adults are also immuno-compromised and highly susceptible to hemocoelic E . coli infections while age-matched wild-type individuals are refractory . Adults that lack Wigglesworthia during larval development exhibit exceptionally compromised cellular and humoral immune responses following microbial challenge , including reduced expression of genes that encode antimicrobial peptides ( cecropin and attacin ) , hemocyte-mediated processes ( thioester-containing proteins 2 and 4 and prophenoloxidase ) , and signal-mediating molecules ( inducible nitric oxide synthase ) . Furthermore , GmmWgm− adults harbor a reduced population of sessile and circulating hemocytes , a phenomenon that likely results from a significant decrease in larval expression of serpent and lozenge , both of which are associated with the process of early hemocyte differentiation . Our results demonstrate that Wigglesworthia must be present during the development of immature progeny in order for the immune system to function properly in adult tsetse . This phenomenon provides evidence of yet another important physiological adaptation that further anchors the obligate symbiosis between tsetse and Wigglesworthia .
Bacteria comprise the most abundant and diverse life form on earth . The ubiquity of bacteria means they have colonized virtually every ecological niche , including habitation within more evolutionarily sophisticated multi-cellular animals . Co-evolution over millions of years has provided an opportunity for beneficial symbiotic associations to develop between phylogenetically distant taxa . Such affiliations are often mutualistic , meaning both partners benefit so that each can successfully inhabit diverse environments that neither could survive in on its own [1] , [2] . Deciphering the mutualistic relationships between prokaryotic bacteria and multi-cellular eukaryotic animals is a rapidly advancing field of research . Performing detailed investigations into the relationships between symbiotic bacteria and higher eukaryotes often involves costly and complex procedures . However , insects have well-documented symbioses that are attractive to study because they have relatively short generation times and are easy and less costly to rear . One insect that harbors multiple symbionts is the tsetse fly , Glossina morsitans . These microbes include the commensal Sodalis , the parasite Wolbachia , and the obligate mutualist Wigglesworthia glossinidia [3] . Molecular phylogenetic analysis indicates that tsetse's symbiosis with Wigglesworthia is ancient , dating back 50–80 million years [4] . The concordant nature of tsetse's obligate association with Wigglesworthia has driven the co-evolution of biological adaptations that are beneficial to both partners . For example , the localization of Wigglesworthia cells within host bacteriocytes provides a protective and metabolically favorable niche for this bacterium [5] . In return tsetse derives benefit from Wigglesworthia in at least two distinct ways . First , tsetse feeds exclusively on vertebrate blood , which is deficient in vitamins essential for survival . In accordance , a large proportion of Wigglesworthia's streamlined ( 700 kB ) genome encodes vitamin biosynthesis pathways that presumably supplement tsetse's restricted diet [5] , [6] . Second , more recent studies indicate that Wigglesworthia may serve an immunologic role in tsetse . These flies are the sole vector of pathogenic African trypanosomes , the causative agent of sleeping sickness in humans [7] . In laboratory experiments infection with immunogenic trypanosomes results in a decrease in tsetse fecundity [8] . Furthermore , parasite infection prevalence is higher in flies that lack Wigglesworthia when compared to age-matched wild-type ( WT ) individuals [9] . Wigglesworthia is thought to influence tsetse's vectorial competence by modulating its host's humoral immune system [10] . Thus , by preventing energetically costly parasite infections , this obligate symbiont may indirectly benefit the reproductive fitness of tsetse . Symbiotic bacteria are rapidly gaining recognition for their important contributions to host development and immunity . The most well-known example of this type of interaction involves the mammalian microbiome , which modulates gut development during early postnatal life and subsequently shapes our mucosal and systemic immune systems [11] , [12] . Symbionts also serve similar functions in invertebrate hosts . For example , light organ morphogenesis in juvenile bobtail squid ( Euprymna scolopes ) initiates only after symbiotic Vibrio fischeri cells have stably colonized this tissue [13] . The pea aphid , Acyrthosiphon pisum , harbors a secondary symbiont , Hamiltonella defensa , which can be infected with a lysogenic bacteriophage ( A . pisum secondary endosymbiont; APSE ) . A . pisum that harbor both H . defensa and APSE are protected from being consumed by a parasitic wasp larvae through the action of phage-encoded toxins [14] . To the best of our knowledge no evidence exists that demonstrates insect symbionts can confer an impending protective phenotype in their adult host by directing immune system development during immature stages . In the present study we investigate the mechanism by which Wigglesworthia contributes to the development and function of cellular and humoral immune responses in adult tsetse . Our results show that the maturation and normal function of adult cellular immune responses in tsetse are severely compromised when Wigglesworthia is absent during larval development . This study reveals an important new facet that further anchors the obligate relationship between tsetse and Wigglesworthia and may serve as a useful model to understand the highly integrated and dynamic relationship between hosts and their beneficial bacterial fauna .
Insects are normally capable of mounting an immune response that combats infection with various groups of bacteria . Interestingly , in comparison to Drosophila , tsetse flies are uniquely susceptible to septic infection with 103 colony-forming units ( CFU ) of normally non-pathogenic Escherichia coli ( E . coli ) K12 [15] . In the present study we further investigated tsetse's unique susceptibility to E . coli infection by subjecting wild-type ( GmmWT ) and adults from two age groups to hemocoelic infections with varying quantities of E . coli K12 . Three-day-old GmmWT individuals ( flies from this age group are hereafter referred to as “young” ) were highly susceptible to this treatment , as 103 CFU resulted in the death of all flies by 8 d post-infection ( dpi; Figure 1A , top graph ) . In contrast , 77% and 55% of 8-d-old WT individuals ( flies 8 d old and older are hereafter referred to as “mature” ) survived for 14 dpi with 103 and 106 CFU of E . coli K12 , respectively ( Figure 1A , middle graph ) . We demonstrated previously that feeding pregnant female tsetse the antibiotic ampicillin results in the generation offspring that lack Wigglesworthia ( GmmWgm− ) but still harbor Sodalis [9] and presumably Wolbachia . In the present study we determined the survival outcome of mature GmmWgm− inoculated with 106 CFU of E . coli K12 ( the dose required to kill ∼50% of mature WT flies; Figure 1A , middle graph ) . In comparison to age-matched WT tsetse , GmmWgm− flies were highly susceptible to this infection . In fact , at 14 dpi , only 1% of these individuals remained ( Figure 1A , bottom graph ) . We reasoned that the dramatic susceptibility of mature GmmWgm− flies to infection with E . coli could result from one of two scenarios . In the first scenario Wigglesworthia may directly contribute to the adult immune response to a foreign micro-organism . In the second scenario , the absence of Wigglesworthia during larvagensis may give rise to GmmWgm− adults with compromised immune functions . To determine if the first scenario is correct we treated mature WT tsetse with the antibiotic tetracycline to eliminate all of their microbiota , including bacteriome-associated Wigglesworthia ( Figure 1B ) , and subsequently challenged these adult flies ( GmmWT/Wgm− ) with 106 CFU of tetracycline resistant E . coli K12 . We found that , similar to their WT counterparts ( GmmWT; Figure 1A , middle panel ) , about 70% of GmmWT/Wgm− individuals survived this infection ( Figure 1C ) . Conversely , GmmWgm− adults were highly susceptible ( Figure 1A , bottom graph ) . This result suggests that when Wigglesworthia is absent from tsetse during larval development subsequent adults are severely immuno-compromised . We demonstrated that when larval tsetse lacks exposure to Wigglesworthia in utero their immune system is highly compromised during adulthood . This finding does not exclude the possibility that treatment of female flies with ampicillin to produce GmmWgm− offspring induces perturbations in other constituents of tsetse's microbiota . Because tsetse's other two known endosymbionts , Sodalis and Wolbachia , are present at similar densities in GmmWT and GmmWgm− adults ( Figure 1D ) , we looked for the presence of uncharacterized endosymbionts or digestive tract-associated microbes that are potentially passed on to developing intra-uterine larvae where they subsequently impact immunity . We generated a clone library containing 16s rRNA gene sequences PCR amplified from 3rd instar GmmWgm− and GmmWT larvae and then sequenced multiple clones from each tsetse line . Our results indicate that the proportion of Wigglesworthia , Sodalis , and Wolbachia in 3rd instar GmmWT larvae is 19% , 75% , and 6% , respectively . As expected no Wigglesworthia 16s rRNA sequences were present in GmmWgm− larvae , and the proportion of Sodalis and Wolbachia sequences present was 71% and 29% , respectively ( Figure 1E ) . No uncharacterized endosymbionts or gut-associated environmental microbes were present in any sample . Taken together , these results suggest that the presence of Wigglesworthia specifically is responsible for enabling immune system maturation in WT tsetse . Our host survival curves indicate that mature GmmWT and GmmWT/Wgm− can survive infection with E . coli while young GmmWT and mature GmmWgm− perish . To determine a cause for the variation in survival we observed between the four groups following infection with E . coli , we monitored bacterial growth dynamics over the course of the experiment in each group . Bacterial number within mature GmmWT and GmmWT/Wgm− peaked at 3 . 8×104 and 1 . 9×104 E . coli , respectively , over the 2-wk period , indicating that these groups appeared able to control their infections . In contrast , E . coli increased exponentially in young GmmWT and mature GmmWgm− and reached a maximum density at 6 dpi of 4 . 2×106 and 8 . 8×106 , respectively ( Figure 1F ) . These results implicate bacterial sepsis as the cause of high mortality observed in young GmmWT and mature GmmWgm− . All of the above-mentioned results taken together indicate that mature GmmWT are considerably more resistant to infection with a foreign microbe than are their younger counterparts . Furthermore , tsetse's obligate mutualist , Wigglesworthia , must be present during the development of immature stages so that mature adults are able to overcome infection with E . coli . To understand the basis of the compromised immunity we observed in mature GmmWgm− flies , we evaluated the expression profile of a set of immunity-related genes from age-matched mature adult GmmWT and GmmWgm− flies that were either uninfected or 3 dpi with E . coli K12 . We included the antimicrobial peptides ( AMPs ) attacin , cecropin , and defensin , which distinctly target gram-negative ( attacin and cecropin ) and gram-positive bacteria ( defensin ) [16] . Our analysis also included thioester-containing proteins ( tep2 and tep4 ) , prophenoloxidase ( PPO ) , and inducible nitric oxide synthase ( iNOS ) . In insects TEPs likely function as pathogen-specific opsonins that bind to bacteria or parasites and promote their phagocytosis/encapsulation [17] , while PPO initiates a proteolytic cascade that results in melanin deposition [18] . iNOS catalyzes synthesis of the signaling molecule nitric oxide ( NO ) , which plays a role in humoral and cellular immunity in Anopheline mosquitoes [19] , reduvid bugs [20] , and Drosophila [21] , [22] by inducing AMP expression and recruiting hemocytes to the site of infection . Our expression results indicate that symbiont status plays little or no role in the expression of immunity-related genes in uninfected adults . In fact , with the exception of the AMP defensin , no significant differences in immunity-related gene expression between mature uninfected GmmWT and GmmWgm− adults ( Figure 2A ) . However , we observed a considerably different profile of immunity-related gene expression when these different fly strains were infected with E . coli K12 . Under these circumstances , all of the genes evaluated ( with the exception of defensin ) were expressed at significantly higher levels in GmmWT compared to GmmWgm− individuals ( Figure 2B ) . Particular striking was the fact that the induction of pathways associated with cellular immunity , such as pathogen recognition ( tep2 and tep4 ) and melanization ( PPO ) , were significantly compromised in mature GmmWgm− adults . The absence of a robust cellular immune response is likely the cause of high mortality among these individuals following E . coli infection . This analysis indicates that Wigglesworthia must be present during the development of immature tsetse in order for immune-related genes to subsequently be expressed in mature E . coli–infected adults . Our analysis of immunity-related gene expression in tsetse suggests that cellular immune pathway functions in adult tsetse are particularly compromised when Wigglesworthia is absent during immature development . The most prominent cellular immune mechanisms include melanization and phagocytosis . These processes , which ultimately result in the removal of foreign invaders in Drosophila [23] and A . aegypti [24] , both arise from distinct crystal cell and plasmatocyte hemocyte lineages , respectively [25] . We investigated the role hemocytes might play in determining the susceptible phenotype we observed in tsetse following infection with E . coli . We infected mature GmmWT individuals with GFP-expressing E . coli and were able to observe that hemocytes had engulfed a large number of the introduced cells by 12 hpi ( Figure 3A ) . We next inhibited phagocytosis by introducing blue fluorescent microspheres directly into tsetse's hemocoel and 12 h later infected the bead-treated individuals with GFP-expressing E . coli . Microscopic inspection of hemocytes harvested 12 hpi with E . coli revealed the presence of internalized microspheres and the absence of engulfed E . coli . This observation indicated that we were successful in blocking hemocyte phagocytosis ( Figure 3B ) . We subsequently maintained our microsphere-injected tsetse for 2 wk with the intention of determining the impact of impaired phagocytosis on host survival outcome . Mature GmmWT flies exhibiting impaired phagocytosis were highly susceptible to infection with both 1×103 and 1×106 E . coli K12 . In fact , by day 12 post-infection , all of these flies had perished regardless of the initial dose used for infection ( Figure 3C ) . This observation contrasts starkly with infection outcome in mature GmmWT that exhibit normally functioning hemocytes ( Figure 1A , middle graph ) . Our results suggest that defects in phagocytosis severely compromise the ability of these tsetse flies to overcome bacterial infection . A notable result of our immunity-related gene expression analysis was a 37-fold decrease in PPO levels in GmmWgm− flies . This enzyme is an essential component of the melanization pathway , and its expression ultimately results in host wound healing and the melanization , encapsulation , and subsequent removal of foreign microorganisms [26]–[28] . In conjunction with the remarkable variation in PPO expression observed between GmmWT and GmmWgm− , we were also able to visually observe the absence of a melanization response to E . coli infection in flies lacking Wigglesworthia . In fact , 30 min post-injection with E . coli , hemolymph was still actively exuding from the inoculation wound of GmmWgm− flies . Conversely , in WT individuals no hemolymph was detectable and melanin was deposited at the wound site ( Figure 4 ) . These results further suggest that hemocyte-mediated cellular immunity provides an imperative defense against the establishment of bacterial infections in tsetse's hemocoel . Furthermore , the absence of this response in GmmWgm− individuals was likely responsible for the compromised host survival phenotype we observed following infection with E . coli . We observed that young GmmWT were markedly more susceptible to infection with E . coli K12 than were their mature counterparts . Furthermore , symbiont status also altered infection outcome , as mature GmmWgm− perished following E . coli infection while age-matched WT individuals survived . These differential infection outcomes appeared to result from disparities in cellular immune system function between the different tsetse lines we examined . Based on these observations we hypothesized that the obligate mutualist Wigglesworthia plays a crucial role in regulating the development of cellular immunity in tsetse during immature stages . To test this hypothesis we quantified the number of circulating and sessile hemocytes present in young and mature adult GmmWT and mature adult GmmWgm− . Our results indicate that a 1 . 4-fold increase in circulating hemocyte number occurs between day 3 and day 8 in WT tsetse , while no significant change in circulating hemocyte number was observed between young and mature GmmWgm− ( Figure 5A ) . Interestingly , mature GmmWT adults harbored 3 . 4× more circulating hemocytes than did mature GmmWgm− adults ( Figure 5A ) . We also looked at sessile hemocyte abundance as a further indicator of Wigglesworthia's impact on the development of cellular immunity in tsetse . In Drosophila , this hemocyte subtype concentrates in large quantities around the anterior end of the fly's dorsal vessel [29] . Thus , we indirectly quantified sessile hemocyte abundance immediately adjacent to the anterior-most chamber of tsetse's dorsal vessel by measuring the fluorescent emission of microspheres that were found engulfed in this region . Young GmmWT adults engulfed 1 . 2× more microspheres than their mature counterparts and 15 . 7× more than mature GmmWgm− adults . Furthermore , mature WT adults engulfed 13 . 2× more microspheres than did age-matched GmmWgm− adults ( Figure 5B ) . Our results demonstrate that Wigglesworthia must be present during the development of immature tsetse in order for cellular immunity to develop and function properly in adults . Thus , we speculate that the absence of hemocytes in adult tsetse should reflect a lack of blood cell differentiation during the development of immature stages . In Drosophila the process of blood cell differentiation , or hematopoiesis , begins in the embryo and proceeds through all larval stages [30] . During Drosophila embryogenesis early hematopoiesis can be distinguished by the expression of the zinc finger transcription factor “Serpent . ” Subsequently , another transcription factor , “Lozenge , ” directs the differentiation of serpent-expressing precursor cells into a specific lineage of hemocytes called crystal cells . To address the relationship between the presence of Wigglesworthia and early hematopoiesis in tsetse , we used qPCR to evaluate the relative number of serpent and lozenge transcripts present in 1st , 2nd , and 3rd instar larvae dissected from pregnant GmmWT and GmmWgm− females . Larval instars L1 , L2 , and L3 from WT females expressed 1 . 7 , 2 . 1 , and 1 . 9 times more serpent transcripts , and 4 , 4 . 4 , and 3 . 9 times more lozenge transcripts , respectively , than did their counterparts from females that lacked Wigglesworthia ( Figure 5C ) . This observed attenuated expression of both serpent and lozenge may account for the depleted hemocyte population , and compromised cellular immune function , we observed in adult GmmWgm− individuals .
In the present study we demonstrate that Wigglesworthia is intimately involved in regulating the maturation and function of tsetse's cellular immune system during immature larval development . We present a model that links the presence of Wigglesworthia in larval progeny with host immune system maturation during development and the subsequent ability of adult tsetse to overcome infection with foreign microbes ( Figure 6 ) . Obligate symbioses between intracellular bacteria and multi-cellular eukaryotes represent millions of years of co-evolution during which time both partners have adapted to increase each other's overall fitness . The association between tsetse and Wigglesworthia is an example of this reciprocal relationship in that neither organism can survive in the absence of the other . We present several lines of evidence indicating that tsetse's resistance to E . coli positively correlates with fly age and symbiont infection status during juvenile stages . Specifically , our results signify that mature GmmWT adults are resistant to E . coli infection , while young GmmWT adults are susceptible . In contrast , both young and old adult flies that lack Wigglesworthia throughout all developmental stages are killed by E . coli infections . Like their WT counterparts , GmmWgm− larvae acquire both Sodalis and Wolbachia while in utero [31] . Although the intrauterine larval environment is otherwise aseptic , adult GmmWgm− can be exposed to a wide range of environmental microbes during adulthood . However , neither the presence of other symbiotic bacteria ( Sodalis and Wolbachia ) in larvae , nor environmental microbes acquired during adulthood , appear to be sufficient to induce immunity in GmmWgm− adults . Additionally , we show that mature GmmWT adults treated with antibiotics to eliminate Wigglesworthia ( GmmWT/Wgm− ) remain resistant to E . coli infections . The resistant phenotype of GmmWT/Wgm− further signifies that obligate Wigglesworthia is not directly responsible for the ability of mature adult GmmWT to overcome septic bacterial infection . Instead , it appears that Wigglesworthia's presence during the maturation of larval progeny stimulates the development of host immunity in adults . Our quantitative analysis of gene expression indicates that GmmWT and GmmWgm− adults exhibit no significant difference in the expression of genes that serve as hallmarks of humoral and cellular immunity in the absence of microbial challenge . However , following infection with E . coli , all pathways were significantly compromised in GmmWgm− versus GmmWT adults . The most notable discrepancy observed between the two tsetse lines involved the expression of prophenoloxidase . Following E . coli infection , the expression of this gene increased 37-fold in WT tsetse but remained virtually unchanged in individuals that lacked Wigglesworthia . In WT insects , PPO , which is an inactive zymogen , is proteolytically cleaved to produce phenoloxidase upon mechanical injury or the presence of foreign pathogens . Phenoloxidase then facilitates the process of melanin deposition [18] . A functional melanotic pathway would likely increase tsetse's resistance to E . coli infection in several ways . First , sequestration of E . coli in a melanotic capsule at the site of inoculation would likely help prevent their dissemination into adjacent host tissues . This phenomenon has been observed in both the hawk moth , Manduca sexta , and Drosophila following infection with pathogenic bacteria ( Photorhabdus luminescens ) and parasitic wasp eggs , respectively [32] , [33] . In both cases the lack of melanization at the wound site severely compromised the host's ability to subsequently overcome the foreign invader . Second , the melanization cascade results in the production of reactive oxygen intermediates that are directly toxic to foreign pathogens . In the flesh fly Sarcophaga peregrina and M . sexta , melanization intermediates such as DOPA exhibit direct antimicrobial activity [34] , [35] . Finally , melanin at the site of inoculation expedites wound healing that could prevent the spread of secondary infections [26] . The E . coli–susceptible phenotype of mature GmmWgm− adults is likely reflective of their blood cell ( hemocyte ) deficit in comparison to E . coli–resistant WT flies . In Drosophila 90%–95% of the total hemocyte population is composed of sessile and circulating plasmatocytes , which are a distinct hemocyte lineage predominantly responsible for engulfing and digesting foreign pathogens [36] . The inability of mature GmmWgm− tsetse to survive infection with E . coli may result from their significantly reduced population of phagocytic hemocytes available to engulf bacteria injected into the hemocoel . In fact , by injecting polystyrene beads as a means of blocking this physiological process , we demonstrate that phagocytosis is a critical component of tsetse's ability to manage septic infection with E . coli in WT flies . Similarly , mutant Drosophila that contain a depleted plasmatocyte population , or have been injected with beads to prohibit phagocytosis , exhibit a remarkable susceptibility to a variety of gram positive and negative bacteria [37] , [38] . Interestingly , Drosophila that lack functional plasmatocytes also exhibit a reduced capacity to activate humoral immune responses , further inhibiting their ability to fight bacterial infection [29] . In Drosophila the crystal cell hemocyte lineage controls the humoral melanization cascade via the release of PPO stored in large cytoplasmic inclusion bodies [30] , [39] . In addition to the dramatic reduction in PPO expression in GmmWgm− tsetse , two further lines of evidence indicate that this fly strain harbors a significantly reduced population of hemocytes that function in a homologous manner to Drosophila crystal cells . First , lozenge expression in all three larval instars from GmmWgm− females is significantly lower than in their GmmWT counterparts . In Drosophila larval crystal cells fail to form in the absence of lozenge expression , yet the differentiation of other hemocyte types proceeds normally [40] . Second , we observed that hemocyte-deficient GmmWgm− flies were unable to produce a viable clot at the site of bacterial inoculation . In mutant Drosophila strains that lack crystal cells , PPO is absent from the hemolymph . Consequently , the melanization cascade fails to initiate and hard clots do not form at wound sites [41] . We discovered that young GmmWT adults harbor significantly less circulating hemocytes than do mature WT adults . This observation implies that circulating hemocyte number in adult WT tsetse increases as a function of age , although the specific mechanism underlying this process in tsetse is currently unknown . In adult Drosophila intact lymph glands are absent and no evidence exists for the de novo synthesis of hemocytes following metamorphosis [30] , [40] . Interestingly , we did observe more subepidermal sessile hemocytes in young compared to mature GmmWT adults , although the number was not statistically significant between the two groups . We speculate that the increased abundance of circulating hemocytes we observed in mature compared to young GmmWT may reflect a shift in the proportion of sessile to circulating cells instead of de novo production of new hemocytes . A comparable process occurs in Drosophila larvae and adults , where the proportion of sessile to circulating hemocytes changes following immune stimulation [42] , [43] . Furthermore , after larval Drosophila receive an epidermal wound , circulating hemocytes are rapidly recruited to the site of injury . These circulating cells are phagocytically active and likely function as a front-line surveillance system against tissue damage and microbial infection [44] . In the mosquito malaria vector Anopheles gambiae , exposure to Plasmodium parasites stimulated an increase in the number of granulocytes circulating in the hemolymph . These primed mosquitoes were subsequently more resistant to infection with pathogenic bacteria than their wild-type counterparts [45] . We propose that young GmmWT adults and mature GmmWig− adults may be susceptible to E . coli infection in part because they harbor significantly less circulating hemocytes than do older WT individuals that are resistant . Our previous studies indicated that GmmWig− adults are highly susceptible to trypanosome infection [9] , [10] and that this phenotype may be modulated by tsetse's humoral immune system [46]–[48] . Studies are ongoing to determine if cellular immunity also modulates tsetse's trypanosome vectorial competence . Neonatal humans ( and presumably other mammals as well ) acquire probiotic gut microbes through the act of breast feeding [49] . These bacteria subsequently stably colonize the naïve intestine where they promote immune system maturation and enhance defense against infection with pathogenic microbes [50] . Most lower eukaryotes , including insects , hatch from an egg deposited into the environment and thus rely mainly on environmental microbes to stimulate their innate immune systems during the development of immature stages . For example , Anopheline mosquitoes and Drosophila are exposed to environmental microbes throughout all stages of their development . Under these natural conditions subsequent adults exhibit potent innate immunity [51] , [52] . However , when these insects are reared under germ-free conditions , they exhibit a severely compromised humoral immune response [51] , [53] . Tsetse flies are unique among other insects because they lead a relatively aseptic existence . Not only do they feed exclusively on sterile vertebrate blood , but they also exhibit a unique viviparous reproductive strategy . During this type of reproduction , all embryonic and larval stages develop within the female's uterus . The protected in utero environment in which viviparous offspring develop limits their exposure to environmental microbes . However , throughout tsetse larvagenesis maternal milk gland secretions provide the developing offspring with nourishment as well as Wigglesworthia , Sodalis , and Wolbachia [31] . Interestingly , recently established Sodalis and Wolbachia appear unable to influence their host's physiology to the same extent as mutualist Wigglesworthia despite their presence throughout larval development . Future studies on this system will focus on determining the chemical and/or metabolic elements provided by Wigglesworthia that stimulate the maturation of its host's immune system during larval development . The results of this study provide evidence of a novel functional role for obligate insect symbionts–host immune system activation during immature developmental stages to ensure robust function during adulthood . We demonstrate that the obligate Wigglesworthia provides tsetse offspring the stimuli necessary for immune system development , a process that exhibits functional parallels with the mammalian system following the transfer of beneficial microbes from mothers to their neonates during that act of breast feeding . This phenomenon represents an adaptation that further anchors the steadfast relationship shared between tsetse and its obligate mutualist . The essential nature of tsetse's dependence on Wigglesworthia provides a potentially exploitable niche to experimentally modulate host immunity , with the intention of diminishing this insect's capacity to vector deadly trypanosomes .
Throughout the text , 3-d-old and 8-d-old tsetse are referred to as “young” and “mature , ” respectively . Wild-type G . m . morsitans ( GmmWT ) were maintained in Yale's insectary at 24°C with 50%–55% relative humidity . These flies received defibrinated bovine blood every 48 h through an artificial membrane feeding system [54] . Two Wigglesworthia-free tsetse lines were generated for use in our experiments . The first , GmmWgm− , was generated by supplementing the diet of pregnant females with 25 µg of ampicillin per ml of blood . GmmWgm− ( offspring of ampicillin-fed females ) adult are devoid of Wigglesworthia throughout all developmental stages [9] . PCR using Wigglesworthia thiamine C-specific primers ( forward , 5′-TGAAAACATTTGCAAAATTTG-3′; reverse , 5′-GGTGTTACATAGCATAACAT-3′ ) confirmed that this tsetse strain lacked Wigglesworthia . The second Wigglesworthia-free tsetse line , GmmWT/Wgm− , was generated by feeding mature WT adults three blood meals supplemented with 40 µg/ml tetracycline . GmmWT/Wgm− individuals thus harbored Wigglesworthia , Sodalis , and Wolbachia throughout the development of all immature stages and early adulthood , but lacked these symbionts thereafter . The absence of Wigglesworthia from mature GmmWT/Wgm− adults was confirmed microscopically by comparing the bacteriome ( Wigglesworthia-harboring organ ) contents of mature WT and GmmWT/Wgm− adults . Septic infection of tsetse was achieved by anesthetizing flies with CO2 and subsequently injecting individuals with live bacterial cells using glass needles and a Narashige IM300 micro-injector . The methods used to produce luciferase-expressing E . coli K12 ( recE . colipIL ) , and the assay used to quantify luciferase expression in vivo , were described previously [15] . GFP-expressing and tetracycline-resistant E . coli K12 were produced via electroporation with pGFP-UV ( Clontech , Mountain View , CA ) and pBR322 ( Promega , Madison , WI ) plasmid DNA , respectively . All flies treated with tetracycline were subsequently infected with tetracycline resistant E . coli K12 . The number of bacterial cells injected and control group designations for all infection experiments are indicated in the corresponding figures and their legends . For all survival experiments , treatments were performed in triplicate , using 25 flies per E . coli treatment replicate . LB media controls were performed once using 25 flies . For analysis of immunity-related gene expression , sample preparation and qPCR were performed as described previously [15] . Quantitative measurements were performed on three biological samples in duplicate and results were normalized relative to tsetse's constitutively expressed β-tubulin gene ( determined from each corresponding sample ) . Fold-change data are represented as a fraction of average normalized gene expression levels in bacteria-infected flies relative to expression levels in corresponding uninfected controls . For symbiont quantification , total RNA was prepared from 40-d-old adult GmmWT and GmmWgm− flies . Symbiont genome numbers were quantified using single-copy Sodalis fliC and Wolbachia groEL . Relative symbiont densities were normalized to tsetse β-tubulin . All qPCR was performed with an icycler iQ real time PCR detection system ( Bio-Rad ) . Values are represented as the mean ( ±SEM ) . qPCR primer sequences are shown in Table S1 . Universal bacterial 16S rRNA gene primers 27F ( 5′-AGAGTTTGATCCTGGCTCA G-3′ ) and 1492R ( 5′-GGTTACCTTGTTACGACTT-3′ ) [55] were used to produce a clone library of 16s rRNA gene sequences found in 3rd instar GmmWT and GmmWgm− larvae . Five individual larvae from each tsetse line were dissecting under sterile conditions and washed in DNase ( Ambion , Austin , TX ) to remove any surface contamination prior to DNA extraction . Genomic DNA was isolated using Holmes-Bonner buffer ( 0 . 1 mol/L Tris-HCl , pH 7 . 5; 0 . 35 mol/L NaCl; 10 mmol/L EDTA , pH 8 . 0; 2% SDS; 7 mol/L Urea ) , purified via phenol-chloroform extraction and precipitated in 100% EtOH . PCR was performed under standard reaction conditions [15] , and the resulting products were cloned in the pGEM-T vector ( Promega , Madison , WI ) . Twenty clone inserts from each larvae ( 100 in total from each tsetse line ) were sequenced using the T7 vector specific primer , and homology to previously described 16s rRNAs was determined using the blastn database . Depending on the subsequent experiment , tsetse hemolymph was collected using one of two methods . For hemocyte quantification , undiluted hemolymph was collected by removing one front fly leg at the joint nearest the thorax and then applying gentle pressure to the distal tip of the abdomen . Hemolymph exuding from the wound was collected using a glass micro-pipette and placed into a microfuge tube on ice . Hemocytes were quantified microscopically using a Bright-Line hemocytometer , and hemocyte numbers are represented as cells per µl of hemolymph . When hemocytes were required for microscopic visualization , hemolymph was collected by employing a modified version of the high injection/recovery method previously developed for use in mosquitoes [56] . In brief , tsetse flies were sedated on ice and injected with 25 µl of chilled anticoagulant buffer [70% MM medium , 30% anticoagulant citrate buffer ( 98 mM NaOH , 186 mM NaCl , 1 . 7 mM EDTA , and 41 mM citric acid , buffer pH 4 . 5 ) , vol/vol] between the last two abdominal schlerites using a glass needle and a Narashige IM300 micro-injector . Following a 30 min incubation on ice , a front leg was removed at the joint most proximal to the thorax . At this point internal pressure forced hemolymph diluted with anticoagulant buffer to be expelled from the wound site ( more liquid could be recovered by applying gentle pressure to the distal end of the abdomen ) . Liquid was collected using a pipette and either placed into a chilled microfuge tube or directly into a 24-well cell culture plate . In the latter case , cells were allowed to adhere to the plate bottom , after which anticoagulant buffer was replaced with MM media . Sessile hemocytes were observed by intra-thoracically injecting young and mature GmmWT , and mature GmmWgm− ( n = 5 of each strain ) , with 2 µl of blue fluorescent ( 365/415 nm ) 0 . 2 µm carboxylate-modified beads ( Invitrogen corp . ) . Prior to use , beads were washed once in PBS and resuspended in 100% of their original volume . Flies were dissected 12 h post-injection to reveal their dorsal vessel and surrounding tissue , which was gently washed 3 times with PBS to remove any potentially contaminating circulating ( non-adherent ) hemocytes . Engulfed microspheres were visualized using a Zeiss steriomicroscope ( Discovery v8 ) equipped with a coaxial fluorescence module . Semi-quantitative comparison of sessile hemocyte number between young and mature GmmWT adults , and mature GmmWT and GmmWgm− adults , was performed by quantifying fluorescent signal intensity ( n = 4 individuals from each group ) using ImageJ software ( http://rsbweb . nih . gov/ij/ ) . Phagocytosis by circulating tsetse hemocytes was observed by intra-thoracically infecting mature GmmWT flies ( n = 10 ) with 1×106 GFP-expressing E . coli K12 . Twelve hours post-infection , hemolymph was collected and hemocytes monitored to determine if they had engulfed the GFP-expressing bacterial cells . Hemolymph samples were fixed on glass microscope slides via a 2 min incubation in 2% PFA . Prior to visualization using a Zeiss Axioscope microscope , slides were overlayed with VectaShield hard set mounting medium containing DAPI ( Vector Laboratories , Burlingame , CA ) . Phagocytosis by tsetse hemocytes was inhibited with blue fluorescent ( 365/415 nm ) 0 . 2 µm carboxylate-modified beads ( Invitrogen Corp . ) . Prior to use , beads were washed once in PBS and resuspended in 100% of their original volume . Inhibition assays were performed by inoculating 8-d-old GmmWT with 2 µl of beads via their thoracic compartment . Twelve hours later , these flies were similarly infected with 1×103 and 1×106 GFP-expressing E . coli K12 ( experiment was performed in triplicate; n = 25 flies per replicate ) . Finally , 12 hours post-infection with E . coli , hemolymph was collected and processed as described above ( these samples were overlayed with VectaShield hard set mounting medium that lacked DAPI ) . Melanization assays were performed by intra-thoracically inoculating mature GmmWT and GmmWgm− ( n = 10 of each strain ) with 1×103 E . coli K12 . Subsequently , three individuals from each group were monitored microscopically every 10 min for the presence of melanin at the wound site . The remaining seven flies from each group were maintained for 2 wk in order to observe infection outcome . Statistical significance of survival curves was determined by log-rank analysis using JMP ( v8 . 02 ) software ( www . jmp . com ) . Statistical analysis of qPCR data was performed by Student's t test using JMP ( v8 . 02 ) software . Statistical significance between various treatments , and treatments and controls , is indicated in corresponding figure legends . | Beneficial bacterial symbionts , which are ubiquitous in nature , are often characterized by the extent to which they interact with the host . In the case of mutualistic symbioses , both partners benefit so that each one can inhabit diverse ecological niches where neither could survive on its own . Unfortunately , little is known about the functional mechanisms that underlie mutualistic relationships . Insects represent a group of advanced multi-cellular organisms that harbor well-documented symbiotic associations . One such insect , the tsetse fly , harbors a maternally transmitted bacterial mutualist called Wigglesworthia that provides its host with essential metabolites missing from its vertebrate blood-specific diet . In this study , we further examine the relationship between tsetse and Wigglesworthia by investigating the interaction between this bacterium and its host's immune system . We have found that when Wigglesworthia is absent from tsetse during the maturation of immature larval stages , subsequent adults are characterized by an underdeveloped cellular immune system and thus highly susceptible to infection with a normally non-pathogenic foreign microbe . These findings represent an additional adaptation that further anchors the steadfast relationship shared between tsetse and its obligate symbiont . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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"Methods"
] | [
"microbiology/innate",
"immunity",
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"infections"
] | 2011 | Tsetse Immune System Maturation Requires the Presence of Obligate
Symbionts in Larvae |
Complex cutaneous and muco-cutaneous leishmaniasis ( CL and MCL ) often requires systemic therapy . Liposomal amphotericin B ( L-AmB ) has a strong potential for a solid clinical benefit in this indication . We conducted a retrospective analysis of data from a French centralized referral treatment program and from the “LeishMan” European consortium database . All patients with parasitologically proven CL or MCL who received at least one dose of L-AmB were included . Positive outcome was based on ulcer closure as per recent WHO workshop guidelines . From 2008 through 2016 , 43 travelers returning from 18 countries ( Old World n = 28; New World n = 15 ) were analyzed with a median follow-up duration of 79 days [range 28–803] . Main clinical forms were: localized CL with one or multiple lesions ( n = 32; 74% ) and MCL ( n = 8; 19% ) . As per published criteria 19 of 41 patients ( 46% ) were cured 90 days after one course of L-AmB . When the following items -improvement before day 90 but no subsequent follow-up , delayed healing ( >3 months ) and healing after a second course of L-AmB- were included in the definition of cure , 27 of 43 patients ( 63% ) had a positive outcome . Five patients ( MCL = 1; CL = 4 ) experienced a relapse after a median duration of 6 months [range 3–27] post treatment and 53% of patients ( 23/43 ) experienced at least one adverse event including severe hypokalaemia and acute cardiac failure ( one patient each ) . In multivariate analysis , tegumentary infection with L . infantum was associated with complete healing after L-AmB therapy ( OR 5 . 8 IC 95% [1 . 03–32] ) while infection with other species had no impact on outcome . In conditions close to current medical practice , the therapeutic window of L-AmB was narrow in travellers with CL or MCL , with the possible exception of those infected with L . infantum . Strict follow-up is warranted when using L-AmB in patients with mild disease .
Tegumentary leishmaniasis ( TL ) occurs when one of the 20 Leishmania species that can infect humans causes lesions of the skin or mucosae . Because TL is observed in patients of all ages , either immunocompetent or immunocompromised , the result is a wide continuum of clinical forms and medical situations for which a multiplicity of treatment approaches have been proposed . Despite the high global burden of cutaneous and muco-cutaneous leishmaniasis ( CL and MCL ) and the rising number of travels to endemic areas , there is still relatively sparse evidence to support an accurate choice between local ( e . g . cryotherapy and/or intralesional antimony , topical paromomycin ) or systemic therapy ( e . g . amphotericin B , miltefosine , pentavalent antimonials , pentamidine , fluconazole ) . In addition , as almost all studies have been conducted in endemic countries , published research may not apply to travelers [1] . National and international recommendations as well as recent IDSA guidelines favor systemic treatment for patients with complex CL defined by a high risk of mucosal involvement ( if this can be accurately determined ) , numerous or very large lesions , disseminated forms , lesions location not compatible with local treatment ( e . g . periorificial , fingers , toes , ears ) , or immunosuppression [1–4] . Among the currently available systemic antileishmanial agents , liposomal amphotericin B ( L-AmB ) has a strong potential for a high clinical benefit in TL . L-AmB has a pivotal role in the management of visceral leishmaniasis ( VL ) as efficacy of 95 to 100% has been reported both in the Indian subcontinent ( where VL is due to L . donovani ) and in Southern Europe ( where VL is due to L . infantum ) [5–7] . The drug has been used off-label for imported CL in various settings and response rates of 75 to 85% have been reported in travelers who had been predominantly infected in the New-World [8 , 9] . Cure rates around 90% were reported in the treatment of MCL with deoxycholate amphotericin B or with liposomal forms [10] . Due to its extensive use in fungal infections and in VL , the safety profile of L-AmB is well-known as well as how to prevent and manage its renal and metabolic adverse events . Nevertheless—possibly because of the high cost of L-AmB—relatively little information has been reported so far on the clinical benefit of L-AmB in CL in general and in Old-World CL in particular . Less than 50 patients infected in the Old World ( including children ) were treated in the three largest published studies [8 , 9 , 11] . Because these studies included predominantly patients without comorbidities , immunosuppression or complex CL , information on rare side effects affecting fragile patients could not be captured . The objective of this retrospective study was to assess the efficacy and safety of L-AmB for the treatment of TL in unselected travelers of all age groups returning from both the Old or New World .
French National Agency regulating data protection ( Commission Nationale de l’Informatique et des Libertés ) approved this observational study ( DR-2013-041; N°912650 ) . All data were recorded anonymously through the approved database . As diagnosis and treatment strategy followed the international or national guidelines ( of each country ) there is no ethical concern on treatment and diagnosis . Patients ( or parents or legal guardians of children ) gave their informed oral or written consent allowing examinations on their samples and publication of anonymized data on clinical findings , treatment received , clinical outcome and laboratory examinations . From 2008 through 2012 data were collected in a French nationwide centralized referral treatment program as reported elsewhere [12] . Briefly , these data were collected by experts providing treatment advice to physicians attending patients with leishmaniasis in France . Expert advice was part of normal medical process and followed the national guidelines [2] . Demographic and clinical data were obtained from treating physicians through a standardized case report form . After at least 45 days after the treatment advice has been provided , the attending physician was contacted and asked to provide follow-up information , using a standardized form , regarding the identification of the infecting Leishmania species , treatment currently administered , treatment outcome , and adverse events . From 2012 through 2016 , data were collected in the European LeishMan ( “Leishmaniasis management” ) database , a multicentre multinational surveillance project on cutaneous and mucosal leishmaniasis . The consortium gathers 17 experts from 12 institutions in 7 European countries ( France , Switzerland , United-Kingdom , Belgium , Netherlands , Germany and Spain ) who aim at improving the management of leishmaniasis through harmonization of medical practices and collection of data in an international surveillance system [13] . Demographic , clinical and biological data were assessed in an electronic case report form and documented before and after treatment . Treatment schedule was left at the discretion of the attending physician , unless he/she explicitly required advice on this point from the expert . All patients with parasitologically confirmed cutaneous or mucosal leishmaniasis who received at least one infusion of liposomal AmB ( Ambisome® ) were included . Only patients with subsequent follow-up were included in the final analysis . Parasitological diagnosis was based on: visualization of amastigotes on stained smear obtained by scrapping or histological tissue sections and/or promastigotes in culture from material obtained by aspiration and/or positive polymerase chain reaction ( PCR ) on material obtained by aspiration or by skin biopsy . Whenever possible , samples and aliquots of positive cultures were sent to the French National Reference Center Leishmaniasis for species identification using a multilocus sequence typing approach based on the analysis of seven single-copy coding DNA sequences or using MALDI-TOF mass spectrometry as reported elsewhere [14] . Success was defined as complete healing within 3 months after starting therapy . Healing was assessed either by closure of the ulceration ( if present ) or disappearance of the induration if no ulceration was present . Failure was defined as incomplete reepithelialization or persistence of erythematous induration at day 90 . Improvement corresponds to partial response before evaluation at day 90 and no subsequent follow-up . A relapse was defined as reappearance of cutaneous or mucosal lesions after complete healing without evidence of reinfection during follow-up . All events , whether related or not to L-AmB therapy following treatment administration were considered as adverse events . Adverse events were considered severe if life threatening . Continuous variable are presented as median [range] and categorical variables as numbers ( frequencies ) . Categorical variables were compared using chi-square test or Fisher’s exact test as appropriate . Differences in mean values between two groups were compared using Mann Whitney test . A two-tailed p-value < . 05 was considered as statistically significant . Univariate analysis was followed by multivariable logistic regression analysis to define predictive factors of complete healing after L-AmB therapy . All statistical analyses were performed using StatView software ( Version 5 . 0 SAS institute Inc . , Cary , NC ) .
From 2008 through 2016 , data from 45 patients who had received at least one infusion of L-AmB for TL were entered into the databases . Two patients had no-follow-up and were excluded from the analysis . The characteristics of 43 travelers returning from 18 different countries are shown in Table 1 . Twenty nine centers from 3 countries ( 26 in France , 2 in Switzerland , 1 in Germany ) participated to the study . Leishmaniasis was mainly acquired in the Old World ( 9 in Europe , 9 in Maghreb , 6 in sub-Saharan Africa , 3 in Middle East , 1 in India ) . 15 patients were infected in the New World ( 10 in French Guiana , 3 in Bolivia , 1 in Brazil , 1 in Peru ) . Median age of the cohort ( which comprised 6 children aged from 1 to 12 years ) was 55 years for adults . Seven patients ( 19% of adults ) had prior cardiovascular history . Among immunocompromised patients ( 5/43; 12% ) , three had human immunodeficiency infection ( HIV ) infection and two received prolonged corticosteroid therapy and/or immunosuppressive treatment for autoimmune diseases . Localized CL ( n = 32; 74% ) was the predominant clinical form , followed by muco-cutaneous leishmaniasis ( n = 8; 19% ) . One subject with HIV infection had CL with simultaneous visceral involvement caused by L . donovani , and two other patients had disseminated form ( L . major; L . guyanensis ) defined as more than 10 skin lesions in two non-contiguous sites . 72% of patients had at least two lesions and 28% ( 12/43 ) had at least one large lesion ( ≥ 40mm ) . Among 32 patients with CL , 10 ( 31% ) had lesions on the face . Median duration of disease was 5 months [range 1–24] but this information was available for only 15 patients . The infecting Leishmania species was identified in 35 patients ( 81% ) . 14 species belonged to the Viannia subgenus ( 40% ) , the other common species were L . infantum ( n = 9; 26% ) and L . major ( n = 6; 17% ) . Median cumulative dose of L-AmB per patient was 20 mg/kg [range 6–56] . Most of patients were treated with the “VL” regimen ( i . e . , daily infusion from day 1 to day 5 followed by one infusion on day 10 ) but data on duration of treatment were missing for 19 patients . Two third of patients ( 30/43; 70% ) received L-AmB as front-line therapy while the others had received prior treatment for leishmaniasis with pentavalent antimony ( n = 6 ) , pentamidine ( n = 4 ) , oral azoles ( n = 3 ) or local treatment ( n = 3 ) . Three patients received concomitant medication before day 90 , with fluconazole ( n = 1 ) or miltefosine ( n = 2 ) during L-AmB therapy due to premature discontinuation of L-AmB for adverse events . No death was reported . Renal toxicity and infusion related reactions occurred in 15 ( 35% ) and 6 ( 14% ) patients respectively . A 81 year-old patient with hypertensive heart disease experienced acute heart failure after hydratation with saline solution during L-Amb therapy . One week after the last infusion a 29 year-old patient developed severe symptomatic hypokaliemia ( 2 . 5mmol/L ) without ECG changes which required hospitalization and intravenous potassium chloride supplementation . While 53% of patients ( 23/43 ) experienced at least one adverse event , seven of them ( 30% ) required modification of L-AmB therapy during follow-up ( discontinuation n = 3; dose delay n = 3; dose reduction n = 1 ) . Median follow-up was 79 [28–803] days . L-AmB therapy was associated with a complete healing ( without relapse ) or improvement before day 90 in 21 patients ( 49% ) ( Table 1 ) . Five patients experienced a relapse after a median of 6 months [range 3–27] post treatment . Of the five patients who relapsed , four ( 80% ) were infected by a Leishmania species belonging to the Viannia subgenus . Of the 22 patients with a negative outcome before day 90 , 3 ( 14% ) had delayed healing after 5 to 18 months , while 3 ( 14% ) healed after a second course ( new cure with 20mg/kg cumulative dose ) of L-AmB , 2 ( 9% ) with pentavalent antimony and 2 ( 9% ) with miltefosine . If delayed healing and healing after a second course of L-AmB are considered positive outcomes the healing rate in L-AmB treated patients was 63% . Table 2 provides effectiveness analysis in relation to subgroups of patients . In univariate analysis ( Table 2 ) , the area where infection was acquired was associated with L-AmB effectiveness ( p≤ . 05 ) . Being infected with L . infantum approached but did not quite achieve significance ( p = . 057 ) . No other clinical or biological factors were associated with the outcome after L-AmB therapy . Of the 9 patients with L . infantum tegumentary leishmaniasis , 5 ( 55% ) had mucocutaneous and 4 ( 45% ) localized cutaneous leishmaniasis . The median number of lesions was 2 [range 1–17] . There were two failures: one mucocutaneous form of the lips and one cutaneous form of the leg . The following variables L . infantum species , area ( New World or Old World ) where infection was acquired , mucocutaneous form , and frontline therapy with L-AmB were included in multivariable conditional logistic regression analysis . In multivariable analysis , only infection with L . infantum was independently associated with complete healing after L-AmB therapy ( OR 5 . 8 IC 95% [1 . 03–32] ) .
In this cohort of unselected travellers with TL , L-AmB was associated with a suboptimal clinical benefit . Following conventional criteria for healing ( excluding patients with improvement before day 90 and no subsequent follow-up ) , only 19 of 41 patients ( 46% ) were cured 90 days after one course of L-AmB and when the most inclusive estimate was used—which integrates improvement before day 90 and no subsequent follow-up , delayed healing ( >3 months ) and healing after a second course of L-AmB—the cure rate reached a still relatively low 63% cure rate ( 27 of 43 patients ) [15] . We also observed clinically significant adverse events . Renal toxicity ( including electrolytic disorders and acute renal failure ) and infusion related events ( fever , chills ) occurred in 35% and 14% of patients respectively . In 7 of 43 patients , discontinuation or modification of the initially prescribed regimen of L-AmB was deemed necessary by attending physicians . Two patients experienced serious adverse events . Acute cardiac failure occurred in an 81 year-old patient with a pre-existing cardiovascular condition , and delayed severe hypokaliemia occurred in a 29 year-old patient without pre-existing condition . In current medical practice , the risk-to-benefit ratio of L-AmB may thus be lower in TL than in VL [5 , 7] . A puzzling question is whether the relatively high risk of severe adverse events observed in this cohort was counterbalanced by the ( inconstantly ) positive impact of treatment , especially in patients with disfiguring but not life-threatening cutaneous lesions . Why would efficacy of liposomal amphotericin B be lower in TL than in VL ? To our knowledge , no study has systematically evaluated skin penetration of L-AmB following systemic administration in human subjects . Penetration of amphotericin B was lower in the skin than in other organs in a rat model suggesting that higher doses may be needed in TL as compared to VL , although this will likely increase toxicity and inevitably increase cost [16] . Predictions are elusive due to the non-linear pharmacokinetics and complex mechanisms of reticuloendothelial uptake and release of liposomal amphotericin B , likely influenced by the presence of skin lesions [17 , 18] . Parasite “resistance” to L-AmB in TL may be another important parameter to explain poor efficacy in this context . Although substantiated so far only by circumstantial evidence , a dormant metabolic state of parasites in skin lesions may also contribute to the suboptimal effectiveness of L-AmB in CL , a process suspected to occur only rarely in VL [19–21] . A species-related difference in drug susceptibility is a third potentially important determinant of outcome in TL and VL . That efficacy was high when TL was due to L . infantum ( approximately 80% ) and low when it was due to other identified species ( around 30% ) provides some substance to this assumption . Of note , variations of susceptibility to L-AmB may even exist at the intra-species level as VL due to L . donovani is less responsive to L-AmB in East Africa than it is in India [22] . Apart from the impact of L . infantum , we found no obvious epidemiological , clinical or laboratory parameter associated with outcome after treatment with L-AmB in TL . L-AmB has been successfully used in multiple case reports where TL was caused by L . infantum but whether TL due to L . infantum is indeed more sensitive to L-AmB—as our multivariate analysis suggests—will requires assessment in larger studies [23 , 24] . In this study , we pooled CL and MCL which increased the power of the analysis to identify potential predictors of outcome . We observed no significant difference in efficacy in patients with or without mucosal involvement which provides some post-hoc justification to this approach . Multinational networks like LeishMan , will help increase the statistical power to determine the potential impact of host characteristics , clinical forms and infecting species on the outcome of each form analyzed separately . Healing rates were also lower in our cohort than in previous studies that had assessed the efficacy of L-AmB in TL , somewhat reminiscent of the decline in efficacy observed across the evaluation process with L-AmB and miltefosine in VL and with miltefosine or azoles in TL [25–27] . We observed a 27% of efficacy of L-AmB in TL acquired in the New World while it had varied from 80% to 100% in previous studies , except in one where low ( 7 . 5 mg/kg ) cumulative dose of L-AmB had been used [9 , 25–29] . To our knowledge , standard regimens of L-AmB have not been comparatively evaluated in TL and so far , randomized controlled studies with this drug have been performed only in VL . In TL , most observations report on a cumulative dose of 20mg/kg , likely by extrapolation from guidelines for VL . This regimen is similar to the median dose used in our study . It has been suggested that a higher cumulative dose ( >30mg/kg ) could be beneficial in disseminated forms caused by L . braziliensis [30] . Toxicity and cost are likely to increase with the cumulative dose used . Regarding patients who acquired TL the Old World , we observed a 58% cure rate while it was 75% and 84% in the two largest studies published so far [8 , 11] . These studies involved patients infected with L . tropica , in keeping with the observation that the three patients infected with L . tropica in our study had also a positive outcome . As mentioned above regarding L . infantum , the infecting species may thus influence the efficacy of L-AmB in TL although others factors such as age , presence of immunosuppression or comorbidities may have also affected the results . The population was diverse in our study which included subjects visiting friends and relatives , expatriates , military personal and tourists while previous studies had included very predominantly young military personal or children . Not least , part of the relatively low cure rate in our study may result from the frequent discontinuation or modification of L-AmB regimen . In summary , while L-AmB may not have been given its best chance of success in our study its medical impact was assessed in conditions close to that prevailing in current medical practice . The therapeutic window of L-AmB , indisputably wider than that of other antileishmanial drugs in VL , appeared unexpectedly narrow in this cohort of unselected travellers with TL , with the possible exception of TL due to L . infantum . The momentum should be maintained to deliver either optimized regimens of existing antileishmanial drugs in TL ( either systemic or local ) , or new drugs which should ideally be amenable to oral administration [31] . Such a perfect drug does not exist at the moment . In the short term , physicians should pay close attention to the potential side effects of L-AmB related to comorbidities and adopt strict clinical and laboratory follow-up when using L-AmB in patients with TL . The risk to benefit ratio of L-AmB in patients with mild CL may be difficult to determine which brings further support to the use of local therapy as front-line approach , as now recommended [2 , 32] . Beside cryotherapy plus intralesional antimony , topical paromomycin has an excellent risk-benefit ratio but is still not available due to unexplained delays in its clinical development despite positive clinical trials [33–35] . In travelers with complex CL or MCL not due to L . infantum , oral miltefosine , pentavalent antimony , pentamidine and L-AmB should be considered as front-line therapy with the choice guided by age of the patient and pre-existing comorbidities . | Cutaneous and muco-cutaneous leishmaniasis ( CL/MCL ) are disfiguring diseases caused by a worldwide distributed parasite called Leishmania and its 20 species . Clinical manifestations span a wide continuum from single nodular lesion to disseminated form with mucosal involvement . Though local treatment with cryotherapy and intralesionnal antimony or topical formulations of paromomycin is generally adequate in most of situations , some patients with complex CL/MCL require systemic therapy . No convenient regimen has been proved to be safe and effective for all infecting species , all clinical forms and all patients ( e . g . children , pregnant women , adults with comorbidities or immunosuppression ) . In this study , the authors examined in returning travelers with CL/MCL the effectiveness of an antifungal agent “liposomal amphotericin B” ( L-AmB ) , which is highly effective in visceral leishmaniasis . Surprisingly , rates of healing were lower than in previous reports in this unselected population that reflects clinical practice in non-endemic countries . The observations also suggest that some Leishmania species ( namely , L . infantum ) may be more susceptible to L-AmB than others . Occurrence of adverse events should raises the question of the benefit-risk balance of L-AmB in CL/MCL . Careful attention to comorbidities and adoption of strict protocols for administration are pre-requisites for the use of L-AmB in patients with CL/MCL . | [
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] | 2017 | Liposomal amphotericin B in travelers with cutaneous and muco-cutaneous leishmaniasis: Not a panacea |
Brain anatomy and physiology support the human ability to navigate a complex space of perceptions and actions . To maneuver across an ever-changing landscape of mental states , the brain invokes cognitive control—a set of dynamic processes that engage and disengage different groups of brain regions to modulate attention , switch between tasks , and inhibit prepotent responses . Current theory posits that correlated and anticorrelated brain activity may signify cooperative and competitive interactions between brain areas that subserve adaptive behavior . In this study , we use a quantitative approach to identify distinct topological motifs of functional interactions and examine how their expression relates to cognitive control processes and behavior . In particular , we acquire fMRI BOLD signal in twenty-eight healthy subjects as they perform two cognitive control tasks—a Stroop interference task and a local-global perception switching task using Navon figures—each with low and high cognitive control demand conditions . Based on these data , we construct dynamic functional brain networks and use a parts-based , network decomposition technique called non-negative matrix factorization to identify putative cognitive control subgraphs whose temporal expression captures distributed network structures involved in different phases of cooperative and competitive control processes . Our results demonstrate that temporal expression of the subgraphs fluctuate alongside changes in cognitive demand and are associated with individual differences in task performance . These findings offer insight into how coordinated changes in the cooperative and competitive roles of cognitive systems map trajectories between cognitively demanding brain states .
In human cognition , internally-generated cognitive control processes modulate attention , facilitate task switching , and inhibit prepotent behavior [1] . One avenue by which the brain may rapidly traverse a cognitive state-space is through its functional interactions—coherent fluctuations in brain activity shaped by the structural connectome [2] . The brain’s distributed functional interactions form a functional network whose architecture is temporally dynamic [3] , conferring adaptivity in the face of environmental pressures or task demands [4] such as those elicited during learning [5] and other tasks demanding executive cognition [6] . Cognitive control processes have been widely reputed to recruit several cognitive systems that include executive , attention , and salience systems that span prefrontal cortices , striatum , parietal regions , and cerebellum [7–12] . The notion that cognitive control involves a heterogenous collection of brain systems is supported by several univariate studies demonstrating concurrent activation of functionally-specialized brain areas across different cognitive control tasks [13] . If patterns of measured brain activity signal involvement of different brain regions across a diverse set of cognitive control tasks , then how do functional brain networks encode and coordinate this task-relevant information to adapt to fluctuations in cognitive demand ( Fig 1A ) ? One mechanistic theory , known as the “adaptive coding model of cognitive control” [17] , posits that brain regions that activate during higher cognitive functions can alter their dynamical properties based on the current goals of the neural system . More recent studies have challenged this hypothesis by presenting data that suggests that changes in the cognitive demands of a task lead to recruitment of mechanistically-specialized brain regions based on an anatomically-defined gradient [18 , 19] . To reconcile these opposing theories of the neuronal basis of cognitive control , [20] applied multivoxel pattern analysis—a machine learning technique for identifying consistent patterns of voxel-wise activation—to the fMRI of subjects as they performed simple and cognitively demanding tasks . The authors found a consistent pattern of activation in frontoparietal brain areas that was specific to highly demanding conditions across multiple cognitive tasks . Their findings support the hypothesis that a consistent group of brain regions activate in response to increases in cognitive demand . However , parallel lines of investigation on the underpinnings of cognitive control in functional brain networks suggest that the integrated cognitive control network dissociates into several , segregated sub-networks that are responsible for different aspects of cognitive control processes [13] . To address these conflicting reports , a data-driven approach that can disentangle parts of functional brain networks that encode cognitive states associated with control tasks—and track their expression alongside changes in cognitive demand—is required . Such a capability would improve our understanding of which components of functional brain networks are important for different facets of cognitive control , and how these components encode shifts between cognitively demanding states . In the present work , we identify components of functional brain networks associated with transitions between cognitively demanding states by using an unsupervised machine learning technique known as non-negative matrix factorization ( NMF ) [21] . NMF decomposes functional brain networks into: ( i ) additive subgraphs that represent clusters of graph edges that track with one another over time , and ( ii ) time-varying coefficients that quantify the degree to which a subgraph is expressed at a given point in time [16 , 22 , 23] . This computational tool allows us to track how groups of functionally interacting brain areas are dynamically expressed during experimentally modulated changes in cognitive demand ( Fig 1B; a discussion regarding the differences between NMF and components analysis can be found in textitMaterials and methods ) . In particular , we ask participants to engage in the following two cognitive control tasks: a response inhibition Stroop task ( Fig 1C; [24] ) and a local-global perception switching task based on classical Navon figures ( Fig 1D; [25] ) . Our methodological approach enables us to address a critical question in cognitive control: “How do brain networks coordinate task-relevant information as individuals adapt to the cognitive demands imposed by a task ? ” To address this question using NMF , we draw upon recent studies that suggest that task-driven reconfiguration of functional brain networks integrates otherwise functionally-specialized and segregated information [26 , 27] . One compelling current theory proposes that transitions between cognitively demanding brain states are facilitated by dynamic changes in the patterns of correlated and anticorrelated brain activity such that anticorrelated fluctuations in brain activity represent segregated brain functions , and correlated fluctuations in brain activity represent integrated brain functions [28 , 29] . Correlated and anticorrelated dynamics may explain how task-relevant information is shared between different regions of the network during cognitively demanding tasks . In this study we construct functional brain networks by applying the Pearson correlation function to block level fMRI collected during cognitive control tasks . By accounting for correlated and anticorrelated functional interactions in the NMF framework , we can determine the likelihood that the functional interactions within a subgraph are collectively correlated or anticorrelated at a particular point in time—providing a perspective on integrated and segregated information processing among composite sets of brain regions . Based on prior studies demonstrating that behavioral tasks can be used to dissociate intrinsic and task-specific architectures of functional brain networks [30] , we first hypothesize that NMF will identify functional subgraphs whose expression is either generalized across the Stroop and Navon tasks or specific to distinct cognitive conditions within and between tasks . In particular , we expect task-general subgraphs to reflect interactions relevant for task saliency and cognitive control processes common to both tasks . We also expect task-specific subgraphs to reflect interactions relevant for stimulus processing and attentional mechanisms necessary for either response inhibition in the Stroop task or task-switching in the Navon task . Building upon recent evidence that functional interactions dynamically reorganize between integrated and segregated network states [26] , we next hypothesize that functional subgraphs will shift their roles between correlated and anticorrelated modes of interaction in response to experimentally driven changes in cognitive demand . Lastly , we hypothesize that changes in subgraph expression during experimental modulation of cognitive demand will reflect inter-individual differences in behavioral performance on the task . Specifically , based on previous theories regarding the behavioral influence of correlated and anticorrelated functional interactions in cognitive control [29] , we expect that components of the frontoparietal and default mode systems will most prominently participate in subgraphs associated with individual differences in performance .
To uncover the topological organization and putative roles of correlated and anticorrelated functional interactions in cognitive control , we first acquire fMRI data as 30 healthy adult human subjects perform Stroop and Navon cognitive control tasks . Two subjects are excluded on the basis of poor performance and technical problems on the day of scanning , leaving 28 subjects for further analysis . In particular , we measure fMRI BOLD signals from 262 functional brain areas ( Fig 2A ) —including cortex , subcortex , and cerebellum [31 , 32]—during three separate conditions of both the Stroop and Navon tasks: fixation , low cognitive demand , and high cognitive demand conditions ( Fig 2B ) . Briefly , the low cognitive demand condition is designed to elicit a neural response associated with performing each task with low cognitive control demands and the high cognitive demand condition is designed to elicit a neural response associated with either task shifting or inhibition cost ( see Material and methods for more details ) . We then construct dynamic functional brain networks for each subject where network nodes represent brain regions and network edges represent the Pearson correlation coefficient between regional BOLD time series ( Fig 2C ) . Specifically , we compute a 262 × 262 adjacency matrix for each of 6 task blocks ( corresponding to 30 seconds of BOLD activity , and comprising several trials ) in each of the 3 task conditions ( fixation , low demand , high demand ) for each of 2 tasks ( Stroop and Navon ) . This process results in 36 block-level adjacency matrices per subject . Importantly , positive Pearson correlations underlie integrated and coherent activation between brain regions or correlated functional interactions , and negative Pearson correlations underlie segregated and discordant activation between brain regions or anticorrelated functional interactions [28] . To separate positively-weighted network edges ( correlated interactions ) from negatively-weighted network edges ( anticorrelated interactions ) , we duplicate the adjacency matrix of each block and separately threshold edge weights either greater than zero or less than zero ( see Materials and methods for details ) . Lastly , we aggregate all functional brain networks into a network configuration matrix ( Fig 2D ) with size 2016 × 34191 . The first dimension of size 2016 corresponds to all combinations of two tasks , three task conditions , six repeated blocks , twenty-eight subjects , and two edge types ( correlated or anticorrelated ) ; the second dimension of size 34191 corresponds to all unique , pairwise edges between the 262 brain regions . We first assess the extent to which task-specific differences in functional network topology are explained by first-order , global network statistics , by comparing the distribution of mean edge strengths across different dimensions of the network configuration matrix ( S1 Fig ) . We find no significant difference in mean edge strength across subjects between blocks during the Stroop task and blocks during the Navon task ( paired t-test , t27 = −1 . 5 , p = 0 . 14; S1B Fig ) . We also find no significant difference in mean edge strength across subjects between blocks during the low cognitive demand condition and blocks during the high cognitive demand condition ( paired t-test , t27 = 0 . 35 , p = 0 . 73; S1D Fig ) . We find a significant decrease in mean edge strength between blocks during the fixation period and blocks during the cognitive control task period ( paired t-test , t27 = 4 . 7 , p6 . 3 × 10−5; S1C Fig ) , suggesting that engaging in cognitive control tasks is associated with a reduction of correlated and anticorrelated BOLD dynamics . Critically , this result suggests that attention-related brain states thought to be associated with the fixation period may be subserved by stronger and more well-defined functional relationships between brain regions than more complex , task-driven brain states . Overall , our findings suggest that differences in functional network topology during cognitive control tasks and control conditions are not driven by first-order differences in mean edge strength of the network . Rather , we expect that differences in the topological organization of correlated and anticorrelated BOLD dynamics may be complex and heterogenously distributed across the functional network . To disentangle patterns of correlated and anticorrelated BOLD dynamics related to cognitive control processes , we extract functional subgraphs and their dynamic expression from functional brain networks . Specifically , we apply an unsupervised machine learning algorithm called non-negative matrix factorization ( NMF ) to the network configuration matrix . This technique enables us to pursue a parts-based decomposition of network edges into additive functional subgraphs with accompanying expression coefficients that measure the degree to which the subgraph is expressed in a particular task block , task condition , subject , and edge type ( Fig 2D ) [22 , 23] . Each subgraph composes a 262 × 262 adjacency matrix and each subgraph’s expression coefficients compose a vector of length 2016 . Thus , subgraphs detail topological components of the functional brain network and temporal coefficients quantify their expression during different phases of the cognitive control tasks . Moreover , each subgraph is associated with a positive expression component associated with correlated BOLD dynamics and a negative expression component associated with anticorrelated BOLD dynamics ( Fig 2E ) . A critical step in using NMF is optimizing model parameters ( number of subgraphs m , sparsity of subgraph edge weights β , and regularization of temporal expression coefficients α ) to ensure generalizability of component subgraphs without overfitting the model on observed data . By designing a four-fold , leave-seven-subjects-out cross-validation scheme , we minimize the average cross-validation error on held-out subjects and find the optimal number of subgraphs to be twelve , the subgraph sparsity to be 0 . 29 , and the regularization of the temporal expression coefficients to be 0 . 56 ( S2 Fig; see Materials and methods for more details ) . For a quality check on the effect of motion confounds on subgraph expression , we refer the reader to S3 Fig . For a test-retest reliability assessment of subgraph reproducibility we refer the reader to S4 Fig . We next rank the twelve subgraphs ( A-L ) in decreasing order of their relative positive or negative expression across all conditions in the cognitive control tasks . Specifically , we compute the difference between the positive expression coefficient corresponding to correlated dynamics and the negative expression coefficient corresponding to anticorrelated dynamics for each task block and average the difference across blocks of each subject ( Fig 2F ) . Intuitively , subgraphs whose mean relative expression values are positive are more likely to capture correlated BOLD dynamics and subgraphs whose mean relative expression values are negative are more likely to capture anticorrelated BOLD dynamics . We refer to specific subgraphs according to their assigned letter for the remainder of the study . We next ask whether the functional subgraphs expressed during the cognitive control tasks reflect functional interactions within and across known cognitive systems . To study the relationship between the functional subgraph architecture and known cognitive brain systems , we assign each of the 262 brain regions into one of nine cognitive systems [33]: dorsal attention , default mode , frontoparietal , limbic , somatosensory , subcortical , ventral attention , visual , and cerebellum . Thus , we re-organize the rows and columns of each subgraph’s 262 × 262 adjacency matrix such that nodes assigned to the same brain system are contiguously ordered , and we visualize the resulting adjacency matrices as circular , ring graphs ( Fig 3; for matrix representation see S5 Fig ) . To quantitatively confirm that each subgraph captures functional interactions that are indeed distributed within and between cognitive systems , we compare the average subgraph edge weight between pairs of nodes of the same or different cognitive systems to a null distribution of the average subgraph edge weight—constructed by permuting subgraph edge weights between nodes and recomputing the average subgraph edge weight for each pair of cognitive systems for 10000 permutations . We find that functional subgraphs cluster interactions between brain regions of the same cognitive system and between brain regions of different cognitive systems ( p < 0 . 05; Bonferroni corrected for multiple comparisons; see S5 Fig ) —implicating a distributed functional architecture underlying the cognitive control tasks . In other words , the functional subgraphs recovered by NMF span several cognitive brain systems defined a priori [33] . Based on the distribution of subgraph edges within and between known cognitive systems , we examine how subgraph topology might underlie different information processes during cognitive control . Interconnected complex systems that underlie distributed information processes—such as those involved in cognitive control—can exhibit core-periphery structure in which a strongly interconnected core of nodes is connected to other nodes in the network periphery , which tend to solely connect with core nodes and remain otherwise isolated from other network regions [34 , 35] . Intuitively , the putative function of the network core is to integrate information from different , specialized systems located in the network periphery [36 , 37] . The core-periphery model has also been extended to accommodate dynamic functional networks in which the network core exhibits less flexible functional connectivity and the network periphery exhibits more flexible functional connectivity [36] . A critical assumption of recent applications of the core-periphery model is that there is a single set of core regions and a single set of periphery regions . It is plausible that brain networks consist of multiple core structures [37–39] that are activated based on ongoing cognitive processes reflected by network subgraphs . To identify core-periphery organization in a subgraph , we compute a core-periphery index ( see Methods ) that quantifies the difference between mean edge strength within a cognitive system ( network core ) and mean edge strength between a cognitive system and all other systems , averaged for each cognitive system . Intuitively , the core-periphery index ranges between −1—stronger edges in the periphery than in the core—and + 1—stronger edges in the core than in the periphery; index values closer to 0 imply equally strong edges in the core and in the periphery characteristic of traditional core-periphery structure . We use a surrogate subgraph model ( 10000 rewiring permutations ) to statistically test whether each cognitive system of each subgraph exhibits core and periphery architecture ( details regarding specific cognitive systems significantly involved in each subgraph may be found in S6 Fig . We find that different functional subgraphs exhibit varying degrees of core-periphery organization ( Fig 3 ) . For example , subgraph A expresses significant core connectivity in seven of nine cognitive systems and significant periphery connectivity in one of nine cognitive systems ( S6 Fig ) . In contrast , subgraph K expresses significant core connectivity in zero of nine cognitive systems and significant periphery connectivity in three of nine cognitive systems ( S6 Fig ) . Intuitively , subgraph A reflects core organization where systems exhibit more centralized topology and subgraph K reflects periphery organization where systems exhibit more decentralized topology . We find that cognitive systems in the remaining subgraphs tend to exhibit both internally centralized connectivity as well as decentralized connectivity to other systems . The differentiation of subgraphs into constituent core-periphery architectures suggests that subgraphs may reflect different modes of integrated and segregated network processes in which task-relevant information may be organized within core cognitive systems and shared with cognitive systems in the network periphery . Logically , we next ask the question “How does the core-periphery organization of a functional subgraph relate to its dynamical expression during cognitive control ? ” To answer this question , we examine the relationship between the core-periphery index of a subgraph and its mean relative expression . We hypothesize that functional subgraphs with stronger edges adjoining brain regions in the network core ( core-periphery index closer to +1 ) are expressed more positively and functional subgraphs with stronger edges adjoining brain regions between the network core and network periphery ( core-periphery index closer to −1 ) are expressed more negatively . Intuitively , subgraphs with stronger edges within the core and weaker edges between the core and periphery will be associated with more correlated BOLD dynamics underlying states of integrated cognitive processes , and subgraphs with stronger edges between the core and periphery and weaker edges within the core will be associated with more anticorrelated BOLD dynamics underlying states of segregated cognitive processes . Using the Spearman’s ρ , we find a significant positive correlation between core-periphery index and relative subgraph expression ( ρ = 0 . 76 , p = 0 . 004; Fig 3 ) . This result supports the hypothesis that subgraphs with greater sensitivity to topology within the network core tend be positively expressed and subgraphs with greater sensitivity to topology between the network core and network periphery tend to be negatively expressed . Importantly , we observe that functional subgraphs with more evenly balanced core-periphery topology ( core-periphery index close to 0 ) also tend to be more positively expressed . Collectively , our findings demonstrate that subgraphs with strong core topology or balanced core-periphery topology are associated with network states in which brain regions exhibit correlated dynamics and that subgraphs with strong periphery topology are associated with network states in which brain regions exhibit anticorrelated dynamics . By examining the relationship between subgraph topology and subgraph expression , we may now begin to bridge theoretical interpretations of subgraph architecture with experimentally driven and empirically observed changes in cognitive brain state . Based on the set of twelve functional subgraphs and their time-varying expression , we next ask “Are functional subgraphs differentially recruited during separate cognitive control tasks ? ” We hypothesize that a functional subgraph is either sensitive to cognitive control processes specific to each task or to cognitive control processes that are shared between the two tasks . To motivate our hypothesis , we examine relative differences in the distributions of mean strength of each edge between all task blocks of the Stroop task and all task blocks of the Navon task ( S7 Fig ) , before extracting functional subgraphs using NMF . Specifically , we compare the strength of an edge during the Stroop task to its strength during the Navon task by computing the mean difference of Fisher’s r-to-Z transformed correlations across subjects , separately for positive correlations and negative correlations . We observe stronger positive correlations within and between the dorsal attention , visual , and cerebellar systems during the Navon task than during the Stroop task , and we observe stronger negative correlations between the default mode system and dorsal attention , visual , and cerebellar systems during the Navon task than during the Stroop task . We use these results to inform our expectation regarding cognitive systems that might be involved in task-specific functional subgraphs . We examine the relationship between the mean relative expression of a subgraph during the Stroop task and the mean relative expression of a subgraph during the Navon task ( Fig 4 ) . We find that the expression of a subgraph during the Stroop task is significantly associated with its expression during the Navon task ( Spearman’s ρ , ρ = 0 . 97 , p = 1 . 3 × 10−7 ) . This result suggests that subgraphs are similarly ranked based on their respective expression values between the two tasks . Critically , this result implies that subgraph expression may follow a consistent hierarchy of expression during two different cognitive control tasks . While the relative relationships between subgraph expression are preserved between the Stroop task and the Navon task , we also identify differences in the magnitude of subgraph expression between the tasks . Specifically , we compare the distribution of relative subgraph expression between the Stroop task and the Navon task for each subgraph . Using paired t-tests and FDR correction for multiple comparisons , we find greater positive expression during the Navon task than during the Stroop task for subgraph B ( t27 = 4 . 4 , p = 1 . 4 × 10−4 ) and subgraph D ( t27 = 2 . 9 , p = 7 . 0 × 10−3 ) , and we find greater negative expression during the Navon task than during the Stroop task for subgraph K ( t27 = 5 . 1 , p = 1 . 4 × 10−5 ) . These findings suggest that ( i ) the Navon task exhibits greater correlated BOLD dynamics within and between dorsal attention , visual , and cerebellar systems ( subgraph B ) and between the default mode system and other broadly distributed cognitive systems ( subgraph D ) than the Stroop task , and ( ii ) the Navon task exhibits greater anticorrelated BOLD dynamics between the default mode system and dorsal attention , visual , and cerebellar systems ( subgraph K ) than the Stroop task . Critically , subgraph D and subgraph K both capture functional relationships between the default mode system and other cognitive systems . However , they exhibit different types of interactions—correlated versus anticorrelated—and involve different sub-regions of the default mode system that engage or disengage with other cognitive systems . This heterogeneity may underlie a multi-faceted functional role for cognitive systems involved in both positively expressed and negatively expressed subgraphs as regions of information integration and information segregation during these tasks . To summarize , our results imply that functional subgraphs follow a general hierarchy of expression during two cognitive tasks that invoke different control processes—pre-potent response inhibition during the Stroop task and perceptual , rule-based task switching during the Navon task . While this hierarchy may establish a task-general functional network organization related to complex cognitive processes , specific processes associated with different forms of cognitive control may be represented through small deviations in subgraph expression that significantly differ between tasks . Accordingly , nine of the twelve subgraphs were not significantly more expressed in any particular task than expected by chance and may implicate functional network components that are expressed during processes that are agnostic to task-specific mechanics , such as arousal . We next ask “How do functional subgraphs adapt to experimentally imposed changes in cognitive demand during the different cognitive control tasks ? ” We hypothesize that a functional subgraph is either sensitive to cognitive control processes specific to the experimentally imposed changes in cognitive demand or to the stimulus and task mechanics that are shared between low and high cognitive demand conditions of each task . To motivate our hypothesis , we examine the relative differences in the distributions of mean strength of each edge between the low demand conditions and high demand conditions of the Stroop task and the Navon task ( S8 Fig ) , before extracting functional subgraphs using NMF . Specifically , we compare the strength of an edge during the low demand condition of a cognitive task to its strength during the high demand condition of the task by computing the mean difference of Fisher’s r-to-Z transformed correlations over subjects , separately for positive and negative correlations . For the Stroop task , we observe: ( i ) stronger positive correlations within the dorsal attention system and between the dorsal attention , cerebellar , default mode , and frontoparietal systems , and ( ii ) stronger negative correlations within the cortical limbic system and between the cortical limbic system and other broadly distributed cognitive systems during the high demand condition compared to the low demand condition . For the Navon task , we observe: ( i ) stronger positive correlations within and between the dorsal attention , visual , and cerebellar systems , ( ii ) stronger positive correlations within the frontoparietal system , and between the frontoparietal system and other broadly distributed cognitive systems , and ( iii ) stronger negative correlations within somatosensory and ventral attention systems , and between somatosensory and ventral attention systems and other broadly distributed cognitive systems during the high demand condition compared to the low demand condition . We use these results to inform our expectation regarding cognitive systems that might be involved in functional subgraphs that adapt to changes in cognitive demand . We first examine the relationship between the mean relative expression of a subgraph during the low cognitive demand condition of a task and during the high cognitive demand condition of a task ( Fig 5 ) . We find that the expression of a subgraph during the low cognitive demand condition is significantly associated with its expression during the high cognitive demand condition for the Stroop task ( Spearman’s ρ , ρ = 0 . 99 , p = 4 . 1 × 10−9 ) and for the Navon task ( Spearman’s ρ , ρ = 0 . 99 , p = 4 . 1 × 10−9 ) , suggesting that subgraphs follow a similar ranked order in their relative expression before and after the increase in cognitive demand . Critically , this result implies that subgraphs follow a consistent hierarchy of expression during the low demand and high demand conditions of each task . While the relative relationships between subgraph expression are preserved between cognitive demand conditions , we also identify differences in the magnitude of subgraph expression between demand conditions . Specifically , we compare the distribution of relative subgraph expression between the low cognitive demand condition and the high cognitive demand condition of each task for each subgraph using paired t-tests and FDR correction for multiple comparisons . For the Stroop task , we find greater positive expression during the high demand condition than the low demand condition for subgraph B ( t27 = 3 . 3 , p = 2 . 7 × 10−3 ) and subgraph E ( t27 = 3 . 2 , p = 3 . 6 × 10−3 ) and greater negative expression during the high demand condition than the low demand condition for subgraph L ( t27 = 2 . 5 , p = 0 . 01 ) . These findings suggest that the Stroop task ( i ) exhibits greater correlated BOLD dynamics during the high demand condition than during the low demand condition within and between dorsal attention , visual and cerebellar systems ( subgraph B ) , and within and between default mode and frontoparietal systems ( subgraph E ) , and ( ii ) exhibits greater anticorrelated BOLD dynamics during the high demand condition than during the low demand condition within the limbic and subcortical systems , and between the limic and subcortical systems and other broadly distributed cognitive systems ( subgraph L ) . For the Navon task , we find greater positive expression during the high demand condition than during the low demand condition for subgraph G ( t27 = 2 . 9 , p = 8 . 2 × 10−3 ) , and we find greater negative expression during the high demand condition than during the low demand condition for subgraph F ( t27 = 2 . 7 , p = 0 . 01 ) . These findings suggest that the Navon task ( i ) exhibits greater correlated BOLD dynamics during the high demand condition than during the low demand condition between frontoparietal and default mode systems and other broadly distributed cognitive systems ( subgraph G ) , and ( ii ) exhibits greater anticorrelated BOLD dynamics during the high demand condition than during the low demand condition within somatosensory and ventral attention systems , and between somatosensory and ventral attention systems and other broadly distributed cognitive systems ( subgraph F ) . Overall , we find that functional subgraphs follow a general hierarchy of expression that remains consistent between low cognitive demand conditions and high cognitive demand conditions , and adaptively shift their expression alongside experimentally invoked changes in cognitive demand . Critically , our findings imply that subgraphs may maintain a robust network representation of each cognitive control task between different states of cognitive demand and may adaptively encode different cognitive control processes via shifts in positive or negative expression such that the overall hierarchical representation of the task remains undisturbed . These shifts in subgraph expression are evidently coordinated through changes in correlated and anticorrelated BOLD dynamics involving select subgraphs . Accordingly , these results suggest that the functional brain network may utilize task-specific control strategies by coordinating antagonistic changes in the co-activation between different cognitive systems during pre-potent response inhibition ( Stroop task ) and during perceptual , rule-based task switching ( Navon task ) . We next examine how the recruitment of functional subgraphs relates to the change in inter-individual performance as participants invoke cognitive control mechanisms . Our approach is based upon prior studies that posit a functional role of antagonistic dynamics between correlated and anti-correlated brain activity in cognitive control processes [28 , 29] . We use the subgraph characterization of the functional network to directly examine how behavioral performance is related to the extent that distinct networks exhibit more correlated or anti-correlated dynamics . To evaluate the change in an individual’s task performance—also known as performance cost—we separately compute mean change in an individual’s reaction time between consecutive blocks of the low demand condition and the high demand condition . Intuitively , a lower reaction time cost indicates better performance and a higher reaction time cost indicates worse performance . Using the reaction time cost as a behavioral marker for inter-individual differences in cognitive control processes , we study the functional role of subgraphs during the following two phases of the cognitive control tasks: ( i ) task activation associated with the low cognitive demand condition , and ( ii ) task control associated with the high cognitive demand condition . To quantify the association between subgraph expression and performance , we first compute each individual’s relative subgraph expression as the difference between the likelihood that a subgraph is positively expressed ( i . e . , functional dynamics are correlated ) and the likelihood that a subgraph is negatively expressed ( i . e . , functional dynamics are anti-correlated ) . We next use Spearman’s ρ to assess the relationship between relative subgraph expression during the low or high demand condition , and the reaction time cost across individuals on each task ( Fig 6A–6D; left ) . Indeed , if the correlation between relative expression and reaction time cost is positive , then individuals who express more correlated dynamics ( and less anti-correlated dynamics ) within a subgraph exhibit poorer performance and individuals who express more anti-correlated dynamics ( and less correlated dynamics ) within a subgraph exhibit better performance . This analysis approach enables us to understand the extent to which behavior is explained by both the degree to which regions in a subgraph engage with one another via correlated dynamics and the degree to which regions in a subgraph disengage from one another via anti-correlated dynamics . We find a diverse set of subgraphs whose relative expression during low demand , task activation conditions or high demand , task control conditions correlate with reaction time cost . For the Stroop task , we find that a lower reaction time cost ( better performance ) is associated with: ( i ) greater negative expression of subgraph A ( ρ = 0 . 44 , p = 0 . 01; uncorrected for multiple comparisons ) during the low demand condition , and ( ii ) greater positive expression of subgraph B ( ρ = −0 . 39 , p = 0 . 04; uncorrected for multiple comparisons ) during the high demand condition . These results suggest that a smaller change in the reaction time between the low demand condition and the high demand condition of the Stroop task is associated with: ( i ) greater anticorrelated dynamics within dorsal attention , default mode , frontoparietal , somatosensory , ventral attention , and visual systems during task activation , and ( ii ) greater correlated dynamics within and between dorsal attention , visual , and cerebellar systems during task control . For the Navon task , we find that a lower reaction time cost ( better performance ) is associated with: ( i ) greater negative expression of subgraph E ( ρ = 0 . 55 , p = 2 . 2 × 10−3; uncorrected for multiple comparisons ) during the low demand condition , and ( ii ) greater negative expression of subgraph E ( ρ = 0 . 47 , p = 0 . 01; uncorrected for multiple comparisons ) and subgraph J ( ρ = 0 . 44 , p = 0 . 02; uncorrected for multiple comparisons ) during the high demand condition . These results suggest that a smaller change in the reaction time between the low demand condition and the high demand condition of the Navon task is associated with: ( i ) greater anticorrelated dynamics within default mode and frontoparietal systems , and between default mode and frontoparietal systems and other broadly distributed cognitive systems during task activation , and ( ii ) greater anticorrelated dynamics within default mode , frontoparietal , and visual systems , and between default mode , frontoparietal , and visual systems and other broadly distributed cognitive systems during task control . In sum , we find that changes in the correlated and anticorrelated BOLD dynamics within and between distributed cognitive systems is associated with cognitive processes during task activation and task control that explain inter-individual differences in performance during cognitive control tasks . Based on these data and our previous result that subgraphs maintain a consistent hierarchical organization in terms of their ranked expression between cognitive demand conditions , our findings suggest that individual variability in behavior during cognitive control may be marked by subtle individual differences in subgraph expression amid a hierarchical order that is defined at the population level . Lastly , we ask whether there are individual brain regions that are more likely to participate in subgraphs associated with task performance . By quantifying the extent to which brain regions participate in subgraphs , we aim to link our analysis with classical univariate approaches for examining functional brain activation during cognitive control tasks . We hypothesize that brain regions commonly associated with executive and higher cognitive functions , such as frontoparietal , default mode , attention , and salience systems are more likely to participate in subgraphs that are associated with task performance . To test this hypothesis , we computed the performance participation score—a nodal measure linking the participation of a node in a subgraph with the relationship between the subgraph and behavioral performance . Specifically , we first compute node participation in a subgraph as the sum of the subgraph edge weights from a node to all other nodes—yielding one node participation score for each of the 262 brain regions in each of the twelve subgraphs [16] . We next compute the sum of a node’s participation scores , weighted by the Spearman’s ρ value between relative subgraph expression and performance cost: that is , nodes of the same subgraph were weighted by the same ρ value . Intuitively , a node with positive participation score tends to become disengaged in the brain network , via anti-correlated dynamics , during better task performance and a node with negative participation score tends to become engaged in the brain network , via correlated dynamics , during better task performance . To determine whether a brain region exhibits a greater participation score than expected by chance , we construct null distributions of regional participation scores by uniformly permuting the edges of each subgraph 10000 times and recomputing the regional participation score for each permutation . We retain regional participation scores that exceeded the 95% confidence interval of the null distribution after using Bonferroni correction for multiple comparisons testing . Using this approach , we find a broad range of brain regions that are significantly involved in correlated and anti-correlated brain activity during improved task performance ( Fig 6A–6D; right ) . For the Stroop task we observe that individuals exhibit lower reaction time cost when ( i ) during the low demand condition , regions within frontoparietal , default mode , subcortical , and visual systems are more disengaged from each other and regions within the cortical limbic system are more engaged with each other ( Fig 6A; right ) , and ( ii ) during the high demand condition , regions within default mode and limbic systems are more disengaged from each other and regions within visual and somatosensory systems are more engaged with each other ( Fig 6B; right ) . For the Navon task we observe that individuals exhibit lower reaction time cost when ( i ) during the low demand condition , regions within frontoparietal , default mode , and visual systems are more disengaged from each other and regions within the cortical limbic system are more engaged with each other ( Fig 6C; right ) , and ( ii ) during the high demand condition , regions within frontoparietal , default mode , and visual systems are more disengaged with each other and regions within subcortical and limbic systems are more engaged with each other ( Fig 6D; right ) . Together , these results demonstrate that brain regions classically considered as key components of executive and higher cognitive functions , such as regions in frontoparietal and default mode systems , tend to be more influential in subgraphs that are associated with task performance . Notably , our approach characterizes the functional role that these brain areas play during task activation and task control based on their participation in correlated and anticorrelated BOLD dynamics . During different phases of cognitive control , regions involved in correlated dynamics may serve as integrators of task-relevant information while regions involved in anticorrelated dynamics may serve as segregators of task-relevant information .
Our non-negative matrix factorization ( NMF ) approach enables us to objectively account for: ( i ) the dissociability of brain networks into composite subgraphs that are associated with specific cognitive control functions , and ( ii ) the flexible and adaptive expression of these putative cognitive sub-networks during fluctuations in cognitive demand . Intuitively , these subgraphs represent clusters of functional interactions whose weights tend to fluctuate together across tasks and across conditions . Unlike other graph partitioning techniques , such as community detection , that pursue a hard partitioning of network nodes into discrete clusters , NMF enables a soft partitioning of the high dimensional set of network edges into subgraphs that allow an edge to participate in multiple network sub-units [16 , 22] . This capability is advantageous for examining how pairs of brain areas functionally interact within different topological contexts . Mathematically , NMF recovers a non-orthogonal spanning set of graph edges whose linear combination—weighted by dynamic expression coefficients—can reconstruct the original space of observed network topologies across the experimental task conditions . In other words , subgraphs represent a set of functional relationships for the cognitive control data from which they were recovered and subgraph expression coefficients represent the encoding of those relationships for the different task conditions ( we refer the reader to [40] for a discussion on neural coding theory ) . Thus from the perspective of network-based encoding of cognitive control tasks , indeed , we find that subgraphs are comprised of functional interactions that are either sensitive to the specific needs of a particular task or generalized to needs common across tasks . These data support the theory that there exist separate task-specific and task-general network architectures [30] . We examine the particular cognitive systems involved in task-specific and task-general subgraphs and find a dual-role for correlated and anticorrelated interactions between traditional cognitive control systems and the default mode system: these systems are positively expressed during cognitive control involving Stroop-based response inhibition and negatively expressed during cognitive control involving Navon-based task switching . Our finding of anticorrelated interactions between cognitive control and default mode systems is well supported by the popular theory that the task-negative , default mode system deactivates as task-positive , executive areas activate [41–43] . On the other hand , our finding of correlated interactions between these systems challenges the notion that these systems must decouple during cognitive control . Prior studies have in fact demonstrated that individuals that exhibit greater integration between the default mode network and executive areas tend to display better behavioral performance during cognitive control tasks that involve switching between task-rules [28 , 44] . Based on these results , we posit that differences in the nature of functional interactions between these systems might be explained by task-specific requirements for cognitive control . Importantly , NMF demonstrates the ability to tease apart functional interactions underlying intrinsic differences in cognitive control processes by recovering task-specific subgraphs . There is a longstanding question in network neuroscience regarding the putative roles of task-specific functional architectures and their relationship to intrinsic functional networks that generalize across tasks [30] . The canonical model posits that task-general processes shape intrinsic functional networks and task-specific processes update subsets of these intrinsic functional connections [30] . A critical assumption has been that networks related to task-specific processes also facilitate behavioral performance of the task . In this study , we present data that support the canonical model yet challenge the assumption that task processes and behavioral metrics of performance on the task stem from the same network structures . First , we find that a robust hierarchy of subgraphs persist between different forms of cognitive control processes ( Fig 4A ) and different levels of cognitive demand ( Fig 5A and 5B ) . Indeed , changes in subgraph expression within the bounds of this hierarchy accompany specific task states , however we also find that the subgraphs that best predict individual differences in behavior are not necessarily those that are modulated by different task conditions . In other words , functional architectures most strongly associated with behavior may represent task-general cognitive functions that are distinct from networks that are differentially expressed between cognitive conditions , consistently across individuals . For example , the Stroop task is designed to recruit general processes related to stimulus perception and color-word discrimination as well as cognitive control processes such as inhibition of the prepotent response to an incongruent stimulus and Navon task is designed to recruit general processes such as perceptual decision making or specific processes such as decision making based on rules that periodically switch . A task-general subgraph that is modulated by lower level perceptual or cognitive processes during low and high task conditions may still be modulated differently across individuals and reflect differences in behavior . Conversely , based on recent work demonstrating that different components of functional brain networks may be highly similar or highly dissimilar across individuals [45] , a task-general subgraph that does not strongly vary with individual differences in behavior might reflect intermediate task processes that are common to the low and high cognitive demand conditions . Indeed , a future study that uses NMF in conjunction with faster imaging modalities may amenably tease apart subgraphs involved with different temporal phases of cognitive control processes . A growing body of literature in network neuroscience has shown that the brain possesses an ability to maintain a homeostasis of its own internal dynamics through antagonistic , push-pull interactions in various areas of healthy cognition [28 , 29 , 46] and disease [47] . Simply , push-pull control strategies may prevent imbalances of activity in complex , interconnected systems like the brain [48 , 49] . A push-pull mechanism would be a critical component of cognitive control in which brain networks must perform two antagonistic functions: ( i ) segregated information processing in functionally-specific domains , and ( ii ) integrated information processing to adapt to environmentally-driven changes in cognitive demand [26] . Our results pertaining to the adaptive shifting in subgraph expression during changes in cognitive demand may be associated with a putative push-pull control mechanism in which: ( i ) subgraphs first establish a consistent hierarchy of expression that enforces a baseline level of expression that remains consistent relative to other subgraphs during cognitive control processes , and ( ii ) subgraphs then shift their expression above or below their baseline—via changes in correlated or anticorrelated BOLD dynamics—depending on cognitive demand . We posit that a push-pull mechanism might internally regulate the direction of change in subgraph expression , collectively across the network: an excessive increase or decrease in subgraph expression might disrupt the hierarchical order of subgraph expression and lead to brain states in which information is overly integrated or overly segregated across the network . In our analysis , we observe that the shift between cognitively demanding brain states involves a change in the interacting roles between brain areas distributed across several cognitive systems: including frontoparietal , default mode , attentional , and cerebellar regions . Recent studies focusing on functional interactions between cerebellum and traditional cognitive control regions [12] have suggested that the cerebellum may subserve cognitive processes related to error correction [50 , 51] . Our results add new insight to this discussion by demonstrating in two different cognitive control tasks that frontoparietal , cerebellar , and sensory systems are involved in subgraphs that significantly change in expression with increasing cognitive demand . We also consider the possibility that regulatory mechanisms involved in cognitive control might also explain differences in individual performance on cognitively demanding tasks . We found that subgraphs may be heterogeneously associated with individual cognitive performance: greater correlated BOLD dynamics and greater anticorrelated BOLD dynamics between regions of subgraphs are associated with improved task performance . These data suggest that cognitive control is associated with enhanced integration and segregation of task-relevant information between different composite sets of brain regions . In addition , we use functional subgraphs to uncover the relationship between functional interactions and sub-processes of cognitive control that differentially contribute to the performance cost associated with an increase in cognitive demand . Namely , we find subgraphs whose expression during task activation is associated with lower performance cost accompanying an increase in cognitive demand , and we find subgraphs whose expression during task control is associated with lower performance cost accompanying an increase in cognitive demand . We speculate that the rich distribution of performance modes exhibited by functional subgraphs implicates a network homeostasis on cognitive control processes [46] . Critically , we contextualize the relationship between network reorganization during task states and its relationship with task performance via the following sequence of events . First , global network correlations decrease between the fixation period and the task . As the network becomes less correlated , select subgraphs become increasingly specialized and exhibit heightened levels of expression relative to non-task related subgraphs . These task-related subgraphs remain highly expressed across individuals and inter-individual differences in expression scale with task performance . Brain regions with greatest levels of participation within task-related subgraphs are putative mediators of the relationship between subgraph expression and performance . In sum , we demonstrate that functional brain networks capably adapt their topological architecture in response to task-driven modulation in cognitive demand . Critically , we observe that cognitive control may not necessarily activate discrete cognitive brain systems , but rather recruit several interconnected systems , in concert , between changes in cognitively demanding brain states . When individuals under- or over-express functional interactions between these cognitive systems they tend to respond more slowly during difficult cognitive tasks , implicating specific brain sub-networks in facilitating or impeding an individual’s ability to transition between states . While we narratively describe cognitive control to be recruited continuously in response to task demands , it is also important to acknowledge that cognitive control functions can be considered to be distinct processes [52] with partially dissociable substrates [53] . Given these broader debates about shared and unique CC mechanisms , we should continue to consider the spatiotemporal signatures of brain activity that distinguish between accounts of CC . Future studies could use NMF-based subgraph analysis to dissect networks involved in tasks where demand is parametrically varied and test whether a continuous or discrete representation of specific CC functions better describes observed network dynamics . Lastly , we focus on the mechanistic role that functional brain networks play in regulating internal dynamics during cognitive control . Our novel approach and findings open new doors for querying how such regulatory mechanisms could be modulated to influence behavior . For instance , can we perturb specific network components to improve the likelihood that an individual is able to access shorter trajectories to switch between low demanding states and high demanding states ? By marrying machine-learning approaches that objectively tease apart concurrent network processes attributed to different facets of cognition with burgeoning neurotechnologies such as neurofeedback [54] , neurostimulation [55] , or pharmacological intervention [56–58] that can exogenously control network dynamics , we can explore how disrupting network components that exhibit task-based adaptation causally influence behavior . The prospect of such scientific inquiry is equally exciting in diseases such as schizophrenia in which patients experience more probable transitions to more disruptive cognitive states .
All participants completed a Stroop task with color-word pairings that were eligible and ineligible to elicit interference effects [24] , and a local-global perception task based on classical Navon figures [25] . For the Stroop task , trials were comprised of words presented one at a time at the center of the screen printed in one of four colors—red , green , yellow , or blue -– on a gray background . For all trials , subjects responded using their right hand with a four-button response box . All subjects were trained on the task outside the scanner until proficient at reporting responses using a fixed mapping between the color and button presses ( i . e . , index finger = “red” , middle finger = “green” , ring finger = “yellow” , pinky finger = “blue” ) . Trials were presented in randomly intermixed blocks containing trials that were either eligible or ineligible to produce color-word interference effects . In the scanner , blocks were administered with 20 trials apiece separated by 20 s fixation periods with a black crosshair at the center of the screen . Each trial was presented for a fixed duration of 1900 ms separated by an interstimulus interval of 100 ms during which a gray screen was presented . In the trials ineligible for interference , the words were selected to not conflict with printed colors ( “far , ” “horse , ” “deal , ” and “plenty” ) . In the trials eligible for interference ( i . e . , those designed to elicit the classic Stroop effect [24] ) , the words were selected to introduce conflict ( i . e . , printed words were “red , ” “green , ” “yellow , ” and “blue” and always printed in an incongruent color ) . In our analysis , we refer to blocks that are eligible ( ineligible ) to produce color-word interference effects as high demand ( low demand ) conditions ( Fig 1B ) . For the Navon task , local-global stimuli were comprised of four shapes—a circle , X , triangle , or square—that were used to build the global and local aspects of the stimuli . On all trials , the local feature did not match the global feature , ensuring that subjects could not use information about one scale to infer information about another . Stimuli were presented on a black background in a block design with three blocks . In the first block type , subjects viewed white local-global stimuli . In the second block type , subjects viewed green local-global stimuli . In the third block type , stimuli switched between white and green across trials uniformly at random with the constraint that 70% of trials included a switch in each block . In all blocks , subjects were instructed to report only the local features of the stimuli if the stimulus was white , and to report only the global feature of the stimuli if the stimulus was green . Blocks were administered in a random order . Subjects responded using their right hand with a four-button response box . All subjects were trained on the task outside the scanner until proficient at reporting responses using a fixed mapping between the shape and the button presses ( i . e . , index finger = “circle” , middle finger = “X” , ring finger = “triangle” , and pinky finger = “square” ) . In the scanner , blocks were administered with 20 trials apiece separated by 20 s fixation periods with a white crosshair at the center of the screen . Each trial was presented for a fixed duration of 1900 ms separated by an interstimulus interval of 100 ms during which a black screen was presented . In our analysis , we refer to blocks that switch between local-global perception as the high demand condition and blocks that do not switch as the low demand condition ( Fig 1C ) . We acquired T1-weighted anatomical scans on a Siemens 3 . 0T Tim Trio for all subjects . Anatomical scans were segmented using FreeSurfer [59] and parcellated using the connectome mapping toolkit [31] into N = 234 cortical and subcortical brain regions . We also included a cerebellar parcellation ( N = 28 brain regions [32] ) by using FSL to nonlinearly register the individual’s T1 to MNI space . Then , we used the inverse warp parameters to warp the cerebellum atlas to the individual T1 . Finally , we merged the cerebellar label image with the dilated cortical and subcortical parcellation image resulting in N = 262 brain regions . Functional magnetic resonance imaging data was acquired on a 3 . 0T Siemens Tim Trio whole-body scanner with a whole-head elliptical coil by means of a single-shot gradient-echo T2* ( TR = 1500 ms; TE = 30 ms; flip angle = 60 degrees; FOV = 19 . 2 cm , resolution 3mm x 3mm x 3mm ) . Preprocessing was performed using FEAT v . 6 . 0 ( fMRI Expert Analysis Tool ) a component of the FSL software package [60] . To prepare the functional images for analyses , we completed the following steps: skull-stripping with BET to remove non-brain material , motion correction with MCFLIRT ( FMRIB’s Linear Image Registration Tool; [60] ) , slice timing correction ( interleaved ) , spatial smoothing with a 6-mm 3D Gaussian kernel , and high pass temporal filtering to reduce low frequency artifacts . We also performed EPI unwarping with fieldmaps to improve subject registration to standard space . Native image transformation to a standard template was completed using FSL’s affine registration tool , FLIRT [60] . Subject-specific functional images were co-registered to their corresponding high-resolution anatomical images via a Boundary Based Registration technique ( BBR [61] ) and were then registered to the standard MNI-152 structural template via a 12-parameter linear transformation . Finally , each participant’s individual anatomical image was segmented into grey matter , white matter , and CSF using the binary segmentation function of FAST v . 4 . 0 ( FMRIB’s Automated Segmentation Tool [62] ) . The white matter and CSF masks for each participant were then transformed to native functional space and the average timeseries were extracted . Based on the commonly accepted notion that smoothing reduces scan-related , spatially-distributed Gaussian noise across voxels and enhances BOLD signal-to-noise ratio , we conducted smoothing by applying a kernel with full-width half-maximum of 6 mm to voxels prior to ROI time series extraction . An important consideration of smoothing is that voxels at the edge of an ROI may contain overlapping information from adjacent ROIs . However , our analysis occurs at the level of the aggregate BOLD activity across many voxels in an ROI , and thus voxel-level precision was not a goal in this study . The white matter and CSF signals were used as confound regressors on the time series along with 18 translation and rotation parameters as estimated by MCFLIRT [63] . To preserve natural anti-correlation in the BOLD signal , we did not regress the global signal [64] . We refer the reader to [65] for additional methodological details regarding data acquisition and pre-processing . We constructed functional brain networks to study the functional interactions between brain regions during the Stroop and Navon cognitive control tasks . To measure functional interactions , we first separately divided the BOLD signal into six low demand blocks , six high demand blocks , and twelve fixation blocks ( before each cognitive demand block ) for each behavioral task of each subject . Each block contained 20 samples or 30 seconds of signals ( Fig 2B ) . We next computed a Pearson correlation coefficient between each pair of BOLD signals from the N brain regions ( graph nodes ) in each of the K experimental blocks . We then aggregated correlations ( graph edges ) into an N × N × K adjacency matrix A for each subject . We note that due to confounding delays in hemodynamic response , it is possible that fixation blocks contain both task-related and task-unrelated activity . To mitigate this concern , we take two steps . First , we align each block with the peak hemodynamic response by shifting analysis windows by 4 TRs , which corresponds to the canonical hemodynamic lag of 6 seconds . Second , we compute NMF-based subgraphs ( see next section ) using fixation blocks to increase the length of the physiologic signal , but we restrict our analysis of the subgraphs specifically to task blocks . To analyze positively correlated ( correlated ) and negatively correlated ( anticorrelated ) functional interactions , we separated positively-weighted edges from negatively-weighted edges for each block k in A using a threshold of zero . This procedure resulted in a thresholded adjacency matrix A* of size N × N × 2 × K where each block k is associated with one N × N matrix with positive edge weights and another N × N matrix with negative edge weights ( Fig 2C ) . We retain all correlation values after the thresholding procedure such that both positive adjacency matrices and negative adjacency matrices are both fully-weighted graphs . An alternate representation of the adjacency matrix A* is a two-dimensional network configuration matrix A ^ * , which tabulates all N × N pairwise edge weights across K blocks , and across positive and negative edge types ( Fig 2D ) . Due to symmetry of A k * , we unravel the upper triangle of A k * , resulting in the weights of N ( N − 1 ) /2 connections . Thus , A ^ * has dimensions N ( N − 1 ) /2 × 2*K . To identify network subgraphs—sets of network edges whose strengths co-vary over experimental task conditions—we applied an unsupervised machine learning algorithm called non-negative matrix factorization ( NMF ) [21] to the network configuration matrix . This technique enabled us to pursue a parts-based decomposition of the network configuration matrix into subgraphs with expression coefficients that vary with time ( Fig 2E and 2F ) . Briefly , NMF holds two distinct advantages to principal components analysis ( PCA ) and independent components analysis ( ICA ) for studying components of interconnected network structures . First , PCA/ICA quantify subgraphs that are statistically orthogonal/independent from each other , while NMF quantifies subgraphs that are statistically redundant such that they can flexibly co-occur with other subgraphs during different brain states . The unique property of NMF to characterize overlapping network structures is conceptually valuable for the analysis of brain graphs , which assume that each node encompasses statistical relationships with all other nodes in the network—this assumption is violated by PCA/ICA . Second , PCA/ICA arbitrarily assign positive and negative weights to subgraphs , while NMF enforces non-negative weights to subgraphs . The non-negative property of NMF uniquely quantifies subgraphs that are additive parts of the network and interpretable on the basis of their positive contribution to the functional network at each point in time—this interpretation is obfuscated by PCA/ICA . For further , in-depth discussion regarding network subgraphs , we refer the reader to [16] . For recent applications of NMF to the study of functional brain networks , please see [22 , 23 , 66 , 67] . To apply NMF to functional networks , we first computed the magnitude of the network configuration matrix A ^ * such that all entries of the matrix were non-negative . We next applied two normalization procedures to account for differences in the magnitude of edge weights between positive correlations and negative correlations and between study participants . First , based on the finding that mean negative correlations are significantly lower in magnitude than mean positive correlations across subjects ( paired t-test; t27 = 20 . 0 , p = 9 . 7 × 10−18; S1E Fig ) , we sought to normalize the distribution of positive edge weights and negative edge weights for each observed graph ( each row of the configuration matrix ) . Therefore , we divided the edge weights in each row of the configuration matrix by their sum such that the weight edge density for each observed graph was equal to one . Second , based on the finding that the distribution of edge weights differs between subjects ( one-way ANOVA; F = 2 . 5 , p = 3 . 5 × 10−5; S1A Fig ) , we sought to standardize the vector of weights associated with each edge ( each column of the configuration matrix ) , separately , for each subject . Therefore , we scaled the weights of each edge by their Euclidean length ( L2-norm ) , separately , for each subject [68] . We also note that BOLD autocorrelation was not removed from the measured edge weights . As NMF is a linear operation and based on a recent study showing that the edge weights before removing BOLD autocorrelation are linearly correlated with edge weights after removing BOLD autocorrelation [69] , we did not expect this procedure to influence NMF analysis . We next formulated the matrix factorization problem A ^ * ≈ W H s . t . W > = 0 , H > = 0 as the decomposition of the network configuration matrix A ^ * into two non-negative matrices W—the weighted subgraph matrix consisting of recurring patterns of functional interactions , or network edges—and H—the dynamic expression matrix consisting of coefficients reflecting the weight of a subgraph during different task conditions of each subject [16] . To quantify W and H , we optimized the following cost function: min W , H1 2 ‖ A ^ − W H ‖ F 2 + α ‖ W ‖ F 2 + β ∑ t = 1 T ‖ H ( : , t ) ‖ 1 2 , ( 1 ) where m ∈ [2 , min ( N ( N − 1 ) /2 , T ) − 1] is the number of subgraphs to decompose , β is a penalty weight to impose sparse temporal expression coefficients , and α is a regularization of the interaction strengths for subgraphs [70] . To solve the NMF equation , we used an alternating non-negative least squares with block-pivoting method with 100 iterations for fast and efficient factorization of large matrices [71] . We initialized W and H with non-negative weights drawn from a uniform random distribution on the interval [0 , 1] . To select the parameters m , β , and α , we pursued a random sampling scheme—shown to be effective in optimizing high-dimensional parameter spaces [16 , 72]—in which we re-ran the NMF algorithm for 1000 parameter sets in which m is drawn from U ( 3 , 50 ) , β is drawn from U ( 0 . 01 , 5 ) , and α is drawn from U ( 0 . 01 , 5 ) ( S2 Fig ) . We evaluated subgraph learning performance based on a four-fold cross-validation scheme in which the twenty eight subjects are uniformly partitioned into folds of seven subjects and , iteratively , three folds are used to identify subgraphs and the held-out fold is used to compute the cross-validation error ( ‖ A ^ − W H ‖ F 2 ) . The optimal parameter set should yield subgraphs that minimize the cross-validation error and reliably span the space of observed network topologies [16] . Based on these criteria , we identified an optimum parameter set ( m ¯ , β ¯ , α ¯ ) that exhibited a low residual error in the bottom 5th percentile of our random sampling scheme ( S2G and S2I Fig ) . Due to the non-deterministic nature of this approach , we integrated subgraph estimates over multiple runs of the algorithm using consensus clustering—a general method of testing robustness and stability of clusters over many runs of one or more non-deterministic clustering algorithms [73] . Our adapted consensus clustering procedure entailed the following steps: ( i ) run the NMF algorithm R times per network configuration matrix , ( ii ) concatenate subgraph matrix W across R runs into an aggregate matrix with dimensions E × ( R * m ¯ ) , and ( iii ) apply NMF to the aggregate matrix to determine a final set of subgraphs Wconsensus and expression coefficients Hconsensus ( we refer the reader to [16] for more details ) . In this study , we set R = 1000 . To investigate putative core-periphery organization in each functional subgraph , we quantify the core-periphery index as a measure of the balance between mean edge strength within each cognitive system and mean edge strength of each cognitive system to all other cognitive systems . Specifically , we define the core-periphery index for a symmetric , subgraph adjacency matrix W* with dimensions N × N using the following equations: core s = 1 | s | ∑ i j [ W i j * ] δ ( s i , s j ) ( 2 ) periphery s = 1 | s | * ( N − | s | ) ∑ i j [ W i j * ] ( 1 − δ ( s i , s j ) ) ( 3 ) core-periphery = 1 9 ∑ s = 1 9 c o r e s − p e r i p h e r y s c o r e s + p e r i p h e r y s ( 4 ) where N is the number of network regions , s is one of nine cognitive systems , |s| is the number of nodes in cognitive system s , si , sj refer to the cognitive system assignments of nodes i and j , and δ ( si , sj ) = 1 if si = sj and δ ( si , sj ) = 0 if si ≠ sj . Intuitively , the core-periphery index is bounded between −1 and +1 , where positive values indicate greater subgraph edge strength within a cognitive system , indicating that the subgraph reflects functional interactions within a network core , negative values indicate greater subgraph edge strength between cognitive systems , indicating that the subgraph reflects functional interactions within a network periphery , values approaching zero imply that a subgraph reflects balanced functional interactions between the network core and the network periphery . To examine the specific cognitive systems that participate in core-periphery organization of each subgraph , we first generate 10000 surrogates of each subgraph by randomly permuting subgraph edges to disrupt system-level architecture . We compute the core score and the periphery score for each cognitive system s of each of the surrogates , separately for each subgraph . Using Bonferroni correction for multiple comparisons testing , we identify subgraph-specific cognitive systems that exhibit significantly greater core and periphery scores than expected by the surrogate model S6 Fig . It is important to consider the reproducibility of subgraphs measured using NMF given different data splits . To quantify the reproducibility of functional subgraphs , we measured the extent to which the pattern of subgraph edge weights measured in one dataset predicts the pattern of subgraph edge weights measured in a second dataset . Specifically , we first divided the whole cognitive control dataset into two datasets such that the first dataset contains the first three experimental blocks across subjects and the second dataset contains the second three experimental blocks across subjects . We next applied NMF using the optimal parameter set to the two datasets ( A ^ 1 corresponds to the network configuration matrix of the first dataset and A ^ 2 corresponds to the network configuration matrix of the second dataset ) , resulting in two subgraph matrices ( W1 and W2 ) . Note that the subgraphs along the columns of W1 may not necessarily be ordered similarly as the subgraphs along the columns of W2 due to the stochastic nature of the NMF algorithm . To reorder subgraphs from the second dataset such that they correspond to the same order as subgraphs from the first dataset , we sought a mapping Xi , j of subgraph W 1 i to subgraph W 2 j , where X is a Boolean matrix that prescribes whether the ith subgraph from the first dataset is uniquely assigned to the jth subgraph from the second dataset . The cost Ci , j associated with assigning W 1 i to W 2 j is equal to ‖ W 1 i − W 2 j ‖ . To determine a unique X , we minimized the cost function ∑i∑j Ci , j Xi , j using the well-known Hungarian algorithm [74] . After calculating an optimal assignment between subgraphs of the two datasets , we measured the similarity in the pattern of edge weights between assigned subgraph pairs ( i , j ) by computing the Pearson correlation coefficient . We compared the true Pearson correlation coefficient of every subgraph pair to a null distribution in which we re-computed the Pearson correlation coefficient between every possible , non-assigned subgraph pair . This approach enabled us to assess the reproducibility of each individual subgraph based on the magnitude of the Pearson correlation similarity measure relative to that expected by chance . | Brain networks support the human ability to navigate a complex space of perceptions and actions through cognitive control . Here we ask , “How do brain networks coordinate task-relevant information as individuals adapt to cognitive demands imposed by a task ? ” We study the fMRI BOLD signal of twenty-eight healthy subjects as they perform two cognitive control tasks—a Stroop interference task and a local-global perception switching task using Navon figures—with low and high cognitive load conditions . We construct functional networks and use a machine learning technique called non-negative matrix factorization to identify topological motifs whose expression fluctuates across different phases of cognitive control processes . We find that motifs stratify the brain network into a hierarchy of distributed functional processes that adapt to changes in cognitive demand and predict individual differences in task performance . These data offer insight into how network interactions linking cognitive systems coordinate transitions between cognitively demanding brain states . | [
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Along the transformation process , cells accumulate DNA aberrations , including mutations , translocations , amplifications , and deletions . Despite numerous studies , the overall effects of amplifications and deletions on the end point of gene expression—the level of proteins—is generally unknown . Here we use large-scale and high-resolution proteomics combined with gene copy number analysis to investigate in a global manner to what extent these genomic changes have a proteomic output and therefore the ability to affect cellular transformation . We accurately measure expression levels of 6 , 735 proteins and directly compare them to the gene copy number . We find that the average effect of these alterations on the protein expression is only a few percent . Nevertheless , by using a novel algorithm , we find the combined impact that many of these regional chromosomal aberrations have at the protein level . We show that proteins encoded by amplified oncogenes are often overexpressed , while adjacent amplified genes , which presumably do not promote growth and survival , are attenuated . Furthermore , regulation of biological processes and molecular complexes is independent of general copy number changes . By connecting the primary genome alteration to their proteomic consequences , this approach helps to interpret the data from large-scale cancer genomics efforts .
Chromosomal aberrations are a hallmark of cancer cells . During transformation cells lose cell-cycle control and fidelity of DNA replication causing multiple changes in DNA copy numbers [1] , [2] . Although chromosomal aberrations are associated with transformation , changes in DNA copy number can cause growth defects rather than cell growth [3] , [4] . Therefore transformation requires specific genomic changes that enable tolerance to genomic instability and promote growth and survival . The identity of these specific altered genes that enable transformation is still unknown , and great efforts are made to achieve a better understanding of these gene changes and their effects . Technological developments in recent years have allowed high resolution genomic analysis using SNP arrays , and large scale projects have mapped the gene copy number changes in thousands of tumor samples [5] , [6] . Another major step necessary for the interpretation of the biological significance of such studies that is missing so far is the analysis of the consequences of these alterations: to what extent they affect protein expression . This in turn would allow investigation and interpretation of potential biological function . Several studies have shown high correlation between the amplifications and deletions and changes in mRNA levels and were therefore able to predict amplifications and deletions based on global transcript measurements [7]–[11] . Still , only a few amplifications were associated with oncogenes , and some deletions with tumor suppressors , while the majority of these alterations could not be associated with known tumor promoting activities [5] , [6] . Furthermore , the effects of co-amplifications and deletions of genes in the same regions as known tumor-related genes , are yet to be discovered . A priori it would be possible that proteins encoded in a given amplicon are uniformly overexpressed in accordance with genome copy number or alternatively , that the expression levels only of selected or none of the proteins changes . These different scenarios have very different implications when trying to assess potential biological and oncological effects of a given amplicon detected in a somatic cancer genome . For better understanding of the general output of chromosomal changes , the protein level therefore has to be globally examined . Such knowledge can be crucial as it can suggest novel potential drivers of transformation and , as already shown in specific cases in the past , help determine treatment modalities and prognosis [12] , [13] . To compare proteomic to genomic alterations in a system-wide manner deep coverage of the proteome is essential as it maximizes the chance to detect and accurately quantify the proteins expressed from amplified or deleted regions . Stable Isotope Labeling by Amino Acids in Cell Culture ( SILAC ) is an accurate method for quantitative mass-spectrometry based proteomics [14] , [15] . Recent advances in SILAC-based proteomics using high resolution mass spectrometry [16] , [17] enabled accurate proteome coverage of the complete yeast proteome [18] and large proportions of the mammalian proteome [19] . Based on these developments , we could now compare cancer cell lines containing multiple chromosomal alterations and normal diploid epithelial cells , and further compare these changes to genomic alterations detected by SNP arrays . This accurate analysis enabled us to find the output of thousands of genes with varying gene dosage , and thereby estimate their regulation and their potential impact .
To study the effects of genomic alterations on the protein level , we performed quantitative proteomic analysis of two aneuploid breast cancer cell lines and normal diploid cells . We SILAC-labeled the MCF7 breast cancer cell line with heavy lysine and arginine to serve as internal standard for quantification . The lysate of the labeled cells was combined with normal mammary epithelial cells ( HMEC ) or with two breast cancer cell lines - HCC2218 , derived from a patient with Stage III ductal carcinoma and HCC1143 , derived from a patient with Stage II ductal carcinoma ( Figure 1 ) . We analyzed each proteome mixture by enzymatic digestion and isoelelectric focusing of the resulting peptides followed by online liquid chromatography mass spectrometry on hybrid linear ion trap Orbitrap mass spectrometers . In total we identified and quantified 72 , 239 SILAC peptide pairs at 99% confidence . Quantification of cancer cell lines against normal cells was computed as the ‘ratio-of-ratios’ of each proteome against the internal MCF7 SILAC standard , requiring at least two quantification events per protein in each experiment . From biological triplicates , we identified and quantified 6 , 735 proteins ( an average of more than 5 , 000 quantified proteins per cell line ) . For the analysis of chromosomal aberrations , we mapped the copy number changes in the genome of HCC2218 , HCC1143 and HMEC with SNP arrays ( Affymetrix- Genome-wide Human SNP Array 6 . 0; Figure 1 ) . Similar to the proteome analysis , we calculated the ratios of the signal in the cancer cell lines compared to the diploid control cells , then matched the chromosomal position with the gene , and determined the change in copy number as the median of the signals of all the probes annotated to the same gene . We matched between the proteins and the genomic data based on the gene name , enabling direct comparison of the level of almost every identified protein and its encoding gene . A density plot of gene copy number of the HMEC indicates that these cells are diploid and therefore can serve as a normal control ( Figure 2A ) . We normalized the proteomic and the genomic data of HCC2218 and HCC1143 cells to the control cells . Overall correlation between the change in gene copy number and the change in protein level determined in this way was low ( 0 . 22 for HCC2218 and 0 . 28 for HCC1143 cells ) . Only 4 . 8% and 7 . 8% of the protein level changes were determined by the copy number changes of the genes , significantly less than the percentage of transcriptome changes explainable by genome differences [9] , [10] , [20] , [21] . This suggests that there is a tighter coupling between gene copy numbers and transcript changes than between gene copy numbers and protein level changes . The remaining changes of protein levels are presumably caused by other mechanisms of regulation of protein expression . The plots of the gene copy number vs . the protein level show that the genome is distributed around integer values corresponding to 0 , 1 , 2 , 3 , 4 gene copies ( Figure 2B and 2C ) . The distribution of the proteins encompassed many higher fold changes and was much less structured . Interestingly , many genes with higher than diploid copy number nevertheless have reduced protein expression and for many genes loss of one copy still resulted in increased protein expression compared to normal cells ( rectangles in Figure 2B ) . Many chromosomal changes can be inferred from mRNA data [7]–[9] . Given the depth and accuracy of our proteome measurement , we wanted to see whether despite the low overall correlation , gene amplifications and deletions can also be directly inferred from proteomic data and to find region-related proteomic changes . We developed a genome profiling algorithm that examines the correlation between the expression levels of proteins that are adjacent in a given chromosomal location . This algorithm orders proteins on each chromosome and checks for significant regional deviations of their log ratios from zero . For that purpose windows encompassing various numbers of adjacent proteins are moved along the chromosome , and the deviation of the window mean from zero is tested by one-sample t-test . A p-value is determined for each window size ranging from 3 proteins to the whole chromosome . The final amplification or deletion profile is then calculated from the window medians of all windows in which the average value differs significantly from zero . At each position each intersecting significant window is considered and among those the value that deviates most from zero is chosen . This value is reported in the amplification/deletion profile at this position . After genome profiling , the correlation between the calculated change in protein amounts at each genome position and the corresponding change in gene copy number was greatly increased ( 0 . 64 and 0 . 59 for HCC2218 and HCC1143 , respectively ) . We plotted the calculated proteomic values against their chromosomal location to visualize amplifications and deletions along the chromosomes ( Figure 3A and 3C ) . The genome profiling algorithm predicted and localized numerous aberrations . In HCC2218 cells we found very high level amplification in chromosomes 1 and 17 , and lower amplifications in chromosomes 5 , 7 , 8 , 14 , 16 , 19 , 20 , 21 . We found only two small deletions in chromosomes 1 and 3 . In HCC1143 cells we predicted amplifications in chromosomes 1 , 6 , 8 , 10 , 19 , 20 , 21 and 22 , and deletions in chromosomes 4 , 5 , 8 , 12 , 16 and 17 . To examine whether our predictions were correct despite the low correlation between genome and proteome , we performed a similar alignment of the genomic data . We plotted the smoothed data of the SNP array ( normalized to the control cells ) directly according to the genomic location . Although not all aberrations had a detected proteomic output; remarkably , in each of the predicted locations , we indeed found a matching change in the SNP array data ( Figure 3B and 3D ) . Thus accurate proteome measurements can indeed detect genome copy number changes , via the regional effects on protein expression level changes . Furthermore , these predicted changes agree with well known breast cancer genomic alterations , such as gains in chromosome 1q , 8q , 16p , 17q , 20q and losses in chromosomes 4q , 8p [22] , [23] . While the correlation between the gene copy number and the proteins was very low , it was still possible that the altered genes would globally affect specific pathways and processes , to confer a growth advantage to the aneuploid cells . We comprehensively analyzed each process to determine to what extent it is regulated on the protein level or on the genomic level . We developed a two-dimensional annotation distribution analysis tool ( see Materials and Methods ) , to determine protein categories with significant co-regulation in the combined space of gene copy number and protein changes . We examined gene-ontology ( GO ) categories , KEGG pathways , protein complexes annotated in the CORUM database and distribution of genes to chromosomes ( Figure 4 ) . The only categories changing at the genome level were the chromosomes themselves and , as shown above , they only have a small overall effect on the proteome level . Almost all other statistically significant categories , including GO , KEGG and CORUM are distributed horizontally along the proteome direction , indicating that they cannot be directly attributed to broad changes in gene dosage ( Table S1 ) . As an example , Figure S1 illustrates the changes in oxidative phosphorylation genes and proteins in HCC2218 . There was a clear increase in the abundance of proteins involved in this process , while most of the corresponding gene copy numbers were constant ( Figure S1 ) . Moreover , there were genes whose copy number changed , but the encoded proteins did not change accordingly . For example NDUFB9 , ATP6V1H and ATP1C1 , were amplified , and a single copy of ATP6V1B2 was deleted , but the protein levels stayed constant . In this case , clearly the copy number of genes belonging to this process had no effect on its functionality . Our two-dimensional annotation analysis further highlighted a number of protein complexes , such as the proteasome , ribosome , spliceosome and NADH dehydrogenase complex . We found that the proteins of these complexes always maintain equal protein ratios , despite variation in the gene copy number of their subunits ( Figure 5A and Figure S2 ) . Interestingly , this is strictly true for the core complexes components , but to a lesser degree for peripheral proteins , which can also be involved in other processes . The 20S proteasome , which includes seven alpha and seven beta subunits , is completely insensitive to gene dosage while the levels of the proteins from the whole 26S proteasome vary slightly ( Figure 5A and 5B ) . Similarly , we found much higher variation in the spliceosome complex than in the 17S U2 snRNP subcomplex ( Figure S2B ) . We further examined whether the determination of the exact ratios of the proteins in a core complex is due to regulation already at the level of mRNA and can be attributed to regulation of transcription or mRNA stability , or on the protein level and could be related to protein translation or degradation . We measured the mRNA levels of the proteasome core complex ( seven alpha subunits and seven beta subunits ) by real-time-PCR in HCC2218 cells . In contrast to the equal protein amounts , we found large variability in the mRNA levels of the subunits ( Figure 5C ) . The correlation between mRNA and genes was 0 . 6 , while the correlation between proteins and their corresponding genes was −0 . 1 . Therefore , the main regulation of the protein amounts for this complex occurs at the protein level , rather than at the mRNA level . In accordance with these results , it has been shown that ribosomal subunits are synthesized in excess and those subunits that do not assemble into the complex are degraded [24] . Our results suggest that this mechanism occurs in many molecular complexes . For these complexes the abundance of the subunits is regulated by the amount of the whole complex , and this regulation is done only on the protein level . We showed above that cellular processes and molecular machines do not obey gene dosage changes . But as primary events in transformation , amplification of deletion of key regulatory genes may impact the functionality of the whole process . Indeed , oncogenes and tumor suppressors are often amplified or deleted in the genome [5] . For such aberrations to affect transformation , the gene copy number change must positively correlate with a protein level change . For example , HCC2218 cells have a known amplification of the ERBB2 gene , and indeed our data show that the protein is >50 fold increased compared to HMEC . We searched whether more of the amplified or deleted genes with correlative protein level changes have known oncogenic or tumor suppressor activities by comparing our data to the Sanger institute ‘cancer gene census’ [25] . Among this list of genes that were amplified , deleted , mutated or translocated in various cancers , we selected those in which changes in genome copy number positively correlated with our measured proteome changes ( Table S2 ) . For instance , among the amplified genes we found AKT1 and CCND1 in HCC1143 cells and we found CDH1 to be deleted in HCC2218 cells . We zoomed-in on the small amplicons encompassing ERBB2 , CCND1 and AKT1 to examine the effects of these amplifications on the expression levels of adjacent genes ( Figure 6 ) . The ERBB2 amplicon is very well studied [26] and includes five genes; of these we quantified three proteins: ErbB2 , C17orf37 and Grb7 , all of which were highly over-expressed ( Figure 6A ) . The significance of ErbB2 and the effects of its inhibition are well known [27] , [28] . Its amplification is examined routinely in the clinic and predicts responsiveness to treatment with trastuzumab . Over-expression of Grb7 , a mediator of receptor tyrosine kinase and integrin signaling , was also shown to correlate with tumor aggressiveness [29] . The function of the gene-product of C17orf37 is still unknown , but its protein overexpression along with ErbB2 and Grb7 makes it an interesting candidate for functional studies in breast cancer . The amplicon surrounding CCND1 gene includes five genes – of them we quantified four ( Figure 6B ) . CCND1 encodes the cell-cycle regulator Cyclin D1 , whose overexpression is known to enhance tumor growth in multiple cancer types [30]–[32] . The same amplification event induced overexpression of Liprinα1 and Cortactin . Overexpression of Liprinα1 may promote cell migration [33] , and Cortactin overexpression was reported to be associated with increased tumor aggressiveness [34] . In contrast , expression of Fas-Associated protein with Death Domain , FADD , was much lower than expected from the gene amplification . FADD is an adaptor protein that mediates signals from death receptors to caspase 8 during apoptosis [35] . Possibly , amplification-induced protein overexpression has deleterious results for cancer cells , which therefore control its overexpression . The amplicon surrounding AKT1 , an oncoprotein that mediates cell growth and survival [36] , is located at the end of chromosome 14 , and includes 11 genes . These contain NUDT14 and MTA1 , which show even higher fold overexpression . MTA ( metastasis-associated protein ) is involved in chromatin remodeling , and its overexpression has been associated with a more aggressive phenotype of some tumors [37] . NUDT14 is a minimally characterized protein implicated in the regulation of carbohydrate metabolism [38] . The high expression of these genes suggests investigation of possible tumor-promoting role in these cells . In contrast , four other amplified genes were not overexpressed as proteins and some of them were even down-regulated . Crip2 and INF2 are actin binding proteins , suggesting a potential role in cell adhesion and migration [39] , [40] . In agreement with the opposing changes of Crip2 gene and protein levels , the promoter of Crip2 was shown to be methylated in cancer cell lines and animal models [41] , offering a possible mechanism to eliminate the effect of the amplification . The functions of AHNAK2 and KIAA0284 are still unknown . Downregulation of proteins encoded by amplified genes suggests that overexpression of these proteins may have negative effects on the cells . Extrapolating from the proteins with a known role in the etiology of cancer , we created a list of potential novel regulators of transformation . We listed the overexpressed proteins encoded by amplified genes in HCC2218 and in HCC1143 cells ( Table S3 ) . These proteins were upregulated as a result of gene amplification , and their overexpression may have given a growth advantage to these cells . In contrast , reduced expression of amplified proteins may point to a negative effect on tumor growth . We performed similar analyses for the deleted regions , and listed the downregulated proteins , which may function as tumor suppressors , and the upregulated protein , which may be important proteins for cell growth . Functional research targeted towards these proteins could lead to identification of novel drivers of transformation and crucial regulatory proteins . We conclude that with high coverage of the proteome and high quantification accuracy , multiple chromosomal aberrations can be predicted directly from the proteomic data . Furthermore , proteomics can determine which genes in an amplified region are expressed at all and which are changing at the endpoint of the gene expression cascade – the level of the proteins . As expected , the expression of some oncogenes and tumor suppressors is affected by gene copy number . However , our data clearly show that in the majority of cases , there is no direct correspondence between the gene copy number change and the corresponding protein change . We suggest that proteomics is a useful complement to widely employed gene copy number analysis . It can determine if genome amplifications or deletions have a downstream effect on the level of the protein - a precondition for a potential impact on the transformation process .
Human mammary epithelial cells ( HMEC ) were obtained from Lonza and cultured in mammary epithelial cell growth medium ( ECACC- Health Protection Agency ) . HCC1143 and HCC2218 cells were obtained from the American Type Culture Collection ( ATCC ) , and grown in RPMI containing 10% FBS . MCF7 cells were obtained from the German Collection of Microorganisms and Cell Cultures ( DSMZ ) . MCF7 cells were SILAC labeled by culturing them in DMEM where the natural lysine and arginine were replaced by heavy isotope labeled amino acids , L-13C615N4-arginine ( Arg10 ) and L-13C615N2-lysine ( Lys8 ) . Labeled amino acids were purchased from Cambridge Isotope Laboratories , Inc , USA . The medium was supplemented with 10% dialyzed serum . Cells were cultured for approximately 8 doublings in the SILAC medium to reach complete labeling . For proteomic analysis each of the cell lines was analyzed in biological triplicates . The first two replicates were lysed with modified RIPA buffer ( 50 mM Tris HCl pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , 1% NP40 , 0 . 25% sodium deoxycholate and protease inhibitors ) at 4°C . Following lysis , lysates were centrifuged at 14 , 000 rpm at 4°C . Proteins were then precipitated over-night with acetone , and resuspended in 8 M urea ( 6 M urea , 2 M thiourea ) . Cells of the third replicate were lysed with a buffer containing 4% SDS , 100 mM Tris-HCl pH 7 . 6 and 100 mM DTT . Lysates were incubated at 95°C for 5 min , and then briefly sonicated . Genomic DNA was isolated from the cells using QIAmp DNA Blood Maxi Kit . DNA was hybridized with the Affymetrix Genome-Wide Human SNP Array 6 . 0 according to the manufacturer's instructions . SNP array analysis was done in the Microarray DNA facility at the Max Planck Institute of Molecular Cell Biology and Genetics , Dresden . Raw files were analyzed with “Copy Number and LOH analysis” algorithm from the Affymetrix Genotyping console . We used the default settings with the HapMap270 as reference , quality assessment and regional GC correction configuration . The ‘SmoothSignal’ column from the Affymetrix software output was used directly for the genome profile in Figure 3 . For the comparison with the proteomic data , we determined the copy number of the gene as the median of the smoothed signal of the probes annotated with the corresponding gene name . These values were normalized to the gene copy number in the control cells , which are always diploid ( Figure 2A ) . Each of the non-labeled samples ( HMEC , HCC1143 or HCC2218 ) was mixed at a ratio 1∶1 with labeled MCF7 cells . Two methods were used for trypsin digest . In-solution digestion was used for the first two replicates , where cells were lysed with RIPA buffer . Filter Aided Sample Preparation ( FASP ) [42] was used when lysis was done with SDS-based buffer . For in-solution digest , proteins were reduced by incubation with 1 mM DTT for 30 min at room-temperature , followed by alkylation with 55 mM iodoacetamide for 30 min at room-temperature in the dark . Next , proteins were digested with Lysyl Endopeptidase ( LysC ) at a concentration of 1∶50 ( w/w ) for three hours . Proteins were then diluted 4 fold in water , and digested with trypsin over-night at a concentration of 1∶50 ( w/w ) . FASP digestion was performed as previously described [42] . Briefly , proteins were loaded on microcon-30 kDa filters . Following two washes with urea , proteins were alkylated with 50 mM iodoacetamide . Filters were washed twice with urea and twice with 40 mM ammonium bicarbonate , and digested over-night with Trypsin ( 1∶50; w/w ) at 37°C . Peptides were desalted on Milli-SPE C18 extraction cartridges ( Millipore ) . mRNA was isolated from HMEC , HCC1143 and HCC2218 using PrepEase RNA Spin Kit ( USB ) . Two micrograms of each mRNA were reverse-transcribed using First strand cDNA Synthesis Kit ( Fermentas ) with oligo-dT primers . For real-time PCR , we used IQ SYBR-green Supermix ( Biorad ) on a C1000 Thermal Cycler ( Biorad ) . Method included 40 cycles of amplification with annealing and elongation temperature of 54°C or 58°C . Primers for GAPDH were used for normalization . List of primers is given below ( 5′-3′ ) : PSMA1:for CTGTTAAACAAGGTTCAGCCAC rev CCAAACACTCCTGACGCATA PSMA2:for TGTTGGAATGGCAGTAGCAG rev TGCAGCCAAAAGGTCTAACA PSMA3:for TGTTGGAATGGCAGTAGCAG rev TGCAGCCAAAAGGTCTAACA PSMA4:for TCAATGAGGACATGGCTTGC rev AGGGACGTTTTCCTCCAAAT PSMA5:for GCTCACATAGGTTGTGCCATG rev CTGGGGTCCTTTCTCATCAA PSMA6:for GGCTATGAGATTCCTGTGGAC rev GAAGCTGGTTGACTCAGTTTGTT PSMA7:for CTTTTGAGAGTCGCGGCGGA rev CCGCACTGTTCTTTCATCCTG PSMB1:for AAGAAGGAAAGGGGGCTGTA rev TCTCTCTCAGCCGCAGAAAT PSMB2:for GTGAGAGGGCAGTGGAACTC rev GTGAGAGGGCAGTGGAACTC PSMB3:for CGGAATGTGTGAGTCCCTCT rev CTGGGAACAGGGTTAGTCCA PSMB4:for GCCAGATGGTGATTGATGAG rev GGGCTTCATAGGCTACACCA PSMB5:for ACTTCCCTTACGCAACATGG rev GCCTAGCAGGTATGGGTTGA PSMB6:for GGCGGACTCCAGAACAACC rev CCAGTGGAGGCTCATTCAGT PSMB7:for CTGTGTCGGTGTATGCGCCA rev GCAACAACCATCCCTTCAGT GAPDH: for TGGTATCGTGGAAGGACTCATGAC rev ATGCCAGTGAGCTTCCCGTTCAGC Peptides were separated according to their isoelectric-point using an Agilent 3100 OFFGEL fractionator ( Agilent , G3100AA ) as described previously[43] . Briefly , we used 13 cm IPG Drystrips , pH 3–10 ( GE Healthcare ) . Strips were rehydrated for 20 min with a solution containing 5% glycerol and 1∶50 dilution of IPG buffer , pH 3–10 ( 20 µl/well ) . Peptides were diluted in 5% glycerol and IPG buffer . A total of 100 µg of peptides were loaded on each strip . Focusing was done for 20 kVh with a miximum current of 50 µA and power of 200 mW . Fractions were acidified by adding 1% TFA , 0 . 5% acetic acid and 3% acetonitrile . Prior to LC-MS analysis peptides were concentrated and desalted on StageTips[44] . Peptides were separated by reverse-phase chromatography on an in-house made 15 cm column ( inner diameter 75 µm , 3 µm ReproSil-Pur C18-AQ media ) , using a nanoflow HPLC system ( Proxeon Biosystems ) . HPLC was coupled on-line via a nanoelectrospray ion source ( Proxeon Biosystems ) to a LTQ-Orbitrap mass spectrometer ( Thermo Fisher Scientific ) . Peptides were loaded onto the column with buffer A ( 0 . 5% acetic acid ) with a flow rate of 500 nl/min , and eluted with 90 min linear gradient at a flow rate of 250 nl/min . After the linear gradient the column was washed with 90% buffer B and re-equilibrated with buffer A . Mass spectra were acquired in the positive ion mode applying a data-dependent automatic switch between survey scan and tandem mass spectra ( MS/MS ) acquisition . Samples were analyzed with a ‘top 5’ method , acquiring one Orbitrap survey scan in the mass range of m/z 300–2000 followed by MS/MS of the five most intense ions in the LTQ . The target value in the Orbitrap was 1 , 000 , 000 ions for survey scan at a resolution of 60 , 000 at m/z 400 using lock masses for recalibration[45] . Fragmentation in the LTQ was performed by collision-induced dissociation with a target value of 5 , 000 ions . Ion selection threshold was 1000 counts . Raw MS files from the LTQ-Orbitrap were analyzed by MaxQuant[14] , [46] ( version 1 . 0 . 14 . 3 ) . MS/MS spectra were searched against the decoy IPI-human database version 3 . 62 containing both forward and reverse protein sequences by the MASCOT search engine ( version 2 . 2 . 04 , Matrix Science ) . Parent mass and fragment ions were searched with maximal initial mass deviation of 7 ppm and 0 . 5 Th , respectively . The search included variable modifications of methionine oxidation and N-terminal acetylation , and fixed modification of cystein carbamidomethylation . Peptides of minimum 6 amino-acids and maximum of two missed cleavages were allowed for the analysis . For peptide and protein identification false discovery rate ( FDR ) was set to 0 . 01 . In case the identified peptides of two proteins were shared by two proteins ( homologs or isoforms ) , the two proteins were reported by MaxQuant as one protein group . Complete protein and peptides lists are given as Table S4 and Table S5 . The algorithm is applied to the log ratios between relative protein levels of a cancer cell to a normal cell . Chromosomal locations are assigned to proteins according to the Ensembl annotation that is included into Uniprot . On each chromosome the sequentially ordered proteins are checked for significant regional deviations of their normalized log ratios from zero . For that purpose windows encompassing various numbers of adjacent proteins are moved along the chromosome , and the deviation of the window mean from zero is tested with a one-sample t-test . Window sizes range from 3 proteins to the whole chromosome in steps of factors of square root of 2 . Each log p-value was transformed in a window-length dependent way to a posterior error probability , applying Bayes rule to two-dimensional histograms . To correct for multiple hypothesis testing , a false discovery rate of 2% was applied by permutation-based estimation on the basis of 10 randomized genomes . The final amplification or deletion profile is then calculated from the window medians of all windows in which the average value differs statistically significantly from zero . At each position each intersecting significant window is considered and among those the value is taken that deviates most from zero . This is then the value of the amplification/deletion profile reported at this position . To obtain copy numbers , these values have to be exponentiated and multiplied by two . Protein ratios and the corresponding gene copy number changes are given in Table S6 . Protein ratios after genome profiling are given in Table S7 . Categorical annotation is supplied in form of Gene Ontology ( GO ) biological process ( BP ) , molecular function ( MF ) and cellular component ( CC ) as well as participation in a KEGG pathway and membership in a protein complex as defined by CORUM . The chromosome of the corresponding gene was considered as an additional protein annotation . For each annotation term proteins are separated into two groups , one containing the proteins annotated with this term and the other containing the complement . A two-dimensional two-sample test then finds significant difference between the two-dimensional means of the two protein populations . Here , the two numerical dimensions consist of log protein ratio and log copy number ratio , but the algorithm would apply to other data types as well . The specific test we use is a two-dimensional version of the non-parametric Mann-Whitney test . Multiple hypothesis testing is controlled by using a Benjamini-Hochberg false discovery rate threshold of 5% . For categories that are significant a two-dimensional difference score is calculated by determining the average rank of the proteins belonging to the category . This average rank is then rescaled to the interval between −1 and 1 . A value of 1 in one of the dimensions would mean that all members of this category are the largest values in this dimension , while a value of 0 means that the ranks of the members of the category are distributed in the same way as the background proteins , having no significant bias towards larger or smaller values . | In the course of cancer development , cells lose regulation of the cell cycle and quality control of DNA replication . As a result , many genomic alterations accumulate , among them amplifications and deletions of chromosomal regions of varying sizes . Oncogenes that drive transformation often reside in amplified regions , while tumor suppressors are deleted , yet for thousands of genes the effect of altering gene copy number is unknown . Since only genomic alterations that ultimately affect protein levels can have functional importance , a global proteomic approach that directly measures such changes is desirable . Here , we examined output of chromosomal alterations on the proteins in a system-wide manner . We analyzed the global protein expression of cancer cells compared to normal cells using mass-spectrometry–based quantitative proteomics and quantified a large part of the expressed proteome . We compared the protein data to genomic data and matched changes in gene copy number to protein expression level changes for each gene . Overall , gene copy number changes explain only a few percent of observed protein expression changes . Knowledge of when genomic and proteomic changes correlate may help in a better understanding of regulatory mechanisms in tumor development . | [
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] | 2010 | Proteomic Changes Resulting from Gene Copy Number Variations in Cancer Cells |
Canonical Wnt signaling plays a rate-limiting role in regulating self-renewal and differentiation in mouse embryonic stem cells ( ESCs ) . We have previously shown that mutation in the Apc ( adenomatous polyposis coli ) tumor suppressor gene constitutively activates Wnt signaling in ESCs and inhibits their capacity to differentiate towards ecto- , meso- , and endodermal lineages . However , the underlying molecular and cellular mechanisms through which Wnt regulates lineage differentiation in mouse ESCs remain to date largely unknown . To this aim , we have derived and studied the gene expression profiles of several Apc-mutant ESC lines encoding for different levels of Wnt signaling activation . We found that down-regulation of Tcf3 , a member of the Tcf/Lef family and a key player in the control of self-renewal and pluripotency , represents a specific and primary response to Wnt activation in ESCs . Accordingly , rescuing Tcf3 expression partially restored the neural defects observed in Apc-mutant ESCs , suggesting that Tcf3 down-regulation is a necessary step towards Wnt-mediated suppression of neural differentiation . We found that Tcf3 down-regulation in the context of constitutively active Wnt signaling does not result from promoter DNA methylation but is likely to be caused by a plethora of mechanisms at both the RNA and protein level as shown by the observed decrease in activating histone marks ( H3K4me3 and H3-acetylation ) and the upregulation of miR-211 , a novel Wnt-regulated microRNA that targets Tcf3 and attenuates early neural differentiation in mouse ESCs . Our data show for the first time that Wnt signaling down-regulates Tcf3 expression , possibly at both the transcriptional and post-transcriptional levels , and thus highlight a novel mechanism through which Wnt signaling inhibits neuro-ectodermal lineage differentiation in mouse embryonic stem cells .
Embryonic stem cells ( ESCs ) are in vitro cultured cells derived from the preimplantation-stage embryo , which possess unconfined capacity for self-renewal and multi-lineage differentiation towards different embryonic germ layers . Pluripotency and self-renewal are two essential features of ESCs , which make them not only a very robust and suitable model for stem cell research , but also a promising source for regenerative medicine . Also , with the emergence of induced pluripotent stem cells ( iPS ) technology , understanding the basic mechanisms governing the embryonic stem state becomes of great interest for safe clinical applications in regenerative medicine and stem cell programming . Among different signaling pathways , Wnt/β-catenin signaling has been shown to play a major role in maintaining self-renewal as well as in regulating ESCs differentiation [1] , [2] , [3] , [4] , [5] , [6] . The canonical Wnt/β-catenin signaling pathway is controlled by post-translational modifications of β-catenin leading to its differential protein stability and sub-cellular localization . In the absence of active Wnt signaling , β-catenin is negatively regulated by the so-called “destruction complex” , consisting of the Apc and Axin scaffolding proteins and the glycogen synthase and casein kinases ( GSK and CK1 ) , resulting in proteolytic degradation and low levels of cytoplasmic β-catenin . Ligand-mediated Wnt signaling activation leads to nuclear translocation of β-catenin where it binds to members of the Tcf/Lef family of transcriptional factors thus modulating the expression of a broad spectrum of downstream target genes [7] , [8] , [9] . In vertebrates , the Tcf/Lef family encompasses four functionally specialized members including Tcf1 ( also known as Tcf7 ) , Tcf3 ( also known as Tcf7l1 ) , Tcf4 ( also known as Tcf7l2 ) and Lef1 [10] . Whereas Tcf1 , Tcf4 and Lef1 are known to activate different Wnt target genes in the context of active Wnt signaling , Tcf3 primarily functions as a transcriptional repressor [5] , [11] , [12] , [13] , [14] , [15] , [16] . Tcf3 is the most abundant Tcf/Lef member in mouse ES cells [14] and is an integral component of the core pluripotency circuit , co-occupying Oct4 , Nanog and Sox2 DNA binding sites [17] , [18] , [19] , [20] . Loss of function experiments have shown that Tcf3 down-regulation enhances self-renewal and confers differentiation resistance in mouse ESCs [14] , [17] , [19] , [20] , [21] , [22] . In fact , both the zebrafish headless/tcf3 mutant and the Xenopus embryo depleted of TCF3 reveal anterior head defects resembling the Wnt-gain of function phenotype [11] , [15] , [16] . Similarly , Tcf3 ablation in mice resulted in expanded axial mesoderm and loss of anterior neural tissues [21] . Tcf3 is ubiquitously expressed through the mouse embryo at embryonic day 6 . 5 ( E6 . 5 ) and is gradually localized in the anterior part of the embryo at E7 . 5 and the anterior neuroectoderm at E8 . 5 [23] , [24] . Although several studies have demonstrated the key role played by Wnt signaling in regulating self-renewal and differentiation of both mouse and human ESCs , the downstream effects through which Wnt exerts these functions have been a matter of controversy . To date , three models have been suggested in this regard: a . Tcf-independent , β-catenin/Oct4 signaling [25]; b . Tcf3 antagonism by nuclear β-catenin which relieves Tcf3 repression and enhances self-renewal . A minimal role for the canonical Tcf/β-catenin signaling has been suggested in this model [6]; and c . synergistic action of Tcf3 antagonism and the canonical β-catenin/Tcf1 signaling [5] . Although these studies have shed some light on the underlying mechanisms through which Wnt signaling controls self-renewal , none of the above-mentioned models explains how this signaling pathway regulates the lineage differentiation potential of ESCs . In order to elucidate the downstream effects of Wnt signaling on lineage commitment and differentiation in embryonic stem cells , we examined several Apc-mutant ESCs harboring different levels of Wnt signaling and compared their gene expression profiles with wild type ESCs . We show that activation of Wnt signaling down-regulates Tcf3 expression in mouse ESCs . We provide evidence that Tcf3 down-regulation represents a main downstream effect through which Wnt signaling directs the differentiation of pluripotent ESCs towards non-neuroectodermal lineages . Moreover , we show that Wnt-mediated repression of Tcf3 involves epigenetic regulation associated with histone modifications and Wnt-mediated induction of miR-211 . Our data demonstrate that Wnt signaling counteracts Tcf3 function at multiple levels , which ultimately ensures the delicate balance between self-renewal and differentiation in mouse ESCs .
To attempt the elucidation of the mechanisms underlying lineage differentiation in the context of Wnt activation , we have derived several ES clones from pre-implantation blastocysts carrying different hypomorphic Apc alleles: Apc1638T/1638T ( ApcTT ) , Apc1638N/1638T ( ApcNT ) , Apc1638N/1638N ( ApcNN ) [26] , [27] , together with Apc+/+ as wild type controls . As previously reported , ApcTT , ApcNT , and ApcNN encode for a gradient of different Wnt signaling dosages [1] , [26] , as also confirmed by TOP-Flash reporter assay [28] with ApcNN showing the highest Wnt activity ( ApcNN≫ApcNT>ApcTT>Apc+/+ ) ( Figure 1A ) . The potential of the Apc-mutant ES cells to differentiate into ecto- , meso- and endodermal lineages was also evaluated and confirmed by the teratoma formation assay followed by immunohistochemistry ( IHC ) analysis , matching our previous results obtained with ES clones obtained by two rounds of gene targeting by homologous recombination [1] . As expected , no expression of neuroectodermal markers ( GFAP , SV2 , and neurofilaments ) was observed in teratomas derived from ApcNN ES cells ( Figure 1B ) . ES cells can be cultured in serum-free medium supplemented with LIF , GSK inhibitor ( CHIRON ) and Mek inhibitor ( PD ) , the so-called 2i medium [29] . Using the serum-free culture supplemented with a single inhibitor , we found that ApcNN cells have the highest colony-forming capacity when cultured in LIF+Mek inhibitor , suggesting that their constitutive Wnt signaling activity replaces the need for additional pathway activation by the GSK inhibitor ( Figure 1C ) . Of note , culturing ApcNN ESCs in medium supplemented with CHIRON reduced the colony formation capacity of these cells suggesting that a very high dosage of Wnt signaling can compromise the growth of ApcNN cells . We also observed that ApcTT and ApcNT cells formed similar number of colonies in different culture conditions independently of CHIRON supplementation , possibly pointing to the Wnt-independent effects of Apc mutations in these cells ( Figure 1C ) . To elucidate the molecular mechanisms underlying the altered cell fate decision in Apc-mutant ES cells , genome-wide transcriptional analysis was performed on the newly derived cells . Unsupervised hierarchical clustering analysis showed that global gene expression in ApcNN ESCs is already influenced before differentiation is induced , resolving ApcNN from WT expression profiles in different branches of the dendogram ( Figure 1D ) . Among the genes differentially expressed between ApcNN ES cells and their wild type counterparts ( Table S1 ) , we found that , unlike other pluripotency markers ( e . g . Oct4 , Nanog , and Sox2 ) , Tcf3 was specifically down-regulated in ApcNN ES cells , an observation which was further confirmed by qRT-PCR and western blot analysis ( Figure 2A and 2B; and Figure S1 ) . Further qRT-PCR analysis revealed that the observed downregulation is specific for Tcf3 but not for other members of the Tcf/Lef family ( Figure S2A ) . Whereas Tcf3 was down-regulated in both ApcNN and ApcMin/Min ESCs , the latter encode for the most severely truncated Apc mutant allele and therefore for a very high level of Wnt signaling , other members of the Tcf/Lef family were exclusively up-regulated in ApcMin/Min ESCs . Accordingly , Wnt activation achieved in wild type cells either by Wnt3a conditioned medium or by a GSK3-small molecule inhibitor ( SB-216763 ) , confirmed that Tcf3 down-regulation is a specific response to canonical Wnt signaling in mouse ESCs ( Figure 2C , 2D , and 2E , and Figure S2B and S2C ) . Moreover , using a gradient of the GSK inhibitor SB-216763 , we observed that unlike the canonical Wnt targets Axin2 and Cdx1 , downregulation of Tcf3 required a higher Wnt signaling level , possibly explaining why Tcf3 downregulation is observed in ApcNN cells but not ApcTT or ApcNT ESCs ( Figure 2F and Figure S1 ) . It has been previously shown that Tcf3 not only functions as a controller of self-renewal in wild type ESCs , but it is also required for proper neurogenesis in zebrafish , xenopus and mice [11] , [16] , [21] . We therefore hypothesized that Tcf3 down-regulation in ApcNN ESCs might mediate the neural differentiation defects observed in these cells . To test this hypothesis , we rescued Tcf3 expression by stably over-expressing its full-length cDNA in ApcNN ES cells ( Figure 3A , 3B ) . Tcf3 over-expression decreased TOP-Flash reporter activity ( Figure 3C ) and , accordingly , reduced the transcript levels of Cdx1 and Brachyury ( T ) , two well-known Wnt downstream targets . Gene expression profiling of Tcf3-expressing ApcNN cells confirmed that Tcf3 effectively reverses the expression pattern of several genes differentially expressed in ApcNN when compared to wild type ESCs ( Figure S3 ) . Since it has been previously reported that Tcf3 over-expression in wild type ESCs induces differentiation under self-renewing conditions [5] , we first assessed whether over expressing Tcf3 in ApcNN ESCs induces similar effects in these cells . As reported above , ApcNN cells can grow in 1i medium ( i . e . in LIF+Mek inhibitor ) in the absence of GSK inhibitor ( Figure 1C ) . To investigate whether Tcf3 can restore their dependency on the GSK inhibitor in serum-free culture , Tcf3-over expressing ApcNN cells were seeded at clonal density under different conditions and subsequently stained for alkaline phosphatase ( AP ) to evaluate the percentage of undifferentiated colonies . We found that , similar to the parental ApcNN cells , Tcf3-rescued clones show the highest colony forming capacity in the presence of LIF and Mek inhibitor ( Figure 3D ) . Moreover , by applying the short term differentiation assay in N2B27 medium , we found that both ApcNN and their Tcf3-rescued counterparts retain expression of the pluripotency markers Nanog while fail to express the early differentiation markers Fgf5 ( Figure 3E ) . Hence , constitutive Wnt signaling prevents differentiation in a short-term assay despite the ectopic Tcf3 expression . We then asked whether rescuing Tcf3 expression in ApcNN cells could affect the neural differentiation potential of these cells . To this aim , we applied the in vitro neural differentiation assay previously described by Bibel et al . [30] . We found that , whereas wild type ESCs readily gave rise to Tuj1-positive cells , no staining could be detected in ApcNN cells , while only few dispersed Tuj1-expressing cells were observed in the Tcf3 rescued clones ( Figure 3F ) . In contrast , a clear increase in Nestin expression was observed in Tcf3 over-expressing cells ( Figure 3G and Figure S4 ) . This suggests that , although Tcf3 could not restore the formation of fully mature Tuj1-proficient neurons , it does affect neural differentiation in vitro in a more subtle fashion towards neural progenitor-like cells . Next , we examined the differentiation potential of Tcf3-rescued ES cells in vivo by teratoma assay . We injected the newly generated clones into recipient isogenic mice to generate teratomas and analyzed them for the expression of different neuroectodermal markers by IHC . Interestingly , in contrast to the control ApcNN teratomas which did not express any neuroectodermal marker ( 0/20 analyzed teratomas ) , approximately 50% of all teratomas generated from different Tcf3 over-expressing ES clones were positive for the same set of markers ( 6/10 , 6/10 , and 4/10 teratomas originated from clones 1 , 2 and 3 , respectively ) ( Figure 4 ) . However , the extent of neural differentiation was lower compared to teratomas originated from wild type ESCs . Unlike neuroectodermal lineages , Tcf3 did not rescue the mesodermal cartilage-differentiation defect . The observed difference in the results obtained by in vivo and in vitro differentiation assay might reflect the presence of different microenvironmental factors and the longer period of differentiation in vivo , which result in a larger extent of neural differentiation in teratomas . Overall , these results indicate that Tcf3 expression in ApcNN cells can partially rescue the neural differentiation defect characteristic of these cells . Next , we then asked whether Tcf3 down-regulation in wild type embryonic stem cells is sufficient to induce neural differentiation defects , characteristic of Wnt-high ESCs . To this aim , teratomas were obtained by subcutaneous transplantation of Tcf3−/− ESCs [14] followed by IHC and qRT-PCR analysis of different neural markers . We observed reduced neural differentiation in Tcf3−/− teratomas when compared to wild type controls ( Figure 5 ) . However , high expression of the pluripotency markers Oct4 and Nanog was also observed in Tcf3−/− teratomas ( Figure 5 ) . IHC analysis of Oct4 also showed that Tcf3−/− teratomas are largely composed of undifferentiated , embryonic carcinoma ( EC ) -like cells , confirming their undifferentiated nature . This is in contrast with ApcNN teratomas where pluripotency markers were down-regulated . These results suggest that Tcf3 down-regulation in wild type ES cells is necessary but insufficient to fully inhibit neural differentiation , and that canonical Wnt signaling is still required for redirecting the differentiation towards non-neuroectodermal lineages . To elucidate the mechanisms underlying Wnt-driven repression of Tcf3 expression , we first analyzed its promoter activity in ApcNN and wild type ESCs to localize the responsible regulatory elements . We employed luciferase reporter constructs containing a 6 . 7 kb genomic fragment upstream of the mouse Tcf3 ATG translation start site as well as a series of different deletion constructs spanning 4 . 5 kb , 3 . 5 kb , 2 . 2 kb , 1 . 2 kb and 176 bp fragments of the same region ( Figure S5A ) [31] . The 4 . 5 kb fragment was previously shown to resemble endogenous Tcf3 expression in mouse embryo as well as embryonic derived neural stem cells [31] . To test whether Wnt signaling affects Tcf3 promoter activity , we transfected the different Tcf3 promoter constructs in ApcNN and wild type ESCs . Likewise , transfected wild type ESCs were also treated with Wnt3a conditioned medium or L-control medium to examine Tcf3 promoter activity . Using both approaches , we found that the Wnt-mediated repression of Tcf3 is not regulated by elements located within the 6 . 7 kb promoter region ( Figure S5A ) . However , we cannot exclude the possibility that long-range enhancer elements located outside the 6 . 7 kb promoter region might still contribute to the observed Tcf3 repression in Wnt context . The mouse Tcf3 promoter contains a large CpG island extending over exon 1 , 2 and 3 . This indicates that DNA methylation may play a role in the regulation of Tcf3 expression [32] . To test whether the observed Tcf3 down-regulation in ApcNN ESCs results from DNA methylation , we employed the bisulfite-conversion method followed by sequencing and methylation-specific PCR to analyze the Tcf3 promoter in ApcNN cells and compare its methylation pattern to wild type ESCs . As depicted in Figure S5B , we found that similar to wild type ESCs , the Tcf3 promoter is unmethylated in ApcNN cells thus suggesting that DNA methylation is unlikely to represent the mechanism underlying Wnt-driven Tcf3 down-regulation in mouse ESCs . Active and repressed promoters are thought to be associated with histone marks , which reflect the gene expression status of the corresponding genes . To test whether Tcf3 down-regulation in ApcNN cells is regulated via chromatin modifications , we performed chromatin immunoprecipitation ( ChIP ) to analyze post-translational histone modifications associated with active and repressed promoters . We studied the active-chromatin marks H3K4me3 and H3-acetylation as well as the repressed-chromatin marks H3K27me3 and H3K9me3 in the Tcf3 promoter and compared the histone modification patterns between ApcNN and wild type ESCs . The immunoprecipitated chromatin was then assessed by qPCR analysis with a panel of specific primers covering a region encompassed between −2 kb to +2 kb from the transcription start site ( TSS ) , as well as 20 kb of the gene body within the Tcf3 locus . In accordance with the observed Tcf3 downregulation in ApcNN cells , we found a decrease in the activating marks H3K4me3 and H3Ac and , to a lesser extent , a slight increase in the repressive marks H3K27me3 and H3K9me3 ( Figure 6 ) . Similarly , 12 h treatment of wild type ESCs with Wnt3a conditioned medium significantly reduced the H3Ac and H3K4me3 activating marks but had no effect on the H3K27me3 and H3K9me3 repressing marks ( Figure S6 ) . These data demonstrate a correlation between Tcf3 expression and histone modifications in its promoter suggesting that Wnt signaling might regulate Tcf3 expression through epigenetic mechanisms . However , the mediator of this regulation still remains elusive . It has been previously shown that members of the core pluripotency circuit are fine-tuned via microRNA-mediated regulation in embryonic stem cells [33] , [34] , [35] , [36] , [37] . Therefore we tested the idea whether Wnt-driven repression of Tcf3 expression might also be mediated , post-transcriptionally , by Wnt-induced miRNAs . To this aim , we profiled the different Apc-mutant ESCs for microRNA expression by using a miRNA array encompassing specific probes for all known mouse miRNAs [38] ( data not shown ) . Of the different candidate miRNAs induced upon Wnt activation , mmu-miR-211 showed a Wnt dosage-dependent up-regulation among the different Apc-mutant ESCs ( Figure 7A ) . Accordingly , activation of Wnt signaling in wild type ESCs either by Wnt3a conditioned medium ( CM ) or by GSK3 inhibition , confirmed that miR-211 is a novel Wnt-regulated microRNA in mouse embryonic stem cells ( Figure 7B and 7C ) . In silico analysis with three software packages , namely Miranda [39] , Targetscan [40] and PicTar [41] , pointed to several potential miR-211 target genes predicted by all three programs . To narrow down the list of potential targets , qRT-PCR analysis was performed on wild type ESCs compared with ApcNN ( Figure S7A ) as well as on wild type ESCs treated with Wnt3a CM ( Figure S7B ) . We excluded those predicted targets that showed up-regulation upon Wnt signaling . Based on these results Sox11 , Sf3b1 and Tcf3 were selected for further analysis . Several stable ESC clones were generated which ectopically over-express miR-211 in an otherwise wild type background ( Figure S7C ) . Western blot analysis showed that , unlike Sox11 and Sf3b1 ( Figure S7D ) , Tcf3 protein level was decreased upon miR-211 ectopic expression ( Figure 7D ) . To confirm that miR-211 directly targets Tcf3 , we cloned the 3′ untranslated region ( 3′UTR ) of the mouse Tcf3 gene in the pmirGLO reporter plasmid ( Figure 7E ) and performed a luciferase-based reporter assay . Transfection of HEK293 cells with the Tcf3-3′UTR reporter plasmid confirmed that Tcf3 is a direct target of miR-211 ( Figure 7F ) . The inhibitory effects of miR-211 were not observed when mutant forms of the 3′UTR , i . e . lacking 7 or 4 nucleotides of the miRNA seed sequence target ( MTR1 and MTR2 respectively ) were used ( Figure 7F ) . We next assessed the differentiation potential of miR-211 over-expressing clones using in vitro neural differentiation assay as well as in vivo teratoma formation . FACS analysis for Tuj1 , a marker for mature neurons and Nestin , revealed that both miR-211 over-expressing ES cells and their wild type controls give rise to similar number of neurons and neural progenitor cells after 13 days of in vitro differentiation , thus suggesting that miR-211 does not affect terminal neural differentiation . As expected , ApcNN cells show a dramatic reduction in mature , Tuj1-proficient neurons ( Figure 7G ) . Teratoma formation assay also confirmed that miR-211 does not suffice to inhibit neural differentiation ( data not shown ) . To evaluate the role of miR-211 at earlier stages of differentiation , we derived embryoid bodies ( EBs ) from miR-211 over-expressing cells and their wild type controls and analyzed lineage differentiation at different time points . EBs derived from wild type ES cells encompass differentiated lineages from the three germ layers , thus providing an in vitro assay recapitulating the early steps of embryonic development . qRT-PCR analysis for different lineage-specific markers indicated that , unlike mesodermal , endodermal and pluripotency markers ( data not shown ) , early neuroectodermal differentiation was specifically attenuated by miR-211 . We found that expression of the primitive ectoderm marker Fgf5 and of the neural progenitor markers Nestin and Pax6 as well as the early neural differentiation marker Sfrp2 were repressed at day 3 of EB formation . Notably , these effects could not be detected at later time points ( day 6 , 9 or 12; data not shown ) . Similar results were obtained at early time points ( i . e . after 24 h ) in N2B27 culture medium , previously described to induce neural differentiation in mESCs [42] ( Figure 7H and 7I ) . These results suggest that miR-211 functions at early stages of neural differentiation and its ectopic expression in wild type ES cells is not sufficient to inhibit further neural commitment as differentiation proceeds . Altogether , our results indicate that miR-211 , a novel Wnt-regulated miRNA , can fine-tune Tcf3 expression and attenuate early neural differentiation in wild type ESCs .
The role of Wnt/β-catenin signaling in controlling self-renewal and lineage differentiation in pluripotent embryonic stem cells has been a matter of controversy . Although both GSK3 inhibitors and Wnt ligands are essential to support ESCs self-renewal , it is yet unclear whether this occurs through β-catenin- and TCF-dependent mechanisms [43] . Among the members of the Tcf/Lef family of transcription factors , Tcf3 and Tcf1 are the most abundant in ES cells . This is of relevance as , while Tcf1 appears to function as a canonical transcriptional activator upon association with β-catenin , Tcf3 acts as a β-catenin-independent transcriptional repressor of self-renewal , suppressing genes such as Nanog , Oct4 and other members of the core pluripotency circuitry [17] , [19] . In this scenario , it is yet unclear how canonical Wnt signaling controls the balance between Tcf1- and Tcf3-mediated gene activation and repression in the regulation of self-renewal and differentiation in ESCs . During the last few months , several studies have been published on the specific roles of β-catenin and Tcf3 in these processes [4] , [5] , [6] , [44] . In the classical Wnt model , Tcf factors bind DNA and repress gene expression in the absence of active Wnt signaling . Activating the signaling pathway leads to the binding of β-catenin to Tcf proteins thus converting them from transcriptional repressors to transcriptional activators . Among the four members of Tcf/Lef family , Tcf3 seems to be different as its repressor function is not directly affected by Wnt signaling . In this perspective , two modes of action have been described for the relief of Tcf3 repression by Wnt signaling: 1 ) Tcf3 phosphorylation by homeodomain interacting protein kinase 2 ( HIPK2 ) which is mediated by β-catenin and results in displacement of Tcf3 from its target sites [45]; and 2 ) direct physical interaction between β-catenin and Tcf3 which displaces Tcf3 and inhibits its repressive role in the context of active Wnt signaling [6] , [46] . Recently , using a knock-in mouse model lacking the β-catenin-interaction domain of Tcf3 , Wu et al have demonstrated that counteracting Tcf3 function is not mediated by the physical interaction between β-catenin and Tcf3 during the first stages of embryonic development [47] . In view of these models , our data suggest that transcriptional and post-transcriptional down-regulation of Tcf3 expression might be yet another mechanism by which Wnt signaling inhibits Tcf3 function . It is worthwhile mentioning , however , that Wnt signaling does not seem to fully suppress Tcf3 expression and that residual levels of Tcf3 are retained even in the most severely truncated Apc mutant alleles ( i . e . ApcMin/Min ESCs; Figure 2A ) which encode for extremely high Wnt signaling dosages . Altogether these observations suggest that Wnt/β-catenin signaling regulates Tcf3 at several levels and by a combination of multiple mechanisms during different stages of embryonic development . Although over-expression of a dominant negative form of Tcf1 or Tcf4 reduced the canonical Wnt reporter activity ( TOP-Flash ) , it failed to rescue the neural differentiation in GSK-null ESCs [25] . Inhibition of β-catenin in GSK3β-null ESCs , however , was sufficient to rescue the neural differentiation defect thus confirming the central role of β-catenin-dependent mechanisms in this process [25] . The partial rescue of neural differentiation by Tcf3 expression in ApcNN cells , as shown here , highlights the distinct role of Tcf3 from other members of the Tcf/Lef family and suggests that a plethora of Tcf3-dependent and -independent mechanisms underlie the Wnt-regulated lineage differentiation in embryonic stem cells . As for self-renewal maintenance in ES cells , Wray et al have shown that a mutant form of β-catenin where the trans-activating domain was deleted , can still maintain self-renewal in mESCs cultured in 2i medium [6] . This suggests that maintenance of self-renewal is mediated by Tcf3 displacement rather than β-catenin signaling in 2i culture . Based on this , one can hypothesize that forced overexpression of Tcf3 in Wnt context could restore the dependency on CHIRON in serum-free culture . Our data show that Tcf3 overexpression in ApcNN cells does not induce differentiation in 2i culture , highlighting the dominant role of Wnt signaling in this process . This is in line with the report by Yi et al . which showed that over expressing Tcf3 in the context of Wnt signaling activation has minimal effect on self-renewal suggestive of a synergistic action of Tcf3 antagonism and β-catenin/Tcf1 signaling [5] . In an attempt to elucidate the mechanisms underlying Tcf3 downregulation in the context of active Wnt signaling , we found that Tcf3 down-regulation does not require DNA methylation but is associated with alterations in histone marks at the core Tcf3 promoter region which are likely to regulate Tcf3 expression . Notably , these modifications occur shortly after Wnt stimulation and it is plausible to think that the chromatin modifications within the Tcf3 locus can trigger the downregulation process of Tcf3 expression which can be stabilized further on via miR-211 function . Epigenetic regulation through histone modification or DNA methylation was also shown previously for other antagonists of Wnt signaling such as DACT3 , sFRPs , WIF1 and DKK-1 in different cancer cells [48] , [49] , [50] , [51] . Further experiments are required to clarify whether this mode of gene repression is a general mechanism for Wnt-induced gene silencing in embryonic stem cells and tumor cells . Although the mediator of the observed chromatin modifications downstream of Wnt signaling remains elusive , we found that the putative cis-acting element , if any , is not located in the 6 . 7 kb promoter region which was previously described to regulate Tcf3 expression in different cell types [31] . Further work is needed to identify and study these cis-acting elements which might be of potential interest for providing further insight into the transcriptional repression downstream of Wnt signaling . As an additional regulatory mechanism , we also described a novel Wnt-induced micro RNA , miR-211 , and demonstrated that it targets Tcf3 in ApcNN ESCs . However , miR-211 over-expression in wild type ESCs does not reduce Tcf3 levels to the same degree as observed in ApcNN ES cells thus suggesting that multiple Wnt-mediated mechanisms are likely to exist . On the other hand , microRNAs usually exert their function by targeting multiple genes and it is plausible that miR-211 inhibits early neural differentiation in mESCs by repressing target genes other than Tcf3 . Further experiments are required to characterize the loss of miR-211 function phenotype in mouse ESCs in order to evaluate the long-term effects on neural differentiation . The observation that Wnt signaling induces miR-211 expression might also be of interest for other disciplines of research and in particular cancer . In line with our observation , a tumor promoting function has recently been described for miR-211 in colorectal cancer cells [52] . Accordingly , miR-211 has also been shown to play a key role in melanoma tumor formation and metastasis , as well as mesenchymal to epithelial transition ( MET ) [53] , [54] , [55] Taken together , we have revealed two downstream effects of Wnt signaling which contribute to the differentiation defects observed upon constitutive activation of canonical Wnt signaling , namely downregulation of Tcf3 expression and induction of miR-211 . These cooperatively contribute to the inhibition of neural differentiation previously observed in Apc-mutant mouse ESCs [1] . We suggest that Wnt signaling represses Tcf3 expression possibly by altering the histone marks at the Tcf3 promoter and by activating miR-211 expression , thus extending our understanding of Tcf3 regulation in stem cells . In the future , additional studies are required to elucidate how these mechanisms contribute to the regulation of Tcf3 expression and , more in general , how Wnt signaling regulates stemness in embryonic and adult stem cells .
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 protocol was approved by the Committee on the Ethics of Animal Experiments of the Erasmus Medical Center ( DEC permit numbers EMC 2351 ) . All efforts were made to minimize suffering . Apc1638N/+ and Apc1638T/+ animals , kept on an inbred C57Bl6/J background , were bred to derive ES cells from pre-implantation blastocyts according to previously described protocols [56] . Cells were cultured on MEFs inactivated by Mitomycin-C ( Sigma ) in Dulbecco's Modified Eagle's Medium ( DMEM , Gibco ) supplemented with 10% fetal calf serum ( FCS , Gibco ) , L-glutamine ( 2 nM , Gibco ) , Na-Pyruvate ( 1 mM , Gibco ) , non essential amino acids ( 0 . 1 mM each , Gibco ) , 2-mercaptoethanol ( 55 µM , Gibco ) and LIF ( 1000 U/ml , Milipore ) . Bruce 4 ESCs were purchased from American Type Culture Collection ( ATCC ) and Tcf3−/− and their wild type control GS1 ESCs were obtained as previously decribed [14] . To stimulate Wnt signaling in wild type ESCs , cells were cultured on gelatin coated dishes and treated with Wnt3a-conditioned medium ( collected from L-cells expressing Wnt3a plasmid ) or L-control medium ( collected from parental L-cells ) . Conditioned media were diluted 1∶1 with ES medium and added to wild type ESCs for different time points . The Gsk-inhibitor SB-216763 was purchased from Sigma , dissolved in DMSO and used at 10 µM final concentration . DMSO was used as control in all the experiments . Stable clones over expressing mmu-miR-211 were generated by transfecting Bruce4 wild type ESCs with miR-211 expressing plasmid pEZX-MR01 ( Genecopoeia ) , or the corresponding empty vector . Several G418 resistant clones ( 200 µg/ml ) were selected and validated for miRNA expression . In order to generate Tcf3 over expressing ESCs , ApcNN ESCs were co-transfected with pCAG-HA-Tcf3-IRES-EGFP ( gift of Dr . Bing Lim , National University of Singapore , Singapore , ) and Hygromycin resistance plasmid . Transfected ES cells were selected for Hygromycin ( 150 µg/ml ) . GFP expression in resistant clones was employed for validation purposes . Several independent clones were isolated and , upon validation by qPCR and western blot analysis for Tcf3 expression , employed for subsequent experiments . The Tcf3-3′-UTR plasmid was obtained by PCR amplification from mouse genomic DNA of a 565 bp fragment encompassing the Tcf3-3′-UTR inclusive of the miR-211 target site ( forward primer 5′-AAATTGAGCTCTCCCCTTGCGCTGTGGTG-3′; reverse primer 5′-AAAAACTCGAGGGTGGGGGAAGGGGCAGA-3′ ) . PCR products were digested with SacI and XhoI and ligated into SacI and XhoI-cut pmirGlo plasmid ( Promega ) . All constructs were sequenced to verify their authenticity . RNA was isolated using the RNeasy Mini Kit ( QIAGEN ) from cells lysed directly on the plate; a DNase step on the column was performed according to manufacturer's instructions . RNA quality was controlled by RNA 6000 Nano LabChip kit ( Agilent Technologies ) . RNA was labeled using the GeneChip One-Cycle Target Labeling kit , hybridized to MOE430 2 . 0 arrays ( Affymetrix ) according to manufacturer's instructions . For data analysis , CEL files were uploaded and normalized using MAS 5 . 0 algorithm in Expression Console software ( Affymetrix , Inc ) . Expression analysis was performed using Partek Genomics Suite 6 . 5 ( ( Partek Inc . , St . Louis , MO ) and Excel 2010 ( Microsoft ) . A robust empirical method coupled with a validation step using qRT-PCR was used to confirm the modulation of gene expressions between different genotypes . A modulation of gene expression was validated when the observed fold-change is ≥1 . 5 and corresponding to none overlapping individual values , not present in the background . The unsupervised hierarchical clustering was performed after MAS 5 . 0 normalization , using Pearson's dissimilarity as distance measure and Ward's method for linkage analysis . ESCs were cultured on 0 . 1% gelatin-coated dishes without MEFs for 2 passages and genomic DNA was isolated using DNeasy Blood & Tissue Kit ( Qiagen ) . 1 µg of genomic DNA was used in bisulfite conversion reaction using EZ DNA Methylation Kit ( Zymo Research ) according to the manufacturer's instructions . Converted DNA was amplified by PCR using specific primers ( Table S2 ) designed with Methyl Primer Express Software 1 . 0 ( Applied Biosystems ) or MethPrimer software [57] . The PCR amplification was carried out using KAPA2G Robust HotStart Taq DNA polymerase ( Kapa biosystems ) and PCR conditions were: 95°C for 3 min and 39 cycles of 95°C for 15 s , 57 or 53°C for 15 sec and 72°C for 15 sec , followed by 10 min at 72°C . PCR products from Region A , B and D were employed in direct sequencing using ABI BigDye Terminator and ABI 3130×l genetic analyzer ( Applied Biosystems ) . ESCs were trypsinized , re-suspended in ES medium and plated on gelatin-coated culture dishes for 30 min to remove MEFs . Non-attached ESCs were resuspended in EB medium ( ESCs medium without LIF ) at a cell density of 2×105 cells/ml and plated on a non-adherent bacterial dish to initiate EB formation . EBs were collected by centrifugation ( 800 rpm for 5 min ) every three days and re-suspended in fresh medium . 1/5 volume of EBs suspension was used for RNA extraction while the remaining EBs were kept in culture until day 12 . Differentiation assays were performed as previously described [42] . Shortly , cells were trypsinized and plated on gelatine-coated dishes in N2B27 medium consisting of DMEM/F12∶Neurobasal medium ( 1∶1 , Gibco ) supplemented with N2 and B27 ( Gibco ) . Cells were harvested after 24 h or 48 h of differentiation for further analysis . Neuronal differentiation of ESCs was induced as previously described [30] . Briefly , ESCs were trypsinized and incubated in ES medium on gelatine-coated dishes for 30 min . to allow attachment of MEFs . Non attached cells were collected and 3×106 cells were cultured in 10 cm . non-adherent bacterial dishes ( Greiner Bio-One ) in EB medium for 8 days . Medium was refreshed every 2 days and 5 µM all-trans retinoic acid ( Sigma ) was added at day 4 and 6 . On day 8 cells were trypsinized and plated on poly-L-ornithine/laminin-coated dishes at a density of 2×105 cells/cm2 in N2 medium . Poly-L-ornithine ( Sigma ) and laminin ( Roche ) were used at final concentrations of 0 . 1 mg/ml and 20 µg/ml , respectively . N2 medium was refreshed after 2 and 24 hrs . from cell plating to remove dead cells . The N2 medium consisted of: DMEM/F12 ( Gibco ) supplemented with L-glutamine ( Gibco ) , Nonessential amino acids ( GIBCO ) , Insulin ( 25 ug/ml , Sigma ) , Progesterone ( 20 nM , Sigma ) , Putrescine ( 100 nM , Sigma ) , Transferrin ( 50 µg/ml , Sigma ) , Bovine serum albumin ( 50 µg/ml , Sigma ) , Sodium selenite ( 30 nM , Sigma ) and Penicillin-Sterptomycin ( Gibco ) . After 48 h from cell plating , medium was changed to N2B27 and refreshed every 2 days . Cells were collected after 5 days of plating for further analysis . Cells were trypsinized and plated on 0 . 1% gelatin-coated dishes for 30 min to remove MEFs . 500 FACS sorted cells were plated on each well of a gelatinized 24-well plate in N2B27 medium supplemented with different combinations of CHIR99021 ( 3 µM , Stemgent ) , PD0325901 ( 1 µM , Stemgent ) and LIF ( 1000 U/ml , Milipore ) . Total number of colonies were counted after 5 days from plating upon staining with alkaline phosphatise ( Milipore ) according to manufacture's instructions . Teratomas were obtained upon subcutaneous injection of 5×106 cells ( in PBS ) into C57Bl6/J , for Apc-mutant ESCs ( and their wild type controls ) , and NOD/SCID , for Tcf3−/− ESCs ( and their wild type controls ) , recipient mice . Teratomas were collected after 2–3 weeks and used for further experiments . RNA was isolated using Trizol ( Invitrogen ) or RNeasy Mini Kit ( QIAGEN ) and treated with DNase ( Ambion ) to remove contaminating genomic DNA . For gene expression analysis cDNA was synthesized using 1 µg RNA and the RevertAid H Minus First Strand cDNA Synthesis Kit ( Thermo ) . microRNA expression analysis was performed using 40 ng of total RNA isolated by Trizol and employed in cDNA synthesis reaction using TaqMan MicroRNA Reverse Transcription kit ( ABI ) . Real-time RT-PCR was performed using Applied Biosystems inventoried assays or TaqMan MicroRNA Assays on a 7900HT ABI real-time PCR system ( Applied Biosystems ) . The Delta-Ct method was used to quantify the mRNA or miRNA relative gene expressions . Actb or snoRNA234 were used for normalization , respectively . qPCR analysis of the selected genes were performed using Fast SYBR Green Master Mix ( ABI ) and the primers listed in Table S2 . Isolated teratomas were fixed in PFA ( 4% ) and embedded in paraffin . Five µm sections were mounted on slides stained by H&E for routine histology . Antibodies employed for IHC analysis included: rabbit anti-GFAP ( 1∶5000 , Z0334 , DAKO , ) ; mouse 2H3 against Neurofilaments ( 1∶50 , Developmental Studies Hybridoma Bank ) ; mouse SV2 against Synaptic vesicles ( 1∶50 , Developmental Studies Hybridoma Bank ) ; mouse A4 . 1025 against Adult myosin ( 1∶50 , Developmental Studies Hybridoma Bank ) ; goat anti-Oct3/4 ( 1∶100 , sc-8629 , Santa Cruz ) . Signal detection was performed using HRP-conjugated Goat anti mouse ( 1∶250 , Jackson ImmunoResearch ) , rabbit-anti-Goat-HRP ( Dako ) or Rabbit Envision kit ( Dako ) . Cells were harvested and fixed in 2% PFA for 20 min , washed with PBS , and permeabilized with 0 . 1% triton X-100 in PBS for 15 minutes . Cells were then incubated in Blocking solution ( PBS , 4% FCS ) for 30 min . , stained overnight at 4°C with the primary antibody , washed and finally incubated with the secondary antibody for 2 h . Confocal analysis was performed with a Zeiss LSM510 confocal microscope . Tuj-1-Alexa488 was detected using a 488 nm laser and BP 500–550 emission filter . DRAQ5 was detected using a 633 nm laser and LP650 nm emission filter . Alexa 488-conjugated monoclonal anti-Tuj-1 was from Covance ( A488-435L ) and was used at 1∶4000 dilution . Flow cytometric analysis was performed with a BD FACSAria III , using a yellow-green laser at 561 nm and a BP582/15 emission filter to detect anti-Nestin-PE antibodies , and 488 nm laser and LP502 and BP530/30 emission filters for Tuj-Alexa-488 antibodies . A Live-Dead-Fixable red staining ( Invitrogen ) was performed before fixation , to exclude dead cells and was detected using a 633 nm laser and BP660/20 emission filter . Alexa 488-conjugated anti-Tuj-1 antibody was used at a 1∶4000 dilution and the mouse anti-nestin antibody was from BD ( 556309 ) and was used at a 1∶500 dilution together with a 2nd Rat-anti-mouse PE-conjugated antibody ( BD; 1∶1000 ) . DRAQ5 was from Biostatus and was used as recommended by the manufacturer . For the β-catenin/TCF reporter assay , 5×105 ES cells were plated on 24-well plates seeded with MEFs and subsequently transfected by Fugene HD ( Roche ) with 250 ng of the TOP-Flash or FOP-Flash reporter constructs [28] together with 25 ng of the Renilla luciferase vector for normalization purposes . Luciferase activity was measured by Dual–Luciferase Reporter Assay System ( Promega ) . Tcf3 promoter activity was evaluated in ApcNN and wild type ESCs similar to β-catenin/TCF reporter assay , as mentioned above and by using Tcf3-promoter constructs ( kindly provided by Nina Solberg , SCI-CAST Innovation Center , Norway ) and pGL3 empty vector a control . To examine the effect of Wnt3a treatment on Tcf3 promoter activity , cells were transfected with luciferase constructs and treated with Wnt3a condition medium or L-control medium for 48 h and luciferase activity was measured using Dual–Luciferase Reporter Assay System ( Promega ) . For the 3′UTR-Luciferase reporter assay , HEK293 cells were plated in 24-well plates at a density of 0 . 5×105 cells per well . Cells were co-transfected with 250 ng of UTR ( or MTR ) reporter plasmid and either mmu-miR-211 mimic or non-targetting oligos ( 40 nM , Dharmacon ) using lipofectamin 2000 ( Invitrogen ) . Twenty-four hrs . after transfection , firefly-luciferase activity was measured by Dual–Luciferase Reporter Assay System ( Promega ) and normalized to the co-expressed Renilla luciferase signal . ES cells were lysed using Cell Lysis Buffer ( 9803 , Cell Signaling ) and a cocktail of protease inhibitors ( 11836170001 , Roche ) . Subsequently , NuPage LDS Sample Buffer ( NP0008 , Invitrogen ) and DTT ( 1 mM ) were added . Primary antibodies employed in western blot analysis included: Tcf3 ( sc-8635 , Santa Cruz ) ; Sox2 ( AF2018 , R&D Systems ) ; Sox11 ( sc-20096 , Santa Cruz ) ; Oct4 ( sc-5279 , Santa Cruz ) ; Dyrk1A G-19 ( G2905 , Santa Cruz ) ; Sap155/Sf3b1 ( D138-3 , MBL ) ; Nanog ( AB5731 , Milipore ) ; β-actin ( A5441 , Sigma ) ; β-tubulin ( ab6046 , Abcam ) . Lysates were loaded on 10% SDS-PAGE ( BIO-RAD System ) , and transferred onto Immobilon-FL PVDF membrane ( IPFL00010 , Millipore ) . Blocking was performed at room temperature using LI-COR Blocking buffer ( Part#927-40000 ) diluted 1∶1 with PBS . Incubation with the first antibody was performed overnight at 4°C . Blots were subsequently incubated with fluorescent-labeled secondary antibodies for 30 min . at room temperature . Goat anti-mouse IgG – IRDye 680 ( 1∶5000 , LI-COR Biosiences ) , Goat anti-rabbit IgG – IRDye 800CW ( 1∶5000 , LI-COR Biosiences ) and Donkey anti-goat-IRDye 800CW ( 1∶5000 , LI-COR Biosiences ) were used as secondary antibodies . Fluorescent signal was detected using LI-COR scanner ( LI-COR Biosiences ) . Two mutant forms of the Tcf3-3′UTR-luc plasmid were generated using QuikChange Lightning Site-Directed Mutagenesis Kit ( Agilent , 210518 ) . We introduced either 7 bp substitutions in the miRNA binding site ( AAAGGGA into CCCTTTC ) to generate the MTR1-Luc plasmid , or 4 bp ( AAAGGGA into cAcGtGc ) to generate the MTR2-Luc plasmid . The following mutagenesis primers were employed in the reaction: For MTR1 , sense primer is 5′-tctgaaatggtccccccccctgcatttccctttcctcaaggtgcctaccactgccttc-3′ and antisense primer is 5′-gaaggcagtggtaggcaccttgaggaaagggaaatgcagggggggggaccatttcaga-3′ . For MTR2 plasmid the sense primer is 5′-gtccccccccctgcatttcacgtgcctcaaggtgcctacc-3′ and the antisense primer is 5′-ggtaggcaccttgaggcacgtgaaatgcagggggggggac-3′ . The mutagenesis reaction was performed according to manufacture's instruction . Briefly , mutant strands were synthesized using the described primers followed by DpnI digestion of the amplification products to remove the parental methylated strands . Digestion reactions were transformed in XL10-Gold Ultracompetent cells and bacterial clones with correct nucleotide substitutions were used for further plasmid extraction . ChIP was performed on wild type and ApcNN ESCs or on wild type ESCs treated for 12 h with Wnt3a conditioned medium or L-control medium ( 1∶1 dilluted with ES medium ) . Briefly , cells were fixed in 1% PFA for 30 minutes at room temperature and PFA was quenched afterwards with 125 mM glycine . Cells were washed with buffer B ( 0 . 25% Triton-X 100 , 1 mM EDTA , 0 . 5 mM EGTA , 20 mM Hepes , pH 7 . 6 ) , buffer C ( 150 mM NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 20 mM Hepes , pH 7 . 6 ) . Cells were then sonicated in ChIP incubation buffer ( 0 . 3% SDS , 1% Triton-X 100 , 0 . 15 M NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 20 mM Hepes , pH 7 . 6 ) using a BioRuptor sonicator ( Cosmo Bio Co . , Ltd ) to obtain DNA fragments 200–700 base pairs . Chromatin was diluted in ChIP dilution buffer ( with 0 . 15% SDS ) and incubated with BSA-blocked protein-A/G Sepharose beads ( Amersham ) and 5 µg antibody overnight at 4°C . Antibodies used in this study include: H3K4me3 ( Abcam , Ab8580-50 ) , H3K27me3 ( Upstate , 07-449 ) , H3K9me3 ( Abcam , Ab8898-100 ) , H3Ac ( Millipore #06-599 ) Beads were washed with buffer 1 ( 0 . 1% SDS , 0 . 1% deoxycholate , 1% Triton-X 100 , 150 mM NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 20 mM Hepes pH 7 . 6 ) , buffer 2 ( 0 . 1% SDS , 0 . 1% deoxycholate , 1% Triton-X 100 , 0 . 5 M NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 20 mM Hepes pH 7 . 6 ) , buffer 3 ( 250 mM LiCl , 0 . 5% deoxycholate , 0 . 5% NP-40 , 1 mM EDTA , 0 . 5 mM EGTA , 20 mM Hepes , pH 7 . 6 ) , and buffer 4 ( 1 mM EDTA , 0 . 5 mM EGTA , 20 mM Hepes , pH 7 . 6 ) . Chromatin was eluted for 30 min at room temperature in elution buffer ( 1% SDS , 0 . 1 M NaHCO3 ) and together with input chromatin , decrosslinked overnight at 65°C in the presence of 200 mM NaCl . DNA was extracted using QIAquick PCR Purification Kit and was used in QPCR analysis using Fast SYBR Green Master Mix ( ABI ) and primers indicated in Table S2 . | The future successes of regenerative medicine largely rely on our knowledge of , and our capacity to manipulate , the cellular and molecular mechanisms governing stem cell differentiation . A growing body of evidence suggests that , in mouse embryonic stem cells , canonical Wnt/β-catenin signaling not only enhances self-renewal but also directs the cell fate decision towards non-neuroectodermal lineages . However , little is known about the mechanisms underlying the differentiation defects caused by constitutive active Wnt signaling . Using a set of Apc-mutant ESCs harbouring different levels of Wnt signaling , we found that , among others , down-regulation of Tcf3 , a key member of the pluripotency circuit , as well as induction of a novel Wnt-regulated microRNA , miR-211 , represent two important downstream effects through which Wnt signaling inhibits neural differentiation in mouse ESCs . We also provide a more detailed picture on how Wnt signaling counteracts Tcf3 function in stem cells by showing that Tcf3 repression , in the context of active Wnt signaling , involves histone modifications at the Tcf3 promoter and the activation of miR-211 , which post-transcriptionally stabilizes Tcf3 downregulation . Understanding the downstream effects of Wnt signaling in ESCs is of both fundamental and translational relevance , as it may be exploited to manipulate ESC differentiation towards specific cell lineages . | [
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] | 2013 | Wnt Signaling Regulates the Lineage Differentiation Potential of Mouse Embryonic Stem Cells through Tcf3 Down-Regulation |
Defective-Interfering RNAs ( DI-RNAs ) have long been known to play an important role in virus replication and transmission . DI-RNAs emerge during virus passaging in both cell-culture and their hosts as a result of non-homologous RNA recombination . However , the principles of DI-RNA emergence and their subsequent evolution have remained elusive . Using a combination of long- and short-read Next-Generation Sequencing , we have characterized the formation of DI-RNAs during serial passaging of Flock House virus ( FHV ) in cell-culture over a period of 30 days in order to elucidate the pathways and potential mechanisms of DI-RNA emergence and evolution . For short-read RNAseq , we employed ‘ClickSeq’ due to its ability to sensitively and confidently detect RNA recombination events with nucleotide resolution . In parallel , we used the Oxford Nanopore Technologies’s ( ONT ) MinION to resolve full-length defective and wild-type viral genomes . Together , these accurately resolve both rare and common RNA recombination events , determine the correlation between recombination events , and quantifies the relative abundance of different DI-RNAs throughout passaging . We observe the formation of a diverse pool of defective RNAs at each stage of viral passaging . However , many of these ‘intermediate’ species , while present in early stages of passaging , do not accumulate . After approximately 9 days of passaging we observe the rapid accumulation of DI-RNAs with a correlated reduction in specific infectivity and with the Nanopore data find that DI-RNAs are characterized by multiple RNA recombination events . This suggests that intermediate DI-RNA species are not competitive and that multiple recombination events interact epistatically to confer ‘mature’ DI-RNAs with their selective advantage allowing for their rapid accumulation . Alternatively , it is possible that mature DI-RNA species are generated in a single event involving multiple RNA rearrangements . These insights have important consequences for our understanding of the mechanisms , determinants and limitations in the emergence and evolution of DI-RNAs .
RNA viruses are extremely diverse and rapidly evolving . Their RNA-dependent RNA polymerases ( RdRps ) readily generate single-nucleotide variants whilst lacking proof-reading capabilities[1] . RdRps are also highly prone to RNA recombination[2]; either through template-switching[3] or through non-replicative end-joining[4] . RNA recombination has been demonstrated to be responsible for the emergence of new strains or species of viruses such as rhinoviruses[5] and dengue virus[6] , and the formation of vaccine-derived poliovirus[7] . Non-homologous RNA recombination is also responsible for the generation of defective RNAs[8 , 9] . These are versions of the parental viral genome that can arise naturally during the course of viral passaging but have been truncated and rearranged by RNA recombination . While not encoding for functional viruses themselves , they can be amplified and co-passaged with the help of the wild-type ‘helper’ virus that provides the necessary machinery for replication , encapsidation and transmission . A defective RNA that accumulates to such an extent as to compete with or otherwise attenuate the replication of the parental virus is known as a Defective-Interfering RNA ( DI-RNA ) [10] . DI-RNAs can attenuate the viral infection via a variety of proposed mechanisms such as the saturation of the viral replicative machinery , sequestration of essential cellular cofactors , and/or induction of innate immune responses[10–14] . DI-RNAs have been well characterized for a number of RNA viruses as they provide valuable tools to molecular virologists by revealing conserved regions and functional domains in the RNA genome such as binding sites for viral or host factors . Moreover , characterizing recombination loci reveal the mechanisms of recombination , impacting our understanding of viral evolution [8 , 15 , 16] . Until recently [17] , due to difficulties in capturing and characterizing DI-RNAs in vivo , DI-RNAs were considered to be a curious epiphenomenon of cell-culturing practices . As a result , our appreciation of the diversity of DI-RNAs and the range of situations in which they could play a role was greatly limited . Increasingly , due to the use and sensitivity of Next-Generation Sequencing ( NGS ) technologies , DI-RNAs have been observed in a multitude of viral systems under laboratory conditions ( e . g . SARS coronavirus[18] , HIV[19] ) , in clinical settings ( e . g . measles[20] , dengue[21] and chronic hepatitis C[22] ) and in metagenomic or ‘wild’ samples ( e . g . West Nile virus[23] , influenza virus[24] ) . Despite this burgeoning range of hosts for DI-RNAs , limitations in NGS technologies including high artifactual recombination rates , short reads and a limited range of bioinformatics tools tailored to viral RNA recombination discovery has hindered our ability to detect and characterize DI-RNAs in complex or clinical samples . Flock House virus ( FHV ) is a positive-sense single-stranded RNA ( +ssRNA ) virus originally isolated from grass grubs in New Zealand[25] and is perhaps the best-studied Alphanodavirus from the Nodaviridae family . FHV infects Drosophila flies and cells in culture as well as medically important genera of insects including mosquitos , ( Anopheles gambiae ) , the tsetse fly ( Glossina morsitans morsitans Westwood ) , and the Chagas vector ( Rhodnius prolixus Stal ) [26] . Infection of these organisms by FHV has been demonstrated to have similar characteristics in terms of viral titer , virus dissemination and mortality as has been shown for fruit fly infections . FHV provides an excellent model system to study +ssRNA virus evolution by virtue of having one of the smallest known eukaryotic virus genomes[27] . Moreover , the viral life-cycle and details of the molecular biology of virus particle assembly , cell entry and particle disassembly are highly-characterized . FHV contains two genomic RNAs . RNA1 ( 3107 nts ) encodes the viral RdRp and RNA2 ( 1400 nts ) encodes the viral capsid protein . RNA1 also expresses a small sub-genomic RNA , called RNA3 , that encodes the B2 protein responsible for inhibition of the anti-viral RNAi machinery[28] . FHV has been demonstrated to form DI-RNAs in multiple independent studies spanning three decades both in cell-culture[8 , 26 , 29–32] and in Drosophila melanogaster[33] . Many of these studies characterized individual DI-RNA genomes through sub-cloning and Sanger sequencing . Intriguingly , many of these DI-RNAs are highly similar . This indicates that either the DI-RNAs have emerged due to a common mechanism of formation or the presence of a common selectivity filter , or both . Our recent NGS studies of RNA recombination in FHV revealed a diverse array of RNA recombination events , suggesting that the genomic landscape of DI-RNAs is highly dynamic and likely contributes significantly to the diversity of viral genomes that form the viral quasi-species[34] . Despite these findings , studies to-date present only a single snap-shot of the DI-RNA genome landscape and do not capture the pathways of their emergence and evolution nor characterize any intermediate DI-RNA species that might arise during these processes . In order to resolve the potential mechanisms of DI-RNA emergence and elucidate the evolutionary pathways that lead to the formation of ‘mature’ DI-RNAs , we performed high-titer serial passaging of FHV in cell culture and characterized the encapsidated RNA using RNAseq . We used Illumina HiSeq sequencing of ClickSeq generated libraries to provide a high-resolution and high-confidence quantification of individual recombination events . We combined this information with long-read Oxford Nanopore Technologies’s ( ONT ) MinION sequencing to resolve the topology of full-length and defective RNA genomes . By combining these data , we aimed to determine the correlation of recombination events within single RNA virus genomes , characterize the distribution of defective RNA genomes , and determine the exact make-up of DI-RNAs during serial passaging of FHV in cell culture . We recently developed the ‘ClickSeq’ method for RNAseq that uses copper-catalyzed alkyne-azide cycloaddition ( CuAAC ) , a click-chemistry reaction , for RNAseq library synthesis[35] . ClickSeq provides a robust platform on which to study RNA recombination in RNA viruses . Artifactual recombination is a common contaminant in NGS library generation and can easily obscure rare or non-canonical recombinant species . ClickSeq does not require template fragmentation and replaces enzymatic ligation steps commonly required in NGS library generation with click-chemistry . ClickSeq works by introducing small amounts of azido-nucleotides ( AzNTPs ) into RT-PCR reactions to generate azido-terminated cDNA transcripts . These cDNA fragments are subsequently mixed with alkyne-labelled DNA adaptors . The addition of a copper catalyst results in the ‘click-ligation’ of the two chemically-functionalized DNA substrates to produce an unnatural triazole-linked single-stranded DNA molecule[36] . ClickSeq prevents template switching during RT-PCR as well as non-specific ligation of RNA fragments . We demonstrated that ClickSeq reduces artifactual recombination to fewer than 3 events per million mapped reads[35] . As a result , ClickSeq provides a superior method for the detection of DI-RNAs and RNA recombination events . The Oxford Nanopore Technologies’s ( ONT ) MinION is a small handheld sequencing device[37] poised to revolutionize the next-generation sequencing field by providing real-time , high-throughput and long-range ( over 882 kbp[38] ) sequences of DNA samples with minimal sample prep . ONT nanopore sequencing has been used to rapidly characterize virus genomes from metagenomic samples[39] , in the midst of Ebola virus outbreaks[40] , and in targeted studies aimed at characterizing sequence variations within influenza virus samples[41] . Highly parallel direct RNA sequencing using Nanopore technology was also recently reported[42] . Due to the higher error-rate[43] of the nanopore sequencing technology compared to other RNAseq platforms , the exact identity of recombination events within single-molecule genomes may be inaccurate . However , long-read nanopore reads provide the distinct advantage of being able to sequence full-length cDNA copies of RNA virus genomes and thus can resolve multiple recombination events within a single RNA virus genome . This study provides a comprehensive analysis of the steps and pathways governing DI-RNA emergence and evolution starting from a plasmid-driven inoculum through to a highly-passaged sample . By combining short-read and long-read sequencing technologies , we determine both the exact identity of RNA recombination sites and their correlation within the viral quasispecies . We find little evidence for the accumulation of intermediate defective RNA species that contain either only one , or smaller , deletions during the course of passaging . Rather , fully formed ‘mature’ DI-RNAs that are characterized by two to three deletions between a limited number of positions in each of the FHV genomic RNAs appear after approximately 9 days of viral passaging and accumulate rapidly . The accumulation of DI-RNAs corresponds with a reduction in the specific infectivity of the viral samples in each passage . This implies that partially formed DI-RNA species are not competitive and cannot accumulate in the manner that mature DI-RNA species do , perhaps due to the epistatic interaction of multiple recombination events . Alternatively , the formation of mature DI-RNAs may occur in a single step involving multiple simultaneous genome rearrangements .
D . melanogaster ( S2 ) cells were grown at 28°C in Schneider’s Drosophila Media supplemented with 10% fetal bovine serum and 1X Penicillin-Streptomycin using standard laboratory procedures . To generate the initial Flock House virus inoculum , S2 cells were plated at 50–70% confluency in a six well plate and were transfected with 2 . 5μg of pMT plasmid containing FHV RNA1 ( NC_004146 ) and 2 . 5μg of pMT plasmid containing FHV RNA2 ( NC_004144 ) using Lipofectamine 3000 Transfection Reagent as per the manufacturer’s protocol . Plasmid transcription was induced 24 hours post transfection with the addition of 50mM CuSO4 . Virus was then allowed to propagate for 3 days post induction . For successive passages ( Passages 1–9 ) , S2 cells were grown in T-25 flasks to 70–90% confluency ( ~1 x 107 cells ) , then infected with 1mL of viral inoculum from the previous passage . Virus was grown for 3 days , then fractions were harvested for viral purification or inoculation of the next passage . To purify virus from each consecutive serial passage , cells and supernatant were subjected to a freeze-thaw cycle in the presence of 1% Triton X-100 to release viral particles from infected cells . Virus particles were then purified on a 30% sucrose cushion by spinning the cell lysate at 40 , 000 RPM for 2 . 5 hours . The viral pellet was resuspended in 10mM Tris ( pH 7 . 4 ) . Virus was further purified by applying resuspended virus atop a 10–40% sucrose gradient and spun at 40 , 000 RPM for 1 . 5 hours . The viral band was collected and subsequently treated with 1 Unit DNase and 1 Unit RNase and incubated at room temperature for at least one hour to remove any cellular nucleic acids not protected by the viral capsid . The virus sample was concentrated on a 100 , 000 NMWL centrifugal filter column and washed with at least 2 volumes of 10mM Tris pH 7 . 4 . Finally , encapsidated viral RNA was extracted using a QIAGEN RNeasy Mini Kit as per the manufacturer’s protocol . Next generation sequencing ( NGS ) libraries were generated using 100ng of RNA using the ‘ClickSeq’ protocol as previously described by Routh et al . [35 , 44 , 45] . Briefly , cDNA is synthesized through RT-PCR initiated from semi-random ( 6N ) primers containing a partial Illumina p7 adapter ( GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNN ) and stochastically terminated by the addition of azido-NTPs ( AzNTP ) at a ratio of 1:35 AzNTP:dNTPs . Subsequently , the p5 Click-Adapter ( 5’-Hexynyl-NNNNAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT , IDT ) was click-ligated onto the azido-terminated cDNA fragment using copper-catalysed azide-alkyne cycloaddition ( CuAAC ) in the presence of TBTA ligand ( Lumiprobe ) and Vitamin C catalyst in 55% DMSO . After purifying the click-linked cDNA with a Zymo DNA clean column , 18 cycles of OneTaq ( NEB ) PCR amplification adds the remainder of the p7 adapter along with the desired TruSeq index sequence . PCR product was cleaned again with a Zymo DNA clean column to remove excess primers and then ran on a 1–2% precast agarose e-gel ( Invitrogen , E-Gel Electrophoresis System ) . cDNA libraries between 400 to 700bp were excised corresponding to insert sizes of 250-550bp and cleaned using the Zymo Research Gel DNA Recovery Kit . Final cDNA libraries were quantified using a QuBit fluorimeter ( Life Tech ) and loaded on a HiSeq 1500 single read rapid run flowcell for 1x150 reads and 7 nucleotides of the index . FHV libraries used for the triplicate study shown in S2 Fig were sequenced on a MiSeq platform with v3 chemistry for 600 cycles ( 2x300 ) . Reads were trimmed to 150nts prior to analysis to emulate the libraries sequenced on the HiSeq . Raw reads were processed by first removing the Illumina TruSeq adaptor using Cutadapt [46] with default parameters . Next , the first 6 nucleotides ( corresponding to the random nucleotides and triazole-linkage included in the Click-Adaptor ) were trimmed and any reads that contained nucleotides with a PHRED score <20 were removed using the FASTX toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/ ) . The remaining reads were aligned end-to-end with Bowtie ( v1 . 0 . 1 ) [47] ( command line parameters: -v 3 –-best ) first to the FHV genome ( NC_004414 and NC_004146 ) and next to host D . melanogaster genome ( fb5_22 ) . The remaining unmapped reads were processed to identify recombination events using the python script ‘ViReMa’ ( Viral Recombination Mapper ) [32] ( command line parameters:--N 1 --X 5 --Seed 25 --Host_Seed 30 --Defuzz 0 --MicroInDel 5 ) . The frequency of a specific recombination event is approximated by dividing the number of reads mapping to this recombination ( N ) by N plus the average of the number of reads mapping to the wild-type genome at each of the recombination coordinates . The Oxford Nanopore Technologies’s ( ONT ) MinION and flowcells were acquired as part of the ONT early-access program . To prepare sequencing libraries for the MinION , 50ng of RNA was reverse transcribed using RNA specific primers that were complimentary to the 3’ end of the respective genome ( RNA1_RP: ACCTCTGCCCTTTCGGGCTA or RNA2_RP: ACCTTAGTCTGTTGACTTAA ) . cDNA was then amplified using the standard Phusion ( NEB ) PCR protocol using genome specific primers ( RNA1_FP: GTTTTCGAAACAAATAAAAC or RNA2_FP: GTAAACAATTCCAAGTTCCA ) for 19 cycles . Excess primers were removed from the PCR product using AMPure XP beads ( Beckman Coulter ) at a ratio of 1:1 AMPure bead:PCR product . Samples were then barcoded and prepared following the manufacture’s protocol ( R9 Native Barcoding Kit I and Nanopore Sequencing Kit ) with adjustments to tailor input cDNA quantities . A target of 1ug of fragmented DNA at approximately 8’000 nts is considered optimal for library generation using this kit . The input amounts for RNA1 ( 3107 bp ) and RNA2 ( 1400 bp ) were thus adjusted to 192ng and 88ng respectively and combined in 46uL water to maintain optimal DNA end molarity . After ligation of barcodes , equal amount of each DNA library ( 9 samples in total ) were pooled and loaded onto a MinION MkIB device equipped with an R9 flow cell . The MinKNOW control software was used to select a 48-hour sequencing protocol and was allowed to proceed for at least 36 hours , until high-quality data accumulation ceased . Raw data was uploaded automatically by Metrichor software for cloud-based base-calling using default settings and quality filtering for 2-dimensional reads . Reads were extracted from HDF5 format files ( fast5 ) using poretools[48] . Full-length ONT reads were mapped to the Flock House virus genome using the pacbio wrapper from the BBMAP v36 suite ( command line parameters: fastareadlen = 6000 vslow = t maxindel = 3100 minid = 0 . 5 local = f ignorebadquality = t usequality = f ) . Alignment SAM files were visualized using the Tablet sequence viewer [49] . SAM files were filtered to ensure that MinION reads mapped from the first 25 nts to the final 25 nts of the reference genome ( accounting for deletions and insertions ) , due to the presence of truncated nanopore reads and mis-priming during the cDNA PCR amplification steps . Errors including substitutions , insertions and deletions were counted using the samtools[50] mpileup command and error rates at each position were calculated by dividing this value by the read depth at this position ( S5 Fig ) . Insertion and deletion events longer that 25 nts were extracted using the CIGAR string of the SAM files using simple in-house scripts . For recombination sites containing ‘fuzz’ , where nucleotides surrounding the putative recombination events are the same for both the acceptor and donor sites , the recombination event was reported as occurring in the middle of the ‘fuzzy’ region , or at the 5’ side of the middle two nucleotides in the orientation of the reference if the fuzzy site contained an even number of residues . This is the same methodology as employed in the ViReMa script[32] used to map recombination event in the ClickSeq data . Insertion events and soft-pads longer than 100 nts were extracted and their nucleotide sequence was analyzed using an online BLASTn search to determine their identity . To annotate the defective genomes detected by MinION nanopore sequencing or recombination events detected by ClickSeq , we use underscores ‘_’ to denote continued mapping , and carets ‘^’ to denote a recombination events . For example , “1_317^1091_1242^2301_3107” indicates an authentic mapping from nt 1 to 317 , then a deletion event removing nts 318 through 1090 , then another authentic mapping from 1091 to 1242 , followed by another deletion removing nts 1243 through 2300 , and finally an authentic mapping from nt 2301 to 3107 . The Shannon Entropy Index is given by: H ( X ) =−∑i=0N−1pilnpi For the ClickSeq data , each recombination event is treated as independent with its probability determined by dividing the number of reads mapping to the present recombination event divided by the average coverage over the whole viral RNA . For the MinION data , each individual read mapping is treated as an individual event with the frequency determined by dividing the number of identically mapping reads divided by total mapped reads . Tissue culture infective dose 50 ( TCID50 ) analyses of the supernatants from each passage were performed using standard protocols [51] . For purified particles of each passage TCID50 was calculated with slight modifications . Specifically , 1 x 105 cells ( S2 ) per well were plated in 96 well format . Virus samples were quantified by measuring the OD260nm . An OD of 4 . 15 corresponds to 1mg virus [52] , which in turn corresponds to 6 . 4 x 1013 virus particles assuming a virion mass of 9 . 4MDa [53] . Purified virus samples were diluted to a starting concentration of 47ng/μL , which corresponds to 3 x 1010 virus particles per 10μL . These quantities were chosen as particle-to-PFU ratios for rescued FHV has previously been reported to be 300–400 particles [26 , 52] . Therefore 3 x 1010 particles per 105 cells corresponds to approximately 1000 PFUs per cell . Eight serial 10-fold dilutions were subsequently made and added to each column of the 96-well plate ( 8 replicate wells per dilution ) , as per standard TCID50 protocols . Virus was allowed to grow for 4 days after which the number of positive wells exhibiting cytopathic effect ( CPE ) were counted . The TCID50 values and effective MOI were calculated using the Reed and Muench Calculator [51] . We further counted the total number of cells that were present in each well after infection using a Guava easyCyte HT ( Millipore ) flow cytometer to provide us with quantifiable amount of cell death . 50μL from each well was diluted in 150μL PBS and injected using manufactures’ protocols . The InCyt v3 . 1 software was used to collect data with the following parameters: collection time: 30sec; flow rate: 0 . 59μL/sec; FSC: 16; SSC: 25; threshold: 0 . The region used to determine live cells was based on scattering features of the negative controls ( wells with no viral infection ) ( S7 Fig ) . All raw Illumina data and demultiplexed MinION nanopore data passing quality filters ( comprising 2D , template and complement strands ) associated with this manuscript are available on the SRA NCBI archive with study number SRP094723 and BioProject number PRJNA352872 .
D . melanogaster ( S2 ) cells in culture were transfected with cDNA plasmids containing each of the Flock House virus genomic RNAs followed by a hepatitis D virus ( HDV ) ribozyme sequence . After induction , the HDV ribozyme regenerates the authentic 3’ end of the positive sense viral RNA , which is thus successfully recognized by the FHV RdRp allowing the initiation of viral replication[54] . We choose to initiate replication with this method to ensure that the starting viral population would be homogeneous containing only the full-length RNAs derived from the plasmid cDNA . After transfection , the viral inoculum was allowed to amplify for 3 days ( Passage number = P0 ) , after which most cells exhibited cytopathic effect . Subsequently , the supernatant from infected cells was collected and a 1mL fraction ( 10% of the total volume ) was used to infect 10mL of fresh S2 cells in triplicate ( Replicates R1 , R2 , and R3 ) . Again , after three days , 1mL of the supernatant was harvested and used to infect fresh S2 cells in series for a total of nine 3-day passages ( Passage numbers = P1 –P9 ) . Therefore , one single inoculum was used to generate three distinct lineages as shown in Fig 1 . For each passage and replicate , including the original inoculum , viral particles were purified over a sucrose cushion and non-encapsidated genetic material was degraded to ensure that the genetic material subsequently analyzed was packaged within the viral capsid . RNA was extracted from the purified viral particles using standard silica-based spin columns . ClickSeq libraries[35] were synthesized from the purified viral genomic RNA and sequenced on an Illumina HiSeq 1500 for 1x150 single-end reads . The inoculum sample was sequenced on a separate flowcell to all other samples to prevent any cross-contamination from incorrect demultiplexing . We obtained 1 . 2–30 . 6 million reads after trimming and quality filtering for each passaged sample , and 41 . 6 million reads for the original inoculum ( Table 1 and S1 Table ) . 1 million reads corresponds to an average coverage of greater than 33’000X across the FHV genome . Reads were aligned to the FHV genome and the host genome ( D . melanogaster , fb5_22 ) using Bowtie end-to-end mapping [47] . As expected , the majority of the reads aligned to FHV RNA1 ( 3107 nts ) and FHV RNA2 ( 1400 nts ) in a ratio reflecting the longer length of RNA1 . As we have observed previously [34 , 55] , 0 . 3–8 . 4% of reads correspond to host RNAs that are encapsidated within the viral particles including mRNAs , ribosomal RNAs and retrotransposons . Interestingly , the amount of encapsidated host RNA increases modestly through later passages ( S1 Fig ) . Subsequently , we further characterized the unmapped reads with the Python script ViReMa ( “Virus Recombination Mapper” ) [32] . ViReMa is a computational pipeline optimized for mapping virus recombination junctions in NGS data with nucleotide resolution by dynamically generating moving read segments . ViReMa is sensitive to many types of RNA recombination events . This includes micro-insertions and deletions ( InDels comprising 5 or fewer nucleotides ) , duplications , deletions , inter-RNA recombination ( denoting recombination between FHV RNA1 and RNA2 ) and virus-to-host recombination events , and reports both the identity and frequency of recombination events . Recombination events that cannot be unambiguously identified due to unmapped read segments , mismatches occurring near to putative recombination events , or reads containing fragments of sequencing adaptors are flagged as ‘other’ ( Table 1 ) [32] . In each genomic RNA we found hundreds of unique recombination events , reflecting a diverse and complex mutational landscape ( S1 Datafile ) . Broadly , we see an increase in the total number of recombination events during serial passaging of FHV and a corresponding increase in the Shannon diversity index ( Fig 2A and 2B ) . Following these mapping procedures , few reads ( 0–1 . 2% , Table 1 and S1 Table ) remained uncharacterized . As in previous studies , these were found to be derived from incorrect demultiplexing of neighboring samples on the HiSeq flow-cell[56] or from contaminants in the RNAseq library generation[57] . Having accounted for almost all of the reads present in each dataset , we can be confident that we are capturing the full range of recombination events and/or other rearrangements present within each sample and thus are not missing important or significant events due to computational limitations . To demonstrate the reproducibility of the ClickSeq approach and to assess the limit in terms of our ability to successfully detect rare recombination events , we generated three replicate ClickSeq libraries from the RNA sample P7R2 , obtained 1x150bp reads and performed the same computational analyses as described above . We mapped 0 . 83M , 1 . 45M and 1 . 18M reads per library , giving an approximate coverage of ~42 , 000–139 , 000x coverage over FHV RNA1 and ~18 , 000–67 , 000x over FHV RNA2 ( calculated from the average coverage over the conserved 5’ and 3’ ends ) . When comparing the frequency of unique recombination events in either RNA1 or RNA2 between any pair of the three replicates , we find excellent correlation ( Pearson >0 . 99 ) even for very infrequent recombination events , as illustrated in the scatter plots in S2 Fig . Events that were found in two replicates but not a third , never exceeded more than 20 mapped reads for RNA1 and 8 reads for RNA2 . If we take these values as a cut-offs , below which we begin to fail to detect events , then we can conservatively estimate that we are reproducibly sensitive to recombinant species that are present at approximately 0 . 048% ( 20/42 , 000 ) of RNA1 population and 0 . 044% ( 8/18 , 000 ) of the total RNA2 population when obtaining ~1M sequence reads . In the inoculum ( P0 ) , less than 0 . 2% of the all the reads mapped to recombination events ( Table 1 , Fig 2A ) . Inspection of these events reveals that they are dispersed throughout each of the genomic RNAs . RNA1 recombination events are the least frequent , with only 22 unique events detected represented by 2920 reads and without an apparent bias toward any specific location . The three most common RNA1 events in the inoculum are 2325^1241 , 1484^1944 , and 2199^393 with 514 , 229 , and 177 mapped reads respectively ( S1 Datafile ) . Read depth for the wild-type genome at these loci ranges from 400K to 1 . 9M reads , therefore these recombination events make-up at less than 0 . 1% of the total viral population . These are not the events that have been previously reported as forming FHV DI-RNAs , moreover none of these events are observed again in subsequent passages , perhaps due to approaching the sensitivity of discovery limit as described above . However , due to the low-rate of artifactual recombination of the ClickSeq approach ( >3 events per millions reads [35] ) , we can be confident that these are not sequencing artifacts[35] . Therefore , these events likely represent non-viable or transient recombination events that arose due to stochastic non-homologous recombination . For RNA2 in the inoculum , the three most frequently observed events were 738^1219 , 738^1222 and 1024^1190 , with 9849 , 5237 , and 3673 mapped reads respectively ( S1 Datafile ) . Coverage in the wild-type RNA2 mapping in these regions ranges between 2 . 6M and 4 . 8M mapped reads , therefore these recombinant species make-up approximately 0 . 2% of the viral RNA2 population . The majority of other recombination events are found to delete a similar region of RNA2 . This region is important as it has previously been reported to be deleted in FHV DI-RNAs . However , in the inoculum we do not observe deletions upstream in the RNA2 gene ( for example 250^513 ) , also reported to be deleted in previously characterized FHV DI-RNAs . Therefore , this dataset suggests that intermediate DI-RNAs with only a single region deleted between ~740–1220 are formed very early during virus passaging ( within 3 days ) . In passages P1-P2 , less than 1 . 3% of the all the reads mapped to recombination events ( Table 1 , Fig 2A ) . Again , these occur throughout each of the genomic RNAs . However , we do begin to see events that have previously been characterized as forming DI-RNAs , such as 311^1104 and 301^1100 , although these events are present at low levels ( 70 and 21 reads in Replicate 1 from a total of 8 . 2M reads mapped to the FHV genome ) ( S1 Datafile ) . In subsequent passages there was a rapid increase in the total proportion of mapped recombination events ( peaking at 11 . 9% in P8R1 ) ( Table 1 , Fig 2A ) . In these later passages for each replicate , it can be seen that the most common recombination events are deletions that span two regions in each genomic RNA including: nts 300–940 and nts 1240–2300 of RNA1; and nts 250–510 and nts 740–1220 of RNA2 , consistent with previous observations of FHV DI-RNAs[31] ( S1 Datafile ) . However , the exact sites of the recombination events , while repeatedly observed over time in each replicate , varied between replicates and each had distinct ‘most popular’ species in the final passages ( Table 2 ) . In some instances we are able to find that a specific event is predominant in one replicate while at low levels in another . Overall , these trends in the frequency of recombination events throughout passaging reveal that once defective RNA species emerge during viral passaging they rapidly accumulate . Despite the predominance of certain recombination events in the later passages , there still remained a large number of infrequent events scattered throughout the viral genome . Many of these are observed only in one passage and not in subsequent passages . Again , these events likely correspond to stochastically generated RNA recombination events that form non-viable defective RNAs . We reasoned that analyzing these events would more accurately reveal the nucleotide preference of the FHV RdRp for RNA recombination as they were not subject to replicative selection ( although they must be packaged by FHV particles ) , unlike the DI-RNAs . Therefore we extracted all recombination events occurring with fewer than 10 mapped reads throughout all FHV passages and replicates ( 20’723 unique events from a total of 11 . 6M possible permutations [8] ) and counted the frequency of nucleotides found both up and down-stream of 5’ and 3’ recombination sites in the reference genome . As shown in Fig 2C , this revealed a preference for A’s 1-3nts downstream of 5’ sites , a preference for U’s 1-3nts upstream of 5’ sites , a weaker preference for a C 1nt up stream of 5’ sites , and an aversion to G’s 2nts both upstream and downstream of 5’ sites . Interestingly , an almost identical trend was observed for the 3’ sites . This trend was maintained also when analyzing only recombination events without ambiguity in the site of recombination ( i . e . sites that lacked ‘fuzziness’ as reported by the ViReMa pipeline[32] ) . This result is similar to what we have previously reported[8] . However , here we provide a much larger dataset and analyze events that we can determine are not amplified in subsequent passages , providing greater confidence that these sites reflect the preference for RNA recombination at these sites rather than the selection of replicatively viable defective RNA species . Since many recombination events resulted in deletions of the viral genome , we were curious to see if the open reading frame ( ORF ) was conserved , as conservation of an ORF has frequently been observed to be a property of defective and defective-interfering RNAs[58] . Moreover , it has previously been shown that cloned DI-RNAs vectors containing eGPF in their putative ORFs do indeed express fluorescent protein[59] although it is not clear whether a functional ORF is essential for DI-RNA formation or propagation . In the earliest passages , only ~33% of deletions removed a multiple of 3 nucleotides ( i . e . they thus conserved the ORF ) , as would be expected if deletion events occurred randomly throughout the genome . However , with continued passaging , there was a general trend toward conservation of the ORF for both RNA1 and RNA2 ( Fig 2D ) , although this was not the case for all replicates . Specifically , while initially showing an increase in ORF conservation , replicate 2 of RNA2 showed a decrease in the conservation of the ORF after passage 4 , in contrast to the other two replicates , and in fact dips below 33% . Closer inspection of individual recombination events shows that this trend is driven by three of the four most common recombination events in replicate 2 passages 4 to 9: 249^517 , 736^1219 , 250^513 and 249^519 ( the latter three events in bold do not maintain the ORF ) . These events are observed in the other replicates , but at a much reduced frequency ( no more than 1% of the total RNA2 recombination events for reps 1 and 3 ) ( S1 Datafile ) . This indicates that DI-RNAs can indeed accumulate without a strict requirement for a functional ORF . However , this analysis only takes a single recombination event into consideration and the Illumina reads are not long enough ( 150bp ) to resolve multiple deletions at the same time . Nevertheless , neither compensatory recombination events including small InDels that might restore the ORF , nor single nucleotide variants at putative stop-codons , were found . Using ViReMa , we were able to calculate the frequency with which each nucleotide was deleted , revealing areas of the viral genome that are conserved during serial passaging and required for DI-RNA replication . We plotted these data to generate recombination profiling maps for each RNA of FHV throughout passaging ( Fig 3 and S2 Fig ) . In the first passage , there is a relatively even distribution of nucleotide deletions along the whole length of the genome with the exception of two frequently excised regions in the 3’ end in RNA1 due to two common recombination events in each of the replicates: 2545^2685 and 2277^2435 . By passage 3 the deletions along the genomic landscape begin to be ‘sculpted’ whereby certain regions are deleted with a greater frequency than others . Passages 5 , 7 , and 9 were sculpted further revealing three major regions that were deleted in RNA1 and two in RNA2 . Interestingly , for both RNA segments , while a range of deletions and rearrangements are generated during early passages , only the deletions that maintain regulatory and control elements are amplified during continued passaging , as previously observed in FHV [32] . These include the 5’/3’ UTRs and internal response elements ( intRE ) of both genomes , as well as the Proximal- and Distal-Subgenomic Control Elements ( PSCE and DSCE ) in RNA1 ( Fig 3 ) , which correlates to the findings that these regions are important and required for RNA replication and encapsidation [29 , 30 , 32 , 60–64] . The short-read ClickSeq data provide in-depth and high resolution details of individual recombination events . However , in order to determine the correlation of these events over time , we used long-read ONT nanopore sequencing , which can characterize full-length wild-type and defective genomes ( Fig 4 and S4 Fig ) . We reverse transcribed and amplified both RNA genes using primers specific to the 5’ and 3’ UTRs of RNA1 and RNA2 from all passages of replicate 2 to obtain cDNA that could be barcoded and analyzed using protocols for 2D sequencing on the ONT MinION . The ClickSeq data shows that these regions were highly conserved during passaging ( Fig 3 ) , therefore we were confident using template-specific primers to these regions would capture both the full-length wild-type virus genomes as well as any defective RNAs . After PCR amplification , we analyzed the MinION cDNA libraries using agarose gel electrophoresis to observe the distribution of cDNA fragments and ratios of full-length to defective RNA genomes ( Fig 5A ) . While the cDNAs from the early passages are predominantly of the expected size for full-length RNA genomes , later passages contain an array of band sizes . This shows that in the early passages the full-length genome is the predominant species while in later passages the truncated version becomes predominant . We also observe species appearing to be larger than RNA2 . It is possible that some of these species correspond to RNA2 homodimers or other complex rearrangements that have previously been observed[65] and would result in an increased molecular weight . Evidence of RNA1 homodimers ( 3107^1 ) , RNA2 homodimers ( 1400^1 ) , and RNA2 to RNA3 heterodimers ( 1400^2720 ) can also be found in the ClickSeq data ( S1 Datafile ) . We pooled the amplified cDNAs from each sample at equimolar ratios and loaded the pooled , barcoded library onto a MinION MkIB device using an R9 flowcell as per the manufacture’s protocol . We ran the standard protocol for obtaining 2-dimensional reads using the MinKNOW control software and collected nanopore reads for approximately 36 hours , upon which the quality and yield of reads dropped substantially . We obtained a total of 169’814 reads , of which 46’183 passed the default ONT filter and were successfully demultiplexed . This yielded between 2688 and 8815 full-length reads per passage and corresponds to approximately 0 . 1 Gigabases of sequence information . This would correspond to ~80’000 1x150bp Illumina reads per sample , assuming even coverage . With this depth , we can build up a comprehensive picture of the full-length genomic landscape of the viral samples , allowing us to resolve DI-RNA species even if they were present at less than 1% of the total viral genomic population . The long-read nanopore sequencing data were aligned to the full-length FHV genome using the BBMAP suite ( https://sourceforge . net/projects/bbmap/ ) . This pipeline tolerates large insertions and deletions in the long-reads , thus allowing us to characterize the overall topology of the defective RNAs . We mapped between 94 and 97 . 5% of the MinION reads from each passage to the FHV reference genome ( Table 3 ) . An example of reads aligned to FHV RNA2 is shown in S4 Fig . The error rate of aligned reads , including single nucleotide mismatches and small InDels , was determined from alignment pileup files . Consistent with recent reports [66] , we found the overall modal and mean error rates for all mapped position was 5 . 0% and 6 . 3% respectively , with 95% of the sites having an error rate better than 13 . 6% ( N95 value = 0 . 864 ) . A histogram of error rates for all mapped positions across all 9 datasets is shown in S5A Fig . Due to the large number of small InDels generated during nanopore sequencing [43] , we also determined the frequency of deletions and insertions of different lengths ( S5B and S5C Fig ) . This shows that small InDels are frequent , but fall quickly in abundance with increasing length . 99 . 8% and 99 . 9% of all MinION deletions and insertions respectively were shorter than 25nts . Therefore , we considered only deletions and insertions of at least 25nts to be likely to be bona fide InDels present in the original viral RNA and corresponding to recombination events comprising defective RNA species rather than a sequencing error . The nanopore data reveals the presence and frequency of large deletions and insertions within defective RNA genomes . From these , we can reconstruct the population of either full-length or defective RNA genomes present in each of the viral passages ( annotated as described in the Methods section ) . The full table of characterized defective RNAs and their frequencies in each passage is detailed in S2 Datafile . In total , we found 6030 and 3639 unique defective RNAs of RNA1 and RNA2 respectively throughout all passages . The frequency of individual recombination events found in both the ClickSeq data and the MinION data were compared and correlation coefficients calculated ( Table 3 ) . The correlation in the earliest passages was poor , due to the low abundance of events in both datasets . However , later passages correlate well with Pearson coefficients reaching 0 . 85 . This is important as it demonstrates that the frequency of recombination events was not biased during cDNA amplification of the full-length or defective viral genomes . Similarly , the Shannon Entropy indices increase during passaging ( Fig 5C ) , consistent with those from the ClickSeq data . The number of deletions in each passage in RNA1 and RNA2 are given in Table 3 and illustrated in Fig 5B . The earliest passage contains very few deletions . In passage 1 , 95 . 8% of the reads map to the full-length genome in its entirety . With subsequent passages , the number of reads containing deletions increases , reaching a plateau at passage 8 with 76 . 0% of the reads containing two deletions and 8 . 2% containing three deletions . DI-RNAs ( e . g . 1_317^1091_1242^2301_3107 ) are easily identifiable as early as passage 2 ( S2 Datafile ) and match well with the expected identities based on our ClickSeq results and previous studies[8 , 31] . By the final passages these species predominate , leaving only a small percentage of full-length wild-type viral RNAs . While we can readily identify mature DI-RNAs containing two or more deletions , few single-reads contain just one deletion ( <6% ) in all of the passages . Moreover , individual species are rarely observed again in subsequent passages ( S6 Fig and S2 Datafile ) . Importantly , most of these single events do not delete the expected regions common to FHV DI-RNAs . Therefore , they may either correspond to sequencing artifacts , or transient defective RNA species generated due to stochastic RNA recombination , similar to the low-frequency events observed in the ClickSeq datasets . In later passages ( beginning at passage 3 ) , we do begin to see the presumptive intermediates ( e . g . 1_317^1091_3107 or 1_1242^2301_3107 ) of mature DI-RNAs ( e . g . 1_317^1091_1242^2301_3107 ) . However , this is after the mature DI-RNAs were first observed , and after the point at which DI-RNAs have begun to accumulate . Indeed , in passages 2 and 3 respectively , mature DI-RNAs make up 1% and 30% of the viral population while the singly-deleted intermediates make up 0% ( unobserved ) and 3% . Together with the observation of rare DI-RNAs in the inoculum with the ClickSeq recombination analysis , these data indicate that single-deletion species do occur early during passaging , but remain poorly abundant and do not accumulate . In contrast , mature DI-RNAs are observed to rapidly accumulate between passages , indicating that they possess a replicative advantage above both wild-type viral genomes and intermediate defective RNA species . In addition to deletions , a small number of defective RNAs first appearing at passage 5 contained insertions . Interestingly , the majority of these comprised short insertions of ~200 nucleotides that were found in first 300nts of the MinION reads and were mapped between nts 19 and 20 of RNA1 . In each case , these inserts corresponded to nts 2300–2513 of RNA1 . This region corresponds to an internal response element ( intRE ) of the Proximal Sub-genomic RNA Control Element ( PSCE ) previously identified as being essential for FHV RNA replication and conserved in DI-RNAs species [60] . The most common deletion in the DI-RNAs in this region of RNA1 for the final passages are from 1242 to 2301 , which retains the intRE . However , there are also a large number of deletions ranging from 1245 to 2514 , which would delete the essential intRE . Closer inspection of the MinION data reveals that the majority of these reads ( >90% ) that contain the 200nt intRE insertion concomitantly contained deletions from 1245–2514 , indicating that these two events are correlated . The ClickSeq data also shows a frequent recombination event , 2513^21 , which appears first in passage 4 and is among the 10 most common events in the final 5 passages ( S1 Datafile ) . This matches precisely the 3’ junction site of the insertion event detected in the MinION data . However , the event 20^2300 corresponding to the 5’ junction site was not detected in our initial ViReMa analysis of the ClickSeq data as this would have required a search seed length of less than 20 nts . Repeating the ViReMa analysis using a seed length of 17 does indeed reveal the presence of the 20^2300 recombination event . This event is rarely observed in either of the other two replicate ClickSeq data ( 7 and 31 total reads across all passages of replicates 1 and 3 respectively ) . These data indicate that in a number of defective RNA genomes , the intRE element has been deleted and subsequently re-inserted at the 5’ end of the defective RNA genome . As the intRE element is required for regulation of RNA replication , presumably this maintains the ability for this highly-rearranged defective RNA to replicate . These data provide a comprehensive overview of the different species of defective RNAs that are present during viral passaging . Illustrating such a complex set of data is a challenge as each sample contains a large number of genome arrangements ( 6030 and 3639 for RNA1 and RNA2 respectively ) and frequencies of these species vary substantially over time . We found that illustrating these data as a stacked area plot gave the most informative summary of the changes of the many different type of DI-RNA species over time . Due to the moderate error-rate of the nanopore read data , the exact identification of a recombination event and thus annotation of that genome may be incorrect . This would result in an over-estimation in the potential number of unique structural variants . Therefore we filtered datasets by requiring genomes to be represented by three or more reads . While removing a lot of noise , this has the drawback that we might be losing rare defective RNAs . Stacked area plots for genomes represented by three or more reads are shown in Fig 6 . The stacked area plots for the unfiltered datasets are shown in S6 Fig . This representation reveals key components of the evolution of the DI-RNA species . The stacked area plot for RNA1 ( Fig 6A ) shows that the composition of DI-RNAs in the viral population changes over time and new species appear at each passage . For example , the most abundant defective RNA1 species in passage 5 is ‘1_317^1091_1242^2301_3107’ but reduces in relative frequency in later passages . The most abundant species in the final passage 9 is ‘1_313^941_1241^2325_3107’ , which appears at low levels as early as passage 2 , but does not begin to accumulate until passage 6 ( S2 Datafile ) . Why this DI-RNA only begins to accumulate at later passages despite being present in the early passages is not clear . The ‘complex DI-RNA’ that deletes the PSCE in RNA1 referred to in the previous section ( ‘1_342^1083_1245^2514_3107’ ) is also observed ( annotated in Fig 6A ) first appearing at passage 5 . As can be seen in the stacked area plot for RNA2 ( Fig 6B ) , the general composition of DI-RNA species is established at passages 4–5 . Subsequently , the relative frequencies of the DI-RNA fluctuate but the overall diversity changes little with few new species appearing after passage 4 . This is also observed when calculating the Shannon Diversity index ( Fig 5B ) whereby entropy reaches a maximum at passage 5 and decreases thereafter . Interestingly , this range of fluctuations resemble the sinusoidal patterns of DI-RNA abundance that have been observed in other studies of RNA viruses where the ratio of the frequency of DI-RNAs to wild-type genome has been measured through longitudinal studies[67] . The reduction in full-length infectious viral genomes and the accumulation of defective RNAs during passaging is likely to correspond to a decrease in the specific infectivity of the virus samples . To determine the effect of defective RNAs characterized by combined ClickSeq and nanopore sequencing of replicate 2 upon specific infectivity , we performed 50% tissue culture infectious dose ( TCID50 ) assays for each passage 1–9 for both the original inoculum used to infect each sample and for the particles purified from each passage [51] . The TCID50 assay is used to determine the dose required to give a 50% chance that cells in culture will be successfully infected as determined by CPE and is typically used to determine viral titer and the effective MOI of the inocula transferred from passage to passage . The results from the TCID50 assay for each passage are shown in Fig 7A and 7B . We found that the TCID50 value ( and thus PFUs ( Plaque Forming Units ) /ml ) drops considerably during passaging by over four orders of magnitude . The corresponding effective MOI ( PFUs per cell ) also drops from 38 . 5 to 0 . 0003 during passaging ( Fig 7B ) . To determine whether the drop in effective MOI was driven by reduced total particle yield or from reduced specific infectivity ( i . e . virus particles per PFU ) , we also performed TCID50 analysis of our purified and quantified virus stocks ( described in methods ) . This allowed us to determine and normalize the number of virus particles delivered per cell between each passage . As the particle-per-PFU ratio has previously been estimated at 300–400 particles-per-PFU [26 , 52] we setup our assay beginning with 300’000 particles per cell in 96 well format and performed 8 10-fold serial dilutions . In this assay , we found that the number of viral particles required to induce CPE decreased by over 400-fold during passaging ( Fig 7A ) with a trend very similar to that for the unpurified inocula . Together , these data indicate virus specific infectivity drops with a corresponding increase in the defective RNA population . There was an exception at passage 7 where TCID50 actually increased ~5 fold from the previous passage . This could be correlated to our observation of a decrease in the amount of defective RNA2 species in the MinION analysis ( Figs 5B and 6B ) . We further characterized each well of the TCID50 assay of our purified particles using flow-cytometry to give a quantitative assessment of cell survival and death in response to virus dose . We calculated the number of live cells that remained after infection at each dilution and for each passage ( Fig 7C and S7 Fig ) . We observed a reduced overall CPE in later passages at the highest virus dose as well as an increase in the number of viral particles require to induce the same amount of CPE ( Fig 7C ) . Together these trends reflect a reduced specific infectivity during viral passaging , in agreement with our TCID50 assays . Interestingly however , for the highest particle concentrations in passages 8 and 9 , we saw less cell death at the highest doses ( 300 , 000 and 30 , 000 particles per cell ) than for cells infected with the same inoculum ( and therefore same ratio of full-length to defective RNAs ) but at a lower dose ( 3 , 000–30 particles per cell ) . This observation indicates the protection of cells from infection and/or CPE when supplied with a large dose of viral particles that contain a large proportion of DI-RNAs .
In this manuscript , we sought to provide a thorough and comprehensive analysis of the frequency and identity of recombination events present during the serial passaging of Flock House virus in cell culture in order to elucidate the pathways and mechanism of DI-RNA emergence and evolution . We began with a homogenous inoculum derived from plasmid cDNAs of each of the FHV genomes . In the inoculum and in the early passages , we find a wide range of low-frequency recombination events corresponding to deletions and duplications that are dispersed through-out the viral genomic RNAs . We can be confident that these species do not constitute sequencing artifacts as we made our RNAseq libraries using ‘ClickSeq’[35] that has previously been demonstrated to reduce artifactual recombination in RNAseq data to fewer than 3 events per million reads . Further confidence in the low rates of artifactual recombination in our study is provided internally by inspecting the numbers of inter-RNA recombination events ( RNA1 to RNA2 and vice versa ) , which are always low . Furthermore , the majority of the detected inter-RNA recombination events correspond to genomic RNA hetero- and homo-dimers , which have previously been characterized as replication intermediates [65] . Within only 2–3 passages , however , deletion events similar to those previously observed in DI-RNAs appear in all three passaging replicates . In subsequent passages , these recombination events begin to accumulate rapidly so as to predominate over full-length viral RNAs . This observation of the emergence of DI-RNA species , followed by their rapid accumulation is consistent with existing theories on the evolution of DI-RNAs that postulate that a wide range of potential DI-RNA species are generated by non-programmed RNA recombination and that only a handful are successfully replicated and thus accumulate[9] . While the short-read data provide high-resolution characterization of individual recombination events , it is through the use of the Oxford Nanopore Technologies ( ONT ) MinION that we are able reconstitute the complex full-length genomic landscape of FHV during passaging and determine the relative abundances of the genomic RNAs in each passage . As a result , we were able to determine that by the final passage only ~2% of the mapped reads are full-length viral RNA1 , which corresponded with a large reduction in specific infectivity . Additionally , the nanopore data revealed complex rearrangements of RNA genomes , including the excision of an entire functional RNA motif and its reinsertion in the 5’UTR of RNA1 . The variation in recombination boundaries of the DI-RNAs suggests that a range of deletions can be tolerated . However , it is important to note that while each replicate is its own distinct lineage , each replicate passaging experiment was derived from the same initial inoculum . We chose to design the experiment this way to determine if the same DI-RNAs would be generated independently or if completely different deletions would arise and be selected for even though the environmental conditions are practically the same . Here , we observe the latter ( Table 2 ) . Few of the RNA1 recombination events observed in the inoculum are observed again in subsequent passages . Additionally , even though we found the event ‘738^1219’ in RNA2 the inoculum , this was not the final predominant species in any replicate . Therefore , the evolution of DI-RNAs was not pre-determined by the presence of rare DI-RNA species in the common inoculum ( a founder-effect ) , but rather by the selection of well-replicating DI-RNAs that arose later during serial passaging . Nonetheless , the final recombination events are highly similar between replicates and to previous reports from different laboratories . Therefore , this indicates that either the DI-RNAs have emerged due to a common mechanism of formation , the presence of a common selectivity filter , or both . In addition to providing a thorough analysis of the pathways of defective RNA formation and evolution , there are two unexpected and critical observations made through this study . First: while we observe a wide range of recombination events early on during passaging , only a limited number of events are subsequently amplified and later define DI-RNAs . Moreover , these limited sets of events are similar between replicates , and to previous studies . This suggests that while a large pool of potential defective RNAs are generated , only a small number are capable of accumulating . Secondly: we do not observe the amplification of DI-RNAs with only one deletion . In contrast , ‘mature’ DI-RNAs accumulate rapidly . Nonetheless , we do find evidence for intermediate DI-RNAs as early as the inoculum sample . This indicates that intermediate defective-species are either non-competitive and do not accumulate or are not formed as a pre-cursor to mature DI-RNAs . These two observations provide important insights into the potential mechanisms of DI-RNA emergence and evolution . While there is a strong selection pressure for DI-RNAs to retain essential functional genomic elements , it is also postulated that a shorter defective RNA would be replicated more quickly and thus more competitively [10] . In our analysis , while the ~250–550 deletion in RNA2 ( for example ) is very common we do not observe the accumulation of deletion events that are smaller than this ( e . g . 300–450 ) . This is despite the fact that we can detect and observe such species in low frequencies both in early and late passages , suggesting that they are indeed generated but are not selectively amplified . This may in part be due to selecting for DI-RNA genomes that are as small as possible , while retaining the minimal amount of genetic material to form functional genetic elements . However , it may also be the result of a negative selection pressure or restrictive barrier that is released only after excising specific portions of the viral genome . An example of this scenario has been demonstrated for tomato bushy stunt virus ( TBSV ) associated DI-RNAs whereby deletion of a translation enhancer functional element removes the competition between translation and replication , thus favoring replication of the smaller DI-RNA[68] . Therefore , the final structure of DI-RNAs may depend both on the retention of essential functional RNA elements , as well as the removal of restrictive barriers that attenuate RNA replication . One model for the evolution of DI-RNAs is through the step-wise accumulation of deletion events through a series of individual recombination events[68] . The MinION data reveals that the defective RNAs that accumulate ( rapidly over the course 2–3 passages ) contain multiple deletion events . However , we do not see the rapid accumulation of the intermediate DI-RNAs , despite evidence for their presence early in viral passaging . This suggests that the mature DI-RNAs have a competitive advantage over their presumed intermediate precursors . If this is the case , the multiple deletions may function epistatically either through an undefined cooperative/additive mechanism or through the release of multiple restriction barriers , as proposed above . If multiple restriction barriers are required to be excised for the formation of DI-RNAs , small or multipartite RNA viruses , such as FHV or influenza[17] , may therefore generate DI-RNAs more readily by requiring fewer intermediate steps than long , monopartite RNA viruses . Moreover , if intermediate defective RNAs fail to accumulate , this reduces the likelihood that mature DI-RNAs can subsequently be generated and may place substantial limitations on the ability of some viruses to generate DI-RNAs altogether . An alternative reason for the rarity of precursor/intermediate defective RNAs is that the mature DI-RNAs are generated in one single event . We are yet to determine the molecular mechanism of recombination that leads to DI-RNA formation . Both template-switching , secondary-structure jumping , and non-replicative mechanisms have been proposed , and indeed these mechanisms need not be mutually exclusive . Our observation of nucleotide preferences at recombination junctions ( Fig 2C ) may arise through any of these potential mechanisms . Alternatively , it is possible that multiple reassembly/deletion events occur in a single step , in a manner reminiscent of chromosome shattering ( chromothripsis ) [69]; or ‘virothripsis’ . Within the confined invaginations of the mitochondrial membranes that form the replication factories of RNA viruses such as FHV[70] , the fragmentation the RNA virus genome followed by incorrect re-stitching of these genome pieces , either through forced-copy choice template switching or a non-replicative mechanism , could create the DI-RNAs observed here including the complex rearrangements observed for RNA1 . A defective-interfering RNA is a defective RNA that has the ability to compete with or otherwise attenuate the replication and proliferation of the wild-type helper virus . In our study we demonstrate that the viral swarm , even after only a few passages , is replete with many varieties of defective RNAs . With a single sequencing experiment , we would not be able to determine whether these defective RNAs are accumulating , diminishing or make-up a static component of the viral quasi-species . However , as we perform serial passaging with sequential sequencing experiments , we can determine which defective RNAs are accumulating ( for example the ‘mature’ DI-RNAs ) and which are not ( e . g . the putative intermediate , or ‘immature’ defective RNAs ) . It would be impractical to validate each of the many hundreds of detected defective RNA species with molecular virological experiments to determine whether they truly can attenuate or interfere with wild-type virus replication and to therefore categorize that species as a defective-interfering RNA . Indeed , we cannot exclude the possibility that multiple DI-RNAs act co-operatively within the viral quasi-species and are mutually dependent upon one another . However , the demonstration here of an accumulation during serial passaging is strong evidence that these species are interfering , as their accumulation essentially dilutes the pool of wild-type functional virus . With this in mind , we believe it would be suitable to describe the mature defective RNAs as defective-interfering RNAs ( DI-RNAs ) , and the ‘immature’ only as defective RNAs . It is remarkable that FHV is able to maintain a viable infection despite being burdened with such a gross excess of DI-RNAs in the final passages presented here . By performing TCID50 assays of the original inocula used between each of the passages and of the particles purified from each passage , we show that there is a dramatic reduction in specific infectivity during passaging corresponding with the increase in the DI-RNA content . DI-RNAs have generally been demonstrated to arise at high MOIs , as was the scenario with our first passages . However , our calculated MOI drops rapidly after DI-RNAs have formed to levels that might be expected during typical in vivo viral passaging scenarios . However , in this experiment we were actually passaging a large number of virus particles between cells , but with a low specific infectivity . It is also interesting to observe that for the final passages there appears to be a protective property of the DI-RNAs as determined by flow-cytometry , but only when administered at the highest doses corresponding to over ~30 , 000 particles per cell . However , the role that DI-RNAs might play in vivo is not clear , as these very high doses may not be physiologically relevant . DI-RNAs for FHV similar to the ones described here have been observed to arise in experimental fruit fly infections [33] and so the mechanism of formation and/or selection is likely to be similar in cell culture and in vivo . However , whether RNA viruses such as FHV have evolved to favor the spontaneous formation of DI-RNAs and if so whether these DI-RNAs play an important role in modifying the life-cycle of the virus , is yet to be determined . | Defective RNAs are versions of a viral genome that arise naturally during viral infections but have been truncated or rearranged by non-homologous recombination . While not encoding for functional viruses , they can be amplified and co-passaged with the wild-type virus , effectively parasitizing the normal viral machinery . Some defective RNAs can replicate so successfully so as to subdue the replication of the wild-type virus , forming a ‘Defective-Interfering RNA’ ( DI-RNA ) . As a result , DI-RNAs may promote the establishment of chronic viral infections , may prolong the period during which the host is infectious , and may even be exploited as antiviral therapies or vaccines . Therefore , understanding the reasons and mechanisms of how DI-RNAs are formed and subsequently evolve is important . Here , we sought to characterize these processes by passaging Flock House virus ( a highly tractable and well-characterized model RNA virus ) in cell-culture for approximately one month and sequencing the viral genomes every three days using a combination of ‘ClickSeq’ ( to resolve recombination events with nucleotide resolution ) and the Oxford Nanopore Technologies MinION ( to characterize full-length and defective genomes ) . This provides a highly detailed characterization of the pathways of DI-RNA emergence and their progression to dominance over the wild-type viral genome . | [
"Abstract",
"Introduction",
"Methods",
"Results",
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] | 2017 | Parallel ClickSeq and Nanopore sequencing elucidates the rapid evolution of defective-interfering RNAs in Flock House virus |
Genome-wide association studies ( GWAS ) examine a large number of markers across the genome to identify associations between genetic variants and disease . Most published studies examine only single markers , which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk . Much knowledge has been accumulated in the literature on biological pathways and interactions . It is conceivable that appropriate incorporation of such prior knowledge may improve the likelihood of making genuine discoveries . Although a number of methods have been developed recently to prioritize genes using prior biological knowledge , such as pathways , most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of a pathway . However , how genes are related with each other in a pathway may be very informative to identify association signals . To make use of the connectivity information among genes in a pathway in GWAS analysis , we propose a Markov Random Field ( MRF ) model to incorporate pathway topology for association analysis . We show that the conditional distribution of our MRF model takes on a simple logistic regression form , and we propose an iterated conditional modes algorithm as well as a decision theoretic approach for statistical inference of each gene's association with disease . Simulation studies show that our proposed framework is more effective to identify genes associated with disease than a single gene–based method . We also illustrate the usefulness of our approach through its applications to a real data example .
In genome-wide association studies ( GWAS ) researchers examine a large number of markers across the genome in many individuals to identify associations between genetic variants and disease , or to prioritize markers for follow up studies . However , most of the times the signals from individual markers are weak and the sample size is not large enough to have adequate power for true discoveries , especially when the minor allele frequency is low . Various approaches have been developed to increase statistical power , including aggregating multiple markers from the same gene or in the same haplotype block region and incorporating information from other sources into the GWAS analysis . It has been found that the gene level analysis has the ability to identify new associations in addition to those identified using individual Single Nucleotide Polymorphisms ( SNPs ) [1] , [2] . Gene-based analyses include those using the most significant SNP within and near a gene [1]; combination statistics ( Fisher , Sidat , and Simes ) from all individual markers [2]; Principal Component Analysis ( PCA ) regressions [3] and the sparse partial least squares regressions [4] . To incorporate prior biological knowledge , one information rich resource is biological pathways . It is believed that genes interact with each other in biological processes , and it is conceivable that they may jointly affect the risk of a complex disease . There exist an abundance of databases containing known gene pathways and protein-protein interactions , such as KEGG , BioCarta , GenMAPP , and HPRD . A number of gene prioritization methods incorporating prior biological knowledge have been developed for GWAS . Some examples include Prioritizer [5] , Endeavour [6] , CGI [7] , CANDID [8] , GeneWanderer [9] , CIPHER [10] , GIN [11] , and the pathway based gene set enrichment approach [1] . These methods have shown that incorporating prior biological information in GWAS is useful . However , they do not consider functional relationships among genes . The general input of these approaches is a list of genes as a set , in which genes are treated as exchangeable without taking into account the regulatory relationships among them . As a result , information from the pathway topology and interactions among genes is usually ignored . However , how genes are functionally related to each other in a pathway may be very informative for GWAS analysis and such information can be utilized to increase the power of detecting real associations . When associations have been firmly established for some genes either through GWAS or prior candidate gene-based studies , we can take advantage of this knowledge to examine other genes related to these known genes through the same pathways they all participate in . In this paper we propose a Markov Random Field ( MRF ) model to incorporate biological pathway information in GWAS . MRF has been considered by several authors to combine data from different sources in genomics studies , e . g . , a spatial normal mixture model [12] for gene expression and CHIP-chip data , a Gamma-Gamma model and MRF for mRNA microarray data [13] , and prioritizing genes by combining gene expression and protein interaction data [7] . However , little has been done in the context for GWAS , with the exception of Li et al . [14] who proposed a hidden MRF for GWAS . But their method is developed in the context of jointly analyzing markers in linkage disequilibrium . We first present a motivating example from a GWAS of Crohn's disease [15] for the proposed method . As will be shown next , the result clearly suggests that genes in the same neighborhood within a pathway tend to show similar association status . This Crohn's disease cohort includes 401 cases and 433 controls , and the Illumina HumanHap300 BeadChip ( Illumina , San Diego ) were used for genotyping . We first mapped SNPs to genes and then applied PCA regressions to obtain gene-level p values of the association tests with Crohn's disease status [3] . More details about this data set are given in the Materials and Methods section . We then obtained pathway and interaction from BioCarta ( http://www . biocarta . com/ ) , GeneMAPP [16] and KEGG [17] . We consider a total of 3 , 735 genes in over 350 pathways . Genes on the same chromosome that are within 1 million base pairs are excluded to avoid effects caused by possible linkage disequilibrium . To see whether genes connected with each other in the same pathway tend to show similar evidence for association , we use a cut-off value 0 . 15 where genes whose p values are below this cut-off are considered interesting and labeled with 1 . Note that we use a relatively loose threshold so that a sufficiently large number of genes are called “interesting” and this loose cut-off also reflects our belief that many genes have weak effects and only show moderate evidence of association . In a pathway , we consider the number of edges connecting a pair of “interesting” genes , which depends on the labels of all genes . We denote this number by . A large value of would suggest that “interesting” genes are more likely to be neighboring genes . To assess the statistical evidence for the tendency to observe large values , we employ a permutation procedure as follows . In each permutation , we randomly permute the “interesting” labels of all genes and derive a permuted statistic and these permuted statistics are used to arrive at an empirical distribution of under the null hypothesis that there is no tendency for neighboring genes to have similar disease association status , i . e . “interesting” or not . We then compare the observed statistic with the empirical distribution . Finally the p value of the observed in this empirical distribution is calculated . A p value close to 0 indicates that “interesting” genes tend to be neighbors . This procedure is repeated for all pathways , and the histogram of p values of for all pathways is plotted in Figure 1 . It is evident that this distribution is highly skewed to the left , which suggests associated genes tend to be neighbors in a given pathway . In the rest of this article , we first introduce our model and statistical inferential procedures . The performance of our methods is then assessed through both simulation studies and real data applications .
We start by considering a simple model in which a pathway is represented by an undirected graph where is a set of genes ( nodes ) and are directly connected} denotes the set of all edges . For the th gene in , let denote the set of its neighbors , and denote the number of its neighbors . Let denote the true association status where The values are referred to as labels of a node hereafter . Let denote the labeling of . Thus is a spatial random vector whose elements may be correlated with each other . Note that each node can be labeled either or , and so there are a total of unique configurations of the pathway . The ultimate goal is to infer the value of based on the pathway topology and the observed association data . To formalize the idea that neighboring genes tend to have similar association status , we need a probability measure so that nodes connected with each other tend to have the same labels . Here we consider a nearest neighbor Gibbs measure [18] that has the following form: ( 1 ) where are the prior parameters or hyperparameters , and are the indicator functions , , and is a normalizing function that is the sum over all possible configurations: ( 2 ) Note that it is prohibitive to evaluate when is large . Here and assign prior weights to edges connecting two non-associated nodes and two associated nodes , respectively . The function will be elaborated in more details in the context of the conditional probability later . In ( 1 ) , the second sum is taken over all edges connecting direct neighbors in which both end nodes are labeled −1 , and the third sum is taken over all edges in which both end nodes are labeled 1 . Positive and will put more weights on configurations in which directly linked nodes have the same labels , which is desirable in our context . The hyperparameter determines the marginal probability of when , i . e . , all nodes are treated as singletons that are independent: The simple form Gibbs measure in ( 1 ) has the Markov property that makes it attractive to model a biological pathway , in which directly linked genes interact with each other . It defines a MRF , which by definition is a probability measure that satisfies , where denote all nodes but , and is the set of all direct neighbors of node . Please see Materials and Methods for details . Now we discuss the posterior distribution of association status after combining the evidence from the observed association statistics at the gene level and the structure of the gene pathway . Before we proceed , it is necessary to present the likelihood function of the observed data . We consider the situation where the observed evidence of association is summarized by values , which are assumed to be conditionally independent given the true association status . Under the null hypothesis of no association , each p-value has a uniform ( 0 , 1 ) distribution . In this article , we consider , where and is the CDF ( Cumulative Distribution Function ) of . Therefore , under the null hypothesis of no association , i . e . , , the density of is . However , if there is association between the gene and disease , i . e . , , the distribution of is usually unknown . For simplicity , we assume that it is from , where is the location parameter and is the scale parameter that usually depends on the true effect size , allele frequencies , and the sample size . To account for the uncertainty about the parameters , we can put prior distributions on and , and marginalize over them to obtain the predictive density of . Here we consider conjugate priors and , or We denote that are hyperparameters . The prior mean of is and its variance is . The prior mean of is and the prior variance is . This prior is of conjugate form so that the integration over and is analytically tractable . We note that the hyperparameters can be estimated from the observed data via an empirical Bayes method ( see Text S2 , Figures S1 and S2 ) . Under this prior setting , the marginal density of is This is equivalent to when 2 , and others . The joint marginal density of is Thus , the posterior distribution of given the observed data is ( 3 ) Similar to the MRF interpretation of the prior distribution ( 1 ) , the posterior also has a nice conditional distribution and is actually a MRF , as will be shown in the Materials and Methods section . When is large , since it is prohibitive to evaluate posterior probabilities on the entire space of configurations , we implement a Markov chain Monte Carlo ( MCMC ) method to sample from the posterior distribution . Naturally a Gibbs sampler is well suited for a MRF . As will be shown later , due to the MRF property , the posterior has a nice closed-form conditional distribution that can greatly facilitate the MCMC . Most GWAS lead to a set of candidate genes/SNPs that will need to be validated in follow-up studies . Therefore , it is important to include as many truly associated genes as possible among the top ranked genes . Our proposed method allows us to rank order genes as detailed below . There are several ways of inferring the labels according to the posterior distribution of . The first one is to use maximum a posteriori ( MAP ) estimate , which is the configuration with the largest posterior probability , a reasonable point estimate for . Let us denote it by . The MAP is the maximizer of the joint posterior distribution: A Gibbs sampler outlined above can be applied to stochastically search for the solution to the above optimization problem . Multiple restarts with different initial configurations are recommended . An alternative approach is to base the estimate on the posterior conditional probability of given the observed data and all the other nodes . We can estimate by maximizing this conditional probability ( MCP ) : ( 4 ) The advantage of this approach is that the above problem is trivial to solve . As will be explained in equation ( 8 ) of the Materials and Methods section , the second term in formula ( 4 ) can be evaluated in closed form . Besag [19] proposed an algorithm known as iterated conditional modes ( ICM ) that iteratively updates . Note that the convergence of ICM is assured because the posterior is proportional to which never decreases at any iteration because the first term is non-decreasing and the second one is a constant . So it is easy to see the ICM will converge to a local maximum in the posterior distribution . Since the ICM runs fast and usually converges in several iterations , multiple restarts with different initial configurations are recommended . Finally the resulting configurations can be compared by evaluating up to a normalizing constant to pick the largest one . The inference can also be based on the marginal posterior probability . Let . We consider a decision rule in the form , where is an indicator function and is the sought decision threshold . If , the decision is positive ( also referred to as discovery ) and gene is called to be associated with the disease . Likewise if the decision is negative . To address the problem of multiple comparisons , we consider loss functions associated with making wrong decisions ( false discoveries and false negatives ) , and solve the decision problem by minimizing the expectation of the loss functions under the posterior distribution . Here we consider two loss functions . First , if we are interested in the 0-1 loss function , we may want to minimize the expected loss ( 5 ) under the posterior distribution of . The solution is . Note that assigns equal loss to the false positive and false negative errors . This is to minimize the expected frequency of making wrong calls for the association status . Note that the performance of the decision rule is based on the frequentist operating characteristic in the Bayesian framework , which is common in medical decision makings [20] . The second loss function we consider is the false discovery rate ( FDR ) : ( 6 ) Suppose the goal is to control the expected FDR , under the posterior distribution , such that it is no more than , i . e . , . If we rank order all genes by their posterior probabilities from the largest to the smallest , and let denote the th order statistics , then the solution is to choose a cut off value where is the largest integer that makes . We should mention that more complicated loss functions can be considered under the framework of our model . See Müller et al . [20] for other examples . First we use simulated data to study the performance of the proposed method . The simulation is based on a simple 6-node network shown in Figure 2 . Genes G1 through G3 are assumed to be associated with the disease ( labeled +1 ) while G4 through G6 are not associated with the disease ( labeled −1 ) . Data are simulated from a disease model as follows . We assume G1 , G2 and G3 have independent effects on disease risk and each has a disease related SNP . The genotypes and minor allele frequencies of these three SNPs are denoted by and , respectively , where for A multiplicative genetic model is assumed for the risk of having the disease . More specifically , for an individual with genotype , the risk is , where is the baseline risk of those carrying two normal alleles in all three genes , and is the relative risk , or effect size , of gene , . For each SNP the Hardy-Weinberg equilibrium ( HWE ) is assumed to hold in the general population so that the genotype probabilities are , , and for , 1 , and 2 , respectively . In the simulation we use three minor allele frequencies = ( 0 . 05 , 0 . 10 , 0 . 15 ) , three disease prevalence values = ( 0 . 05 , 0 . 10 , 0 . 15 ) , and six effect sizes = ( 1 . 05 , 1 . 10 , 1 . 15 , 1 . 20 , 1 . 25 , 1 . 30 ) . As a result , there are a total of 54 settings of , for each of which we first let and , and then calculate the baseline risk , and finally obtain the conditional distribution of the genotypes given the disease status . Then genotypes of G1 , G2 and G3 of 600 cases and 600 controls are simulated according to the conditional genotype distribution . The values of the three causal genes are calculated from a logistic regression of the data . For G4 through G6 , the values are simulated from Uniform ( 0 , 1 ) . The power of detecting the true association depends on the disease model . In this case , larger values of relative risk , MAF and prevalence corresponds to association tests with higher power . In the simulation we set the hyperparameters = ( −1 , 0 . 25 , 0 . 01 ) where more weights are assigned to edges connecting two associated genes . This corresponds to a prior belief that the probability of association is roughly between 0 . 35 and 0 . 50 . The hyperparameters are set to ( 3 , 1 , 10 , 1 ) where a large value of puts a large prior variance on , which allows a wide range of values for both and . For each simulated data set , the posterior probabilities are enumerated since there are only 64 possible configurations in this simple example . The simulation is repeated 500 times . We compare the proposed method using the posterior mean with the one using the value , and apply cut-off values of 0 . 7 and 0 . 05 for posterior probabilities and values , respectively . For each simulated data set , we calculate the false positive rate ( FPR ) , sensitivity ( Sens . ) , and false discovery rate ( FDR ) by thresholding on values and posterior probabilities . In addition , genes can be rank ordered by the two methods and the area under the Receiver Operating Characteristic curve ( AUC ) can be calculated . The average values of the three rates plus the AUC over the 500 simulated data sets are shown in Table 1 . As can be seen , the proposed method of the posterior probability has higher sensitivity , smaller false discovery rate , and higher AUC than the value thresholding in every setting of the prevalence , MAF and effect size , while the FPR of both methods are controlled at 0 . 05 . The second simulation study is based on the network shown in Figure 3 . This network was adapted from BioCarta “Human Rho cell motility signaling pathway” and we deleted a few genes that are either absent from our Crohn's disease data or not connected to others . We assume three different sets of truly associated genes , plotted in triangles , rectangles and pentagons , each of which contains three , five , and seven nodes , respectively . To simulate different levels in the power of the association tests , for each gene with , the value is computed from a two-sided test where scores are randomly drawn from , and , respectively , corresponding to the power 0 . 16 ( low ) , 0 . 32 ( median ) and 0 . 51 ( high ) in the association tests . The values for are generated randomly from Uniform ( 0 , 1 ) as before . To examine the effects of hyperparameters of the network , we consider eight priors , listed in Table 2 , that roughly form four main groups indexed by numbers 1 through 4 , and two subgroups indexed by letters and . For each set of hyperparameters a Gibbs sampler is run to draw samples from the corresponding prior distribution , and we can estimate , the prior mean , and and where , the probabilities of edge linking two nodes with identical labels . The averages of the estimated probabilities are listed in the last three columns of Table 2 . The average prior means of all nodes are about 0 . 05 , 0 . 15 , 0 . 25 , and 0 . 4 , respectively for the four main groups . Roughly speaking , it means that group 1 is in favor of a small number , and group 4 a large number , while groups 2 and 3 in between , of nodes labeled with +1 . Furthermore , values of in subgroup are larger than those in subgroup , meaning that nodes with identical labels are more likely to be next to each other apriori in subgroup than subgroup , as can be seen from the last two columns in Table 2 . On the other hand , because the posteriors are found to be insensitive to the hyperparameters when is large , they are set to ( 3 , 1 , 10 , 1 ) as in the previous example . We simulate 200 data sets for each combination of the three power settings ( low , median and high ) and three truly associated sets ( 3 , 5 , and 7 nodes ) . For each data set , we run eight Gibbs samplers using eight different hyperparameters described above . Each Gibbs sampler is run with 100 restarts and each start contains 100 steps . We compare the average AUC of 200 simulated data sets using value and the posterior mean and plot the results in Figure 4 . In general , the AUC of the proposed method is larger than that using values alone . It achieves good AUC if the prior mean is close to the truth , especially when the power is low . For example , in the middle column panels where there are 5 truly associated genes , prior settings 2 and 3 , favoring median number of truly associated nodes , outperform prior settings 1 and 4 . Similarly , in the right panel where the true model contains 7 genes , prior settings 3 and 4 , which are in favor of large models , perform better than the other prior settings . Furthermore , priors in subgroup b are better than subgroup a in general . It is not surprising because the priors in subgroup b encourages nodes labeled with +1 to group together , which agrees with the simulation setting . To evaluate the control of the false positive rates and the false discovery rates of the proposed methods in relatively large pathways with only a few associated genes , we conduct a third simulation study based on a simulated network shown in Figure 5 that contains 60 nodes . We consider three truly associated gene sets , namely ( 2 , 11 , 19 ) , ( 2 , 11 , 19 , 41 ) , and ( 2 , 11 , 19 , 20 , 41 ) , and label them as models 1 , 2 and 3 in Table 3 . Similar to the previous study , we simulate values from scores randomly drawn from , and , corresponding to weak , median and strong associations , respectively . Three prior settings are considered for , namely ( −1 . 5 , 0 . 15 , 0 . 02 ) , ( −1 . 50 , 0 . 10 , 0 . 01 ) and ( −2 , 0 . 2 , 0 . 01 ) , whose average prior probability is approximately 0 . 2 , and average prior probabilities for are roughly 0 . 13 , 0 . 11 , and 0 . 08 , respectively . For the proposed method , we consider three decision rules . The first one ( PM1 ) uses the posterior mean with a cut-off value as in ( 5 ) , the second one is MCP as in ( 4 ) , and the third one ( PM2 ) is the method to control the FDR at 0 . 1 as in ( 6 ) . Then we compare them with the value method ( P value ) with a cut-off value set at 0 . 05 and the correction method ( BH ) of Benjamini & Hochberg ( 1995 ) [21] . For each scenario we simulate 100 data sets , and run a Gibbs sampler with 100 restarts where each start contains 100 iterations . For each simulated data set , we calculate the FPR , sensitivity ( Sens . ) , FDR , and AUC as before . Table 3 lists the average values of the 100 simulation runs . In general PM1 and MCP control the FPR below the 0 . 05 level and have lower FDR than the value while achieving better or similar power as the value method . In terms of controlling FDR , PM2 controls the FDR around 0 . 1 , and it has smaller FPR or better power than the BH method in most cases when it achieves similar or better FDR . We use one Crohn's disease [15] data set to further evaluate the performance of the proposed method . Details of this data can be found in the Materials and Methods section . We run our algorithm on 289 pathways that have at least 20 genes with non-missing values . The hyperparameters are chosen such that the average prior mean is roughly between 0 . 2 and 0 . 4 based on the simulation findings . To evaluate the performance , we consider 32 target genes that are confirmed to be related to the Crohn's disease [22] . Among these genes , 10 genes can be mapped to 66 pathways . In Figure 6 we plot the AUC values of the rankings by values on the axis and posterior means on the axis for pathways containing three or more target genes . A majority of AUC values are improved if genes are rank ordered by the posterior mean . The average AUC based on values is 0 . 568 while on posterior means is 0 . 613 . To see what causes the rank changes of genes in the posterior probability , in Figure 7 we show the Human IL-2 Receptor Beta Chain in T cell Activation pathway from BioCarta . Genes in this pathway are densely connected . To aid visualization , we randomly remove some edges . Significant genes whose values are below 0 . 05 are colored in cyan , genes with improved ranks are colored in light blue and others are colored in pink . It can be clearly seen that genes colored in light blue have more connections with the significant genes , and are more heavily linked among themselves , compared to other genes in the pathway . Genes that have many interactions with each other may play important roles in the biological processes in the pathway . When they are connected to many significant genes , it might be reasonable that they are more likely to associate with the disease than other genes .
In this article we introduced a Bayesian method to incorporate prior knowledge of biological pathways into GWAS . This approach uses a MRF as a prior distribution to model the interactions among genes that participate in the same pathway . We showed that the posterior distribution is also a MRF and can be sampled via a Gibbs sampler . Inferences based on the posterior distribution allow us to combine data from the association study with prior information of biological pathways . In particular , this framework considers the topology of all genes in a pathway , which has not been fully utilized in many of the existing methods . The simulation studies and real data example suggest that the proposed method has higher power to identify genes associated with disease . One limitation of the MRF model is that the Gibbs sampler tends to move around local maxima for a long time and thus can be slow in convergence to the posterior distribution . We recommend to run the Markov chain Monte Carlo with multiple random restarts , and examine the sampling distribution of network statistics , like the number of genes labeled with +1 and the proportion of edges linking genes with identical labels . In our studies , we found that a Markov chain initially moves very rapidly from its starting state , usually within the first 10 to 20 steps , before it reaches some steady states and stabilizes for a long period thereafter . We suggest running 100 Gibbs steps for each random starting state , and conducting the simulation with 100 restarts . The computing time of this scheme typically takes a few minutes on a PC for a pathway of about 30 genes . We should also mention that the characteristics of the MRF defined in ( 1 ) depend on both the hyperparameters and the structure of the network under consideration . Consequently there does not exist a set of hyperparameters that can be suitable for all pathways . To assist the specification of hyperparameters , we provide an algorithm of estimating hyperparameters based on a conditional empirical Bayes approach in Text S2 . It is recommended that these values would be used in initial attempts and it would be better to test several other variants of hyperparameters , possibly through fine-tuning the initial values . It is helpful to draw samples from the prior distribution to assess the effects o f different prior settings . One limitation of pathway-based analysis is that not all the genes can be associated with pathways . It is likely with knowledge accumulation , more genes will be mapped to pathways . An R package is under construction and will be made publically available soon .
The nearest neighbor Gibbs measure on gene pathways in formula ( 1 ) defines a MRF and its conditional distribution has a logistic regression form as shown below . To see that the posterior distribution is also a MRF , note that for node , Thus , the conditional posterior distribution of given all other nodes only depends on its neighbors , which means the posterior distribution is also a MRF . The conditional posterior log odds of is ( 9 ) where is the marginal likelihood ratio . Therefore , ( 9 ) is the product of the marginal likelihood ratio , reflecting the evidence from the data for association with the disease , and the conditional prior odds , reflecting the effect from interactions among neighboring genes from the biological pathway . To make it clear , we can rewrite ( 9 ) in the form of a system of auto-logistic regression equations: ( 10 ) where There are a few observations . First , it is easy to see that the posterior conditional logit form in ( 9 ) is the same as the prior conditional logit in ( 8 ) except its intercept is . Thus , the observed log likelihood ratio provides a fixed additive effect to the prior logit . Second , the coefficient matrix is symmetric , i . e . , . If gene and are not neighbors , then and they are conditionally independent . On the other hand , if they are neighbors , then the impact between each other is equal . Third , genes and are in general correlated in their joint posterior distribution , even if they are not neighbors and are conditionally independent . Moreover , the more common neighbors they share with each other , the stronger the correlation between the two . To sample from the posterior distribution , here we implement a Gibbs sampler that is well suited for a MRF . The algorithm is described as follows . First we set an initial value for , say . Then in step , we update the labels sequentially for according to ( 10 ) : to obtain from . In each cycle we may want to randomize the order in which the nodes are updated . The Crohn's disease [15] data set is used to evaluate the performance of the proposed method in the Results section . Crohn's disease is a type of inflammatory bowel disease characterized by chronic inflammation of discontinuous segments of the intestine . The disease is found to be related to the interaction of several factors including genetic susceptibility , the intestinal microbial flora of the patient , the patient's immune response to these microbiota , and environmental triggers [25] . It has been well established that Crohn's disease has a strong genetic component [26] . The cohort used in the analysis includes 401 cases and 433 controls . SNPs with a call rate greater than 0 . 9 , minor allele frequency greater than 0 . 01 , and HWE value greater than 0 . 001 are kept , while subjects with a call rate less than 0 . 95 are removed from the analysis . Finally 397 cases and 431 controls remain in the analysis . SNPs are considered being mapped to a gene if their physical locations are within 10 kb from the start or end point of the gene as given by Refseq annotation at the NCBI website . Gene level values are obtained by regressing disease status on PCA components that account for at least 85% of the variation [27]–[29] . The pathways and genes in each pathway as well as the gene-level p values can be found at http://bioinformatics . med . yale . edu/group/software . html . | Statistical methods used in most GWAS are based on the analysis of single markers . Prior biological information about markers , genes , and pathways is not commonly incorporated in the detection of associated disease loci . Recently a number of methods have been developed to incorporate such information , and it has been shown that they may make use of prior biological knowledge in association analysis . However , most of these methods ignore the regulatory relationships and functional interactions among genes . In this article , we propose a statistical method that can explicitly model the interactions of genes in a neighborhood defined by the topology of a pathway . Simulation studies and a real data example show that the proposed method can improve the power of identifying associated genes when they are in the neighborhood of other genes whose association has been firmly established in previous studies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"mathematics/statistics",
"genetics",
"and",
"genomics/bioinformatics",
"computational",
"biology/genomics"
] | 2011 | Incorporating Biological Pathways via a Markov Random Field Model in Genome-Wide Association Studies |
Small mammals serve as most important reservoirs for Leptospira spp . , the causative agents of Leptospirosis , which is one of the most neglected and widespread zoonotic diseases worldwide . The knowledge about Leptospira spp . occurring in small mammals from Germany is scarce . Thus , this study’s objectives were to investigate the occurrence of Leptospira spp . and the inherent sequence types in small mammals from three different study sites: a forest in southern Germany ( site B1 ) ; a National Park in south-eastern Germany ( site B2 ) and a renaturalised area , in eastern Germany ( site S ) where small mammals were captured . DNA was extracted from kidneys of small mammals and tested for Leptospira spp . by real-time PCR . Positive samples were further analysed by duplex and conventional PCRs . For 14 positive samples , multi locus sequence typing ( MLST ) was performed . Altogether , 1213 small mammals were captured: 216 at site B1 , 456 at site B2 and 541 at site S belonging to following species: Sorex ( S . ) araneus , S . coronatus , Apodemus ( A . ) flavicollis , Myodes glareolus , Microtus ( Mi . ) arvalis , Crocidura russula , Arvicola terrestris , A . agrarius , Mustela nivalis , Talpa europaea , and Mi . agrestis . DNA of Leptospira spp . was detected in 6% of all small mammals . At site B1 , 25 small mammals ( 11 . 6% ) , at site B2 , 15 small mammals ( 3 . 3% ) and at site S , 33 small mammals ( 6 . 1% ) were positive for Leptospira spp . Overall , 54 of the positive samples were further determined as L . kirschneri , nine as L . interrogans and four as L . borgpetersenii while five real-time PCR-positive samples could not be further determined by conventional PCR . MLST results revealed focal occurrence of L . interrogans and L . kirschneri sequence type ( ST ) 117 while L . kirschneri ST 110 was present in small mammals at all three sites . Further , this study provides evidence for a particular host association of L . borgpetersenii to mice of the genus Apodemus .
Leptospira spp . are helical-shaped bacteria and form a particular group of causative agents for the zoonotic disease Leptospirosis . Leptospira spp . are transmitted through infected urine of small mammals or contaminated water via the direct contact to skin lesions or conjunctivae [1] . Small mammals are described as the most important maintenance reservoirs in nature and thus as an essential vector for several pathogenic Leptospira spp . [2 , 3 , 4 , 5] . Leptospirosis is considered the most widespread zoonotic disease worldwide , which is of emerging concern [6] . In the past , Leptospirosis was described to be a disease of occupational risk for harvesters , miners , veterinarians and rodent control workers in Europe [2 , 7] . Nowadays , it is increasingly linked to recreational outdoor activities , such as water sports and adventure travels [5 , 8] . However , partially due to the broad variety of clinical symptoms , which are nonspecific , the awareness for this disease is not yet present especially in temperate regions [5 , 9] . The estimated incidence of clinical cases per year is 0 . 2 / 100 , 000 in Germany [10] . Severe cases associated with rats have also been reported [8 , 11] . Recently , human cases , which were linked to contaminated water or soil , occurred in Austria [12 , 13 , 14] . Furthermore leptospirosis outbreaks were reported among triathletes and strawberry harvesters in Germany [15 , 16] . The clinical severity of Leptospira spp . infection depends on the virulence of the infecting Leptospira serovar as well as on the health status of the patient [3] . The taxonomy of Leptospira spp . is complex . To date , ten different pathogenic Leptospira species with more than 300 serovars , grouped in 20 serogroups are known [17] . The term serogroup is of taxonomic importance and defines groups with antigenetically related serovars . However identical serovars may belong to different Leptospira species [2] . Duplex PCR [18] and detailed sequence typing are used for the characterisation of Leptospira spp . strains and genotypes while the microscopic agglutination test ( MAT ) which is important for the categorisation of serovars , is still the gold standard in routine diagnostics [19] . Most commonly , human clinical cases in Europe are caused by L . interrogans and/or Leptospira spp . serovar Grippotyphosa [12 , 13 , 16 , 20] . A recent study from Poland reported also antibody titres in humans against the serovars Australis , Autumnalis , Hebdomadis , Hardjo , Sejroe , Zanoni , Bataviae , Bratislava , Canicola and Grippotyphosa , belonging to 3 species , L . interrogans , L . borgpetersenii and L . kirschneri[21] . Studies from Germany and France reported high prevalences for Leptospira spp . in small mammals which are likely responsible for simultaneous human leptospirosis cases [16 , 20] . So far there are only a few studies reporting moderate to high prevalences in small mammals , beavers ( Castor fiber ) and wild boars ( Sus scrofa ) from Germany [5 , 8 , 22] . Little is known about the prevalence and the geographic distribution of pathogenic Leptospira spp . in rodent maintenance hosts in Germany . Possible host-pathogen associations were not further determined thus far . Therefore , this study’s objectives were:
Small mammals were trapped with Sherman live animal traps ( H . B . Sherman Traps , Inc . , Tallahassee , Fla . , U . S . A . ) in 2012 at site B1 , in 2010 at site B2 and from 2010 to 2012 at site S . Traps , baited with apple slices , were placed for at least two consecutive nights per month and site and were checked twice a day . For site B1 , 50 traps were set up between July and October in 2012 . At site B2 in plot sizes of 18 x 18 m , 16 traps were laid out in a grid . The traps were checked twice on two successive days once a month , from May to October 2010 . At site S , small mammals were captured with at least 20 traps per subdivided area per month in August and October in 2010 and from March to June in 2011 ( site E , H , I ) and further in November 2011 and from March to October in 2012 ( site E , F , G , H , I ) . Collected animals were euthanized in accordance with the German Animal Protection Act and stored at −80°C . Detailed trapping procedures were published elsewhere [26 , 27 , 28] . Necropsy was carried out with collection of biometric data of all small mammals and kidneys were collected . All small mammals were morphologically identified with taxonomic keys [29] . Further , a conventional PCR targeting the partial mitochondrial cytochrome b gene [30] yielding an amplicon of 354 bp was performed with 37 small mammal DNA samples from site B1 ( 17 . 1% ) and 36 DNA samples from site S ( 6 . 7% ) including all bycaught small mammal species , which were not rodents , in order to verify morphological identification and to verify successful DNA extraction . Depending on the initial kidney size of the small mammals , kidney samples weighed 0 . 01–0 . 05g and were homogenized either by cutting the samples into small pieces each with a sterile scalpel ( site B1 ) or by the use of the Precellys 24 Tissue Homogenizer ( Bertin Technologies , Montigny-le-Bretonneux , France ) for which 600–800 μl phosphate buffered saline ( PBS , pH = 7 . 2 ) and 0 . 6 g of sterile ceramic beads , ( PeqLab Biotechnologie GmbH , Erlangen , Germany ) sized 1 . 4 mm , were added to the samples in advance ( sites S and B2 ) . DNA was extracted either with the Maxwell 16 LEV Blood DNA Kit ( Promega GmbH , Mannheim , Germany ) and the corresponding Maxwell 16 System ( site B1 ) or manually with the QIAamp DNA Mini Kit ( Qiagen , Hilden , Germany ) as recommended by the manufacturers ( sites S and B2 ) after addition of 300 μl lysis buffer and 30 μl proteinase K to each sample and incubation overnight at 56°C in a thermomixer ( Eppendorf , Hamburg , Germany ) . For all samples , quantity and quality of the extracted DNA samples were determined with a spectrophotometer ( NanoDrop 2000c respectively NanoDrop ND-1000 , Peqlab Biotechnologie GmbH ) .
Altogether , 1213 small mammals of eleven different species ( 737 M . glareolus , 12 Microtus agrestis , 431 either A . flavicollis or A . sylvaticus , one Sorex coronatus , seven Sorex araneus , four Crocidura russula , three Arvicola terrestris , two Talpa europaea , two Mustela nivalis , seven Apodemus agrarius , seven Microtus arvalis ) were captured ( Table 1 ) . Altogether , 216 small mammals were captured at site B1 ( three species ) , 456 at site B2 ( four species ) and 541 at site S ( ten species ) . DNA was extracted from one kidney of all 1213 animals . From altogether 1213 small mammals , 73 tested positive by real-time PCR ( 5 . 9%; 95%CI: 4 . 7–7 . 4 ) ( Table 2 ) . Regarding the different sites , 11 . 6% ( 95%CI: 8–16 . 7 ) of the small mammals from site B1 ( n: 25/216 ) , 3 . 3% ( 95%CI: 1 . 9–5 . 44 ) from site B2 ( n: 15/456 ) and 6 . 1% ( 95%CI: 4 . 32–8 . 49 ) from site S were positive ( n: 32/542 ) . Interestingly along the altitude gradient of B2 at two sampling sites at low altitude ( both small beech forests at 379 m and 412 m a . s . l . , Fig 1 ) most small mammals of B2 were PCR positive ( n: 12 ) . However , Leptospira spp . were found up to an altitude of 1 . 298 m a . s . l . ( see Fig 1 ) . From the altogether 73 PCR-positive samples , 67 could be further determined by duplex PCR . The other six samples did not yield any of the two amplicons most likely due to the high CT value achieved by real-time PCR ( >39 ) . Fifty-four ( 80 . 3%; 95%CI: 69–88 . 3 ) of these 67 samples were identified as L . kirschneri . Four of the other 13 ( 19 . 7%; 95%CI: 11 . 8–31 ) samples were further determined as L . borgpetersenii , and 9 as L . interrogans by conventional PCR of the partial gyrB gene and amplicon sequencing . Leptospira borgpetersenii was exclusively and L . interrogans was mainly found in Apodemus spp . ( n: 7/9 , 77 . 8% , 95%CI: 44 . 3–94 . 7 ) . Only two M . glareolus were positive for L . interrogans ( Table 2 ) . Leptospira kirschneri was mainly detected in positive small mammals ( 34 of 36 positive M . glareolus , 94 . 4%; 95%CI: 81–99 . 4; 14 of 25 positive Apodemus spp . , 56% , 95%CI: 37 . 0–73 . 35 ) . Apodemus agrarius , Mustela nivalis and Mi . arvalis , though captured in small numbers , showed high prevalences for L . kirschneri ( 42 . 86%; 95%CI: 15 . 75–75 . 02; 50%; 95%CI: 9 . 5–90 . 5 and 37 . 5%; 95%CI: 13 . 5–69 . 6 respectively ( Table 1 ) ) . Leptospira kirschneri was obtained in four of the five rodent species ( A . agrarius , Mi . arvalis , M . glareolus , A . flavicollis ) . In contrast , L . interrogans and L . borgpetersenii were detected only in A . flavicollis and M . glareolus . All other investigated small mammal species were negative . Whereas most L . kirschneri-positive rodents were found at site S ( n: 27/54 , 50 . 94%; 95%CI: 37 . 88–63 . 88 ) , the majority of other pathogenic Leptospira spp . -positive samples were found at site B2 ( n: 10/13 , 76 . 92%; 95%CI: 49 . 06–92 . 5 ) ( χ2 = 30 . 0823; p< 0 . 00001 ) , at a sampling site 379 m a . s . l . From 67 samples tested positive by duplex PCR , for 14 a complete MLST covering all seven housekeeping genes could be determined for rodents captured at all three study sites ( ten M . glareolus , three Apodemus spp . and one A . agrarius ) . Altogether sequence types for eleven L . kirschneri and three L . interrogans positive samples were detected . All Leptospira interrogans positive samples were found to be ST 24 ( Table 2 ) . For L . kirschneri two different sequence types were detected . Four samples were positive for ST 117 and seven for ST 110 . While ST 110 could be detected at all three sites , ST 117 was only detected at site S . Here , ST 117 could be detected in A . flavicollis , A . agrarius and M . glareolus whereas ST 110 could only be detected in M . glareolus . Leptospira interrogans ST 24 could be detected in A . flavicollis and M . glareolus but exclusively at site B2 ( Table 2 ) .
This study focussed on pathogenic Leptospira species in small mammals from selected habitats in Germany . Studies on prevalences for Leptospira spp . in mammals in Europe are rare and focussed mainly on larger rodent species such as rats ( Rattus norvegicus ) which are considered to be the major source of Leptospira infection for humans [34 , 35] . High prevalences ( 20–88% ) in Rattus norvegicus have been reported from different European countries such as Turkey , France and Denmark [36 , 37 , 38] . Studies from Germany , Switzerland , the Netherlands , Croatia and Austria showed the occurrence of Leptospira spp . in a wide range of different small mammal species including M . glareolus , Apodemus spp . , Mi . arvalis , Mus musculus , Castor fiber and Sorex spp . ( 2 . 9–71 . 4% ) [5 , 22 , 39 , 40 , 41 , 42 , 43] . This study’s prevalences show a similar wide range in prevalence regarding the investigated rodent species ( 5 . 3–42 . 9% ) . The highest prevalence in small mammals was detected at site B1 , a forest in southern Bavaria in comparison to the other two study sites . A recent German study showed high prevalences of leptospiral DNA in Mi . arvalis and A . agrarius ( 12–14% ) which are supposed to be the most common carrier hosts for L . kirschneri [5] . In this study the highest prevalence was also found in both of these rodent species for L . kirschneri . Leptospira kirschneri was detected in almost all investigated rodent species ( M . glareolus , Mi . arvalis , A . flavicollis , A . agrarius ) with the exception of Mi . agrestis suggesting that this Leptospira species has a broad host range and is well adapted to a number of different small mammal species . Additionally , L . kirschneri was found in Mustela nivalis but not in Sorex spp . which therefore may play a subordinate role as maintenance host for L . kirschneri . Human leptospirosis case reports caused by L . kirschneri are scarce . A recent study from Poland , however , reported antibody titres against ten serovars belonging to L . kirschneri , L . borgpetersenii and L . interrogans in several healthy humans [21] . This argues for asymptomatic infections and a less pathogenic potential than in other pathogenic Leptospira species . Leptospira kirschneri is known to cause unspecific clinical symptoms in dogs including diarrhoea , lethargy and dehydration [44] . Human cases caused by L . kirschneri may likewise display such unspecific illness and Leptospirosis may well be overlooked or kept undiagnosed . Nevertheless , this Leptospira species should be taken into account as a possible cause of disease in mammals other than dogs and humans . Leptospira interrogans is known to cause severe symptoms such as pneumonia , hepatitis and kidney failure in humans and dogs [45 , 46] . The hazardous impact of L . interrogans to human health was recently described in France in human cases with symptoms such as lumbar myalgia and pneumonia [20] . Moreover unspecific clinical symptoms such as lethargy and fever in humans with previous outdoor activities ( e . g . strawberry harvesters , triathletes ) were reported in Germany and Austria [12 , 13 , 15 , 16] . High prevalences ( 33 . 3–100% ) were detected in Mi . arvalis , Mus musculus and Rattus norvegicus which occurred sympatrically to human Leptospirosis outbreaks in France and Germany [16 , 20] . In former studies Mi . arvalis was also pointed out to be one of the most important maintenance hosts for L . interrogans among small mammal species [47 , 48] . In the current study , however , this highly pathogenic Leptospira species was mostly detected in Apodemus spp . and exclusively at site B2 , at four locations in a national park , which leads to the assumption that L . interrogans in contrast to L . kirschneri ( at least of ST 110 , see below ) rather occurs focally . Leptospira borgpetersenii and in particular serovar Hardjo type Hardjobovis is reported as the most causative leptospiral agent for infertility and abortion in cattle from North America [49] . Leptospira borgpetersenii strains were described to be associated with Mus musculus [50] . In the present study this Leptospira species was detected at all three sites but , exclusively and significantly more often in Apodemus spp . than in any other small mammal species which suggests that certain L . borgpetersenii strains have probably a host preference for the genus Apodemus in the investigated habitats . To the authors’ knowledge , this study is providing first evidence on sequence types of Leptospira spp . in rodents from Germany . Leptospira kirschneri ST 110 was the most widespread and most common ST in this study . In Germany former studies reported diseases in humans caused by the serogroup Grippotyphosa , declared as “mudfever” and associated with fieldwork activities , such as strawberry harvesting [16] . Leptospira kirschneri ST 117 was formerly found in A . agrarius and A . flavicollis collected in Croatia [42] . In our study this ST was also found in both of Apodemus species and additionally in M . glareolus . In Spain Leptospira interrogans ST 24was detected in dogs and wild carnivores such as Vulpes vulpes showing clinical signs , thus several carnivore species were suggested to be not maintenance but dead end hosts for Leptospira interrogans [51] . Further ST 24 was detected in A . flavicollis from Croatia [42] . In the present study , this sequence type was found in two different rodent species ( M . glareolus , A . flavicollis ) . It should be taken into account that the comparison of our results between sites and species is limited due to different DNA extraction methods and as the animals examined were not caught in the same years . In summary , in our study at three sites in Germany pathogenic Leptospira spp . were detected in high prevalences in four of five investigated rodent species . Therefore humans could during leisure time activities get into contact with these pathogenic Leptospira spp . if respective transmission conditions are optimal . Regarding the Leptospira spp . prevalences this study’s results suggest a host preference for L . borgpetersenii in Apodemus spp . Moreover a broad host spectrum was detected for L . kirschneri which was the most common species detected in this study . Besides this study is proving first evidence of L . kirschneri ST 110 and 117 as well as L . interrogans ST 24 in rodents from Germany . | Leptospirosis is one of the most widespread zoonotic diseases and is caused by Leptospira spp . Small mammals often serve as maintenance hosts . We evaluated host-pathogen relations for Leptospira species and sequence types in different small mammal species captured at three German study sites . Leptospira spp . was detected in 5 . 9% of the captured small mammal species and at all investigated study sites . While a particular host association was observed for L . borgpetersenii to Apodemus spp . , a broad host spectrum was detected for L . kirschneri which was the most common species detected in small mammals from this study . Besides this study is proving first evidence of L . kirschneri sequence types ( ST ) 110 and 117 as well as L . interrogans ST 24 in rodents from Germany . A focal occurrence of L . interrogans and L . kirschneri ST 117 was detected . In contrast , L . kirschneri ST 110 was present in most small mammal species at all three sites . | [
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] | 2016 | Prevalence and Genotype Allocation of Pathogenic Leptospira Species in Small Mammals from Various Habitat Types in Germany |
Plasmodium and soil transmitted helminth infections ( STH ) are a major public health problem , particularly among children . There are conflicting findings on potential association between these two parasites . This study investigated the Plasmodium and helminth co-infections among children aged 2 months to 9 years living in Bagamoyo district , coastal region of Tanzania . A community-based cross-sectional survey was conducted among 1033 children . Stool , urine and blood samples were examined using a broad set of quality controlled diagnostic methods for common STH ( Ascaris lumbricoides , hookworm , Strongyloides stercoralis , Enterobius vermicularis , Trichuris trichura ) , schistosoma species and Wuchereria bancrofti . Blood slides and malaria rapid diagnostic tests ( mRDTs ) were utilized for Plasmodium diagnosis . Out of 992 children analyzed , the prevalence of Plasmodium infection was 13% ( 130/992 ) , helminth 28 . 5% ( 283/992 ) ; 5% ( 50/992 ) had co-infection with Plasmodium and helminth . The prevalence rate of Plasmodium , specific STH and co-infections increased significantly with age ( p < 0 . 001 ) , with older children mostly affected except for S . stercoralis monoinfection and co-infections . Spatial variations of co-infection prevalence were observed between and within villages . There was a trend for STH infections to be associated with Plasmodium infection [OR adjusted for age group 1 . 4 , 95% CI ( 1 . 0–2 . 1 ) ] , which was more marked for S . stercoralis ( OR = 2 . 2 , 95% CI ( 1 . 1–4 . 3 ) . Age and not schooling were risk factors for Plasmodium and STH co-infection . The findings suggest that STH and Plasmodium infections tend to occur in the same children , with increasing prevalence of co-infection with age . This calls for an integrated approach such as using mass chemotherapy with dual effect ( e . g . , ivermectin ) coupled with improved housing , sanitation and hygiene for the control of both parasitic infections .
Parasitic infections such as Plasmodium present a major public health problem among children in Africa [1 , 2] and its coexistence with Soil Transmitted Helminth ( STH ) infections is common [3 , 4] . Multipasitism is a norm among children in developing countries including United Republic of Tanzania [5 , 6] . It is defined as a concurrent infection in a single host with two or more species whereas monoinfection consists of only one infection from a single species [7] . Variety of environmental and host related factors can influence the structure and dynamics of the parasite communities which make up these multiple infections [8–10] . These conditions include poverty , environmental contamination with infected faeces containing helminth eggs , water bodies , lack of effective preventive measures [11] and immunity of the host . In addition , overlap of Plasmodium infection and other pathogens depends on the conditions that favour multiple parasitic species survival and transmission such as exposure related risk and within host interactions between co-infecting species [3 , 11 , 12] . There is mounting evidence indicating that helminth infections increase susceptibility to Plasmodium infection [13–15] . On the other hand , some studies showed that specific STH like Ascaris lumbricoides are protective against Plasmodium disease and its severe manifestations [16] . Previous epidemiological studies have shown coexistence of Plasmodium and helminth infections with spatial heterogeneity in their distribution [3 , 4 , 6 , 17] . In Tanzania , cross-sectional surveys conducted among school and preschool children showed that multiple parasitic infections are common [5 , 6 , 17] . Study by Kinung’hi et al showed that the prevalence of malaria parasites tended to increase with increasing number of co-infecting helminth species as compared to helminth free children although the difference was not statistically significant [6] . In Tanzania , global strategies to control malaria are being conducted by the National malaria control program ( NMCP ) via Long Lasting Insecticide Impregnated Nets ( LLINs ) , Intermittent Preventive Treatment in Pregnancy ( IPTp ) and prompt treatment with artemether/lumefantrine . The helminth control is done via mass drug administration ( MDA ) , chemotherapy based morbidity control campaigns . The planning for prevention and control program are designed to focus on a single infection approach despite occurrence of co-infections [18] . There is an underestimation of the burden of infection and lack of understanding how these parasitic infections interact [18] . This underlines the importance of investigating the epidemiology of co-infections in different geographical locations where different pattern of infections are expected . In the present study we aimed to determine the relation between Plasmodium and STH co-infections among children aged 2 months to 9 years living in Bagamoyo district , coastal region of Tanzania . Knowledge of the magnitude and on the common risk factors for co-infections should guide the development of focused integrated control programs targeting multiple infections endemic in each country .
The study was conducted under the IDEA study protocol which was approved by the institutional review boards of the Swiss Tropical and Public Health Institute ( Swiss TPH; Basel , Switzerland ) and the Ifakara Health Institute ( IHI; Dar es Salaam , United Republic of Tanzania ) . The ethical approval for the conduct of the study was granted by the Ethikkomission beider Basel ( EKBB; Basel , Switzerland; reference number: 257/08 ) and the National Institute for Medical Research of Tanzania ( NIMR; Dar es Salaam , United Republic of Tanzania; reference number: NIMR/HQ/R . 8a/Vol . IX/1098 ) . The local district , community , school teachers and health authorities were informed during sensitization meetings about the purpose , procedures , risk and benefits of the study prior to the start . Written informed consent was obtained from the parents/guardians of children prior to study procedures after explaining them to the group . Illiterate parents/ guardians were asked to bring witness who participated within the discussion prior to obtaining their thumbprints and witness signature . Participants infected with helminth and/or malaria or other medical conditions received appropriate treatment/referral according to the national treatment guidelines of Tanzania . Bagamoyo is a district in the coastal region of Tanzania where Ifakara Health Institute ( IHI ) through its branch , Bagamoyo Research and Training Centre ( BRTC ) works in close collaboration with the Bagamoyo District hospital ( BDH ) officials to ensure quality health care delivery using its research platforms . The BRTC study area covers about 1160 square kilometres . The eastern border of the study area is formed by the Indian Ocean , with the Ruvu river forming part of the western and northern borders . The area extends for approximately 7 km on either side of a road running westwards for 62 kilometres . To the south is an uninhabited forest reserve . According to the 2012 Tanzania National Census , the population of the Bagamoyo District was 311 , 740 which can be reached by dirt road throughout the year; all are within an hour drive [20] . The main rainy season is from March to May , with a second period from November to December , although occasional rain occurs at all times of the year . Average rainfall is 1200 to 2100 mm per year . There is year round grassland vegetation or subsistence agriculture throughout the study area . According to meteorological statistics the average temperature for the region is about 28°C . Majority of the people are either subsistence farmers who cultivate rice , maize and cassava , or fish from the sea or the Ruvu River and its tributaries . Agriculture employs 76% of the population [20] . The survey was conducted in the western rural area including hamlets within the villages of Kiwangwa , Msata , Mkange and Magomeni . The settlements are located about 20 to 60 km from Bagamoyo town . The inhabitants of the villages are mainly smallholder farmers engaged in food crop production such as pineapples , cassava , maize , vegetables and in fishing and salt mining . The prevalence of malaria within the western study area is still high compared to Bagamoyo town with seasonal variations secondary to malaria interventions through research and Tanzania National Malaria Control program ( NMCP ) [21] . Research on malaria drugs and vaccine have been conducted in Bagamoyo town since 2005 through IHI and its collaborative partners . The rural water supply is mostly from dams and ponds [22] highly contaminated with fecal coliform bacteria [23] . Eighty four percent of the communities have soil based latrines [22] . The latter resemble to simple pit latrines but without floor nor hygiene cover slab , nor lid covering the hole . The study is part of the IDEA project , an African-European Research initiative , funded by European community , with the aim of dissecting the immunological interplay between poverty related diseases ( malaria , TB and HIV ) and helminth infections[24] . The present community cross-sectional survey was conducted at the start of the IDEA malaria project to provide baseline data to inform further prospective immune-epidemiological studies of malaria infected individuals . Study population included a random sample of healthy children as regarded by their parents/guardians , aged 2 months to 9 years inclusively , whose parents/guardians where informed about the study through Village Health Care Workers ( VHCW ) and agreed to come for screening at the meeting points . The villages were purposely selected based on the environmental conditions favouring both malaria and helminth survival and transmission . In the malaria arm , a sample size of 100 children with asymptomatic Plasmodium parasitemia was required . The malaria prevalence being 10% within the study area [25] , we enrolled about ~1000 children from the community survey [26] . Standardized questionnaires were used to collect information on demographics , vital and clinical signs and symptoms to ensure that they were free of common diseases at that point in time . Parents or guardians where asked about interventions implemented within the Tanzanian National program , namely the use of long-lasting impregnated bednets ( LLINs ) and prior anti-helminth treatment . Participant recruitment and data collection was done between July 2011 and November 2012 covering an entire year and thus including seasonal variation [26] . All children had a finger prick to obtain about 1ml of blood which was collected in an Ethyl Diamine Tetra acetic Acid ( EDTA ) tube for malaria slide and full blood count which were performed at the main BRTC laboratory . Malaria rapid diagnostic test ( SD BIOLINE , SD standard diagnostics , inc . Korea ) and hemoglobin level using HemoCue hemoglobinometer ( EKF diagnostic GmbH , Germany ) were done in the field for inclusion/exclusion criteria and immediate management of the children with malaria and severe anemia . Additionally , each participant was provided with i ) two clean containers ( 100mls ) for stool and urine samples ii ) a plastic pocket with an adhesive tape ( 50 x 20mm ) and a glass slide . All labelled with participant identification number . Parents/guardians were instructed on how to apply the adhesive tape and advised to collect sufficient amount of fresh stool and urine . The filled containers and adhesive tape slide were collected by the VHCW at a predefined meeting point in the village centre , the next day before noon and submitted to the Helminth Unit ( HU ) of the BRTC where all stool and urine samples were examined by experienced technicians . Thick and thin blood films were prepared , air dried and Giemsa stained for detection and quantification of malaria parasites according to the IHI laboratory Standard Operating Procedures ( SOP ) . To detect malaria parasites , 200 fields were examined . Parasite density expressed per μl of blood was calculated by multiplying a factor of 40 to the number of parasites counted , assuming 8 , 000 leucocytes per μl of blood [27] . All slides were read by two independent qualified technicians . In case of discrepancy between two readers , a third reader was requested . The final result was the geometric mean of the two geometrically closest readings out of the three . For cases of positive/negative discrepancy the majority decision was adopted . If the test results were positive , the final one was then taken as the geometrical mean of the two positive results . Duplicate Kato-Katz thick smear slides using a 41 . 7 mg template , adhesive tape slides , Baermann and FLOTAC methods were used to diagnose intestinal helminth [28] . Microhaematuria was examined using a dipstick ( Hemastix; Siemens Healthcare Diagnostics , Eschborn , Germany ) and for S . haematobium eggs by urine filtration ( hydrophilic polycarbonate membrane filter; pore size 20 micron , diameter 13mm; Sterlitech , Kent , WA , United States of America ) . Binax NOW Filariasis rapid immunochromatic test ( ICT ) card ( inverness medical professional diagnostics; ME; United States of America ) was performed at the HU to detect W . bancrofti antigen using whole blood All Kato-Katz thick smear , adhesive tape and urine filtration slides were stored in boxes and 10% of slides re-examined for quality control by the senior experienced personnel after 3–6 months [28] . The helminth species specific results derived by each method were entered into an electronic data base using Microsoft ACCESS 2010 . Double entry of the clinical and laboratory data was done using the DMSys software ( FDA approved for ICH/GCP clinical trials ) . The two datasets were transferred into STATA format merged and cleaned . Data analysis was performed using STATA version 11 . 0 software ( Stata Corp LP; College Station , Texas , USA ) . For duplicate Kato-Katz methods , the average of the two slides multiplied by a factor 24 was done to obtain egg per gram of stool ( EPG ) . S . stercoralis and E . vermicularis intensity expressed as larvae counts and number of eggs counted respectively . Helminth infection intensity was categorized according to WHO criteria [29] . To investigate the relationship between Plasmodium and helminth , only the children who had both Plasmodium and helminth results were included . Table 1 defines the terms used in the analysis for easiness of interpretation . Geographical information system ( arcgis 10 ) was used to map the distribution of Plasmodium and helminth monoinfections/co-infections among children in the coastal region of Bagamoyo . Harmonization of the identified hamlets within the studied villages was achieved using the known registered villages and hamlets/streets names from the Tanzania shape file ( http://openmicrodata . wordpress . com/2010/12/16/tanzania-shapefiles-for-eas-villages-districts-and-regions/ ) . Hamlets with low numbers ( less than 10 ) were systematically merged to the nearest hamlet within the same village . Baseline characteristics were presented within age groups ( children less than 3 years , preschool children aged 3–5 years and school-aged children from 6–9 years ) . The categorization was chosen to explore the age dependency variability considering the ongoing malaria and mass drug administration helminth programs with different approaches based on age , mainly focusing on under five and above five years of age . Crude odds ratios ( ORs ) including 95% confidence interval and p-values were calculated for variables potentially associated with infections/co-infections . To investigate risk factors , different models were explored with all Plasmodium , Plasmodium monoinfection , helminth mono- and mixed infections , and Plasmodium + helminth co-infections . The association between helminth species and Plasmodium infection was explored to further study co-infection patterns . Variables that were associated in bivariate analysis with a p-value level of < 0 . 05 were considered in multiple logistic regression models . The association between Plasmodium and helminth co-infections was subsequently investigated using negative binomial regression estimation after comparing the conditional means and variances of variables , all variances greater than means signifying over dispersion of data . To further investigate the age dependency relationship , Mantel—Haenszel stratified odds ratios ( ORs ) were conducted . A more detailed description and analysis of the helminth distribution is provided in the published paper [26] . The present one focuses on the relationship of soil transmitted helminth ( STH ) and Plasmodium as prevalence of other helminth like W . bancrofti and S . hematobium were low among the studied children .
A total of 1033 children were recruited . Of these , 41 ( 3 . 9% ) did not submit stool samples which left 992 children as study analysis population ( Fig 1 ) . Demographic characteristics and intervention coverage are described in Table 2 . Overall median age was 4 . 7 years with 25th of 2 . 3 and 75th quartiles of 6 . 5; 459 ( 46% ) were represented by children above five years of age . Among children aged five years and above 185 ( 40 . 3% ) were not schooling at the time of the survey . 494 ( 49 . 8% ) were males . Eight hundred and twenty ( 82 . 7% ) of the study population slept under a long lasting insecticide impregnated net ( LLIN ) the night before the survey . The parents/guardians reported use of albendazole and mebendazole past six months in 411 ( 41 . 4% ) and 188 ( 18 . 9% ) of their children respectively . Prevalence of Plasmodium , helminth and co-infections are shown in Fig 1 and Table 3 . Out of the 992 children included in the analysis , 130 ( 13 . 1% ) were infected with Plasmodium species , 283 ( 28 . 5% ) had helminth infection and 50 ( 5% ) harbored both infections ( co-infected ) . The prevalence of Plasmodium and helminth monoinfection were 8 . 1% ( 80/992 ) and 23 . 5% ( 233/992 ) respectively . E . vermicularis was the most prevalent single helminth infection 116 ( 11 . 7% ) followed by hookworm 60 ( 6 . 1% ) and S . stercoralis 42 ( 4 . 2% ) . The prevalence of Plasmodium , STH and co-infections increased with age , older children were mostly affected ( Fig 2 and 3A and Table 3 ) . This was especially true for the most prevalent infections , namely E . vermicuralis and hookworm infections , co-infected or not with Plasmodium ( Fig 3B and 3C ) . The only exception was the prevalence of S . stercoralis monoinfection which was slightly higher in children below five years of age ( Table 3 and Fig 3D ) . Co-infection with Plasmodium and S . hematobium was found in two children ( 0 . 2% ) and co-infection with Plasmodium and T . trichura in only one child ( 0 . 1% ) , all above five years of age . Two children ( 0 . 2% ) had positive ICT for W . bancrofti infection . Fig 4 shows administrative map of Tanzania locating Bagamoyo district within coastal region and the spatial distribution of monoinfection/co-infections prevalence in the four villages studied namely Kiwangwa , Mkange , Msata and Magomeni . Spatial heterogeneity of infection prevalence was observed between and within villages . Fig 5 shows the distribution of monoinfection and co-infections among the hamlets of the four studied villages . There were significantly different prevalences of helminth ranging from 44 . 7% in Mkange to 26 . 3% in Kiwangwa . The prevalence of Plasmodium infection ranged from 15 . 4% in Kiwangwa to zero in Magomeni village , with co-infection prevalence being higher in the hamlet of Kiwangwa , Kiwangwa Msinune ( p = 0 . 028 ) , Kiwangwa Bago and Mkange Matipwili ( Table 4 and Fig 4 and 5 ) . There was a pattern for Plasmodium to be associated with helminth infection [OR = 1 . 7 , 95% CI ( 1 . 1–2 . 5 ) ] , which was marked for S . stercoralis monoinfection [OR = 2 . 5 , 95% CI ( 1 . 2–5 . 2 ) , p = 0 . 0146] as shown in Table 4 . The effect was statistically significant in a multivariate negative binomial regression model when other factors were considered [IRR = 2 . 0 , 95% CI ( 1 . 0–4 . 00 ) , p = 0 . 034] as shown in Table 5 . The risk of Plasmodium , STH and Plasmodium STH co-infections increased with age , with older children ( >5 years ) being more affected compared to younger children ( <3 years and 3–5 years ) . The differences were statistically significant by bivariate and multivariate negative binomial regression analysis ( Tables 4 and 5 ) . Overall there was an age pattern for Plasmodium to be associated with STH [multivariate negative binomial regression [IRR = 2 . 9 , 95% CI ( 1 . 7–5 . 1 ) ] , which was more marked for S . stercoralis [IRR = 3 . 9 , 95% CI ( 1 . 2–13 . 1 ) ] , and especially in young children . Mantel—Haenszel stratified ORs ( Table 6 ) indicated that the risk of Plasmodium infection with S . stercoralis was higher among younger children aged below 3 years ( stratum specific OR = 9 . 2 , 95% CI ( 0 . 8–105 . 5 ) when exploring for confounding effect of age groups ( M-H adjusted OR = 2 . 2 , 95% CI ( 1 . 1–4 . 3 ) , p = 0 . 0266; homogeneity of ORs , p = 0 . 3774 ) . Compared to the results with other type of STH , S . stercoralis infection showed increased risk of Plasmodium among younger age group but the homogeneity test suggests no difference in the odds between age groups . In addition , children who were not attending school although they should have according to their age had increased risk of Plasmodium and co-infections by bivariate analysis although the statistical association was lost in a multivariate analysis . Male and female children were equally affected ( Table 4 ) . The geometric mean Plasmodium parasite count decreased with age as shown in Table 3 . Most of the studied children had low intensity helminth infections . Moderate and heavy STH infections intensity were noted among older children . Generally , there were no significant correlation between Plasmodium and STH densities . There was a trend for a negative correlation between Plasmodium parasite density and S . stercoralis larvae count ( r = -0 . 0786 , p = 0 . 8082 ) and a positive correlation with a hookworm ( r = 0 . 2123 , p = 0 . 5560 ) and E . vermicularis ( r = 0 . 0418 , p = 0 . 7095 ) infections .
The current study provides baseline epidemiological data of Plasmodium and STH infections in the whole population of children , including the young ones who are rarely surveyed for the latter . To increase sensitivity , different diagnostic methods for helminth infection were used including adhesive tape slides for E . vermicularis and Baermann technique for S . stercoralis . This enabled us to have full range of helminth pattern within the area where malaria transmission occurs throughout the year and National malaria and helminth control programs are undertaken . Results show that infection with Plasmodium , STH and co-infections are common among children aged below five and above five years living in Bagamoyo district , Tanzania . Despite high coverage of LLINs ( above 80% ) , pockets of Plasmodium infection remain in west side areas like Kiwangwa compared to Magomeni village which is closer to Bagamoyo town where malaria prevalence was documented to be low [21] . This spatial variation could be explained by behavioral factors such as outdoor activities , coverage and effective use of LLINs in the Magomeni village and easy access to health care and hence effective treatment . The geometric mean Plasmodium parasite count decreased with advancing age as expected in most of the malaria endemic countries in relation to development of antimalarial specific immunity [30 , 31] but the malaria infection prevalence increased with age . These findings may be an indication of a shift of Plasmodium infection towards older age group as observed in Muheza district , Tanzania [32] and other parts of Africa [33–35] where malaria transmission tends to decline and acquisition of immunity is thus delayed . Prevention strategies need thus to take into account the older age group too in the momentum of malaria elimination [36] . The association of STH and Plasmodium infections highlights the extent of the burden of parasites in older age groups . Our results show a definite increase of parasite prevalence , especially STH , with age , as do most of the previous studies [5 , 6 , 17 , 37 , 38] . The prevalence and pattern of co-infections observed in Bagamoyo differ from those reported in Magu [6] and Mvomero districts [17] , Tanzania . Our results show lower prevalence of helminth and Plasmodium helminth co-infections with predominance of E . vermicularis , hookworm and S . stercoralis . The method of detection might be the reason for these differences . Indeed , we used adhesive tape slides for E . vermicularis and Baermann technique for S . stercoralis together with Kato-Katz technique , which detected these specific worms , mostly missed in other surveys . Factors such as exposure and intervention coverage may also explain the types of helminth infection isolated and high prevalence in Magu and Mvomero districts . In Tanzania , published reports on mass drug administration ( MDA ) , mostly under National Lymphatic Filariasis Elimination Program ( NLFEP ) , have shown coverage to fluctuate [39] and its effectiveness to vary depending on the chemoprophylaxis used and duration between cycles [40 , 41] . The uptake variations could results into persistence of parasites in certain areas and subgroups . In this study , an increased risk of STH , Plasmodium and co-infections was observed among the school-aged children who were not schooling . These may have missed the opportunity to be dewormed , either at school level or within the under-fives program . These children may also be exposed to other risk factors in the environment , or behave differently in terms of sanitation and hygiene . The latter have not been assessed in the present study , which represents a definite limitation . Such a burden in this age group is not only deleterious for them but also acts as a reservoir of infection within the population [42] . Co-infection patterns increased with age as predicted from the age specific prevalence rates by the simple probability model except for S . stercoralis co-infection . The exposure of S . stercoralis and Plasmodium co-infections was significantly different from the other species of helminth . The results show that children with S . stercoralis had twice the risk of Plasmodium infection , with even higher odds among children below 3 years of age , as compared to those with other species of STH . This observation requires further exploration as reports on the age profile for S . stercoralis infection are rare and still conflicting [43–46] . A study done in Côte d’Ivoire showed that hookworm and S . stercoralis had an almost parallel shape of age prevalence pattern [38] , but it did not stratify the <5 children into smaller age categories . This could be the reason for the masked trend compared to what was observed in the present study where a negative correlation between S . stercoralis larvae and Plasmodium parasitemia was shown compared to positive correlation with hookworm and E . vermicularis , although not significant . The observed relation between Plasmodium and S . stercoralis infection may arise due to biological associations , whereby S . stercoralis being an early life and chronic infection , starts in the first years and persists through older aged groups promoting establishment and/or survival of Plasmodium infection potentially through Th2 lymphocytes immune modulation [47] . It has been repeatedly shown that hookworm tends to exaggerate Plasmodium infection but knowledge on the immunomodulation with S . stecoralis co-infection is still scarce . Depending on the pro and anti-inflammatory responses mounted within the host , immune response could either promote or inhibit Plasmodium infection [16 , 47 , 48] . The equilibrium with S . stercoralis could have been maintained through its intrinsic characteristic to persist for many years in asymptomatic immunocompetent host surviving through low grade autoinfection cycles [47] . In this study , children were not tested for HIV infection which is among the risk factor for S . stercoralis hyperinfection syndrome [49–51] . The immaturity and predominance of Th2 response among younger children could also explain the increased risk of Plasmodium co-infection with S . stercoralis [52] . Heterogeneity of infection prevalence within and between villages indicate that other factors apart from biological association determine the co-infection patterns . Behavioural such as outdoor activities and walking barefooted , sanitation , hygiene and socio-economic factors could explain the variations [38] . Considering the type of STH species isolated and their modes of transmission , exposure factors conducive to both parasites are suspected to be main contributors of Plasmodium and STH co-infections within the studied population [11] . Previous studies done within Bagamoyo district in 2004 suggested that availability of safe water is a serious problem with public health consequences [22] . Up to 40% of people reporting water to be not easily accessible [22] and 70 . 8% of the water sources were contaminated with fecal coliforms [23] . The situation has not much changed , at least in the rural areas , just few kilometers from the Bagamoyo town . Recent studies conducted within the area showed high rates of water contamination [53 , 54] . Both hookworm and S . stercoralis are transmitted via skin penetration in poorly maintained latrines and sites of promiscuous defecation . As of E . vermicularis direct transfer of eggs into mouth , inhalation and retroinfection are possible in areas of poor hygiene and scarcity of water [55 , 56] . All these could contribute to high reinfection rates [57] and persistence of chronic infection post treatment . Overall , the results of this study demonstrate that both Plasmodium and STH exhibit marked age dependency in infection patterns . In main land Tanzania , control program against helminth has been implemented through expanded program of immunization ( EPI ) using mebendazole or albendazole among <5 years children and community based Global Program to Eliminate Lymphatic Filariasis ( GPELF ) using ivermectin plus albendazole among school-aged children and adult population . Under five years children have been targeted by interventions against malaria via antenatal and later postnatal programs where LLINs are distributed . Generally , school-aged children have been rather neglected and therefore not well covered by both control programs . In the current study , the heaviest load of helminth infection was detected among children aged above five years underlying the importance of deworming program to be focused on this age group as suggested by the WHO in order to reduce morbidity and transmission of helminth [29] . Integrated control approaches emphasizing on health education , improvement of environmental sanitation and hygiene coupled with improved housing and access to water , chemoprophylaxis [29 , 42] and LLINs distributions are required considering the pattern and types of infections within the area to interrupt transmission of both STH and Plasmodium among both the school-aged children but also the under-fives . Frequent and effective antihelminth administrations at least twice a year with a drug like ivermectin which has shown to reduce both helminth and malaria transmission could be prioritized to reduce the burden of co-infection in school-aged children [58] . Potential safety and additional impact of ivermectin to reduce malaria requires further exploration considering the risk of co-infection early in childhood with S . stercoralis [58] . The risk of Plasmodium with S . stercoralis infection among young children requires more investigation to better understand this singular interaction . | Parasitic infectious agents rarely occur in isolation and multiparasitism is a norm specifically in children living in endemic areas of Tanzania . We studied the pattern and predictors of Plasmodium and STH co-infections in rural Bagamoyo district , coastal region of Tanzania . Parents/guardians of healthy children aged 2 months to 9 years who were willing to participate in the study were invited from the community . Stool , urine and blood were examined for helminth and Plasmodium parasites . We found that children aged above five years and those who are not schooling had the greatest burden of co-infection with Plasmodium and helminth parasites . The risk of being co-infected with Plasmodium increased with age with all the common types of STH isolated ( E . vermicularis , hookworm and S . stercoralis ) . Younger children had a significantly higher risk of having Plasmodium when co-infected with S . stercoralis . Integrated control approaches including health education , environmental sanitation and hygiene , novel chemoprophylaxis as well as long lasting Impregnated Nets ( LLINs ) distributions should be implemented considering the pattern and types of infections within the area in order to interrupt transmission of both parasites among young and school-aged children . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Distribution and Risk Factors for Plasmodium and Helminth Co-infections: A Cross-Sectional Survey among Children in Bagamoyo District, Coastal Region of Tanzania |
Type IV pilus ( T4P ) systems are complex molecular machines that polymerize major pilin proteins into thin filaments displayed on bacterial surfaces . Pilus functions require rapid extension and depolymerization of the pilus , powered by the assembly and retraction ATPases , respectively . A set of low abundance minor pilins influences pilus dynamics by unknown mechanisms . The Vibrio cholerae toxin-coregulated pilus ( TCP ) is among the simplest of the T4P systems , having a single minor pilin TcpB and lacking a retraction ATPase . Here we show that TcpB , like its homolog CofB , initiates pilus assembly . TcpB co-localizes with the pili but at extremely low levels , equivalent to one subunit per pilus . We used a micropillars assay to demonstrate that TCP are retractile despite the absence of a retraction ATPase , and that retraction relies on TcpB , as a V . cholerae tcpB Glu5Val mutant is fully piliated but does not induce micropillars movements . This mutant is impaired in TCP-mediated autoagglutination and TcpF secretion , consistent with retraction being required for these functions . We propose that TcpB initiates pilus retraction by incorporating into the growing pilus in a Glu5-dependent manner , which stalls assembly and triggers processive disassembly . These results provide a framework for understanding filament dynamics in more complex T4P systems and the closely related Type II secretion system .
Vibrio cholerae is a Gram-negative bacterial pathogen that causes the human diarrheal disease cholera , which afflicts millions of people each year [1] . Cholera is marked by copious watery diarrhea that can lead to dehydration , shock , organ failure and death as little as 24 hours after infection [2] . The severe diarrhea is caused by cholera toxin , an ADP ribosylating enzyme that is transported from the cytoplasm across the inner membrane via the Sec machinery and from the periplasm across the outer membrane via the Type II secretion ( T2S ) system [1 , 3–5] . Colonization of the small intestine by V . cholerae requires a second virulence factor , the toxin co-regulated pilus ( TCP ) , which self-associates to induce bacterial aggregation and microcolony formation [6–8] . In addition to building a pilus filament , the TCP assembly apparatus acts like a T2S system , exporting a soluble protein , TcpF , across the outer membrane [9 , 10] . TCP is also the primary receptor for the lysogenic bacteriophage CTXϕ , which carries the cholera toxin genes ctxAB and is responsible for converting V . cholerae from a harmless marine microbe to a deadly human pathogen causing pandemic disease [11] . Understanding TCP biology is essential for understanding V . cholerae pathogenesis and designing prevention and treatment strategies for cholera disease . TCP are members of the Type IV pilus ( T4P ) class , which are expressed by many Gram-negative bacteria as well as some Gram-positive species and archaea [12–16] . T4P perform a myriad of functions including adhesion , microcolony formation , phage and DNA uptake , twitching motility and protein secretion . They are polymers comprised primarily of a single protein subunit , the major pilin . X-ray crystal structures of full length major pilins reveal a small protein with a curved ~53-residue α-helical spine , α1 , embedded via its C-terminal half , α1C , in an antiparallel β-sheet within the globular C-terminal domain of the protein [14 , 17–20] . The N-terminal half of α1 , α1N , is an exposed mostly hydrophobic stalk with an invariant glutamate at position 5 . Pilin subunits are arranged in the pilus filament in a helical array with the α1Ns associated in the hydrophobic core of the pilus and Glu5 positioned to neutralize the positively charged N-terminal amine ( N1+ ) of an adjacent subunit [18 , 21 , 22] . Two T4P sub-classes have emerged , Type IVa ( T4a ) and Type IVb ( T4b ) . The T4b pili are present on enteric bacteria such as V . cholerae , enterotoxigenic Escherichia coli ( ETEC ) , enteropathogenic E . coli ( EPEC ) and Salmonella Typhi as well as the Flp pili and R64 thin pilus [23 , 24] . The T4b pili are distinguished from the T4a pili of Myxococcus xanthus , Pseudomonas aeruginosa and the pathogenic Neisseria in having fewer assembly components , all of which are encoded on a single gene cluster , a longer signal peptide and mature region for the major pilin and a distinct connectivity for the β-sheet in the globular domain [14] . Polymerization of the major pilin subunits into surface-displayed pili is accomplished by an assembly machinery that spans the bacterial envelope . This machinery is comprised of as few as 10 proteins to 30 or more components [16] . Despite recent major structural advances [25 , 26] our understanding of T4P assembly remains incomplete . Major pilins are synthesized as prepilins , which are translocated across the inner membrane and are simultaneously processed by a dedicated prepilin signal peptidase that removes the signal peptide , leaving the subunits anchored in the inner membrane via their hydrophobic α1N [27–30] . Pilin subunits are thought to dock into the base of a growing pilus , which allows α1 to transition seamlessly from the acyl phase of the inner membrane to the hydrophobic core of the pilus where it is surrounded by its neighboring α1s [18] . The docked pilin is prevented from diffusing away from the pilus by extrusion of the growing pilus a short distance of 8–10 Å out of the membrane , equivalent to the axial rise per subunit [18 , 21 , 22] , which opens up a gap around the base of the filament for the next subunit to dock . Polymerization is powered by ATP hydrolysis by the assembly ATPase located on the cytoplasmic side of the inner membrane [31–34] . Efficient pilus assembly requires the highly conserved Glu5 , as amino acid substitutions at this position substantially reduce or eliminate pilus assembly [22 , 35–37] . We have proposed that an electrostatic attraction between Glu5 on the incoming pilin subunit and the positively-charged N-terminal amino group on the terminal subunit in the growing pilus ( N1+ ) contributes to subunit docking by neutralizing these charges in the acyl phase of the lipid bilayer prior to their extrusion from the membrane as part of the pilus [18 , 22] . A bridge of electron density is observed connecting the N-terminal α-helices in the recent ~ 6 Å reconstruction of the Neisseria meningitidis T4P cryo-electron microscopy reconstruction , supporting the existence of a Glu5:N1+ salt bridge in the assembled pilus [21] . In Gram-negative bacteria T4P filaments grow across the periplasm and through the outer membrane via a complex conduit comprised of an outer membrane secretin channel and a network of proteins that connect this channel to the inner membrane assembly platform [38–41] . In addition to the assembly ATPase , most T4P systems possess a second “retraction” ATPase , often called PilT , which is required to depolymerize or retract the pili by an unknown mechanism [33 , 42–46] . Retraction allows twitching motility , which pulls the bacteria along surfaces , and can draw bound substances like DNA and bacteriophage into the cell [47–53] . Retraction has been demonstrated , both directly and indirectly for P . aeruginosa , Neisseria gonorrhoeae , M . xanthus and Streptococcus sanguinis T4P by total internal reflection fluorescence microscopy , optical tweezers and elastic micropillars assays [52 , 54–61] . N . gonorrhoeae pili can retract at an astonishing rate of ~1 μm/sec , equivalent to removing ~1000 pilin subunits per second . N . gonorrhoeae and M . xanthus T4P retract with forces of 100–150 pN in a PilT-dependent manner and thus PilT is considered to be the strongest molecular motor known [58] . Pilus retraction likely occurs by reversing pilus assembly , whereby subunits translocate from the base of the growing pilus into the inner membrane . The T4P system is structurally and functionally related to the T2S system , whereby major “pseudopilins” assemble into a pseudopilus that grows from the inner membrane through the periplasm , driven by an assembly ATPase [4 , 62–64] . While T4P are long thin filaments displayed on the bacterial surface , T2S pseudopili normally do not grow across the outer membrane: they are thought to form periplasmic pilus stubs that are rapidly assembled and disassembled , producing a piston-like motion to extrude substrates through the secretin channel [64–68] . Pseudopili can , however , be induced to grow into long surface-displayed pili when the major pseudopilin or the entire T2S operon are overexpressed [69 , 70] . The T2S major pilins resemble Type IV pilins , having a hydrophobic N-terminus with Glu5 and a canonical pilin fold [71] . Despite the model for a dynamic pseudopilus , T2S systems lack a retraction ATPase to facilitate depolymerization . In addition to the major pilin , the T4P and T2S systems each possess several minor pilins , pilin-like proteins that share N-terminal sequence homology and are structurally similar to their corresponding major pilins but are much less abundant in the cell . Minor pilins are known to be involved in T4P and T2S dynamics and functions but a mechanistic understanding is still lacking . Four core minor pilins , GspH , GspI , GspJ and GspK , are common to both systems , though their names differ depending on the system and bacterial species . T4P GspHIJK orthologs in P . aeruginosa , N . gonorrhoeae and EPEC are required for pilus assembly in an otherwise wild type ( WT ) background [72–74] , but some pili are produced in minor pilin mutants that also lack the retraction ATPase , PilT [75–78] . Thus , it has been proposed that the minor pilins function as effectors of pilus homeostasis in the presence of the retraction ATPase rather than as essential initiators of pilus assembly [76] . Some T4P systems also possess one or more non-core minor pilins that are dispensable for pilus assembly but are required for pilus functions , including PilE in P . aeruginosa [77] , PilV in N . gonorrhoeae [79] and ComP , PilX and PilV in N . meningitidis [80–86] . The T4P minor pilins localize to the pilus fraction and are shown by immunogold labeling and transmission electron microscopy ( immunogold TEM ) in some cases to be incorporated into the pilus [74 , 82] . Structural studies on these minor pilins reveal the canonical pilin fold , consistent with their ability to incorporate into the pilus filament [77 , 80 , 82 , 87–89] . Like the minor pilins of the T4P system , the T2S system minor pilins function in initiation of filament assembly [68 , 70 , 90–93] . Glu5 is conserved in most T2S minor pilins and was shown for at least one of these , PulH of Klebsiella oxytoca , to be necessary for secretion [94] . ETEC GspI , GspJ and GspK were crystallized in a ternary complex that is proposed to assemble at the tip of the pseudopilus [4 , 95] , consistent with a role in initiating pseudopilus assembly . However this minor pilin tip complex may also prevent growth of the pseudopilus through the secretin channel . The largest of the ETEC minor pseudopilins , GspK , has a bulky α-helical domain inserted between the first two strands of the pilin domain β-sheet . This large protein at the tip of the pilus may provide a steric or chemical signal that stalls pseudopilus assembly upon contact with the secretin channel [95] . ETEC GspK is a member of the GspK family of pilin-like proteins that are larger than their corresponding minor pilins and lack a position 5 glutamate [96] . Consistent with the proposed role in pseudopilus growth arrest , the GspK minor pilin XcpX was shown to control pseudopilus length in a P . aeruginosa T2S mutant overexpressing the major pseudopilin XcpT: longer more abundant pseudopili were produced in this strain when xcpX was deleted whereas fewer pseudopili were produced when xcpX was overexpressed [97] . Blocking passage of the pseudopilus through the secretin channel may trigger disassembly , producing a piston-like motion that extrudes protein substrates across the secretin channel independent of a retraction ATPase . The closely related V . cholerae TCP and the ETEC CFA/III T4b pili provide comparatively simple systems in which to study the role of the minor pilins in filament dynamics as their assembly machinery has fewer than a dozen components , with only a single minor pilin . The functions associated with these pili—secretion , aggregation and phage transduction—suggest dynamic filaments that rapidly assemble and retract , as seen for the more complex T2S and T4P systems , yet they , like the T2S systems , lack a retraction ATPase . We show here that V . cholerae TCP are indeed retractile and that retraction is induced by the single minor pilin , TcpB . We further show that TcpB , like its ETEC counterpart CofB [98 , 99] , induces pilus assembly . Efficient TCP retraction but not assembly requires the conserved position 5 glutamate , consistent with TcpB having both a tip-associated localization , like the GspK family pseudopilins , but also incorporating into the growing pilus in the place of a major pilin . Our findings in the T4b pilus system provide important insights for understanding pilus dynamics in the more complex T4P and T2S systems .
TcpB is encoded in the tcp operon immediately downstream of the tcpA gene encoding the major pilin subunit , TcpA ( Fig 1A ) . TcpB is predicted to have a 7-amino acid signal peptide , which is considerably shorter than the 25-residue signal peptide of TcpA , and a 423 amino acid mature protein ( 46 kDa ) , which is much larger than the 199-residue TcpA ( 20 kDa , Fig 1B and 1C ) . The TcpB mature N-terminus shares amino acid sequence similarity with the N-terminal 25-residue polymerization domain of TcpA , including the conserved Glu5 ( Fig 1B ) , but differs from TcpA in the C-terminal region . Importantly , tcpA has a putative rho-independent transcription termination site at its 3’ end ( Fig 1A ) [100] , allowing it to be transcribed at higher levels than tcpB and other genes in the tcp operon . Apart from TcpA , TcpB is the only pilin-like protein encoded in this operon . Previous reports indicate that TcpB is essential for TCP production [101–103] . We show here that the ΔtcpB strain can in fact assemble pili but at very low levels . Low magnification TEM images show abundant thick TCP bundles for V . cholerae WT strain O395 , whereas few TCP are observed for the ΔtcpB mutant ( Fig 2A ) . Nonetheless , individual TCP filaments are morphologically indistinguishable between the two strains . Pili from the WT and ΔtcpB strains were probed with polyclonal anti-TcpA primary antibodies and gold-labeled secondary antibodies and imaged by TEM . Though much less abundant , the ΔtcpB pili observed by TEM are gold labeled ( Fig 2B ) , confirming that they are indeed TCP . To evaluate whether TcpB influences pilus production by acting at the level of TcpA expression and/or stability , V . cholerae overnight cultures were examined by SDS-PAGE and immunoblotting . TcpA levels in whole cell culture samples ( WCC ) , which represent the total protein within the cells and in the supernatant , are comparable in the WT O395 and ΔtcpB strains ( Fig 3A ) , suggesting that TcpB is required for pilus assembly rather than for optimal expression and stability of TcpA . We also looked at the levels of TcpA in the culture supernatant after homogenizing the cells to shear off their pili ( sheared cell supernatant , SS ) . Immunoblotting of the SS fraction with anti-TcpA antibodies showed low levels of TcpA for WT V . cholerae compared to the WCC fraction . This is likely due to the tendency of TCP to bundle , which makes them difficult to separate from the cells as the bundles pellet with the cells even at low g-forces . Nonetheless , TcpA levels were further reduced in the ΔtcpB mutant , comparable to that of a ΔtcpC mutant lacking a functional secretin channel . TcpA levels in the ΔtcpB SS fraction were restored when TcpB was expressed on a plasmid , ptcpB , from a rhamnose-inducible promoter PRHA ( Fig 3A ) . Taken together , the immunoblots and TEM results suggest that TcpB , like the closely related ETEC CofB and other minor pilins [76 , 78 , 92 , 98 , 104] , is involved in initiating pilus assembly . TCP-mediated functions of TcpF secretion and bacterial autoagglutination were analyzed for the ΔtcpB mutant and complemented strain . TcpF secretion is assayed by immunoblotting cleared culture supernatant ( CS ) , in which overnight V . cholerae cultures are centrifuged and filtered to remove bacteria . TcpF secretion is abrogated in the ΔtcpB strain , comparable to that of the ΔtcpC strain , and is restored when TcpB is expressed ectopically ( Fig 3A ) . Autoagglutination in overnight cultures can be assessed visually by examining the size and compactness of the macroscopic aggregates and the clarity of the supernatant , and more quantitatively by measuring the optical density ( 600 nm ) of the culture supernatant , where the more complete the autoagglutination the lower the OD600 ( Fig 3B ) . This assay serves as an in vitro readout for V . cholerae microcolony formation [8 , 35] . Like TcpF secretion , autoagglutination is disrupted in the ΔtcpB strain and restored in the tcpB-complemented strain . The loss of pilus functions in the ΔtcpB mutant is consistent with a disruption of TCP assembly . Because TcpB is homologous to TcpA in its N-terminal segment , and to ETEC CofB , which has a pilin domain [98] , this minor pilin may incorporate into the pilus filament , most likely at its tip , consistent with its role in initiating pilus assembly . To test if TcpB co-localizes with TCP , WCC and SS fractions were immunoblotted with anti-TcpB antibody . As expected , TcpB is detected in the WCC fraction of WT and ΔtcpC strains and in the ΔtcpB+ptcpB strain , but not in the ΔtcpB strain ( Fig 3A ) . While the intensities of the TcpA and TcpB bands cannot be directly compared as they are detected by different antibodies , the differences are nonetheless consistent with TcpB being produced in much lower levels than TcpA , as expected for a minor pilin . No TcpB was detected in the SS fraction for any of the strains ( Fig 3A ) . However , based on the high ratio of TcpA:TcpB in the WCC fraction it is unlikely that TcpB would be detected in the SS fraction as so little TcpA is present in this fraction . Since the immunoblots for TcpA and TcpB from sheared supernatant fractions were inconclusive regarding co-localization of TcpB with the pili , we looked for TcpB in a more concentrated pilus sample . The V . cholerae mutant , RT4225 , has a His181Ala substitution in the major pilin TcpA [35] . TCPH181A pili can readily be purified by ammonium sulfate precipitation of the overnight culture supernatant [8 , 22 , 105] . This is likely because the pili don’t bundle and thus do not precipitate with the cells , and because they grow unusually long and fall off the bacteria into the culture supernatant during overnight growth . We can load much larger quantities of purified TCPH181A onto a gel ( Fig 4A ) compared to our sheared supernatant fractions from WT V . cholerae , which increases our chance of detecting the low abundance TcpB . Purified TCPH181A were examined by SDS-PAGE and immunoblotting with anti-TcpA and TcpB antibodies . TcpB was detected in the TCPH181A pilus preparation when loaded in much larger amounts than that required to visualize the major pilin TcpA ( Fig 4B ) , indicating that it is indeed a minor component of the pilus . V . cholerae RT4225 produces TcpA and TcpB in levels comparable to that of WT V . cholerae O395 , as shown by immunoblots of whole cell culture ( Fig 4B ) . To ensure that the TcpB present in the TCPH181A preparation is not a contaminant of the V . cholerae envelope , this fraction was probed with antibodies against membrane components of the TCP assembly machinery: the outer membrane protein TcpC and the inner membrane protein TcpE . These proteins are almost undetectable even at the highest TCPH181A concentration loaded . These results indicate that TcpB co-localizes with the pilus but is present in much lower levels than TcpA , consistent with it being a minor pilin . To determine the TcpA:TcpB stoichiometry in the TCPH181A fraction , each protein was quantified by comparing the intensities of the immunoblot bands with those of purified recombinant N-terminally truncated ( ΔN- ) TcpA and TcpB of known concentrations using densitometry . Band densities of known amounts of ΔN-TcpA and ΔN-TcpB were normalized and their averages were plotted; these plots were used to determine the amount of TcpA and TcpB in the TCPH181A sample based on their densities ( S1A and S1B Fig ) . The TCPH181A sample was found by densitometry to contain 12±1 mg/ml of TcpA and 4±1 μg/ml of TcpB , giving a mass ratio of 3000:1 and a molar ratio of ~ 7000:1 TcpA:TcpB . WT TCP bundles are typically ~ 2–3 μm in length , whereas individual TCPH181A pili appear to be longer but cannot be measured precisely as they extend past the edges of TEM images at magnifications at which they can be resolved ( S1C Fig ) . Based on TCP having an axial rise of 8 . 4 Å [22] , a ~ 6 μm-long pilus would be comprised of ~ 7000 TcpA subunits and only a single TcpB molecule . Since TcpB is involved in initiating pilus assembly we reason that this single minor pilin is located at the pilus tip . Interestingly , despite the very small amount of TcpB in the purified pilus fraction , TcpB is readily detected in whole cell culture by immunoblots ( Figs 3A and 4B ) , indicating that more TcpB is made than is incorporated into the pili . We proposed previously that TCP might be retractile despite lacking a retraction ATPase , based on their functions in TcpF secretion , autoagglutination and phage uptake [106] . We wondered if TcpB might be involved in this process in addition to its role in initiating pilus assembly . Our hypothesis is that TcpB induces pilus retraction by randomly incorporating into a growing pilus in place of TcpA and stalling assembly , causing the pilus to spontaneously disassemble . Stalling could occur by TcpB blocking addition of subsequent major pilins into the growing pilus or by preventing passage of the pilus through the secretin channel , as was proposed for the ETEC minor pseudopilin GspK [95] . To test if TCP are indeed retractile we performed an elastic micropillars assay , which has been used to demonstrate and quantify retraction events for T4P of N . gonorrhoeae [54 , 107] and Streptococcus sanguinis [57] . Bacterial microcolonies are placed on an array of elastic micropillars and the movements of the tips of the micropillars under and adjacent to a microcolony are observed by differential interference contrast microscopy ( Fig 5A ) . To assess TCP retraction in V . cholerae we employed a mutant lacking the flagellin gene , ΔflaA , to eliminate non-pilus-mediated micropillar movement that might result from flagellar rotation . Movements of micropillars directly under and within a few microns of microcolonies were observed in the direction of the microcolonies , consistent with TCP binding and retraction ( Fig 5B and 5C and S1 and S2 Movies ) . The force exerted on the micropillars , F , is the product of the calibrated elastic spring constant of the micropillars , k , and the displacement , X , of micropillars from their resting position ( Fig 5A , 5D–5F ) . Although many micropillars motions were recorded , only those movements whereby the micropillars returned to their resting position were included in the force calculations . Micropillars motions occur in single pulls lasting a few seconds ( Fig 5D ) and also in more complex pulls that last tens of seconds ( Fig 5E ) . Such complex patterns showing different local force maxima and plateaus could be explained by the pulling of multiple pili , perhaps within TCP bundles , as seen for N . gonorrhoeae T4P [54] . This study does not distinguish between movements induced by individual pili and pilus bundles . The average force for recorded pulling events is 4 ± 1 . 7 pN ( n = 447 , Fig 5F ) , which is substantially weaker than the 50–100 pN forces observed for individual T4P from N . gonorrhoeae and S . sanguinis [54 , 57 , 58 , 107] . Next , we examined the V . cholerae ΔtcpB/ΔflaA strain , which produces very few pili , to demonstrate that the micropillars movements observed for V . cholerae ΔflaA are pilus-mediated . Since this strain does not aggregate , these bacteria were treated with anti-lipopolysaccharide antibody prior to the micropillars assay to promote cell-cell aggregation and microcolony formation to better mimic the V . cholerae ΔflaA conditions . No micropillar movements were observed under these artificially induced V . cholerae ΔtcpB/ΔflaA microcolonies ( Fig 5G–5I and S3 Movie ) . These results confirm that the micropillars movements observed for the V . cholerae ΔflaA strain are pilus-dependent , supporting our hypothesis that TCP are retractile despite lacking a retraction ATPase . To determine whether or not TcpB influences pilus retraction it was necessary to identify a V . cholerae tcpB mutant capable of assembling TCP at WT levels , but not retracting them . We showed previously that the conserved Glu5 of major pilin TcpA is necessary for efficient TCP assembly [22] . We reasoned that TcpB should not require Glu5 to initiate pilus assembly as it would be the first subunit in the pilus , but Glu5 would be necessary for TcpB to incorporate into the growing pilus to initiate retraction . Retraction-deficient TCP would be unable to pull bacteria into compact microcolonies , to produce a piston-like motion to secrete TcpF , or to draw CTXϕ phage into the bacterium . To test the role of TcpB Glu5 in pilus assembly , retraction and functions we generated a series of tcpB mutants with position 5 substitutions and examined their ability to rescue pilus assembly and functions in a ΔtcpB mutant . Expression of the TcpB Glu5 variants in a ΔtcpB mutant restored pilus assembly: these strains produce WT or better levels of TCP , as assessed by TEM ( Fig 6A ) . However , pilus-mediated TcpF secretion and autoagglutination are impaired in the complemented strains ( Fig 6B and 6C ) . The degree of functional impairment depends on the nature of the Glu5 substitution: the ΔtcpB mutant complemented with the most conserved Glu5 substitution to the negatively charged aspartate ( E5D ) showed a slight reduction in both TcpF secretion and autoagglutination compared to the WT strain; the Glu5Gln ( E5Q ) variant , which results in loss of the negative charge but retains polarity , showed poor TcpF secretion and autoagglutination; and the Glu5Val ( E5V ) variant , which has a hydrophobic side chain at position 5 , was substantially impaired for TcpF secretion and autoagglutination . These differences cannot be attributed to defective TcpB expression for the Glu5 variants , as TcpB-E5X levels are comparable to the WT rescue and in all cases are higher than for endogenous expression in the WT O395 strain ( Fig 6B ) . Gao et al . showed that the V . cholerae chromosomal Glu5Val mutant is deficient in autoagglutination , TcpF secretion and CTXϕ transduction [102] . Thus , TcpB Glu5 is not required for pilus assembly but is required for optimal pilus functions . Interestingly , while low levels of TcpB expression ( induction with 0 . 001% rhamnose ) restored TcpF secretion to varying degrees depending on the nature of the Glu5 substitution , high levels ( 0 . 1% rhamnose ) were detrimental for both the WT and the mutant tcpB complementation ( Fig 6B ) . Induction with 0 . 001% rhamnose apparently provides the optimal level of TcpB expression for TCP functions . TCP bundles were quantified for both expression levels , revealing a substantial reduction in piliation for cells induced with 0 . 1% rhamnose , compared to those induced with 0 . 001% rhamnose ( see Fig 7B ) . Thus , overexpression of TcpB disrupts pilus assembly , which impacts pilus functions . Even lower levels of rhamnose were tested for the complementation of the ΔtcpB strain with WT ptcpB , but these did not result in improved autoagglutination levels ( S2 Fig ) . Together these results imply that a precise TcpA:TcpB stoichiometry must be attained for optimal pilus assembly and functions . We hypothesized that the TcpB Glu5 variants are defective in inducing TCP retraction , explaining their impaired autoagglutination and TcpF secretion . To test this hypothesis we performed the micropillars assay on the V . cholerae tcpB-E5V mutant , which has the most severe functional defects . For these experiments we introduced the mutation encoding the Glu5Val substitution into the tcpB gene within the V . cholerae large chromosome via allelic exchange . Consistent with our results with ectopically expressed TcpB-E5V in the ΔtcpB strain ( Fig 6A–6C ) , the chromosomal tcpB-E5V mutant produces pili that match WT TCP in morphology , bundling and quantity , as assessed by TEM ( Fig 7A and 7B ) , but is impaired for both TcpF secretion and autoagglutination ( Fig 7C and 7D ) . Interestingly , further deletion of flaA in the tcpB-E5V strain to eliminate flagella and non-pilus-mediated motility restored autoagglutination , though TcpF secretion remained impaired . Autoagglutination was also restored in another double mutant , tcpB-E5V/ΔmotAB , which is non-motile but does produce flagella ( S3 Fig ) . Micropillars pulling events were very infrequent for the tcpB-E5V/ΔflaA mutant ( Fig 7E–7G and S4 Movie ) despite it being piliated at WT levels ( Fig 7A ) . Thus , the single amino acid substitution in TcpB , Glu5Val , appears to disrupt TCP retraction without disrupting TcpB-mediated pilus assembly . We propose that V . cholerae autoagglutination and microcolony formation are driven by dynamic cycles of elongation and retraction of TCP . Initially , weak complementary interactions among extended pili bring the bacteria in contact with one another [8] , followed by pilus retraction to form tight aggregates . Without retraction the forces of flagellar rotation will disrupt the pilus:pilus interactions and prevent aggregation . Conversely , without flagellar rotation , weak pilus:pilus interactions may persist long enough for inefficient TcpB-E5V-mediated retraction or spontaneous non-TcpB-mediated retraction to occur , allowing the tcpB-E5V/ΔflaA mutant to autoagglutinate during overnight growth .
Our results represent an important conceptual advance in understanding pilus assembly and retraction and the role of the minor pilins in pilus dynamics for both the T4P and T2S pseudopilus systems . First , we show that the single minor pilin of the V . cholerae TCP system , TcpB , is required for efficient pilus assembly . Second , TCP are retractile despite lacking a retraction ATPase . Third , TCP retraction is induced by the minor pilin TcpB . Fourth , TcpB-mediated pilus retraction but not assembly requires TcpB-Glu5 . Finally , retraction facilitates TCP-mediated processes of autoagglutination and TcpF secretion . The role of the single minor pilin in initiating pilus assembly was shown previously in the CFA/III and Longus T4b pilus systems of ETEC [98 , 99 , 108] , which are closely related to V . cholerae TCP in sequence , structure , gene synteny and pilus functions [98 , 109] . We proposed that the ETEC minor pilin CofB forms the first subunit in the growing pilus and is thus tip-associated [98] . CofB has a well-defined pilin domain that resembles its corresponding major pilin , CofA , in fold and size , but has an extended C-terminal region comprised of a β-repeat domain followed by a β-sandwich domain , which are connected by flexible linkers ( Fig 8A ) [98 , 99] . Each domain is stabilized by one or more disulfide bonds . While sequence similarity between CofB and TcpB is low , alignment of the conserved N-terminal α1s and 6 cysteines reveals additional shared residues ( Fig 8B ) and suggests a similar structure for TcpB , but lacking the first β-repeat sub-domain and having only a single linker ( Fig 8C ) . The tip of the pilus is the only location that this bulky pilin could occupy without undergoing a conformational change and without having the C-terminus protrude from the pilus . As tip-associated pilins TcpB and CofB could recruit major pilins and/or trigger opening of the outer membrane secretin channel to allow passage of the pilus to the cell surface ( Fig 8D ) . While we have not directly demonstrated that TcpB is tip-associated , we show here that it co-localizes with purified TCP with approximately one molecule per pilus , consistent with this minor pilin incorporating into the pilus tip to initiate pilus assembly rather than acting at the level of the trans-envelope pilus biogenesis machinery . It is important to note that TcpB is not essential for pilus assembly as small amounts of pili are observed in the ΔtcpB mutant . It may be that the major pilin can itself nucleate TCP assembly and/or trigger opening of the secretin channel but does so inefficiently . We show here that the non-motile but piliated V . cholerae strain ΔflaA induces micropillars movements , whereas the non-piliated ΔtcpB/ΔflaA strain does not , implying that micropillars movements are pilus-mediated . T4P-mediated micropillars movements have been demonstrated for N . gonorrhoeae and S . sanguinis [54 , 57 , 111] , and T4P retraction has been demonstrated by optical tweezers experiments for N . gonorrhoeae and M . xanthus [52 , 54 , 56 , 58 , 59 , 61] . Piliation is not sufficient for micropillars pulling as strain tcpB-E5V/ΔflaA makes better than WT levels of pili but micropillars movements were rarely observed for this strain , demonstrating a role for Glu5 in TCP retraction but not assembly . An alternative explanation for the lack of micropillars movements in tcpB-E5V/ΔflaA , that the Glu5Val substitution destabilizes the pili causing them to break off the cells , was ruled out based on the following observations: ( i ) TCPTcpB-E5V bundles are more likely than WT TCP to be found by TEM attached to cells; ( ii ) TCPTcpB-E5V levels in the overnight culture supernatant are approximately the same as TCPWT; and ( iii ) TCPTcpB-E5V bundles tend to be longer than those of TCPWT , consistent with efficient pilus growth but inefficient retraction . Our results with the tcpB-E5V strain , together with those of Gao et al . [102] show that pilus production alone is insufficient to mediate TCP functions of autoagglutination , TcpF secretion and CTXϕ uptake . Impaired functions in the tcpB-E5V strain correlate with lack of retraction , suggesting that TCP retraction is central to all of these processes: to produce the very tight , spherical aggregates observed for autoagglutinated V . cholerae in vitro and V . cholerae microcolonies in the intestines [35 , 112]; to produce a piston-like motion to efficiently secrete TcpF; and to draw the filamentous CTXϕ into the periplasm . While the non-piliated ΔtcpB mutant is completely deficient in autoagglutination , TcpF secretion and phage uptake , the piliated tcpB-E5V mutant is able to perform these functions , though at substantially reduced levels , suggesting that pilus retraction occurs in this strain , perhaps inefficiently in a TcpB-E5V dependent manner , or by spontaneous TcpB-independent retraction . Nonetheless , it was surprising that the non-motile tcpB-E5V/ΔflaA mutant autoagglutinates as well as the WT strain . This observation highlights the disruptive force of flagellar rotation , which works against autoagglutination and microcolony formation . However TcpF secretion requires efficient retraction whether the cells are motile or not . It stands to reason that a pilus undergoing rapid cycles of elongation and retraction would extrude substrate more efficiently , whereas a pilus that grows long and then retracts would be more effective in establishing bacterial aggregates . TCP retract with forces averaging ~4 pN , enabling V . cholerae to move against moderate hydrodynamic flows to form compact aggregates and to draw phage into the periplasm . These forces are considerably weaker than the >100 pN forces exerted by N . gonorrhoeae and M . xanthus T4P [54 , 56 , 58] , which perform twitching motility in addition to aggregation and phage uptake . The N . gonorrhoeae and M . xanthus T4P systems possess a retraction ATPase , which may influence the force , rate and frequency of the retraction events . How might this minor pilin perform dual antagonistic roles in pilus biogenesis ? We propose that TcpB incorporates at the pilus tip to initiate pilus assembly and remains at the pilus tip as the filament grows . Additionally TcpB incorporates transiently into the growing pilus where it causes assembly to stall , triggering disassembly or retraction . TcpB anchored in the inner membrane would occasionally insert into the growing pilus via its pilin domain , in place of TcpA ( Fig 8D ) . The flexible linker would allow the bulky C-terminal domain to move out of the way of the pilin domain , forcing it onto the pilus surface where it would interfere with passage of the pilus through the secretin complex or perhaps prevent addition of the next major pilin , effectively stalling pilus assembly . Stalling would prevent incremental extrusion of the pilus so TcpB would eventually diffuse back into the inner membrane . As a result , the pilus would collapse a short distance into the membrane , whereupon the next pilin subunit would diffuse away , etc . , disassembling the pilus one subunit at a time . The frequency of retraction events would depend on the molar ratio of TcpA to TcpB . This ratio would define the average length of the pilus: the more TcpB present the more frequent the retraction events and the fewer and shorter the pili . Our model infers that pilus retraction occurs as a consequence of the dynamic instability of the growing pilus filament , which requires energy from ATP hydrolysis to assemble but disassembles spontaneously , melting into the inner membrane one subunit at a time when assembly is interrupted , in this case by TcpB . This processive melting mechanism has been proposed by others [53] , but was ruled out for T4P systems possessing a retraction ATPase as retraction visibly slows when ATP is depleted , suggesting an active role for the retraction ATPase [58] . While the mechanism of PilT-mediated retraction is not well understood , it clearly differs markedly from TcpB-mediated retraction , which , rather than utilizing chemical energy from ATP hydrolysis , appears to be a passive mechanical process that exploits the potential energy stored in the pilus . Such a model explains why Glu5 is necessary for TcpB to induce pilus retraction but not pilus assembly . Docking of pilin subunits into a growing pilus is driven in part by charge complementarity between the N-terminal amine on the terminal subunit in the pilus and Glu5 on the incoming pilin ( Fig 8D ) . As the first pilin subunit in the pilus , TcpB would need a positively charged N-terminus to neutralize the negatively charged Glu5 on an incoming TcpA , but would not itself need Glu5 . However , to incorporate into the growing pilus TcpB would need Glu5 to neutralize N+ on the terminal TcpA at the base of the filament . Because TcpB-E5V can incorporate at the pilus tip it can initiate pilus assembly , thus the tcpB-E5V mutant is piliated , but TcpB-E5V cannot efficiently incorporate into the growing pilus to induce retraction , explaining the lack of micropillars motions and aberrant functions for this strain . The sequence and functional similarities between V . cholerae TcpB and ETEC CofB suggest that the highly homologous ETEC minor pilins CofB and LngB also induce retraction of their respective T4b pili , CFA/III and Longus , respectively , to mediate aggregation and secretion . It is remarkable that a single minor pilin accomplishes both initiation and retraction , whereas four or more minor pilins work together to prime and in some cases to arrest pilus assembly in the more complex T4a pilus and T2S systems [74 , 76 , 77 , 92 , 93] . The extended C-terminal regions of the V . cholerae and ETEC minor pilins likely impart specialized functions accomplished by multiple minor pilins in the more complex systems , such as recruiting major pilins to initiate pilus assembly and/or opening the secretin channel . CofB , and by inference , TcpB , have flexible structures plus Glu5 that allow them to ( i ) incorporate at the tip of the pilus to initiate assembly , ( ii ) to pass through the secretin complex when tip-associated , and ( iii ) to incorporate into the growing pilus to trigger retraction . In contrast , the T2S minor pilin GspK is bulky but rigid and has a hydrophobic amino acid at position 5 . While GspK in complex with GspHIJ appears to initiate pseudopilus assembly , its rigid structure might prevent its passage through the secretin complex [95] , preventing growth of this pilus across the outer membrane , and triggering retraction . Rapid cycles of polymerization and retraction would serve to efficiently extrude substrates across the outer membrane . Importantly , its bulk , rigidity and lack of Glu5 would prevent GspK from incorporating at any site other than at the tip of the pilus . GspK is the only minor pilin of the GspHIJK cluster that lacks a glutamate at position 5 . In fact , for most T2S and T4a pilus systems , as well as the EPEC T4b system of bundle-forming pili , only one of the four or five minor pilins has hydrophobic residue at position 5 , the others having Glu5 . In the T2S systems and the Gram positive T4P systems this minor pilin is also substantially larger than its corresponding major pilin , typical of GspK family pilins , and is encoded at the 3’ end of the minor pilin gene cluster . These features may represent signatures to identify minor pilins that form the first subunit in a nascent filament . Interestingly , for two of the minor pilins of the K . oxytoca T2S system , Glu5 has been implicated in interactions with inner membrane components of the pseudopilus assembly machinery , demonstrating a critical and complex role for this residue in filament formation [94] . The minimalist V . cholerae and ETEC T4b systems may represent precursors for other T4P and T2S systems . In these systems all genes necessary for T4b pilus assembly are clustered on a single operon . The V . cholerae tcp operon is present on a mobile genetic element , the Vibrio pathogenicity island [7] , and the ETEC cof operon and lng operons are present on large virulence plasmids [113 , 114] . These bacteria could pass their pilus operons to other bacteria during gut infections . Additional minor pilin genes likely arose via gene duplication . The T2S system appears more closely related to the V . cholerae and ETEC T4b pilus systems as T2S genes are also located on a single operon , they lack a retraction ATPase and they encode a large GspK family subunit . V . cholerae TcpB and ETEC CofB are large like the GspK family pilins but they have a flexible C-terminal region and Glu5 , allowing them to incorporate at any point during pilus assembly and induce retraction . Our results suggest that the T4b pili , like the T4a pili and T2S pseudopili , are dynamic filaments that rapidly assemble and disassemble to perform their multiple and diverse functions , with each system having evolved distinct mechanisms to facilitate pilus dynamics . The V . cholerae minor pilin TcpB represents a simple but elegant multifunctional protein responsible for TCP dynamics and functions .
Bacterial strains used in this study are described in Table 1 . All strains were maintained at -80°C in lysogeny broth ( LB ) containing 20% glycerol ( v v-1 ) . V . cholerae strains were grown under classical , TCP-expressing conditions of LB ( starting pH of 6 . 5 ) at 30°C with aeration as previously described [7 , 115] . Where appropriate , strains were grown with antibiotics at the following final concentrations: ampicillin ( Ap ) 100 μg/ml , gentamicin ( Gm ) 30 μg/ml , kanamycin ( Km ) 45 μg/ml , and streptomycin ( Sm ) 100 μg/ml , spectomycin ( Sp ) , tetracyclin ( Tet ) . All DNA manipulations were performed using standard molecular and genetic techniques [116 , 117] . Expression of tcpB and tcpB mutants from ptcpB was induced using rhamnose at the indicated concentrations . Plasmids used in this study are listed in Table 1 . Expression vector pJMA10 is derived from pBAD22 and constructed as follows . Primers ABN26/27 were used to amplify the rhamnose promoter PRHA from E . coli BL21 chromosomal DNA . The PCR amplified region was then cloned into pBAD22-TOPO ( ATCC ) , replacing the araC promoter region to generate pJMA10 . This vector was further modified to remove the endogenous NcoI restriction digest sequence from the multiple cloning site ( MCS ) to prevent a possible frameshift mutation . Primers NcoDEL-F-NheI and NcoDEL-R-KpnI were used to PCR amplify a 1 . 7 kb fragment of pJMA10 upstream of the MCS that contains a unique NheI restriction site . The fragment downstream of the MCS was directly digested from the purified plasmid . Both fragments were digested with restriction enzymes NheI and KpnI , and ligated together using T4 DNA ligase ( New England Biolabs ) to generate the corrected plasmid pJMA10 . 1 . Both pJMA10 and pJMA10 . 1 were verified by DNA sequencing . The vector expressing the minor pilin TcpB was derived from expression vector pJMA10 . 1 , which contains an ApR marker . The gene fragment encoding TcpB was PCR amplified with primers TcpB-F-KpnI/-R-XbaI from V . cholerae WT strain O395 genomic DNA . The PCR product was purified , digested with KpnI and XbaI , and ligated into pJMA10 . 1 at the KpnI/XbaI restriction sites using T4 DNA ligase . The construct ptcpB was verified by DNA sequencing and transformed into V . cholerae strains for complementation testing . Pilin expression was induced using rhamnose at indicated concentrations . The conserved Glu5 residue in TcpB was changed to Asp , Gln and Val on ptcpB to generate ptcpBE5D , ptcpBE5Q and ptcpBE5V respectively . Forward primers TcpB-E5D/E5Q/E5V-F-KpnI were used individually with reverse primer TcpB-R-XbaI to PCR amplify from ptcpB the tcpB gene fragments encoding the corresponding amino acid substitutions E5D , E5Q and E5V . PCR products were purified and subcloned into pJMA10 . 1 as previously described . All plasmids were verified by DNA sequencing and transformed into V . cholerae ΔtcpB and cells were grown on LB-Sm/Ap plates . The ΔtcpB/flaA and tcpB-E5V/ΔflaA chromosomal mutants were generated by allelic exchange . pTRNS101 encoding tcpB-E5V [102] and pTK2 encoding tcpB with a central deletion [103] were used to introduce the E5V substitution and the ΔtcpB deletion into the V . cholerae ΔflaA strain TJK189 [118] by allelic exchange [119] . Fractions of overnight cell cultures were analyzed by SDS-PAGE and immunoblotting to compare TcpA , TcpB and TcpF production in the various V . cholerae strains and conditions . Whole cell culture ( WCC ) fractions used to assess total protein were obtained by resuspending overnight cultures by vortexing . Sheared supernatant ( SS ) fractions were obtained by homogenizing the resuspended cells using an IKA ULTRA-TURRAX T8 . 01 disperser ( IKA-Werke ) at the maximum setting for 20 seconds [8] . Cell debris was removed by centrifugation at 13 , 000 x g for 30 minutes at RT using a bench top microfuge , with the supernatant representing the SS fraction . For analysis of secreted TcpF in the culture supernatant ( CS ) , overnight cultures were centrifuged at 3000 x g for 10 minutes at RT to remove the cells and the culture supernatant containing secreted TcpF was filtered through a 0 . 22 μm syringe-drive filter ( Pall ) to remove remaining cells . Each fraction was mixed with Laemmli sample buffer ( 60 mM Tris pH 6 . 8 , 5% 2-mercaptoethanol , 2% SDS , 10% glycerol , 0 . 02% bromophenol blue ) and boiled for 10 minutes prior to loading 25 μl onto 15% SDS polyacrylamide gels . The PageRuler Unstained Protein Ladder ( Fermentas ) was included for mass markers . Proteins were transferred onto polyvinylidene difluoride ( PVDF ) membrane ( Bio-Rad ) at 4°C in transfer buffer ( 25 mM Tris , 192 mM glycine , 20% methanol ) with a wet transfer apparatus ( Bio-Rad ) . The membrane was blocked for 1 hour at RT with non-fat dried milk ( 5% w/v ) in Tris-buffered saline with 0 . 1% Tween . Proteins were detected with rabbit polyclonal antisera raised against TcpA peptide 174–199 [120] , TcpB peptide 64–78 ( Pacific Immunology ) and mouse monoclonal antisera for TcpF [10] . Goat-anti-rabbit or goat-anti-mouse secondary antibodies conjugated to horseradish peroxidase ( Jackson ImmunoResearch ) were used to bind the primary antibody . The protein mass markers were detected with Strep-Tactin-HRP conjugate ( IBA ) . Immunoblots were visualized by enhanced chemiluminescence using the SuperSignal West Pico substrate ( Thermo Fisher Scientific ) . Immunoblots were digitized using the FujiFilm LAS 4000 imager . Expression and purification of ΔN-TcpA displaying an N-terminal hexahistidine tag ( His ) and linker are described elsewhere [17] . The gene fragment encoding ΔN-TcpB ( residues 25–423 ) was PCR amplified with primers TcpB25-vc-fpcr-nde1 and TcpB25-vc-rpcr-BamH1 from V . cholerae WT strain O395 genomic DNA , followed by digestion with restriction enzymes NdeI and BamHI . The product was ligated into expression vector pET15b ( Novagen ) , which encodes a His-tag and linker with a thrombin cleavage site . The construct was transformed into SHuffle T7 Express lysY Competent E . coli ( New England Biolabs ) by heat shock . The cells were grown in LB broth supplemented with ampicillin ( 100 μg/ml ) to OD600 of 0 . 1 in a shaking incubator 37°C , and protein expression was induced by adding isopropyl β-D-1-thiogalactopyranoside ( IPTG , final concentration to 0 . 1 mM ) . The temperature was reduced to 14°C and cells were grown for a further 20 hours and harvested by centrifugation ( 5000 x g , 30 min , 4°C ) . The culture supernatant was discarded and the cell pellet was resuspended in lysis buffer ( 50 mM Na2HPO4/NaH2PO4 pH 7 . 4 , 20 mM Tris HCl pH 7 . 4 , 100 mM NaCl , EDTA-free protease inhibitor cocktail [Roche] , and 1 mg/ml lysozyme ) and gently stirred at room temperature for an hour . Cells were lysed by sonication and cell debris was removed by centrifugation at 40 , 000 x g for 1 hour at 4°C . Purification was carried out at 4°C . The cell lysate was filtered through 0 . 4 μm polyethersulfone membrane and loaded onto a Ni-NTA column pre-equilibrated with wash buffer ( 50 mM Na2HPO4/NaH2PO4 pH 7 . 4 , 30 mM imidazole and 100 mM NaCl ) . After washing the column with 10 column volumes of wash buffer , the N-terminal His tag was cleaved off of the TcpB bound to the Ni-NTA column by thrombin digestion and the protein was eluted with elution buffer ( 50 mM Na2HPO4/NaH2PO4 pH 7 . 4 , 250 mM imidazole , 100 mM NaCl ) . The protein was further purified by size exclusion chromatography using a HiPrep 26/60 Sephacryl S-100 HR column ( GE Healthcare ) in buffer containing 20 mM Tris-HCl , pH 7 . 4 , 100 mM NaCl , 0 . 5 mM EDTA , 0 . 5 mM EGTA and concentrated to 10 mg/ml using a stirred-cell concentrator ( Millipore ) . The purity was estimated to be >95% . V . cholerae strain RT4225 expressing a mutation in tcpA encoding a His181Ala substitution [35] , was grown on a LB plate containing ampicillin ( LB/Ap ) . A single colony was used to inoculate 1 ml of LB/Ap , which was incubated at 37°C on a rotary shaker for 30 min . The cell culture was diluted to OD600 = 0 . 01 and 400 μl was used to inoculate 200 ml of LB , pH 6 . 5 , 0 . 4 mM IPTG , 100 μg/ml Ap in a 2 L-flask , which was incubated at 30°C for 18 hours shaking at 250 rpm . Four ml of 0 . 5 M EDTA ( pH 8 ) and 2 ml of 0 . 1 M histidine-HCl were added to the culture , which was centrifuged at 5000 xg for 15 minutes at 4°C to pellet cells . The supernatant was centrifuged again at 5000 xg for 10 minutes at 4°C to pellet residual cells . After transferring the supernatant to new tubes , solid ammonium sulfate ( AmSO4 ) was added to 10% saturation and the solution was incubated at 4°C for 2 hours on a rocker . The solution was centrifuged at 10 , 000 xg for 30 minutes at 4°C , and AmSO4 was added to 30% saturation . The solution was incubated at 4°C for one hour on the rocker then centrifuged at 10 , 000 xg for 30 min at 4°C to pellet the pili . The pellet was resuspended in 200 μl of PBS containing 10 mM EDTA and dialyzed exhaustively using a 3500 kDa molecular weight cut-off membrane against precooled PBS , 10 mM EDTA . Cells were grown in 2 mL LB with antibiotics ( streptomycin , ampicillin as required ) for 2 hours at 37°C with rotation and cell concentrations were normalized to OD600 of 0 . 01 . Normalized cultures were diluted 1/500 in 3 mL LB ( pH 6 . 5 ) and grown overnight on a Ferris wheel rotator at 30°C with antibiotics and rhamnose as required . Overnight cultures were allowed to settle at room temperature for 15 minutes , after which the autoagglutination phenotype was assessed visually and the OD600 of the culture supernatant was measured . Experiments were performed in triplicate . ΔN-TcpA and ΔN-TcpB concentrations were determined by UV absorbance ( 260 nm ) using a Nanodrop spectrophotometer ( Thermo Fisher Scientific ) . Known amounts of each protein ( standards ) and fixed volumes of purified TCPH181A were separated on a 15% SDS polyacrylamide gel . Proteins were transferred onto PVDF membrane and immunoblotted using anti-TcpA and anti-TcpB antibodies . Protein bands were scanned with a FujiFilm LAS4000 imager and analyzed using ImageJ [121] . Band densities of the ΔN-TcpA and ΔN-TcpB standards were normalized to the densest standard band in that blot to make averaging possible . The averages of relative densities from two replicates were plotted against their protein amounts and the plot was used to interpolate the amount of native TcpA or TcpB present in the purified TCPH181A sample . V . cholerae cells were grown under TCP-expressing conditions . Carbon-coated grids ( CF-300 , Electron Microscopy Science ) were inverted on top of a 20 μL drop of sample on Parafilm and incubated for 10 minutes . Following incubation , grids were transferred to drops of Tris-buffered saline with 0 . 1% Tween ( TBST ) . Grids were then stained with 3% uranyl acetate and imaged on a Hitachi 8100 STEM operating at 120 kEv . For immunogold labeled samples , grids were inverted on top of a 25 μl drop of overnight cell culture on Parafilm in a humidified chamber to prevent evaporation and incubated at 30°C for 10 min . Following incubation , grids were transferred to drops of fixative ( 4% paraformaldehyde/0 . 2% glutaraldehyde in 0 . 2 M sodium cacodylate pH 7 . 4 ) for 1 hour . The grids were washed with Tris-buffered saline with 0 . 1% Tween ( TBST ) and blocked for 1 hour in bovine serine albumin ( BSA ) in TBST . Samples were incubated with primary rabbit antibody raised against TcpA ( peptide 174–199 [120] , 1:50 dilution in TBST 1% BSA ) , washed in TBST , and then incubated for 30 minutes with gold-conjugated anti-rabbit secondary antibody ( 1:60 dilution in TBST 1% BSA ) ( Jackson Immunoresearch , Electron Microscopy Sciences ) . The final wash in TBST was followed by staining with 3% uranyl acetate . Samples were imaged on a Hitachi 8100 STEM at 200 kV with the exception of Fig 2B , which was imaged on a JEOL 100CX TEM at 100 kV . Micropillars assays were performed as previously reported [107] using PoMPs ( Polyacrylamide MicroPillars ) . Briefly , microfabricated silica molds were inverted onto 15 μL of a mixture of 20% polyacrylamide and 0 . 2% Bis-acrylamide with 1/10 volume of ammonium persulfate and 1/1000 volume of TEMED on activated 25 mm diameter round coverslips ( Warner Instruments ) . Reticulation of the hydrogel and removal of the silica mold yielded a hexagonal array of polymerized micropillars spaced 3 μm center to center with a spring constant ( k ) of 25 +/- 4 pN/μm . The micropillars hydrogel was activated with sulfo-SANPAH crosslinker ( Invitrogen ) according to the manufacturer's recommendations , then the micropillars were treated with poly-L-lysine ( 30 μg/ml in phosphate buffered saline ) for one hour at 37°C . After 3 washes in water the micropillars were coated for one hour with a 1/10 ( V/V ) solution of 20 nm carboxylated beads in water ( Molecular Probes ) . After two washes with water and one with phosphate buffered saline , the coverslips supporting the micropillars were mounted at the bottom of an observation chamber ( Attofluor chamber , Invitrogen ) . Two mls LB ( pH 8 . 5 ) and 5 μL suspension of V . cholerae overnight culture was added to the chamber and incubated at 30°C for one hour . Ten Hz movies of the pillars tips were recorded using a 60X objective on an inverted microscope with an environmental chamber at 30°C ( TiEclipse , Nikon ) . Positions of the micropillar tips were analyzed in the movies using a custom-designed cross-correlation algorithm [122] . The position of a non-deflected micropillar away from any colony was used as a reference to compensate for any global movement of the chamber . The forces exerted on the micropillars were calculated based of the displacement of the pillars and their calibrated spring constant using a commercial software package Matlab ( Mathworks Inc . Natick , MA ) and Igor Pro ( WaveMetrics , Inc . , Lake Oswego , OR ) . The V . cholerae suspensions used to perform the micropillar assays were prepared as follows: ΔflaA , ΔtcpB/ΔflaA and tcpB-E5V/ΔflaA strains were streaked onto LB agar plates and grown for 24 hours at 37°C , a single colony was touched with a 2 mm sterilized loop and the bacteria attached to the loop were transferred to 1 ml of LB ( pH 6 . 5 ) . Ten μl of this solution was used to inoculate 3 ml LB ( pH 6 . 5 ) in a culture tube , which was rotated at 30°C for 12 to 15 hours . For V . cholerae strain ΔtcpB/ΔflaA , 1 μl of anti-lipopolysaccharide antibody was added to induce bacterial aggregation and the suspension was rotated an additional 30 minutes prior to the micropillar assay . | Bacterial pathogens utilize a number of highly complex and sophisticated molecular systems to colonize their hosts and alter them , creating customized niches in which to reproduce . One such system is the Type IV pilus system , made up of dozens of proteins that form a macromolecular machine to polymerize small pilin proteins into long thin filaments that are displayed on the bacterial surface . These pili have a remarkable array of functions that rely on their ability to ( i ) adhere to many substrates , including host cell surfaces , pili from nearby bacteria , DNA and bacterial viruses ( bacteriophage ) , and ( ii ) to depolymerize or retract , which pulls the bacteria along mucosal surfaces , pulls them close together in protective aggregates , and can even draw in substrates like DNA and bacteriophage for nutrition and genetic variation . For most Type IV pilus systems , retraction is an energy-driven process facilitated by a retraction ATPase . We show here that in the simplest of the Type IV pilus systems , the Vibrio cholerae toxin-coregulated pilus , a pilin-like protein initiates pilus retraction by what appears to be mechanical rather than enzymatic means . Our results provide a framework for understanding more complex Type IV pili and the related Type II secretion systems , which represent targets for novel highly specific antibiotics . | [
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] | 2016 | The Vibrio cholerae Minor Pilin TcpB Initiates Assembly and Retraction of the Toxin-Coregulated Pilus |
Most bacteria contain both eukaryotic-like Ser/Thr kinases ( eSTKs ) and eukaryotic-like Ser/Thr phosphatases ( eSTPs ) . Their role in bacterial physiology is not currently well understood in large part because the conditions where the eSTKs are active are generally not known . However , all sequenced Gram-positive bacteria have a highly conserved eSTK with extracellular PASTA repeats that bind cell wall derived muropeptides . Here , we report that in the Gram-positive bacterium Bacillus subtilis , the PASTA-containing eSTK PrkC and its cognate eSTP PrpC converge with the essential WalRK two-component system to regulate WalR regulon genes involved in cell wall metabolism . By continuously monitoring gene expression throughout growth , we consistently find a large PrkC-dependent effect on expression of several different WalR regulon genes in early stationary phase , including both those that are activated by WalR ( yocH ) as well as those that are repressed ( iseA , pdaC ) . We demonstrate that PrkC phosphorylates WalR in vitro and in vivo on a single Thr residue located in the receiver domain . Although the phosphorylated region of the receiver domain is highly conserved among several B . subtilis response regulators , PrkC displays specificity for WalR in vitro . Consistently , strains expressing a nonphosphorylatable WalR point mutant strongly reduce both PrkC dependent activation and repression of yocH , iseA , and pdaC . This suggests a model where the eSTK PrkC regulates the essential WalRK two-component signaling system by direct phosphorylation of WalR Thr101 , resulting in the regulation of WalR regulon genes involved in cell wall metabolism in stationary phase . As both the eSTK PrkC and the essential WalRK two-component system are highly conserved in Gram-positive bacteria , these results may be applicable to further understanding the role of eSTKs in Gram-positive physiology and cell wall metabolism .
Regulatory Ser/Thr phosphorylation has been assumed to be largely absent from prokaryotes . However , recently , phosphoproteomic analysis has identified numerous ( ~50 ) proteins phosphorylated on Ser or Thr residues in both Gram-positive and Gram-negative bacteria [1] . Likely candidates for the enzymes responsible for these modifications are bacterial proteins with structural and mechanistic similarities to eukaryotic Ser/Thr kinases ( eSTKs ) and phosphatases ( eSTPs ) [2 , 3] . These enzymes have been identified in phylogenetically diverse bacteria , and eSTKs may be directly responsible for a number of the observed Ser/Thr phosphorylations . Thus , how these kinases regulate cellular physiology remains unclear , but their conservation across the majority of sequenced bacterial species suggests that they play an important role . Regulatory phosphorylation in bacteria has largely been studied in the context of two-component systems ( TCS ) that govern the expression of a regulon in response to a physiological signal . These signaling systems include a sensor histidine kinase , which when activated transphosphorylates a cognate response regulator , typically a DNA-binding protein , on a conserved aspartate residue . Once phosphorylated , the response regulator dimerizes and binds specific sequences in the promoters of the genes in its regulon . Hints that eSTKs might similarly be involved in transcriptional regulation come from studies demonstrating that mutations in eSTKs cause large-scale changes in gene expression [4–6] . However , in contrast to TCS systems which typically use response regulator phosphorylation to regulate gene expression , eSTKs and the eSTPs generally lack identified DNA binding motifs , suggesting they likely mediate changes in gene expression indirectly . Although eSTKs and eSTPs are also not generally associated with a specific partner transcription factor , at least some of the observed transcriptional changes may be due to direct phosphorylation of a transcription factor . As the regulons governed by some TCSs have been extensively studied , overlap between transcriptional changes caused by mutations in eSTKs and eSTPs and certain TCS systems has been observed [7 , 8] . A plausible mechanism for these effects is that an eSTK phosphorylates components of a TCS system and thereby affecting gene expression . Consistently , eSTKs from a number of organisms have been shown to phosphorylate TCS proteins , and these modifications affect expression of genes in the respective TCS regulon [7 , 8] . All sequenced Gram-positive bacteria contain a single membrane-bound eSTK comprised of an extracellular domain composed of three or four PASTA repeats and a cytoplasmic domain with extensive homology to eukaryotic Ser/Thr kinases [3 , 9] . The PASTA ( Peptidoglycan binding and Ser/Thr kinase associated ) motif was originally identified in penicillin-binding proteins that are responsible for peptidoglycan polymerization , suggesting that it mediates peptidoglycan recognition [10] . Consistent with this hypothesis , isolated PASTA domains interact in vitro with a peptide corresponding to the peptidoglycan stem peptide [11–13] . In the Gram-positive bacterium B . subtilis , the PASTA-containing eSTK is PrkC , which is required for spore germination [14] and for regulation of the production of a secreted muramidase [15] in response to muropeptides . PrkC is transcribed during all phases of growth , and is co-transcribed with the eSTP PrpC [16] . Thus , PrkC could play a role in the regulation of peptidoglycan metabolism in B . subtilis . A signaling system that is known to be important for peptidoglycan metabolism is the WalRK TCS , a well conserved , essential TCS found in low-GC-Gram-positive bacteria including B . subtilis [17–19] . Mutations in the response regulator WalR that alter its function significantly result in the generation of B . subtilis L-forms , variants that lack cell wall [20] . The essentiality of WalR likely derives from its regulation of at least one WalRK regulon member , although the identity of that gene , or combination of genes , is not known for most WalRK-containing species ( see S . pneumoniae for an exception [21] ) . This essentiality is unusual; WalRK is the only essential TCS ( out of 34 ) in B . subtilis [17] . The core WalRK regulon conserved in different species comprises ~10 genes involved in cell wall metabolism , including several peptidoglycan hydrolases [18] . In B . subtilis , ~20 WalRK regulon genes have been identified through several approaches including the use of a hybrid regulator [22] , an IPTG-inducible WalRK operon [23 , 24] , and ChIP [25] . Differential regulation of WalRK regulated genes is observed upon treatment with various cell wall-targeting antibiotics , suggesting that the WalK activating ligand is a molecule derived from the cell wall [18] . As the eSTK PrkC and the TCS WalRK have functional overlap , it is plausible that these two types of signaling systems converge in Gram-positive bacteria . Consistent with this hypothesis , deletion of the PrkC homologs in S . pneumoniae ( StkP; [5] ) , S . mutans ( PknB; [6] ) , S . pyogenes ( Stk; [26] ) and S . aureus ( PknB; [4] ) resulted in the down-regulation of genes known to be under control of either WalR or its homolog VicR . In the model Gram-positive B . subtilis , yocH , a well-characterized member of the WalRK regulon encoding a cell wall hydrolase , is regulated by both WalR and PrkC . WalR directly binds the yocH promoter in vitro [22] , and yocH transcription is activated by WalR expression in vivo [27] . Intriguingly , yocH also demonstrates PrkC-dependent activation in response to cell wall-derived muropeptides [15] . Thus , these data suggest a functional interaction between PrkC and the WalRK TCS . Here we use in vitro and in vivo approaches to demonstrate that , consistent with this hypothesis , PrkC regulates the WalRK TCS through phosphorylation of WalR , resulting in the regulation of genes involved in cell wall metabolism by an eSTK .
Prior work demonstrated that expression of the WalR regulon gene yocH is controlled both by WalR binding to PyocH [22 , 27] and by PrkC through an unknown mechanism [15] . These observations suggest two possible mechanisms of PrkC dependent yocH activation: 1 ) PrkC acts on the WalRK TCS to regulate yocH and consequently other members of the WalR regulon , or 2 ) PrkC acts through another ( unknown ) mechanism on yocH ( Fig 1A ) . The first hypothesis predicts that changes in PrkC activity resulting in the activation of yocH would also result in transcriptional changes across the WalR regulon , namely increased activation of genes activated by WalR , as well as increased repression of genes repressed by WalR . The second hypothesis predicts that changes in PrkC activity that activate yocH may have little , or no consistent , effect on the expression of other members of the WalR regulon . To test these alternate hypotheses , we first determined when PrkC activity has the strongest effect on yocH expression . We constructed a transcriptional fusion of the yocH promoter region to the luxABCDE operon , originally from P . luminescens , optimized for expression in Gram-positive bacteria [28] and integrated it into an ectopic locus in the chromosome . As the luminescence signal from the luxABCDE operon is relatively unstable , it can be used to measure both increases and decreases in gene expression [29] . Therefore , we used the PyocH-luxABCDE transcriptional reporter ( hereafter referred to as PyocH-lux ) to continuously monitor changes in yocH expression throughout growth in rich media ( LB ) in a plate reader . Under these conditions , the doubling time for the WT strain harboring the PyocH-lux reporter is roughly 22 minutes in exponential phase , comparable to the optimal growth rate of B . subtilis in LB at 37°C . We measured yocH expression as well as the corresponding growth curves ( OD600 ) in the plate reader every 5 minutes in four genetic backgrounds: wild type ( ‘WT’ ) , ΔprpC ( no phosphatase ) , ΔprkC ( no kinase ) , and Δ ( prpC-prkC ) ( neither phosphatase nor kinase ) . Under these conditions , the growth of these four strains is consistent with the previously reported stationary phase survival defect of ΔprkC strains and increased longevity of ΔprpC strains ( Fig 1B ) [30] . To highlight the consistent similarity between the ΔprkC and Δ ( prpC-prkC ) strains , indicating that the observed ΔprpC phenotypes are epistatic to prkC , ΔprkC mutants are grouped by color ( Fig 1 , shades of blue ) . Furthermore , these experiments also demonstrated that while there is no growth phenotype for ΔprkC and ΔprpC mutant strains during log phase growth , mild differences begin to appear during the transition to stationary phase and become more pronounced later in stationary phase prior to the onset of lysis ( Fig 1B ) . Since transcriptional changes near the onset of lysis may be complicated to interpret , we chose to examine PrkC-dependent changes in gene expression at four characteristic growth points , as determined by position on the growth curve and corresponding measured OD600 ( arrows indicate points for WT , Fig 1B ) : 1 ) exponential phase ( OD600~0 . 1 ) , 2 ) late log ( OD600~0 . 2 ) , 3 ) transition phase ( OD600~0 . 3 ) , and 4 ) stationary phase ( OD600~0 . 5 ) . We picked OD600~0 . 5 as a representative point for stationary phase measurements , as we observe comparable growth rates for all genetic backgrounds near this OD600 , and it is prior to the onset of lysis . Note that OD600 values measured by the plate reader differ from those measured by a standard laboratory spectrophotometer . See materials and methods for more details . To characterize PrkC-dependent yocH expression at each of these characteristic growth phases , we compared the normalized luminescence ( relative luminescence , RLU , normalized by OD600 ) of the PyocH-lux reporter in each genetic background at each characteristic growth phase ( Fig 1C ) . Interestingly , although prkC and prpC are expressed throughout all growth phases [16] , mutants in prkC and prpC did not exhibit strong changes in yocH expression until stationary phase ( Fig 1C ) . To determine more precisely when in the growth cycle changes in yocH expression begin to appear , we plotted normalized luminescence as a function of OD600 for data acquired at 5 minute intervals for each genetic background ( Fig 1D ) , yielding a synchronized picture of gene expression as a function of growth state . For clarity , these graphs show only the pre-lysis data ( to maximum OD600 ) for each strain . In WT cells , yocH expression is high in exponential phase and falls in stationary phase ( Fig 1C and 1D , gray ) , consistent with prior work [31] . Interestingly , this trend was reversed in a ΔprpC background beginning at the exit from log phase ( OD600~0 . 3 , Fig 1D , ii ) . This effect is PrkC dependent as both ΔprkC and Δ ( prpC-prkC ) strains show very similar expression profiles ( compare shades of blue , Fig 1D ) . The WT expression profile ( gray ) lies between ΔprpC and ΔprkC , and is strongly biased towards ΔprkC . This suggests that under these conditions , PrkC exerts a relatively small average effect on yocH expression in WT cells , ~3 . 5 fold ( S1 Fig ) , when compared to the total possible PrkC-dependent effect observed in a ΔprpC background . The difference in expression between ΔprpC and ΔprkC backgrounds suggests that PrkC is capable of exerting a >20 fold change on yocH expression in stationary phase . If PrkC regulates yocH expression through a WalR-dependent mechanism ( Fig 1A ) , we would expect it to control expression of other genes in the WalR regulon . Two genes repressed by WalR were found to have the highest average WalR occupancy of their promoters in a ChIP study [25]: iseA ( yoeB ) , encoding an inhibitor of autolysin activity [32] , and pdaC ( yjeA ) , a peptidoglycan deacetylase [33] . To determine if PrkC regulates iseA and pdaC under the same conditions where we observed PrkC-dependent regulation of yocH , we constructed PiseA-luxABCDE and PpdaC-luxABCDE transcriptional reporters . Using these reporters and the same growth conditions and techniques used to monitor yocH expression , we measured iseA and pdaC expression using normalized luminescence as a function of OD600 ( Fig 2A ) . Strikingly , we observed that both iseA and pdaC exhibit strong PrkC-dependent repression at the same OD600 and growth phases where yocH exhibited PrkC-dependent activation . That is , beginning at the exit from log phase ( OD600~0 . 3 ) and becoming most significant in stationary phase , both iseA and pdaC show increased repression in a ΔprpC background that is relieved in both ΔprkC and Δ ( prpC-prkC ) backgrounds ( Fig 2A ) . We examined this growth phase dependence by extracting the normalized luminescence at four specific optical densities from the continuous data ( Fig 2B ) . Similarly to what we observed for yocH , the WT strains have expression profiles in stationary phase between the ΔprpC and ΔprkC backgrounds , consistent with opposing regulation . In strains carrying a ΔprpC mutation , PiseA-lux in stationary phase is expressed ~4x less and PpdaC-lux is expressed >10x less than in strains carrying Δ ( prpC-prkC ) mutations ( Fig 2B ) . As yocH is activated by WalR and both iseA and pdaC are repressed by WalR , these data strongly support a model ( Fig 1A ) where PrkC acts through the WalRK system to transcriptionally regulate yocH , iseA , pdaC , and other members of the WalR regulon . We tested whether inducible heterologous PrkC expression complements a ΔprkC mutation specifically in stationary phase by comparing the expression of the WalR reporters in strains carrying an inducible copy of prkC ( Pspac-prkC ) to their expression in WT and ΔprkC backgrounds ( Fig 3 ) . To confirm that these results represent systematic changes in expression from transition phase through early stationary phase ( OD600~0 . 3–0 . 5 ) , we also compared the normalized luminescence as a function of OD600 for each strain and IPTG concentration ( S2 Fig ) . Without the addition of the inducer IPTG , the strain to test complementation , ΔprkC Pspac-prkC , appears similar to ΔprkC for all three promoters ( compare two lightest shades of blue , Figs 3 and S2 ) . Under these conditions , the small amount of “leaky” expression without inducer is sufficient to complement expression of PyocH , and nearly complement PiseA ( compare gray and lightest shade of blue , Figs 3 and S2 ) . At 10 μM IPTG , Pspac-prkC complements the ΔprkC deletion for the expression of PpdaC , and PyocH and PiseA also display similar expression to the wild type ( compare gray and blue , Figs 3 and S2 ) . For each promoter , the effect of increasing inducer concentration was consistent with increasing WalR activity specifically in stationary phase: increased repression of pdaC and iseA , and increased activation of yocH . Although ΔprkC Pspac-prkC was grown in the continuous presence of inducer , changes in gene expression for pdaC , iseA , and yocH did not appear until early stationary phase , consistent with the previous observation that the PrkC-dependent effect on the WalR regulon is growth phase specific ( Figs 1 and 2 ) . Thus , a low level of prkC expression in trans is sufficient to complement expression of pdaC , iseA , and yocH in stationary phase . The eSTK prkC is co-transcribed with its cognate eSTP prpC . Previous work suggested that the kinase activity of PrkC and the phosphatase activity of PrpC have opposing modes of action on B . subtilis physiology . Specifically , mutants in prkC and prpC have opposing stationary phase lysis phenotypes ( [30] and Fig 1B ) and overexpression of PrpC was shown to prevent PrkC mediated induction of yocH [15] . Consistent with these observations , prkC and prpC mutations have opposing effects on the expression of the WalR regulon genes yocH , iseA , and pdaC in stationary phase ( Figs 1 and 2 ) . We therefore sought to determine if the expression phenotypes for WalR regulon genes in a ΔprpC mutant background reflect high net PrkC kinase activity . To test this , we overexpressed prkC from Pspac-prkC ( in a strain lacking endogenous prkC ) using higher levels of IPTG induction than were necessary to complement the ΔprkC mutation ( Fig 3 ) . Higher levels of induction ( 100 μM or 1 mM IPTG ) of Pspac-prkC caused expression of PyocH to increase and expression of PiseA and PpdaC to decrease ( Fig 4 ) , consistent with further increases in WalR activity . These effects are similar to those observed in the presence of a ΔprpC mutation ( green ) suggesting that kinase overexpression overwhelms the phosphatase activity under these conditions ( Fig 4 ) . This further suggests that the ΔprpC expression phenotypes observed for yocH , iseA , and pdaC in stationary phase ( Figs 1 and 2 ) are caused by high levels of PrkC-dependent WalR phosphorylation , generated by the absence of phosphatase activity . With 100 μM or 1 mM IPTG induction ( shades of dark blue ) , the reporters in the ΔprpC mutant ( green ) background all appear similar ( Fig 4 ) , suggesting that the reporters in the ΔprpC background reflect the maximal kinase activity for that condition . Although the prpC-prkC transcript is detected from exponential through stationary phase in LB [16] , the heterologous overexpression of PrkC ( Fig 4 ) produces only mild expression phenotypes prior to stationary phase . This observation , combined with the data indicating that PrkC-dependent transcriptional changes in the WalR regulon occur primarily in stationary phase ( Figs 1 and 2 ) , suggests that these effects are not simply caused by changes in prkC expression . As PrkC is membrane-bound and does not contain any predicted DNA binding domains , the observed co-regulation of three separate genes in the WalR regulon is likely to be indirect . One potential mechanism is a direct interaction of PrkC with WalR resulting in the phosphorylation of WalR on Ser or Thr residue ( s ) . We examined this possibility by taking advantage of the previous observation that the cytoplasmic domain of PrkC autophosphorylates in vitro [34] and observed that PrkC robustly transphosphorylated WalR ( Fig 5A ) . Mass spectrometry analysis of a PrkC-WalR kinase reaction performed with cold ATP suggested that WalR Thr101 was the only phosphorylated residue . To verify this interpretation , we generated mutant WalR proteins containing either a T101S or a T101A mutation and repeated the in vitro kinase assays . Strikingly , PrkC did not transphosphorylate either WalR T101S or WalR T101A mutant proteins , suggesting PrkC has a high degree of specificity for Thr101 ( Fig 5A ) . WalR Thr101 is located at the dimerization interface of the receiver domain ( Fig 5B , red ) . Work with M . tuberculosis eSTKs revealed specificity for particular phosphoacceptors based on sequences surrounding the phosphosite [35] . A ClustalO alignment of the 34 B . subtilis response regulators identified eight response regulators with a Thr or Ser residue at the position homologous to WalR Thr101 ( PhoP , YvrH , PsdR , YkoG , YclJ , YrkP , LytT , and CssR; Fig 5C ) . In all of these response regulators , the Ser or Thr residue is surrounded by well-conserved residues ( Fig 5C , bold ) , including those located on the surface exposed loop from the +1 to +6 position ( Fig 5B , yellow ) . However , despite this strong conservation , when we purified the eight response regulators and performed in vitro kinase assays with PrkC , we observed that PrkC much more robustly phosphorylated WalR than the other response regulators following 10’ incubation ( Fig 5D ) . Increasing the incubation time of the reactions to 30’ did not increase the phosphorylation of any other substrates ( S3A Fig ) . We verified that similar amounts of total protein were loaded for each response regulator ( S3B Fig ) , and that none of the response regulators exhibited a background signal in the absence of PrkC ( S3C Fig ) . The ability of PrkC to affect the expression of WalR-dependent genes and to phosphorylate WalR in vitro on Thr101 suggests that this modification occurs in vivo . To detect WalR Thr phosphorylation in vivo , we immunoprecipitated FLAG-tagged WalR from OD600 matched stationary phase LB cultures of wild type ( WT ) , ΔprpC , and ΔprkC strains . As a control for non-specific immunoprecipitation of proteins , strains lacking a FLAG-tagged protein were also used . Each sample was run on a gel cast with Phos-tag acrylamide , allowing for the separation of phosphorylated proteins by SDS-PAGE ( Fig 6 , top ) . To determine if any shifted bands observed were dependent on Phos-tag , and therefore indicative of phosphorylation , a control gel without Phos-tag was run in parallel ( Fig 6 , bottom ) . Since Thr phosphorylations are relatively heat stable compared to Asp phosphorylations , samples were boiled prior to gel loading . Visualized by subsequent Western blotting for the FLAG-tagged protein , a Phos-tag-dependent second band was observed in the WT sample , was significantly enhanced in the ΔprpC sample , but did not appear in the ΔprkC sample ( Fig 6 , top ) . This result indicated the presence of a heat stable PrkC-dependent WalR phosphorylation in vivo . To verify that this PrkC-dependent WalR phosphorylation was on the same residue observed in vitro , mass spectrometry was performed on WalR-FLAG immunoprecipitated from ΔprpC cells . In two separate experiments , phospho-peptides containing Thr101 were found by mass spectrometry , providing further evidence that this modification occurs in vivo ( S4 Fig ) . To confirm that the experimentally observed phospho-peptides and unmodified peptides had masses that are consistent with the WalR Thr101 region , the experimentally determined masses were compared to both the theoretically predicted masses and the masses observed from synthetic peptides ( S4 Fig ) . PrkC-dependent WalR Thr101 phosphorylation was observed in vivo , suggesting that WalR Thr101 phosphorylation was responsible for the PrkC-dependent gene expression phenotypes observed for yocH ( Fig 1 ) . Therefore , we generated a strain containing a walR T101A mutation at the endogenous locus . Since the WalR T101A mutant may exhibit PrkC independent changes in expression compared to wild type WalR , direct comparison of yocH expression between waRT101A and walRWT backgrounds cannot be performed . Therefore , we generated PyocH reporter strains in the walRT101A background in otherwise WT , ΔprpC , and Δ ( prpC-prkC ) backgrounds to test for PrkC dependence . In parallel , we measured the expression of yocH using normalized luminescence as a function of OD600 in both walRT101A and walRWT backgrounds ( Fig 7A ) . Log phase yocH expression was comparable between walRWT and walRT101A , indicating that the walRT101A mutant is functional at the yocH promoter in vivo ( Fig 7A , compare OD600<0 . 3 , left and right ) . Strikingly , in the walRT101A background , PrkC-dependent yocH activation in stationary phase was lost , with the WT , ΔprpC and Δ ( prpC-prkC ) backgrounds all showing similar expression profiles ( Fig 7A , left ) . This is in contrast to the walRWT background , particularly in stationary phase ( OD600>0 . 4 ) , where the strongest PrkC-dependent activation is observed in a walRWT background ( Fig 7A , right inset ) . At the same stationary phase OD600 used in Figs 1–4 , we found that in the walRWT background , a >20 fold difference is observed between the ΔprpC and Δ ( prpC-prkC ) backgrounds , whereas no significant difference is observed in the walRT101A mutant background ( Fig 7B ) . In the 40 min prior to OD600~0 . 5 ( ‘Stationary Phase’ ) we observed comparable growth rates for WT , ΔprpC , ΔprkC , Δ ( prpC-prkC ) in both walRWT ( S5 Fig , top ) and walRT101A ( S5 Fig , bottom ) backgrounds . To confirm that WalR Thr101 is also required for the PrkC-dependent repression of iseA and pdaC , we performed experiments similar to those described above for yocH . Log phase expression of iseA and pdaC in walRT101A and walRWT backgrounds was comparable , suggesting that WalR T101A is also functional at these promoters as a repressor ( S6 Fig ) . Whereas both PiseA and PpdaC show strong PrkC-dependent repression in the walRWT background in stationary phase , in the walRT101A background , the repression is at least ~10 fold relieved between ΔprpC and ΔprkC ( Fig 8 ) . Consistent with a loss of PrkC-dependent activity at these promoters in stationary phase , the loss of repression in the walRT101A mutant is primarily due to the reduction of repression in the ΔprpC background , not to an increase in expression in a ΔprkC background . The residual-PrkC dependent effect on these promoters may be due to additional PrkC activity not dependent on WalR Thr101 phosphorylation that is sensed by WalK ( S6 Fig ) . As iseA and pdaC were identified as the two sites in the B . subtilis chromosome with the highest WalR occupancy [25] , it is possible that they are more sensitive than yocH to WalRK activity .
Here , we provide the first known mechanism for PrkC-dependent gene regulation in B . subtilis by demonstrating that PrkC phosphorylates the response regulator WalR on Thr101 both in vitro and in vivo . Overall , this work supports a model ( Fig 9 ) where PrkC , a PASTA-domain-containing eSTK , is activated and phosphorylates WalR at Thr101 . This is supported by the in vitro data demonstrating direct phosphorylation of WalR Thr101 by PrkC ( Fig 5 ) , as well as the presence of PrkC-dependent phosphorylation observed at Thr101 in vivo ( Figs 6 and S4 ) . Transcriptional data from the WalR-dependent genes yocH , iseA , and pdaC ( Figs 1 , 2 , 7 and 8 ) demonstrates that this secondary phosphorylation increases both the activation and repression of WalR regulon genes ( Fig 9 , bottom ) . In ΔprpC strains , increases in WalR Thr101 phosphorylation are observed in vivo , and changes in expression of WalR-dependent genes is consistent with high levels of PrkC activity ( Figs 4 and 6 ) , demonstrating that PrpC and PrkC have opposing modes of action . The effect of PrkC on the expression of the WalR regulon is most significant as growth slows in stationary phase ( Figs 1 and 2 ) . As continuous expression of prkC in trans does not cause changes in gene expression during rapid growth , the mild effect of PrkC on the WalR regulon in log phase is likely not due to lack of expression . Rather , it suggests that PrkC regulation of the WalRK system becomes significant as the activity of the WalRK system begins to decrease in stationary phase [31] . Thus , by acting as a second sensor for the system , PrkC can provide an input to increase WalR activity even as WalK activity decreases . Since both PrkC and WalK are hypothesized to sense cell wall-related processes , and both are highly conserved signaling systems in Gram-positive bacteria , this may suggest that they have separate sensory inputs in vivo , such as sensing different aspects or different steps in cell wall growth and metabolism . The PASTA domain of PrkC interacts in vitro with muropeptides [13] , suggesting that it may directly sense some aspect of the cell wall . WalK localizes to septal regions , and it has been hypothesized that some aspect of cell wall synthesis during active division is required to keep WalK activity high [18 , 36–39] . Taken together , separate sensory inputs working through PrkC and WalK may regulate the expression of genes responsible for cell wall metabolism by phosphorylation of WalR on two different sites . WalR Thr101 , the site of PrkC phosphorylation both in vitro and in vivo , is in the receiver domain at the α4-β5-α5 dimerization interface ( Fig 5B ) . This region is important for both activity and regulation of protein-protein interactions in OmpR family response regulators such as WalR [40 , 41] . In response regulators of this type , this region has been shown to undergo important conformational changes when Asp phosphorylation occurs [42] . Therefore WalR Thr101 phosphorylation may impact the structural conformation of WalR in complex ways , including by altering the mechanism by which WalK-mediated Asp phosphorylation activates WalR . However , the gene expression data presented here suggests that the net effect of WalR Thr101 phosphorylation is consistent with an increase in WalR activity . The high degree of sequence conservation between B . subtilis response regulators with conserved Ser or Thr residues in the receiver domain ( Fig 5C ) suggests that the specificity of PrkC for WalR is mediated by a region which is not conserved . Previous work dissecting the in vitro specificity of six different eSTKs in M . tuberculosis suggested that phosphorylation is enhanced with highly hydrophobic residues at the +3 and +5 positions surrounding the phosphosite [35] . Given that this region is surface exposed and the +3 site is conserved among the B . subtilis response regulators , the residue at the +5 position may govern specificity . This possibility is consistent with data that residues not directly adjacent to the phosphorylation site can affect the specific phosphorylation of response regulators by histidine kinases [43 , 44] . Other PrkC substrates including EF-Tu , EF-G , CpgA , YezB , YkwC , YvcK , and several metabolic enzymes [14 , 45–49] have been reported . However , evidence suggesting that PrkC-dependent phosphorylation of a transcription factor is important for gene regulation is relatively scarce . Recently , the transcription factor AbrB was found to be phosphorylated in vitro on S86 by PrkC and the two other B . subtilis eSTKs YabT and PrkD , resulting in a decrease in AbrB DNA binding activity [50] . However , AbrB phosphorylation in vivo was dependent on the presence of all three eSTKs , making the precise signal sensed and the dominant kinase responsible for physiologically relevant phosphorylation unclear . Numerous examples of eSTK-dependent phosphorylation of transcription factors , including response regulators , have been reported in other Gram-positive bacteria ( for recent reviews , see [2] and [8] ) . Although most of these examples are derived from in vitro observations , in vivo phosphorylation has been observed for both S . pneumoniae RR06 [51] and S . pyogenes CovR [52] . Examples in which specific in vitro phosphosites have been identified include S . aureus GraR [53] and VraR [54] , M . tuberculosis DosR and Rv2175c [55–57] , S . pneumoniae RitR [58] and RR06 [51] , and Group A and Group B Streptococci CovR [52 , 59] . In some of these examples ( e . g . , RitR [58] , GraR [53] and VraR [54] ) , the eSTK phosphorylates the response regulator in the DNA binding domain , suggesting that the effects on gene expression are caused by changing the affinity of the response regulator for its DNA binding sites . Alternatively , response regulators can be phosphorylated in the receiver domain ( e . g . , CovR [52 , 59] , DosR [55] , and VraR [54] ) . Of these , the most similar example to the WalR Thr101 phosphorylation we observed here is S . aureus VraR , which is phosphorylated in vitro on four residues including Thr106 located at the dimerization interface . The essentiality of WalRK and its homologues in many low G+C Gram-positive bacteria , combined with its effect on virulence [60] and antibiotic resistance [61 , 62] , has made mutations in the WalRK system in clinical pathogens of particular interest . Of note , single nucleotide substitutions within walR cause an increase in vancomycin resistance [63] . Whole genome sequencing of clinical isolates of VISA ( vancomycin intermediate S . aureus ) has identified WalR mutations ( walR T101S and walR T101R ) at the homologous Thr to the PrkC-WalR Thr phosphosite reported here [64] . This observation suggests that WalR Thr101 plays an important role in antibiotic resistance in S . aureus . In conclusion , our characterization of PrkC-dependent phosphorylation of WalR helps to clarify the role of PrkC in stationary phase B . subtilis physiology and gene regulation . The intersection of the eSTK-eSTP pair PrkC-PrpC with the essential WalRK TCS system provides two sensory inputs to regulate the essential process of cell wall metabolism in B . subtilis . Parallels with closely related systems in Gram-positive pathogens suggest that this mechanism of regulation may be conserved and relevant to further understanding the role of eSTK phosphorylation of response regulators in Gram-positive physiology in the future .
Bacillus subtilis strains were constructed by transformation into B . subtilis 168 trpC2 ( PB2 ) obtained from Chet Price [30] and its derivatives unless otherwise noted . Strains used in each figure are listed in S1 Table , and details of strain and plasmid construction are in S2 , S3 and S4 Tables . B . subtilis strains were transformed with 5–10 μl of plasmid DNA or 1–2 μl of genomic DNA using the two-step method [65] . Genomic DNA was prepared using the Wizard Genomic DNA Purification Kit ( Promega ) according to the manufacturer’s instructions . Cultures were grown in LB ( Lennox ) . Early log phase cultures were grown in LB inoculated from single colonies obtained from plates grown overnight at 37°C . The cultures were diluted 1:30 ( 150 μl final volume ) into fresh LB in 96-well plates and grown at 37°C with continuous shaking for >20 h in a Tecan Infinite 200 plate reader . Measurements of luminescence and OD600 were taken at 5 min intervals . Media only and lux- controls were used for background subtraction for OD600 and luminescence respectively . All cultures were grown in triplicate . Note that OD600 values measured in the Tecan plate reader differ from the spectrophotometer by both a scale factor and an offset , making OD600~0 . 1 measurement in the plate reader correspond to OD600~0 . 4 in a standard laboratory spectrophotometer . WalR-FLAG was immunoprecipitated from 25 ml ( Phos-tag experiment ) or 600 ml ( mass spectrometry experiment ) cultures at OD600~0 . 4 using anti-FLAG M2 magnetic beads ( Sigma M8823 ) according to the manufacturer’s instructions with the following modifications . Initial lysis to break down the cell wall was performed in 50 mM Tris pH 7 . 5 , 150 mM NaCl , 0 . 1% NP-40 , 5 μg/ml DNaseI , 3 mM MgCl2 , 1x Halt phosphatase inhibitor cocktail ( Pierce ) , 1 mM PMSF , and 1 mg/ml lysozyme . After 20 min of lysis on ice with periodic vortexing , cell pellets were spun down and resuspended in lysis buffer without lysozyme and applied to 0 . 1 mm silica beads ( BioSpec , 11079101z ) in pre-chilled screw cap tubes . Cells were lysed using a FastPrep-24 5G instrument ( MP Biomedicals ) using 4 runs of 6 . 5 m/s for 40 seconds with samples placed on ice for 3 min between each run . Lysates were cleared by centrifugation at 19 , 000 x g for 20 min at 4°C . Elutions from the anti-FLAG M2 magnetic beads were performed competitively by addition of the 3x-FLAG peptide ( Sigma F4799 ) . 10% resolving gels for SDS-PAGE were cast with ±37 . 5 μM Phos-tag acrylamide ( Wako , AAL-107 , 304–93521 ) , and 5% stacking gels were prepared as per the manufacturer’s instructions . Prior to gel loading , 35 μL of each sample was boiled at 100°C for 15 seconds to remove any residual heat labile Asp phosphorylation . Approximately 30 ng of WalR-FLAG was loaded per lane . Gels were run and transferred as recommended by the manufacturer with the following modifications: SDS-PAGE gels were loaded and run in parallel on a Bio-Rad mini-protean gel apparatus at constant voltage ( 150 V ) for approximately 100 min ( an additional ~20 minutes after the dye front ran off the gel ) at 4°C . Gels were fixed twice for 15 min in transfer buffer supplemented with 5 mM EDTA to remove Mn2+ from the gel , followed by a 10 min incubation in transfer buffer . Transfer to PVDF membranes was performed using a wet tank method ( Bio-Rad Mini Trans-Blot ) at a constant 350 mA for 80 min . Subsequent Western blotting was performed using standard protocols with an anti-FLAG-Peroxidase antibody ( Sigma-Aldrich s#SAB4200119 ) . PrkC kinase reactions were performed in kinase buffer ( 50 mM Tris-HCl pH 7 . 5 , 50 mM KCl , 10mM MgCl2 , and 0 . 5 mM DTT ) . All reactions were performed in the presence of 400 μM cold ATP and 2 μCi ( ~ 20 nM ) of γ-32P-ATP . The eSTK was used at a final concentration of 1 μM and substrates were added at a final concentration of 4 μM . Reactions were incubated at 37°C for 30 min unless otherwise indicated . 5X SDS-PAGE buffer was added to the reactions and subsequently boiled at 95°C for 5 min . Samples were run on a 12% SDS-PAGE gel and dried for 30 min at 80°C in a gel dryer . Dried gels were exposed to a phosphoscreen and visualized using a Typhoon Scanner ( GE Healthcare ) . Additional Materials and Methods , including protein purification and mass spectrometry for the detection of WalR phosphorylation both in vitro and in vivo , can be found in S1 Text . | A central question in bacterial physiology is how bacteria sense and respond to their environment . The archetype of bacterial signaling systems is the two-component signaling system composed of a sensor protein histidine kinase that activates a transcription factor response regulator in response to a specific signal . In addition , bacteria also have signaling systems composed of eukaryotic-like Ser/Thr kinases and phosphatases . Even though these systems do not have dedicated transcription factors , they are capable of affecting gene expression . Here we show that a eukaryotic-like Ser/Thr kinase conserved in all sequenced Gram-positive bacteria converges with an essential two-component signaling system to regulate gene expression in the model organism Bacillus subtilis . We show that this eukaryotic-like Ser/Thr kinase phosphorylates the response regulator of a highly conserved and essential two-component signaling system , thereby increasing its activity . This phosphorylation results in the regulation of genes involved in the essential process of cell wall metabolism . Given that bacterial cell wall metabolism is the target of many known antibiotics , and mutations in both of these signaling systems change the antibiotic sensitivity of a number of important Gram-positive pathogens , we expect that our analysis will suggest novel insight into the emergence of antibiotic resistance . | [
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] | [] | 2015 | The Eukaryotic-Like Ser/Thr Kinase PrkC Regulates the Essential WalRK Two-Component System in Bacillus subtilis |
Vibrio parahaemolyticus is a foodborne pathogen that has become a public health concern at the global scale . The epidemiological significance of V . parahaemolyticus infections in Latin America received little attention until the winter of 1997 when cases related to the pandemic clone were detected in the region , changing the epidemic dynamics of this pathogen in Peru . With the aim to assess the impact of the arrival of the pandemic clone on local populations of pathogenic V . parahaemolyticus in Peru , we investigated the population genetics and genomic variation in a complete collection of non-pandemic strains recovered from clinical sources in Peru during the pre- and post-emergence periods of the pandemic clone . A total of 56 clinical strains isolated in Peru during the period 1994 to 2007 , 13 strains from Chile and 20 strains from Asia were characterized by Multilocus Sequence Typing ( MLST ) and checked for the presence of Variable Genomic Regions ( VGRs ) . The emergence of O3:K6 cases in Peru implied a drastic disruption of the seasonal dynamics of infections and a shift in the serotype dominance of pathogenic V . parahaemolyticus . After the arrival of the pandemic clone , a great diversity of serovars not previously reported was detected in the country , which supports the introduction of additional populations cohabitating with the pandemic group . Moreover , the presence of genomic regions characteristic of the pandemic clone in other non-pandemic strains may represent early evidence of genetic transfer from the introduced population to the local communities . Finally , the results of this study stress the importance of population admixture , horizontal genetic transfer and homologous recombination as major events shaping the structure and diversity of pathogenic V . parahaemolyticus .
Vibrio parahaemolyticus is a Gram-negative halophilic bacterium that naturally inhabits marine and estuarine environments throughout the world . While many strains of V . parahaemolyticus are strictly environmental , some groups are pathogenic and may cause gastroenteritis in human [1] . V . parahaemolyticus is the leading human pathogen of bacterial food-borne diseases associated with the consumption of raw or undercooked seafood [2] . Recently , V . parahaemolyticus has gained notoriety due to global dissemination of infections [3] . The rise of infections was initially linked to the emergence of gastroenteritis throughout Asia associated with a single clone of the O3:K6 serovar in 1996 [4] . The subsequent detection of this clone causing infections in Peru [5] , [6] , and Chile [7] in 1997 indicated the pandemic expansion of O3:K6 clone infections . Afterwards , gastroenteritis cases associated with the O3:K6 clone were reported in many other countries and different areas of the world , such as the United States , Russia , Mozambique , Mexico , and Spain [3] . In addition to rapid dissemination of the pandemic clone , a parallel rise of human cases associated with other genetic groups has been reported in recent years . Specific genetic groups have been described as being associated with infections in particular geographic regions of the world . Consequently , the presence of local clones and serovars prevail among clinical cases , such as O4:K12 in the Pacific coast area of the United States [8] , O4:K8 in the Peruvian coast [5] , O4:K13 in Africa [9] , and O4:K11 in the northeast of Spain [10] . The majority of clinical cases of V . parahaemolyticus have been associated with strains bearing the thermostable direct hemolysin ( tdh ) and/or TDH-related hemolysin ( trh ) . Therefore , the presence of one or both hemolysins has been considered to be a conventional marker of V . parahaemolyticus virulence [11] , [12] . However , it has been reported recently that not only the presence of hemolysins but also other virulence factors such as the type III secretion system ( T3SS ) have been involved in the cytotoxicity and enterotoxicity of V . parahaemolyticus [13] , [14] . Whole genome sequencing of the clinical V . parahaemolyticus strain RIMD2210633 revealed the presence of two sets of genes encoding two different T3SSs , named T3SS1 and T3SS2 , distributed in each chromosome [15] . A functional analysis of these two T3SSs revealed that T3SS1 is involved in cytotoxic activity , while T3SS2 has been related to enterotoxicity [13] . Consequently , the presence of T3SS2 has been consistently associated with pathogenicity of V . parahaemolyticus in humans . The T3SS2 found in the second chromosome of V . parahaemolyticus ( T3SS2α ) is part of a large pathogenicity island ( PAI ) of approximately 80 kb that also includes the tdh-genes . In the trh-positive strain TH3996 , a novel PAI that is inserted in a distinctive pathogenic island containing a homologous T3SS2 ( T3SS2β ) has recently been described [16] . Comparative genome analysis of V . parahaemolyticus predicted that RIMD2210633 pathogenesis is associated with the presence of eight pathogenicity islands ( VpaI ) [17] . However , to date , VpaI that include T3SS2 have only been functionally characterized in infection models [13] , [16] . Genomic analyses also revealed that different types of pathogenicity islands and mobile elements are the major structural differences between trh-positive and tdh-positive strains , including the pandemic clone [18] . Vibrio parahaemolyticus has not been routinely investigated in Peru and infections caused by this organism are rarely reported to the surveillance system . The mandatory investigation of V . cholerae in clinical laboratories after the dramatic emergence of cholera epidemic in 1991 in Peru contributed to the identification of other pathogenic Vibrio species . Vibrio strains isolated from hospitals and regional public health laboratories throughout the country were shipped to the Instituto Nacional de Salud ( INS , Lima , Peru ) for final identification and characterization . This extraordinary repository has been decisive for identification and evaluation of the impact of V . parahaemolyticus infections on the population , especially in remote regions and small villages along the Peruvian coast where seafood consumption constitutes the nutritional base of population . The arrival of the pandemic clone to Peru in 1997 resulted in a major shift in the epidemic dynamics of V . parahaemolyticus in the region , replacing the seasonal and local self-limited infections attributed to native genetic groups by the generalization of infections exclusively caused by pandemic strains distributed across the country [5] , [6] . Due to the environmental nature of V . parahaemolyticus , this overturn in population dominance and the subsequent population admixture may be expected to lead to an unpredictable impact on populations of pathogenic V . parahaemolyticus in Peru . To test this hypothesis , we investigated the population genetics , genomic variation and pathogenic islands distribution in a complete collection of non-pandemic strains recovered from clinical sources in Peru over the pre- and post-emergence of the pandemic clone . Pandemic and non-pandemic clinical strains representative of the entire period of study were subjected to serotyping and genotyping analysis by Multilocus Sequence Typing ( MLST ) and Variable Genomic Region ( VGRs ) analysis .
Peruvian V . parahaemolyticus strains were recovered from the collection of the National Reference Laboratory for Enteropathogens at the Instituto Nacional de Salud ( INS ) in Peru . This collection comprised 46 strains from the previously studied period of 1994 to 2005 [6] and 10 additional strains from the period of 2006 to 2007 . To extend the comparison , a Chilean group , comprised of 13 strains from Antofagasta and Puerto Montt [19] , [7] , and 20 Asiatic strains , corresponding to 18 from Japan , one from Bangladesh and one from Thailand [20] , [21] , were included in the analysis ( Table 1 ) . The reference strain to V . alginolyticus ATCC17749 was also added to the panel of strains . DNA extraction of V . parahaemolyticus strains was performed using an overnight culture in trypticase soy broth at 37°C using the Chelex-100 method [22] . Strains were confirmed by the presence of the V . parahaemolyticus species-specific genes toxR as described previously [23] . Additionally , the presence of the genes tdh and trh was determined according to procedures described by Tada et al . [24] . Group-specific PCR for the detection of the toxRS sequence of strains belonging to the pandemic clone of V . parahaemolyticus was performed as previously described [6] , [25] . Serotyping Lipopolysaccharide ( O ) and capsular ( K ) serotypes were determined by a commercially available antisera scheme ( Denka Seiken Corp . , Tokyo , Japan ) . MLST analysis was performed as previously described [21] , based on internal fragments of seven housekeeping genes: recA , gyrB , dnaE , dtdS , pntA , pyrC , and tnaA . Sequences of both strands were determined by custom sequencing ( Macrogen Inc . , Seoul , South Korea ) . All chromatograms were assembled , manually edited and trimmed in Bionumerics 5 . 1 ( Applied-Maths , Kortrijk , Belgium ) . Allele numbers were assigned to each strain by comparing the nucleotide sequence at each locus to all known corresponding alleles available at the V . parahaemolyticus MLST Database ( http://pubmlst . org/vparahaemolyticus/ ) . Novel sequence variants and sequence types ( ST ) were deposited in this database , and ST assignment was performed using the MLST website tools ( http://pubmlst . org ) . Nucleotide sequences of MLST locus corresponding to STs generated from the study were also deposited in GenBank under accession numbers KC542949–KC543109 . Population genetic relationships among V . parahaemolyticus strains included in this study were performed based on the MLST allelic profiles by a minimum spanning tree analysis implemented in BioNumerics 5 . 1 software ( Applied-Maths , Sint Maartens-Latem , Belgium ) . Strains were grouped according to priority rules adopted from the BURST algorithm [26] , with the highest priority given to profiles with the largest numbers of single locus variants or double-locus variants in the case of a match . Clonal complexes were defined as groups with a maximum neighbor distance of one change and a minimum size of two strains . Variable genomic regions ( VGRs ) were identified by comparative genome analysis based on a complete genome sequence ( RIMD2210633 ) and two draft genomes of V . parahaemolyticus ( AQ4037 and AQ3810 ) retrieved from the NCBI database ( http://www . ncbi . nlm . nih . gov/ ) . Genomic sequence data were analyzed by MUMmer 3 . 0 [27] to identify non-redundant genomic sequences . The identified sequences were listed as VGRs and used in further analyses . To determine the presence and distribution of VGRs among the strains included in this study , we carried out 31 different PCR assays using specific primers targeting the 23 regions identified by comparative analysis ( Table 2 ) . The PCR assays were performed in a 50-µl reaction volume containing 10 ng of genomic DNA , 1 U of Taq DNA polymerase ( Roche , Mannheim , Germany ) , 0 . 2 µM of each primer ( Sigma-Aldrich , Sigma-Aldrich , St . Louis , MO ) , and 200 µM of each deoxynucleoside triphosphate ( Roche , Mannheim , Germany ) . PCR cycling conditions consisted of an initial denaturation step at 94°C for 3 min followed by 30 cycles of denaturation at 94°C for 50 s , 50 s at the annealing temperature , which was variable for each region ( see Table 2 ) , and extension step at 72°C for 1 min . A final extension step consisted of 10 min at 72°C . Phylogenetic relationships were inferred using ClonalFrame v1 . 1 software [28] . MLST loci sequences of unique STs were input into ClonalFrame using the default options . Two independent ClonalFrame runs were performed consisting of 500 , 000 iterations . The first 100 , 000 iterations in each run were discarded , and the phylogeny and additional model parameters were sampled every 100 generations in the last 400 , 000 iterations . The phylograms sampled from the two different runs were concatenated and summarized in a 50% majority rule consensus tree constructed by ClonalFrame GUI [28] . The convergence of the Markov Chain Monte Carlo ( MCMC ) in both runs was proven based on the Gelman-Rubin test as implemented in ClonalFrame [29] . To visualize potential associations between the phylogeny of housekeeping genes and the distribution of VGRs , we performed a transversal clustering analysis . For this purpose , the concatenated sequences of 67 strains were grouped by the mean of their phylogenetic relationship using ClonalFrame ( 50% majority consensus ) , while the VGR data were grouped using a UPGMA algorithm mean of their value ( 0 = absent and 1 = present ) , resulting in a data matrix ordered according to both phylogenetic relationship and VGR clustering .
A total of 324 V . parahaemolyticus strain records were retrieved from the INS database from 1994 to 2007 . These strains were obtained from gastroenteritis cases that occurred in different regions of Peru and were subsequently submitted to the INS in Lima for identification and storage . The overall distribution of V . parahaemolyticus cases over the 14 years of study ( Figure 1 ) showed a characteristic seasonal pattern with annual peaks of incidence concurring with the warmest months . This epidemic pattern was uniquely disturbed in the course of the austral winter of 1997 when V . parahaemolyticus cases dramatically increased coincidently with an anomalous rise of temperature . The highest incidence of cases was observed from July 1997 to May 1998 with two peaks during September 1997 and February 1998 . The epidemic dynamics were restored from 1999 onwards . From the 324 cases recorded over the period 1994–2007 , only 56 V . parahaemolyticus strains could be recovered from the INS collection . Distinct serovar dominances were detected over different periods of the study . From 1994 to 1996 , infections were associated with serovars O4:K8 and O5:KUT . This serovar dominance abruptly changed during the winter of 1997 , with the emergence of infections caused by strains belonging to the pandemic clone . Finally , an undefined pattern of serovar dominance was detected after the emergence of the pandemic clone in 1997–98 . In this post-pandemic period , pandemic strains were identified together with O4:K8 strains as well as multiple serovars not previously detected ( O1:K33 , O1:KUT , O3:K30 , O3:K58 , O3:KUT , O5:KUT , O6:KUT and OUT:KUT ) . Pandemic strains represented the largest group of strains ( n = 38 , 67 . 9% ) detected from 1997 onwards , and O3:K6 was the most frequent serovar among the pandemic strains ( n = 31 , 55 . 4% ) , although serovars O3:K58 and O3:KUT were also identified in the pandemic group . Serovar O4:K8 was the second most frequent group of strains over the whole period ( n = 11 , 19 . 6% ) , whereas the remaining serovars ( n = 14 ) represented 25% of the total strains . The population genetic structure of V . parahaemolyticus strains representative of Peru , Chile , and Asian countries was analyzed by MLST ( Fig . 2 ) . MLST profiles of V . parahaemolyticus ( n = 89 ) were categorized into 23 sequence types ( STs ) . The recA gene of strains included within the ST265 ( n = 8 , O4:K8 serotype ) showed an unexpected large PCR product of approximately 1500 bp ( 773 bp in the original ) with a nucleotide identity diverging 18–19% from the characteristic sequence of V . parahaemolyticus . The Peruvian group was split into 9 different STs ( Fig . 2B ) . Minimum spanning tree analysis identified a single clonal complex consisting of ST88 ( n = 3 ) and ST265 ( n = 8 ) both of which included O4:K8 strains differing in a single locus . All of the strains belonging to the pandemic complex were grouped in ST3 ( n = 24 ) , whereas the remaining strains were included in 6 unrelated STs . Minimum spanning tree of the 23 STs resulting from the MLST analysis of the 89 strains showed a similar topography and group discrimination . Peruvian , Chilean and Asian pandemic strains shared the sequence of the 7 loci and were assigned to a single ST ( ST3 ) . Strains from Peru and Chile shared two additional STs: ST65 composed by O1:KUT strains and ST64 including strains belonging to O3:KUT and O1:KUT serovars . Three other clonal complexes ( CC ) were identified among trh+ Asian strains: one CC included STs 1 , 83 and 82; a second group consisted of ST96 and ST91; and a third CC was shared by STs 87 and 14 . The remaining strains were clustered in different and unrelated STs with differences in more than 5 loci . The nucleotide sequences of 23 unique STs identified among the V . parahaemolyticus , as well as homologous sequences of V . alginolyticus ( outgroup ) , were concatenated , and their phylogenetic relationships were inferred by ClonalFrame . The clonal genealogy inferred from the data revealed a star-like topology of V . parahaemolyticus delineated into two evident lineages , with the rest of the STs remaining unresolved ( Figure 3 ) . The pandemic lineage consisted of all of the pandemic strains from Asia , Chile and Peru ( ST3 ) , as well as other STs that included Asian pre-pandemic strains of diverse serotypes and the three variants of hemolysin-related genotypes . A second lineage consisted of trh+ strains of STs 1 , 82 and 83 , all of them from Asia . The genealogy showed the influence of recombination on the genetic diversification of V . parahaemolyticus with a central node ( node A ) from which all of the branches diverged . This specific topology indicated an unresolved phylogeny due to the non-identification of a common ancestor . ST88 and ST265 , comprising all the O4:K8 strains were grouped in a single lineage . ST89 and ST93 , which included Peruvian strains , also shared a common linage , as well as ST92 and ST63 , sequence types composed of Asian and Chilean strains , respectively . For each branch of the reconstructed genealogy , ClonalFrame identified fragments that were likely imported . The influence of mutation and recombination events in the generation of polymorphisms was further investigated in the two cluster nodes ( A and B ) indicated in Figure 3A . Node A showed a high probability of importing events consistent with recombination substitutions , while divergence in node B , which included the Pandemic lineage , most likely originated as a result of point mutations ( Fig . 3B ) . ClonalFrame analysis shows that the relative impact of recombination versus point mutation expressed as a ratio ( r/m ) was approximately 1 . 45 , and that the relative frequency of recombination in comparison to point mutation ( ρ/θ ) was approximately 0 . 20 . To extend our analysis of the phylogenetic relationships and to understand the impact of genomic variation in the evolutionary history of V . parahaemolyticus , the distribution of VGRs was assessed by PCR assays in the 67 strains . A transversal clustering analysis was performed to visualize the evolutionary significance of the presence of genomic regions within the strain collection ( Fig . 4 ) . ClonalFrame majority-rule consensus tree building with the whole set of strains revealed the same group structure as resolved by the clonal genealogy . However , analysis of VGR data showed a specific clustering of VGRs with three different groups ( A , B , and C ) ( Fig . 4 ) . Cluster A grouped five different genomic regions almost exclusive for trh+ strains with the exception of two tdh+ strains ( U5474 and AQ3810 ) . The presence of TTSS2β was only detected in strains bearing the trh gene . This cluster also included regions HGT20 and HGT22 encoding for the Type I restriction system , which was only identified in trh+ Asian strains . Cluster B included 6 genomic regions; two of them ( HGT3 and HGT3A ) correspond to ORFs coding the type 6 secretion system ( T6SS1 ) , which were present in all of the clinical strains with the exception of 4 tdh+/trh− non-pandemic strains isolated in Peru , Chile , and Asia . T3SS2α was singularly detected in those strains with genotype tdh+/trh− but was not present in those strains positive for the tdh and trh gene . Cluster C consisted of the majority of VGRs ( 20 genomic regions ) , which were uniformly distributed among almost all the pandemic strains . Some of these regions were partially present in all Asian strains phylogenetically related to pandemic strains , while those were less frequent in the remaining non-pandemic strains and distributed with an undefined pattern . Non-pandemic Peruvian strains showed a shift in the distribution of the VGRs included within this cluster from the pre-pandemic period to 1999 and afterwards . Genomic regions such as HGT6A ( ribonuclease R ) , HGT12 ( VPaI2 ) , HGT9 ( site-specific recombinase ) , HGT14 ( hypothetical protein ) , HGT16B ( phage genes ) , HGT13 ( type IV pilin ) , HGT18 ( hypothetical protein ) and HGT19 ( hypothetical protein ) , which are characteristic of pandemic strains , were exclusively identified in those non-pandemic strains from Peru isolated after the arrival of the pandemic clone in 1997 . Finally , none of the VGRs investigated were found in V . alginolyticus ATCC17749 .
Vibrio parahaemolyticus represents an intriguing food-borne pathogen and poses a significant threat to public health in Peru . However , despite this concern for public health , the knowledge of the molecular epidemiology and genetic structure of this pathogen remains incomplete . Epidemiology of V . parahaemolyticus in Peru have been historically associated with sporadic outbreaks linked with seafood consumption . Since 1983 , clinical strains were dominated by the presence of local serovars such as O4:K8 that was isolated from both clinical and environmental samples [30] . These infections were characteristically related to auto-limiting outbreaks detected along the coastal regions over summer months . This epidemic pattern shifted in 1997 with the unexpected arrival of pandemic V . parahaemolyticus to Peru [5] . The presence of the O4:K8 serovar in pre- and post-pandemic periods allowed for the identification of this group as the dominant population among clinical strains in Peru and the cause of recurrent outbreaks during the warmest months [31] , [5] . The totality of strains recovered from infections in 1997 and 1998 belonged exclusively to the pandemic clone . After the period of the infections associated with the pandemic clone in 1997–98 , the dominance of this clone in clinical infections began to decline and a mix of different serovars began to emerge . This specific epidemiological trend of arrival and rapid decline of pandemic V . parahaemolyticus infection in Peru clearly contrasts with the infection dynamics found in Chile , the neighboring country where infections associated with the pandemic clone started first in 1997 and subsequently in 2004 , and where the pandemic clone has dominated the clinical isolations since the second epidemic radiation in 2004 [32] . The arrival of the pandemic clone to Peru provided a unique opportunity for testing the potential impact of an introduced genetic group on the structure and genetic variability of local pathogenic populations . One particular feature of the epidemiology of V . parahaemolyticus in Peru after the appearance of the pandemic clone was the sudden apparition of diverse serovars not detected previously . Serovars O1:K33 , O1:KUT , O3:K30 , O3:K58 , O3:KUT , O5:KUT , O6:KUT and OUT:KUT had not been isolated prior to 1998 . Similar results were found in a previous retrospective study carried out in Peru covering the 1993–2002 period [6] , which reported the presence of serovars O3:K68 , O3:K58 , OUT:K6 , O6:K18 , O11:KUT , O11:K15 and OUT:KUT after 1998 . The flourishing of serovars and genotypes in Peru may be related to the particular vehicle for the introduction and propagation of pandemic strains . The epidemic dissemination of this pandemic clone along the coast of Peru corresponded with the expansion and dynamics of the poleward propagation and the receding of tropical waters linked to the 1997 El Niño event [5] , [33] . Another important aspect to be considered to understand the possible genetic impact of the pandemic clone on Peruvian local populations is the comparative analysis of phylogeny inferred from MLST sequences and the distribution of variable regions in the panel of strains . The pattern of VGR distribution forming cluster C showed a conserved presence for all regions among strains belonging to the pandemic clone , which provides an additional evidence for the highly clonal nature of this phylotype [34] , [18] . On the contrary , a sparse presence of VGRs in cluster C was observed , showing an undefined pattern among non-pandemic strains . A detailed analysis of variations and phylotypes showed that VGRs in cluster C are only present in strains isolated after the arrival of the pandemic clone in 1997 . This specific pattern of distribution and the connection between these strains and the arrival of the pandemic clone may suggest a common origin for all of these groups . However , the presence of a region coding hypothetical genes ( VPA0434 and VPA0435 ) in one O4:K8 strain also raises the possibility of a local horizontal transfer from pandemic strains to local Vibrio communities in Peru . Horizontal gene transfer appears to be the major force shaping both the genomic variation and virulence of V . parahaemolyticus , as evidenced in previous studies linking the acquisition of pathogenicity islands with the emergence of the pandemic clone [17] , [18] . The results of transversal clustering analysis revealed that the presence of T3SS , T6SS and mannose-sensitive hemagglutinin ( MSHA ) pilus were clustered together in most clinical isolates showing a clear association with the pathogenicity of these strains , suggesting that these regions are conserved in pathogenic strains and could be a good marker of pathogenicity . The evolutionary history of pathogenic lineages of V . parahaemolyticus has been analyzed previously by different approaches [21] , [17] , [35] . However , the previous analysis of population structure was not sufficiently integrated within the epidemic dynamics prevailing in a specific region so that there could be an adequate evaluation of the shift in genotype and population dominance over different periods . ClonalFrame genealogy inferred a star-shaped tree with long terminal branches showing a primary diversification affecting all of the STs , likely as a result of recombination events [36] . A defined lineage grouped all of the pandemic strains as well as most of the Asian genotypes showing a hierarchical pattern recently evolved from a common ancestor , likely due to the course of successive point mutations . The overall results evidenced the influence of recombination events in the diversification of most pathogenic V . parahaemolyticus genotypes . Homologous recombination in housekeeping genes has been found naturally in Vibrio , and it is an important driver of diversification in this genus [37] . ClonalFrame analyses of the whole concatenated dataset showed rates of recombination of 1 . 45 and 0 . 20 for r/m and ρ/θ , respectively . These data suggest an intermediate rate of recombination among the strains characterized [38] . This estimate of recombination frequency suggests that recombination is relatively rare compared to other species , such as Streptococcus uberis ( ρ/θ , 9 . 0 ) [39] and Clostridium perfringens ( ρ/θ , 3 . 2 ) [40] , but it is approximately similar to that observed for other groups , such as lineage I of Listeria monocytogenes ( ρ/θ , 0 . 13 ) [41] , This particular feature may be related to the exclusive use of pathogenic strains in the study , which are characterized by a clonal diversification [38] . To conclude , the results of this study describe the epidemiological impact caused by the introduction of the pandemic clone in Peru on the epidemiology and structure of the local population of V . parahaemolyticus . The presence of genomic regions characteristic of the pandemic clone in other non-pandemic strains provides early evidence of genetic transfer from the introduced population to the local communities . Additionally , the genetic relationships based on MLST and VGR analyses support the epidemiological connection between pandemic and non-pandemic strains isolated in both Peru and Chile . The phylogenetic and genomic analysis performed allowed us to determine the recent origin of the pandemic clone lineage , probably caused by successive acquisition of genomic regions , as well as the influence of recombination events in the diversification of non-pandemic pathogenic V . parahaemolyticus . Ultimately , these results provide a preliminary framework about evolutionary history of V . parahaemolyticus . Recent advances in high throughput sequencing are revolutionizing the field of population genetics of human pathogens . Application of fine-scale analysis based on whole genome sequences in future studies of pathogenic bacteria will contribute to improve our knowledge of the epidemic dynamics and routes of dispersion of Vibrio diseases [42] , [43] . | Infections caused by Vibrio parahaemolyticus have increased significantly over the last two decades , with cases now regularly reported globally . The emergence of cholera at global scale has brought the attention toward other Vibrio diseases in developing countries . This was the situation in Peru , where the investigation of V . cholerae in hospitals and regional public health laboratories after the dramatic emergence of cholera epidemic in 1991 enabled the identification of other pathogenic Vibrio throughout the whole country . The submission of all these bacteria to the Instituto Nacional de Salud ( INS , Lima , Peru ) for characterization generated an extraordinary repository of records and isolates which have been decisive for sizing the impact of V . parahaemolyticus infections on the population . The present study addresses , for first time , the impact of the arrival of a non-endemic population of V . parahaemolyticus on the genetic structure and virulence attributes of local populations . The detection of the pandemic clone of V . parahaemolyticus to Peru in 1997 changed not only the epidemic dynamics of this pathogen , but also the population structure and genetic variation of native populations through population admixture , horizontal genetic transfer and homologous recombination between native and introduced populations of pathogenic V . parahaemolyticus . | [
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] | 2013 | Molecular Epidemiology and Genetic Variation of Pathogenic Vibrio parahaemolyticus in Peru |
Using the genomic sequences of Drosophila melanogaster subgroup , the pattern of gene duplications was investigated with special attention to interlocus gene conversion . Our fine-scale analysis with careful visual inspections enabled accurate identification of a number of duplicated blocks ( genomic regions ) . The orthologous parts of those duplicated blocks were also identified in the D . simulans and D . sechellia genomes , by which we were able to clearly classify the duplicated blocks into post- and pre-speciation blocks . We found 31 post-speciation duplicated genes , from which the rate of gene duplication ( from one copy to two copies ) is estimated to be 1 . 0×10−9 per single-copy gene per year . The role of interlocus gene conversion was observed in several respects in our data: ( 1 ) synonymous divergence between a duplicated pair is overall very low . Consequently , the gene duplication rate would be seriously overestimated by counting duplicated genes with low divergence; ( 2 ) the sizes of young duplicated blocks are generally large . We postulate that the degeneration of gene conversion around the edges could explain the shrinkage of “identifiable” duplicated regions; and ( 3 ) elevated paralogous divergence is observed around the edges in many duplicated blocks , supporting our gene conversion–degeneration model . Our analysis demonstrated that gene conversion between duplicated regions is a common and genome-wide phenomenon in the Drosophila genomes , and that its role should be especially significant in the early stages of duplicated genes . Based on a population genetic prediction , we applied a new genome-scan method to test for signatures of selection for neofunctionalization and found a strong signature in a pair of transporter genes .
As proposed almost four decades ago , gene duplication is one of the major sources to create genetic novelty [1] . Gene duplication followed by the fixation of a mutation providing a slightly different function should be a possible scenario of the evolution of new gene function via duplication ( i . e . , neofunctionalization of a duplicated gene ) . To understand the contribution of this mechanism to genomic evolution , we need to answer at least two fundamental questions: “How often does gene duplication occur ? ” and “What are the signatures of natural selection operating on a mutation providing neofunctionalization ? ” Using the Drosophila genomes as a model , this article addresses these two questions with special attention to gene conversion between duplicated genes . Gene conversion is one outcome of a recombination event , which is usually modeled as a copy-and-paste event [2] , [3] . Interlocus gene conversion transfers a DNA fragment in one region to the corresponding place in another paralogous region; subsequently , the transferred region becomes identical . With frequent gene conversion , the paralogous regions keep their sequences very similar for a long time , resulting in the well-known phenomenon of concerted evolution [4] , [5] , [6] , [7] . Although concerted evolution was first demonstrated more than 30 years ago [8] , its genomic impact has been unveiled only recently . It is increasingly recognized that interlocus gene conversion can be a genome-wide phenomenon in a wide range of organisms from yeast to higher eukaryotes [9] , [10] , [11] , [12] , although the extent depends on species . There are strong reasons why it is important to elucidate the role of gene conversion after gene duplication in order to address the above two questions . A simple ad-hoc method of estimating the gene duplication rate is to count gene-pairs of low divergence ( presumably young ) in the genome [13] . This method works only when the nucleotide divergence between the duplicated genes follows the molecular clock [14] , in which case gene pairs with low divergence are indeed young . However , Teshima and Innan [15] theoretically demonstrated that this method will cause a serious overestimation of the gene duplication rate when a number of duplicated genes undergo concerted evolution and Gao and Innan [11] showed that this is the case for the yeast genome ( Saccharomyces cerevisiae ) . In such a situation , because the divergence between duplicated genes does not necessarily reflect their ages , other methods should be used . In the study of Gao and Innan [11] , a comparative genomic approach was used , in which genomic sequences of several closely related species of S . cerevisiae [16] , [17] were involved . The gene duplication rate was estimated by directly mapping duplication events on a phylogeny of those species , which was two orders of magnitude lower than the divergence-based estimate . Now , recent genome sequence data of Drosophila [18] provide the second opportunity to evaluate the role of interlocus gene conversion in eukaryotes by using comparative genomic approaches . Such followup studies are important to examine the generality of the conclusion obtained from yeasts [11] . The situation of the Drosophila genome data is similar to that of yeast . There is a completed genome sequence data available for a model species ( D . melanogaster in fruit flies and S . cerevisiae in yeasts ) , and its relatives' genomes are sequenced at various levels in quantity and quality . Therefore , in our comparative genomic study , the finished D . melanogaster genome [19] plays the key role , as well as in other studies [e . g . ] , [18] , 20 , 21 , 22 . In other words , the D . melanogaster genome serves as a reliable template to understand the genomic organization of the other species , especially when most of the 11 newly sequenced genomes are not yet assembled into chromosomes ( exceptions are D . simulans and D . yakuba ) [19] . Gene duplications in Drosophila have been extensively studied in various scales by using the comparative genomic data [18] . For example , Hahn et al . [22] investigated the pattern of gene duplication and loss in gene families that are defined as groups of homologous genes . Some gene families consist of hundreds of copy members . Based on the changes in the copy number along evolutionary history , the rates of duplication and loss were estimated . Heger and Ponting [21] also performed comprehensive evolutionary analysis of homologous genes across the 12 species and found an excess of low-divergence duplicated genes in the terminal branches of the 12-species tree , which was in agreement with the observation of Lynch and Conery [13] . However , in those long-term evolutionary analyses , it was very difficult to elucidate the role of gene conversion because it plays significant roles in early stages of duplicated genes . This article primarily focuses on the patterns of nucleotide evolution in relatively young duplicates , where gene conversion is likely to be active . We restrict our analysis to duplication events , by which single-copy genes become two-copy duplicated genes ( 1→2 duplication ) to exclude ambiguity caused by multiple complex duplications in large multigene families . While some large families exhibit evidence for expansion in size and rapid amino acid changes [22] , the molecular evolution of two-copy duplicates is relatively slow . This makes it possible to trace the history of duplicates at the DNA level in the D . melanogaster subgroup , from which we performed a fine-scale analysis of the duplicated genomic regions including non-coding regions . We were able to identify what part of the genome was duplicated in D . melanogaster and D . simulans , from which we inferred when the duplication event occurred ( i . e . , whether it was before or after the speciation of the two species ) . With these data , we demonstrated a significant role of gene conversion between young duplicated genes , and obtained an estimate of the gene duplication rate , which is much lower than that of the divergence-based method used by Lynch and Conery [13] . The comparative genomic data are also used to detect the signatures of natural selection for neofunctionalization . The neofunctionalization process can be initiated by a single beneficial mutation , which provides a slightly different function so that selection works to maintain this mutation . However , it is usually very difficult to detect the signature of selection in DNA sequence data , unless a number of nonsynonymous nucleotide substitutions occur at a faster rate than synonymous substitutions [e . g . , 23] . Recently , Teshima and Innan [24] proposed a novel idea to detect signatures of neofunctionalization , which works best when the duplicated regions are undergoing concerted evolution . When there is gene conversion between duplicated genes , a newly arisen neofunctionalized mutation could be erased by gene conversion . Therefore , the neofunctionalized mutation can be stably maintained in the population only when its selective advantage is much larger than the rate of gene conversion [25] . Under these conditions , deleterious ( at least less beneficial ) gene conversion is immediately eliminated from the population . Teshima and Innan [24] found that the maintenance of a neofunctionalized mutation through the balance of strong selection and gene conversion continues for a relatively long time . In this period , a local peak of the divergence between the duplicates emerges because of the lack of paralogous DNA exchanges in this region . This high level of divergence accumulated around the site of the neofunctionalized mutation is contrasted with low divergence in regions away from the site . Therefore , Teshima and Innan [24] suggested the possibility of using this signature of selection in a genome scan for recent neofunctionalization . The idea was applied to our data , and we found a strong signature of recent neofunctionalization in a pair of transporter genes .
Sixty three pairs of two-copy duplicated genes with synonymous divergence KS<0 . 2 were identified in the D . melanogaster genome ( see Methods ) . This KS cutoff value was chosen such that almost all duplicated genes in the D . melanogaster genome that appeared after the speciation of D . melanogaster and D . simulans can be detected . Note that the average KS between the two species is around 0 . 12 [21] , so that the probability that KS between duplicates exceeds 0 . 2 should be very low . Then , the locations of these genes on the D . melanogaster genomic sequence were visually examined , and by using the BLASTN algorithm we identified duplicated genomic regions ( blocks ) that encompass the identified duplicated genes . It was found that the 63 duplicated genes belong to 55 duplication blocks: some of them are next to each other and belong to the same duplication blocks ( summarized in Tables 1 and 2 ) . Almost all duplicates are located on the same chromosomes . For each pair of the duplicated blocks , the one that is close to the telomere of the left arm of the chromosome was assigned to Xm and the other was assigned to Ym . These results are consistent with those of Fiston-Lavier et al . [27] . We identified the orthologous regions of these duplicated blocks in the D . simulans and D . sechellia genomes , and the results are shown in Tables 1 and 2 . For the 25 blocks in Table 1 , there is only one orthologous region , while the orthologous regions of both the duplicated blocks are found in the D . simulans and/or D . sechellia genomes for the remaining 30 blocks ( Table 2 ) . The orthologs of Xm and Ym are denoted by Xs and Ys , respectively , in Table 2 . The locations for Xs and Ys are those in the D . simulans genome if Xs and Ys are found in this species , otherwise the locations are those in the D . sechellia genome . The relative chromosomal locations and orientations of the blocks in the two species are consistent with each other for most of the duplicated blocks . Considering that it is very unlikely that the identical size of duplication occurred at the same genomic location and in the same orientation independently on the lineages leading to D . melanogaster and D . simulans ( D . sechellia ) , it may be reasonable to consider that the duplicates in Table 2 were created by a single duplication event before the speciation of the two species . Therefore , these duplicates are referred to as “pre-speciation duplicates” . Note that the difference in orientation for Pre12 can be explained by a large inversion difference on chromosome 3R [28] . Pre5 and Pre11 are only exceptions , for which the possibilities of independent duplications cannot be ruled out although single duplication plus inversion will also explain them . In the following analysis , we treat all duplicated blocks in Table 2 as pre-speciation duplicates , but exclusion of Pre5 and Pre11 has very little effect on our conclusions . The duplicates in Table 1 are called “post-speciation duplicates” because they likely arose after the speciation of D . melanogaster and D . simulans . For all post- and pre-speciation blocks , NJ trees on the basis of nucleotide divergence are constructed with D . yakuba homologs as an outgroup ( Figure S1 and S2 ) . The phylogenetic relationship is relatively simple for the post-speciation blocks ( Figure S1 ) : D . melanogaster has two copies while D . simulans and D . sechellia have one copy each , suggesting that the duplication events occurred after the speciation of D . melanogaster and the other two species . For the pre-speciation duplicated blocks , in most cases , phylogeny includes two duplicates in D . melanogaster and their orthologs in D . simulans and D . sechellia ( Figure S2 ) . Figure 2 shows the distributions of KS for the two classes of duplicated blocks . The overall distribution is L-shaped as reported by Lynch and Conery [13] , mainly due to the excess of duplicated blocks with low KS . Almost all post-speciation blocks have KS<0 . 1 except for Post25 . The tree for Post25 in Figure S1 shows that the duplicates in D . melanogaster are most closely related each other . It seems that the divergence is high only in synonymous sites in the coding region . KS for the pre-speciation blocks are also low . If the two duplicated blocks have accumulated substitutions independently ( i . e . , a molecular clock holds for the paralogous divergence ) , the expectation of KS for the pre-speciation blocks is larger than KSspecies , which is the orthologous divergence at synonymous sites . The genome-wide average of KSspecies is 0 . 12 [21] . Although there should be variation in KSspecies across genes , our observation is quite unlikely under a molecular clock model , indirectly suggesting that those duplicated genes are undergoing concerted evolution by gene conversion . The role of gene conversion can be directly and clearly documented by examining the shape of the gene tree of the duplicated genes . If the duplicated blocks X and Y in the two species are currently undergoing concerted evolution , the two paralogous regions in each species are more closely related than the orthologous pairs , as illustrated in Figure 1C . Without gene conversion , the orthologous pairs should be more closely related ( Figure 1B ) . It is also possible that only one paralogous pair is undergoing concerted evolution while the other is not ( Figure 1D ) . Based on this idea , we investigated the shapes of the trees in Figure S2 , which is summarized in Table 3 . Out of the 28 blocks to which the analysis can be applied ( excluding two blocks with no outgroup sequence available ) , 14 exhibited evidence for gene conversion for both species ( i . e . , the tree shape in Figure 1C ) , and evidence for gene conversion is obtained for either of the two species ( i . e . , Figure 1D ) for 10 blocks . It seems that the effect of gene conversion in D . simulans and D . sechellia is not as extensive as that in D . melanogaster , because nine of the 10 blocks have the Xm-Ym cluster ( Table 3 ) . However , this can be simply explained by the ascertainment bias of our sampling of duplicates: our sample is biased toward those with low paralogous divergence in D . melanogaster . No evidence for gene conversion is obtained in four blocks . The power to detect evidence for gene conversion should increase if we perform a window-analysis of the tree shape . This is because the tree shapes in Figure S2 ( also summarized in Table 3 ) reflect the average evolutionary relationship over the entire region ( block ) . Therefore , this approach could potentially miss signatures of gene conversion when occurring only in local regions . In other words , the approach can detect evidence for gene conversion when it frequently occurs in most of the analyzed region . The results of the window analysis are also shown in Figure S2 , where regions with red bar have tree shapes illustrated in Figure 1C ( evidence for gene conversion in both species ) , while regions with blue bar have tree shapes illustrated in Figure 1B ( no evidence for gene conversion ) . We observe that the tree shape changes across duplicated regions , indicating that different regions have different evolutionary histories . This is expected because gene conversion tracts should be much smaller than the duplicated regions . It is also suggested that there could be substantial local variation in the activity of gene conversion . Overall , there is evidence for extensive gene conversion . We found that in most of the analyzed blocks , the regions of red bar ( i . e . , supporting the tree shape of Figure 1C ) distribute along the entire region . All blocks investigated have at least one local region ( window ) supporting the tree shape of Figure 1C . A drawback of this analysis is that the relative effect of other noises , including multiple mutations , would be large because phylogeny is constructed for short regions ( windows ) . In other words , a small number of sites with multiple mutations could mimic the real evolutionary history of the duplicated blocks . Therefore , we apply a statistical test that incorporates the effect of multiple mutations . The null hypothesis is set such that the evolutionary history in the entire duplicated region follows the tree shape of Figure 1C , so that the observation could be explained without gene conversion when the effect of multiple mutations is taken into account . The P-value is the rejection probability of this null hypothesis; therefore , a smaller P-value indicates a stronger evidence for gene conversion . The statistical analysis is based on the alignment of the four sequences , Xm , Ym , Xs , and Ys ( Figure 3 ) . We focus on two types of informative sites in the alignment , denoted by type-C and type-N sites ( Figure 3A ) . The former is a biallelic site at which the same nucleotide is shared by the two paralogous sequences in each species , while the latter is that at which the same nucleotide is shared by the two orthologous sequences ( Figure 3A ) . A type-C site parsimoniously supports a tree with gene conversion ( i . e . , the left tree in Figure 3B ) , while a type-N site supports a tree with no gene conversion ( i . e . , the right tree in Figure 3B ) . Let j and k be the observed numbers of type-N and type-C sites , respectively . The presence of type-C sites ( k>1 ) parsimoniously suggests that ( at least a part of ) the duplicated block experienced gene conversion , but multiple mutations could also explain it , especially when k≪j . The statistical test examines if the observed number ( k ) can be explained by multiple mutations assuming no gene conversion ( see Methods ) . As shown in Table 3 , the P-value is less than 0 . 05 for almost all pre-speciation blocks ( 29/30 ) , most of which exhibit very strong evidence with P<0 . 0001 . The exception is Pre19 for which only one informative site is available; thus , almost no statistical power is expected . We use our list of 1→2 duplications to estimate the rate of gene duplication . Note that our interest is in the long-term duplication rate , that is , the rate at which a duplicate arises by mutation and becomes fixed in the population . As mentioned in the Introduction , our focus is limited to two-copy duplicates to perform the fine-scale analysis at the DNA level . Therefore , the rate we estimate can be considered to be the rate at which a single-copy gene becomes two-copy duplicated genes . In this sense , the rate we are interested in is quantitatively different from those estimated in other articles [13] , [22] . We have identified 63 gene duplications by which single-copy genes became two-copy genes . It was found that 31 of them are in the 25 post-speciation blocks , indicating that these 1→2 duplications occurred after the speciation of D . melanogaster and D . simulans , which was roughly 2 . 3 million years ago [29] . It can be estimated that a 1→2 duplication occurs every 0 . 074 million years , or the duplication rate per gene is 1 . 0×10−9 , given that there are about 13 , 000 single-copy genes in the genome . The advantage of this phylogeny-based method is that it is robust to the effect of gene conversion , which could cause a serious overestimation of gene duplication rate when estimated by counting duplicated genes with low divergence [15] . To investigate this effect of gene conversion , we estimated the 1→2 duplication rate following the method of Lynch and Conery [13] . We found 25 two-copy duplicated genes with synonymous divergence KS<0 . 01 . Their ages should be smaller than 2 . 3×106×0 . 01/0 . 12 = 1 . 9×105 years . Thus , the divergence-based method produced the duplication rate per gene as 10 . 0×10−9 , which was roughly 10 times larger than our estimate . Figure 4 displays the evolutionary changes in the size of duplicated blocks , the number of genes in each block , and the length of the intervening sequence between each pair . To understand their evolution over time , we used two methods to measure time . The first is the paralogous synonymous divergence ( KS ) . Although KS is not a very good measure because of gene conversion ( see above ) , theory predicts that KS at least shows a positive correlation with time since the duplication event [15] . Second , we directly compared the two classes of duplicates for the three characteristics of interest . The relationship between KS and the block size is shown in Figure 4A . The sizes of duplicated blocks with KS<0 . 01 ranges from 1 kb to 35 kb , while the size is generally smaller than 2 kb for those with KS>0 . 1 . KS and the block size show a strong negative correlation , and Pearson's correlation coefficient is r = −0 . 288 , which is highly significant ( p<0 . 0001 , permutation test ) . It is also found that the average block size of the post-speciation blocks is significantly larger than that of the pre-speciation blocks ( p = 0 . 0012 , permutation test ) , indicating that young blocks are likely to be large . Additionally , the number of genes ( denoted by sX and sY in Tables 1 and 2 ) in a block also decreases with increasing KS ( r = −0 . 396 , p<0 . 0001 , permutation test ) . The average number of genes in the post-speciation blocks is significantly larger than that of the pre-speciation blocks ( p = 0 . 0124 , permutation test ) . It seems that young duplicated blocks have more genes . We found unannotated pseudogenes in several blocks , which resulted in sX≠sY in Tables 1 and 2 , suggesting that pseudogenization of redundant duplicated copies is underway . We also found that some orthlogs in D . simulans and/or D . sechellia have frameshift mutations ( see Table 2 ) . Insertion/deletion is the mechanism to affect the size of duplicated blocks . Petrov et al . [30] reported that the deletion rate may be higher than the insertion rate in retrotransposons in the D . melanogaster genome . If this can be applied to duplicated regions , the biased pressure toward deletion would partly explain the observed decay of the sizes of duplicated blocks . The decay of the sizes of blocks could also be simply explained by technical limitations to identify the real duplicated regions . It may be easy to imagine that the accumulation of nucleotide mutations and small insertion/deletions around the edges of the duplicated regions could result in misidentification of the duplicated regions; usually , the “identifiable” region is smaller than the real region . We propose that the decay of “identifiable” duplicated blocks can be enhanced by the combination of two opposing forces , mutation ( including small indels ) and gene conversion . Obviously , the former increases the divergence between duplicates , the latter decreases the divergence , and their balance determines the divergence between paralogs [31] , [32] , [33] . It may be reasonable to assume that the spatial distribution of the mutation rate would be roughly uniform , but there could be a substantial amount of local variation in the gene conversion rate . Because interlocus gene conversion is a kind of recombination event [2] , we expect that the rate of paralogous synapses may be lower around the edges due to decreased sequence identity . As a consequence , the rate of gene conversion would be low around the edges . The divergence in these regions possibly increases more rapidly in comparison with that in regions far from the edges . This contrast in the pressure of homogenization by gene conversion could result in the misidentification of duplicated regions . This process predicts two outcomes . ( i ) The length of the intervening sequence between “identifiable” duplicated blocks ( denoted by I ) increases over time . This can be well documented if all duplication occur tandemly with no intervening region ( i . e . , I = 0 ) , but this is not the case in practice . Nevertheless , the prediction of increased intervening sequences may still be supported because all duplicated blocks with I = 0 are in the post-speciation class , and almost all ( 10/11 ) duplicated blocks with I = 0 have KS<0 . 01 ( Tables 1 and 2 ) . However , because many other mutational mechanisms are involved in the length evolution of intervening sequences , the relative contribution of the decay of duplicated block to the growth of intervening sequences may not be large . ( ii ) The second outcome would be seen in the distribution of the nucleotide divergence between duplicated blocks . The decay of the identifiable duplicated blocks could be visualized if the divergence is elevated around the edges when a high level of identity is observed in the middle of the block . Figure S2 illustrates the distribution of the paralogous divergence ( blue line ) , which shows that many pre-speciation duplicated blocks have elevated divergence around the edges . Two examples with very clear patterns are picked up and shown in Figure 5 . The first example is Pre6 , which encompasses the Bob ( Brother of Bearded ) genes , and the second is Pre16 with the Amy ( amylase ) genes . In both , the divergence between paralogs is high around the edges of the identified blocks . Because the spatial distribution of orthologous divergence between the two species is not necessarily U-shaped in both the cases , the relaxation of negative selection outside the coding regions alone cannot explain the observation . The latter case is a typical example of duplicated genes with strong evidence for long-term concerted evolution by gene conversion [34] , [33] . The two duplicates are shared by the D . melanogaster subgroup , indicating that the duplication occurred at least ∼10 million years ago . Such a long-term concerted evolution was achieved by frequent gene conversion: the rate has been estimated to be roughly 100 times higher than the synonymous mutation rate [32] , [33] , [35] . Thus , we have demonstrated that the size of “identifiable” duplicated blocks will shrink over time together , which can be explained by the accumulation of point mutations and ineffective gene conversion around the edges . The upshot is that it is difficult to know the real sizes of old duplicated blocks . An acceleration in amino acid-changing substitutions ( KA ) after gene duplication is usually considered as a signature of neofunctionalization , although the relaxation of negative selection could also elevate the rate of non-synonymous substitutions . As shown in Tables 1 and 2 , KA is smaller than KS in most cases , indicating the operation of purifying selection . Although several blocks have KA>KS , the ratio KA/KS is not significantly higher than 1 . Asymmetry of the evolutionary rate after gene duplication is another signature of neofunctionalization . Our data provide an opportunity to investigate the rate of molecular evolution in the original vs . derived copies . Since Ohno proposed his model of evolution of genetic novelty by gene duplication [1] , this hypothesis has been challenged by many researchers [36] , [37] , [38] . Ohno's neofunctionalization model describes the process such that after a duplication , as long as one copy maintains the original function , the other is completely free from purifying selection . Therefore , Ohno's prediction has been tested for many species by looking at the symmetry ( or asymmetry ) of the evolutionary rate after gene duplication . However , those analyses did not specify which duplicates are original and which are derived copies . Here , with the availability of the genome sequences of D . simulans and D . sechellia , we were able to confidently define duplicates as original or derived copies for six of the post-speciation blocks ( see Methods ) . We performed a relative rate test [39] by using the MEGA 3 . 1 program package [40] for genes in those six blocks , but we did not observe any significant trend in the acceleration of substitutions in the lineages of the original and derived copies . Teshima and Innan [24] recently proposed a new test for detecting signature of neofunctionalization . Using this simple non-parametric test , we performed a genome scan for recent neofunctionalization in D . melanogaster . The test can be best applied to relatively old duplicated blocks that are currently undergoing concerted evolution . In our data , the pre-speciation blocks with strong evidence for gene conversion should be suitable for this analysis . Because a simple search for locally diverged regions may capture false positives created in regions of less functional importance , we focused on the distributions of type-C and type-N sites . A cluster of type-N sites would be considered as a signature of neofunctionalization , which can be emphasized when there are many type-C sites in the surrounding regions of the cluster . A simple sliding-window analysis ( see Methods ) found such a pattern in one of the pre-speciation blocks . Figure 6 shows the distributions of type-C and type-N sites in Pre28 ( below and above the horizontal axis , respectively ) . The observation is very well-consistent with the theoretical expectation with selection . There are two clusters of type-N sites , which are surrounded by regions with abundant type-C sites . A forward simulation ( see Methods ) showed that the probability that a peak of divergence with >15% appears in a 1600 bp region is very low ( P<0 . 0001 ) , suggesting that selection may be working at the two locations . The two clusters are located in the coding regions of CG18281 ( region X ) and CG17637 ( region Y ) , which belong to the major facilitator superfamily . The members in the major facilitator superfamily transport small solutes such as sugar and drugs in response to chemiosmotic ion gradients [41] . These two genes have conserved homologs among many metazoan organisms . A BLAST-based conserved domain search ( CD search ) showed that these two proteins contain arabinose or drug efflux domains of bacteria in their N-terminal regions [42] . Figure 6 also shows the distributions of the paralogous divergences for the two species . As expected , two peaks of divergence are observed at the same locations in both the distributions . The red line in Figure 6 is the distribution of do , the divergence between the orthologous pairs , which is roughly flat across the region , indicating that the peaks of divergence are not due to the relaxation of purifying selection . This is also supported by an excess of non-synonymous type-N sites especially for the first peak around position 800 ( 14/20 ) , indicating that the amino acid differences between duplicates may be preferred by selection . The distributions of the paralogous divergences for the two species are nearly identical , indicating that the peaks have been maintained by selection at least since the speciation of the two species . This is also well-supported by phylogenetic trees in Figure S3 . In the regions excluding the two peaks , the two paralogs in D . melanogaster are closely related to each other ( Figure S3C ) . In contract , the tree for the first peak is consistent with the species tree ( Figure S3A ) . The branch lengths in the tree in Figure S3A are overall longer than those in Figure S3B , suggesting an accelerated evolutionary rate in the region around the first peak . A similar pattern is also observed for the second peak , although the resolution of the tree is not very clear because the region is short ( Figure S3B ) .
The pattern of recent 1→2 gene duplications in the D . melanogaster genome was investigated with special attention to interlocus gene conversion . Our fine-scale analysis with careful visual inspections enabled accurate identification of duplicated blocks . The orthologous parts of those duplicated blocks were also identified in the D . simulans and D . sechellia genomes , by which we were able to clearly classify most blocks into post- and pre-speciation duplicated blocks . Our analysis demonstrated that a number of duplicated blocks undergo concerted evolution by gene conversion . Almost all pre-speciation duplicated blocks exhibited strong signatures of gene conversion ( Table 3 , Figure S2 ) . Gene conversion and unequal crossingover are usually considered the major mechanisms of concerted evolution . In this study , we focused only on gene conversion because unequal crossingover is not relevant . Our fine-scale identification of recent duplicated blocks showed that the synteny around the duplicated blocks are very well-conserved among D . melanogaster , D . simulans and D . sechellia , indicating that there is no evidence of unequal crossingover . The decay of duplicated blocks over time was observed . We found that ( 1 ) the length of duplicated blocks is large for young duplicates ( post-speciation blocks ) , ( 2 ) young duplicated blocks include more genes , ( 3 ) all duplicated blocks with no intervening sequences ( I = 0 ) belong to the post-speciation class . In addition to biased deletion rate , which may be possible for D . melanogaster [30] , we postulate that the degeneration of gene conversion around the edges enhances the divergence between duplicates , causing the misidentification of the real duplicated region; usually , the “identifiable” region is smaller than the real region . Our hypothesis is supported by the elevated paralogous divergence around the edges of duplicated regions as shown in Figures 5 and S2 . Thus , we provided several lines of evidence that gene conversion plays a crucial role after gene duplication in the D . melanogaster genome . Although most of the duplicated blocks analyzed in this study were located close together on the same chromosome , interlocus gene conversion can occur between different chromosomes . By looking at the polymorphism data in a pair of duplicated genes located on chromosomes 3 and X , Arguello et al . [43] showed clear evidence that the pair has been undergoing long-term concerted evolution by gene conversion . Polymorphism data analysis is much more powerful to detect interlocus gene conversion , and there are a number of duplicated gene pairs with strong signatures of recent gene conversion in D . melanogaster [33] , [35] . It seems that interlocus gene conversion is a genome-wide phenomenon . Therefore , its effect should be taken in account in any kind of evolutionary analysis of gene duplication . We estimated the 1→2 gene duplication rate to be 1 . 0×10−9 per gene per year by using a phylogeny-based method . The method is robust to the effect of gene conversion , which is a great advantage . In contrast , a divergence-based method [13] , which uses information from only a single genome , is very sensitive to gene conversion because it reduces the divergence between duplicated genes . We found that the divergence-based method provides an estimate of gene duplication rate about 10 times higher than that provided by the phylogeny-based method . The degree of overestimation is not as serious as in yeast , for which overestimation by the divergence-based method is about two orders of magnitude [11] . It should be note that the original estimate of Lynch and Conery is 2 . 3×10−9 per gene per year , which is only twice higher than ours even when they included small multigene families with sizes up to five . The reason for this is that they found only 10 duplicated gene pairs with KS<0 . 01 probably because of the incompleteness of the D . melanogaster genome at the time . This study focuses only on 1→2 duplications because our primary purpose was to perform a fine-scale analysis at the DNA level including non-coding regions , which has not been done in previous large-scale analysis in Drosophila [21] , [22] . In this sense , this study is different from others , including that of [13] , [22] , and [21] , who analyzed gene families with various sizes . The rates of duplication ( as defined above ) depend on the size of the multigene family . The duplication rate of single-copy genes ( i . e . , 1→2 duplication rate ) should be lower than the rates of larger families ( e . g . , 2→3 , 3→4 , … duplication rates ) , when selection is working on copy number . For example , if over-expression by a duplicated extra copy is deleterious , the extra copy is subject to negative selection , and this selective pressure is stronger for single-copy genes [44] . Although Hahn et al . [22] reported lineage-specic expansion of gene families , we did not observe such expansion in our data , indicating that the copy-number evolution in small families is more stable than that in large ones . Nevertheless , the estimate of Hahn et al . [22] is 1 . 0×10−9 per gene per year , which is quantitatively consistent with ours . This is because their estimate is based on net copy size changes over a long evolutionary time , so that it does not reflect some duplications canceled out by losses . Our estimate ( 1 . 0×10−9 per gene per year ) is quantitatively more consistent with an estimated rate of new gene formation through DNA-level duplication by Yang et al . [45] . Their estimate ( 0 . 12×10−9 ) is several times lower than ours because they ignore tandem duplications . They found that most of those events are 1→2 duplications . It may be possible to extend our analysis to a larger gene family although technically more difficult [46] , but description of such an analysis is beyond the scope of this article . Note that we define the duplication rate as the rate at which a single-copy gene is duplicated and fixed in the population . Although our estimates assumed that all identified duplicated blocks are fixed in the D . melanogaster population , it is possible that some of them are still polymorphic ( i . e . , copy-number polymorphism ) . If so , our estimates would be overestimated . If we exclude duplicates with too low KS ( say , KS<0 . 01 ) , our estimate turns out to be 0 . 4×10−9 per year , which can be considered as the lower limit of our estimate because this treatment might be too drastic: all duplicates with KS<0 . 01 are considered to be polymorphic . Indeed , only two of our post-duplicates are found to be polymorphic in a recent survey of copy-number variation by Emerson et al . [47] , but this number may be underestimated because of their experimental strategy: Because Emerson et al . designed their research to map the regions of copy-number variation on the reference genome of D . melanogaster , it might not be optimized to detect copy-number variation in the reference genome itself . Here , we arbitrarily defined duplicated genes as those with synonymous divergence less than 0 . 2 . This definition is to cover duplicate pairs that could potentially exchange DNA sequences frequently by gene conversion . This cutoff value should not be unreasonable , according to our previous theoretical work [15] , together with the observation in yeast [11]: KS for duplicated genes with evidence for gene conversion in yeast is usually less than 0 . 2 . Our results are robust to this arbitrary cutoff value . For example , there is a very minor quantitative change in the estimate of gene duplication rate when the cutoff value is set as 0 . 3 because there are very few duplicated gene pairs with 0 . 2<KS<0 . 3 . Neofuntionalization is one of the most important selective processes after gene duplication . To infer the action of natural selection , we first focused on the synonymous and nonsynonymous divergences ( KS and KA ) between duplicated genes , but we found no strong signature of selection for neofunctionalizations . There could be at least two reasons for this . First , such KS−KA analysis works best for relatively long-term molecular evolution , during which a substantial number of nucleotide substitutions accumulate . Therefore , the methods would not have sufficient statistical power for our data with recent duplicated genes , especially when active gene conversion between duplicated genes retards the paralogous divergence . More importantly , gene conversion complicates the neofunctionalization process at the DNA level . When the duplicated genes undergo concerted evolution by gene conversion , which should be the case for many of the duplicated genes we analyzed , selection does not automatically result in the acceleration of nonsynonymous substitutions in the entire gene . The acceleration of substitutions will be limited to a narrow region around the target; therefore , KS−KA-based methods using the divergence in the entire gene should result in a lack of power . Instead , Teshima and Innan [24] suggested a possibility to focus on the spatial distribution of the divergence to detect signature of recent neofunctionalization . According to this idea , we found a strong signature in a pair of transporter genes ( CG18281 and CG17637 ) . This result indicates the promising possibility for applying this method as a genome scan for signatures of selection for neofunctionalization in other species . The advantage of our method is that it is possible to infer what parts of the genes are subject to selection .
Drosophila genome Release 3 . 1 ( dm3 ) was used for the identification of duplicated genes . A total of 13 , 165 non-redundant protein sequences were in the database . All protein sequences were used as queries to search against all the others by using the BLASTP program with a cutoff value of e<10−10 . We filtered out pairs of protein sequences with lower similarity than the criteria of Gu et al . [48] , which is the protein identity α>0 . 3 if the alignable region β>150 bp , otherwise α≥0 . 06+4 . 8 β0 . 32[1+exp ( β/1000 ) ] . The duplicated genes detected in this screening process were aligned by using ClustalW [49] . The nucleotide divergence was estimated by using the method of Li-Pamilo-Bianchi [50] , [51] , and gene pairs with KS>0 . 2 were screened out . In this analysis , we use 63 duplicated genes . Genes with no homologs with KS<0 . 2 are considered as single-copy genes , and we found that the D . melanogaster genome has 12959 single-copy genes . We identified the duplicated genomic regions ( blocks ) that involved those duplicated genes , using the BLASTN algorithm followed by visual inspection . The duplicated blocks were located on the latest version ( Release 5 . 3; dm3 ) of the D . melanogaster genome , with the annotation data at the UCSC genome browser website ( http://genome . ucsc . edu/ ) . For these duplicated blocks , their orthologs were searched in other species in the D . melanogaster subgroup ( Figure 1 ) . All aligned sequences are provided in Dataset S1 . The DNA sequences of duplicated blocks in D . melanogaster and their orthologs were aligned together with an outgroup sequence from D . yakuba by using ClustalW [49] . Pairwise nucleotide distances [Kimura's distance , 52] were computed , from which an NJ tree was constructed ( Figures S1 and 2 ) . For the post-speciation duplicated blocks , it may be possible to infer which of the duplicates the original copy was , if the intervening sequence between the duplicates is relatively long ( i . e . , I≫0 ) . The intervening sequence was searched against the genome sequence of D . simulans ( or D . sechellia ) . If the sequence has homology to the upstream of the D . simulans homolog , the downstream copy of D . melanogaster would be the original copy , and vice versa . For each block , we first estimated the number of nucleotide substitutions per site between the two orthologous pairs , p0 . Given this estimate , we consider k̅ , the expected number of type-C sites in the duplicated block under a simple two-allele model with 0 and 1 . The expected number of sites at which each of the X and Y regions experienced a mutation since speciation is roughly given by , where L is the length of the duplicated block . At such a double-mutated site , the resultant pattern of the two alleles ( 0 and 1 ) depends on the branches on which the mutations occurred . The left and middle trees in Figure 3C consider cases where the two duplicated regions had the same allele , 0 , at the speciation event . In the left tree , both the two mutations occurred in the lineages leading to the same species ( i . e . , D . simulans in this example ) , so that the current allelic status for ( Xm , Xs , Ym , Ys ) is ( 0 , 1 , 0 , 1 ) and the site becomes a type-C site . On the other hand , in the middle tree , the two mutations occurred in the lineages leading to different species ( i . e . , in this example , in the D . simulans lineage at X and in the D . melanogaster lineage at Y ) , resulting in ( Xm , Xs , Ym , Ys ) = ( 0 , 1 , 1 , 0 ) . This pattern cannot be explained by a single mutation even with gene conversion and is referred to as a type-M site in Figure 3A . Thus , because the probabilities that a mutation occurs in the two lineages are half at both X and Y , a double-mutated site becomes a type-C site with probability 1/2 when X and Y had the same allele at the speciation event . Similar logic holds for the case where X and Y had different alleles at the speciation event , and the probability to become a type-C site is again 1/2 ( see Figure 3C ) . Therefore , the expected number of type-C sites is given by . Our statistical test examines whether the observed number of type-C sites is significantly larger than this expectation , that is , the P-value is given by ( 1 ) assuming the Poisson distribution of mutations . For simplicity , we employed a two-allele model , although the real sequence has four nucleotides . This method underestimates the P-values because the probability that a double-mutated site appears as a type-C site is much smaller than 1/2: in most cases , it becomes a triallelic site . Thus , our treatment is conservative in terms of detecting gene conversion . To detect signatures of selection , we used a sliding window approach . We set the window size = 200 bp . For each window , the numbers of type-C and type-N sites are computed , and compared with those in the surrounding regions ( 200 bp in each direction ) . In practice , a 2×2 contingency table is obtained and Fisher's exact P-value is computed . With a cutoff of P<0 . 0001 , we found two peaks of the paralogous divregence , and both of them are in Pre28 . A forward simulation was performed following Teshima and Innan [15] , and it was found that a peak of divergence ( >15% in a 200 bp window ) appears in a 1600 bp region with probability <0 . 0001 . | Eukaryote genomes have a number of duplicated genes , which could potentially coevolve by exchanging DNA sequences by interlocus gene conversion . However , the extent of gene conversion on a genomic scale is not well understood , except that an extensive role of gene conversion was reported in yeast . Here , we show a second evaluation of the role of gene conversion by analyzing multiple genomes in the D . melanogaster subgroup . We found that most of young duplicated genes have experienced gene conversion , although not as extensively as yeast . We further performed fine-scale analysis of duplicated DNA sequences and estimated the gene duplication rate . Our estimate turned out to be much smaller than that of a commonly used method , which usually causes an overestimation when gene conversion is active . The role of positive selection for neofunctionalization was inferred by applying a novel test . Our results suggest that interlocus gene conversion could be a crucial mutational mechanism in the evolution of duplicated genes in eukaryote genomes and that the effect of gene conversion should be taken into account when analyzing molecular evolution of duplicated genes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"genetics",
"and",
"genomics/comparative",
"genomics",
"genetics",
"and",
"genomics/population",
"genetics"
] | 2008 | Duplication and Gene Conversion in the Drosophila melanogaster Genome |
Lipopolysaccharide ( LPS ) is a major component on the surface of Gram negative bacteria and is composed of lipid A-core and the O antigen polysaccharide . O polysaccharides of the gastric pathogen Helicobacter pylori contain Lewis antigens , mimicking glycan structures produced by human cells . The interaction of Lewis antigens with human dendritic cells induces a modulation of the immune response , contributing to the H . pylori virulence . The amount and position of Lewis antigens in the LPS varies among H . pylori isolates , indicating an adaptation to the host . In contrast to most bacteria , the genes for H . pylori O antigen biosynthesis are spread throughout the chromosome , which likely contributed to the fact that the LPS assembly pathway remained uncharacterized . In this study , two enzymes typically involved in LPS biosynthesis were found encoded in the H . pylori genome; the initiating glycosyltransferase WecA , and the O antigen ligase WaaL . Fluorescence microscopy and analysis of LPS from H . pylori mutants revealed that WecA and WaaL are involved in LPS production . Activity of WecA was additionally demonstrated with complementation experiments in Escherichia coli . WaaL ligase activity was shown in vitro . Analysis of the H . pylori genome failed to detect a flippase typically involved in O antigen synthesis . Instead , we identified a homolog of a flippase involved in protein N-glycosylation in other bacteria , although this pathway is not present in H . pylori . This flippase named Wzk was essential for O antigen display in H . pylori and was able to transport various glycans in E . coli . Whereas the O antigen mutants showed normal swimming motility and injection of the toxin CagA into host cells , the uptake of DNA seemed to be affected . We conclude that H . pylori uses a novel LPS biosynthetic pathway , evolutionarily connected to bacterial protein N-glycosylation .
Lipopolysaccharide ( LPS ) is a prevalent macromolecule in the outer membrane of Gram negative bacteria and represents an important virulence factor . LPS is composed of three parts: lipid A which is embedded in the outer membrane , the core oligosaccharide , and the O antigen [1] . Lipid A is also known as endotoxin , which refers to the induction of fatal reactions of the human immune system at very low LPS concentrations . Bound to lipid A is the core oligosaccharide , which is relatively well conserved among closely related bacteria . The O antigen represents the outermost region of the LPS . The O antigen of Helicobacter pylori contributes in several respects to the virulence of this human gastric pathogen , which is recognized by the World Health Organization as a Type 1 carcinogen [2] . H . pylori mimics carbohydrate structures present on human epithelial cells , blood cells , and in secretions , by incorporating Lewis antigens on its O chains [3] . In most strains , both , Lewis x ( Lex ) and Lewis y ( Ley ) , can be found in certain regions of the O antigen . Some strains also display Lewis a ( Lea ) and b ( Leb ) antigens or can have alternative O antigen structures [4] . H . pylori profits from this molecular mimicry , as Lex and Ley interact with the C-type lectin DC-SIGN on dendritic cells , which signals the immune system to down-regulate an inflammatory response [5] . The amounts of Lewis antigens and their location on the H . pylori O polysaccharide are variable , differing between strains and also between cells from the same isolate [3] , [6] . This is due to the phase variable expression of the H . pylori fucosyltransferases , enzymes required for the synthesis of Lewis antigens [7] . Evidence suggests that the O antigen structures of H . pylori strains are adapted to the individual human host , enabling the establishment of a chronic infection [8] , [9] . Unlike most bacteria , the genes involved in LPS biosynthesis in H . pylori are not arranged in a single cluster , but rather found in various locations distributed throughout the chromosome . Nevertheless , many enzymes required for H . pylori LPS biosynthesis have been identified and characterized . These include glycosyltransferases responsible for the addition of the monosaccharide building blocks in the assembly of the O polysaccharide [4] , as well as several proteins involved in the synthesis and modification of the lipid A-core [10] . The pathway used for the assembly and translocation of the O antigen in H . pylori remained uncharacterized . In all characterized LPS biosynthetic pathways , the O polysaccharide is assembled onto the undecaprenyl phosphate ( UndP ) lipid carrier by specific glycosyltransferases located in the cytoplasmic compartment of the bacteria [1] . Several initiating enzymes have been characterized that transfer a sugar phosphate from a nucleotide activated donor to UndP , forming a pyrophosphate linkage . One of the common initiating enzymes is WecA , a UDP-GlcNAc:undecaprenyl-phosphate GlcNAc-1-phosphate transferase [11] , [12] . Other glycosyltransferases sequentially add monosaccharides at the non-reducing end of the growing glycan chain . The lipid-linked glycan is subsequently translocated to the periplasm where the O polysaccharide is transferred from undecaprenyl pyrophosphate ( UndPP ) onto the lipid A-core . This last step is catalyzed by the O antigen ligase WaaL . Three LPS biosynthesis pathways are known to date [1] . They are distinguished by three different mechanisms for O antigen polymerization and translocation . In the polymerase-dependent pathway , only short O antigen subunits are assembled in the cytoplasm . These subunits are translocated to the periplasm by the flippase Wzx , where they are polymerized by Wzy with assistance of the chain length regulator Wzz , before the complete O antigen is transferred to the lipid A-core . In the two remaining pathways , the ABC transporter-dependent and the synthase-dependent pathway , the entire O polysaccharide is synthesized at the cytoplasmic side of the inner membrane . The flippase in the ABC transporter-dependent pathway consists of two different polypeptides , Wzm and Wzt . Wzm forms a channel in the inner membrane for the passage of the lipid-linked O antigen , and Wzt provides energy through its ATPase activity . The C-terminal domain of Wzt is required for substrate recognition , and often displays specificity towards the structure of the endogenous O chain [13] . In the third pathway , the key enzyme is the synthase WbbF , which has glycosyltransferase activity and is also required for the translocation of the UndP-linked O antigen to the periplasm [1] . Some exopolysaccharides and capsules are synthesized via pathways that resemble one of these three LPS biosynthesis pathways [14] . In some Gram negative bacteria , including Campylobacter jejuni which is closely related to H . pylori , the cell surface is covered with lipooligosaccharides ( LOS ) instead of LPS [1] . LOS and LPS are equivalent macromolecules; however , LOS lacks the O polysaccharide and is limited to a short oligosaccharide bound to the lipid A-core [1] . Generally , the oligosaccharide moiety is directly assembled onto the lipid A in the cytoplasm and UndP is not required for LOS biosynthesis . The goal of this investigation was to determine the pathway used by H . pylori for the assembly of the Lewis antigens onto the lipid A-core . We found that these polysaccharides are assembled as typical O antigens onto the UndP carrier . Surprisingly , for the membrane translocation of the lipid-linked glycan , H . pylori employs an enzyme which has not been previously found to be involved in LPS synthesis , but instead is used by other bacteria in the biosynthesis of N-glycoproteins . This translocase , named Wzk , has no strict structural requirement for its substrates , a characteristic that enables H . pylori to produce O antigens of various structures and lengths according to the phenotype of the infected host .
In order to identify genes possibly involved in H . pylori LPS biosynthesis , a genome search was performed using the sequences of enzymes known to participate in LPS biosynthetic pathways . The search resulted in the identification of homologs of the E . coli wecA and waaL genes . The proteins encoded in the H . pylori genes JHP1488 in strain J99 and HPG27_1518 in strain G27 are 22% identical to E . coli WecA and 94% identical to each other . The genes JHP0385 and HPG27_389 encode polypeptides which are 19% identical to the O antigen ligase WaaL and 95% identical to each other . The overall homology between WecA and WaaL sequences from different organisms is low . Nevertheless these proteins , including the H . pylori homologs identified , share similar membrane topologies and a few conserved key residues . Alignments of the protein sequences are shown in Figure S1 and S2 . Intriguingly , the H . pylori genome seemed to lack homologs of wzx , wzt , wzm or wbbF , which encode the flippase proteins involved in O antigen translocation in the known pathways . Furthermore , sequences encoding an O antigen polymerase Wzy , or a chain length regulator Wzz could not be found . Thus , the canonical LPS pathways are incomplete in H . pylori . To address whether the identified genes were indeed involved in H . pylori LPS biosynthesis , mutants in both H . pylori strains , J99 and G27 , were constructed by inserting a chloramphenicol-resistance cassette into the putative wecA and waaL open reading frames . For the generation of complemented strains , each gene was re-introduced into the recA gene on the chromosome of the corresponding mutant strain . This location in the genome was selected to prevent further recombinations , a procedure expected to stabilize the mutations . We took advantage of the H . pylori natural competence for the construction of the mutant strains . Interestingly , this procedure was not successful for the generation of complemented strains , as no colonies were recovered on the selective plates after transformation . However , complemented cells were efficiently obtained by electroporation . Monoclonal antibodies reacting with Lewis antigens were used to visualize the presence or absence of O antigens on the cell surfaces by fluorescence microscopy ( Figure 1 ) . Not all wild type cells reacted with the antibody ( Figure 1B ) . This is due to the high frequency of phase variation in the fucosyltransferase genes ( 0 . 2–0 . 5% ) reported by Appelmelk et al . [15] . Lewis antigens could also be detected on flagella ( Figure S3 ) , confirming the presence of LPS in the membranous sheaths covering these organelles [16] . This was shown previously by electron microscopy [17] but , to our knowledge , not by fluorescence microscopy . Importantly , all the mutant cells were devoid of Lewis antigens ( Figure 1D , F ) , suggesting the participation of the putative WecA and WaaL in H . pylori LPS biosynthesis . To obtain further evidence for the involvement of the putative WecA and WaaL in H . pylori LPS biosynthesis , the LPS of all strains was purified , separated by SDS-PAGE and visualized by silver staining ( Figure 2A ) and Western blotting , using monoclonal anti-Lex ( Figure 2B ) and anti-Ley antibodies ( Figure 2C ) . Only rough LPS without O chains and Lewis antigens was produced by the mutant strains ( Figure 2 , lanes 2 , 4 ) , demonstrating that both targeted genes are essential for O antigen display . However , whereas the complementation of the putative wecA mutants was successful , the production of smooth LPS was restored only at minimal levels after reintroduction of the putative waaL gene into the chromosome of the waaL mutant strain ( Figure S4 ) . Based on the evidence presented we annotated JHP1488 and HPG27_1518 as wecAHP . Similarly , JHP0385 , as well as HPG27_389 were named waaLHP . The availability of E . coli strains with specific mutations in LPS biosynthesis genes allowed us to test for activity of WecAHP and WaaLHP by recombinant expression in these mutant strains . E . coli strains carrying either a mutation in wecA or waaL were transformed with plasmids carrying the corresponding H . pylori homolog gene ( pIH22 and pIH52 , respectively ) . As shown in Figure 3 , wecAHP complemented O antigen synthesis in the E . coli wecA mutant , which confirms its role as a UDP-GlcNAc: undecaprenyl-phosphate GlcNAc-1-phosphate transferase . On the contrary , although WaaLHP could be expressed in E . coli , it was unable to restore smooth LPS production in an E . coli waaL mutant ( data not shown ) . The transfer of O antigens onto the lipid A-core generally requires a strain specific core structure , and heterologous expression of O antigen ligases is therefore often not functional [1] , [18] , [19] . E . coli and H . pylori core regions are structurally different [20] , which could explain the lack of activity of WaaLHP in E . coli . As full complementation of the H . pylori waaLHP mutant strain could not be achieved and the enzyme was not functional with the E . coli lipid A-core as an acceptor , we tested O antigen ligation activity by performing an in vitro assay . WaaLHP containing a C-terminal deca-His tag ( encoded in pIH52 ) was expressed in the E . coli waaL mutant strain CLM24 [21] . Expression in this strain was selected to prevent possible contamination with the E . coli O antigen ligase . Membranes containing the enzyme were solubilized with detergents and WaaLHP was purified by nickel affinity chromatography as described in Material and Methods ( Figure S5 ) . As the band corresponding to purified protein had an apparent molecular weight of about 36 kD instead of the expected 50 kD , the identity of the polypeptide was confirmed by mass spectrometry . As seen with silver staining ( Figure 4A , lane 1 ) , lipid A-core from E . coli co-purified with the H . pylori ligase . Lipid A-core from the H . pylori waaLHP mutant strain was purified and used as an acceptor structure . O antigen ligases typically show relaxed specificity towards the structure of the O polysaccharide [1] , a property that allows the use of diverse UndPP-linked glycans as substrates for the in vitro assay . Due to the presence of multiple bands , LPS containing a polymerized O antigen is often indistinguishable from the corresponding UndPP-bound O antigen in SDS-PAGE and Western blot analysis . We reasoned that the use of a short oligosaccharide of defined length would facilitate the interpretation of results . Therefore , the UndPP-linked heptasaccharide derived from the C . jejuni N-linked protein glycosylation pathway was selected as substrate . This lipid-linked glycan can be synthesized in E . coli cells carrying the plasmid pACYCpglBmut [22] , which contains all the enzymes required for the assembly of the heptasaccharide , and has been successfully shown to be a suitable substrate for in vitro glycosylation [23] , [24] . All reactants were mixed and incubated at 37°C overnight . Reaction products appeared as bands of higher molecular weight in SDS-PAGE ( Figure 4A , lane 4 ) and positively reacted with the HR6 antibody , which recognizes the C . jejuni heptasaccharide ( Figure 4B , lane 4 ) . The reaction mixtures were subjected to mild acid hydrolysis . In such conditions , the reaction product is stable , whereas the substrate is hydrolyzed ( Figure S6 ) . After acid hydrolysis , the HR6-reacting bands corresponding to LPS were still present ( Figure 4C , lane 4 ) , whereas the bands corresponding to the UndPP-linked heptasaccharide substrate were no longer observed ( Figure 4C , lanes 2 , 4 , 5 , 7 ) . These results demonstrated the successful transfer of the C . jejuni heptasaccharide onto the H . pylori lipid A-core acceptor , thereby confirming the O antigen ligase activity of WaaLHP . The E . coli lipid A-core present in the fraction containing the recombinant H . pylori ligase was not an appropriate WaaLHP acceptor , as activity was dependent on the presence of H . pylori lipid A-core ( Figure 4 , lanes 4 , 7 ) . It is notable that unlike the O antigen ligase of Pseudomonas aeruginosa [19] , WaaLHP did not require ATP as an energy source . The presence or absence of ATP in the in vitro reaction did not noticeably affect ligation efficiency ( Figure S7 ) . From the previous experiments we concluded that the H . pylori Lewis antigens are synthesized onto the UndPP carrier via a pathway initiated by WecAHP and involving WaaLHP for the transfer of the glycans onto the lipid A-core . It was puzzling that no gene encoding one of the common O antigen translocases could be found in the H . pylori genome . Therefore , we searched for the presence of alternative flippases belonging to other biosynthetic pathways . A gene homolog to pglK ( formerly named wlaB ) which encodes a flippase involved in the translocation of the UndPP-linked heptasaccharide during protein N-glycosylation in C . jejuni [25] was found in the H . pylori genome . The genes annotated as JHP1129 in strain J99 and HPG27_1153 in strain G27 encode polypeptide sequences which are 97% identical to each other and share 37% identity with C . jejuni PglK ( see alignments in Figure S8 ) . No evidence for the presence of N-glycoproteins has been found in H . pylori , and therefore , the homolog of C . jejuni PglK was our principal candidate as the H . pylori O antigen translocase . We investigated the flippase activity of the putative H . pylori translocase through complementation experiments in E . coli described by Alaimo et al . [25] . A gene cluster encoding the complete C . jejuni N-linked protein glycosylation machinery , which includes the PglK flippase , was introduced into an E . coli mutant strain devoid of known glycan flippases . These cells were also transformed with plasmid pIH18 expressing AcrA , a C . jejuni acceptor protein that carries two N-glycosylation sites . Glycosylation of AcrA in E . coli was detected through the appearance of two extra bands of higher molecular weight in Western blots , immunoreactive to the AcrA and the C . jejuni glycan-specific HR6 antibodies ( Figure 5A , B ) . Glycosylation of AcrA was abolished when the translocase PglK was absent ( Figure 5A , B , lane 2 ) , but was restored in the presence of the putative H . pylori translocase encoded in pIH23 ( Figure 5A , B , lane 3 ) , indicating that the activities of PglK and its H . pylori homolog ( named Wzk , according to the current official nomenclature of LPS genes [26] ) are interchangeable . Further evidence of the Wzk translocase activity was obtained by demonstrating its capability to restore the flipping of O antigen in an E . coli wzx mutant strain ( Figure 5C , D , lane 3 ) . Taken together , these results indicate that Wzk is a translocase for UndPP-linked glycans , equivalent to PglK . Structurally different glycans were translocated by Wzk ( Figure S9 ) , indicating a relaxed substrate specificity of this enzyme . To demonstrate the involvement of Wzk in H . pylori LPS biosynthesis , the wzk gene was mutated as described previously for wecAHP and waaLHP . Fluorescence microscopy showed that Lewis antigens were not present on the surface of H . pylori wzk mutant cells ( Figure 1J ) . The absence of Lex and Ley antigens on purified LPS from the mutant strain was shown by Western blot analysis ( Figure 6B , C , lane 2 ) . SDS-PAGE followed by silver staining showed that the LPS of the wzk mutant did not contain O antigens ( Figure 6A , lane 2 ) . Upon complementation with either wzk or its homolog pglK , the synthesis of smooth LPS was restored ( Figure 6 , lanes 3 , 4 ) . Taken together , these results demonstrated that Wzk is an essential component in the LPS biosynthesis pathway in H . pylori , responsible for the translocation of the O antigen . Although in their native hosts PglK and Wzk participate in different pathways , both enzymes can be functionally exchanged , being able to translocate glycans of diverse structures and lengths . Our fluorescence microscopy experiments allowed us to confirm the presence of LPS on the membranous sheaths covering H . pylori flagella , raising the possibility of a connection between LPS integrity and motility . We tested this hypothesis by comparing the swimming activity of the O antigen deficient strains relative to the wild type strains . After growth on soft agar plates , no significant difference between the diameters of colony expansion was detected ( results not shown ) . It was concluded that the absence of O antigens in the H . pylori LPS has no adverse effect on flagellar function in vitro . As mentioned above , the protocol for natural DNA uptake was not successful in the construction of the complemented strains . One possible reason is a reduced natural competence of the H . pylori O antigen mutants . The natural competence of H . pylori depends on the ComB type IV secretion system [27] . As H . pylori possesses additional type IV secretion systems , we examined if the presence of O antigens on the bacterial surface might be required for the function of these machineries . One additional H . pylori type IV secretion system is encoded in the cag pathogenicity island [28] . The presence of this gene cluster in H . pylori correlates with enhanced virulence , as the Cag type IV secretion system builds a needle-like device for the injection of a single known effector protein , CagA , directly into the host cells . Within the host , CagA is tyrosine phosphorylated and interferes with cell signaling pathways [28] . We compared the efficiency of CagA injection into human gastric epithelial cells between wild type and O antigen mutant H . pylori strains . AGS gastric epithelial cells were infected with H . pylori cells from an overnight grown liquid culture . Cells were harvested four hours after infection , when many epithelial cells displayed an elongated morphology , a typical effect following CagA translocation [28] . AGS cell membrane fractions were collected and analyzed by Western blotting , using anti-CagA ( Figure 7A ) and anti-phosphotyrosine ( Figure 7B ) antibodies . All H . pylori strains injected similar amounts of CagA . We concluded that mutations interfering with the synthesis of smooth LPS in H . pylori may reduce natural competence , but do not affect type IV secretion systems in general .
With the display of Lewis antigens on the O chains , the LPS plays a unique role in H . pylori colonization . The fact that the genes involved in LPS biosynthesis are not grouped in a single locus in the H . pylori chromosome is probably one of the reasons why the pathway for the biosynthesis of this key macromolecule remained to be determined . The objective of this investigation was to elucidate the LPS biosynthetic pathway in H . pylori . Genomic analysis revealed that none of the previously characterized pathways for LPS biosynthesis is complete in H . pylori , suggesting that this bacterium uses an alternative strategy . One possibility was that the synthesis of LPS took place directly on the lipid A-core as it occurs with the LOS biosynthesis in C . jejuni and Neisseria spp . [1] . However , using a combination of genetic , biochemical and microscopy techniques , we showed that the O antigen is assembled onto a polyisoprenoid lipid carrier . Figure 8 illustrates our model of H . pylori LPS biosynthesis . WecAHP initiates this pathway by transferring a GlcNAc-phosphate from UDP-GlcNAc to UndP . The resulting molecule , UndPP-GlcNAc , serves as an acceptor for the assembly of the O chain backbone , composed of alternating GlcNAc and Gal residues . Some of these linear polysaccharides are decorated at selected locations through the activity of various fucosyltransferases , producing the Lewis antigens [4] . After translocation to the periplasm by Wzk , the O chain is attached onto the lipid A-core acceptor by the action of the O antigen ligase WaaLHP . H . pylori LPS biosynthesis follows a novel pathway , differing from all the established LPS pathways in the translocation of the O chain . We found that this step is accomplished by Wzk , which is not related to any described translocase involved in O antigen synthesis . Instead , Wzk is homolog to C . jejuni PglK , responsible for UndPP-heptasaccharide flipping during protein N-glycosylation . Wzk and PglK are related to the lipid A-core flippase MsbA , with the closest sequence similarity among known ATP transporters . ATPase activity of PglK has been reported by Alaimo et al . [25] . As the C-terminal Walker domains are well conserved between PglK and Wzk , Wzk most likely also possesses ATPase activity . All ABC transporter-dependent LPS pathways described to date require two polypeptides , Wzm and Wzt , for the translocation of the O chains [1] . The only homology of these proteins to Wzk is found in the ATP binding domains of Wzt . Wzm possesses several transmembrane domains and is proposed to form a channel in the inner membrane . Wzt provides the energy for the flipping mechanism through its ATPase activity . In H . pylori , the flippase Wzk is the only polypeptide required and sufficient for translocation of UndPP-linked glycans . The ability of Wzk to translocate Lewis antigens , the C . jejuni heptasaccharide , as well as the E . coli O16 antigen , demonstrates that Wzk activity is independent of the length or the composition of the translocated UndPP-linked sugars . In most bacteria , the genes involved in O antigen biosynthesis are clustered in a single locus , which facilitates horizontal gene exchange and regulation of O antigen synthesis [1] . In contrast , the three genes investigated in this work , as well as the other genes involved in O antigen biosynthesis in H . pylori , are located in separate loci dispersed along the chromosome . H . pylori exhibits a high rate of DNA uptake and genetic variability [29] . An independent location of the genes requires individual gene regulation , which could be beneficial for H . pylori by allowing more diversity and flexibility in the LPS structure . It is particularly intriguing that the position of the H . pylori wzk gene is located in close proximity to tRNA genes , which are known to be hot-spots for the insertion of mobile genetic elements [30] . On the contrary in C . jejuni , PglK is encoded as part of the pgl-cluster , responsible for N-linked protein glycosylation [22] . Interestingly , the oligosaccharyltransferase PglB , also encoded in the pgl-locus , has homologs in eukaryotes and archaea but not in H . pylori , which does not possess this general glycosylation machinery [31] . Different scenarios can be advanced to describe the origin of the Wzk-like translocases . H . pylori may have discarded its pgl-cluster , yet retained wzk to act on the synthesis of LPS . Alternatively , the wzk gene could have been adopted by other epsilon- or delta proteobacteria to produce N-glycoproteins . Subsequently , some of these organisms , like C . jejuni , may have lost their LPS cluster , producing LOS instead . In either case , bacteria appear to favor the dedication of their lipid-linked glycans exclusively to one biosynthetic pathway , either protein glycosylation or LPS biosynthesis . We encountered difficulties with natural transformation of O antigen mutant H . pylori cells and electroporation was required for the construction of strains for complementation experiments . One possible explanation is a loss of natural competence for DNA uptake . A similar observation has been reported for a C . jejuni LOS mutant strain [32] . However , in another study C . jejuni LOS and capsule mutants displayed increased DNA uptake ability [33] . Although the exact role of O chains in H . pylori natural competence remains unclear , we demonstrated that the presence of O antigens does not have a general inhibitory effect on H . pylori type IV secretion systems , because the type IV secretion apparatus encoded in the cag pathogenicity island was functional in O chain deficient H . pylori mutants . Alternatively , the lack of O antigens may induce a stress response , resulting in the induction of DNA restriction enzymes , which could digest the foreign DNA before or after uptake into the cells . Due to the presence of LPS on the H . pylori flagella , we investigated the possible role of O antigens in motility . H . pylori mutant strains were not defective in in vitro swimming motility compared to wild type strains . However , the presence of LPS on the flagellar surface may still play a role in vivo . The O antigens may have a protective function by shielding the flagella against components of the host immune defense , and by actively down-regulating flagellin-specific activation of the innate immune system via the interaction between Lewis antigens and DC-SIGN . A central role of the Lewis antigens in H . pylori pathogenicity is their interaction with DC-SIGN , which results in modulation of the host immune defense [5] . As Lewis antigen expression is phase variable due to the reversible switching off of the fucosyltransferases , H . pylori maintains a balance between activation and repression of the host immune system [9] . In addition , the fucosylated locations along the O chain backbone are finely adapted to the host phenotype [8] . With this mechanism , a permanent infection can be established , which in rare cases results in gastric cancer . The O antigen translocase Wzk , which we show here is essential for the cell surface expression of Lewis antigens , could be an attractive target for the design of antibiotics effective against H . pylori and possibly C . jejuni infections . Interestingly , Wzk is the first protein common to both , LPS biosynthesis and protein N-glycosylation , supporting an evolutionary connection between these pathways .
NCBI BLASTP ( default settings ) was used for the search of putative WecA , WaaL and lipid-linked glycan translocase polypeptide sequences encoded in the H . pylori genome . Global sequence identities were calculated with LALIGN ( default settings , global ) ( http://www . ch . embnet . org/software/LALIGN_form . html ) . Oligonucleotides used for DNA amplification are listed in Table S1 . H . pylori strains J99 [37] and G27 [38] served as parental strains for the construction of O antigen mutants . H . pylori mutant strains were generated through natural transformation with the plasmids pGEM-HPwecA-CAT for wecA mutants , pGEM-HPwaaL-CAT for waaL mutants and pIH40 for wzk mutants , respectively , resulting in the disruption of the targeted genes through insertion of a chloramphenicol resistance cassette by homologous recombination . Mutant strains were recovered as single colonies after growth on selective plates containing chloramphenicol . Complementation was achieved through electroporation of the mutant strains for the uptake of the plasmids pIH42 for wecA complementation , pIH53 for waaL complementation and pIH43 for wzk complementation , respectively . Following homologous recombination , the complemented genes were inserted into the chromosome of the H . pylori mutant strains , disrupting the recA gene . Complemented colonies were selected on plates containing chloramphenicol and kanamycin . All strains were verified with PCR analysis . H . pylori strains were grown on brucella broth agar plates , supplemented with 10% heat inactivated fetal bovine serum , or on brain heart infusion agar with 10% horse serum . The antibiotics vancomycin ( 5 µg/ml ) , cycloheximide ( 100 µg/ml ) , trimethoprim ( 10 µg/ml ) and amphotericin B ( 8 µg/ml ) were added and cells were incubated at 37°C under micro-aerobic conditions , obtained by adding a CampyGen gas pack ( Oxoid ) to an anaerobic jar . O antigen mutant strains were selected with chloramphenicol ( 25 µg/ml ) . Kanamycin ( 20 µg/ml ) was added for the selection of complemented strains . Liquid cultures were grown overnight in brucella broth supplemented with 10% heat inactivated fetal bovine serum and the appropriate antibiotics at 37°C with 160 rpm rotation . E . coli strains were grown overnight in LB broth at 37°C with rotation at 200 rpm . Microscope cover glasses were prepared for the attachment of cells using ( 3-aminopropyl ) triethoxysilane ( Sigma ) according to Strähle and coworkers [39] . Overnight H . pylori cultures were adjusted to an optical density at 600 nm wave length ( OD600 ) of 1 . 0 per ml . Cells were washed with PBS and an equivalent of 0 . 4 OD600 was applied to each cover glass . After 30 min of incubation on ice , the cell suspension was removed and cells were fixed with 4% paraformaldehyde for 10 min at room temperature . Staining and microscopy procedures were conducted as described by Couturier and Stein [40] , whereby a monoclonal anti-Ley antibody ( 1/200 ) ( Calbiochem ) and a secondary Alexa Fluor 488 goat anti-mouse antibody ( 1/500 ) ( Molecular Probes ) were used for staining . Small scale LPS extraction using hot phenol was performed following the procedure described by Marolda et al . [41] , with the exception that ethyl ether was replaced by ethanol for the washing of the LPS pellet . The LPS was run on a 15% SDS-PAGE and visualized by the silver staining method described by Tsai and Frasch [42] , or by Western blotting , using monoclonal mouse anti-Lex and anti-Ley antibodies ( Calbiochem ) , or rabbit anti-O16 antigen antiserum ( Statens Institute , Denmark ) . After incubation with a secondary goat anti-mouse IgM IRDye-800 antibody or a goat anti-rabbit IRDye-800 antibody , respectively ( LI-COR Biosciences ) , the blots were scanned with an Odyssey infrared imaging system ( LI-COR Biosciences ) . E . coli serogroup O16 laboratory strains produce rough LPS without O antigen due to a mutation inactivating the rhamnosyltransferase responsible for the addition of the second sugar residue in the O chain assembly . Smooth LPS production can be restored with the addition of a plasmid pMF19 containing the rhamnosyltransferase gene [43] . E . coli W3110 transformed with pMF19 served as a positive control , producing long chain O16 LPS . E . coli strain CLM37 has a mutation in wecAEC , and therefore is unable to assemble O chains [44] . CLM37 was transformed with pMF19 and empty vector pEXT20 to serve as negative control in the experiment testing for WecAHP activity . WecAHP activity was examined in CLM37 transformed with pMF19 and pIH22 . The E . coli O antigen ligase mutant strain CLM24 was transformed with pMF19 and either pIH52 or pEXT20 for the analysis of WaaLHP activity or for the corresponding negative control , respectively . To test the ability of H . pylori Wzk to flip UndPP-linked glycans in an N-glycosylation pathway , E . coli strain SCM7 [25] containing mutations in oligosaccharide translocases was transformed with pIH23 ( containing wzk ) , pIH18 ( encoding the acceptor protein AcrA ) and pACYCpglKmut ( encoding the C . jejuni glycosylation machinery with a mutation in the translocase gene pglK [44] ) . In the negative control strain the empty vector pEXT20 was transformed instead of pIH23 . The positive control strain was transformed with pACYCpgl ( containing the intact C . jejuni glycosylation machinery ) instead of pACYCpglKmut . To further examine if Wzk has O antigen translocase activity , the E . coli flippase mutant strain CLM17 [45] was transformed with pMF19 and pIH23 , or the vector control pEXT20 . LPS profiles were analyzed by silver staining as described above . Western blotting was used to determine the glycosylation status in the Wzk activity tests . As primary antibodies , either an anti-AcrA antibody [22] , recognizing the acceptor protein , or the antiserum HR6 ( S . Amber and M . Aebi , manuscript in preparation ) , reacting with the C . jejuni glycan , were applied . After incubation with a secondary goat anti-rabbit IRDye680 antibody ( LI-COR Biosciences ) , bands were visualized with an Odyssey imaging system ( LI-COR Biosciences ) . The protein band corresponding to the putative WaaLHP was excised from a coomassie stained gel ( Figure S5 ) . The protein was in-gel digested using trypsin ( Promega ) according to Shevchenko et al . [46] . Peptide fragments were eluted from the gel piece , desalted using zip-tipC18 ( Millipore ) according to the supplier protocol and dissolved in 0 . 1% formic acid . Peptides were separated with a LC/MSD Trap XCT ( Agilent Technologies ) and the resulting mass spectrum was used for the identification of the protein by the Mascot search engine ( www . matrixscience . com ) using the NCBInr database . The E . coli O antigen ligase mutant CLM24 was transformed with pIH52 which encodes waaLHP with a C-terminal deca-histidine tag . Cells were grown overnight at 37°C with 0 . 2 mM IPTG for the production of the O antigen ligase . The protocol described by Faridmoayer et al . [24] for the purification of an oligosaccharyltransferase was used for the purification of WaaLHP . Briefly , membrane fractions were solubilized with 2% elugent ( Calbiochem ) in phosphate buffer , pH 7 . 2 . Elugent concentration was diluted to 1% and the membrane fraction loaded unto a nickel agarose column ( Qiagen ) with 20 mM imidazole . The washing solution contained 50 mM imidazole and 0 . 5% DDM ( Anatrace ) . Ligase was eluted with 250 mM imidazole in the presence of 0 . 5% DDM . The lipid A-core from waaLHP mutant cells was obtained as described above and utilized as acceptor in the in vitro assay . UndPP-linked glycans serving as substrates were produced by E . coli CLM24 cells transformed with pACYCpglBmut [22] . A crude UndPP-glycan extraction was performed according to Ielpi et al . [47] . Purified WaaLHP ( 4–5 µg ) , purified LPS ( 1 . 2 µg , estimated using the method described by Osborn [48] ) , and an extract containing the UndPP-glycan ( 20% v/v ) were incubated overnight at 37° . The volume was adjusted to 50 µl with reaction buffer as previously described for in vitro glycosylation [24] ( 50 mM Tris-HCl pH 7 . 5 , 100 mM sucrose and 1 mM MnCl2 ) , with or without ATP ( 2 mM ) . Reaction products were visualized by silver staining or Western blotting using the glycan specific antibody HR6 . In addition , mild acid hydrolysis was performed similar to the method described by Ielpi et al . [47] , by incubation in 1% acetic acid at 80°C for 30 min to destroy the pyrophosphate linkage of the substrate . H . pylori wild type and O antigen mutant cells from overnight cultures , grown either on plates or in liquid media , were spotted on soft brucella broth agar plates ( 0 . 3% agar ) and incubated at 37°C under micro-aerobic conditions . Swimming motility was analyzed after 3–6 days by comparison of the colony growth diameters . The procedure for the CagA translocation assay was previously described by Cendron et al . [49] . Briefly , AGS cells were grown overnight in 10 cm culture dish plates and later infected with H . pylori cells with a multiplicity of infection of 100∶1 for four hours . After incubation , AGS cells were washed , harvested and fractionated . Membrane fractions were analyzed by Western blotting , using anti-CagA ( 1∶2000 , kindly provided by Antonello Covacci ) , and anti-phosphotyrosine ( 1∶2000 , anti-PY99 , Santa Cruz Biotechnology ) antibodies . Analysis with an anti-h-Met antibody ( 1∶1000 , C-28 , Santa Cruz Biotechnology ) showing the general host membrane protein h-Met served as loading control . | Bacterial surfaces are decorated with glycans . The human stomach pathogen Helicobacter pylori exposes lipopolysaccharide ( LPS ) containing Lewis antigens that mimic human glycan structures . H . pylori alters its Lewis antigen display in adaptation to the individual host . Lewis antigens can interact with human dendritic cells , thereby inducing a suppression of the immune response and facilitating a chronic H . pylori infection . Whereas three general LPS biosynthesis pathways are known , the route of LPS assembly in H . pylori remained to be elucidated . We identified and characterized two components of the H . pylori LPS pathway , WecA and WaaL , which demonstrated that , as in other bacteria , the glycan is initially assembled onto a polyprenoid lipid carrier . This intermediate then has to cross a membrane barrier , requiring specialized translocases . H . pylori does not employ a translocase from common LPS pathways . We show that instead H . pylori uses a translocase named Wzk , which is involved in protein N-glycosylation in other bacteria . Wzk was able to translocate various glycan structures . The identification of Wzk as the H . pylori translocase involved in LPS biosynthesis indicates an evolutionary connection between LPS and glycoprotein biosynthesis pathways . | [
"Abstract",
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] | 2010 | Helicobacter pylori Lipopolysaccharide Is Synthesized via a Novel Pathway with an Evolutionary Connection to Protein N-Glycosylation |
Lyme disease in humans is caused by several genospecies of the Borrelia burgdorferi sensu lato ( s . l . ) complex of spirochetal bacteria , including B . burgdorferi , B . afzelii and B . garinii . These bacteria exist in nature as obligate parasites in an enzootic cycle between small vertebrate hosts and Ixodid tick vectors , with humans representing incidental hosts . During the natural enzootic cycle , infected ticks in endemic areas feed not only upon naïve hosts , but also upon seropositive infected hosts . In the current study , we considered this environmental parameter and assessed the impact of the immune status of the blood-meal host on the phenotype of the Lyme disease spirochete within the tick vector . We found that blood from a seropositive host profoundly attenuates the infectivity ( >104 fold ) of homologous spirochetes within the tick vector without killing them . This dramatic neutralization of vector-borne spirochetes was not observed , however , when ticks and blood-meal hosts carried heterologous B . burgdorferi s . l . strains , or when mice lacking humoral immunity replaced wild-type mice as blood-meal hosts in similar experiments . Mechanistically , serum-mediated neutralization does not block induction of host-adapted OspC+ spirochetes during tick feeding , nor require tick midgut components . Significantly , this study demonstrates that strain-specific antibodies elicited by B . burgdorferi s . l . infection neutralize homologous bacteria within feeding ticks , before the Lyme disease spirochetes enter a host . The blood meal ingested from an infected host thereby prevents super-infection by homologous spirochetes , while facilitating transmission of heterologous B . burgdorferi s . l . strains . This finding suggests that Lyme disease spirochete diversity is stably maintained within endemic populations in local geographic regions through frequency-dependent selection of rare alleles of dominant polymorphic surface antigens .
Borrelia burgdorferi sensu lato ( s . l . ) , the spirochetal agents of Lyme disease , comprise several closely related genospecies of pathogenic bacteria that are maintained in nature in an enzootic cycle involving Ixodes ticks and a wide range of vertebrate hosts . While these spirochetes can cause Lyme disease in humans , most natural reservoir hosts become persistently infected without signs of disease [1–5] . Ixodes ticks feed once per life stage and the corresponding blood meal is required for the immature larval and nymphal stages to molt , and for the adult females to lay eggs . Acquisition of B . burgdorferi s . l . typically occurs when larval ticks feed on an infected host . Ingested spirochetes colonize the larval tick midgut , survive through the molt and are subsequently transmitted to new vertebrate hosts by feeding nymphs . Persistent infection of these vertebrate hosts and subsequent acquisition of B . burgdorferi by feeding larval ticks complete the infectious cycle . Ingestion of host blood by infected ticks stimulates spirochete replication and induces changes that are critical for transmission of B . burgdorferi s . l . to the vertebrate host and survival in this disparate environment [6–10] . In contrast to the highly infectious phenotype of spirochetes in replete ticks , a recent study from our lab demonstrated that spirochetes colonizing unfed ticks are viable , but essentially non-infectious [11] . We will use the term “pathogenic potential” rather than “virulence” , as proposed by Casadevall [12] , to describe the infectious phenotype of wild-type B . burgdorferi s . l . in an experimental mouse-tick cycle because infection does not cause disease in these rodent hosts . We conclude that in addition to stimulating spirochete replication , exposure to vertebrate blood during tick feeding also induces phenotypic changes that conditionally prime B . bugdorferi s . l . for subsequent infection of a vertebrate host , thereby dramatically enhancing the pathogenic potential of tick-borne spirochetes . In nature , multiple strains of B . burgdorferi s . l . are stably maintained at high prevalence in both the tick vector and reservoir hosts sharing the same local geographic area [13–20] . Such diversity is observed within the single genospecies of Lyme disease spirochete , B . burgdorferi s . s , that predominates in North America , and among strains of the three genospecies , B . burgdorferi , B . afzelii and B . garinii , that co-exist and cause Lyme disease in Eurasia . Laboratory and field studies indicate that infection of vertebrates with B . burgdorferi s . l . elicits strain-specific protective immunity [21–30] . In an endemic region , infected nymphal ticks will occasionally feed on infected hosts carrying the same ( homologous ) or different ( heterologous ) B . burgdorferi s . l . strains . In the current study , we have extended our previous observation of conditional priming of B . burgdorferi s . l . during tick feeding to investigate how host immune status impacts the pathogenic potential of spirochetes within infected ticks . We show that blood from an infected host can have either a profoundly negative or positive impact on the infectious phenotype of the Lyme disease spirochete within feeding ticks , depending upon the similarity of the strains . This dichotomous response to host blood prevents super-infection by the homologous B . burgdorferi s . l . strain , while promoting infection by heterologous strains . Significantly , this study demonstrates for the first time that protective immunity against the Lyme disease spirochete , like induction of pathogenic potential , takes effect within the feeding tick vector prior to transmission , through a neutralization mechanism that does not require bacterial killing nor utilize tick midgut components .
We assessed the impact of the immune status of the blood-meal host on the infectious phenotype of the Lyme disease spirochete within the tick vector , as schematically diagrammed in Fig 1 and outlined as follows . Groups of infected nymphs were fed to repletion on naïve mice , on mice infected with the homologous B . burgdorferi strain ( B31 ) , or on mice infected with a heterologous B . afzelii strain ( PKo ) . Wild-type ( WT ) and immune-deficient laboratory mice were utilized in separate experiments . Fed nymphs were collected at drop-off and the presence of OspC+ spirochetes in the tick midgut was analyzed by IFA using several ticks from each experimental group . The remaining fed nymphs were pooled per individual mouse and crushed to yield infected tick homogenates . Aliquots of these homogenates were plated to quantify viable spirochetes and thereby calculate the average spirochete load per fed tick in each pool and inocula . Serial dilutions of fed tick homogenates containing defined numbers of viable spirochetes were injected into naïve WT mice to assess the impact of the host blood meal / immunity on the pathogenic potential of spirochetes within infected ticks . Ingestion of host blood promotes spirochete replication in infected nymphs [31] . To determine whether the acquired immune response of an infected blood-meal host impacts spirochete replication in the tick midgut , we quantified the spirochete burden in infected nymphs after they fed to repletion on naïve , homologously infected , or heterologously infected wild-type ( WT ) mice . The results from four independent experiments were combined and the mean number of viable spirochetes per tick in each mouse/tick cohort was estimated ( Fig 2 ) . The spirochete burdens in infected ticks fed on naïve or heterologously infected mice were similar ( 6 . 4×104 vs 5 . 8×104 , respectively ) , and approximately 4-fold greater than the spirochete burden in the same cohort of infected ticks fed on mice infected with the homologous strain ( 1 . 6×104 ) . These results demonstrate a modest strain-specific impact on spirochete burden when infected nymphs feed upon immune hosts . In addition to fostering replication , spirochetes in the tick midgut undergo biological changes in response to ingested blood that enable their subsequent transmission and infection of the vertebrate host [7–11 , 32] . Therefore , we compared the infectious dose of spirochetes derived from infected ticks fed on naive versus immune hosts to measure their relative pathogenic potentials . Fed tick homogenates containing known numbers of viable organisms ( as outlined in Fig 1 and described above ) were used to needle-inoculate naive mice with defined doses ranging from 10−105 spirochetes per mouse , using 3 to 6 mice per dose . B . burgdorferi infection in mice was determined by seroconversion and isolation of spirochetes from tissues; the data from several experiments were combined and are presented in Table 1 . Spirochetes in homogenates prepared from infected nymphs were highly infectious after ticks fed on naïve mice , or after feeding on mice infected with the heterologous strain: 5/6 mice became infected following injection of ~10 organisms , and 59/63 mice were infected with doses ranging up to 105 organisms from these sources ( Table 1 ) . In contrast , the infectivity of viable spirochetes in infected nymphs was dramtically reduced after ticks fed on mice infected with the homologous strain , where only 6/29 mice were infected with doses ranging from 102 to 105 organisms ( Table 1 ) . This outcome indicates an approximately 104-fold difference in the infectious dose of viable spirochetes derived from ticks fed on homologously infected hosts ( ≥ 105 organisms ) versus naïve or heterologously infected hosts ( ≤ 10 organisms ) . These results demonstrate that the immune status and infection history of the vertebrate host on which infected nymphs feed will strongly impact the pathogenic potential of the viable spirochetes they can transmit . OspC is an essential outer surface lipoprotein of B . burgdorferi that is required for spirochete survival at the initial stage of mammalian infection [32 , 33] . Expression of ospC is induced by environmental cues that spirochetes encounter in the midgut of feeding ticks and is a hallmark of a global adaptive response that prepares B . burgdorferi for host infection [7 , 34–36] . Therefore , as a measure of the host-adaptive response , we also assessed whether OspC was present on spirochetes in ticks fed on immune hosts . A subset of infected nymphs fed on naïve or infected WT mice were subjected to analysis by IFA , using a polyclonal anti-B . burgdorferi antiserum to visualize the spirochete population in dissected tick midguts , and a monoclonal antibody to identify the subset of host-adapted spirochetes producing OspC ( Fig 3 ) . Negative control images of an uninfected tick midgut , or infected midguts without primary or secondary antibodies , demonstrate specificity of antibody staining relative to background autofluorescence ( S2 Fig ) . All groups of infected ticks contained OspC+ spirochetes ( Fig 3 ) , irrespective of the naïve or infected status of their blood-meal hosts or stark differences in the infectious phenotypes of these spirochetes ( Table 1 ) . These data , coupled with the data in Fig 2 and Table 1 , demonstrate that the blood meal from an infected host neutralizes homologous spirochetes in the infected tick midgut without killing them , blocking their host-adaptive response , or eliminating OspC+ organisms . We next assessed whether the acquired immune response of infected mice was responsible for the observed neutralization of homologous spirochetes in fed ticks . Rag1 KO mice are incapable of mounting an adaptive immune response due to a lack of functional B and T lymphocytes . Hence , blood meals from both naïve and infected Rag1 KO mice lack antibodies to B . burgdorferi . Similar to previous experiments , we quantified the mean number of viable spirochetes per tick in cohorts of infected nymphs fed upon naïve , heterologously and homologously infected Rag1 KO mice and found that the infection status of the blood-meal host did not affect the spirochete burden in any group of ticks ( ~ 3–5 x104 CFU/tick ) ( Fig 4 ) . We then inoculated naïve wild-type mice with defined doses of spirochetes derived from infected nymphs fed on Rag1 KO mice , as in previous experiments , and monitored infection in recipient mice . No difference in infectivity was observed among spirochetes from ticks fed on naïve , heterologously or homologously infected Rag1 KO mice ( Table 2 ) . Some mice became infected with as few as 10 organisms from each source , and all mice ( 5/5 per group ) were infected with doses of 103 organisms or higher , regardless of the infectious status of the blood-meal host . This outcome contrasts sharply with what we previously observed with spirochetes in ticks fed on immune-competent WT mice ( Table 1 ) . To confirm that this outcome stemmed from the immune-deficient status of the Rag1 KO blood-meal host , and not differences in the genetic backgrounds of strains ( inbred C57Bl/6 , “B6” versus outbred Swiss-Webster , “RML” ) , we conducted a similar experiment using immune-competent WT B6 mice . As observed previously with immune-competent RML mice , the infectious phenotype of viable spirochetes in ticks fed on homologously infected WT B6 mice was severely attenuated ( 1/18 mice infected with doses ranging from 10 to 104 tick-derived spirochetes ) , whereas most mice were infected ( 32/36 ) with similar doses of spirochetes derived from ticks fed on naive or heterologously infected B6 mice ( S1 Table ) . We next performed a similar experiment using immune-deficient muMT- mice ( B6 background ) , which contain T cells , but lack mature antibody-producing B cells ( Table 3 ) . As with Rag1 KO mice , the infection history of the muMT- blood-meal host had no impact on the pathogenic potential of spirochetes in fed ticks . Some mice were infected at the lowest dose of 10 spirochetes from all 3 sources , and all mice were infected with doses of 103 and higher ( Table 3 ) . These results establish that antibodies , which are not present in blood ingested from muMT- mice , comprise the strain-specific neutralizing component of homologously infected host blood seen in previous experiments with wild-type mice . The previous experiments identify strain-specific antibodies as the component of host blood that neutralizes spirochetes within the midguts of infected nymphs . However , we questioned whether some aspect of the tick midgut environment might also contribute to spirochete neutralization . To address this possibility , we utilized highly infectious homogenates prepared from infected nymphs fed upon naive mice ( infectious dose of ~10 spirochetes , Table 1 ) . We briefly exposed aliquots of this tick homogenate to an equal volume of serum from naive or infected mice for 30 minutes at room temperature . We also incubated an aliquot of the same infected tick homogenate with PBS as an untreated control . We then injected naive mice with the equivalent of 103 organisms present in the original homogenate to assess the impact of serum treatment on spirochete infectivity and plated to determine the viability of spirochetes in the inoculum after incubation with serum or PBS . This ex vivo experiment fully reproduced the results of previous experiments with spirochetes derived from infected nymphs fed directly upon immune blood-meal hosts ( Table 4 ) . Spirochetes retained an infectious phenotype after incubation with PBS ( 5/5 mice infected ) or exposure to sera from naive or heterologously infected mice ( 9/10 mice infected ) , whereas infectivity was ablated ( 0/5 mice infected ) when the same tick homogenate was incubated with serum from an homologously infected host ( Table 4 ) . Plating confirmed that spirochete viability was not reduced by incubation with serum , irrespective of the immune status of the host from which they were obtained . This experiment demonstrates that strain-specific antibodies can directly attenuate the pathogenic potential of host-adapted spirochetes without impacting their viability or requiring exogenous tick factors . This brief exposure to serum does not allow sufficient time for a global adaptive response in gene expression and protein synthesis . Hence the mechanism of neutralization appears to be an immediate and direct consequence of antibody binding to the spirochetal outer surface . We directly compared the antibody responses of B31- and PKo-infected wild-type mice by immunoblot analysis with whole cell lysates of both strains , including clonal derivatives of strains B31 and PKo that make or lack OspC ( Fig 5 ) . Antibodies in sera of mice infected with either strain recognized multiple proteins in both lysates , indicating a fairly broad and cross-reactive immune response accompanying infection , but with stronger recognition of more proteins in lysates from the homologous versus heterologous strain ( Fig 5A , compare top and bottom panels ) . Significantly , OspC was only detected by homologous sera , indicating strict strain-specific recognition of this abundant surface protein by the polyclonal antibody response of B31- and PKo-infected hosts ( Fig 5B ) . This result is consistent with previous reports [37–40] and indicates that antibodies recognizing OspC could contribute to the observed strain-specific neutralization of spirochetes in the tick midgut , albeit without killing them . Finally , we determined the ELISA serum antibody titers of infected mice against whole cell lysates of both strains ( Fig 5C ) . Consistent with the immunoblot results , infection with either strain elicited a robust immune response , but with higher ELISA titers to homologous than heterologous strain lysates ( >1x105 vs 1–2 . 5x104 , respectively ) ( Fig 5C ) . The above analyses were conducted with whole-cell lysates of vitro grown organisms . It’s possible that cross-reactive protein antigens detected by immunoblot or ELISA analyses would not be accessible for antibody binding on intact spirochetes in the tick midgut . To address this possibility , we also compared antibody recognition of spirochetes in fed nymphs by IFA analysis , using infected mouse sera . Spirochetes in infected tick midguts stained brightly with both homologous and heterologous immune sera , whereas none were visualized by naïve mouse sera ( Fig 6 ) . This result indicates that spirochetes in the tick midgut are not “invisible” to immune recognition by the blood meal of a heterologously infected host , but that these cross-reactive antibodies do not impact the pathogenic potential of tick-borne spirochetes , whereas neutralization stems from strain-specific antibodies , such as those recognizing OspC . The above experiments assess the pathogenic potential of spirochetes in ticks fed on naïve and infected mice , but do not evaluate the outcome for these blood-meal hosts . Previous reports indicate that super-infection by tick bite only occurs when an immune ( infected ) host is challenged by ticks carrying a different B . burgdorferi strain [21 , 22 , 26 , 29 , 30 , 38] . We confirmed this outcome in our experimental system using unmarked strain PKo , and two infectious clones of strain B31 ( S9 and A3* ) that can be easily distinguished by their characteristic antibiotic resistance phenotypes . Primary mouse infections were established with either B31-S9 or PKo , as confirmed by seroconversion and isolation of spirochetes from ear punch biopsies . Infected mice were subsequently challenged by tick bite with nymphs infected with B31-A3* , and super-infection assessed several weeks later . The results were as predicted: all mice infected with strain PKo became super-infected with strain B31-A3* ( 10/10 ) , whereas none of the mice infected with B31-S9 ( 0/20 ) were super-infected following a parallel challenge with B31-A3* -infected ticks . These results , coupled with our previous experiments , lead us to conclude that strain-specific antibody recognition/neutralization of spirochetes occurs within the tick midgut prior to transmission and prevents host super-infection by the same B . burgdorferi strain .
We and others have previously shown that activation of B . burgdorferi for vertebrate infection initiates when spirochetes in the midgut of a feeding tick encounter host blood [7–11 , 32] . In the current study we demonstrate that the immune status of the host is a critical variable in the activation phenomenon we have termed “conditional priming”: blood from an infected ( immune ) host can also neutralize virulent spirochetes during tick feeding , but only when both vector and host are infected with the same B . burgdorferi strain , as depicted in Fig 7 . In nature , multiple strains of B . burgdorferi co-exist in the same endemic area , and a high proportion of reservoir hosts are infected , sometimes with multiple strains [13 , 14 , 16 , 18 , 19] . Previous studies have demonstrated that Lyme disease patients can become re-infected by a different B . burgdorferi strain following effective antibiotic therapy [24 , 30] . Our current study indicates that ingested host blood specifically targets and neutralizes ( without killing ) homologous spirochetes within feeding ticks , while enhancing the pathogenic potential of heterologous strains . OspC is a highly polymorphic surface protein that is recognized by strain-specific neutralizing antibodies in infected hosts [37 , 39–45] . Multiple ospC alleles are stably maintained in natural B . burgdorferi populations , presumably through some form of balancing selection [15 , 22 , 43 , 46–50] . The results of our current study indicate that rare ospC alleles would confer a fitness advantage in endemic populations , consistent with maintenance of the observed polymorphism of OspC through negative frequency-dependent selection . We have referred to “strain-specific” neutralizing antibodies , protective immunity , etc . , but the heterologous strains employed in the current study represent 2 distinct genospecies of the Lyme disease spirochete , B . burgdorferi and B . afzelii , with laboratory mice ( Mus musculus ) as the reservoir host . This experimental model resembles the enzootic cycle in Eurasia , in which several genospecies of Lyme disease spirochete co-exist and infect a variety of reservoir hosts , including wild M . musculus [18] . A single genospecies , B . burgdorferi , predominates in North America , with Peromyscus sp . as major reservoir hosts . However , diversity is stably maintained within local populations of B . burgdorferi in North America [14 , 15 , 20] , consistent with immune-mediated selection of polymorphic surface antigens among spirochetes of the same genospecies . Infected nymphs contained similar spirochete burdens after feeding on naïve or heterologously infected mice ( Figs 2 and 4 ) . This result demonstrates that spirochetes acquired during the nymphal blood meal contribute only slightly to the colonized tick midgut population . We noted a slight reduction in spirochete load when infected nymphs fed on homologously infected WT mice ( Fig 4 ) , which does not match the dramatic difference ( >10 , 000-fold ) in the infectious dose of viable spirochetes derived from these separate mouse/tick cohorts ( Table 1 ) . Theoretically , some component of the tick midgut could influence the pathogenic potential of spirochetes . However , all spirochetes compared in the current study were exposed to a similar tick midgut environment before and during feeding , irrespective of the infectious/immune status of the nymphal blood-meal host . Likewise , strain-specific neutralization of tick-derived spirochetes through in vitro exposure to immune sera ( Table 4 ) , directly demonstrates that ingested antibodies , not tick midgut components , attenuate the infectious phenotype of tick-borne spirochetes . As an essential surface component and hallmark of host-adapted spirochetes , neutralization of OspC+ spirochetes by the strain-specific host immune response could explain the depletion of infectious spirochetes in fed ticks . However , OspC+ spirochetes were detected in the midguts of ticks fed on homologously infected ( immune ) hosts ( Fig 3 ) . If strain-specific antibody recognition of OspC comprises the neutralizing element of host immunity , antibody binding must somehow obscure OspC’s essential function . Alternatively , additional surface components of B . burgdorferi could be targets of neutralizing immunity , as infected mouse sera recognize a number of proteins in a strain-specific fashion ( Fig 5 ) . It is puzzling that genetically identical , wild-type spirochetes in the midgut of an infected tick do not uniformly undergo a host-adaptive response with respect to OspC induction during tick feeding ( Figs 3 and S3 ) [51–53] . This could reflect exposure of individual spirochetes to different cues in the local tick midgut micro-environment , but spirochetes that lack OspC are not infectious , even when successfully transmitted to a naïve host [32 , 33 , 51] . Perhaps this seemingly imperfect genetic program confers a fitness advantage under negative frequency-dependent selection , as our current study demonstrates that host-adapted , infectious spirochetes are selectively neutralized when ticks ingest blood from an immune host carrying the same strain ( ospC allele ) . A previously marketed Lyme disease vaccine for humans ( Lymerix ) [54] targeted OspA , which is a relatively well-conserved surface component of spirochetes colonizing the tick midgut [7 , 52 , 55 , 56] . Vaccination with OspA elicits antibodies that recognize spirochetes in the tick midgut and prevent transmission [57–60] . Although OspA represents an effective vaccine target , conditional priming of spirochetes during tick feeding results in down-regulation of OspA and induction of OspC , as required for host infection and subsequent larval acquisition [32 , 52] . Consistent with this scenario , B . burgdorferi-infected mice do not make antibodies against OspA , and hence OspA is not a target of strain-specific neutralizing immunity in the current study . OspC has also received attention as a vaccine candidate because it is an indispensable and abundant surface component of the Lyme disease spirochete that naturally elicits a strong neutralizing immune response during host infection [42 , 61–66] . However , the high degree of OspC polymorphism among B . burgdorferi strains in local endemic regions , and resulting strain-specific immune response , present a substantial challenge to an OspC-based vaccine [15 , 40 , 67 , 68] . These challenges were addressed by Earnhart and colleagues with a multivalent chimeric Lyme disease vaccine that incorporates neutralizing linear epitopes from multiple OspC types [69] . These investigators have recently proposed to include a linear epitope of OspA that elicits bactericidal antibodies , but does not encompass a putative autoimmune epitope [70] . A vaccine targeting both OspA and OspC would theoretically neutralize both host-adapted ( OspA- , OspC+ ) and uncommitted ( OspA+ , OspC- ) spirochetes in the tick midgut , prior to transmission to a mammalian host . It is well established that the Lyme disease spirochete must undergo an adaptive response during tick feeding in order to infect the vertebrate host [6–11] . We previously found that transmission of B . burgdorferi during the nymphal blood meal represents a bottleneck through which only a random subset of infectious tick-borne spirochetes can pass and successfully infect naïve hosts [71] . In the current study we demonstrate that the immune response of an infected vertebrate host specifically ablates the infectious phenotype of homologous spirochetes within feeding ticks , prior to transmission and without killing them . These findings provide insight into the evolutionary processes that shape the natural diversity of the Lyme disease spirochete and expose a point in the transmission cycle that is inherently restricted and highly vulnerable to the vertebrate immune response .
The Rocky Mountain Laboratories , National Institute of Allergy and Infectious Diseases , National Institutes of Health , Animal Care and Use Committee ( RML , NIAID , NIH , IACUC; USDA Permit Number: 51-F-0016 Customer #441 , PHS number: A-4149-01 ) approved study protocols for work conducted in strict accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All infection studies were performed in an Animal Biosafety Level 2 facility according to protocols reviewed and approved by the RML Institutional Biosafety Committee and the RML IACUC . All work in this study adhered to the institution’s guidelines for animal husbandry , and followed the guidelines and basic principles of the Public Health Service Policy on Humane Care and Use of Laboratory Animals . Mice were anesthetized by isoflurane inhalation prior to inoculation or blood withdrawal . Mice were sedated with a ketamine/xylazine cocktail for tick feeding experiments . Mice were euthanized by isoflurane inhalation followed by cervical dislocation . Strains B31-A3-68Δe02 ( S9 ) and B31-A3-lp25Gm ( A3* ) are infectious clonal derivatives of B . burgdorferi sensu strictu type strain B31 ( ATCC35210 ) [1 , 72 , 73] . Strains B31-S9 and B31-A3* carry antibiotic resistance cassettes that permit growth in streptomycin or gentamicin , respectively , and differ in the presence ( A3* ) or absence ( S9 ) of endogenous restriction/modification systems [73] . Wild-type ( WT ) strain PKo is an infectious B . afzelii strain originally isolated from human skin [74] . Spontaneous B31 and PKo variants that no longer make OspC were used as control lysates on some immunoblots . Liquid cultures were inoculated from frozen stocks and propagated with minimal passage in Barbour-Stoenner-Kelly II ( BSK II ) medium containing gelatin and 6% rabbit serum , and supplemented with 50μg/ml streptomycin ( S9 ) or 40μg/ml gentamicin ( A3* ) , where appropriate . Viable spirochetes were quantified as colony forming units ( CFUs ) in solid BSK medium incubated at 35°C with 2 . 5% CO2 [75] . Immune-competent RML mice are derived from a colony of Swiss-Webster mice established at NIH in 1935 and maintained at the Rocky Mountain Laboratories with an outbreeding program designed to intentionally maintain genetic diversity . RML mice reject autologous grafts , demonstrating MHC diversity within the colony . Immune-competent C57BL/6J ( "B6" ) and immune-deficient B6 . 129S7-Rag1tm1Mom/J ( "Rag1 KO" ) and B6 . 129S2-Ighmtm1Cgn/J ( “muMT- ) inbred mice were obtained from Jackson Laboratories , Bar Harbor , ME . C57Bl/6 is a non-albino inbred mouse strain that is not derived from Swiss-Webster mice . Mice ( six to eight weeks old ) used in tick feeding experiments were infected by needle inoculation with a dose of approximately 104 spirochetes ( 8×103 intraperitoneally and 2×103 subcutaneously ) . Spirochetes were enumerated using a Petroff-Hausser chamber and diluted in BSK II for the inocula . Mice were bled three weeks after injection and assessed for seroconversion , as described below . All experiments assessing the infectivity of spirochetes in tick homogenates by needle inoculation of naïve mice , as described below , were conducted with homogenates prepared from B31-S9-infected nymphs fed upon naïve , PKo-infected ( heterologous ) or B31-S9-infected ( homologous ) mice . The subset of experiments addressing mouse super-infection by tick-bite challenge was conducted with B31-A3*-infected nymphs fed upon PKo-infected ( heterologous ) or B31-S9-infected ( homologous ) mice . Whole-cell lysates of B31-S9 were separated by electrophoresis through 12% polyacrylamide gels and transferred to nitrocellulose membranes . Blots were blocked with 5% nonfat milk in TBST ( Tris-buffered saline , 0 . 1% Tween 20 ) for 1 h at room temperature , followed by incubation with mouse sera ( 1:200 dilution in TBST ) at 4°C overnight . Blots were washed 3 times with TBST , 15 minutes per wash , and then incubated for 1–2 h with peroxidase-conjugated sheep anti-mouse IgG or goat anti-mouse polyvalent immunoglobulins ( 1:10 , 000 dilution in TBST ) ( Sigma-Aldrich , St Louis , MO ) . Blots were again washed 3 times in TBST before incubation with enhanced chemiluminescent reagents ( SuperSignal , Pierce Thermo Scientific , Rockford , IL ) and exposure to X-ray film . Multiple proteins were recognized by sera of infected mice , while no bands were present on immunoblots using sera from pre-immune or uninfected mice . A typical immunoblot with infected ( seropositive ) and uninfected ( seronegative ) sera is shown in S1 Fig . The relative serum antibody titers of infected WT mice against homolgous or heterologous strains were determined by enzyme-linked immunosorbent assay ( ELISA ) . 5 mice per group were injected with ~104 strain B31-S9 or strain PKo spirochetes . Pre-immune sera was obtained from all mice prior to injection and infected sera obtained 3 weeks after inoculation . Pooled sera from all 5 mice in each group was used to determine antibody titers before and after infection . Stationary phase cultures of B31-S9 and PKo were harvested by centrifugation , washed twice in phosphate-buffered saline ( PBS; pH 7 . 4 ) and resuspended in PBS at 1/10th of the original culture volume . Whole cell lysates of spirochetes were prepared by sonication of PBS suspension on ice using a Heat Systems Ultrasonic Processor ( XL-2015 ) sonicator ( Misonix , Farmingdale , NY ) at 40% amplitude with four repetitions of 20 s each . The total protein concentration of B31-S9 and PKo lysates was estimated using Bradford reagent ( Sigma-Aldrich ) and normalized to 1 mg/ml with PBS . Immulon 2 HB 96-Well ELISA plates ( Thermo Fisher Scientific , Bothell , WA ) were incubated overnight at 4°C with 1 μg of B31-S9 or PKo lysate per well in coating buffer ( 0 . 1 M carbonate-bicarbonate buffer , pH 9 . 6 ) . Non-specific binding sites were blocked with 5% non-fat dry milk in PBS containing 0 . 2% Tween-20 ( PBST ) . Serial dilutions of each pooled sera ( 100 μl/well ) were added to triplicate wells in blocking buffer and incubated for 2 h at 37°C . HRP-conjugated rabbit anti-mouse IgG was then added to each well ( 1:10 , 000 ) and incubated at 37°C for 1 h . Wells were washed several times with PBST and color was developed using ABTS 2-Component Microwell Peroxidase Substrate Kit ( SeraCare , Milford , MA ) . The reaction was terminated by the addition of 1% SDS in water and absorbance measured at 405 nm with an ELISA plate-reader ( Labsystems Multiskan Plus , Thermo Fisher Scientific ) . Baseline absorbances were determined for each dilution of pooled pre-immune sera , in triplicate , against B31-S9 and PKo lysates . The threshold for positive sero-reactivity was set at 3 standard deviations above the mean absorbance of pre-immune serum at each dilution . The titer of infected mouse sera against heterologous and homologous strains represents the highest dilution at which absorbance above this baseline cut-off was achieved . Infected RML mice were infested with 100–150 larval Ixodes scapularis ticks ( Oklahoma State University ) . Fully engorged larvae were collected daily as they dropped off the host . Eight to ten days later , several fed larvae per mouse were mechanically disrupted as described below , and cultured in BSKII medium to confirm colonization of 80–100% of ticks . The remainder of each infected larval cohort was allowed to molt into nymphs . Cohorts of strain B31-infected nymphs were allowed to feed to repletion on naïve or infected mice ( ~20 nymphs/mouse ) and collected at drop-off . Several ticks from each mouse were used in IFA analyses , as described below and shown in Figs 3 and S3 . The remaining fed nymphs for each mouse were pooled , surface-sterilized by sequential immersion for 5 minutes in 3% hydrogen peroxide and 70% ethanol , and homogenized in BSK medium in sterile 1 . 5 ml microfuge tubes with disposable plastic pestles ( Kimble Chase , Rockwood , TN ) . Aliquots of pooled tick homogenates for each mouse were plated to enumerate viable bacteria as CFU and the remainder frozen at -80°C . The average spirochete burden per tick for each pool of ticks fed on an individual mouse was estimated , as shown in Figs 2 and 4 . Enumeration of viable spirochetes as CFU was repeated when tick homogenates were thawed , serially diluted , and inoculated into naive RML mice , as described below . The relative infectious dose or pathogenic potential of spirochetes in tick homogenates was assessed in several independent experiments . In each experiment , groups of 3 to 6 naïve wild-type RML mice received 10-fold-increasing doses of tick-derived spirochetes , ranging from approximately 10 to 105 organisms per mouse . Mice were bled and euthanized 4 weeks after inoculation , and ear , bladder , and ankle joint tissues were harvested and incubated in BSKII medium . Mouse infection was determined by seroconversion and isolation of spirochetes from tissues ( Tables 1–4 , S1 ) . A representative immunoblot with positive and negative sera is shown in S1 Fig . Aliquots of a highly infectious homogenate prepared from B31-infected nymphs fed upon naive mice ( infectious dose ~10 spirochetes , Table 1 ) were exposed to equal volumes of sera from naive or infected mice for 30 minutes at room temperature . An aliquot of the same infected tick homogenate was incubated with PBS as an untreated control . 103 organisms of each treated homogenate were injected into naïve mice to assess the impact of serum exposure on spirochete infectivity , as described above . A dilution of each inoculum was also plated to confirm the viability of spirochetes after incubation with serum or PBS . Spirochetes in dissected tick midguts were visualized by immunofluorescence assay ( IFA ) on a fluorescent microscope ( 20X magnification ) with a polyclonal rabbit anti-B . burgdorferi primary antiserum and a TRITC-labeled secondary antibody ( Kirkegaard & Perry Laboratories , Gaithersburg , MD ) , while synthesis of OspC by these spirochetes was examined using a mouse monoclonal anti-OspC primary antibody {provided by Robert Gilmore; [61]} and a FITC-labeled secondary antibody ( Kirkegaard & Perry Laboratories , Gaithersburg , MD ) . Specificity of antibody staining was confirmed using uninfected tick midguts or by omission of primary or secondary antibodies ( S2 Fig ) as negative controls . Differences in the outcome of infection in mice ( Tables 1 , 2 , 3 , 4 and S1 ) were analyzed by Fisher exact test and P values < 0 . 05 were considered significant . Spirochete burdens in ticks ( Figs 2 and 4 ) were analyzed using the GraphPad software PRISM 7 and P values were calculated using non-parametric rank order test . | Lyme disease is a tick-borne infection of humans that is caused by a spirochetal bacterium called Borrelia burgdorferi . It is a zoonosis , which means that these bacteria exist in nature outside of people . Many different strains of B . burgdorferi are stably maintained in the same local population of infected wild animals and ticks . Once infected , people and animals are immune to re-infection by the same strain , but can become infected with a different B . burgdorferi strain . We previously assumed that factors in the blood of an immune host recognized closely related bacteria and neutralized them after they were transmitted by a feeding tick . However , in the current study we found that this actually occurs within feeding ticks , even before the Lyme disease spirochete enters the immune host . Conversely , when ticks and animals are infected with different strains of B . burgdorferi , blood ingested by feeding ticks enhances the spirochete’s ability to re-infect the host . These findings provide insight into the evolutionary processes that shape the natural diversity of the Lyme disease spirochete and expose a highly vulnerable stage within the tick vector for targeting effective vaccines . They also identify a way to test whether a potential Lyme disease vaccine would protect against tick-borne spirochetes . | [
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] | 2018 | Infection history of the blood-meal host dictates pathogenic potential of the Lyme disease spirochete within the feeding tick vector |
Alternative splicing of genes is an efficient means of generating variation in protein function . Several disease states have been associated with rare genetic variants that affect splicing patterns . Conversely , splicing efficiency of some genes is known to vary between individuals without apparent ill effects . What is not clear is whether commonly observed phenotypic variation in splicing patterns , and hence potential variation in protein function , is to a significant extent determined by naturally occurring DNA sequence variation and in particular by single nucleotide polymorphisms ( SNPs ) . In this study , we surveyed the splicing patterns of 250 exons in 22 individuals who had been previously genotyped by the International HapMap Project . We identified 70 simple cassette exon alternative splicing events in our experimental system; for six of these , we detected consistent differences in splicing pattern between individuals , with a highly significant association between splice phenotype and neighbouring SNPs . Remarkably , for five out of six of these events , the strongest correlation was found with the SNP closest to the intron–exon boundary , although the distance between these SNPs and the intron–exon boundary ranged from 2 bp to greater than 1 , 000 bp . Two of these SNPs were further investigated using a minigene splicing system , and in each case the SNPs were found to exert cis-acting effects on exon splicing efficiency in vitro . The functional consequences of these SNPs could not be predicted using bioinformatic algorithms . Our findings suggest that phenotypic variation in splicing patterns is determined by the presence of SNPs within flanking introns or exons . Effects on splicing may represent an important mechanism by which SNPs influence gene function .
The sequencing of the human genome [1 , 2] and subsequent work describing sequence variation amongst human populations [3] has provided the necessary resources for large-scale studies of the effects of genetic variation on human gene expression . Identifying functionally important variation has the potential for increasing understanding of gene regulation and for providing efficient markers to study the effects of variation in gene expression on human disease risk [4] . Experimentally demonstrating the potential functional effects of DNA polymorphism is difficult , as these effects may be both tissue and stimulus specific . Significant efforts have focused on transcriptional regulation , because of the strong suspicion that the majority of human phenotypic variation is due to regulatory variants [5 , 6] . Novel allele-specific transcript quantification approaches to candidate genes [7 , 8] have been employed , along with broader approaches to investigate the absolute levels of expression of thousands of genes [9 , 10] . Using these methods , several cis-acting SNPs that correlate with gene expression have been identified . However , fine mapping these effects and determining the mechanisms underlying the associations has been more difficult [11] . In this study , we used a different approach—that of evaluating effects on splicing efficiency—to study the effects of common genetic polymorphism on gene function . The vast majority of human genes are comprised of three or more exons that need to be efficiently spliced together to form mature mRNA . Variation in this process occurs naturally and is thought to be an important mechanism whereby different protein products can be derived from the same gene sequence [12] . Single base changes that affect splicing can have dramatic effects on gene function and can cause disease , usually because the splice mutation results in a shift in the amino acid reading frame . Most commonly observed alternative splicing events preserve the reading frame and have more subtle effects on protein function [13] . There are an increasing number of examples in which the genetically determined modulation of alternative splicing has been implicated in common complex disease traits , such as the associations between the G protein-coupled receptor ( GPRA ) and asthma susceptibility [14] , cytotoxic T lymphocyte antigen 4 ( CTLA4 ) and autoimmune disease [15] , and the CD45 ( leucocyte common ) antigen and infectious and autoimmune diseases [16 , 17] . The potential effects of common SNPs on splicing isoforms have been suggested by bioinformatic analysis of expressed sequence tags [18] . In a small number of genes , these potential effects have been demonstrated experimentally [19–21] . Here , we used lymphoblastoid cell lines ( LCLs ) from the Centre d'Etude du Polymorphisme Humain ( CEPH ) as an experimental model system to investigate the relationship between variation in simple cassette exon splicing events and genotypic diversity . We sought to determine ( 1 ) whether individual variation in splicing patterns was commonly observed , ( 2 ) if any observed phenotypic variation could be explained by genetic differences among individuals , and ( 3 ) whether any genetic differences could be localised and the functional element identified .
Our initial aim was to investigate whether there was variation among individual LCLs in simple cassette exon events . These events were defined as the occurrence of complete exon skipping in two or more mRNA isoforms . We used a strategy of exon selection that we believe increased the likelihood of detecting allele-specific effects on alternative splicing . We argue that for genes in which common SNPs affect splicing , at least two mRNA transcript isoforms of that gene will be relatively commonly observed . Conversely , where only one transcript isoform has been observed and documented , the likelihood of a SNP-related splicing event is reduced . We identified 2 , 281 simple cassette exon events from the European Bioinformatics Institute Alternative Splicing Database ( EBI-ASD ) in which each transcript isoform had been observed in at least two clone libraries . From these , we selected the 250 genes with the highest expression levels in LCLs as detected by global microarray analysis . We carried out reverse transcriptase PCR ( RT-PCR ) analysis of these 250 genes and found that in LCLs both transcript isoforms were present in 70 ( 28% ) of the genes . We proceeded to investigate whether the amount of different isoforms varied between 22 different LCLs . Of the 70 events that produced both full-length and exon-skipped products , we found that 18 ( 26% ) showed significant variation among cell lines , in which at least one cell line showed a ratio of PCR products that differed by more than 10% of the mean value for the entire sample set of 22 cell lines ( 10% difference in relative abundance is the lower limit of sensitivity of the detection assay ) . These 18 events were retested using RNA derived from an independent round of cell culture . Six events , centered around genes CASP3 , CD46 , IFI16 , RBM23 , SH3YL1 , and ZDHHC6 , demonstrated repeatable and consistent variation of the splicing pattern among different cell lines . The genes and exons for each of these six events are listed in Table 1 . None of the skipped exons resulted in a shift in the reading frame of the mRNA . We did not investigate the remaining 12 events; these provided inconsistent results , as splicing isoforms were present only at very low intensity or in only one or two cell lines . We next investigated the relationship between DNA sequence variation and observed differences in splice isoforms among LCLs . We looked at the correlation between SNP genotype and splicing pattern over the 500-kb region surrounding each of the six splicing events that showed consistent variation among the LCLs . Two sources of SNP genotyping data were used . First , we analysed SNP genotypes from the International HapMap Project [3] . Second , we resequenced the skipped exons and 150 bp of the flanking introns for each event in each of the 22 cell lines . Resequencing did not identify any SNPs that were not already identified on the HapMap resource . Approximately 350 SNPs were available for each gene at an average density of 0 . 7 SNPs per kb . For each of the six events , highly significant correlations between SNPs and the observed splicing pattern were identified ( Figure 1 ) . The maximum values for the Pearson's statistic were 0 . 76 ( p < 10−4 ) for the CASP3 event and over 0 . 86 ( p < 10−6 ) for the other five events . For five of the six events , the SNP nearest the intron–exon boundary showed the strongest correlation with splicing pattern . For the ZDHHC6 event , a slightly higher value for the Pearson's statistic was seen for a group of three SNPs lying over 50 kb away from the gene and in very strong linkage disequilibrium ( LD ) ( R2 = 0 . 95 ) with the SNP nearest the intron–exon boundary . When we studied the ZDHHC6 SNP nearest the intron–exon boundary in the minigene system ( see below ) , we observed a direct effect of this SNP on splicing efficiency , suggesting that this SNP , rather than the more distant group of three SNPs , was responsible for the observed variation in splice pattern . To test whether the identified SNP accurately predicted the variation in splice pattern , we selected a new set of nine unrelated LCLs in which there was at least one example of each of the possible SNP genotypes . For each of the six genes , the splicing pattern observed was accurately predicted by the SNP genotype ( Figure S1 ) . The correlations between splice pattern and individual SNPs are highly significant even after allowing for correction for multiple comparisons . If we use a simple Bonferroni correction for the 350 SNPs that were tested for each simple cassette exon event , all results remain significant at the 0 . 05 level . This level of correction is overly conservative , since the LD relationship among the SNPs means that they are not independent of one another . Furthermore , it is remarkable that for five of the six events it is the SNP closest to the intron–exon boundary that is the strongest predictor of splicing phenotype . When we analysed the effects of the SNP nearest the intron–exon boundary of each event , a clear effect of genotype on relative abundance of each product was found . The measured ratios of the two splice products are plotted by genotype in Figure 2 . The magnitude of allele-specific effect was similar for each gene and represented an approximately a 2-fold additive effect on the ratio of splice isoforms . In four out of the six events , the minor allele was associated with an increased abundance of mRNA with the exon-skipping event . For the other two ( RMB23 and ZDHHC6 ) , the minor allele was associated with an increased abundance of the full length mRNA . These data suggest that cis-acting variation is directly modulating the pattern of observed alternative splicing at these loci . For five of the six events , there is an apparent dose-dependent effect with larger effects seen in homozygotes compared with heterozygotes . For the CASP3 event , the effect of the SNP on relative abundance of the two transcript isoforms was only seen in the homozygous state . The effect of the CASP3 SNP in the homozygous state was nevertheless clear-cut and repeatable . We were puzzled as to why we were unable to detect an effect when the SNP was present on only one chromosome since a cis-acting mechanism of action seems most likely . One possible explanation is that when only one chromosome carries the splicing SNP , up-regulation of expression from the other chromosome compensates for the loss of the full-length product . To test this hypothesis , we quantified the relative abundance of CASP3 transcripts derived from each chromosome in LCLs from 16 unrelated CEPH individuals heterozygous for the rs4647603 CASP3 exonic SNP . These experiments showed that all 16 heterozygous individuals had a higher relative abundance of transcripts containing the rs4647603 G allele compared to those with the A allele ( on average 2 . 7 times more G than A , Figure S2 ) . In the homozygous state , the A allele is associated with increased exon skipping . In the heterozygous state , the relative proportions of the full-length and exon-skipped products appear unchanged . The higher relative abundance of CASP3 transcripts containing the rs4647603 G allele suggests increased expression of CASP3 derived from this chromosome and is consistent with an effect of the rs4647603 A allele on splicing in the heterozygote state . Splice site signal scores from the donor and acceptor sites of the test exons predicted to show alternative splicing were compared with those from a genome-wide set of constitutively spliced exons ( Figure 3 ) . As a group , the test exons had significantly ( p = 1 × 10−5 ) weaker splice site signal scores than those from constitutive exons . The difference was greatest for the exons in which alternative splicing was experimentally demonstrated . The effect was seen in both donor and acceptor sites and was slightly more pronounced at the donor sites . Although the differences in splice site strength were statistically significant , there was extensive overlap in splice site scores between the groups ( Figure 3 ) . The potential effects on exonic splice enhancer strength of the four exonic SNPs shown to correlate with splice pattern were tested using four different prediction algorithms ( see Materials and Methods ) . For two SNPs , no effects were predicted by any of the four models tested . For the other two SNPs , the results were contradictory ( different models showed both increased and decreased splice enhancer activity ) . There were no differences in the number of SNPs in the 50-bp regions around the intron–exon junction for the 180 exons that did not show alternative splicing in our experimental model , compared to the 70 exons that did . This suggests that using the position of known SNPs to select for exons with allele-specific splicing patterns is unlikely to be fruitful . Furthermore , of the six exons that showed allele-specific splicing patterns , three showed splicing patterns that were correlated with SNPs situated more than 50 bases from the intron–exon junctions . To investigate whether SNP genotype directly defined splice isoform pattern , we carried out minigene analysis in two genes . In ZDHHC6 , the test SNP was situated in the middle of the exon , 99 bp away from the intron–exon boundary . In SH3YL1 , the test SNP was also exonic , but in this case only 2 bp away from the intron–exon boundary . For each gene , we independently cloned two fragments that differed only by the alleles of the SNP correlated with exon skipping . Each fragment consisted of the alternatively spliced exon plus 180 bp of intronic sequence on each side . The fragments were inserted into a minigene splicing vector that was used to transfect HEK293T cells . After 48 h , mRNA was extracted from the cells and the relative abundance of mRNA ( full length and alternative spliced ) transcripts derived from the minigene plasmid was determined . For both genes we observed that the SNP allele associated with increased exon skipping in the LCL experiments was also associated with increased exon skipping in the minigene system ( Figure 4 ) . For four genes ( SH3YL1 , ZDHHC6 , IFI16 , and RNPC4 ) there were between four and ten SNPs identified on the HAPMAP resource that were in complete LD with the SNP nearest the intron–exon boundary . All but one of these SNPs lay more than 2 kb away from the exon of interest and are not testable using the minigene system . We cannot discount an effect of these more distant SNPs on the observed allele-specific splicing event .
This study describes reproducible phenotypic variation in splicing among individuals , in each case arising from a simple cassette exon event that is associated with genotypic variation in SNPs close to the corresponding intron–exon boundaries . Our starting point was to screen for phenotypic variation in splicing in 22 lymphoblastoid cell lines , and then to identify SNPs associated with this phenotypic variation . Interestingly , the splicing-associated SNPs identified experimentally in this study did not show any clear difference in position or sequence context from other SNPs that were not associated with splicing variation . The mechanisms by which alternative splicing is regulated are poorly understood . Exon recognition and splicing requires the presence of basic “classic” splice sites ( the branch point , polypyrimidine tract , and the 3′ and 5′ splice sites ) . The efficiency of the splicing process can be affected in some exons by the presence of auxiliary or modulating elements ( Figure 5 ) . The consensus sequences for the known modulating elements are degenerate and frequently found throughout the genome . DNA sequence variation can modulate alternative splicing , and to date attention has focused on disease-causing cis-acting mutations affecting the use of constitutive and alternative splice sites , together with trans-acting variants that affect the basal splicing machinery and factors regulating splicing [22] . In contrast to mechanistic studies of disease process , our study started from the premise of defining simple cassette exon events in which there was significant variation among a panel of LCLs and relating this to genotypic diversity . We found consistent variation in six out of 70 simple cassette exon events , and for each of these six events we found a clear relationship between genotype and splice phenotype . Analysis of SNPs typed by the International HapMap project and those derived experimentally by resequencing showed that the SNPs with the strongest correlation were those closest to the intron–exon boundaries of the splicing events . For two of the SNPs we carried out minigene experiments , and both showed cis-acting effects on gene splicing using this system . It is perhaps not surprising that we were unable to detect any specific patterns in the sequence context of the six SNPs identified in this study , given the apparent degenerate nature of consensus sequences that bind splice modulator proteins . Overall the splice-site strengths of the exons that were predicted to be skipped by the EBI-ASD database were weaker than those of constitutive exons , and those that we were able to demonstrate to have alternative splicing in our experimental system had the weakest splice site strength . However , there was significant overlap among the groups , and splice site strength cannot be used to identify the most likely exons to study . Equally , the presence of SNPs close to the intron–exon boundaries did not differ between those exons that did and did not show alternative splicing , suggesting that selecting exons to study according to whether there is a “splice site SNP” ( defined for example on Ensembl as a SNP lying within 10 bp of the intron–exon junction ) will not enrich for those SNPs that actually affect the splicing process . Only two out of the six SNPs identified in this study were within 10 bp of an intron–exon junction . The exonic SNPs that correlated with splice pattern in this study showed no consistent effects on splice enhancer strength using four different predictive models . Thus , the sequence context or position of the SNPs would not identify those likely to influence splicing efficiency . A different approach to identify allele-specific alternative splicing events that does not rely on the sequence context or the position of SNPs is to identify allele-specific RNA isoforms from EST databases [18] . This approach requires the presence of an exonic SNP not involved in the alternative splicing event to be in high LD with the functional splicing SNP and limits its broad applicability . When applied to our data using HAPMAP SNPs , only the events in ZDHHC6 and RBM23 have the potential of being identified . The EST method is prone to false-positive results , particularly for low frequency SNPs , if there are insufficient representative ESTs available in the database . We suggest that an experimental approach , rather than a bioinformatic approach , will be necessary to identify splicing phenotype-associated SNPs , at least until more is learned about how these SNPs exert their functional effects . We believe that identifying alternative splicing events is the essential first step in this experimental approach . While splice enhancers and suppressors are found in constitutive exons and their flanking introns , these exons by definition are not observed to show alternative splicing . This suggests either that no SNPs occur within functionally important splice elements or that the splice enhancer/suppressor signals are not required for the accurate splicing of these exons . The relative positions and sequence context of experimentally identified splicing SNPs can be used to refine predictive algorithms and may provide new insights into which of the many exonic and intronic splice modulator sequences present in every gene are functionally important in regulating the splicing process . Our method of isoform quantification and pooling strategy meant that our ability to detect rare events was limited . Dilution experiments determined that both the full-length and exon-skipped transcript products were detectable even when their starting concentrations differed by 100-fold . Thus , provided that both transcripts were present in at least one of the 22 cell lines , and the minor transcript was present at an abundance of 30% or greater , the event would be detected . If the rare transcript was present in three or more cell lines , the sensitivity increased to a lower abundance of 10% . The method we used is not readily scalable to whole genome analysis . Microarray-based approaches to the analysis of alternative splicing have been published [23 , 24] . These approaches can analyse the splicing patterns of many thousands of exons and have been used to distinguish splicing patterns seen in different tissues . Interpretation is complex , and for some arrays sensitivity is low and false positive rates are high . Although it is likely that the technology will improve , these approaches have not yet been shown to have the sensitivity to detect the level of variation we observed in this study , particularly for low-abundance isoforms . The advantage of the system we describe is targeted amplification of the splicing event of interest , which we believe provides greater sensitivity . Nevertheless , use of an array-based approach is likely to become the most efficient method to identify allele-specific splicing effects at a whole genome level . For the splicing phenotypes , our experiments using the minigene system suggest that the SNP closest to the intron–exon boundary that shows correlation with the splicing phenotype is very likely to be the functional element . For four of the genes in this study there were additional SNPs in complete LD with the SNP nearest the intron–exon boundary , and although most were over 2 kb away from the exon-skipping event it is possible that the presence of these SNPs influence the splicing process . Further work is needed to define the consequences of the loss of these exons on the functional activities of the encoded protein isoforms and in the levels of expression . There is already evidence that biological consequences of the alternative splicing event we describe in CD46 are likely to be important . CD46 is a cell-surface glycoprotein involved in regulation of complement activation and it acts as a receptor for several pathogens including measles virus , Streptococcus pyogenes , Neisseria gonorrhea , and Neisseria meningitidis [25] . CD46 is known to have two protein isoforms with distinct cytoplasmic tails of 16 or 23 amino acids generated by alternative splicing of exon 8 [26] . These different tails have pivotal effects on the intracellular precursor processing of , and signal transduction by , the CD46 protein [26–28] . We have demonstrated that the inclusion of exon 8 is strongly associated with the presence of a nearby SNP ( rs2724374 ) , and whether or not this SNP is directly functionally responsible for the pattern , rs2724374 is a genetic marker for what appears to be an important functional protein isoform . Variants of CD46 have been associated with outcome in hemolytic uremic syndrome [29] , but genetic association studies using the rs2724374 SNP have not been reported . For CASP3 we have shown that , in individuals who are heterozygous for the splicing SNP rs4647603 , there appears to be compensatory upregulation of expression of the full-length CASP3 isoform derived from the other chromosome . This suggests some functionally important difference between the two CASP3 isoforms . The consequences of the allele-specific splice events we have defined are summarised in Table 2 . In this study we focused on only one form of splicing variation in a relatively small number of genes . Larger-scale whole genome studies investigating additional splicing patterns , such as alternative donor and acceptor sites , will be needed to determine the extent of SNP-associated splicing phenotypes . Our findings raise the possibility that SNP effects on splicing may be at least as prevalent in the genome as those on overall gene expression [11] . SNPs that predict splicing phenotypes are likely to be important markers to include in genetic association studies of complex diseases .
A number of different publicly available databases of observed mRNA transcripts are available . We used the EBI-ASD ( www . ebi . ac . uk/asd ) , which is a database of computationally delineated alternative splice events derived from alignments of expressed sequence tags or cDNA sequences with the corresponding genomic sequences for each gene . Using this resource , we identified transcripts where at least two isoforms are detected in which complete exons ( called simple cassette exon events on the EBI-ASD ) are skipped . These events generally result in transcripts that differ sufficiently in size to be readily distinguished by simple agarose electrophoresis . Primers were designed in the flanking exons , and product sizes for the full length and exon-skipped products were calculated . LCLs from 22 unrelated CEPH individuals selected from the HapMap collection were obtained from the Coriell Institute for Medical Research . Cells were cultured at 37 °C in a 5% CO2 environment using RPMI 1640 cell culture medium with 10% fetal calf serum , 200 mM L-glutamine , penicillin , and streptomycin . Cell density was maintained between 200 , 000 and 800 , 000 cells/ml . DNA and RNA were each extracted from 10 million cell aliquots . Constitutive expression levels in CEPH cell lines were defined for pooled RNA from four LCLs using an Affymetrix human U133A expression microarray ( Affymetrix , http://www . affymetrix . com ) . RNA was extracted from cell pellets using TRIREAGENT ( Sigma-Aldrich , http://www . sigmaaldrich . com ) , chloroform , isopropanol , and ethanol precipitation . Total RNA was quantified using UV spectrophotometry . mRNA was extracted from 20 μg of total RNA aliquots using the Dynabeads mRNA purification kit ( Invitrogen , http://www . invitrogen . com ) , and was cDNA synthesised using Stratascript reverse transcriptase ( Stratagene , http://www . stratagene . com ) with oligo ( dT ) primers . 1 μl of cDNA was derived from 100 ng of total RNA . Parallel reverse transcriptase negative controls were generated in all cDNA syntheses . PCRs were carried out at standard conditions ( 30 cycles , melting at 94 °C , annealing at 58 °C , and extension at 72 °C , each for 30 s ) using BioTaq DNA polymerase ( Bioline , http://www . bioline . com ) . Primers were designed in the exons flanking the simple cassette exon event . Two products of different lengths were predicted to be amplified , one including the cassette exon and a shorter product lacking the cassette exon . The products were resolved on 2% agarose gels . Pooled cDNA from all 22 cell lines was used to test each set of primer pairs . Identification of the expected full length product and the shorter product lacking the cassette exon ( and no other products ) was used to confirm that the predicted alternative splicing event was detectable in our experimental system . Primer sets showing the two expected RT-PCR products were subsequently taken forward to determine if there was variation in the proportion of the two products among different individual cell lines . Detection of variation among cell lines was carried by performing RT-PCR on RNA from each cell line separately . For each cell line , the relative amount of each of the two RT-PCR products ( representing the full length and skipped mRNA ) was quantified using image analysis of the products visualised on ethidium bromide gels ( ImageQuant software; Amersham Biosciences , http://www4 . gelifesciences . com ) . Since both RT-PCR products were amplified by the same primer sets , the RT-PCR was truly competitive , allowing the accurate determination of their relative abundance [30] . To assess the robustness of the ethidium bromide–based quantification method it was compared with quantification using a fluorescence-based technique . Primer pairs with a 5′ FAM modification were used to amplify exon-skipping events from six different genes using cDNA from nine different LCLs . The amplicons ranged in size from 137 bp to 617 bp . The relative amounts of PCR products for each of the 54 reactions were quantified using GeneScan software ( Amersham Biosciences ) . The ratios of quantified products from this method showed excellent correlation with those derived from image quantification of ethidium bromide–stained gels ( correlation coefficient 0 . 93 ) . We determined the sensitivity of the ethidium bromide quantification system using known starting concentrations of DNA fragments of different lengths , and then quantifying the resulting amplicons . We were able to show that over a range of different product signal intensities , differences in the ratios of the different sized starting material of 10% or greater could be detected reliably ( Figure S3 ) . The sensitivity of this method was independent of cycle number . Each of the 22 cell line samples was assayed in duplicate . The mean ratio of the abundance of the two RT-PCR products from each primer set was calculated for each cell line . When the relative abundance for an individual cell line differed by more than 10% from the average value for the full set of 22 samples , the experiment was repeated using a fresh aliquot of cell culture material . Those events that gave consistent differences in the repeat analysis were then analysed further . Genotypes for SNPs positioned within 250 kb on either side of the exon-skipping event were downloaded from the International HapMap Project Web site ( http://www . hapmap . org ) [3] . For each event with reproducible variation , we also resequenced the skipped exon and 150 base pairs of the flanking introns to determine if additional SNPs close to the event could be identified , using DNA from each of the 22 LCLs . Sequencing was carried out using purified PCR products generated with M13-tagged primers . For each splicing event with reproducible variation , we calculated Pearson's correlation between the ratio of band intensities for the two RT-PCR products and the SNP genotype . In this analysis , we have assumed that any functional SNPs will be cis-acting , and thus expect to see an effect that is more pronounced in homozygotes than in heterozygotes . Thus , for the purposes of the correlation analysis , genotypes were coded 1 , 2 , and 3 to represent the genotypes AA , Ab , and bb , where A represents the major allele and b represents the minor allele . The value of Pearson's correlation was determined for each SNP in each 500-kb region . Splice donor and acceptor sequences were scored using a position specific score matrix ( PSSM ) method [31] . Alignments of mRNA and EST sequences to the reference human genomic assembly ( version: hg17 ) were taken from the University of California Santa Cruz genome database ( http://genome . ucsc . edu ) and used to define a population of well-supported ( appearing in more than nine transcripts ) constitutive splice sites . These were used to train the PSSMs , considering three exonic , six intronic nucleotides at the splice donor , and three exonic , 18 intronic nucleotides for the splice acceptor PSSM [31] . We compared the splice site scores of the 250 exons predicted by EBI-ASD to show exon skipping ( subdivided into those that showed exon skipping in our experimental model and those that did not ) with the splice site scores of 7 , 431 exons that were always found in mRNA transcripts ( constitutively present ) randomly selected from the genome . We sought to determine if splice site strength could predict those exons that were likely to be skipped . We also sought to determine if the SNP density near to the intron–exon boundaries differed in those exons that showed alternative splicing compared to those that did not . Finally , the sequence context of SNPs correlated with specific splice patterns was analysed to determine whether they affected know splice enhancer or silencer elements , using four published algorithms: http://ast . bioinfo . tau . ac . il/ESR . htm [32] , http://genes . mit . edu/burgelab/rescue-ese [33] , http://cubweb . biology . columbia . edu/pesx [34] , and http://rulai . cshl . edu/tools/ESE [35] . Both allelic forms of the SNPs showing correlation with splice patterns in the ZDHHC6 and SH3YL1 genes were cloned into a minigene splicing vector ( pALTER MAX modified splice vector , http://www . promega . com ) . Within this modified splicing vector , the multiple cloning site ( MCS ) of the conventional pALTER MAX minigene vector was replaced by an insert , so that the MCS falls within an intron instead of being within expressed sequence . The new insert contains the 5′ donor splice site from the human β-globin gene intron 1 , the MCS , and the 3′ acceptor splice site from the intron of an immunoglobulin gene . When a PCR product with primers designed within introns is used , all donor and acceptor splice sites are present and thus the construct is spliced correctly . The fragments cloned consisted of the exon plus an average of 180 bases of flanking intron . All inserts were confirmed by fluorescent sequencing . HEK293T cells were transfected following the manufacturer's protocol ( FuGENETM6 , Boehringer Mannheim ) . Cells ( 3 × 105 ) were transfected with 1 μg of DNA and were harvested after 48 h . The relative abundance of the full length and alternatively spliced mRNA derived from the plasmid was analysed using the same methodology as described for the CEPH cell RNA . Allele-specific differences in CASP3 expression were determined using a transcribed marker polymorphism ( rs4647603 ) in the exon of interest to distinguish the relative abundance of transcript containing this exon arising from the two alleles . Sixteen unrelated CEPH individuals heterozygous for the transcribed marker were selected from the HapMap collection and obtained from the Coriell repository . RNA and cDNA from each individual were prepared as described above . DNA was extracted using a purification kit ( Blood and Cell Culture DNA , Nucleon BACC2; Tepnel , http://www . tepnel . com ) . For each individual , data were obtained from nine replicates from each of two independent cultures . Allele-specific transcript quantification was carried out by single nucleotide primer extension and MALTI-TOF analysis using a SpectroREADER MassArray ( Sequenom , http://www . sequenom . com ) mass spectrometer as described previously [7] . RNA ratio values were normalized with the ratios observed for genomic DNA .
The National Center for Biotechnology Information ( NCBI ) Entrez Gene ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=gene ) accession numbers for the genes discussed in this paper are CASP3 , 836; CD46 , 4179; IFI16 , 3428; RBM23 , 55147; SH3YL1 , 26751; and ZDHHC6 , 64429 . | Genetic variation , through its effects on gene expression , influences many aspects of the human phenotype . Understanding the impact of genetic variation on human disease risk has become a major goal for biomedical research and has the potential of revealing both novel disease mechanisms and novel functional elements controlling gene expression . Recent large-scale studies have suggested that a relatively high proportion of human genes show allele-specific variation in expression . Effects of common DNA polymorphisms on mRNA splicing are less well studied . Variation in splicing patterns is known to be tissue specific , and for a small number of genes has been shown to vary among individuals . What is not known is whether allele-specific splicing events are an important mechanism by which common genetic variation affects gene expression . In this study we show that allele-specific alternative splicing was observed in six out of 70 exon-skipping events . Sequence analysis of the relevant splice sites and of the regions surrounding single nucleotide polymorphisms correlated with the splicing events failed to identify any predictive bioinformatic signals . A genome-wide study of allele-specific splicing , using an experimental rather than a bioinformatic approach , is now required . | [
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The RNase III enzyme DICER generates both microRNAs ( miRNAs ) and endogenous short interfering RNAs ( endo-siRNAs ) . Both small RNA species silence gene expression post-transcriptionally in association with the ARGONAUTE ( AGO ) family of proteins . In mammals , there are four AGO proteins ( AGO1-4 ) , of which only AGO2 possesses endonucleolytic activity . siRNAs trigger endonucleolytic cleavage of target mRNAs , mediated by AGO2 , whereas miRNAs cause translational repression and mRNA decay through association with any of the four AGO proteins . Dicer deletion in mouse oocytes leads to female infertility due to defects during meiosis I . Because mouse oocytes express both miRNAs and endo-siRNAs , this phenotype could be due to the absence of either class of small RNA , or both . However , we and others demonstrated that miRNA function is suppressed in mouse oocytes , which suggested that endo-siRNAs , not miRNAs , are essential for female meiosis . To determine if this was the case we generated mice that express a catalytically inactive knock-in allele of Ago2 ( Ago2ADH ) exclusively in oocytes and thereby disrupted the function of siRNAs . Oogenesis and hormonal response are normal in Ago2ADH oocytes , but meiotic maturation is impaired , with severe defects in spindle formation and chromosome alignment that lead to meiotic catastrophe . The transcriptome of these oocytes is widely perturbed and shows a highly significant correlation with the transcriptome of Dicer null and Ago2 null oocytes . Expression of the mouse transcript ( MT ) , the most abundant transposable element in mouse oocytes , is increased . This study reveals that endo-siRNAs are essential during meiosis I in mouse females , demonstrating a role for endo-siRNAs in mammals .
The RNase III enzyme DICER is responsible for biosynthesis of short-interfering RNAs ( siRNAs ) and microRNAs ( miRNAs ) . DICER processes long double-stranded RNA ( dsRNA ) precursors into 21–23 bp-long duplexes known as siRNAs [1] . miRNAs are encoded by specific genomic loci and are processed from endogenous hairpin-shaped transcripts that are initially cleaved in the nucleus to a 70-bp miRNA precursor ( pre-miRNA ) by the Microprocessor complex , which is composed of the RNase III enzyme DROSHA and its partner , DiGeorge syndrome critical region 8 ( DGCR8 ) . The pre-miRNA is exported to the cytoplasm , where DICER cleaves the loop region of the molecule to generate the mature miRNA duplex [2] . Although both siRNAs and miRNAs are synthesized as duplexes , only one of the two strands , the ‘guide’ strand , is incorporated into the multi-protein complex RNA-induced silencing complex ( RISC ) ; the other strand ( ‘passenger’ strand ) is discarded [3] . The guide strand recognizes a target mRNA by Watson-Crick base pairing and , based on the degree of sequence complementarity between the small RNA and target mRNA , either endonucleolytic cleavage or translational repression of the target mRNA follows [4] . In animals , siRNAs are perfectly complementary to their targets , and hence trigger mRNA cleavage , whereas miRNAs are usually only partially complementary and silence gene expression by translational repression and mRNA decay . Although it was initially postulated that mRNA levels did not change substantially in response to animal miRNAs , it was later shown that mRNA destabilization , prompted by deadenylation and decapping by the mRNA degradation machinery , is the main mode of regulation by mammalian miRNAs [5] . ARGONAUTE ( AGO ) proteins are at the core of RISC . In mammals , there are four AGO proteins ( AGO1–4 ) . All four can bind small RNAs and trigger translational repression , but only AGO2 possesses endonucleolytic activity and is the catalytic component of RISC [6] . We previously demonstrated a role for small RNAs during meiosis in mouse oocytes . Mice with an oocyte-specific deletion of Dicer are infertile due to defects during meiosis I [7 , 8] . Dicer-deficient females have morphologically normal ovaries and oocytes , produce normal numbers of oocytes , and ovulate similar numbers of eggs . However , Dicer null oocytes display meiotic catastrophe , with multiple disorganized meiotic spindles and severe chromosome congression defects . Expression of a subset of transposable elements is increased and the transcriptome is widely perturbed in Dicer null oocytes , with 18 . 4% of transcripts mis-regulated [7] . Deep sequencing of small RNAs demonstrated the presence of DICER-dependent miRNAs and endogenous siRNAs ( endo-siRNAs ) , as well as DICER-independent PIWI interacting RNAs ( piRNAs ) in mouse oocytes [9 , 10] . Two populations of endo-siRNAs were found , one that corresponds to transposon-rich loci and another that maps to protein-coding genes . Interestingly , we found that some siRNAs are processed from dsRNAs formed by hybridization of transcripts from protein-coding genes to antisense transcripts from homologous pseudogenes and that these endo-siRNAs regulate the expression of endogenous genes . Therefore , the phenotype of Dicer null oocytes could be due to the absence of miRNAs or endo-siRNAs , or both . Using mRNA reporters , we assayed the ability of miRNAs to silence gene expression , looking at both translational repression and transcript levels . We found that miRNA activity decreases during oocyte growth and is suppressed in the full-grown oocyte . Furthermore , the very modest translational repression observed is not accompanied by message degradation [11] . Similarly , Suh et al . generated an oocyte-specific deletion of Dgcr8 and found that Dgcr8 null oocytes , which lack mature miRNAs , have a normal transcriptome and undergo normal meiotic maturation , fertilization , and embryonic development; consistent with these findings , Dgcr8 null mice have no discernable phenotype [12] . These two studies suggest that most likely endo-siRNAs , and not miRNAs , have an essential role during female meiosis . It has recently been reported that mouse oocytes express a truncated DICER isoform , DICERO , which lacks the N-terminal DExD helicase domain , and which processes long dsRNAs much more efficiently than the somatic DICER isoform ( DICERS ) , which is also expressed , albeit at lower levels [13] . The phenotype of DicerO null mice is virtually identical to the phenotype of mice with an oocyte-specific deletion of Dicer ( which lack both DICERS and DICERO ) . Although DICERO can produce both miRNAs and endo-siRNAs when ectopically expressed in embryonic stem ( ES ) cells , miRNA levels appear slightly increased in DicerO null oocytes , suggesting that likely siRNAs are responsible for the observed phenotype . Whether this role of endo-siRNAs is mediated by endonucleolytic cleavage of mRNA targets remains unknown . To test directly the role of endo-siRNAs through endonucleolytic cleavage in mouse oocytes , we expressed a catalytically inactive knock-in allele of Ago2 specifically in oocytes to disrupt the function of endo-siRNAs . We find that female mice expressing a catalytically inactive AGO2 ( but not active AGO2 ) in their oocytes are infertile due to meiosis I defects . The phenotype is virtually identical to that in Dicer null females—female sterility , defects in spindle formation and chromosome congression , increase in abundance of transposable elements , and widespread changes in the transcriptome—and using live cell imaging , we characterize in more detail the meiotic defects . This study demonstrates a functional role for endogenous siRNAs through endonucleolytic cleavage in mammals and adds support to the evolutionary pressure to conserve ARGONAUTE endonucleolytic activity in animals .
To eliminate the function of siRNAs we generated mice carrying a catalytically inactive form of AGO2 in their oocytes using a knock-in allele of Ago2 in which the catalytic DDH motif was mutated to ADH ( Ago2ADH ) [14] . This mutation inhibits endonucleolytic cleavage without affecting small RNA binding [6] . However , because mice carrying this allele die shortly after birth , we utilized a breeding scheme using Ago2ADH mice , Ago2fl/fl mice , and mice expressing Cre recombinase driven by the oocyte-specific Zp3 promoter to produce Ago2fl/ADH; Cre/+ females , referred to as Ago2ADH ( S1 Fig . ) . These crosses also generated Ago2 null mice . Ovarian morphology in Ago2ADH females was normal , with follicles at different stages of development , as well as corpora lutea , indicating that ovulation had occurred ( Fig . 1A ) . After hormone stimulation , Ago2ADH females yielded similar numbers of full-grown oocytes as their wild-type ( Ago2fl/fl ) or heterozygous ( Ago2fl/ADH ) counterparts; similar numbers were also present in Ago2 null females ( Fig . 1B ) . This result indicated that siRNA function is not required for oocyte development or response to hormones . However , Ago2ADH females were unable to produce any offspring during a 6-month mating trial with several wild-type males , indicating female sterility . To ascertain if oocytes carrying an Ago2ADH allele are incapable of endonucleolytic cleavage of small RNA targets , an RNAi assay was performed with Ago2ADH females . Full-grown oocytes were microinjected with c-Mos siRNA and 40 h later c-Mos mRNA levels were quantified by qRT-PCR . Whereas oocytes derived from Ago2fl/fl or Ago2fl/ADH females exhibited ~90% decrease in c-Mos transcript levels in c-Mos siRNA-treated oocytes compared to control oocytes , oocytes obtained from Ago2ADH females only showed a mild reduction ( ~10% ) in c-Mos levels ( Fig . 1C ) . These results demonstrated that oocytes from Ago2ADH females had extremely reduced AGO2 catalytic activity . This residual endonucleolytic activity may be due to persistent wild-type AGO2 levels that were present prior to Cre excision , because both mRNAs and proteins are often stable in oocytes . To assess if AGO2 catalytic activity was required for meiotic maturation , full-grown oocytes were in vitro matured and spindle morphology was determined by immunofluorescence . Oocytes derived from Ago2fl/fl ( Fig . 2A ) or Ago2fl/ADH ( Fig . 2B ) females matured normally to metaphase II , as evidenced by the barrel-shaped meiotic spindle and extrusion of the first polar body . However , oocytes collected from Ago2ADH ( Fig . 2C ) or Ago2 null ( Fig . 2D ) females exhibited abnormal , disorganized spindles , with unaligned chromosomes . Some oocytes derived from Ago2ADH females extruded a polar body; nevertheless , upon closer examination it became clear that meiotic maturation was also abnormal in these oocytes , because partitioning of chromosomes between egg and polar body had not faithfully occurred ( Fig . 2E , F ) . To characterize better the meiotic defects , oocytes were microinjected with cRNAs encoding Aurora kinase A ( AURKA ) fused to EGFP ( to label spindle poles ) and histone H2B fused to mCherry ( to label chromosomes ) and live imaging was performed during meiotic maturation ( S1–S3 Movies ) . In Ago2fl/fl or Ago2fl/ADH oocytes ( S1 Movie , Fig . 3A-B ) , the chromosomes remained centrally located and formed a sphere right after germinal vesicle breakdown ( GVBD ) . In contrast , in Ago2ADH oocytes ( S2 Movie , Fig . 3G-H ) , the chromosomes did not congress and instead scattered , covering a large area of the oocyte . Ago2fl/ADH oocytes proceeded to form a barrel-shaped metaphase I spindle , with chromosomes tightly aligned at the metaphase plate ( Fig . 3C ) . Homologous chromosomes then separated at anaphase I ( Fig . 3D ) , and migrated to opposite poles at telophase I ( Fig . 3E ) , followed by cytokinesis , resulting in extrusion of the first polar body , completion of meiosis I and arrest at the metaphase stage of meiosis II ( Fig . 3F ) . In contrast , in most Ago2ADH oocytes , the chromosomes remained dispersed and never aligned , and oocytes failed to enter anaphase I ( Fig . 3G-L , S2 Movie ) . In a few Ago2ADH oocytes , after an initial dispersion of the chromosomes at GVBD , most chromosomes managed to align and form a metaphase I spindle , but there were always a few misaligned chromosomes , which resulted in a failure to enter anaphase and dispersion of chromosomes ( S3 Movie ) . The severe spindle defects observed in Dicer null oocytes have also been described in Ago2 null oocytes [15] . Although in the latter study the defect was attributed to reduced levels of miRNAs , it was later demonstrated that oocytes devoid of miRNAs have normal meiotic spindles [12] . By utilizing an allele of Ago2 that can bind small RNAs , but is catalytically inactive , we show that spindle formation and chromosome congression depend on the action of endo-siRNAs . Live imaging technology revealed that the defects start during GVBD , when chromosomes and microtubule organizing centers ( MTOCs ) scatter instead of forming a sphere [16] , resulting in a long , abnormal spindle with unaligned chromosomes that fail to progress to anaphase I . The mechanism that links siRNAs with the spindle defects remains unknown . Given that the transcriptome of Ago2ADH oocytes is widely perturbed ( see below ) , it is unlikely that a single protein is responsible for this phenotype . Because the levels of a subset of transposons are increased in Dicer-deficient oocytes [7 , 12] , we investigated if this was also the case in the absence of AGO2 catalytic activity . Quantitative RT-PCR of the most abundant transposons in mouse oocytes revealed a significant increase in the levels of mouse transcript ( MT ) , a member of the MaLR family of non-autonomous retrotransposons , in Ago2ADH and Ago2 null oocytes ( Fig . 4 ) . No significant differences were observed for the short interspersed repetitive elements ( SINEs ) , long interspersed repetitive element 1 ( LINE1 or L1 ) , or intracisternal A-particle ( IAP ) . This result differs somewhat from what we had previously described in Dicer null oocytes , where not only MT , but also Sine B1 and B2 elements were increased . This difference is likely due to differences in genetic background . We found that after re-deriving the Dicer null line , only MT levels were increased in oocytes ( S2 Fig . ) , in agreement with a previous study [12] , with DicerO null mice [13] , and with Ago2ADH oocytes . PIWI family mutants are male sterile , but female fertile in mouse , indicating that the piRNA system is not essential during oogenesis [17] . This female fertility is not the case in flies and fish , where mutants that disrupt the piRNA system are female sterile [17] . The presence of endo-siRNAs that map to transposons in mouse oocytes likely explains why piRNAs are not essential in females , because both piRNAs and endo-siRNAs repress transposable elements in mouse oocytes . Because MT transcripts account for ~13% of all transcripts in the oocyte [18] , a 3-fold increase in abundance is substantial and emphasizes the importance of siRNA action through endonucleolytic cleavage in transposon control . Dicer-deficient oocytes exhibit dramatic changes in their transcriptome , as assayed by microarray analysis , with thousands of transcripts up- and down-regulated compared to wild-type oocytes [7 , 12] . To determine if the same molecular phenotype exists in the absence of AGO2 catalytic activity , we performed high-throughout RNA sequencing ( RNA-seq ) in full-grown Ago2fl/fl , Ago2ADH , and Ago2 null oocytes , as well as Dicer wild-type ( WT ) and knockout ( KO ) oocytes . We found extensive changes in transcript levels in Ago2ADH and Ago2 null oocytes . Using a false discovery rate ( FDR ) of 1% , 6441 transcripts were mis-regulated in Ago2ADH vs . Ago2fl/fl oocytes ( 3199 up-regulated and 3242 down-regulated ) and 6142 transcripts were mis-regulated in Ago2 null vs . Ago2fl/fl oocytes ( 3050 up-regulated , 3092 down-regulated ) . Similarly , 6767 transcripts were mis-regulated in Dicer KO vs . WT oocytes ( 3195 up-regulated , 3572 down-regulated ) . Interestingly , although similar numbers of transcripts were down-regulated and up-regulated , as we had described for Dicer null oocytes , when the dataset was filtered by fold-change , a different picture surfaced . Of those transcripts whose abundance changed at least two-fold , the percentages that were up-regulated vs . down-regulated were 69%/31% in Ago2ADH vs . Ago2fl/fl oocytes , 68%/32% in Ago2 null vs . Ago2fl/fl , and 62%/38% in Dicer KO vs . WT oocytes . This finding indicates that the magnitude of change is greater in those transcripts that are up-regulated . This is indeed the case , as shown in S3 Fig . , where the absolute values of fold-changes for the different comparisons were plotted for up-regulated and down-regulated transcripts . Because Cre-mediated recombination to excise the floxed allele of Ago2 and impair endo-siRNA function occurs in small , growing oocytes , and we utilized full-grown oocytes in our study , most likely the changes that we observe in the transcriptome are not only primary to disruption of siRNA function , but represent a complex array of downstream effects . Interrogating the transcriptome in growing oocytes should provide a better picture of the direct targets of endo-siRNAs . As expected , there was an excellent correlation between the Ago2ADH and Dicer datasets ( Fig . 5A ) . Of the 3242 transcripts that were down-regulated in Ago2ADH vs . Ago2fl/fl oocytes , 2385 ( 74% ) were also down-regulated in Dicer KO vs . WT oocytes . Similarly , of the 3199 transcripts that were up-regulated in Ago2ADH vs . Ago2fl/fl oocytes , 2165 ( 68% ) were also up-regulated in Dicer KO vs . WT oocytes . Comparable numbers were obtained when Ago2 null and Dicer datasets were compared ( S4 Fig . ) . Also as expected , the transcriptome of Ago2ADH and Ago2 null oocytes was very similar , with only 33 transcripts ( 24 genes ) whose abundance differs between these two groups , one of them being Ago2 itself ( S5 Fig . , S1 Table ) . Accordingly , the overlap between genes up-regulated compared to Ago2fl/fl oocytes in both groups or down-regulated in both groups is quite high ( 79–84% , S6 Fig . ) . Although the overlap between genes mis-regulated in Ago2ADH and Dicer KO oocytes is quite high , there are many genes that are regulated differently in both groups . One possible explanation for these differences is that endo-siRNAs could have additional functions not mediated through AGO2-dependent endonucleolytic cleavage of target mRNAs . Also , AGO2 could cleave other , yet uncharacterized , DICER-independent small RNAs . Given that a population of endo-siRNAs in oocytes derives from protein-coding genes , it was postulated that these small RNAs regulate expression of their precursor mRNAs [9 , 10] . To test this hypothesis , we analyzed our RNA-seq data for the transcript levels of the 20 genes that produce the largest number of siRNAs in oocytes [9] . The vast majority ( 15/20 ) are up-regulated in the absence of AGO2 catalytic activity ( Fig . 5B ) , demonstrating a functional role for endo-siRNAs in the regulation of endogenous transcripts through endonucleolytic cleavage . The RNA-seq data were validated by performing qRT-PCR on several transcripts for which expression was either significantly increased , decreased , or unchanged in Ago2ADH oocytes , obtaining very similar results ( Fig . 5C ) . We had previously demonstrated that the transcripts levels of genes that make siRNAs were increased in Dicer null oocytes [9] , indicating a gene regulatory role for these small RNAs . Nevertheless , it was not clear if transcript regulation was due to endonucleolytic cleavage or if the mere production of siRNAs was diminishing the relative abundance of the transcript . Our results demonstrate that the action of siRNAs is through endonucleolytic cleavage of target mRNAs . To gain insight into specific pathways that could be affected in Ago2ADH oocytes , gene ontology analysis of mis-regulated transcripts was performed using the database for annotation , visualization and integrated discovery ( DAVID ) . For genes that are up-regulated in Ago2ADH oocytes , cell cycle , cell division , and regulation of translation , as well as microtubules and ribosomes were enriched ( S7 Fig . ) ; very similar categories were over-represented among genes up-regulated in Dicer KO oocytes ( S8 Fig . ) . Many more categories were enriched among the genes that are down-regulated in Ago2ADH oocytes ( S9 Fig . ) ; these include RNA binding , nucleotide binding , cell cycle , chromosome , and transcription . And there was also an excellent correlation with those categories enriched for genes that are down-regulated in Dicer KO oocytes ( S10 Fig . ) . Although the miRNA pathway is dispensable in mouse oocytes , we were interested in determining if miRNA levels were normal in Ago2ADH oocytes , because Ago2 null oocytes have reduced miRNA levels [15] . The concentration of 5 abundant miRNAs was assayed in oocytes of different Ago2 genotypes . Mature miRNA levels were significantly decreased in both Ago2ADH and Ago2 null oocytes ( S11A Fig . ) . Consistent with this finding , the modest miRNA-mediated translational repression , as assayed using luciferase reporters , was also reduced ( S11B Fig . ) . Although AGO proteins stabilize mature miRNAs ( and hence AGO loss leads to miRNA turnover ) , the catalytic activity of AGO2 is not required for this effect [19–21] . There are at least two possible explanations for the discrepancy with our results . First , Ago2ADH oocytes contain only one allele of Ago2 and hence the amount of protein is likely half the amount present in wild-type oocytes . Although Ago3 is the most abundant Ago transcript in mouse oocytes ( S12A Fig . ) , Ago2 levels are substantial and a decrease in available AGO protein concentration may affect miRNA stability . Also , the levels of the other Ago transcripts are unchanged in both Ago2ADH and Ago2 null oocytes ( S12B Fig . ; only Ago2 and Ago3 transcript levels are shown because Ago1 and Ago4 mRNA levels are extremely low , undetectable in many samples , but are not up-regulated in Ago2ADH oocytes ) . Second , the aforementioned studies were performed in somatic cells , which lack endo-siRNAs . Because the catalytic activity of AGO2 is required for passenger strand cleavage and siRNA unwinding [22–25] , in Ago2ADH oocytes siRNA duplexes would remain associated with AGO2 , preventing miRNA binding and thus leading to more rapid miRNA turnover . The Zp3-driven Cre recombinase utilized to delete the floxed allele of Ago2 is active very early during oocyte growth [26] , which takes ~ 3 weeks during which time transcription starts to decrease around mid-growth such that the full grown oocyte is transcriptionally inactive . Therefore , a small difference in miRNA stability can result over time in a highly significant decrease in miRNA levels . However , because mice whose oocytes are depleted of miRNAs show no discernable phenotype [12] , the phenotype of Ago2ADH mice cannot be attributed to differences in oocyte miRNA levels . In mammals , endo-siRNAs have only been described in mouse oocytes , ES cells , and male germ cells [9 , 10 , 27 , 28] . A physiological role for endo-siRNAs , however , has not been demonstrated in mammals . Mouse oocytes and ES cells lack the interferon response , an anti-viral defense mechanism against long dsRNA [29 , 30] , and germ cells in the testis have also been suggested to be insensitive to interferon and hence tolerate dsRNA precursors that could generate endo-siRNAs [28] . In the mouse testis , ablation of Dicer or Drosha in germ cells leads to abnormal spermatogenesis , but male mice with a germ cell-specific ablation of Ago2 show no phenotype [31 , 32] , suggesting that miRNAs are essential for spermatogenesis , but endo-siRNAs are dispensable in the male germline . In contrast , we find that endo-siRNAs are essential in the female germline in mouse . The presence in oocytes of DICERO that efficiently generates siRNAs from long dsRNA precursors , coupled with the absence of an interferon response , makes the mouse oocyte a privileged environment for siRNA action and may explain why this highly specialized cell relies on the siRNA pathway to regulate gene expression and protect genomic integrity . Given that DICERO is only expressed in mouse and rat oocytes , but not other rodent or non-rodent species [13] , this essential role of siRNAs in oocytes may be restricted to the Muridae family . Because most animal miRNAs silence their targets by translational repression , often linked to mRNA decay , but not by endonucleolytic cleavage , it has been puzzling that one mammalian AGO protein ( AGO2 ) has retained catalytic activity . The finding that the catalytic activity of AGO2 is required for biosynthesis of one miRNA , miR-451 [14] , and that this small RNA is essential for erythropoiesis [33] provided an answer to this conundrum . Our findings of an essential role of siRNAs through endonucleolytic cleavage during female meiosis strengthen the idea of evolutionary pressure that at least one AGO retain catalytic activity .
Ago2fl/+ animals [20] were crossed to Ago2ADH/+ mice [14] . The resulting Ago2fl/ADH females were crossed to Zp3-Cre males ( Jackson Laboratories ) and their progeny were intercrossed to produce Ago2fl/ADH; Cre/+ ( Ago2ADH ) mice ( S1 Fig . ) . These crosses also generated Ago2 null mice . To determine fertility , two Ago2ADH and two Ago2fl/ADH female mice were bred with several males of proven fertility for a period of 6 months . Oocyte-specific Dicer null mice have been described [7] . All animal experiments were approved by the Institutional Animal Use and Care Committee of the University of Pennsylvania ( protocol number 803551 ) and were consistent with National Institutes of Health guidelines . Four- to six-week-old female mice were primed by intraperitoneal injection of 5 IU of equine chorionic gonadotropin ( eCG ) 48 h before oocyte collection . Full-grown , germinal vesicle ( GV ) -intact cumulus-enclosed oocytes were collected as previously described [34] . The collection medium was bicarbonate-free minimal essential medium ( Earle’s salt ) supplemented with polyvinylpyrrolidone ( 3 mg/mL ) and 25 mM HEPES , pH 7 . 3 ( MEM/PVP ) . Germinal vesicle breakdown was inhibited by including 2 . 5 μM milrinone [35] . The oocytes were transferred to CZB medium [36] containing 2 . 5 μM milrinone and cultured in an atmosphere of 5% CO2 in air at 37°C until microinjection was performed . In experiments in which oocyte maturation was assessed , after collection the oocytes were transferred to milrinone-free CZB medium and cultured for 16h in an atmosphere of 5% CO2 in air at 37°C . GV oocytes were microinjected with approximately 5 pL of either siRNAs or cRNAs in MEM/PVP containing 2 . 5 μM milrinone as previously described [37] . c-Mos siRNA ( CTGAACATTGCAAGACTAC; Dharmacon ) was microinjected at 50 μM . For live imaging experiments , oocytes were microinjected with Aurka-Gfp cRNA ( 590 ng/μl ) and H2b-mCherry cRNA ( 1035 ng/μL ) . miRNA reporters and firefly luciferase cRNAs were microinjected at 0 . 05 μg/μl . For immunohistochemistry , whole ovaries were fixed for 16h in Bouin’s fixative , embedded in paraffin , sliced to 10-μm sections , and stained with hematoxylin and eosin . Immunofluorescence was performed as previously described [38] . The meiotic spindle was stained with a mouse anti- ( -tubulin monoclonal antibody conjugated to AlexaFluor 488 ( 1:100; Life Technologies ) , the cortical actin cap was visualized with Alexa Fluor 633-conjugated phalloidin ( 1:500; Life Technologies ) . DAPI ( Sigma ) and TO-PRO3 ( Life Technologies ) , both at 1 . 5 μg/mL , were used to label DNA and were added to the mounting medium ( Vectashield , Vector Laboratories ) . cRNAs encoding AURKA-GFP and H2B-mCherry were synthesized as described [39] . Oocytes were microinjected with Aurka-Gfp and H2b-mCherry cRNAs , cultured for 5 h in CZB + milrinone , and then transferred to individual drops of milrinone-free CZB medium , where meiotic maturation was assessed through live imaging , as described [39] . Images of individual cells were acquired every 18 min during 16 h and processed using NIH ImageJ software . Total RNA was extracted from 20 full-grown oocytes using Trizol ( Life Technologies ) , according to the manufacturer’s protocol , except that 2 ng of Egfp RNA was added to the Trizol at the beginning of RNA isolation to serve as an exogenous normalization gene . cDNA was prepared by reverse transcription of total RNA with Superscript II and random hexamer primers . One oocyte equivalent of the resulting cDNA was amplified using TaqMan probes and the ABI Prism Sequence Detection System 7000 ( Applied Biosystems ) . Two replicates were run for each real-time PCR reaction; a minus template served as control . Quantification was normalized to Egfp within the log-linear phase of the amplification curve obtained for each probe/primer using the comparative CT method ( ABI PRISM 7700 Sequence Detection System , User Bulletin 2 , Applied Biosystems , 1997 ) . The TaqMan gene expression assays used were: Mm00445082_m1 ( Vav3 ) , Mm00551650_m1 ( Tbcd ) , Mm00441071_m1 ( Rangap1 ) , Mm00620601_m1 ( Oog4 ) , Mm00786153_s1 ( Lcp1 ) , Mm00725286_m1 ( Optn ) , Mm00433565_m1 ( Gdf9 ) , Mm00508001_m1 ( Adar1 ) , Mm00459008_m1 ( Stx19 ) , Mm00556276_m1 ( Frmd3 ) , Mm00462977_m1 ( Ago1 ) , Mm03053414_g1 ( Ago2 ) , Mm01188534_m1 ( Ago3 ) , and Mm00462659_m1 ( Ago4 ) . For Ubc9 , Egfp , and c-Mos , custom TaqMan Gene Expression Assays were used that had the following primers and probes: Ubc9 forward primer 5′-CAGGTGAGAGCCAAGGACAAA-3′ , Ubc9 reverse primer 5′-GGCCCACTGTACAGCTAACA-3′ , Ubc9 probe 5′-CTGGCCTGCATTGATC-3′; Egfp forward primer: 5′-GCTACCCCGACCACATGAAG-3′ , Egfp reverse primer: 5′-CGGGCATGGCGGACTT-3′ , Egfp probe: 5′-CAGCACGACTTCTTC-3′; c-Mos forward primer: 5′-GGGAACAGGTATGTCTGATGCA-3′ , c-Mos reverse primer: 5′-CACCGTGGTAAGTGGCTTTATACA-3′ , c-Mos probe: 5′-CCGAGCCAAACCCTC-3′ . RNA isolation and reverse transcription were performed as above . Real-time PCR was done using one oocyte equivalent per reaction and SYBR Green master mix . β-actin served as an internal control for normalization . Primer sequences were: MT . fwd: 5’-TGTTAAGAGCTCTGTCGGATGTTG-3’; MT . rev: 5’-ACTGATTCTT CAGTCCCAGCTAAC-3’; SineB1 . fwd: 5’-GTGGCGCACGCCTTTAATC-3’; SineB1 . rev: 5’-GACAGGGTTTCTCTGTGTAG-3’; SineB2 . fwd: 5’-GAGATGGCTCAGTGGTTAAG-3’; SineB2 . rev: 5’-CTGTCTTCAGACACTCCAG-3’; Line L1 ORF2 . fwd: 5’-TTTGGGACACAATGAAAGCA-3’; Line L1 ORF2 . rev: 5’-CTGCCGTCTACTCCTCTTGG-3’; IAP LTR . fwd: 5’-TTGATAGTTGTGTTTTAAGTGGTAAATAAA-3’; IAP LTR . rev: 5’-AAAACACCACAAACCAAAATCTTCTAC-3’; actin . fwd: 5’- CGGTTCCGATGCCCTGAGGCTCTT-3’; actin . rev: 5’-CGTCACACTTCATGATGGAATTGA-3’ . miRNA levels were assayed using the TaqMan MicroRNA Cells-to-CT kit ( Life Technologies ) , following the manufacturers’ instructions , with slight modifications . Briefly , 9 . 1 μl of lysis solution was added to a tube containing 50 previously frozen full-grown oocytes . The samples were incubated for 8 min at room temperature and then 0 . 9 μl of stop solution was added , followed by a 2 min incubation at room temperature . Reverse transcription was performed using MultiScribe reverse transcriptase and following a multiplex protocol where the different miRNA-specific primers are mixed at a final concentration of 250 nM each . The resulting cDNA was diluted 10 times and real-time PCR was performed as described for mRNAs , using snoRNA202 as normalizing control . The following small RNA TaqMan assays were used: 000391 ( mmu-miR-16–5p ) , 000580 ( mmu-miR-20a-5p ) , 000602 ( mmu-miR-30b-5p ) , 002459 ( mmu-miR-106a-5p ) , 002406 ( mmu-let-7e-5p ) , and 001232 ( snoRNA202 ) . Twenty oocytes were lysed in 5 μL of NuGen lysis buffer . Each tube contained oocytes derived from 3 or 4 different animals of the same genotype and collection was performed three times to obtain 3 replicates per group . The groups were: Ago2fl/fl , Ago2ADH , Ago2 null , Dicer WT and Dicer KO . The lysate was used for cDNA synthesis using the Ovation RNA-Seq System V2 ( Nugen ) according to the manufacturer’s protocol . The resulting cDNA was fragmented into 200bp using Covaris shearing , and the Ovation Ultralow DR Multiplex System ( Nugen ) was used for library construction . The size and concentration of the resulting libraries were checked on Bioanalyzer , quantified by qPCR and sequenced on Illumina HiSeq 2000 with PE50 . Sequencing reads were mapped to the mm10 refGene transcriptome and genome using TopHat v2 . 0 . 3 [40] with options ‘--read-mismatches 1 --read-gap-length 1 --read-edit-dist 1 --max-multihits 100 --no-discordant --b2-very-sensitive --transcriptome-max-hits 100 --library-type fr-unstranded --no-coverage-search --no-novel-juncs’ for 36bp reads and ‘--read-mismatches 3 --read-edit-dist 3—max-multihits 100 --b2-very-sensitive --transcriptome-max-hits 100 --library-type fr-unstranded --no-coverage-search --no-novel-juncs’ for 50bp reads . Read counts were computed using htseq-count ( http://dx . doi . org/10 . 1101/002824 ) with options ‘--stranded = no -mode = intersection-strict’ . Differential expression analysis was performed using the DESeq R package ( version 1 . 10 . 1 ) [41] . Gene ontology ( GO ) analysis was performed using the Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) online resource [42 , 43] and using only the molecular function , cellular component , and biological process terms in the gene ontology database . The RNA-seq data have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE57514 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE57514 ) . Oocytes were microinjected with reporters that contain four bulged miR-30c sites ( RL-4xB ) downstream of the Renilla luciferase coding sequence . As a control , a reporter where the four miR-30c sites were mutated ( RL-4xM ) was used [11 , 44] . For normalization , firefly luciferase cRNA was coinjected with the Renilla luciferase reporters . The experiments were performed as previously described [11] . All experiments were replicated at least three times , except for luciferase assays , which were performed twice . Data were analyzed by ANOVA , followed by Bonferroni post-test . RNA-seq data were analyzed using a Chi-square test . A p< 0 . 05 was considered statistically significant . | In animals , the three main classes of small RNAs are microRNAs , short interfering RNAs , and PIWI-interacting RNAs . All three RNA species silence gene expression post-transcriptionally through interaction with the ARGONAUTE family of proteins . In mammals in particular , microRNAs are ubiquitously expressed , are essential for development , and perform numerous functions in a variety of cells and tissues . piRNAs are expressed almost exclusively in the germline , and are essential for male fertility and defense against transposons . Endogenous siRNAs are only expressed in germ cells and embryonic stem cells and have not been ascribed a functional role . By engineering a mouse that expresses a modified ARGONAUTE protein , we disrupt the function of endo-siRNAs exclusively in oocytes and find that females are infertile . Oocytes with an impaired siRNA pathway fail to complete meiosis I , and display severe spindle formation and chromosome alignment defects . Their transcriptome is widely perturbed and expression of the most abundant transposon is increased . These findings indicate that endo-siRNAs are essential for female fertility in mouse , are required for spindle formation , chromosome congression , and defense against transposons . This study unequivocally demonstrates an essential function for siRNAs in mammals , mediated through endonucleolytic cleavage of targets , and provides an explanation for the selective pressure that one AGO protein retains catalytic activity . | [
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Essential Role for Endogenous siRNAs during Meiosis in Mouse Oocytes |
Modulation of host DNA synthesis is essential for many viruses to establish productive infections and contributes to viral diseases . Human cytomegalovirus ( HCMV ) , a large DNA virus , blocks host DNA synthesis and deregulates cell cycle progression . We report that pUL117 , a viral protein that we recently identified , is required for HCMV to block host DNA synthesis . Mutant viruses in which pUL117 was disrupted , either by frame-shift mutation or by a protein destabilization-based approach , failed to block host DNA synthesis at times after 24 hours post infection in human foreskin fibroblasts . Furthermore , pUL117-deficient virus stimulated quiescent fibroblasts to enter S-phase , demonstrating the intrinsic ability of HCMV to promote host DNA synthesis , which was suppressed by pUL117 . We examined key proteins known to be involved in inhibition of host DNA synthesis in HCMV infection , and found that many were unlikely involved in the inhibitory activity of pUL117 , including geminin , cyclin A , and viral protein IE2 , based on their expression patterns . However , the ability of HCMV to delay the accumulation of the mini-chromosome maintenance ( MCM ) complex proteins , represented by MCM2 and MCM4 , and prevent their loading onto chromatin , was compromised in the absence of pUL117 . When expressed alone , pUL117 slowed cell proliferation , delayed DNA synthesis , and inhibited MCM accumulation . Knockdown of MCM proteins by siRNA restored the ability of pUL117-deficient virus to block cellular DNA synthesis . Thus , targeting MCM complex is one mechanism pUL117 employs to help block cellular DNA synthesis during HCMV infection . Our finding substantiates an emerging picture that deregulation of MCM is a conserved strategy for many viruses to prevent host DNA synthesis and helps to elucidate the complex strategy used by a large DNA virus to modulate cellular processes to promote infection and pathogenesis .
The manipulation of host DNA synthesis is a critical step for many DNA viruses , including human cytomegalovirus ( HCMV ) , to establish productive infection leading to disease [1] , [2] , [3] . HCMV is a prototypical β-herpesvirus , a ubiquitous pathogen , and one of the most common causes of birth defects in newborns and life-threatening disease in immunocompromised individuals . Despite its slow replication kinetics , HCMV can efficiently infect , persist , and establish latency in humans . To accomplish this , HCMV encodes at least 166 annotated genes , many of which act to hijack and deregulate key cellular processes , such as cellular DNA synthesis and cell cycle control [4] . HCMV infection induces a cellular environment conducive for DNA replication but in the meantime specifically blocks host DNA synthesis , thereby arresting host cells in a pseudo-G1 phase [5] , [6] . It is proposed that such modulation allows the virus to divert resources , such as energy , nucleotide pools , and cellular DNA replication enzymes , exclusively for viral DNA replication . Consistent with this notion , cells that actively replicate their DNA fail to support a lytic HCMV infection . Instead , they progress through S phase and arrest at the next G1-phase after mitosis to initiate HCMV replication [7] , [8] . HCMV encodes multiple factors , both stimulatory and inhibitory , to tightly regulate host and viral DNA replication for a successful infection ( reviewed in [5] , [6] , [9] ) . Over-expression of HCMV protein pp71 , IE1 , IE2 , or pUL97 inactivates pRb-family proteins , activates expression of E2F-dependent S-phase genes , and promotes G1/S- transition . On the other hand , pUL69 [10] , [11] and IE2 [12] are required for HCMV to block cellular DNA synthesis . Over-expression of IE2 inhibits cyclin A transcription , induces p16 expression , and arrests cells in S phase [13] , [14] , [15] , [16] . The mechanism for the cell-cycle arresting activity of pUL69 remains unknown . These viral factors must act in conjunction at multiple regulatory levels in order for HCMV to manipulate the host cell cycle . Neither IE2 nor pUL69 alone blocks cellular DNA synthesis during infection , because a mutant HCMV expressing one but not the other fails to arrest host cells at a pseudo-G1 phase [10] , [12] . In addition to cellular proteins involved in cell cycle control , HCMV directly targets DNA replication factors , such as mini-chromosome maintenance ( MCM ) complex [17] , [18] . MCM , a hetero-hexameric protein complex composed of MCM2-MCM7 , is recruited by Cdc6 and Cdt1 to replication origins , forming pre-replication complexes ( pre-RC ) during late M- to G1- phase to allow replication licensing . Once activated by cyclin-dependent kinase 2 and Dbf4-dependent kinase ( Dbf4/cdc7 ) in S phase , MCM acts as a replicative helicase to unwind the origin and initiate DNA replication [19] . HCMV inhibits loading of MCM onto chromatin to prevent cellular replication licensing [17] , [18] , and this inhibitory activity appears to correlate with virus-induced early accumulation of geminin , a negative regulator of the assembly of pre-replication complexes [18] . We previously identified a protein product of HCMV early gene UL117 , termed pUL117 [20] . We showed that pUL117-deficient HCMV replicated viral DNA at slightly reduced levels but was markedly delayed in the maturation of viral replication compartments and severely attenuated in growth in human foreskin fibroblasts [20] . In this study , we investigated the potential role of pUL117 in manipulating host cells during HCMV infection . We report that pUL117 is necessary and sufficient to reduce the accumulation of MCM proteins . During HCMV infection pUL117 may also have a direct role in preventing MCM loading onto chromatin . Importantly , knockdown of MCM proteins restored the ability of pUL117-deficient virus to block cellular DNA synthesis . Thus , targeting MCM function is a mechanism for pUL117 to help block cellular DNA synthesis during HCMV infection . These findings reveal a novel strategy used by HCMV to usurp the host DNA replication machinery , and provide new insight into the complex host-pathogen interactions fundamental to the biology and pathogenesis of the virus .
We previously discovered that pUL117 was required for the proper maturation of nuclear viral replication compartments and efficient HCMV infection in primary human foreskin fibroblasts ( HFFs ) [20] . Here , we asked if pUL117 functioned by targeting host factors involved in cellular DNA replication and cell cycle control . HCMV infection blocks cellular DNA synthesis and predominantly arrests permissive cells in a phenotypically G1-like phase [5] , [6] . To test whether this activity required pUL117 , actively growing HFFs were mock-infected , infected with wild type virus ( ADwt ) or pUL117-deficient virus ( ADdlpUL117 ) . Nocodazole was added 8 hours post infection ( hpi ) to prevent cell division , and the DNA content was analyzed by flow cytometry-based cell cycle analysis at 48 hpi ( Fig . 1A ) . 45% of mock-infected cells reached G2/M by this time point . As anticipated , wild type HCMV infection efficiently blocked host DNA synthesis with only 24% of cells reaching G2/M at 48 hpi , and this population of cells likely represented uninfected bystanders or abortively-infected cells . However , pUL117-deficient virus failed to block host DNA synthesis , allowing 52% of cells to progress to G2/M . In wild type virus infected cells , broadening of the G1 peak was observed as a result of viral DNA synthesis [21] . Broadening of the G1 peak was less evident in ADdlpUL117 infected cells at this time point , likely due to the slight delay of viral DNA synthesis [20] . To eliminate potential complications due to viral DNA synthesis , we also analyzed the DNA content of cells after infection in the presence of ganciclovir ( GCV ) , a specific inhibitor of HCMV DNA synthesis ( Fig . 1B ) . The results showed that GCV , at concentrations of 13 and 30 µg/ml , blocked the broadening of the G1 peak , indicative of viral DNA synthesis inhibition . Thus , while wild type HCMV maintained its ability to block host DNA synthesis , mutant virus failed to do so and cells entered S phase and progressed to G2/M phase . As all HCMV viruses used in this study were tagged with GFP , we specifically analyzed the DNA content of GFP-positive infected cells by sorting cells based on GFP expression . Sorting was efficient as 99% of sorted cells were GFP positive ( data not shown ) . Upon ADwt infection , only 6% of infected cells reached the S- or G2/M- phase , whereas upon ADdlpUL117 infection , 16% and 38% of cells reached S and G2/M , respectively ( Fig . 2A ) . Thus , unlike ADwt infection , the mutant virus did not block host DNA synthesis . It is known that abortive HCMV infection is unable to block host DNA synthesis [22] , [23] , [24] . However , this was not the case here because pUL117-deficient virus established productive infection , producing infectious progeny even though the kinetics of its viral DNA replication was slightly delayed in HFFs [20] . This was further supported by the observation that 93–94% of wild type and mutant virus infected cells that were GFP-positive also expressed early viral protein pUL44 , confirming that both viruses were able to establish productive infection equally well ( Fig . 2B and Figure S4 ) . In this study , we used HCMV-driven GFP or viral proteins pUL44 ( DNA polymerase processivity factor ) or IE1/IE2 ( immediate early proteins ) interchangeably as markers of productive infection as they all identified the same cell populations during infection ( data not shown ) . To examine the effect of pUL117 on host DNA synthesis more closely during infection , we gated infected cells by IE1/IE2 and analyzed their DNA content at both 24 and 48 hpi ( Fig . 2C ) . Infection was carried out in the presence of GCV to prevent viral DNA replication . At 24 hpi , although slightly more mutant virus-infected cells ( 6% and 16% ) than wild type virus-infected cells ( 2% and 15% ) progressed to S-phase and G2/M phase , the mutant infection still blocked host DNA synthesis quite efficiently when compared to mock infection ( 3% and 28% ) ( Fig . 2C ) . However , as expected , while ADwt infection maintained this inhibitory activity at 48 hpi , the mutant virus was unable to block host DNA synthesis of actively growing HFFs at this time point . To better define how pUL117 modulates host DNA synthesis , we examined its impact on the DNA profile of HFFs that were synchronized at G0 prior to HCMV infection , a condition that has been widely used to investigate the modulation of cell cycle and cellular DNA synthesis by HCMV . We synchronized subconfluent HFFs at G0 by serum starvation , infected them with mock or recombinant HCMV in the presence of serum , GCV , and nocodazole , and analyzed the DNA content of infected cells that were gated for expression of viral protein pUL44 ( Fig . 3 ) . More than 99% of cells infected with either wild type or mutant virus were pUL44 positive , indicating a robust productive infection ( Fig . 3A ) . The overall cell cycle progression of serum-stimulated quiescent mock-infected cells or cells infected with mutant virus was delayed compared to actively growing cells . Nonetheless , as anticipated , while a total of 35% of mock-infected cells moved into S-phase ( 7% ) and G2/M-phase ( 28% ) at 48 hpi , wild type HCMV blocked host DNA synthesis , resulting in only 18% of pUL44+ cells moving into S-phase ( 9% ) and G2/M-phase ( 9% ) . In contrast , pUL117-deficient virus failed to block host DNA synthesis , and 34% of pUL44+ cells moved into S-phase ( 19% ) and G2/M-phase ( 15% ) ( Fig . 3B ) . This defect was further confirmed by direct measurement of DNA synthesis with 3H thymidine incorporation at 48 hpi ( Fig . 3C ) . The amount of GCV-resistant , replicating DNA in cells infected with mutant virus ( 232% ) was ∼30-fold higher than that in cells infected with wild type virus ( 8% ) . The resistance of DNA replication in pUL117 deficient virus-infected cells to GCV indicated that we were selectively monitoring cellular DNA synthesis . In support of this , GCV was found to inhibit viral DNA synthesis equally well in cells infected with wild type or mutant virus ( Fig . 3D ) . GCV treatment resulted in ∼80-fold reduction of viral DNA accumulation in both wild type and mutant virus infected cells at 48 hpi . Importantly , in the presence of GCV , the residual viral DNA in mutant virus infected cells was 2 . 6-fold less than that in wild type virus infected cells . Furthermore , the elevated DNA content seen in cells infected with mutant virus as compared to wild type virus was recapitulated when viral DNA replication was inhibited by another specific viral DNA synthesis inhibitor , phosphonoacetic acid ( PAA ) ( Figure S1 ) . It was noted that , similar to infection in actively growing HFFs ( Fig . 2C ) , in G0-synchronized HFFs the inability of pUL117-deficient virus to block host DNA synthesis at 24 hpi was less pronounced than that at 48 hpi ( Fig . 3B and Figure S1 ) . It is also important to note that progress through S-phase was delayed during infection of mutant virus as compared to mock-infected cells at 48 hpi , both in actively growing HFFs ( Fig . 2C , 23% of infected cells versus 12% of mock ) and in G0-synchronized HFFs ( Fig . 3B , 19% of infected cells versus 7% of mock ) . These results suggest that other viral factors , such as IE2 and pUL69 , are able to block host DNA synthesis at early times and they may also institute additional blockades in S phase in the absence of pUL117 at times after 24 hpi . Collectively , these results are consistent with the notion that HCMV encodes multiple factors to modulate the DNA replication machinery of host cells , and importantly , our results demonstrate that pUL117 is required for HCMV to block host DNA synthesis at times after 24 hpi . A major strategy employed by many DNA viruses to replicate their genomes is to promote host cell entry into S-phase in order to utilize the cellular resources needed for viral DNA synthesis . The HCMV UL97 protein has cyclin-dependent kinase ( CDK ) activity , allowing the virus to inactivate Rb-family proteins and activate transcription of S-phase genes [1] , [25] . Earlier studies have also shown that HCMV induces the expression of cellular genes needed for DNA replication , such as the ones encoding the pre-RC components [17] , [18] . Thus , HCMV may have an “intrinsic” ability to stimulate cellular DNA synthesis , and that this stimulatory activity is counteracted by pUL117 . This hypothesis was supported by the observation that when infecting cycling cells , ADdlpUL117 infection promoted a greater number of cells to move out of G1 compared to mock-infected cells at 48 hpi ( Fig . 2C ) and 72 hpi ( data not shown ) . To test the hypothesis directly , we synchronized HFFs in G0 by contact inhibition and serum starvation , infected them with HCMV in the presence of GCV and nocodazole but without serum ( to prevent serum-stimulated re-entry of cells into the cell cycle ) , and then analyzed infected cells for their DNA content by flow cytometry and for DNA synthesis by 3H thymidine incorporation ( Fig . 4 ) . At 96 hpi , while very few mock infected ( 2% ) or wild type virus-infected cells ( 6% ) entered S phase , pUL117-deficient virus stimulated entry of host cells into S phase ( 21% ) ( Fig . 4A ) . Direct measurement of thymidine incorporation confirmed elevated levels of DNA replication in mutant virus-infected cells under conditions of serum starvation and contact inhibition ( Fig . 4B ) . This result suggests that HCMV has the intrinsic ability to drive host cells to initiate cellular DNA synthesis , but this stimulatory activity is blocked by pUL117 . It was conceivable that viral stocks of ADdlpUL117 used for infection in our study might differ from those of wild type virus in composition or virion structures , and this physical difference of the seed stocks rather than the lack of pUL117 expression during infection would directly alter the modulation of cellular DNA synthesis when cells were infected . This was unlikely since there was no evidence for the presence of pUL117 in HCMV virions [26] and the mutant virus was capable of blocking cellular DNA synthesis at early times during infection ( Figs . 2C and 3A ) . Nonetheless , to provide direct evidence we made a recombinant HCMV in which the UL117 ORF was tagged with a FKBP destabilization domain ( ddFKBP ) at its amino terminus ( ADpFKBP-UL117 ) . The fusion protein pFKBP-UL117 was directed for rapid degradation by ddFKBP , but could be stabilized by the ligand Shield-1 ( Shld1 ) that binds to ddFKBP [27] , [28] . This approach has recently been used to generate conditional HCMV mutants in which ddFKBP was fused to three essential viral genes to regulate accumulation of these fusion proteins and viral growth [29] . This approach provided a rapid and reversible way to regulate the accumulation of pUL117 in infections from the same viral stock . We predicted that infection would be pUL117-deficient without Shld1 ( pFKBP-UL117 unstable ) but would be wild type with Shld1 ( pFKBP-UL117 stable ) . HCMV virus prepared without Shld1 was used to infect actively growing HFFs incubated with or without Shld1 , and viral protein accumulation and cell cycle distribution of infected cells were analyzed at 48 hpi . Shld1 only had negligible effect on accumulation of viral proteins pUL117 , pUL117 . 5 , and IE1 ( Fig . 5A ) , modulation of the cell cycle progression ( Fig . 5B ) , or replication of ADwt or ADdlpUL117 ( data not shown ) . With Shld1 , the pFKBP-UL117 fusion protein was stabilized and ADpFKBP-UL117 effectively blocked host DNA synthesis . In contrast , without Shld1 , the accumulation of the FKBP-UL117 fusion protein was greatly reduced ( Fig . 5A ) and recombinant virus failed to prevent host DNA synthesis ( Fig . 5B ) . Thus , altered modulation of host DNA synthesis in pUL117-deficient virus was the direct result of abrogation of pUL117 expression rather than physical differences between wild type and mutant viral stocks . To dissect the mechanism of pUL117 function , we examined several key cellular factors that regulate cellular DNA replication and are perturbed by HCMV infection . We were particularly interested in events occurring at times prior to the failure of the pUL117-deficient virus to block host DNA synthesis ( i . e . , 0–36 hpi ) as misregulation of these events was the most likely cause of the defect . HCMV up-regulates p16 at late times [13] , [30] , inhibits cyclin A accumulation [8] , [31] , [32] , and prevents MCM complex loading onto chromatin [17] , [18] . When infecting G0-synchronized HFFs in the presence of serum , HCMV induced p16 accumulation and expressed another viral inhibitory protein IE2 independent of pUL117 ( Fig . 6A and Figure S2 ) . HCMV also regulated the accumulation of two other cellular regulatory proteins , p53 [31] and p21 [32] , independent of pUL117 ( Figure S2 ) . Both wild type and pUL117-deficient viruses inhibited accumulation of cyclin A during infection ( Fig . 6A ) . This was anticipated because the mutant virus expressed IE2 , which has been known to inhibit cyclin A accumulation [14] , and was consistent with S-phase delay observed in mutant virus infection ( Figs . 2C and 3A ) . Interestingly , mutant virus appeared to inhibit cyclin A to an even greater extent than wild type virus ( Fig . 6A ) . This inhibition could not explain elevated cellular DNA seen in mutant virus infection because in normal cells cellular DNA synthesis and S-phase progression correlates with cyclin A accumulation . This result also suggested that the cell cycle proceeded , albeit with delayed kinetics , in fibroblasts even when cyclin A level was reduced . Indeed , it has been shown that depletion of cyclin A does not prevent proliferation of fibroblasts [33] or cellular DNA synthesis [34] . Thus , we found no evidence for a role of viral protein IE2 , cellular cell cycle regulatory proteins p16 , p21 , p53 , or cyclin A in elevated cellular DNA synthesis in pUL117-deficient virus infection . In striking contrast , the ability of HCMV to modulate the MCM complex , a key component of replication licensing , was altered in the absence of pUL117 ( Fig . 6A ) . In mock-infected cells , MCM proteins , represented by MCM2 and MCM4 , were almost undetectable at G0 ( 0 h ) but were expressed and recruited to chromatin when cells re-entered the cell cycle ( 12–36 h ) . As reported previously [17] , [18] , HCMV infection slightly reduced MCM accumulation at early times ( 12–24 h ) but induced their overall levels at late times ( 30–36 h ) , and MCM accumulation in chromatin fractions from wild type HCMV-infected cells was markedly reduced . The purity of our fractions was confirmed by demonstrating that cellular protein lamin A was exclusively present in chromatin fractions but not in soluble fractions , and the vast majority of viral protein IE1 was present in soluble fractions but not in chromatin fractions ( Fig . 6C ) . pUL117 mutant virus infection induced even higher MCM accumulation and failed to efficiently block MCM recruitment to chromatin as compared to wild type virus infection ( most evident at 24–30 hpi ) . This difference between wild type and mutant virus infection was apparent for both MCM2 and MCM4 , although the effect on MCM4 was greater . The altered accumulation of MCM proteins was likely to occur at a post-transcriptional level as MCM4 transcript accumulated at similar levels during HCMV infection independent of pUL117 ( Fig . 6D ) . To better define the effect of pUL117 on MCM accumulation and chromatin loading , we quantified total and chromatin bound MCM protein levels at 24 and 30 hpi . It was clear that both total and chromatin bound MCM levels markedly increased in mutant virus infection relative to wild type virus infection ( Fig . 6B ) . At 24 hpi , the increase of chromatin-bound MCM protein levels paralleled the increase of total MCM levels in mutant virus infection . However , at 30 hpi , chromatin bound MCM2 and MCM4 increased by 3 . 2- and 4 . 1- fold whereas total MCM2 and MCM4 only increased by 1 . 7- and 1 . 8- fold , respectively , in the absence of pUL117 . These results suggest that pUL117 inhibits replication licensing by reducing accumulation of total MCMs ( e . g . 24 hpi ) , and additionally , may by targeting MCM chromatin loading ( e . g . 30 hpi ) during HCMV infection . An earlier study suggests that the premature accumulation of geminin , a substrate of the anaphase-promoting complex ( APC ) , may block MCM chromatin loading in HCMV infection [18] . Therefore , we examined the accumulation of geminin and two other APC substrates , cdc6 and cyclin B1 , in mutant virus infection . As compared to mock infection , elevated levels of all three APC substrates were evident as early as 12 hpi and persisted throughout the course of infection examined , consistent with previous reports ( Fig . 7 ) [18] , [35] , [36] . However , this premature accumulation of APC substrates during HCMV infection was independent of pUL117 expression . In fact , these APC substrates accumulated to even greater levels in the absence of pUL117 at 36 and 48 hpi , perhaps due to the increased transcription of these genes at late S and G2 phases in cells infected with the mutant virus . Thus , geminin is unlikely to be targeted by pUL117 to modulate MCM chromatin loading . Taken together , our results suggest that pUL117 helps to block cellular DNA synthesis during HCMV infection , at least in part , by preventing the accumulation and loading of MCM onto chromatin and subsequent replication licensing . We next wanted to test if pUL117 was sufficient to inhibit the accumulation and loading of MCM onto chromatin to block host DNA synthesis . We created HFFs expressing a fully functional GFP-tagged pUL117 ( HF-GFP/UL117 ) [20] or GFP control ( HF-GFP ) by retroviral transduction . To test the hypothesis , transduced , G0-synchronized cells were stimulated into active growth by re-seeding at sub-confluency with serum , and then analyzed for the rate of DNA synthesis , MCM modulation , and cell proliferation ( Fig . 8 ) . In GFP/UL117-expressing cells , the rate of cellular DNA synthesis decreased ( 54% ) compared to GFP-expressing cells ( 100% ) ( Fig . 8C ) . Moreover , the accumulation and chromatin loading of MCM proteins were markedly reduced in these cells ( Fig . 8A ) . Finally , cell proliferation of GFP/UL117-expressing cells was substantially slower relative to GFP-expressing control cells ( Fig . 8D ) . The effect of pGFP/UL117 was unlikely due to non-specific toxic effect resulting from over-expression , since the level of pGFP/UL117 was much lower than that of GFP control in transduced cells ( Figure S3 ) . Quantitative immunoblot analysis indicated that both the total and chromatin bound MCMs are indeed markedly reduced in pGFP/UL117 expressing cells , but the magnitude of reduction in the former paralleled that in the latter ( Fig . 8B ) . Thus , pUL117 alone exerts its effect primarily by inhibiting MCM accumulation . During HCMV infection , it may act with other viral factors to exert its additional inhibitory effect to further reduce MCM chromatin loading ( Fig . 6B ) . Modulation of MCM function has been suggested as one mechanism for HCMV to block host DNA synthesis [17] , [18] . Our study establishes a role for pUL117 in inhibiting MCM function and host DNA synthesis during HCMV infection . We wanted to directly test the role of MCMs in pUL117-mediated inhibition of cellular DNA synthesis . We knocked down MCM2 and MCM4 by siRNA transfection , infect transfected HFFs with HCMV , and examined cellular DNA content in pUL44-positive cells at 48 hpi . MCM-specific siRNAs reduced MCM2 and MCM4 proteins by ∼80% whereas control siRNA only had a negligible effect ( Fig . 9A ) . As expected , wild type virus blocked cellular DNA synthesis regardless of siRNA transfected ( Fig . 9B ) . Also as expected , control siRNA had a minimal effect on the cellular DNA content in pUL117-deficient virus infected cells as 33% of mock- and 36% of control siRNA- transfected cells reached S and G2/M phases ( Fig . 9B ) . In great contrast , transfection of MCM-specific siRNAs restored the ability of pUL117-deficient virus to block cellular DNA synthesis . In mutant virus infection , while 36% of control siRNA-transfected cells reached S and G2/M phases , only 13% of MCM-specific siRNA-transfected cells did so . This inhibitory effect was as potent as that seen during wild type virus infection , in which 13% of cells also reached S and G2/M phases . Thus , targeting MCM function is one key mechanism for pUL117 to block host DNA synthesis . Together , our results provide evidence that viral protein pUL117 reduces accumulation of MCMs and helps to prevent their loading onto chromatin . This activity is critical for HCMV to block cellular DNA synthesis during infection .
The DNA virus HCMV has an intrinsic ability to stimulate DNA synthesis ( Fig . 4 ) , but host DNA synthesis is specifically blocked during infection . Such modulation is pivotal for herpesviruses to establish a successful lytic infection [2] . HCMV proteins pUL69 and IE2 are two known viral proteins that exert this regulatory activity . Inhibition of DNA replication licensing was also proposed as one mechanism for HCMV to block host DNA synthesis [17] , [18] . However , the viral factors targeting replication licensing have not been documented . Here , we identified pUL117 as a novel viral factor that inhibits replication licensing by targeting the MCM helicase and blocks cellular DNA synthesis upon HCMV infection . pUL117 expression is sufficient to inhibit MCM protein accumulation and reduce cellular DNA synthesis ( Fig . 8 ) . During infection , it may also act with other viral factors to exert its additional inhibitory effect to further reduce MCM chromatin loading ( Fig . 6B ) . Importantly , MCM knockdown by siRNA restores the ability of pUL117-deficient virus to block cellular DNA synthesis ( Fig . 9B ) . Thus , targeting MCM complex is one key mechanism for pUL117 to block host DNA synthesis in HCMV infection . This study also provides evidence to support the previously proposed hypothesis that inhibition of replication licensing is important for HCMV to block cellular DNA synthesis . It is also noteworthy to mention that other viral factors , such as IE2 [15] , [16] and pUL69 [10] , [11] , contribute to the ability of HCMV to block host DNA synthesis . This is consistent with our results that HCMV was still able to largely prevent host DNA replication at early times and impose additional , post-G1 blocks at late times in the absence of pUL117 ( Figs . 2C and 3A ) . This may also explain why targeting MCM is so efficient to block cellular DNA synthesis in HCMV infected cells but not in uninfected cells . MCMs are normally loaded onto chromatin in a great excess over DNA synthesis initiation events [37] , [38] . Knockdown of MCMs by RNAi may not have an immediate effect as minimally licensed chromatin continues to support DNA replication [39] , [40] . This is consistent with our result that knockdown of MCM2 and MCM4 only has a subtle effect on the DNA content of uninfected cells ( Fig . 9B ) . However , HCMV encodes several other inhibitory proteins ( i . e . IE2 and pUL69 ) and introduces many changes during infection , which may render cellular DNA synthesis hyper-sensitive to reduced MCM levels . Our results substantiate the general strategy of HCMV to use multiple viral proteins to subvert related cellular processes for its own replication . As chromatin loading of MCMs precedes the initiation of DNA synthesis at S-phase , it is conceivable that targeting MCM complex by pUL117 enables HCMV to block cellular DNA synthesis while maintaining an S-phase like environment . During infection , several viral proteins , such as pp71 , IE1 , IE2 , and pUL97 inactivate pRb , create a cellular environment conducive to DNA synthesis , and stimulate transcription of E2F-responsive genes , including MCM proteins ( reviewed in [5] ) [1] . On the other hand , accumulation of MCM proteins is delayed and chromatin loading of MCM proteins is reduced in HCMV-infected cells [17] , [18] . In addition , HCMV also appears to modulate post-licensing events by blocking hyper-phosphorylation of MCM2 at 24–48 hpi and inducing hyper-phosphorylation of MCM4 at 48–72 hpi in G0-synchronized HFFs [18] . We have also observed MCM4 hyper-phosphorylation at late times during mutant and wild type HCMV infection ( data not shown ) . However , the roles of such modulation need to be further tested . pUL117 appears to target MCM complex primarily by reducing its total levels when expressed alone or during HCMV infection at early times ( i . e . 24 hpi ) . However , the reduction in MCM loading is significantly greater than the reduction in their total accumulation at 30 hpi . Therefore , pUL117 may have other means to target MCM loading in addition to reducing their total accumulation . The exact mechanism for this remains to be elucidated as co-immunoprecipitation studies failed to reveal evidence for a direct interaction of pUL117 with pre-RC components ( data not shown ) . Furthermore , we cannot rule out the possibility that pUL117 may also target other cellular factors to excise its full inhibitory activity . How does this inhibitory activity of pUL117 impact the replication cycle of HCMV ? We previously showed that pUL117-deficient virus was defective in growth and maturation of viral nuclear replication compartments [20] . It is intriguing to speculate that the pUL117-mediated inhibition of host DNA synthesis may prevent downstream detrimental responses to unscheduled cellular DNA synthesis to allow the development of replication compartments , a critical event of the viral infection cycle . Work is in progress to dissect the molecular basis of pUL117 activity and the engagement of MCM in replication compartment maturation during HCMV infection . DNA viruses employ sophisticated strategies to subvert host DNA replication machinery and cell cycle control pathways to promote their own replication . By driving host cells into an S-phase like environment , viruses promote the accumulation of DNA synthesis components and other cellular factors required for their own production . However , many viruses inhibit host DNA synthesis at critical stages of their replication cycle . Examples include adeno-associated virus ( AAV ) [41] , human papillomavirus ( HPV ) [42] , [43] , and herpesviruses [2] . How do these viruses block cellular DNA synthesis in an S-phase like environment ? Viruses can accomplish this by modulating an array of DNA replication factors , and MCM proteins emerge as key targets in this strategy . AAV modulates multiple factors required for cellular DNA synthesis . It reduces cyclin-dependent kinase activity and sustains hypophosphorylation of pRb [41] , [44] , [45] . It also encodes viral DNA replication protein Rep that binds to MCM proteins [46] and inhibits cellular DNA synthesis [44] , [45] . It is conceivable that Rep hijacks MCM proteins for viral DNA replication , and simultaneously prevents MCM loading onto host chromatin . Over-expression of the HPV E4 protein prevents MCM protein loading onto host chromatin and inhibits cellular DNA synthesis [42] . Among herpesviruses , Epstein-Barr virus ( EBV ) induces phosphorylation of MCM4 to inhibit the DNA helicase activity of MCM complex during lytic infections [47] . When over-expressed , an EBV-encoded protein kinase phosphorylates MCM4 and causes cell cycle arrest . However , to our knowledge , the roles of these viral proteins in MCM modulation in the context of virus infection have not been reported . We have identified pUL117 as a viral factor that manipulates MCM during HCMV infection and our results exemplify deregulation of MCM function as a conserved strategy for diverse viruses to prevent host DNA synthesis . Understanding how pUL117 inhibits MCM at a mechanistic level will not only help to elucidate how diversified virus species have evolved to converge on common cellular targets , but may also shed new understanding on how eukaryotic DNA replication is regulated .
pYD-C160 and pYD-C305 are pRetro-EBNA based retroviral vectors [48] that express GFP and the GFP-UL117 fusion protein , respectively . Primary antibodies used in this study include: α-pUL117 [20] , α-IE1 [49] ( a gift from Thomas Shenk at Princeton University ) , α-pUL44 ( Virusys ) , α-IE1/IE2 ( a gift from Jay Nelson at Oregon Health and Science University ) , α-actin , α-GFP , α-MCM2 ( all three from Abcam ) , α-p16Ink4a ( Neomarkers ) , α-MCM4 ( BD Pharmingen ) , α-lamin A ( Biolegend ) , α-cdc6 ( Upstate ) , α-cyclin B1 ( Thermo Scientific ) , α-cyclin A , α-geminin , α-p53 , α-p21 ( all four from Santa Cruz Biotechnology ) . Shld1 was a gift from Thomas Wandless at Stanford University . Ganciclovir ( GCV ) , phosphonoacetic acid ( PAA ) , and propidium iodide ( PI ) were purchased from Sigma . [Methyl-14C] and [Methyl-3H] thymidines were purchased from Perkin Elmer . siLentFect lipid reagent was purchased from Bio-Rad . siGENOME Non-Targeting siRNA #2 and siGENOME SMARTpool siRNA targeting human MCM2 and MCM4 were purchased from Thermo SCIENTIFIC . The MCM2 targeted sequences are: 5′-GAA GAU CUU UGC CAG CAU U-3′ , 5′-GGA UAA GGC UCG UCA GAU C-3′ , 5′-GCC GUG GGC UCC UGU AUG A-3′ , and 5′-GGA UGU GAG UCA UGC GGA U-3′ . The MCM4 targeted sequences are: 5′-GGA CAU AUC UAU UCU UAC U-3′ , 5′-GAU GUU AGU UCA CCA CUG A-3′ , 5′-CCA GCU GCC UCA UAC UUU A-3′ , and 5′-GAA AGU ACA AGA UCG GUA U-3′ . Primary human foreskin fibroblasts ( HFFs ) were propagated in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 10% fetal calf serum . To generate HFFs expressing the GFP-UL117 fusion protein ( HF-GFP/UL117 ) or GFP ( HF-GFP ) , retrovirus stocks were made from transfection of retroviral vector pYD-C305 or pYD-C160 , respectively , and then used to transduce HFFs as previously described [50] . Various recombinant HCMV AD169 viruses were reconstituted from transfection of corresponding BAC-HCMV clones as described [20] . Viral stocks were prepared by ultra-centrifuging of infected culture supernatant through 20% D-sorbitol cushion and re-suspending pelleted virus in serum-free medium . pUL117-deficient frame-shift mutant virus ADdlpUL117 was created and previously described as ADinUL117C19-1nt [20] . The recombinant HCMV virus ADpFKBP-UL117 was reconstituted from the BAC-HCMV clone in which UL117 was tagged with a FKBP destabilization domain ( ddFKBP ) sequence at its 5′-end by linear recombination as described previously [20] . The DNA fragment containing the ddFKBP coding sequence used for recombination was amplified by PCR from plasmid pENTR221-FKBP [27] using a primer pair ( 5′-CGT GTG GAG CCG TAG ACG ATC TGG ACG TGG TCC TGG GAG AAC ATG ACC ATT TCT TCC GGT TTT AGA AGC T-3′ and 5′-CCC TCA GCC TCT GTG TTC CCA ACA CGT CCC CCC GCT GAG CGT TGG CGG CGA TGG GAG TGC AGG TGG AAA C-3′ ) . To prepare actively growing HFFs , cells synchronized at G0/G1 by contact inhibition were stimulated into the cell cycle by re-seeding at sub-confluency with serum-containing medium for 36 hours , a time where ∼90% of cells were at G1 phase ( data not shown ) . To prepare G0-synchronized HFFs , sub-confluent or confluent cells were starved in serum-free medium for 72 hours . Cells were then infected with recombinant HCMV at a tissue culture infectious dose 50 unit ( TCID50 ) of 5 , with or without serum ( 10% ) and specific viral DNA replication inhibitors ( GCV [30 µg/ml unless indicated otherwise] or phosphonoacetic acid ( PAA ) [100 µg/ml] ) as needed . If GCV or PAA was used , new GCV- or PAA- containing medium was replenished every 48 hours to maintain its efficacy . Where necessary , nocodazole ( 100 ng/ml ) was added at 8 hpi to prevent the second round of the cell cycle progression . To knockdown MCM2 and MCM4 in HFFs , subconfluent HFFs were mock-transfected , or transfected with control siRNA or siGENOME SMARTpool siRNA targeting human MCM2 and MCM4 by siLentFect lipid reagent according to manufacturer's instruction ( Bio-Rad ) . Transfected cells were starved in serum-free medium for 3 days and then infected as described above in serum ( 10% ) -containing medium in the presence of GCV ( 30 µg/ml ) . Nocodazole ( 100 ng/ml ) was added at 8 hpi to prevent the second round of the cell cycle progression . To determine the cellular DNA content , HFFs were removed from culture dish by trypsinization , collected by low-speed centrifugation , fixed , and permeabilized in ice-cold 70% ethanol overnight . Cells were then stained with propidium iodide ( PI ) only , or double-stained with PI and α-IE1/IE2 or α-pUL44 to identify lytically infected cells . Infected cells that were unsorted , sorted by virus-driven GFP expression , or gated by antibody staining to IE1/IE2 or pUL44 were determined for their DNA content by cell-cycle analysis with flow-cytometry . Percentages of cells in each cell cycle compartment were calculated using the Cell Quest software . To measure the rate of DNA synthesis , HFFs were first labeled with [14C] thymidine overnight . For infection , cells were then starved in serum-free medium for 72 hours before infected with HCMV in the presence of GCV ( 30 µg/ml ) . For over-expression , cells were then transduced with expressing retrovirus , starved in serum-free medium for 72 hours , and subsequently stimulated into the cell cycle by sub-confluent re-seeding in serum ( 10% ) -containing medium . At proper times , infected or transduced cells were labeled with [3H] thymidine for 2 hours , DNA was recovered by trichloroacetic precipitation , washed with phosphate-buffered saline , and [3H] and [14C] incorporation were measured by a liquid scintillation counter . The amount of [3H] incorporated was normalized to that of [14C] incorporated . Experiments were performed in biological triplicate and the P value associated with Student's paired t-test with a two-tailed distribution was used for statistical analysis . To prepare intra-cellular DNA for quantification by realtime-quantitative PCR , HCMV-infected HFFs were collected at various times post infection , re-suspended in 2× lysis buffer ( 200 mM NaCl , 20 mM Tris [pH 8 . 0] , 50 mM EDTA , 0 . 2 mg/ml proteinase K , 1% sodium dodecyl sulfate [SDS] ) that was spiked with 28 ng/ml of a human ATF4 cDNA-containing plasmid [50] , and incubated at 55°C overnight . DNA was extracted with phenol-chloroform , precipitated with ethanol , and re-suspended in nuclease-free water ( Ambion ) . Viral DNA was quantified by realtime-quantitative PCR as previously described [20] using a TaqMan probe ( Applied Biosystems ) and primers specific for the HCMV UL54 gene [12] . Cellular and spiked DNA was quantified with SYBR Advantage qPCR Premix ( Clonetech ) and primer pairs specific for the human β-actin gene [20] and the human ATF4 cDNA [50] , respectively . pUL117-deficient virus and wild type virus differentially modulated cellular DNA synthesis , and therefore the spiked ATF4 cDNA that was in ∼1 , 000-fold excess to genomic DNA was used to normalize samples . The accumulation of viral DNA was normalized by dividing UL54 gene equivalents by ATF4 cDNA equivalents . The accumulation of pUL117-deficient viral DNA in the presence of GCV was set as 1 . Protein accumulation was analyzed by immunoblotting [20] or flow cytometry . For immunoblotting , cells were collected , washed , and lysed in the sodium dodecyl sulfate ( SDS ) -containing sample buffer . Proteins from equal cell numbers were resolved by electrophoresis on a SDS-containing polyacrylamide gel , transferred to a PVDF membrane , hybridized with primary antibodies , reacted with HRP-conjugated secondary antibodies , and visualized by SuperSignal West Pico Chemiluminescent Substrate ( Thermo Scientific ) . For quantitative immunoblotting , total cell lysate and chromatin fractions from equal number of cells were diluted in serial two-fold dilutions and then analyzed by immunoblotting for MCM2 and MCM4 . The intensity of protein band signals was determined by imageJ software ( NIH ) . Only dilutions that produce protein bands with the intensity within the linear range were used for calculation . To further reduce systematic variations , protein bands from different samples with the most similar intensity were used . The ratio of the MCM in sample A to that in sample B is calculated based on the intensities and dilution factors of the samples: MCM of A / MCM of B = ( intensity of A/intensity of B ) * ( dilution factor of A/dilution factor of B ) . For flow cytometry , infected HFFs were collected and fixed with 1% paraformaldehyde , labeled with α-pUL44 and Alexa fluor 647 labeled anti-mouse antibody ( Invitrogen ) , and analyzed for expression of pUL44 and HCMV-driven GFP by flow cytometry . Transcript accumulation was analyzed by reverse transcription-coupled realtime-quantitative PCR as previously described [50] . Total RNA was extracted using the Trizol reagent ( Invitrogen ) and treated with the TURBO DNA-free reagent ( Ambion ) . cDNA was reverse transcribed with random hexamer primers using the High Capacity cDNA Reverse Transcription Kit ( Applied Biosystems ) , and quantified by real-time qPCR using the SYBR Advantage qPCR Premix ( Clontech ) and primer pairs specific for human genes MCM4 ( 5′-TGT GGC AGC ATG CAA AGA A-3′ and 5′-GGG TCA ATA AAA CGC TGA AGA AA-3′ ) [51] and GAPDH ( 5′-CTG TTG CTG TAG CCA AAT TCG T-3′ and 5′-ACC CAC TCC TCC ACC TTT GAC-3′ ) . The amount of MCM4 was normalized using GAPDH as the internal control . Chromatin fractions were prepared to analyze the loading of MCM as previously described [52] . Cells were collected , washed twice with cold phosphate-buffered saline , and lysed in modified CSK buffer ( 10 mM PIPES , pH 6 . 8 , 100 mM NaCl , 300 mM sucrose , 3 mM MgCl2 , 0 . 5% Triton X-100 ) containing protease inhibitors ( Protease Inhibitor Cocktail , Roche ) , 1 mM PMSF , phosphatase inhibitors ( Phosphatase Inhibitor Cocktail 1 and 2 , Sigma ) , and 1 mM ATP . Cells were incubated on ice for 10 min and then centrifuged at 850×g for 5 min at 4°C . The pellet was washed , re-suspended in lysis buffer by sonication , dissolved in SDS-containing sample buffer , and analyzed by SDS-PAGE and immunoblotting . | Inhibition of host DNA synthesis is pivotal for many viruses to establish productive infection and cause disease . Human cytomegalovirus ( HCMV ) is the top viral cause of birth defects in newborns and leads to life-threatening diseases in individuals with compromised immunity . HCMV blocks host DNA synthesis and creates a cellular environment to replicate its own genome . We report here that pUL117 , a novel viral protein that we recently identified , is required for HCMV to block host DNA synthesis . Mechanistically , pUL117 is necessary and sufficient to reduce the accumulation of the mini-chromosome maintenance ( MCM ) complex , a replicative helicase that unwinds the origin and initiates cellular DNA replication . During HCMV infection pUL117 may also have a direct role in preventing MCM loading onto chromatin . Importantly , knockdown of MCM proteins restored the ability of pUL117-deficient virus to block cellular DNA synthesis . Thus , targeting MCM function is a mechanism for pUL117 to help block cellular DNA synthesis during HCMV infection . Several proteins encoded by other viruses have also been reported to subvert MCM function by distinct mechanisms and inhibit host DNA synthesis when over-expressed in host cells . Therefore , MCM has emerged as a conserved target for viruses to prevent host DNA synthesis . Our results illustrate a novel strategy that HCMV uses to manipulate this critical cellular factor during infection . This study helps to elucidate the sophisticated strategies used by a large DNA virus to modulate cellular processes to promote infection and pathogenesis and may also shed light on the regulation of eukaryotic DNA replication . | [
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] | 2010 | Human Cytomegalovirus Protein pUL117 Targets the Mini-Chromosome Maintenance Complex and Suppresses Cellular DNA Synthesis |
Typical bacterial strain differentiation methods are often challenged by high genetic similarity between strains . To address this problem , we introduce a novel in silico peptide fingerprinting method based on conventional wet-lab protocols that enables the identification of potential strain-specific peptides . These can be further investigated using in vitro approaches , laying a foundation for the development of biomarker detection and application-specific methods . This novel method aims at reducing large amounts of comparative peptide data to binary matrices while maintaining a high phylogenetic resolution . The underlying case study concerns the Bacillus cereus group , namely the differentiation of Bacillus thuringiensis , Bacillus anthracis and Bacillus cereus strains . Results show that trees based on cytoplasmic and extracellular peptidomes are only marginally in conflict with those based on whole proteomes , as inferred by the established Genome-BLAST Distance Phylogeny ( GBDP ) method . Hence , these results indicate that the two approaches can most likely be used complementarily even in other organismal groups . The obtained results confirm previous reports about the misclassification of many strains within the B . cereus group . Moreover , our method was able to separate the B . anthracis strains with high resolution , similarly to the GBDP results as benchmarked via Bayesian inference and both Maximum Likelihood and Maximum Parsimony . In addition to the presented phylogenomic applications , whole-peptide fingerprinting might also become a valuable complementary technique to digital DNA-DNA hybridization , notably for bacterial classification at the species and subspecies level in the future .
The most common techniques for bacterial classification and identification are conventional DNA:DNA hybridization ( DDH ) [1] , comparison of 16S or 23S rRNA gene sequences or 16S–23S rRNA spacer regions [2] , multi-locus sequence typing ( MLST ) [3] and rep-PCR fingerprinting [4] , among others [5] . For decades , the technique of choice to identify and classify species has been DDH with a similarity value of 70% DDH as the species delimitation threshold [6] . In microbial taxonomy , DDH is mandatory whenever the 16S rRNA gene sequence similarity between two strains is above 97% for confirming that these do not belong to the same species . This threshold has recently been increased by proposing values of between 98 . 2 and 99 . 0% , depending on the phylum [7] . Conventional DDH has limitations , for instance , that it is only available in a few specialized molecular laboratories world-wide and it is particularly biased to experimental errors [8] . Due to this and because of the availability of whole-genome sequencing , this facilitated the development of bioinformatics alternatives to conventional DDH [9] . Here , the Genome-to-Genome Distance Calculator web service ( GGDC; freely available at http://ggdc . dsmz . de/ ) currently provides the highest in silico correlation to conventional DDH–without sharing the aforementioned drawbacks–which is a crucial requirement for any such in silico method to maintain consistency in prokaryotic species delineation [10] . The GGDC server incorporates the latest version [[10] of the Genome-BLAST Distance Phylogeny method ( GBDP ) —a highly optimized tool for the calculation of intergenomic distances—and estimates digital DNA-DNA hybridization values ( dDDH values ) from these distances under recommended settings [10] . Among other useful data , the dDDH values are reported along with confidence intervals , which are important for assessing the statistical uncertainty inherent to all model-based approaches [10] . In this way , GGDC can be reliably used for both species and subspecies delimitation [11] . The GBDP method incorporates several optimizations to avoid potentially biased results caused by elements such as paralogous genes or low-complexity regions . It is also robust against the use of incomplete genome sequences [10] and can be applied to both nucleotide and amino acid data . Finally , it includes a pseudo-bootstrapping procedure [10] for the calculation of replicate intergenomic distances , which can be further used in phylogenetic applications to assess branch support values as shown earlier [11–13] . Matrix Assisted Laser Desorption/Ionization Time Of Flight Mass Spectrometry ( MALDI-TOF MS ) has been applied as an alternative approach to identify and discriminate between species and strains [14–16] . This alternative is typically adopted when there is limited genetic variability within or across the species under study , and assumes the presence and detection of species/strain specific peptides through comparison of their mass-to-charge ratio . In this way this method supports species/strain differentiation . However , many of these differential peptides may not be detected due to their low abundance or other physicochemical properties , i . e . , those methods are limited in such a way that it only explores a subset of the total peptidic variability . To overcome this limitation , we have designed a novel in silico peptide fingerprinting methodology suitable for phylogeny inference . This methodology follows the same general principle of existing mass spectrometry approaches but it uses whole genome data and in silico protein digestion , i . e . , it does not involve any conventional experimentation . Furthermore , the analysis stands on the shoulders of well-established software tools , namely PSortB [17] , mzJava [18] , SPECLUST [19] and MrBayes [20] . The aim is to be able to generate a valid and manageable list of peptides that are potentially specific to each strain . This list could then be further investigated using in vitro approaches , such as LC-MS/MS , towards the identification of biomarkers , strain specific peptides and the development of application-specific detection methods . Our case study covers a subset of strains belonging to the Bacillus cereus group [21] . More precisely , the case study covers B . thuringiensis , B . anthracis and B . cereus ( senso stricto ) strains , which are known to share high genetic similarity [22] . Such strains are conventionally classified according to other features , such as their pathogenic potential or the presence of plasmids [23] . From a taxonomic point of view , separation of the three Bacillus species is still a subject of controversy among scientists . However , a recent large-scale whole-genome sequence-based study using GBDP elucidated the taxonomy within the B . cereus group and showed that B . thuringiensis , B . anthracis and B . cereus ( senso stricto ) species are indeed belonging to individual phylogenetic groups [12] . Other strains originally attributed to one of these three species , were either misclassified or belong to other novel species within the cluster . The results of the GBDP phylogenomic analysis serve as a good baseline , representative of what can currently be achieved with a state-of-the-art phylogenomic analysis as exemplified for the B . cereus group . Currently , a method to infer bacterial taxonomy in silico through the use of peptidomes is missing . The development of such a method is appealing as it would complement GBDP analysis . Additionally , establishing the comparison and identification of unique peptides on an exemplary microbial data set would aid in the separation of closely related strains . Moreover , in silico peptidome fingerprinting is able to reduce whole proteome data into smaller binary matrices , which is of advantage when handling larger bacterial datasets . The amount of data may be decreased using different peptidome subsets without losing phylogenetic signal . Main results are discussed in this manuscript .
All the sequence data used in this study were retrieved from the BioProject collection of the National Center for Biotechnology Information ( NCBI ) , using their public FTP site ( ftp://ftp . ncbi . nih . gov/genomes/bacteria/ ) [24] . Our study focused on the complete genomes of Bacillus anthracis , Bacillus cereus and Bacillus thuringiensis whose BioProject accession numbers are listed in Table 1 . Genetic data was obtained from * . fna files , whereas proteomes for in silico digestion were obtained from * . faa archives . Bacillus subtilis subsp . natto BEST195 was selected as an outgroup . For efficiency and to increase the flexibility in the analyses , protein data were stored in an in-house database . Subcellular localization defines the putative localization of the protein in the cell . This information is relevant because , for instance , extracellular proteins are used by the bacterium to communicate with its environment and thereby could help in bacterial differentiation . The subcellular localizations of the proteins were predicted using the standalone version of the PSortB v3 . 0 tool , following the developer guidelines [17] . The subsets corresponding to chromosomal proteins and plasmids were stored in the in-house database . Bacterial proteomes were obtained for all the Bacillus strains used in this work . The open-source Java library mzJava from ExPASy ( http://mzjava . expasy . org ) supported protein digestion [18] . For the purposes of the present analysis , three proteases representing the major intestinal endoproteases were used: trypsin , chymotrypsin and pepsin ( low specificity model , pH>2 ) . Resulting peptides , denominated peptidomes , were also stored in the in-house database . Five different datasets were considered in our study: i ) whole proteomes using GBDP for calculating intergenomic distances ( GBDP ) , ii ) peptides with a length > 28 amino acids obtained from cytoplasmic proteins ( Cyto28-more ) , iii ) peptides with a length comprised between 51 and 60 amino acids obtained from cytoplasmic proteins included in the pI range 4 . 5–5 . 5 ( Cyto_PI_51–60 ) , iv ) peptides with a length higher than 60 amino acids obtained from cytoplasmic proteins included in the pI range 4 . 5–5 . 5 ( Cyto_PI_60-more ) , and v ) peptides obtained from extracellular proteins ( Extracellular ) . For the four last subsets , three different methodologies were used to infer phylogenies , Bayesian ( MB ) , Maximum Likelihood ( ML ) and Maximum Parsimony ( MP ) . The consensus peak set among all the strains was obtained in two steps . First , the list of the total peptides for each strain was subdivided based on peptide length for indexing purposes . Then , the molecular weight and isoelectric point of the selected peptides were calculated using an in-house customised tool adapted from the SIB Bioinformatics Resource Portal ( http://web . expasy . org/compute_pi/ ) . In the case of peptides obtained from extracellular proteomes , all peptides were kept for analysis . SPECLUST , a public web-based tool , was used to identify representative and reproducible peak masses that are present in a collection of spectral profiles [18] . This tool calculates the mass difference between two peaks taken from different peak lists and determines whether or not the two peaks are identical , taking into account some measurement uncertainty ( σ ) . In the present study , the measurement uncertainty was set empirically to 3 . 0 Da . In addition , the pairwise cut-off was set to 0 . 6 , i . e . , a peak was considered shared between two spectra if it was matched in the alignment of the spectra with a peak match score greater than 0 . 6 ( corresponding to a 0 . 5 Da mass difference ) . The consensus spectra matrix was translated to a binary matrix ( 0s and 1s , representing absence or presence of a given peptide mass respectively ) in NEXUS file format [25] . MrBayes , the model-based phylogenetic inference tool using Bayesian statistics , was utilised to generate a consensus tree [20] . The consensus binary file obtained from the previously generated SPECLUST consensus file was used as input . The phylogeny was inferred through the restriction data type implemented in MrBayes ( with state 0 or 1 representing the absence or presence of a consensus peptide throughout the strain peptidomes ) . For the purpose of our study , we assumed that the frequencies of these two possible states had a Dirichlet ( 1 . 00 , 1 . 00 ) prior parameter . Bayesian analysis was performed in two independent runs using four Markov chains and 1 , 000 , 000 generations . When necessary , the number of generations was incremented for chain convergence diagnosis . The potential scale-reduction factor , printed at the end of the analysis , was used as convergence diagnosis . A majority-rule consensus tree ( 50% ) was obtained after discarding the initial 25% of the trees ( burnin = 250 ) , where the log-likelihood values of the analysis ( log probability of the data given the parameter values ) are frequently not yet stabilized . Using this command , MrBayes plots the number of generations ( each corresponding to a phylogenetic tree ) versus its log probability . Usually , the first sampled trees show trends towards increasing or decreasing log-likelihood values , which results in inadequate sampling from the posterior probability distribution Maximum likelihood ( ML ) and maximum parsimony ( MP ) phylogenies were inferred using the DSMZ phylogenomics pipeline [11] . A multiple sequence alignment was created with MUSCLE [26] , and ML and MP trees were inferred from it with RAxML [27] and TNT [28] , respectively . For ML , rapid bootstrapping in conjunction with the autoMRE bootstopping criterion [29] and subsequent search for the best tree was used; for MP , 1000 bootstrapping replicates were used in conjunction with tree-bisection-and-reconnection branch swapping and ten random sequence addition replicates . A whole-genome phylogeny ( based on the proteome data ) was inferred using the latest version of the Genome-BLAST Distance Phylogeny ( GBDP ) method [11 , 30] . Here , pairwise proteome comparisons ( including pseudo-bootstrap replicates ) were done under the greedy-with-trimming algorithm and further recommended settings [13] . The tree was inferred using FastME v2 . 07 with TBR post-processing [31] . The species and subspecies clustering was conducted on the nucleotide data ( i ) with the help of the Genome-to-Genome Distance Calculator ( GGDC ) , ( ii ) established ( sub- ) species distance cut-offs [11 , 12] , and ( iii ) the OPTSIL clustering tool [32] , in analogy to a recent study [12] . The Interactive Tree Of Life ( iTOL ) web-based tool was utilised to visualize the phylogenetic trees [33] . Using the tree files generated previously , the annotation was performed , highlighting the BCG ( Bacillus Cereus Group ) notation as reported before by Li et al . [12] . Posterior probabilities or branch support values were included when equal or above 60% . The inferred trees were compared amongst themselves and with the pseudo-bootstrapped whole-proteome GBDP phylogeny [13] . The topological comparison was based on pairwise weighted Robinson-Foulds distances , which were calculated using the RaxML tool [27 , 34] . Visualisation was supported by the packages ggplot [35] and ggdendro [36] for the statistical language R [37] .
As illustrated in Fig 3 , the GBDP proteome tree recovered all species with high support and showed insignificant subspecies conflicts . Most notably , this tree has an average branch support of 84 . 7% ( Table 2 ) and confirms previous results of a nucleotide-based GBDP analysis [12] . Moreover , the OPTSIL clustering method [32] yielded eight species clusters as well as ten subspecies clusters ( excluding the outgroup of B . subtilis ) . For instance , the cluster BCG01 contained some “B . cereus” and “B . thuringiensis” strains , which in fact belong to B . anthracis based on the dDDH estimates ( see Supplementary S3 File ) . In turn , cluster BCG03 ( B . cereus ) included two “B . thuringiensis” strains: “B . thuringiensis BMB171” and “B . thuringiensis serovar kurstaki HD73" . This is in accordance with a recent study on the taxonomic situation of the B . cereus group [12] . In summary , three major groups were identified: ( i ) BCG01 containing traditional and anomalously assigned strains of B . anthracis , ( ii ) a group encompassing the three related BCG03 ( B . cereus ) , BCG04 ( B . thuringiensis ) and BCG17 and , ( iii ) a group formed by BCG10 , BGC12 and BCG20 comprising three potential novel species [12] . Finally , “B . thuringiensis MC28” was classified into BCG09 , which has been proposed as a novel species [12] . The phylogenies of the peptidome datasets resulting from all possible combinations of the three human proteases were evaluated based on MB , ML and MP criteria ( see Supplementary S1 File ) . We also investigated proteins with different subcellular location as a possible way of reducing the amount of proteomic data input . In the case of extracellular proteins , all the resulting peptides were used in the analysis , but in the case of cytoplasmic peptidomes , the high number of peptides was further reduced by means of amino acid length and pI value filtering . Specifically , we considered three length bins , i . e . 28-more , 51–60 and 60-more amino acids , and those proteins with a pI between 4 . 5 and 5 . 5 , which corresponds to the pI exhibited by most of the housekeeping and metabolic enzymes , as deduced from as deduced from 2 dimensional electrophoresis experiments [38] . In addition , genes coding for many of these proteins , such the β-subunit of RNA polymerase ( rpoB ) , the β-subunit of ATP synthase F0F1 ( atpD ) , or the chaperonin GroEL ( groEL ) are frequently used in multilocus sequence typing approaches [39] . Interestingly , this pI range do not correspond with the normal cytoplasmatic pH in mesophilic organisms such as Escherichia coli or Bacillus subtilis , which is slightly alkaline ( 7 . 0–7 . 8 ) over an external pH ranges of 5 . 0–9 . 0 [40–44] was determined by means of a flow cytometry with the fluorescent probe 5 ( and 6- ) -carboxyfluorescein ester . As an example , we can say that the dataset including peptides with more than 60 amino acids comprised approximately 1 , 000 peptides per strain ( Suppl . S2 File ) , which contrasts with the 320 , 000–411 , 000 peptides obtained after proteome digestion for the different strains concerned in this study , and results in an obvious reduction of data input . So , the hereafter presented results relate to the extracellular peptide dataset ( Fig 4 ) , the cytoplasmic dataset containing peptides with 28 or more amino acids ( Fig 5 ) , and the cytoplasmic datasets containing peptides with 51–60 amino acids or more than 60 amino acids and pI values within the range 4 . 5–5 . 5 ( Figs 6 and 7 , respectively ) . Interestingly , other filtering criteria , such as charge to mass amino acid ratio , may be implemented as mean as reducing the proteomic input . The four peptide subsets were loaded in MrBayes and used to infer phylogenies . At the end of the Bayesian analysis , the average standard deviation of split frequencies after 1e06 generations suggested a good convergence of the analyses , as in all cases it was lower than 0 . 01 ( Cyto28-more: 0 . 004; Cyto_PI_51–60: 0 . 007; Cyto_PI_60-more: 0 . 004; Extracellular: 0 . 006 ) . Convergence of the analyses was confirmed by calculating the potential scale reduction factor ( PSRF ) of the total tree length ( TL ) and the stationary phase frequencies ( pi ) of the two possible states of our binary model ( 0 or 1 ) . In all cases the PSRF values converged to 1 . 000–1 . 001 at the end of the analysis , indicating a good phylogenetic tree sampling from the posterior distribution . A summary of the results of the phylogenetic inference is found in Table 2 . The ML analyses yielded and subsequently used “Uncorrected+GAMMA” as best model during the inference . Since the ML , MP and MB trees were very similar within each peptidome dataset in terms of weighted topological distance ( see below ) , only the MB-based trees are shown while discussing the different datasets . The remaining ML and MP trees are shown in Suppl . S1 File . Pairwise weighted Robinson-Foulds distances supported the assessment of topological differences among the five trees at the light of the four methods of analysis ( Fig 2 ) . More specifically , the differences observed between the trees inferred from whole proteomes ( GBDP analysis ) and Cyto28-more , cyto_PI_60-more cyto_PI_51–60 and Extracellular subsets ( applying the MB , ML and MP criteria ) . Significance of conflict between two trees was assumed when a bipartition implied by one tree was found incompatible with a bipartition implied by the other tree , with both receiving ≥95% support . Similarly , disagreement with the monophyly of a species or subspecies was only considered if the conflicting branches had ≥95% support . As an initial observation we can say that the MB [Cyto_PI_60-more] and [Cyto28-more] trees showed no significant conflict with the GBDP tree . However , there are some interesting discrepancies between several trees . For example , in contrast to ML and MP trees , the MB [Cyto28-more] tree ( Fig 5 ) showed significant conflict in terms of subspecies assignments within BCG01 ( B . anthracis ) cluster . Another example is the conflict between the MB [Extracellular] tree and some of the MB cytoplasmic trees regarding the placement of “B . thuringiensis serovar kurstaki HD73” . Specifically , in the [Extracellular] tree ( Fig 4 ) the “B . thuringiensis serovar kurstaki HD73” is placed next to the BCG04 ( B . thuringiensis ) cluster with high support while in the MB [Cyto_PI_60-more] ( Fig 6 ) and [Cyto28-more] ( Fig 5 ) trees it is part of the BCG03 ( B . cereus ) group . Likewise , the MB [Cyto_PI_51–60] tree ( Fig 6 ) significantly deviated from the GBDP proteome tree by placing B . anthracis H9401 as sister group of all other highly virulent B . anthracis strains instead of as sister group of B . anthracis CDC 684; and , by forming a well-supported group ( 96% ) comprising “B . thuringiensis MC28” , the cluster BCG04 ( B . thuringiensis ) and the cluster BCG17 ( B . thuringiensis ) . Noteworthy , these arrangements received no support in the ML and MP analyses of the [Cyto_PI_51–60] dataset . See Supplementary S1 File for details . The comparison of the peptidome-based phylogenetic trees allowed us to gain a better understanding about the information provided by the different sets of peptides . The four peptide subsets produced similar results regarding the identification of quite unrelated strains ( e . g . , B . subtilis subsp . natto BEST195 ) , and established a species grouping as close as the one suggested by Liu et al . using 224 genomes of strains belonging to the B . cereus group [12] . Classically , B . thuringiensis strains have been considered an insect pathogen , affecting mainly members of the orders Lepidoptera , Diptera and Coleoptera [23] . Spores from these strains include large crystal protein inclusions , which are cleaved by the insect mid-gut proteases producing the active toxin forms . The action of this toxin leads to the complete destruction of the intestinal epithelium . In turn , the BCG03 cluster corresponds to B . cereus , which is an opportunistic human pathogen and food-borne bacterium that causes two forms of poisoning , one characterised by diarrhea and abdominal pain , and the other involving nausea and vomiting [45 , 46] . Some “B . thuringiensis” strains also clustered in BCG03 , because they share certain genetic similarity with B . cereus ATCC 14579T , namely genetic regions such as a putative polysaccharide capsule cluster [47] . B . anthracis ( BCG01 cluster ) is the etiological agent of anthrax , a fatal disease for herbivores and mammals that is best known for its use as biological weapon [48] . Strains from this species can be classified according to different phenotypical tests . For instance , these strains are non-motile , penicillin-sensitive , and produce an extracellular capsule of poly-γ-D-glutamic acid [49] . Toxins responsible for anthrax symptoms and other virulence factors necessary for complete virulence are codified into two large plasmids , denominated pXO1 and pXO2 [50] . Two strains of “B . thuringiensis” also clustered within the BCG01: “B . thuringiensis Al Hakam” , and “B . thuringiensis serovar konkurian” . Indeed both strains have been shown to be more related to the B . anthracis cluster . The genome of these strains contain no homologues of the known B . thuringiensis insecticidal genes cry , cyt , or vip and , even if these ever existed , the plasmid ( s ) encoding for these genes may have been lost during in vitro culture [50 , 51] . Therefore , classification of these two strains as B . thuringiensis strains may not be correct , as previously reported in [12] . Other cluster identified in our analysis was BCG17 , a putative novel species . This contained “B . cereus G9842” together with other two “B . thuringiensis” strains . The G9842 strain was isolated from stool samples of an emetic outbreak that involved three individuals in Nebraska ( 1996 ) and the genome was sequenced by the J . Craig Venter Institute ( http://www . ncbi . nlm . nih . gov/bioproject/17733 ) . The isolate was characterised by MLST typing using the MLSTDB scheme as sequence type 56 ( http://pubmlst . org/bcereus/ ) . Interestingly , the sequence type 56 was quite unrelated to the major clade of pathogenic B . cereus isolates and was suggested as representative for a novel pathogenicity group within the B . cereus group [52] . Peptidome fingerprinting confirms the new affiliation to B . thuringiensis . The peptidome of strain G9842 , shared a high homology with the other “B . thuringiensis strains” , so it is plausible that these two isolates lost the plasmids containing the insecticide genes and acquired certain virulence factors , which allow them to act as pathogens in the human host . Finally , phylogenetic techniques consistently grouped “B . thuringiensis serovar finitimus” individually , and it has been proposed as representative for the novel species BCG20 [12] . This strain contains several cry genes encoding for crystal proteins and located in two plasmids [53] . The chromosome of this strain has been shown to be closer phylogenetically to B . anthracis Ames than to B . cereus ATCC 14579T [12 , 54] . Given the close distance of “B . thuringiensis serovar finitimus” to the other BCG groups containing “B . cereus” strains , such as BCG10 and BCG12 , we speculate that this strain may be a B . cereus strain that acquired the plasmids from a B . thuringiensis donor . Another important aspect of the evaluation of our peptidome similarity method is the computational complexity induced by each processing step and the resulting processing time eventually , although available computational power will be of course decisive . The running time of future in silico experiments can thus be extrapolated , especially that of significantly larger datasets . We computed the processing time of each of the main steps for the whole Bacillus dataset ( i . e . 32 genomes ) and for four subsets , representing a large dataset ( i . e . 24 genomes ) , a medium-large dataset ( i . e . 16 genomes ) , a medium-small dataset ( i . e . 8 genomes ) and a small dataset ( i . e . 4 genomes , which is the smallest possible dataset that one can use for phylogenetic inference ) . In particular , we randomly sampled without replacement four sets of 24 , 16 , 8 and 4 genomes , and calculated the average running time . Here , we present the average times , but details on the different runs can be found in S4 File . Table 3 summarises the running times taken by the steps of protein localization , which is performed by PsortB , and protein digestion , which is performed by ExPASy MzJava . Protein localization is the most time consuming task and , in particular , the processing of larger datasets may take several days . Although this may be considered somewhat time consuming , this step enables further filtering of the peptide dataset that , in turn , may reduce considerably the data matrices to be computed and speed up the subsequent steps of analysis . The running times of steps leading to the generation of the NEXUS files are negligible compared to those of previous steps ( Table 4 ) . For most of the sample sets both steps took less than 15 minutes to execute . A large running time ( > 2 hours ) was observed for the SPECLUST run over the whole dataset of cytoplasmic peptides with 28 or more amino acids , which comprises a total of 121 , 632 peptides . One of the potential applications of our pipeline is to accept , as input , experimental peptide mass profiles . If traced back , our application allows detection of differential peptide profiles , providing a robust tool to discriminate not only strain-specific peptides , but true intraspecies differences among a set of biological replicates or even microorganism-phenotype variations such as those occurring between biofilm and planktonic populations . In this regard , the negative effect of certain peptide families on bacteria through different mechanisms is well known [55 , 56] . In this regard , our pipeline will just provide a candidate peptide list , but experimental approaches such as MS/MS experiments will never detect peptides that are inhibiting own bacterial growth . Rather , such experimental approaches will validate the presence of those certain strain-specific peptides , either free or most probably encoded in a “carrier protein” . Generation of a potential strain-specific peptide list together with its experimental identification , may facilitate development of different approaches focused on the identification of given strain , such as a dairy starter or a probiotic that has to be traced through the human gut during clinical intervention studies . This can be accomplished , for instance , with the use of high-resolution mass spectrometers or antibody-based protocols targeting these specific peptides . Whereas our bioinformatic approach will reliably produce the same results , conventional methods might yield different results even if applied on the same organisms , due for instance to phenotype-variations or the use of transient input data . In addition , the big advantage of the in silico method is accuracy , reproducibility and speed , whereas the disadvantage is that it might not get the experimental peptidome as we simply consider all proteins encoded in a genome and not only those that are actively produced by the organism while being measured . Overall , results show that our phylogenetic method based on peptidome similarity , as opposed to genome-sequence homology , is complementary to the proteome-based GBDP analysis . Most notably , our peptidome-based phylogeny analysis supported already reported taxonomic discrepancies within the B . cereus group . Our peptidome-based method has the advantage of reducing larger amounts of proteomic data to small matrices ( by a factor of 320 ) without losing too much phylogenetic signal . Our pipeline can be also applied to other peptide datasets originated from viruses , eukaryotic species or even metaproteomes with the inclusion of few modifications regarding the prediction of the protein subcellular location . This could be of interest for developing more efficient applications aimed at managing very large bacterial datasets , such as those generated in epidemiologic studies . | Molecular based differentiation of bacterial species is important in phylogenetic studies , diagnostics and epidemiological surveillance , particularly where unusual phenotype makes the classical phenotypic identification of bacteria difficult . Typical bacterial differentiation methods are often challenged by a high genetic similarity among strains . For decades , the technique of choice to classify and identify bacteria was DNA-DNA hybridization ( DDH ) . The boosting of whole-genome sequencing technology facilitated the development of bioinformatics alternatives that could assist a much wider number of laboratories and are less biased to experimental errors . Currently , the Genome-to-Genome Distance Calculator web service , implementing the Genome-BLAST Distance Phylogeny ( GBDP ) method , provides the highest correlation to conventional DDH . Our methodology shows that whole peptide fingerprinting may complement the outputs of GBDP , i . e . experimental mass spectra may be used to cluster the bacteria , and more specifically it has been found useful for bacterial classification at the species and subspecies level . In addition , we present here how peptidome subsets obtained from in silico digestion of the peptidomes , is an efficient way to maintain the phylogenetic signal whilst reducing the total amount of data , making this methodology suitable for handling large data sets as in the case of epidemiologic studies . | [
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] | 2016 | Improving Phylogeny Reconstruction at the Strain Level Using Peptidome Datasets |
Mouse Embryonic Stem ( ES ) cells express a unique set of microRNAs ( miRNAs ) , the miR-290-295 cluster . To elucidate the role of these miRNAs and how they integrate into the ES cell regulatory network requires identification of their direct regulatory targets . The difficulty , however , arises from the limited complementarity of metazoan miRNAs to their targets , with the interaction requiring as few as six nucleotides of the miRNA seed sequence . To identify miR-294 targets , we used Dicer1-null ES cells , which lack all endogenous mature miRNAs , and introduced just miR-294 into these ES cells . We then employed two approaches to discover miR-294 targets in mouse ES cells: transcriptome profiling using microarrays and a biochemical approach to isolate mRNA targets associated with the Argonaute2 ( Ago2 ) protein of the RISC ( RNA Induced Silencing Complex ) effector , followed by RNA–sequencing . In the absence of Dicer1 , the RISC complexes are largely devoid of mature miRNAs and should therefore contain only transfected miR-294 and its base-paired targets . Our data suggest that miR-294 may promote pluripotency by regulating a subset of c-Myc target genes and upregulating pluripotency-associated genes such as Lin28 .
Embryonic stem cells , which are derived from the inner cell mass of the blastocyst , hold great clinical promise because of their unique capacity to both self-renew and differentiate into potentially any cell type . Understanding the molecular controls of pluripotency is key to realising their therapeutic potential . While the general importance of small RNAs in gene regulation has been recognised in plants and animals , much remains to be understood about the specific role of small RNAs in ES cells . miRNAs and small interfering RNAs ( siRNAs ) are a class of small ( ≈20–25 nucleotide ) non-coding RNAs that direct sequence-specific post-transcriptional repression of target mRNAs . Mature miRNAs and siRNAs are generated from double stranded RNA ( dsRNA ) precursors by the RNase III enzyme Dicer [1] , [2] . The mature small RNA is then incorporated into a protein of the Argonaute family [3] , [4] . This RNA-protein complex forms the core of the effector complex referred to as the RNA-induced silencing complex ( RISC ) . Within the RISC , the small RNA acts a guide to direct Argonaute proteins to complementary target transcripts to elicit the cleavage , degradation or translational repression of their targets depending on their degree of complementarity [5] . Several studies implicate miRNAs in the control of early embryonic development and maintenance of the pluripotent stem cell state . Disruption of the single Dicer1 gene in mice leads to early embryonic lethality around E7 . 5 [6] . Dicer1 mutant embryos have greatly reduced expression of Oct-4 in the epiblast , implying a lack of pluripotent cells , and it is not possible to derive ES cells from Dicer1-null blastocysts . However , conditional deletion of Dicer1 from established ES cells results in an impaired capacity to differentiate , as well as a profound initial proliferation defect that is overcome with time [7] , [8] . Moreover , large scale cloning and sequencing efforts have revealed a subset of miRNAs that are unique to ES cells [9]–[11] . The miR-290-295 cluster ( Figure 1A ) consists of 6 miRNAs that share a similar 5′ region from nucleotides 2–8 , known as the ‘seed’ sequence , which is thought to be the primary specificity determinant for target recognition in most miRNAs [12] . The miR-290-295 cluster accounts for the majority of all miRNAs expressed in undifferentiated ES cells but decreases after ES cells differentiate [9] . Recent evidence suggests that there are functional differences between miRNAs from the miR-290-295 cluster . Only miR-291-3p , miR-294 and miR-295 can promote the G1-S transition of the cell cycle and the induction of pluripotency [13] , [14] . Furthermore , miR-293 expression and seed sequence differs markedly from the other members of this family , indicating the need to re-examine previous inferences based on whole miR-290-295 overexpression studies [15]–[17] . To identify targets and study the function of individual miRNAs in ES cells , we used Dicer1-null ES cells in which we replaced endogenous Ago2 with a transgenic Ago2-myc ( Dicer1Δ/Δ+Ago2-myc ) ( Figure 1B ) . In this Dicer1-null background , there are no mature miRNAs [11] , which enables the reintroduction of any single miRNA at a time . To investigate the role of miR-294 in ES cells , we profiled the transcriptome of Dicer1-null ES cells transfected with miR-294 . In addition , we used a biochemical approach that exploits the direct interaction of the mature miRNA to its mRNA target within the Ago protein of the RISC complex [18] . Immunoprecipitation of Ago2-myc from Dicer1-null ES transfected with miR-294 , followed by RNA-sequencing of associated RNAs should lead to identification of direct miR-294 targets . Our strategy overcomes the problem of immunoprecipitating RISC complexes that contain many different miRNAs with their corresponding targets [19]–[21] . This approach is applicable to the study of any single miRNA or combination of miRNAs , potentially in any cell type . We describe here the results of the microarray and RNA-Sequencing analyses , and how miR-294 integrates into the ES cell regulatory network .
First , we established Dicer1-null ES cells that lack all mature miRNAs ( Figure S1B , S1C ) , into which we can reintroduce one specific miRNA of interest at a time . Previous studies have shown that the miR-290-295 cluster might have an important role in the pluripotency of ES cells [14] , [16] , [17] amongst which miR-294 is one of the most abundantly expressed miRNAs ( Figure S1A ) [11] . We decided to investigate the potential role of miR-294 in pluripotency . Metazoan miRNAs typically bind to partially complementary sites in the 3′ untranslated region ( 3′ UTRs ) of their target mRNAs to direct translational repression or mRNA destabilisation [22]–[24] . Overexpression of a miRNA can affect hundreds of mRNAs and messages that are downregulated tend to have significant enrichment of sequences complementary to the corresponding seed of the miRNA [16] , [17] , [24] . To identify the global effects of miR-294 in ES cells , we analysed the transcriptomes of Dicer1Δ/Δ+Ago2-myc ES cells transfected with miR-294 compared to Dicer1Δ/Δ+Ago2-myc ES cells transfected with a cel-239b control miRNA , which has minimal sequence identity to mouse miRNAs . The transcriptomes were profiled using Illumina microarrays ( GEO accession code: GSE20048 ) . The relative signal intensities of each of the 15735 probes across the six samples were plotted as a heat map ( Figure 1C ) , which revealed global downregulation and upregulation of probes in each sample transfected with miR-294 . To select differentially expressed genes , probes were ranked by the log odds ratio ( B-statistic ) of each probe showing differential expression , and plotted against the log fold change in a ‘volcano’ plot ( Figure 1D ) . A B-statistic >5 ( and corresponding to an adjusted p-value <0 . 0001 ) was selected as a cut-off for differential expression ( Figure 1C ) . Using this conservative cut-off gives , with very high confidence , 162 upregulated ( Table S1 ) and 248 downregulated differentially expressed genes upon miR-294 transfection . To determine whether the downregulated transcripts contain miR-294 seed matches , we used the k-mer composition analysis tool Sylamer [25] to search for overrepresentation of sequence motifs in the 3′ UTRs . Each gene on the array ( for which a 3′ UTR sequence was annotated ) was ranked from most downregulated to most upregulated according to log fold change . If enrichment of the seed sequence in the 3′ UTRs correlates with the ranking of genes according to their fold change , then one would expect to observe a sharp peak in overrepresentation of the miRNA seed for the top-ranked genes . The resulting enrichment analysis plot shows that a strong signal is evident for 6-mer words corresponding to the seed region of miR-294 ( Figure 1E ) , peaking approximately at gene 1000 in the ranked list . Conversely , this sequence is depleted in upregulated genes . The maximum of the first enrichment peak can be chosen as a threshold , above which , genes can be considered candidate targets if they contain the appropriate seed sequences matches . This yielded a more relaxed list of 487 predicted targets , which have at least one 6-mer present in their 3′ UTR ( Table S2 ) . This Sylamer list contains all of the conservative list of predicted targets ( 127 out of 248 have at least one 6-mer in their 3′ UTR ) selected using a B-statistic >5 ( Table S2 ) . In summary , there was overrepresentation of the miR-294 seed sequence in the most downregulated genes , indicating that many of the observed gene expression changes are likely to be consequences of miR-294 expression . To discover direct targets of miR-294 , we used a biochemical approach to isolate mRNAs targeted by miRNAs within the Ago2 protein of the RISC . To facilitate the immunoprecipitation of Ago2 , we replaced endogenous Ago2 with a myc-epitope tagged Ago2 . We also constructed a catalytically-inactive mutant Ago2-myc ( Ago2-myc-MUT ) by point-mutating residues Q633R and H634A in the PIWI domain to capture the subset of target mRNAs that would otherwise be sliced by Ago2 [26] , [27] . Point mutations Q633R and H634A were previously shown to abolish mouse Ago2 cleavage-activity without affecting siRNA binding [27] . First , we confirmed the specificity of Ago2-myc immunoprecipitation ( IP ) ( Figure S2A ) , and that immunoprecipitation of Ago2-myc from Ago2flox/flox;Dicer1flox/flox ES cells does retain miRNAs ( Figure S2B ) . We then assessed the enrichment of known targets using Dicer1-null ES cells transfected with miR-294 . We found enrichment of Cdkn1a , a known miR-294 target [13] , in immunoprecipitated RNA from Dicer1Δ/Δ+Ago2-myc ES cells transfected with miR-294 , compared to cel-239b ( Figure S2C ) . Following RNA-immunoprecipitation of miR-294-programmed Ago2-myc , RNA from the INPUT ( total RNA ) and IP ( Figure S3A ) were subjected to library preparation and sequenced by SOLiD ( GEO accession code: GSE20199 ) . A comparison of the global gene expression changes detected by microarray and by RNA-Sequencing of the INPUT revealed a similar trend ( Figure 2A ) . Furthermore , there was overrepresentation of the miR-294 seed sequence in the most downregulated genes of the sequenced INPUT ( Figure 2B ) , indicating that miR-294 was functional and that the transfection efficiency was sufficiently high ( Figure S3B ) to bring about gene expression changes . An examination of the IP vs . INPUT ratios for each sample revealed an overall greater dynamic range of enrichment for samples transfected with miR-294 compared to cel-239b ( Figure S4 ) . The pattern was similar for the catalytically-inactive Ago2-myc . This result was expected since miRNA-directed slicing of the target mRNA is thought to occur rarely in metazoans [12] . However , the overall extra enrichment could not be accounted for by the overrepresentation of miR-294 seed sequence matches in the 3′ UTRs of enriched genes ( Figure 3A ) . There is a surprising general tendency for hexamers with a higher GC content to be overrepresented in the 3′ UTRs of enriched transcripts ( Figure 3A and 3B ) . This effect is also observed in samples transfected with the control cel-239b ( Figure 3C ) . However , the GC effect is markedly stronger in the samples transfected with miR-294 . To ascertain whether this extra IP enrichment contains miR-294 targets , the correlation between our data and computational miR-294 target predictions was tested . The fraction of genes ranked by p-value ( based on the likelihood of enrichment ) that have a TargetScan prediction was calculated . For samples transfected with miR-294 , approximately one-tenth ( 0 . 1 ) are predicted targets by TargetScan for the top 1000-most enriched genes ( Figure 4A ) . For samples transfected with cel-239b , the cumulative fraction is considerably lower ( 0 . 04–0 . 06 ) ( Figure 4A ) . Thus , there is a correspondence between the degree of observed enrichment and the TargetScan computational target predictions . In contrast , there was no correlation with miRanda [28] target predictions ( Figure 4B ) . To have a genome-wide benchmark for identifying miR-294 targets , we compared enriched genes in the IP ( selected using a p-value <0 . 003 cut-off ) with our microarray-predicted targets . There is a statistically significant correlation between IP enrichment and the microarray-predicted targets ( Figure 4C and 4D ) . The trend is similar to the correlation between IP enrichment and TargetScan predictions ( Figure 4A ) . Thus , miR-294 targets are enriched in the IP but there is also a non-seed-match-specific binding effect that correlates with hexamer GC content . In summary , a substantial proportion of enriched genes with miR-294 seed matches are not , however , detected in the microarray target predictions . These genes could be direct targets whose transcript levels remain unchanged by miR-294 overexpression and are therefore , not detected by microarray analysis . Alternatively , the basis of this extra enrichment could represent novel targets with non-canonical seed matches , non-specific associations , or technical limitations . To gain an insight into the biological role of miR-294 , we performed a Gene Ontology ( GO ) analysis on the upregulated and downregulated genes ( with at least one 6-mer in their 3′ UTR ) selected from the microarray ( B-statistic >5 ) . The top ten terms included enrichment for genes involved in cell cycle ( regulation of the G1-S transition ) , and development and transcription ( Figure 5A ) . The miR-290-295 cluster has been described as a ‘Trojan horse’ inside ES cells to bring about differentiation [29] . If this were indeed the case , then one would expect to find an enrichment of differentiation-associated terms in the upregulated genes . This is not the case . Instead , the majority of terms associated with differentiation were more enriched in the downregulated genes . In addition , Lin28 was upregulated by miR-294 transfection into Dicer1Δ/Δ+Ago2-myc ES cells ( Figure 5C ) . Lin28 is considered to be important for stem cell maintenance by blocking the processing of let-7 [30] , [31] , a critical miRNA involved in differentiation [32] . Furthermore , Lin28 , in conjunction with Nanog , Oct-4 and Sox2 can reprogram human fibroblasts into pluripotent cells [33] . No pluripotency genes were detected amongst the downregulated genes . This is consistent with a potential role of miR-294 in the maintenance of the pluripotent state because co-expression of miRNAs that directly target the pluripotency factors would be detrimental to ES cells . In keeping with cell proliferation as the top-ranking functional GO category ( Figure 5B ) , miR-294 has previously been reported to be able to substitute for c-Myc , but not Oct-4 , Sox2 or Klf4 , in the reprogramming of mouse embryonic fibroblasts ( MEFs ) into induced Pluripotent Stem ( iPS ) cells [14] . The authors posited that miR-294 acts as a downstream target of c-Myc , which binds to the promoter region of mir-290-295 . To test the alternative ( and not exclusive ) possibility that miR-294 may also substitute for c-Myc by regulating shared targets , a GeneGo ( GeneGo Inc ) network was generated using the downregulated and upregulated genes ( with a B-statistic >5 ) from the microarray as an input list . The resulting networks revealed enrichment for a subset of targets of the c-Myc network ( Figure 6 ) . c-Myc was a central node with direct regulatory connections to target genes , but it was not itself a target of miR-294 . However , many c-Myc target genes were downregulated directly or upregulated indirectly by miR-294 . This suggests that miR-294 and c-Myc regulate an overlapping set of target genes , which is consistent with their comparable roles during iPS induction [14] . Thus , from the GeneGo network , miR-294 appears to act synergistically with c-Myc on a subset of targets . However , miR-294 may also repress a subset of targets that are induced by c-Myc . Since the reprogramming process is not identical when miR-294 is substituted for c-Myc [14] , this might perhaps explain some of the observed differences between them during the derivation of iPS cells . This network motif , in which c-Myc both activates a target , and inhibits it via miR-294 , is described as “incoherent feedforward” regulation [34] . Our network also includes a previously hypothesised incoherent feedforward loop involving Oct-4 , miR-294 and Lefty1/Lefty2 [35] . The functional significance of the incoherent feedforward loop has not been fully delineated but it can accelerate the response following a stimulus by decreasing the target concentration , fine tune steady state levels and buffer against perturbation [34] . In conclusion , miR-294 may promote pluripotency through regulating a shared subset of c-Myc target genes rather than simply being a downstream effector of c-Myc , and through the upregulation of pluripotency-associated genes , such as Lin28 .
Our findings show that miR-294 regulates a subset of c-Myc target genes , and upregulates Lin28 . We did not observe substantial overlap between Oct-4 , Nanog or Sox2 regulated genes and miR-294-regulated transcripts . Thus , the effects on c-Myc and Lin28 are distinct from the other core pluripotency factors , consistent with previous data that proposed functional differences between targets of Myc and that of the other core pluripotency factors [36]–[38] . Furthermore , our conclusions are supported by a recent study [39] . However , we did not detect upregulation of c-Myc mRNA by miR-294 in our microarray results , but rather Mycl1 , which belongs to the Myc family of transcription factors . This difference could be a result of the different microarray platforms used for the transcriptome analyses . Indeed , we identified other networks that were enriched for miR-294-regulated transcripts , which included Sp1 , Esr1 , Hnf4-alpha , p53 and Stat3 . The integration of miRNAs into transcriptional networks offers versatility to the regulatory outputs: it enables miRNAs to affect the dynamical properties of transcription factor targets , modulate the strength of transcription factors , and increase the robustness of transcription factor networks . With respect to c-Myc , this model may provide insights on how the initial proliferation defects of Dicer1-null ES cells can be compensated with time [7] , [8] , and indicates a resetting of the transcriptional network in the absence of miRNAs . This is the first study to use Dicer1-null ES cells combined with a biochemical approach to identify targets of a single miRNA at a time . A surprising finding was that there was a GC bias in hexamer composition in the 3′ UTRs of enriched transcripts . This effect was much greater for samples transfected with miR-294 than with cel-239b , implying that there is a biological basis for this association . Such transcripts with no predicted canonical seed matches may reflect binding to miRNAs that don't follow the rules of seed-driven target recognition or non-specific associations . Recently , a role for seed sequence-independent regulation has emerged for miR-328 , which acts through its C-rich region to titrate a translational inhibitor , hnRNP E2 protein , from its target mRNA [40] . However , the possibility of non-canonical targets would require careful verification . A pertinent issue for the biochemical approach is the ability of miRNAs to induce mRNA destabilisation independently of Ago2-catalysed slicing , as this could hamper the isolation of mRNAs that are being degraded . One possible way to address this in future work is to explore different Ago2 mutants that cannot interact with factors that bring about mRNA destabilisation , such as GW182 [41] . The use of such a mutant might improve the likelihood of retaining intact mRNAs and increase the efficiency of the pull down . Despite a large discrepancy between miRNAs found in human and mouse ES cells , there are homologs of the miR-290-295 cluster in human ES cells ( miR-371 , miR-372 and miR-373 ) [10] , alluding to an important role of these miRNAs in the pluripotency of mammalian ES cells . Given the expression of miR-294 in primordial germ cells [42] and its function in pluripotency , it will be interesting to determine the effects of miR-290-295 conditional deletion upon the germline .
Mice carrying floxed alleles of Ago2 or Dicer1 were described previously [43] , [44] . Ago2flox/flox;Dicer1flox/flox ES cells were derived from Ago2flox/flox;Dicer1flox/flox blastocysts and cultured on feeders in media supplemented with leukaemia inhibitory factor . Ago2flox/flox;Dicer1flox/flox ES cells were transfected with linearised plasmids of pCAG-Ago2-myc , pCAG-Ago2-myc-MUT or pCAG-myc , and selected with G418 . To excise the floxed alleles of Ago2 and Dicer1 in Ago2flox/flox;Dicer1flox/flox stable clones , ES cells were transfected with Cre-GFP plasmid ( Addgene ) . After 24 hr , GFP-positive cells were flow-sorted ( FACSAria , BD Biosciences ) . 2000 or 4000 cells/10 cm dish were plated to enable picking of single colonies . The genotype of individual clones was determined by PCR using primers: Dicer-FN 5′-GGT TAC ATG GCT AGA CTC AAA GC-3′; Dicer-RN 5′-AGG TGC CTT TCG TTT AGG AAC-3′; Dicer-FWF 5′-AAA GCA GAA CTC TAA TGC CCC-3′ . It was further confirmed by profiling the expression of mature miRNAs from the miR-290-295 cluster ( Figure S1C ) . ES cells were maintained in ES cell medium , in the absence of feeders on gelatinised tissue culture plates . ES medium consisted of:: DMEM/F12 ( Invitrogen ) , 15% FCS ( Gibco ) , 2 mM L-glutamine ( Gibco ) , 0 . 1 mM non-essential amino acids ( Gibco ) , 1 mM sodium pyruvate ( Sigma ) , 0 . 12% sodium bicarbonate ( Sigma ) , 10 µM β-mercaptoethanol ( Gibco ) , 50 µg/ml penicillin/streptomycin ( Gibco ) , and 2×103 U/ml LIF ( Chemicon ) . The Ago2 coding sequence was amplified from 129Sv/Ev mouse ES cell cDNA by PCR using primers: Ago2-5′ 5′- AGA ATT CAT GTA CTC GGG AGC CGG CCC CGT TCT T-3′; Ago2-3′ 5′-ATG CGG CCG CTC ACA GAT CCT CTT CTG AGA TGA GTT TTT GTT CAG CAA AGT ACA TGG TGC GCA G-3′ to contain a carboxy-terminal myc-epitope tag . Q633R and H634A point-mutations were introduced by site-directed mutagenesis using the QuiKChange kit ( Stratagene ) . For the mammalian expression constructs , wild-type and point-mutated Ago2-myc in pCAGIG were digested with Sal1-Not1 to include the CAG promoter upstream of the Ago2-myc coding sequence and ligated into pEGFP-1 ( Clontech ) , which was pre-digested with Sal1-Not1 to remove the EGFP coding sequence . Transfections were performed using the Mouse ES Cell Nucleofection Kit ( Lonza ) , and program A23 of Nucleofactor I apparatus ( Lonza ) , as specified by the manufacturer's instructions . Transfections of miRNA mimics mmu-miR-294 and cel-239b ( Dharmacon ) was performed as described in [17] . Briefly , for microarray analysis , approximately 4×106 Dicer1-null ES cells were resuspended in 90 µl mouse ES cell Nucleofection Solution . 300 pmol of miR-294 and 1 µg pEGFP-1 ( Clontech ) plasmid ( which served as a control for transfection efficiency ) were diluted in 10 µl Nucleofection Solution , mixed with the cells and transferred to a Lonza cuvette . For RNA-IP experiments , cells were transfected with 300 pmol of miR-294 or cel-239b control miRNA ( Dharmacon ) and harvested 12–16 hr after transfection for immunoprecipitation . Cells were lysed in lysis buffer ( 50 mM Tris-HCl pH 8 . 0 , 150 mM NaCl , 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , and protease inhibitors ) . Equal concentration of the lysed proteins were separated on polyacrylamide-SDS gels , transferred onto PVDF Hybond membrane and probed with the following primary antibodies: anti-Ago2 ( Abnova , 1∶1000 ) , Lin28 ( Cell Signaling Technology , 1∶1000 ) , Tubulin ( Sigma , 1∶5000 ) . This was followed by incubation with horseradish peroxidase-conjugated secondary antibodies . ECL kits ( Amersham ) were used for detection . Cells were grown on Lab-Tek chamber slides and fixed with 4% paraformaldehyde in PBS for 15 minutes at room temperature . Cells were blocked in 1% BSA/0 . 1% Triton X-100 in PBS and incubated O/N at 4°C with the following primary antibodies: anti-Eomes ( eBioscience , 1∶200 ) , anti-Oct3/4 ( BDBiosciences , 1∶200 ) . This was followed by incubation with AlexaFluor secondary antibodies ( 1∶500; Molecular Probes ) and DAPI ( 1∶1000; Sigma ) , for 1 hr at room temperature . miRNA- and RNA-immunoprecipitation was performed in native conditions as described [45]–[47] . 2 . 5×107 cells were pelleted and resuspended in 220 µl ice-cold Polysomal Lysis Buffer [100 mM KCl , 5 mM MgCl2 , 10 mM HEPES ( pH 7 . 0; Gibco ) , 0 . 5% NP40 , 1 mM DTT , 200 U Recombinant RNasin Ribonuclease Inhibitor ( Promega ) , 200 U SUPERase•In RNase inhibitor , and Protease Inhibitor Cocktail] . The lysate was passed through a 27G needle five times , incubated on ice for 30 min at 4°C and then transferred to –80°C to promote lysis . The lysate was then thawed on ice , centrifuged ( 15 min , maximum speed , 4°C ) , and the supernatant was transferred to a new tube . 400 U DNase ( Roche ) was added and incubated on 30 min at 4°C . 20 µl of the lysate was saved for the INPUT at –80°C . The remaining lysate was diluted in 800 µl NT2 buffer [50 mM Tris-Hcl ( pH 7 . 4 ) , 150 mM NaCl , 1 mM MgCl2 , 0 . 05% NP40 , Protease Inhibitor Cocktail , 200 U Recombinant RNasin Ribonuclease Inhibitor] and pre-cleared for 2 hr at 4°C with 20 µl Dynabeads-Protein A ( Invitrogen ) , which had been pre-blocked in 0 . 5% BSA +1 mg/ml yeast tRNA ( Ambion ) . The supernatant was incubated with 20 µl anti-myc ( Cell Signaling Technology ) antibody O/N at 4°C and 200 U Recombinant RNasin Ribonuclease Inhibitor . The next day , the RNA/antibody complex was precipitated by addition of 50 µl Dynabeads-Protein A for 2 hr at 4°C . The beads were washed with 1 ml NT2 buffer four times and then resuspended in 100 µl NT2 buffer and transferred to a fresh tube . 80 µl of NT2 buffer was added to 20 µl of the stored INPUT . RNA was extracted with 1 ml Trizol according to the manufacturer's protocol and RNA was resuspended in 20 µl H2O . To amplify mRNA 20 cycles for sequencing , 1 µl of IP ( 10–40 ng/µl ) and 1 µl of INPUT ( diluted to 0 . 5–2 . 5 ng/µl ) were used . For sequencing of mRNAs , the single cell method was modified according to [48] . For 220-plex microRNA expression profiling , 220 miRNAs were reverse transcribed , amplified and then analysed by Q-PCR , as described [49] , [50] . Quantitative PCR was performed on an ABI PRISM 7000 sequence detection system ( Applied Biosystems ) . For SYBR Green fluorescent nucleic acid dye , primers were designed in order to achieve product lengths between 50 and 100 bp . TaqMan probes were used for detection of mature miRNAs by Q-PCR . The reaction conditions were: 95°C 10 min , 40× ( 95°C 15 s , 60°C 1 min ) . Data was normalised to HPRT , or INPUT RNA amount in the case of RNA- and miRNA-immunoprecipitation experiments , using the ΔΔCT method [51] . Feeder-free Ago2Δ/Δ;Dicer1Δ/Δ+Ago2-myc ES cells were transfected in triplicate with 1 µg of pEGFP-1 and 300 pmol of miR-294 mimic or C . elegans cel-239b control miRNA and harvested 24 hr later . Cells were sorted for GFP expression and total RNA was isolated using the RNeasy Mini kit ( Qiagen ) . 1 µg of total RNA from each triplicate ( six samples in total ) was sent to Cambridge Genomic Services , Cambridge University for sample processing and hybridisation to MouseWG-6 v2 . 0 Illumina microarrays . Cambridge Genomic Services performed the quality control analysis , gene selection , and data normalisation . Microarray data analysis was carried out using the R language with Bioconductor packages [52] and custom-written code . GO and network analysis were performed using the online software MetaCore from GeneGo Inc . Microarray data were deposited in the Gene Expression Omnibus ( GEO ) database ( Accession Code: GSE20048 ) . The amplified cDNAs from the IP and INPUT were subjected to SOLiD library preparation by ABI . The cDNAs were then sequenced by ABI's next-generation sequencing SOLiD system . RNA-Sequencing reads were mapped to RefSeq transcripts using ABI's pipeline , as described [48] . To visualise the data , we typically plotted the fraction of reads for a given gene which came from the IP library: Differential representation was measured as the probability that was drawn from a binomial distribution withand Sequencing data were deposited in the GEO database ( Accession Code: GSE20199 ) . | Stem cells in plants and animals contain many small RNAs , which help to regulate differentiation into diverse cell types . Mutation in a gene necessary for the maturation of small RNAs in plants causes the stem cells ( called meristem cells ) to remain in an indeterminate , overproliferating state . Similarly in worms , a small RNA called lin-4 miRNA prevents “stem cell–like cells” appearing at inappropriate times . Thus , it is important to determine the precise functions of key individual small RNAs in embryonic stem cells . To address this , we created embryonic stem cells lacking all miRNAs into which we introduced a single miRNA . We discovered that a single miRNA could affect the expression of many genes in stem cells , which in turn regulate key properties of stem cells . These together help establish an intricate network of gene regulation in stem cells that defines their properties . Our findings are of broad interest because different miRNAs have critical functions in diverse cell types in developing embryos . It is important to understand the function of these molecules also because misregulation of miRNA function underlies some human diseases , including cancers . | [
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] | 2010 | Genome-Wide Identification of Targets and Function of Individual MicroRNAs in Mouse Embryonic Stem Cells |
The checkpoint kinases ATM and ATR are redundantly required for maintenance of stable telomeres in diverse organisms , including budding and fission yeasts , Arabidopsis , Drosophila , and mammals . However , the molecular basis for telomere instability in cells lacking ATM and ATR has not yet been elucidated fully in organisms that utilize both the telomere protection complex shelterin and telomerase to maintain telomeres , such as fission yeast and humans . Here , we demonstrate by quantitative chromatin immunoprecipitation ( ChIP ) assays that simultaneous loss of Tel1ATM and Rad3ATR kinases leads to a defect in recruitment of telomerase to telomeres , reduced binding of the shelterin complex subunits Ccq1 and Tpz1 , and increased binding of RPA and homologous recombination repair factors to telomeres . Moreover , we show that interaction between Tpz1-Ccq1 and telomerase , thought to be important for telomerase recruitment to telomeres , is disrupted in tel1Δ rad3Δ cells . Thus , Tel1ATM and Rad3ATR are redundantly required for both protection of telomeres against recombination and promotion of telomerase recruitment . Based on our current findings , we propose the existence of a regulatory loop between Tel1ATM/Rad3ATR kinases and Tpz1-Ccq1 to ensure proper protection and maintenance of telomeres in fission yeast .
Telomeres , the nucleoprotein protective structures at ends of eukaryotic chromosomes , are essential for stable maintenance of eukaryotic genomes [1] . In most eukaryotic species , telomeric DNA is made up of short repetitive G-rich sequences that can be extended by the specialized reverse transcriptase telomerase , to overcome the inability of semi-conservative DNA replication machineries to fully replicate ends of linear DNA [2] . While most of the telomeric G-rich repeats are composed of double-stranded DNA ( dsDNA ) , telomeres end with G-rich 3′ single-stranded DNA ( ssDNA ) , known as G-tail . Both dsDNA and ssDNA portions are important for maintaining functional telomeres as they provide binding sites for telomeric repeat sequence-specific binding proteins , as well as various DNA repair and checkpoint proteins , that are critical for proper maintenance of telomeres . In mammalian cells , the shelterin complex , composed of TRF1 , TRF2 , TIN2 , RAP1 , TPP1 and POT1 , plays critical roles in the stable maintenance of telomeres [1] . TRF1 and TRF2 bind specifically to telomeric dsDNA G-rich repeats via their C-terminal myb-like DNA binding domain , while POT1 binds to the telomeric G-tail via its N-terminal OB-fold domains [1] . On the other hand , RAP1 , despite the fact that it is evolutionarily related to the budding yeast dsDNA telomeric repeat-binding protein Rap1 , cannot directly bind to DNA , and it is recruited to telomeres via its interaction with TRF2 [1] . Likewise , TIN2 is recruited to telomeres by its ability to interact with both TRF1 and TRF2 [3] . TIN2 plays a central role in the formation of the shelterin complex through its ability to interact with the POT1 binding partner TPP1 . Previous studies have shown that TRF2 is essential for preventing fusion of telomeres by non-homologous end-joining ( NHEJ ) and for attenuating ATM-dependent checkpoint signaling [4] . On the other hand , POT1 is critical for protection of telomeres against nucleolytic processing and for attenuating ATR-dependent checkpoint signaling [4] . The POT1-TPP1 sub-complex was also found to interact with the telomerase complex and to increase processivity of telomerase [5] , [6] . Fission yeast Schizosaccharomyces pombe is an attractive model system for understanding how the shelterin complex contributes to telomere function since this organism utilizes proteins that show a high degree of conservation to the mammalian shelterin subunits [7] . In contrast , the more extensively studied budding yeast Saccharomyces cerevisiae , while providing unparalleled detailed molecular understanding on how telomere maintenance is regulated , cannot provide much insight into how the shelterin components might contribute to telomere function , since budding yeast lacks shelterin and relies on evolutionarily unrelated alternative protein complexes to maintain telomeres [8] , [9] . The S . pombe shelterin complex is composed of Taz1 , Rap1 , Poz1 , Ccq1 , Tpz1 and Pot1 [7] . Taz1 directly binds to telomeric dsDNA G-rich repeats via its myb DNA-binding domain , and is thought to fulfill functions analogous to mammalian TRF1 and TRF2 [10] . Rap1 , like mammalian Rap1 , does not bind directly to telomeric DNA , but it is recruited to telomeres through its interaction with Taz1 [11] , [12] . Poz1 , the functional counterpart of mammalian TIN2 , connects Taz1 to the G-tail binding protein Pot1 by simultaneously interacting with Rap1 and the Pot1 interaction partner Tpz1 [7] . Deletion of taz1+ , rap1+ or poz1+ causes massive telomerase-dependent expansion of the G-rich repeat-tract length , and thus they are implicated in the negative regulation of telomerase activity [7] , [13] . Tpz1 , an ortholog of mammalian TPP1 , interacts with Pot1 via its N-terminus , and with Poz1 and Ccq1 via its C-terminus [7] . Thus , Tpz1 is the central protein necessary for the formation of the Pot1 sub-complex , composed of Pot1 , Tpz1 , Ccq1 and Poz1 . Pot1 and Tpz1 are both essential for protecting telomeres in fission yeast since deletion of pot1+ or tpz1+ results in rapid and complete loss of telomeric G-rich repeats and chromosome circularization [7] . Ccq1 is required for telomerase-dependent telomere maintenance as well as inhibition of checkpoint responses and recombination at telomeres [7] , [14] . While an ortholog of Ccq1 has not been identified in mammalian cells , analogous proteins that are critical for telomerase recruitment and inhibition of checkpoint and repair responses at telomeres might await discovery in mammalian cells . The telomere protection function fulfilled by Pot1 and Tpz1 appears to be provided redundantly by Poz1 and Ccq1 , since poz1Δ ccq1Δ cells , but not poz1Δ or ccq1Δ single deletion cells , rapidly lose telomeres and circularize chromosomes [7] . Similar to pot1Δ or tpz1Δ cells , S . pombe cells deleted for either Stn1 or Ten1 rapidly lose telomeres and circularize chromosomes [15] . Fission yeast Stn1 and Ten1 are evolutionarily conserved to S . cerevisiae Stn1 and Ten1 , which are essential for telomere capping in budding yeast . Budding yeast Stn1 and Ten1 form a complex with the telomeric G-tail binding protein Cdc13 , and the Cdc13-Stn1-Ten1 complex has been proposed to represent a telomere-specific replication protein A ( RPA ) -like complex [16] . Since Pot1 does not appear to be in the same complex as Stn1 and Ten1 , fission yeast cells seem to utilize two independent capping complexes to protect telomeres [15] , [17] . Higher eukaryotic cells may also utilize both Pot1 and Stn1 complexes to protect telomeres since the Stn1 ortholog in Arabidopsis was found to be important for telomere protection , and potential Stn1 orthologs have been identified in mammalian genomes based on sequence analysis [15] , [16] , [18] . Telomere proteins , such as TRF2 and POT1 , inhibit DNA damage and/or DNA replication checkpoint signaling regulated by ATM and ATR kinases [4] . Paradoxically , checkpoint and DNA repair proteins are also essential for stable telomere maintenance . In fact , cells simultaneously lacking both ATM and ATR pathways suffer severe telomere dysfunction in a wide variety of organisms , including budding and fission yeasts , Arabidopsis and Drosophila [19]–[23] . In budding yeast , where the shelterin complex is absent , studies have uncovered redundant roles for Tel1ATM and Mec1ATR in promoting telomerase recruitment via phosphorylation of Cdc13 to enhance the interaction between Cdc13 and the Est1 subunit of telomerase [24] . However , no molecular details of telomere defect ( s ) caused by simultaneous loss of ATM and ATR pathways were available for the organisms that utilize telomerase , shelterin , and the Stn1 complex to maintain telomeres . Therefore , we utilized fission yeast to define the nature of telomere dysfunction in cells lacking both Tel1ATM and Rad3ATR . Our analyses implicate a defect in efficient accumulation of the shelterin complex subunits Tpz1 and Ccq1 to telomeres as the main cause of telomere dysfunction in tel1Δ rad3Δ cells , which exhibit defects in both telomere protection and telomerase recruitment .
In budding yeast , a telomere maintenance defect observed in tel1Δ mec1Δ double mutant cells can be suppressed by deleting Rif1 or Rif2 ( Rap1 interacting factors ) or by reducing Rap1 accumulation at telomeres . These observations suggested that the requirement of Tel1ATM and Mec1ATR for telomere maintenance could be bypassed simply by making telomeres more accessible to telomerase by removing inhibitory regulators of telomerase [25] . Moreover , tel1Δ mec1Δ cells lost their viability slower than telomerase RNA mutant ( tlc1Δ ) cells , and tel1Δ mec1Δ tlc1Δ cells lost their viability with a rate comparable to tlc1Δ cells . Thus , the telomere maintenance defect observed in tel1Δ mec1Δ cells may entirely be attributable to the failure of the double mutant cells to efficiently recruit telomerase to telomeres [25] . In contrast , our previous analyses suggested that fission yeast lacking Tel1ATM and Rad3ATR are likely to be defective in telomerase recruitment and other additional functions such as telomere protection [26] . This prediction was made based on the following observations . First , tel1Δ rad3Δ cells lost their viability faster than telomerase mutant ( trt1Δ ) cells . Second , tel1Δ rad3Δ trt1Δ and tel1Δ rad3Δ cells lost their viability at comparable rates , suggesting telomere defects observed in tel1Δ rad3Δ cells include a defect in telomerase function . Third , Taz1 deletion ( taz1Δ ) , which allows trt1Δ cells to stably maintain telomeres by recombination and thus should be able to suppress chromosome circularization if telomerase recruitment is the only defect caused by tel1Δ rad3Δ , could not suppress chromosome circularization of tel1Δ rad3Δ cells [26] , [27] . However , since taz1Δ cells show more severe telomere defects than rap1Δ or rif1Δ cells [13] , [28] , we tested if rap1Δ or rif1Δ could suppress chromosome circularization of tel1Δ rad3Δ cells . Fission yeast Rap1 and Rif1 show sequence homology to budding yeast Rap1 and Rif1 , respectively , and rap1Δ and rif1Δ cells carry elongated telomeres , suggesting that they are important for negative regulation of telomerase in fission yeast [11] . However , neither rap1Δ nor rif1Δ was able to suppress the chromosome circularization phenotype of tel1Δ rad3Δ cells ( Figure 1A ) . These results thus establish that mutations of telomerase inhibitors cannot suppress the telomere maintenance defect of tel1Δ rad3Δ , and further support the notion that Tel1ATM and Rad3ATR may contribute to telomere protection . Next , we tested more directly if loss of Tel1ATM and Rad3ATR causes defects in telomere protection . In order to reliably examine changes in telomere structure or recruitment of various telomere-associated factors in tel1Δ rad3Δ cells prior to chromosome circularization , we first developed a new plasmid-based system that allowed us to utilize younger generation tel1Δ rad3Δ cells for our experiments , rather than performing meiotic crosses to create tel1Δ rad3Δ cells ( Figure 2A ) . In this system , we took advantage of the fact that tel1Δ rad3Δ cells carrying a Rad3-plasmid grow significantly slower upon loss of the plasmid , and thus form smaller colonies when grown on non-selective media plates ( Figure 2B ) . For our experiments , we chose multiple small colonies , individually confirmed to be tel1Δ rad3Δ based on their inability to grow on media lacking histidine ( loss of his3+ marker ) or media containing hydroxyurea ( loss of rad3+ ) ( Figure 2B ) . These freshly derived tel1Δ rad3Δ cells were then pooled and grown in liquid culture to obtain sufficient amount of cells at early generation to perform our biochemical analyses . Based on Southern blot analysis , we estimate that the average telomere length of tel1Δ rad3Δ cells utilized in our experiments is shorter than wt cells , but comparable or even slightly longer than rad3Δ cells ( Figure 2C ) . Furthermore , based on amplification cycle numbers for input samples in our quantitative PCR analyses for chromatin immunoprecipitation ( ChIP ) assays , we can ensure that tel1Δ rad3Δ cells utilized in our experiments have not yet circularized their chromosomes , since primer annealing sites are completely lost after chromosome circularization [26] . We first examined changes in telomeric G-tail length by carrying out a series of non-denaturing native dot blot hybridization experiments using G-rich or C-rich strand specific probes for genomic DNA samples prepared from wt , tel1Δ , rad3Δ and tel1Δ rad3Δ cells . We found that the native hybridization signal for the probe that specifically anneals to the G-rich strand of telomeres ( normalized against denatured sample ) , but not for the probe specific for the C-rich strand , increased significantly in tel1Δ rad3Δ cells ( Figure 3A ) . Thus , we conclude that the telomeric G-tail is significantly elongated in tel1Δ rad3Δ cells , compared to wt , tel1Δ , or rad3Δ cells . The increase in G-tail length may be caused by a decrease in protection of the telomeric C-rich strand against degradation , or a delay in the arrival of lagging strand DNA polymerases at telomeres [17] . We next monitored recruitment of the largest subunit of RPA ( replication protein A ) Rad11 and the homologous recombination ( HR ) DNA repair proteins Rad51 and Rad52 ( Rhp51 and Rad22 in fission yeast , respectively ) by quantitative ChIP assays . Based on Western blot analyses , expression levels for all analyzed proteins did not change significantly , when tel1 and/or rad3 were deleted . We found that Rad11RPA , Rhp51Rad51 , and Rad22Rad52 are all recruited to telomeres at significantly higher levels in rad3Δ and tel1Δ rad3Δ cells ( Figure 3B–3D ) . While Rad22Rad52 recruitment to telomeres was comparable between rad3Δ and tel1Δ rad3Δ cells , Rad11RPA and Rhp51Rad51 recruitment to telomeres was significantly higher in tel1Δ rad3Δ than in rad3Δ cells . Since rad3Δ cells carry much shorter telomeres than wt cells [19] , [26] ( Figure 2C ) , increased incidences of cells experiencing critically short telomeres may be responsible for increase in telomere association of RPA and HR repair factors in rad3Δ cells . In contrast to HR repair proteins , telomere recruitment of Ku80 , involved in NHEJ repair , was not greatly affected by deletion of tel1 and/or rad3 ( Figure 3E ) . The observed increase in telomere binding for RPA and Rad22Rad52 , but not Ku , would be consistent with the notion that chromosome circularization in tel1Δ rad3Δ cells might occur by single strand annealing rather than NHEJ , much like in pot1Δ cells [29] . Since we observed an increase in G-tail length and recruitment of HR repair factors in tel1Δ rad3Δ cells , we suspected that the integrity and/or recruitment of telomere capping complexes might be affected by the loss of Tel1ATM and Rad3ATR . Accordingly , we monitored changes in the association of the Pot1 sub-complex ( composed of Pot1 , Tpz1 , Poz1 and Ccq1 ) and the Stn1 complex ( composed of Stn1 and Ten1 ) by quantitative ChIP assays . Previous studies have established that these complexes are likely to be independent , but both are essential for telomere protection in fission yeast [7] , [15] , [17] , [30] . Western blot analyses indicated that expression levels for all analyzed proteins are not greatly affected by deletion of tel1 and/or rad3 ( Figure 4 ) . While we did not observe any major changes in Stn1 recruitment to telomeres ( Figure 4E ) , we observed subunit specific changes in recruitment of the Pot1 sub-complex to telomeres when Tel1ATM and Rad3ATR were eliminated . While Pot1 recruitment to telomeres was increased in tel1Δ rad3Δ cells ( Figure 4A ) , recruitment of Tpz1 and Ccq1 was significantly reduced in tel1Δ rad3Δ cells ( Figure 4B , 4C ) , and recruitment of Poz1 was not significantly affected among different genetic backgrounds ( Figure 4D ) . Therefore , it appears that simultaneous loss of Tel1ATM and Rad3ATR differentially affects individual subunits of the Pot1 sub-complex . It is also worth noting that the increase in telomere association for RPA ( ∼9 fold ) is much greater than for Pot1 ( ∼2 fold ) in tel1Δ rad3Δ cells . Given that Ccq1 and Tpz1 association to telomeres was decreased while Pot1 association was increased , we wondered if the integrity of the Pot1 sub-complex is compromised in tel1Δ rad3Δ cells . Therefore , we performed pairwise co-immunoprecipitation ( IP ) experiments among different subunits of the Pot1 sub-complex in wt and tel1Δ rad3Δ cells ( Figure 5 ) . Surprisingly , we did not observe any obvious changes in interactions . One possible explanation might be that asynchronous fission yeast cell cultures contain a large excess of the Pot1 sub-complex that is not bound to telomeres and thus is not regulated by Tel1/Rad3 . If only the telomere-bound Pot1 sub-complex stability is affected in tel1Δ rad3Δ cells , co-IP assays may not be able to detect changes in complex stability . It is currently unknown if fission yeast cells contain a large pool of telomere unbound Pot1 sub-complex , but we have previously shown that telomere association of Pot1 is cell cycle regulated and occurs maximally during late S-phase [17] . Alternatively , since previous studies have demonstrated that Ccq1 can interact with the heterochromatin modulator SHREC complex [7] , [31] , loss of Ccq1 from telomeres might be caused by loss of interaction between SHREC and Ccq1 without affecting the stability of the telomere-bound Pot1 sub-complex . However , we found that tel1Δ rad3Δ cells appear to have intact heterochromatin based on the intact telomere-specific silencing of a marker gene ( Figure 6 ) . Previous studies have indicated that recruitment of Pot1 can occur independently of its N-terminal OB fold domain , required to bind G-tails at 3′ ends of telomeres , and that Rap1-Poz1 interaction can promote recruitment of the Pot1 sub-complex to the dsDNA portion of telomeres [7] , [32] . In fact , based on microscopic observation [32] , a majority of Pot1 may be associated with dsDNA portion of telomeric and sub-telomeric regions , and only a small fraction of the Pot1 sub-complex is bound to the extreme 3′ ends of telomeres . Therefore , we currently favor the notion that Tel1ATM and Rad3ATR are especially important for stabilizing the Pot1 sub-complex bound close to the 3′ ends of telomeres , but bulk of the Pot1 sub-complex , bound to the dsDNA portion of telomeres ( or unbound to telomeres ) , are not significantly affected by simultaneous deletion of Tel1ATM and Rad3ATR kinases . Ccq1 was recently found to be important for telomerase-dependent telomere maintenance in fission yeast [7] , [14] . Moreover , Ccq1 and Trt1TERT can be co-immunoprecipitated , and Tpz1 pull down experiments can bring down active telomerase in a Ccq1-dependent manner . Since we found reduced association of Tpz1 and Ccq1 to telomeres in our quantitative ChIP analyses ( Figure 4 ) , we next examined if recruitment of telomerase to telomeres is affected by loss of Tel1ATM and Rad3ATR . We found that telomere association of both the telomerase catalytic subunit Trt1TERT and its regulatory subunit Est1 are significantly reduced in tel1Δ rad3Δ cells ( Figure 7 ) , much like in ccq1Δ cells [14] ( Figure S1 ) . The loss of ChIP signals were not due to loss of the telomerase complex subunits since comparable expression levels of Trt1TERT and Est1 were detected by Western blots in all genetic backgrounds tested . We next examined if interactions among telomerase , Tpz1 and Ccq1 are disrupted in tel1Δ rad3Δ . Indeed , the Ccq1-dependent interaction between the telomerase RNA subunit TER1 and Tpz1 , as well as interaction between Ccq1 and TER1 were abolished in tel1Δ rad3Δ cells ( Figure 8A ) . The loss of interaction between Tpz1-Ccq1 and telomerase is not due to disruption of the telomerase complex or degradation of telomerase RNA in tel1Δ rad3Δ cells , since we can pull down comparable amounts of telomerase RNA when the telomerase catalytic subunit Trt1TERT was used for IP ( Figure 8B ) . Taken together , our data indicate that the telomerase complex ( Trt1-Est1-TER1 ) can no longer be recruited to telomeres in the absence of Tel1ATM and Rad3ATR due to the disruption of the Pot1 sub-complex and its interaction with telomerase .
In this paper , we investigated the nature of telomere dysfunction caused by simultaneous deletion of the two major checkpoint kinases Tel1ATM and Rad3ATR in fission yeast . Results reported here support a model depicted in Figure 9A . We showed that tel1Δ rad3Δ cells accumulate longer G-tails ( Figure 3A ) , suggesting possible defects in either protection against degradation of the C-rich strand or in coordination of leading and lagging strand synthesis at telomeres . The observed increases in recruitment of RPA , Rad51 and Rad52 to telomeres ( Figure 3B–3D ) further support the notion that tel1Δ rad3Δ cells are defective in protection of telomeres . Analysis of telomere complexes suggests that tel1Δ rad3Δ cells are defective in efficient accumulation of the shelterin subunits Tpz1 and Ccq1 ( Figure 4 ) . Moreover , we determined that tel1Δ rad3Δ cells were unable to recruit telomerase to telomeres due to a defect in interaction between Tpz1-Ccq1 and telomerase ( Figures 7 and 8 ) . The loss of interaction between Tpz1-Ccq1 and telomerase may be due to direct role ( s ) of Tel1/Rad3 in promoting this interaction , or could be indirectly caused by inefficient accumulation of Tpz1-Ccq1 at telomeres . It should also be noted that our data do not rule out the possibility that Tel1ATM and Rad3ATR phosphorylate different sets of substrates at telomeres . Given that ccq1Δ cells were previously found to be defective in both protection of telomeres and recruitment of telomerase [7] , [14] , our data is consistent with the notion that all telomere defects observed in tel1Δ rad3Δ may primarily be caused by the failure to properly accumulate Ccq1 at telomeres . Ccq1 was also recently shown to be essential for suppressing Rad3ATR-dependent G2 checkpoint activation by telomeres [14] . Thus , it appears that fission yeast Tel1ATM and Rad3ATR promote accumulation of their own inhibitor Ccq1 to ensure that telomeres do not cause permanent cell cycle arrest . The regulatory loop formed by Tel1/Rad3 and the Pot1 sub-complex ( Figure 9B ) ensures that telomeres that transiently become de-protected would preferentially activate Tel1/Rad3 pathways to promote recruitment of Tpz1 and Ccq1 , and to re-establish proper protection of telomeres . An analogous regulatory loop appears to exist in Drosophila , where retrotransposons have replaced telomerase and neither the shelterin complex nor the Stn1-Ten1 complex exist , since ATM and ATR are redundantly required to promote recruitment of the telomere capping protein HOAP to telomeres [22] , [23] . We suspect that mammalian ATM and ATR might also be involved in promotion of telomere capping by affecting the recruitment of the shelterin complex components . Similar to budding yeast , where Tel1ATM and Mec1ATR are redundantly required to promote interaction between the G-tail binding protein Cdc13 and telomerase , our data demonstrate that fission yeast Tel1ATM and Rad3ATR are redundantly required to recruit telomerase to telomeres by promoting the interaction between the Pot1 sub-complex and telomerase . In budding yeast , phosphorylation of Cdc13 by Tel1ATM/Mec1ATR kinases promotes Cdc13-Est1 interaction to facilitate telomerase recruitment [24] . Tel1ATM/Rad3ATR kinases may also promote interaction between the Pot1 sub-complex and telomerase by phosphorylation of the Pot1 sub-complex subunits in fission yeast . Mammalian POT1-TPP1 has also been implicated in recruitment of telomerase to telomeres [6] . Thus , future studies may uncover an involvement of mammalian ATM/ATR in promoting the interaction between POT1-TPP1 and telomerase .
Fission yeast strains used in this study were constructed by standard techniques [33] and are listed in supplemental Table S1 . For tel1Δ::LEU2 , rad3Δ::LEU2 , pku80Δ::ura4+ , taz1Δ::ura4+ , rap1Δ::ura4+ , rif1Δ::ura4+ and rhp51Δ::ura4+ , original deletion strains were described previously [11] , [26] , [34]–[36] . For rad11-FLAG , pku80-myc , pot1-myc , poz1-myc , stn1-myc and trt1-myc , original tagged strains were described previously [17] , [37] , [38] . Primers listed in Table S2 were used to construct ccq1Δ::hphMX , ccq1-myc , ccq1-FLAG , tpz1-myc , tpz1-FLAG , est1-myc and rad22-myc by PCR-based methods [39]–[41] . The plasmids pREP41H-rad3 and pREP42-myc-rad3 were used to complement tel1Δ rad3Δ strains to maintain telomeres . pREP41H-rad3 carries rad3+ under the control of the medium strength nmt1 promoter and a his3+ marker , while the pREP42-myc-rad3 carries myc-rad3+ under the control of the medium strength nmt1 promoter and an ura4+ marker [42] , [43] . tel1Δ rad3Δ strains carrying either pREP41H-rad3 or pREP42-myc-rad3 were grown in YES liquid culture for 16 hours prior to plating onto YES plates in order to promote loss of plasmid . Small colonies were picked and simultaneously streaked on YES , YES+5 mM HU , and PMG UAL ( -His ) or HAL ( -Ura ) plates to verify the loss of rad3+ and the selection marker . Several colonies that were sensitive to HU and did not grow on PMG selection plates were pooled and inoculated in YES liquid culture , and grown overnight to obtain sufficient cells for subsequent experiments . Pulsed-field gel electrophoresis of NotI-digested chromosomal DNA was performed to monitor chromosome circularization as previously described [26] . For telomere length analysis , genomic DNA samples were digested with EcoRI , separated on a 1% agarose gel , and probed with telomere probe [44] as previously described . Native dot blot analyses were performed as described [45] , with minor modifications . DNA was blotted onto Hybond-XL membrane ( GE ) using the BioRad Bio-Dot Microfiltration System . Hybridization was performed in Church buffer [0 . 25 M sodium phosphate buffer pH 7 . 2 , 1 mM EDTA , 1% BSA , 7% SDS] at 45°C overnight with probes annealing to the G-rich strand [848: CGT GTA ACC ACG TAA CCT TGT AAC CCG ATC] or to the C-rich strand [847: GAT CGG GTT ACA AGG TTA CGT GGT TAC ACG] [46] . Cells were processed for ChIP and analyzed as previously described [17] . Monoclonal anti-myc ( 9B11; Cell Signaling ) and anti-FLAG ( M2-F1804; Sigma ) antibodies and polyclonal anti-Rad51 antibody ( A-92 , Santa Cruz ) were used . Percent precipitated DNA values ( % ppt DNA ) were calculated based on ΔCt between Input and IP samples after performing several independent triplicate SYBR Green-based real-time PCR ( Bio-Rad ) using telomere primers jk380 and jk381 [17] . Cell extracts were prepared in lysis buffer [50 mM Tris pH 8 . 0 , 150 mM NaCl , 10% glycerol , 5 mM EDTA , 0 . 5% NP40 , 50 mM NaF , 1 mM DTT , 1 mM PMSF , 0 . 2 mM APMSF , 1 mM Na3VO4 , ‘Complete’ protease inhibitor cocktail] using glass beads . Extracts were preincubated with 100 µg/ml Ethidium bromide for 30 min on ice . Proteins were immunoprecipitated using either monoclonal anti-myc antibody ( 9B11 , Cell Signaling ) or monoclonal anti-FLAG antibody ( M2-F1804 , Sigma ) , and Dynabeads ( Invitrogen ) . Immunoprecipitated proteins were analyzed by Western blot analysis . Proteins in whole cell extract or from immunoprecipitations were analyzed by western blot using either monoclonal anti-FLAG antibody ( M2-F1804 ) or monoclonal anti-myc antibody ( 9B11 ) . Anti-Cdc2 antibody ( y100 . 4 , Abcam ) was used for loading control . Experiments were performed essentially as described [37] . Cell extracts were prepared in TMG100 buffer [10 mM Tris pH 8 . 0 , 1 mM MgCl2 , 100 mM NaCl , 10% glycerol , 1 mM EDTA , 0 . 1 mM DTT , 2 mM PMSF , 0 . 2 mM APMSF , 1 U/µl RNasin ( Promega ) , and ‘Complete’ protease inhibitor cocktail] using glass beads . IPs were performed with 4 mg of whole cell extract in the presence of 0 . 5% v/v Tween20 using monoclonal anti-myc antibody ( 9B11 ) and Dynabeads ( Invitrogen ) . Beads were subsequently washed with TMG100 buffer and treated with 0 . 4 mg/ml Proteinase K in [10 mM Tris pH 8 . 0 , 100 mM NaCl , 1% SDS , 10 mM EDTA] at 37°C for 30 min . RNA was isolated using ‘Total RNA Isolation’ Kit ( Clontech ) . RNA was reverse transcribed using M-MLV Reverse Transcriptase ( Ambion ) with Primer 1016 [GAT CCA TGG ATC TCA CGT AAT G] , and subsequently subjected to triplicate SYBR Green-based real-time PCR analysis with primers 1015 [CAG TGT ACG TGA GTC TTC TGC CTT] and 1017 [CAA AAA TTC GTT GTG ATC TGA CAA GC] . Control reactions were also performed without reverse transcriptase to ensure that the PCR signal reflected RNA and not contaminating DNA . In order to determine statistical significance of our data , two-tailed Student's t-tests were performed , and P values ≤0 . 05 were considered as statistically significant differences . | Stable maintenance of telomeres is critical to preserve genomic integrity and to prevent accumulation of undesired mutations that might lead to formation of tumor cells . Fission yeast cells serve as a particularly attractive model system to study telomere maintenance mechanisms , since proteins critical for telomere maintenance are highly conserved between fission yeast and humans . Previous studies have shown that the checkpoint kinases ATM ( Tel1 ) and ATR ( Rad3 ) are required for stable maintenance of telomeres in a wide variety of organisms . Here , we investigated the molecular basis for telomere dysfunction in fission yeast cells lacking ATM and ATR kinases . Our results show that fission yeast ATM and ATR are redundantly required to promote efficient recruitment of telomere protection complex subunits to telomeres , which in turn promote recruitment of telomerase needed to maintain telomeres . Human ATM and ATR kinases might similarly promote telomere protection and telomerase recruitment by promoting recruitment of telomere protection complex subunits . | [
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] | 2009 | Fission Yeast Tel1ATM and Rad3ATR Promote Telomere Protection and Telomerase Recruitment |
Leptospirosis occurs worldwide , but the global incidence of human disease and its mortality are not well understood . Many patients are undiagnosed and untreated due to its non-specific symptoms and a lack of access to diagnostics . This study systematically reviews the literature to clarify the mortality from untreated leptospirosis . Results will help quantify the global burden of disease and guide health policies . A comprehensive literature search was performed to identify untreated patient series . Included patients were symptomatic , but asymptomatic patients and those who had received antibiotics , dialysis or who were treated on Intensive Care Units were excluded . Included patients had a confirmed laboratory diagnosis by culture , PCR , or serological tests . Data was extracted and individual patient series were assessed for bias . Thirty-five studies , comprising 41 patient series and 3 , 390 patients , were included in the study . A high degree of bias within studies was shown due to limitations in study design , diagnostic tests and missing data . Median series mortality was 2 . 2% ( Range 0 . 0 – 39 . 7% ) , but mortality was high in jaundiced patients ( 19 . 1% ) ( Range 0 . 0 – 39 . 7% ) , those with renal failure 12 . 1% ( Range 0-25 . 0% ) and in patients aged over 60 ( 60% ) ( Range 33 . 3-60% ) , but low in anicteric patients ( 0% ) ( Range 0-1 . 7% ) . This systematic review contributes to our understanding of the mortality of untreated leptospirosis and provides data for the estimation of DALYs attributable to this disease . We show that mortality is significantly higher in older patients with icteric disease or renal failure but is lower in younger , anicteric patients . Increased surveillance and accurate point-of-care diagnostics are required to better understand the incidence and improve diagnosis of disease . Empirical treatment strategies should prioritize early treatment to improve outcomes from leptospirosis .
Leptospirosis is a bacterial zoonosis caused by pathogenic Leptospira species which are transmitted to humans by exposure to water containing the urine of infected mammals , predominantly rodents [1] . The disease occurs worldwide and over 853 , 000 cases and 48 , 000 deaths are estimated to occur each year [2] . Incidence is highest in tropical regions , including the Asia-Pacific , Latin America , and the Caribbean , where there is an estimated incidence of >10 cases per 100 , 000 population per year [3] Around one billion people are thought to reside in urban slum areas where frequent outbreaks occur following heavy seasonal rains [4] , most notably the recent epidemics in Nicaragua in 2007 and the Philippines in 2009 [5] . Despite the wide availability of effective antibiotic treatment , leptospirosis remains under recognized , mainly due to its non-specific clinical manifestations within a wide differential diagnostic spectrum . The current gold standard diagnostic techniques: culture and the microscopic agglutination test ( MAT ) , are expensive , not useful for early diagnosis , require considerable expertise , and are impractical in resource poor settings . To date there is no widely deployable and reliable point-of-care test , meaning a large proportion of patients are never diagnosed or treated [6] . Outbreaks are often confused with viral infections , such as dengue fever , leading to delays in treatment and increased mortality [6–8] . Knowledge of the mortality from untreated leptospirosis is important for our understanding of the global burden of disease and the calculation of Disability Adjusted Life Years ( DALYs ) , and will inform empirical fever treatment strategies and economic analyses [9] . The DALYs from leptospirosis are currently unknown and our understanding of the untreated mortality from leptospirosis remains limited . Mortality is thought to depend on host factors such as age , and bacterial factors such as serotype or inoculum size [4 , 6] but current estimates of mortality vary widely according to the clinical presentation , from 0% in patients with non-severe disease [10] to over 50% in those with Severe Pulmonary Haemorrhagic Syndrome ( SPHS ) [4 , 11] . This review aims to improve these estimates and better define the untreated mortality from leptospirosis , based on a comprehensive systematic review of previously published literature .
This review included all studies that contained untreated patients with leptospirosis . Patients were defined as untreated if they had received no leptospirosis-effective antibiotic treatment , were not treated with convalescent serum or admitted to an Intensive Care Unit ( ICU ) , and did not receive dialysis . Included patients were of all ages and presented with symptoms consistent with leptospirosis and a confirmed diagnosis through either identification of leptospira by culture or direct microscopy , diagnosis by Polymerase Chain reaction ( PCR ) or serology through MAT . Patients admitted to hospital for supportive treatment ( including IV fluids ) were included in the analysis . All study designs and articles in all languages were included in the search . Studies were excluded if the diagnosis was on a clinical basis alone , there was no confirmed laboratory diagnosis for all patients in a clinical series , or if patients had asymptomatic infection . Studies with fewer than 10 patients were excluded to reduce patient selection bias . Inclusion and exclusion criteria are summarized in S1 Table . The primary outcome of the analysis was mortality from leptospirosis . Secondary outcomes were total days of fever , clinical signs and symptoms , and laboratory results for liver and kidney function where available . This review followed the PRISMA statement for systematic reviews ( S1 Checklist ) . Studies were identified through electronic resources , by scanning reference lists of relevant articles , and from library index catalogues , resulting in a comprehensive collection of published and peer reviewed full text articles . The electronic search was performed using Ovid MEDLINE ( 1946–Present ) , Embase Classic ( 1947–Present ) , and Global Health ( 1910–Present ) on 28th July 2014 ( S1–S3 Figs ) and results reviewed manually . The search term used were: “Leptospirosis or leptospira or leptospir*; Weil's disease; Weil’s Syndrome; Swamp Fever; Mud fever; Autumn fever; Akiyami disease; Swineherds disease; Rice field fever; Cane cutters fever; Haemorrhagic Jaundice; Stuttgart disease; Canicola fever; Fort Bragg fever; icterohemorrhagic fever; seven day fever; dairy farm fever” and “Mortality or death” . Authors were not contacted regarding further information , no unpublished or grey literature was obtained , and studies were excluded if the full text was not available . Duplicate articles were removed using the reference manager “Mendeley” ( 2008–14 Mendeley Ltd , Version 1 . 12 . 1 ) . One author ( AT ) reviewed the title and abstract , and papers were excluded if they did not fit the eligibility criteria . If there was doubt as to whether a paper was appropriate for inclusion then the whole paper was acquired and reviewed for eligibility . One author ( AT ) extracted all available data and used Google Translate to translate non-English articles . Several articles contained more than one patient series , which were extracted separately , and all series were reviewed to prevent duplication of patient series . Treated patient subgroups were separated from untreated patient subgroups and excluded , and the whole patient series was excluded if separate outcomes were not clearly defined . There is no standardised method for assessing bias in non-interventional observational studies , and an existing data extraction sheet [12] was modified to create standardized criteria to assess bias within each study . Bias was graded according to patient selection and study design , diagnostic criteria , missing data and missing outcomes ( S2 Table ) . To grade diagnostic certainty , we adapted diagnostic criteria from the WHO [13] and a grading system from Phommasone et al . [14] . Grade I diagnosis was identification of spirochetes through microscopy or culture , PCR , or a 4-fold rise in MAT titre , Grade II a single high MAT titre of ≥1:400 in an endemic region or ≥1:100 in a non-endemic region , and Grade III a single high titre MAT with no specified titre or a confirmed diagnosis but no record of the diagnostic method . Due to differences in methodology , inclusion criteria , missing data , and bias across patient series , a statistical meta-analysis was not performed . Each patient series was defined as a separate population and the median and range were used to summarize outcomes across patient series . The primary outcomes of the review was measured as the median mortality across all patient series and termed the “median series mortality” . Secondary outcomes were measured as the median value across patient series . For graphs , 95% Confidence Intervals ( CI ) of mortality were estimated using the Wilson score method [15] . Data was mapped using an image from NASA—Visible Earth .
Twenty-five articles were in English , 3 in Dutch , 3 in French , 2 in German , 1 in Spanish and 1 in Danish . The 41 patient series consisted of 25 retrospective patient series , 8 prospective patient series , 3 randomised control trials ( RCTs ) , 3 non-randomised control trials ( NRCTs ) and 2 summaries of case reports , and were published between 1917 and 1984 . Information on the location of the patient series was present for all case series ( Fig 2 ) . Eighteen series were located in Europe , 15 in Asia , 7 in the Americas and 1 in Africa . The median ( range ) number of patients in each series was 32 ( 10–459 ) . All 41 patient series were designed to assess the clinical symptoms and outcome of leptospirosis . Each patient series was assessed for bias , and a summary of methodological quality across each criterion is displayed in S4 Fig , with further details for each series displayed in S5 and S6 Tables . Many patient series were limited by non-standardized study design and incomplete data . Diagnostic tests were at high risk of bias in 44% ( 18/41 ) of studies due to use of a single high admission titre with no confirmed cut-off titres , or no confirmed method of diagnosis for patients . Mortality data was available for all ( 41/41 ) patient series , for a total of 3 , 390 patients . Median series mortality was 2 . 2% ( range 0–39 . 7% ) with a total of 314/3 , 390 deaths , and a wide variation in mortality across series . No deaths were reported in 16/41 patient series , but a mortality of 20% or more was reported in 7/41 patient series ( Fig 3 ) . Data for demographics , secondary outcomes and laboratory results are displayed in Table 2 , but were not available from all patient series . Information on secondary outcomes were described in between 2–38 ( 4 . 8–92 . 7% ) studies for each outcome , but often had heterogeneous definitions or were not reported numerically , meaning that data could not be extracted from many articles . Available data showed that fever , headache and myalgia occurred in nearly all patients , while conjunctival suffusion was reported in over half of the patients . Haemorrhagic symptoms ranged from epistaxis to more severe bleeds , but details of haemorrhage were not always specified so it is not possible to report on the incidence of severe haemorrhage or SPHS . Mortality varied according to year and location of patient series and study design , with information present for all patient series ( 41/41 ) . There was a wide range in mortality by year of study but no evident trend across time ( S5 Fig ) . Between locations there was a wide range in mortality with median series mortality in Africa 9% ( range n/a ) , 0 . 0% ( range 0–39 . 7% ) in the Americas , 1 . 0% ( range 0–20% ) in Asia , and 8 . 8% ( range 0–26 . 3% ) in Europe ( S6 Fig ) . Median series mortality was high in 25 retrospective case series ( 8 . 0% ( 0 . 0–39 . 7 ) ) , and 2 patient series summarizing case reports ( 22 . 4% ( 20 . 0–24 . 8 ) ) but lower in 6 controlled trials ( 0 . 0% ( 0–8 . 7% ) ) and 8 prospective case series ( 2 . 7% ( 0 . 0–19 . 6 ) ) ( S7 Table ) . Median series mortality varied according to diagnostic grade and serovars . Mortality was lowest in patients with a grade I diagnosis and highest for those with a grade III diagnosis ( Table 3 ) . Across series with a grade II and III diagnosis the majority of patients were male and had a similar median age , while jaundice was highest in those with a grade III diagnosis , and lowest in those with a grade I diagnosis . Median series mortality was highest for serovar Icterohaemorrhagiae at 13 . 6% ( 0–34 . 3% ) , compared to 0 . 0% ( 0–50 . 0% ) for Canicola and other serovars . Infections with serovar Icterohaemorrhagiae had a higher frequency of jaundice compared to other serovars ( Table 4 ) . Data on mortality by age was available in 13/41 studies for 838 patients . Median series mortality was 0% ( 0–25% ) in 7 series containing 51 patients aged 0–15 , 16 . 3% ( 0–34 . 1% ) in 11 series containing 308 patients aged 16–45 , 36 . 7% ( 16 . 7–66 . 7% ) in 6 series containing 70 patients aged 45–59 , and 60 . 0% ( 33 . 3–60 ) in 3 series containing 23 patients aged over 60 . Two large patient series , which were not incorporated due to differences in age stratification also showed a low mortality in children and a high mortality in older age groups . Smith [44] demonstrated a low mortality of 1% in 105 children aged 0–20 years compared to 50% in 18 patients aged 51 years or over , while Walch-Sorgdrager , [49] using the same cohort as Schuffner [41] , demonstrated a mortality of 60% in 15 patients aged 60 years and over compared to 7 . 1% in 210 patients aged 10–40 years . Data on mortality by sex of patient was available in 21/41 patient series . Across 8 patient series the median series mortality for 227 female patients was 0% ( range 0–40% ) , compared to 8 . 7% ( range 0 . 0%- 39 . 7% ) in 21 series containing 1077 male patients . No data was available on the untreated mortality of leptospirosis in pregnant women . Frequency of jaundice was reported in 37/41 patient series , ranged from 0% to 100% , and was associated with increased mortality ( Fig 4 ) . In 8 patient series where the incidence of jaundice was 0% , median series mortality was 0% ( range 0–1 . 7% ) with 0 . 3% ( 1/348 ) overall mortality; while in 9 patient series where incidence of jaundice was 100% , the median series mortality was 19 . 1% ( range 0 . 0–39 . 7% ) , with 21 . 6% ( 143/662 ) overall mortality . Data on renal function was reported in 12/41 studies and mortality increased with higher frequency of renal failure . In 4 patient series , with a total of 137 patients , where 29 . 8% ( 0 . 0–44 . 9% ) patients had renal failure , median series mortality was 0% ( 0–3 . 4% ) , while in 8 patient series , with a total of 349 patients , where 80 . 5% ( 52 . 0–100% ) patients had renal failure; the median series mortality was 12 . 1% ( range 0–25 . 0% ) . Data on mortality in patients with meningitis was present in 12/41 studies for 188 patients with 4 deaths reported overall . Median series mortality in patients with meningitis was 0% ( range 0–25% ) , with a low median incidence of jaundice in these patients of 12 . 2% ( 0–100% ) .
Leptospirosis remains a major , under appreciated and under recognized infection whose burden of disease falls disproportionately on those in poor and developing regions of the world [61] . This review clarifies the untreated mortality from leptospirosis and shows that despite wide variation , it is of high significance in elderly , jaundiced patients and/or those with renal failure , but much lower in younger , anicteric patients . The results from this study will support the quantification of DALYs from leptospirosis and may be used to guide empirical treatment strategies . A greater understanding of the true incidence of disease through increased surveillance should be encouraged to understand the pathogenicity of local serovars and guide local empirical treatment strategies , while improved genotypic based tests are required to more accurately predict the virulence of local strains [62] . The development of accurate and inexpensive point-of-care antigen based diagnostic tests for the diagnosis of leptospirosis early in its disease course would prevent the development of complications or death in this easily treatable disease [60 , 63] . Strategies for managing the disease should stress the importance of early empirical treatment of fever with effective antibiotics such as penicillin and doxycycline , which are cheap and widely available . | Leptospirosis is a common cause of fever in the developing world but often goes undiagnosed and untreated due to its non-specific clinical features and the limited availability of point-of-care diagnostics . This review systematically evaluated available literature to clarify the mortality from untreated leptospirosis . Untreated patients were defined as patients not receiving antibiotics , dialysis , or treatment on an Intensive Care Unit . All patients had a confirmed laboratory diagnosis of leptospirosis through culture , PCR or serological tests . Results showed that mortality from untreated leptospirosis is significant in older patients and those who develop complications such as jaundice and renal failure , but mortality is low in younger patients and those with anicteric disease . There was a high degree of bias within studies due to limitations in diagnostics and missing data . The data presented in this review , when coupled with improved understanding of the true incidence of the disease , will help estimate the burden of disease from leptospirosis . Increased surveillance and accurate point-of-care diagnostics are required to better understand the incidence of disease and outcomes from leptospirosis . Empirical treatment strategies of undifferentiated fever should focus on early treatment of fever to reduce mortality from leptospirosis . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | A Systematic Review of the Mortality from Untreated Leptospirosis |
To understand the molecular basis of how hosts evolve resistance to their parasites , we have investigated the genes that cause variation in the susceptibility of Drosophila melanogaster to viral infection . Using a host-specific pathogen of D . melanogaster called the sigma virus ( Rhabdoviridae ) , we mapped a major-effect polymorphism to a region containing two paralogous genes called CHKov1 and CHKov2 . In a panel of inbred fly lines , we found that a transposable element insertion in the protein coding sequence of CHKov1 is associated with increased resistance to infection . Previous research has shown that this insertion results in a truncated messenger RNA that encodes a far shorter protein than the susceptible allele . This resistant allele has rapidly increased in frequency under directional selection and is now the commonest form of the gene in natural populations . Using genetic mapping and site-specific recombination , we identified a third genotype with considerably greater resistance that is currently rare in the wild . In these flies there have been two duplications , resulting in three copies of both the truncated allele of CHKov1 and CHKov2 ( one of which is also truncated ) . Remarkably , the truncated allele of CHKov1 has previously been found to confer resistance to organophosphate insecticides . As estimates of the age of this allele predate the use of insecticides , it is likely that this allele initially functioned as a defence against viruses and fortuitously “pre-adapted” flies to insecticides . These results demonstrate that strong selection by parasites for increased host resistance can result in major genetic changes and rapid shifts in allele frequencies; and , contrary to the prevailing view that resistance to pathogens can be a costly trait to evolve , the pleiotropic effects of these changes can have unexpected benefits .
The presence of a parasite elicits strong selection pressures for the host to evolve increased resistance and the parasite to overcome host defences . This can drive rapid changes in allele frequencies in both organisms and result in “Red Queen” evolution , where both species must constantly evolve just to maintain a fitness status quo [1] . Generation times , population sizes , mutation rates and migration rates all affect the evolutionary potential of hosts and parasites , and these factors mean that in many cases the parasite will be evolving faster than the host [2] . Therefore the host is under constant selection to evolve new forms of resistance to the parasite , and this makes host resistance an excellent model to study the evolution of adaptation . Identifying the genes underlying the evolution of resistance can provide insights into this process , revealing the types of mutation involved , the nature of selection acting on resistance , and the molecular mechanisms involved in evolving resistance to infection . A substantial amount of work has been done to study the genetics of host-parasite co-evolution in plants , and we have a broad knowledge of plant resistance ( R ) gene genetics [3] . Unfortunately this is not true for the animal kingdom , especially invertebrates . Aside from a handful of studies on disease vectors , much of the work on invertebrates tends to be purely phenotypic or has not been done with naturally co-evolving systems . Identifying the genes causing variation in the resistance of invertebrates to viruses will allow us to get at many of the mechanisms underlying the evolution of resistance and provide insights to the nature of co-evolution . The antiviral immune defences of Drosophila have been the target of much research in recent years , with RNAi , autophagy and other pathways proving to be important [4]–[7] . However , on an evolutionary timescale , changes to the immune system are not the only way in which hosts can defend themselves against viruses . Several insects , including Drosophila melanogaster , have developed a symbiosis with the bacterium Wolbachia that provides resistance to a range of RNA viruses [8]–[11] . Viruses also rely on the host cellular machinery for all stages of their replication cycle , and changes to these host factors may also lead to the evolution of resistance , for example by blocking entry in host cells [12] . The discovery of genes causing variation in resistance can also allow us to infer the selection pressures acting on host alleles during co-evolution [3] , [13] . Co-evolution can result in two main forms of selection: new resistance alleles may continually arise by mutation and be fixed by directional selection , or negative frequency-dependent selection can maintain polymorphisms of resistant and susceptible alleles [1] , [13]–[14] . To complicate matters , selection pressures on host alleles can be very dynamic , not only depending on allele frequencies in the parasite [15] , but also on changing environmental conditions . It is also of interest to understand the genetic architecture of resistance and the nature of the mutations involved . For example , is the resistance level primarily controlled by alleles of small or large effect , and is it the result of regulatory or coding changes or both ? By addressing all of these questions , the identification of host genes experiencing strong selection will therefore help to develop better models of co-evolution . We have investigated the genetics of resistance to the sigma virus , the only naturally occurring host-specific parasite known in D . melanogaster [16]–[17] . Host specificity is important , as when a parasite infects a single host species there is particularly strong selection for reciprocal adaptation , and such “tight” co-evolution simplifies the arduous task of understanding how co-evolution operates . The sigma virus is a member of the rhabdovirus family , and has a negative-sense RNA genome [18] . It is only transmitted vertically from parent to offspring [18] . In this study we have investigated a resistance gene called ref ( 3 ) D , which had previously been mapped between two visible markers on the right arm of the 3rd chromosome [19] .
The two fly lines that we began our experiments with differed dramatically in their resistance to the sigma virus —11 days after injection less than 5% of the flies from the resistant OOP line showed the symptom of being paralysed by CO2 , compared to over 95% of flies from the susceptible 22a line ( Figure 1 ) . Previous work has mapped a gene called ref ( 3 ) D , which affects sigma virus replication , to the third chromosome of this fly stock [19] . However , it also contains a gene with an allelic variant that reduces transmission of the sigma virus through sperm , so we first removed this allele to avoid complications in identifying ref ( 3 ) D . This was accomplished by crossing OOP and 22a to generate a line with a recombination event between the suspected locations of each gene ( 92–94 cM region ) . The resulting line was homozygous for both the resistant allele of ref ( 3 ) D and the allele of the other gene that results in high rates of transmission through sperm , and this was used in subsequent experiments . To map ref ( 3 ) D , we produced lines that carried a homozygous third chromosome that was a recombinant between the resistant and susceptible stocks . We used molecular markers to screen 191 recombinant flies to identify those that had recombined in a 12 cM interval believed to contain the gene , and created 21 homozygous recombinant lines in this anticipated region . These lines were injected with the sigma virus and genotyped with molecular markers across the region ( Figure 2A ) . There was a clearly bimodal distribution of infection rates , with some lines being highly resistant and others highly susceptible . Furthermore , there was a perfect association between infection rates and genotype across a 182 kb region ( Figure 2A; Wilcoxon Rank Sum Test: W = 110 , P = 1 . 2×10−4 ) . This process was repeated to generate recombinants in the 2 cM interval that contains the resistance gene . This time we screened 1920 flies for informative recombinants and 32 new homozygous recombinant lines were generated in this new region . Again , after injecting the virus these could be clearly categorized into resistant and susceptible lines . After genotyping the lines , this experiment reduced the region where there is a perfect association between genotype and phenotype to 60 kb ( Figure 2B; Wilcoxon Rank Sum Test: W = 256 , P = 1 . 5×10−6 ) . To select for recombinants in this smaller region we used phenotypic markers rather than molecular markers . We combined two P-elements carrying eye-color markers to produce a susceptible mapping stock ( 2GT1 ) , crossed this to a resistant fly line , and selected recombinants that carried just one of the two markers . Using this approach we generated 10 lines that were homozygous for the recombinant chromosome . As before , these lines were assayed for resistance to the sigma virus and genotyped for several markers across the 60 kb candidate region . This reduced the region that could contain the gene to 36 kb ( Figure 2C; Wilcoxon Rank Sum Test: W = 16 , P = 0 . 04 ) . To map the gene within in this region , we induced site-specific recombination in males using P-elements . In this experiment we crossed transposable element lines that were susceptible to the sigma virus ( data not shown ) to a resistant line , and induced recombination at the location of the P-element . We successfully produced four recombinants that were viable as homozygotes . To control for the effects of genetic background , lines that lacked a recombination event were also generated using the same crossing scheme , so they either had the susceptible chromosome containing the transposable element or the resistant chromosome . To check that recombination had occurred , we scored molecular markers flanking the transposable element positions in each line . We injected the recombinant lines and respective controls with the sigma virus ( Figure 3 ) , and found that there was a striking difference between the resistance of recombinants between two sites located just 3089 bases apart in the published genome ( 3R:21155073 . . 21158162 , D . melanogaster genome version 5 . 31 . ) . This region contains all of CHKov2 plus the 3′ end of CG10669 in the published genome sequence ( part of the fifth exon , all of the sixth exon and the 3′UTR ) . To identify the polymorphisms that could be causing resistance , we sequenced the region around CHKov2 and found that there had been a complex rearrangement in the resistant line ( Figure 4C , highly resistant line ) . The susceptible line had a gene order that is the same as the published Drosophila genome ( Figure 4B , note that this is described as ‘resistant’ in the figure as a more susceptible allele is described below ) . As is the case in the published genome sequence , in both our resistant and susceptible lines a naturally occurring Doc transposable element has inserted into the protein coding region of CHKov1 , which is a paralog and neighbour of CHKov2 . Previous research has shown that this insertion results in two short transcripts being produced , which are predicted to encode truncated proteins [20] . However , in the resistant line there are two duplications , both of which involve partial sequences of both CHKov1 and CHKov2 . The first duplication includes a large portion of the 5′ end of CHKov1 ( including some upstream intergenic sequence ) and approximately two-thirds of the 3′ end of CHKov2 . The second duplication is in the reverse orientation , and includes all of CHKov2 and the 5′ end of CHKov1 ( compared to the first duplication , this includes less of the Doc element insertion , exactly the same protein coding region and an identical region of the upstream intergenic sequence ) . It is highly likely that this rearrangement is causing the difference in resistance , as in the region mapped by male recombination there is only one single nucleotide polymorphism ( SNP ) outside of the rearrangement that differs between the resistant and susceptible lines . This rearrangement could confer resistance to viruses either by altering the expression of the genes involved , or due to coding changes ( the only coding sequence which is altered is the truncation of one of the duplicates of CHKov2 ) . We therefore used quantitative rtPCR to examine whether the expression of CHKov1 or CHKov2 is different in the resistant and susceptible flies . It has previously been shown that neither of the CHKov genes change expression after injection with sigma [21] , so any novel changes in expression could be attributed to the rearrangement . Six days after injection with the sigma virus CHKov2 expression was 5 . 6-fold greater in the resistant lines than the susceptible lines ( Wilcoxon Rank Sum Test: W = 86 , P = 0 . 0001 ) and 12 days after injection it was 9 . 6-fold greater ( Wilcoxon Rank Sum Test: W = 88 , P = 2 . 6×10−5 ) . In contrast there was no evidence for a change in the expression of CHKov1 , despite this gene being amplified to three copies in the resistant line ( 1 . 9 fold greater expression in susceptible lines on day 6 , Wilcoxon Rank Sum Test: W = 24 , P = 0 . 11; 1 . 4 fold greater expression in susceptible lines on day 12 , Wilcoxon Rank Sum Test: W = 29 , P = 0 . 24 ) . In the experiments above we have used a symptom of infection — paralysis on exposure to CO2 — to test if flies are infected . To check whether the resistance gene is reducing viral titres rather than simply altering CO2 sensitivity itself , we used quantitative PCR to estimate the relative copy number of the viral genome in resistant and susceptible flies . Using the same samples that we used to examine gene expression , we found that there was an approximately 79–fold decrease in sigma virus load in resistant lines 6 days after the virus was injected ( Wilcoxon Rank Sum Test: W = 0 , P = 3×10−5 ) and a 138–fold decrease after 12 days ( Wilcoxon Rank Sum Test: W = 0 , P = 3×10−5 ) . As the rearrangement of the CHKov1 and CHKov2 genes that confers resistance to the sigma virus was originally found in a natural population in Europe , we examined its frequency in nature . To do this we used Freeze 1 of the Drosophila Genetic Reference Panel ( DGRP ) ( http://www . hgsc . bcm . tmc . edu/project-species-i-Drosophila_genRefPanel . hgsc ) , which is a set of highly inbred North American fly lines whose genomes have been sequenced . As the genome sequences were produced from short-read data , rearrangements and transposable element insertions are not reliably assembled . We therefore used PCR to genotype all the lines for both the Doc element in CHKov1 and the complex rearrangement . The Doc insertion was present in most of the lines ( 155 were homozygous for the insertion , 29 were homozygous without it , and 8 were heterozygous , likely due to insufficient inbreeding . ) , but the rearrangement was not found in any of the 192 lines tested . Therefore this rearrangement is not an important cause of virus resistance in this population . As the truncated version of CHKov1 has been duplicated in the most resistant allele ( Figure 4C ) , we tested whether the Doc element insertion in CHKov1 was itself associated with resistance . We injected 11870 flies from 186 of the DGRP lines with the sigma virus and tested them for infection with the CO2 assay 13 days later . We found that the insertion is associated with a highly significant drop in infection rates ( Bayesian generalised linear mixed model: P<0 . 001 ) . Using this statistical model , we estimate that the Doc insertion is associated with a 52% drop in infection rates from 82% to 30% ( 95% C . I . on drop: 42%–64% ) . It should be noted that the susceptible line used in the mapping experiment above contains the Doc insertion . Therefore the three alleles in this region shown in Figure 4 have a hierarchy of resistance , with the ‘rearranged’ allele being most resistant ( Figure 4C ) and the Doc insertion having intermediate resistance ( Figure 4B ) . The sequence in Drosophila simulans has neither the Doc insertion nor the rearrangement , indicating that the most susceptible allele is the ancestral state ( Figure 4A ) , the allele of intermediate resistance arose next following the Doc insertion , and then a rearrangement occurred that lead to a further increase in resistance . To examine whether any other polymorphisms in this region are associated with resistance we used the data from the genome sequences of the DGRP lines . Using 150 lines whose genomes have been sequenced we examined the 60 kb region which we mapped in our first set of experiments ( Figure 2B ) . In the regions flanking CHKov1 we found that 32 of 468 SNPs in the region were significantly associated with resistance to the sigma virus after Bonferroni correction ( Figure 5A ) . However , there is extensive linkage disequilibrium between the Doc insertion and surrounding sites ( see below; [20] ) , so all of these associations could all be caused the same polymorphism . We therefore repeated the analysis , but this time included the presence or absence of the Doc insertion in the model . We found that none of the associations were significant ( Figure 5B ) , so the most parsimonious interpretation is that a single polymorphism in this region is causing resistance . As the Doc insertion has such a dramatic effect on the protein encoded by CHKov1 , this is most likely to be the cause of resistance . The mapping data together with these association studies therefore provide strong evidence that there are two different polymorphisms in this region that make flies resistant involving the Doc insertion and its subsequent duplication . However , we still wished to confirm that none of the other SNPs associated with resistance in the DGRP lines could contribute to the difference between the resistant and susceptible lines used in the mapping experiments . We therefore sequenced this entire 60 kb region from both the resistant and susceptible lines ( OOP and 22a ) , and identified 191 SNPs and 11 indels that differed between these lines and were present in the DGRP genomes . None of these polymorphisms were significantly associated with resistance to sigma ( after corrections for multiple testing; Figure 5B ) , and only two of them fell within a 30 kB region around the duplication implicated in resistance . This confirms that different genetic changes are affecting resistance in the DGRP lines and causing the difference between the two lines we used in the mapping experiments . Previous studies have examined the pattern of genetic variation around the Doc insertion in CHKov1 [20] , but the sequences of all 192 DGRP lines provides us with a more complete dataset . We found that there is extensive linkage disequilibrium between the Doc insertion and surrounding sites that extends at least 25 kB to the 3′ end of the gene and a much shorter distance in the 5′ direction ( Figure 6 ) . In the region where sites are in linkage disequilibrium with the Doc insertion , there is greater genetic variation among the susceptible chromosomes than the resistant chromosomes ( Figure 6 ) , despite the resistant allele being most common . These observations are consistent with the conclusion of Aminetzach et al [20] that the Doc insertion has recently increased in frequency under directional selection .
We have found that two events have led to successive increases in resistance to the sigma virus ( Figure 4 ) . The first of these is a Doc transposable element insertion into the coding sequence of CHKov1 . The second is a complex rearrangement that results in two duplications of CHKov1 and the Doc element , further increasing resistance to sigma . As infection with the sigma virus reduces the fitness of infected flies [22] , it is likely that selection for resistance to this common pathogen has led to the major structural changes in this gene and large shifts in resistance to the sigma virus . The first of these events , involving the insertion of the Doc element , caused the infection rate in our experiments to drop from 82% in flies with the susceptible allele to 30% in flies with the insertion . Transposable element insertions are known to be important in causing a number of major-effect mutations that are important in adaptations such as insecticide resistance [23]–[24] . In contrast to most of these changes , which tend to affect the regulatory regions upstream of genes [23] , this Doc element has inserted into an exon and is expected to cause major changes to the structure of the protein . In its ancestral state , CHKov1 is comprised of four exons that produce a single transcript . Previous research has shown that , by interrupting the original transcript , this Doc insertion results in two derived transcripts being produced , each of which contains both Doc element sequence and CHKov1 sequence [20] . Assuming these transcripts are translated , this is likely to result in the protein losing its original enzymatic function , as neither of the new transcripts include the two protein domains encoded by the original transcript ( a choline kinase domain and the PFAM domain DUF227 ) [20] . The second event to occur was a complex rearrangement of this region , which resulted in an even greater increase in resistance to the sigma virus than the original Doc insertion . The rearrangement leaves the fly with two full copies of CHKov2 , a partial copy of CHKov2 , and three full copies of the first derived transcript of CHKov1 caused by insertion of the Doc element ( Figure 4 ) . The simplest explanation of how this rearrangement increases resistance is that the amplification of the region coding for the first derived transcript of CHKov1 increases the expression of this new gene , and this in turn increases resistance . However , we were unable to find any evidence for the expression of CHKov1 changing , suggesting that this is not the case . Furthermore , the coding region of CHKov1 is unaffected by the rearrangement . However , the rearrangement is associated with a 6- to 9-fold increase in expression of CHKov2 , suggesting that this may be the cause of resistance . CHKov2 is a paralog of CHKov1 which also has a predicted choline kinase activity [20] , so it is possible that the two genes could both have antiviral effects through a similar mechanism . These complex , sequential modifications to the CHKov1 region are similar to a series of alleles of the gene Cyp6g1 which increase resistance to the pesticide DDT [25] . In the case of Cyp6g1 , successive increases in resistance to DDT were caused by the insertion of an Accord transposable element into the promoter followed by a gene duplication event and the insertion of an HMS-Beagle transposable element and a partial P-element [25] . Together with our results , this suggests that both transposable element insertions and gene-duplications can be important sources of major-effect mutations that contribute to phenotypic evolution . It is well known that genes that increase resistance to pathogens often have pleiotropic effects on other components of fitness . For example , in Drosophila , selection for increased resistance to parasitoid wasps results in a decrease in competitive ability [26] and flies that are resistant to bacteria have reduced fecundity [27] . As these pleiotropic effects tend to be harmful , it is commonly thought that resistance to pathogens is a costly trait to evolve , and these costs are assumed in many theoretical models of coevolution [1] . However , previous research has found that the Doc element insertion in CHKov1 increases resistance to organophosphate insecticides [20] . Therefore , contrary to received wisdom , this pleiotropic effect of this antiviral resistance allele would appear to be beneficial to the fly . Although CHKov1 is involved in pesticide and viral resistance , the molecular basis of these effects are not clear . Neither CHKov1 nor CHKov2 appear to be part of an induced response to the sigma virus , as they are not upregulated in infected flies [21] . It has been suggested that CHKov1 , which contains a choline kinase domain , might make flies resistant to organophosphates by affecting choline metabolism in general or the target of organophosphate insecticides , acetylcholine esterase [20] . If this is the case , it is possible that it could be linked to the mechanism of virus resistance as Rhabdoviruses use acetylcholine receptors to enter cells [28] . Did the Doc insertion initially function as a defence against viruses or insecticides ? Previous work has shown that there is extensive linkage disequilibrium between the Doc insertion and surrounding sites [20] . These observations , which we confirmed using a much larger dataset , provide compelling evidence for a partial selective sweep in which the Doc insertion has very recently increased in frequency . However , the number of sequence changes that have accumulated in the Doc element suggest that the insertion occurred approximately 90 , 000 years ago , which long predates the use of insecticides [20] . The most recent common ancestor of present-day sigma virus isolates existed roughly 2 , 000 years ago [17] , and the infection may have been present in fly populations for much longer than this . Therefore , the Doc element would initially have only played a role in defending flies against viral infection , but these flies found themselves with an unexpected advantage once organophosphate insecticides were introduced . The duplication of this region that resulted in the allele with the highest level of virus resistance has occurred very recently . There are only 2 sequence differences between our mapping lines in the 30 kB region surrounding the duplication , compared with over 550 polymorphisms among the DGRP lines . For this reason it is unsurprising that this highly resistant allele is still rare in the wild ( although we have not tested flies from the population where this allele was first found ) . It is possible that given sufficient time this allele may replace the partially resistant allele that dominates today's populations . Taken together , our results show that successive changes to the same genomic region have caused large shifts in the resistance of flies to the sigma virus . These mutations have all resulted in substantial structural changes to the genes involved , and the first of them has swept through populations under directional selection . This has not only increased the resistance of flies to viral infection , but it may also have pre-adapted flies to the introduction of insecticides in the middle of the last century .
A susceptible ( 22a ) and resistant ( OOP ) fly line was provided by Didier Contamine . The third chromosome of OOP is derived from the Paris line [19] and carries both the resistant allele of the ref ( 3 ) D gene and an allele of a gene called ref ( 3 ) V which reduces the transmission of the virus through sperm [19] . The remaining chromosomes of OOP are from the susceptible Oregon R lab stock . Before attempting to map ref ( 3 ) D we first separated it from ref ( 3 ) V by crossing OOP and 22a . The F1 progeny were then crossed to TM6B , Tb/Sb , and the resulting TM6B , Tb/+ male progeny back-crossed to the balancer stock . These flies were then genotyped using molecular markers located at 92 cM and 94 cM on the standard genetic map . As these markers lie between ref ( 3 ) D and ref ( 3 ) V , this allowed us to identify a recombinant that carried the resistant allele of ref ( 3 ) D but not ref ( 3 ) V . To map ref ( 3 ) D we created stocks that carried homozygous chromosomes that were recombinants between the resistant and susceptible chromosomes . We crossed the resistant stock to 22a , and crossed the F1 progeny to TM6B , Tb/Sb . Single male TM6B , Tb/+ progeny were then crossed back to the balancer . A few days after setting up this cross the males were removed from the tube and genotyped using molecular markers at 80 cM and 92 cM , which flank the region thought to contain ref ( 3 ) D [19] . This allowed us to retain just the 21 genotypes that had recombined in this region . In the next generation we crossed sibling TM6B , Tb/+ flies , and then selected for homozygous recombinants in the subsequent generation . Once we had mapped the gene to a smaller region ( see below ) , we then repeated the experiment using different molecular markers to produce another 33 recombinants between 86 cM and 88 cM . To select recombinants in even smaller regions we used phenotypic markers flanking the region of interest rather than molecular markers . First , we selected two lines , w1118; P {GT1}BG02256 and w1118;P {GT1}jigr1BG00794 , which carry P-elements flanking the region of interest . These elements both carried the mini-white gene , and flies that carry a single heterozygous element have lighter colored eyes than flies carrying two heterozygous elements [29] . This allowed us to cross them and select a 3rd chromosome mapping line that carries both elements ( 2GT1 ) . This was then crossed to a resistant 3rd chromosome recombinant line ( D2-6 ) generated in the experiment described above . Recombinants between 2GT1 and D2-6 were then generated as in the previous experiment , except that this time the 3rd chromosome recombinant lines were balanced with w−;TM3 , Sb/H and recombinants were detected from their eye color . Ten homozygous 3rd chromosome recombinant lines were generated along with controls with either no recombination event or a recombination event outside the region of interest . To generate recombinants at defined sites in the vicinity of the resistance gene we used P-element-induced male recombination [30] . Four different lines with transposable element insertions ( P-elements ) ( Text S1 ) were used with a resistant line to generate recombinants via male induced recombination . The crossing scheme was kindly provided by Kevin Cook ( Text S1 ) and w−;TM3 , Sb/H was used to balance the lines . Non-recombinant lines with either the 3rd chromosome derived from the susceptible P-element line or the resistant parental stock used in this cross ( see Text S1 ) were generated as controls . All four transposable insertion lines contained the second allelic variant ( Figure 4B ) of CHKov1 ( data not shown ) . DNA was extracted using either a protocol using Chelex resin ( Sigma-Aldrich , St Louis ) [31] or a Tissue Genomic DNA Kit ( Metabion , Munich ) . Genotyping was done using microsatellites , indels , SNP specific primers or via sequencing ( Table S1 ) . To score length differences in indels and microsatellites , short PCR products were run on 2% agarose gels , while larger products were run on 1% agarose gels . PCR products for sequencing were cleaned up by incubating with the enzyme Exonuclease I and Shrimp Alkaline Phosphotase at 37°C for 1 hr , followed by a 15 min incubation at 72°C to deactivate the enzymes . The sequencing reaction consisted of 25 cycles of 95°C ( 30 sec ) , 50°C ( 20 sec ) and 60°C ( 4 min ) using BigDye reagents ( ABI ) . Sequencing was carried out at either Source BioScience LifeSciences ( Cambridge ) or The GenePool ( Edinburgh ) . The Hap23 strain of the sigma virus [32] was extracted from an infected line of D . melanogaster ( Om ) , and this extract was used in all assays except one . One hundred flies were ground in 1 ml of Ringer's solution , centrifuged at 13000 rpm for 30 seconds , and the supernatants from several replicate tubes mixed together . The viral extract was then separated into small aliquots and stored at −80°C . When this ran low , the same procedure was followed , this time using susceptible flies two weeks after they were injected with the previous stock of sigma virus . This new stock was tested on susceptible and resistant lines and then used in the 3rd chromosome 2GT1 experiment . Female D . melanogaster were injected in the abdomen with sigma virus until slight extension of the proboscis was observed . They were then maintained on either Lewis media or apple juice-agar media . Flies were tipped onto new media two days after injection and then two more times before they were tested for infection . The flies were then exposed to 100% carbon dioxide for 15 minutes at 12°C on day 10 after injection ( the first two recombinant assays ) or day 14 ( all subsequent assays ) . Flies were given 2 hours to recover from the carbon dioxide and then the number of dead or paralyzed individuals was counted as well as the total number of individuals in each vial . Four replicate vials each containing approximately 15 flies on average were used in each experiment except for the first recombinant assay with the third chromosome line ( three replicates ) . DNA for sequencing was extracted using the kit described above . The majority of the 59 . 6 kb region on chromosome 3 that we had identified by mapping using recombinant lines ( 3R:21126075 . . 21185688; release 5 . 31 of the Drosophila genome ) was sequenced from both OOP and 22a ( GenBank accession numbers JN247668–JN247669 ) . Primer pairs were designed to amplify these regions in overlapping fragments ( Table S1 ) , and the sequencing was performed as described above . The sequencing of a small region involving the genes CHKov1 and CHKov2 was made more difficult by a complex rearrangement in which certain sequences had been duplicated . This region was therefore sequenced by designing PCR primers that amplified just single copies of the duplicated region . Diagnostic PCR primers were designed to genotype flies for a Doc element insertion in CHKov1 and a complex rearrangement involving CHKov2 . The forward primer CHK2-8F ( 5′ GCAGCACGATCGTCAAATAG 3′ ) and the reverse primer CHK2-8R ( 5′ AATGCTTCAAAGGTTTTGTTGA 3′ ) were used to detect the absence of the insert near CHKov2 . The forward primer CHK2-7F ( 5′ TCTTCTCATCTTCCGGGACT 3′ ) and the reverse primer FlipR ( 5′ GTAGTTACTGGACCACAAGTTGAAG 3′ ) were used to identify the presence of the 5′ end of the insertion near CHKov2 . The forward primer CHK_F ( 5′ CTCTTGGCTCCAAACGTGAC 3′ ) and reverse primer CHK_R ( 5′ AAGGCAAACGACGCTCTT 3′ ) were used to detect the absence of the Doc1420 element in CHKov1 . The forward primer Doc1420_F ( 5′ CTTGTTCACATTGTCGCTGAG 3′ ) was used with the reverse primer CHK_R to detect the presence of the Doc1420 element in CHKov1 . The genotype of another resistance gene , ref ( 2 ) P , was scored using the PCR test described in [33] . To examine the expression of candidate resistance genes and estimate viral titers we used quantitative rtPCR . Four biological replicates of 8 resistant and 11 susceptible recombinant lines were injected with sigma virus , and RNA was extracted from two of the replicates after 6 days and the other two replicates after 12 days ( 1 resistant line missing second 12-day replicate ) . From each biological replicate we extracted RNA from 10 individuals using Trizol ( Invitrogen ) following the manufacturer's instructions . RNA was reverse transcribed into cDNA using MMLV ( Invitrogen ) and random hexamer primers . Viral load was determined using quantitative PCR using SYBR Green and the forward primer DmelSV_F1 ( 5′ TTCAATTTTGTACGCGGAATC 3′ ) and reverse primer DmelSV_R1 ( 5′ TGATCAAACCGCTAGCTTCA 3′ ) , which amplify a region of the viral genome spanning the L gene and 5′ trailer ( and therefore amplify genomic RNA but not mRNA ) . Expression of CHKov1 was measured using the forward primer CHKoV1-qPCR-F1 ( 5′ GAACTCCGTGGGATCGACTA 3′ ) and reverse primer CHKoV1-qPCR-R2 ( 5′ CATGGGACAGGTGTTTGTCA 3′ ) . These primers span the first intron of the gene , and amplify a region of the gene that is present in the truncated form of the gene ( described below ) . Expression of CHKov2 was measured using the forward primer CHK2_3F ( 5′ CACCAAAAATCTCCGTGGTT 3′ ) and reverse primer qPCR_Chkov2_3_R ( 5′ TCGTTCTCATAAGCGACTATACATC 3′ ) . Expression of Actin 5C was used as a control in all assays using the primers qActin5c_for2 ( 5′ GAGCGCGGTTACTCTTTCAC 3′ ) and qActin5c_rev2 ( 5′ AAGCCTCCATTCCCAAGAAC 3′ ) . We performed three technical replicates of each PCR and used the mean of these in subsequent analyses . To test which naturally-occurring polymorphisms are associated with resistance we used the Drosophila Genetic Reference Panel , which is a panel of highly inbred fly lines from North America whose genomes have been sequenced ( http://www . hgsc . bcm . tmc . edu/project-species-i-Drosophila_genRefPanel . hgsc ) . To measure the resistance of these lines , we injected 186 of the lines with the virus and tested them for infection 13 days later . In total we tested 11870 flies for infection , and on average 4 different replicate vials of each line containing an average of 16 flies were tested . As far as was possible , each replicate vial of each line was injected on a different day and on each day we used different combinations of lines . R version 2 . 11 . 1 was used for statistical analyses . Our data from the infection experiments consists of numbers of infected and uninfected flies , which we treat as a binomial response in a generalized linear mixed model . The parameters of the model were estimated using the R library MCMCglmm [34] , which uses Bayesian Markov chain Monte Carlo ( MCMC ) techniques . To test for an association between Doc1420 status and resistance to sigma virus we used the model:Where νi , j is the probability of flies in vial i from line j being infected . β is a vector of the fixed effects of ref ( 2 ) P genotype and Doc1420 genotype , and XiT is a row vector relating the fixed effects to vial i . αj is a random effect of line j . The residual , εi , j , includes over-dispersion due to unaccounted for heterogeneity between vials in the probability of infection . The estimated effect of Doc1420 on infection rates was back-transformed from logits into a proportion , and the number quoted in the text is based on estimates for lines that have the susceptible allele of ref ( 2 ) P . The 95% highest posterior density of the MCMC sample was used as an estimate of the credible intervals ( C . I . ) of parameters . This Bayesian approach is computationally intensive and slow to implement , so when testing larger numbers of SNPs from the DGRP dataset for effects on resistance we used a maximum likelihood method . The model was essentially the same as that described above except the SNP in question was included as a fixed effect ( and Doc1420 status was not always included ) . The model was fitted using the R function lmer , and the significance of the fixed effects was assessed using the Wald statistic . When sample sizes are small this can give anti-conservative results [35] , but this should not be important in our analysis as common SNPs were found to be highly significant ( see below ) . For each fly line in which we measured viral titres or gene expression by quantitative RT-PCR , we first calculated ΔCt as the difference between the cycle thresholds of the gene of interest and the endogenous control ( actin 5C ) . The viral titre or gene expression in resistant flies relative to susceptible flies was calculated as 2−ΔΔCt , where ΔΔCt = ΔCtresistant−ΔCtsusceptible , where ΔCtresistant and ΔCtsusceptible are the means of the ΔCt values of the resistant and susceptible lines . To assess whether these differences were statistically significant , we used a Wilcoxon Rank Sum Test to compare ΔCt in the resistant lines and the susceptible lines . This calculation assumes that the PCR reactions are 100% efficient . To check whether this assumption is realistic we used a dilution series to calculate the PCR efficiency . Using this approach we found that the actin PCR is 103% efficient , the virus PCR is 101 . 5% efficient , the CHKov1 PCR is 100 . 0% efficient and the CHKov2 PCR is 102 . 5% efficient . | Though much is known about host–parasite coevolution in plants , relatively little is understood in animals . Most studies using animal systems have focused on either generalist parasites or those that do not naturally occur in the host . The sigma virus is specific to Drosophila melanogaster , which provides the unique opportunity to study natural coevolution in a well-established model organism . In order to gain a better understanding of host–parasite coevolution , we have set out to identify novel viral resistance genes using the sigma-Drosophila system . Here we identify two successive mutations that provide increasing resistance to the sigma virus . The first of these , a transposable element insertion within a gene called CHKov1 , is already known to provide resistance to insecticides . There is evidence that the novel gene product resulting from this insertion has been under positive selection pressure long before the use of pesticides . Two duplications of this gene region have resulted in further resistance to sigma virus . We believe that selection for resistance to the sigma virus led to the added benefit of resistance to insecticides . | [
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] | 2011 | Successive Increases in the Resistance of Drosophila to Viral Infection through a Transposon Insertion Followed by a Duplication |
Flavivirus nonstructural protein 5 ( NS5 ) consists of methyltransferase ( MTase ) and RNA-dependent RNA polymerase ( RdRp ) domains , which catalyze 5’-RNA capping/methylation and RNA synthesis , respectively , during viral genome replication . Although the crystal structure of flavivirus NS5 is known , no data about the quaternary organization of the functional enzyme are available . We report the crystal structure of dengue virus full-length NS5 , where eight molecules of NS5 are arranged as four independent dimers in the crystallographic asymmetric unit . The relative orientation of each monomer within the dimer , as well as the orientations of the MTase and RdRp domains within each monomer , is conserved , suggesting that these structural arrangements represent the biologically relevant conformation and assembly of this multi-functional enzyme . Essential interactions between MTase and RdRp domains are maintained in the NS5 dimer via inter-molecular interactions , providing evidence that flavivirus NS5 can adopt multiple conformations while preserving necessary interactions between the MTase and RdRp domains . Furthermore , many NS5 residues that reduce viral replication are located at either the inter-domain interface within a monomer or at the inter-molecular interface within the dimer . Hence the X-ray structure of NS5 presented here suggests that MTase and RdRp activities could be coordinated as a dimer during viral genome replication .
Flaviviridae includes at least 70 mosquito- and tick-borne viral species , some of which act as the etiological agents of human diseases such as dengue virus ( DENV ) , West Nile virus ( WNV ) , Japanese encephalitis virus ( JEV ) , and yellow fever virus ( YFV ) . Dengue is the most prevalent mosquito-borne virus , with nearly 400 million annual cases worldwide [1] . Infection with one of four dengue virus serotypes ( DENV1-4 ) can lead to febrile illness and flu-like symptoms , or can progress to the more severe dengue hemorrhagic fever or dengue shock syndrome . Development of the more severe hemorrhagic fever is more likely if recovery from infection by one dengue serotype is followed by subsequent infection by a second serotype [2] . There are currently no effective antiviral drugs for the treatment of flavivirus infections , and no vaccine is available for protection against severe diseases caused by any of the DENV serotypes or WNV [3] . The 10–11 kb positive ( + ) sense flaviviral genome consists of a single open reading frame ( ORF ) flanked by 5’ and 3’ untranslated regions ( UTR ) . The 5’ terminal nucleotide A is modified by the addition of a type 1 cap ( N7MeG5’-ppp-5’A2’OMe ) . The viral ORF is translated into a single polyprotein that is co- and post-translationally cleaved by host and viral proteases to produce ten proteins , including three structural proteins ( capsid , pre-membrane , and envelope ) and seven nonstructural ( NS ) proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , and NS5 ) [4] . All NS proteins , along with the viral RNA and cellular factors , form a viral replication complex embedded within virus-induced membrane vesicles located at the rough endoplasmic reticulum of infected cells [5–8] . NS3 and NS5 are the key enzymes in the replication complex , as together they account for all catalytic activities required for genome replication and RNA capping at the 5’ UTR . NS3 consists of an N-terminal serine protease domain , which requires NS2B as a cofactor , and a C-terminal helicase domain possessing three distinct activities: RNA helicase , nucleoside triphosphatase , and 5’ RNA triphosphatase [4] . NS5 , the largest NS protein at 103 kDa , consists of an N-terminal methyltransferase ( MTase ) domain possessing three activities necessary for cap synthesis ( guanylyltransferase , guanine-N7-methyltransferase , and nucleoside-2’O-methyltransferase ) and a C-terminal RNA-dependent RNA polymerase ( RdRp ) domain that carries out de novo RNA synthesis [9–14] . Replication of the ( + ) strand RNA genome is an asymmetric and semi-conservative process wherein the antigenome is present only as a double-stranded ( ds ) RNA replication intermediate [15] . After ( + ) strand RNA synthesis by the NS5 RdRp , the cap structure is added to the 5’ end of the genome by four enzymatic reactions catalyzed by NS3 and NS5 [16] . First , the 5’ γ-phosphate of the ( + ) RNA is cleaved by NS3 5’-RNA triphosphatase activity to produce a diphosphate-terminated RNA ( pp-5’A ) . Next , the NS5 MTase catalyzes the transfer of a GMP moiety from GTP to the RNA ( G5’-ppp-5’A ) . Finally , NS5 MTase catalyzes sequential guanine-N7- and nucleoside-2’O-methylations using S-adenosyl-L-methionine ( SAM ) as a methyl donor and produces a type 1 cap structure ( N7MeG5’-ppp-5’A2’OMe ) [16 , 17] . The physical linkage of two distinct catalytic domains within NS5 suggests that RNA synthesis and genome capping activities may be coupled during viral replication [18] . Communication between the MTase and RdRp domains has indeed been observed in several flaviviruses via reverse genetic , biochemical , and structural experiments [19–24] . Here we present the crystal structure of full-length DENV3 NS5 . A total of eight copies of full-length NS5 are arranged as four dimers within the crystallographic asymmetric unit ( ASU ) . All eight molecules have essentially the same relative orientation of the MTase and RdRp domains , and this arrangement differs from that seen in JEV NS5 [25] . In the structure presented here , the linker region between the MTase and RdRp domains is fully resolved , and comparison with the JEV NS5 structure suggests that this region acts as a hinge that allows NS5 to adopt multiple conformations . Furthermore , many residues that reduce viral replication are located at the inter-domain and inter-molecular interfaces , and thus the domain-domain interactions within a monomer as well as monomer-monomer interactions within a dimer likely play a role during viral genome replication . The DENV NS5 structure will aid the development of structure-based inhibitors that would interfere with inter-domain or inter-molecular interactions .
The crystallographic ASU contains eight copies of full-length DENV3 NS5 , in which four sets of dimers ( AB , CD , EF , and GH ) are arranged in a saddle-like shape ( Fig 1 ) . The AB and CD dimers are related to the GH and EF dimers , respectively , by a 2-fold non-crystallographic symmetry ( NCS ) axis . Superimposition of the eight NS5 monomers and four NS5 dimers within the ASU yields rmsd values ranging from 0 . 4 to 1 . 9 Å , and from 0 . 8 to 2 . 3 Å , respectively , indicating that all copies of NS5 are nearly superimposable with one another . Thus the relative arrangement of MTase and RdRp domains within a monomer and the arrangement of monomers within a dimer are conserved in all eight molecules . These eight molecules within the ASU are subject to entirely different packing forces , hence the observed arrangement is not an artifact of crystallization ( S1 Fig ) . Individual MTase and RdRp domains in full-length NS5 have conserved protein folds . The MTase ( residues 1–262 ) adopts the SAM-dependent methyltransferase fold composed of four helices surrounding a central 7-stranded β-sheet ( Fig 2A ) , similar to previously determined flavivirus MTase structures [13] . The active site , containing a catalytic K61-D146-K180-E216 ( KDKE ) motif , is positioned in the center of the β-sheet . The MTase core fold is surrounded by N- and C-terminal extensions that interact with each other . Although no SAM or S-adenosyl-L-homocysteine ( SAH , a byproduct of the methyltransferase reaction ) were added during purification or crystallization , SAH is clearly visible in the electron density in the active site ( S2 Fig ) . The C-terminal RdRp ( residues 273–900 ) adopts the canonical right hand polymerase fold , consisting of fingers , palm , and thumb subdomains ( Fig 2A ) . The palm subdomain contains a catalytic G662-D663-D664 ( GDD ) metal-binding motif . Flavivirus RdRps initiate RNA synthesis de novo , without the need of a primer . The priming loop in the thumb subdomain is proposed to stabilize the de novo initiation complex and occlude access to the template-binding channel [26] . During elongation , however , the priming loop is proposed to open to allow exit of dsRNA products , requiring significant movement . The NS5 construct used to solve the crystal structure ( NS5-Δ6 ) contained a six-residue deletion ( 795WSIHAH800 ) in this priming loop , which was rationally designed to eliminate the need for this movement . This protein has higher polymerase activity than the wild-type ( WT ) protein when a subgenomic RNA is used as a template [27] ( Fig 2B ) . Thus the structure represents a biologically relevant form of the polymerase . The shortened priming loop of the protein follows continuous density in the electron density map , suggesting that the deletion did not significantly affect the protein fold . Three areas of the DENV RdRp had been unresolved in the previous crystal structures [28–30] . While this manuscript was in preparation , a DENV NS5 structure was published [31] , and thus was included in comparison . First , the region containing motif G ( residues 408–417 ) in the fingers subdomain is disordered . Motif G is proposed to form a part of the template-binding channel and regulate RNA template binding and translocation [25] . Three monomers ( B , C , and E ) out of the eight copies in the DENV NS5 ASU have connected density for a long loop near the entrance to the template-binding channel ( Fig 2C ) . This is the first time that motif G has been fully resolved in the DENV RdRp structures . The conformation of the loop is similar to that observed in JEV NS5 [25] ( Fig 2C ) . Second , the fingertip region formed by residues 454 to 469 ( motif F ) is disordered in all eight copies . Motif F is well ordered in JEV NS5 and forms part of the template-binding channel near the MTase-RdRp interface ( Fig 2C ) . Unexpectedly , when JEV and DENV RdRp domains are overlaid , motif F in JEV NS5 sterically clashes with the MTase in the DENV NS5 structure due to differences in the MTase and RdRp interface ( see below ) . This suggests that motif F adopts several conformations depending on the interactions between MTase and RdRp domains . Finally , the C-termini ( residues 884–899 ) of four monomers ( A , D , F and G ) were clearly resolved for the first time . The C-terminus forms an α-helix and interacts with the MTase of a neighboring NS5 ( Figs 2C and 3B ) . Involvement of the C-terminus is further discussed in the intermolecular interactions below . When the eight RdRp domains are overlaid , the biggest difference is the orientation of the thumb subdomain relative to the palm and fingers subdomains ( Fig 2E ) . For example , the thumb subdomain in monomer B is rotated ~5° around a hinge point at residues G699 and W700 relative to the other monomers . This results in a 6 Å movement of an α-helix in the thumb , as calculated by the program DynDom [32] . When the RdRp domains were further compared to other flavivirus RdRp structures , movement of the thumb subdomain relative to the palm and fingers subdomains ranges up to 13 Å ( Fig 2E ) . Such flexibility of the thumb subdomain could control the size of the template-binding channel to accommodate either an ssRNA template or a dsRNA product during RNA synthesis . The unusually large number of molecules in the crystallographic ASU allows us to examine interactions of NS5 domains without the influence of crystal contacts . Although the individual NS5 molecules are not restricted by crystal lattice contacts , all eight copies of NS5 have the same relative orientation of the MTase and RdRp domains . Looking at the canonical right hand orientation of the RdRp , the MTase is located behind the RdRp near the template entry channel , opposite the priming loop ( Fig 2A ) . In this arrangement of two domains , the active site of the MTase and the dsRNA exit of the RdRp face in opposite directions , exposed to solvent . The MTase interacts mainly with the fingers subdomain of the RdRp , and buries ~1000 Å2 of surface area as calculated by the program PISA [33] . Two areas of NS5 contribute to the inter-domain interface—the linker between the MTase and RdRp domains , and the interface involving two helices on the periphery of the MTase core and the backside of the fingers subdomain ( Figs 2A and S3 ) . The MTase and RdRp domains are connected by a 10-residue linker ( residues 263–272 ) . The linker has very low sequence identity among flavivirus NS5 proteins ( one identical residue out of 10 residues ) , and was structurally unresolved in both the crystal structures of individual MTase and RdRp domains , as well as in the full-length JEV NS5 structure [25 , 30] . Hence the linker was predicted to be flexible . In the current structure , the linker is well ordered , and has complete density connecting the MTase and RdRp domains ( Fig 2D ) . The linker is in an extended conformation and makes extensive interactions with each domain . R262 and E267 form hydrogen bonds with the MTase core ( A92 , L94 , K95 , V97 , N112 , and Y119 ) ( Fig 2D ) . Residues P268 , E269 , and N272 interact primarily with the fingers subdomain of the RdRp ( R361 , K595 , and N574 ) ( Fig 2D ) . The average temperature factor for the linker ranges from 80 to 127 Å2 amongst the eight monomers , and is similar to the temperature factor of the neighboring residues in each monomer . The interactions between the linker and NS5 domains support previous observations that incorporation of the linker ( residues 263–272 ) into the DENV1-4 RdRp domains improves thermal stability compared to the RdRp domain alone [30] . Adjacent to the linker region , there are additional molecular contacts at the MTase and RdRp interface . These include a cation-pi interaction between R68 and F348 , and salt bridges between E67 and R352 , and between E252 and R352 ( S3 Fig ) . Hydrogen bonding interactions are also observed between Q63 and R352 , G93 and K300 , and D256 and E356 . Most of the residues involved in the domain-domain interactions are conserved , and thus the MTase and RdRp interactions are likely preserved in DENV NS5 from all four serotypes . Several residues located in the MTase and RdRp domain interface have previously been mutated and shown to reduce viral replication . For example , virus containing the double mutation K356A/E357A in DENV4 NS5 ( corresponding to K355/E356 in DENV3 NS5 ) reduced viral replication 100-fold relative to the WT virus [22 , 34] . Similarly , no virus was detected after transfection of DENV2 RNA transcripts containing R353A/K358A ( R352/K357 in DENV3 ) or E357A/K358A/D360A ( E356/K357/D359 in DENV3 ) in NS5 [34] . A F349D substitution in DENV2 NS5 ( F348 in DENV3 NS5 ) also severely impaired viral replication [22] . Thus the MTase and RdRp interactions shown in the crystal structure are likely important for viral replication . Flavivirus NS5 is proposed to adopt multiple conformations from compact to more extended forms based on small-angle X-ray scattering ( SAXS ) [35] . Crystal structures of JEV and recently solved DENV NS5 ( PDB entry 4V0Q ) indeed show different arrangements of MTase and RdRp domains along with different linker conformations [25 , 31] . It was thus interesting that the arrangement of MTase and RdRp domains in our DENV NS5 structure was similar to those in the 4V0Q structure . Despite the different crystallization conditions ( 1 . 0 M succinic acid and 1% monomethylether 2000 in our condition vs . 0 . 2 M calcium acetate and 10–20% PEG 8000 in the 4V0Q structure ) and symmetry of crystals ( P3221 with 8 molecules in ASU vs . P21212 with one molecule in ASU ) , the two DENV NS5 structures can be superimposed with rmsd values of 1 . 0–2 . 5 Å for each of the eight NS5 monomers . The 10-residue linkers , including a short 310 helix , are also similar and they can be superimposed with rmsd values of 0 . 4–0 . 6 Å ( Fig 2C ) . We next compared the NS5 crystal structure to the SAXS profile , which showed an average radius of gyration ( RG ) and maximum dimension ( Dmax ) of 34 and 125 Å , respectively . The RG and Dmax of both DENV NS5 structures are calculated to be 31 and 98 Å , respectively , using the program CRYSOL [36] . Thus , the crystal structure would correspond to a compact form of NS5 [35] . Since both structures have the same domain arrangements within the monomer despite the protein’s ability to adopt multiple forms in solution , the compact form of NS5 may be a preferred structure at high protein concentration . The eight molecules of NS5 in the crystallographic ASU consist of four dimers ( AB , CD , EF , and GH in Fig 1 ) , in which the relative arrangement of both monomers is fixed ( type I dimer ) . The four dimers can be superimposed with rmsd values ranging from 0 . 8 Å ( between AB and GH dimers ) to 2 . 3 Å ( between EF and GH dimers ) . The periphery of the core MTase domain in one NS5 molecule interacts with the base of the palm subdomain in the second NS5 molecule , and buries 1100–1200 Å2 of surface area between monomers in each pair ( Fig 3A ) . This extensive intermolecular dimer interaction even includes two hydrogen bonds between main chain atoms of S128 in the MTase of one monomer and G526 in the RdRp of the neighboring monomer as in a short β-sheet ( Fig 3A ) . MTase residues E112 , S117 , and V124 in one monomer interact with RdRp residues H701 , N676 , and R673 in the neighboring monomer , respectively . P113 forms an additional hydrogen bond with H701 . The type I dimer is also observed in the recently solved DENV NS5 structure ( PDB code 4V0Q ) [31] . Although the NS5 structure was reported to be a monomer , i . e . , one molecule in the ASU , the structure shows a crystallographic dimer that is nearly identical to our type I dimer . The crystallographic dimer can be superimposed with the type I dimer with an rmsd of 2 . 0 to 3 . 1 Å ( for 1599–1671 Cα carbons ) . As mentioned previously , crystallization conditions and the symmetry of the crystals are significantly different between the two crystal forms . Crystal contact areas of the five dimers ( four dimers in the current structure and the crystallographic 4V0Q dimer ) were determined using the program CONTACT with a 4 Å cutoff . The crystal contacts were also quite different for each of the five dimers ( S1 Fig ) . Thus , assembly of DENV NS5 into the type I dimer is not an artifact of crystallization . Because of the arrangement of dimers within the 8 copies of NS5 in the ASU , there is a second type of dimer interaction ( type II dimer ) only observed between monomers A and F , and between D and G ( Fig 3B ) . In the type II dimer , the tip of the thumb subdomain of one NS5 molecule ( where the fingertips and the thumb domain meet ) interacts with the same area in the neighboring molecule . This interface includes mostly hydrophobic interactions and buries ~1600 Å2 of surface area . Interestingly , only the four NS5 monomers in type II dimers ( A , D , F , and G ) have the extended density for the C-terminus ( residues 884–899 ) , contributing to the highly buried surface area . The C-terminus of each of the four monomers forms an α-helix and is located near the C-terminal α-helix ( residues 228–243 ) in the neighboring MTase ( Figs 2C and 3B ) . The C-terminus of flavivirus NS5 is thought to be flexible because the amino acid sequences are less conserved ( < 25% identity ) , and it has not been observed in any crystal structures . The current structure indicates that the C-terminus of NS5 can be ordered and mediate interactions with other NS5 molecules . The two types of NS5 dimer interactions with significant interface areas ( >1 , 000 Å2 ) suggest that NS5 may function as a dimer in the viral replication complex . Although recombinant NS5 is a monomer in solution [35 , 37] , cellular interactions between NS5 molecules have been shown using pull-down and fluorescence resonance energy transfer ( FRET ) assays [19 , 38] . For instance , the WNV RdRp domain pulls down the MTase domain and full-length NS5 , indicating that inter-domain and inter-molecular interactions exist for NS5 in cells [19] . FRET assays also show that NS5 homo-oligomerizes via its RdRp domain [38] . Thus , if multiple copies of NS5 are present in the replication complex , a specific dimer of NS5 , such as one shown in the crystal structure , may be formed preferentially . We thus investigated whether any of the NS5 residues located at one of the dimer interfaces have been shown to be important for viral replication . Indeed , several mutations in or near the type I dimer interface severely reduced viral replication . Individual mutations of P113D and W121D in DENV2 NS5 almost completely disrupted viral replication [22 , 23] . Both P113 and W121 are conserved in all DENV NS5 sequences , and interact with H701 and G260 at the type I dimer interface , respectively ( Fig 3A ) . Several paired charge-to-alanine mutations in DENV4 NS5 led to viral attenuation in mice [34] . A double mutant K524A/K525A or K525A/D526A in DENV4 NS5 ( K523/I524/P525 in DENV3 ) reduced viral replication by ~100 fold [34] . Other pairs such as E642A/R643A and D654A/R655A in DENV4 , corresponding to K641/K642 and E653/R654 located near the dimer interface in DENV3 NS5 , reduced viral replication by ~10 , 000 fold ( Fig 3A ) . Thus the intermolecular interactions observed in the NS5 type I dimer seem to be important for viral replication . None of the residues in the type II dimer interface have been included in published mutagenesis studies , and thus it is not clear whether the type II dimer also plays a role in viral replication . The structure of DENV NS5 was compared to the JEV NS5 structure [25] . As in the DENV NS5 structure , the JEV MTase is positioned behind the RdRp when looking at the canonical right hand orientation of RdRp , and interacts with the fingers subdomain ( Fig 4 ) . However , the relative orientations of MTase and RdRp in DENV and JEV NS5 differ significantly . With the MTase of DENV and JEV NS5 superimposed ( rmsd = 0 . 99 Å for 253 Cα atoms ) , the RdRp domains are related by a rotation of 102° and a ~5Å translation as calculated by DynDom [32] ( Fig 4A ) . This twisting motion would require significant bending of residues 262–272 ( the linker ) , using residues 260–262 ( 260GTR262 ) as the pivot ( Fig 4B ) . The ‘GTR’ sequence is conserved in flavivirus NS5 , and individual substitutions of G261A , T262V , and R263L in DENV2 and JEV NS5 greatly impair viral replication and virus production [22] . Thus , flavivirus NS5 likely uses the GTR pivot to sample different conformations , repositioning the MTase and RdRp domains during viral replication . Due to the different arrangements of MTase and RdRp domains , DENV and JEV NS5 have different domain interfaces ( Fig 4C ) . The domain interface in JEV NS5 buries ~860 Å2 surface area and centers around a hydrophobic core comprised of six residues: P113 , L115 , and W121 in the MTase domain , and F351 , F467 , and P585 in the RdRp domain . When individual mutations of the six residues were introduced into infectious JEV or replicon systems , virus replication was significantly impaired , while their polymerase activities were largely unaffected [22 , 23] . We mapped the corresponding residues on the DENV NS5 structure to determine whether they are involved in domain-domain interactions . The conserved RdRp residues F348 and P584 are found in the MTase and RdRp domain interface in the DENV NS5 monomer , similar to those involved in the intra-molecular interactions in the JEV NS5 monomer . F464 is disordered in the DENV NS5 structure . However , MTase residues P113 , P115 ( corresponding to L115 in JEV NS5 ) and W121 are not involved in domain-domain interactions in the DENV NS5 monomer ( Fig 4C ) . Instead , the loop containing these MTase residues is part of the monomer-monomer interface in the NS5 type I dimer ( Figs 3A and 4C ) . Thus , the same MTase region mediates intra- and inter-molecular interactions with the RdRp domain in JEV and DENV NS5 , respectively . This surprising discovery again suggests that the inter-molecular interactions in the DENV NS5 dimer ( type I ) are likely important for viral replication . NS5 is the most conserved protein in flavivirus , and sequence identities among DENV1-4 NS5 range from 74 ( DENV2 vs . DENV4 ) to 84% ( DENV1 vs . DENV3 ) . Despite the high sequence identity , chimeric NS5 , wherein the MTase and RdRp from different DENV serotypes or from DENV and WNV ( 66% sequence identity ) are combined in a single polypeptide , is not capable of carrying out replication [39 , 40] . We have previously shown that a DENV2 infectious RNA containing an NS5 chimera with DENV2 MTase ( residues 1–270 ) replaced by the DENV4 MTase was severely impaired and unable to accumulate viral RNA and virus particles [39] . Repeated passages of the chimeric RNA-transfected cells yielded viruses that contain a mutation in either NS5 MTase ( K74I ) or NS3 helicase ( D290N ) . Thus , serotype-specific interactions either between the MTase and RdRp domains and/or between NS5 and other components of the replication complex are required for efficient viral RNA synthesis . To determine whether inter-domain interaction within NS5 plays a significant role in viral replication , we replaced the entire NS5 with DENV4 NS5 in the full-length DENV2 infectious RNA . Surprisingly , replication of the DENV2 RNA containing the DENV4 NS5 was delayed only slightly compared to the wild-type DENV2 RNA , significantly faster than replication of the chimeric RNA containing the DENV4 MTase ( Fig 5A ) . This suggests that the serotype-specific interactions between MTase and RdRp domains are the major determinant of efficient viral replication , rather than interactions between individual NS5 domains and RNA or other proteins . In light of the DENV NS5 structure presented here , we analyzed whether virus serotype-specific interactions exist between the MTase and RdRp domains that could explain the low viral replication of the NS5 chimera , and whether serotype-specific residues accumulate at a particular protein surface that would facilitate serotype-specific protein interactions . Multiple amino acid sequences of four DENV serotypes were analyzed to identify individual residues that are specific to each serotype . Serotype-specific NS5 residues were identified such that the residues are conserved within one DENV serotype , but not conserved among different serotypes . A total of 119 residues were identified as serotype-specific , and 38 positions out of 119 were identified for more than two serotypes ( Fig 5B ) . These 38 residues were then mapped on the DENV3 NS5 structure ( Fig 5C ) . Most of the residues were located on the protein surface , with several residues in the domain-domain and monomer-monomer interfaces . For example , K96 , E296 , and linker residues H263 and N265 are found in the domain-domain interface ( Fig 5C ) . The more surprising discovery , however , was that many serotype-specific residues clustered on one face of the dimer , while the opposite face of the dimer , where the active sites of NS5 are exposed to solvent , contained few serotype-specific residues ( Fig 5C ) . The best-documented example of serotype-specific differences in DENV NS5 is their nuclear localization . DENV2 and DENV3 NS5 have a functional nuclear localization signal ( NLS , residues 369–406 ) that is recognized by importin-α/β , and predominantly localize to the nucleus [41 , 42] . By contrast , DENV4 NS5 does not have a functional NLS , and localizes to the cytoplasm [43] . We mapped the NLS sequence on the NS5 structure to determine whether it is located on the serotype-specific side of the dimer ( Fig 5C ) . The NLS indeed mapped to the serotype-specific surface , and four residues within the NLS—residues 375 , 383 , 385 , and 404—were identified as serotype-specific ( Fig 5B ) . Thus , serotype-specific residues accumulate on a particular NS5 dimer surface that is likely mediate NS5-protein interactions that are specific for each serotype .
Genome replication in flavivirus is carried out by a membrane-bound viral replication complex that consists of viral NS proteins , viral RNA , and unknown host proteins [7] . Neither the exact composition of the replication complex nor the stoichiometry of viral NS proteins within the replication complex is currently known . The DENV NS5 structure reported here shows two dimer types with significant interface areas between the monomers ( > 1 , 000 Å2 ) . In particular , the type I dimer is formed by all eight NS5 molecules in the crystallographic ASU , even though each of these eight molecules is in a different chemical environment in the crystal ( S1 Fig ) . The type I dimer was also observed in recently solved DENV NS5 structure [31] , and thus the DENV NS5 clearly assembles into the dimer . Many NS5 residues involved in the monomer-monomer interface in the type I dimer are important for viral replication , suggesting that the NS5 dimer may be the biological unit in the replication complex . Recombinant NS5 has been shown to be a monomer in solution [35 , 37] . However , these experiments were performed in membrane-free , high salt ( 400 mM NaCl ) buffers that do not necessarily reflect the cellular environment . In the membrane-bound replication complex , where free diffusion is limited to a 2-D surface , NS5 may form a higher order oligomer . Cellular interaction and pull-down assays indeed demonstrate that NS5 interacts with itself [19 , 38] . Furthermore , NS5 is anchored to the membrane by its interaction with NS3 in the replication complex [38] . NS3 itself does not contain any membrane-associated region , but the N-terminal protease domain requires the cofactor NS2B that forms a dimer on the membrane [38] . Thus , the membrane anchored NS5-NS3-NS2B complex could contain a dimeric form of NS5 . Other viral polymerases such as poliovirus 3D , which is also a monomer in solution , oligomerize in order to facilitate efficient binding of RNA [44] . Disruption of the protein-protein interface of the poliovirus 3D oligomers led to low viral replication in infected cells , suggesting the biological significance of oligomerization [44] . The physical linkage of the MTase and RdRp domains in a single polypeptide of NS5 suggests that RNA synthesis and capping may be coupled during viral genome replication . In light of the dimer structure , we compared the NS5 monomer and dimer in terms of the relative orientation of the RdRp dsRNA exit and MTase active site , which could be important for the coordination of RdRp and MTase activities ( Fig 6 ) . The location of the ssRNA-binding site in the MTase domain of the full-length NS5 was identified based on superposition of the MTase domains in our structure with the structure of the isolated MTase domain-RNA complex [45] ( PDB code 2XBM ) . In the NS5 monomer , the dsRNA exit site of the RdRp and the active site of the MTase face opposite directions in both the DENV and JEV NS5 structures ( Fig 4 ) . Thus , the NS5 monomer model would require a large conformational change to pass the RNA from the RdRp directly to the MTase . Consequently , monomer models with multiple large conformational changes have been proposed [25 , 46] . Using small-angle X-ray scattering , monomeric NS5 has been shown to adopt multiple conformations in solution ranging from compact to more extended forms [35] . The DENV and JEV NS5 structures clearly indicate that NS5 adopts different arrangements of MTase and RdRp domains related by rotation along the linker ( Fig 4 ) . However , multiple large conformational changes could be limited in the replication complex , where NS5 is bound by viral and host proteins on the membrane . The NS5 dimer could thus allow coordination between MTase and RdRp domains within and across the NS5 molecules without requiring large conformational changes inherent in the monomer model . In the DENV NS5 dimer structure , the dsRNA exit site of the RdRp in one monomer and the MTase active site of its partner in the NS5 dimer face the same direction ( Fig 6 ) . The distance from the dsRNA exit of one monomer to the entrance to the MTase active site of its partner is also considerably closer than the distance to its own MTase . Thus the dsRNA product of the RdRp could easily access the MTase active site of its neighbor in the NS5 dimer ( Fig 6 ) . The dimeric form could still allow small rearrangements of MTase and RdRp domains . How flavivirus coordinates RNA synthesis and 5’-RNA capping is not well understood . It is currently not clear how NS5 modulates multiple interactions with different RNA forms ( plus , minus , double stranded , and capped RNA ) . In addition , following ( + ) strand RNA synthesis , the 5’ triphosphate end of the RNA must be dephosphorylated by NS3 5’ RNA triphosphatase activity prior to the cap addition , so NS5 is also required to interact with NS3 for viral replication . Thus , NS5 may need the flexibility to form a monomer and dimer to carry out multiple functions in the replication complex . Future effort will be directed toward validating these NS5 models . The current structure provides a framework to test how NS5 coordinates multiple reactions within a single polypeptide , and to design NS5 inhibitors of dengue virus replication .
DENV3 NS5 constructs Δ2 and Δ6 with the priming loop shortened by either two ( 797IH798 ) or six amino acids ( 795WSIHAH800 ) , respectively , were designed to make NS5 more amenable to crystallization . The plasmid encoding DENV3 NS5-Δ2 was generated from the wild-type NS5 clone ( 900 residues ) using the QuikChange II site-directed mutagenesis kit ( Agilent Technologies ) with the oligonucleotide primers 5’ CCTACAAGCAGAACGACATGGTCTGCTCACCATCAGTGGATGACTAC 3’ ( forward ) and 5’ GTAGTCATCCACTGATGGTGAGCAGACCATGTCGTTCTGCTTGTAGG 3’ ( reverse ) . The plasmid containing NS5-Δ2 was then used to produce NS5-Δ6 by another round of site-directed mutagenesis using the oligonucleotide primers 5’ CCCACGAGCAGAACGACACATCAGTGGATGACTACAG 3’ ( forward ) and 5’ CTGTAGTCATCCACTGATGTGTCGTTCTGCTCGTGGG 3’ ( reverse ) . After DNA sequence confirmation of the NS5-Δ2 and Δ6 plasmids , the plasmids were transformed into BL21-CodonPlus-RIL Escherichia coli cells ( Stratagene ) . His-tagged NS5 proteins were purified as previously reported with minor differences [35] . Briefly , 1 L of Luria Broth medium supplemented with 25 μg/ml chloramphenicol and 50 μg/ml kanamycin was inoculated with 5 mL of start culture . After reaching an OD600 of 0 . 8 , the cells were induced by the addition of 1 mM isopropyl 1-thio-β-D-galactopyranoside and grown at 18°C overnight . Pelleted cells were lysed by sonication in lysis buffer [50 mM sodium phosphate pH 8 . 0 and 1 M NaCl , supplemented with 50 μg/mL ribonuclease A , 100 μg/mL deoxyribonuclease A , and one tablet of protease inhibitor cocktail ( Roche Applied Science ) ] . NS5 was purified first by affinity chromatography using TALON cobalt affinity resin ( Clontech ) and an imidazole gradient of 5 to 150 mM in elution buffer ( 25 mM sodium phosphate , pH 7 . 0 and 500 mM NaCl ) . Fractions containing NS5 were concentrated to ~1 mL and further purified by size exclusion chromatography using a HiLoad 16/60 Superdex 200 preparative grade column ( GE Healthcare ) in 20 mM Tris-HCl pH 7 . 0 , 300 mM NaCl , and 1 mM DTT . NS5 containing fractions were pooled . The protein concentration was determined using a Nanodrop 1000 spectrophotometer ( Thermo Scientific ) with a theoretical molar extinction coefficient of 217 , 000 M-1cm-1 and a molecular weight of 104 , 000 Da . Polymerase activity assays for wild-type , Δ2 , and Δ6 NS5 were performed using the subgenomic RNA template as previously described [27] . Briefly , the assays were conducted in a standard reaction mixture ( 50 μL ) containing 50 mM Tris-HCl ( pH 8 . 0 ) , 50 mM NaCl , 5 mM MgCl2 , template RNA ( 0 . 2 μg; 0 . 2 pmol ) , 500 μM ( each ) ATP , CTP , and UTP , 10 μM unlabeled GTP , 10 μCi of [α-32P]GTP , and 100 μM DTT . The subgenomic RNA template ( 719 nt ) contains both 5’- and 3’-UTR regions of the DENV2 genome . The reaction was started by adding 10 μg ( 100 pmol ) of purified NS5 before incubation at 37°C for 1 h . The reaction was terminated by acid phenol-chloroform extraction , followed by purification on a Bio-Rad P-30 column to remove the unincorporated nucleotides . Radioactive RNA products were analyzed by formaldehyde-agarose gel electrophoresis and visualized by autoradiography . Band intensities were measured with a PhosphoImager ( Molecular Dynamics ) . NS5-Δ2 and Δ6 proteins were concentrated to ~10 mg/mL and screened using the sitting drop vapor diffusion method in 96-well plates using a Phoenix RE liquid handling robot ( Rigaku ) . Several conditions produced small crystals within 2 weeks for Δ2 and Δ6 , which were further optimized . The best-diffracting NS5-Δ6 crystals were lens-shaped , and grew to full size within 3–7 weeks in 1 . 0 M succinic acid ( pH 7 . 0 ) , 0 . 1 M HEPES pH 7 . 0 , and 1% PEG monomethylether 2000 . Crystals were harvested by cryo-cooling in liquid nitrogen after soaking for ~20 seconds in well solution supplemented with 25% ethylene glycol . X-ray diffraction data were collected at 100 K at the Advanced Photon Source beamline 21 ( Argonne National Laboratory , Chicago ) . Four datasets were collected from three crystals . Reflections were indexed and integrated using HKL2000 , and four datasets were scaled and merged together using SCALEPACK [47] . A 3 . 6 Å resolution cutoff was applied using CC1/2 and CC* values of 0 . 157 and 0 . 520 , respectively , in the highest resolution shell ( 3 . 66–3 . 60 Å ) [48] . The crystals belonged to space group P3221 with a = b = 215 . 3 Å , c = 480 . 7 Å , and contained eight NS5 monomer in the ASU with a corresponding solvent content of 68% . The initial structure solutions were obtained by molecular replacement with DENV3 MTase ( residues 7–262 , PDB code 3P97 ) and RdRp ( residues 272–883 , PDB code 4HHJ ) as search models using the program PHASER in the Phenix suite [49] . Seven MTase and seven RdRp solutions were found , and the eighth MTase was placed manually . After refinement with seven RdRp solutions and eight MTase , the eighth RdRp solution was found using Phenix . The eighth RdRp domain ( monomer H in Fig 1 ) has the weakest density and is missing 133 residues . Upon initial structure solution , continuous electron densities in the 2Fo-Fc map were clearly visible between the C-terminus of MTase and the N-terminus of RdRp , indicating which copies of the MTase and RdRp were part of each of eight intact polypeptide chains . The distance between the terminal ends of each corresponding MTase/RdRp pair was ~24 Å ( distance between Cα atoms of R262 and N272 ) , excluding the possibility of any other domain arrangement and making the assignment of continuous protein chains unambiguous . The 2Fo-Fc map also indicated that either SAM or SAH is bound to the MTase active site . We modeled SAH in the density , because SAH was copurified in several high-resolution MTase structures [13 , 50] , and the density for the additional methyl group of SAM was missing ( S2 Fig ) . Additionally , the RdRp contains two metal ions coordinated by tetrahedral geometry with one metal-binding site consisting of H712 , H714 , C728 , and C853 in the thumb subdomain , and the second site consisting of E437 , H441 , C446 , and C449 in the fingers subdomain . Since no metal was added during crystallization , the metal ions must have come from the growth medium and been copurified with the protein . Based on the tetrahedral geometry , coordinating residues , and the positions where zinc atoms have previously been identified in the structures of other flavivirus RdRps [20 , 29] , zinc atoms were modeled into the electron density . Manual model building was carried out using Coot [51] , and iterative cycles of refinement were carried out using phenix . refine . Initially , a global NCS was used during refinement owing to the presence of eight copies of the NS5 monomer in the ASU , which was relaxed to a torsional NCS during subsequent rounds of refinement . TLS ( translation , liberation , and screw motion ) refinement was also used with TLS groups automatically defined by the TLSMD server [52] . The final model contains eight full-length NS5 , eight SAH , and sixteeen zinc atoms with the R and Rfree factors of 23 . 8 and 27 . 4% , respectively ( Table 1 ) . A Ramachandran plot shows 95 . 4 and 4 . 5% of residues in allowed or generally allowed regions , and 0 . 1% outliers . The final model and structure factors were deposited to the Protein Data Bank with accession code 5CCV . The buried surface areas and interface residues between MTase and RdRp domains and between NS5 monomers were determined using the program PISA and Contact , respectively , in the CCP4 suite [33 , 53] . Separate domains were delineated by the following residue ranges: MTase , residues 7–271 in DENV3 and residues 5–270 in JEV; RdRp , residues 272–892 in DENV3 and residues 274–895 in JEV . The rmsds of two structures were calculated using Pymol [54] . The conformational differences between the DENV and JEV NS5 structures were analyzed using the DynDom protein domain motion analysis program [32] . The maximum dimension of the NS5 monomer was calculated by CRYSOL [36] . Construction of a full-length cDNA of DENV2 ( New Guinea C strain ) and its NS5 chimera containing the DENV4 MTase in yeast/Escherichia coli shuttle vector were previously described [39 , 55] . The replacement region was amplified by PCR using primers , 5’ TCCATCATGAAGAAC ACAACCAACACGAGAAGGGGAACTGGGACCACAGGAGAG 3’ ( forward ) and 5’ GACCTG ACTTCTAGCCTTGTTTCATGTTAGTTTTGCCTTTTACAGAACTCCCTCACTCT 3’ ( reverse ) , and pRS424 DENV4 cDNA ( GenBank accession number M14931 . 2 ) . The amplified DNA fragment was mixed with the StuI and AatII-double digested pRS424-FLDV2 cDNA encoding full-length DENV2 RNA . Yeast recombination method was used [55] to create a chimera virus cDNA having DENV4 full-length NS5 in DENV2 backbone . The chimera plasmid was linearized using the BcgI enzyme at the 3′-end of the viral sequence and was used as the template for in vitro transcription catalyzed by SP6 RNA polymerase ( Epicenter Biotechnologies ) in the presence of the 7-MeGpppG cap analog . The DENV2 RNA ( ∼3 μg ) containing either the wild type ( DENV2 ) NS5 , a NS5 chimera ( DENV4 MTase and DENV2 RdRp ) , or DENV4 NS5 were transfected by electroporation ( Amaxa Nucleofector II system , Amaxa Biosystems , Cologne , Germany ) into BHK-21 cells ( American Type Culture Collection , Manassas , VA ) , as previously described [39] . Briefly , ∼1 × 106 cells were resuspended in 100 μl of Ingenio solution ( Mirus Bio , Madison , WI ) . After pulsing , cells were carefully transferred into prewarmed complete medium ( Dulbecco's modified Eagle's medium ( DMEM ) , supplemented with 10% fetal bovine serum and 1× streptomycin/penicillin ) , and allowed to recover for 5 min at 37°C in an incubator . Cells were resuspended in 10 ml of complete DMEM and incubated in a T-12 . 5 flask . On days 2 and 9 , cells were trypsinized and transferred into a T-25 and T-75 flask , respectively . This procedure was repeated using one-third of the trypsinized cells from a T-75 flask every 5–7 days . For immunofluorescence assay , RNA-transfected cells at the end of indicated time periods were seeded into a slide ( LabTek ) , and fixed by treatment with acetone . Cells were incubated with a 1:200 dilution of 7E11 , a monoclonal antibody against DENV2 NS1 . Fluorescein isothiocyante ( FITC ) -labeled , goat anti-mouse immunoglobulin G conjugate ( Kirkegaard & Perry Laboratories ) was used as a secondary antibody at a 1:100 dilution . Immunofluorescence photomicrographs ( ×200 magnification ) were acquired using a Leitz Diaplan microscope coupled to the Leica/Wild MPS48 automated photographic system . The numbers and intensities of positive cells were compared utilizing the ImageJ program ( National Institutes of Health ) , as previously described . Serotype-specific residues were identified in two steps . First , seven to ten NS5 sequences from each DENV serotype were randomly selected for multiple sequence alignment using Clustal W [56] . DENV1 NS5 sequences include GenBank codes AHI43715 . 1 , ACW82945 . 1 , ACJ04223 . 1 , AGN94878 . 1 , and AAK29447 . 1 , and UniProt codes P33478 . 1 and P17763 . 2 . DENV2 NS5 sequences include UniProt codes P07564 . 2 , Q9WDA6 . 1 , P14337 . 2 , P12823 . 1 , P29991 . 1 , P14340 . 2 , and P29990 . 1 , and GenBank codes ABY65725 . 1 , AII99332 . 1 and AHB63929 . 1 . DENV3 NS5 sequences include GenBank codes ABV54900 . 1 , YP_001621843 . 1 , AHG23213 . 1 , Q99D35 . 1 , ACY70817 . 1 , AAS49486 . 2 , ABV54900 . 1 , ABV03585 . 1 , YP_001621843 . 1 , and ABV54900 . 1 . DENV4 NS5 sequences include GenBank codes GNWVDF , AHG23274 . 1 , ACW83012 . 1 , ACQ44391 . 1 , ABO45246 . 1 , AEX91754 . 1 , and AGI95993 . 1 . Serotype-specific residues that are conserved in each serotype , but different among serotypes were selected . Next , the virus variation resource at NCBI ( http://www . ncbi . nlm . nih . gov/genome/viruses/variation/dengue/ ) was used to remove residues that have some variations in each serotype [57] . | Many plus-strand RNA viruses encode a viral RNA polymerase and capping enzymes to synthesize a 5’-capped RNA genome . However , how these two activities are coordinated during viral replication is not understood . In flaviviruses , polymerase and capping enzymes are encoded in a single multifunctional protein , where separate domains within the polypeptide are responsible for these activities; flavivirus NS5 , composed of the polymerase and methyltransferase domains , carries out viral RNA synthesis , 5’-RNA capping , and RNA cap methylations . Previous NS5 monomer structures were unable to provide mechanistic insight into how the two domains communicate or the quaternary organization of the functional enzyme . We have determined the crystal structure of dengue virus NS5 and show that the NS5 dimer is likely the biological assembly of NS5 , and RNA synthesis and RNA capping may be coordinated by the dimer . We found that essential interactions between the two NS5 domains can be maintained either within a monomer or via inter-molecular interactions within a dimer , and thus NS5 can adopt multiple conformations while preserving necessary interactions between the methyltransferase and polymerase domains . Using dengue virus , we additionally determined that such specific interaction between the two NS5 domains is the major determinant of viral replication . | [
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] | 2016 | Dengue Virus Nonstructural Protein 5 (NS5) Assembles into a Dimer with a Unique Methyltransferase and Polymerase Interface |
The polymeric mucin component of the intestinal mucus barrier changes during nematode infection to provide not only physical protection but also to directly affect pathogenic nematodes and aid expulsion . Despite this , the direct interaction of the nematodes with the mucins and the mucus barrier has not previously been addressed . We used the well-established Trichuris muris nematode model to investigate the effect on mucins of the complex mixture of immunogenic proteins secreted by the nematode called excretory/secretory products ( ESPs ) . Different regimes of T . muris infection were used to simulate chronic ( low dose ) or acute ( high dose ) infection . Mucus/mucins isolated from mice and from the human intestinal cell line , LS174T , were treated with ESPs . We demonstrate that serine protease ( s ) secreted by the nematode have the ability to change the properties of the mucus barrier , making it more porous by degrading the mucin component of the mucus gel . Specifically , the serine protease ( s ) acted on the N-terminal polymerising domain of the major intestinal mucin Muc2 , resulting in depolymerisation of Muc2 polymers . Importantly , the respiratory/gastric mucin Muc5ac , which is induced in the intestine and is critical for worm expulsion , was protected from the depolymerising effect exerted by ESPs . Furthermore , serine protease inhibitors ( Serpins ) which may protect the mucins , in particular Muc2 , from depolymerisation , were highly expressed in mice resistant to chronic infection . Thus , we demonstrate that nematodes secrete serine protease ( s ) to degrade mucins within the mucus barrier , which may modify the niche of the parasite to prevent clearance from the host or facilitate efficient mating and egg laying from the posterior end of the parasite that is in intimate contact with the mucus barrier . However , during a TH2-mediated worm expulsion response , serpins , Muc5ac and increased levels of Muc2 protect the barrier from degradation by the nematode secreted protease ( s ) .
Immune mediated elimination of gastrointestinal ( GI ) parasitic nematodes has been a subject of considerable investigation [1] . Hyperplasia of goblet cells that produce the secreted mucosal barrier is one of the most prominent features of the TH2-type immune response necessary for the expulsion of these pathogens from the intestine [1] , [2] . However , until recently , definition of the precise role of goblet cells in host protection remained elusive , especially with regards to the major secreted component of goblet cells , the mucins , which are pivotal to the formation of the mucus layer that overlies the intestinal epithelium . Using established gastrointestinal nematode models Trichuris muris , Trichinella spiralis and Nippostrongylus brasiliensis , we have recently demonstrated that mucins are critical in resolving infection [3] , [4] , [5] . The major intestinal mucin Muc2 plays a significant role in the concerted protective worm expulsion mechanism and in its absence T . muris expulsion is significantly delayed [4] . Additionally , the Muc5ac mucin , not usually expressed in the murine intestine but induced post-infection during a TH2-type immune response , was demonstrated to be necessary for intestinal worm clearance [3] . Furthermore , Muc5ac was shown to directly affect the viability of the nematode . Bearing in mind that under field conditions GI nematodes can survive for long periods of time , it raises the question of how these parasites interact within the mucosal barrier and subvert the responses against them . It is well established that GI nematodes secrete a variety of molecules ( Excretory Secretory Products , ESPs ) into the surrounding niche . These can be highly immunogenic , although , their functions in vivo are not well described [6] . T . muris infection in the mouse provides a unique tractable model that can be used to examine the interaction of parasites with the mucosal barrier during both acute ( worm clearance by TH2 immune response ) and chronic infection ( lack of worm clearance by TH1 immune response ) [6] . ESPs are thought to be very well-conserved and similar in terms of their antigenicity in the different Trichuris species . A study by Drake and co-workers has shown that a major 43 kDa protein secreted by the Trichuris nematode has the ability to induce ion-conducting pores in a lipid bilayer [7] , whereas other studies have attributed the tunnel formation through the intestinal epithelium to protease activity ( zinc metalloproteases , thiol protease and phenol oxidase ) present in ESPs [8] , [9] . In this study , we aimed to investigate the effect of the T . muris ESPs on the mucus barrier and in particular , on mucins which are an essential part of the co-ordinated TH2-mediated worm expulsion response . Our findings demonstrate for the first time that serine protease ( s ) secreted by the nematode have the ability to degrade Muc2 and depolymerise the mucin network . In contrast , the mucus barrier formed during worm expulsion is protected from ESP degradation . Particularly , Muc5ac mucin , which is necessary for worm expulsion , was resistant to ESP protease activity . Interestingly , in the mice that expel the nematodes , up-regulation of host serine protease inhibitors ( Serpins ) is observed which probably provides an additional level of protection of mucins , particularly Muc2 , from degradation . These data , therefore , demonstrate that GI nematode parasites produce protease ( s ) that degrade the major structural scaffold of the mucus barrier during chronic infection , resulting in a more porous mucus barrier , which in turn can aid establishment and persistence of nematode infection and/or exacerbate inflammation .
BALB/c ( Harlan Olac ) mice were maintained in the Biological Services Unit at The University of Manchester in a conventional clean Helicobacter hepaticus- and norovirus-free facility . All mice ( 6–12 wk old ) were kept in sterilized , filter-topped cages , and fed autoclaved food in the SPF facility . The protocols were employed at the University of Manchester and were performed in accordance with guidelines set and approved by the Animal Procedures Committee and Home Office Scientific Procedures Act ( 1986 ) , United Kingdom under the personal licence issued to SZH ( No . PIL: 40/9777 ) . The techniques used for T . muris maintenance and infection were described previously [10] . Mice were orally infected with approximately 150 eggs for a high dose infection and <15 eggs for a low dose infection . Worm burdens were assessed by counting the number of worms present in the caecum . Worms present in the caecum on day 12 confirmed that infection had established in both groups of mice ( high dose and low dose infection ) . Adult worms present in the caecum of the low dose infected group of mice , on day 35 post infection , confirmed chronic infection ( Figure S1 ) . ESPs were isolated ex vivo from adult T . muris nematodes using the method previously described [11] . ESPs were separated into <5 kDa , 5–50 kDa , 50–100 kDa and >100 kDa fractions using size exclusion Amicon columns ( Millipore Pty Ltd , Australia ) . Human intestinal adenocarcinoma LS174T cells ( European Collection of Cell Culture , Salisbury , U . K ) were used as a source of glycosylated MUC2 . Cells were cultured with complete medium containing DMEM , 2 mM L-glutamine , 100 U/ml penicillin , 100 µg/ml streptomycin and 10% heat inactivated FBS ( all from Invitrogen , Paisley , U . K ) at 37°C in a humidified incubator ( 95% air with 5% CO2 ) . When confluent , cells were gently scraped until dislodged from the flasks surface and rinsed with approximately 5 ml of complete media . The harvested cells were dispersed using a 25G needle and resuspended at approximately 1×106 cells/ml [12] . MKN45 gastric cancer cells that express mucin-specific chaperones but no endogenous MUC2 were transfected with the murine Muc2 N-terminal D3 domain ( rMuc2-D3 ) consisting of 3 FLAG tags at the N-terminus and a myc tag at the C-terminus as previously described [13]; rMuc2-D3 was obtained from cell lysates using TRIS-HCl and Triton-X lysis buffer . Conditioned media was collected from transfected MKN45 cells and concentrated to obtain secreted rMuc2-D3 . Immunodetection was carried out using a polyclonal antibody raised against a murine Muc2 ( mMuc2 ) [4] or human MUC2 ( hMUC2 ) [14] . Commercially available 45M1 antibody was used for the detection of mouse Muc5ac [3] and , mouse monoclonal FLAG antibody-clone 2 ( Sigma-Aldrich , UK ) and c-Myc antibody ( 9E10 ) was used to detect rMuc2-D3 [13] . To isolate the mucus from mice , the caecum was gently flushed with PBS to remove the faecal matter , subsequently scraped lightly with a cell scraper into equal volumes of PBS and stored at −80°C until required . To isolate the mucus from LS174T cells , media was removed and cells were washed vigorously with ice cold PBS , mucus was stored at −80°C until required . Mucus , mucins and rMuc2-D3 were incubated at 37°C with the ESPs for various time points ( as specified ) at final concentration of 50 µg/ml or above . Control samples were not treated with the ESPs , but were incubated at 37°C for the maximum time point . ESPs were heat inactivated at 100°C before incubation or incubated at 4°C with mucus as negative controls . ESP activity was quenched using the protease inhibitors: ethylenediaminetetraacetic acid ( EDTA ) , N-ethylmaleimidide ( NEM ) , Lupeptin , Chymostatin and Antipain at 50–150 µg/ml ( Sigma-Aldrich , UK ) . Caecal tissue was cut longitudinally , washed with PBS and kept hydrated with PBS in a 6 well plate . ESPs were applied topically at 50 µg/ml concentration for 2 h prior to analysis . 0 . 1 µm blue fluorescently labelled polymer microspheres ( Dukes Scientific , UK ) were placed on top of the luminal surface of the caecum ( set as a reference ) and their position analysed using a Nikon C1 Upright confocal microscope [4] . Five measurements were taken per caecum . 3D optical stacks were taken every 5 µm and combined to obtain a Z-stack at the time points stated . All images were analysed using the EZ-C1 freeviewer software ( version 3 . 9 ) . Isolated mucus samples were solubilised in 6 M Urea and subsequently reduced using 50 mM dithiothreitol ( DTT ) and carboxylmethylated using 125 mM iodoacetamide prior to electrophoresis on a 1% ( w/v ) agarose gel . Whole mucus samples were separated by 0 . 7% agarose gel electrophoresis for 22–24 h before reduction in SSC ( 0 . 6 M NaCl , 60 mM sodium citrate ) containing 0 . 1 M DTT for 30 min . The fractions were taken from the top of the tubes , analysed by slot blotting or agarose gel electrophoresis followed by western blotting on to a nitrocellulose membrane [15] . Mucins were detected by PAS staining or mucin-specific antisera . Staining intensity was measured using a GS-800 calibrated densitometer ( Bio-Rad Laboratories , U . K ) . 6–8 M guanidinium chloride ( GuCl ) gradients were formed in centrifuge tubes using an MSE gradient maker connected to a Gilson Minipuls 2 peristaltic pump . Mucin samples ( in 4 M GuCl ) were loaded onto the tops of the gradients and centrifuged in a Beckman Optima L-90K Ultracentrifuge ( Beckman SW40 rotor ) at 40 , 000RPM for 2 . 75 h for mucus and 3 . 50 h for purified mucins at 15°C . The refractive index of each fraction was measured using a refractometer; all gradients were comparable ( data not shown ) . Data from 3–5 mice is presented as the percentage of the area under the curve ( AUC ) of fractions ( Fr ) 1–8 , 9–14 and 15–18 , with and without treatment . Mucins were purified using isopycnic density gradient centrifugation as previously described [16] . In brief , solubilised mucus was purified using caesium chloride ( CsCl ) /4M GuCl density gradient at a starting density of 1 . 4 g/ml , centrifuged in a Beckman Optima L-90K Ultracentrifuge ( Beckman Ti70 rotor ) at 40 , 000RPM for 65 h at 15°C . Periodic Acid Schiff's ( PAS ) rich fractions were pooled , dialysed into 0 . 2 M GuCl before being subjected to a second CsCl density gradient centrifugation ( at a starting density of 1 . 5 g/ml ) [16] . The PAS-rich fractions pooled after the second CsCl density gradient was subjected to anion exchange chromatography as described previously using a Resource Q column [17] . Samples were eluted with the starting buffer ( 20 mM Tris-HCl at pH8 ) for 15 min ( 0 . 5 ml/min ) , followed by a linear gradient ( 60 min ) up to 0 . 4 M Lithium perchlorate-10 mM piperazine at pH5 in 6 M Urea containing 0 . 02% 3-[ ( chloamidopropyl ) dimethylammonio]-1-propanosulphonate [17] . The secreted intestinal mucus barrier was isolated using the method previously described [5] . Samples were digested with trypsin and analysed using a Q-Tof micro mass spectrometer . MS-MS data were subsequently analysed using the Uni-Prot/Swiss-Prot databases [18] . RNA was isolated from caecal epithelial cells as previously described . cDNA was generated using an IMPROM-RT kit ( Promega ) and SYBR Green PCR MasterMix ( ABgene ) was used for quantitative PCR using the ABI HT7900 PCR machine . Primer efficiencies were determined using cDNA dilutions and genes of interest were normalised against the housekeeping gene , β-actin , and expressed as a fold difference to uninfected naïve message levels . The following primer sequences were used to determine the levels of Serpinb6a ( TGGACAAGATGGACGAAGAAGA , CCAACTTGTAGAGGGCATCGTT ) ; Serpina3k ( GAGCAAAGGGCAAGACCA , CAGCCACATCCAGCACAG ) and Serpinb1a ( TTCGCCTTGGAGCTGTTC , ATGCCTCCTGTAGCAGCG ) . Results are expressed as the mean ± SEM . Statistical analysis was performed using Prism v 3 . 2 ( GraphPad Software ) . The statistical significance of different groups was assessed by using non-parametric tests . P<0 . 05 was considered to be significant .
ESPs isolated from T . muris are a complex mixture of components ( Figure S2A ) that have been shown to contain a variety of enzymes [7] . We sought to determine whether the ESPs released from the T . muris nematode have a direct effect on the mucus barrier and its mucin components that are essential for the expulsion of this nematode from the host . To this end , intestinal mucus isolated from uninfected mice was incubated with ESPs for 24 h at 37°C , subsequently , treated and untreated ( control ) mucus samples were subjected to rate zonal centrifugation . This analysis revealed a significant change in sedimentation behaviour of the mucins after exposure to ESPs and indicated a degradative effect on the mucins that resulted in a reduction in their size; demonstrated by their lower sedimentation rate ( Figure 1A–B ) . The PAS staining ( carbohydrate assay ) profile was comparable to the mMuc2 antibody reactivity as expected since Muc2 has been shown to be the predominant intestinal mucin [19] . In addition to the major PAS and mMuc2 staining peak ( fractions 9–14 ) , a more minor PAS and mMuc2 staining species had sedimented to the bottom of the gradient ( fractions 15–18 ) . These high density mucins most likely represent the ‘insoluble’ Muc2 component of the mucus gel , as characterised previously by Carlstedt et al . [20] . ESPs did not have a degradative effect on Muc2 if heat inactivated prior to incubation or if the incubation was performed at 4°C ( Figure 1C ) , suggesting that enzymes within the ESPs , most likely with proteolytic activity , were responsible for the degradation of mucin polymers . Additionally , the degradative effect was dose-dependent; with a significantly slower sedimentation rate observed with increasing concentration of ESPs ( Figure S2B–C ) . Mucins present within the mucus barrier are large disulphide bond-mediated polymers that determine the rheological properties of the mucus layer . When polymeric mucins are reduced to monomers , the mucus layer loses its viscous properties , thus highlighting the importance of mucin polymeric structure for the barrier properties of mucus . The significantly slower sedimentation rate of mucins after ESP treatment indicated that the mucins had a decreased size ( Figure 1B ) , which would be predicted to alter the network properties of the mucus barrier and decrease mucus viscosity . To assess alterations in the mucin network , we measured the movement of fluorescently labelled beads within the mucus barrier . This demonstrated that the diffusion rate of the beads was significantly higher when the mucus layer was treated with ESPs ( Figure 1D ) . Furthermore , this alteration in the mucin network was prevented when the mucus layer was concomitantly treated with ESP and protease inhibitors ( PI ) . Collectively , these experiments suggested that ESPs were capable of degrading Muc2 , resulting in a more porous mucus network . Previous studies from our laboratory have shown that the mucin composition of the mucus barrier is altered during T . muris worm expulsion ( day 21 pi ) . Specifically , the mucus barrier is composed of Muc5ac in addition to Muc2 [3]; there are increased amounts of Muc2; and the mucins are differentially glycosylated [5] . Overall , this results in a less porous network [4] . Therefore , we sought to determine whether these changes in the mucus barrier during worm rejection affected the ability of the ESPs to degrade the mucins . To this end , mucus isolated during the course of acute ( high dose infection in BALB/c mice ) and chronic ( low dose infection in BALB/c mice ) infection , was treated with ESPs ( Figure 2A–D ) . Untreated and ESP-treated mucus was subjected to rate zonal centrifugation prior to fractionation and analysis . ESP treatment altered the sedimentation profiles of mucins from mice with chronic and acute infection on day 14 pi . ( Figure S3A–D ) , as previously observed with ESP-treated mucus from uninfected naïve mice ( Figure 1A–B ) . However , ESPs were unable to depolymerise/degrade the mucins present within the mucus isolated from mice with acute infection on day 21 pi . ( Figure 2A–B ) This correlates with the changes mentioned above that occur within the mucus barrier during the worm expulsion process . Interestingly , the sedimentation profile of mucins in mucus on day 21 from mice with acute infection remained unaltered even when treated with up to 300 µg/ml concentrations of T . muris ESPs ( data not shown ) . In contrast , mucins in the isolated mucus from chronically infected mice on day 21 pi . were readily degraded by 50 µg/ml of T . muris ESPs ( Figure 2C ) . It is important to note that a small proportion of the mucins present within the mucus isolated from the mice with chronic infection on day 21 appeared degraded before in vitro treatment ( Figure 2C–D ) . However , more strikingly , mucins present within the mucus isolated from mice on day 42 pi . with chronic infection were almost completely degraded without ESP treatment ( Figure 2E–F ) as compared to those isolated from the mice with an acute infection . These data suggest that mucins present within the mucus barrier during long term chronic infection are degraded in vivo and , additionally , may be more prone to degradation by parasite proteases than those produced in mice able to expel the worms . Surprisingly , mucins present within the mucus isolated on day 56 pi . of acute infection , which is 35 days after the expulsion of the nematode , did not fully degrade when treated with ESPs ( Figure S3E–F ) . This suggested that the changes in the barrier [3] , [4] that occur during the co-ordinated expulsion response are maintained for some time after expulsion . A clear difference was observed in the ESPs ability to alter the mucin component of the mucus barrier produced during worm expulsion ( acute infection; day 21 pi ) compared to the chronically infected mice ( Figure 2 ) . As we have previously reported , in addition to goblet cell hyperplasia and elevated levels of Muc2 , IL-13 induced de novo expression of the Muc5ac mucin is also observed during T . muris expulsion [3] . Immunohistochemistry ( using Muc2 and Muc5ac-specific antibodies ) of caecal tissue and mass spectrometry analysis of caecal mucus confirms the increased levels of Muc2 production in acute infection and demonstrates that even during acute infection Muc2 is the predominant mucin ( Figure 3A & B ) . Therefore , to investigate the effect of the ESPs specifically on mucins in more detail , mucins were purified from the mucus isolated on day 21 pi . of mice with acute infection . In brief , isolated mucus ( pooled from 5 mice ) was reduced and carboxymethylated and , the resultant mucin monomers were purified from the mucus by two isopycnic density gradient centrifugation steps . Firstly centrifugation in CsCl/4M GuCl was used to separate mucins from lower buoyant density proteins ( Figure S4A ) [16] , followed by a second centrifugation in CsCl/0 . 2M GuCl to separate mucins from nucleic acids ( Figure S4B ) and then the distinct Muc2- and Muc5ac-rich fractions were further purified using anion exchange chromatography ( Figure S4C ) . Fractions enriched in monomeric Muc2 and Muc5ac were pooled from the anion-exchange column ( Figure S4C ) , treated with 50 µg/ml of ESPs for 24 h and , analysed by rate zonal centrifugation ( Figure 3C–D ) . Fractions analysed by slot blotting revealed that ESPs degraded Muc2 , since a significant amount of Muc2 was present in the fractions at the top of the gradient after ESP-treatment ( Figure 3C ) . In contrast , the sedimentation profile of the Muc5ac-rich fraction was unaltered after treatment with ESPs with up to 300 µg/ml ( Figure 3D ) . This implied that ESPs specifically degrade the intestinal mucin , Muc2 , but are unable to degrade the Muc5ac mucin , which we have previously shown to aid the co-ordinated worm expulsion process by reducing the nematode's vitality [3] . To further explore the effect of ESPs on the MUC2 glycoprotein and in particular its degree of polymerisation , the human intestinal LS174T cell line , which synthesises and secretes mature glycosylated MUC2 , was utilised as a source of MUC2 . MUC2 isolated was treated with 50 µg/ml of ESPs for 4 , 6 , 24 , 48 and 72 h were subjected to rate zonal centrifugation , which showed a time dependent shift in MUC2 distribution to the top of the gradient and a loss of staining intensity with ESP treatment ( Figure 4A ) . Interestingly , the putative ‘insoluble’ mucin content , present at the bottom of the gradient in the control samples also gradually decreased after ESP-treatment and no ‘insoluble’ fraction was observed after 72 h of treatment ( Figure 4A ) . Untreated and ESP treated MUC2 was subjected to agarose gel electrophoresis ( Figure 4B ) , transferred onto a nitrocellulose membrane and probed with hMUC2 antibody . The unreduced MUC2 can be seen as at least three bands in the control samples that likely represent different multimeric forms of MUC2 ( Figure 4B ) . Importantly , after ESP treatment the intensity of the slower migrating multimeric ( i , ii ) MUC2 bands were decreased significantly ( Figure 4B–C ) . After 72 h of ESP-treatment the intensity of the fastest migrating MUC2 band ( iii ) decreased further ( Figure 4C ) . An overall loss of MUC2 antibody staining was also noted , suggesting ESPs initially affect the polymerisation of MUC2 and over time degrade/cleave the MUC2 protein core . Proteolytic activity of ESPs was probably responsible for degrading/depolymerising the mucin network as it was demonstrated to be temperature dependent ( Figure 1C ) and blocked by protease inhibitors ( Figure 1D ) . ESPs secreted by the T . muris nematode contain several different proteases including cysteine , serine and metalloproteases [21] . Therefore , to determine which specific protease ( s ) were responsible for degrading and altering the polymerisation of MUC2 , ESPs were incubated in the presence of specific protease inhibitors ( Table S1 ) , prior to the treatment of MUC2 . Note that the control samples contained a mixture of all the stated protease inhibitors . MUC2 was significantly degraded despite the presence of aprotinin , ethylenediaminetetraacetic acid ( EDTA ) and N-ethylmaleimidide ( NEM ) ( Figure 4D–E ) , which inhibit chymotrypsin/trypsin , metallo- and cysteine proteases , respectively . However , it was noted that NEM , may have a partial effect on the ESP enzymatic activity as the fastest migrating band of MUC2 ( iii ) was intact and a low reactivity with the slower migrating MUC2 band ( ii ) was also observed ( Figure 4D–E ) . More strikingly , after treatment with the serine protease inhibitors chymostatin and antipain , the ESPs were unable to alter the abundance of all forms of MUC2 ( Figure 4D–E ) . There was a slight difference in the electrophorectic migration of the MUC2 bands noted after ESPs treatment that could not be prevented with protease inhibitor treatment , suggestive of a potential role of ESP in de-glycosylating mucins or activity of another protease in the ESPs . Therefore , overall , these data demonstrate that the depolymerisation activity of ESPs is due to serine proteases . The serine protease with the enzymatic activity against mucins was fractionated from the ESPs by performing size fractionation into the following fractions <5 kDa , 5–50 kDa , 50–100 kDa and >100 kDa . MUC2 was treated with the ESP-fractions and analysed using rate zonal centrifugation . This demonstrated that 50–100 kDa ESP-fraction had activity against MUC2; whereas , no other ESP-fraction affected MUC2 ( Figure 5A ) . To ascertain whether the active 50–100 kDa fraction cleaves and depolymerises murine Muc2 , the N-terminal D3 polymerisation domain ( rMuc2-D3 ) of Muc2 was expressed in human MKN45 gastric cells which express mucin-specific chaperones and no endogenous MUC2; rMuc2-D3 is present intracellularly as a monomer and dimer and secreted mainly in its dimeric/multimeric forms [13] . The active 50–100 kDa ESP-fraction depolymerised the dimeric form of rMuc2-D3 protein ( Figure 5C ) , similar to that observed when rMuc3-D3 was analysed after treatment with a reducing agent ( Figure 5B ) . rMuc2-D3 isolated from cell lysates and secretions in conditioned media was then treated with the active ESP-fraction ( Figure 5D ) and analysed by SDS-PAGE/Western blot ( detected using the anti-Myc antibody ) . In addition to the multimeric forms of rMuc2-D3 being reduced to monomers , smaller cleavage products were also observed ( highlighted with arrows; Figure 5D ) , confirming that ESPs not only depolymerised the murine Muc2 mucin but also degraded/cleaved it into smaller fragments . Importantly , the other ESP-fractions did not have this effect on rMuc2-D3 protein ( Figure 5C ) , and treatment with the serine protease inhibitor antipain inhibited this activity ( data not shown ) , confirming that serine protease ( s ) present in the 50–100 kDa fraction were responsible for depolymerising/degrading Muc2 . Worm expulsion is near complete by day 21 pi . [6] in the mice with the high dose infection ( Figure S1 ) , and our data clearly show that at this stage the mucins , and in particular Muc2 , present within the mucus barrier were somehow protected from the degradative effects exerted by ESPs . This raised the possibility of the presence of protective non-mucin components such as protease inhibitors which may be secreted by the host within the mucus barrier to hinder the ESP activity . To this end , we isolated the secreted mucus from mice during the course of acute and chronic infection using a method previously described [5] . The proteins present within mucus were identified using trypsin digestion followed by tandem mass spectrometry ( MS ) analysis . Tandem MS analysis identified three members of the serine protease inhibitor family ( Serpins ) , Serpinb6a , Serpina3k and Serpinb1a , to be present in the mucus barrier during infection . Interestingly , Serpins were not detected in the uninfected and chronically infected mice on day 14 pi . , with 1–2 unique peptides identified on day 21 pi . of chronic infection ( Figure 6A , B ) . In contrast , the mucus barrier isolated during acute infection , contained higher levels of serpins on day 14 and day 21 pi . RT-PCR analysis demonstrated that mRNA levels encoding Serpinb1a , Serpinb6a and Serpina3k ( Figure 6B ) , identified in the mucus barrier with MS analysis , were elevated in the caecal epithelium of mice during the TH2-mediated immune response; however no major changes were observed during chronic infection . The host therefore potentially up-regulates serine protease inhibitors during the worm expulsion process in order to prevent mucin degradation in vivo , and subsequent inflammation and aid the rejection of the nematode from the intestine .
Mucins are an essential part of the TH2-mediated immune response that enforces intestinal expulsion of the nematode , Trichuris muris [1] , [3] , [4] . In this study , we demonstrate how the Trichuris nematode has evolved ways to promote its survival within its intestinal niche by degrading mucins; the molecular framework of the host protective mucus barrier . This is the first study to describe that serine protease ( s ) released by the nematode can cleave the polymerising domain of Muc2 , the major intestinal mucin , leading to a more porous mucus barrier network . Here , we clearly demonstrate that the Muc2 mucin , but not Muc5ac , was degraded by secretions from T . muris nematodes; and serine protease inhibitors are present in the mucus barrier during worm expulsion , which may hinder the degradative effects of ESPs . The degradation of Muc2 , but not Muc5ac , by parasite proteases provides a plausible explanation for our previous observation that , whilst Muc2 aids worm expulsion [4] only Muc5ac is essential for expulsion [3] . Therefore , components of the ESPs are produced by the nematode to alter the properties of the mucus barrier and thus facilitate its own survival and/or improve the conditions within its niche . The Trichuris nematodes are extremely successful within the host , because they not only have the ability to survive an immune attack but are thought to actively subvert the immune responses generated by the host by exerting immunomodulatory effects [6] . These nematodes have been shown to produce complex secretions containing proteases and proteins that have immunogenic properties , thus described as ‘excretory secretory antigen’ , but which are likely to have a protective function [7] , in particular in maintaining infection within its mucosal niche . Mucins are responsible for the physical properties of the mucus barrier . These large heavily glycosylated molecules polymerise , mediated by disulphide bond formation involving the cysteine-rich domains at the N- and C-termini of the mucin polypeptide , which gives the barrier its viscous properties [22] . The parasite proteases have evolved the ability to act on the polymerising D3 domain of the intestinal mucin , Muc2 , which is thought to be largely protease resistant [23] , due to the high level of intra-molecular disulphide bonding . The dimeric form of a recombinant mucin protein was depolymerised into monomers after ESP-treatment and this was prevented if serine proteases inhibitors were used and a smaller fragment than the monomeric form was also observed . Clearly , the depolymerisation/degradation of mucins impacts on the properties of the mucus barrier , as the barrier was more porous after ESP treatment , which also corroborates our previous finding of a more porous mucin network in mice with chronic infection [4] . The host has developed ways to counteract the depolymerising ability of ESPs . We observed a significant difference in the ESPs ability to degrade mucins isolated during acute and chronic infection . As described previously , under the TH2-mediated immune response the mucin component of the mucus barrier changes during worm expulsion with a prominent increase in Muc2 [4] , de novo expression of the mucin Muc5ac [3] and change in glycosylation observed [5] . ESPs were unable to degrade the mucins in mucus isolations on day 21 of acute infection , which is the peak of the immune response [6] . Interestingly , when the mucins were purified we could show that the ESPs have the ability to degrade mouse and human Muc2/MUC2 , but not the Muc5ac mucin , which is not usually expressed in the intestinal epithelium . It is plausible that these nematodes have evolved a capability to degrade the major mucin in the intestine , Muc2 , whilst being unable to degrade Muc5ac . This suggests that the Trichuris proteases are specifically acting on a peptide sequence within the MUC2/Muc2 protein core that appears conserved between mouse and human . The presence of the IL-13 induced protease-resistant Muc5ac during nematode expulsion will maintain mucus viscosity and retention of anti-helminthic factors in the mucus and subsequently be detrimental for nematode viability as previously demonstrated [3] . Interestingly , in addition to Muc5ac and increased levels of Muc2 , we show that Serpins were upregulated and were present within the mucus barrier of mice resistant to chronic infection . The presence of Serpins , and possibly other protease inhibitors in the mucus barrier during worm expulsion could explain why mucins , in particular Muc2 , were protected from degradation when treated with ESPs but Muc2 when purified , was susceptible to degradation . Another possibility for the lack of degradation of mucus could be due to the increased concentration of mucins and other proteins within the mucus barrier [4] . The increased levels of proteins could result in more competition for ESPs to cleave sites and , therefore , make the mucins less susceptible to degradation as illustrated previously in respiratory mucus [24] . The changes to the properties and composition of the mucus barrier could hinder ESPs activity , which could also explain the decrease in vitality of the nematode during worm expulsion [3] , [4] . This is not the first time pathogen exo-products have been shown to degrade mucins , several other pathogens have adopted a similar mechanism to survive within the mucosal layer . Helicobacter pylori secretes ‘mucinases’ which allow its corkscrew-like motion through the mucus layer [25] . Protozoan parasites such as Entamoeba histolytica [26] , Trichomonas vaginalis [27] and Naegleria fowleri [28] all release cysteine proteases which have the ability to degrade mucins . We demonstrated that cysteine protease inhibitors ( NEM and aprotinin ) only very partially limited the activity of Trichuris ESPs to degrade MUC2 . Treatment with chymostatin and antipain inhibited the depolymerisation of MUC2 implicating trypsin and/or serine protease activity . However , since degradation was not inhibited by aprotinin ( cysteine and trypsin protease inhibitor ) , it is most likely that serine protease activity is responsible for degrading Muc2/MUC2 . ESPs contained serine protease ( s ) of molecular weight range 50–100 kDa with the depolymerising activity against the gel-forming mucins . Interestingly , serine proteases of molecular weight of 85 and 105 kDa have been reported to be isolated from T . muris ESPs previously [21] . Whilst serine proteases appeared to be the major Muc2 protease , our data imply that cysteine proteases present in the ESPs were in part responsible for the affects observed on the insoluble Muc2/MUC2-gel . Serine and cysteine proteases , therefore , may act in concert to disrupt the polymeric mucin network . Supporting , this hypothesis further is the presence of Serpins within the mucus barrier prior to and during worm expulsion , which may hinder the ability of ESPs to break down the mucus barrier . There is an added level of unique complexity in the assembly of the intestinal MUC2: an uncharacterised ‘non-reducible linkage’ which results in an ‘insoluble’ gel enabling MUC2 to form a barrier resistant to the harsh environment of the intestine [20] . In addition , other proteins such as Fc Ig binding protein ( Fcgbp ) have been shown to associate with Muc2/MUC2 and could potentially act as a cross-linkers [19] . Interestingly , for the first time we demonstrate that Trichuris ESPs can degrade the Muc2/MUC2 polymers into smaller subunits and may have a further effect on the MUC2 protein as there was a change in the electrophorectic migration suggesting ESPs may be involved in de-glycosylating/degrading the MUC2 protein . Interestingly , as the mucins produced in vivo during acute infection are protected , the mucins present in the mucus barrier of susceptible mice also have reduced glycosylation [5] , which may make them more prone to the effects of ESPs . Taken together , the data suggests that the serine protease activity of the ESPs cleaves the mucin polymerising domain ( Figure 7 ) resulting in mucin monomers [29] which can be cleaved/degraded into smaller fragments ( Figure 7 ) . This will subsequently result in a mucus barrier that is more porous and , will therefore , exacerbate inflammation and aid persistence of infection . Although the stability and turnover of the ESPs is not known in vivo , ESPs can clearly increase the porosity of the mucus layer by depolymerisation of mucins , which would hinder the retention of host protective factors within the mucus barrier . Furthermore , bearing in mind the niche in which Trichuris lives there will be a major interface between the adult parasite and secreted mucins within the mucus layer via the posterior half of the worm , which protrudes out of the epithelium into the caecal lumen . The posterior section of adult worms is involved in mating and ultimately egg deposition , and it is tempting to speculate that modification of the mucus barrier properties , perhaps via the proteolytic activity described here would and , allow optimal mobility of the posterior end of the worm , facilitating efficient mating and egg laying during chronic infection . Many questions remain unanswered including identification of the specific protease ( s ) and the cleavage site on the mucin , the site of protease ( s ) production and the details of the host anti-protease response . Answering these will deepen our understanding of the host-parasite relationship of this group of ubiquitous and important gastrointestinal dwelling nematodes . | Gastrointestinal parasitic worm infections cause significant morbidity , affecting up to a third of the world's populationand their domestic pets and livestock . Mucus , the gel-like material that blankets the surface of the intestine , forms a protective barrier that is an important part of our innate immune system . The whipworm Trichuris is closely associated with the intestinal mucus barrier . The major structural component of this barrier , large glycoproteins known as mucins play a significant role in the expulsion of these worms in a mouse model . Using mice that get longterm chronic infections and others able to expel the worms from the intestine , we uncover a novel role for products secreted by the worms . Enzymes secreted by whipworms can disrupt the mucin network that gives mucus its viscous properties . Moreover , we unravel that worm products are unable to degrade forms of mucins present in the mucus barrier during worm expulsion , suggesting that these enzymes may be released by the worm as part of its regime to improve its niche and survival in the host . However , the host is capable of producing mucins and other protective molecules that protect the mucus barrier from degradation and are detrimental to the viability of the worm . | [
"Abstract",
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] | 2012 | Serine Protease(s) Secreted by the Nematode Trichuris muris Degrade the Mucus Barrier |
Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex , quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging . Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex , receiving thalamo-cortical visual inputs . The 45 , 000-neuron model was subjected to a battery of visual stimuli , and results were compared to published work and new in vivo experiments . Simulations reproduced a variety of observations , including effects of optogenetic perturbations . Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons . Interestingly , after extreme simplification the model still performed satisfactorily on many measurements , although quantitative agreement with experiments suffered . These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations .
Although our knowledge of the cortex has been improving dramatically thanks to the ongoing revolution in experimental neuroscience methods , the field is still far from an overall understanding of cortical circuits and their specific function . One essential component necessary to address this problem is the development of data-driven quantitative models that integrate experimental information and enable predictive simulations under a wide range of realistic in-vivo-like conditions–following the dictum attributed to Richard Feynman , “What I cannot create , I do not understand” [1] . Whereas detailed data-driven models of cortical tissue have been reported , in particular , [2] and [3] , modeling applications at the biophysical level to an in-vivo-like regime have been fewer ( although see , e . g . , [4] and references therein ) . A typical systems neuroscience experiment involves a battery of different stimuli and , ideally , perturbations of the investigated circuit . Reproducing this in simulations of a data-constrained cortical model has proven challenging . To investigate the feasibility of in-vivo-like comprehensive simulations , and to build a platform for further studies , we set out to simulate a set of visual physiology experiments in the mouse primary visual cortex ( area V1 ) , with the emphasis on the first step in the cortical processing of visual information–namely , modeling the V1 input layer , the layer 4 ( L4 ) . We decided early on to replicate a small set of what we consider to be canonical physiological findings characterizing cells in L4 of V1 . Given the thousands or more of published experiments carried out over the years in this region of cortex , our list is small , non-exclusive and may be considered idiosyncratic by some . However , we believe it is critical to start somewhere solid before generalizing indiscriminately . Our list of explored phenomena includes the approximately log-normal distributions of firing rates [5] , orientation selectivity [6 , 7 , 8 , 9] , oscillatory population dynamics [10 , 11 , 12 , 13 , 14 , 15] , sparsity of responses to natural stimuli [16] , amplification of thalamic inputs by recurrent connections [17 , 18 , 19 , 20 , 21 , 22 , 23] , preferential connectivity among similarly tuned neurons [24 , 25 , 26 , 27 , 28 , 29] , and a number of others . The model was constructed in a data-driven fashion from what is known about the L4 circuit organization . Indeed , we think of it as a consistent summary of our collective anatomical and physiological knowledge about this region of the nervous system . Of course , it is not the most compact such summary ( e . g . in terms of Kolmogorov complexity ) nor was it meant to be that . To the extent that we can reproduce physiology across scales–from post-synaptic potentials to local field potentials–we would argue that we understand the phenomena that we observe experimentally . Although the model was biophysically and anatomically detailed , we also used simplifications when appropriate , typically choosing computationally inexpensive approximations for biological mechanisms . A crucial component was a set of filters that represented visual information processing from image to the output of the lateral geniculate nucleus ( LGN ) of the thalamus , which projects to L4 . This feature enabled one to use arbitrary movies as visual stimuli . We presented the same or similar sets of stimuli to the model and to mice in experiments and then compared the in silico and in vivo responses . We asked three major questions: ( 1 ) How well does our model reproduce experimentally observed neural responses from the above list ? ( 2 ) What are the major mechanisms that determine the neuronal activity patterns ? And ( 3 ) , how does the ability to reproduce experimental recordings depend on the level of granularity of the model ? To address ( 1 ) , we assessed neuronal responses to artificial ( e . g . , drifting gratings ) and naturalistic ( e . g . , movies ) stimuli and selected a number of features of these responses that are generally considered important and interesting in the field . We then benchmarked the model performance on these features against the experimental data . Whereas typically models are developed to explain a specific phenomenon and may aim to reproduce 1–2 observed quantities , the key element in our study was to observe generalization over a wide variety of visual stimuli and response features . We found that our simulations reproduced many of experimental observations ( with some exceptions ) under a range of different stimuli . For ( 2 ) , we performed in silico experiments to investigate how individual neurons process inputs from different sources and how recurrent connections shape the network activity . This approach relied in part on simulated optogenetic experiments paralleling in vivo optogenetic studies . The most striking observation was that tuning properties of neurons were critically affected by the functional connectivity rules . For question ( 3 ) , we introduced two much simplified versions of our model , where biophysical neuron models were replaced by point-neuron models with either instantaneous or time-dependent synaptic action , and compared simulations of these simplified networks to the biophysical model . We found that , although the simplified network models qualitatively reproduced the trends observed in the biophysical simulations ( as also reported by [30] ) , the quantitative agreement with experiment suffered . The time-dependent synaptic kinetics in the simplified model allowed for better agreement with the biophysical model and experiment , such as , for example , in terms of producing oscillations in a gamma range . No circuit in the brain exists in isolation , but including all the brain complexity in the model is impossible at present due to absence of data and inadequate computing resources . Therefore , we attempted to build a model of L4 of V1 , which can be handled with available resources and for which a substantial amount of information could be found . The good performance of the model compared to the experiment may indicate a relative compartmentalization of L4 computation ( see Discussion ) ; this should not be expected everywhere in the cortex . Our L4 model provides a comprehensive characterization of activity and mechanisms in this cortical circuit and may serve as a stepping-stone for future , more sophisticated studies of all cortical layers . To enable this , we make the software code , the model , and simulation results publicly available ( see SI ) .
The network ( Fig 1A and 1B ) consisted ( see Methods ) of models of individual neurons [31] from an early version of the Allen Cell Types Database [32] , employing compartmental representation of somato-dendritic morphologies ( ~100–200 compartments per cell ) and 10 active conductances at the soma that enabled spiking and spike adaptation . Although recent additions to the Allen Cell Types Database include models of neurons with active conductances in the dendrites as well , those models are very computationally expensive , which was prohibitive for the breadth of our study ( see below ) . In addition , in terms of somatic spike output , the current versions of such models do not exhibit much better performance than the models with active conductances restricted to the soma [32] . Thus , we used these latter , computationally cheaper models . The cells were distributed uniformly in a cylinder ~400 μm in radius , representing the central portion of V1 , and 100 μm height with density of 200 , 000 mm-3 [33] . Five single neuron models represent five “types” of neurons–three major excitatory groups as determined by Cre-lines ( Scnn1a , Rorb , Nr5a1 , 85% of all cells ) and two groups of parvalbumin-positive fast-spiking interneurons ( 15% , denoted as PV1 and PV2 ) , which form the majority of interneurons in L4 [34] . All 10 , 000 biophysical cells were exact copies of these five models . The cell models correspond to regular-spiking excitatory cells and fast-spiking interneurons ( PV+ ) ; whereas non-PV+ interneurons do exist in L4 , they are a relative minority [34] , and therefore were neglected for simplicity . Furthermore , 35 , 000 much simpler leaky-integrate-and-fire ( LIF ) neurons , with only two groups–excitatory and inhibitory–were placed around the biophysically detailed “core” to prevent boundary artefacts ( see SI ) . The complete model accounted for over half of V1 L4 cells ( 45 , 000 out of ~70 , 000 ) . Below , we primarily focus on the properties of the biophysical core circuit . Three independent instantiations were generated using different random seeds . The connectivity and inputs into these three model instantiations were distinct , but all followed the rules described below . All simulations were performed using the Python 2 . 7 code ( an early version of the BioNet package [35] ) employing NEURON 7 . 4 [36] . Recurrent connections ( Fig 1C ) were established randomly according to probability decaying with intersomatic distance ( e . g . , [37] ) . Recent experiments [25 , 26 , 27 , 29] showed that excitatory neurons in the mouse V1 L2/3 exhibit more likely and stronger connections if neurons prefer similar stimuli ( “like-to-like connectivity” ) . We sought to investigate how such a rule affects neural activity . The rule ( Fig 1D ) was applied to all excitatory cells by assigning a preferred orientation to each excitatory neuron ( which was also used to select LGN inputs , see below ) and using the difference between such orientations to compute a probability of connection . A similar like-to-like rule was applied to the amplitude of synaptic weights ( Fig 1E ) . The like-to-like rules were parameterized to correspond approximately to the observations for L2/3 [25] ( Fig 1F ) and did not apply to pairs containing one or more inhibitory neurons . See Methods ( section “Connectivity within the Network” and “Synaptic characteristics” ) for further details . We directly converted visual stimuli to spikes of LGN cells ( which provide the input to L4 ) via space-time separable linear-nonlinear Poisson ( LNP ) filters ( Fig 1G; see , e . g . , [38 , 39 , 40] ) of ON , OFF , and ON/OFF types ( see Methods ) , all of which produced relatively transient responses . Such a considerable simplification of the wide variety of response types that the LGN cells exhibit ( see , e . g . , [41 , 42 , 17] ) was necessary to make the model tractable , especially because rules of connectivity from various functional LGN cell types and L4 in the mouse are largely unknown . Because the retinotopy of the complete model did not cover the entire field of view , we employed 9 , 000 LGN filters–approximately half of the mouse LGN . Based on the experimental data about receptive fields of L4 neurons [6 , 7 , 17] , we used retinotopy of L4 cells to “pool” inputs from LGN filters with similar retinotopy , and the assigned preferred orientation ( see above ) to establish the geometry of the ON and OFF subfields ( Fig 1G; see Methods ) . This helped to establish orientation selectivity in the combined LGN inputs to individual L4 cells [17] . Due to the distance dependence of recurrent connections and retinotopy of LGN inputs , the L4 cells that were connected to each other had a higher chance to receive inputs from the same LGN cells , in comparison with unconnected L4 cells ( see Methods ) . This is consistent with recent experimental observations for L4 and L2/3 cells [27] . The numbers of synapses for recurrent connections were chosen based on the literature ( see Methods ) . Using electron microscopy ( EM ) , we found ( Fig 1H ) that LGN synapses constitute 15–30% of all synapses in the neuropil in L4 ( see Methods for details ) , consistent with observations for mouse S1 and M1 [43] . This corresponds to over 1 , 000 synapses from the LGN per excitatory L4 neuron ( given ~8 , 000 synapses total per mouse V1 cell [33] on average , we assume relatively small L4 neurons to receive ~6 , 000 synapses ) . Thus , the number of LGN-to-L4 synapses is an order of magnitude higher in the mouse than in the cat V1 ( for which this number has been long established to be ~100 [44] ) . These new data determined the numbers of LGN-to-L4 synapses in the model ( see Methods for further details ) . For the majority of connections , multiple synapses per connection were assigned , typically in the 3–7 range ( see Methods ) . Synaptic dynamics was described by a sum of two decaying exponentials . We assumed no short-term plasticity as it is plausible that in vivo synapses operate at steady-state conditions [18] . Long-term plasticity was neglected because simulations covered several seconds at most . To account for inputs from the rest of the brain and for different brain states , we introduced externally generated waves of background activity sweeping across the L4 model ( Fig 2A ) , inspired by reports of activity propagation over cortical surface in vivo [45] . Poisson spike generators were distributed within the cortical plane and were activated whenever the wave swept through their positions , sending spikes to the nearby L4 cells . This enabled the L4 model ( Fig 2B ) to operate with an extremely simple analogue of distinct cortical states ( see , e . g . , [46 , 47 , 48] ) , which we call “Bkg . on” and “Bkg . off . ” ( see Methods ) . The random generation of the background waves , as well as random generation of background spike trains from the wave profiles and of LGN spike trains from deterministic filter outputs were the sole sources of indeterminacy , leading to differences between individual trials . After the steps above , all parameters were fixed except the synaptic weights ( conductance amplitudes ) . The LGN-to-L4 weights were selected to produce experimentally observed excitatory currents from LGN [17] . The weights for recurrent connections were then manually optimized , while constraining post-synaptic potentials and currents ( PSPs and PSCs ) to the experimentally reported range for mouse cortex [49] ( see Methods ) . The weights were not adjusted individually; instead , we uniformly scaled all weights belonging to a particular connection type , such as , for example , excitatory-to-Rorb connections or inhibitory-to-Nr5a1 connections . Optimization was limited to two training stimuli–a single trial of a drifting grating and one of a gray screen , 500 ms each . The target was to reproduce the mean spontaneous firing rate and the rate in response to the preferred grating ( Rmax ) , while avoiding synchronous epileptic-like activity . Published data from [6] were used for this optimization , before our own experiments were finalized . After optimizing the synaptic weights using this limited training set , we applied the model without any further modification to all the other stimuli , which served as a test set . The exact synaptic weights obtained during the optimization stage may not represent a unique solution , due to degeneracy [50] . Nevertheless , it is reassuring that synaptic properties from our optimized models were consistent with published experimental reports ( see S2 Fig ) , exhibiting , e . g . , current amplitudes of ~10–30 pA for excitatory and ~40–60 pA for inhibitory synapses [49] or voltage amplitudes of ~1 mV [51] for L4 excitatory-to-excitatory connections ( as measured at the soma ) . How well does the model reproduce experimentally observed neural responses in vivo ? The answer will depend on the types of stimuli and features of responses to those stimuli that one chooses for comparison . We reasoned that a successful model should be versatile; in other words , it is not sufficient to be able to model responses to one type of stimuli well , and instead one should strive to reproduce features across many types of stimuli . Therefore , our battery of visual stimuli included gray screen , a variety of drifting gratings , 10 natural images , 3 natural movie clips , 2 types of full-field flashes , and 4 moving-bar stimuli ( see example responses in Fig 2C–2E; see also Methods , S3 and S4 Figs , and S2 Table ) . Altogether , ~3 , 600 simulations were carried out . Our own experiments used for benchmarking consisted of two series of extracellular electrophysiological recordings in mouse V1 employing multi-electrode silicon probes . In the first [7] , responses to drifting gratings with a variety of spatial and temporal frequencies were measured , in both awake and anesthetized mice . In the second ( previously unpublished ) , spontaneous activity ( responses during gray screen presentation ) as well as responses to natural movies , natural images , full-field flashes , and other stimuli were recorded in awake mice . We chose several features of visual responses that are generally considered important and are often reported in the field of cortical visual physiology and compared their values between experiment and simulations ( Fig 3 ) . It should be noted that , although the measured metrics differed slightly among the three excitatory cell types ( Scnn1a , Rorb , and Nr5a1 ) , as well as among the two inhibitory types ( PV1 and PV2 ) , we do not ascribe significance to these differences . This is because during model construction and optimization no experimental data was available on distinctions in response features or connectivity between these L4 cell populations , and , in absence of either , we assumed the same optimization targets for all excitatory or inhibitory cells ( which , however , allowed for ~1 Hz rate deviation from the target , resulting in different types settling at slightly different activity levels ) . The first characteristic we checked is whether neuron firing rates follow a positively-skewed , log-normal-like distribution , which is a ubiquitous hallmark feature of brain activity in vivo [5] . Such distributions were indeed observed in our simulations for spontaneous activity and all types of visually-driven responses ( Figs 3A and S5A ) . Consistent with published reports , our simulated rate distributions spanned 2–3 decades and may widen further in longer simulations ( for instance , spontaneous rates were computed from 20 trials of 500 ms each , resulting in the lowest possible rate of 0 . 1 Hz ) . Also in agreement with literature [5] , individual neurons tended to keep their low or high firing rates across different stimulus conditions ( Fig 3A , top ) . The positive skewness of firing rate distribution ( i . e . , the third standardized moment , 〈 ( ( f −〈f〉 ) /σf ) 3〉 , where f is the firing rate and σf is its standard deviation , both computed in the linear ( i . e . , not log ) firing-rate space ) was typically between 0 and 4 ( S5A Fig ) . While it is hard to expect an exact match of the experimental skewness distributions from our relatively rough model , it is reassuring that the model reproduced the overall experimental trends of firing rate distributions with positive skewness in the 0–4 range . Note that a normal distribution has zero skewness , whereas log-normal distribution has positive skewness . The mechanisms underlying log-normal like distributions of activity of individual neurons in vivo are not well understood [5] , and may involve both cell-intrinsic and network phenomena . One major mechanism proposed based on point-neuron network simulations [52] involves a transformation of an approximately normal distribution of total inputs to a cortical cell by a rectified and expansive input-output nonlinearity , which results in a heavy-tailed distribution of firing rates . This , combined with the log-normally distributed strength of background inputs in our model ( see S1C Fig ) , likely underlie our observations . Another aspect of activity that we checked was whether the magnitude of responses was consistent with the experiment; presumably , it is important for network computations that cells of certain types fire at certain rates in response to particular stimuli . For this purpose , we considered the rates of spontaneous activity and maximal rates ( Rmax ) in response to drifting gratings ( Fig 3B; see Methods for definitions ) . The spontaneous rates were 0 . 5–1 . 0 Hz for excitatory and 1 . 5–2 . 0 Hz for inhibitory cells , broadly consistent with our experimental measurements and published data [6] , and Rmax levels were also similar to experimental ones . Furthermore , the L4 responses to gratings are known to exhibit orientation and direction selectivity ( e . g . , [6 , 7] ) . We found that the orientation selectivity index ( OSI; see Methods ) for excitatory cells was ~0 . 4–0 . 5 on average , in excellent agreement with the experiments ( Fig 3B ) . For inhibitory cells , OSI was ~0 . 05 on average , whereas the experimental value was ~0 . 3 . The relatively low OSI values were due to non-selective thalamic inputs and recurrent connectivity for inhibitory cells ( see Methods ) , which were chosen that way at model construction to conform to an often-expressed view that excitatory cells are tuned and inhibitory fast-spiking cells are not . However , recent experiments [34] , as well as our own data ( Fig 3B ) , suggest moderate levels of tuning for these neurons , and therefore future models will need to reflect these observations better . Nevertheless , qualitatively the model already captures the trend of better tuning of excitatory cells compared to inhibitory fast-spiking cells . Another often-observed phenomenon is that the orientation tuning width of cortical excitatory neurons stays approximately constant with respect to contrast ( see , e . g . , [6 , 8 , 38 , 53] ) . This is considered important because simple feedforward models typically produce broadening of tuning curves with contrast , and , thus , contrast invariance may reflect mechanisms inherent to cortical processing . Interestingly , it has been shown that responses in L4 of mouse V1 do not simply maintain tuning width , but actually exhibit sharpening of tuning curves and increase of OSI with contrast for excitatory cells ( and broadening of tuning curves/reduction of OSI for inhibitory cells ) [9] . We found strong orientation tuning in excitatory cells for contrast as low as 10% in our model ( S5C Fig ) . The changes in tuning curve width ( characterized as differences of the half-width at half-height at high and low contrasts , ΔHWHH ) were centered relatively narrowly around zero ( S5D Fig; 4+/-5 degrees for 80% vs . 10% and –2+/-4 degrees for 80% vs . 30% contrasts on average ) . Thus , our model of excitatory and inhibitory recurrent connections was adequate for preventing broadening of tuning curves , but not sufficient to produce sharpening of tuning curves at high contrasts [9] . We further characterized OSI changes with contrast and found on average slight increase in OSI of excitatory cells and more substantial decrease in OSI of inhibitory cells ( S5E Fig ) . Whereas this trend is consistent with experimental observations [9] , it is not sufficiently strong in our model , most likely due to the insufficiently sharp tuning of inhibitory cells mentioned above . Thus , the model captures the overall tendency of cortical circuits to prevent broadening of tuning curves with contrast , but should be improved in the future to capture the more delicate properties of tuning sharpening and broadening in distinct cell populations . By contrast to OSI , the model performed poorly on the direction selectivity index ( DSI; Fig 3B ) , which was close to zero in simulations , whereas experimentally it was ~0 . 4–0 . 5 . This was due to the extreme simplicity of the LGN inputs , which were constructed based on experimental data , but at this point did not include all types of LGN activity observed experimentally [7 , 17 , 21] . In particular , for simplicity , we did not include the sustained LGN responses ( i . e . , all LGN filters produced transient responses ) , whereas the most recent experimental data suggest a critical role for the interplay between sustained and transient LGN inputs in generating direction selectivity in V1 [23] . Proper incorporation of direction selectivity is the subject of ongoing work on the next generation of the model . Another important aspect of neuronal activity in vivo is the population-level oscillatory rhythms , which are observed in a variety of frequency bands . Whereas oscillations at many of such frequencies are likely caused by non-local interactions in the brain , and therefore cannot be expected to arise in an isolated L4 model , some of them may be generated locally . Indeed , our simulations exhibited oscillations in the 15–50 Hz range ( sometimes referred to as “mouse gamma” ) , with a peak at ~20 Hz ( Fig 3C ) . This is consistent with extracellular electrophysiology data [10] , which exhibits a particularly strong peak at similar frequencies in L4 . Global luminance changes present another challenge to models , due to simultaneous engagement of all cells by these stimuli . We studied responses to 50 ms long full-field flashes and observed , in both experiment and simulations , that a sharp and fast peak in activity was evoked by the first white-to-gray transition , followed by a second smaller and wider peak ( Figs 2D and 3D ) . The magnitude of the first peak was about 2–4 times higher in simulation than in the experiment , whereas that of the second peak was approximately the same , and the time course of the response was uniformly ~2-fold faster in simulations ( Fig 3D and 3E and S5B Fig ) . These differences were most likely again caused by the absence of sustained LGN responses in the model . Importantly , however , in both simulations and experiments , the second peak was delayed–instead of 50 ms ( the flash duration ) , it appeared 100 ms after the first ( 200 ms in the experiment ) . This reflects a known phenomenon of suppression following luminance change , where the second peak corresponds to release from inhibition ( e . g . , [54 , 55] ) . Thus , qualitatively the model reproduces well the critical features of cortical responses to global luminance change , which likely affect perception of visual stimuli ( such as in , e . g . , luminance-induced visual masking ) . Finally , artificial stimuli such as gratings are quite different from natural images and movies and it is an important question to ask whether a model reproduces differences in response statistics between artificial and natural stimuli . We did observe in simulations that responses to a natural movie ( e . g . , Fig 2E ) were very distinct from those to a horizontally drifting grating ( Fig 2C; both panels use the same ID labels ) . For a grating , cells with certain IDs respond strongly throughout the simulation ( as they prefer the grating’s orientation ) . By contrast , natural movies evoked episodes of concerted responses in distinct populations of cells ( which share similar orientation preference ) . Notably , such concerted responses were mostly absent when movie frames were shuffled in time ( Fig 2E , bottom ) . To quantify the differences , we computed lifetime sparsity for each cell following Vinje and Gallant [56] ( see Methods ) . Sparsity was higher for movies than for gratings ( Fig 3F ) in both simulations and experiments , consistent with previous observations ( including Ca2+ imaging [16] ) : 0 . 77+/-0 . 16 in simulation vs . 0 . 71+/-0 . 22 in experiments for movies and 0 . 64+/-0 . 14 vs . 0 . 65+/-0 . 25 for gratings . Thus , the model correctly accounts for the distinction in sparsity between these two classes of stimuli . To investigate how trial-to-trial neural variability in the L4 model responses compares with experimental observations for gratings and natural movies , we computed several metrics ( see Methods for details ) . The coefficients of variation of inter-spike intervals ( S6A Fig ) were consistent between the experiment and the model , as was the trend that spontaneous activity and drifting gratings produced lower values than natural movies . The model was also consistent with reports of stimulus-induced suppression of variability [57] , as the Fano factor ( S6B Fig ) tended to be lower for stimulus-evoked responses ( especially , for gratings ) than for spontaneous activity . This effect , however , was weak for excitatory neurons in the model and was not clearly identifiable in our experimental recordings ( S6B Fig ) , where it might have been obscured by the noise due to relatively small number of recorded cells . The signal correlations were widely distributed in both simulation and experiment ( S6C Fig ) , with the median close to 0 for gratings and close to 1 for spontaneous activity . The signal correlations for natural movies tended to be higher than for gratings , although only slightly so in the experiment and more substantially in simulation . The noise correlations ( S6D Fig ) were more narrowly distributed and on average close to zero in both simulation and experiment . We further used the model to shed light on the mechanisms that determine patterns of neural activity and computations performed by the L4 circuit . One important question is to what extent the L4 activity and computations are inherited from the input regions ( here , LGN ) vs . being shaped by intrinsic , recurrent connectivity ( see , e . g . , [58] ) . To investigate this , in addition to regular ( “Full” ) simulations , we performed a number of simulations where recurrent and background connections were removed , so that neurons received LGN inputs only ( “LGN only” ) . Using in silico voltage clamp ( see Methods ) , we measured ( Fig 4A ) synaptic excitatory currents at the cell body–from the LGN only , ILGN , and the total , Itot – and found that , for preferred directions of 2 Hz drifting gratings , the fraction of Itot contributed by ILGN was 0 . 41+/-0 . 05 for excitatory cells , in good agreement with experiment ( 0 . 36+/-0 . 02 [17] ) . Also in agreement with experiment [17] , the mean ILGN was untuned , since individual LGN filters were mostly untuned [17] ( but see [19 , 20] ) , whereas mean Itot and Isub = Itot = ILGN ( i . e . , the current due to recurrent connections ) were well tuned to the grating orientation ( Fig 4B ) , and F1 components ( i . e . , the amplitude of the mode at the stimulus frequency; see Methods ) of all of these currents were tuned . Besides these features that were consistent with experimental recordings [17] , we observed one distinction: the F1 component of Isub was substantially smaller than that of ILGN or Itot ( Fig 4B ) and the temporal dynamics of Isub was in antiphase with that of ILGN ( S7A Fig ) . This turned out to be a space clamp artefact , where a stronger ILGN current increases the membrane voltage at the synapses , resulting in weaker driving force for recurrent excitation and , therefore , antiphase relationship of ILGN and Isub; simulations where LGN current was removed from the recorded cells exhibited Isub with no oscillations at the grating frequency ( S7A Fig ) . Thus , the overall picture was that the ILGN mean was untuned whereas its F1 component was; the recurrent connections added current that was tuned overall , but was not time-modulated ( whereas in the experiment [17] it is time-modulated ) ; and the resulting total current was tuned in both mean and F1 . The inhibitory currents were mostly untuned at the level of both the mean and F1 ( Fig 4B ) ; the F1 did show a preference to orientation , but its magnitude was only a few percent of the mean and the current was not strongly modulated in time ( S7A Fig ) . Comparing mean Itot and ILGN of individual cells ( Fig 4C ) for all grating conditions ( i . e . , not only the preferred one shown in Fig 4A ) and different contrasts , we observed a complex dependence . The relationship of Itot with ILGN was essentially linear along the contrast dimension . The quality of the linear fit , Itot=AILGN+B , was r2 = 0 . 76 for excitatory cells for all conditions , and 0 . 97 for a specific grating direction . This is consistent with the prediction of an earlier , much simpler self-consistent model [58] ( see also [59] ) . For the orientation dimension , the dependence was highly non-linear ( r2 = 0 . 18 ) . The latter was the consequence of the mean of ILGN being untuned to orientation , whereas the tuned current from recurrent connections made the mean of Itot highly tuned ( Fig 4B ) . Due to the same reason , the amplification factor A was approximately 2 across all conditions ( since B was very small on average , A can be thought of as the inverse of the LGN contribution described above; across all conditions 1/A = 0 . 54+/-0 . 04 , Fig 4C ) , whereas for the preferred orientation it was 1/0 . 41 = 2 . 44 ( as the LGN contribution at the preferred orientation was 0 . 41+/-0 . 05 , Fig 4A ) . How do these relationships between currents shape the properties of the spiking output ? We found that the OSI in L4 at the level of spikes was inherited from the combined LGN inputs to each cell [17] , producing weakly selective output , and was strongly increased by recurrent connections ( Figs 4D and S7B ) . Generally , the relationship between the rates in Full and LGN only simulations ( S7C Fig ) was not close to linear ( attempting a linear fit fFull=AffLGN+Bf , where fFull and fLGN are the firing rates in Full and LGN only simulations , and the parameters Af and Bf are marked with the subscript “f” to distinguish them from the parameters of linear fit of currents above , we found r2 = 0 . 5 for all conditions and 0 . 65 for one grating direction for excitatory cells ) . The firing rates at fixed contrast and TF exhibited a much more linear relationship ( r2 = 0 . 88 ) , but only locally–the good linear fit required negative values of parameter Bf ( -4+/-3 Hz on average ) , which is non-physiological given that Bf is the firing rate of the cell under the conditions when the firing rate induced by the LGN-only input is zero . Thus , overall the relationship was non-linear , but close to linear past the firing threshold for LGN-only inputs . Interestingly , the overall effect of recurrent connections on the spiking output in our model was that of suppression , as the Full-network firing rates at non-preferred orientations tended to be smaller than LGN-only rates ( Fig 4D and S7C Fig , top right ) , and only at the preferred orientations Full-network firing reached the same rates as in the LGN-only case . Consequently , Rmax values were approximately the same in the Full and LGN-only cases ( S7B Fig ) . To investigate the L4 circuit mechanisms further , we performed in silico optogenetic silencing of LGN inputs and of a subset of the circuit ( see Methods ) . Despite the significant recurrent amplification , L4 activity shut down rapidly when the LGN spiking was silenced ( Fig 4E ) , in agreement with an analogous experiment [18] . Although the time course of activity decay was faster in the model ( 2 . 3 vs . 9+/-2 ms , likely due to the absence of excitatory stimulation from other cortical regions ) , otherwise the effect of LGN silencing was the same . The widespread and powerful intracortical inhibition appears to be the most plausible driving force behind this effect . We then separately silenced the Scnn1a population of excitatory cells in simulations and conducted analogous experiments in vivo ( using Archaeorhodopsin-mediated silencing on randomly selected trials during presentation of TF = 2 Hz drifting gratings , while extracellular multielectrode recordings were performed , see Methods ) . Results were characterized by converting firing rates with ( fp ) and without ( fcontrol ) perturbation to the optogenetic modulation index ( OMI ) , OMI=fp−fcontrolfp+fcontrol Simulations qualitatively agreed with experiment ( Figs 4F , S8A and S8B ) in terms of the OMI distribution over L4 excitatory cells ( few inhibitory cells were recorded experimentally ) , which was approximately bimodal , with one “lobe” concentrated close to -1 ( totally silenced neurons ) and the other near 0 ( weak or no effect ) . Due to challenges of recording form the thin layer 4 , a small number of cells was obtained in the experiment , but they all showed a consistent trend of OMI values belonging to one of these two lobes ( S8A Fig ) . Whereas weak and/or sparse silencing of Scnn1a cells in simulations did not result in a two-lobe distribution , a strong and dense silencing did ( S8C Fig ) , consistent with the experimental perturbation that attempted to silence as many Scnn1a cells as possible . Simulations showed ( Fig 4F ) that the left lobe in the OMI distribution was due to near-complete silencing of Scnn1a cells . This silencing reduced the amount of excitation in the network and resulted in moderate decrease of firing of inhibitory cells . The net effect is that the firing rates of the other excitatory cell types ( Rorb and Nr5a1 ) remained almost unaffected–yielding the OMI lobe with values close to zero . We investigated the effect of the logics of connections between L4 excitatory cells on the neural activity and computation . In our regular models , a “like-to-like” ( “L” ) rule [25 , 26 , 24 , 29] was used for both the connectivity and synaptic weights of excitatory-to-excitatory connections , based on the anticipated orientation tuning of the cells ( Fig 1C–1E ) ; because the rule applied to both synaptic connectivity and amplitude distributions , we refer to this set as “LL” . We then studied three alternative sets of models . In one , both the connectivity and weights were randomly ( “R” ) assigned independently of cell tuning ( “RR” ) . The remaining two sets had the random rule applied to connectivity and like-to-like to weights ( “RL” ) , or vice versa ( “LR” ) . Each set consisted of 3 models . Besides the “R” or “L” rules , everything else was exactly the same between the four sets , including the probabilistic distance-dependent connectivity ( Fig 1C ) . Synaptic weights for all models were tuned following the same procedure ( tuning only involved scaling of weights uniformly across populations , thus not affecting the “L” vs . “R” property , which applies to individual cell pairs ) , resulting in similar levels of activity for the training stimulus ( grating ) in all models . We then assessed how functional properties differed . We found that OSIs were extremely reduced in the RR set , with LR being only slightly better tuned , whereas RL came close to the original well-tuned LL set ( Fig 5A ) . Because the mean LGN current was untuned in all models , the critical amplification of orientation selectivity ( Fig 4B–4D ) came from the lateral L4 connections , which were well tuned in LL and RL sets and poorly tuned in LR and RR sets ( Fig 5B ) . As a result , the total current as well as spiking output was well tuned in LL and RL sets and barely tuned in LR and RR ( Fig 5 ) . This suggests a bigger role of synaptic weights than connection probability in shaping network responses [60] , which can be easily understood since the “L” rule for weights results in low contributions from non-like-to-like connections , even if such connections are present , thus effectively enforcing the “L” type connectivity . How does the ability to reproduce experimental recordings depend on the level of granularity of the model ? To address this question , we built two versions of a radically simplified model , where biophysical neurons and bi-exponential synapses were replaced by LIF neurons and either instantaneous “charge-dump” synapses ( using NEURON’s IntFire1 function ) or synapses with exponential time dependence of synaptic current ( NEURON’s IntFire4 function ) . These all-LIF models used exactly the same cell-to-cell connections and inputs as the biophysical models and were optimized using the same protocol ( see Methods ) . By contrast to another recent study [30] , our coarse-graining procedure did not aim to replicate results of the biophysical simulations , but , rather , aimed to match certain experimental observables using the point-neuron networks in the same way as we did for the biophysical network; nevertheless , both that study and ours agree in that results of the simplified models were quite similar to those of the biophysical models . The results of the all-LIF simulations were qualitatively similar to the results of biophysical simulations ( Fig 6 ) , but specific quantitative distinctions were obvious . The overall appearance of responses ( Fig 6A ) and mean values of spontaneous rates and Rmax were similar to the biophysical case ( Fig 6B ) . However , there were apparent differences in the way spontaneous rates were distributed ( Fig 6B ) : the bottom three quartiles of cells in all-LIF simulations exhibited no spontaneous firing , whereas in the biophysical case that was true for half or less of the cells; nevertheless , the means across cells were similar . The OSI values were elevated in the IntFire1 case ( Fig 6B ) . Most noticeable , the gamma oscillation was largely absent in the IntFire1 models ( Fig 6C ) . This was likely due to instantaneous synapses in IntFire1 , since gamma oscillations [11 , 12 , 13 , 14 , 15] are thought to be strongly dependent on the properties of inhibitory perisomatic synapses [11 , 14] , and especially their time constants . Thus , removal of the appropriate synaptic kinetics from the large and distributed network model like ours may be expected to disrupt gamma oscillations . By contrast , the IntFire4 model employed a simple form of synaptic kinetics ( see Methods ) and produced oscillations in the range of 10–20 Hz , i . e . , close to ~20 Hz gamma oscillation in the biophysical model ( Fig 6C; note that the weighted multi-unit activity metric used here is only a proxy for the actual LFP that can be computed for the biophysical model , as shown in Fig 3C , but not for point-neuron models; nevertheless , both metrics show similar trends for the biophysical model , such as the large peak at ~20 Hz ) . Further distinctions were observed for sparsity values ( Fig 6D ) . The overall trend of higher sparsity for movies than for gratings was reproduced by both all-LIF models , but they both also exhibited higher sparsity values than the biophysical case and the experiment ( Fig 3F ) . Perhaps the most significant difference with the biophysical model was observed for the magnitude of cortical amplification ( Fig 6E ) for excitatory cells ( interestingly , inhibitory cells did not show substantial differences ) . The LGN contribution was 0 . 41+/-0 . 05 in the biophysical model and 0 . 36+/-0 . 02 in experiment [17] , whereas it was 0 . 53+/-0 . 13 for IntFire1 and even higher , 0 . 68+/-0 . 12 , for the otherwise more realistic IntFire4 . The distributions of amplification values were also quite different in all-LIF models ( Fig 6E ) , exhibiting multiple peaks–apparently for the multiple excitatory cell types–instead of a single peak in the biophysical model . Finally , the functional connectivity had the same consequences in the all-LIF models as in the biophysical case–the orientation tuning was high for models with the LL or RL rules , and low for LR and RR ( Fig 6F ) . On the other hand , the small difference between the LL and RL cases , observed in the biophysical model , was mostly eliminated , and overall the OSIs were higher for the IntFire1 model and , in the RR and LR cases , for IntFire4 as well .
The promise of data-driven neuroscience modeling lies in harnessing computing power to establish a platform for discovery that would work hand-in-hand with experiments . For that promise to materialize , models need to be biologically realistic in recapitulating knowledge about brain structure as well as in reproducing in vivo activity . Doing either is difficult , and doing both together is more difficult yet , and is less widely practiced ( for some powerful examples , see [2 , 3 , 4] ) . We described here a model of the L4 in mouse V1 that was built using a high degree of biological realism and simulated in a framework of an in silico visual physiology experiment . We asked how well the model reproduces activity observed in vivo across a variety of stimuli , which mechanisms underlie the activity and computation in the modeled L4 circuit , and how the levels of simplification used in the model affect its performance . Our software code , the model , and simulation results are made publicly available ( see SI ) to enable further efforts in modeling in vivo activity and function . Despite the small training set of stimuli for which our model was optimized–a gray screen and a single grating presentation for 0 . 5 s–it generalized well to a large test set of stimuli . The model reproduced major features of in vivo observations with respect to , e . g . , the magnitude of responses to gratings , orientation selectivity , prominence of gamma oscillations , long-tailed distributions of firing rates , lifetime sparsity for gratings and movies , trends in variability of neural responses , magnitude of cortical amplification , and effect of optogenetic perturbations of the LGN or the Scnn1a population in L4 ( Figs 3 and 4 ) . Such an agreement across stimulus classes and types of observation is remarkable given that , although the model included a significant degree of biological realism , many simplifications were used: neurons possessed active conductances only at the soma , the variety of neuron models was limited to five unique single cell morphologies and sets of membrane conductances , synapses had no short-term plasticity , LGN inputs were simplified , connectivity was established via simple probabilistic rules , most known interneuron cell types [34] were absent , and , finally , and probably most importantly , the influence of most of V1 ( L1 , L2/3 , L5 , and L6 ) and the rest of the brain was reduced to extremely simple background states . This may suggest that many of L4 computations are produced by its local network , and other layers may play a primarily modulatory role–such as gain control exerted by L6 [61]–as far as L4 activity is concerned . Perhaps even more instructive than successes , a number of deficiencies were observed . In terms of reproducing in vivo activity , the most important issues were the absence of direction selectivity and too fast responses to full-field flashes ( Fig 3B and 3E ) . Both appear to be due to the extreme simplicity of the LGN inputs–in particular , the absence of sustained LGN responses in the model . This provides an immediate direction for improving the model , especially based on the more recent experimental results [23] , which is the aim of ongoing work on the next model generation . In applying the model to study mechanisms underlying the operation of the L4 circuit ( Figs 4 and 5 ) , we found the following principles . Tuning in L4 cells arose from combination of convergent LGN inputs , but to reach physiological levels of selectivity , the functional like-to-like connectivity was essential ( Figs 4D , 5 and S7B ) . This suggests that like-to-like rules , experimentally observed in L2/3 [25 , 26 , 24 , 29] , are likely to be found in L4 as well , and may play an essential role in determining functional information processing in the cortex . The amplification of excitatory current due to recurrent connections was by 2-3-fold ( Fig 4A , 4B and 4C ) , in quantitative agreement with experiment [17] , and was linear [58 , 59] along the contrast dimension , but highly non-linear along the orientation dimension ( Fig 4C ) . Despite this strong excitatory amplification , the whole circuit was controlled by even stronger inhibition , which readily shut down the activity in the absence of the LGN input [18] ( Fig 4E ) . Another insight again followed from a model deficiency: the cortical component of the excitatory current ( “Sub” ) was orientation-tuned , but poorly modulated in time ( Figs 4B and S7A ) , whereas in the experiment it was modulated at the grating frequency and matched the phase of LGN input [17] . The like-to-like connectivity we used enables orientation tuning of the “Sub” current , but does not take phase into account . Thus , for a given target cell , all source cells supply different phases , explaining why the “Sub” current was not modulated at the grating frequency . The fact that in the experiment it is modulated in phase with LGN current suggests more sophisticated like-to-like connectivity rules that include information about phase [17] . This is consistent with data from L2/3 [25 , 24] showing that similarity in orientation preference is a good but not only predictor of connectivity and with theories ( e . g . , [28] ) that lateral connections optimally enhance features such as extended lines . A further simplification of our network model , replacing all biophysical neurons by LIF units , preserved most general trends in features of neuronal activity , but quantitative agreement with experiment suffered . The levels of orientation selectivity and sparsity of responses were altered ( Fig 6B , 6D and 6F ) . The oscillations at the level of population activity were eliminated when instantaneous synaptic kinetics was used and partially rescued with non-instantaneous synapses ( Fig 6C ) . Although further exploration is necessary to find out how one can match the oscillations spectrum of the biophysical model with a simpler network model , this result supports the importance of synaptic kinetics for generating oscillations ( e . g . , [11 , 14] ) . Perhaps the most substantial difference with the biophysical model was observed in the magnitude of cortical amplification ( Fig 6E ) , hinting that mechanisms shaping activity and computation in the L4 circuit may not be well represented by these simpler models . Overall , however , it is clear that the simplified models captured well the major aspects of network dynamics . This suggests that in many cases more complex models may not be necessary to gain important insights about brain networks or to fit the relationship between visual stimuli and neuronal responses ( just like in the stationary domain , a single hidden-layer network containing a finite number of neurons can approximate any measurable function under some mild assumptions [62 , 63] ) . However , it is likely that neuronal point-model approximations will be insufficient to capture dendritic nonlinearities , such as synaptic saturation , veto-type inhibition , NMDA or calcium spikes and so on , that may be critical to neuronal selectivity and plasticity . For the discrepancies that we did observe , many reasons are possible . These include , for example , dendritic filtering and distinct distributions of synapse types over dendritic trees in the biophysical , but not all-LIF models; imperfect match of synaptic kinetics ( the IntFire1 model used instantaneous synapses and IntFire4 a mixture of single- and double-exponential synapses , by contrast to double-exponential synapses of the biophysical case , see Methods ) ; and much more sophisticated somatic mechanisms in the biophysical model ( mostly Hodgkin-Huxley based [31] , as opposed to simple integrate-and-fire mechanisms in both IntFire1 and IntFire4 ) , which enabled spike adaptation , after-spike hyper- and depolarization , and other nonlinear phenomena . Exploring how these different factors contribute to specific differences under systematic , data-driven constrains of our model network is an exciting direction for future studies , which will be enabled by the code and model data that we publicly share ( see SI ) . For example , if IntFire1 and IntFire4 are not sufficient to capture all aspects of dynamics and mechanisms , would the performance improve substantially in the case where all neurons are still represented as points , but are supplied with Hodgkin-Huxley mechanisms ? This could shed light , for example , on roles of synapse distributions over dendritic arbors . There are also neuron models that employ much simplified mechanisms that can account for such phenomena as spike adaptation or dendritic computations ( e . g . , [64 , 65 , 66] ) , and such models may furnish a better match with the biophysical simulations and experiments . Furthermore , although we observed mismatch in certain features between the all-LIF and biophysical models , we cannot claim the all-LIF models are incapable of reproducing these features . A different optimization procedure than our relatively straightforward training on a small dataset ( a short trial of grating and another of spontaneous activity , see Methods ) could result in a better match . These considerations , and the fact that we did obtain mostly consistent results , support broad applicability of point-neuron models . Similar conclusions were reached in another recent study [30] , even though their coarse-graining approach was different ( the simpler model was optimized to match results of the complex model , whereas we optimized both the simple and complex models to match experiment ) and the point neuron models were more complex than ours ( the “Generalized Integrate-and-Fire” model vs . our simple Leaky Integrate-and-Fire model ) . Thus , while our results exhibiting , e . g . , a poor match of cortical amplification , show that it is important to exercise caution in applying simplified models especially when one attempts to gain quantitative understanding of mechanisms , it is likely that point-neuron models will be sufficient to describe major qualitative features of cortical networks . Ultimately , it is useful to apply a variety of multi-granular modeling techniques to study brain circuits–from abstract multi-layered , machine learning networks to population coding statistical models , point models , and biophysically detailed models reported here [67 , 68] . As mentioned above , we here describe the biophysical model as it is the easiest to directly interpret in terms of biological variables that can be queried experimentally and that yields a concise summary of our present-date understanding of the anatomy and the physiology of L4 ( and the limits of this knowledge ) .
All experiments , animal treatment and surgical protocols were carried out with authorization from the Institutional Animal care and Use Committee of the Allen Institute in accordance with the Public Health Service ( PHS ) Policy on Humane Care and Use of Laboratory Animals . The software code used to build models , perform simulations , and perform analysis is included in this published article in its supplementary files and at https://github . com/AllenInstitute/arkhipov2018_layer4 . All simulations were performed with the parallelized code ( available in Supplementary Files ) written in Python 2 . 7 and employing NEURON 7 . 4 [36] as a simulation engine . Simulations were carried out typically on 120 CPU cores ( Intel Xeon CPU E5-2620 v2 , 2 . 10GHz; 128 GB RAM per a 24-core node ) , requiring ~1 hour to simulate 1 s . Visual stimuli for the simulations , as well as experiments ( see below ) , were chosen primarily from the stimulus set in the Allen Brain Observatory [69] . Data analysis was performed on all simulations carried out for a given stimulus , as described in S2 Table . That typically involved at least two independent models ( usually three models ) and multiple trials . Data from these multiple models and trials were typically combined together to produce summary plots . Summary figures that present box plots of various analyzed quantities adhere to the following standard . The box bottom and top represent the lower boundary of the second quartile of the data and the top boundary of the third quartile , respectively , thus marking the inter-quartile range ( IQR ) ; the median is shown in red . Whiskers mark +/-1 . 5 IQR from the bottom and top of the plot , as long as there are data points in that range . The rest of the data ( outliers ) are shown as separate dots or other symbols . In cases when the plot range does not include the whole span of the data ( which was sometimes necessary due to the log-normal-like distribution of the data points over several orders of magnitude ) , the outliers outside of the plot range are indicated by an arrow . The numbers next to such arrows ( “N1/N2” ) indicate the number of such outliers ( N1 ) and the total number of data points ( N2 ) . The mean and s . e . m . of the data are shown on these plots as circles with error bars ( thick black lines ) . | How can we capture the incredible complexity of brain circuits in quantitative models , and what can such models teach us about mechanisms underlying brain activity ? To answer these questions , we set out to build extensive , bio-realistic models of brain circuitry by employing systematic datasets on brain structure and function . Here we report the first modeling results of this project , focusing on the layer 4 of the primary visual cortex ( V1 ) of the mouse . Our simulations reproduced a variety of experimental observations in response to a large battery of visual stimuli . The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 –in particular , the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion . Simplification of neuronal models led to specific deficiencies in reproducing experimental data , giving insights into how biological details contribute to various aspects of brain activity . To enable future development of more sophisticated models , we make the software code , the model , and simulation results publicly available . | [
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] | 2018 | Visual physiology of the layer 4 cortical circuit in silico |
Influenza A virus ( IAV ) is a seasonal pathogen with the potential to cause devastating pandemics . IAV infects multiple epithelial cell subsets in the respiratory tract , eliciting damage to the lungs . Clearance of IAV is primarily dependent on CD8+ T cells , which must balance control of the infection with immunopathology . Using a virus expressing Cre recombinase to permanently label infected cells in a Cre-inducible reporter mouse , we previously discovered infected club cells that survive both lytic virus replication and CD8+ T cell-mediated clearance . In this study , we demonstrate that ciliated epithelial cells , type I and type II alveolar cells can also become survivor cells . Survivor cells are stable in the lung long-term and demonstrate enhanced proliferation compared to uninfected cells . When we investigated how survivor cells evade CD8+ T cell killing we observed that survivor cells upregulated the inhibitory ligand PD-L1 , but survivor cells did not use PD-L1 to evade CD8+ T cell killing . Instead our data suggest that survivor cells are not inherently resistant to CD8+ T cell killing , but instead no longer present IAV antigen and cannot be detected by CD8+ T cells . Finally , we evaluate the failure of CD8+ T cells to kill these previously infected cells . This work demonstrates that additional cell types can survive IAV infection and that these cells robustly proliferate and are stable long term . By sparing previously infected cells , the adaptive immune system may be minimizing pathology associated with IAV infection .
Influenza A virus ( IAV ) is a negative sense segmented RNA virus causing significant morbidity and mortality annually . IAV has a broad tropism in the respiratory tract infecting club cells , ciliated cells , type I and type II alveolar cells among others [1] . IAV drives destruction of infected cells directly through lytic virus replication and indirectly from the innate and adaptive immune response’s efforts to eliminate the infection . However , we and others , have previously shown IAV infected pulmonary epithelial cells can survive acute IAV infection [2–4] . Survivor cells express a robust interferon simulated gene ( ISG ) signature , facilitating a transient non-specific antiviral environment in the lung , and protecting against secondary viral infection [2 , 4] . Despite the extensive damage caused by viral replication and antiviral immune responses , the lung is able to recover through significant remodeling and repair . While the turnover of pulmonary epithelial cells in the steady state is slow , after injury epithelial cells undergo proliferation to prevent vascular leakage and differentiation to restore lung function [5] . In addition , rare pulmonary progenitor cells are also critical for the repair of the upper and lower airway [6–9] . CD8+ T cells are critical for the control of acute IAV infections . CD8+ T cells use perforin and granzyme as well as FasL and TRAIL to specifically kill IAV infected cells [10–12] . There are several mechanisms by which cells can evade CD8+ T cell-mediated control . Cells can downregulate MHC-I , a strategy employed by quiescent stem cells [13] , tumor cells , and cells infected with some viruses [14] . The inhibitory ligand , PD-L1 , can bind to PD-1 on CD8+ T cells and inhibit T cell effector functions . During IAV infection epithelial cells upregulate PD-L1 resulting in decreased IAV titers [15 , 16] . CD8+ T cells can also control virus infected cells without destroying the cell . After hepatitis B virus infection , CD8+ T cells secrete IFN-γ and TNF-α to stop viral replication and cure infected hepatocytes [17 , 18] . It is unclear if survivor epithelial cells are inherently resistant to cytolysis , downregulate MHC-I , or if non-cytolytic control of IAV facilitates their survival and evasion from CD8+ T cell-mediated elimination . In this study we demonstrate that in addition to club cells , ciliated epithelial cells , type I and type II alveolar cells can survive IAV infection . Surviving cells undergo enhanced proliferation compared to uninfected cells after IAV clearance and survivor cells are stable in the lungs until at least 99 days post infection . When we investigated how the adaptive immune system fails to kill survivor cells , we discovered that survivor cells are not inherently resistant to CD8+ T cell killing , do not employ active mechanisms to dampen the response and demonstrate that they lack IAV antigen . These results suggest that survivor cells rapidly clear the infection to evade killing from the adaptive immune system . This study sheds new light on the biology of cells that survive acute IAV infection .
We generated a H1N1 strain ( A/Puerto Rico/8/1934 ) of IAV expressing Cre recombinase ( Cre ) ( IAV_Cre ) downstream of the PA gene . IAV_Cre showed similar replication kinetics to the wt PR8 virus ( S1A and S1B Fig ) and was cleared between 10 and 12 days post infection ( dpi ) ( S1C Fig ) . We had previously determined that club cells were the primary cell type surviving IAV infection [2] . However , digestion , processing , sorting and lysing of lung epithelial cells for RNA-seq could bias cell types detected . To determine if a different digestion method could result in a greater breadth of lung epithelial cells recovered , we compared two distinct digestion methods for flow cytometry analysis [2 , 19] . Transgenic mice containing a Cre-inducible fluorescent reporter protein were infected with IAV_Cre . With a dispase-based lung digestion , 6-fold more epithelial cells and 10-fold more reporter+ epithelial cells were recovered from the lung ( S1D and S1E Fig ) . Therefore , we used this digestion method for all experiments analyzing lung epithelial cells by flow cytometry . We next determined the presence of reporter+ cells by microscopy and flow cytometry . Reporter+ lung epithelial cells were observed by flow cytometry at multiple time points after IAV_Cre infection ( Fig 1A and S1F Fig ) . There were large numbers of reporter+ epithelial cells early after infection ( 4–8 dpi ) , which decreased by 12 dpi , likely to due to lytic IAV replication and CD8+ T cell mediated killing ( Fig 1B ) . The number of reporter+ epithelial cells remained stable after clearance , and long-term survivor cells could be detected as late as 99 dpi ( Fig 1B ) . We observed similar trends for reporter+ cells detected by microscopy ( Fig 1C and 1D ) . Reporter+ cells were observed both in the large airways and in the lung parenchyma ( Fig 1C ) . At later time points ( 15–30 dpi ) reporter+ cells were frequently observed in clusters with other reporter+ cells . The percentage and number of reporter+ cells with a reporter+ neighbor cell increased from 7 to 30 dpi ( Fig 1E and S1G–S1H Fig ) . As the expression of the fluorescent reporter is a permanent heritable change in the DNA , a reporter+ cell with a reporter+ neighbor may be the result of a parental infected cell undergoing division . Surviving cells could have been infected during the first wave of replication or later after induction of antiviral immune responses , or a combination thereof . To determine if survivor cells could be generated late during IAV infection we infected Cre-inducible reporter mice with wt PR8 , then infected with IAV_Cre at 5 or 7 dpi and quantified reporter+ epithelial cells on 10 dpi . We observed detectable levels of reporter+ cells when IAV_Cre was delivered at 5 or 7 dpi ( Fig 1F ) , demonstrating that survivor cells can be generated late during the acute phase of IAV infection , after the induction of antiviral immune responses . We next assessed if other cell types are able to survive IAV infection and evade CD8+ T cell mediated killing , or if survival is a property specific to club cells , as suggested by our previous study [2] . We stained for CD24 ( ciliated cells ) , and podoplanin ( type I alveolar cells ) , to assess cells by flow cytometry ( S2 Fig ) . Total ciliated cells and type I alveolar cells are relatively stable after IAV infection ( S2B , E ) . We observed reporter+ cells expressing CD24 or podoplanin at multiple timepoints ( Fig 2A and 2B , S2C and S2F Fig ) . We assessed two other cell types by fluorescence microscopy , club cells ( CC10+ ) and type II alveolar cells ( SPC+ ) ( S3 Fig ) . We observed reporter+ cells expressing CC10 or SPC after clearance ( Fig 2C and 2D and S3E and S3F Fig ) . Together these data demonstrate that in addition to club cells diverse pulmonary epithelial cells can survive acute IAV infection and CD8+ T cell-mediated killing . We observed that the number of survivor cells increased with time ( Fig 1B ) , and are present in clusters after viral clearance ( Fig 1E ) , indicative of cell division . To assess if the survivor cells are proliferating , we treated mice with BrdU for 6 days prior to euthanasia . We observed a low level of BrdU incorporation in naïve lung epithelial cells ( Fig 3A and 3B and S4A and S4B Fig ) . After IAV clearance the level of BrdU incorporation increased in both wt PR8 and IAV_Cre infected mice when compared to naïve epithelial cells ( Fig 3B ) . Interestingly , a greater percentage of survivor cells had incorporated BrdU than total lung epithelial cells at both 21 and 27 dpi . These data demonstrate that survivor cells are proliferating after IAV clearance , and are doing so at a higher rate than bystander cells after clearance . These data may explain the long-term stability of the surviving cell populations . To determine if the immune system exerts bias in the cell subsets that survive IAV infection , we assessed which cells are infected and survive in a primary mouse epithelial cell culture devoid of innate and adaptive immune cells . Tracheal epithelial cells from reporter mice were differentiated at air liquid interface , and infected with IAV_Cre and collected at 1 and 8 dpi , to quantity infected and survivor cell , respectively . In this system , IAV is cleared by 8 dpi ( S5D Fig ) . Using confocal microscopy we demonstrated that the cell types that were initially infected went on the become survivor cells at similar ratios ( S5A–S5C Fig , Fig 4A and 4B ) . These data suggest that in the absence of immune cell killing , a broad array of cell types can survive acute lytic replication . To determine if these results were consistent in an in vivo model , we assessed reporter+ cells in mice treated with CD4 and CD8α depletion antibodies or control antibody . These data demonstrated that while the CD8+ T cell depletion increased the overall number of reporter+ cells there were no major alterations in surviving cell populations ( Fig 4C and S5E Fig ) . Together these data suggest that CD8+ T cells do not fail to survey and kill specific cell types . To understand how survivor cells circumvent destruction by IAV-specific CD8+ T cells , we assessed our RNA-seq data for molecules known to protect cells from T cell killing [2] . We observed an upregulation of CD247 ( PD-L1 ) mRNA in reporter+ cells at 5 dpi ( S6A Fig ) [2] . PD-L1 is an inhibitory ligand for PD-1 , expressed on CD8+ T cells , which suppresses CD8+ T cell signaling and killing of target cells [20] . We confirmed increased surface protein expression of PD-L1 on reporter+ cells in the lung on 10 and 13 dpi during CD8+ T cell-mediated clearance ( Fig 5A ) . Additionally , IAV-specific CD8+ T cells in the lung expressed high levels of PD-1 ( Fig 5B ) . To determine if survivor cells use PD-L1 to inhibit CD8+ T cell-mediated killing , we treated IAV infected mice with PD-L1 blocking antibody . We chose to block PD-L1 instead of PD-1 , to avoid potential interactions between PD-1 and PD-L2 , which is expressed on cells other than pulmonary epithelial cells [20] . PD-L1 blocking antibody was given on 2 , 5 and 8 dpi and assessed survivor cells at 10 dpi ( S6B Fig ) . There was no difference in the number of survivor cells between control treated and anti-PD-L1 treated mice ( Fig 5C ) . Even when we performed a dual blockade of PD-L1 and PD-1 , there was no difference in the number of survivor cells ( S6C Fig ) . These results demonstrate that survivor cell evasion of CD8+ T cell killing is not mediated by PD-L1-PD-1 interactions and suggest there is another mechanism of escape . Viruses can suppress the surface expression of MHC-I to evade CD8+ T cell-mediated clearance . We assessed the mRNA expression of MHC-I ( β2M ) in lung reporter- and reporter+ epithelial cells after IAV_Cre infection . Greater b2m mRNA levels were observed on reporter+ cells , compared to reporter- epithelial cells ( S6D Fig ) [2] . MHC-I ( H-2Kb ) was also upregulated on the surface of reporter+ cells , when compared to total epithelial cells after infection ( Fig 6A ) . It is possible that survivor cells clear IAV infection and no longer display IAV peptide-MHC-I , thereby rendering them invisible from IAV-specific CD8+ T cells . It is also possible that survivor cells are inherently resistant to cytolysis . To assess whether antigen-specific CD8+ T cells can kill survivor cells we used a system where IAV_Cre permanently induces a CD8+ T cell antigen . To accomplish this , we utilized the just eGFP death-inducing ( JEDI ) CD8+ T cell system , and Cre-inducible eGFP-expressing mice [13 , 21 , 22] . JEDI T cells recognize eGFP on MHC-I ( H-2Kd ) , and specifically deplete eGFP expressing cells [13 , 21] . To use JEDI T cells in our system we crossed B10 . D2 ( H-2Kd ) mice to Cre-inducible eGFP expressing mice [21 , 22] . F1 mice express H-2Kd in all cells and will express eGFP in cells infected with IAV_Cre . Reporter+ cells will express eGFP even after IAV is cleared , so we can determine if the survivor cells are resistant to cytolysis or lack viral antigen . H-2Kd reporter mice were infected with IAV_Cre , and JEDI T cells were transferred and stimulated with Adenovirus expressing eGFP ( AD_eGFP ) and eGFP200-208 peptide . Survivor cells were then assessed at 10 dpi ( S6E Fig ) . As a control , AD_eGFP was administered to mice and eGFP expression in the lung was restricted to the endothelial ( CD31+ ) compartment and was absent from epithelial cells ( S6F Fig ) . Therefore , AD_eGFP priming will not induce CD8+ T cell-mediated killing of pulmonary epithelial cells . While the addition of JEDI T cells did not alter the number of reporter- cells in the lung there was a significant decrease in reporter+ cells ( Fig 6B and 6C ) . These data demonstrate that survivor cells are susceptible to killing by antigen-specific CD8+ T cells , and that survivor cells are not inherently resistant to cytolysis . We previously demonstrated that survivor cells do not have infectious virus by 10 dpi [2] . However , antigen can have a long half-life and these cells may still present IAV peptide . To determine if survivor cells lack IAV-antigen , we performed an in vitro proliferation assay . We generated a new reporter virus containing the GP33 epitope from lymphocytic choriomeningitis virus glycoprotein . The new reporter virus infects epithelial cells to a similar level as the IAV_Cre , and induces a robust GP33-specific CD8+ T cell response ( S6G and S6H Fig ) . By incubating in vitro activated GP33-specific P14 TCR-Tg CD8+ T cells with sorted lung CD45- CD31- reporter+ cells from IAV_Cre/NA_GP33 infected mice , we can directly assess if the epithelial cells are presenting antigen . P14 CD8+ T cells mixed with 4 dpi reporter+ cells undergo division , indicating these epithelial cells express IAV antigen ( S6I Fig ) . In contrast , 10 dpi reporter+ cells fail to induce P14 CD8+ T cell division ( S6I Fig ) . These data suggest that loss of IAV-antigen may account for the ability of survivor cells to avoid detection and killing by IAV-specific CD8+ T cells .
In this study , we sought to elucidate how epithelial cells that survive the lytic phase of IAV infection are able to avoid CD8+ T cell mediated killing . We observed that multiple epithelial cell types could survive IAV infection , suggesting that survival is not a cell specific mechanism , and that CD8+ T cells are able to survey all areas of the lung . Survivor cells upregulate PD-L1 , but do not use PD-L1 to suppress CD8+ T cell killing . Survivor cells are not inherently resistant to CD8+ T cell killing , do upregulate MHC-I , and no longer express IAV antigen by the time they are under CD8+ T cell surveillance . In our original study , we identified club cells as the primary lung epithelial cell type that survived IAV infection . By incorporating fluorescent microscopy and using an alternative lung digestion method for flow cytometry analysis , we determined that multiple distinct cell types could survive IAV infection . We observed that in addition to club cells , ciliated cells , type I and type II alveolar cells can become survivor cells . We found increased numbers of ciliated epithelial cells and type I alveolar cells long-term compared to acute infection . However , it is difficult to determine if increased presence of these cell types represents a bias in the cells that can survive , increased proliferation , or preferential differentiation into these cell types . In a similar system , Schwemmle and colleagues also observed type I and type II survivor cells [3] . These cells are widespread throughout the lung , requiring CD8+ T cells to survey not only a broad array of cells types , but anatomical locations . The indelible Cre-inducible reporter system allowed us to follow survivor cells out to 99 dpi , demonstrating the longevity and stability of survivor cells . This may be partially due to the slow turnover of many lung epithelial cells [5] and/or capacity to proliferate . As transient antiviral immunity is mediated by surviving club cells [2 , 4] , we propose that this immunity is lost as survivor cells proliferate and daughter cells lose the ISG expression profile that was acquired during infection . IFN signaling can induce epigenetic changes [23] , but it is unclear if these epigenetic changes are heritable in lung epithelial cells . One interesting finding of our study is increased proliferation of survivor cells compared to bystander cells within the same lung at multiple different time points . Although the benefit of this proliferation is unclear , one potential advantage of increased survivor cell division is that it could aid in the repopulation of the respiratory epithelium . Proliferation of phenotypically altered survivor cells could also alter the pulmonary cytokine and chemokine environment and modulate susceptibility to secondary viral infection [4] . Survivor cells may also proliferate in order to enhance the repair of DNA damage that occurs during viral infection . Some DNA damage repair pathways only function during cell proliferation and DNA repair has been recently demonstrated to help aid in infected cell survival [24] . IAV uses several distinct mechanisms to disrupt cell cycle progression and cell division has been shown to decrease IAV replication [25–27] . Survivor cells could initiate cell division as an antiviral mechanism to blunt virus replication and to dilute antigen to prevent killing by the adaptive immune system . Interestingly , we have previously found that cells infected early have increased activation of genes involved in cell cycle [19] . The cellular events that occur for a survivor cell to be generated are still unclear . Survivor cells can be generated early during infection as well as later during the acute phase after type I and III IFN responses have been generated . As small amounts of Cre recombinase are needed to express the fluorescent reporter , infected cells undergoing abortive replication may turn on the reporter . However , these cells are still impacted by IAV replication as demonstrated by altered ISG profiles [2 , 4] . These cells could still express IAV antigen and be targeted by CD8+ T cells . The efforts of the immune system to control IAV drive significant pathology within the lung [28 , 29] . Eliminating the virus without killing infected cells could be one way of ameliorating this affect . CD8+ T cells have been shown to control hepatitis B virus , sindbis virus , lymphocytic choriomeningitis virus , and simian immunodeficiency virus in the absence of cell lysis [17 , 18 , 30–33] . Another method to curb pathogenic T cell responses in the lungs is though engagement of inhibitory ligands . Airway epithelial cells increased the expression of PD-L1 after IAV infection [15 , 16] . PD-L1 blockade enhanced CD8+ T cell-mediated clearance of infected cells [16] , increased CD8+ T cell numbers and reduced IAV titers [15] . Similar results were observed in PD-1 KO mice during acute IAV infection [34] . Therefore , we hypothesized that this axis may be responsible for protecting surviving cells . However , PD-L1 blockade or systemic blockade of PD-L1 and PD-1 did not result in increased frequency or number of surviving cells . This could be a result of redundancy in inhibitory ligand mediated suppression as others have demonstrated [35 , 36] . While inhibitory ligands clearly play a role in IAV clearance and pathogenesis they do not appear to be involved in promoting survival of previously infected cells . Our data demonstrate that survivor cells are not inherently resistant to CD8+ T cell killing . We propose that survivor cells no longer express IAV antigen , thereby rendering the survivor cell invisible to an antigen-specific CD8+ T cell . This is consistent with our in vitro proliferation assay where only reporter+ cells from 4 , and not 10 , dpi could activate IAV antigen-specific CD8+ T cells . There are several mechanisms that could drive this . Cell division could dilute the amount of antigen between two daughter cells , survivor cells could have cleared IAV protein/antigen , and/or survivor cells could have had an abortive infection . Interestingly , while in the presence of JEDI T cells and constitutive antigen there was a substantial decrease in the number of survivor cells , but not all were eliminated . These remaining cells could evade CD8+ T cell cytolysis through loss of antigen processing and presentation pathways and/or multiple redundant inhibitory mechanisms . Together the data presented herein demonstrate that a broad array of epithelial cells are capable of surviving acute lytic virus replication . As these cells are not resistant to killing , these epithelial cells must rapidly clear the infection to evade CD8+ T cell mediated cytolysis . This antiviral strategy may be critical for preventing immunopathology .
C57Bl/6J , B6 . Cg-Gt ( ROSA ) 26Sortm14 ( CAG-tdTomato ) Hze/J , B6 . 129 ( Cg ) -Gt ( ROSA ) 26Sortm4 ( ACTB-tdTomato , -EGFP ) Luo/J[22] , and B10 . D2-Hc0 H2d H2-T18c/oSnJ ( B10 . D2 ) mice were purchased from The Jackson Laboratory . JEDI mice were obtained from Brian Brown ( Mount Sinai ) . CD45 . 1 P14 TCR-Tg mice were provided by Dr . Masopust ( University of Minnesota ) . For IAV infection , mice were anesthetized using a weight-based dose of ketamine/xylazine delivered intraperitoneally ( i . p . ) . Mice were infected intranasally with 40 plaque forming units ( PFUs ) of PR8 , 100 PFU of IAV_Cre , 500 PFU of IAV_Cre/NA_GP33 or 40 PFU IAV_NA_GP33 . For sequential infections , mice were infected with 40 PFU of PR8 or 100 PFU of IAV_Cre . PR8 infected mice were infected on 5 or 7 dpi with 100 , 000 PFU of IAV_Cre . During infection , all mice having weight loss exceeding 25% of their starting weight were sacrificed . For cell proliferation experiments , mice were injected i . p . with 1 mg BrdU ( Sigma ) once , and given BrdU in drinking water at 0 . 8 mg/mL for 6 days prior to euthanasia . At indicated dpi lungs were harvested and lung cells were processed and surface staining was performed . Cells were fixed and permeabilized with the BD Cyotfix/Cytoperm kit as per the manufacturer’s instructions , incubated with 1 mg/mL DNase I ( Sigma-Aldrich ) for 60 min at 37°C washed and stained with anti-BrdU ( clone MoBU-1 ) ( Life Technologies ) . For CD4 and CD8 depletion experiments , IAV infected mice were treated on 2 , 5 , and 8 dpi with 100 μg of control IgG ( SF128 ) or 100 μg of anti-CD4 ( GK1 . 1 ) and 100 μg of anti-CD8 ( 2 . 43 ) . For PD-L1 blockade experiments , IAV infected mice were treated on 2 , 5 and 8 dpi with 250 μg of control IgG ( SF128 ) , anti-PD-L1 ( M1H6 ) antibody , or anti-PD-L1 and PD-1 ( J43 ) delivered i . p . [37] . Single cell suspensions were washed once with 1 X PBS and stained with a fixable viability dye for 30 min on ice , Ghost Dye™ Red 780 or UV 450 ( Tonbo Biosciences ) . Cells were washed one time with FACS buffer ( ice-cold HBSS supplemented with 2% bovine serum ) , stained with surface marker Abs , then washed twice before multiparameter flow cytometric detection on a BD LSRFortessa ( Becton Dickinson , San Jose , CA ) . H-2Db-PA224 ( SSLENFRAYV ) and H-2Db-NP366 ( ASNENMETM ) tetramers were obtained from the National Institutes of Health Tetramer Core Facility . H-2Db-GP33 tetramer was a gift from Dr . Vezys . Directly conjugated fluorescent Abs used include: CD24 ( clone M1/69 ) , CD44 ( clone IM7 ) , CD45 ( alone 30-F11 ) , CD45 . 1 ( clone A20 ) , CD45 . 2 ( clone 104 ) , and CD279 ( PD-1 ) ( clone 29F . 1A12 ) ( Biolegend ) ; CD31 ( 390 ) ( BD Bioscience ) ; CD247 ( PD-L1/B7-H1 ) ( clone M1H5 ) , EpCAM ( clone G8 . 8 ) and Podoplanin ( eBio8 . 1 . 1 ) ( eBioscience ) ; CD8-α ( clone 53–6 . 7 ) and F4/80 ( clone BM8 ) ( Tonbo ) . B10 . D2 mice were lethally irradiated ( 1100 rad ) and were injected with 5 x 106 bone marrow cells isolated from JEDI mice . At least 8 weeks later , spleen and lymph nodes ( cranial , axillary , brachial , inguinal and mesenteric ) were harvested , RBCs were lysed and CD8+ T cells were negatively selected using the mouse CD8+ T cell isolation kit from Stem Cell Technologies following the manufacturer’s instructions . 1 x 106–5 x 106 JEDI T cells were transferred intravenously ( i . v . ) into IAV_Cre infected B6 . 129 ( Cg ) -Gt ( ROSA ) 26Sortm4 ( ACTB-tdTomato , -EGFP ) Luo/J x B10 . D2 F1 mice . To activate JEDI CD8+ T cells , recipient mice were injected i . v . with 1 x 108–2 x 108 transducing units of Adenovirus encoding enhanced green fluorescent protein ( AD_eGFP ) with 10 μg of eGFP200-208 peptide . Mice were euthanized and lungs were inflated via intratrachial injection with 2 mL dispase ( Corning ) and 0 . 5 mL 1% low melt agarose ( Lonza ) and were incubated under an ice pack for 2 minutes . Lungs were removed , transferred to 1 mL dispase and incubated at room temperature for 45 minutes . Lungs were minced into small pieces , and transferred to DMEM with DNaseI ( Sigma-Aldrich ) 95 U/L and rocked for 10 minutes at room temperature . Lungs were homogenized in a GentleMACS dissociator , filtered through 70 μm filter mesh to generate a single cell suspension , RBCs were lysed and cells were stained for flow cytometry as described above . Mice were euthanized and lungs were harvested , minced into small pieces and washed twice with harvest buffer ( ice-cold RPMI 1640 supplemented with 5% bovine serum , 4 mM L-glutamate and 10 mM HEPES ) . Lungs were incubated in a solution of RPMI 1640/ 10% bovine serum/ 2 mM MgCl2/ 2mM CaCl2/ 10 mM HEPES/ 4 mM L-glutamate medium containing 100 U/mL of collagenase type I ( Worthington ) for 45 min at 37°C . Lung pieces were then incubated in a solution of RPMI 1640/ 10% bovine serum/ 10 mM HEPES/ 4 mM L-glutamate medium containing 1 . 3 mM EDTA ( Calbiochem ) for 45 min at 37°C . Single cell suspensions of all tissues were generated using a Gentle MACS dissociator and cells were passed through 70 μm filter mesh , RBC lysed , and stained for flow cytometry as described above . Spleen and lymph nodes ( cranial , axillary , brachial , inguinal and mesenteric ) were harvested from a CD45 . 1 P14 TCR-Tg mouse . RBCs were lysed and CD8+ T cells were negatively selected using the mouse CD8+ T cell isolation kit from Stem Cell Technologies following the manufacturer’s instructions . 1 x 106 P14 T cells were placed in 24 well plates pre-treated with anti-CD3 and anti-CD28 ( 10 μg/mL of each ) . P14 T cells were incubated at 37°C for 48 hours in the presence of 0 . 0025 μg/mL IL-12 ( R&D Systems ) . After 48 hours , cells were washed and plated at 5 x 105 cells/mL in the presence of 10 U/mL recombinant human IL-2 for 24 hours . P14 T cells were labeled with CellTrace Violet ( CTV; Invitrogen ) per the manufacturer’s instructions . P14s were mixed at a 10:1 ratio with sorted CD45- CD31- reporter+ cells from the lungs of B6 . 129 ( Cg ) -Gt ( ROSA ) 26Sortm4 ( ACTB-tdTomato , -EGFP ) Luo/J mice infected with IAV_Cre/NA_GP33 4 or 10 days prior . After 48 hours , cells were stained for flow cytometry . Cell cultures were maintained in RPMI 1640 supplemented with 10% FCS . 4mM L-glutamine , 0 . 1 mM non-essential amino acids , 1 mM sodium pyruvate , 100 U/mL penicillin and streptomycin and 10 mM HEPES . In-Fusion primers ( Takara Bio , Inc . ) were designed for insertion of a PA2A site followed by Cre and the complete 5’ vRNA packaging signal ( 184 nts ) into PA of influenza A/Puerto Rico/8/1934 ( PR8 ) [2 , 38] . PA_Cre was then cloned into pDZ rescue system via In-Fusion HD cloning . NA_GP33 was inserted into the stalk of NA at a position amenable to insertion via infusion cloning as described [39 , 40] . All viruses were rescued via HEK293T transfection and amplified in embryonated chicken eggs as previously described [41] . Rescued viruses were sequence confirmed and titered on Madin-Darby canine kidney ( MDCK ) cells ( ATCC ) . MDCK cells were maintained in Dulbecco’s modified Eagle medium ( DMEM ) with 10% fetal bovine serum ( FBS ) and 1% penicillin-streptomycin ( pen-strep ) . Infections were carried out in infection medium ( phosphate buffered saline [PBS] with 10% CaMg , 1% pen-strep , 5% bovine serum albumin [BSA] ) at 37°C for 1 hr . Infection medium was replaced with an agar overlay ( MEM , 1 mg/mL tosylsulfonyl phenylalanyl chloromethly ketone [TPCK] trypsin , 1% DEAE-dextran , 5% NaCO3 , 2% agar ) , and cells were cultured at 37°C for 40 h and then fixed with 4% formaldehyde . Blocking and immunostaining were done for 1 hr at 25°C in 5% milk using the following antibodies: polyclonal anti-IAV PR8/34 , 1:5 , 000 ( V301-511-552 ) , and peroxidase rabbit anti-chicken IgG , 1:5 , 000 ( 303-035-003; Jackson Immuno Research ) . TrueBlue peroxidase substrate ( 50-647-28; Kirkegard & Perry Laboratories ) was used as directed for detection of virus plaques . B6 . Cg-Gt ( ROSA ) 26Sortm14 ( CAG-tdTomato ) Hze/J mice previously infected with PR8 or IAV_Cre were euthanized at indicated dpi . Lungs were harvested and fixed in 2% PFA for two hours , immersed overnight in 30% sucrose , inflated and flash frozen in optimum cutting temperature compound ( O . C . T . ) . 7 μm sections were cut from each block with a Leica CM 1860 cryostat and stained prior to imaging on a Leica DM6000B EPI fluorescent microscope ( violet LED ) . Histo-cytometry was performed using Imaris ( Bitplane ) by creating surfaces for the reporter+ cells ( tdTomato ) and markers ( SPC or CC10 ) , and performing a distance transformation to detect double positive cells . Reporter+ cells were manually counted in ImageJ . Neighbor cells were defined as two or more reporter+ DAPI+ cells within the proximity of one DAPI-stained nucleus to one another . Abs used were: CC10 ( polyclonal ) ( Abcam ) , proSP-C ( polyclonal ) ( Millipore Sigma ) , goat anti-rabbit ( polyclonal ) ( ThermoFisher Scientific ) . Tracheal epithelial cells were collected from the lungs of Cre-inducible reporter mice and plated on 4 μm pore transwell membranes ( Corning ) . Cultures were grown in mTec+ for 2 days of proliferation then switched to mTec-serum free media for differentiation . After 21 days of differentiation , the pseudostratified cultures were infected with IAV_Cre then harvested at 1 dpi ( to measure actively infected cells ) and 8 dpi ( to measure survivor cells after the clearance of viral infection ) . These cultures were then fixed with 2% PFA and antigen retrieval was performed in a citrate buffer . Samples were then stained with antibodies for active infection ( HA , PY102 ) , basal stem cells ( Krt5 , Biolegend , clone Poly19055 , cat 905501 ) , ciliated cells ( FoxJ1 , eBioscience , clone 2A5 , cat 14–9965 ) , secretory cells ( SSEA-1 , Biolegend , clone MC-480 , cat 125611 ) using tdTomato as an endogenous marker of infection . Wholemount membranes were mounted in Prolong Diamond ( Life Technologies ) then imaged on a SP5 inverted confocal microscope ( Leica ) and images processed using Fiji . GraphPad Prism ( version 6 . 0h , GraphPad Software , La Jolla , CA ) was used to determine statistical significance using as Student unpaired two-tailed t test . A p value of < 0 . 05 was considered statistically significant . Care and use of the animals was in accordance with The guide for the Care and Use of Laboratory Animals from the National Research Council and the USDA Animal Care Resource Guide . All experimental protocols involving the use of mice were approved by the Institutional Animal Care and Use Committee at the University of Minnesota ( protocol: 1708-35040A . approved 09/14/2017; expires 09/13/2020 ) . Eggs were obtained from Charles River Laboratories and were grown at 37°C until 12 days of embryonation . | Influenza A virus is a seasonal respiratory pathogen that can cause severe lung damage and death . We previously made the discovery that cells infected with influenza virus do not have a death sentence . An infected cell can survive both influenza virus infection and the immune response to eliminate the virus , specifically CD8+ T cells which are required for virus clearance . Here , we investigated how an infected cell could survive the CD8+ T cell immune response . We used an influenza virus expressing a recombinant protein that permanently labels infected cells in inducible reporter mice . This system allowed us to detect actively infected cells , as well as cells that had survived influenza virus infection and CD8+ T cell-mediated killing , called survivor cells . We demonstrate that survivor cells do not actively block CD8+ T cell effector function and are not inherently resistant to CD8+ T cell-mediated killing . Our data suggest that survivor cells have lost influenza virus antigen and are rendered invisible to virus-specific CD8+ T cells . Our research provides important new insight into the mechanism of how survivor cells can be generated . This could be a mechanism by which the host is protecting the lung from greater pathology during influenza virus infection . | [
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] | 2019 | Long-term surviving influenza infected cells evade CD8+ T cell mediated clearance |
The interplay between polycomb and trithorax complexes has been implicated in embryonic stem cell ( ESC ) self-renewal and differentiation . It has been shown recently that WRD5 and Dpy-30 , specific components of the SET1/MLL protein complexes , play important roles during ESC self-renewal and differentiation of neural lineages . However , not much is known about how and where specific trithorax complexes are targeted to genes involved in self-renewal or lineage-specification . Here , we report that the recruitment of the hSET1A histone H3K4 methyltransferase ( HMT ) complex by transcription factor USF1 is required for mesoderm specification and lineage differentiation . In undifferentiated ESCs , USF1 maintains hematopoietic stem/progenitor cell ( HS/PC ) associated bivalent chromatin domains and differentiation potential . Furthermore , USF1 directed recruitment of the hSET1A complex to the HoxB4 promoter governs the transcriptional activation of HoxB4 gene and regulates the formation of early hematopoietic cell populations . Disruption of USF or hSET1A function by overexpression of a dominant-negative AUSF1 mutant or by RNA-interference-mediated knockdown , respectively , led to reduced expression of mesoderm markers and inhibition of lineage differentiation . We show that USF1 and hSET1A together regulate H3K4me3 modifications and transcription preinitiation complex assembly at the hematopoietic-associated HoxB4 gene during differentiation . Finally , ectopic expression of USF1 in ESCs promotes mesoderm differentiation and enforces the endothelial-to-hematopoietic transition by inducing hematopoietic-associated transcription factors , HoxB4 and TAL1 . Taken together , our findings reveal that the guided-recruitment of the hSET1A histone methyltransferase complex and its H3K4 methyltransferase activity by transcription regulator USF1 safeguards hematopoietic transcription programs and enhances mesoderm/hematopoietic differentiation .
Embryonic stem cells ( ESCs ) have the ability to differentiate into any cell type of the body and therefore offer a great tool for studying processes involved in cellular differentiation . ESCs also provide a great potential for application of regenerative medicine that is based on two key properties of stem cells: self-renewal and differentiation . Recent genome-wide chromatin studies revealed that the pluripotency of ESCs is maintained by unique chromatin signatures [1] , [2] . To maintain the stemness properties of ESCs , pluripotency associated genes such as Oct4 , Sox2 , and Nanog are marked by high levels of H3K4me3 whereas many silenced lineage-specific genes are either marked by bivalent H3K4me3/H3K27me3 or by H3K27me3 alone [3]–[9] . In particular , bivalent domains , a unique chromatin feature of stem cells and some differentiated cell lineages , mark developmental genes that are primed to be activated [2] . Bivalent domains were observed in the clusters of Hox genes and other genes that are required for early development [3] , [10] . Aberrations in Hox gene expression often result in abnormal development and malignancy . Although it has been suggested that both polycomb ( PcG ) and trithorax ( TrxG ) group complexes play an important role in ESC self-renewal and differentiation [7] , [8] , [11] , [12] , the mechanisms by which specific TrxG proteins and the modification of H3K4me3 are targeted to specific gene loci and initiate differentiation of particular cell lineages still remain unknown . In mammalian cells , the conserved SET domain-containing hSET1/MLL TrxG family complexes specifically methylate histone H3K4 [13] . In addition to the SET domain-containing catalytic subunit , hSET1/MLL complexes comprise several integrated subunits , WDR5 , RBBP5 , ASH2L , and HCF1 , that are required for the enzymatic activity [14] , [15] . Deletion of any one of the core subunits drastically reduces global H3K4 methylation [14] , suggesting that hSET1/MLL complexes play a critical role in shaping the landscape of global H3K4 methylation . Although they share common structural subunits , the hSET1/MLL complexes contain distinct enzymatic subunits ( hSET1A , hSET1B , MLLI , MLL2 , MLL3 or MLL4 ) . MLL1 is required for definitive hematopoiesis [16] , but loss of Mll1 reduces H3K4 methylation only at the HoxC loci and has little effect on other Hox gene loci [17] . In contrast , MLL3/4 has been linked to adipogenesis [18] . These results suggest that the enzymatic subunits of the TrxG complexes may have cell-type specific functions . Furthermore , it has been shown that Dpy-30 , a mammalian core subunit of the SET1/MLL-like complex , controls neuronal differentiation of ESCs but not self-renewal [12] . In contrast , WRD5 mediates ESC self-renewal and reprogramming [11] . Both DPY-30 and WDR5 are shared by all of the hSET1/MLL complexes . It is still unknown how integration of different enzymatic subunits of the complex , hSET1A , hSET1B , MLL1 , MLL2 , MLL3 , or MLL4 , affects regulation of ESC pluripotency versus lineage differentiation . During hematopoiesis , Hox genes are critical for maintaining the balance between self-renewal and differentiation of hematopoietic stem/progenitor cells ( HS/PCs ) . The Hox genes are associated with bivalent domains in undifferentiated ESCs [3] . The sequential expression of Hox genes during embryonic development is regulated and maintained epigenetically by PcG and TrxG group regulators [19] . Ectopic induction of HoxB4 in primitive ESCs leads to hematopoietic cell fate specification [20] , [21] , suggesting that HoxB4 plays an important role in the switch of the balance between self-renewal and differentiation of ESCs towards the hematopoietic lineage . In addition , HoxB4 has been shown to induce both murine and human hematopoietic progenitor cells and to enhance multilineage hematopoietic engraftment of lethally irradiated mice [22]–[25] . In contrast to the HoxB4 gene , the anterior HoxB genes , B2 , B3 , B5 , and B6 are dependent on MLL1 for transcriptional activation [26] . How the HoxB4 gene is dynamically activated to specify ESC fate during early hematopoiesis remains largely unclear . It was reported that USF1 and USF2 heterodimers interact with the HoxB4 promoter and activate hematopoietic expression of the HoxB4 gene in response to cytokine-mediated self-renewal and expansion of HSCs [27] , [28] . The formation of a NF-Y/USF protein complex is essential for full HoxB4 promoter activity and a potent inducer of HoxB4 gene in hematopoietic cells [29] , [30] . By protein affinity purification , we found that USF1 is associated with the hSET1A complex and establishes active chromatin boundaries containing high H3K4 methylation levels in erythroid cells [31] . This raises the possibility that although USFs are ubiquitous transcription factors , they might be involved in regulating hematopoietic differentiation by forming active chromatin domains and recruiting transcription complexes at hematopoietic specific genes . The roles of USF1 and hSET1A protein complexes in stem cell fate commitment and differentiation remain to be determined . Here , we report that the collaboration of USF1 with the hSET1A but not the MLL protein complex is required for mesoderm specification and subsequent hematopoietic cell differentiation . Although hSET1A depletion did not affect ESC self-renewal , it blocks mesoderm differentiation and subsequent hematopoietic differentiation by decreased H3K4me3 levels and transcription preinitiation complex formation at the hematopoietic associated HoxB4 gene . Transcription factor USF1 , which recruits hSET1A , maintains HoxB4 transcription . Disruption of USF1 prevents hSET1A recruitment , mesoderm development , and further differentiation into hematopoietic cells . Interestingly , Ectopic expression of USF1 enhances mesoderm differentiation and further hematopoietic progenitor formation . Thus , our data reveal that the guided-recruitment of hSET1A and H3K4 methylation patterns by the DNA-binding protein USF1 initiates a lineage transcription program and counteracts ESC self-renewal .
We previously showed that affinity purified USF1 complexes contained multiple components of the hSET1A complex and possessed H3K4 histone methyltransferase ( HMT ) activity to maintain chromatin barrier function [31] . Given that hematopoietic expression of the HoxB4 gene is also dependent on transcription factor USF1 [27] , [30] , we sought to examine whether the recruitment of hSET1A complex by USF1 is required for USF1-mediated HoxB4 promoter activation in hematopoietic cells . A 367 ( −276∼+90 ) bp DNA fragment including the proximal HoxB4 promoter containing a USF1 binding site was cloned into the episomal pRep4-luciferase vector . This vector was introduced into K562 cells ( Figure 1A ) . Consistent with the positive role of USF1 in HoxB4 activation , coexpression of USF1 activated the HoxB4 promoter driven luciferase reporter activity by 1–2 fold compared to the vector control . Expression of dominant negative AUSF1 , which interferes with endogenous USF DNA binding , reduced reporter gene activity by 79% compared to the vector control ( Figure 1B and S1A–C ) . Introduction of siRNAs targeting the core components of the hSET1A complex , hSET1A , HCF1 , or ASH2L ( Figure 1C ) , resulted in a complete loss of USF1-mediated transcription activation of the HoxB4 promoter compared to the scrambled siRNA control ( Figure 1B ) . Reduction of PRMT1 expression , which is required for the USF1 mediated activation of a β-globin promoter driven reporter in K562 cells ( Figure S1C ) and expression of the endogenous β-globin gene [32] , did not affect HoxB4 promoter activity ( Figure 1B ) . As a control , hSET1A and PRMT1 did not activate HDAC1 promoter driven luciferase reporter activity ( Figure S1B ) suggesting that hSET1A and PRMT1 may regulate a unique set of target genes . The reduction of reporter activity mediated by hSET1A KD resulted from the loss of hSET1A complex recruitment and H3K4me3 levels at the reporter gene , but not from reduced USF1 binding ( Figure 1D ) . In addition , USF1 , the hSET1A complex , and H3K4me3 are associated with and required for HoxB4 expression in erythroleukemia K562 cells ( Figure S1D–F ) . Taken together , the data suggest that the specific recruitment of the hSET1A complex is critical for USF1 mediated transcriptional activation of the HoxB4 gene in the hematopoietic lineage . The hSET1A complex contains several core components . To determine which core subunit , WDR5 , ASH2L , RBBP5 , or hSET1A , is involved in the direct interaction between USF1 and the hSET1A complex , glutathione-sepharose bead conjugated GST-USF1 was incubated with individual Flag-tagged hSET1A components following baculovirus-mediated expression and purification ( Figure 1E Bottom ) . Although USF1 weakly ( if any ) associated with WDR5 , it strongly interacted with ASH2L ( Figure 1E Top ) . As a control , GST alone did not interact with ASH2L ( Figure S1G and S1H ) . Furthermore , the deletion analysis showed that the basic helix-loop-helix domain of USF1 mediated its interaction with ASH2L ( Figure S1G and H ) . Because ASH2L is a shared subunit by all of the hSET1/MLL-like complexes , we further tested the specificity of the USF1 and hSET1A interaction by analyzing co-precipitated USF1-associated proteins in extracts derived from K562 cells that overexpress Flag-tagged USF1 ( Figure S1I ) . USF1 specifically interacted with hSET1A , but not with the MLL complex ( Figure 1F ) . It was reported that CFP1 is also a component of the hSET1A complex that specifically targets H3K4me3 to CpG islands in ESCs [33] . To test if this component is present in the USF1 associated hSET1A complex , we carried out coimmunoprecipitation assay in Flag-USF1 expressing K562 cells ( Figure 1G ) . Although both USF1 and CFP1 interact with hSET1A , USF1 and CFP1 did not associate with each other ( Figure 1G ) indicating that the CFP1 component is excluded from the USF1 associated hSET1A complex . To ascertain that ASH2L mediates USF1 recruitment of the hSET1A core complex , hSET1A core components , hSET1A , WDR5 , RBBP5 , plus HCF1 , were co-expressed in baculovirus-infected insect SF9 cells in the presence or absence of the ASH2L component ( Figure 1H , bottom ) . Reconstitution of the hSET1A complex was verified in SF9 cells ( Figure S1J ) . USF1 pulled down all of the core hSET1A components in the presence but not in the absence of ASH2L ( Figure 1H , Top ) indicating that ASH2L bridges the association of USF1 with the hSET1A complex . The hSET1A complex is responsible for H3K4me3 patterns that mark transcriptionally active chromatin . Having implicated hSET1A in hematopoietic transcription of HoxB4 in K562 cells , we carried out un-biased ChIP-seq and RNA-seq analyses in human primary CD34+ HSCs and CD36+ progenitors to investigate whether targeting hSET1A and its HMT activity are critical for HoxB4 transcription during hematopoiesis . RNA-seq data revealed that HoxB4 was actively transcribed in both CD34+ and CD36+ cells ( Figure 2A and B ) . Consistent with the transcription status of the HoxB4 gene in these cells , H3K4me3 and RNAPII were enriched around the transcription start site ( TSS ) of the HoxB4 gene compared to the IgG control which did not bind to the locus ( Figure 2A and B ) . Furthermore , the hSET1A and RBBP5 components of the hSET1A complex were also associated with the TSS ( Figure 2B ) , suggesting that recruitment of the hSET1A complex causes promoter H3K4 methylation and transcriptional activation of HoxB4 expression in hematopoietic cells . Moreover , the hSET1A complex , H3K4me3 , and RNAPII were enriched in the highly transcribed β-actin gene but not in the silenced MyoD1 gene ( Figure S2 and S3 ) . To assess whether the interaction between USF1 and hSET1A is important for HoxB4 expression during hematopoiesis , we also performed USF1 and USF2 ChIP-seq analyses in CD36+ erythroid precursor cells ( Figure 3A ) . The binding of USF correlated with H3K4me3 patterns at the anterior HoxB loci especially at the HoxB4 transcription start site ( TSS; Figure 3A ) . Next , we examined the effect of USF1 and hSET1A in the regulation of HoxB4 expression and hematopoiesis using an AUSF1 transgenic mouse model [34] . AUSF1 expressing males die at E11 . 5–12 . 5 with severe defects in yolk sac hematopoiesis [34] . The c-Kit positive primitive hematopoietic progenitor cell population was consistently reduced by 24 . 80% in four AUSF1 transgenic embryos ( Figure 3B and C ) . Reduction of USF1 DNA binding activity in transgenic embryos led to a significant decrease in the expression of HoxB4 , Tal1 , as well as CD41 which is the earliest embryonic HS/PC marker [35]–[37] , but not hSET1A ( Figure 3D ) . The reduction of HoxB4 expression was accompanied by a reduction of hSET1A recruitment by 66% at the HoxB4 promoter region ( Figure 3E ) , suggesting that USF1 guides the hSET1A complex to activate HoxB4 transcription during primitive hematopoiesis . Taken together , our data demonstrate that USF1 cooperates with the hSET1A complex to regulate hematopoietic-specific HoxB4 transcription during early embryonic hematopoiesis . It has been reported that the ability of ESCs to differentiate into different cell lineages including hematopoietic precursors depends on the balance between self-renewal and cell-fate specification [2] . Lineage commitment-associated genes are modulated in part by bivalent chromatin domains that keep developmental genes in a repressed but poised configuration [3] . To understand the molecular mechanisms governing pluripotent stem cell differentiation into hematopoietic cells , we explored the possibility that HS/PC-specific genes including HoxB4 , Tal1 , and Runx1 are repressed by bivalent chromatin in ESCs and induced by the hSET1A complex during differentiation . A sequential-ChIP analysis was carried out in undifferentiated cells to determine whether H3K4me3 and H3K27me3 co-occupy the promoters of transcription factors associated with early hematopoiesis . Figure 4A shows that in undifferentiated mESCs , HoxB4 and Tal1 , which are required for the early onset of hematopoiesis , were marked by both H3K4me3 and H3K27me3 ( Figure 4A , bottom , and S4A ) while the late erythroid stage gene EPB4 . 2 was associated only with H3K27me3 ( Figure 4A ) . The Runx1 gene is also critical for HS/PC function and contains two promoters [38] . Interestingly , its P1 promoter , which is activated and required for late myeloid lineage differentiation , was associated with high levels of H3K27me3 alone ( Figure 4A ) . In contrast , the P2 promoter , which is exclusively active at the HSC stage [38] , was marked by bivalent chromatin domains ( Figure 4A , bottom ) . Thus , our data indicate that the promoters of HS/PC-associated genes are repressed by bivalent domains in undifferentiated ES cells . Given that ectopic activation of the HoxB4 gene in ESCs leads to hematopoietic fate specification [20]–[25] , we further reasoned that the guided recruitment of hSET1A to the HoxB4 locus by USF1 may initiate HoxB4 transcription and hematopoietic differentiation of ESCs . To test this possibility , ESCs were induced to differentiate into hematopoietic cells by addition of stem cell factor ( SCF ) , IL3 , and IL6 ( Figure S4B ) . Under these conditions , ESCs differentiated into hematopoietic cells and expressed lineage markers and hematopoietic specific transcription factors ( Figure S4C–F ) . Time course ChIP-qPCR analyses indicated that , although USF1 and hSET1A levels were not increased during differentiation ( Figure S4D ) , USF1 binding , hSET1A recruitment , and H3K4me3 levels were increased and remained at high levels at the HoxB4 promoter after day 10 of differentiation ( Figure S5A–C ) . As expected , expression of ESC-specific transcription factor Oct4 was dramatically reduced while hematopoietic-associated HoxB4 was gradually increased upon differentiation ( Figure 4B ) . HoxB4 expression reached its peak on day 10 of differentiation and remained at a high level ( Figure 4B ) . Consistent with the HoxB4 expression pattern , the recruitment of USF1 and hSET1A to the HoxB4 promoter increased upon differentiation ( Figure 4C , 4D , and S5A–C ) . In contrast , MLL1 and MLL2 did not bind to the HoxB4 promoter in either K562 or ESCs ( Figure S5D and data not shown ) . Indeed , sequential ChIP analyses showed that USF1 and hSET1A co-occupied the HoxB4 promoter only in hematopoietic cells differentiated from ESCs , but not in undifferentiated ESCs ( Figure 4D ) . The recruitment of hSETA1 was coincident with a significant increase of H3K4me3 at the active HoxB4 promoter ( Figure 4E ) . Consistent with hSET1A recruitment and H3K4me3 patterns , the binding of RNAPII was also significantly elevated by 8 . 2 fold at the HoxB4 promoter upon induction compared to undifferentiated ESCs ( Figure 4F ) . These data suggest a functional relationship between hSET1A recruitment and activation of hematopoietic lineage-associated genes during ESC differentiation . Having shown that USF1 activity is important for HoxB4 expression in differentiation of ES cells ( Figure 4C–F and S5A–C ) , we next examined the biological effect of USF1 on stem cell pluripotency and differentiation by enforced expression in ES cells of AUSF1 , a dominant negative USF1 mutant that competes with endogenous USF for DNA-binding ( Figure 5A ) . Disruption of USF1 DNA binding activity affected neither expression of stemness genes Oct4 and Nanog ( Figure S6A ) nor the self-renewal ability of ESCs as examined by AP staining ( Figure S6B ) . In addition , we tested the relationship of enforced AUSF1 expression and changes in bivalent H3K4 and H3K27 trimethylation patterns at HS/PC-specific genes in ESCs . Suppression of USF DNA-binding activity in ESCs resulted in a specific inhibition of H3K4me3 levels within HS/PC-associated bivalent chromatin marks in the HoxB4 , Tal1 , and Runx1P2 gene loci ( Figure 5B ) , but did not reduce the H3K27me3 levels at these genes ( Figure 5C ) . Interestingly , AUSF1 embryoid bodies ( EBs ) appeared smaller compared to those harboring the vector control ( Figure S6C ) , suggesting that loss of USF activity compromises lineage differentiation programs . We next examined the expression of endoderm , mesoderm , and ectoderm markers in both control and AUSF1 EBs ( Figure 5D–F ) . To our surprise , inhibition of USF1 DNA binding activity in early embryonic development resulted in a specific reduction of primitive mesoderm markers , Brachyury ( T ) and FLK1 , with only minor effects on endoderm and ectoderm markers , Gata4 , Gata6 , Fgf5 , and nestin ( Figure 5D–F ) . Further analysis indicated that USF1 does not directly regulate both T and FLK1 ( Figure S6D ) . Several mesoderm markers , Mesp1 , Snail2 , Eomes , and Nkx2-5 , were also significantly inhibited by ectopic expression of AUSF1 ( Figure S6E ) . The effect of USF1 in EB differentiation was biased toward the mesoderm which can differentiate to hematopoietic lineages . This is consistent with the disruption of H3K4me3 levels at HS/PC-associated chromatin domains by enforced expression of AUSF1 . The mouse mesoderm derived aorta-gonad mesonephros ( AGM ) region is considered to be the first site of hematopoiesis [39] and hematopoietic potential is segregated within the Flk1+ compartment in the murine EB system [40] , [41] . The hemogenic endothelium stage cells ( Tie2hic-Kit+ population ) developed from hemangioblast give rise to HS/PCs at the onset of hematopoiesis [37] , [42]–[45] . AUSF1 specifically inhibited Flk1 expression in the mesoderm ( Figure 5 ) suggesting that USF1 may be involved during early hematopoietic commitment steps . We next explored the effect of AUSF1 expression on cytokine induced hematopoietic differentiation of ES cells ( Figure S4B ) . Ectopic expression of AUSF1 did not affect co-activator hSET1A expression in undifferentiated or differentiated ESCs ( Figure 6A ) . However , it specifically inhibited differentiation of hematopoietic progenitors in three independent AUSF1 ESC clones ( Figure 6B , C , and S7A ) . Compared to cells harboring the pcDNA vector control , cells expressing AUSF exhibited a reduction in c-Kit and Tie2 double positive hemogenic endothelium cell population by 64% ( down to 5 . 4% from 15 . 1%; Figure 6B ) . These cells represent hematopoietic potential at the onset of hematopoiesis [37] , [43] . Furthermore , suppression of USF1 activity significantly reduced the number of CD41+c-Kit+ HS/PCs ( down from 4 . 7% to 0 . 3% ) ( Figure 6C ) and reduced Sca1+c-Kit+ hematopoietic cells by 75% ( Figure S7A and 7B ) . In addition to the inhibition of hematopoietic cell populations , the HS/PC markers and transcription factors associated with the onset of hematopoiesis such as HoxB4 , Tal1 , and Runx1 were downregulated by the inhibition of USF1 activity ( Figure 6D ) . AUSF1 only dimerized with wt USF1 but not with TAL1 ( Figure S7C ) , as expected [46] . Therefore , AUSF1 did not interfere with the DNA binding activity of hematopoietic specific bHLH transcription factor TAL1 ( Figure S7C and D ) . Thus , the data show that USF1 is critical for the initial expression of hematopoietic transcription factors and markers . To further elucidate the underlying mechanism by which USF1 regulates hematopoietic associated HoxB4 transcription , USF1 binding , hSET1A recruitment , H3K4me3 levels , and RNAPII loading at the HoxB4 locus were examined in hematopoietic cells differentiated ( days 13 ) from ESCs either harboring a pCDNA4 vector or expressing AUSF1 . Enforced expression of AUSF1 suppressed HoxB4 transcription by an average of 61 . 8% compared to the vector control ( Figure 6D ) . In contrast , AUSF1 did not affect hSET1A expression ( Figure 6A and S6A ) . Consistent with the decrease in HoxB4 transcription ( Figure 6D ) , AUSF1 expression eliminated the binding of USF1 and consequently the recruitment of hSET1A at the HoxB4 promoter ( Figure 6E and F ) . The elimination of hSET1A recruitment was accompanied by a dramatic loss of H3K4me3 levels ( reduced by 66 . 5% on average ) and a decrease in Pol II binding at the HoxB4 promoter ( Figure 6G and H ) . These data reveal that USF1 activates lineage-specific transcription by recruiting the hSET1A complex to hematopoietic regulatory elements . The above data indicate that the activity of USF1 during mesoderm differentiation and subsequent transcription activation of HoxB4 correlates with its ability to recruit the hSET1A complex ( Figure 5 and 6D–G ) . To ascertain whether hSET1A , but not MLL1 , is required for USF1-mediated transcription activation during differentiation , hSET1A was silenced in ES cells by retroviral-mediated shRNA targeting ( Figure 7A ) . Compared to the pSuper scramble control , three individual ES clones showed a substantial decrease in hSET1A expression ( Figure 7A ) . ESCs transduced with retroviruses harboring the pSuper scramble control or the hSET1A-specific shRNAs were differentiated into embryoid bodies ( EBs ) by withdrawal of LIF ( Figure S4B ) . Similar to the ectopic expression of AUSF1 , downregulation of hSET1A did not alter ESC self-renewal properties and expression of the Oct4 and Nanog genes ( Figure S8A and S8B ) , but impaired EB size ( Figure 7B ) . Furthermore , we examined the expression levels of three germ layer lineage markers in the scramble and hSET1A KD EBs by qRT-PCR ( Figure 7C–E ) . Both mesoderm and endoderm markers were largely abolished in the hSET1A KD EBs . However , ectoderm markers were not suppressed by the hSET1A KD . Further analyses of the hematopoietic population derived from EBs following a switch to suspension culture with hematopoietic differentiation medium for 6 ( for Tie2+c-Kit+ hemogenic endothelium population ) or 10 ( for CD41+c-Kit+ population ) days revealed that similar to the effect of AUSF1 , the Tie-2+c-Kit+ hemogenic endothelium cell population , which is prone to hematopoiesis , was significantly decreased by 41 . 4% in the hSET1A KD cells ( Figure 7F ) . In addition , both CD41+c-Kit+ as well as Sca-1+c-Kit+ hematopoietic cell populations were reduced by 85% and 43 . 5% in the hSET1A KD cells compared to cells expressing the scrambled shRNA ( Figure 7G , S8C , and S8D ) , respectively . Expression of transcription factors associated with the onset of hematopoiesis was also inhibited by the hSET1A KD ( Figure 8A ) . Interestingly , in both AUSF1 and hSET1A KD EBs primitive hematopoiesis markers εy and βH1 were significantly downregulated ( Figure 8B ) indicating that USF1 and the hSET1A complex in part work together to regulate early hematopoiesis . Transcription of the HoεxB4 gene is inhibited by both expression of AUSF1 and hSET1A KD ( Figure 6D and 8A ) . Disruption of the hSET1A complex may lead to a loss of H3K4 methylation that subsequently impairs recruitment of the RNAPII transcription pre-initiation complex to USF1 target genes such as HoxB4 gene . To test this hypothesis , ChIP-qPCR was performed in the pSuper scramble control and hSET1A KD ESC-derived hematopoietic cells . H3K4me3 levels at the HoxB4 promoter were significantly reduced by 55% and 58% in two selected hSET1A depleted clones , respectively ( Figure 8C ) . TAF3 is a component of the TFIID complex and specifically recognizes the H3K4me3 mark at promoters , thereby transmitting the histone methylation signal to the recruitment of RNAPII [47] . Thus , we tested whether reduction of H3K4me3 at the HoxB4 promoter by hSET1A KD results in subsequent decreased recruitment of TAF3 or RNAPII . Consistent with the observed changes in H3K4me3 patterns , levels of HoxB4 promoter bound TAF3 and RNAPII were also reduced 66% and 85% in hSET1A depleted clones , respectively ( Figure 8D and 8E ) . This is consistent with a previous report demonstrating that the PHD domain of TAF3 recognizes the H3K4me3 mark [47] . Taken together , our data demonstrate that recruitment of the hSET1A complex and its HMT activity plays a critical role in activating a lineage-specific transcription program during differentiation of ESCs . Finally , we tested whether USF1 induces lineage differentiation by inducing transcription factors associated with the early onset of hematopoiesis . Flag-tagged USF1 was stably expressed in murine ES cells ( Figure S9A ) . We used RT-qPCR to survey three lineage markers in control and USF1 overexpressing EBs upon withdrawal of LIF for 6 days ( Figure 8F ) . As expected , only mesoderm markers ( Figure 8F ) , but not endoderm or ectoderm markers ( Figure S9B ) , were upregulated by overexpression of USF1 supporting the notion that USF1 plays an important role in mesoderm development . To further examine the effect of USF1 overexpression on hematopoietic differentiation , both control and USF1 expressing EBs were collected and induced with hematopoietic cytokines to differentiate these cells along the hematopoietic lineage ( Figure S4B ) . FACS analysis revealed that USF1 overexpression increased the CD41+c-Kit+ hematopoietic cell population from three independent experiments ( Figure 8G and S9C ) . The CD41+c-Kit+ population was increased by 42 . 8% in USF1 expressing cells compared to cells harboring the vector control ( from 3 . 9% in control cells to 5 . 57% in USF1 overexpressing cells ) ( Figure S9D ) . Although c-Kit is present in all hematopoietic cell populations , only the earliest embryonic HSCs express CD41 [35] . Consistent with the role of CD41 in embryonic hematopoiesis , both primitive hematopoietic markers , εy and βH1 , were significantly upregulated in the USF1 overexpressing ES cell clones ( Figure S9E ) . The levels of Tal1 and HoxB4 that associate with the early onset of hematopoiesis were also significantly stimulated by 2 . 8 and 3 fold in cells overexpressing USF1 , respectively ( Figure 8H ) . Thus , our data suggest an important role of USF1 and hSET1A in directing lineage fate determination during ESC differentiation .
The dynamic balance of H3K4me3 and H3K27me3 at genes involved in the commitment and development of multiple lineages is thought to play a critical role in maintaining the differentiation potential of ESCs [1] , [3]–[5] . However , it remains unknown how chromatin modifying enzymes that catalyze these modifications are targeted to lineage-specific genes to allow lineage commitment of ESCs . Do variations in the enzymatic subunit compositions of the MLL1 , MLL2 , MLL3 , MLL4 , hSET1B , or hSET1A complexes play different roles in ESC self-renewal versus differentiation ? It has been suggested that hSET1A and hSET1B play non-redundant roles in the regulation of chromatin structure and gene expression [48] . We show that the hSET1A HMT complex is essential for ESC differentiation , but not self-renewal . We demonstrate that hSET1A is recruited to hematopoietic associated genes by transcription factor USF1 through its interaction with ASH2L , a core component of hSET1/MLL complexes . By doing so , USF1 and hSET1A cooperatively shift the balance of bivalent chromatin modifications to H3K4me3 , thereby driving lineage differentiation . In this regard , we provide evidence demonstrating that the DNA-sequence specific transcription factor USF1 initiates and enforces hematopoietic associated HoxB4 expression in differentiating ESCs by guiding the hSET1A complex and corresponding H3K4me3 to the HoxB4 promoter . H3K4me3 is then recognized and bound by TAF3 and RNAPII in differentiating ESCs ( Figure 6 and 8 ) . It is particularly interesting that TAF3 also controls ESC lineage commitment by regulating the balance of transcription programs involved in ESC pluripotency and commitment [49] . Select components of the hSET1/MLL complexes have been implicated in both regulating stem cell self-renewal and lineage-fate commitment [11] , [12] . H3K4me3 appears to be required for maintaining high level expression of pluripotency associated genes and for transcriptional induction of lineage-specific regulators . The differential roles of hSET1/MLL complexes in ESC commitment and self-renewal may rely on the specific loci to which the different complexes are recruited . All of the hSET1/MLL complexes consist of shared structural core subunits but vary in their specific enzymatic and several additional subunits [50] , [51] . In this regard , it is interesting to note that transcription factor USF1 , which is critically involved in activating the HoxB4 gene in the hematopoietic lineage , is associated with the hSET1A complex , but not with the MLL complex . Furthermore , MLL1 is required for the activation of other anterior HoxB genes but not HoxB4 in acute myeloid leukemia , and deletion of MLL1 does not affect HoxB4 transcription [17] , [26] . Thus , we propose that the different hSET1/MLL complexes play unique roles during differentiation and development . Our data demonstrate that USF1 specifically interacts with the hSET1A complex via the ASH2L subunit , which is also common to the other MLL complexes . Thus , there must be additional determinants for this interaction , likely complex-specific subunits , that prevent stable USF1 interactions with other ( e . g . , MLL1 ) complexes . Apart from the ESC core transcriptional network of genes ( Oct4 , Nanog , and Sox2 ) that regulate self-renewal , factors controlling transcription programs that guide lineage commitment are largely unknown . In order to simultaneously maintain both stem cell identity and differentiation potential , ES cells possess unique bivalent chromatin domains at gene loci expressing developmental regulators [3]–[5] . Alteration of either H3K4 methylation or H3K27 methylation may perturb commitment of ESCs . USF1 controls cell fate specification through regulation of H3K4 methylation marks at lineage-specific genes although USF1 does not recruit hSET1A to these loci in undifferentiated ESCs . Interestingly , USF1 can function as a chromatin barrier to maintain active histone modifications within chromatin domains in hematopoietic cells [31] , [52] , which may be involved in protecting HS/PC-associated bivalent genes in undifferentiated ESCs and ascertain that these genes are primed to be induced in response to differentiation . In this case , it is interesting to note that USF1 also interacts with another H3K4 HMT , SET7/9 which may contribute to the barrier activity of chicken HS4 [52] . In agreement with this notion , the ectopic expression of dominant negative AUSF1 in ESCs and in transgenic mice inhibits differentiation of mesoderm and further hematopoietic lineages ( Figures 3 , 5 and 6 ) . Although USF1 is a ubiquitous transcription factor/chromatin barrier protein , it has been implicated in hematopoiesis and β-globin gene regulation through association with a variety of cofactors that include the hSET1A complex [31] , [34] ( this report ) . Thus , an interesting question is what underlies selective differentiation bias toward hematopoiesis . The most plausible explanation is that the disruption of USF1 activity broadly impairs gene expression patterns especially those critical for mesoderm development . Some of the USF1 targets are transcription factors required for hematopoietic commitment and differentiation . Consistent with this mechanism , both inhibition of USF1 DNA binding and depletion of hSET1A led to a decrease in expression levels of transcription factors associated with the onset of hematopoiesis , HoxB4 , Tal1 , and Runx1 ( Figure 6 and 8 ) . In particular , the homeodomain gene HoxB4 has been shown to promote engraftment of murine bone marrow HSCs and enhance self-renewal of HS/PCs from human cord blood [20] , [21] , [23]–[25] . Involvement of USF1 in HoxB4 activation [27] suggests a role of USF in regulating hematopoietic differentiation . Ectopic expression of USF1 enhances formation of the CD41+c-Kit+ HS/PC subpopulation and activates expression of hematopoietic associated HoxB4 and Tal1 genes supporting its role in hematopoiesis ( Figure 8 ) . Interestingly , overexpression of NF-Ya , a transcription factor that forms a complex with USF and binds to the HoxB4 promoter [30] , also specifically promoted hematopoiesis of hESCs ( personal communication with Dr . Stephen Emerson ) . It is possible that NF-Ya cooperates with USF to activate the HoxB4 promoter by dramatically increasing the affinity of the protein complex to the HoxB4 promoter chromatin in hematopoietic cells [27] , [30] . Finally , induced recruitment of the hSET1A HMT complex by USF1 to the HoxB4 promoter upon hematopoietic differentiation ( Figure 4 ) likely leads to alterations in chromatin structure and stabilize transcription complexes at hematopoietic promoters . In mammals , HoxB4 is expressed in the stem cell fraction of the bone marrow . Ectopic expression of the HoxB4 gene in bone marrow hematopoietic cells leads to a dramatic expansion and increased self-renewal of HSCs [21] , [23] , [24] . Furthermore , HoxB4 expression promotes the transition from embryonic stem cells into definitive HSCs with increased long-term engraftment potential [20] , [25] , suggesting that HoxB4 is an early hematopoietic regulator associated with hematopoietic fate specification of ESCs . Our data reveal that HoxB4 and other hematopoietic associated genes are modulated by bivalent chromatin domains ( Figure 3 ) . The main action of USF1 and the co-regulator hSET1A during differentiation of ESCs is to ascertain activation of the HoxB4 gene as well as other hematopoietic regulators that shifts the decision of differentiation versus self-renewal to reinforce hematopoietic commitment . However , it still remains to be determined whether HoxB4 is sufficient for driving ESC differentiation . It is reported that HoxB4 deficient mice exhibit mild reduction in progenitors and stem cells in fetal liver and bone marrow [53] . In contrast , mice deficient in both HoxB3 and HoxB4 or compound deletion of Hoxa9/HoxB3/HoxB4 display severe hematopoietic defects with a marked decrease in HSC regeneration and proliferation [54] , [55] suggesting that other Hox genes may compensate for the loss of HoxB4 . Nevertheless , the data presented here support the notion that activation of HoxB4 confers HSC expansion by coordinating the stem cell response to commitment and differentiation signals [20] , [22] , [25] .
K562 cells were cultured in RPMI1640 media supplemented with 10% fetal bovine serum ( FBS ) . Murine ESCs were maintained at a density between 1×105 and 5×105 cells/ml in 5% CO2 at 37°C as described [56] . Constructs used for shRNA expression were generated by subcloning shRNA oliginucleotides into the pSuper . retro . puro vector following the manufacturer's instruction ( Oligoengine ) . Infectious viruses were produced in PhoenixA package cells using calcium phosphate transfection; supernatant was collected after 48 hrs post-transfection to infect cells , and cells were selected by puromycin resistance . The AUSF1 transgenic mice have been described previously [34] . All animal experiments were approved by the IACUC committee and conform to the regulatory guidelines . The pcDNA4-AUSF1 expressing construct was described previously [31] . The pcDNA3 . 1-Flag-USF1 expressing vector was cloned by fusing Flag tag to the N-terminal of human USF1 cDNA . ESCs were transfected with these plasmids using Lipofectamine 2000 reagent ( Invitrogen ) and selected with 400 ng/ml Zeocin for AUSF1 and with 500 µg/ml G418 for Flag-tagged USF1 ( Invitrogen ) . For luciferase reporter assays , K562 cells were transfected with a pREP4-hHoxB4-luc reporter , an expression vector for USF1 , AUSF1 , or siRNAs targeting individual components of the hSET1A complex , hSET1A , HCF1 , or ASH2L . A CMV-driven renilla luciferase plasmid was used as transfection control . Transfected cells were cultured for 48 hrs and subjected to luciferase and ChIP assays . In vitro hematopoietic differentiation of ESCs was performed as described previously [56] with minor modifications ( Figure S4B ) . Briefly , mESCs were dispersed into single cell suspension by trypsinization with 0 . 05% trypsin and resuspended in EB media ( IMDM supplemented with 15% FBS , 180 mg/ml transferrin , 4 . 5×10−4 M ( MTG ) , 50 ng/ml ascorbic acid , and 1% penicillin/streptomycin ) with 5×104 cells/ml . 20 µl of a drop containing 1000 ESCs were seeded on the cover of the tissue culture dish by the hanging drop method . The EBs were collected for suspension culture in polyHEMA-coated dishes on day 3 . The EBs were then supplemented with 40 ng/ml SCF on day 4 . Fresh media containing 40 ng/ml mSCF , 20 ng/ml IL3 , and IL6 ( Peprotech ) was replaced on day 7 and then changed with fresh media every two days . The EBs were harvested at different time points , and single cell suspensions were prepared for FACS analysis , RNA extraction , and ChIP . FLAG tagged cDNAs encoding hSET1A , ASH2L , WDR5 , RBBP5 and HCF1 were cloned into the pFastBac1 vector as described [14] and proteins were expressed in SF9 cells using the Bac-to-Bac Baculovirus Expression System ( Invitrogen ) . Flag tagged fusion proteins were purified by FLAG immunoaffinity purification as described [14] . To reconstitute the hSET1A core complex , SF9 cells were co-infected with baculoviruses expressing FLAG-tagged hSET1A , ASH2L , RBBP5 , WDR5 , and HCF1 . The GST-pull down assay was carried out as described [31] . Briefly , equal amounts of GST-USF1 fusion protein was incubated with reconstituted hSET1A core complex or individual components of the complex in 500 µl binding buffer ( PBS , 10% glycerol , 1% Triton X-100 , 1 mM EDTA , 1 mM DTT ) at RT for 1 hour and proteins were captured by glutathione beads preconjugated with GST-USF1 . Captured proteins were washed three times and detected by western blotting ( WB ) . Immunoprecipitation was carried out as described previously [31] . USF1 ( H-86 ) , MLLn , and MLLc antibodies were purchased from Santa Cruz Biotechnology ( Santa Cruz , CA ) . hSET1A ( A300-289A , A300-290A ) , RbBP5 ( A300-109A ) , ASH2L ( A300-489A ) , HCF1 ( A301-399A ) , and TAF3 antibodies were from Bethyl Laboratories ( Montgomery , TX ) . Mouse FLAG antibody was bought from Sigma . Single cell suspensions obtained from E10 . 5 yolk sacs of the AUSF1 embryos treated with collagenase ( Stem Cell Technologies ) was subjected to FACS analysis using antibody against CD117 PE ( c-Kit ) ( BD Biosciences ) . Single cell suspension obtained from differentiated EBs was subjected to FACS analysis using antibodies against CD117 PE ( c-Kit ) , Sca-1 PE-cy7 , CD41 FIFC ( BD Biosciences ) , CD117 FIFC , or Tie2 PE ( eBioscience ) . Briefly , cells were resuspended in PBS containing 2% FBS , passed through a 70-µm cell strainer , and incubated on ice with indicated antibodies for 30 min . After a series of washes to remove unbound antibodies , cells were subjected to FACS using a BD LSRII system ( BD Biosciences ) . Total RNA was prepared by using the RNeasy mini isolation kit according to the manufacturer's instruction ( Qiagen , MD , USA ) . 1 µg RNA was reverse transcribed by using the Superscript II reverse Transcriptase ( Invitrogen ) . cDNA was analyzed by real-time PCR ( qRT-PCR ) using a CFX 96 real time PCR Detection System ( Bio-Rad ) . Primer sequences are listed in the Supplemental Information ( Table S1 ) . ChIP assays were performed as described previously [31] using antibodies specific for transcription factors , modifying enzymes , and various histone modifications . Antibodies against RNAPII , H3K4me3 and H3K27me3 were purchased from Millipore ( Millipore ) . Other antibodies were described above . The relative enrichment was determined by the following equation: 2Ct ( IP ) −Ct ( Ref ) . In addition , Micro-ChIP was used to analyze EBs ( 1×104 cells/IP ) using Dynal bead conjugated protein A or protein G ( Invitrogen ) . The sequential ChIP assays were carried out as described previously [32] with minor modifications . Briefly , chromatin prepared from 3×106 cells was first immunoprecipitated with USF1 or H3K27me3 antibody . The USF1 or H3K27me3 selected chromatin complexes were eluted , dialyzed , and subsequently immunoprecipitated with hSET1A or H3K4me3 antibody , respectively . The bound protein-DNA complexes were reverse cross-linked , purified , and analyzed by qPCR . Primary human CD34+ cells were isolated and differentiated to CD36+ cells as described [10] . ChIP-Seq and RNA-seq assays were performed as outlined previously [10] and described briefly below . For ChIP-Seq analysis , the cells were cross-linked with 1% formaldehyde , followed by sonication to fragment chromatin to sizes ranging from 200 to 500 bp . Chromatin fractions from 1 to 5 million cells were used for chromatin immunoprecipitation using 2 micrograms of specific antibodies . Following reverse cross-linking and purification , the ChIP DNA was ligated to Illumina ChIP-Seq adaptors , amplified using the Illumina primers , and sequenced on the Illumina GAII platform . For RNA-Seq assays , total RNA was isolated from cells and reverse transcribed . The cDNA samples were fragmented to 100 to 300 bp by sonication , ligated to Illumina adaptor , and sequenced on the GAII platform , similar to the ChIP-Seq libraries . Sequence reads of 25-bp were obtained , mapped to the human genome ( hg18 ) and processed as described previously [57] . The sequence reads have been deposited in the NCBI Short Read Archive ( GSE12646 ) . | Embryonic stem cells ( ESCs ) are capable of differentiating into any type of cell or tissue of the body . It is important to understand how developmental genes are controlled during differentiation of ESCs into specific cell types . The hSET1/MLL histone modifying protein complexes add methyl groups to lysine 4 on the N-terminal tails of the DNA associated protein histone H3 and activate gene expression . Although the hSET1/MLL enzymatic complexes play a role in activating genes involved in ESC growth and differentiation , how and where these activities are targeted to remains unclear . In this report , we demonstrate that DNA binding factor USF1 interacts with and brings the hSET1A enzymatic complex to its target gene , HoxB4 , during blood cell specification and differentiation . Consistent with the function of HoxB4 in early blood cell formation , we found that the inactivation of USF1 or hSET1A activities leads to a block in the differentiation of blood cells and causes reductions in methylation levels of H3K4 and expression of HoxB4 , without impairing the self-renewal capability of ESCs . Taken together , our findings reveal that the collaboration between the hSET1A enzymatic complex and DNA binding regulator USF1 activates developmental genes that control cellular differentiation programs during development . | [
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] | 2013 | USF1 and hSET1A Mediated Epigenetic Modifications Regulate Lineage Differentiation and HoxB4 Transcription |
Insertions and deletions ( indels ) cause numerous genetic diseases and lead to pronounced evolutionary differences among genomes . The macaque sequences provide an opportunity to gain insights into the mechanisms generating these mutations on a genome-wide scale by establishing the polarity of indels occurring in the human lineage since its divergence from the chimpanzee . Here we apply novel regression techniques and multiscale analyses to demonstrate an extensive regional indel rate variation stemming from local fluctuations in divergence , GC content , male and female recombination rates , proximity to telomeres , and other genomic factors . We find that both replication and , surprisingly , recombination are significantly associated with the occurrence of small indels . Intriguingly , the relative inputs of replication versus recombination differ between insertions and deletions , thus the two types of mutations are likely guided in part by distinct mechanisms . Namely , insertions are more strongly associated with factors linked to recombination , while deletions are mostly associated with replication-related features . Indel as a term misleadingly groups the two types of mutations together by their effect on a sequence alignment . However , here we establish that the correct identification of a small gap as an insertion or a deletion ( by use of an outgroup ) is crucial to determining its mechanism of origin . In addition to providing novel insights into insertion and deletion mutagenesis , these results will assist in gap penalty modeling and eventually lead to more reliable genomic alignments .
Despite the significance of insertions and deletions ( indels ) for human genetic disease [1] and genome evolution [2–5] , the mechanisms of their mutagenesis are not completely understood . Both replication and recombination have been proposed as potential contributors; however , their relative roles in the formation of indels are presently unknown . On the one hand , the importance of replication is supported by the overrepresentation of repeats prone to slipped mispairing [6 , 7] and of polymerase pause sites in the vicinity of small indels [8] . Male mutation bias observed for indels in rodents is also consistent with their generation by errors in DNA replication [9] , because in the germline males undergo more rounds of replication than females [10 , 11] . On the other hand , while the role of recombination has received much less attention in the literature , its impact may yet be considerable since several motifs known to be associated with recombination are enriched in the proximity of indels [8] . The predominantly maternal origin of indels causing several genetic diseases [12 , 13] , taken together with a higher recombination rate in females than in males [14] , also points toward the involvement of recombination . In addition to replication errors and recombination , transcription [15] and aberrations in repair [7] might also cause or facilitate the genesis of indels . Since the rates of all these processes fluctuate across the genome , regional variation in indel rates is expected and its examination should be useful for inferring the mechanisms of insertion and deletion mutations . Unlike for substitution rates ( e . g . , [16–18] ) , a detailed investigation of regional variation in small insertion and deletion rates has not yet been performed . Hardison and colleagues [16] studied regional variation in several different measures of DNA alterations including a rough estimate of the number of bases deleted in either the human or mouse lineages . However , this measure comprised deletions of all sizes; small versus large deletions might be originating by different molecular mechanisms [9] . Additionally , only pairwise relationships between these rough deletion rates and other genomic factors were considered and thus the correlations among these factors were not taken into account . Another study [19] investigated variation in insertion rates of interspersed repeats , yet regional variation in either small insertion or small deletion rates was not examined . Makova et al . [9] analyzed only interchromosomal ( and not intrachromosomal ) variation in insertion and deletion rates between mouse and rat . Regional variation in human–mouse indel rates was noted by Lunter and colleagues [20]; however , only GC content and chromosomal location were considered to account for this variation and no discrimination was made between insertions and deletions ( and they also might be caused by different molecular processes ) . Finally , additional studies focused on indels situated in genes ( e . g . , [4 , 5 , 8] ) , which are likely to be affected by selection , leaving indels occurring in neutrally evolving regions largely underanalyzed . Thus , prior to the present study , a systematic analysis of regional variation in small insertion and ( separately ) deletion rates in neutrally evolving parts of the genome has not been conducted . Here , we scrutinize patterns of neutral regional variation in rates of small indels across the human genome , and contrast the inferred molecular mechanisms contributing to the generation of insertions versus deletions . We identified small ( less than or equal to 30 bp ) insertions and ( separately ) deletions at neutrally evolving ( see below ) interspersed ancestral repeats ( ARs ) in the whole-genome human–chimpanzee comparison employing macaque sequences as an outgroup . Next , we applied the methods of multiple regression to rigorously examine genomic factors determining regional variation in rates of these mutations that occurred in the human lineage since the human–chimpanzee divergence .
We established a computational pipeline to detect small indels that occurred in the human lineage since its divergence from chimpanzee , and used the recently sequenced macaque genome [21] as an outgroup to ascertain their polarity in the human–chimpanzee–macaque MULTIZ alignments [22] . Substitution rate matrix and gap penalties were derived for these specific alignments ( Materials and Methods , Table S1 ) ; gap attraction was found to be minimal ( Figure S1 ) . We focused our analyses on indels ≤30 bp because they occur only within ( not between ) alignment blocks , and thus can be inferred consistently . Also , similar to other studies ( e . g . , [5] ) , we gave priority to analyses of human-specific indels because of the higher coverage and quality of the human genome as compared with the chimpanzee genome . To increase the accuracy of indel inference and to minimize false positives , we introduced several levels of filtering ( Materials and Methods , Table S2 ) . The analysis was restricted to indels occurring within ARs , which have been widely employed as a model of neutral evolution [16 , 20] . The resulting dataset consisted of a greater number of human-specific deletions ( a total of 237 , 405 ) than insertions ( a total of 143 , 904 ) , with size distributions following an exponential decay in both cases ( Figure S2 ) , in agreement with other studies [3 , 8 , 9] . Insertion and deletion rates , measured here in number of events per base , are conservative because of strict filtering and as a result are lower compared with other studies ( e . g . , [4] ) . Human-specific insertion and deletion rates are strikingly lower for chromosome X ( insertions mean 1 . 2 × 10−4 , standard deviation ( sd ) 3 × 10−5; deletions mean 1 . 9 × 10−4 , sd 4 × 10−5 ) than for autosomes ( insertions mean 1 . 6 × 10−4 , sd 3 × 10−5; deletions mean 2 . 7 × 10−4 , sd 4 × 10−5—see also Figure 1 ) . Chromosome Y was excluded from our analysis because a female macaque was sequenced [21] . Significant variation in indel rates is also observed among autosomes ( p < 3 × 10−16 , Kruskal-Wallis test over 1-Mb windows; Figure 1; see below ) . Similar to nucleotide substitutions rates [16–18] , small insertion and deletion rates show substantial regional variation ( standard deviations computed on 1-Mb windows are 3 . 2 × 10−5 for insertions and 4 . 5 × 10−5 for deletions; Figure S3 ) . Interestingly , a positive association is evident between insertion and deletion rates ( Figure 2A ) , suggesting that at least some of the processes underlying these two types of mutations are shared [8 , 23] . To infer the underlying molecular mechanisms contributing to variation in indel rates , we investigated various genomic features as predictors of insertion and deletion rates in 2 , 568 1-Mb nonoverlapping windows throughout the human genome ( Table S3; Materials and Methods ) employing a regression analysis . We used the “best subset regression” selection technique to identify linear and quadratic terms for inclusion in our regression models , followed by pruning of terms that were not significant after correction for multiple testing ( see Materials and Methods for more details ) . The two resulting multiple regression models , which explain 32% and 27% of the observed variation in deletion and insertion rates , respectively , are summarized in Table 1 ( plots of observed rates versus fitted values from the models , with prediction bands superimposed , are given in Figure 3 ) . For each significant predictor , we calculated its relative contribution to variability explained ( RCVE ) , i . e . , the relative amount a predictor contributes to the overall variability explained by the full model in the context of all other predictors . Additionally , we calculated its variance inflation factor—to check for potential estimate deterioration due to multicollinearity ( Table 1; Materials and Methods ) . None of the predictors had variance inflation factor greater than 10; thus , in spite of correlations among predictors ( unpublished data ) , the models are not adversely affected by multicollinearity ( Materials and Methods ) . Thus , we are able to elucidate the individual contributions of each predictor to explaining variability in insertion/deletion rates in spite of correlations between some of them . In choosing a window size to perform our main analysis , we struck a balance among various important considerations . Larger windows increase accuracy in the computation of insertion and deletion rates , and thus reduce the error variability carried by these measurements . This fact is reflected in the share of variability explained by our regression models increasing steadily with the window size , for both indels ( Table 2 ) . We find no evidence of improved regression performance at sub-Megabase scales ( e . g . , 100 Kb , 500 Kb ) , as would be the case if some of the predictors considered in our analysis indeed carried much better explanatory power at such scales . On the other hand , using very large windows ( e . g . , 5 Mb , 10 Mb ) likely causes us to “average out” meaningful variation in rates and predictors , and because of the decreased number of windows , induces overfitting in our regression analysis . 1-Mb windows represent a good compromise in this respect , and are chosen for our main analysis—however , results for different scales are provided in Table 2 , and some comparisons are discussed below . The choice of 1-Mb windows is also supported by autocorrelation considerations . Gaffney and Keightley [17] argued that 1 Mb is a “natural” unit of variation for murine substitution rates because the partial autocorrelations among these rates computed in 100-Kb windows are very large at small lags , and rapidly fall to become nonsignificant at lags larger than 10—implying that at scales larger than 1 Mb , similarities among substitution rates can be explained as propagations of similarities at smaller scales [17] . Partial autocorrelation functions computed for human insertion and deletion rates in 100-Kb windows ( Figure S4 ) suggest that the “natural” variation unit may be somewhere between 1 Mb and 3 Mb . Contrary to expectation [8] , the results in Table 1 indicate that some significant predictors are shared by insertions and deletions , while others are not . Factors common to both types of mutations include a categorical variable indicating autosomal versus X-chromosomal location ( the X chromosome/autosome indicator ) , divergence , GC content , male recombination rate , distance to the telomere , and SINE count . In addition , insertion rates correlate with female recombination rates , poly ( A/T ) content , CpG island counts , and gene content; and deletion rates correlate with LINE counts . The X chromosome/autosome indicator is one of the top predictors for both insertion and deletion rates . The lower indel rates for chromosome X and the higher rates for autosomes corroborate the importance of replication errors in generating indels [9]; chromosome X spends less time in the male germline , and thus undergoes fewer replications than autosomes [10 , 11] . The male-to-female mutation rate ratio ( alpha; [10] ) is ∼7 and ∼17 for insertions and deletions , respectively . These values are , respectively , close to and higher than ∼6 , the corresponding ratio in the numbers of male versus female germline cell divisions for human [10] , but should be considered preliminary because alpha in closely related species is highly affected by polymorphisms in the ancestral population at the time of speciation [24] . Ancestral polymorphism can lead to an underestimation of divergence and hence obscure estimation of alpha , because the observed divergence between the species pair is in fact a sum of the true fixed divergence and of the ancestral polymorphism [24] . Currently , we cannot properly account for this effect due to the lack of sufficient data for indel polymorphisms; an initial map of human indel polymorphisms indicates equal levels of variation at chromosome X and autosomes [25] , which is likely influenced by a deficit of X chromosome traces ( a larger study of nucleotide substitution variation found significantly lower polymorphism level on X than on autosomes [26] ) . Additional indel polymorphism discovery through resequencing ( on chromosome X in particular ) will be useful in clarifying this issue . Recombination can also contribute to the observed differences in indel rates between X and autosomes; despite similarity in average recombination rates between these two types of chromosomes in humans [27] , the adjusted recombination rate of X is two-thirds that of autosomes ( accounting for the fact that X spends only two-thirds of the time in a recombining sex ) . By contrast , location on a particular autosome is at best a minor determinant of these rates . Adding chromosomal labels other than X to the regressions leads to either slight or no increase in the total share of explained variability , and to inconsistent results at various scales that are difficult to interpret biologically ( specifically , different autosomes appear significant at different window sizes; Table S6 ) . Moreover , the inclusion of autosomal labels does not alter results relative to other predictors ( Table S6 ) . All further discussion is therefore based on the regressions presented in Table 1 , which do not include autosomal labels . A significant positive association between deletion or insertion rates and divergence ( Table 1 , Figure 2B ) , also reported elsewhere [4 , 16 , 23] , supports the claim that some regions of the genome possess elevated mutability for several types of mutations [16] . The correlation between indel and nucleotide substitution rates can also be interpreted as evidence for the importance of replication in mutagenesis of indels; it is accepted that replication errors cause a substantial portion of nucleotide substitutions [11 , 28] . Note that divergence is a stronger predictor of deletion as compared with insertion rates . The association between GC content and both insertion and deletion rates again emphasizes the role of replication in generating indels . Indeed , the curvilinear relationship that is observed by plotting indel rates against GC content ( Figure 2C ) supports the origin of indels via replication slippage; GC-poor and GC-rich regions have a high occurrence of mononucleotide runs leading to increased slippage , and as a result to elevated indel rates [20] . Additionally , GC content is known to correlate with replication timing [29] . While both insertions and deletions are associated with male recombination rates , insertions are additionally and more strongly associated with female recombination rates ( Table 1 ) . These observations imply that more insertions than deletions are linked to recombination , and that recombination-mediated insertions might occur preferentially during female meiosis . There are established differences between the sexes in terms of meiosis and recombination that could contribute to these observations . For instance , compared with males , females display recombination rates that are higher on average and have a distinct intrachromosomal distribution: female rates are higher at centromeres , and not as elevated at telomeres [14 , 30] . Additionally , female meiosis is known to be more error-prone and to have 2-fold longer synaptonemal complexes than male meiosis [31] . Notably , in mouse , knockouts of several proteins important for double-strand break formation and repair lead to more pronounced defects for spermatogenesis than oogenesis [31] . Thus , our results are consistent with mechanistic differences between the two types of mutations that might be caused by such sexual dimorphism in recombination rates and/or in mammalian meiosis . Puzzlingly , similar to nucleotide substitutions [32] , both insertion and deletion rates increase near the telomeres , with a stronger effect for insertions than for deletions ( Table 1 ) . Several other predictors in the model co-vary with distance to telomeres—e . g . , recombination rate [27] and GC-content [33]—but the multiple regression approach allows us to evaluate each predictor , factoring out overlapping effects of other predictors included in the model ( see above ) . Thus , we have evidence that yet-unidentified factors may contribute to higher rates of both small indels and nucleotide substitutions near the telomeres [18 , 32] . Chromosome ends are known to possess special properties that provide clues to explaining elevated mutation rates in their vicinity . For instance , some distal regions do not exhibit the association between early replication and open chromatin that is observed in other regions of the genome [34] . Moreover , subtelomeric regions are enriched in sites undergoing meiotic nonhomologous end-joining , one of the major mechanisms of double-strand break repair in mammals [35] . These regions also have elevated rates of mitotic sister chromatid exchange , indicating that distal regions of the chromosome are subject to double-stranded breaks and/or repair at much greater frequencies than internal regions [36] . Faulty repair can potentially lead to indel formation . The incidence of SINEs appears to be strongly associated with both insertion and deletion rates , albeit in opposite ways ( Table 1 ) . A positive association between insertion rates and SINE counts might reflect the prominent role that SINEs play in promoting non-allelic homologous recombination ( particularly between Alus [37] ) , and is consistent with the importance of recombination for this type of mutation ( see above ) . Conversely , deletion rates display a strong negative association with SINE counts . Nucleotide substitution rates are also known to correlate negatively with the occurrence of SINEs [19] . The exact explanation for this phenomenon is unclear , however; SINEs are known to accumulate in different ( usually GC-rich ) portions of the genome as compared with other repeats [38] . Similarly to nucleotide substitutions [19] , deletions ( but not insertions ) are positively associated with occurrence of LINEs ( Table 1 ) . Insertion rates rise with increasing content of poly ( A/T ) runs ( Table 1 ) , which have been connected with high recombination rates [14] and are locally enriched near indel sites [6 , 8] . Because of their repetitive nature , poly ( A/T ) runs are also expected to be frequent sites of replication slippage events [7] . Furthermore , insertion rates are correlated with gene content and CpG islands ( Table 1 ) , although these predictors explain only a small fraction of the overall variability . Fitting regressions for different window sizes allows us to investigate the scales at which each genomic feature is the most highly connected to indel rates ( Table 2 ) . For instance , divergence , which is claimed to “naturally” vary at ∼1 Mb scale [17] , has higher RCVE at smaller scales . Similarly , GC content , known to vary even more locally [39] , loses its significance at larger window sizes . In contrast , the share of variability in deletion rates explained by SINEs increases with window size . The predictive power of the X/autosomal indicator and recombination rates ( sex-averaged , male- or female-specific ) , though fluctuating in magnitude , remains significant at almost all scales considered in Table 2 ( recombination rates are not significant at 0 . 1-Mb windows for deletions ) . In addition to considering different scales , we performed regressions ( at the 1-Mb scale ) for 1-bp human-specific indels , which constitute roughly 50% of our total dataset ( Table S4 ) , and ( separately ) for autosomal windows only ( Table S5 ) . The results of these analyses were largely consistent with our main findings . However , the total share of explained variability was lower than for the regressions summarized in Table 1 . The lower total share of variability explained for the 1-bp analysis can be attributed to the fact that the smaller number of indel events available in each window decreases the accuracy in the calculation of the rates . Despite the significance of the X chromosome/autosome indicator ( Table 1 ) , an analysis of autosomal-only windows confirms the differences in the relative contributions of the remaining genomic factors contributing to variation in indel rates ( Table S5 ) . Notably , the significance of the predictors is very similar to the genome-wide analysis reported in Table 1 . The decrease in total share of variability explained can be attributed to the importance of the observed differences in indel rates between X and autosomes . To further explore differences in the biological mechanisms contributing to indels , we identified and analyzed 1-Mb windows having extremely different insertion versus deletion rates . We selected 25 windows skewed toward insertions and 25 windows skewed toward deletions , each group constituting ∼1% of our original dataset ( Materials and Methods; Figure 2A ) . Contrasting such windows allows us to highlight factors more important for one type of mutation versus the other . This analysis confirmed our main results ( Table 1 ) . For instance , the incidence of SINEs is significantly higher in windows skewed toward insertions than in ones skewed toward deletions ( p < 0 . 0001 , one-tailed randomization test comparing two medians; Materials and Methods ) , verifying the opposite effect of SINE count on the two types of mutations . Female recombination rates , found to be positively associated with insertion rates , are significantly higher in windows skewed toward insertions than in windows skewed toward deletions ( p = 0 . 0051 ) . Similarly , inspection of windows with extremely different indel rate versus divergence allows us to identify genomic factors having stronger association with either indels or nucleotide substitutions ( Figure 2B; Materials and Methods ) . For instance , windows skewed toward nucleotide substitutions are significantly closer to telomeres than windows skewed toward indels ( p = 0 . 0033 ) , suggesting a greater importance of distance to telomere for nucleotide substitutions than indels . On the contrary , LINE counts are significantly higher in windows skewed toward indels than in windows skewed toward substitutions ( p = 0 . 0116 ) , suggesting a stronger association with deletions than divergence ( insertions are likely unaffected by LINE count; Table 1 ) . In a separate analysis , we investigated relationships between insertion or deletion rates and the proportion of a window occupied by so-called most conserved elements ( i . e . , content ) . Many of these elements are likely to be functional and include protein-coding exons , other transcribed regions , and conserved noncoding sequences potentially important for gene regulation and other cellular processes [40] . Interestingly , we found a sizeable negative correlation ( Pearson's correlation coefficient r = −0 . 26 , p < 0 . 0001; Figure 4 ) between deletion rates and content of most conserved elements ( the correlation for insertion rates was much weaker; r = −0 . 053 , p = 0 . 0070; Figure 4 ) . In agreement with this , the content of most conserved elements was significantly lower in windows skewed toward deletions than in windows skewed toward insertions ( p < 0 . 0409 ) . The indel rates studied here are estimated at ARs , known to have very little overlap with most conserved elements [40] . In spite of this , these results suggest , intriguingly , that regions of the genome dense in highly conserved ( and likely functional ) elements evolved to have low deletion rates , while still tolerating a certain amount of insertions . At the same time , we have evidence that these regions might tolerate more indels than substitutions—the content of most conserved elements is significantly lower in windows skewed toward substitutions than in windows skewed toward indels ( p = 0 . 0004 ) . The above discussion of genomic factors suggestive of similarities and differences in the mutagenesis of insertions and deletions leads to the following conclusions . First , our regression analyses are consistent with the importance of replication for generating both indels [7 , 9] . Indeed , many shared significant predictors either alter the probability of replication errors ( e . g . , GC content and the X/autosomal indicator , although the latter can also be affected by recombination rate differences ) or are caused by them ( e . g . , divergence ) . Next , while slipped mispairing has received most of the attention as a model for small indel mutagenesis , our results indicate that replication alone cannot account for all indel events . In contrast to analyses focusing on genes and emphasizing the role of replication [8] , we find that recombination rates are significant predictors for both insertions and deletion rates in the neutrally evolving regions of the genome , contradicting a recent study [41] . Finally , the differences between genomic factors significantly associated with insertions and deletions suggest that the relative contributions of replication versus recombination are unequal for these two types of mutations . The trends we observe for deletions are closer to those reported for nucleotide substitutions in other studies ( e . g . , positive association with male recombination rates and negative association with SINE occurrence [19] ) . In agreement with this , divergence is a stronger predictor for deletions than for insertions ( Table 1 ) . Since many nucleotide substitutions result from errors in DNA replication [11 , 28] , it is plausible that a large fraction of deletions are caused by replication-associated mechanisms as well . However , some deletions are probably caused by recombination as suggested by the significance of male recombination rates in our regression analysis , as well as by another study [42] . In stark contrast , female recombination rate and other recombination-related forces ( e . g . , positive association with SINE frequency ) are strongly connected to insertions . Thus , even though these patterns are difficult to quantify as a whole , our results suggest that replication-related factors are mostly important for deletions , while recombination-related effects are more pronounced for insertions . This is consistent with observations of unequal rates of insertions versus deletions , and distinct motifs for insertion and deletion hotspots [6 , 8] . Differentiating between insertions and deletions using the macaque sequence as an outgroup has implied that nontrivial mechanistic differences exist between the two types of mutations . The importance of recombination and replication to indel formation conveyed here warrants evaluation in future studies . Conceivably , such studies will also allow us to discriminate between the roles of replication and repair . Our in-depth investigation of neutrally evolving indels provides important insights into indel mutagenesis , with its implications for understanding human genetic diseases . In addition , it will aid in the development of better gap modeling techniques , which are crucial for improving alignment methodology and thus for inferences on genome evolution .
Alignment methods and parameters are critical for the identification of indels . Here we use the human–chimpanzee–macaque ( hg18–panTro2–rheMac2 ) three-way genome alignments that were produced by the MULTIZ algorithm [22] and are available at the University of California Santa Cruz Genome Browser ( UCSC ) ( http://genome . ucsc . edu ) . This BLASTZ-based local alignment tool was used to generate alignments of other mammalian genomes , and thus allows a comparison of our results with those of other studies , which employ comparisons of more diverged genomes ( e . g . , [9] ) . Correct placement of gaps in an alignment depends on the interplay between substitution rate matrix and gap penalties . Our pilot analysis indicated that the default MULTIZ matrix and gap penalties derived from human–mouse alignments are inappropriate for human–chimpanzee alignments . We therefore derived new parameters ( Table S1 ) : the substitution rate matrix was obtained by a probabilistic scoring scheme [43] , and the corresponding matrix-specific gap penalties were obtained by empirical testing , similar to other studies [44] . Although a rigorous statistical procedure to derive alignment gap penalties is currently unavailable , developing such procedure represents an active area of research [45–47] . If indel events are independent , intergap distances are expected to follow a geometric distribution [20] . However , an under-representation of short intergap distances ( <20 bp ) due to gap attraction has been noted for human–mouse alignments [20] . For alignments used in the present study , gap attraction is minimal and only occurs for intergap distances smaller than 4 bp ( Figure S1 ) . Consequently , we do not adjust gaps manually . MAF-formatted alignment blocks were restricted to human coordinates of ARs using Galaxy [48] . ARs were defined as RepeatMasker [49] annotations of DNA elements , LTRs , LINEs , and SINEs , excluding elements active since the human–macaque divergence time , namely L1PA1-A7 , L1HS , and AluY [38 , 50 , 51] . Custom PERL scripts ( available upon request ) were developed for the computational pipeline to identify and filter indels . A human-specific deletion was identified as a gap of one or more consecutive columns within a MAF local alignment block , covered by a nucleotide base at the orthologous position in both the chimpanzee and macaque genomes . Conversely , a human-specific insertion was defined as one or more consecutive nucleotides in human covered by gaps in both the chimpanzee and macaque genomes . Filtering of putative indels was further performed to remove potential false positives ( Table S2 ) . First , we excluded indels occurring in overlapping blocks of local alignments to avoid scoring the same loci more than once , which might result in indel rate inflation . This effectively removed most of the indels located in segmental duplications . In the final dataset , only ∼1% of indels overlapped with coordinates of segmental duplications as annotated by the UCSC Genome Browser ( as a comparison , ∼5% of the genome consists of segmentally duplicated regions; [52] ) . Second , microsatellites , simple repeats , and regions of low complexity as annotated by RepeatMasker for each of the three genomes were eliminated because they are usually difficult to sequence , assemble , and align accurately . Third , we required a minimum of three bases flanking each side of a gap to have a quality Phred score of 20 or higher in both the chimpanzee and macaque draft genomes . Indels violating this criterion were excluded . Fourth , gaps occurring at orthologous locations but having unequal length in different species were filtered out because such instances represent either sequence errors or multiple indel events [53 , 54] . Insertion and deletion rates were then calculated as the number of events per aligned ( ungapped ) human base in ARs covered by three-way alignments , correcting the denominator for nucleotides excluded due to filtering . To investigate regional variation in indel rates , we divided the human genome ( hg18 ) into nonoverlapping windows and estimated counts or content ( fraction of bases of the window ) for various genomic features to create a set of potential predictors ( Table S3 ) . The features ( based on the hg18 annotations in the UCSC Genome Browser ) were calculated at the level of 100-kb windows , and aggregated as needed when considering larger windows . The calculation of recombination rates is an exception to this rule , since different sources were used for different window sizes . For 1- , 5- and 10-Mb windows , sex-specific recombination rates were obtained from the UCSC Genome Browser deCODE data track [14] for build hg18 . For this track , marker DNA is aligned to each new assembly , thus the coordinates are taken into account when determining the corresponding physical distance ( the UCSC Genome Bioinformatics Group , personal communication ) . For 0 . 1- and 0 . 5-Mb windows , the computationally predicted recombination rates obtained from linkage disequilibrium analysis of human SNP data [55] were utilized , as sex-specific rates are not available at these scales . Human–macaque divergence was used instead of human–chimpanzee divergence because of a strong effect of ancient polymorphisms on the latter [24] , and was calculated at ARs using a REV model , as implemented in the baseml module of PAML [56] . In addition to various quantitative predictors obtained as counts and frequencies , we considered an indicator variable which labels each window as belonging to X ( “1” ) or autosomes ( “0” ) —this is also listed in Table S3 . Some of our fits also included labels for specific autosomes ( see Table S6 ) . The male-to-female mutation rate ratio , or alpha , was calculated according to [57] . Windows were excluded from the analysis at two stages of filtering; first , if they lacked data due to low sequencing coverage ( “N” content >50% of the window ) or if they lacked sufficient aligned AR coverage ( <20% of the window ) . Second , additional windows were excluded due to lack of recombination data and/or human–macaque divergence estimates . As the X chromosome is unique in having distinct ordered physical and evolutionary blocks or “strata” ( based on divergence from Y; [58] ) , windows located in pseudoautosomal and evolutionary stratum 5 regions of this chromosome were also excluded as they did not evolve as truly X-like over the evolutionary time examined . For instance , for 1-Mb windows , the initial total was 3 , 010 , the total after the first filtering 2 , 614 , and the total after the second filtering 2 , 568 . All computations were conducted using the R statistical package [59] . Standard regression diagnostics were used to identify and remove outliers ( Cook's distances , standardized residuals greater than 3 or smaller than −3 ) , assess goodness of fit for each model ( plots of residuals versus fits , normal probability plots ) , and evaluate predictors ( added variable plots , t-tests and general linear F-tests , partial R2 and similar measures , variance inflation factors ( VIF ) ) ; see [60] for details on these diagnostic techniques . In addition , the best subset regression selection procedure was employed to identify subsets of linear and quadratic terms to include in final regression models . For both insertion and deletion rates ( separately ) , model selection was performed at the 1-Mb scale ( and similarly at other scales ) , with the following approach . We started with the pool of predictors in Table S3 , and formed an overall set of terms comprising all linear and quadratic terms in the quantitative predictors and the X/autosomal indicator . Including squared terms allowed us to account for possible curvatures in the relationship with the response . This overall set of terms was subjected to a “best subset” selection procedure , which identified subsets with smallest Mallow's Cp value [60] . Similar to an adjusted R2 , Mallow's Cp selects subsets based on a balance between small mean square error ( MSE ) for the corresponding regressions , and parsimony ( small number of terms ) . Next , the regressions corresponding to the best subsets for insertion and deletion rates were further “pruned , ” eliminating terms whose coefficients were not significant after a Bonferroni correction [60] or which carried large VIFs ( see below ) . This led to the final models summarized in Table 1 . Adding autosomal labels as indicators to these regressions was also considered ( Table S6 ) , but produced only minor improvements in terms of R2 , with inconsistency in the results when refitting the models for different window sizes ( Table S6 ) . While many of the quantitative predictors are correlated , our regression fits for both indels are not adversely affected by multicollinearity , as shown by the relatively low values of the VIFs in Table 1 . VIFs are commonly used to diagnose multicollinearity . The VIF of a term in a regression measures how much the variance of its estimated coefficient increases relative to what it would have been if all predictors were orthogonal [60] . High values of VIF ( greater than ten ) indicate that the accuracy of the regression coefficient estimates is eroded by collinearity ( intuitively , that the least square solution is “less stable” because of the linear interdependencies among the predictors ) . VIF value of 1 means orthogonal ( uncorrelated ) predictors . All predictors from our final regressions reported in Tables 1 , 2 , and S4–S6 had VIFs lower than 10 . To assess the contribution of each individual predictor to the explanation of the total variability in the response , we use RCVE: Here and SSRfull are the R2 ( share of variability explained ) and the regression sum of squares of the full model ( includes all significant terms ) , while and SSRreduced are the corresponding quantities for the model obtained from the full model dropping all terms involving the predictor of interest ( i . e . , both linear and quadratic terms where applicable ) . The RCVE expresses the relative contribution of a predictor in the context of all other predictors included in the full model . Therefore , if the predictors are correlated , because of “overlaps” in the contribution , the numerators of the RCVEs do not add up to , and the RCVEs do not add up to 1 . The RCVE is a close relative of a more commonly used measure called partial R2—the formula for the latter has SSEreduced ( the error sum of squares for the reduced model ) instead of SSRfull , at the denominator . We find the RCVE to be more intuitive than the partial R2 because it uses the same denominator for all predictors in the same model . We evaluated the predictors in our models using the standard partial R2 , with very similar results and consistent conclusions ( unpublished data ) . We also checked whether residuals from our final regression models presented troublesome spatial autocorrelations among adjacent windows on each chromosome . Diagnostic plots of the residuals' partial autocorrelation function ( PACF ) for various lags ( here lags are measured in number of adjacent 1-Mb windows ) showed no substantial evidence against the assumption of independent errors on which the regression fits rely ( autocorrelation parameter values <0 . 2 do not violate the assumption of independent error terms ) ; moreover , the partial autocorrelation in residuals drops substantially compared with the partial autocorrelation in the response ( unpublished data ) . To further explore differences between indels , as well as substitutions , we considered a number of features ( some chosen among the predictors in our regression analysis , and some novel—e . g . , most conserved element content obtained from the UCSC Genome browser [40] ) , and compared them between groups of windows presenting very extreme behaviors in terms of these mutations . The groups were identified as follows . We ranked all windows used in the 1-Mb regression analysis according to insertion and deletion rates separately . Next , we computed the difference between each window's ranks in terms of insertion and deletion rates , and selected windows in the ∼1% left and right tails of the distribution of rank differences . These two groups ( 25 1-Mb windows each ) represent genomic locations extremely skewed toward deletions ( versus insertions ) and toward insertions ( versus deletions ) , respectively . Note that this rank analysis is completely nonparametric and robust to the nature of the relationship between the two mutation types . Median values of some regression predictors and other variables ( e . g . , fraction of a window covered by most conserved elements ) were calculated for the two groups . To test whether differences in medians between the two groups were significant , we used a randomization procedure . We randomly sampled ( without replacement ) two groups of 25 windows each , and computed the differences in medians between them for all variables considered . Repeating this 10 , 000 times allowed us to construct empirical null distributions for each difference in medians for variables of interest , and thus empirical p-values . The same approach was used to identify windows extremely skewed toward indels ( versus substitutions ) and toward substitutions ( versus indels ) , and to test for differences in medians for various variables between these two groups . The windows analyzed in this section were randomly distributed among and within chromosomes ( i . e . , did not cluster to specific regions in the genome ) . | Insertions and deletions ( indels ) represent a significant source of evolutionary change . In this manuscript , the authors investigate the patterns of genome-wide rate variation for indels that occurred in the human lineage since its divergence from chimpanzee . Earlier work suggested that insertion and deletion rates are correlated , implying that some genomic factors might affect both types of mutations and thus their patterns of variation across the genome . However , sequences evolving under and without selection were considered together . The present study represents the first attempt to quantify the levels of variation in neutral indel rates in the framework of multiple regression analysis . The finding that insertion versus deletion rates correlate with different genomic features suggests that these two types of mutation are caused in part by distinct molecular mechanisms . This conclusion has direct implications for understanding human genetic diseases , since a large number of them are caused by indels , and contributes to the growing recognition of the importance of fine-scale rearrangement in shaping genome evolution . | [
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] | 2007 | A Macaque's-Eye View of Human Insertions and Deletions: Differences in Mechanisms |
Polyglutamine expansion causes diseases in humans and other mammals . One example is Huntington's disease . Fragments of human huntingtin protein having an expanded polyglutamine stretch form aggregates and cause cytotoxicity in yeast cells bearing endogenous QN-rich proteins in the aggregated ( prion ) form . Attachment of the proline ( P ) -rich region targets polyglutamines to the large perinuclear deposit ( aggresome ) . Aggresome formation ameliorates polyglutamine cytotoxicity in cells containing only the prion form of Rnq1 protein . Here we show that expanded polyglutamines both with ( poly-QP ) or without ( poly-Q ) a P-rich stretch remain toxic in the presence of the prion form of translation termination ( release ) factor Sup35 ( eRF3 ) . A Sup35 derivative that lacks the QN-rich domain and is unable to be incorporated into aggregates counteracts cytotoxicity , suggesting that toxicity is due to Sup35 sequestration . Increase in the levels of another release factor , Sup45 ( eRF1 ) , due to either disomy by chromosome II containing the SUP45 gene or to introduction of the SUP45-bearing plasmid counteracts poly-Q or poly-QP toxicity in the presence of the Sup35 prion . Protein analysis confirms that polyglutamines alter aggregation patterns of Sup35 and promote aggregation of Sup45 , while excess Sup45 counteracts these effects . Our data show that one and the same mode of polyglutamine aggregation could be cytoprotective or cytotoxic , depending on the composition of other aggregates in a eukaryotic cell , and demonstrate that other aggregates expand the range of proteins that are susceptible to sequestration by polyglutamines .
A variety of human neurodegenerative disorders are associated with expansions of polyglutamine ( poly-Q ) repeats in certain proteins [1] , [2] . One well known example is Huntington's disease ( HD ) , which is caused by an expansion of the poly-Q stretch , located within the N-terminal stretch of the essential protein called huntingtin ( Htt ) [3] . Poly-Q expansion promotes formation of aggregates by the proteolytic Htt fragments containing an expanded poly-Q stretch [4] , [5] . As the poly-Q-expanded N-terminal region of Htt is shown to aggregate and produce HD-like neurodegeneration in the mouse model , it is clear that this region is sufficient for reproducing the characteristic features of poly-Q aggregation and toxicity [6] , [7] . Poly-Q associated pathologies can not be explained solely by the loss of the cellular function of a respective protein , e . g . Htt ( for review , see [1] ) . Sequestration of other essential proteins by poly-Q aggregates was proposed to be a possible mechanism for toxicity [1] , [8] . However , different experimental models suggested different candidates for sequestration [9]–[12] , which decreased enthusiasm for the sequestration model . To complicate matters further , expanded poly-Q proteins form various types of aggregates in mammalian cells [13] , [14] . In the case of Htt , both nuclear and cytoplasmic aggregates were found [4] , [15] , [16] . Their contributions to poly-Q pathogenicity remain a topic of intense discussion [17] , [18] . At least , most researchers agree that one type of cytoplasmic aggregated structure , so-called “aggresome” , plays a cytoprotective role via assembling poly-Q expanded Htt at one site and possibly promoting its autophagy-dependent clearance [19]–[22] . The aggresome is located perinuclearly , associated with the centrosome , and assembled with participation of the microtubular cytoskeleton . Other misfolded proteins can also be sequestered into an aggresome , indicating that this structure serves as a universal quality control depot for aggregating proteins [19] , [23]–[27] . Experimental assays for studying the molecular mechanism of poly-Q aggregation and toxicity have been developed in the yeast Saccharomyces cerevisiae [28]–[34] . It has been shown that cytoplasmic aggregation and toxicity of the chimeric protein , generated by a fusion of the expanded poly-Q stretch of Htt to the green fluorescent protein ( GFP ) , is facilitated by the presence of the endogenous yeast prions , [PIN+] and/or [PSI+] [30] , [35] . In the absence of a prion , aggregates of this construct were rare , and no significant cytotoxicity was detected . However , in the presence of a prion , multiple peripherally located aggregates were formed , and cytotoxicity was observed [30] . The prions [PIN+] and [PSI+] are self-perpetuating aggregates of the endogenous yeast proteins Rnq1 ( unknown function ) and Sup35 ( translation termination , or release factor , also called eRF1 ) , respectively ( for review , see [36] ) . Both of these proteins contain QN-rich prion domains ( PrDs ) that are responsible for aggregation properties ( for review , see [37] , [38] ) . It is likely that pre-existing prion aggregates nucleate aggregation of poly-Q expanded huntingtin . In the case of the Rnq1 prion , it was shown that poly-Q aggregates sequester some cytoskeletal components and inhibit endocytosis , which apparently contributes to cytotoxicity [39] . Inhibition of endocytosis was also detected in mammalian cells expressing poly-Q [40] . As mammalian Htt has been proposed to play a role in vesicle trafficking [41] , these results are likely relevant to human HD . Flanking sequences modulate poly-Q toxicity [32] , [42] . In yeast strains containing the Rnq1 prion , cytotoxicity was eliminated by using a longer Htt fragment , which includes a proline ( P ) -rich stretch in addition to poly-Q . This P-rich stretch was shown to target aggregated poly-Q protein into a single perinuclear microtubule-dependent deposit , co-localized with the spindle body ( yeast counterpart of a centrosome ) and therefore resembling a mammalian aggresome [42] . The cytoprotective role of the aggresome , as opposed to cytotoxicity of some other types of aggregates , recapitulates the situation previously observed in mammalian cells [20] , [21] , [23] . While the prion form of Sup35 protein ( [PSI+] ) also promotes poly-Q toxicity in the yeast assay [35] , the mechanism for this toxicity has not been studied in detail previously . In our current work , we demonstrate that [PSI+]-dependent poly-Q toxicity is not counteracted by aggresome formation , but is ameliorated by an increased dosage of some components of the translational termination machinery . These data show that targets of poly-Q toxicity and the cytoprotective potential of the aggresome depend on the composition of endogenous aggregated proteins in a eukaryotic cell .
To distinguish between the different patterns of poly-Q aggregation in yeast , we have employed the previously described constructs ( Figure 1A ) that produce the N-proximal region of Htt , fused to the FLAG epitope at the N-terminus and the green fluorescent protein ( GFP ) at the C-terminus . The N-terminal Htt region included the poly-Q stretch , which is either followed ( poly-QP ) or not followed ( poly-Q ) by the P-rich region . The poly-Q expanded versions ( 103Q and 103QP ) contained a stretch of 103 glutamine residues , which corresponds to a severe form of Huntington's disease , while control non-aggregating versions ( 25Q and 25QP ) contained 25 glutamine residues . As there was no difference in the effects of 25Q and 25QP , some of the figures show only the 25Q control . As described previously [30] , the 103Q construct was toxic to yeast strains containing either [PIN+] ( Rnq1 protein in a prion form ) or [PSI+] ( Sup35 protein in a prion form ) , with a combination of both prions showing an additive effect ( Figure 1B ) . Also in agreement with previous observations [42] , the 103QP construct was not toxic to the strains containing only Rnq1 prion . Surprisingly , the 103QP construct was toxic to the strains containing the Sup35 prion , independently in the presence or absence of Rnq1 prion ( Figure 1B ) . Fluorescence microscopy confirmed that 103QP preferentially formed a single perinuclear aggregate deposit ( aggresome ) in the cells containing Rnq1 and/or Sup35 prions , while 103Q produced multiple peripheral aggregates ( Figure 1C ) . Therefore , the ability of poly-QP to form an aggresome was not affected by the Sup35 prion , however , amelioration of toxicity by the aggresome was impaired . These data show that the mechanism of polyglutamine toxicity , promoted by the Sup35 prion , is different from the mechanism of polyglutamine toxicity promoted by the Rnq1 prion . Unless stated otherwise , all further experiments were performed in the strains containing the [PIN+] prion , thereby comparing the [PSI+] and [psi−] derivatives so that we could distinguish the effects attributed specifically to the [PSI+] prion . Notably , when Sup35NM , tagged with DsRed , and 103QP-GFP are co-overproduced in the [PSI+] strain , most of the Sup35NM-DsRed is eventually assembled into one large deposit , that is either partially or completely overlapping with the 103QP aggresome ( Figure 1D ) . This indicates a possibility of sequestration of Sup35 by the aggresome , and agrees with the previous observation [43] , confirmed by us ( Figure 1E ) that polyglutamines promote aggregation of a fraction of Sup35 , even in a [psi−] strain . Notably , 103QP toxicity in the [PSI+] cells was ameliorated by introducing the Sup35 derivative ( designated Sup35C ) that lacks the N-terminal ( prion ) and middle domains and therefore , is functional but unable to be incorporated into the prion aggregates ( Figure 1F ) . Expression of Sup35C also decreased toxicity of 103Q in the [PIN+ PSI+] strain down to the levels observed in the [PIN+ psi−] strain . However , Sup35C did not affect toxicity of 103Q in [PIN+ psi−] . These data confirm that in the [PSI+] strain ( but not in the [PIN+ psi−] strain ) , sequestration of Sup35 contributes to polyglutamine toxicity . Next , we looked for other genetic factors influencing the [PSI+]-dependent polyglutamine toxicity . As the ubiquitin-proteasome system ( UPS ) is known to influence poly-Q effects in mammalian cells , we have studied poly-Q toxicity in the yeast strain with a deletion of the UBC4 gene , coding for one of the major yeast ubiquitin-conjugating enzymes [44] . Ubc4Δ did not improve growth in the presence of polyglutamines ( Figure 2A ) however the ubc4Δ 103Q strain produced spontaneously arising fast-growing papillae ( Figure 2B ) . Three independent papillae were analyzed further , and each was confirmed to stably reproduce the anti-toxic phenotype ( Figure 2B and 2C ) , and also to ameliorate toxicity of 103QP ( Figure 2D ) . These derivatives were named AQT for Anti-polyQ Toxicity , with respective phenotype designated as Aqt+ . Specific effect of AQT on toxicity caused by expanded polyglutamines was especially pronounced after longer periods of incubation ( Figure 2C ) . All AQT derivatives retained the [PIN+] and [PSI+] prions ( data not shown ) . In each derivative , the Aqt+ phenotype was dominant ( see Figure 2E as an example ) and segregated in a Mendelian fashion in meiosis ( Table S1 ) . All pairwise genetic crosses between three independent AQT derivatives produced 4 AQT: 0 wild-type pattern of segregation in the vast majority of tetrads ( Table S2 ) , indicating that all AQT derivatives are formally confined to a single genetic locus . Reintroduction of the wild-type UBC4 gene into the AQT strain decreased but did not completely eliminate amelioration of toxicity , indicating that ubc4Δ strengthens the Aqt+ phenotype but is not required for its manifestation ( Figure 2F ) . Despite their anti-toxic effect , AQT derivatives retained the typical mode of cytologically detectable aggregation for both 103Q ( multiple peripheral aggregates , Figure 3A ) and 103QP ( single perinuclear aggregate , Figure 3B ) , indicating that amelioration of toxicity is not due to a lack of aggregation . Overproduction of Sup35 protein or its prion domain , Sup35N , is known to inhibit growth of the [PSI+] strains [37] , [38] . This effect was ameliorated in AQT derivatives ( Figure 3C ) . The AQT strains also exhibited additional phenotypes that were not directly related to amelioration of toxicity , such as compensation of the ubc4Δ-mediated temperature sensitivity and loss of the invasive growth capability ( Figure S1 ) . AQT also slightly increased the growth of the [PIN+ psi−] ubc4Δ strain expressing 103Q , especially after shorter incubation periods ( Figure S1D ) , possibly due to increased robustness of the AQT ubc4Δ strain in the stressful conditions . Genetic crosses aimed at characterizing the inheritance of AQT revealed that AQT is centromere-linked ( Figure S2A ) . Moreover , when the original AQT derivative , obtained in the strain bearing the ubc4Δ::HIS3 disruption was mated to the wild-type strain bearing the ubc4Δ::KanMX disruption ( causing resistance to the antibiotic G418 ) , both AQT and KanMX markers segregated 2∶2 in meiosis as expected , while majority of tetrads did show 3∶1 or 4∶0 segregation for the His+ phenotype , indicative of the presence of two copies of the HIS3 allele in the cross ( Figure 4A ) . Notably , all AQT spores ( 76 total ) obtained from tetrads with a 2∶2 ratio for KanMX were His+ . The simplest scenario compatible with these ratios is that the AQT derivative is a disomic of chromosome II , where the ubc4Δ:: HIS3 allele is located . As chromosome segregation is controlled by the centromere , this would also explain the centromere linkage of AQT . Indeed , fractionation of the yeast chromosomes via CHEF ( Contour-clamped Homogeneous Electric Field ) gel electrophoresis confirmed that each of the three independent AQT derivatives contains an additional copy of the chromosome II band ( Figure S2B ) . Extra-copy of chromosome II also co-segregated with AQT in tetrad analysis ( data not shown ) . Microarray-based analysis of genomic DNA of the three original AQT derivatives and two AQT meiotic segregants confirmed that each of these strains contains an extra copy of every piece of the coding material in chromosome II ( Figure 4B ) . Overall , our data demonstrate that AQT is associated with an extra copy of chromosome II . Sequential deletion analysis of the extra copy of chromosome II in the AQT strain confined the gene responsible for amelioration of toxicity to the region of the right arm , located between positions 528161 and 537490 ( Figure 4C ) . This region contains 5 ORFs , including the essential gene SUP45 , that codes for a translation termination factor Sup45 , or eRF1 , working together with Sup35 ( eRF3 ) [45] . We have disrupted the copy of the SUP45 gene , located on the duplicated chromosome II in the AQT strain , and have shown that this disruption eliminates the anti-toxicity effect on both 103Q and 103QP ( Figure 4D ) . Notably , other phenotypes , associated with chromosome II disomy but not related to amelioration of polyglutamine toxicity in the [PSI+] strain , including slightly increased growth of the [PIN+ psi−] strain in the presence of 103Q , were not affected by sup45Δ ( Figure S1B , S1C and S1E ) . Western blot analysis confirmed that the AQT derivative contains more Sup45 protein , compared to the isogenic wild type strain ( Figure 4E ) . This increase was more profound in the ubc4Δ than in the UBC4+ background . This explains why the AQT effect was better seen in ubc4Δ . Thus , an increase in Sup45 levels due to the presence of an extra-copy of the SUP45 gene is responsible for the anti-toxic ( Aqt+ ) phenotype in the [PSI+] background . Next , we checked if an increase in the Sup45 levels , produced by means other than duplication of chromosome II , would also ameliorate the [PSI+]-dependent polyglutamine toxicity . Indeed , introduction of the centromeric plasmid bearing the SUP45 gene under its own ( Figure 5A ) or galactose-inducible ( Figure 5B ) promoter ( in the latter case , under inducing conditions ) ameliorated toxicity of both 103Q and 103QP . Anti-toxic effect of plasmid-borne SUP45 was clearly detected in both ubc4Δ and UBC4+ backgrounds , indicating that it is less sensitive to the presence of Ubc4 protein , compared to the chromosomal extra copy . For both plasmids , Sup45 overproduction was confirmed by protein analysis ( Figure 5C and 5D ) . Ability of the extra-copy of SUP45 to ameliorate polyglutamine toxicity was abolished by a deletion of 19 C-terminal amino acids ( Figure 5E ) , that impairs Sup45 function in translation and interaction with Sup35 [46] , or by the missense mutation sup45-103 , T62C ( Figure 5F ) that also impairs Sup45 function in translation termination [47] . Thus , Sup45 ability to ameliorate toxicity depends on the same sequence elements that control its function in translational machinery . As both polyglutamines and prion form of Sup35 form SDS-resistant polymers in the yeast cells , we have checked if patterns of their aggregation are influenced by the presence of an extra copy of SUP45 . Both 103Q and 103QP proteins exhibit a broad range of distribution of the SDS-resistant polymers by size , as demonstrated by semi-denaturing agarose gel electrophoresis ( SDD-AGE ) , with 103QP containing more protein in the higher molecular weight ( MW ) fraction ( Figure 6A ) . This result confirms that the aggresome , formed by 103QP , contains insoluble protein aggregates , in contrast to the juxtanuclear quality control compartment ( JUNQ ) observed in the yeast cells with a defect of the ubiquitin-proteasome system [48] . Neither 103Q nor 103QP polymer distribution was significantly affected by AQT ( Figure 6A ) . In the wild type [PSI+] cells containing non-expanded polyglutamines ( 25Q ) , Sup35 prion polymers were distributed within a relatively narrow range of sizes ( Figure 6B ) . However , in the presence of either 103Q or 103QP , size range of the Sup35 polymers was increased and higher molecular weight ( MW ) polymers were accumulated , suggesting that some Sup35 could be associated with 103Q ( or 103QP ) polymers , therefore partly following their distribution . Notably , the Sup35 polymer size range became narrower in the presence of AQT , and the high MW fraction , which depends on 103Q/QP , disappeared ( Figure 6B ) . This suggests that the extra dosage of Sup45 somewhat counteracts the increase in size of the Sup35 polymers and possibly , their interaction with polyglutamines . We have also checked effects of polyglutamines and gene dosage on patterns of Sup45 aggregation . Sequestration of Sup45 by the Sup35 prion aggregates is known to contribute to cytotoxicity of overproduced Sup35 in [PSI+] strains [49] . We could not detect aggregate-associated Sup45 by SDD-AGE ( data not shown ) , apparently because it is not converted into an amyloid form and is therefore released after SDS treatment . However , centrifugation analysis demonstrated that presence of either [PSI+] prion or 103Q protein resulted in the shift of a fraction of Sup45 protein to the pelletable ( aggregate-associated ) form , with both prion and 103Q together having an additive effect ( Figure 6C ) . 103QP did not exhibit any observable effect on Sup45 aggregation in the [psi−] strain , however it further increased Sup45 aggregation in the presence of [PSI+] . Remarkably , proportion of the pelletable versus soluble Sup45 was decreased in the AQT ( disomic ) [PSI+] strain expressing 103Q or 103QP , compared to the identical strain not possessing disomy ( Figure 6D ) . This showed that an increase in Sup45 levels counteracted its sequestration by aggregates . Overall , our data indicate that both release factors , Sup35 and Sup45 , are sequestered by polyglutamine aggregates in the [PSI+] cells , and that excess Sup45 not only restores supply of functional Sup45 but also changes the mode of Sup35 aggregation . As our data point to sequestration of release factors as a mechanism of polyglutamine toxicity , we have checked if polyglutamines increase translational readthrough of stop codons . For this purpose , the chimeric constructs bearing a stop codon between the PGK1 and lacZ ORFs have been employed . Surprisingly , no increase in translational readthrough ( measured by β-galactosidase activity ) has been detected in the presence of 103Q ( Table S3 ) . One possible explanation of these data is that damage to translational machinery , caused by the aggregation and sequestration of release factors in the presence of polyglutamines , is so severe that translation is arrested and not proceeding beyond the stop codon . Another ( but not mutually exclusive ) possibility is that cytotoxicity is related to non-translational functions of Sup35/45 . Indeed , it has been reported that the immediate consequence of the severe shortage of a release factor in yeast is not translational defect per se , but cytoskeleton damage leading to cell death [50] .
Our data demonstrate that mechanism of polyglutamine toxicity depends on the prion composition of the cell . In fact , it appears that polyglutamine protein is not the toxicity agent itself , but rather amplifies the effects of the endogenous prion aggregates by sequestering them and making them more rigid . In case of Rnq1 prion , sequestration of Rnq1 by polyglutamines also leads to sequestration of the cytoskeletal proteins , interacting with Rnq1 , and subsequent impairment of endocytosis [39] , [40] . In this case , toxicity is relieved by re-localization of polyglutamines to an aggresome that removes polyQ ( and possibly Rnq1 ) from the endocytic sites [42] . However , in case of Sup35 prion , relocalization is not sufficient for amelioration of toxicity . This could be due to the fact that Sup35 itself is an essential protein , and/or due to its normal distribution all over the cytoplasm , making it impossible to define specific toxicity sites . Additive action of Rnq1 and Sup35 prions on 103Q toxicity in the absence of the P-rich region also confirms that their cytotoxic effects are at least partly independent of each other . Remarkably , our data show for the first time that the aggresome is not always cytoprotective . Moreover , it is possible that formation of the aggresome in the cell containing an essential protein in the form of self-perpetuating amyloid ( prion ) is itself cytotoxic due to sequestration of this essential protein . However , it remains uncertain whether toxicity is primarily driven by sequestration of Sup35 into an aggresome , or by its sequestration into the smaller polyglutamine aggregates remaining in the cytoplasm . While some Sup35 is definitely detected in the aggresome ( Figure 1D ) , we don't know if the functional fraction of Sup35 is sequestered there . Moreover , while it is obvious that some fraction of Sup35 should retain function in the [PSI+] strain , as elimination of Sup35 is lethal [51] , it remains unknown whether this functional component of Sup35 is represented by residual non-aggregated Sup35 , smaller oligomers , or both . However , it is more likely that a fraction of oligomeric Sup35 remains functional , as amount of monomeric Sup35 retained by the strong [PSI+] strains , that is essentially at the limit of detection , seems too low for maintaining viability . Indeed , Sup35C regions are not included in the amyloid core and may stay enzymatically active , so that only size , composition and/or location of the aggregate would modulate its functionality in the cellular context . Changes in the distribution of Sup35 polymers by size in the presence of polyglutamines ( Figure 6B ) , clearly show that certain alterations of Sup35 aggregation patterns , making them more similar to poly-Q aggregation patterns , coincide with toxicity . Amelioration of [PSI+]-dependent cytotoxicity by extra-dosage of the Sup35 functional partner , Sup45 , confirms that toxicity results from sequestration of release factor ( s ) by polyglutamine aggregates . Possibly Sup35 , containing a QN-rich domain , is sequestered directly , while Sup45 is sequestered via its interaction with Sup35 . Indeed , ability of polyglutamines to facilitate aggregation of endogenous QN-rich proteins even in a non-prion strain has been reported previously [43] , [52] and confirmed by us ( Figure 1E and Figure 6D ) , and it was shown that Sup35 prion aggregates produced at high levels cause toxicity via sequestering Sup45 [49] . In agreement with these data , AQT ( i . e . , extra copy of SUP45 ) ameliorates both polyglutamine toxicity ( Figure 2 ) and toxicity of excess Sup35 ( Figure 3C ) in the [PSI+] cells . There is probably a competition for the Sup35/Sup45 complex between polyglutamine aggregates and functional sites ( ribosome etc . ) at which the Sup35/Sup45 complex is supposed to act . Therefore , an increased abundance of Sup45 not only increases a proportion of non-sequestered Sup45 but also partly counteracts sequestration of Sup35 , that can be seen as a change in size distribution of the Sup35 polymers ( Figure 6B ) . Hence , the antitoxic effect of excess Sup45 . The AQT derivatives were originally detected in the strain , lacking the major ubiquitin-conjugating enzyme Ubc4 . One obvious reason for this is that amelioration of toxicity by AQT is more pronounced in the absence of Ubc4 ( Figure 2F ) , making detection of the anti-toxic papillae easier . It is possible that Ubc4 promotes ubiquitination and subsequent degradation of a fraction of excess Sup45 , that agrees with a more profound increase in Sup45 protein levels , which was detected in the ubc4Δ strain bearing an extra-copy of SUP45 , in comparison to the isogenic UBC4+ strain ( Figure 4E ) . In addition , ubc4Δ may influence patterns of polyglutamine aggregation and/or ability of polyglutamines to sequester other proteins . Indeed , defects of the ubiquitin system are known to promote aggresome formation in mammalian cells [20] , and ubc4Δ influences formation and aggregation of the [PSI+] prion yeast , as well as levels of some Hsps and patterns of their interactions with prion aggregates [53] . Another , although not necessarily exclusive explanation of increased AQT appearance in ubc4Δ cells is that a lack of Ubc4 may affect chromosome segregation and/or recombination , therefore increasing the frequency of chromosome non-disjunction . Ubiquitination and ubiquitin-dependent protein degradation are involved in regulation of DNA repair and chromosome segregation [54] , [55] . Ubc4 is implicated in ubiquitination of histones [56] , and ubc4Δ is shown to affect proper segregation of some yeast plasmids [57] . Persisting variations of the chromosome II size in AQT strains ( for example , see Figure S2B ) and occasional appearance of weak additional bands on the CHEF gels of the ubc4Δ strains ( data not shown ) speak in favor of a detrimental effect of ubc4Δ on chromosome stability . It is possible that the presence of the foreign DNA ( KanMX insertion ) on chromosome II of the ubc4Δ strain aids in destabilization of this specific chromosome . Irrespective of the mechanism of the ubc4Δ effect , this deletion is required for neither maintenance of the chromosome II extra copy nor toxicity amelioration . The UBC4+ strains with an extra copy of chromosome II were obtained by genetic cross and dissection and continued to maintain a disomy ( data not shown ) , and amelioration of toxicity by excess Sup45 was still detected in the UBC4+ strain , at a lower level in case of chromosome II extra copy ( Figure 2F ) and at more profound level for the plasmid-mediated excess Sup45 that is less sensitive to the presence of Ubc4 ( Figure 5 ) . Involvement of translational machinery in HD has been suspected from some results in mammalian systems [58] . It remains unknown if polyglutamines can sequester the human homologs of Sup35 and Sup45 ( respectively , eRF3 and eRF1 ) , as mammalian ortholog of Sup35 does not have a QN-rich domain . However , our results could be relevant to mammalian systems in a more general way . About 40% of the variation in the age of HD onset , in cases where the polyglutamine repeat is of the same length , is due to DNA variation [59] . Our work provides a potential explanation for such a variation by demonstrating that changes in the abundance of the sequestered protein ( s ) , occurring via alteration of either gene dosage or gene expression , can modulate polyglutamine toxicity . A non-DNA component of variations in polyglutamine toxicity can be explained by differences in the composition of other aggregated proteins ( e . g . endogenous self-perpetuating aggregates or prions ) present in the cell . Our results show that prion composition of the cell not only drives polyglutamine toxicity but also determines a pathway via which polyglutamines influence cell physiology , as proteins already associated with the other aggregates are more likely to be sequestered by polyglutamines . Mammalian cells contain a variety of proteins with the prion-like QN-rich domains , and machinery for propagation of the QN-rich protein aggregates exists in mammals [60] . Protein aggregation can also be induced by oxidative damage and other stresses . It was reported that artificially generated β-rich aggregates may sequester other proteins [61] . It is therefore entirely possible that organisms or tissues ( or both ) differ by the aggregate composition , and this in turn influences their susceptibility to polyglutamine disorders . Composition of endogenous aggregates may also modulate which proteins are sequestered by polyglutamines , as proteins associated with other aggregates interacting with polyglutamines are more likely to be sequestered , like Sup45 in the cells containing the Sup35 prion . This could explain why different groups are coming out with different conclusions in regard to both mechanisms of polyglutamine toxicity and contributions of different types of polyglutamine aggregates .
The Saccharomyces cerevisiae strains , used in this study and listed in Table S4 , are derivatives of GT81 series [62] of the prototype haploid genotype ade1 his3 leu2 lys2 trp1 ura3 , with different mating types and various prion compositions . The individual gene deletions were made by using PCR-mediated transplacement with the cassette bearing either Schizosaccharomyces pombe HIS5 gene , an ortholog of S . cerevisiae HIS3 gene ( thus designated in this paper as HIS3 ) , or bacterial kanr gene , which causes resistance to G418 in yeast [63] . Spontaneous AQT mutants were initially obtained in the strain GT349 ( MATa ubc4Δ::HIS3 [PIN+ PSI+] ) , as described in Results . Standard yeast media , procedures ( including transformation , phenotype scoring , velveteen replica plating , mating and sporulation ) , and growth conditions were used [64] . Yeast cultures were grown at 30°C except for the temperature-sensitivity assays ( employing 39°C ) . Tetrad dissection was performed by using the MSM System 300 micromanipulator from Singer Instrument Co . Ltd . Analysis of yeast chromosomes by CHEF ( Contour-clamped Homogeneous Electric Field ) is described in Text S1 . Polyglutamine toxicity was detected as growth inhibition on the synthetic dropout medium with galactose instead of glucose where polyglutamine constructs were selectively maintained and induced . As most of our polyglutamine constructs were expressed from plasmids bearing the URA3 marker , the plasmid-selective galactose medium ( -Ura/Gal ) was used , unless stated otherwise . Polyglutamine toxicity becomes more evident after longer incubation periods , as also confirmed by growth curves ( see Figure 2C ) . Typically , velveteened plates were scanned following 5–10 days of incubation after a second passage on galactose medium , while spotted from solution ( without dilutions ) plates were scanned after 3–5 days , and serial decimal dilutions spotted onto galactose medium were scanned after 2–3 weeks . Major plasmids used in this study are described in Text S1 . A list of plasmids is available in Table S5 . Strategy of making chromosomal deletions is described in Text S1 . Fluorescence microscopy was performed according to standard techniques , as described in Text S1 . Protein isolation and electrophoresis are described in Text S1 . Semi-Denaturing Detergent-Agarose Gel Electrophoresis ( SDD-AGE ) , used to fractionate the SDS-resistant protein polymers according to their sizes , was performed according to the standard protocol [65] with slight modifications . Proteins were diluted in 2% SDS , incubated for 5 min at room temperature before loading , run in the 1 . 5% agarose gel with 0 . 1% SDS in 1X TAE buffer containing 0 . 1% SDS , transferred to nitrocellulose membrane ( Whatman ) by capillary blotting , and reacted to appropriate antibody . Assay for β-galactosidase activity was performed according to the standard protocol [66] , except that cell debris was removed by centrifugation to avoid light scattering before the OD reading at 420 nm was taken . Description of antibodies used in this study can be found in Text S1 . Gene copy number was determined by hybridization to the complete DNA microarray of the Saccharomyces cerevisiae genome , as described previously [67] . Detailed information can be found in the Text S1 . | Polyglutamine diseases , including Huntington disease , are associated with expansions of polyglutamine tracts , resulting in aggregation of respective proteins . The severity of Huntington disease is controlled by both DNA and non–DNA factors . Mechanisms of such a control are poorly understood . Polyglutamine may sequester other cellular proteins; however , different experimental models have pointed to different sequestered proteins . By using a yeast model , we demonstrate that the mechanism of polyglutamine toxicity is driven by the composition of other ( endogenous ) aggregates ( for example , yeast prions ) present in a eukaryotic cell . Although these aggregates do not necessarily cause significant toxicity on their own , they serve as mediators in protein sequestration and therefore determine which specific proteins are to be sequestered by polyglutamines . We also show that polyglutamine deposition into an aggresome , a perinuclear compartment thought to be cytoprotective , fails to ameliorate cytotoxicity in cells with certain compositions of pre-existing aggregates . Finally , we demonstrate that an increase in the dosage of a sequestered protein due to aneuploidy by a chromosome carrying a respective gene may rescue cytotoxicity . Our data shed light on genetic and epigenetic mechanisms modulating polyglutamine cytotoxicity and establish a new approach for identifying potential therapeutic targets through characterization of the endogenous aggregated proteins . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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"and",
"Methods"
] | [
"biology"
] | 2012 | Polyglutamine Toxicity Is Controlled by Prion Composition and Gene Dosage in Yeast |
Lymphatic filariasis ( LF ) and human immunodeficiency virus ( HIV ) are major public health problems . Individuals may be co-infected , raising the possibility of important interactions between these two pathogens with consequences for LF elimination through annual mass drug administration ( MDA ) . We analysed circulating filarial antigenaemia ( CFA ) by HIV infection status among adults in two sites in northern Malawi , a region endemic for both LF and HIV . Stored blood samples and data from two geographically separate studies were used: one a recruitment phase of a clinical trial of anti-filarial agent dosing regimens , and the other a whole population annual HIV sero-survey . In study one , 1 , 851 consecutive adult volunteers were screened for HIV and LF infection . CFA prevalence was 25 . 4% ( 43/169 ) in HIV-positive and 23 . 6% ( 351/1487 ) in HIV-negative participants ( p=0 . 57 ) . Geometric mean CFA concentrations were 859 and 1660 antigen units per ml of blood ( Ag/ml ) respectively , geometric mean ratio ( GMR ) 0 . 85 , 95%CI 0 . 49-1 . 50 . In 7 , 863 adults in study two , CFA prevalence was 20 . 9% ( 86/411 ) in HIV-positive and 24 . 0% ( 1789/7452 ) in HIV–negative participants ( p=0 . 15 ) . Geometric mean CFA concentrations were 630 and 839 Ag/ml respectively ( GMR 0 . 75 , 95%CI 0 . 60-0 . 94 ) . In the HIV-positive group , antiretroviral therapy ( ART ) use was associated with a lower CFA prevalence , 12 . 7% ( 18/142 ) vs . 25 . 3% ( 67/265 ) , ( OR 0 . 43 , 95%CI 0 . 24-0 . 76 ) . Prevalence of CFA decreased with duration of ART use , 15 . 2% 0-1 year ( n=59 ) , 13 . 6% >1-2 years ( n=44 ) , 10 . 0% >2-3 years ( n=30 ) and 0% >3-4 years treatment ( n=9 ) , p<0 . 01 χ2 for linear trend . In this large cross-sectional study of two distinct LF-exposed populations , there is no evidence that HIV infection has an impact on LF epidemiology that will interfere with LF control measures . A significant association of ART use with lower CFA prevalence merits further investigation to understand this apparent beneficial impact of ART .
Human immunodeficiency virus ( HIV ) and parasitic infections affect widely overlapping populations in sub-Saharan Africa . Of the estimated 35 million people infected with HIV worldwide at the end of 2013 , about 70% were from sub-Saharan Africa [1] . Parasitic infections , including lymphatic filariasis ( LF ) are also widespread in sub-Saharan Africa , raising the possibility of clinically significant interactions between the two pathogens . It has been suggested that HIV and parasitic co-infections may have bidirectional deleterious interactions by affecting susceptibility to HIV , impacting on HIV progression and potentially worsening clinical outcomes of filarial infection [2] . Previous in-vitro studies have shown helminth infections to increase susceptibility of peripheral blood mononuclear cells to HIV infection [3] . In addition deworming can result in increases in CD4+ cells and reduction in plasma HIV-1 RNA concentrations [4] . Derangements in the immune response associated with HIV-infection might also be expected to alter susceptibility to , or complications from , filarial infection or other helminths such as Strongyloides [5] . To date , there are few studies that have investigated LF and HIV co-infection and to our knowledge , none have been on a large population scale . A cross-sectional study of 907 adults undertaken in Tanga region of Tanzania reported increased circulating filarial antigen ( CFA ) concentration in HIV-positive persons [6] , although a further evaluation of this group of individuals did not support any association between HIV and Wuchereria bancrofti infection [7] . Similarly , in urban southern India , no quantitative difference in W . bancrofti CFA levels by HIV status was found in a study of 432 HIV-positive and 99 HIV-negative patients [8] . Malawi embarked on a programme of mass drug administration ( MDA ) for LF control and elimination in 2009 [9] . Concerns that the programme may be less effective in areas of high HIV and LF prevalence prompted this study in Karonga , a district in the northern region of Malawi which was known to be highly endemic for LF infection [10] . Karonga is bordered by Lake Malawi to the east , the Songwe river to the north ( which also forms the boundary with Tanzania ) , and by the Nyika plateau and escarpment to the west and south . The population is rural and dependent on subsistence agriculture including rice growing and fishing from the lake . Two previous studies had been undertaken by co-authors in the district and both had serum samples stored with approval for later testing . The first was the recruitment phase of a randomised controlled clinical trial that investigated alternative schedules and dosing regimens of ivermectin and albendazole use in MDA programmes ( study 1 ) . Findings from this clinical trial are reported elsewhere [11] . The second study was nested within a comprehensive population-based HIV survey which enabled a longitudinal assessment of CFA in a whole adult population ( study 2 ) . In this paper we report the prevalence and relationship of LF and HIV infections from these two studies .
The ICT card test ( Binax , Portland , ME ) [16] was only used as the screening test for LF infection in study 1 . This is a portable rapid point-of-care test suitable for screening in non-laboratory settings and was used in accordance with manufacturer’s instructions . Microfilaria counting was done on ICT card test positive individuals in study 1 by the nucleopore membrane filtration technique [17] . This involves filtering a measured volume of venous blood through a 5μM pore size nucleopore filter . After filtration , the filter is removed , placed on a glass slide and mounted on a light microscope for examination and counting of microfilariae . Filarial antigenaemia was quantified in all plasma samples in both study 1 and study 2 by means of the Og4C3 antigen-capture ELISA ( TropBio , Australia ) [18] . Samples were analysed in accordance with the manufacturer’s instructions and expressed as antigen units per ml of blood ( Ag/ml ) based on control samples supplied with the ELISA kits . Samples with CFA concentration more than 128Ag/ml were considered clear positives for CFA . Samples with CFA concentration of 32Ag/ml and below were considered negatives . Samples with a titre between 32 and 128Ag/ml were considered indeterminate . In both studies HIV testing used whole blood rapid diagnostic tests according to Malawian national guidelines [15] with demonstrated high accuracy in community settings in Karonga [14] . The initial screening test was with Determine TM HIV-1/2 ( Abbott Japan Co Ltd , Japan ) and confirmatory testing was done with UniGold TM HIV-1/2 ( Trinity Biotech PLC , Ireland ) . Samples with a non-reactive screening test were considered negative and those with a reactive screening and confirmatory test were considered positive . Where the screening and confirmatory tests were discordant , a tie breaker using a third rapid test , ( SD Bioline , Korea ) was used . Study forms were checked , coded , double entered and verified using Microsoft Access software . Statistical analysis was done in Stata 12 software ( StataCorp , Texas , USA ) . Continuous variables were log transformed prior to analysis to achieve an approximate normal distribution . Linear regression was used for crude and adjusted analyses with results expressed as geometric mean ratios ( GMR ) and their 95% confidence intervals . The association of age , gender and CFA status with HIV positivity was estimated using χ2 tests for crude analyses and a logistic regression model for adjusted Odds Ratios ( OR ) . In a risk factor analysis for CFA positivity , logistic regression was used to estimate crude and adjusted ORs . Variables were retained in the model if significant associations were identified in the unadjusted estimates . Geographic identifiers for groups of survey villages were also incorporated in the models to adjust for geographic confounding as previous surveys had indicated heterogeneity of CFA prevalence across the region [19] . Rather than a binary variable , HIV was treated as a categorical variable in the model with the HIV—negative group and two HIV-positive groups based on ART use at the time of blood sampling . A sub-group analysis to investigate the effect of cotrimoxazole and duration of ART use on CFA prevalence was performed on the HIV-positive group only . Logistic regression models were used to estimate odds ratios . Difference in CFA prevalence with increased use of ART was investigated with a χ2 test for linear trend with odds ratios derived from a 2xn table . Adjusted odds ratios were estimated with logistic regression . The National Health Sciences Research Committee of the Malawi Ministry of Health ( protocol numbers 495 and 419 ) and the Ethical Committee of the London School of Hygiene and Tropical Medicine ( protocol numbers 5344 and 5081 ) gave ethical clearance for both studies 1 and 2 . Study participants in both studies were consented for storage and later testing of samples at the time of enrolment . This covered testing for HIV and other diseases of local significance . The National Health Sciences Research Committee ( protocol number 908 ) and the Ethical Committee of the Liverpool School of Tropical Medicine ( protocol number 11 . 77 ) approved the additional analysis conducted on stored samples .
From the estimated total of 36 , 643 adults of the target villages , 1 , 851 individuals were eligible and consented to participate . Of these 1 , 851 individuals screened for LF antigen by the ICT card test , 447 ( 24 . 2% ) were CFA positive ( Fig 1 ) . A total of 1 , 656 individuals accepted HIV testing and 169 ( 10 . 2% ) of these were HIV-positive . HIV-positive individuals tended to be older ( Table 1 ) . CFA positivity was present in 43 ( 25 . 6% ) of HIV-positive and 351 ( 23 . 6% ) of HIV-negative ( crude OR 1 . 11 , 95% CI 0 . 77–1 . 60 ) with an LF/HIV co-infection prevalence rate of 2 . 6% . CFA positivity did not differ by HIV infection status ( Table 1 ) . There was heterogeneity in the prevalence of CFA by village location , median 24 . 4% , range 15 . 7–33 . 3% ( Pearson χ2 22 . 7 , p<0 . 01 , 9 degrees of freedom ) . Data on the use of antiretroviral and cotrimoxazole was incomplete in the context of this study . Microfilaria counting was done in the 311 ( 69 . 6% ) LF antigen positive individuals who were eligible and gave consent for night blood sampling . The remainder either refused or left before follow up . HIV prevalence in those lost to follow up was broadly similar to those sampled ( 10 . 3% vs . 9 . 3% respectively , χ2 test p = 0 . 90 ) . Microfilariae were present in 49 . 5% of the 311 sampled individuals . Microfilarial detection and levels did not differ by HIV infection status ( Fig 1 and Table 1 ) . Of 311 stored baseline night blood plasma samples , 290 ( 93 . 2% ) were CFA positive using the Og4C3 antigen-capture ELISA . CFA was positive in 26 ( 89 . 7% ) of HIV-positive individuals , 231 ( 93 . 9% ) of the HIV-negative individuals and 33 ( 97 . 1% ) of HIV-unknown individuals respectively ( p = 0 . 47 ) . The geometric mean CFA concentration levels by HIV status were 859 and 1660 for HIV-positive and HIV-negative respectively ( GMR 0 . 85 , 95% CI 0 . 49–1 . 50 ) . CFA and MF counts showed reasonable positive correlation ( Pearson correlation coefficient r = 0 . 56 , p<0 . 01 ) . The total eligible population of 15 year olds and older in the KDHSS at the baseline survey was 11 , 756 . From this group , 7 , 863 ( 66 . 9% ) underwent HIV testing and consented to storage of their blood sample . Of these , 1 , 875 ( 23 . 9% ) individuals were CFA positive by the Og4C3 ELISA . HIV infection was identified in 411 ( 5 . 2% ) participants . HIV-positive adults tended to be older and more likely to be female ( Table 2 ) . CFA positivity was present in 86 ( 20 . 9% ) of HIV-positive and 1789 ( 24 . 0% ) of HIV-negative ( crude OR 0 . 84 , 95% CI 0 . 66–1 . 07 ) with an HIV/LF co-infection prevalence rate of 4 . 6% . In the female participants , CFA positivity was present in 17 . 8% of the HIV-positive and 19 . 8% of the HIV-negative ( OR 0 . 88 , 95% CI 0 . 64–1 . 21 ) and in the male participants , 26 . 8% of the HIV-positive and 29 . 4% of the HIV-negative ( OR 0 . 88 , 95% CI 0 . 60–1 . 28 ) respectively . Geometric mean CFA concentration was lower in the HIV-positive individuals by 25% although this association was weakened when adjusted for age and sex ( Table 2 ) . Several risk factors were associated with an increased prevalence of CFA ( Table 3 ) . These included male gender , age between 30 and 39 and lower quality housing , whilst decreased CFA prevalence was associated with higher levels of education ( p<0 . 01 , χ2 for linear trend ) the availability of piped tap water or the use of lake water and the use of antiretrovirals . Bed net ownership was high , however ownership or the number of nets owned in the household was not associated with CFA prevalence . Individuals were found in all 21 reporting groups , with a median of 314 participants ( range 129–820 ) . There was considerable heterogeneity in the prevalence of CFA by reporting group , median 23 . 2% range 5 . 7–37 . 2% ( Pearson χ2 354 . 7 , p<0 . 01 , 20 degrees of freedom ) . Of the 411 HIV-positive adults , 142 ( 34 . 5% ) were taking antiretroviral therapy ( ART ) and 117 ( 28 . 5% ) were using cotrimoxazole prophylaxis ( CTX ) with only 4 of the 117 taking CTX without ART at the time of sampling . In 6 of the 411 individuals , information on ART and/or CTX use at the time of sampling was unavailable . ART consisted of Lamivudine , Stavudine and Nevirapine ( Triomune-30 ) in 94% of cases with Zidovudine or Efavirenz substitutions in the remainder . No protease inhibitors were in use . In the HIV-positive group , ART use was associated with a lower prevalence of CFA when compared to those not on ART [12 . 7% vs . 25 . 3% ( OR 0 . 43 , 95% CI 0 . 24–0 . 76 ) ] . Similarly , CTX use was associated with lower CFA prevalence [12 . 8% vs . 24 . 1% ( OR 0 . 46 , 95% CI 0 . 25–0 . 85 ) ] . In a multivariable model incorporating ART and CTX use along with age , sex and geographical location , the adjusted odds ratio for ART use was 0 . 47 ( 95% CI 0 . 17–1 . 31 ) and for CTX use 0 . 92 ( 95% CI 0 . 31–2 . 71 ) . When the ART treated group were further sub-divided by year since treatment started , there was a significant trend to decreased prevalence of CFA with increasing time on treatment; 25 . 3% no treatment ( n = 265 ) , 15 . 2% year 1 treatment ( n = 59 ) , 13 . 6% year 2 treatment ( n = 44 ) , 10 . 0% year 3 treatment ( n = 30 ) and 0% year 4 treatment ( n = 9 ) , ( p<0 . 01 χ2 for linear trend ) . This relationship persisted after adjustment for age , gender and reporting group . In the HIV-positive individuals with detectable CFA , the geometric mean concentration of CFA was not significantly different between those off and on ART , 647 vs . 512 Ag/ml respectively , GMR 1 . 27 , 95% CI 0 . 76–2 . 08 ( Fig 2 ) , nor did the GMC differ by ART duration category 647 , 392 , 762 , 516 & 0 Ag/ml for no treatment , year 1 , 2 , 3 & 4 of treatment respectively .
We present data from two separate studies undertaken in Karonga district , northern Malawi . In both studies a high LF and HIV prevalence was measured with HIV co-infection rates of 2 . 6% and 4 . 6% among those who were CFA positive and 15 years and older . We found no evidence that HIV is associated with an increased risk of LF infection . Initial findings from study 1 , a clinical trial not powered to definitively test the impact of HIV on LF infection , revealed a tendency to lower CFA and microfilarial density in the HIV-positive adults . Subsequent investigation of these parameters in the much larger population sample revealed a tendency to lower CFA prevalence in the HIV-positive group , attributable to significantly lower CFA prevalence in the ART treated sub-group , a finding that persisted following adjustment for key potential confounders and showed a significant trend to lower CFA prevalence with duration of ART use . There was no significant effect of CTX therapy , when analysed in a multivariable model . CFA concentration was also persistently lower in the HIV-positive group although at a level of uncertain clinical or public health significance . Previous studies have reported divergent findings with some showing an association between LF and HIV infections but these have tended to be small samples and in selected populations [6–8] . In contrast to these studies , our second study had a larger sample taken from a whole population survey , including a high proportion of the at-risk population in an area with high prevalence rates of LF and HIV . The findings in relation to ART use are novel and we are unaware of other studies that have investigated this association . Individuals receiving ART may represent a select group of the HIV-positive population who have better health seeking behaviour , may be more educated , live in better accommodation and/or may live in close proximity to health providers . However , as this work was undertaken in the context of a demographic survey we were able to investigate these potential confounders by adjusting for reported educational status , housing quality , access to clean water and geographic location . The finding of ART associated with a lower CFA prevalence appears robust . An explanation for these findings remains less clear and merits further work . Residual confounding or an unrecognised selection bias remains possible , but seems unlikely given the highly significant lower CFA prevalence with duration of ART therapy . The crude association of CTX with lower CFA prevalence seems adequately explained by concomitant use of ART , and there is no evidence to support either sulphonamides or trimethoprim , the components of CTX , as effective antifilarial agents . If LF infection adversely impacts on the success of ART therapy , then over time the prevalence of CFA positivity in this group will reduce as the LF/HIV co-infected die . There is no evidence from the Malawi national HIV programme that outcomes from ART treatment are worse in regions of the country endemic for LF compared to those with low LF prevalence . Helminth infections have been linked to increased viral load in non-ART treated individuals [21] but not to evidence of faster HIV progression [22] . Similarly , LF infection had no significant effect on HIV disease progression in a study of W . bancrofti and HIV coinfections in south India [23] . Altered diagnostic accuracy of the Og4C3 ELISA in the presence of ART has not been reported . ART has been rarely linked to false negative HIV results in children and adults but this is more likely to be due to low levels of virus and/or antibody than a direct inhibitory effect . The reduction in CFA prevalence by ART treatment duration and the antigen capture nature of the Og4C3 ELISA would be difficult to explain by ART inhibition of the assay . Immune reconstitution as a result of ART does not adequately explain our finding either as there is a similar prevalence of CFA in the HIV-negative and the HIV-positive untreated . There is no precedent for immune recovery following ART leaving the immune system in a more competent state than an HIV-negative person . ART treatment is an imprecise proxy marker of duration of HIV infection . If the natural history of LF in the HIV-positive is a steady fall in antigenaemia could this explain the association ? We do not have accurate seroconversion dates for the majority of this population so are not able to fully consider this possibility . However with ART use the “natural history” of HIV is dramatically altered and it might be expected that any tendency to lower antigenaemia with time would also be altered and this would be inconsistent with our findings . The most plausible explanation for this finding is a direct filaricidal activity of the major ART agents . We are unaware of any data on the effect of Lamivudine , Stavudine or Nevirapine on helminths . Further evaluation of these molecules as antihelminthics would be appropriate . Of the other factors associated with CFA positivity all have been reported previously , providing reassurance that the epidemiology of LF disease in Karonga is not unique and results are generalizable to other similar regions . One surprise was the lack of association with bed net ownership . However most households possessed bed nets limiting the power of any comparison , and during this survey we did not specifically ask about usage , or condition of the nets , thus limiting the value of this finding . More detailed evaluation of this will be needed in future work . Both of our studies had some degree of selection bias , but it is unlikely that this has fundamentally altered our findings . In study 1 , we targeted villages known historically to have a high prevalence of LF infection . If participation by HIV-positive individuals was reduced because of perceived stigma associated with an HIV test , we may have had reduced power to identify an association between LF and HIV . However , a similar finding in the much larger study 2 provides consistency . In study 2 , we know HIV-positive adults were under-represented . Adults who knew their status from earlier HIV testing studies or through routine service provision in the district , declined participation [20] . However it is difficult to see a mechanism whereby LF co-infection would disproportionately lead to non-participation by HIV-positive adults and in particular ART treated HIV-positive adults thereby obscuring the true association . More females than males were included in both studies . This may represent the easier access to females at the time of recruitment since females are more likely to be at home . Although we know men are more likely to be infected with LF in this population , we do not think this under-representation has meaningfully affected the LF/HIV association . Sub-group analyses showed similar odds ratios for the LF/HIV association by gender in study 2 suggesting no major effect modification . The measurement of our exposure ( HIV ) and outcome endpoints ( LF status ) were based on accurate and well described tests and we do not believe these have introduced significant bias into the study . We used different tests for assessment of circulating filarial antigen in the two studies with different sensitivities and specificities , the ICT card test with sensitivity and specificity reportedly close to 100% and the Og4C3 ELISA test with 100% sensitivity and specificity of at least 94% [24–26] . There was some disparity between these two tests identified in study 1 . This is consistent with previous studies that have reported overall agreement between the ICT and Og3C4 tests but different sensitivities and specificities [24 , 25] . In study 2 , we were not able to assess MF counts due to the use of a stored sample collection . Whilst we cannot categorically rule out an association between MF density and HIV , data from study 1 showed a positive correlation between CFA levels and MF density . Previous studies have also shown a positive correlation between CFA levels and MF density [26 , 27] . This implies that the CFA relationship will broadly apply to MF counts . In summary , we did not demonstrate a significant detrimental association between LF and HIV in these studies that will have a negative impact on plans to eliminate lymphatic filariasis . However ART treated adults had significantly lower CFA prevalence , a finding that merits further careful evaluation to exclude an adverse impact of LF on HIV , or the potential of antiretrovirals as molecules with antihelminthic properties . | Lymphatic filariasis ( LF ) and HIV are both major public health problems worldwide and where they co-exist have the potential to interact . The main strategy for LF elimination is annual mass drug administration ( MDA ) . A particular concern is whether HIV , through its impact on the immune system , will interfere with the effectiveness of this approach to control and eliminate LF . We report findings from cross-sectional studies in two separate populations in northern Malawi where both HIV and LF are common . One group ( 1 , 851 individuals ) were studied at enrolment into a trial of anti-LF treatments , whilst the other study used samples stored from adult participants in a whole population HIV survey ( 7 , 863 individuals ) . Between 5–10% of the study participants were HIV-positive and 24% were LF-infected . We found no evidence that LF infection was more or less common in HIV-positive adults in either population . However , we identified robust evidence that antiretroviral therapy use was associated with lower LF prevalence rates . We have no evidence to suggest HIV will have a detrimental effect on LF control . On the contrary , the evidence suggests that antiretroviral therapy may have beneficial effects and merits further careful evaluation of the anti-filarial properties of these compounds . | [
"Abstract",
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] | [] | 2015 | Human Immunodeficiency Virus, Antiretroviral Therapy and Markers of Lymphatic Filariasis Infection: A Cross-sectional Study in Rural Northern Malawi |
Strongyloides stercoralis is a worldwide disseminated parasitic disease that can be transmitted from solid organ transplant ( SOT ) donors to recipients . We determined the serological prevalence of S . stercoralis among deceased individuals from endemic areas considered for SOT donation , using our institution’s serum bank . Retrospective study including all deceased potential donors from endemic areas of strongyloidiasis considered for SOT between January 2004 and December 2014 in a tertiary care hospital . The commercial serological test IVD-Elisa was used to determine the serological prevalence of S . stercoralis . Among 1025 deceased individuals during the study period , 90 were from endemic areas of strongyloidiasis . There were available serum samples for 65 patients and 6 of them tested positive for S . stercoralis ( 9 . 23% ) . Only one of the deceased candidates was finally a donor , without transmitting the infection . Among deceased individuals from endemic areas considered for SOT donation , seroprevalence of strongyloidiasis was high . This highlights the importance of adhering to current recommendations on screening for S . stercoralis among potential SOT donors at high risk of the infection , together with the need of developing a rapid diagnostic test to fully implement these screening strategies .
Strongyloides stercoralis is a parasitic infection which is endemic in large parts of Latin America , Asia and Africa with an estimated prevalence of more than 400 million infected [1] . Due to its characteristic life cycle , individuals can carry the infection lifelong with mild or no symptoms , unless treated . However , strongyloidiasis can result in a life-threatening disease in immunosuppressed individuals , such as solid organ transplant ( SOT ) recipients under immunosuppressive drugs and/or steroids [2] . Most commonly , individuals become infected by contacting infective larvae when walking barefoot . In recent years , cases of transmission between SOT donors to recipients have been reported [3–7] . In these reports , strongyloidiasis in donors was commonly missed by physicians , possibly due to asymptomatic carriage and/or failure to diagnose severe forms of the disease . Prior testing or screening in donors had not been performed . Currently , the American Society of Transplantation recommends systematic screening of S . stercoralis in donors and candidate recipients from endemic areas and/or with unexplained eosinophilia [8] . However , the increasing reports of transmission from donors to recipients suggest that these guidelines may not be followed by all Organ Procurement Organizations ( OPO ) worldwide . Data from the New York Organ Donor Network showed a strongyloidiasis serological prevalence of 4 . 3% among 233 eligible potential donors screened [9] . However , there are no other data published on strongyloidiasis prevalence among deceased donors considered for SOT . Thus , the aim of this study was to determine the serological prevalence of strongyloidiasis among candidates to SOT donation from endemic areas in a tertiary care hospital in Spain with authorization for organ donor procurement and transplantation .
This is a retrospective study including all deceased individuals from endemic areas for strongyloidiasis who were considered for organ donation between January 2004 and December 2014 in Hospital Clínic , a tertiary care hospital in Barcelona , Spain . Although the prevalence rates are probably very different within continents and countries , including rural and urban areas , donors born in Latin America , Africa and Asia were considered to be from an endemic area for strongyloidiasis . The main objective of the study was to determine the seroprevalence of S . stercoralis among candidates for deceased organ donation . This study was approved by the hospital’s Ethics Committee . Since 2004 , the Transplantation Coordination Unit has been registering in a database all deceased individuals who are considered for deceased organ donation . A questionnaire is filled with demographic data ( age , sex and country of origin ) , comorbidities ( arterial hypertension , diabetes mellitus , HIV and HCV infection ) and organ procurement data ( cause of death , absolute or relative contraindications for organ donation and organs procured ) . Deceased organs are only procured if the donor does not have absolute contraindication for donation like active disseminated cancer or disseminated infection from any origin , with multiple organ failure , and after family consent from the deceased donor . Several microbiological serologies are performed to exclude potentially transmittable diseases and a frozen serum sample from all deceased organ donors remain under custody in the Microbiology Department of the hospital , for at least 30 years after donation . Using the serum samples of potential donors from endemic areas for strongyloidiasis , we performed a S . stercoralis serological test . The commercial test IVD-ELISA ( IVD Research , Carlsbad , CA ) , which detects IgG antibodies by using somatic antigens from larvae of the parasite , was used ( sensitivity 91 . 2% , specificity 99 . 1% [10] ) . Currently , IVD-ELISA is the available test in our hospital . The sample is defined as positive if the absorbance/0 . 2 ( i ) > 1 . 1 . All analyzed samples were anonymized . Stata version 13 . 1 ( Stata Corporation , College Station , TX , USA ) was used for statistical analyses . Categorical variables were described by counts and percentages , whereas continuous variables were expressed as means and standard deviations ( SD ) or medians and interquartile ranges ( IQRs ) .
During the period 2004–2014 , 1025 deceased individuals were evaluated as potential deceased organ donors . Of these , 90 donors ( 8 . 78% ) were native from endemic areas for strongyloidiasis . Most were males ( 63/90 cases; 70% ) and median age of all cases was 41 years old ( IQR 33–51 ) . More than half of the deceased organ donors from endemic areas had been born in Latin America ( 49/90 cases; 54 . 44% ) , whereas the rest were from Southeast Asia ( 24/90 cases; 26 . 67% ) and Africa ( 17/90 cases; 18 . 89% ) . Finally , only in 65 of the 90 cases , available serum samples were found and analysed for S . stercoralis serology and 6/65 cases ( 9 . 23% ) were found to have a positive test for strongyloidiasis . S . stercoralis serology indices ranged between 1 . 84 and 9 . 32 . As shown in Table 1 , four were male and age range was between 22 and 62 years . From the 6 positive cases , three individuals were native from Latin America ( 2 from Brazil and 1 from Ecuador ) , two from Africa ( Senegal and Ghana ) and one from Southeast Asia ( Philippines ) . Between the seropositive deceased donors , only one was considered suitable for donation and 2 kidneys , two lungs , heart and liver were procured and transplanted . Given these results , we investigated whether there had been transmission of strongyloidiasis to SOT recipients from the infected deceased donor . Transplant units from other centres were informed and we were only able to evaluate two recipients who were transplanted in our hospital in 2007 . They were a 50-year-old and 55-year-old male recipients who had received a kidney and liver transplant , respectively . Strongyloidiasis serology and stool culture were negative in both recipients .
In this article , we found that the seroprevalence of strongyloidiasis among deceased individuals from endemic areas who are evaluated as potential donors was high . Apart from the data published by Abanyie et al . [9] , there are no previous reports of the seroprevalence of strongyloidiasis from OPOs’ or Procurement Hospital serum banks . Strongyloidiasis can be transmitted from donors to SOT recipients , although this is probably uncommon . The true risk of transmission and related factors are difficult to determine , although the parasite burden and the stage of the infection could play an important role [7] . However , since most SOT recipients will be immunosuppressed for the rest of their lives , the possibility of becoming infected by transmission from the donor represents a serious public health issue . AST guidelines clearly recommend screening both donors and recipients from endemic areas [8] , but this is probably not universally followed in most procurement hospitals and centers with SOT programs in USA or Europe , as has been recently published [11] . Reasons for this may be related with little number of donors and/or recipients native from endemic areas , the lack of standardized serological tests in some laboratories or economic issues . Despite all these potential difficulties , physicians in charge of procurement centers and SOT programs should definitely incorporate AST recommendations when testing for donor-to-recipient transmissible diseases . In our hospital , following the present study results and a previous reported case of transmission [7] , screening policies have been redefined . Clinicians in charge of transplantation programs have been encouraged either to screen potential recipients at risk or refer them to the Tropical Medicine Department Outpatient Clinic . A similar approach has been suggested for donors who may undergo elective transplant surgery . Despite clinicians’ awareness of the problem , more difficulties have been faced in implementing an efficient strongyloidiasis screening strategy in urgent donations , given the characteristics of these procedures and the current available diagnostic tests . In every case , a strict protocol is currently followed according to the individualized donor’s or recipient’s risk of infection , prompting exhaustive investigations prior or during the SOT donation , with rapid antiparasitic treatment initiation , if necessary . Strongyloidiasis , a neglected tropical disease , is one of the most prevalent infections worldwide . In recent years , it has become an important health problem in non-endemic settings , where high prevalence in specific populations has been encountered [12 , 13] . The increasing frequency of migration flows of people from endemic areas in recent years may have resulted in these individuals becoming SOT donors more often . Similarly , many endemic countries have national transplant programs and the number of organ procurements may increase in the coming years . Clinicians should also take into account that strongyloidiasis has been described in temperate areas , such as Spain , especially among the elderly [14] , who may be potential recipients prone to develop severe forms if immunosuppressed . All these considerations make it even more reasonable to incorporate AST recommendations in the daily clinical practice of deceased and living donors and SOT recipients in endemic and non-endemic settings . In this line , the available serological tests currently available are far from being optimal [10] . Two commercial tests ( IVD-ELISA and Bordier-ELISA ) based on Enzyme-Linked Immunosorbent Assay ( ELISA ) are among the most common used tests . These present high sensitivity ( 73–100% ) , which can be lower in immunocompromised individuals [15] . Moreover , these tests show cross-reactivity with other nematode infections , potentially causing false-positive results . In our study , cross-reactivity was probably limited , since positive samples had high serological indices [10] . Another limitation of these tests is that they are often time-consuming and require specific expertise , which may add difficulties to systematic screening in the urgency of transplantation . All these inconvenients in the serological diagnosis of strongyloidiasis support the importance of focusing research efforts on the development of better diagnostic tools in the near future [16] . It is clear that there is a need for a rapid , validated and robust test , if systematic screening needs to be implemented among the deceased and living donors and SOT recipient population . Our study has several limitations . It was a retrospective study performed in a single centre , thereby limiting the generalizability of the results . Apart from the high number of missing serum samples , the lower proportion of individuals from Africa and Asia may have affected our results . Finally , the existence of false positives due to a co-infection by other nematodes is also difficult to rule out . In conclusion , we found a high seroprevalence of strongyloidiasis in individuals from endemic settings evaluated as potential deceased donors for SOT . Our data reinforce the importance of following current guidelines recommending systematic screening of potential donors from endemic areas . More research is urgently needed to develop rapid diagnostic tests which can be used in daily clinical practice . | Strongyloidiasis is a neglected tropical disease caused by a parasite which is endemic in most parts of the world . It can cause a life-threatening disease among immunosuppressed individuals and can be transmitted from solid organ transplant donors to recipients . We retrospectively investigated the prevalence of strongyloidiasis among deceased individuals from endemic areas who were considered for solid organ transplant donation in our center , by performing a serological assay using our institution’s serum bank . We found a high prevalence of strongyloidiasis among these deceased candidates to donation , but only one of the six who tested positive was finally a donor , without transmitting the disease to recipients . Our results should encourage physicians to adhere to current guidelines which recommend active screening of strongyloidiasis in potential solid organ transplant donors from endemic areas who may be infected . There is a clear need for a rapid diagnostic test to fully implement systematic screening among these individuals . | [
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] | 2018 | High seroprevalence of Strongyloides stercoralis among individuals from endemic areas considered for solid organ transplant donation: A retrospective serum-bank based study |
Dicer ribonucleases of plants and invertebrate animals including Caenorhabditis elegans recognize and process a viral RNA trigger into virus-derived small interfering RNAs ( siRNAs ) to guide specific viral immunity by Argonaute-dependent RNA interference ( RNAi ) . C . elegans also encodes three Dicer-related helicase ( drh ) genes closely related to the RIG-I-like RNA helicase receptors which initiate broad-spectrum innate immunity against RNA viruses in mammals . Here we developed a transgenic C . elegans strain that expressed intense green fluorescence from a chromosomally integrated flock house virus replicon only after knockdown or knockout of a gene required for antiviral RNAi . Use of the reporter nematode strain in a feeding RNAi screen identified drh-1 as an essential component of the antiviral RNAi pathway . However , RNAi induced by either exogenous dsRNA or the viral replicon was enhanced in drh-2 mutant nematodes , whereas exogenous RNAi was essentially unaltered in drh-1 mutant nematodes , indicating that exogenous and antiviral RNAi pathways are genetically distinct . Genetic epistatic analysis shows that drh-1 acts downstream of virus sensing and viral siRNA biogenesis to mediate specific antiviral RNAi . Notably , we found that two members of the substantially expanded subfamily of Argonautes specific to C . elegans control parallel antiviral RNAi pathways . These findings demonstrate both conserved and unique strategies of C . elegans in antiviral defense .
Innate immunity is active immediately upon pathogen attack and represents an ancient defense mechanism conserved in diverse multicellular organisms . Innate immunity is initiated by pattern recognition receptors ( PRRs ) that recognize conserved molecular patterns associated with microbes . Well-characterized PRR families include the transmembrane Toll-like receptors ( TLRs ) and the cytosolic NOD-like receptors ( NLRs ) and RIG-I-like RNA helicase receptors ( RLRs ) , all of which contain members in vertebrates that recognize viral single- and/or double-stranded RNAs as the pathogen signatures 1–3 . Recognition of pathogens by PRRs typically triggers protein-protein interactions of PRRs with downstream signaling factors leading to the nucleus translocation of a transcriptional factor such as NF-κB and the subsequent transcription of immunity effector genes . The Dicer family of ribonucleases also recognizes viral RNA like these PRRs to initiate the viral immunity in plants and invertebrates that is mechanistically related to RNA silencing or RNA interference ( RNAi ) . Unlike TLR and RLRs , however , Dicer further processes the viral RNA trigger into small RNAs of 21–24 nucleotides to guide specific antiviral silencing [4] . In addition to two type III RNase domains and a dsRNA-binding domain ( dsRBD ) , Dicer contains an RNA binding domain called PAZ and an N-terminal RNA helicase domain that is closely related to RLRs [5] , [6] . The Dicer family proteins produce small interfering RNAs ( siRNAs ) and microRNAs ( miRNAs ) in many eukaryotes , which are loaded in an Argonaute ( AGO ) -containing effector complex to silence gene expression by RNA cleavage , translational arrest , or methylation of DNA and chromatin . In fungi , plants and Caenorhabditis elegans , siRNAs are further amplified in a process that depends on de novo synthesis of dsRNA by cellular RNA-dependent RNA polymerase ( RDR ) . Genetic requirements of the Dicer-initiated viral immunity have been characterized more extensively in Arabidopsis thaliana and Drosophila melanogaster using known mutants in various RNA silencing pathways [7] . The prevailing model for antiviral silencing against RNA viruses is that it acts via the canonical dsRNA-siRNA pathway of RNAi . This is supported by the detection of virus-derived siRNAs ( viRNAs ) of two polarities covering the entire length of viral genomic RNAs in the infected cells and the identification of the siRNA-producing Dicers in the biogenesis of viRNAs in both D . melanogaster and A . thaliana [4] , [8]–[14] . Almost all of the genes known to participate in A . thaliana antiviral silencing have been implicated in the RDR-dependent synthesis of dsRNA in transgene-induced RNA silencing [4] , [15]–[20] . In D . melanogaster , antiviral silencing induced by distinct positive-strand RNA viruses including Flock house virus ( FHV ) , requires the same set of the core components of the canonical RNAi pathway that are dispensable for the biogenesis and function of miRNAs [13] , [14] , [21] , [22] . A recent study found that viral dsRNA produced during initiation of FHV progeny RNA synthesis is recognized by Dicer-2 ( DCR-2 ) and diced into viRNAs to trigger antiviral silencing [23] . Infection of mammalian cells with some DNA viruses induces production of virus-derived miRNAs capable of silencing the antisense mRNAs of the cognate viruses [24] . However , there is currently no evidence for the production of viral siRNA in mammals in response to RNA viruses , suggesting that RNA viruses are sensed by unrelated PRRs in invertebrates and vertebrates [4] . C . elegans is an excellent model system for studying many aspects of biology , including host responses to bacterial pathogens [25] , [26] . C . elegans lacks NLRs and NF-κB-like transcriptional factors but encodes a single TLR . C . elegans also encodes a family of Dicer-related helicases ( DRH ) , DRH-1 , DRH-2 and DRH-3 , which are highly homologous to the DExD/H box RNA helicase domain found in Dicer and the mammalian RLR family composed of RIG-I , MDA5 and LGP2 [6] , [27] , [28] . The RNA silencing machinery of C . elegans is characterized by a single Dicer ( dcr-1 ) , 4 RDRs ( eg , ego-1 and rrf1–rrf-3 ) and 27 AGOs [29] , [30] . Both miRNAs and siRNAs are produced by DCR-1 whereas PIWI-interacting RNAs ( piRNAs , also known 21U RNAs ) is Dicer-independent . Processing both endogenous ( endo ) and exogenous ( exo ) dsRNA into siRNAs further requires the dsRNA-binding protein RDE-4 although distinct AGO and RDR proteins participate in endo and exo siRNA pathways [6] , [31]–[35] . The C . elegans family of AGOs , the largest of any organisms examined to date , is divided into three subfamilies . The AGO and PIWI subfamilies are required for the biogenesis of miRNAs and piRNAs , respectively , but ergo-1 in the PIWI subfamily has an essential role in the production of endo-siRNAs [31] , [36]–[38] . The third subfamily is worm-specific and contains 18 members , many of which such as rde-1 , ppw-1 , C04F12 . 1 , sago-1 , and csr-1 , act in parallel or sequentially to mediate the exo-siRNA pathway [39]–[42] . The exo-siRNA pathway requires amplification by rrf-1 in the soma and ego-1 in the germline whereas rrf-3 is essential for the biogenesis of endo-siRNAs [34] , [43] . Interestingly , exo-RNAi is enhanced in worm mutants defective for several components of the endo-siRNA pathway including eri-1 , ergo-1 and rrf-3 , suggesting antagonism between the two siRNA pathways [27] , [30] , [34] , [41] , [44] , [45] . A natural virus for C . elegans is not known . However , cultured primary cells and living animals of C . elegans can be infected respectively by Vesicular stomatitis virus ( VSV ) and Vaccinia virus and living animals support complete replication of the FHV RNA genome engineered to be transcribed from an integrated transgene [46]–[49] . Infection of VSV , which contains a negative-strand RNA genome , is associated with the production of VSV-specific small RNAs and is potentiated in both nematode cells derived from exoRNAi-defective C . elegans mutants ( rde-1; rde-3 , rde-4 , and rrf-1 ) and wild-type cells depleted of either DCR-1 or C04F12 . 1 , but is inhibited in rrf-3 and eri-1 mutants that exhibit an enhanced exoRNAi response [47] , [48] . The positive-strand genome of FHV is divided into two RNAs . RNA1 ( 3 . 1 kb ) encode the viral RNA-dependent RNA polymerase ( RdRP ) and can self-replicate independently of RNA2 ( 1 . 4 kb ) , which encodes the capsid protein . Although FHV is a natural insect virus , it replicates efficiently on outer mitochondria membranes of diverse eukaryotic cells , indicating conserved mitochondria targeting of the viral replication complex [50]–[52] . FHV RNA3 ( 387 nt ) is a product of RNA1 replication and acts as mRNA of the B2 protein , a viral suppressor of RNAi ( VSR ) essential for FHV infection in D . melanogaster [13] , [14] , [21] , [53] . We showed that replication of a B2-deficient FHV mutant occurred robustly in the rde-1 mutant nematodes but was severely inhibited in wild-type nematodes , indicating restriction of FHV replication by the exo-RNAi pathway in C . elegans [46] . These studies together strongly indicate that C . elegans encodes an active antiviral RNAi pathway that is induced by either direct viral infection [47] , [48] or replication of a viral RNA genome initiated intracellularly [46] , the latter of which bypasses the initial steps such as cell entry that occur in viral infection of natural hosts . Here we describe development of a transgenic C . elegans strain for the genetic characterization of the antiviral RNAi pathway . Use of the reporter strain in a feeding RNAi screen led to the identification of a largest set of putative genes in antiviral RNAi pathway in any organism . In particular , we showed that drh-1 and drh-2 of the three nematode drh genes participated in the regulation of antiviral RNAi . An extensive genetic analysis indicated that unlike mammalian RLRs , C . elegans drh-1 acts downstream of virus sensing and viRNA biogenesis and that exogenous and antiviral RNAi pathways have distinct genetic requirements . Both the conserved and unique strategies of C . elegans in antiviral defense are discussed .
We have previously described a derivative of the infectious full-length cDNA clone of FHV RNA1 , pFR1gfp [53] , in which eGFP coding sequence replaces most of the VSR B2 coding sequence ( Figure 1A ) . The inserted eGFP fused with the N-terminal 23 codons of B2 , is expressed only from the recombinant RNA3 produced during replication of FR1gfp , but not directly from FR1gfp because its initiation codon is more than 2 . 7 kb away from the 5′-terminus of FR1gfp RNA ( Figure 1A ) . FR1gfp is defective in RNAi suppression due to loss of B2 expression but not in replication so that productive FR1gfp replication and expression of eGFP from the FHV replicon occur only after antiviral RNAi is suppressed by either co-expression of B2 or genetic disruption of the antiviral RNAi pathway in cultured fruit fly and mosquito cells [53] . To develop a model suitable for genetic screens to identify new components in antiviral RNAi , we generated C . elegans strains carrying a chromosomally integrated transgene that codes for FR1gfp under the control of a heat-inducible promoter . We found that no green fluorescence or only a tiny green spot in the pharynx area was observed in FR1gfp worms after heat induction of the FHV replicon transgene ( Figure 1B , top left ) . In contrast , bright green fluorescence was detected throughout the animal body after FR1gfp worms were fed on the E . coli food that expresses rde-1 dsRNA , which depletes mRNA of rde-1 in a process referred as feeding RNAi ( Figure 1B , top middle ) . Abundant expression of eGFP was also observed in FR1gfp worms after a loss-of-function rde-1 allele was introduced into FR1gfp worms by genetic crosses ( data not shown ) . Northern blot hybridizations confirmed the abundant accumulation of the chimeric RNA1 and RNA3 in FR1gfp worms after rde-1 depletion , but FR1gfp replication was inhibited in FR1gfp worms without rde-1 depletion ( Figure 1C , compare lanes 1/2 and 4/5 ) . Productive replication of FR1gfp replicon was similarly rescued by the loss-of-function rde-1 allele in the second FR1gfp worm strain in which the FR1gfp transgene was integrated at a different chromosomal location ( Figure 2B , and data not shown ) . Thus , as found previously in cultured fruit fly and mosquito cells [53] , productive FR1gfp replication and detection of extensive eGFP expression in FR1gfp worms depend on the genetic disruption of the antiviral RNAi pathway , suggesting that FR1gfp worms could be screened for new components of the pathway by feeding RNAi . To test this idea , we searched for antiviral RNAi factors among genes shown previously to play a role in exo-RNAi [30] , [54] . The genes identified using the candidate gene approach include firstly dcr-1 , rde-1 , and rde-4 , which , together with rrf-1 and C04F12 . 1 , were shown previously to be required for antiviral RNAi against VSV and for exo-RNAi [30] , [41] , [47] , [48] . A specific role of these genes in worm antiviral RNAi was verified by demonstrating genetic rescue of the B2-deficient FHV replicon in FR1gfp worms following introduction of genetic loss-of-function mutations in rde-1 , rde-4 , rrf-1 or C04F12 . 1 by genetic crosses ( Figure 2A ) . These results showed that the B2-deficient FHV replicon transcribed from a nuclear transgene was silenced in FR1gfp worms by the same set of genes known to participate in worm antiviral immunity against VSV infection . Thus , the FR1gfp worm strain provided an alternative model to the VSV infection system for the genetic characterization of the RNAi-mediated antiviral immunity in C . elegans , including the use of FR1gfp worms for feeding RNAi screens . In addition to dcr-1 , rde-1 , and rde-4 , 32 of 98 candidate genes tested were required for silencing the viral replicon in C . elegans in three independent feeding RNAi screens ( Figure 1B/1C , Table S1 ) . Our result suggests that most of the RNAi factors identified from previous genetic and feeding RNAi screens , including mut-7 and mut-16 [55] , may not contribute to antiviral RNAi . Nevertheless , our screen provided the largest set of putative antiviral RNAi factors in any organism ( Table S1 ) , which include rsd-2 ( Figure 1B/1C ) , required for systemic RNAi [56] , and drh-1 . We focused on drh-1 identified from the feeding RNAi screen and its related gene drh-2 because of their homology to the mammalian RLR family of cytosolic sensors for RNA viruses [2] , [6] , [27] . drh-1 encodes one of the three closely related worm members of the RLR family [6] , [27] . To verify the result from feeding RNAi , we identified presumptive null alleles of drh-1 ( tm1329 ) and drh-2 ( ok951 ) and introduced them respectively into FR1gfp worms by genetic crosses ( Figure 2C ) . We did not examine drh-3 in this study because drh-3 ( tm1217 ) mutant worms are sterile [27] . We found that drh-1 mutant worms exhibited no visible morphological and developmental phenotypes . However , drh-2 worms were noticeably smaller than wildtype worms at the same developmental stages and were late by ∼8 hours in laying eggs ( data not shown ) , suggesting that drh-2 may have a role in development . Northern blot hybridizations detected mRNAs specific to drh-1 and drh-2 in wildtype worms and deletion of 483- and 789-nucleotide genomic DNA respectively in drh-1 and drh-2 mutants caused visible shifts of the corresponding mRNAs ( Figure 2E ) . However , neither was transcriptionally induced 48 hours after the initiation of FR1gfp replication or earlier ( Figure 2E and data not shown ) . We found that RNAs 1 and 3 of the B2-deficient FHV replicon accumulated to high levels in drh-1 mutant worms as compared to wild-type N2 worms ( Figure 2A , compare lanes 3/4 and 9/10 ) . Thus , an essential role for drh-1 in antiviral silencing revealed by feeding RNAi experiments was confirmed by an independent approach . By comparison , the accumulation levels of the replicon RNAs 1 and 3 were similar in drh-1 , rde-1 and rde-4 worms , but were higher in these worms than in rrf-1 and C04F12 . 1 mutant worms in at least five independent experiments ( Figure 2A ) . These results indicate that antiviral silencing against the FHV replicon was inhibited in drh-1 worms as effectively as in rde-1 and rde-4 mutants , and more effectively than in rrf-1 and C04F12 . 1 mutants ( Figure 2A ) . In contrast , the FHV replicon was not rescued in drh-2 mutants ( Figure 2A , lanes 11–12 ) . Although longer exposure revealed low accumulation levels of the FHV replicon in wild-type worms , the accumulation of the FHV replicon was either undetectable or lower in drh-2 mutants than in wildtype worms ( Figure 2A , compare lanes 17/18 and 21/22 ) . Apparently this effect is not transgenic allele specific in that the same effect was also observed for another FR1gfp transgenic allele ( Figure 2B , compare lane 28 and lane 25 ) . As expected , fosmid WRW0640F2 , which contains both drh-1 and drh-2 wild-type alleles , was able to rescue drh-1 and drh-2 function in the corresponding mutants when assayed for FR1gfp replication ( data not shown ) . These results show that drh-1 is as important as rde-1 and rde-4 in the worm antiviral RNAi against FHV , whereas drh-2 may negatively regulate this immunity . Similarly reduced accumulation of the FHV replicon was also observed in worms defective for ergo-1 ( Figure 2A , lanes 23 and 24 ) , which encodes an AGO that is required for the biogenesis of endo-siRNAs but is antagonistic to exo-RNAi [41] . Several lines of evidence indicate that drh-2 and ergo-1 act as negative regulators of antiviral RNAi rather than positive regulators of FHV replication . FR1gfp is defective in RNAi suppression but not in replication in fly and mosquito cells [53] and in worms because FR1gfp replicated to high levels in several single mutants including rde-4 ( Figure 2A ) and robust replication of FR1gfp was not inhibited by the drh-2 and ergo-1 alleles in rde-4;drh-2 ( see the last section of Results below ) and rde-4;ergo-1 ( data not shown ) double mutants . In addition , both drh-2 and ergo-1 negatively regulate exogenous RNAi ( Figure 3 , see below ) . We further performed time course analysis of the accumulation of the FHV replicon in wild-type and seven worm mutants 2 , 6 and 16 hours after transcriptional induction of FR1gfp ( Figure 2F ) . Primary transcripts of FR1gfp initially accumulated to comparable levels in wild-type and all of the seven mutant worm strains examined at two hours after heat induction , indicating that none of these mutant alleles had a major effect on the transcription of the FR1gfp locus [30] . Six hours after induction , FHV RNA1 and its replication product RNA3 were detectable in rde-1 , rde-4 and drh-1 mutants , but not in wild-type , rrf-1 , C04F12 . 1 , drh-2 , or ergo-1 mutants . At 16 hours after induction , productive replication of the viral replicon also became visible in rrf-1 worms but not in C04F12 . 1 , drh-2 , or ergo-1 worms ( Figure 2F ) . These data indicate that the viral RNA clearance was triggered by viral RNA replication in an RNAi-dependent process that might require early and simultaneous participation of DRH-1 , RDE-1 and RDE-4 . DRH-1 was first identified as an interacting protein of DCR-1 and subsequent affinity purification coupled with mass spectrometry implicates all three DRH proteins as DCR-1 interactors [6] , [27] . drh-3 mutant worms were defective in germline RNAi but were wild-type in somatic RNAi; however , both germline and somatic RNAi was dramatically reduced in worms treated with either drh-1 or drh-2 ( or both ) dsRNAs [6] , [27] , [54] , [57] . While these data indicate that drh-3 is essential for germline RNAi , it was uncertain if a specific member of the DRH family plays an essential role in somatic RNAi since depletion of the homologous drh genes by dsRNA lacks specificity . We found that drh-1 mutant worms , either with or without the FR1gfp transgene , remained as sensitive as wild-type worms to feeding RNAi targeting either the maternally expressed skn-1 ( data not shown ) or dpy-13 ( Figure 3C ) , and to microinjection of dsRNA targeting muscle specific gene unc-22 , which produces both the light ( twitching ) and severe ( paralysis ) knockdown phenotypes ( Figure 3A/3B ) . Thus , drh-1 is dispensable for somatic RNAi , which is in contrast to its essential role in antiviral silencing . Nevertheless , we found a slightly lower percentage of the injected worms exhibiting paralysis in drh-1 worms than in wildtype worms ( Figure 3A/3B ) , and the difference detected was statistically significant ( P<0 . 05 ) , illustrating that drh-1 worms exhibited a weak deficiency in somatic RNAi . These results suggest that DRH-1 may be more efficient to mediate RNAi in the soma of wild-type worms , but this function of DRH-1 is genetically redundant and could be substituted for by another DRH member in drh-1 mutant worms . Surprisingly , drh-2 mutant worms exhibited enhanced sensitivity to the injection RNAi targeting unc-22 as compared to wild-type worms and the difference was more obvious when low concentration of dsRNA was injected ( Figure 3A/3B ) . Feeding RNAi targeting dpy-13 led to mild dumpy phenotype in wild-type and drh-1 worms , but drh-2 worms displayed severe dumpy phenotype as did worm mutants known to exhibit an enhanced RNAi phenotype such as ergo-1 ( Figure 3C ) and eri-1 [45] , [47] , [48] . In contrast to wildtype and drh-1 worms , rde-1 worms were resistant to dpy-13 RNAi as expected ( Figure 3C ) . These results indicate that somatic RNAi is partially suppressed by drh-2 in wild-type worms , which is similar to its inhibitory role in antiviral RNAi . drh-3 is required for the biogenesis of endo-siRNAs , but not for miRNAs or piRNAs [27] , [57] . Production of endo-siRNAs is also dependent on ergo-1 , which inhibits both somatic RNAi and antiviral silencing . To investigate the mechanism of drh-1 and drh-2 , we further determined if either was involved in the biogenesis of the three classes of C . elegans small RNAs . We found that neither drh-1 nor drh-2 was required for the biogenesis of miRNAs ( Figure 4A , lanes 5 and 6 ) , which is also known to be independent of rde-4 . Minor changes in the accumulation of miRNAs observed occasionally in drh-1 mutant worms were not reproducible ( Figure 2D/3A/3B/5B ) . The rde-4-dependent endo-siRNAs K02E2 . 6 and X-cluster siRNAs also accumulated to wild-type levels in both drh-1 and drh-2 mutants ( Figure 4A/4B ) . The recently characterized piRNAs also accumulated to wild-type levels in both drh-1 and drh-2 mutants as in rde-4 , rde-1 , rrf-1 , ppw-2 and ergo-1 mutants ( Figure 4B ) . Thus , drh-1 and drh-2 do not appear to contribute to the biogenesis of any of the known endogenous small RNAs in C . elegans , suggesting that drh-2 negatively regulates somatic RNAi and antiviral silencing in a mechanism distinct from ergo-1 . An antiviral RNAi component may function in virus sensing , the biogenesis or the antiviral activity of viRNAs [4] . FHV-specific viRNAs of the antigenomic polarity , ( - ) viRNAs , were detectable in both drh-1 and rde-1 mutants ( Figure 2D , lanes 2 and 4 ) . Probing for viRNAs of the genomic polarity resulted in a smear and no discrete bands were detected in any of the worm strains tested either before or after transcriptional induction of FR1gfp ( data not shown ) . FHV viRNAs accumulated to much lower levels in worms than in fruit flies [13] , [23] . ViRNAs in worm cells infected with VSV were only detected by the RNase protection assay [47] , which is far more sensitive than Northern blot hybridization [58] . We found that ( - ) viRNAs were undetectable in wild-type , drh-2 or ergo-1 mutant worms ( Figure 2D , lanes 1 and 5; Figure 5B , lane 8 ) , which is likely due to the inhibition of the viral replication ( Figure 2A , lanes 3/4 , 11/12 , 23/24 ) and consequently lower levels of viral dsRNA for dicing in these worms . The FHV replicon replicated to similarly high levels in rde-1 , rde-4 and drh-1 mutant worms ( Figure 2A ) , but viRNAs were not detectable in rde-4 worms , unlike in rde-1 and drh-1 worms ( Figure 2E ) . These results therefore show that RDE-4 is essential for the production of FHV viRNAs whereas either DRH-1 or RDE-1 is dispensable . However , since viRNAs produced in drh-1 and rde-1 worms were not able to inhibit the replication of the VSR-deficient viral replicon ( Figure 2A ) , we further conclude that both RDE-1 and DRH-1 are required for the antiviral activity of viRNAs . The observations that the viral RNA trigger was detected and processed into viRNAs in rde-1and drh-1 mutant worms ruled out a direct role of either gene in virus sensing . To determine the epistatic relationships among the identified antiviral RNAi components , we constructed seven double knockout worm mutants by genetic crosses . First , rescue of the VSR-deficient viral replicon was stronger in rde-1 worms than in the mutant worms defective for C04F12 . 1 ( Figure 5C ) , which encodes an AGO closely related to rde-1 in the expanded group of AGOs specific to C . elegans . However , the viral replicon replicated to higher levels in the rde-1:C04F12 . 1 double mutant than in either single mutant ( Figure 5C , compare lane 4 and lanes 2 and 3 ) . Thus , there is an additive effect of the two mutant alleles in blocking antiviral RNAi , indicating parallel AGO pathways for antiviral silencing in C . elegans . Second , the viral replicon did not accumulate to higher levels in rde-4;drh-1 double mutant than in either rde-4 or drh-1 single mutant ( Figure 5A , compare lane 6 and lanes 3 and 4 ) . viRNAs were not detectable in rde-4;drh-1 worms ( Figure 5B , lane 5 ) , which was similar to rde-4 worms but distinct to drh-1 worms . These results illustrated that rde-4 and drh-1 act in the same antiviral RNAi pathway and placed rde-4 in the upstream of drh-1 . Third , abundant viRNAs were detected in rde-1;drh-1 worms as was found in rde-1 and drh-1 single mutants , further confirming the above conclusion that neither gene is essential for viRNA production . Fourth , we found that the viral replicon replicated to similar levels in rde-1 , rde-4 single mutants and rde-1;drh-2 and rde-4;drh-2 double mutants ( Figure 5A , compare lanes 11 and 12 with lanes 8 and 9 ) . This result showed that the drh-2 mutant allele failed to enhance antiviral RNAi when either rde-4 or rde-1 was not functional , suggesting that the rde-4-initiated pathway was targeted by drh-2 for negative regulation . Fifth , the accumulation levels of the viral replicon were similar in the ergo-1;drh-1 double mutant and the drh-1 single mutant ( Figure 5E ) , indicating that the ergo-1 mutant allele also became ineffective in enhancing antiviral RNAi when rde-4-initiated pathway was inactive . However , we found that the ergo-1;drh-1 double mutant exhibited enhanced sensitivity to somatic RNAi as did the ergo-1 single mutant ( Figure 5D ) , further demonstrating that drh-1 was dispensable for exo-RNAi .
Our genetic analysis confirms earlier observations that antiviral RNAi in C . elegans overlaps the canonical dsRNA-siRNA pathway of RNAi [46]–[48] . For example , 35 known RNAi factors participate in the worm antiviral RNAi against FHV ( Table S1 ) . Antiviral RNAi against FHV is enhanced in both ergo-1 and drh-2 mutant worms that exhibit enhanced RNAi . Furthermore , this work together with previous studies shows that ( i ) antiviral RNAi induced by either FHV or VSV requires dcr-1 , ( ii ) production of both VSV and FHV siRNAs was dependent on rde-4 and ( iii ) neither rde-1 nor drh-1 is essential for the biogenesis of viRNAs . These findings revealed a role of RDE-4 , most likely with DCR-1 in a previously identified complex , in the sensing of viral RNA trigger and in the biogenesis of viRNAs . Whereas these data support a shared biogenesis pathway for viRNAs , exo- and endo-siRNAs in C . elegans [30] , an indispensable role for rde-4 in the viRNA biogenesis is distinct from D . melanogaster in which DCR-2 but not the rde-4 homolog ( R2D2 ) , is essential for viRNA production [14] . Primary siRNAs processed directly from exogenous dsRNA are not sufficient abundant to detect by Northern blot hybridizations in rde-1 worms [35] , [43] , [58] , in contrast to FHV viRNAs . This difference is probably due to the robust supply of viral dsRNA from active viral RNA replication in rde-1 worms whereas the amount of exogenous dsRNA is limited in the absence of RDR-dependent dsRNA synthesis . Furthermore , our feeding RNAi screens indicate that a majority of the known exoRNAi factors may not participate in the worm antiviral RNAi against FHV ( Table S1 ) . In addition , whereas drh-1 is essential for antiviral RNAi against FHV , it is dispensable for exoRNAi . These data suggest that antiviral RNAi and exoRNAi pathways in C . elegans are genetically distinct even though both are initiated by RDE-4 and DCR-1 . An increased inhibition of the worm antiviral RNAi against FHV was observed in rde-1;C04F12 . 1 double mutant than in either single mutant , indicating that FHV is targeted by two parallel AGO-dependent antiviral RNAi pathways in C . elegans . This conclusion is also supported by previously observations that knockdown of either rde-1 or C04F12 . 1 individually enhances the VSV accumulation [48] and that RNAi suppression by B2 further increases FHV accumulation in rde-1 worms [46] . Both RDE-1 and C04F12 . 1 belong to the substantially expanded subfamily of AGOs found only in worms and many members in this subfamily have been shown to act either in parallel or sequentially in exo-RNAi [41] , [42] . Thus , expansion of this AGO subfamily in C . elegans may represent a unique strategy of host adaptation to viral infection , distinct or in addition to the strategy used by insects in which the evolution rate of antiviral RNAi factors DCR2 and AGO2 is much faster than that of their miRNA pathway counterparts DCR1 and AGO1 [61] , [62] . Several lines of evidence illustrate that drh-1 has a specific , non-redundant role in the rde-4-dependent antiviral RNAi pathway in C . elegans . Either depletion of drh-1 mRNA by feeding RNAi or a genetic lesion in drh-1 blocked antiviral RNAi against FR1gfp , an FHV-based replicon that does not express VSR B2 and thus exhibits a specific defect to suppress antiviral RNAi . B2 is a dsRNA/siRNA-binding protein which is located both inside the viral replication complex to inhibit the dicing of nascent dsRNA replicative intermediates into viRNAs and in the cytoplasm to inhibit the activity of viral siRNAs [13] , [23] , [46] , [63] , [64] . The antiviral RNAi against FHV was inhibited in drh-1 worms as efficiently as in rde-4 and rde-1 mutant worms . A time course analysis further suggests that DRH-1 , RDE-4 and RDE-1 may all participate in the early induction of antiviral RNAi . Finally , epistatic analysis showed that the FHV replicon did not replicate to higher levels in rde-4:drh-1 double mutant than in either single mutant , demonstrating that drh-1 specifically acts in the rde-4-initiated antiviral RNAi pathway . Notably , inhibition of antiviral RNAi by the drh-1 mutant allele did not prevent the production of viRNAs in either drh-1 single mutant or rde-1:drh-1 double mutant worms , indicating that defects of drh-1 worms in antiviral RNAi were similar to rde-1 mutants but distinct to rde-4 worms . These data together show that drh-1 of C . elegans acts downstream of dcr-1 and rde-4 in the antiviral RNAi pathway and does not play a critical role in the sensing of the viral RNA trigger . Previous knockdown experiments have established a genetic requirement for the DRH family of genes in the canonical RNAi [6] , [27] . Use of drh-3 mutant worms has further shown that DRH-3 mediates germline RNAi and endo-RNAi , but is dispensable for somatic RNAi [27] , [57] . Our results show that neither drh-1 nor drh-2 plays a role in the biogenesis of miRNAs , endo-siRNAs and piRNAs and that drh-1 mutant worms supported RNAi in the soma with only a negligibly reduced efficiency . Thus , drh-1 is essential for antiviral RNAi but is largely dispensable for both endo-RNAi and somatic RNAi . In contrast , drh-2 mutant worms exhibited enhanced response to both somatic RNAi and antiviral RNAi . Isolation of double drh knockout mutants , which is not possible to achieve by genetic crosses between the single mutants because of their close proximity , will be necessary to determine if the observed somatic RNAi in drh-1 worms is mediated by drh-3 and/or drh-2 . Current models indicate that DRH-1/2 and DRH-3 participate in the biogenesis of exo-and endo-siRNAs , respectively , in distinct complexes with DCR-1 and that primary siRNAs thus generated are loaded in a particular AGO to guide RDR-dependent amplification of secondary siRNAs required for specific degradation of target mRNA [27] , [34] , [41] , [58] , [65] . Results presented in this work indicate that DRH family proteins play an essential role in siRNA pathways by acting downstream of the DCR-1/RDE-4-dependent siRNA biogenesis to mediate both specific and redundant siRNA pathways . Since in vivo interactions of DRH proteins with DCR-1 and RDE-4 have been detected [6] , [27] , we propose that DRH protein facilitate the binding of primary siRNAs to AGOs , the amplification of secondary siRNAs , or the targeting and cleavages of the mRNA . The essential role of DRH-3 and DRH-1 specifically in the endo-siRNA/germline exo-siRNA and viRNA pathways , respectively , may be due to tissue and cell-specific localization of DRH proteins and of dsRNA trigger and mRNA targets . In contrast , somatic RNAi may be mediated redundantly by DRH-1 , possibly with DRH-3 , which is supported indirectly by the observation that although all of the known endo-RNAi worm mutants are defective in the biogenesis of endo-siRNAs , enhancement of somatic RNAi were detected in rrf-3 , eri-1 and ergo-1 mutants , but was not reported for drh-3 mutant worms . DRH-2 may compete with DRH-1 for binding to the same or a similar set of co-factors , thereby inhibiting DRH-1-dependent antiviral RNAi and somatic RNAi . This hypothesis is consistent with our observation that the drh-2 mutant allele failed to enhance antiviral RNAi against FHV in rde-1 and rde-4 mutant worms that are defective in the drh-1-dependent viRNA pathway . Our demonstration that the viral immunity is regulated by drh-1 and drh-2 in C . elegans indicates an evolutionarily conserved antiviral role of the RLR family between worms and mammals . In mammals , RLR members RIG-I and MDA5 are essential for controlling infection of two distinct sets of ssRNA viruses . Current models envision that recognition of specific viral RNA forms by the C-terminal repressor domain activates a signaling cascade via two caspase activation and recruitment domains ( CARD ) at the N-terminus that culminates in the transcription of cytokine genes and broad-spectrum immunity [1]–[3] . The third RLR member LGP2 is analogous to DRH-2 since it shares homology with RIG-I and MDA5 in the helicase and repressor domains without the N-terminal CARD domains , and appears to repress RIG-I signaling but contribute to MAD5 signaling [1]–[3] . Although the N-terminal regions of DRH-1 and DRH-3 are highly homologous , neither contains a CARD domain or is transcriptionally induced upon viral replication . Our genetic analysis further suggests that drh-1 directs antiviral RNAi downstream of both the sensing and the processing of the viral RNA trigger into viRNAs . This may explain why DRH proteins of C . elegans do not contain CARD domains , which mediate downstream signaling events by protein-protein interactions in mammals .
The Bristol strain N2 was used as the standard wild-type strain . Alleles used in this study are all derived from N2 and include rde-1 ( ne300 ) , rde-4 ( ne337 ) , drh-1 ( tm1329 ) , drh-2 ( ok951 ) , rrf-1 ( pk1417 ) , ergo-1 ( tm1860 ) , and C04F12 . 1 ( tm1637 ) . The genotypes of rde-1 and rde-4 worms were confirmed using skn-1 feeding RNAi . The genotypes of the rest worm strains containing single or double mutations were confirmed by PCR and/or feeding RNAi targeting skn-1 . Transgene construct carrying FR1gfp replicon was a derivative of pFR1-3 by replacing the NcoI-SacI fragment of FHV RNA1 by the full length enhanced GFP coding region as described previously [46] , [53] . This created a translational fusion of GFP with the N-terminal 23 amino acids of B2 and deletion of approximately 200 nucleotides from the B2 ORF . Animals were made transgenic by gonadal microinjection following standard protocol as described [46] . Briefly , FR1gfp plasmid ( final concentration 5 µg/µl ) was mixed with the rol-6D plasmid pRF4 ( final concentration 100 µg/µl ) for injection into wild-type N2 animals . Generation of worm integrants carrying FR1gfp transgene and assay for viral replication was carried out as described previously [46] . Injection and feeding RNAi were carried out as previously reported . Briefly , for unc-22 dsRNA preparation , unc-22 single-stranded RNAs of both polarities were in vitro transcribed from a cDNA fragment amplified using T7 promoter-tagged primer unc22T7plus ( TAATACGACTCACTATAGGAGTTGGGAGAGGATGAAGCT ) and primer unc22T7minus ( TAATACGACTCACTATAGGCCACCGTTGTCACGTGGAGGA ) . For injection RNAi , unc-22 dsRNA at 25 µg/ml or 100 µg/ml in water was delivered into intestine of young adult worms through microinjection . The injected worms were then transferred onto fresh NGM plates 8 hours post microinjection . Progenies produced between 8 and 32 hours post microinjection were scored for unc-22 phenotype . Feeding RNAi targeting skn-1 or dpy-13 was performed by feeding worms on E . coli . HT115 strains that express dsRNA corresponding to skn-1 and dpy-13 , respectively . IPTG at final concentration of 1 mM was used for the induction of dsRNA expression . The P-value on the differences of unc-22 RNAi phenotype ( paralysis ) between worm strains was calculated using unpaired t test calculator ( http://www . graphpad . com/quickcalcs/ttest1 . cfm ? FormatSD ) . Total RNA was prepared using the TRI Reagent method ( MRC , Inc . ) . Small RNAs were enriched using the mirVana kit ( Ambion ) . For high molecular viral RNA analysis , 3 to 6 µg total RNA per lane was fractionated in 1 . 2% agarose gel . For small RNA analysis , 30 to 50 µg of enriched small RNAs per lane was resolved using 15% acrylamide denaturing gel along with chemically synthesized and end-labeled siRNAs as size markers . After electrophoreses , the RNA samples were transferred onto Hybond N+ membrane ( Amersham Biosciences ) and UV crosslinked using 1 . 8×105 µJ/CM2 as output power ( SpectroLinker ) . For small RNA analysis , membranes were hybridized with 32P-labeled oligo DNA probes in PerfectHyb buffer ( Sigma ) . For northern blot detection of drh-1 and drh-2 transcripts , 32P-labelled DNA probes were prepared using genomic DNA fragments amplified by PCR . Primer tm1329_internal_b ( ATACTCTGCCTCGAGCCGAT ) and primer Tm1329minus ( TCAGTCGTATCTCCAATTTTCGA ) were used to amplify genomic DNA as the drh-1 specific probe , while primer drh-21780plus ( AGTAGCATTCGTTCGAGAGTT ) and primer Ok951minus ( TTGCTTTCCTGGACATGAAGTG ) were used to generate the drh-2 specific probe . Sequences for oligo probes used for the detection of endogenous small RNAs were: miR-238 , CTGAATGGCATCGGAGTACAAA; miR-58 , ATTGCCGTACTGAACGATCTCA; miR-2 , GCACATCAAAGCTGGCTGTGATA; lin-4 , TCACACTTGAGGTCTCAGGGA; Let-7 , AACTATACAACCTACTACCTCA; X-cluster siRNA , CGCGTATCTATTCAATTGAAT; K02E2 . 6 siRNA , ATCAGTTACTTGCCAATTTC; and 21U-1 , CACGGTTAACGTACGTACCA Transgenic lines in either drh-1 ( tm1329 ) or drh-2 ( ok951 ) background that carried an extrachromosomal array corresponding to WRW0640F2 were produced with microinjection , and were crossed respectively with drh-1 ( tm1329 ) and drh-2 ( ok951 ) animals homozygous for FR1gfp transgene . The F1 progenies in either drh-1 ( tm1329 ) or drh-2 ( ok951 ) background were then examined for GFP expression after being maintained at 20°C ( drh-1 ) or 25°C ( drh-2 ) for 36 hours after induction of the FR1gfp replicon replication . The complete loss or significant reduction of green fluorescence in F1 progenies that carried both FR1gfp and the WRW0640F2 extrachromosomal array , as compared to that in F1 animals that carried FR1gfp only , was considered as successful drh-1 function rescue . Likewise , increased GFP expression in F1 progenies that carried FR1gfp transgene only as compared to F1 progenies that carried both FR1gfp transgene and a WRW0640F2 extrachromosomal array , was considered as evidence for the restoration of drh-2 function . GFP fluorescence images were collected using a CANON G2 digital camera mounted on an Olympus IMT-2 microscope . | The genome of Caenorhabditis elegans encodes three Dicer-related helicases ( DRHs ) highly homologous to the DExD/H box helicase domain found in two distinct families of virus sensors , Dicer ribonucleases and RIG-I-like helicases ( RLRs ) . Dicer initiates the specific , RNAi-mediated viral immunity in plants , fungi and invertebrates by producing virus-derived small interfering RNAs ( siRNAs ) . By contrast , mammalian RLRs trigger interferon production and broad-spectrum viral immunity , although one of the three RLRs may act as both a negative and positive regulator of viral immunity . In this study we developed a transgenic C . elegans strain for high-throughput genetic screens and identified 35 genes including drh-1 that are required for RNAi-mediated viral immunity . Genetic epistatic analyses demonstrate that drh-1 mediates RNAi immunity downstream of the production of viral siRNAs . Notably , we found that drh-2 functions as a negative regulator of the viral immunity . Thus , both nematode DRHs and mammalian RLRs participate in antiviral immune responses . Unlike mammalian RLRs , however , nematode DRH-1 employs an RNAi effector mechanism and is unlikely to be involved in direct virus sensing . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"virology/host",
"antiviral",
"responses",
"immunology/innate",
"immunity"
] | 2009 | An RIG-I-Like RNA Helicase Mediates Antiviral RNAi Downstream of Viral siRNA Biogenesis in Caenorhabditis elegans |
Recent years have seen progress in the development of statistically rigorous frameworks to infer outbreak transmission trees ( “who infected whom” ) from epidemiological and genetic data . Making use of pathogen genome sequences in such analyses remains a challenge , however , with a variety of heuristic approaches having been explored to date . We introduce a statistical method exploiting both pathogen sequences and collection dates to unravel the dynamics of densely sampled outbreaks . Our approach identifies likely transmission events and infers dates of infections , unobserved cases and separate introductions of the disease . It also proves useful for inferring numbers of secondary infections and identifying heterogeneous infectivity and super-spreaders . After testing our approach using simulations , we illustrate the method with the analysis of the beginning of the 2003 Singaporean outbreak of Severe Acute Respiratory Syndrome ( SARS ) , providing new insights into the early stage of this epidemic . Our approach is the first tool for disease outbreak reconstruction from genetic data widely available as free software , the R package outbreaker . It is applicable to various densely sampled epidemics , and improves previous approaches by detecting unobserved and imported cases , as well as allowing multiple introductions of the pathogen . Because of its generality , we believe this method will become a tool of choice for the analysis of densely sampled disease outbreaks , and will form a rigorous framework for subsequent methodological developments .
Statistical methods for analyzing detailed epidemiological data collected during infectious disease outbreaks have seen rapid development in recent years [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] . These methods probabilistically reconstruct likely transmission links between cases using data on the timing of symptoms and , where available , contact tracing data or other proximity information . The resulting transmission trees allow estimation of the number of secondary infections generated by each case , and thus of the transmission intensity ( characterized by the reproduction number , R ) over time . Pathogen genetic sequence data provides valuable additional information on potential transmission links between cases in a disease outbreak , particularly when reliable contact tracing data is not available . Indeed , using sequence data alone to estimate transmission rates during epidemics is increasingly frequent [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] . As genetic sequences can now be obtained nearly in real-time [19] , [20] , this new source of information opens up exciting perspectives not only for understanding past outbreaks , but also for unraveling the transmission routes of ongoing outbreaks and subsequently adapting public health responses . Integrated analysis of both epidemiological and sequence data clearly would maximize our ability to reconstruct transmission trees , but there are methodological and computational challenges . These challenges center on constructing and evaluating a unified likelihood for both the genetic and epidemiological data . One of the first attempts at integrated analysis [21] used phylogenetic trees to constrain the set of transmission trees then explored by an epidemiological transmission tree inference algorithm . An alternative approach [22] highlighted limitations of phylogenetic methods for reconstructing densely sampled outbreaks , and proposed an alternative graph theoretic approach for reconstructing ‘genetically parsimonious’ transmission trees , i . e . trees implying the smallest number of genetic changes amongst the sampled isolates . While simple and fast , this method also has a number of limitations: dates of infection are not inferred , the probability of a given transmission event cannot be assessed , and unobserved cases or multiple introductions of the disease cannot be detected . Substantive methodological developments have been made by Ypma et al . [23] and subsequently by Morelli et al . [24] , both of which proposed unified likelihoods for genetic and epidemiological data to analyze livestock disease outbreaks ( avian influenza H7N7 [23] and foot-and-mouth disease [24] ) . However , those methods require that the outbreak has a single introduction event and that all cases are observed , which limits their applicability to restricted epidemic contexts . Here we introduce a novel and generic framework for the reconstruction of disease outbreaks based on pathogen genetic sequences and collection dates . We use the distribution of the generation time ( i . e . time interval between a primary and a secondary infection ) [7] , [8] to define the epidemiological likelihood of a given transmission tree . This is coupled with a simple model of sequence evolution defining the probability of the genetic changes observed between the pathogen genomes along a chain of transmission . Our model is embedded within a Bayesian framework allowing estimation of dates of infections , mutation rates , separate introductions of the pathogen , the presence of unobserved cases , and the transmission tree . Estimate of the effective reproduction number over time , R ( t ) , can also be obtained . As an improvement over previous approaches [23] , [24] , our method does not require all cases to be observed or there to be a single introduction event which triggers an outbreak . After evaluating the performance of our method using simulated outbreaks , we illustrate our approach by analyzing the 2003 Severe Acute Respiratory Syndrome ( SARS ) outbreak in Singapore [10] , [11] , [25] . Our method is implemented in the package ‘outbreaker’ for the R software [26] and represents the first widely available tool for the reconstruction and analysis of disease outbreaks from genomic data .
We analysed simulated outbreaks to assess the performance of our method under a variety of conditions , including different basic reproduction numbers ( R0 ) , sampling coverage , rates of evolution , and generation time distributions , with our base scenario resembling an influenza-like illness ( Table 1 ) . The outbreak size varied from 10 to nearly 200 infections in a fixed population of 200 susceptible hosts ( plus imported cases ) , with a median sample size of 110 ( quartile range: [66–132] , Fig . S1 ) . Wherever applicable , reported results refer to the marginal distributions . Transmission trees were overall very well reconstructed , with 70% to 90% of true ancestries being recovered in most simulation settings ( Fig . 1 and Table S1 in Text S1 ) . Better results were achieved when the sampling coverage was high ( compare settings ‘Base’ to 75% , 50% and 25% of missing cases ) . In the absence of genetic information , the transmission tree was very difficult to infer ( setting ‘No mutation’ ) . Differences in basic reproduction numbers ( settings ‘Low R’ and ‘High R’ ) and in the shape of the generation time distribution ( settings ‘Short generation’ and ‘Long generation’ ) induced some variation in the proportions of successfully recovered ancestries , although these remained satisfying in every case ( Fig . 1 and Table S1 in Text S1 ) . Dates of infections were inferred with accuracy in most settings ( Fig . S2 and Table S1 in Text S1 ) . However , this result was mostly driven by the shape of the generation time distribution , with broader distributions leading to greater uncertainty in the dates of infection ( Fig . S2 ) . While perfectly inferred in fully sampled outbreaks , the number of generations between ancestor and descendents became ambiguous as the proportion of missing cases increases ( Table S1 in Text S1 ) . Mutation rates were also mostly well estimated ( Table S1 in Text S1 , Fig . S3 ) , albeit with a tendency to over-estimation . This bias was stronger when sampling grew sparser ( settings with 75% and 50% missing cases ) , and to a lesser extent when the number of imported cases grew large ( setting ‘Many imports’ ) . Detailed investigation of individual simulations suggested that misdetection of imported cases and increased numbers of erroneous ancestries may be responsible for over-estimating the mutation rates in these settings . The inference of sampling coverage varied largely amongst different simulation settings ( Table S1 in Text S1 , Fig . S4 ) : well recovered in fully sampled outbreaks , it was largely overestimated in sparse samples ( settings with 75% , 50% and 25% missing cases ) , and slightly underestimated with longer generation time . The detection of imported cases showed excellent specificity and good sensitivity pooling results across the simulated datasets examined , with a majority of simulations exhibiting perfect results ( Fig . 2 ) . However , substantial variations were observed between simulation settings ( Fig . S5 , Table S1 in Text S1 ) . Unsurprisingly , detection of imported cases was more difficult when imported cases were more frequent and when a higher fraction of cases was unobserved . With longer generation times , the larger numbers of mutations accumulated between ancestors and descendents made the detection of genetic outliers , and thus of imported cases , nearly impossible ( Fig . S2 ) . While our model does not explicitly estimate the effective reproduction number ‘R’ ( i . e . , the number of secondary cases per infected individual ) , this quantity can easily be computed from the posterior trees . Our ‘base’ simulations show that reliable estimates of R at an individual level can be obtained when genetic information is available ( Fig . 3 , left ) . In contrast , such inference was impossible in the absence of genetic data ( Fig . 3 , right ) . To gain a better understanding of disease outbreak dynamics , identifying systematic heterogeneity in R across cases is also essential . To assess whether our approach could detect such heterogeneity , we implemented two types of simulations in which there were systematic differences in infectivity between groups of hosts . In a first set of simulations , the host population was divided into two groups of equal sizes ( e . g . adults and children ) with low and high infectivity ( infectivity in one group was twice that of the other group , with equal susceptibility ) . In the second setting , we included rare ( 5% ) super-spreaders , who had the same susceptibility to infections as non super-spreaders , but were 13-fold more infectious . In both sets of simulations , infectivity was fixed for each individual at the beginning of the simulations . The classification of individuals into super-spreaders and regular spreaders was considered as known when comparing estimated reproduction numbers . Results showed that our method was able to recover contrasted infectivity between different groups ( Fig . 4 , S6 , 7 , 8 , 9 ) . In the simulations with equally-sized groups , the overall distributions of R for each group were almost perfectly recovered ( Fig . 4 , top panel ) , while values of R at an individual level were also well estimated ( Fig . S6 ) . Importantly , when ignoring the genetic information , differences between groups were barely detectable ( Fig . 4 and S7 ) . Similar results were observed in simulations including super-spreaders ( Fig . 4 , bottom panel ) , in which estimates of R values at an individual level were excellent when using genetic information ( Fig . S8 ) , and very poor without it ( Fig . S9 ) . The reconstruction of average R values over time was not improved by the inclusion of genetic information ( Fig . S10 , S11 ) , which is unsurprising as this mainly depends on correctly inferring the dates of infections , which was unaffected by the absence of genetic data ( Fig . S1 , S2 ) . We analyzed data collected during the beginning of a SARS outbreak which took place in Singapore in 2003 [10] , [25] . Previous studies proposed different reconstructions of this outbreak based on indirect contact tracing information and genetic data , and while all agreed on the necessity to combine these two streams of information , a clear consensus on the initial transmission tree has not been reached [10] , [11] , [25] . Here , we aimed to reconstruct the early stage of this outbreak using 13 full SARS genomes collected from the putative index patient and primary and secondary cases , and previously published estimates of the generation time distribution [27] ( Fig . S12 ) . The genetic diversity amongst isolates was limited , with less than 15 mutations separating any pair of genomes ( Fig . S13 ) . For most cases , transmission events could not be readily inferred from the phylogenetic tree ( Fig . S14 ) . According to previous estimates of the mutation rate [25] , we expect that most direct transmissions ( >99% ) will exhibit between 0 and 5 mutations . Using this result , we performed a simple graph analysis to derive possible clusters of direct transmissions , which suggested the existence of one main cluster of cases that may be linked directly , the remaining 4 isolates falling into three groups ( Fig . S15 ) . However , this crude analysis only relied on genetic diversity , and did not take into account information on the collection dates of the isolates or on the duration of the infectious period . We used outbreaker to exploit all these data simultaneously . Results of the inferred likely scenarios ( Fig . 5 and 6 ) show that for half of the cases , a well-supported ancestor can be identified from the data ( see also Fig . S16 ) . These correspond to all of the first and second generations of infections ( Sin2677 , Sin2679 , Sin2748 , Sin2774 ) and to the last sampled case ( Sin850 ) . Ancestries of most cases were compatible with a single generation , although one or two unobserved infections may have taken place between Sin849 and Sin850 ( Fig . S17 ) . We found no evidence for separate index cases after Sin2500 , in agreement with contact tracing information [10] , [11] , [25] . However , the small number of cases may impair the detection of outliers and thus the identification of imported cases , so that multiple introductions of the pathogen cannot be ruled out . The most recent investigation of this outbreak suggested a dual introduction of the pathogen , with a separate index case ( Sin2679 ) nearly 20 days after the initial index case Sin2500 [10] , [11] , [25] . This may be deemed surprising as this case is genetically close to some preceding cases ( Fig . S14 , S15 ) . Here , our results suggest that Sin2679 would in fact be part of the second generation of infection , and was infected by Sin2748 ( Fig . 5 and 6 ) . Indeed , while the collection dates of Sin2748 and Sin2679 are relatively close , the generation time of SARS ( Fig . S12 ) may have allowed for this transmission to occur . Closer examination of the patterns of mutations between Sin2500 , Sin2748 and Sin2679 bring further support to this scenario ( Fig . 6 , Data S3 ) . Indeed , the four mutations separating Sin2500 from Sin2679 are the simple addition of the mutations accumulated on the chain of transmission , from Sin2500 to Sin2748 ( position 26 , 430: a→g ) , and from Sin2748 to Sin2679 ( 18 , 284: c→a; 19 , 086: t→c; 23 , 176: c→t ) .
Building on past work [23] , [24] , we have presented a flexible analytical framework for the reconstruction of densely sampled outbreaks from epidemiological and sequence data . We extended previous work by accounting for unobserved cases and proposing a new approach for identifying multiple introductions of the pathogens based on the detection of genetic outliers . Our method is also the first tool for outbreak reconstruction widely available as a free software ( the R package ‘outbreaker’ ) and able to run on standard desktop computers . The analysis of simulated data suggests that our approach will be applicable to a wide range of pathogens with various basic reproduction numbers , generation time distributions , and genetic diversity . We have shown how our approach can be used to infer effective reproduction numbers at an individual level . Importantly , this allows for detecting differences in infectivity of different groups of cases , and for the identification of super-spreaders . Our results suggest that while epidemiological data may suffice for the estimation of mean aggregated quantities such as the mean effective reproduction number , R , genetic data are useful to tease individual heterogeneities apart . As in other tree reconstruction methods [2] , [7] , [28] , [29] , we did not explicitly model the population of susceptible individuals . This is because information on individuals who were not infected during the outbreak ( the “denominator” data ) is quite often unavailable . Compared with case-only analyses , availability of denominator data also makes it possible to estimate the force of infection and risk factors for infection [4] . We note that our framework could easily be extended to model the uninfected population . This could be done by modifying our likelihood so that the probability of the time of infection of a case would be based on an explicit model of the force of infection; individuals not infected during the outbreak would also contribute to the epidemiological likelihood as is standard in such situations [4] . Integrating and validating these additional features in our approach will be the subject of future research . Our method relies on several assumptions which can be used to define the scope of its possible applications . The most important element in this respect is the proportion of cases represented in the sampled data , and thus often the scale of the epidemics considered . Our approach aims to reconstruct ancestries in closely related cases . As such , it should be most useful for detailed outbreak investigations . While the reconstruction of transmission tree seems relatively robust to large proportions of unobserved cases ( up to 75% of missing cases , Fig . 1 ) , our method is clearly tailored to densely sampled outbreaks , and not meant for the analysis of large-scale , more sparsely sampled epidemics . In such cases , phylogenetic methods are preferred as they explicitly reconstruct unobserved common ancestors of the sampled pathogen genomes , and can be used to infer , if not the transmission tree , the past dynamics of the disease [30] , [31] , [32] . One of the novelties of our approach is the detection of imported cases , which are identified as genetic outliers . While this method should be useful to detect separate introductions of different pathogenic lineages in an epidemic , it may be sensitive to other events prone to creating genetic outliers , such as sequencing errors or recombination . Care should therefore be devoted to ensuring data quality and filtering out polymorphism due to recombination . Moreover , the assumption that imported cases are genetically distinguishable from other cases may not always be true , especially when multiple introductions take place from a closely related lineage . Such cases cannot be detected by genetic data only , and would require other sources of information ( e . g . contact tracing ) to be considered . In this respect , an interesting feature of outbreaker is the ability to fix known imported cases ( as well as any other known transmissions ) before reconstructing the transmission tree . Another important point is that following a previous , widely-used approach for the analysis of outbreaks [7] , we assume the distributions of the generation time and of the time from infection to sample collection to be known . In some situations such as outbreaks of new emerging pathogens , accurate estimates of the generation time may not be readily available . In this case , a conservative approach should allow for a wide range of possible times to infection , at the expense of increased uncertainty in the inferred ancestries . As our method is numerically efficient for the analysis of small outbreaks , we suggest testing different generation time distributions to assess the robustness of the results . As a longer-term alternative , our approach could be extended to include an explicit parameterization and estimation of the generation time distribution . More fundamentally , the use of a generation time distribution also implies that our method is less appropriate for diseases in which long periods of asymptomatic carriage are frequent . For instance , bacteria such as Staphylococcus aureus can cause infections after months of asymptomatic colonization of the host , but may equally cause outbreaks of cases linked by only a few days [12] , [33] . In such cases , the collection dates of isolates effectively carry less information about possible transmissions , which would hamper our current approach . However , our model could be adapted to the analysis of carried pathogens by incorporating specific data on known exposures ( e . g . shared occupancy on a hospital ward ) [34] , [35] , [36] . Moreover , carried pathogens are also more likely to cause multiple colonizations of the host , resulting in several lineages coexisting within the same patient . Our model assumes that a single pathogen genome exists within each host , and is therefore not designed to account for multiple infections . A simple workaround would consist in duplicating cases of multiple infections into single infections , assuming that multiple infections are made of independent , single colonization events . However , this would not allow for disentangling multiple infections from mere within-host evolution of a single lineage . A more satisfying approach would consist in modeling explicitly the evolution of isolates within host , but this will likely result in a much more complex model and is beyond the remit of our current approach . A major simplification made in our model , that could be relaxed in future work , is that we do not consider within host diversity of pathogens . Within-host diversity is particularly prominent in pathogens that infect a host for a long time relative to their within-host replication cycle ( e . g . HIV or Hepatitis C Virus ) , pathogens that can be carried for a long time ( e . g . Staphylococcus aureus ) , pathogens where the infectious inoculum is large ( e . g . blood-transmitted HIV ) , or super-infection is frequent ( e . g . Streptococcus pneumoniae in hyper-endemic settings ) . Limited host diversity leads us to assume that genomes sampled from infectors are effectively ancestral to genomes sampled from secondary cases , allowing us to equate phylogenetic and transmission trees . This substantially reduces the complexity of the inferential problem , and reduces by orders of magnitude the dimensionality of the space of linked augmented variables to be explored . The assumption of no within-host diversity will likely be appropriate for acute infectious pathogens in outbreaks , but will also be relatively appropriate for situations where there is a strong bottleneck on diversity upon transmission and limited opportunities for superinfection , such as sexually transmitted HIV . Inclusion of within-host diversity in the model inference is an important but likely complex task , though efficient approximations may be possible . A related development will be the inclusion of multiple samples per individual , used to sample cross-sectional and longitudinal genetic diversity within infected hosts . Another somewhat simpler extension would be the inclusion of a ‘relaxed’ molecular clock , which would allow accounting for heterogeneities in mutation rates amongst different pathogen lineages . Finally , we wish to emphasize the importance of including all available prior information in the analysis . Because the estimates of parameters governing an outbreak are often correlated , accurate knowledge of one can be used to refine the estimation of the others . For instance , specifying known transmission chains or imported cases will improve the estimation of the mutation rates , as well as the overall reconstruction of the transmission tree . Conversely , fixing the mutation rate to its ‘true’ value ( or a good estimate thereof ) is likely to improve the detection of imported cases . As currently implemented , our method allows for fixing any parameter as well as individual ancestries , which are used in the likelihood computations but not changed during the MCMC . This feature should be especially useful for incorporating known transmission events or introductions of the pathogen into the population , based for instance on clinical investigations and contact tracing information . However , results of contact tracing studies should always be considered cautiously , and could be contradicted by the analysis of corresponding sequences , as illustrated by the SARS outbreak in Singapore . There are other promising avenues for incorporating various streams of information into our approach . The likelihood of our model allows for additional ‘plug-in’ terms for individual transmissions , which could be used to model spatial dispersion processes as well as movement over a contact network . Therefore , we hope that the present method will not only be applied widely , but also motivate further developments for the investigation of infectious disease outbreaks .
Thirteen previously published full SARS genomes [10] , [25] ( Data S1 ) were obtained from Genbank and aligned using MUSCLE [39] . The resulting alignment contained 29 , 731 columns , 39 of which were polymorphic ( Data S2 ) . We used a generation time distribution modeled as a discretized gamma distribution with a mean of 8 . 4 days and a standard deviation of 3 . 8 days [27] , using the function DiscrSI from the R package EpiEstim [29] . The same distribution was used for the the time to collection . Details of the parameters used to run outbreaker are provided in Supporting Methods . The statistical confidence in determining the ancestry of a given case was quantified using the entropy of the frequencies of the posterior ancestors . With different ancestors of posterior frequencies ( ) , the entropy is defined as: ( 11 ) The entropy is 0 if one of the , is 1 , indicating high confidence in allocation of an ancestry , while larger values of the entropy indicate poorer confidence . | Understanding how infectious diseases are transmitted from one individual to another is essential for designing containment strategies and epidemic prevention . Recently , the reconstruction of transmission trees ( “who infected whom” ) has been revolutionized by the availability of pathogen genome sequences . Exploiting this information remains a challenge , however , with a variety of heuristic approaches having been explored to date . Here , we introduce a new method which uses both pathogen DNA and collection dates to gain insights into transmission events , and detect unobserved cases and separate introductions of the disease . Our approach is also useful for identifying super-spreaders , i . e . , cases which caused many subsequent infections . After testing our method using simulations , we use it to gain new insights into the beginning of the 2003 Singaporean outbreak of Severe Acute Respiratory Syndrome ( SARS ) . Our approach is applicable to a wide range of diseases and available in a free software package called outbreaker . | [
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] | 2014 | Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data |
In mammalian meiotic prophase , the initial steps in repair of SPO11-induced DNA double-strand breaks ( DSBs ) are required to obtain stable homologous chromosome pairing and synapsis . The X and Y chromosomes pair and synapse only in the short pseudo-autosomal regions . The rest of the chromatin of the sex chromosomes remain unsynapsed , contains persistent meiotic DSBs , and the whole so-called XY body undergoes meiotic sex chromosome inactivation ( MSCI ) . A more general mechanism , named meiotic silencing of unsynapsed chromatin ( MSUC ) , is activated when autosomes fail to synapse . In the absence of SPO11 , many chromosomal regions remain unsynapsed , but MSUC takes place only on part of the unsynapsed chromatin . We asked if spontaneous DSBs occur in meiocytes that lack a functional SPO11 protein , and if these might be involved in targeting the MSUC response to part of the unsynapsed chromatin . We generated mice carrying a point mutation that disrupts the predicted catalytic site of SPO11 ( Spo11YF/YF ) , and blocks its DSB-inducing activity . Interestingly , we observed foci of proteins involved in the processing of DNA damage , such as RAD51 , DMC1 , and RPA , both in Spo11YF/YF and Spo11 knockout meiocytes . These foci preferentially localized to the areas that undergo MSUC and form the so-called pseudo XY body . In SPO11-deficient oocytes , the number of repair foci increased during oocyte development , indicating the induction of S phase-independent , de novo DNA damage . In wild type pachytene oocytes we observed meiotic silencing in two types of pseudo XY bodies , one type containing DMC1 and RAD51 foci on unsynapsed axes , and another type containing only RAD51 foci , mainly on synapsed axes . Taken together , our results indicate that in addition to asynapsis , persistent SPO11-induced DSBs are important for the initiation of MSCI and MSUC , and that SPO11-independent DNA repair foci contribute to the MSUC response in oocytes .
During meiotic prophase in yeast and mammals , the induction of DNA double-strand breaks ( DSBs ) by the transesterase SPO11 precedes stable pairing and synapsis of homologous chromosomes [1] , [2] . Synapsis between chromosomes is achieved by the formation of a specific protein complex , consisting of lateral elements along the chromosomal axes that contain SYCP2 , SYCP3 [3] , [4] , different components of the cohesin complex [5] , [6] , and ( before synapsis is achieved , on axial elements ) the HORMA-domain proteins HORMAD1 and HORMAD2 [7] , [8] . Lateral elements are connected by a central element containing SYCP1 [9] and several other meiosis-specific proteins , including SYCE1 , SYCE2 [10] and TEX12 [11]; reviewed by Yang and Wang [12] . Parallel to synaptonemal complex formation , meiotic DSBs are repaired , thereby facilitating homologous chromosomes interaction and the achievement of complete synapsis . In male mammals , the X and Y chromosomes form a very special pair; they can synapse only in their short pseudoautosomal regions , and localize to the periphery of the nucleus . Furthermore , the XY chromatin is silenced , forming the XY body , by a process named meiotic sex chromosome inactivation ( MSCI ) . This requires the expression of the histone variant H2AX [13] . The checkpoint kinase ATR phosphorylates H2AX at S139 , generating γH2AX [14] . γH2AX is the earliest known marker of MSCI . This specific histone modification is also found in somatic cells , usually at sites of DNA DSB repair [15] . Interestingly , H2AX phosphorylation in response to DNA damage has been coupled to reduced levels of RNA polymerase II activity in somatic cells [16] . MSCI is considered a specialized form of a more general mechanism termed meiotic silencing of unsynapsed chromatin ( MSUC ) , which silences unsynapsed chromatin in male and female meiotic prophase cells [17]–[19] . The exact cascade of events that leads to this transcriptional silencing is not known , but it has been established that there is a tight correlation between the presence of unsynapsed chromosomal axes coated by HORMAD1 and HORMAD2 ( the two mammalian orthologs , of the yeast protein Hop1 [7] , [20] , [21] ) , the accumulation of ATR along these axes , the formation of γH2AX , and the transcriptional silencing . Indeed , it was recently reported that efficient accumulation of ATR on the XY body requires the HORMAD1 and HORMAD2 proteins [22] , [23] . Many DNA repair proteins accumulate at the XY body , together with histone modifications such as specific methylation , sumoylation and ubiquitylation ( reviewed by Inagaki et al . [24] ) . The accumulation of DSB repair proteins may be caused by delayed or stalled DSB repair in regions that fail to synapse . Persistent meiotic DSBs can indeed be observed on the X , but not on the Y chromosome , via immunocytochemical detection of the homologous recombination proteins RAD51 and its meiosis-specific paralogue DMC1 [25]–[28] . RAD51 and DMC1 have DNA-dependent ATPase activity and form filaments on single-stranded resected DNA-ends at DSB repair sites , and are essential for homologous recombination repair in mitotic and meiotic cells , respectively [29]–[32] . Evidence for a relationship between meiotic DSBs and homologous synapsis is provided by the observation that synapsis is severely affected in the absence of SPO11-induced meiotic DSBs [33] , [34] . Some heterologous synapsis can occur in Spo11 knockout meiocytes , but both spermatocytes and oocytes do not proceed beyond a zygotene-like stage [33] , [34] . In Spo11 knockout spermatocytes , a pseudo XY body is formed , which most often does not localize to the X and Y chromosomes , but to part of the unsynapsed chromatin [35] , [36] . It has been defined as a condensed chromatin structure that , similar to the XY body , is marked by γH2AX and ATR , and is transcriptionally silenced [35] , [37] . Based upon these characteristics , it has been proposed that the pseudo XY body is a manifestation of MSUC [37] . However , in Spo11 knockout spermatocytes , HORMAD1 and HORMAD2 coat all unsynapsed axes , but the pseudo XY body forms only on part of the unsynapsed chromatin , indicating that somehow the MSUC response is not complete [7] , [8] In addition , although more than 60% of the spermatocyte nuclei in Spo11 knockout testes contain a pseudo XY body , only 11% show clear accumulation of ATR along the unsynapsed axes in the pseudo XY body , compared to 100% ATR accumulation along the axes of true XY bodies in wild type spermatocytes [23] . The restriction of MSUC to only part of the unsynapsed chromatin is surprising , and raises the possibility that , apart from asynapsis , also other mechanisms may contribute to the activation of MSUC and MSCI . Since all known players in these processes function also in DNA repair we hypothesized that persistent DSBs on unsynapsed axes may contribute to the activation of MSUC and MSCI . This would then suggest that , even in the absence of SPO11 , perhaps some damage-induced DSBs are frequently present , and could play a role in restricting the MSUC response to those areas that contain both unsynapsed axes and DNA damage . This notion is supported by the fact that radiation-induced DSBs in mouse leptotene cells enhance the efficiency of MSUC of a small translocation bivalent that carries a heterologous region of approximately 35–40 Mb [38] . In addition , recent data also provide a link between DSB repair , the checkpoint kinase ATM , and transcriptional silencing of surrounding chromatin in somatic cells [39] . Herein , we have generated a mouse model with a point mutation , which inactivates the catalytical site of SPO11 . We used this mouse model to obtain more insight in the relation between the presence of DSBs and MSUC . As expected based on our hypothesis , we found that SPO11-independent DNA repair foci are present in spermatocytes and oocytes . Moreover , we observed de novo induction of DNA repair foci in zygotene-like SPO11-deficient oocytes . Together with the results of a thorough analysis of the relationship between the localisation of DSB repair proteins and the MSUC response , our data reveal a direct link between the presence of persistent damage and the activation of MSUC and MSCI .
We used a Spo11 knock-in mouse model in which the catalytically active tyrosine ( Tyr ) 138 residue is replaced by a phenylalanine ( Phe ) ( Spo11YF/YF ) ( Figure S1A , B ) . In yeast and plants , mutation of the analogous Tyr residue abolished meiotic DSB formation [40]–[42] , and a similar mouse mutant was recently described [43] . Presence of the point mutation and normal expression of the mutant protein were verified by sequence analyses , RT-PCR , and Western blot analyses ( Figure S1C , D , E ) . The amount of mutant and/or wild type SPO11 protein in the testis of +/+ , +/YF and YF/YF animals was comparable . Identical to the Spo11 knockout [33] , [34] , male and female Spo11YF/YF mice are infertile , and leptotene and zygotene nuclei display global absence of markers of DSB formation and repair ( Figure 1A , B , and C ) . Spermatocytes and oocytes reach a zygotene-like stage with variable degrees of heterologous synapsis ( Figure S2A , B , C ) . We analyzed the formation of meiotic DSBs in wild type , heterozygote and homozygote Spo11YF/YF mice through immunocytochemical analysis of the formation of RAD51 foci . The number of RAD51 foci was quantified in leptotene and zygotene spermatocyte and oocyte nuclei ( Figure 1 ) . In wild type leptotene , many DSBs are formed , concomitant with the assembly of short patches of axial element along the chromosomal axes ( Figure 1A , B , left panels , 1C ) . In zygotene , repair of meiotic DSBs occurs , parallel to the pairing of homologous chromosomes . Axial elements of paired homologous chromosomes then synapse ( and are therefore termed lateral elements ) , through the formation of the central element of the synaptonemal complex ( SC ) ( Figure 1A , B , left panel ) . The number of RAD51 foci gradually decreases , from leptotene to zygotene ( Figure 1 A , C ) , as has been observed before [26] . It should be noted that , in mouse , male meiosis induction occurs throughout postpubertal life , whereas female meiosis is initiated only once during embryonic development ( around embryonic day 13 ( E13 ) ) . Oocytes progress through leptotene and zygotene in 15–20 h [44] , [45] . At E17 , the vast majority of oocytes has reached the pachytene stage , and around E19 , oocytes enter diplotene , reaching the first meiotic arrest . Spermatocytes require a longer time span between leptotene ( induction of DSBs ) and early pachytene ( synapsis ) of approximately 48 h [46] . In Spo11+/YF leptotene spermatocyte nuclei , the number of RAD51 foci was approximately 30% lower compared to wild type ( Figure 1C ) . However , in zygotene nuclei , no difference in the number of RAD51 foci between wild type and heterozygote nuclei was observed ( Figure 1C ) . Similar to the males , the number of RAD51 foci was lower in Spo11+/YF leptotene oocytes , compared to the wild type , and a small difference between the wild type and heterozygote oocytes was still observed at zygotene ( Figure 1C ) . MLH1 is mismatch repair protein that is a well-known marker of crossover sites [47] , and functions in the resolution of joint molecules at the final phase of crossover formation [48] . The number of MLH1 foci was not different between wild type and Spo11+/YF spermatocytes ( Figure 1D ) . In Spo11YF/YF animals , a few RAD51 foci were observed on the axial elements in leptotene and zygotene-like spermatocytes ( average foci number 12±4 . 4 , n = 54 ) and oocytes ( average foci number 5±3 . 7 , n = 50 ) ( Figure 1A–C ) . Surprisingly , from E17 . 5 onwards , when oocytes should have reached the pachytene stage , we observed de novo RAD51 accumulation ( Figure 1E ) , in oocytes from Spo11YF/YF mice . These RAD51 foci formed along most of the length of one or more axes ( Figure 1B , lower panel , right ) . Such marked accumulation of RAD51 is also observed in wild type and Spo11+/YF pachytene oocytes ( Figure 1B , lower panel , left ) , but in a relatively small proportion of the nuclei ( around 20% , see also below ) . To confirm the specificity of this pattern of RAD51 accumulation , we also used a commercial RAD51 antibody previously reported to mark RAD51 foci in spread meiotic nuclei [49] . This antibody yielded a similar pattern of RAD51 accumulation in oocytes ( Compare Figure 1B to Figure S3 ) . To ensure that the RAD51 foci that are observed in Spo11YF/YF spermatocytes and oocytes are not caused by remnant SPO11 activity , we also analysed RAD51 localisation in Spo11 knockout meiocytes . As expected , the pattern of RAD51 foci staining in Spo11 knockout spermatocytes and oocytes was similar to what was observed in meiocytes of Spo11YF/YF animals ( Figure S4 ) . This confirms that the observed RAD51 foci in our Spo11YF/YF model are SPO11-independent . Extensive asynapsis is thought to elicit an MSUC response , which can be observed in Spo11−/− spermatocytes as a γH2AX positive domain in the nucleus [36] , [37] . This domain has been termed pseudo XY body , since it does not necessarily include chromatin from the X and Y chromosomes . Similar to what has been described for Spo11 knockout mice , we observed one or two pseudo XY bodies in late zygotene-like spermatocytes from Spo11YF/YF mice ( Figure S5A ) . In addition to γH2AX , other components of the DNA repair machinery are known to accumulate on the unsynapsed axes of the pseudo XY body ( BRCA1 , TOPBP1 ) , or on the surrounding chromatin ( MDC1 ) in Spo11 knockout spermatocytes [37] , [50] , and this was also observed for the pseudo XY bodies in Spo11YF/YF spermatocytes ( Figure S5B–D ) . As recently reported , pseudo XY body-like structures can also be detected in Spo11 knockout oocytes [22] , and even wild type oocytes have been reported to contain a MSUC region in a small percentage of the pachytene oocytes that fails to correctly synapse all chromosomes [51] . We also observed areas of MSUC in a minority of wild type and Spo11+/YFoocytes at E16 . 5 and E17 . 5 ( Table 1 ) . In addition , in Spo11YF/YF ovaries we observed a γH2AX-positive chromatin domain in about 14% of oocytes at E16 . 5 ( Table 2 ) , and in more than 80% of oocytes derived from Spo11YF/YF ovaries at E17 . 5 ( Table 2 ) . The transcriptional silencing in the XY body can be immunocytochemically visualized as an area that is relatively depleted of RNA polymerase II [17] . To verify that the γH2AX domain detected in SPO11-deficient spermatocytes and oocytes is a transcriptionally silenced region , we performed RNA polymerase II ( RNA pol II ) staining and indeed observed a depletion of this enzyme from the areas enriched for γH2AX in Spo11−/− and Spo11YF/YFspermatocytes and oocytes ( Figure 2A and B ) . To verify the results , we quantified the relative average intensity of RNA pol II staining in the γH2AX domain in oocytes , and compared it to the relative intensity in the true XY body of wild type pachytene spermatocytes ( Figure 2C ) . Despite the fact that we observed variable depletion levels within each of the three analysed categories , the relative average level of RNA pol II in γH2AX domains of wild type ( 0 . 77±0 . 16 , n = 30 ) and Spo11YF/YF ( 0 . 76±0 . 18 , n = 30 ) oocytes is similar , and also comparable to what is observed for the XY body in male wild type spermatocytes ( 0 . 69±0 . 14 , n = 30 ) ( Mann-Whitney , confidence interval p<0 . 001 ) , indicating a significant transcriptional silencing . Based on these results , we will refer to the γH2AX domains that are observed in both Spo11YF/YF and Spo11+/+ oocytes as pseudo XY bodies . Having established that both SPO11-independent DNA repair foci and pseudo XY bodies are present in SPO11-deficient spermatocytes and oocytes , we subsequently analysed whether these foci are indeed associated with the MSUC areas . Such an association would be expected , if SPO11-independent DNA damage , present on part of the unsynapsed axes , plays a role in nucleating the formation of the pseudo XY body . To investigate this , we performed co-immunostaining experiments for RAD51 to visualize DSB repair sites , γH2AX to visualize the pseudo XY body and SYCP3 to assess the stages of the cells . Due to the severe impairment of meiotic prophase progression in Spo11YF/YF animals , spermatogenesis is arrested at stage IV , but spermatocytes never reach a true pachytene stage . We performed our analyses on a subpopulation of spermatocytes which displayed one or more areas of ( heterologous ) synapsis and showed no signs of SC fragmentation , in order to select healthy spermatocytes which had already entered the zygotene stage . First of all we determined the frequency of spermatocytes with RAD51 foci and with a pseudo XY body . We split our population ( n = 240 ) in four classes ( Figure 3A ) : 1 ) cells having both a pseudo XY body and RAD51 foci; 2 ) cells having only a pseudo XY body; 3 ) cells having only RAD51 foci; and 4 ) cells lacking both a pseudo XY body and RAD51 foci ( Figure 3A , B ) . The results indicate that the vast majority of nuclei ( 78 . 3% ) contain both a pseudo XY body as well as RAD51 foci . Although RAD51 is a well-known marker of sites of DSB repair [52] , it may also accumulate on ssDNA that is formed in a different context of DNA damage , such as observed during collapse of a replication fork in S phase [53] . To obtain additional evidence for the presence of DNA damage in Spo11YF/YF spermatocytes , we performed the same analysis by staining for two more markers of DNA damage and repair: DMC1 and RPA . DMC1 is the meiosis-specific homolog of RAD51 which participates in the process of repair of meiotic DSBs via homologous recombination . Hence , we expected the results for DMC1 and RAD51 to be similar . Indeed , comparable percentages of the analyzed nuclei were found to fall in each of the four classes ( Figure 3A ) . In addition , we observed colocalization between RAD51 and DMC1 foci in the γH2AX domains ( Figure S6A ) . Unlike RAD51 and DMC1 , RPA is not a recombinase but a single-stranded DNA ( ssDNA ) binding protein which takes part in many processes involving DNA metabolism ( reviewed by Sakaguchi et al . [54] ) . At meiotic DSBs , the dynamics of RPA foci differ from those of DMC1 , and although both proteins are enriched on the XY body , this occurs at different developmental time points ( Figure S7 ) . Nevertheless , similar to what was found for RAD51 and DMC1 , 72 . 3% of the cells ( n = 108 ) showed presence of both RPA foci and γH2AX domains ( Figure 3A , lower panel ) . The high percentages of cells with a pseudo XY body and DNA damage markers , provided an indication for a possible correlation between the presence of DNA damage , in particular DSBs , and the formation of the pseudo XY body . To further test the hypothesis for such a correlation , we determined the colocalization between each DNA repair marker and the γH2AX domain , in the fraction of spermatocytes that was positive for both of these features . We counted similar average numbers of RAD51 , DMC1 and RPA foci ( 5 . 7 , 5 . 2 and 6 . 4 , respectively ) in the nuclei , and the percentages of colocalization with the γH2AX domain ( s ) ranged between 70 . 8% ( RAD51 ) and 82 . 2% ( DMC1 ) ( Figure 3B ) . Furthermore , up to 89–98% of the analysed pseudo XY bodies contained at least one focus of RAD51 , DMC1 or RPA ( Figure 3B ) . To validate that the frequent localization of RAD51 in the pseudo XY body is not coincidental , we compared the relative area of the nucleus that was positive for γH2AX ( pseudo XY body ) to the fraction of RAD51 foci that was found inside that area . We observed that the fraction of RAD51 that localized inside the pseudo XY body ( more than 70% ) was much larger than the fraction of the nucleus that was taken up by this chromatin domain ( 20% of the total area ) . In addition , there was no specific correlation ( Pearson linear correlation coefficient [Pcorr] = 0 . 0704 ) between the size of the pseudo XY body and the percentage of RAD51 foci that was found in the pseudo XY body ( Figure 3C ) . In Spo11 knockout spermatocytes , a similar pattern of colocalization between RAD51 , DMC1 , and RPA foci and the pseudo XY body was observed ( Figure S8 ) . The localised presence of DNA repair foci in one or a few pseudo XY bodies indicates that DNA damage in spermatocytes tends to concentrate in a single , transcriptionally silenced area . To test this hypothesis , we induced exogenous DSBs in Spo11YF/YF spermatocyte nuclei by whole-body irradiation , and analysed the presence of DSB markers at different time points following the treatment . We observed approximately 120 ( ±5 . 3 , n = 30 ) RAD51 foci and a nucleus-wide accumulation of γH2AX at 1 h following irradiation . Interestingly , 48 hours after irradiation , we still observed extensive H2AX phosphorylation emanating from the many RAD51 foci ( Figure 4A ) . However , 120 h following irradiation , when cells that were irradiated at leptotene would have progressed to pachytene in a wild type background , a pseudo XY body was observed in about 90% ( n = 70 ) of the analysed nuclei ( Figure 4B ) . These pseudo XY bodies always contained RAD51 foci ( 25 . 1±1 . 73 , n = 50 ) , and the majority of the radiation-induced RAD51 foci that are still present at this time point ( 65 . 7% ) localized in the pseudo XY body ( Figure 4A ) . These data show that the persistent radiation-induced DSBs tend to relocalize in a specific nuclear subdomain . This phenomenon is in accordance with the colocalization of unsynapsed or partially synapsed translocation chromosomes , carrying persistent meiotic DSBs , with the XY body [38] . To confirm that the pseudo XY body in these irradiated spermatocytes is an MSUC area , as observed in non-irradiated Spo11YF/YF spermatocytes , we performed co-immunostaining for γH2AX and RNA pol II . We detected a depletion of this enzyme in the areas enriched for γH2AX , indicating that they are transcriptionally silenced ( Figure 4C ) . Next , we asked if RAD51 , DMC1 , and RPA foci also preferentially localized in the pseudo XY bodies in E17 . 5 Spo11YF/YF oocytes . As discussed above , RAD51 was found to accumulate extensively on some chromosomal axes , often coating them completely , so that single foci could not be easily resolved . Such marked accumulation was not observed for DMC1 or RPA , which are forming fewer foci ( average number of 5 . 6±2 . 3 , n = 20 and 7 . 4±6 . 9 , n = 30 , respectively ) . Despite this difference in foci pattern , the percentage of oocyte nuclei that contained both a γH2AX domain and RAD51 foci ( 79 . 2% , n = 120 ) was similar to the percentage of oocyte nuclei with a γH2AX domain and RPA foci ( 83 . 1% , n = 89 ) ( Figure 5A , upper and lower panel respectively ) . In contrast , only 25 . 9% of the analysed Spo11YF/YF oocytes ( n = 54 ) displayed DMC1 foci , but all these cells also had a γH2AX domain . The rest of the nuclei had only a pseudo XY body ( 57 . 41% ) or were negative for both DMC1 and γH2AX ( 16 . 67% ) ( Figure 5A , middle panel ) . In the group of nuclei that contained both RAD51 foci and a γH2AX domain , the pseudo XY body always contained RAD51 foci that coated part of the axes ( Figure 5B ) . Also , in E17 . 5 Spo11YF/YF oocytes that contained a pseudo XY body and DMC1 or RPA foci , more than 90% of the pseudo XY bodies contained DMC1 or RPA foci , respectively . Conversely , the vast majority of RAD51 , DMC1 , and RPA foci in this subgroup of nuclei were located in the pseudo XY body , similar to what was observed for Spo11YF/YF spermatocyte nuclei . Furthermore , the DMC1 foci were found to colocalize with some of the ( more abundant ) RAD51 foci in the pseudo XY bodies of oocytes ( Figure S6B ) . For comparison , these analyses were also performed on Spo11 knockout E17 . 5 oocytes and this provided similar results ( Figure S8 , right ) . Interestingly , also in wild type and Spo11YF/+ oocyte nuclei , RAD51 coats the axial elements in γH2AX-positive domains ( Table 1 ) . These pseudo XY bodies were observed in approximately 20% of pachytene oocytes , similar to what was previously reported by Koutznetsova et al . [51] who observed BRCA1 and ATR on unsynapsed axes in around 15% of the oocyte population from E17 wild type embryos . To analyse this further , we studied the localisation of other proteins involved in homologous recombination ( DMC1 and RPA ) in relation to the formation of a γH2AX domain . Again we divided the oocyte population in four subgroups , based on the detection of γH2AX and the three DNA repair markers . As expected , the majority of pachytene oocytes showed complete synapsis of all chromosomes and no clear γH2AX-positive domain . Around 20–30% of nuclei showed pseudo XY bodies , as defined by the presence of one or a few distinct γH2AX-positive domains ( Figure 6A ) . Approximately half of the pachytene nuclei lacked both γH2AX domains and RAD51 or DMC1 foci , whereas no nuclei were found without RPA foci ( Figure 6A , B ) . We did not observe any pseudo XY body in nuclei without RAD51 foci , but 13% of the nuclei contained a γH2AX domain but no DMC1 foci ( Figure 6A ) . RPA is known to mark DSB repair spots after RAD51-mediated strand invasion and during homologous recombination , to protect the ssDNA regions generated during this process [55] . This explains the fact that RPA foci are always present in E17 . 5 oocyte nuclei which are at a mid-meiotic stage and have not yet completed the homologous recombination process at all DSB repair sites . Also , since RPA is engaged in completing recombination at synapsed autosomal sites , a relatively small fraction of the RPA foci colocalizes with pseudo XY bodies . In contrast , most DMC1 and RAD51 foci localize to γH2AX domains , similar to what was found for Spo11YF/YF oocyte nuclei ( Figure 6B ) , although DMC1 foci are found more frequently and in higher numbers in pseudo XY bodies in Spo11+/+ compared to Spo11YF/YF oocytes . DMC1 foci colocalized with RAD51 foci when both were present in the pseudo XY body ( Figure S6C ) . Since we observed some differences between the patterns of RAD51 and DMC1 accumulation in pseudo XY bodies of wild type oocytes , we wondered whether pseudo XY bodies that contain both DMC1 and RAD51 foci differ from those that show only RAD51 foci . First , we analysed the relation between DMC1 accumulation , formation of the pseudo XY body and synapsis , using an antibody directed against the central element protein TEX12 . The results in Figure 7A and B show that DMC1 foci in oocyte pseudo XY bodies localize mainly ( 58 . 6% ) on unsynapsed axes ( inferred from the absence of TEX12 , and placement of DMC1 foci in an axis-like pattern ) , and rarely ( 12 . 8% ) on synapsed areas ( Figure 7B ) . It is important to note that 28 . 6% of oocytes with a pseudo XY body did not show any DMC1 foci ( Figure 7A , B ) and that all these nuclei were also characterized by complete synapsis ( based on the presence of 20 TEX12-positive bivalents ) ( Figure 7B ) . In contrast , RAD51 always coats the chromosomal axes of the pseudo XY body , irrespective of synapsis ( Figure 7C ) . These observations prompted us to further analyse the occurrence of pseudo XY bodies in association with complete synapsis . For this , we used an antibody directed against the HORMAD1 protein , together with anti-TEX12 as well as anti-γH2AX to identify the pseudo XY body . As reported previously , HORMAD1 covered all unsynapsed axes at zygotene , and was lost once the cells reached complete synapsis at pachytene [8] ( Figure 8A ) . Conversely , TEX12 gradually accumulated as synapsis progressed , consistent with earlier reports [11] ( Figure 8A ) . When we analysed the pachytene population in more detail , we observed unsynapsed axes that were positive for HORMAD1 in a pseudo XY body in 9 . 8% of the pachytene nuclei , and another 13 . 1% that showed partial ( 5 . 7% ) or no ( 7 . 4% ) colocalisation of the pseudoXY body with HORMAD1 ( Figure 8B ) . Whenever HORMAD1 was absent from the pseudo XY body , TEX12 was present , indicating complete synapsis . To verify that synapsis was complete in the nuclei that lacked HORMAD1 but contained a pseudo XY body , we measured the total length of synapsed axes , visualized as TEX12 stretches , in pachytene oocyte nuclei . We found that the total SC length was comparable in pachytene oocytes without any HORMAD1 staining , independent of the presence of a pseudo XY body . On the contrary , the total synapsis length was significantly lower in pachytene oocyte nuclei which showed both a pseudo XY body and HORMAD1 ( Figure 8C ) . Finally , to confirm that these pseudo XY bodies elicit true meiotic silencing , despite the absence of asynapsis , we performed a triple staining for RNA polII , TEX12 and γH2AX . As shown in Figure 8D and 8E , RNA polII is depleted from the pseudo XY body , irrespective of synapsis .
A point mutation in the Spo11 gene that results in the replacement of Tyr 138 by Phe in the catalytic site of the enzyme leads to the absence of detectable SPO11-dependent meiotic DSBs in oocytes and spermatocytes . This observation is in accordance with recent findings of Boateng et al . [43] , who analysed a mouse mutant carrying a mutation in the Spo11 gene that leads to replacement of both Tyr 137 and Tyr138 by Phe . Although having half the amount of functional SPO11 is sufficient to generate a normal number of crossovers , as evidenced by the analysis of MLH1 foci in Spo11+/YF spermatocytes and oocytes , the dynamics of DSB induction was clearly altered . The lower number of RAD51 foci that was observed in leptotene Spo11+/YF oocytes and spermatocytes may indicate that fewer breaks are made . However , near normal numbers of RAD51 foci are observed in zygotene Spo11+/YF spermatocytes and oocytes . These data are consistent with the homeostatic control mechanism that has been observed in yeast [56] and mouse spermatocytes , allowing maintenance of normal crossover frequencies when the number of DSBs is reduced [57] . In addition , or alternatively , the recently identified feedback mechanism , requiring ATM activity , which regulates the number of breaks that can be formed by SPO11 [58] may ensure that a similar level of DSB formation is reached in the heterozygote , albeit with different kinetics when compared to the wild type . In the absence of SPO11 , no meiotic DSBs are formed , and accumulation of RAD51 , DMC1 and RPA proteins is therefore not expected . Nevertheless we observed significant numbers of RAD51 , DMC1 and RPA foci in Spo11YF/YF and Spo11−/− oocytes and spermatocytes that preferentially localized in the pseudo XY body , identified on the basis of the γH2AX staining pattern . In Spo11YF/YF oocytes , we observed a clear increase in the number of RAD51 foci in oocytes at E17 . 5 , compared to oocytes at E16 . 5 . However , the number of DMC1 and RPA foci was much lower than the number of RAD51 foci in these nuclei . The number of DMC1 foci in particular would be expected to follow the same pattern as RAD51 , because DMC1 has been reported to participate in the formation of recombination filaments [26] . Nevertheless , it has been recently shown that the dynamics of accumulation of DMC1 and RAD51 are different when extra DSBs are induced by a supplemental copy of the SPO11β-isoform [57] . Cole et al . [57] suggested that , in this situation , the extra DSBs may be more likely to engage in a mitotic pathway of HR repair , and thus less likely to recruit DMC1 . In oocytes that completely lack a synaptonemal complex , DMC1 was found to be lost from persistent DSBs , whereas RAD51 foci were still observed [59] . Based on this , it was suggested that DMC1 can only stably associate with meiotic DSBs in the context of synapsed chromatin and normal progression of repair [59] . Our own observations also indicate that DMC1 is lost from SPO11-induced DSB repair sites before RAD51 ( data not shown ) . Together , these observations are in accordance with the notion that the sites that recruit RAD51 foci in E17 . 5 oocytes can no longer recruit DMC1 with equal efficiency . This may be due to differences in the composition of the repair complexes at ( persistent ) DSBs in late compared to early pachytene oocytes , or is possibly caused by a drop in the level of DMC1 protein expression . It is important to establish if the DNA repair foci represent actual sites of DNA damage . The increase in the number of RAD51 foci in oocytes between E16 . 5 and E17 . 5 may be due to a DNA-damage independent association of RAD51 to chromosomal axes , or foci formation might be induced by the specific chromatin structure that is formed upon γH2AX formation , which would explain why the foci tend to colocalize in a single subnuclear region . However , we have observed that radiation-induced DSBs , that localize throughout the nucleus , first lead to a nucleus wide accumulation of γH2AX , and subsequently to a more concentrated presence of RAD51 foci and γH2AX in a specific subdomain of the nucleus ( the pseudo XY body ) . In addition , it is known that in spermatocytes that carry autosomes with a pairing problem , meiotic DSBs persist on the unsynapsed regions , in association with MSUC , and these regions then also tend to colocalize with the XY body , indicating that persistent DSBs in the context of MSUC have a tendency to reside together in a single nuclear area [38] . The preferred presence of DMC1 and RPA in addition to RAD51 in the pseudo XY bodies supports the hypothesis of the presence of a DNA damage event . One particular feature of the SPO11-independent repair foci in Spo11YF/YF oocytes is their inefficient processing . In fact , in oocytes from E17 . 5 Spo11YF/YF mice , RAD51 appears to coat unsynapsed axial elements , so that individual foci are no longer clearly observed , indicating that the RAD51 filament formation is not regulated as in a normal homologous DSB repair event . Upon replacement of RPA by RAD51/DMC1 , and subsequent persistence of a DSB without further processing to a recombination intermediate , such an axis-wide pattern for RAD51 may develop , possibly due to an abnormal regulation of the foci dynamics , compared to conventional DSB repair events . The spreading of RAD51 along axial elements may result from spreading of RAD51 onto double-stranded DNA , a phenomenon that has also been described for persistent DSBs in yeast [60] . Based upon these considerations , we favour the conclusion that the SPO11-independent DNA repair foci represent true sites of persistent DNA damage . To explain what might cause spontaneous DNA damage in Spo11YF/YF and knockout spermatocytes and oocytes , and possibly also in wild type meiocytes , different mechanisms can be proposed . First , during S phase in somatic cells , and most likely also in meiocytes , DSBs can form at stalled replication forks . In human cells , 50 endogenous DSBs have been proposed to occur in every cell cycle [61] . Most of these DSBs will be repaired before the cells enter G2 , but some may persist , and the number of persisting breaks appears to vary between different cell types [62] , [63] . A second mechanism that could generate endogenous DSBs is transcription-associated recombination ( TAR ) . The causes of DSBs that form in association with ongoing gene transcription are thought to be related either to generation of stalled replication forks in association with transcription , or to increased accessibility of DNA during transcription , making it more vulnerable to DNA-damaging agents ( reviewed by [64] , [65] ) . Meiocytes are post S phase cells , and leptotene , zygotene , and early pachytene spermatocytes and oocytes display a low level of RNA synthesis , making TAR an unlikely source of RAD51 foci in these cells [66] , [67] . A third possible endogenous source of DSBs is impaired topoisomerase II activity . Inhibition of topoisomerase II activity in pachytene spermatocytes has been found to result in DSB formation , indicating that topoisomerase II is indeed functional in meiocytes [68] . Fourth , endonuclease activity of ORF2 , encoded by Line1 transposons , generates DSBs during the transposition of mobile elements in the genome [69]–[71] . Derepression of transposons has been shown to cause SPO11-independent DNA damage in Mael mutant spermatocytes [72] . In wild type oocytes and spermatocytes , transcription of Line1 elements is transiently derepressed at the onset of meiosis [73] . Finally , we cannot exclude that DNA damage may occur as a result of unknown environmental or endogenous factors such as reactive oxygen species ( ROS ) . ROS generation has been described for normal rat spermatocytes [74] , but it is not clear to what extent such damage also results in RAD51 foci formation . In Spo11YF/YF spermatocytes , it appears most likely that some or all of the SPO11-independent RAD51 foci result from carry-over of spontaneous DSBs that were induced in the previous S phase . In oocytes this may also occur , and the observed de novo generation of RAD51 foci post S phase in Spo11YF/YF oocytes indicates that ( additional ) spontaneous DSBs in oocytes may arise either from impaired topoisomerase II activity or from ORF2 mediated endonuclease activity in cells that should have progressed already to pachytene . Such SPO11-independent DNA damage may also be induced in wild type pachytene oocytes , but the close proximity of the homologous template in these oocytes may facilitate homologous recombination repair of most of the de novo induced DNA damage . In Spo11YF/YF oocytes the appropriate template for repair is not directly available due to almost complete lack of homologous chromosome pairing . This difference in homologous template availability readily explains the higher relative frequency of pseudo XY body formation in Spo11YF/YF oocytes compared to oocytes from wild type or heterozygote littermate controls . At present , it is not clear whether the persistent repair foci are resolved at some later time point , or whether the persistent presence of these foci and the associated γH2AX signaling triggers a checkpoint that induces apoptosis . Daniel et al . [22] reported increased apoptosis of oocytes in ovaries of newborn Spo11 knockout mice compared to controls . In addition , it has been reported that only 10–20% of the normal number of oocytes is present in Spo11 knockout ovaries at postnatal days 4 and day 8 [34] , [75] . This percentage nicely corresponds to the 19% of oocytes that do not contain a pseudo XY body at E17 . 5 in our Spo11YF/YF model . However , although these data confirm that oocytes with a pseudo XY body are lost shortly after birth , cell death may also be caused by a so-called synapsis checkpoint , mediated by HORMAD proteins , rather than by a DNA repair checkpoint [22] , [23] , [76] . Our analyses of RAD51 and DMC1 foci in relation to MSUC and synapsis in pachytene oocytes from Spo11+/YF and wild type E17 . 5 embryos has shown that two different types of equally silenced pseudo XY bodies exist in wild type pachytene oocytes . Approximately two-third of the pseudo XY bodies accumulate DMC1 as well as RAD51 and form on unsynapsed chromatin ( Type I ) , whereas one-third accumulate RAD51 , but little or no DMC1 , and form on synapsed chromatin ( Type II ) . We propose that the Type I pseudo XY bodies represent sites that contain persistent SPO11-induced DSBs in areas that failed to synapse , whereas the Type II pseudo XY bodies represent sites where SPO11-independent damage has persisted that elicited a MSUC response , independent of synapsis . The percentage of cells with γH2AX accumulation in a pseudo XY body is highly reduced in Spo11−/− Hormad1−/− or Spo11−/− Hormad2−/− double mutant spermatocytes [22] , [23] . This illustrates the important role of HORMAD proteins in the MSUC response . Yet the localization of HORMAD1 to all unsynapsed chromatin in Spo11 knockout spermatocytes [22] , [23] ) , and the presence of some nuclei with a proper MSUC response in Spo11−/− Hormad1−/− spermatocytes indicate that , apart from HORMAD proteins , an additional localizing event is needed for pseudo XY body nucleation . Taken together , these and our observations support the hypothesis that both asynapsis , detected by HORMADs , and persistent SPO11-independent DNA repair foci are involved in the induction of H2AX phosphorylation and the establishment of meiotic silencing in pseudo XY bodies in Spo11YF/YF oocyte nuclei . We would like to propose that MSCI in wild type spermatocytes is then also triggered by both persistent DSBs , in this case SPO11-dependent , and the presence of unsynapsed chromatin ( schematically presented in Figure 9 ) . If RAD51 accumulation is as extensive as observed in pseudo XY bodies in oocytes , HORMADs may not even be required , and enough ATR may be recruited by the DNA repair machinery itself , to elicit the MSUC response , as indicated by the existence of pseudo XY bodies that lack HORMAD1 in oocytes . Despite the more prominent RAD51 accumulation on axes of the pseudo XY body in oocytes as compared to spermatocytes , we propose that the mechanism of pseudo XY body formation in Spo11YF/YF spermatocytes occurs in a similar fashion . The differences in the pattern of RAD51 accumulation may be caused by the fact that Spo11YF/YF spermatocytes are eliminated at stage IV of the spermatogenic cycle , whereas Spo11YF/YF oocytes appear to proceed normally throughout the stage that should correspond to pachytene and are eliminated later [34] . Perhaps , the few spontaneous DSBs in Spo11YF/YF spermatocytes modulate the MSUC response in a slightly different way , compared to the responses elicited by the more extensive accumulation of endogenous DSBs in Spo11YF/YF oocytes . Still , the MSUC response in both Spo11YF/YF spermatocytes and oocytes is characterized by the same intense γH2AX accumulation and by the presence of RAD51/DMC1 and RPA foci . It is interesting to note that such foci can also be observed on the unsynapsed axes of the X chromosome in wild type spermatocytes , as a hallmark of persistent DSBs . HORMAD proteins may be instrumental to spread the MSUC response along the chromosomal axes into areas that lack persistent DSBs , such as the Y chromosome . In somatic cells , formation of γH2AX chromatin domains has also been coupled to transcriptional silencing , in the context of radiation-induced damage [16] . More recently , Shanbhag et al . [39] analysed the effect of persistence of an endonuclease-dependent DSB on transcriptional activity in the neighbouring genes . They observed that H2AX phosphorylation spreads along the DNA surrounding the DSB , and that the accumulation of this histone modification correlated with reduction of RNA polymerase II activity . Persistent DSBs were shown to trigger the silencing of neighbouring genes , and the mechanism was termed DSB-induced silencing in cis ( DISC ) [39] . This mechanism , that occurs in somatic cells , might have some aspects in common with MSUC and MSCI in meiocytes . In conclusion , this study has revealed the presence of SPO11-independent DNA repair foci in oocytes and spermatocytes . In addition , we show that unrepaired DSBs most likely are the initial trigger of both MSCI and MSUC in spermatocytes and oocytes . For wild type oocytes , the possible presence of de novo induced DNA damage in a substantial part of the oocyte population may contribute to the massive loss of such oocytes around birth . For spermatocytes , the few SPO11-independent breaks that are present will most likely be rapidly repaired once homologous chromosome pairing is obtained with the help of the 200 or more SPO11-induced DSBs . The MSUC and MSCI response may be less unique than previously thought , and actually represent an extreme and adapted form of DISC . Therefore , knowledge about the molecular basis of meiotic silencing may also be relevant for our understanding of DNA damage-induced chromatin modifications in somatic cells .
All animal experiments were approved by the local animal experiments committee DEC Consult . All animals were housed in IVC cages under supervision of the Animal Welfare Officer . Any discomfort of animals was daily scored by the animal caretakers . No more than mild or moderate discomfort of animals was expected from the treatments , and no unexpected discomfort was observed . All animal experiments were approved by the animal experiments committee DEC-Consult . Spo11 mutant mice were generated through a two-step recombination strategy as described by Soukharev et al . , [77] . First , two heterospecific lox sites flanking the selectable marker hygromycin , replacing exons 4–8 , were placed in the Spo11 gene , in ES cells by homologous recombination . Next , a targeting vector containing the same heterospecific lox sites flanking exon 4–8 of Spo11 with the point mutation generating Y138F at exon 4 was used to replace the selection marker by a site-specific double cross-over event ( Figure S1A ) . The final modified Spo11 locus carries a loxP site between exons 3 and 4 , the point mutation generating Y138F at exon 4 , and a lox511 site between exons 8 and 9 . ES cells carrying a single modified Spo11 allele were used for blastocyst injection to generate chimeras , and heterozygotes were obtained upon germ line transmission of the mutated allele . Correct targeting was verified using Southern blotting with 5′and 3′probes outside the targeted region ( Figure S1B ) , and sequencing ( Figure S1C ) . This Spo11 allele has been registered at Mouse Genome Informatics ( MGI ) as Spo11<tm1Bdm> ( Allele Accession ID: MGI:5432496 ) . Wild type , heterozygote and homozygote Spo11 mutant mice were kept on a FVB background . To genotype the animals , the following primers were used: forward , 5′CTGGTCGATGCAGATCCCTACGG3′; reversed , 5′TAGATGCACATTATCTCGATGCC3′ ( Figure S1B ) Spo11 knockout mice carried the Spo11tm1M allele described in [34] . For the analysis of radiation-induced DSBs in spermatocytes , Spo11YF/YF male adult mice were exposed to 5Gy whole body radiation and sacrificed 1 h , 48 h , and 120 h after the treatment to collect the testes . For primary antibodies , we used mouse monoclonal antibodies anti-phosphorylated H2AX , anti-BRCA1 , anti-TOPBP1 , anti-MDC1 , anti-phospho H2AX ( all from Upstate ) , anti-DMC1 ( DMC1-specific ) , anti-RAD51 , anti-RNA Polymerase II ( all from Abcam ) ; rabbit polyclonal antibodies anti-RAD51 ( recognizing both DMC1 and RAD51 ) [78] , anti-RPA ( gift from P . De Boer , described in Schaarmidt et al . , ( [79] ) , anti-SYCP3 ( gift from C . Heyting ) , anti-HORMAD1 ( gift from A . Tóth ) and anti-phosphorylated H2AX ( Upstate ) ; rat polyclonal anti-SYCP3 [80]; guinea pig anti-TEX12 ( gift from Christer Höög ) . SPO11 antibody ( Spo11L56S9 ) was raised from rabbits immunized with GST-Spo11α produced by the service of recombinant protein of CRBM ( UMR5237-CNRS ) . For secondary antibodies , we used a goat anti-rabbit IgG alexa 405/488/546/633 , goat anti-mouse alexa IgG 350/488/546/633 , goat anti-rat IgG alexa 546 , goat anti-guinea pig 405/555 ( Molecular Probes ) . RNA was extracted and reverse transcribed according to standard procedures . PCR amplifications were performed with forward primer 5′AATAGTCGAGAAGGATGCAACA3′and reversed primer 5′TAGATGCACATTATCTCGATGC3′ Immunoprecipitations were carried out with rabbit polyclonal anti-SPO11 antibody , followed by western blot detection with the same primary antibody and Trueblot secondary antibody ( eBioscience ) . Testes were fixed and stained with hematoxilin and eosin using standard histological methods . Testis tissues were processed to obtain spread nuclei for immunocytochemistry as described by Peters et al . ( 1997 ) [81] . Spread nuclei of spermatocytes were stained with antibodies mentioned above . Before incubation with antibodies , slides were washed in PBS ( 3×10 min ) , and non-specific sites were blocked with 0 . 5% w/v BSA and 0 . 5% w/v milk powder in PBS . Primary antibodies were diluted in 10% w/v BSA in PBS , and incubations were overnight at room temperature in a humid chamber . Subsequently , slides were washed ( 3×10 min ) in PBS , blocked in 10% v/v normal goat serum ( Sigma ) in blocking buffer ( supernatant of 5% w/v milk powder in PBS centrifuged at 14 , 000 rpm for 10 min ) , and incubated with secondary antibodies in 10% normal goat serum in blocking buffer at room temperature for 2 hours . Finally , slides were washed ( 3×10 min ) in PBS ( in the dark ) and embedded in Prolong Gold with or without DAPI ( invitrogen ) . Fluorescent images were observed by using a fluorescence microscope ( Axioplan 2; Carl Zeiss ) equipped with a digital camera ( Coolsnap-Pro; Photometrics ) . To distinguish zygotenes from aberrant pachytenes , we used specific parameters defined in Figure S9 . Aberrant pachytene oocytes , have also been described in previous publications [7] , [51] , and are characterized by the presence of one to three chromosome pairs lacking synapsis . We also included rare nuclei in which some chromosomes are entangled and not fully synapsed . Normal ( late ) zygotene nuclei are characterized by a higher proportion of homologs that have not completed synapsis , compared to what is observed in the aberrant pachytenes , and SYCP1/TEX12 patches can be observed which have not yet converged to become a single complete central element . In addition to specific characteristics of the SC , the labelling patterns of the repair associated recombinase RAD51 and phosphorylated H2AX are also helpful to distinguish late zygotenes from aberrant pachytenes . Single , isolated RAD51 foci are observed in zygotene nuclei , whereas multiple closely adjacent foci are present in aberrant pachytenes . H2AX phosphorylation , occurs in a nucleus-wide pattern at zygotene . In contrast , aberrant pachytene oocytes have one to three bright and defined γH2AX domains . Fluorescent images were taken under identical conditions for all slides , and images were analyzed using the ImageJ ( Fiji ) software ( Rasband , W . S . , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA [http://rsb . info . nih . gov/ij/] ) . Confocal imaging was performed on a Zeiss LSM700 microscope ( Carl Zeiss , Jena ) : we used 63× oil immersion objective lens ( N . A . 1 . 4 ) , pinhole 1AU . DAPI was excited at 405 nm and imaged with a short pass filter ( SP ) 490 nm; Alexa 488 was excited at 490 nm and imaged SP 555 nm; Alexa 546 was excited at 555 nm and imaged SP 640 nm; Alexa 633 was excited at 639 nm and for the imaging no filter was required . Imaging of nuclei immunostained for RAD51 or DMC1 or RPA and SYCP3 was performed with the same exposure time for each nucleus . Images were analysed without any manipulation of brightness and contrast . Foci were subsequently counted using Image J software , including the Fiji plug-in . We used the analyze particles function and set the threshold manually , in order to include the smallest visible focus in the analysis . The average area of one RAD51 focus was assessed to be 40–50 pixels , therefore foci with an area larger than 100 pixels were counted as multiple foci to allow approximate quantification of RAD51 foci also when it was observed as a continuous signal along the axial elements . Measurement of synaptonemal complex length was performed using a homemade ImageJ macro . The macro generates a skeletonized image of the original picture and measures the length of that skeleton . Relative quantification of the RNA polII levels in the ( pseudo ) XY body was performed comparing the average intensity per pixel area in the γH2AX domain with the average intensity in a non-heterochromatic nuclear area of the same shape and size . | Meiosis is a special cell division that generates genetically divergent haploid germ cells . At the very beginning of this process , during meiotic prophase , the enzyme SPO11 generates hundreds of DNA double-strand breaks ( DSBs ) . Meiotic DSBs are repaired via a mechanism that requires the presence of an intact homologous template . This repair process stimulates homologous chromosome pairing , and the formation of a protein complex that connects the paired chromosome axes , reaching a state called synapsis . Male mammals carry a pair of largely heterologous sex chromosomes , the X and Y , which show delayed DSB repair and extensive asynapsis . In addition , the X and Y chromosomes are transcriptionally silenced by a mechanism named Meiotic Sex Chromosome Inactivation ( MSCI ) . This mechanism is a specialization of a more general silencing mechanism , named Meiotic Silencing of Unsynapsed Chromatin ( MSUC ) , that is induced when any pairing problem between homologous chromosomes results in asynapsis , in male as well as female meiotic prophase cells . Here , we demonstrate that in addition to asynapsis , the persistent presence of DNA repair foci is a hallmark of meiotic silencing . In addition , we show that SPO11-independent DNA repair foci form during normal oocyte development . We propose that these foci represent sites of unrepaired DSBs that are capable of inducing transcriptional silencing , irrespective of synapsis . | [
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] | 2013 | SPO11-Independent DNA Repair Foci and Their Role in Meiotic Silencing |
The vertebrate adaptive immune system provides a flexible and diverse set of molecules to neutralize pathogens . Yet , viruses such as HIV can cause chronic infections by evolving as quickly as the adaptive immune system , forming an evolutionary arms race . Here we introduce a mathematical framework to study the coevolutionary dynamics between antibodies and antigens within a host . We focus on changes in the binding interactions between the antibody and antigen populations , which result from the underlying stochastic evolution of genotype frequencies driven by mutation , selection , and drift . We identify the critical viral and immune parameters that determine the distribution of antibody-antigen binding affinities . We also identify definitive signatures of coevolution that measure the reciprocal response between antibodies and viruses , and we introduce experimentally measurable quantities that quantify the extent of adaptation during continual coevolution of the two opposing populations . Using this analytical framework , we infer rates of viral and immune adaptation based on time-shifted neutralization assays in two HIV-infected patients . Finally , we analyze competition between clonal lineages of antibodies and characterize the fate of a given lineage in terms of the state of the antibody and viral populations . In particular , we derive the conditions that favor the emergence of broadly neutralizing antibodies , which may have relevance to vaccine design against HIV .
It takes decades for humans to reproduce , but our pathogens can reproduce in less than a day . How can we coexist with pathogens whose potential to evolve is 104-times faster than our own ? In vertebrates , the answer lies in their adaptive immune system , which uses recombination , mutation , and selection to evolve a response on the same time-scale at which pathogens themselves evolve . One of the central actors in the adaptive immune system are B-cells , which recognize pathogens using highly diverse membrane-bound receptors . Naive B-cells are created by processes which generate extensive genetic diversity in their receptors via recombination , insertions and deletions , and hypermutations [1] which can potentially produce ∼1018 variants in a human repertoire [2] . This estimate of potential lymphocyte diversity outnumbers the total population size of B-cells in humans , i . e . , ∼1010 [3 , 4] . During an infection , B-cells aggregate to form germinal centers , where they hypermutate at a rate of about ∼10−3 per base pair per cell division over a region of 1-2 kilo base pairs [5] . The B-cell hypermutation rate is approximately 4–5 orders of magnitude larger than an average germline mutation rate per cell division in humans [6] . Mutated B-cells compete for survival and proliferation signals from helper T-cells , based on the B-cell receptor’s binding to antigens . This form of natural selection is known as affinity maturation , and it can increase binding affinities up to 10–100 fold [7–9] , see Fig 1A . B-cells with high binding affinity may leave germinal centers to become antibody secreting plasma cells , or dormant memory cells that can be reactivated quickly upon future infections [1] . Secreted antibodies , which are the soluble form of B-cell receptors , can bind directly to pathogens to mark them for neutralization by other parts of the immune system . Plasma B-cells may recirculate to other germinal centers and undergo further hypermutation [8] . Some viruses , such as seasonal influenza viruses , evolve quickly at the population level , but the adaptive immune system can nonetheless remove them from any given host within a week or two . By contrast , chronic infections can last for decades within an individual , either by pathogen dormancy or by pathogens avoiding neutralization by evolving as rapidly as B-cell populations . HIV mutation rates , for example , can be as high as 0 . 1–0 . 2 per generation per genome [10] . Neutralizing assays and phylogenetic analyses suggest an evolutionary arms race between B-cells and HIV populations during infection in a single patient [11–15] . Viruses such as HIV have evolved to keep the sensitive regions of their structure inaccessible by the immune system e . g . , through glycan restriction or immuno-dominant variable loops [16 , 17] . As a result , the majority of selected antibodies bind to the most easily accessible regions of the virus , where viruses can tolerate mutations and thereby escape immune challenge . Nonetheless , a remarkably large proportion of HIV patients ( ∼20% ) eventually produce antibodies that neutralize a broad panel of virions [18 , 19] by attacking structurally conserved regions , such as the CD4 binding site of HIV env protein [14 , 20–23] . These broadly neutralizing antibodies ( BnAbs ) , can even neutralize HIV viruses from other clades , suggesting it may be possible to design an effective HIV vaccine if we can understand the conditions under which BnAbs arise [14 , 20 , 23–27] . Recent studies have focused on mechanistic modeling of germinal centers in response to one or several antigens [7 , 28] , and elicitation of BnAbs [27 , 29] . However , these studies did not model the coevolution of the virus and B-cell repertoire , which is important to understand how BnAbs arise in vivo . Modeling of such coevolution is difficult because the mechanistic details of germinal center activity are largely unknown [15 , 30] , and the multitude of parameters make it difficult to identify generalizable aspects of a model . While evidence of viral escape mutations and B-cell adaptation has been observed experimentally [11–14] and modeled mechanistically [27 , 29] , it is not clear what are the generic features and relevant parameters in an evolutionary arms race that permit the development , or , especially , the early development of BnAbs . Phenomenological models ignore many details of affinity maturation and heterogeneity in the structure of germinal centers and yet produce useful qualitative predictions [15 , 30 , 31] . Past models typically described only a few viral types [27 , 28] , and did not account for the vast genetic diversity and turnover seen in infecting populations . A recent study by Luo & Perelson [30] described diverse viral and antibody populations , relying primarily on numerical simulations . In this paper , we take a phenomenological approach to model the within-host coevolution of diverse populations of B-cells and chronic viruses . We focus on the chronic infection phase , where the immune response is dominated by HIV-specific antibody-mediated mechanisms , which follow the strong response by the cytotoxic T-lymphocytes ( i . e . , CD8+ killers T-cells ) , around 50 days after infection [32] . During the chronic phase , population sizes of viruses and lymphocytes are relatively constant but their genetic compositions undergo rapid turnover [33] . We characterize the interacting sites of B-cell receptors and viruses as mutable binary strings , with binding affinity , and therefore selection , defined by matching bits . We keep track of both variable regions in the viral genome and conserved regions , asking specifically when B-cell receptors will evolve to bind to the conserved region , i . e . , to develop broad neutralization capacity . The main simplification that makes our analysis tractable is that we focus on the evolution of a shared interaction phenotype , namely the distribution of binding affinities between viral and receptor populations . Specifically , we model the effects of mutations , selection and reproductive stochasticity on the distribution of binding affinities between the two populations , which is similar to the approach of quantitative genetics [34] . Projecting from the high-dimensional space of genotypes to lower dimension of binding phenotypes allows for a predictive and analytical description of the coevolutionary process [35] , whilst retaining the salient information about the quantities of greatest biological and therapeutic interest . Using this modeling approach we show that the evolution of the binding affinity does not depend on details of any single-locus contribution , but is an emerging property of all constitutive loci . Even though the coevolution of antibodies and viruses is perpetually out of equilibrium , we develop a framework to quantify the amount of adaptation in each of the two populations by defining fitness and transfer flux , which partition changes in mean fitness . We discuss how to measure the fitness and transfer flux from time-shifted experiments , where viruses are competed against past and future antibodies , and we show how such measurements provide a signature of coevolution . We use these analytical results to interpret empirical measurements of time-shifted neutralization assays from two HIV-infected patients [11] , and we infer two qualitatively different regimes of viral-antibody coevolution . We discuss the consequences of competition between clonal B-cell lineages within and between germinal centers . In particular , we derive analytic expressions for the fixation probability of a newly arisen , broadly neutralizing antibody lineage . We find that BnAbs have an elevated chance of fixation in the presence of a diverse viral population , whereas specific neutralizing antibody lineages do not . We discuss the implications of these results for the design of preventive vaccines that elicit BnAbs against HIV .
B-cell receptors undergo mutation and selection in germinal centers , whereas viruses are primarily affected by the receptors secreted into the blood , known as antibodies . Our model does not distinguish between antibodies and B-cells , so we will use the terms interchangeably . To represent genetically diverse populations we define genotypes for antibodies and viruses as binary sequences of ±1 , where mutations change the sign of individual loci . Mutations in some regions of a viral genome are highly deleterious , e . g . at sites that allow the virus to bind target cell receptors , including CD4-binding sites for HIV . To capture this property we explicitly model a conserved region of the viral genome that does not tolerate mutations , so that its bits are always set to +1 . We let viruses have variable bits at positions i = 1 … ℓ , and conserved bits at positions i = ℓ + 1 , … , ℓ + ℓ ^; while antibodies have variable bits at positions i = 1 … ℓ + ℓ ^; see Fig 1B . Naive B-cells generate diversity by gene rearrangements ( VDJ recombination ) , which differentiates their ability to bind to different epitopes of the virus; and then B-cells diversify further by somatic hypermutation and selection during affinity maturation . We call the set of B-cells that originate from a common germline sequence a clonal lineage . A lineage with access to conserved regions of the virus can effectively neutralize more viral genotypes , since no escape mutation can counteract this kind of neutralization . The binding affinity between antibody and virus determines the likelihood of a given antigen neutralization by an antibody , and therefore it is the key molecular phenotype that determines selection on both immune and viral populations . We model the binding affinity as a weighted dot product over all loci , which for antibody Aα chosen from the genotype space α ∈ 1 … 2 ℓ + ℓ ^ and virus Vγ with γ ∈ 1 … 2ℓ has binding affinity E t o t C ( A α , V γ ) = ∑ i = 1 ℓ κ i C A i α V i γ ︸ variable viral region + ∑ i = ℓ + 1 ℓ + ℓ ^ κ ^ i C A i α ︸ conserved viral region ≡ E α γ C + E ^ α C ( 1 ) where , A i α = ± 1 denotes the ith locus of the α antibody genotype , and V i γ the ith locus of the γ viral genotype . Matching bits at interacting positions enhance binding affinity between an antibody and a virus; see Fig 1B . Similar models have been used to describe B-cell maturation in germinal centers [27] , and T-cell selection based on the capability to bind external antigens and avoid self proteins [36 , 37] . The conserved region of the virus with Vi = 1 is located at positions i = ℓ + 1 , … , ℓ + ℓ ^ for all viral sequences . Consequently , the total binding affinity is decomposed into the interaction with the variable region of the virus , E α γ C and with the conserved region of the virus , E ^ α C . We call the lineage-specific binding constants { κ i C ≥ 0 } and { κ ^ i C ≥ 0 } the accessibilities , because they characterize the intrinsic sensitivity of an antibody lineage to individual sites in viral epitopes . We begin by analyzing the evolution of a single antibody lineage , and suppress the C notation for brevity . Coevolution with multiple antibody lineages is discussed in a later section . Both antibody and viral populations are highly polymorphic , and therefore contain many unique genotypes . While the binding affinity between a virus Vγ and an antibody Aα is constant , given by eq ( 1 ) , the frequencies of the antibody and viral genotypes , xα and yγ , and all quantities derived from them , change over time as the two populations coevolve . To characterize the distribution of binding affinities we define the genotype-specific binding affinities in each population , which are marginalized quantities over the opposing population: Eα ⋅ = ∑γ Eαγ yγ for the antibody Aα , and E . γ = ∑α Eαγ xα for the virus Vγ . We will describe the time evolution of the joint distribution of Eα ⋅ , E ^ α , and E⋅ γ , by considering three of its moments: ( i ) the mean binding affinity , which is the same for both populations E = ∑ α E α · x α = ∑ γ E · γ y γ , ( ii ) the diversity of binding affinity in the antibodies , M A , 2 = ∑ α ( E α · - E ) 2 x α and ( iii ) the diversity of binding affinities in the viruses , M V , 2 = ∑ γ ( E · γ - E ) 2 y γ . Analogous statistics of binding affinities can be defined for the conserved region of the virus , which we denote by E ^ for the mean interaction , and M ^ A , 2 for the diversity across antibodies . The diversity of viral interactions in the conserved region must always equal zero , M ^ V , 2 = 0 . We first characterize the affinity maturation process of a single clonal antibody lineage coevolving with a viral population , which includes hypermutation , selection , and stochasticity due to population size in germinal centers , i . e . , genetic drift . We focus initially on understanding the ( rescaled ) mean binding affinity ε , ε ^ between a clonal antibody lineage and the viral population , since this is a proxy for the overall neutralization ability that is commonly monitored during an infection . Combining genetic drift with mutation and selection , and assuming a continuous-time and continuous-frequency process , results in a stochastic dynamical equation for the evolution of rescaled mean binding affinity in the variable region , d d τ ε = - 2 θ a + θ v ( N A / N v ) ε + s a m A , 2 - s v m V , 2 + m A , 2 + N a N v m V , 2 χ ε ( 4 ) and in the conserved region , d d τ ε ^ = - 2 θ a ε ^ + s a m ^ A , 2 + m ^ A , 2 χ ε ^ ( 5 ) where χε and χ ε ^ are standard Gaussian noise terms , and time τ is measured in units of the antibody coalescence time Na . Our analysis neglects the correlation between the variable and the conserved regions of the virus , which is due to physical linkage of the segments . In Section B . 4 of S1 Text we show that a difference in evolutionary time-scales between these regions reduces the magnitude of this correlation . As eqs ( 4 and 5 ) reflect , mutations drive the mean affinity towards the neutral value , zero , whereas selection pushes it towards positive or negative values . The efficacy of selection on binding affinity is proportional to the binding diversity mA , 2 , mV , 2 in each of the populations . If a population harbors a large diversity of binding affinities then it has more potential for adaptation from the favorable tail of the distribution , which contains the most fit individuals in each generation [40 , 41] . It follows that selection on viruses does not affect the evolution of their conserved region , where the viral diversity of binding is always zero , m ^ V , 2 = 0 . In Section B . 3 of S1 Text and S2 Fig we study the evolution of the higher central moments in detail . The dynamics in eqs ( 4 and 5 ) simplify in the regime where selection on individual loci is weak ( NSκ < 1 ) , but the additive effects of selection on the total binding affinity are substantial ( 1 ≲ s ≪ θ−1 ) . This evolutionary regime is , in particular , relevant for HIV escape from the humoral neutralizing antibody response [39] , that follows the initial strong response to cytotoxic T-lymphocytes [42] . In this parameter regime , the binding diversities are fast variables compared to the mean affinity , and can be approximated by their stationary ensemble-averaged values ( S3 Fig ) , which depend only weakly on the strength of selection even for substantial selection s ∼ 1: 〈mA , 2〉 ≃ 4θa and 〈mV , 2〉 ≃ 4θv . Higher-order corrections ( Section B . 3 of S1 Text and S2 Fig ) show that strong selection reduces binding diversity . The ensemble-averaged mean binding affinities relax exponentially towards their stationary values , 〈 ε 〉 ≃ 2 ( s a θ a - s v θ v ( N a / N v ) ) θ a + θ v ( N a / N v ) ≡ 2 Δ s a v ( 6 ) 〈 ε ^ 〉 ≃ 2 s ^ a ( 7 ) where Δsav is an effective selection coefficient for binding affinity in the variable region , combining the effect of selection from both populations and accounting for their distinct genetic diversities . The stationary mean binding affinity quantifies the balance of mutation and selection acting on both populations . A strong selection difference between two populations Δsav ≫ 1 results in selective sweeps for genotypes with extreme values of binding affinity in each population , and hence , reduces the binding diversity . We validated our analytical solution for stationary mean binding , with corrections due to selection on binding diversity ( Section B . 3 of S1 Text ) , by comparison with full , genotype-based Wright-Fisher simulations across a broad range of selection strengths ( Materials and Methods , S1 and S2 Figs ) . The weak dependence of binding diversity on selection allows for an experimental estimation of the stationary rescaled mean binding affinity , using measurements of the binding affinity distribution and neutral sequence diversities . The rescaled binding affinity can be approximated as: ε ≈ 〈 E 〉 / 〈 M A , 2 〉 / 4 θ a and ε ^ ≈ 〈 E ^ 〉 / 〈 M ^ A , 2 〉 / 4 θ a . Fig 2 demonstrates the utility of this approximation , and it shows that heterogeneous binding accessibilities , κi , drawn from several different distributions , do not affect stationary mean binding . Only the total magnitude of the accessibilities is relevant , as it determines the effect of selection on the whole phenotype . Although we have formulated a high-dimensional stochastic model of antibody-antigen coevolution in polymorphic populations , we can nonetheless understand the long-term binding affinities , which are commonly measured in patients , in terms of only a few key parameters . In Section B . 5 of S1 Text we numerically study non-linear fitness landscapes , and their effect on the stationary mean binding and rate of adaptation ( S4 Fig ) . While the results differ quantitatively , we can qualitatively understand how the stationary mean binding affinity depends on the form of non-linearity . The antagonistic coevolution of antibodies and viruses is a non-equilibrium process , with each population constantly adapting to a dynamic environment , namely , the state of the opposing population . As a result , any time-independent quantity , such as the stationary mean binding affinity studied above , is itself not informative for the extent of coevolution that is occurring . For example , a stationary mean binding affinity of zero ( equivalently Δsav = 0 in eq ( 6 ) ) can indicate either neutral evolution or rapid coevolution induced by equally strong selection in antibody and viral populations . To quantify the amount of adaptation and extent of interaction in two coevolving populations we will partition the change in mean fitness of each population into two components . We measure adaptation by the fitness flux [43–45] , which generically quantifies adaptation of a population in response to a changing environment ( in this case the opposing population ) ; see schematic Fig 3 . For our model , the fitness flux of the antibody population quantifies the effect of changing genotype frequencies on mean fitness , and is defined as ϕ A ( t ) = ∑ α ∂ x α F A ( t ) d x α ( t ) / d t , where FA denotes the mean fitness of antibodies , and the derivative dxα ( t ) /dt measures the change in frequency of the antibody Aα . The forces of mutation , drift , and selection all contribute to fitness flux , however the portion of fitness flux due to selection equals the population variance of fitness , in accordance with Fisher’s theorem [40] . The second quantity we study , which we term the transfer flux , measures the amount of interaction between the two populations by quantifying the change in mean fitness due to the response of the opposing population ( schematic Fig 3 ) . The transfer flux from viruses to antibodies is defined as TV→A ( t ) =∑γ∂yγFA ( t ) dyγ ( t ) /dt . Analogous measures of adaptation and interaction can be defined for the viral population ( see Section C of S1 Text ) . The fitness flux and transfer flux represent rates of adaptation and interaction , and they are typically time dependent , except in the stationary state . The total amount of adaptation and interaction during non-stationary evolution , where the fluxes change over time , can be measured by the cumulative fluxes over a period of time: Φ A ( τ a ) = N a ∫ t ′ = 0 t ϕ A ( t ′ ) d t ′ and T V → A ( τ a ) = N a ∫ t ′ = 0 t T V → A ( t ′ ) d t ′ , where time τa = t/Na is measured in units of neutral coalescence time of antibodies Na . In the stationary state , the ensemble-averaged cumulative fluxes grow linearly with time . For coevolution on the fitness landscapes given by eqs ( 2 and 3 ) , the ensemble-averaged , stationary cumulative fitness flux and transfer flux in antibodies are 〈 Φ A ( τ a ) 〉 = - 2 θ a s a 〈 ε 〉 + s a 2 〈 m A , 2 〉 τ a ( 8 ) 〈 T V → A ( τ a ) 〉 = - 2 θ v s a 〈 ε 〉 - s a s v 〈 m V , 2 〉 ( N a / N v ) τ a ( 9 ) Note that the factor ( Na/Nv ) τa in eq ( 9 ) , which is a rescaling of time in units of viral neutral coalescence time τv = t/Nv , emphasizes the distinction between the evolutionary time scales of antibodies and viruses . The first terms on the right hand side of eqs ( 8 and 9 ) represent the fitness changes due to mutation , the second terms are due to selection , and the effects of genetic drift are zero in the ensemble average for our linear fitness landscape . Notably , the flux due to the conserved region of the virus is zero in stationarity , as is the case for evolution in a static fitness landscape ( i . e . , under equilibrium conditions ) . In the stationary state , the cumulative fitness and transfer fluxes sum up to zero , 〈ΦA ( τa ) 〉 + 〈TV→A ( τa ) 〉 = 0 . Fitness flux and transfer flux are generic quantities that are independent of the details of our model , and so they provide a natural way to compare the rate of adaptation in different evolutionary models or in different experiments . In the regime of strong selection sa , sv ≳ 1 , non-linearity of the fitness function results in a more narrow distribution of fitness values in the antibody population , and hence , reduces the rate of adaptation and fitness flux; see S4 Fig . In the following section we show how to use fitness and transfer flux to detect signatures of significant antibody-antigen coevolution . Measuring interactions between antibodies and viruses isolated from different times provides a powerful way to identify coevolution . These “time-shifted” neutralization measurements in HIV patients have shown that viruses are more resistant to past antibodies , from which they have been selected to escape , and more susceptible to antibodies from the future , due to selection and affinity maturation of B-cells [11–13] . We can predict the form of time-shifted binding assays under our model; see Section D of S1 Text for details . The rescaled time-shifted binding affinity between viruses at time t and antibodies at time t + τ is given by ετ ( t ) = ∑α , γ Eαγ yγ ( t ) xα ( t + τ ) /E0 and ε ^ τ ( t ) = ∑ α E ^ α x α ( t + τ ) / E ^ 0 for the variable and the conserved region , respectively . The corresponding viral mean fitness at time t against the antibody population at time t + τ is N v F V ; τ ( t ) = - s v ( ε τ ( t ) + ε ^ τ ( t ) ) . The slope of the time-shifted viral fitness at the time where the two populations co-occur ( i . e . , τ = 0 ) , approaching from negative τ , i . e . , from the past , measures the amount of adaptation of the viral population in response to the state of the antibody population , and it is precisely equal to the fitness flux of viruses: ∂τ FV;τ ( t − τ ) |τ = 0− = ϕV ( t ) . The slope approaching from positive time-shifts , i . e . , from the future , measures the change in the mean fitness of the viral population due to adaptation of the antibody population , and it is precisely equal to the transfer flux from antibodies to viruses ∂ τ F V ; τ ( t ) | τ = 0 + = T A → V ( t ) . Similarly , we can define time-shifted fitness with antibodies as the focal population; see Section D of S1 Text . In stationarity , the sum of fitness flux and transfer flux is zero on average , and so the slopes from either side of τ = 0 are equal , as in Fig 4 and S5 Fig . Note that these relationships between time-shifted fitness and the flux variables hold in general , beyond the specific case of a linear fitness landscape . In a non-stationary state , the fitness flux and transfer flux are not balanced , and so 〈FV;τ ( t ) 〉 has a discontinuous derivative at τ = 0 ( S6 Fig ) . Therefore , observation of such a discontinuity provides a way to identify stationarity versus transient dynamics , given sufficient replicated experiments . Whether in stationarity or not , the signature of out-of-equilibrium evolution is a positive fitness flux and negative transfer flux . For time-shifted fitness , this means that for short time shifts , where dynamics are dominated by selection , viruses have a higher fitness against antibodies from the past , and have lower fitness against antibodies from the future . This is true even when one population is evolving neutrally and the other has substantial selection , as shown in Fig 4A . For long time shifts , the sequences are randomized by mutations and the fitness decays exponentially to the neutral value . When selection and mutation are substantial on both sides the time-shifted fitness curve has a characteristic “S”shape—a signature of coevolution , whose inflected form can be understood in terms of the fitness and transfer fluxes . In Section D of S1 Text we analytically derive the functional form of the time-shifted binding affinity and fitness dependent on the evolutionary parameters . Fig 4A and 4B and S5 Fig show good agreement between Wright-Fisher simulations and our analytical predictions given by eqs . ( S102 , S103 ) in S1 Text for the stationary time-shifted binding affinity and viral fitness . We can use our analytical results to interpret empirical measurements of time-shifted viral neutralization by a patient’s circulating antibodies . We analyzed data from Richman et al . [11] on two HIV-infected patients . We approximated the fitness of the virus against a sampled serum ( antibodies ) as the logarithm of the neutralization titer FV ≃ −log titer; here titer is the reciprocal of antibody dilution where inhibition reaches 50% ( IC50 ) [46] . A signature of coevolution can sometimes be obscured when the fitnesses of antibodies and viruses also depend on time-dependent intrinsic and environmental factors , such as drug treatments [46] . Therefore , we used fitness of a neutralization-sensitive virus ( NL43 ) as a control measurement to account for the increasing antibody response during infection , shown in S7 Fig . The relative time-shifted viral fitness in Fig 4C for the two HIV patients ( TN-1 and TN-3 ) , match well with the fits of our analytical equations ( see Materials and Methods and Section F of S1 Text ) . The inferred parameter values indicate two distinct regimes of coevolutionary dynamics in the two patients . In patient TN-1 , viruses and antibodies experience a comparable adaptive pressure , as indicated by the “S-curve” in Fig 4C ( blue line ) , whereas in patient TN-3 , adaptation in viruses is much stronger than in antibodies , resulting in an imbalanced shape of the time-shifted fitness curve in Fig 4C ( red line ) . We describe the inference procedure and report all inferred parameters in Section F of S1 Text . The resolution of the data [11] allows only for a qualitative interpretation of coevolutionary regimes . A more quantitative analysis can be achieved through longer monitoring of a patient , detailed information on the inhibition of viral replication at various levels of antibody dilution , and directed neutralization assays against HIV-specific antibody lineages . B-cells in the adaptive immune system are associated with clonal lineages that originate from distinct ancestral naive cells , generated by germline rearrangements ( VDJ recombination ) and junctional diversification [1] . Multiple lineages may be stimulated within a germinal center , and also circulate to other germinal centers [8] . Lineages compete for activation agents ( e . g . , helper T-cells ) and interaction with a finite number of presented antigens [8] . We extend our theoretical framework to study how multiple lineages compete with each other and coevolve with viruses . This generalization allows us to show that lineages with higher overall binding ability , higher fitness flux , and lower ( absolute ) transfer flux have a better chance of surviving . In particular , we show that an antibody repertoire fighting against a highly diversified viral population , e . g . , during late stages of HIV infection , favors elicitation of broadly neutralizing antibodies compared to normal antibodies . The binding preference of a clonal antibody lineage C to the viral sequence is determined by its site-specific accessibilities { κ i C , κ ^ i C } , defined in Fig 1B . The distribution of site-specific accessibilities over different antibody lineages P C ( { κ i C , κ ^ i C } ) characterizes the ability of an antibody repertoire to respond to a specific virus . Without continual introduction of new lineages , one lineage will ultimately dominate and the rest will go extinct within the coalescence time-scale of antibodies , Na ( Fig 5A ) . In reality , constant turn-over of lineages results in a highly diverse B-cell response , with multiple lineages acting simultaneously against an infection [47] . Stochastic effects are significant when the size of a lineage is small , so an important question is to find the probability that a low-frequency antibody lineage reaches an appreciable size and fixes in the population . We denote the frequency of an antibody lineage with size N a C by ρ C = N a C / N a . The growth rate of a given lineage C depends on its relative fitness F A C compared to the rest of the population , d d t ρ C = ( F A C - F A ) ρ C + ρ C ( 1 - ρ C ) N a χ C ( 10 ) where F A = ∑ C F A C ρ C is the average fitness of the entire antibody population , and χ C is a standard Gaussian noise term . For the linear fitness landscape from eq ( 2 ) , the mean fitness of lineage C is F A C = S a ( E C + E ^ C ) . The probability of fixation of lineage C equals the asymptotic ( i . e . , long time ) value of the ensemble-averaged lineage frequency , P fix ( C ) = lim t → ∞ 〈 ρ C ( t ) 〉 . Similar to evolution of a single lineage , the dynamics of a focal lineage are defined by an infinite hierarchy of moment equations for the fitness distribution . In the regime of substantial selection , and by neglecting terms due to mutation , a suitable truncation of the moment hierarchy allows us to estimate the long-time limit of the lineage frequency , and hence , its fixation probability ( see Section E of S1 Text ) . For an arbitrary fitness function , fixation probability can be expressed in terms of the ensemble-averaged relative mean fitness , fitness flux and transfer flux at the time of introduction of the focal lineage , Pfix ( C ) /P0fix≃1+〈 Na ( FAC ( 0 ) −FA ( 0 ) ) 〉+Na23〈 ϕAC ( 0 ) −ϕA ( 0 ) 〉−NaNv〈 | TV→AC ( 0 ) | − |TV→A ( 0 ) |] 〉 ( 11 ) where P0fix is the fixation probability of the lineage in neutrality , which equals its initial frequency at the time of introduction , P 0 fix = ρ C ( 0 ) . The first order term that determines the excess probability for fixation of a lineage is the difference between its mean fitness and the average fitness of the whole population . Thus , a lineage with higher relative mean fitness at the time of introduction , e . g . , due to its better accessibility to either the variable or conserved region , will have a higher chance of fixation . Moreover , lineages with higher rate of adaptation , i . e . , fitness flux ϕ A C ( t = 0 ) , and lower ( absolute ) transfer flux from viruses | T V → A C ( t = 0 ) | tend to dominate the population . For evolution in the linear fitness landscape , we can calculate a more explicit expansion of the fixation probability that includes mutation effects . In this case , the fixation probability of a focal lineage can be expressed in terms of the experimentally observable lineage-specific moments of the binding affinity distribution , instead of the moments of the fitness distribution ( see Section E of S1 Text ) . With our multi-lineage model , we can understand the conditions for emergence of broadly neutralizing antibodies ( BnAbs ) in an antibody repertoire . Similar to any other lineage , the progenitor of a BnAb faces competition with other resident antibody lineages that may be dominating the population . The dominant term in the fixation probability is the relative fitness difference of the focal lineage to the total population at the time of introduction . Lineages may reach different fitnesses because they differ in their scale of interaction with the viruses , E 0 C in the variable region and E ^ 0 C in the conserved region; see Section E of S1 Text for details . Lineages which bind primarily to the conserved region , i . e . , E ^ 0 C ≫ E 0 C , are not vulnerable to viral escape mutations that reduce their binding affinity . Such BnAbs may be able to reach higher fitnesses compared to normal antibodies which bind to the variable region with a comparable scale of interaction . The difference in the mean fitness of the two lineages becomes even stronger , when viruses are more diverse ( i . e . , high MV , 2 ) , so that they can strongly compromise the affinity of the lineage that binds to the variable region; see eq ( 11 ) . If the invading lineage has the same fitness as the resident lineage , then the second order terms in eq ( 11 ) proportional to the fitness and transfer flux may be relevant . A BnAb lineage that binds to the conserved region has a reduced transfer flux than a normal antibody lineage , all else being equal . The difference in transfer flux of the two lineages depends on the viral diversity MV , 2 , and becomes more favorable for BnAbs when the viral diversity is high . Overall , a BnAb generating lineage has a higher advantage for fixation compared to normal antibodies , when the repertoire is coevolving against a highly diversified viral population , e . g . , during late stages of HIV infection . In Fig 5B we compare the fixation probability of a BnAb lineage , that binds only to the conserved region , with a normal antibody lineage that binds only to the variable region . In both cases we assume that the emerging lineage competes against a resident population of normal antibodies . We compare our analytical predictions for fixation probability as a function of the initial state of the antibody and viral populations given by eqs . ( S140 , S141 ) in S1 Text , with Wright-Fisher simulations of coevolving populations ( numerical procedures detailed in the Materials and Methods ) . Increasing viral diversity M2 , V increases the fixation of BnAbs , but does not influence fixation of normal lineages . For low viral diversity , fixation of BnAbs is similar to normal Abs , and therefore they might arise and be outcompeted by other antibody lineages .
We have presented an analytical framework to describe coevolutionary dynamics between two antagonistic populations based on molecular interactions between them . We have focused our analysis on antibody-secreting B-cells and chronic infections , such as HIV . We identified effective parameters for selection on B-cells during hypermutation that enhance their binding and neutralization efficacy , and conversely parameters for selection on viruses to escape antibody binding . The resulting “red-queen” dynamics between antibodies and viruses produces a characteristic signature of coevolution in our model , i . e . , viruses are resistant to antibodies from the past and are susceptible to antibodies from the future . We used our results to infer modes of immune-viral coevolution based on time-shifted neutralization measurements in two HIV-infected patients . Finally , we have shown that emergence and fixation of a given clonal antibody lineage is determined by competition between circulating antibody lineages , and that broadly neutralizing antibody lineages , in particular , are more likely to dominate in the context of a diverse viral population . Luo and Perelson [30] found that competition between lineages caused BnAbs to appear late in their simulations . In addition , they found that multiple viral founder strains dilutes the competition of BnAbs with specific antibodies , leading to higher chance of BnAb appearance . The assumptions of their simulations differ in many ways from those of our model , and yet their overall finding agrees with our analytical results: BnAbs fix more readily when there is a large diversity of viral binding . In contrast to Luo and Perelson’s simulations which made assumptions about the immunogenicity of BnAbs , our analytic results show explicitly how differences in fitness of antibodies and the efficacy of viral escape affect competition between antibody lineages . Our model is simple enough to clarify some fundamental concepts of antibody-antigen dynamics . However , understanding more refined aspects of B-cell-virus coevolution will require adding details specific to affinity maturation and viral reproduction , such as non-neutralizing binding between antibodies and antigens [15 , 48] , epitope masking by antibodies [49] and spatial structure of germinal centers [8] . Importantly , viral recombination [38 , 39 , 50] and latent viral reservoirs [51] are also known to influence the evolution of HIV within a patient . Similarly , the repertoire of the memory B-cells and T-cells , which effectively keep a record of prior viral interactions , influence the response of the adaptive immune system against viruses with antigenic similarity . While our analysis has focused on coevolution of chronic viruses with the immune system , our framework is general enough to apply to other systems , such as bacteria-phage coevolution . Likewise , the notions of fitness and transfer flux as measures of adaptation are independent of the underlying model . Bacteria-phage interactions have been studied by evolution experiments [52 , 53] , and by time-shifted assays of fitness [54 , 55] , but established models of coevolution typically describe only a small number of alleles with large selection effects [56] . In contrast , our model offers a formalism for bacteria-phage coevolution where new genotypes are constantly produced by mutation , consistent with experimental observations [54] . Similarly , our formalism may be applied to study the evolution of seasonal influenza virus in response to the “global” immune challenge , imposed by a collective immune landscape of all recently infected or vaccinated individuals . Time-shifted binding assays of antibodies to influenza surface proteins are already used to gauge the virulence and cross-reactivity of viruses [57] . Quantifying the fitness flux and transfer flux , based on these assays , is therefore a principled way to measure rates of immunologically important adaptation in these systems . One central challenge in HIV vaccine research is to devise a means to stimulate a lineage producing broadly neutralizing antibodies . Common characteristics of BnAbs , such as high levels of somatic mutation or large insertions , often lead to their depletion by mechanisms of immune tolerance control [58] . Therefore , one strategy to elicit these antibodies is to stimulate the progenitors of their clonal lineage , which may be inferred by phylogenetic methods [59] , and to guide their affinity maturation process towards a broadly neutralizing state . Understanding the underlying evolutionary process is necessary to make principled progress towards such strategies , and this study represents a step in that direction . For example , our results suggest that a vaccine based on a genetically diverse set of viral antigens is more likely to stimulate BnAbs .
Simulations of the full genotype model ( Wright-Fisher dynamics ) were implemented as follows . Viral and antibody populations consist of genotypes as strings of ±1 with length ℓ + ℓ ^ . Binding interactions are calculated between all pairs of antibodies and viruses as in eq ( 1 ) , which define the fitness as in eqs ( 2 and 3 ) . Genotypes within an antibody lineage share the same accessibilities , { κ i , κ ^ i } . For each generation , a poisson distributed number of mutations occur , with each mutation flipping the sign of a site . Each generation is replaced by their offspring which inherit their parents’ genotype . Each parent generates a binomially distributed number of offspring , with probability proportional to the exponential of its fitness , with the constraint that the total number of individuals remains constant Na in antibodies and Nv in viruses , which is equivalent to multinomial sampling . Note that we define fitness as “malthusian” , which means that fitness is the relative growth rate of genotypes , and the expected number of offspring is proportional to the exponential of fitness . Simulation parameters for all figures are Na = Nv = 103 , ℓ = ℓ ^ = 50 , θa = θv = 1/50 , and all κ i = κ ^ i = 1 , unless otherwise stated . Populations are initialized with all individuals having the same randomly generated genotype . To measure quantities in the stationary state ( Figs 2 and 4 ) simulations are run for 104 Na generations , and quantities are averaged from samples every Na generations . Data from the beginning of the simulations are omitted from the calculations , where the cutoff is τ = 2 μ a - 1 , the correlation time for the mean binding ( S3 Fig and Section B . 4 of S1 Text ) . To produce the simulations shown in Fig 5B , the newly emerging antibody lineages compete with the resident population as follows . First , the resident lineage is evolved with the virus for 50Na generations to build up diversity . Simultaneously , the invading lineage is evolved with the virus , except that the viral fitness is determined only by the resident lineage . This ensures that invading lineages can marginally bind to the viral population , and are functional lineage progenitors; a process that happens prior to affinity maturation in germinal centers . The pre-adaptation of the invading lineage can also be interpreted as initial rounds of affinity maturation in germinal centers isolated from competition with adapted antibody lineages . Then the two antibody lineages are combined with resident at 90% and invader at 10% , with a total size of 103 , and the state of the system is recorded . The two lineages are evolved until one is extinct , repeated over 100 replicates to estimate the fixation probability . The whole procedure is repeated 103 times for ensemble averaging . The invader , is either a normal lineage with all κi = 1 and κ ^ i = 0 or a BnAb that binds only to the conserved region , κi = 0 and κ ^ i = 1 . Simulations are written in julia and code is available at https://github . com/jotwin/coevolution . The data from Richman et al . [11] provides time-shifted measurements of viral neutralization by a patient’s circulating antibodies . We approximated time-shifted viral fitness as the log-ratio of neutralization titer ( up to a constant c0 ) for plasma ( antibodies ) sampled at time t + τ against viruses sampled at time t , relative to the control titer of the same plasma against the neutralization-sensitive virus ( NL43 ) [46] , FV;τ ( t ) = −log ( titer ( Vt , At+τ ) /titer ( NL43 , At+τ ) ) + c0; titer is the reciprocal of antibody dilution where inhibition reaches 50% ( IC50 ) [11] . Measuring neutralization efficacy relative to NL43 control virus is necessary to account for the increasing ( non-stationary ) antibody response during infection , shown in S8 Fig . Fig 4C shows the time-shifted relative mean fitness FV;τ ( t ) averaged over all time-points t , evaluated for two patients ( TN-1 & TN-3 ) , after linearly interpolating the raw data to produce equal time shifts ( 3 months for TN-1 and 6 months for TN-3 ) . We fit the data to the analytical expression given by eqs . ( S102 , S103 ) in S1 Text , by minimizing the mean squared error after scanning over four composite evolutionary variables: ( i ) nucleotide diversity , which we infer to be equal for antibodies and viruses θa ≃ θv = θ , ( ii ) selection component of the fitness flux in the viral population S v 2 M V , 2 , ( iii ) selection component of the transfer flux from antibodies to viruses , −Sa Sv MA , 2 ( Nv/Na ) , and ( iv ) the constant c0 . Due to the functional form of time-shifted fitness given by eqs . ( S102 , S103 ) in S1 Text , brute force parameter scanning is necessary for a convergent solution . Further details of data analysis and estimates of the fitted variables are given in Section F of S1 Text . | We normally think of evolution occurring in a population of organisms , in response to their external environment . Rapid evolution of cellular populations also occurs within our bodies when the adaptive immune system works to eliminate infections . Some viruses , such as HIV , are able to evolve as quickly as our immune response , resulting in a chronic infection with both viral and immune populations perpetually adapting . Here we develop a mathematical description of this coevolutionary process , discover key parameters that govern the distribution of interactions between the two populations , introduce principled measures of adaptation , and analyze the conditions under which highly potent broadly neutralizing antibodies will emerge and dominate the immune response . | [
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] | 2016 | Host-Pathogen Coevolution and the Emergence of Broadly Neutralizing Antibodies in Chronic Infections |
Different biomolecules have been identified in bacterial pathogens that sense changes in temperature and trigger expression of virulence programs upon host entry . However , the dynamics and quantitative outcome of this response in individual cells of a population , and how this influences pathogenicity are unknown . Here , we address these questions using a thermosensing virulence regulator of an intestinal pathogen ( RovA of Yersinia pseudotuberculosis ) as a model . We reveal that this regulator is part of a novel thermoresponsive bistable switch , which leads to high- and low-invasive subpopulations within a narrow temperature range . The temperature range in which bistability is observed is defined by the degradation and synthesis rate of the regulator , and is further adjustable via a nutrient-responsive regulator . The thermoresponsive switch is also characterized by a hysteretic behavior in which activation and deactivation occurred on vastly different time scales . Mathematical modeling accurately mirrored the experimental behavior and predicted that the thermoresponsiveness of this sophisticated bistable switch is mainly determined by the thermo-triggered increase of RovA proteolysis . We further observed RovA ON and OFF subpopulations of Y . pseudotuberculosis in the Peyer’s patches and caecum of infected mice , and that changes in the RovA ON/OFF cell ratio reduce tissue colonization and overall virulence . This points to a bet-hedging strategy in which the thermoresponsive bistable switch plays a key role in adapting the bacteria to the fluctuating conditions encountered as they pass through the host’s intestinal epithelium and suggests novel strategies for the development of antimicrobial therapies .
Temperature is a prominent signal used by pathogens to adjust their virulence and host survival programs during infection . Different biomolecules can act as thermosensors , including DNA , RNA and regulatory proteins . They all detect changes in temperature through thermally induced conformational changes [1–3] . The velocity and reversibility of thermosensors enable rapid adaptation to the temperature shifts encountered when transitioning between different hosts or environments . The precise thermosensation mechanism of several molecular thermometers was uncovered using population level analyses . However , bulk-scale methods are insufficient for characterizing key features of this process , such as sensor dynamics and quantitative outcome in individual cells . Here , we addressed these features by single-cell level analyses using the Yersinia regulator protein RovA as an example for a thermosensing molecule that controls virulence [4 , 5] . This approach is important as during transition processes genetically identical populations can generate phenotypic heterogeneity , which supports persistence of pathogens in fluctuating environments ( bet-hedging ) via fitness improvement of the whole population by cooperativity or division of labor [6–11] . One example is bistability , in which isogenic bacteria exist in two distinct phenotypic states ( ON or OFF ) driven by divergent gene expression profiles in response to nutrient shifts and stress conditions [6 , 10 , 12–14] . A binary distribution of phenotypes can be generated by feedback-based circuitry in combination with non-linear responses , e . g . by cooperativity in DNA binding of a regulator , [6 , 14] , a characteristic also observed for the thermoresponsive virulence regulator RovA . RovA is active and autoregulated at moderate temperatures ( 20–25°C ) and binds cooperatively to a high-affinity site upstream of the distal rovA promoter ( P2 ) and activates rovA and invA transcription . When the RovA amount has reached a certain threshold , RovA binds to a low affinity site downstream of the proximal rovA promoter ( P1 ) to prevent uncontrolled rovA induction ( Fig 1A ) . An upshift to 37°C induces a reversible conformational change in RovA that leads to a strong reduction of its DNA-binding capacity and renders this regulator susceptible to proteolysis by the Lon protease [4 , 5 , 15] ( Fig 1A ) . Since autoregulatory features , which can generate a bistable output of a genetic system , are combined with a thermosensing element [12–14] , we hypothesized a novel type of ‘thermo-controllable’ bistable switching device for the control of Yersinia virulence .
To prove our hypothesis we first tested for the occurrence of distinct bacterial subpopulations by measuring rovA expression at a single-cell level . The rovA promoter was fused to egfpLVA , encoding a green fluorescence protein derivative ( eGFPLVA ) with a brighter fluorescence but reduced stability . Upon shifting from 25°C to 37°C , eGFPLVA-expressing Yersinia demonstrated successive reduction in eGFPLVA synthesis , that corresponded to the average RovA level ( Fig 1B–1D ) . Two distinct subpopulations showing no ( OFF ) or high ( ON ) eGFPLVA production at growth temperatures between 30°C and 34°C were detected in the wild-type ( Fig 1B–1D ) . No ON subpopulation could be detected when rovA-eGFPLVA was expressed in a rovA mutant , confirming that expression of the reporter depends on active RovA ( S1A Fig ) . Immunofluorescently labeled RovA-dependent adhesin InvA [16] exhibited a similar bimodal staining pattern ( S1B and S1C Fig ) . Time-lapse microscopy revealed that individual bacteria can spontaneously switch ( average time of 2–3 h at 32°C ) from one state to the other , demonstrating reversibility of the switching process ( Fig 1E , S1–S3 Videos ) . We quantified the switching dynamics by measuring rovA expression and intracellular RovA amounts under stable physiological conditions in chemostat cultures over many generations . Quantification of bacteria in the RovA ON and OFF state after transitioning between 25°C and 37°C revealed a dependence of the system’s output on present and past inputs ( hysteretic behavior ) and showed that activation and deactivation of RovA synthesis occurred at strikingly different times scales ( Fig 2A and 2B ) . Thermal upshift caused a rapid decrease in rovA expression with a bimodal RovA distribution and a continuous decrease in the RovA+ subpopulation over 3–4 h ( Fig 2A and 2B ) . In contrast , activation of rovA was delayed and the RovA+ population increased very slowly upon thermal downshifting , indicating that the remaining amount of RovA at 37°C was insufficient to allow rapid autoinduction . In summary , this demonstrated the presence of a new , highly precise thermoresponsive bistable switch with an exceptional hysteretic behavior . We devised mathematical models to derive information about the underlying drivers dictating the temperature-dependent bistability of RovA ( Fig 3A–3C , S1 Text , S2–S4 Figs ) . Our deterministic model is based on ordinary differential equations for the temporal change in RovA concentrations ( dr/dt ) in response to temperature ( Τ ) in a continuous deterministic manner . The temporal change of RovA concentration was described by a sigmoidal regulation function with a basal permanent RovA production rate α0 and a RovA-induced RovA production rate α . The feedback loops were coupled and influenced by an activating DNA-binding constant ka and a repressive DNA-binding constant kr . Cooperative RovA binding was included by the Hill coefficients ha and hr . The RovA degradation rate was included as δ . Our experimental results revealed that the DNA binding constants and the degradation rate of RovA were temperature-dependent , and thus a function of the temperature , described by Τ ( ka ( Τ ) , kr ( Τ ) , and δ ( Τ ) ) which leads to the resulting model: drdt=α0+α⋅rhaka ( Τ ) ha+rhakr ( Τ ) hrkr ( Τ ) hr+rhr−δ ( Τ ) ⋅r We used experimentally determined kinetic parameters to calculate the corresponding values for all temperatures and nonlinear regression to estimate the DNA-binding constants , Hill coefficients and degradation rates . To obtain the production rates α0 and α , we carried out stochastic modeling to fit data obtained by temperature shift experiments ( S1 Text , S3 Fig ) . A stochastic , individual-based version of the deterministic model was used to elucidate the mechanisms determining hysteresis ( Fig 3A ) . The parameters obtained from chemostat experiments ( α = 0 . 7 nM/min and α0 = 0 . 002 nM/min ) predict a number of approximately 35 nM of free RovA per cell ( ≈25 RovA dimers ) , which contribute to rovA regulation ( S1 Text , S3 Fig ) . Determination of RovA molecule numbers in Y . pseudotuberculosis expressing the ProvA-egfpLVA fusion at 25°C revealed an average of 400 RovA molecules per cell , which corresponds to approximately 275 nM RovA ( S3B Fig ) . A higher concentration of RovA molecules than the predicted 35 nM is expected , since not all RovA molecules within the bacterial cell are available for autoregulation , as ( i ) only a fraction of RovA molecules is in the active form and ( ii ) a certain number of RovA dimers is also likely to be bound at different locations on the bacterial chromosome . Furthermore , the bacterial population is still in the ON state at 30°C ( Fig 1D ) , while RovA amounts are considerably decreased compared to 25°C ( 40–50% ) . This indicates that less than 275 nM of RovA is sufficient to trigger RovA autoinduction in the entire population . Our model further predicts that net production of RovA tends to be zero at 37°C as a result of the loss of DNA-binding and increased degradation rate . Consequently , RovA amounts are rapidly ( within 3 h ) reduced to only a few molecules . Upon downshift to 25°C , protein activation combined with the positive feedback loop can reactivate RovA synthesis , but the positive circuit is active only when sufficient RovA molecules cooperate . A simulation of the autoactivation circuit with six RovA molecules ( S1 Text ) correlates perfectly with the experimental RovA data ( S3E Fig ) . When less than six active RovA molecules are present per bacterial cell and the net production rate is very low , the population basically follows a neutral birth-death process until the critical number of active RovA molecules is produced through stochastic processes , including random fluctuations and transcriptional noise . Once this threshold is reached , the bacterial cell switches rapidly to the RovA ON state due to the positive feedback loop . Because of the time interval required to reach the critical RovA number by stochastic forces , a significantly longer time period is needed to drive the population from the RovA OFF into the RovA ON state ( Figs 2 and 3A , S1 Text , S3 Fig ) . A stimulus-response diagram generated by calculating the steady-state concentration of RovA at various temperatures revealed bistable response behavior with hysteresis from 27°C to 37°C highly similar to the experimental data ( Fig 3C ) . The model predicted that bistability was mainly caused by the positive feedback loop , whereby the inhibitory RovA binding site reduced only the response time and degree of bistability ( Fig 1A , S1 Text , S4 Fig ) . A numerical approach was used to describe the influence of δ , α and α0 on the bistable behavior ( S1 Text , Fig 3D ) . The model predicted that the thermally induced increase in degradation was crucial for temperature-responsiveness , and that α0 was critical for RovA bistability , whereby either extremely high or extremely low degradation and production rates abolished bistability and maintained the system in a monostable state . To challenge our analysis and mathematical predictions , we first proved whether the rovA regulatory region is essential for bistability . We replaced the rovA promoter in ProvA-egfpLVA with the constitutive Prho promoter , analyzed eGFPLVA expression in Y . pseudotuberculosis strain YPIII and found that the substitution of ProvA by Prho eliminated bistability and resulted in a unimodal population with strong eGFPLVA production from 25°C to 37°C ( S5A–S5C Fig ) . Moreover , we tested the influence of different RovA mutant proteins on bistability and found that RovA variants carrying amino acid substitutions in the thermosensing region ( G116A , SG127/128IK ) , the Lon protease recognition site ( P98S ) and their combination ( P98S/SG127/128IK/G116A ) [5] did not abrogate bimodal rovA expression . The overall eGFPLVA intensity of the different ON subpopulations was comparable ( S5D Fig ) , but the temperature range for bistability was broader and shifted toward higher temperatures ( Figs 1D and 4 ) . Notably , a mutation eliminating the Lon recognition site ( P98S ) of RovA had no or only a very weak influence on bimodal rovA expression at higher temperatures . This can be explained by the fact that at 37°C the majority of RovA dimers targeted by Lon is inactive , i . e . RovA is partially defolded which abolishes its DNA-binding functions and autoactivation [4] . As shown in Fig 1C and 1D Yersiniae harboring the rovA-eGFPLVA reporter were predominantly in the OFF state at 37°C in vitro . However , rovA transcripts were identified in infected lymphatic tissues of mice by in vivo RNA-Seq analysis , in particular during their persistence stage in the caecum [26] , indicating that additional parameters induce rovA transcription during infection . It is known that rovA expression is strongly affected by changes in carbon source availability involving the carbon storage regulator system ( Csr ) and the cAMP receptor protein Crp , which are transmitted through the LysR-type regulator RovM ( Fig 5A ) [17 , 18] . We therefore tested whether a deletion of rovM influences the distribution of RovA ON and OFF cells . Strikingly , bimodal expression of rovA was fully preserved but shifted toward higher temperatures ( Fig 5 ) . In the absence of RovM a significantly higher amount of RovA ON cells was observed in particular at temperatures ranging from 34°C to 36°C ( Fig 5B and 5C ) . Moreover , a very small fraction of RovA ON cells was detectable at 25°C , but not at all tested higher temperature when RovM was overexpressed ( Fig 5D and 5E ) . Obviously , the observed bistable phenotype correlates with our mathematical models and appears very robust , as temperature-responsive switching was not abolished by fundamental changes in RovA stability . Moreover , the RovA ON/OFF cell ratio is adjustable by a temperature-independent regulator ( RovM ) . This allows the pathogen to modulate the outcome in host tissues at constant temperatures according to nutrients . Observed robustness of the bistable switch suggested a bimodal expression of rovA during infection . To obtain direct evidence for phenotypic heterogeneity in vivo , mice were orally challenged with Y . pseudotuberculosis using a dual fluorescence reporter system ( Ptet-mCherry , ProvA-egfpLVA ) . We observed two subpopulations in the Peyer’s patches and the caecal lymph nodes , with low numbers of RovA ON bacteria randomly distributed within microcolonies within tissue lesions ( Fig 6A and 6B , S6A and S6B Fig ) . There was a statistically significant increase in the RovA ON cell population in the caecum when bacteria expressed the thermotolerant variant RovAP98S/SG127/128IK/G116A ( Fig 6B ) , verifying a shift of bistability towards higher temperatures in vivo . In contrast , no eGFPLVA-expressing bacteria were detected in the absence of the rovA promoter or in a rovA mutant strain , demonstrating that in vivo expression of the reporter depends on RovA ( S6C and S6D Fig ) . Mice were then infected with a lethal dose ( 2×108 bacteria ) of Y . pseudotuberculosis wild-type or mutants producing the more stable RovA variants . Mice infected with wild-type bacteria displayed typical signs of the infection ( e . g . weight loss , piloerection and lethargy ) after 5–10 days . In contrast , none of the mice infected with a rovA-deficient strain or strains producing stabilized RovA variants developed severe disease symptoms , with 40–60% of the mice still alive after 15 days ( Fig 6C ) . Infections with all mutants resulted in a statistically significant reduction in tissue colonization ( Figs 6D and S7 ) . This difference was pronounced for the mesenteric lymph nodes ( Fig 6D ) from which >1000-fold fewer bacteria producing RovAP98S/SG127/128IK/G116A were recovered . Smaller but statistically significant effects were observed in mice infected with mutants producing moderately stable RovA variants RovAP98S and RovAG116A ( Fig 6D ) indicating that variation of RovA bistable properties , which increase RovA+ subpopulations reduces pathogenicity . RovA+ cells , which express the colonization factor invasin , can efficiently invade lymphatic tissues [19–21] , but its presence also renders the bacteria more susceptible to immune responses [22 , 23] . A transcriptome analysis further revealed that also other surface-exposed pathogenicity factors , e . g . the afimbrial adhesin PsaA as well as lipopolysaccharide synthesis genes are activated by RovA of Y . pseudotuberculosis [24] . Although beneficial for the initiation of the infection , they are likely to trigger innate immunity-mediated antimicrobial responses when expressed in deeper tissues . In addition , several general stress adaptation genes ( ibpAB , uspA , cspB , C1-3 , D , E ) are activated by RovA , which could support survival in the lumen of the intestine and/or in the external environment . The analysis of the RovA regulon further uncovered different metabolic programs for the wild-type and a rovA mutant [24] , which may endow the RovA OFF population with a better fitness within lymphatic tissues . In fact , multiple enzymes of the pyruvate-TCA cycle ( icdA , sucDCB , gltA , acnAB , aceE , F ) are down-regulated in a rovA mutant , whereas several enzymes of the amino acid and nucleotide transport and metabolism are induced [24] . Different metabolic programs in the RovA ON and OFF population could contribute to the beneficial effect of bistable RovA expression as they adapt the bacterial metabolism to the distinct nutritional conditions in the intestinal tract or the lymphatic tissues . In summary , the discovered thermo-responsive bistable switch enables expression of an alternative virulence program in a small subpopulation within a single infection site . Transcriptional specialization supports survival and pathogenesis as it primes the bacteria to environmental uncertainty encountered at two critical stages when they cross the intestinal layer: ( i ) shortly after host entry , when Yersinia colonizes the intestinal tract , of which only a subset invades the Peyer’s patches [25] , and ( ii ) during persistence in the caecum , which is a potential reservoir from which the bacteria re-emerge in the intestinal lumen after expulsion from damaged tissues ( Fig 6E ) [26 , 27] . This new form of bet-hedging complements other types of heterogeneous host-pathogen interactions ( i . e . slow-growing variants which are more resistant to antibiotics , or populations subsets formed within the complex tissue landscape as a response to varying local conditions faced outside and inside a bacterial microcolony [28–33] ) , and ( ii ) opposes recent approaches to targeting virulence traits such as adhesion and virulence-relevant regulatory processes to combat bacteria-mediated diseases [34–37] . Based on our study , detailed knowledge of present pathogen subsets and their distinct virulence programs including single-cell expression profiles of potential virulence targets in infected tissues are imperative for the development of successful anti-microbial therapies .
The strains used in this work are listed in S1 Table . For batch culture experiments bacteria were routinely grown in Luria-Bertani ( LB ) broth to exponential growth phase ( OD600nm = 0 . 5–0 . 6 ) at temperatures ranging from 25°C to 37°C under aerobic conditions . If necessary , antibiotics were added at the following concentrations: carbenicillin 100 μg ml-1 , chloramphenicol 30 μg ml-1 and kanamycin 50 μg ml-1 . All DNA manipulations , transformations , restriction digestions and ligations were performed using standard genetic and molecular methods . The plasmids used in this work are listed in S1 Table . Oligonucleotides used for PCR and sequencing were purchased from Metabion and are listed in S2 Table . Plasmid DNA was isolated using QIAprep Spin Miniprep Kit ( Qiagen ) . DNA-modifying enzymes and restriction enzymes were purchased from Roche or New England Biolabs . PCRs were done in a 50 μl mix for 29 cycles using Phusion High-Fidelity DNA polymerase ( New England Biolabs ) . Purification of PCR products was routinely performed using the QIAquick PCR Purfication Kit ( Qiagen ) . All constructed plasmids were sequenced by the in-house facility . For construction of a RovA-dependent gfp reporter , the rovA promoter region along with 170 nts of rovA coding region ( -622 to +170 ) and the egfpLVA gene were PCR amplified from plasmid pYPL using primers 158 and II525 . The PCR product was digested with SalI and NotI and ligated with T4-DNA ligase ( NEB ) into pFU76 of the pFU vector series [38] , yielding plasmid pKH87 . This plasmid was subsequently digested with SacI and AvrII for the exchange of the R6K origin of replication against the origin 29807 from plasmid pFU33 , resulting in plasmid pKH70 . To generate a plasmid for constitutive egfpLVA expression the promoter region of the rho gene was PCR amplified from Y . pseudotuberuclosis YPIII genomic DNA from nucleotide -433 ( primer IV490 ) to nucleotide -21 ( primer IV491 ) and egfpLVA was amplified with primers II525/IV483 from plasmid pKH70 . The two fragments were inserted into the same backbone as pKH70 using AatII and NotI yielding plasmid pFS5 . To perform Quick-change mutagenesis ( Stratagene ) on rovA , the rovA+ plasmid pFS6 was generated . To do so , rovA was amplified with primers III784/III947 and ligated into SalI/SphI sites of the pJet1 . 2 cloning vector ( Thermo Scientific ) . Quick-change mutagenesis of pFS6 was performed using primer pairs II379/II380 and II624/II625 resulting in plasmids pFS7 and pFS14 , respectively . The modified versions of the rovA gene from pFS7 and pFS14 were transferred into the suicide mutagenesis plasmid pDM4 using SalI and SphI , yielding pFS8 and pFS16 . Furthermore , Quick-change mutagenesis with pFS7 was performed with primers II624/II625 to generate plasmid pFS23 , which was subsequently used to perform Quick-change mutagenesis with primers II626/II627 to obtain plasmid pFS24 . Subsequently , pFS24 was digested with SalI/SphI and the insert was ligated into the suicide plasmid pDM4 to obtain pFS28 . For constitutive expression of Ptet-mCherry , plasmid pFU76 was cut with KpnI/AvrII and ligated into pZE21 resulting in plasmid pFS42 . mCherry was amplified with primers V842/V843 from plasmid pTB23 , cut with SalI/NotI and ligated into pFS42 generating plasmid pFS43 . The origin of replication p15a was amplified from plasmid pAKH120 with primers V521/V522 and cut with AvrII/SacI . Additionally , the chloramphenicol cassette was amplified from pFU228 with primers V519/V520 , cut with AatII/SacI and both fragments were ligated into pFS43 to obtain pFS48 . Construction of the Y . pseudotuberculosis rovA mutant strains YP269 , YP270 and YP287 was performed by integration of the suicide plasmids pFS8 , pFS16 or pFS28 in the rovA locus of strain YP107 . E . coli strain S17-1λpir harbouring the plasmids were used for conjugation and the resulting transconjugants were identified by plating on Yersinia selective agar ( Oxoid ) supplemented with chloramphenicol . Expression of the sacB gene , which is also encoded on the integrated plasmids , is induced when the bacteria are plated on LB agar with 10% sucrose . This results in a growth reduction of the bacteria . Derivatives which have lost the plasmid due to a second recombination event were identified as more rapidly growing clones on 10% sucrose plates and presence of the individual rovA mutant genes was verified by PCR and sequencing with primers 135/151 as described [16] . Batch cultures: Y . pseudotuberculosis YPIII harboring a RovA-dependent egfpLVA reporter ( pKH70 ) was grown over night at different temperatures ranging from 25°C to 37°C in liquid LB broth . A fresh culture was started by inoculating pre-warmed medium with over night culture in a 1:50 dilution and incubated at identical temperatures until the cultures reached an OD600nm = 0 . 6 . Subsequently , 1 ml of culture was harvested by centrifugation for western blotting and flow cytometry . For flow cytometry cell pellets were rapidly fixed in 4% para-formaldehyde for 20 min at 25°C . Pellets were washed twice with 1 x PBS and at least 100 . 000 cells were analyzed by a LSRII flow cytometer ( BD Biosciences ) . Data were acquired with the FACS Diva software ( BD Biosciences ) and further analyzed with FlowJo v9 . 7 . 2 ( Treestar ) . Continuous culture: YPIII pKH70 was aerobically grown in Vario 500 mini-bioreactors ( Medorex ) . The bacteria were pre-cultured for 12 h at 25°C under aerobic conditions in LB medium containing carbenicillin ( 100 μg ml-1 ) . The bioreactor was filled with 210 ml LB medium containing carbenicillin . Antifoam 204 ( Sigma-Aldrich ) , an entirely organic antifoaming agent , was added to the medium at a concentration of 0 . 02% ( vol/vol ) . Pre-cultures were washed twice in fresh LB medium ( 25°C ) . The bioreactor was inoculated with the pre-culture ( final OD600nm: 0 . 2 ) and run in batch mode at 25°C under continuous stirring ( 400 rpm ) . Cultivation was switched to continuous mode ( 25°C ) at a growth rate of μ = 0 . 32h-1 . After crucial processing parameters , i . e . ( i ) OD600nm: 4 . 6 ± 0 . 35; ( ii ) pO2:—45–55%; and ( iii ) pH 8 . 0 remained constant , samples were taken for western blotting and flow cytometry . Subsequently , temperature was shifted to 37°C for an 8 h period and shifted back to 25°C for additional 18 hours . Samples were taken in 1 h intervals for western blotting and flow cytometry . At least 105 fixed cells were analyzed by a LSRII ( BD Biosciences ) flow cytometer and the data were extracted and analyzed as described above . Batch cultures: Poly-L-lysine solution ( Sigma ) was diluted 1:10 in sterile filtered 1 x PBS and spotted onto acid washed microscopy slides ( VWR ) . After 2 hours incubation at room temperature the slides were rinsed with ultra pure water and air dried over night . In 4% para-formaldehyde fixed Yersinia cell suspensions ( OD600nm 0 . 6 ) were diluted 1:10 in 1 x PBS . This dilution was spotted onto the poly-L-lysine-coated microscopy slides and incubated for 30 min at room temperature . The slides were washed 3 times with 1 x PBS and cells were blocked for 1 h in 1 x PBS containing 2% BSA . For immunostaining of invasin ( InvA ) , slides were then washed with 1 x PBS before addition of the anti-InvA42 monoclonal mouse IgG ( 1:1 . 000 in 1 x PBS containing 1% BSA ) . After 1 h incubation at room temperature the slides were washed 3 times with 1 x PBS . The secondary antibody ( goat anti-mouse IgG , Cy5 conjugate , Invitrogen ) was added in a 1:1 . 000 dilution and slides were incubated for an additional hour at room temperature . Slides were washed 3 times in 1 x PBS . Coverslips were mounted using SlowFade Gold ( Life Technologies ) , covered with a glass slide and analysed with an Axiovert II fluorescence microscope ( Zeiss ) with an Axiocam HR digital charge-coupled device ( CCD ) camera ( Zeiss ) and the AxioVision program ( Zeiss ) and the software ImageJ ( https://imagej . nih . gov/ij/ ) . Infected tissue: Y . pseudotuberculosis YPIII and YP287 harboring a ProvA::egfpLVA fusion ( pKH70 ) and a Ptet::mCherry expression construct ( pFS48 ) as well as Y . pseudotuberculosis YPIII harboring only pFS48 , which served as negative control , were grown in LB medium at 25°C overnight . Mice were infected orally with 2 x 108 bacteria . After three days mice were sacrificed by CO2 asphyxiation . For cryosections , the Peyer’s patches and caeca were frozen in Tissue-Tek OCT freezing medium ( Sakura Finetek ) on dry ice . Sections of 6–10 μm were prepared using a Microm HM 560 cryostat ( Thermo Scientific ) and mounted on SuperFrost Plus slides ( Thermo Scientific ) . Air-dried sections were fixed for 20 min in ice-cold 4% para-formaldehyde and washed twice with PBS . For visualization of nuclei in the fixed tissue , samples were stained and mounted with Roti Mount Flour Core 49 , 6-diamidino-2-phenylindole ( DAPI , Roth ) . Tissues were imaged and localization of Yersiniae in the infected tissues was analyzed using a fluorescence microscope ( Axiovert II , Zeiss ) with 25 x and 40 x objectives , an Axiocam HR digital charge-coupled device ( CCD ) camera ( Zeiss ) and the ZEN program ( Zeiss ) . Total number of mCherry-positive bacteria and the number of the cells expressing also eGFPLVA was counted in 40 randomly chosen tissue sections of the Peyer’s patches and the caecum of three infected mice , and the percentage of cells expressing eGFPLVA was calculated . For time-lapse microscopy bacteria were grown over night in LB medium at 32°C in the presence of carbenicillin . A fresh culture was started by inoculating pre-warmed LB medium with an over night culture in a 1:50 dilution and was grown at the same temperature to OD600nm of 0 . 6 . Subsequently , 1 μl of bacterial culture was distributed on a microdish ( IBIDI ) , overlaid with a thin agarblock ( 2% LB-agar with carbenicillin ) and covered with a glass slide . For live cell imaging , shutters were computer-controlled , synchronized with the HR camera and opened only during exposure time to reduce photobleaching of eGFPLVA and photodamage of the cells . Starting from single cells or cell doublets , eGFPLVA fluorescence was recorded over several generations . Each imaging cycle consisted of one fluorescence frame to track eGFPLVA expression , followed by one phase-contrast frame to monitor also those cells , which do not express eGFPLVA . The temperature of the microscope chamber was controlled by the Heating Unit XL S and the Incubator XL S ( Zeiss ) . A stable focus was ensured over several hours of imaging by using the Definite-Focus system ( Zeiss ) . Captured images were processed using the Axiovision or ZEN software ( Zeiss ) and the software ImageJ ( https://imagej . nih . gov/ij/ ) . For the detection of RovA , RovM and H-NS , bacterial whole cell extracts were prepared from equal amounts of bacteria and separated on SDS-polyacrylamide gels , and blotted onto nitrocellulose membranes . Subsequently , membranes were blocked in 1 x TBST containing 3% BSA ( blocking buffer ) . Primary anti-RovA [4] , anti-RovM [17] and anti-H-NS [15] antibodies were added in a 1:4 . 000 dilution in blocking buffer . The secondary antibody , anti-rabbit IgG conjugated with horseradish peroxidase , was supplied in a 1:8 . 000 dilution in blocking buffer and the immunological detection of the proteins was performed as described previously[15 , 17] . His-tagged RovA was overexpressed with BL21λDE3 pLW2 and purified as described earlier [4] . EMSAs were performed as described [4] . The DNA fragments of the rovA regulatory regions including either RovA binding site I or II were amplified with the primer pairs 153/296 and 178/V99 . For competitive EMSAs DNA fragments containing either RovA binding site I or II were mixed in equimolar amounts . Pre-incubation of recombinant RovA with the DNA fragments and native gel electrophoresis were performed at 25°C and 37°C , respectively . To determine the amount of RovA molecules per cell Y . pseudotuberculosis YPIII harbouring a RovA-dependent egfpLVA reporter ( pKH70 ) was grown over night at 25°C in liquid LB broth . A fresh culture was started by inoculating pre-warmed medium with over night culture in a 1:50 dilution and incubated at identical temperatures until the cultures reached an OD600nm = 0 . 6 . Subsequently , 1 ml of culture was harvested by centrifugation for western blotting . The bacterial pellet was resuspended in 60 μl 1 x SDS loading dye , heated to 95°C for 10 min , cooled on ice and centrifuged for 5 min at 10 . 000 g . 10 μl of supernatant ( bacterial cell extract from approx . 108 bacteria ) were loaded onto 15% polyacrylamide SDS gels . In parallel 1 and 3 ng of recombinant RovA were loaded . Western blotting was performed as described . For survival and organ burden experiments , 6–7 week old female Balb/c mice were purchased from Janvier ( Saint Berthevin Cedex , France ) and housed under specific pathogen-free conditions in the animal facility of the Helmholtz Centre for Infection Research , Braunschweig . After 16 hours of starvation , mice were orally infected with approximately 2 x 108 colony forming units ( cfu ) of Y . pseudotuberculosis YPIII or the different isogenic rovA mutant strains using a gavage needle . Bacteria were grown over night in LB medium at 25°C , washed and resuspended in PBS . For survival experiments infected mice were monitored for 14 days on a daily basis to determine the survival rate , the body weight and health status . For organ burden experiments , mice were euthanized by CO2 asphyxiation three days after infection . Peyer’s patches , caecum , MLNs , liver and spleen were isolated . Subsequently , all organs were weighed and homogenized in PBS at 30 . 000 rpm for 30 sec using a Polytron PT 2100 homogenizer ( Kinematica , Switzerland ) . To determine the bacterial load of the organs serial dilutions of the homogenates were plated on LB plates with triclosan ( Calbiochem ) . The cfu were counted and are given as cfu per g organ/tissue . To assure presence of the reporter plasmids during infection serial dilutions of Peyer’s patches and caecum of four infected mice were plated in parallel on LB plates containing either triclosan ( total bacteria ) or a combination of triclosan , chloramphenicol and carbenicillin . The cfu were counted and are given as percentage of cfu , normalized to the amount of total bacteria . Animal housing and all animal experiments were performed in strict accordance with the German Recommendations of the Society of Laboratory Animal Science ( GV-SOLAS ) and the European Health Recommendations of the Federation of Laboratory Animal Science Associations ( FELASA ) . The animal care and use protocols adhered to the German Animal Welfare Act , Tierschutzgesetz ( TierSchG ) and were approved by the Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit: animal licensing committee permission no . 33 . 9 . 42502-04-12/1010 . Animals were handled with appropriate care and all efforts were made to minimize suffering . Statistical tests were performed with Prism 5 . 0c ( GraphPad Software ) . Mann-Whitney test was used to compare wild-type and the rovA mutants in the organ burden experiments . The survival was statistical analyzed by the log-rank ( Mantel-Cox ) test . The amount of green bacteria in microcolonies in the infected tissues was compared between wild-type and the rovA mutant using Students t-test . p values < 0 . 05 were considered significant . | The ability of pathogens to sense temperature changes when they enter their mammalian hosts from the environment is crucial to optimize their fitness and adjust expression of their virulence programs . Until now it has been assumed that all cells within a population participate in the thermo-triggered adaptive response . Here , we show that a small subpopulation of an enteric pathogen does not follow thermo-induced reprogramming when the bacteria pass the intestinal epithelial layer . Observed heterogeneity is promoted by a new type of bistable switch , implicating a highly precise , thermoresponsive control element . Moreover , we demonstrate that this regulatory implement is important for virulence as it prepares the pathogen for sudden , unpredictable fluctuations encountered during host entry and exit . | [
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] | 2016 | A Precise Temperature-Responsive Bistable Switch Controlling Yersinia Virulence |
In addition to the previously characterized viruses BK and JC , three new human polyomaviruses ( Pys ) have been recently identified: KIV , WUV , and Merkel Cell Py ( MCV ) . Using an ELISA employing recombinant VP1 capsid proteins , we have determined the seroprevalence of KIV , WUV , and MCV , along with BKV and JCV , and the monkey viruses SV40 and LPV . Soluble VP1 proteins were used to assess crossreactivity between viruses . We found the seroprevalence ( +/− 1% ) in healthy adult blood donors ( 1501 ) was SV40 ( 9% ) , BKV ( 82% ) , JCV ( 39% ) , LPV ( 15% ) , KIV ( 55% ) , WUV ( 69% ) , MCV strain 350 ( 25% ) , and MCV strain 339 ( 42% ) . Competition assays detected no sero-crossreactivity between the VP1 proteins of LPV or MCV or between WUV and KIV . There was considerable sero-crossreactivity between SV40 and BKV , and to a lesser extent , between SV40 and JCV VP1 proteins . After correcting for crossreactivity , the SV40 seroprevalence was ∼2% . The seroprevalence in children under 21 years of age ( n = 721 ) for all Pys was similar to that of the adult population , suggesting that primary exposure to these viruses likely occurs in childhood .
Polyomaviruses are small , non-enveloped dsDNA viruses that occupy replicative niches in a variety of vertebrates , and have been extensively studied as oncogenic agents in experimental systems . Five human polyomaviruses have now been identified: BKV [1] , JCV [2] , KIV [3] , WUV [4] , and most recently , Merkel Cell Polyomavirus ( MCV ) [5] . BKV and JCV were discovered in 1971 [1] , [2] and are apparently ubiquitous as determined by serology studies , infecting over 80% of some populations by adulthood . Primary infections with BKV and JCV are still not well characterized . Both BKV and JCV persist for life , and their tissue sanctuaries may include mononuclear blood cells ( BKV , JCV ) and cells of the proximal renal tubule ( BKV ) . Reactivation of these viruses in immunocompromised individuals , results in hemorrhagic cystitis , nephropathy ( BKV ) and progressive multifocal leukoencephalopathy ( JCV ) . KIV and WUV were isolated from respiratory specimens by PCR methods , indicating a potential for both disease and transmission via the respiratory route . They have been detected in populations from 4 continents , suggesting a global distribution [6]–[14] . PCR evidence suggests a low prevalence for both KIV and WUV genomes in respiratory samples from individuals [15] . Neither KIV nor WUV DNA has been detected in blood or urine , however , KIV has been detected in fecal specimens [3] . MCV was recently discovered in a Merkel cell carcinoma , integrated into the host cell genome in a manner suggesting a possible relationship to oncogenesis [5] . Interestingly , MCV has a close sequence relationship to the primate Lymphotropic Polyomavirus ( LPV ) . LPV was first isolated in 1979 from an African Green Monkey B-lymphoblastoid cell line [16] . Cellular tropism for LPV includes continuous lines of B lymphoblasts of both human and monkey origin [17]–[19] . Original serologic evidence suggested that an LPV-like virus infection may occur in humans and more recent assays using specific VP1 reagents have suggested that 15–25% of humans are seropositive for LPV VP1-reactive antibodies [20] , indicating that exposure to LPV or a virus antigenically related to LPV occurs in the human population . The sequence similarity between LPV and MCV raises the possibility that the seroepidemiology of LPV may be coincident with that of MCV . Another primate virus , SV40 , also has been detected in human tissues , but its prevalence and relationship to human disease is controversial . Little is known about the primary illness associated with infection or potential disease associations of the newly discovered human polyomaviruses . Serologic studies indicate that exposure to BKV and JCV initially occurs during childhood , however , it is unknown when exposure occurs for LPV , KIV , WUV and MCV . In order to study exposure to these viruses in humans , we used recombinant polyomavirus VP1 capsid proteins expressed in E . coli in an ELISA assay similar to that described previously for HPV serotype analysis [21] , [22] . Our serological results provide data on the prevalence and age-related timeline for infection with the recently discovered polyomaviruses , KIV , WUV , and MCV , as well as for those previously identified: SV40 , LPV , BKV , and JCV .
Sera from 1501 adults over the age of 21 were tested for VP1-reactive antibodies using recombinant VP1 capsomeres from SV40 , BKV , JCV , LPV , MCV , KIV , and WUV . Seroprevalence values for KIV and WUV were 55% and 69% , respectively . Although seroprevalence values for both KIV and WUV were high in our population , infection with these viruses was not always coincident . We detected differential seroreactivity to 2 different isolates of MCV: 350 ( 25% ) and 339 ( 46% ) . Before assessment of crossreactivity , we detected a seroprevalence of 9% for SV40 and 82% for BKV . These values are consistent with previous reports [23] , [24] . Thirty-nine percent of the population had JCV VP1-reactive antibodies , and 15% of the population had antibodies against LPV VP1 . The LPV seroprevalence was not coincident with the MCV seroprevalence for either isolate , and the respective VP1 proteins did not compete for seroreactivity , as described below . Our data indicate that there may be an age-related waning of BKV VP1 specific antibodies , however , our data do not suggest an age-related waning for any of the other 6 polyomaviruses tested ( Figure 1 ) . We did not detect a difference in seroprevalence with respect to gender for any of the 7 polyomaviruses tested ( Table 1 ) . The pediatric population consisted of 721 study subjects under the age of 21 . As shown in Figure 1 , seroprevalence values for these individuals were SV40 ( 9% ) , BKV ( 73% ) , JCV ( 21% ) , LPV ( 14% ) , MCV isolate 350 ( 23% ) and MCV isolate 339 ( 34% ) , KIV ( 56% ) and WUV ( 54% ) . Antibodies to all the VP1 antigens tested were detected in children between 1 and 3 years of age in our study population ( Table 1 ) . These data indicate that exposure to all 7 polyomaviruses may occur early in childhood . Our assay allows testing for possible crossreactivity between VP1 proteins by preincubation of sera with soluble VP1 proteins prior to the specific ELISA antigen . No serologic crossreactivity was detected between the VP1 proteins of LPV and either isolate of MCV or between the VP1 proteins of KIV and WUV ( Figure 2 ) . Interestingly , there were 164 sera in our population that were seroreactive to the VP1 protein of MCV isolate 350 , but not to 339 . Also , 560 samples were seroreactive to isolate 339 , but not to isolate 350 . Antibody crossreactivity was observed between the VP1 proteins of SV40 and BKV . Therefore , competition assays with soluble BKV and JCV VP1 capsomeres were performed for all SV40 positive samples to determine the extent of SV40 seroreactivity due to crossreactivity with these other viruses . Two hundred samples were found to have SV40 VP1-reactive antibodies , 5 of these samples were not found to have seroreactivity to the VP1 proteins of either BKV or JCV , while 195 samples exhibited coincident seroprevalences with SV40 and BKV and/or JCV ( Figure 3 ) . Of the 195 coincident samples , 83 ( 43% ) samples were competed using VP1 pentamers from both BKV and JCV , while 62 ( 32% ) samples were competed using only BKV VP1 pentamers , and 7 ( 3% ) samples were competed using only JCV VP1 pentamers ( Figure 3 ) . The ELISA reactivity of 43 ( 22% ) of the 195 coincident samples were not competed with VP1 capsomeres of either BKV and/or JCV . Therefore , a total of 48 samples ( 2% of the study population ) exhibited SV40 “specific” antibodies ( Figure 3 ) . Of these 48 samples , 16 ( 33% ) were from individuals less than 21 years of age , 20 ( 42% ) were obtained from individuals between the ages of 21 and 55 years , and 12 ( 25% ) were obtained from individuals over the age of 55 years ( Table S1 ) . It was not possible from these de-identified samples to determine if the latter group had received SV40-contaminated polio vaccine .
We have determined the seroprevalence for three recently identified human polyomaviruses ( KIV , WUV , and MCV ) and confirmed the seroprevalence of two previously known human polyomaviruses ( BKV and JCV ) and two monkey polyomaviruses ( SV40 and LPV ) in a human population , using a VP1 capsomere-based ELISA . Our study evaluates both large adult ( n = 1501 ) and pediatric populations ( n = 721 ) to determine the overall prevalence and age of first exposure to these viruses . Additionally , our assay evaluated potential serocrossreactivity occurring between these viruses . Previous studies of human Py serology have used a variety of assays , including hemaglutination inhibition ( HI ) [25]–[28] and a virus-like particle ( VLP ) ELISA-based assay [20] , [29]–[31] , which may not be directly comparable . The hemaglutination inhibition assay requires either intact virions or VLPs and only evaluates a subset of antibodies . HI may be less sensitive for determining BKV and JCV VP1 seroreactivity , as compared to enzyme immunoassays [32] . Moreover , not all polyomaviruses exhibit hemaglutination ( e . g . , SV40 ) and HA has not yet been assessed for KIV , WUV and MCV . A VLP-based ELISA may present conformational epitopes and increased specificity over HI . However , while the VLP-based assay may measure only a subset of antibodies , the capsomere assay has the advantage of measuring all VP1-reactive antibodies , and the use of a casein-glutathione conjugate sterically projects the capsomeres from the well surface , allowing their full exposure to the sera . Nonetheless , seroprevalence determinations are likely somewhat dependent on specific conditions of the assay . Our observed seropositivity for both WUV ( 69% ) and KIV ( 55% ) was high , despite a low reported detection rate in respiratory tract isolates using PCR [7] , [12] , [13] , [33] , [34] . The PCR data likely represent active infection or ongoing co-infection , rather than overall exposure rates . From the age stratification data , it appears that primary infection with these viruses occurs during early childhood , with 35% positive between ages 1–3 for WU and 32% positive for KIV . There was no cross-reactivity between WUV and KIV , which may have been expected given only 65% amino acid identity between their VP1 proteins [4] . We found differential seroreactivity to MCV isolates 350 ( 25% ) and 339 ( 42% ) . However , these are not true viral isolates but rather PCR amplified sequences , since no infectious virus has yet been characterized for any of the new human polyomaviruses . The PCR amplifications may have detected defective genomes or variants with minor sequence mutations that occurred after viral integration ( specific mutational events have been reported for the Merkel large T-antigen protein [35] , and VP1 mutations might be anticipated if they affected productive infection ) . The differential reactivity in our analysis may therefore have resulted from one PCR variant having a “less native” conformation of the recombinant VP1 protein used in the assays ( although sufficiently native to generate VP1 pentamers by electron microscopy ) or mutated in critical amino acids that affect a specific epitope . Interestingly , there were 164 sera in our population that were seroreactive to the VP1 protein of MCV isolate 350 , but not to 339 . Also , 560 samples were seroreactive to isolate 339 , but not to isolate 350 , suggesting that both isolates of MCV may circulate in the human population . It remains to be determined when authentic , infectious isolates are characterized whether there is actual strain variation in serology . However , as discussed below for JCV , strain differences likely will not substantially affect overall seroprevalence data . If these are actually different strains of MCV , the differential prevalence may indicate differential geographic exposure frequencies to these MCV isolates [36] , [37] . Also , MCV isolate 339 may contain a sero-dominant MCV VP1 epitope not present in isolate 350 . Five amino acid differences occur between the VP1 proteins of MCV 350 and 339 . Based on alignment with the VP1 primary amino acid sequence of SV40 and the known structure of SV40 VP1 , MCV 339 and 350 do not vary with respect to the surface VP1 exposed variable loop regions thought to comprise the major antigenic determinants , however differences in amino acids occurring close to the surface may indirectly affect loop conformation ( Figure S2 ) . Specifically , H288 of MCV 350 is D288 in isolate 339 ( Figure S2 ) . Based on alignment with known SV40 VP1 structure , this amino acid difference occurs close to the surface near the HI variable loop . It remains to be determined whether the difference in seroprevalence between MCV isolates 350 and 339 is maintained in suspect disease populations . Our data also support previous studies suggesting that the human population has been exposed to an LPV–like virus , antigenically similar to the primate LPV VP1 [20] , [38] , and LPV-like sequences have recently been detected in the white blood cells of immunocompromised individuals [39] . Although the recently discovered MCV exhibits a high degree of sequence similarity with LPV ( Figure 4 ) our competition results indicate that MCV and LPV are antigenically distinct viruses . While the seroprevalence of BKV in our study population is consistent with previous reports [23] , [24] , the JCV seroprevalence ( 39% ) was somewhat lower [30] , [31] . There may be several reasons for this finding: 1 ) epidemiologic evidence suggests that JCV exposure may differ geographically [24] , 2 ) JCV VP1- specific antibodies may not have as high affinity for the JCV VP1 proteins compared to BKV VP1-specific antibodies , resulting in an overall decreased sensitivity of the JCV assay , 3 ) although only the MAD-11 JCV genotype has been found to be serologically distinct [40] , our assay used only genotype 2B and antibodies other than against genotype 2B may bind that VP1 protein with lower affinity , reducing the sensitivity of the assay . BKV VP1 IgG antibodies previously have been observed to cross-react with SV40 VP1 [20] . Using recombinant VP1 proteins to compete potential seroreactivity between BKV and SV40 , we found the seroprevalence of SV40 to be approximately 2% ( Figure 3 and Table S1 ) . In the population of individuals who were seropositive for SV40 and either BKV and/or JCV ( n = 195 ) , SV40 seroreactivity could be competed with VP1 pentamers of both BKV and JCV in 43% of the coincident population . SV40 seroreactivity could be competed with BKV VP1 capsomeres alone in 32% of coincident samples , while 3% of coincident samples could be competed only with JCV VP1 capsomeres . If there was no specific seroreactivity we did not assign seropositivity to SV40 , since we assume that some antibodies must be specific to SV40 to indicate infection . Indeed , we identified 48 individuals with “specific” SV40 antibodies , supporting our assumption that the SV40 serological response is not restricted only to cross-reactive antibodies with BKV or JCV . It is interesting to speculate that BKV infection might confer protection against SV40 , an attribute that may have had a selective advantage at one time . Although we cannot explain the residual SV40 serology of 2% , it is possible that a yet unidentified human polyomaviruses may account for this reactivity . The external surface variable loop domains of the VP1 proteins may present the dominant epitopes of these viruses . For example , comparison of the loop regions of SV40 and BKV reveals a high degree of similarity . Specifically , the BC loops of SV40 and the BK strain of BKV share 53% identity , while the DE and HI loops exhibit 71% and 80% identity , respectively . Comparison of the loops of LPV and MCV , and those of KIV and WUV , reveals fewer similarities ( Figure 4 ) ; LPV and MCV exhibit 21% identity in the BC loop , and 26% and 20% in their respective DE and HI loops . KIV and WUV share 33% and 38% respective identity in the BC and DE loops , and only 20% identity in the HI loop . These differences may account for the lack of crossreactivity between these viruses . Our pediatric population exhibits similar seroprevalence values compared to the adults , indicating that primary infection with polyomaviruses occurs in childhood ( Figure 2 ) . We found seropositivity to BKV , WUV , KIV , LPV , and MCV appears in early childhood whereas that for JCV occurs in pre-adolescence ( Figure 1 ) . Additionally , our data suggest there may be an age-related waning of BKV VP1 specific antibodies , however , our data do not indicate an age-related waning for any of the other 6 polyomaviruses assayed ( Figure 1 ) . While the adult samples are likely representative of the demographics in the Denver area , and reflect healthy individuals within the guidelines for blood donation , the pediatric samples were obtained from inpatients and outpatients at The Children's Hospital Denver and although exclusion criteria were employed , some individuals may have had co-morbid or intercurrent illnesses . Although animal models have suggested that polyomaviruses could be human tumor viruses [41]–[44] , no substantive cause-effect relationships have yet been established . The debate over SV40 induction of human tumors remains controversial [45] , and our data do not support a permissive human infection with SV40 , such as seen with the other human polyomaviruses . If SV40 infects humans , it is very limited in scope . Furthermore , if a specific tumor type is presumed attributable to SV40 infection , serological validation now would be an essential factor in the analysis . The discovery of MCV integrated in a specific tumor [5] is a possible indication of the ability of these viruses to contribute to tumorigenesis , but perhaps only in a subset of cell types and only in immunosuppressed individuals . The identification of the human counterpart to LPV may also reveal a connection to human cancer . However , given the high seroprevalence of these viruses , serological support of etiologic connections to specific diseases may be problematic . Nonetheless , these ubiquitous viruses appear to be significant human pathogens in immunosuppressed populations .
Plasma samples from healthy adult blood donors were obtained ( May and June , 2007 ) from Bonfils Blood Center ( Denver ) , and pediatric plasma samples were obtained from The Children's Hospital ( Denver ) using protocols approved by the Colorado Multiple Institutional Review Board . Consent from human participants was not obtained . Samples were de-identified and analyzed anonymously . To ensure a healthy pediatric population for determination of baseline exposure , the pediatric population excluded patients who had received intravenous immunoglobulin in the preceding year and/or patients who had undergone blood , platelet , or plasma transfusions within two months of obtaining the sample . Children less than one year of age were excluded from the analysis because of possible confounding maternal antibodies . pGEX4T3 plasmids ( GE Healthcare ) encoding the VP1 capsid proteins for SV40 , LPV , BKV , JCV , KIV , WUV and MCV isolate 339 were obtained and subsequently used for protein production . The MCV isolate 350 vp1 gene was synthesized according to the published sequence ( Genbank accession number NC_010277 ) ( Genscript ) and subcloned into the BamHI and XhoI restriction sites of the pGEX4T3 expression vector ( GE Healthcare ) . Subcloning was verified through DNA sequencing analysis of regions flanking the insert using the primers: 5′-GCATGGCCTTTGCAGGGC-3′ and 5′-CGACACTGGCAGAGGCCC-3′ . VP1 proteins from SV40 , BKV ( BK strain ) , JCV ( genotype 2B ) , LPV , KIV , WUV and MCV were expressed in E . coli and purified using affinity chromatography . Briefly , 400 ml cultures of E . coli containing the pGEX VP1 expression plasmids were grown at 37°C until OD600 = 4 . 0 . Cultures were cooled to room temperature and induced with 1 . 25 mM IPTG . Cells were then grown at room temperature to an OD600 of 8 . 0 . Cells were lysed using a French Press . A clarified lysate was chromatographed using a GSTrap FF affinity column ( GE Healthcare ) to bind recombinant GST-VP1 capsomeres . Columns were washed with 3 column volumes of Superdex buffer ( 40 mM HEPES , pH 7 . 4; 200 mM NaCl; 5% glycerol; 1 mM EDTA , 5 mM DTT , pH 7 . 2 ) , and GST-VP1 capsomeres were eluted from the column with 10 column volumes of Superdex buffer supplemented with 10 mM reduced glutathione . Protein fractions were combined , buffer exchanged , aliquoted , and stored at −80°C for use in capture ELISA assays . Soluble VP1 proteins used in competition assays were purified as described above . After buffer exchange , GST-VP1 capsomeres were incubated with glutathione sepharose beads ( GE Healthcare ) for 1 hr at 4°C . GST tags were cleaved with 50 U of thrombin in 10 ml lysis buffer ( 40 mM HEPES , pH 7 . 4; 200 mM NaCl; 5% glycerol; 1 mM EDTA ) , supplemented with 10 mM DTT . Cleaved VP1 pentamers were collected , concentrated , aliquoted , and stored at −80°C for use in competition assays . N-terminal GST-tagged pentameric VP1 capsomeres were captured on polysorp 96-well plates ( Nunc ) using a casein-glutathione conjugate [22] . Plasma samples were diluted 1∶50 in block buffer ( 5% ( w/v ) evaporated milk powder; 0 . 05% ( v/v ) Tween 20 in phosphate buffered saline , pH 7 . 4 ) and incubated with the immobilized GST-VP1 antigen . IgG antibodies were detected using an HRP-labeled secondary antibody , and tetramethylbenzidine with hydrogen peroxide as a substrate . Positive control antibodies were: anti-LPV VP1 monoclonal antibody for LPV VP1 antigen and human sample 637 for both MCV VP1 antigens . We provided BKV VP1 pentamers to Bio-Synthesis Inc , for production of an anti-BKV polyclonal antibody . At a concentration of 1∶50 , 000 , the BKV polyclonal rabbit sera reacted with SV40 , BKV , JCV , KIV , and WUV VP1 antigens . Negative controls included capture of GST alone and subsequent incubation with control antibodies for each antigen . ELISA assays for 90 samples were repeated for all 8 antigens tested . Reproducibility was assessed based on agreement of seropositivity for each sample run on different days . Kappa statistics indicated a high degree of agreement between samples: SV40 ( 0 . 8515 ) , LPV ( 0 . 8218 ) , BKV ( 0 . 8668 ) , JCV ( 0 . 8062 ) , WUV ( 0 . 9076 ) , KIV ( 0 . 8851 ) , MCV isolate 350 ( 0 . 9446 ) , MCV isolate 339 ( 0 . 7976 ) . Net absorbance values were calculated by subtracting the mean absorbance value of the negative control from the mean raw absorbance value read at 450 nm . COVs were determined by ranking net absorbance values and determining the inflection point for each antigen ( Figure S1 ) . The COV for the VP1 antigens of SV40 , BKV , JCV , LPV , KIV , and MCV isolate 350 VP1 antigens was 0 . 2 . The COV for the VP1 antigen of MCV isolate 339 was 0 . 22 . The COV for WUV VP1 antigen was 0 . 25 . To determine sera crossreactivity between two or more VP1 proteins , soluble VP1 proteins were purified and plasma samples were pre-adsorbed using these proteins to compete potential crossreactive antibodies in the ELISA assay . Specifically , VP1 capsomere proteins were added to a 1∶50 dilution of plasma and incubated for 1 hr at 4°C prior to incubating samples with immobilized test antigen . Initially , VP1 capsomeres were titrated to determine the concentration needed to effectively compete crossreactive antibodies ( Figure 2 ) . From this titration experiment it was determined that 3 . 0 µg/ml of competing soluble VP1 was sufficient for the competition assays . Competition assays for SV40 seroprevalence were performed on those samples that exhibited coincident seroreactivity for BKV and/or JCV ( Figure 3 ) . Seroprevalence values were rounded to the nearest integer . Chi square and Fischer's exact tests were performed to determine whether there was a difference in seroprevalence for any of the VP1 antigens tested with regard to gender . We stratified the pediatric and adult population by age , as shown in Table 1 . Significant differences in cumulative seroprevalence among the age categories were determined by comparing standard errors in each group ( Figure 1 ) . SAS version 9 . 0 was used for all statistical analyses performed . | Polyomaviruses occupy a replicative niche in animals from birds to humans . Two human polyomaviruses , BKV and JCV , were discovered in 1971 and within the last two years , three new polyomaviruses have been found in humans: KI ( KIV ) , WU ( WUV ) , and Merkel Cell ( MCV ) polyomavirus . MCV was identified in Merkel Cell carcinomas , a rare skin cancer . To date , it has not been determined what percentage of the human population is exposed to KIV , WUV , and MCV , and when initial exposure to these viruses occurs . We determined that initial exposure to KIV , WUV , and MCV occurs in childhood , similar to that for the known human polyomaviruses BKV and JCV , and that their prevalence is high . We also found evidence that a monkey virus , Lymphotropic Polyomavirus ( LPV ) , likely has a serologically related human counterpart . Another monkey polyomavirus , SV40 , was found at ∼2% prevalence , a level that does not support its role in human disease . | [
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] | 2009 | Seroepidemiology of Human Polyomaviruses |
Existing theory of host-parasite interactions has identified the genetic specificity of interaction as a key variable affecting the outcome of coevolution . The Matching Alleles ( MA ) and Gene For Gene ( GFG ) models have been extensively studied as the canonical examples of specific and non-specific interaction . The generality of these models has recently been challenged by uncovering real-world host-parasite systems exhibiting specificity patterns that fit neither MA nor GFG , and by the discovery of symbiotic bacteria protecting insect hosts against parasites . In the present paper we address both challenges , simulating a large number of non-canonical models of host-parasite interactions that explicitly incorporate symbiont-based host resistance . To assess the genetic specialisation in these hybrid models , we develop a quantitative index of specificity applicable to any coevolutionary model based on a fitness matrix . We find qualitative and quantitative effects of host-parasite and symbiont-parasite specificities on genotype frequency dynamics , allele survival , and mean host and parasite fitnesses .
Parasitism is one of the main lifestyles in nature and a major source of evolutionary pressure . Despite its central place in evolutionary ecology , however , the details of the genetic architecture underlying resistance and infectivity are not known for most host-parasite associations . Mathematical models of host-parasite coevolution compensate for the missing data by making explicit , first-principle assumptions about the interaction of host and parasite genotypes . Two such classic assumptions , and consequently two classic families of models , are known as Matching Alleles ( MA ) and Gene For Gene ( GFG ) . The MA models , inspired by vertebrate immune systems [1] , assume that an exact , lock-and-key match between host and parasite genotypes is required for successful infection . The GFG models , based on studies of plant disease [2] , [3] , postulate that an infection takes place if every “resistance” allele of the host is countered by a “virulence” allele of the parasite . Perhaps the farthest-reaching difference between the two is in the genetic specificity of the interaction . Under MA parasites exhibit full genetic specialisation to their host: a single parasite genotype can only infect a single host genotype . Under GFG , on the other hand , the number of host genotypes that a parasite may infect depends of the number of virulence alleles it has , and ranges from one genotype to all . The perfect specificity of MA interactions readily results in negative frequency-dependent selection and persistent cyclic dynamics of genotype frequencies in hosts and parasites ( “Red Queen dynamics” ) , which in turn underpin the Red Queen Hypothesis ( RQH ) for the evolution of sexual reproduction [4] , [5] . In contrast , in the GFG models the parasite carrying all virulence alleles takes over the population , at least until costs of infectivity and resistance are assumed [3] , [6] . One problem with the existing theory is that there is a mounting number of natural systems for which the interactions between host and parasite genotypes have been disentangled and found to be neither of the MA nor the GFG kind [7]–[10] . Coupled with the uncertainty as to the extent to which plant disease data supports the GFG model [3] , [11] , these findings cast doubt on the generality and explanatory power of interaction models as simple as MA or GFG . Agrawal and Lively [12] tackled this problem by considering a range of non-standard genetic interactions spanning a particular continuum between MA and GFG , and found that MA-like behaviour , in particular Red Queen dynamics , is sufficiently common among non-standard models to support the generality of the RQH . Their work has since been generalised by Engelstädter and Bonhoeffer [13] , who sampled a much broader range of interaction patterns . While they were able to confirm the pervasiveness of Red Queen dynamics , they also found effects that are entirely absent from the MA and GFG models , showing that non-standard models should not be ignored . We aim to build on these two studies to address a new challenge to the existing theory of host-parasite interactions: the discovery of bacterial endosymbionts that increase their hosts' resistance to parasites [14]–[17] . When a symbiont protects a host against a parasite , one has to consider the coevolution of three , not two , species , with all the resulting complications . In particular , it is now important to distinguish between host-parasite and symbiont-parasite genetic interactions . It is for example possible for one to be of the GFG , and the other of the MA kind . Indeed , specialisation of symbiont strains to parasite genotypes and apparent lack of host-parasite specialisation characterises the protection against parasitoid wasps that the endosymbiotic bacterium Hamiltonella defensa lends to aphids [18] , [19] . Another important aspect that models of such systems should address is the spread of symbionts in , and loss from , the host populations , and the coevolutionary impact of these processes . In this paper we build a generic model of the coevolution of hosts , symbionts and parasites . We incorporate as independent , tunable parameters the strength of the reciprocal selection acting on hosts and parasites , the fitness penalty for harbouring symbionts , the efficiency of horizontal and vertical transmission of symbionts , and the genetic interactions among all players . In a straightforward extension of the standard approach , the last factor is subsumed in a real-valued matrix , and we randomly sample many such matrices and simulate our model for each . In this way we are able to cover a range of potential host-symbiont-parasite systems and , importantly , decouple the effects of genetic specialisation patterns from the other factors . In addition , we analyse separately a collection of matrices describing protective symbionts acting within the established MA and GFG frameworks . Throughout , we do not treat genetic specialisation as a binary property; instead , we devise a numerical index of specificity . We find that specificity as defined in this paper strongly influences important coevolutionary outcomes of the models , such as the genotype frequency dynamics , maintenance of allelic diversity and mean host and parasite fitness . Our simulations show that these characteristics depend also on symbiont-related processes , especially the reliability of their maternal inheritance .
We assume that hosts and parasites reproduce asexually and consider one haploid locus and two alleles in each of the three protagonists: host , symbiont and parasite; we also model hosts without symbionts . This gives rise to two parasite genotypes: and , and six combined host-symbiont genotypes or associations: , , , , and . The blank “” denotes absence of symbiont , and so the symbiont-free hosts and are also formally considered to be associations . We subsume any individual interaction pattern of the host-symbiont and parasite genotypes in a 62 master matrix denoted . Each entry in this matrix falls in the interval and is interpreted as the degree of resistance of the particular host-symbiont association to the particular parasite genotype . To give a concrete example , if , then every host carrying the symbiont will suffer only 20% of the maximal potential fitness damage from the parasite . For the majority of the analyses we rely on random generation of such matrices in order to cover a wide range of possible host-parasite relationships ( see “Model sampling and simulation” ) . We use three additional parameters in our coevolutionary setup: the maximum strength of selection that the parasites can exert on hosts ( e . g . means the host can have zero fitness as a result of infection , that is be sterilised or killed before it reproduces ) , the corresponding parameter representing the strength of selection on parasites ( which can be interpreted as the maximal fitness penalty for failing to infect a host ) , and the fitness penalty the hosts pay for harbouring protective symbionts; see Table 1 for an overview of parameters and their values . These parameters are used to derive the host and parasite fitness matrices , and respectively , from the master matrix as follows ( see also Figure 1 ) : ( 1 ) ( 2 ) ( Here , and throughout the paper , we use lowercase letters to refer to the entries of the matrix denoted by the corresponding uppercase letter . ) Each entry of the fitness matrix specifies the relative fitness consequence of the interaction between a particular host-symbiont association and a particular parasite genotype . Again , to give a concrete example , means that the fitness of the host-symbiont association when faced with the parasite genotype is half that of a symbiont-free uninfected host . By definition , host and parasite fitness values are fully anti-correlated in our model , reflecting the antagonistic nature of host-parasite relationships ( but see also [13] ) . Instead of generalising the concept of genetic specialisation to three species , we prefer to analyse the specificity of genetic interactions between pairs of species separately . To this end we transform the master matrix into three matrices , and , each time averaging out the contribution of one species ( symbiont , host and parasite , respectively ) . Thus , each of these matrices serves as a proxy for the genetic interaction of the remaining two species . Formally , they are defined by: ( 3 ) ( 4 ) ( 5 ) In this paper we focus on and . Figure S1 contains the corresponding results for . Our basic assumption in formalisation of specificity is that a relationship between two coevolving species and is specific if there are two genotypes of the species , say and , and two of the species , and , such that is better adapted than to , but is better adapted than to —or analogously with s and s swapped around . This is the same as saying that there is a genotypegenotype interaction between and , or that the reaction norms for two or two genotypes cross . This definition is also easily expressible in terms of interaction matrices . Taking the as an example , we say that the interaction it subsumes is specific if and only if but , or this condition holds with / or / switched in a consistent manner . When is specific , we define its index of specificity , , to be the minimal additive disturbance necessary to bring into a non-specific form . Formally: ( 6 ) where is the set of matrices such that is non-specific . The above definition can be generalised to cover arbitrary and matrices . These constructions are described in Text S1 . In the remainder of the paper we are only concerned with the specificity of and . For these , and for any matrices in general , observe that ranges between and , with only for non-specific matrices such as the GFG matrix , and only for the MA matrix and the Inverse Matching Alleles matrix . The IMA model [20] assumes that the host is resistant if and only if it can match all parasite alleles; in the 22 case it is formally equivalent to the MA model because their matrices are mirror images of each other . For square matrices with , the GFG and IMA matrices acquire intermediate specificity , while the MA matrix remains the most specific . With the exception of the MA- and GFG-based host-symbiont-parasite relationships analysed in “Protective symbionts in the MA and GFG frameworks” , we kept the range of investigated relationships as broad as possible by generating a large number of random master matrices . Because we are interested in the effects of specificity , our goal was to have two collections of matrices , each uniformly distributed with respect to one of the specificity scores . Ideally , one would generate enough matrices by sampling the entries independently from the uniform distribution ( i . u . d . ) on [0 , 1] , and then select the two matrix collections from this sample . Unfortunately , i . u . d . sampling yields no high-specificity matrices in reasonable time , because their entries have extreme values and are highly dependent on each other ( see Text S2 ) . To overcome this problem , we separated the [0 , 1] interval into ten non-overlapping sub-intervals of length 0 . 1 , and for each sub-interval we randomly generated master matrices until we had 400 with the specificity score falling in it . For intervals up to [0 . 6 , 0 . 7] the matrices were obtained by i . u . d . sampling . For the remaining three the matrices were independently sampled from a symmetric bimodal distribution with modes 0 and 1 ( probability density of being for , for , and otherwise ) , ensuring polarised matrix entries , which is a characteristic property of high-specificity matrices . Of all matrices we further required that the symbionts do not impair host resistance , which translates into the simple criterion: for all , and . We performed this procedure twice , once for HP-specificity and once for SP , obtaining two sets of 4000 matrices distributed in an approximately uniform fashion with respect to and ; see Figure 2 . We considered three values of each of the three supplementary parameters , , and ( see Table 1 for an overview of parameters ) , thus deriving 27 pairs of fitness matrices for each master matrix ( see Figure 1 ) . For each pair of fitness matrices we simulated 20000 generations of coevolution , the first 10000 of which were considered the burn-in period and discarded . We worked with infinite population sizes , meaning that we tracked frequencies of host-symbiont and parasite genotypes rather than population sizes . At each generation the symbionts colonised the symbiont-free hosts at the mass-action basal rate , then selection was allowed to operate , and finally the constant fraction of of symbiont-harbouring hosts lost the symbionts . The selection step was performed as follows ( see also [13] ) : assume that is the vector of host-symbiont association frequencies , and the vector of parasite genotype frequencies . Then the post-selection frequencies and are given by:The numerator in the above expressions gives the fitness of genotype , obtained as the sum of the th row of the fitness matrix entries , weighed by the frequencies of the genotypes of the antagonists . The underlying assumption is that the more common a particular opponent is , the more the performance against it contributes to fitness . The denominator , which is independent of , is the mean host or parasite fitness and ensures that the frequencies add up to one after selection . For each master matrix we simulated and analysed 108 models , because there are 108 auxiliary parameter combinations ( Table 1 ) . Each model was first simulated for 10 randomly chosen starting frequency vectors , and then once more for equal starting frequencies of all genotypes , with the results of the last run used for evaluation . Approximately 10% of the models were ambiguous , in that the results of the 11 simulations were not consistent with each other . We also had trivial models: whenever , the symbiont-bearing hosts inevitably went extinct , since the cost of symbionts exceeded any damage the parasites could inflict and we assumed the horizontal transfer to be rare . Unless noted otherwise , the ambiguous models are included in the analyses that follow , but the trivial ones are not .
We begin by considering two simple models incorporating symbiotic protection into the Gene For Gene and Matching Alleles frameworks . We assume that the hosts' innate resistance to the parasites follows one of these two classic principles , but the symbionts may confer a partial degree of resistance to the non-resistant hosts . We further assume that the symbiotic protection is specialised: symbiont only protects against the parasite , and against . This leads to two master matrices: and . Setting , and remembering that harbouring symbionts ( regardless of whether they are needed for protection ) comes at the cost , we derived two pairs of host and parasite fitness matrices using equations ( 1 ) and ( 2 ) . We then simulated the two models for different combinations of and , under perfect maternal inheritance of symbionts ( ) and without horizontal transfer events ( ) . We found that despite the presence of symbionts , the dynamical behaviour of the models is similar to that of their classic symbiont-free counterparts . All MA models exhibit strong oscillations of host and parasite allele frequencies , while in the GFG models the allele frequencies stabilise . The balance of cost and protection quality determines the fate of symbionts in both models . In the MA model , the symbionts become fixed in the population if ; otherwise they go extinct . The corresponding condition for the GFG model , , is less strict because the symbiont-provided protection is essential against the parasite ; for the same reason the symbiont is more common than in this model . These formulae can be derived using the definition of the host fitness matrix and considering when the symbionts confer a net fitness benefit to their hosts , and were corroborated by the simulations . We now give an intuitive specificity-based interpretation of these results . The host-parasite specificity is high in the MA models: , and therefore the negative frequency-dependent selection drives the oscillations of host and parasite alleles easily regardless of whether the symbionts are present . In the GFG models the host-parasite specificity is zero: , and thus in the absence of symbionts the allele frequencies are stable . When the symbionts are present , the moderate symbiont-parasite specificity for high ( ) could be expected to result in oscillations ( see “Allelic diversity and frequency dynamics” ) . However , in these models there is also the host-symbiont association that nullifies this effect of specificity because it is more resistant than any other to both parasites . In the remainder of the paper we substantiate and expand these intuitions by linking specificities to coevolutionary outcomes of models based on randomly generated master matrices . In classical models incorporating genetic specialisation such as the MA model , pronounced oscillations of genotype frequencies often ensure indefinite maintenance of at least two genotypes . Here , we examine to what extent these effects reappear in our three-species setup . We say that a model cycles if the frequency of at least one host-symbiont association has six or more local extrema over the assessment period , and the amplitude between the extrema does not decrease; also , we declare an association lost from the population if its mean frequency over the assessment period is less than 10−3 . We found that while both the host-parasite and symbionts-parasite specificities broadly promote cycling and diversity ( Figure 3 ) , they do so in qualitatively different ways . For HP specificity , the prevalence of cycling and the mean number of host-symbiont associations maintained in the model reach maximum values for master matrices with intermediate values of . For SP-specificity , both measures increase across the entire range of , with one exception ( see below ) . In both cases , models based on non-specific matrices maintain the fewest associations on average and are the least likely to cycle . The fidelity of vertical transmission of symbionts has a strong quantitative effect on the fate of host-symbiont associations and on their dynamics . Perfect symbiont inheritance ( ) is often necessary to maintain symbiont infections , but it can also lead to the extinction of the symbiont-free hosts and , as these populations often depend on the influx of hosts from the infected lineages ( see also [21] ) . Fewer models oscillate when than when , likely due to the interference of the symbiont-free hosts with the frequency-dependent selection driving the cycles . This hypothesis is consistent with the effect disappearing for master matrices with extreme , where the host-parasite relationship becomes determined by host and parasite alleles only , and no significant interference is possible from hosts differing only in their symbiont infection state . For high values of on the other hand , we found that the trend is reversed and perfect vertical transmission of symbionts results in less frequent cycling . Here , the probable explanation is that the symbiont-free hosts cannot engage in cycling because they necessarily go extinct unless they are replenished via symbiont loss ( see Text S2 for more on matrices of high specificity ) . The number of maintained associations and the prevalence of cycling are virtually the same for models differing only in the presence of weak horizontal transmission of symbionts ( or ) . We turn now to the question of allelic diversity , that is the maintenance of the individual host , symbiont and parasite alleles . We found that the genetic specialisation between antagonist species strongly promotes allelic diversity in these species ( Figure 4 ) . Moderate and high host-parasite specificity all but guaranteed the survival of both host and both parasite alleles . We found a similar effect of on symbiont and parasite alleles , but the mean symbiont allele diversity taken across all analysed models increases considerably more slowly due to the cost associated with harbouring symbionts that is built into most of these models ( see Methods ) . There is no effect of the specialisation on the allelic diversity in the non-specialised species , that is and do not influence the likelihood of loss of symbiont and host alleles , respectively . Again , we found no impact of the horizontal transmission of symbionts on the allelic diversity in any of the three species . The presence of symbionts results in novel kinds of Red Queen dynamics ( Figure 5 ) . Traditionally , this term refers to persistent oscillations of both host and parasite genotype frequencies driven by the genetic composition of the antagonist population ( also known as negative frequency-dependence ) . Under considerable symbiont-parasite specificity , oscillations of symbiont allele frequencies may replace those of the host alleles . The result is a dynamical pattern that would be regarded as cryptic if one were unaware of the existence of symbionts: the host allele frequencies remain stable but those of the parasite oscillate . Another possibility is that one symbiont allele is lost , but the other periodically rises and falls in frequency in the host population due to its specialisation to one of the parasite alleles . Lastly , we investigated the dependence of mean host and parasite fitnesses on the host-parasite and symbiont-parasite specificities , and on the parameters of the model . Mean fitness within the host population can be regarded as a measure of how well the hosts are adapted on average to resist infection by the parasites , and analogously for the parasites and their mean fitness . As a general rule , mean parasite fitness increases and mean host fitness decreases with increasing specificity ( see Figure 6 ) . From the host population's perspective this effect can be attributed to the specificity widening the gap between the fitnesses of well-adapted and maladapted associations ( see Text S2 ) , and the contribution of the latter to the mean fitness in the presence of efficient parasites . However , as the entries of fitness matrices become more and more polarised for high or extreme specificities , the selection against the maladapted associations becomes more and more swift . Consequently they cease to contribute to the mean fitness and the trend is halted or even reversed . The fidelity of symbiont inheritance plays a similar role to that discussed in “Cycling and the fate of host-symbiont associations” . Imperfect inheritance maintains a small yet stable population of symbiont-free hosts even when they are severely selectively disadvantaged , e . g . for high values of , and the mean host fitness is reduced . On the other hand , when host-parasite specificity is high , symbiont-free hosts enjoy protection similar to that of their symbiont-bearing counterparts but do not pay the fitness penalty for symbiosis , and thus their influx increases the mean host fitness . We found no influence of horizontal transmission of symbionts on the mean fitness of either species throughout our analyses . We observed the mean host fitness decrease with increasing strength of selection the hosts are under ( ) and the costs of symbiont protection ( ) . Similarly , mean parasite fitness is sensitive to , with stronger selection leading to lower fitness . These findings were entirely expected , since high values of , and make for low average entries in the fitness matrices . The antagonistic nature of the relationship was visible in that the increases of mean host fitness generally coincided with decreases of mean parasite fitness , and vice versa . This too can be traced back to fitness matrices , more precisely to the anti-correlation of and .
Genetic specialisation has been recognised in the literature on host-parasite interactions as a fundamental concept in its own right [22] . To the best of our knowledge , we provided here the first method of quantifying it in the context of coevolutionary modelling . By basing the construction on the concept of fitness matrices central to host-parasite theory [23] , [24] , we ensured that our method is applicable to a wide range of models , extant and future . For actual biological systems for which fitness matrices can be approximated experimentally , for example in factorial experiments , our index can be used directly to generate concrete predictions about the coevolutionary dynamics and genetic diversity . Our work was inspired by that of Agrawal and Lively [12] , who analysed host-parasite coevolutionary dynamics for a range of non-standard fitness matrices . Their setup was based around a single parameter so that yielded the MA model , the GFG , and intermediate values gave non-standard matrices . However , Agrawal and Lively's is not an extrinsic measure of a matrix , and therefore an independent characterisation of a relationship , but an intrinsic assumption used to construct it . In particular , if costs of resistance and virulence and the selection strength are fixed , the value of determines the matrix . Thus , our work is more general because it makes it possible to talk about the specificity of arbitrary matrices , and because we base our conclusions on a much wider range of models . In these respects it resembles a study of Engelstädter and Bonhoeffer [13] , where antagonicity , another property of arbitrary fitness matrices , was developed to analyse coevolutionary dynamics . Our specificity index is related to the notions of nestedness and matrix temperature introduced by Atmar and Patterson [25] to study the extinction of species in fragmented habitats , later used for structural analysis of plant-animal interaction networks [26] , [27] . In this approach , one starts with a presence-absence matrix , that is a binary matrix where 1 denotes an existing plant-animal interaction , or the presence of a species in a habitat , and 0 the absence thereof . The matrix is rearranged so that the rows and columns corresponding to more generalist species or more hospitable habitats appear higher ( rows ) and farther to the left ( columns ) . The matrix temperature is then obtained by penalising deviations of this rearranged matrix from the fully nested matrix , where ones appear only above a generalised diagonal and zeroes only below . A fully nested matrix is non-specific by our definition , but many non-specific matrices are not fully nested . Hence , matrices of high specificity will tend to have high temperature , but temperature may be different for matrices of the same specificity and vice versa . Importantly , specificity is defined for arbitrary matrices while temperature applies to binary matrices only . The similarity of the two measures suggests nevertheless that they capture facets of an essential property of biological interaction networks , and therefore that our specificity index may be applicable more widely than only to host-parasite coevolution . Our setup explicitly included a heritable symbiotic species increasing the hosts' resistance to the parasites . Such beneficial symbionts can play fundamental roles in the the ecology and evolution of their hosts , highlighting the need for comprehensive treatment of the forces shaping symbionts' own spread and evolution [28] . Defensive symbionts are transmitted from mother to offspring with very high fidelity , with the link between protection and maternal transmission also strongly supported by theory [29]–[31] , but lateral transfer appears to be relatively rare on the ecological timescale [32] , [33] . Accordingly , we analysed the impact of occasional vertical loss and occasional horizontal transfer of symbionts on the coevolution of hosts and parasites . We found that a small population of symbiont-free hosts maintained exclusively by the sporadic failure of vertical transmission can disrupt the Red Queen dynamics driven by the specialisation of parasite alleles and host-symbiont associations . Sporadic lateral transfer had no effect on the results of simulations , but given the well-documented role of lateral transfer in the interspecific spread of bacterial symbionts [34] , [35] , we believe that better estimates of the basal rate of transfer ( ) ought to be obtained before discounting its role in the coevolutionary dynamics of the three interacting species . Still , it seems plausible to us that horizontal transfer is important in establishing the initial symbiont infections , but not in their subsequent fate in the host populations , which is governed mainly by the cost-benefit trade-off . The inclusion of protective symbionts as the third species highlighted the interplay of coevolutionary antagonicity and specificity . The genetic specificity between antagonist species had stronger effects on the vital properties of the system such as cycling and maintenance of alleles than the specificity of the mutualist host-symbiont relationship ( Figure S1 ) . This result dovetails with that of Engelstädter and Bonhoeffer [13] , who showed that antagonicity of interaction promotes allelic diversity . We wish to point out , however , that our model is simplistic in two important respects . First , it ignores the fact that in addition to providing protection from parasites and pathogens , maternally transmitted symbionts may also manipulate the host reproductive phenotype in various ways ( reviewed by Engelstädter and Hurst [36] ) , and thus the relationship between the host and the symbiont may be less mutualistic than envisaged here . Second , our model is fully deterministic , and as such it does not incorporate genetic drift . However , when the genotypes of two antagonist species are highly specialised to each other but not to the third one , drift can be expected to play a significant role in the evolution of the latter . Our work may have interesting implications for the Red Queen Hypothesis ( RQH ) —the idea that host-parasite coevolution underlies the evolution of sex and recombination [1] , [4] , [24] . We assumed in our model that hosts reproduce asexually . As a consequence , defensive symbionts and host resistance genes are predominantly co-inherited , except for horizontal transmission events that we assumed to be rare . This lack of recombination , in tandem with the strong epistatic interactions between the symbionts and the nuclear genes that are implicit in many of our master matrices , can create pronounced fluctuations of the linkage disequilibrium ( LD ) between the nuclear locus and the symbiont “locus” when diversity is maintained at both loci ( results not shown ) . In conventional Red Queen models considering only nuclear loci , such LD fluctuations are a prerequisite for recombination modifiers to be under positive selection . In our model , sexual reproduction would entail free recombination between the nuclear and the symbiont locus due to their different modes of inheritance ( Mendelian vs . maternal ) . Therefore , we speculate that modifier alleles inducing sexual reproduction may be selected for under some of our host-symbiont-parasite interaction matrices . This tripartite version of the Red Queen represents an exciting avenue for future research . | Coevolution between hosts and parasites is believed to be central to a number of biological phenomena , most notably the observed patterns of biodiversity and the origins of sexual reproduction . However , classical mathematical models of host-parasite coevolution account neither for the hosts' use of bacterial symbionts for protection from parasites , nor for the potential and observed complexity of genetic interactions between the coevolving species . In this article we address both challenges by simulating a large number of models of host-symbiont-parasite coevolution based on randomly generated genotype interaction patterns . We demonstrate that the degree of “specificity” between the genotypes of the interacting species is a major factor influencing the outcome of coevolution . We also observe that the symbionts may take over from the hosts the coevolutionary arms race against the parasites . Overall , our results make clear that the complex interaction patterns and the defensive symbionts can both play vital roles in host-parasite coevolution . An additional contribution of the article is a numerical index of specificity , applicable to a wide range of existing and future coevolutionary models . | [
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] | 2012 | On Genetic Specificity in Symbiont-Mediated Host-Parasite Coevolution |
The Ty1 retrotransposons present in the genome of Saccharomyces cerevisiae belong to the large class of mobile genetic elements that replicate via an RNA intermediary and constitute a significant portion of most eukaryotic genomes . The retromobility of Ty1 is regulated by numerous host factors , including several subunits of the Mediator transcriptional co-activator complex . In spite of its known function in the nucleus , previous studies have implicated Mediator in the regulation of post-translational steps in Ty1 retromobility . To resolve this paradox , we systematically examined the effects of deleting non-essential Mediator subunits on the frequency of Ty1 retromobility and levels of retromobility intermediates . Our findings reveal that loss of distinct Mediator subunits alters Ty1 retromobility positively or negatively over a >10 , 000-fold range by regulating the ratio of an internal transcript , Ty1i , to the genomic Ty1 transcript . Ty1i RNA encodes a dominant negative inhibitor of Ty1 retromobility that blocks virus-like particle maturation and cDNA synthesis . These results resolve the conundrum of Mediator exerting sweeping control of Ty1 retromobility with only minor effects on the levels of Ty1 genomic RNA and the capsid protein , Gag . Since the majority of characterized intrinsic and extrinsic regulators of Ty1 retromobility do not appear to effect genomic Ty1 RNA levels , Mediator could play a central role in integrating signals that influence Ty1i expression to modulate retromobility .
Retrotransposons have been extensively characterized as catalysts of evolutionary change and agents of genome instability [1–6] . In humans , retrotransposons have been shown to be upregulated in cancerous cells [7] , and have been implicated in tumorigenesis [8] . Long terminal repeat ( LTR ) retrotransposons are of particular interest because they are the evolutionary progenitors of retroviruses [9] , and have been found to be influenced heavily by host factors that control retroviral propagation [10–17] . Research focused on the LTR-retrotransposons in Saccharomyces cerevisiae has been fundamental to our understanding of the mechanism by which LTR-retrotransposons replicate , and how they interact with the host genome [2 , 6 , 18] . Ty1 is the most abundant and active of these LTR-retrotransposons , with 31 copies in the haploid genome of the common reference strain BY4741 and a mobility rate of approximately 1x10-7 to 1x10-5 per Ty1 element in each cell generation [19] . Retrotransposition initiates with transcription of the element ( Fig 1A & 1B ) . The U3 region of the 5′ LTR contains promoter sequences recognized by DNA binding transcriptional activators , as well as a TATA box sequence ( TATAAAAC ) ( Fig 1B ) . A second putative TATA box sequence at the 5’ LTR , with sequence TATTAACA , does not conform to identified functional TATA element sequences [18 , 20] . The 5 . 7kb Ty1 transcript contains two partially overlapping open reading frames , GAG and POL , the latter of which is translated only when a specific +1 frameshift event occurs in the overlapping region ( Fig 1B ) . This frameshifting event enables production of two translational products , the p49-Gag protein and the p199-Gag-Pol polyprotein . Ty1 protease , a factor that is encoded in the POL ORF , processes p49-Gag to p45-Gag as an integral part of Ty1 protein maturation . The Gag-Pol polyprotein is processed to yield p45-Gag , protease , reverse transcriptase and integrase . In addition to its protein coding function , the Ty1 transcript serves as a template for reverse transcription of the element . Ty1 protein processing and reverse transcription of Ty1 RNA occur within a cytoplasmic capsid of Gag protein known as the virus-like particle ( VLP ) ( Fig 1A ) [18] . Following reverse transcription , Ty1 cDNA is transported back to the nucleus and integrated into the host genome through the activity of integrase ( Fig 1A ) [21 , 22] . Ty1 elements also produce a second transcript that initiates at +1000 bp in the Ty1 element , 762 bp downstream of the Ty1 transcription start site ( TSS ) ( Fig 1B ) . This transcript , now termed Ty1i , is readily observed in spt3 and snf5 mutants in which transcript levels of full-length Ty1 are severely reduced , but is difficult to detect against the background of Ty1 in wild type cells [23–25] . Ty1i RNA is important in copy number control ( CNC ) , which is defined by a copy number-dependent decrease in Ty1 retrotransposition observed both in S . cerevisiae and its close relative , S . paradoxus [24 , 26 , 27] . CNC is enforced by a dominant , trans-acting regulatory protein known as p22-Gag that is translated from Ty1i RNA [24 , 28–31] . This truncated Gag protein , as well as its proteolytically processed form , p18-Gag , retains the ability to associate with p49- or p45-Gag . However , incorporation of p22-Gag into the VLP ( where it is processed to p18-Gag ) disrupts nucleocapsid formation , thereby halting Ty1 protein maturation and production of Ty1 cDNA [24 , 28 , 29 , 32] . Ty1 relies extensively on autoregulatory factors and host factors to successfully complete its mobility cycle and limit its mobility so as not to destabilize the host genome [18 , 33] . Host factors that regulate Ty1 mobility include subunits of the Mediator transcriptional co-activator complex [34–39] . Mediator plays a crucial role in the formation of the PIC at all Pol II transcribed genes , in part by acting as a bridge between DNA binding transcriptional activator proteins and the RNA Pol II transcription machinery [40–47] . In Saccharomyces cerevisiae , Mediator is a 1 . 4 MDa complex composed of 25 individual subunits organized into four modules ( Fig 2A ) [48–51] . The core Mediator complex contains the “head , ” “middle , ” and “tail” modules , while a fourth kinase module is transiently associated with the core complex in a context-specific manner [52] . The tail domain is generally responsible for Mediator’s association with transcriptional activator proteins , while the head and middle are involved in association of RNA Polymerase II ( Pol II ) and pre-initiation complex ( PIC ) formation [42 , 50] . Several studies have implicated individual Mediator subunits as either activators or repressors of Ty1 retromobility . While no concerted effort to determine the role of all Mediator non-essential subunits on Ty1 mobility has been reported , individual studies have reported that loss of individual Mediator subunits affects a step in retrotransposition between transcription and integration while having minor effects on Ty1 transcript levels [18 , 34–39] . This conclusion is strikingly incongruous with Mediator’s canonical role as a transcriptional regulator . To date , no mechanistic characterization of Mediator’s positive and negative influences on Ty1 mobility has been undertaken , and an explanation for its apparent post-transcriptional function in Ty1 mobility has been elusive . In this study , we systematically determine the effects of deleting non-essential subunits of the Mediator complex on various steps in Ty1 retrotransposition , from Ty1 and Ty1i RNA expression to completion of the retrotransposition event . We show that deletion of Mediator complex subunits results in substantial , module-specific effects on the level of Ty1 retrotransposition . Consistent with previous findings , we find that Mediator subunit deletions have minimal effects on the levels of Ty1 RNA and Gag protein , but do result in substantial changes in the level of unintegrated cDNA that correspond to changes in the level of retrotransposition . We also report that deletion of individual subunits of the tail module triad , Med2-Med3-Med15 , increases recruitment of Mediator and Pol II to a secondary promoter within the Ty1 GAG ORF . Use of this internal promoter results in expression of Ty1i RNA , whose translation product , p22-Gag , is a potent inhibitor of VLP formation and Ty1 cDNA synthesis . In contrast , loss of Mediator head module subunits Med18 or Med20 decreases Mediator association with the internal Ty1i promoter and results in increased Ty1 mobility . Thus , Mediator subunits control a post-transcriptional step in Ty1 mobility by modulating transcription of Ty1i RNA . Based on these observations , we propose a mechanism in which Mediator regulates Ty1 retromobility by controlling the balance between utilization of Ty1 and Ty1i promoters .
Previous genetic screens determined that individual Mediator subunits influence Ty1 retromobility through post-transcriptional mechanisms; however , the screens employed different assays for retromobility , and differed in their identification of specific Mediator subunits contributing to Ty1 retromobility [34 , 35 , 37 , 38 , 53] . To systematically investigate the role of all non-essential subunits of Mediator in Ty1 retromobility , a collection of strains , each containing a deletion of a non-essential Mediator subunit , was generated from a BY4741 progenitor strain containing a chromosomal his3AI-marked Ty1 element ( S1 Table ) . These strains were then subjected to an established quantitative retromobility assay in which cells that sustain a retromobility event are detected as His+ prototrophs ( Fig 2B ) [19] . A mutant lacking the SAGA complex component Spt3 was chosen as a negative control for Ty1 retromobility due to the well-characterized requirement for Spt3 in Ty1 transcription and mobility [25 , 54] . Results from this assay indicate that Mediator influences Ty1his3AI retromobility in a profound , module-specific manner ( Fig 2C ) . Deletion of genes encoding subunits in the head or middle module increased Ty1his3AI retromobility approximately 100-fold . Conversely , deleting any subunit from the Med2-Med3-Med15 tail module triad resulted in a retromobility level that was less than 1% of that of the wild-type strain , and below detection limits . The lack of Ty1his3AI retromobility in these mutants is not due to the loss of HIS3 expression , since His+ prototrophs form readily when Ty1his3AI is driven by a heterologous promoter in med3Δ and med15Δ mutants , as demonstrated below . The Med2-Med3-Med15 subunits are direct targets of DNA-binding activator proteins [42 , 47 , 55 , 56] , and deletions of these subunits exhibit similar phenotypes [56–58] . In contrast , two other tail module subunits that exhibit distinct phenotypes when deleted , Med5 and Med16 , do not appear to affect Ty1 retromobility . The transiently associated kinase module also does not substantially influence Ty1 retromobility . Kinase module deletion strains were omitted from further analysis in this work . The disparate effects of deleting subunits in different modules is consistent with previous data indicating that the Mediator complex regulates gene expression in a module-dependent manner [58] . Given Mediator’s role as a transcriptional co-activator , we first sought to determine whether the changes in Ty1 mobility observed in the Mediator subunit deletion strains were caused by altered Ty1 transcript levels . Changes in Ty1 RNA levels in head , middle or tail module gene deletion strains relative to the wild-type strain , visualized by northern blot analysis , were modest and not statistically significant ( p>0 . 1 , one-way ANOVA ) ( Fig 3A ) . These data indicate that Mediator head , middle and tail subunits do not regulate Ty1 retromobility by affecting the steady-state level of genomic Ty1 transcripts . A series of non-coding antisense transcripts are also expressed from Ty1 elements via an internal promoter ( Fig 1B & 1C ) . These transcripts initiate from distinct positions within the first 700 bp of the Ty1 element , and their expression is enhanced in certain hypomobile strains , most notably in an spt3Δ mutant [27 , 31] . Levels of Ty1 antisense transcripts ( Ty1AS RNA ) were measured to determine whether Mediator was altering expression of these transcripts and thereby influencing Ty1 mobility ( Fig 3A ) . A modest increase of approximately 2-fold in Ty1AS RNA was observed in the Mediator tail subunit deletion strains relative to the wild-type strain ( Fig 3A ) . An increase of a similar magnitude was observed in the hypermobile med31Δ strain , while a 10-fold increase was measured in the hypomobile spt3Δ strain . Based on the lack of correlation with altered retromobility , Ty1AS RNA expression is not likely to be a major contributing factor to tail-mediated repression of Ty1 retromobility . To independently confirm that the substantial changes in retromobility in Mediator subunit deletion strains were not a result of minor changes in Ty1 RNA levels , or in alterations to Ty1 polyadenylation that would result in translational defects , the levels of Gag , the major product of Ty1 RNA translation , were measured using western blot analysis ( Fig 3B and S1 Fig ) . As with Ty1 RNA and Ty1AS RNA , there were only moderate ( <2-fold ) changes in the levels of Gag protein in Mediator subunit deletion strains relative to the wild-type strain . Together , these data support the argument that Mediator regulates a post-transcriptional step in Ty1 retromobility , and does so without altering the steady-state level of Gag ( Fig 3A & 3B ) . Assessing the level of unintegrated cDNA provides an indication of the efficiency with which Ty1 proteins and RNA have assembled into functional VLPs and carried out reverse transcription . This assay involves the electrophoretic separation of SphI-digested genomic DNA , which is subsequently probed for Ty1 sequences at the 3′ end of POL [15 , 37] , allowing for the visualization of bands representing the junction between the 3′ end of each Ty1 element and flanking genomic DNA . Differences in the size of the bands are due to the different location of SphI sites in DNA flanking Ty1 at different locations . In this assay , the smallest band represents unintegrated Ty1 cDNA because of the absence of flanking genomic DNA . We performed this assay to compare the ratio of unintegrated cDNA to genomic Ty1 DNA in wild-type yeast to that in Mediator mutants . We observed increased cDNA levels for med18Δ , med20Δ , med31Δ , and med1Δ mutants , while loss of tail module triad subunits Med2 , Med3 , or Med15 reduced cDNA to nearly undetectable levels ( Fig 3C ) . Thus , both hyper- and hypomobile mutants exhibited cDNA levels that correlated well with changes in Ty1his3AI mobility ( Fig 2C ) . The magnitude of changes in Ty1 cDNA levels are not as great as those of Ty1 retromobility because the cDNA assay measures steady-state levels , whereas the retromobility assay measures accumulated events . Together , these data indicate that Mediator core subunits regulate a post-transcriptional step in Ty1 mobility , such that deletion of Mediator complex genes alters the accumulation of Ty1 cDNA without substantially altering overall Ty1 transcript or Gag protein levels . Mediator mutants might affect Ty1 activity indirectly by altering the expression of a host factor ( s ) that controls a post-transcriptional step in retrotransposition . If this were the case , similar effects on retromobility would be observed whether Ty1his3AI RNA was expressed from the LTR promoter or a heterologous promoter . Alternatively , Mediator mutants could affect Ty1 activity in cis by acting at the genomic Ty1 promoter or an internal Ty1 promoter . In this case , the effects might be suppressed by expression of Ty1his3AI RNA from a heterologous promoter . To differentiate between these alternatives , the Ty1 promoter in the U3 region of the LTR was swapped for the transcriptionally robust TEF1 promoter sequence in a CEN-plasmid-based Ty1his3AI element ( Fig 4A ) . The TEF1 promoter was chosen based on findings that deletions of non-essential Mediator subunits do not alter TEF1 expression substantially [59 , 60] . The effects of Mediator subunit deletions on retromobility of the TEF1 promoter-driven Ty1his3AI element on a CEN-plasmid versus the LTR promoter-driven Ty1his3AI element on a CEN-plasmid were then compared ( Fig 4B ) . Deletion of the tail module triad gene MED3 reduced retromobility of the plasmid-borne Ty1his3AI element more than 600-fold ( Fig 4B ) , which is consistent with the effect of this deletion on a chromosomal Ty1his3AI element ( Fig 2C ) . In contrast , retromobility of the PTEF1-driven Ty1his3AI element was reduced 6-fold or less by deletion of MED3 or MED15 ( Fig 4B ) . Together , these results indicate that the Mediator tail triad module controls Ty1his3AI retromobility in an LTR-dependent fashion . Middle or head module gene deletions increased retromobility of the plasmid-borne Ty1his3AI element to a lesser extent than that of the chromosomal Ty1his3AI element ( ≤12-fold versus >100-fold; compare Fig 4B and Fig 2 ) , possibly because retromobility of Ty1his3AI on the CEN-plasmid is already substantially higher than that of the chromosomal element . Correspondingly , deletion of the head subunit gene MED20 or middle subunit gene MED31 or MED1 increased retromobility of the plasmid-driven PTEF1-Ty1his3AI and Ty1his3AI elements to a similar extent ( Fig 4B ) . Consequently , we could not determine from these data whether Mediator head and middle subunits regulate Ty1his3AI retromobility in cis or in trans . Regulation of retromobility by the Mediator tail module triad when Ty1his3AI RNA is driven from the LTR versus the TEF1 promoter suggests that Mediator acts in cis on Ty1his3AI . To further test this idea , the CEN-plasmid bearing PTEF1-Ty1his3AI was introduced into congenic wild-type and med15Δ strains that harbor a chromosomal Ty1kanMXAI element . The effect of the med15Δ deletion on retromobility of the chromosomal Ty1kanMXAI element , which is measured by determining the frequency of G418R prototroph formation , was compared to its effect on retromobility of the plasmid-borne PTEF1-Ty1his3AI element ( Fig 4C ) . Deleting MED15 had a modest effect on PTEF1-Ty1his3AI retromobility , but it reduced Ty1kanMXAI retromobility >200-fold to a level below detection . The differential effects of deleting MED15 on these distinct Ty1 elements in the same cells suggested to us that some dependence on the LTR promoter was involved; indeed , PTEF1-driven Ty1his3AI RNA is selectively and significantly increased in a med15Δ mutant ( S2 Fig ) . Taken together , these data suggest that Mediator , via its tail module triad , functions directly at Ty1his3AI to enhance retromobility by a mechanism that depends at least partially on the U3 region of the Ty1 LTR . The observation that the Mediator tail module triad is a potent regulator of retromobility that functions in cis at Ty1 without significantly influencing Ty1 RNA levels led us to consider the possibility that Mediator regulates the balance between the expression of the internal Ty1i transcript , which encodes a dominant negative inhibitor of retromobility , and expression of the genomic Ty1 transcript . The post-transcriptional effects on Ty1 retromobility caused by increasing Ty1i expression [24] are consistent with those observed in Mediator tail triad deletion mutants . We therefore sought to determine whether expression of Ty1i RNA is altered by deletion of Mediator subunits . Ty1i RNA is not easily detected in northern blots of total cellular mRNA ( Fig 3A ) , as it is obscured by the highly abundant Ty1 transcript [24] . Only about 15% of Ty1 RNA is polyadenylated [35]; therefore polyA+ RNA of the wild-type strain and Mediator deletion strains was subjected to northern analysis to achieve better separation of the Ty1 and Ty1i transcripts , as previously reported [24] . This analysis revealed an increase in Ty1i RNA in all three mutants with a tail module triad gene deletion , consistent with reduced retromobility in these mutants , while med5Δ yeast , which showed no change in retromobility , had Ty1i RNA levels similar to those observed in wild-type yeast ( Fig 5A ) . In contrast , the level of genomic Ty1 RNA was altered only minimally by deletion of a tail module triad gene or MED5 . Neither the level of Ty1i RNA nor the level of Ty1 RNA in head and middle subunit deletion strains was substantially altered relative to those in the wild-type strain ( Fig 5A ) . Given the low level of Ty1i RNA in the wild-type strain , we were unable to determine whether deletion of a head or middle subunit results in a decrease in Ty1i RNA that could account for the hypermobility phenotypes of these mutants . Previous work from the Morse lab compared genome-wide occupancy of Pol II in wild type and med3Δ med15Δ yeast , although not in single tail module triad gene deletion mutants [61] . The effects of the med3Δ med15Δ mutation on growth and genome-wide transcription are similar to those of the single Mediator tail module triad subunit deletions [57] , and so we used this data to compare Pol II occupancy at all Ty1 elements in wild type and med3Δ med15Δ yeast ( Fig 5B ) . The results show that Pol II occupancy in med3Δ med15Δ yeast is reduced exclusively in the first 1 kb of the Ty1 element . Pol II occupancy in the med3Δ med15Δ double mutant and wild-type strain became equivalent near the Ty1i transcription start site ( TSS ) and remained so until the transcription end site ( TES ) of both Ty1 sense-strand transcripts ( Fig 5B ) . ( Close inspection of Fig 5B shows that Pol II ChIP signal at the Ty1i TSS region is actually steeply increasing in med3Δ med15Δ yeast , reaching levels equivalent to those seen in wild type yeast at about +200 relative to this TSS . This is consistent with occupancy becoming equivalent close to the TSS , because the fragments analyzed in ChIP experiments vary from 200 to about 500 bp in length , so that decreased occupancy at a particular location results in decreased ChIP signal on either side of that location [62] ) . The decreased occupancy by Pol II over the first ~700 bp of Ty1 in yeast lacking tail module triad subunits indicates that the Mediator tail module plays a critical role in establishing the relative occupancy of Pol II at the Ty1 and Ty1i TSS , consistent with the altered ratio of Ty1i RNA to Ty1 RNA levels observed in tail module triad mutants ( Fig 5A ) . The elevated Ty1i RNA levels observed in Mediator tail module triad mutants would be predicted to give rise to increased levels of the retromobility inhibitor , p22-Gag and its cleavage product , p18-Gag . To test this prediction , we performed western blotting using an anti-p18 polyclonal antibody [24] . Elevated levels of p22-Gag were observed in med2Δ and med3Δ yeast , in accord with the increased Ty1i levels in these mutants , whereas virtually no p22-Gag was detected in the wild-type strain or Mediator head and middle module mutants ( Fig 5C ) . Unexpectedly and in contrast to previous observations in Saccharomyces paradoxus [24] , levels of p18-Gag were similar in all strains and were not correlated with p22-Gag or Ty1 retromobility levels in the wild-type strain or any Mediator subunit deletion strain . Nonetheless , these data demonstrate that diminished Ty1 retromobility is accompanied by elevated Ty1i RNA and p22-Gag levels in Mediator tail module mutants . Taken together with the increased occupancy of Pol II at Ty1i relative to Ty1 in med3Δ med15Δ yeast ( Fig 5B ) and previous findings that p22-Gag blocks post-transcriptional steps in Ty1 mobility , these results suggest that loss of Mediator tail module triad subunits blocks Ty1 retrotransposition at a post-transcriptional step by causing increased Pol II recruitment to the Ty1i promoter relative to the Ty1 promoter . Results presented so far indicate that the opposing effects of deletion of subunits from the Mediator head module ( med18Δ and med20Δ ) and tail module triad ( med2Δ , med3Δ , and med15Δ ) on Ty1 mobility occur via mechanisms that operate at a similar stage in the Ty1 life cycle ( Fig 3 ) . Decreased mobility in mutants lacking subunits from the tail module triad is accompanied by increased expression of Ty1i , and similarly decreased occupancy of Pol II in the first 1kb of Ty1 , suggesting a causal mechanism ( Fig 5 ) . However , Ty1i RNA and p22-Gag levels are very low in wild type yeast , and we have not been able to detect any decrease of this already low level in med18Δ or med20Δ mutants . To investigate the mechanism by which tail module triad and head module deletions exert opposing effects on Ty1 mobility , we used ChIP-seq to examine Mediator association with the proximal promoter regions of Ty1 and Ty1i in wild type yeast and Mediator mutants . For this purpose , we used kin28 Anchor Away ( kin28-AA ) strains harboring appropriate deletions of Mediator subunits . The kin28-AA conditional mutation allows eviction of Kin28 from the nucleus to the cytoplasm by addition of rapamycin [63 , 64] . Eviction of Kin28 prevents phosphorylation of Ser5 of the Pol II C-terminal domain ( CTD ) . This impedes release of engaged Pol II from the proximal promoter of active genes , thus stabilizing association of Mediator , allowing robust ChIP signals to be observed at gene promoters [64 , 65] . ChIP-seq against myc-tagged Med17 from the head module or Med15 from the tail module reveals peaks of Mediator association at both Ty1 and Ty1i TSS in wild type yeast ( Fig 6 and S3 Fig ) . In a med2Δ med3Δ med15Δ mutant , Med17 association with the Ty1 TSS is decreased relative to its association with the Ty1i TSS ( Fig 6 ) , consistent with increased Ty1i RNA levels and increased occupancy by Pol II at Ty1i compared to Ty1 ( Fig 5B and S4 Fig ) in tail module triad deletion mutants . In contrast , occupancy by Med17 ( from the head module ) and Med15 ( tail ) at Ty1i is virtually eliminated in med18Δ ( S3 Fig ) and med20Δ ( Fig 6 ) mutants , while Ty1 occupancy remains robust . Western blotting showed that Med17-myc expression was about 1 . 6-fold greater in med18Δ and med20Δ mutants and about 2 . 3-fold greater in med2Δ med3Δ med15Δ yeast than in wild type yeast ( S5 Fig ) . We were unable to reliably detect Med15-myc in western blots , possibly due to poor transfer of this large protein . These results suggest that the relative occupancy levels by Mediator , and presumably by the PIC , at Ty1 and Ty1i proximal promoters are dictated by Mediator subunits , and play a critical role in governing Ty1 mobility . Our results indicate that the presence of the Mediator tail module triad prevents expression of Ty1i RNA . We considered two mechanisms by which this might occur . First , tail-module dependent recruitment of Mediator to the Ty1 promoter could lead to enhanced Ty1 transcription , and this could repress the downstream Ty1i promoter by read-through effects . In this mechanism , the polymerase moving from the Ty1 TSS could disrupt transcription factor or PIC binding to the Ty1i promoter [66–68] . In the second mechanism , the Mediator tail could act as a direct repressor of the Ty1i promoter . To distinguish between these two mechanistic possibilities , we asked whether increased Ty1i expression was still observed in tail module triad deletion mutants under conditions where transcription from the Ty1 promoter was strongly repressed . To this end , we employed a plasmid-based Ty1 element under control of the GAL1 promoter , which is strongly repressed in glucose medium ( Fig 7 ) [69] . The 2μM-plasmid-borne GAL1-Ty1 element ( pGTy1 ) that we used also has an internal deletion within the POL ORF , which facilitates resolution of Ty1ΔPOL RNA from genomic Ty1 mRNA , thus permitting a direct comparison between endogenous Ty1 RNA and plasmid-derived Ty1ΔPOL RNA by northern blot or single strand cDNA synthesis followed by PCR analysis . We confirmed that Ty1ΔPOL and Ty1iΔPOL RNAs are expressed at very low levels in cells grown in glucose by using a PCR assay that distinguishes these transcripts from endogenous Ty1 RNA . To this end , a reverse primer spanning the ΔPOL deletion was used with either of two forward primers located 250bp upstream or 250bp downstream of the Ty1i TSS ( Fig 7 , top ) . The upstream ( blue in Fig 7 ) primer amplifies only Ty1ΔPOL cDNA , while the Ty1i primer ( red in Fig 7 ) amplifies both Ty1 and Ty1i products . Use of these primers precluded use of real time PCR; instead , aliquots from the reactions were removed at two cycle intervals for analysis by gel electrophoresis ( S6 Fig ) . Results from two replicate experiments using this strategy are depicted in Fig 7B . Taking into account the differential amplification observed using a genomic DNA control ( Fig 7B , bottom panel , and S6 Fig ) , Ty1ΔPOL transcript levels appear lower than or comparable to levels of Ty1ΔPOL plus Ty1iΔPOL transcripts . More importantly , we estimate that these transcripts are present at less than 1% of the abundance of ACT1 mRNA ( see Methods ) . We conclude that pGTy1ΔPOL is transcribed at very low levels in cells grown in glucose medium , as expected . To examine the effect of Mediator subunit deletions on Ty1i RNA expression from pGTy1ΔPOL , levels of Ty1 and Ty1i RNA from endogenous Ty1 elements and Ty1ΔPOL and Ty1iΔPOL RNA from pGTy1ΔPOL were measured by northern blotting ( Fig 7C and 7D ) . Deletion of Mediator subunits had little effect on levels of endogenous genomic Ty1 RNA , as expected . Ty1i and Ty1iΔPOL transcripts were barely detectable in the wild-type strain and head and middle subunit deletion strains; however , Ty1i and Ty1iΔPOL RNAs were markedly increased in strains lacking the tail module subunits , MED2 or MED3 ( Fig 7C and 7D ) . Therefore , disruption of the Mediator tail increases Ty1iΔPOL RNA expression , even when expression from the upstream Ty1 promoter is repressed . This indicates that the tail module triad does not suppress expression of Ty1i RNA via read-through inhibition from the upstream Ty1 promoter . Taken together with the altered occupancy of Pol II and Mediator over Ty1 seen in yeast lacking tail module triad subunits ( Fig 5B and Fig 6 ) , these results suggest that the Mediator tail module triad acts to direct PIC formation preferentially to the Ty1 TSS relative to the Ty1i TSS .
Previous studies provided evidence that loss of various non-essential Mediator subunits affected Ty1 mobility [34–39 , 53] , but they did not systematically characterize the role of Mediator in this process , nor did they provide an explanation for how a transcriptional activator could exert its effects post-transcriptionally . By examining the effects of deletion of each non-essential Mediator subunit on Ty1 retromobility , we show that Mediator functions as both an activator and repressor of Ty1 retromobility ( Fig 2B ) . Effects of Mediator subunit deletions on retromobility correlated well with alterations in Ty1 cDNA levels , but not with changes in Ty1 RNA levels or Gag protein levels , as shown previously for some subunit deletions [35–38] . These findings led us to examine the effect of Mediator subunit deletions on expression of Ty1i , which encodes a dominant inhibitor of Ty1 cDNA synthesis [24] . We found that Ty1i expression was increased in mutants lacking tail module triad subunits , in which retrotransposition is reduced to undetectable levels . Relative occupancy of Pol II at Ty1 and Ty1i in med3Δ med15Δ yeast was altered in favor of Ty1i , and Mediator occupancy in yeast lacking tail module triad subunits was also altered to favor the Ty1i promoter . Taken together , these findings indicate that loss of tail module triad subunits alters the balance of utilization of Ty1 and Ty1i promoters , resulting in increased Ty1i/Ty1 RNA and concomitant inhibition of retromobility . Deletion of head and middle module subunits had opposite effects to those seen with tail module triad subunit deletions , namely , increased retrotransposition and Ty1 cDNA levels . It thus seemed likely that these mutations also exerted their effects by altering the balance of expression between Ty1 and Ty1i . The low levels of Ty1i present in wild type cells made determination of any reduction in these levels challenging; furthermore , only a very small fraction of a given population of yeast cells undergo retrotransposition , and currently there is no way to assess the molecular properties of that specific subpopulation . Nonetheless , we were able to observe altered Mediator occupancy in favor of Ty1 relative to Ty1i in med18Δ and med20Δ mutants , supporting the idea of a common mechanism . These findings are summarized in the model depicted in Fig 8 . The Ty1 LTR contains a TATA box within its U3 region ( Fig 1 ) , and Ty1 expression is Spt3-dependent ( Figs 3 & 5 ) [25] , indicating that Ty1 belongs to the SAGA-dominated class of genes . This class is enriched in highly regulated genes such as stress-response genes , and is characterized by a promoter structure that is distinct from that of the largely constitutively-active , TATA-less , TFIID-dominated genes [20 , 70] . In contrast to Ty1 , Ty1i is transcribed in spt3Δ yeast and is therefore not dependent on SAGA; furthermore , the region upstream of the Ty1i TSS lacks any consensus TATA element , indicating that Ty1i belongs to the class of TFIID-dominated genes . Genes whose activation depends on the Mediator tail module triad are relatively enriched for TATA-containing , SAGA-dominated family members [57]; in accordance with this observation , we propose that mutants lacking tail module triad subunits have reduced efficiency of PIC formation at the Ty1 promoter ( Fig 6 ) , which may permit increased utilization of the TATA-less Ty1i promoter . Alternative explanations are possible; for example , loss of the tail module may confer a general up-regulation of TFIID-dominated promoters , including that of Ty1i . Conversely , we propose that the TATA-less Ty1i promoter depends on head and middle module subunits for Mediator association and PIC formation , consistent with our Mediator ChIP-seq results . Finally , we suggest that replacing the TATA-containing Ty1 promoter with the strong , TFIID-dominated TEF1 promoter allows Ty1 transcription to be dominant over transcription from the Ty1i promoter even when tail module triad subunits are deleted , so that no increase in Ty1ihis3AI RNA is observed for this reporter ( S2 Fig ) . Our model and our ChIP-seq results indicate substantial Mediator occupancy at both the Ty1 and the Ty1i promoter in wild-type yeast , despite the apparent differences in transcriptional output from these two promoters . There are several possible explanations for this disparity . First , Mediator occupancy was measured under conditions of Kin28 depletion , potentially obscuring any differences caused by different efficiencies in facilitating PIC formation and productive transcription at the two promoters . Second , Ty1i RNA may be substantially less stable than Ty1 RNA . Indeed , Ty1 RNA is known to be unusually stable [71 , 72] . Third , transcription from Ty1i may be inefficient in spite of the apparent presence of Mediator at the Ty1i proximal promoter . This may be altered in Mediator head and middle module mutants , or it may be that a minor change in Ty1i RNA abundance leads to a significant change in the small population of cells that undergo transposition . It is also possible that the head and middle modules additionally influence other steps in the retrotransposition life cycle . For example , loss of head and middle module subunits may alter Ty1 RNA localization in such a way as to prevent retrotransposition . This possibility is supported by observed increases in ribonucleoprotein foci known as retrosomes in med20Δ , med18Δ , med9Δ , and med1Δ mutants [35] . The molecular mechanism underlying the contrasting dependence of Ty1 and Ty1i promoters on Mediator subunits remains to be determined , but is likely to involve Ty1 promoter elements that are present upstream of the Ty1i proximal promoter . Ty1 possesses an unusual promoter architecture that includes transcription factor binding sites both upstream of the Ty1 TSS and sites within the ORF , but upstream of the Ty1i TSS ( Fig 1C ) [18] . The latter region includes sites for Ste12 , Tec1 , Tye7 , Mcm1 , Rap1 , and Tea1; intriguingly , Med15 has been shown to negatively regulate activation of a reporter gene by this intra-ORF region via the Mcm1 site [73] . Conceivably , interaction of Mediator with this region may be sufficient to inhibit PIC formation at the Ty1i proximal promoter , in a manner dependent on the tail module triad , even when the Ty1 proximal promoter is inactive , as is the case for pGTy1ΔPOL in yeast grown in glucose medium . Retroelements are nearly ubiquitious in extant genomes , and their expression is governed by disparate mechanisms , including expression of truncated ORFs and altered TSS utilization [74 , 75] . We are not aware of any precedent for Mediator affecting the balance between two promoters in the way we have reported here; future studies will be required to determine whether similar mechanisms apply in other cases in yeast or other organisms . Regarding physiological relevance , retrotransposition frequency is responsive to environmental stress , and Mediator is subject to stress-dependent phosphorylation that affects gene expression [76] . This suggests a possible mechanism for regulating Ty1 retromobility during stress that deserves exploration . An unanticipated finding of this study was that retromobility of an LTR-driven Ty1his3AI element on a low copy CEN-plasmid was over 30 times higher than that of the active chromosomal element , Ty1his3AI-3114 ( Compare Fig 2B to Fig 4B ) . Because the amount of Ty1his3AI RNA relative to total Ty1 RNA is a direct determinant of the frequency of Ty1his3AI retromobility , these data may indicate that the LTR promoter is more active on the CEN-plasmid , possibly due to reduced nucleosome occupancy at the LTR [77 , 78] . Potential differences in Ty1 or Ty1i RNA expression between chromosomal and CEN-plasmid elements warrants further investigation , as these might explain why different screens for Ty1 regulators have yielded largely non-overlapping gene sets [18] . A frequent finding among studies of extrinsic and intrinsic regulators of Ty1 is that the retromobility level is altered without a change in the level of Gag protein [18] . For example , treatment of cells with the DNA damaging agents 4-Nitroquinoline 1-oxide and γ-rays , severe adenine starvation , reduced growth temperature and telomere erosion in the absence of telomerase induce Ty1 retromobility , and in some cases , increase Ty1 RNA without altering steady-state Gag levels [18 , 79–81] . In addition , treatment of cells with γ-factor , the absence of 5′ to 3′ mRNA degradation proteins , and increased Ty1 copy number all restrict Ty1 mobility with minor if any effects on Gag protein levels [15 , 24 , 82 , 83] . Increased Ty1 copy number and the absence of 5′ to 3′ mRNA decay factors leads to increased Ty1i RNA expression [24 , 31 , 33] , but the role of Ty1i RNA in these other phenomena has not yet been determined . Perhaps the Mediator complex and Ty1i/Ty1 promoter balance play a role in these regulatory processes . Saccharomyces cerevisiae lacks RNA interference [84] , which invites speculation that Mediator has assumed the function of central coordinator of Ty1 retrotransposon activity by integrating diverse extrinsic and intrinsic signals and modulating the balance between Ty1i and Ty1 expression .
Strains used in this study are derivatives of BY4741 . Genotypes of each strain are provided in S1 Table . Strains containing a chromosomal his3AI-[Δ1]-marked Ty1 element ( Ty1his3AI-3114 ) were described previously [19 , 85] . Recombination of the his3AI-[Δ1] allele with the his3Δ1 allele present in strain BY4741 derivatives does not result in a functional HIS3 allele [36] . Strains containing Mediator subunit gene deletions were constructed via lithium acetate transformation with a KanMX allele as described [86 , 87] . A chromosomal Ty1kanMXAI element was introduced into strain BY4741 by inducing the expression of a Ty1kanMXAI element fused to the GAL1 promoter on a yeast URA3-marked 2μ vector ( plasmid pGTy1kanMXAI [39] ) , as described previously [19] . Briefly , a transformant of strain BY4741 harboring plasmid pGTy1kanMXAI was grown on SC-URA 2% glucose 2% raffinose agar at 20°C . Twenty single colonies were picked and struck for single colonies on SC-URA 2% glucose and grown at 30°C . A single Ura+ colony harboring cells that had maintained the pGTy1kanMXAI plasmid throughout galactose-induction was picked from each streak and struck for single colonies on YPD and grown at 30°C . A single colony was picked from each YPD streak and used to make a 1 cm2 patch on YPD at 30°C . Each patch was replicated to a fresh YPD agar and grown for three days at 20°C , and then replicated to YPD agar containing 200 μg/ml G418 and grown at 30°C for three days . Strains harboring a chromosomal Ty1kanMXAI element were identified by the appearance of G418R papillae . A strain with a representative number of G418R papillae , JC6464 , was chosen for further analysis . A med15Δ:URA3 derivative of strain JC6464 was constructed by PCR-mediated gene disruption . Plasmid Ycp50-Ty1his3AI-[Δ1] is a URA3-CEN plasmid containing a Ty1his3AI-[Δ1] element , constructed by replacing the ClaI fragment containing the his3AI allele in pBDG633 with a ClaI fragment containing his3AI-[Δ1] and kindly provided by Dr . David Garfinkel [88] . Plasmid pBJC1250 is a LEU2- CEN vector containing a Ty1his3AI-[Δ1] element wherein the U3 region of the 5′ LTR was replaced with a TEF1 promoter ( herein referred to as PTEF1-Ty1his3AI; see Fig 5 ) . Plasmid pBJC80 , herein referred to as pGTy1ΔPOL , has been described previously [69] . Ty1 retromobility was determined as previously described [19] . Individual colonies from strains were grown in triplicate in YPD broth at 30°C overnight . Each culture was then diluted in quadruplicate by a factor of 1000 in YPD broth and grown at 20°C to an optical density beyond log growth phase . 1μL of a 1:1000 dilution of each of the resulting 12 cultures was plated on YPD agar to provide an accurate representation of the cell density . In parallel , 100μL to 1mL of each culture was plated on SC-HIS agar . All plates were grown at 30°C for 3–4 days . Mobility frequency was calculated as a ratio of the number of His+ colony forming units to the number of colony forming units in each culture as represented by the number of colonies growing on YPD agar . For strains for which no His+ prototrophs were observed , mobility was reported as an upper limit equal to the ratio of ( 1/the total number of colony forming units in all three biological replicates ) . For strains containing a plasmid Ty1his3AI element , the above protocol was modified such that cultures were grown in their respective selective media ( SC-URA or SC-LEU with 2% glucose ) at 30°C until confluent , diluted 1:1000 and grown at 20°C in YPD until confluent , and plated on their respective dropout media ( SC-URA or SC-LEU with 2% glucose ) as well as the corresponding media lacking histidine ( SC-URA-HIS or SC-LEU-HIS with 2% glucose ) . For strains containing a chromosomal Ty1kanMXAI element and plasmid pBJC1250 containing the PTEF1-Ty1his3AI element , the above protocol was modified such that cultures were grown in SC-LEU 2% glucose at 30°C overnight . Cultures were diluted 1:1000 into YPD broth and separated into 12 independent cultures that were grown for three days at 20°C . A 1:1000 dilution of each culture was plated on YPD agar to determine the number of colony forming units in each culture , and aliquots of each culture were plated on YPD agar containing 200 μg/ml G418 to determine the number of G418R colony forming units per culture , and SC-HIS 2% glucose to determine the number of His+ colony forming units per culture . Cells were grown in YPD broth at 20°C to mid-log phase . Total cellular RNA was extracted using a hot phenol/chloroform extraction protocol [89] . PolyA+ RNA was purified from 250μg to 1mg of total cellular RNA using the Magnetic mRNA Isolation Kit ( New England Biolabs ) following the manufacturer’s protocol . A 20μg aliquot of total cellular RNA was separated on a 1% Seakem GTG agarose gel as previously described [90] . Three to six μg of poly A+ RNA was separated on an 0 . 8% Seakem GTG agarose gel as previously described [90] . Separated RNA was transferred to a Hybond XL membrane ( GE Healthcare ) using a gradient of 6X SSC to 10X SSC overnight at room temperature . Synthesis of 32P-labeled RNA riboprobes was carried out in vitro using SP6 or T7 polymerase ( New England Biolabs ) . Membranes were incubated with probes in NorthernMax PreHyb Buffer ( Ambion ) at 65°C overnight . Images were scanned using a Typhoon 9400 scanner , and quantified using ImageQuant software ( Molecular Dynamics , Sunnyvale , CA ) . Cultures were grown at 20°C in YPD for one cell doubling ( OD600 0 . 3 to OD600 0 . 6 ) after dilution from overnight cultures grown at 30°C . Protein was extracted from total cell lysates as previously described [91] and resolved on a 10% SDS-PAGE gel . When resolving p18- and p22-Gag , a 15% SDS-PAGE gel was used . Protein was then transferred to a polyvinylidene difluoride ( PVDF ) membrane . Membranes were blocked in a 5% nonfat milk solution dissolved in phosphate buffered saline ( PBS ) with 0 . 1% TWEEN 20 . Membranes were then incubated in 0 . 5% nonfat milk in PBS with 0 . 1% TWEEN 20 with a 1:7500 dilution of affinity-purified anti-VLP antisera [53] to detect Gag , a 1:5000 dilution of a polyclonal antibody specific to p18-Gag ( a gift from David Garfinkel , described in [24] ) , a 1:7500 dilution of anti GAPDH monoclonal antibody ( Thermo Fisher Scientific ) , or a 1:5000 dilution of anti actin monoclonal antibody ( Abcam ) . Med17-myc was detected using a 1:1000 dilution of a monoclonal antibody to c-myc ( Sigma-Aldrich ) . Membranes were subsequently incubated with horseradish peroxidase ( HRP ) -conjugated secondary antibodies ( Millipore ) . Following terminal washes , membranes were incubated with SuperSignal West Pico chemiluminescence substrate ( Pierce , Thermo Fisher Scientific ) , and exposed to film ( Kodak ) . Antibody was stripped from membranes as described previously [92] . Images were developed on film using a Model SRX-101A Medical Scanner ( Konica Minolta ) and scanned using a Cannon MP480 scanner . Protein bands were quantified using ImageJ ( NIH ) . Quantification was performed using film exposed for different durations to ensure that measurements were done within the linear response range . Cultures were grown past log growth phase at 20°C in YPD broth . Total genomic DNA was isolated as previously described [15 , 93] , and digested with Sph I endonuclease . Digested genomic DNA was then fractionated by gel electrophoresis on a 1% GTG agarose gel and subjected to Southern blot analysis with a 32P-labeled riboprobe specific for POL as described previously [15 , 37] . Polyadenylated RNA was used to synthesize cDNA using the First Strand cDNA Synthesis Kit ( Affymetrix ) . 100ng of polyA+ mRNA was used per reaction , as were 0 . 5μM concentration each of primers specific to the ΔPOL region of pGTy1ΔPOL ( 5′-CCACCCATAATGTAATAGATCTATCGATTCTAGAC-3′ ) and to ACT1 ( 5′-ATCGTCCCAGTTGGTGACAATACC-3′ ) . Reactions were performed according to manufacturer protocols , and run at 44°C for 1 hour , followed by incubation at 92°C for 10 minutes . For comparison of levels of full-length Ty1 and Ty1i transcripts generated from pGTy1ΔPOL ( Fig 6 ) , cDNA was PCR amplified using primers binding 250 bp upstream of the Ty1i TSS ( 5′-GATTCATCCTCAGCGGACTCTG-3′ ) , or 250 bp downstream of Ty1i TSS ( 5′-AGAAGAATGATTCTCGCAGC-3′ ) , and the ΔPOL region of the element ( 5′-CCACCCATAATGTAATAGATCTATCGATTCTAGAC-3′ ) . PCR product was isolated at different cycle numbers and compared with amplification of ACT1 ( forward primer: 5′-GGTTCTGGTATGTGTAAAGCCGGT-3′; reverse primer: 5′-ATCGTCCCAGTTGGTGACAATACC-3′ ) to control for relative cDNA abundance . A control set of PCR amplifications was performed using genomic DNA prepared from the same strain . Expression of pGTy1ΔPOL relative to expression of ACT1 was estimated as follows: Amplification of ACT1 cDNA was observed at 5–6 fewer cycles than the average amplification for Ty1 and Ty1i , whereas amplification of gDNA was about equal to this average ( Fig 6B ) , thus indicating conservatively a 32-fold greater amount of ACT1 transcript . The amplicon for ACT1 is about 200 bp , while the amplicons for Ty1ΔPOL and Ty1iΔPOL are each about 3 kb , and so an equal intensity for ACT1 and Ty1ΔPOL corresponds to about a 15-fold greater molar quantity of ACT1 . Combining these ratios indicates that ACT1 transcript levels are approximately 450 times greater than those for Ty1ΔPOL . For analysis of Mediator occupancy at Ty1 elements ( Fig 8 ) , chromatin immunoprecipitation followed by high throughput sequencing ( ChIP-seq ) was performed using strains in which Mediator subunits Med15 or Med17 carried 13-myc epitope tags and which were engineered to allow Kin28 inactivation by the anchor away technique ( S1 Table ) [63] . For anchor away experiments , yeast were grown in YPD to an OD600 of 0 . 8 . Rapamycin was then added to a concentration of 1 μg/mL ( from a 1 mg/mL stock in ethanol stored at -20°C for not more than one month ) and cultures allowed to grow one hr at 30°C prior to cross-linking . ChIP against epitope-tagged Mediator subunits was carried out as described previously [61] , using 2 μg of anti-myc antibody ( clone 9E10 , Sigma ) and protein G sepharose beads for capture ( GE Healthcare ) . Library preparation for Illumina sequencing was performed using the NEBNext Ultra II library preparation kit ( New England Biolabs ) according to manufacturer’s directions . Libraries were bar-coded using NEXTflex barcodes ( BIOO Scientific ) and sequenced on the Illumina NextSeq platform at the Wadsworth Center , New York State Department of Health . Unfiltered paired-end sequencing reads were aligned to the S . cerevisiae reference genome ( Saccer3 ) by using BWA [94] . Up to one mismatch was allowed for each aligned read; reads mapping to multiple locations were retained and randomly assigned . Because full-length Ty1 elements share sequences with 266 Ty1 delta elements ( 263 of which are <350 bp in length ) , some reads from the first ~350 bp of Ty1 elements will also be mapped to these elements . Duplicate reads were removed based on paired end information . Occupancy profiles for Ty1 elements were generated by averaging the signal of all 31 Ty1 elements; thus , behavior of individual elements cannot be assessed . The occupancy is plotted on the window from 2kb upstream of TSS to 2kb downstream of TES ( Figs 5 and 6 ) . In each Ty1 element ORF , from TSS+1kb to TES-1kb , the region is divided into 100 bins and the average occupancy of each bin was calculated . For the flanking regions , the occupancy was calculated for each base pair . ChIP-seq data has been deposited in Arrayexpress under accession number E-MTAB-5824 . Data used in Fig 4B is available from the NCBI Sequence Read Archive under accession number SRP047524 . | Retrotransposons are mobile genetic elements that copy their RNA genomes into DNA and insert the DNA copies into the host genome . These elements contribute to genome instability , control of host gene expression and adaptation to changing environments . Retrotransposons depend on numerous host factors for their own propagation and control . The retrovirus-like retrotransposon , Ty1 , in the yeast Saccharomyces cerevisiae has been an invaluable model for retrotransposon research , and hundreds of host factors that regulate Ty1 retrotransposition have been identified . Non-essential subunits of the Mediator transcriptional co-activator complex have been identified as one set of host factors implicated in Ty1 regulation . Here , we report a systematic investigation of the effects of loss of these non-essential subunits of Mediator on Ty1 retrotransposition . Our findings reveal a heretofore unknown mechanism by which Mediator influences the balance between transcription from two promoters in Ty1 to modulate expression of an autoinhibitory transcript known as Ty1i RNA . Our results provide new insights into host control of retrotransposon activity via promoter choice and elucidate a novel mechanism by which the Mediator co-activator governs this choice . | [
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] | 2018 | The Mediator co-activator complex regulates Ty1 retromobility by controlling the balance between Ty1i and Ty1 promoters |
Respiratory Syncytial Virus ( RSV ) is a highly pathogenic member of the Paramyxoviridae that causes severe respiratory tract infections . Reports in the literature have indicated that to infect cells the incoming viruses either fuse their envelope directly with the plasma membrane or exploit clathrin-mediated endocytosis . To study the entry process in human tissue culture cells ( HeLa , A549 ) , we used fluorescence microscopy and developed quantitative , FACS-based assays to follow virus binding to cells , endocytosis , intracellular trafficking , membrane fusion , and infection . A variety of perturbants were employed to characterize the cellular processes involved . We found that immediately after binding to cells RSV activated a signaling cascade involving the EGF receptor , Cdc42 , PAK1 , and downstream effectors . This led to a series of dramatic actin rearrangements; the cells rounded up , plasma membrane blebs were formed , and there was a significant increase in fluid uptake . If these effects were inhibited using compounds targeting Na+/H+ exchangers , myosin II , PAK1 , and other factors , no infection was observed . The RSV was rapidly and efficiently internalized by an actin-dependent process that had all hallmarks of macropinocytosis . Rather than fusing with the plasma membrane , the viruses thus entered Rab5-positive , fluid-filled macropinosomes , and fused with the membranes of these on the average 50 min after internalization . Rab5 was required for infection . To find an explanation for the endocytosis requirement , which is unusual among paramyxoviruses , we analyzed the fusion protein , F , and could show that , although already cleaved by a furin family protease once , it underwent a second , critical proteolytic cleavage after internalization . This cleavage by a furin-like protease removed a small peptide from the F1 subunits , and made the virus infectious .
Human respiratory syncytial virus ( RSV ) belongs to the Paramyxoviridae , a family of enveloped viruses with a negative-stranded RNA genome . It is a ubiquitous human pathogen that causes severe respiratory tract infections affecting mainly children and the elderly worldwide . Despite ongoing efforts , there are no available vaccines or treatments except passive immunoprophylaxis [1] . A better understanding of virus/host cell interactions is critical for the development of new therapeutic strategies . RSV particles produced in tissue culture are heterogeneous in size and shape . Some are rounded with a diameter of 100–300 nm , others filamentous with a length up to 10 µm [2] . The nucleocapsid is helical and contains in addition to the RNA the nucleoprotein N , the viral polymerase L , its cofactor-phosphoprotein P , and the transcription processivity factor M2-1 . The matrix protein M is believed to form a layer on the inside of the viral envelope [3] . The lipid envelope is derived from the plasma membrane ( PM ) of the infected host cell , and contains three viral glycoproteins; the major attachment protein G , the fusion protein F , and a small hydrophobic protein SH . Cell attachment of RSV is mediated by G and F , which bind to cellular glycosaminoglycans [4] . That G and SH are not essential for replication in cell culture [5] , indicates that the F protein can support both attachment and fusion . In vivo , RSV targets airway epithelial cells , and in the human mucociliary epithelium it infects ciliated cells from the apical surface [6] . Previous studies on RSV entry employing a lipid-dequenching assay suggested that RSV , as most other paramyxoviruses , fuses its membrane directly with the PM of target cells [7] . That RSV entry is pH-independent is consistent with this view [8] . On the other hand , Kolokoltsov and coworkers concluded , that RSV uses clathrin-mediated endocytosis ( CME ) to infect HeLa cells because a targeted siRNA screen revealed clathrin light chain , Eps-15 , and AP-2 as important cellular factors in RSV infection [9] . In a recent publication , San-Juan-Vergara et al . [10] argued that in primary NHEB cells RSV entry is a two-step process; RSV docks to cholesterol-rich PM domains facilitating hemifusion between the viral envelope and the PM followed by endocytosis and complete fusion in endosomes . To determine the pathway of RSV entry into HeLa and A549 cells , we developed quantitative fluorescence-activated cell sorting ( FACS ) assays and complemented them with confocal microscopy to monitor cell binding of RSV , endocytosis , fusion , and infection . We tested the effects of inhibitors and other perturbants . Our results indicated that RSV infected the cells by an endocytosis-mediated mechanism that fulfilled the criteria of macropinocytosis . After uptake into macropinosomes , a second proteolytic cleavage in F served as a ‘cue’ for penetration by membrane fusion .
In our studies , we used a recombinant RSV strain called rgRSV that expresses GFP [11] enabling us to quantify infection by FACS . The virus was grown in HEp-2 cells , and to minimize exposure to broken cells , harvested from the cell supernatant before cytopathic effects were observed . The quality of virus purified by gradient centrifugation was confirmed by SDS-PAGE ( Fig . 1A ) . When the purified particles were examined by indirect immunofluorescence ( IIF ) using antibodies to the F and the N proteins , we found three different particle populations . Half of the particles represented intact virions because in addition to F ( green ) they contained N ( red ) ( Fig . 1B ) . Of these , 30% also stained with phalloidin ( blue , pseudocolored white ) indicating the presence of actin filaments as previously reported ( Fig . 1B arrowheads ) [12] . The remaining particles constituted capsid-free envelopes ( VLPs ) . They stained for F but not for N . Since we did not detect free capsids that would stain only for N or P ( data not shown ) , we used the presence of the capsid antigens to distinguish between intact RSVs and VLPs . When purified virus preparations were incubated with HeLa cells at 4°C , immunoblotting after SDS-PAGE showed that more than half of the input N and P associated with the cells indicating that RSV binding in the cold was efficient ( Fig . 1C ) . To measure infection , RSV was added to HeLa cells for 1 h and infection was continued for additional 3–8 h before measuring GFP expression by FACS ( Fig . 1D ) . The fraction of cells expressing GFP increased with time and with increasing multiplicity of infection ( moi ) . In cells infected at moi of 10 , GFP expression was detected as early as 3 h post-infection ( hpi ) ( 28% GFP positive cells ) and after 5 hpi 80% of the cells were infected . At a moi of 3 , GFP expression was delayed by about 3 h . To follow the fate of the cell-bound particles in the cold after warming to 37°C , IIF with anti-F and anti-N antibodies was used . Actin filaments were labeled with phalloidin to visualize cell boundaries . Confocal Z-stack image series in the orthogonal view revealed that after 30 min virus particles containing N and F were present not only on the cell surface but also deep inside the cytoplasm ( Fig . 2A ) . This indicated that viral particles and VLPs were endocytosed . After binding in the cold , cell-associated viruses and VLPs can be removed from the cell surface by brief incubation with trypsin in the cold that does not affect cell attachment ( Fig . 2B , 0 min ) [10] . We found that when cells after virus binding in the cold were incubated at 37°C , an increasing fraction of the cell-associated particles became trypsin resistant ( Fig . 2B , 15–60 min ) . Quantitation using spot detection software Imaris showed that after 60 min , 77% VLP and 70% RSV-containing spots were , in fact , resistant to trypsin ( Fig . 2C ) . That the total number of RSV- and VLP-containing spots decreased over time was probably caused by the accumulation of multiple particles in common endocytic vacuoles that represented single spots . Of the anti-F stained spots , 47% stained for N indicating that they were intact viruses . To confirm that RSV was endocytosed in an intact form , it was important to determine whether the endocytosed particles also contained the viral lipid envelope . Purified RSV was therefore labeled with a lipophilic fluorescent dye , DiOC , which partitions into the viral membrane . It is fixable with formaldehyde , and can be quenched by the membrane-impermeable dye , trypan blue ( TB ) [13] . After labeling , 80% of re-purified particles contained detectable DiOC ( data not shown ) . When added to cells and incubated at 37°C , the RSV-DiOC particles were visible as discrete fluorescent spots , and of these some were quenched when TB was added ( Fig . 3A ) . FACS analysis showed that , 50% of the fluorescence was resistant to TB after 30 min , and full resistance was reached in about 180 min ( Fig . 3B top ) , indicating that internalization of RSV and VLPs was rapid and complete . Importantly , when the intracellular accumulation of F and N proteins was measured in parallel ( Fig . 3B bottom ) , internalization of both antigens and DiOC ( Fig . 3B top ) followed similar kinetics . To monitor fusion of RSV with cellular membranes , we used a method developed by Sakai and coworkers [14] . In this case , RSV was labeled with two fluorescent lipids , R18 ( red ) and DiOC ( green ) . Concentrations were used at which the R18 quenches the fluorescence signal emitted by the DiOC . Therefore , when allowed to bind to cells and viewed live by confocal microscopy , the labeled viruses were initially all red ( Fig . 3C , 0 min ) . However , after about 60 min at 37°C , yellow and green intracellular spots became apparent increasing in numbers over time , because after fusion , the two lipids were diluted out and the green fluorescence of DiOC was no longer quenched by R18 ( Fig . 3C ) . Some of the spots showed a ring-like fluorescence indicating that the DiOC was localized in the limiting membrane of intracellular vacuoles . Quantitative FACS analysis showed that the dequenching of DiOC became detectable already after 30 min at 37°C ( Fig . 3D ) . It reached a half maximal level at 90 min , and plateaued after 240 min . Treatment of cells with TB during FACS analysis revealed that more than 90% of the fluorescent DiOC failed to be quenched by this membrane impermeable agent confirming that the DiOC was localized in intracellular organelles . From the time course , it was apparent that the fusion events occurred on the average 50 min after endocytosis . Our interpretation of these results was that the virus particles and VLPs that bound to the cell surface were endocytosed . Endocytosis was rapid and efficient , and the internalized viruses accumulated in endocytic vacuoles . After a lag period , the viral envelopes underwent fusion with vacuolar membranes . To bring infection into the picture , cells with virus bound in cold were transferred to 37°C . At indicated times , they were placed on ice , incubated with trypsin to strip away surface-attached RSV , re-plated and incubated for 10 additional hours to allow infection to proceed and GFP to be expressed . FACS analysis demonstrated that in cultures that had been incubated at 37°C before trypsinization , the fraction of GFP-expressing cells increased with time . Maximum infection was reached within 180 min , and the half time was around 30 min ( Fig . 3E ) . That the time course coincided perfectly with the time course of virus endocytosis ( Fig . 3B ) implied that productive infection depended on endocytosis . If RSV entry and infection depended on endocytosis as indicated by our experiments , we expected perturbants that inhibit endocytosis to block internalization and infection . In the experiments that followed , endocytosis of RSV was quantified by measuring the amount of the incoming N protein that was trypsin resistant using FACS analysis 1 h after warming . Infection was scored as a percentage of cells expressing GFP 6 hpi . It is important to mention that the dose-dependence ( Fig . S1 ) and toxicity ( data not shown ) of each inhibitor was carefully determined . For clarity for most of the inhibitors we will present data at a single concentration where we found a strong effect but low cytotoxicity . Since RSV has been reported to enter cells by CME in HeLa cells [9] , we tested five CME inhibitors: chlorpromazine [15] , pitstop-2 [16] , and three inhibitors of dynamin-2 ( dynasore , dyngo-4a and dynole-34-2 ) [17] . None of them influenced RSV endocytosis , although , internalization of the well-characterized CME cargo protein transferrin ( Trf ) was efficiently inhibited by all ( Fig . 4A ) . With the exception of pitstop-2 , which was too toxic in the prolonged infection assay , none of the agents inhibited RSV infection ( Fig . 4B ) . Infection by Semliki Forest virus ( SFV ) , a virus known to depend on CME , was efficiently blocked by all ( Fig . 4B ) [18] . RSV infection has been reported to be insensitive to an increase in endosomal pH [9] . This was confirmed by the lack of influence of bafilomycin A , ammonium chloride , and monensin on RSV infection ( Fig . 4C ) . As expected all three agents blocked infection by SFV , which needs low endosomal pH to trigger fusion [19] . The small reduction in RSV infection observed for ammonium chloride and monensin may reflect the importance of a balanced vacuolar environment for productive RSV infection . Taken together , the results indicated that RSV endocytosis and infection did not depend on CME nor did it require acidification . When RSV was bound to HeLa cells in the cold and the cells warmed to 37°C , rapid and dramatic changes in cell shape and actin distribution were observed ( Fig . 5A ) . The number of actin stress fibers decreased , the cells rounded up , and transient blebs filled with actin formed on the cell surface ( Fig . 5A , 30 min ) . These changes were clearly visualized by live cell imaging ( Movie S1 ) . The cell morphology and actin distribution returned to normal within 2 hpi . When the ratio of G ( globular ) and F ( fibrous ) actin was determined , it was found that 30 min after addition of RSV the ratio of G to F actin was 2∶1 compared to 1∶2 in control cells and in cells 2 h after virus addition ( Fig . 5B ) . This indicated that addition of virus resulted in transient actin depolymerization . Disruption of actin filaments with cytochalasin D and latrunculin A as well as filament stabilization by jasplakinolide were found to strongly reduce RSV infection , whereas SFV infection was enhanced ( Fig . 5C ) . RSV endocytosis was also significantly decreased ( Fig . 5D ) . Inhibition of Cdc42 ( pirl1 ) , inhibited RSV infection effectively , while inhibitors of Rac1 ( NSC23766 ) , RhoA ( CT04 ) and its effector ROCK ( Y27632 ) had only a moderate effect ( Fig . 5E ) . Both infection and endocytic uptake were reduced when some of Cdc42's downstream effectors were inhibited including PAK1 ( IPA-3 ) , N-Wasp ( wiskostatin ) , and moderately when Arp2/3 ( CK-869 ) was targeted ( Fig . 5E , F ) . Nocodazole and taxol that interfere with microtubules had no effect on RSV or SFV infection ( Fig . 5D ) . These results demonstrated that actin and its regulators played a critical role during RSV endocytosis and infection . F-actin was transiently depolymerized , resulting in the formation of blebs . In addition , Cdc42 , PAK1 , and N-Wasp were required for RSV internalization and infection . The formation of blebs , the involvement of actin , and the role of Cdc42 and PAK1 suggested that infectious entry of RSV occurred by macropinocytosis as recently shown for several other viruses [for reviews see [20] , [21]] . One of the characteristic features of macropinocytosis is an elevation in the uptake of extracellular fluid [22] . Indeed , when serum-starved HeLa cells were exposed to RSV , we observed that the internalization of 10 kDa dextran-AF488 , a soluble , fluorescent tracer added to the medium , increased by 50% and 120% at low and high moi , respectively , over mock treated cells ( Fig . 6A ) . The elevation was significantly higher than in serum-stimulated cells . IIF showed that majority of endocytosed viruses stained by anti-F and -P antibodies were localized in large , dextran-AF488 filled , intracellular vacuoles ( Fig . 6B ) . Macropinosome formation requires Na+/H+ exchanger ( NHE ) activity to modulate Rho GTPases at the PM [23] . Inhibition of NHE by EIPA ( an amiloride derivative ) has become one of the diagnostic criteria for macropinocytosis . EIPA inhibited RSV internalization and infection by 90% ( Fig . 6C ) . In addition , pretreatment of cells with EIPA blocked the increase in fluid phase uptake induced by RSV ( Fig . 6D ) . Taken together , these results demonstrated that RSV induces macropinocytosis and uses it for virus endocytosis and infection . Macropinocytosis is usually initiated by activation of receptor tyrosine kinases ( RTKs ) or integrins , followed by the activation of a spectrum of cellular signaling factors [24] . Accordingly , we found that RSV infection was significantly decreased by two broad range protein kinase inhibitors; staurosporine ( Ser/Thr kinases ) and a multi-target protein tyrosine kinase inhibitor , genistein ( Fig . 7C , D ) . To test whether RTKs were involved , we used a human phospho-RTK array comprising antibodies against 42 different phosphorylated RTKs . Lysates from cells exposed to RSV for 15 min , and lysates from mock-treated control cells were used as probes . The epidermal growth factor receptor ( EGFR ) was the only RTK for which activation was detected; a five-fold increase in phosphorylation compared to control ( Fig . 7A ) . When the EGFR was depleted using siRNA , greater than 50% reduction in infection was observed ( Fig . 7B ) . We found , moreover , that EGFR inhibitors ( CAS879127-07-8 , iressa ) significantly decreased RSV infection ( Fig . 7C ) . Inhibition of PI3K ( wortmannin , Ly294002 , PI-103 ) , a downstream effector of EGFR , also reduced infection ( Fig . 7C ) . EGFR inhibitors had little effect on SFV . That the PI3K inhibitors boosted SFV infection was consistent with a distinct entry mechanism for this virus . In addition , inhibition of PKC ( rottlerin , calphostin C ) decreased RSV ( Fig . 7D ) . Although the effect on SFV was smaller , it suggested a role for PKC in the entry of both viruses . Finally , since non-muscle myosin II is thought to mediate closure of macropinosomes , we tested the effects of a myosin II inhibitor ( blebbistatin ) , and a myosin light chain kinase inhibitor , ( ML-7 ) [25] . Both reduced RSV infection with little effect on SFV ( Fig . 7E ) . All the inhibitors that decreased infection also reduced RSV endocytosis ( Fig . 7C–E bottom ) . Depending on the compound , RSV internalization was reduced by 60–90% . None of the inhibitors affected RSV cell binding ( Fig . S2 ) . Thus , we concluded that infectious RSV cell entry and endocytosis were associated with activation of EGFR and its downstream signaling partners including PI3K and PKC . Combined with the requirement for myosin II , these findings were consistent with productive RSV internalization by macropinocytosis . In addition , we performed a series of experiments in A549 cells ( Fig . S3 ) . They revealed changes in actin morphology and polymerization after addition of RSV , and a role of EGFR , NHE , Cdc42 , Pak1 , and other factors similar to HeLa cells in RSV infection . That internalization and infection were clearly dependent on the same cellular processes and factors in A549 cells indicated that entry by macropinocytosis was not HeLa cell specific . Intracellular trafficking of macropinosomes is not well characterized , but it has been shown that like endosomes , they acidify and acquire Rab5 followed by Rab7 before fusing with endolysosomes [26] . Wild type ( WT ) GFP- Rab5 and GFP-Rab7 as well as various constitutively active ( C/A ) and dominant negative ( D/N ) mutants of the Rabs were transiently expressed in HeLa cells . After 15 min post warming , we observed that some of the incoming RSV colocalized with GFP-Rab5 WT positive vacuoles ( Fig . 8A ) . Colocalization was even more evident in cells expressing the C/A GFP-Rab5 mutant Q79L that exhibits enlarged Rab5-positive vacuoles that fail to undergo further maturation [27] . There was no detectable colocalization with Rab7 at this time . After 120 min , some colocalization with GFP-Rab5 WT was still observed . In cells expressing the D/N GFP-Rab5 S34N , we noted accumulation of F- and P-stained particles inside large vacuoles in the perinuclear space . Colocalization of RSV with GFP-Rab7 WT was also detected . To determine whether Rab5 and Rab7 played a role in infection , we infected cells expressing GFP-tagged constructs of Rab5 , Rab7 , and their mutants with RSV-A2 . To determine the fraction of infected cells among cells expressing GFP-tagged Rabs , we stained the cells with anti-N-AF647 . FACS analysis revealed that the D/N -GFP Rab5 ( S34N ) was the only Rab construct that caused a significant decrease in RSV infection when overexpressed ( Fig . 8B ) . We confirmed this result by imaging of rrRSV expressing red fluorescent protein ( Fig . S4 ) . Together with our imaging data , these results indicated that RSV depends on Rab5 GTP for infection but does not require Rab7 . Infectious penetration is thus likely to be determined during early stages of macropinosome maturation . It is noteworthy that expression of the C/A Rab5 ( Q71L ) , which is known to generate enlarged Rab5-containing endosomes and prevent endosome maturation and trafficking to lysosomes [27] , did not affect infection . Pretreatment of cells with PIKfyve inhibitor ( CAS 371942-96-7 ) had no effect on the RSV infection while SFV infection was decreased by 50% ( Fig . 8C ) . By generating PtdIns ( 3 , 5 ) P2 , PIKfyve is involved in the maturation of endosomes and macropinosomes [28] . This suggested that full maturation of macropinosomes was not required for RSV . These results demonstrated that after pinching off from the PM , macropinosomes containing RSV acquired Rab5 and later Rab7 . Maturation of macropinosomes involving Rab5 was evidently a critical step in infection , whereas later stages in maturation coordinated by Rab7 and PIKfyve were not essential . Since acidification of macropinosomes was not needed for infection , we speculated that RSV required some other intracellular cue to trigger fusion . The F is unique among paramyxovirus fusion proteins in having two cleavage sites for furin-like proteases generating in addition to F1 and F2 ( 50 and 20 kDa , respectively ) a soluble 27 amino acid ( aa ) peptide ( p27 ) [29] , [30] . The p27 peptide is located between F2 and F1 N-terminal to the fusion peptide in F1 ( Fig . 9D ) . We hypothesized that removal of this peptide after endocytosis might be required to activate the F protein . Experiments in which cells were pretreated with a membrane permeable furin inhibitor , dec-RVKR-CMK , prior to addition of RSV indicated that a protease was indeed involved . Dec-RVKR-CMK treatment reduced infection by about 80% ( Fig . 9A ) . That a membrane impermeable furin inhibitor , α-PDX , had no effect on infection suggested that the activating proteolysis did not occur on the cell surface . A broad range protease inhibitor leupeptin ( serine , cysteine , threonine proteases ) caused only a slight increase in infection . That the inhibition of infection by dec-RVKR-CMK involved the F protein , was confirmed using a recombinant virus strain ( RSVΔSHΔG ) that lacks the SH and G glycoproteins [5] . Infection by this mutant virus was also blocked by dec-RVKR-CMK ( Fig . 9A ) . When dec-RVKR-CMK inhibitor was applied 1 h after the virus inoculation , there was no inhibition indicating that the critical proteolytic step coincided with entry ( Fig . 9B ) . Fusion assays with R18/DiOC labeled RSV and RSVΔSHΔG revealed that dec-RVKR-CMK impaired viral fusion ( Fig . 9C ) . Its inhibitory effect was comparable to the effect of EIPA , which blocked virus internalization ( Fig . 6C ) . To confirm the presence of the p27 peptide in the purified virus , we used a targeted mass spectrometry approach based on selected reaction monitoring ( SRM ) ( Fig . 9E ) . As a negative control , we analyzed HEp-2 cells extracts used to produce the virus . In trypsin digested virus preparations , we detected 2 peptides corresponding aa 113–131 and 109–123 of F protein both spanning p27 peptide ( Fig . 9D ) . Neither peptide was present in HEp-2 control samples . SRM transitions of the targeted peptides are included as supporting information in Table S1 . Finally , SDS-PAGE was used to monitor changes in the F protein itself during entry . Blotting with an anti-F1 antibody revealed a protein band in the isolated virus migrating with a molecular weight of 50 kDa , confirming that the F protein had been cleaved at least once already in the producer cells ( Fig . 9F ) . When virus bound to cells in the cold were allowed cell enter for 1 . 5 h at 37°C , the mobility of the cell-associated F1 became faster ( 48 kDa ) indicating that further processing had occurred . The reduction in size of around 2 kDa was consistent with the loss of p27 at the N terminus of F1 . Importantly , in cells pretreated with dec-RVKR-CMK , the processing of the 50 kDa F1 protein did not occur . As expected , α-PDX and leupeptin did not influence the processing step . When the time course of F1 processing was followed , we observed that some processed F1 was detectable already 15 min after cell warming ( Fig . 9G ) . It peaked at 60 min when the precursor was fully consumed . That the amount of F1 protein gradually decreased at later time points was probably due to lysosomal degradation explaining the decrease in band intensity in the endocytosis lanes in Fig . 9F . These results indicated that to become fusion competent and infectious , the F1 protein underwent a second , highly efficient cleavage by a furin-like convertase in an endocytic compartment . The time course indicated that the processing of F1 occurred soon after endocytosis preceding fusion by about 30 min . To address whether our results applied to cell types infected by RSV in vivo , we tested polarized epithelial cells 16HBE14o obtained from human bronchial biopsies [31] . After 9 days in culture , the distribution of the tight junction marker , ZO-1 , showed that the cells had reached a polarized phenotype ( Fig . 10A ) . After making certain that RSV could infect 16HBE14o cells from the apical side ( Fig . 10A ) , we tested the effects of nine diagnostic inhibitors previously used in HeLa cell experiments ( Fig . 4B , 5CE , 6C , 7C , 9A ) . They inhibited dynamin , macropinocytosis , and furin proteases . RSV infection was quantified by an image-based approach that detected the fraction of GFP-expressing cells . In agreement with our findings in HeLa cells , inhibition of dynamin by dynasore had no effect on RSV infection in 16HBE14o cells; it even boosted infection . EIPA and seven other inhibitors of macropinocytosis decreased infection in a dose-dependent fashion indicating that macropinocytosis was involved in entry . The need for F1 processing was confirmed by dec-RVKR-CMK , which was found to decrease infection by as much as 95% .
Paramyxoviruses are generally thought to infect cells by fusing directly with the PM [32] , [33] . That paramyxovirus particles can also be endocytosed is , however , also clear . This has most recently been documented for Sendai , Nipah , RSV , Newcastle Disease viruses and for a lentivirus vector pseudotyped with measles virus glycoproteins [9] , [34] , [35] , [36] , [37] , [38] . Which of the two pathways – fusion at PM or fusion after endocytosis - leads to infection is not clear . In our experiments , we found that intact RSV was rapidly and efficiently endocytosed with capsid , glycoproteins , and the lipid envelope intact . The RSV and capsid-free VLPs accumulated within cytoplasmic vacuoles with a half time of about 30 min followed by fusion in the vacuoles with the half time of around 90 min . A sensitive fusion assay using R18/DiOC-labeled fluorescent viruses showed that fusion occurred intracellularly . No fusion of viruses with the PM was detected , and no formation of syncytia by fusion-from-without was observed even after exposing cells to high moi ( data not shown ) . The significant delay between RSV internalization and fusion could at least in part be explained by the requirement for post-endocytic cleavage of F protein . Perturbations that inhibited endocytic uptake caused a dramatic reduction in infection confirming a role for endocytosis in infection . EIPA inhibited both endocytosis and infection by 95% , and a similar level of inhibition was observed for agents that interfered with actin dynamics , a variety of kinases , and myosin II . The inhibitory effect of Rab5 D/N expression was also consistent with a role for endocytosis in infectious entry . Proteolytic activation of the F protein necessary for fusion and infection occurred in intracellular compartments . We concluded that RSV and VLPs were efficiently endocytosed , that penetration by membrane fusion occurred in endocytic vacuoles , and that at least 90% of infection was caused by endocytosed viruses . That the endocytic mechanism responsible for the entry was macropinocytic was demonstrated by the following observations: ( 1 ) Strong dependence of endocytosis and infection on actin dynamics; ( 2 ) Transient activation of blebbing , loss of stress fibers , and cell shape changes after virus addition to cells; ( 3 ) Activation of EGFR phosphorylation and involvement of this receptor and its downstream signaling factors including PI3K , PKC , Cdc42 , PAK1 , and N-Wasp in virus endocytosis and infection; ( 4 ) Elevation of fluid uptake in the presence of virus , and the internalization of viruses together with fluid phase markers into large vacuoles; ( 5 ) Inhibition of endocytosis and infection by EIPA , an inhibitor of the NHE exchangers . Taken together , the observations satisfied all the main criteria currently used to define macropinocytosis [20] , [21] . When inhibitor studies were performed using polarized physiologically relevant epithelial cells ( human bronchial epithelium cells , 16HBE14o ) , infectious entry of RSV was found to depend on the actin cytoskeleton , on cell signaling , and on a furin-like protease activity as also observed in HeLa cells . The results indicated that infection of these polarized epithelial cells monolayers derived from human bronchial tissue involved macropinocytosis and proteolytic activation of the F protein . Macropinocytosis is a clathrin-independent mechanism for the uptake of fluid and cell-associated particles within large , uncoated vesicles formed at the PM [24] . In most cell types , it is transiently induced by the activation of RTKs and downstream signaling factors [39] . In recent years , several viruses have been shown to use it for infectious cell entry . As recently reviewed [21] , the best-described examples include large viruses such as vaccinia , Ebola , adeno 35 , and Kaposi sarcoma-associated viruses . Interestingly , Nipah virus , a paramyxovirus of the Henipavirus subfamily , also belongs to this group . It uses EphrinB2A as a receptor , the phosphorylation of which is required for macropinocytic internalization and infection in CHO-K1 and VeroE6 cells [38] . We found that EGFR phosphorylation was activated by RSV , and that inhibitors such as iressa targeting this receptor blocked endocytosis and infection . It is noteworthy that iressa was only inhibitory when present during the first hour of virus cell contact confirming that its effect was entry-specific ( data not shown ) . When the EGFR was depleted using siRNA , infection decreased only by 50% suggesting that other RTKs may be able to compensate in long-term experiments . Downstream effectors of EGFR such as PI3K and PKC were also important for RSV endocytosis and infection . Infection has previously been shown to promote cell survival mediated by PI3K/NFκB . In A549 cells , PI3K activation and phosphorylation of its effector Akt occurs within 30 min after RSV addition [40] . Interestingly , it has also been demonstrated that RSV binding to NHEB cells induces PKC-α phosphorylation and translocation to the PM , while inhibition of PKC-α , as confirmed here , blocks RSV uptake and infection [41] . Our results contradicted a previous report proposing that RSV entry in HeLa cells occurs by CME [9] . The authors based their interpretation on hits such as clathrin and associated proteins in a targeted siRNA silencing screen against factors involved in endocytosis . ( The hits also included actin modulators such as PAK1 but their function in entry was not addressed ) . However , since the read-out was infection after 20 h , a role for CME in post-endocytic steps in the RSV infectious cycle could not be excluded . In our experiments , we did not observe inhibition of RSV endocytosis or infection by five different agents that block CME: chlorpromazine , dynasore , pitstop-2 , dyngo-4a , and dynol-34-2 . Importantly these agents efficiently inhibited SFV , a virus that enters via CME . That dynasore fails to inhibit RSV infection was also recently reported by others [10] . While macropinosomes are still poorly characterized , there is evidence that they undergo a maturation process similar to that of endosomes involving acidification , association with Rab5 and Rab7 , and fusion with late endosomes or endolysosomes [42] . We noted that some of the vacuoles containing RSV were in fact Rab5- and later Rab7-positive . Over-expression of a D/N Rab5 mutant inhibited infection suggesting that RSV penetration required passage through ‘early’ macropinosomes that contained Rab5 . The lack of inhibition by Rab7 mutants , a PIKfyve inhibitor , and nocodazole , all known to inhibit vacuolar maturation , implied that macropinosome maturation beyond the Rab5 positive stages was not necessary . Finally , our results provided a likely molecular explanation for the endocytosis requirement exhibited by RSV . Unlike other paramyxoviruses , the F protein in RSV has two activating cleavage sites [29] . Our mass spectroscopy analysis and western blots showed that while F in the isolated virus had been cleaved in the A-site ( RARR ) generating F2 and F1 , it had not been cleaved at the more C-terminal B site ( KKRKR ) . The second cleavage occurred after endocytosis . Inhibition of the second cleavage by dec-RVKR-CMK inhibited RSV fusion and infection . That dec-RVKR-CMK is a furin inhibitor suggested that the protease in question belonged to the furin family of convertases . The enzyme was evidently acid-independent , and active in early Rab5 –positive macropinosomes . Cleavage at the B site was most likely important because after removal the p27 peptide ‘cap’ from the N-terminus , the hydrophobic fusion peptide is rendered the most N-terminal sequence in F1 . In other class I viral fusion proteins , including other paramyxovirus F proteins , the fusion sequence is invariably N-terminal [43] . In conclusion , we confirmed that RSV requires two cleavages in its F protein for infectivity and showed that the second cleavage occurs during cells entry . Infectious entry depends on endocytosis , which the virus induces by transiently activating macropinocytosis . The virus most likely meets the enzyme that generates the second cleavage in Rab5 positive macropinosomes , and fusion occurs after some delay in these vacuoles . In this respect , the virus resembles Ebola and SARS viruses , the fusion proteins of which are also activated within endocytic vacuoles by proteases [44] , [45] . It is interesting to note that the F of Nipah virus , which has a single monobasic cleavage site in its F , is activated after endocytic uptake by cathepsin [46] , [47] . Inhibitors of cathepsins block infection , and cathepsin double knock-out cells are not infected . The infectious entry of other paramyxoviruses ( and other viruses that have pH-independent membrane fusion ) may thus be endocytosis-dependent and the mechanisms more complex than previously assumed . For RSV , it will now be important to analyze the molecular features of the entry process in more detail , to identify the protease ( s ) , and to determine whether the intracellular route is relevant also in vivo . Being inducible and highly regulated , the macropinocytic process may prove more amenable to inhibition than other endocytic mechanisms , and therefore more easily targeted by therapeutics .
HeLa , A549 and HEp-2 cells were obtained from the ATCC and cultured in DMEM supplemented with 10% fetal calf serum ( FCS ) , 1 mM Hepes , 1% Glutamax ( Invitrogen ) . Transformed bronchial epithelial 16HBE14o cells obtained from Dr . D . Gruenert [31] , [48] , were grown in RPMI 1640 medium supplemented with 10% FCS , 1% l-glutamine and 1% NEA for at least 9 days before infection . Recombinant SFV-ZsGreen stocks were kindly provided by Dr . G . Balistreri [49] . RSV-A2 was purchased from ATCC . Recombinant RSV strains expressing GFP ( rgRSV , rgRSVΔSHΔG ) or RFP ( rrRSV ) were kindly provided by Drs . M . Peeples and P . Collins [11] , [50] . RSV was produced in HEp-2 cells . Virus was collected form cell culture supernatant . The inhibitors used included: PI-103 ( Alexis Biochemicals ) , dynasore , dyngo-4a , dynol-31-2 , pitstop-2 ( Ascent Scientific ) , pirl1 ( Chembridge ) , wiskostatin ( Enzo ) , CAS 879127-07-8 , CAS 371942-96-7 , dec-RVKR-CMK , LY294002 , NSC23766 , staurosporine , taxol , wortmannin , Y27632 , α-PDX ( Calbiochem ) , bafilomycin A , blebbistatin , calphostin C , chlorpromazine , cytochalasin D , EIPA , genistein , IPA-3 , jasplakinolide , latrunculin A , leupeptin , ML7 , monensin , NH4Cl , nocodazole , and rottlerin ( Sigma ) . Antibody and fluorescent dyes that were used comprised: anti-N monoclonal MAB858-3-5 and anti-RSV goat polyclonal AB1128 ( Millipore ) , anti-F monoclonal ab43812 ( abcam ) , anti-P rabbit polyclonal ( 3-V Biosciences , Menlo Park , USA ) , anti-ZO-1 , goat anti-mouse , goat anti-rabbit , donkey anti-goat AF-conjugated , 10 kDa dextran- AF488 , phalloidin AF-conjugated , R18 , DiOC , and transferrin-AF488 ( Molecular Probes ) , goat anti-mouse , goat anti-rabbit HRP-conjugated ( Bio-Rad ) . Expression plasmids encoding GFP-tagged Rab5 , Rab7 and its mutants were kindly provided by Dr . M . Zerial ( Max Planck Institute , Dresden , Germany ) . HEp-2 cells ( 50–60% confluent ) in T175 flasks were infected with RSV ( moi 0 . 1 ) in 8 ml serum free DMEM-Hepes medium ( DMEM , 1 mM Hepes ) for 1 h at 37°C , and then inoculum was replaced with complete medium ( DMEM , 10% FCS , 1% Glutamax , 1 mM Hepes ) . After 48 h , the cell supernatant was collected , clarified by centrifugation , aliquoted , snap frozen , and stored in −80°C for experiments that did not require purified virus . For virus purification the method of Ueba [51] was modified as follows . The pre-cleared cell supernatant was centrifuged ( 20 . 000 rpm , 90 min , 4°C , SW32 Ti rotor , Beckman Optima 90-K ultracentrifuge ) through 8 ml 20% w/v sucrose cushion in HBSS-Hepes buffer ( HBSS , 25 mM Hepes ) . Pellets were gently washed , reconstituted in 20% sucrose and centrifuged in a 35 , 45 , 60% sucrose step gradient ( 35 . 000 rpm , 90 min , 4°C , SW41 rotor , Beckman Optima 90-K ultracentrifuge ) . An opalescent virus band was collected from the 35 and 45% interface , overlaid over a 30–60% continuous sucrose gradient and centrifuged ( 35 . 000 rpm , 4 h , 4°C , SW41 rotor , Beckman Optima 90-K ultracentrifuge ) . The virus fraction at about 45% sucrose was harvested , diluted in the HBSS-Hepes and pelleted by additional centrifugation ( 20 . 000 rpm , 90 min , 4°C SW41 rotor , Beckman Optima 90-K ultracentrifuge ) . Virus pellets were resuspended in HBSS-Hepes , snap frozen , and stored in −80°C . All RSV stocks were titered by infecting HEp-2 cells with serial dilutions of the virus in 96 well plates . Infection was allowed to proceed for 18–22 h at 37°C . Fixed cells were assessed by microscopy for GFP expression , or stained with RSV anti-N antibody ( AF-488 ) to detect infected cells of rgRSV or RSV-A2 respectively . Protein assay ( Bio-Rad ) was used to measure the amount of protein . When the influence of pharmacological inhibitors was tested , cells were preincubated with a medium containing inhibitors for 30 min at 37°C before virus binding or infection ( except Rho GTPase and protease inhibitors that required 1–5 h of preincubation , and pitstop-2 which was preincubated for 10 min ) . Inhibitory compounds at indicated concentration were continuously present during all of the steps of infection and internalization experiments . DiOC ( 6 µg ) was added to freshly purified RSV ( 25 µg ) in 1 ml of HBSS-Hepes buffer and incubated at room temperature while gently shaking for 1 h . To remove unincorporated dye , virus was filtered through 0 . 45 µm syringe filter ( Millipore ) and snap frozen as 100 µl aliquots , stored in −80°C , and used within 2 weeks . Labeled RSV-DiOC was titered in HEp-2 cells as described above . For the RSV-DiOC endocytosis assay , cells were detached with versene solution ( 0 . 53 mM EDTA , pH 8 . 0 ) , washed with PBS and chilled on ice . Purified RSV-DiOC , diluted in DMEM-Hepes was bound to the cells on ice for 1 h . The inoculum was washed away with PBS; the cells were resuspended in complete medium and transferred to 37°C . After desired times , the cells were fixed in 4% formaldehyde , washed , and resuspended in FACS buffer ( PBS , 0 . 2% FCS , 5 mM EDTA ) . For the FACS data acquisition ( BD Biosciences , Canto II ) samples were divided into two . One sample was acquired directly and the other after addition of trypan blue to a final concentration of 0 . 01% ( Invitrogen ) . Alternatively , after internalization cells were treated with 0 . 5% trypsin for 10 min on ice . Than washed with PBS , fixed in 4% formaldehyde , permeabilized with 0 . 01% Triton X-100 , stained with anti-F or anti-N antibody at 4°C over night followed by the AF-647 secondary antibody staining . Mean fluorescence intensity measured by flow cytometry ( BD Biosciences , Canto II ) was normalized to the mock control without bound virus . Purified RSV ( 25 µg ) was resuspended in 0 . 5 ml of HBSS-Hepes buffer before adding of DiOC ( 3 . 3 µM ) and R18 ( 6 . 7 µM ) mixture . Labeling was performed for 1 h at room temperature while gently shaking . To remove unincorporated dye , virus was filtered through 0 . 45 µm syringe filter ( Millipore ) and used freshly for the fusion assay . For the RSV-R18/DiOC fusion assay , cells were detached with versene solution , washed with PBS and chilled on ice . Purified RSV-R18/DiOC , diluted in DMEM-Hepes was bound to the cells on ice for 1 h . The inoculum was washed away with PBS; the cells were resuspended in complete medium and transferred to 37°C . After desired times , the cells were fixed in 4% formaldehyde , washed , and resuspended in FACS buffer . For the FACS data acquisition ( BD Biosciences , Canto II ) samples were divided into two . One sample was analyzed directly and the other after addition of trypan blue to a final concentration of 0 . 01% ( Invitrogen ) . Subconfluent cells were infected with RSV ( moi ∼3 ) for 1 h at 37°C . Unbound virus was washed with PBS , cells were supplemented with complete medium and infection carried for 6 h . Then , cells were trypsinized , fixed in 4% formaldehyde , resuspended in FACS buffer . GFP expression determined by FACS ( BD Bioscience , Canto II ) and analyzed by FlowJo 9 . 1 . Results of the infection in the presence of tested compounds were normalized to the solvent treated control cells . Experiments with ZsGreen-SFV infection were performed in parallel under identical conditions , with the exception that cells were harvested after 4 h . RSV ( moi ∼3 ) was bound to subconfluent cells for 1 h at 4°C . Than cells were washed with PBS , supplemented with medium containing 1 mM cycloheximide and incubated for 1 h at 37°C . Cells were moved on ice and treated with ice cold 0 . 5% trypsin for 10–20 min , washed with PBS , fixed in 4% formaldehyde , permeabilized with 0 . 1% Triton X-100 and stained with anti-N antibody over night at 4°C followed by the goat anti-mouse AF staining . The MFI ( mean fluorescence intensity ) of AF staining determined by FACS ( BD Bioscience , Canto II ) and analyzed by FlowJo 9 . 1 . Results were normalized to the solvent treated control cells and presented as arbitrary units ( A . U . ) . For the Trf-AF488 ( 2 µg/ml ) internalization was performed in parallel in identical conditions with the exception that uptake was carried for 20 min . Adherent cells were detached with versene solution , washed with PBS , chilled on ice , and pelleted . Cell pellets were resuspended in DMEM-Hepes , containing the virus inoculum , and incubated 1 h at 4°C . When the effect of inhibitors was tested , cells were preincubated with media containing inhibitor for 30 min , and inhibitor was continuously present at each step of the experiments . Cells were washed with PBS to remove unbound virus , fixed with 4% formaldehyde , permeabilized with 0 . 01% Triton X-100 , and stained with RSV anti-F over night at 4°C , followed by the secondary antibody labeling . Samples were reconstituted in FACS buffer and analyzed by flow cytometry ( BD Biosciences , Canto II ) . Mean fluorescence intensity values were normalized to the mock control without bound virus . Cells were detached with versene solution , washed with PBS , chilled on ice , and pelleted . Virus input ( purified virus stock ( moi ∼10 ) diluted in DMEM-Hepes ) was divided in two portions . One was mixed with the cells and the other left for further analysis as virus input . Virus was incubated with the cells for 1 h at 4°C . The cells were pelleted by centrifugation and the supernatant collected as an unbound viruses sample . The cell pellets were washed in PBS and resuspended in DMEM-Hepes of equal volume to the collected supernatant . The SDS-PAGE loading buffer was added to all samples , followed by denaturation for 10 min at 95°C . An equal volume of each sample was separated by the SDS-PAGE and subjected to western blot with anti-P or anti-N antibody . Western blots were quantified by densitometry with QuantityOne software ( Bio-Rad ) . Subconfluent cells seeded in 24 well plates were starved over night with serum free DMEM-Hepes medium . Purified RSV was bound to pre-chilled cells for 1 h at 4°C . Virus inoculum was washed and cells were pulsed with the serum free DMEM-Hepes medium containing 10 kDa dextran-AF488 ( 0 . 5 mg/ml ) for 15 or 60 min at 37°C . The cells were washed with 10 mM NaOAc , 50 mM NaCl pH 5 . 5 followed by PBS wash . The cells were trypsinized , fixed in 4% formaldehyde and analyzed by flow cytometry ( BD Biosciences , Canto II ) . For confocal microscopy , cells were fixed in 4% formaldehyde , permeabilized with 0 . 1% Triton X-100 , and stained with RSV specific antibodies . Images were acquired with Zeiss LSM510 laser scanning confocal microscope . For transfection , cells were trypsinized , pelleted , and electroporated with 2 µg of plasmid DNA using Amaxa ( Nucleofactor solution R , program I-13 ) . Cells were seeded on 12 mm cover glass in 24 well plates for imaging or in the 6 well plates for FACS analysis . Experiments were performed after 12 h of transient expression . The experiment was performed according to the protocol provided by the F/G actin in vitro assay kit producer ( Cytoskeleton Inc . cat # BK037 ) . In brief , subconfluent cells in 3 . 5 cm dishes were inoculated with purified rgRSV ( moi 30 ) for 30 or 120 min at 37°C . Cells were washed , lysed , and clarified by centrifugation . Supernatants were centrifuged ( 53000 rpm , 1 h , 37°C , TLA120 . 2 rotor , Beckman Optima TLX ultracentrifuge ) , the resulting supernatants were collected ( G-actin ) , and the pellets ( F-actin ) were resuspended in equal to supernatant volume of water containing actin depolymerizing reagent provided in the kit . Equal volume of each sample was resolved by SDS-PAGE and western blots were developed with an anti-actin antibody . To measure ratio of the G and F actin western blots were analyzed by densitometry with QuantityOne software ( Bio-Rad ) . For the siRNA experiment , 3000 cells were revers transfected with Lipofectamine RNAiMAX ( Invitrogen ) with siRNA ( siCtrl scrambled , siRNA_1 ATAGGTATTGGTGAATTTAAA , siRNA_2 AAGCTCACGCAGTTGGGCACT , NM_005228 , NM_201282 , NM_201283 , NM_201284 , Qiagen ) in the optical bottom 96 well plates . Cells were infected with rgRSV ( moi 0 . 3 ) 72 h post transfection . After 18 h cells were fixed with 4% formaldehyde and counterstained with DAPI . For the infection assays cells were plated in optical bottom 96 well plates . After infection with rgRSV for 18–20 hours , the cells were fixed with 4% formaldehyde and counterstained with DAPI . Nine images per well were acquired with automated MD2 microscope that autofocuses at each image acquisition ( 10× objective ) . The total cells number and the number of infected cells expressing GFP were scored by the MatLab-based infection counter software described previously [52] . Human phospho-RTK array kit was obtained from R&D Systems ( cat # ARY001 ) and experimental procedure was followed according the manufacturers guidelines . In brief , serum-starved subconfluent cells in 3 . 5 cm dish were inoculated with purified RSV ( moi 30 ) or null virus prep extract for 15 min at 37°C . Then , cells were lysed and pre-clarified by centrifugation . The supernatant samples were incubated with the antibody array , and developed with provided reagents . Developed arrays were analyzed by densitometry with QuantityOne software ( Bio-Rad ) . Subconfluent cells in 12 well plates were chilled on ice for 20 min . RSV ( moi 10 ) in DMEM-Hepes was bound to cells for 1 h at 4°C . Post binding samples were washed and lysed in RIPA buffer ( 50 mM Tris , 150 mM NaCl , 2 mM EDTA , 1% NP-40 , 0 . 1% SDS , pH 7 . 4 ) . Remaining samples were washed with PBS supplemented with complete medium and transferred to 37°C for 1 h; following cells were trypsinized , washed and lysed with RIPA buffer . All samples were separated on the 10% Bis-Tris gels ( Invitrogen ) . Western blots were probed with anti-F antibody ( 1∶500 ) , HRP conjugated goat ant-mouse ( 1∶2000 ) and developed with super signal west pico ( Pierce ) . 0 . 8×105 cells were seeded on 12 mm coverslips in the 24 well plate 24 h prior experiment . Plates were chilled on ice and then RSV was bound to cells in DMEM-Hepes for 1 h at 4°C . Viruses were washed away and cells were supplemented with complete medium and transferred to 37°C . At desired times cells were fixed in 4% formaldehyde , permeabilized with 0 . 1% triton X-100 and incubated with 10% goat serum for 30 min . Cells were then stained with appropriate primary and secondary antibody . Coverslips were mounted to the glass slides with prolong gold anti-fade reagent ( Invitrogen ) . Immunofluorescence images were captured with LSM Zeiss 510 microscope with the confocal laser scanning set up ( objectives 60 or 100× ) . Per experiment , 10–15 images were captured and processed by ImageJ . For the virus particles detection Imaris software was used set up to detect particles larger than 0 . 5 µm and quality greater than 15 . Live cell imaging was performed with Olympus CellR , 20× objective with DIC setting , equipped with 37°C incubator . Hep2 cells or gradient purified RSV particles were lysed in denaturing buffer containing 8 M urea and 100 mM NH4HCO3 . Lysates were briefly sonicated and proteins were reduced with 12 mM DTT for 30 min at 32°C and alkylated with 40 mM iodoacetamide for 45 min at 25°C . The samples were diluted 1∶5 with 100 mM NH4HCO3 and digested with sequencing-grade porcine trypsin ( Promega ) at an enzyme/substrate ratio of 1∶100 . The digestion was performed overnight and stopped with formic acid at final concentration of 2% . The peptide mixtures were desalted on Sep-Pak C18 cartridges ( Waters ) , eluted with 80% acetonitrile , vacuum centrifuged until dryness and resuspended in 0 . 15% formic acid . For each peptide , Q1 and Q3 masses , as well as collision energies ( CE ) for peptide fragmentation were calculated using Skyline ( v1 . 3 , MacCoss Lab Software ) . Double and triple charged product ions from the y-and b-series and transitions in a mass range of 350–1250 Da were considered ( see full list of SRM transitions in the supporting information Table S1 ) . The peptide samples were measured in SRM mode on a triple-quadrupole/ion trap mass spectrometer ( 5500QTrap , ABSciex ) equipped with a nano-electrospray ion source . For the chromatographic separation of the peptides , the instrument was coupled with an Eksigent Nano LC system ( ABSciex ) connected to a 15-cm fused silica column , 75 µm inner diameter ( BGB Analytik ) , packed in-house with Magic C18 AQ , 5 mm beads ( Michrom Bioresources ) . The peptide mixtures were loaded from an autosampler cooled to 4°C ( ABSciex ) and separated with a linear gradient of acetonitrile/water containing 0 . 1% formic acid from 5 to 35% acetonitrile in 30 min , with a flow rate of 350 nl/min . SRM analysis was conducted with Q1 and Q3 operated at unit resolution ( 0 . 7 m/z half maximum peak width ) with up to 70 transitions per run ( dwell time , 30 ms; cycle time , 2 . 5 s ) . Data were analyzed with the software Skyline . Peak area of the SRM peaks was used for quantitation , after confirming co-elution and shape similarity of the transitions monitored for each peptide . Outlier transitions ( e . g . , shouldered or noisy transition traces ) were not considered in the calculations . Results are presented as the sum of the areas of all SRM peaks for a given peptide . All experiments were performed at least in triplicate , and presented as normalized values with +/− standard deviation ( SD ) . | Respiratory Syncytial Virus ( RSV ) is a highly pathogenic paramyxovirus . We developed assays for RSV endocytosis , intracellular trafficking , membrane fusion , and infection . The results showed that RSV was rapidly and efficiently internalized , and that acid-independent membrane fusion occurred intracellularly after endocytosis . Cell biological studies demonstrated that endocytosis was macropinocytic , and that it was required for infection . The process involved activation of the EGF receptor and its downstream effectors including Cdc42 , Pak1 , and myosin II . RSV induced transient actin rearrangements accompanied by plasma membrane blebbing , elevated fluid uptake , and internalization of intact RSV particles into large macropinosomes . Expression of a dominant negative Rab5 mutant but not Rab7 decreased infection indicating that RSV penetration is intracellular , and takes place in Rab5 positive macropinosomes before fusion with endolysosomal compartments . The reason why RSV , unlike most paramyxoviruses , depended on endocytic entry was found to be the need for activation of the F protein by a second proteolytic cleavage . It occurred after endocytosis , and involved most likely a furin-like , vacuolar enzyme . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biology"
] | 2013 | Host Cell Entry of Respiratory Syncytial Virus Involves Macropinocytosis Followed by Proteolytic Activation of the F Protein |
CD8 T cells protect the host from disease caused by intracellular pathogens , such as the Toxoplasma gondii ( T . gondii ) protozoan parasite . Despite the complexity of the T . gondii proteome , CD8 T cell responses are restricted to only a small number of peptide epitopes derived from a limited set of antigenic precursors . This phenomenon is known as immunodominance and is key to effective vaccine design . However , the mechanisms that determine the immunogenicity and immunodominance hierarchy of parasite antigens are not well understood . Here , using genetically modified parasites , we show that parasite burden is controlled by the immunodominant GRA6-specific CD8 T cell response but not by responses to the subdominant GRA4- and ROP7-derived epitopes . Remarkably , optimal processing and immunodominance were determined by the location of the peptide epitope at the C-terminus of the GRA6 antigenic precursor . In contrast , immunodominance could not be explained by the peptide affinity for the MHC I molecule or the frequency of T cell precursors in the naive animals . Our results reveal the molecular requirements for optimal presentation of an intracellular parasite antigen and for eliciting protective CD8 T cells .
CD8 T cells play a critical role in immune-mediated protection against intracellular apicomplexan parasites . Antigenic determinants recognized by CD8 T cells are short peptides of 8 to 10 amino acids presented by class I molecules of the major histocompatibility complex ( MHC I ) . Antigenic peptides are typically degraded by cytosolic proteasomes , transported into the endoplasmic reticulum ( ER ) , trimmed by ER-resident aminopeptidases and loaded on peptide-receptive MHC I molecules [1] . The spectrum of peptides that can theoretically be presented by a given MHC I is far larger than the peptides that actually elicit CD8 T cell responses . Furthermore , not all the peptide-MHC I complexes that can be recognized are equal: rather they elicit a hierarchy of specific CD8 T cells . This phenomenon of “selection and ranking” is termed immunodominance . Immunodominant peptide-MHC I elicit the most abundant cognate T cell populations , whereas subdominant peptide-MHC I induce less abundant T cells ( reviewed in [2] , [3] ) . Knowledge of the mechanisms that enhance immunogenicity and determine immunodominance hierarchy is central to design of optimal vaccines . Mechanisms of immunodominance have been widely studied in the context of viral infections . The dominant position in the hierarchy has been positively correlated with 1 ) efficiency of peptide generation by the antigen processing pathway , e . g . due to proteasomal activity [4] , ER aminopeptidase activity [5] or the nature of epitope-flanking sequences [6] ) , 2 ) antigen abundance [7] , 3 ) ability of the antigen-presenting cells ( APCs ) to stimulate T cells , e . g . dendritic cells ( DCs ) versus non-professional APCs [8] , 4 ) MHC binding affinity [4] , [9] and 5 ) size of the naïve pool of specific T cells [9] , [10] , [11] . This latter parameter is increasingly being considered as a good predictor of immunodominance hierarchy , although , like the other parameters , it does not seem to be absolute [12] . During infection by intracellular parasites , the parameters that promote immunogenicity of a protein and that determine T cell immunodominance remain largely unknown . Unlike viruses , parasite-derived antigens are not synthesized by the host cell translation machinery , thus bypassing a preferential linkage between protein synthesis and MHC I presentation [13] . Moreover , except for antigens that may be directly injected into the host cytoplasm ( e . g . T . gondii rhoptry proteins ) , most antigens from parasites that live in vacuoles are segregated from the cytosol by one or more membranes . We hypothesize that , despite the greater genomic complexity of apicomplexan parasites relative to viruses [14] , these key differences could determine the limited number of hitherto characterized antigenic peptides from Plasmodium yoelii [15] Theileria annulata [16] or T . gondii [17] parasites . In the present study , we addressed the causes and consequences of immunodominance during T . gondii infection . T . gondii is a widespread intravacuolar parasite that can cause severe disease in humans [18] . T . gondii replicates in a specialized parasitophorous vacuole ( PV ) and CD8 T cells play a protective role , especially against toxoplasmic encephalitis which is caused by the persistence of cysts in the brain [19] . We previously identified a decamer peptide ( HF10 , derived from the GRA6 protein ) presented by the Ld MHC I molecule and recognized by a large CD8 T cell population during toxoplasmosis [17] . Two other epitopes , also presented by Ld , have been reported: the ROP7-derived IF9 and the GRA4-derived SM9 peptides [20] . Although the source antigens for each of these epitopes are contained in T . gondii secretory organelles , the GRA6-specific response appeared immunodominant based on its magnitude [17] . The molecular mechanisms for the potent immunogenicity of GRA6-derived HF10 epitope are not known . We generated transgenic parasites that do or do not express the GRA6-derived HF10 epitope and established that even in the absence of the immunodominant GRA6-specific CD8 T cell response , the subdominant GRA4 and ROP7 responses remain poorly immunogenic and fail to protect mice from toxoplasmosis . We show that the location of the epitope at the C-terminus of the GRA6 antigenic precursor is a critical parameter that allows efficient processing , determines immunodominance and provides protection during chronic stage .
In order to study the pathophysiological consequences of HF10 immunodominance , we generated parasites that do or do not express the GRA6-derived HF10 epitope . We took advantage of the genetic diversity among three common T . gondii strains ( type I , II and III ) . While the GRA4-derived SM9 and ROP7-derived IF9 peptides are conserved ( data not shown and ToxoDB . org ) , the GRA6-derived HF10 peptide is polymorphic between type II and type I/III strains . Within the last 10 residues of GRA6 , four non-synonymous single-nucleotide polymorphisms differentiate the type II sequence ( HF10: HPGSVNEFDF ) from the type I/III sequence ( HY10: HPERVNVFDY ) ( Fig . 1A and Fig . S1 in Text S1 ) . We noted that instead of a phenylalanine ( F ) , the C-terminal residue in GRA6III is a tyrosine ( Y ) , a residue not expected to be an appropriate anchor residue for Ld binding [21] . To evaluate the ability of HF10 and HY10 to bind to Ld , we used an MHC I stabilization assay . TAP-deficient RMA-S cells display empty , unstable MHC I molecules and addition of exogenous peptides that can bind to MHC I can stabilize their expression on the cell surface , as read out by flow cytometry . Expression of Ld on the surface was stabilized by addition of HF10 at a 1000-fold lower concentration compared to HY10 ( Fig . 1B ) , which confirmed its poor Ld binding capacity . Given that a type III strain like CEP , expresses HY10 ( and not HF10 ) , we inferred that it would provide a useful “HF10-null” background to analyze immunodominance in vivo . Therefore we engineered CEP parasites to stably express type II or ( as a control ) type I versions of GRA6 ( designated CEP+GRA6II and CEP+GRA6I respectively ) . To facilitate tracking of parasites and infected cells , we used a CEP strain previously modified to express the GFP and the luciferase reporter genes [22] . We assessed the amount of transgenic GRA6 protein expressed by CEP+GRA6II and CEP+GRA6I parasites by immunoblot , using an antibody that detects all forms of GRA6 ( I , II and III ) . The slower migration of GRA6II allowed us to discriminate between endogenous GRA6III and transgenic GRA6II . We confirmed expression of GRA6II , at slightly higher levels as compared to endogenous GRA6III in the same parasites . Transgenic GRA6I and endogenous GRA6III were undistinguishable thus precluding a precise analysis of the GRA6I transgene level . ( Fig . 1C ) . Next , we infected B10 . D2 mice ( H2d MHC haplotype ) with CEP+GRA6II and CEP+GRA6I parasites and 3 weeks post-infection , we measured the CD8 T cell response induced by HF10 and HY10 peptides . As observed with the type II Pru strain [17] , nearly 25% of CD8 T cells from CEP+GRA6II-infected spleens produced IFN-γ in response to HF10 peptide . In contrast , no response was detected above background in CD8 T cells from CEP+GRA6I-infected mice after restimulation with the HF10 or HY10 peptides ( Fig . 1D ) . We conclude that CEP strains are “HF10-null” and suitable for assessing the immunogenicity of various transgenes . We used these transgenic parasites to confirm the immunodominance hierarchy among the 3 known natural peptides presented by Ld and to analyze the consequences of HF10 absence on T cell responses to the other antigens . Four weeks post-infection , we examined the T . gondii-specific CD8 T cell response in the spleen using peptide-loaded Ld dimers ( Fig . 2A ) . As expected , a large fraction of HF10-specific CD8 T cells were detected only in CEP+GRA6II-infected mice ( 9 . 5%+/−3 . 7% , mean +/− s . d . , p = 10−4 ) . The IF9-specific CD8 T cells were found at a much lower frequency ( 0 . 9+/−0 . 8% , p = 0 . 11 ) and SM9-specific CD8 T cells were hardly detectable . Interestingly , even in the absence of HF10 ( such as in CEP+GRA6I-infected mice ) , the frequencies of IF9- and SM9-specific CD8 T cells did not increase , suggesting that the subdominant status of IF9 and SM9 was not the result of competition ( also called immunodomination ) exerted by HF10-specific T cells . Similar results were obtained when we assessed IFN-γ production by effector CD8 T cells following in vitro restimulation ( Fig . 2B ) . Likewise , the epitope-specificity of CD8 T cells in brain infiltrates showed the same HF10>IF9>SM9 hierarchy ( Fig . 2C ) . While the above experiments define the immunodominance hierarchy among already known epitopes , unknown epitopes could also play a role in the parasite-specific response . To analyze the entire repertoire of T . gondii-specific CD8 T cells , we used parasite-infected , rather than peptide-pulsed , APCs to restimulate T cells ex vivo . The magnitude of IFN-γ response elicited by parasite-infected macrophages ( Fig . 2D ) was no higher than that observed after peptide restimulation ( see Fig . 2B ) . Thus , CD8 T cells of other specificities do not play a major role in our experimental model system . Taken together , our data demonstrate that the presence of GRA6II in the parasites triggers a strong and dominant HF10-specific CD8 response in the spleen and brain of chronically infected animals but does not negatively affect ( “immunodominate” ) the subdominant SM9 and IF9 responses . We have previously reported that immunization of H2d mice with HF10-loaded bone marrow-derived dendritic cells protects against lethal type II parasite challenge [17] . We predicted that presence of HF10 may decrease parasite burden . To test this hypothesis , we took advantage of the luciferase expression to analyze parasite dissemination by bioluminescence imaging in BALB/c mice ( H2d ) . Regardless of the presence of HF10 , all strains were cleared by day 13 ( Fig . 3A , B ) . CEP+GRA6II parasites appeared to be cleared slightly earlier than control CEP ( HXGPRT+ ) and CEP+GRA6I parasites , although this difference did not reach statistical significance ( Fig . 3A , B ) . In addition , parasite signal in the brain was detected only in mice infected with control CEP or CEP+GRA6I and was never observed with CEP+GRA6II ( Fig . S2 in Text S1 ) . This suggested that the control of parasitemia by HF10-specific T cells may be more effective at the chronic stage , when parasites are found mostly as brain cysts . When we measured parasitemia in chronically infected B10 . D2 mice at 4 weeks post-infection , we found a significantly higher proportion of splenocytes harboring parasites in CEP+GRA6I-infected mice ( Fig . 3C ) . Accordingly , the number of brain cysts in CEP+GRA6I-infected mice was nearly 5 times higher than in mice infected with the HF10-expressing parasites ( Fig . 3D ) . These results could not be attributed to an intrinsic growth difference between clones since they behaved comparably in a plaque assay in vitro ( Fig . S3A in Text S1 ) . Furthermore , the influence of HF10 on cyst number was visible only in B10 . D2 mice and not in C57BL/6 mice , which have a different MHC haplotype ( H2b ) and therefore do not elicit HF10-specific T cells ( Fig . S3B in Text S1 ) . Combined , the data show that the HF10-specific response has a modest protective effect on parasite control during acute toxoplasmosis but is essential for controlling parasite load during chronic infection . To uncover possible causes of HF10 immunodominance , we used the MHC I stabilization assay described above ( see Fig . 1B ) and compared the affinity for Ld of HF10 to other Ld-restricted peptides . These other peptides were derived either from T . gondii ( SM9 , IF9 ) , from a mouse minor antigen ( QL9 ) or from a mouse cytomegalovirus protein ( YL9 ) ( Fig . 4A ) . HF10 affinity appeared ∼10-fold higher than that of IF9 , QL9 and YL9 but fell in the same range as the T . gondii subdominant peptide SM9 . Therefore , the dominance hierarchy did not correlate with peptide affinity for MHC I . We next assessed whether abundance of peptide-specific T cells in the repertoire of naïve mice may control immunodominance . We employed a tetramer-based enrichment procedure [23] , [24] to enumerate naïve T cells isolated from spleen and lymph nodes of uninfected mice and specific for each of the 3 epitopes . Numbers of T cell precursors were analyzed by flow cytometry after gating on a population of live dump− ( dump = B220 , F4/80 , MHC II ) CD3+ CD8α+ cells ( Fig . S4 in Text S1 ) . Surprisingly , we observed an inverse correlation between the number of naïve T cells per mouse and the immunodominance ( Fig . 4B ) , with the frequency of HF10-specific CD8 T cells around 10- and 3-fold lower than SM9- and IF9-specific CD8 T cells respectively ( Fig . 4C ) . Thus the immunodominance of HF10 cannot be explained by a high starting precursor frequency of specific T cells . Having ruled out two plausible hypotheses , we wondered whether HF10 immunodominance might be related to processing efficiency . We noted that HF10 is located at the very C-terminus of GRA6II , a position that may facilitate processing since no C-terminal cut is required . To test the importance of epitope position , we changed the C-terminus of HF10 by extending GRA6II with one or more amino acids . We first transfected C-terminally extended versions of GRA6II in mouse fibroblasts . Extensions were either single amino acids ( lysine , K ; leucine , L ; proline , P ) or several residues such as the GRA6I/III-derived HY10 peptide or the entire GFP . We used CTgEZ . 4 T cell hybridomas , a β-galactosidase-inducible reporter cell line [17] , to read-out HF10 presentation . The response of CTgEZ . 4 T cells was mildly decreased ( GRA6II-K ) , severely disrupted ( GRA6II-L ) or totally abrogated ( GRA6II-P , GRA6II-HY10 , GRA6II-GFP ) ( Fig . 5A ) . These data suggest that the C-terminal location determines optimal processing and presentation of the HF10 peptide when the precursor protein is expressed ectopically by the antigen-presenting cell . To assess the impact of HF10 position in T . gondii , we used CEP parasites expressing longer versions of GRA6II , extended either by a leucine ( GRA6II-L ) or by the HA tag ( GRA6II-HA ) . First , we verified that transgene levels were comparable by Western blot ( Fig . 5B ) . To investigate whether these additional C-terminal residues might perturb GRA6 transport , we took advantage of the HA tag and evaluated the subcellular distribution of transgenic GRA6-HA , as compared to total GRA6 . Analysis of the overlap between HA and the GRA2 and GRA5 dense granule proteins in extracellular tachyzoites ( Fig . S5A , B in Text S1 ) and in infected fibroblasts ( Fig . S5C , D in Text S1 ) , indicated that GRA6II-HA is packaged in the dense granules and secreted in the vacuolar space , as known for wild-type GRA6 [25] . Although the distribution of GRA6II-L could not be directly assessed , we inferred from the above data that the extra leucine did not alter protein transport either . When used to infect bone marrow-derived macrophages ( BMDMs ) , the CEP+GRA6II-L and CEP+GRA6II-HA transgenic parasites led to similar infection rates ( data not shown ) but HF10 presentation was abrogated ( Fig . 5C ) . Finally , we examined the importance of HF10 C-terminal location in vivo . We infected mice and analyzed the induction of HF10-specific CD8 T cells in the spleen ( Fig . 5D ) and the brain ( Fig . 5E ) at chronic stage . In accordance with our in vitro findings , only the CEP+GRA6II parasites elicited a detectable HF10-specific response . The absence of HF10-specific response in mice infected by CEP+GRA6II-L and CEP+GRA6II-HA was consistent with a dramatically higher cyst burden in their brains ( Fig . 5F ) . We conclude that the precise C-terminal location of HF10 is required for optimal processing and presentation by T . gondii-infected APCs and for eliciting parasite-specific T cells that could provide in vivo protection . We further assessed whether the location of an epitope at the GRA6II C-terminus may be sufficient for enhancing presentation and immunogenicity . We generated CEP parasites expressing the subdominant SM9 peptide either at the C-terminus of GRA6II ( CEP+GRA6II-SM9Cter ) or , as a control , internally within GRA6II ( CEP+GRA6II-SM9internal ) ( Fig . 6A ) . The selected clones expressed comparable levels of transgenes ( Fig . 6B ) . We measured SM9 presentation at the surface of parasite-infected cells using a new β-galactosidase-inducible T cell hybridoma specific for Ld-SM9 complex ( BDSM9Z ) ( Fig . 6C ) . Interestingly , although the natural SM9 precursor , GRA4 , was expressed in type III parasites ( data not shown ) , the presentation of Ld-SM9 complexes remained below detection in CEP-infected BMDMs . SM9 presentation was also undetectable when the peptide was placed at an internal position within GRA6 . In contrast , BDSM9Z T cells were strongly stimulated when SM9 was located at GRA6 C-terminus ( Fig . 6C ) . HF10 presentation by infected BMDMs was abrogated by the presence of SM9 at the C-terminus but not by the presence of SM9 at the internal position ( Fig . 6D ) , consistent the C-terminal extension studies described above ( Fig . 5C ) , . In conclusion , placing the SM9 peptide at the C-terminus of GRA6II was sufficient to enhance its presentation by parasite-infected cells in vitro . We next measured the SM9 and HF10-specific CD8 T cell responses in the spleen ( Fig . 6E ) and brain ( Fig . 6F ) of mice infected for 3 weeks with the transgenic parasites . A SM9-specific response was hardly detectable in mice infected with the control CEP . Infection with CEP+GRA6II-SM9internal elicited SM9-specific CD8 T cells but these T cells were between 3-fold ( spleen , Fig . 6E ) and 5-fold ( brain , Fig . 6F ) more abundant when SM9 was grafted at GRA6 C-terminus . This difference was not due to reduced infectivity of the CEP+GRA6II-SM9internal parasites since HF10-specific CD8 T cells were abundant in those mice ( Fig . 6E , F ) . Similar results were obtained in mice immunized with irradiated tachyzoites ( Fig . S6 in Text S1 ) . To ask whether the enhanced SM9-specific response participates in parasite control , we enumerated the brain cysts in the 3 groups . As compared to mice infected with control CEP , the parasite load was lower when either a strong HF10- or a strong SM9-specific response was elicited ( Fig . 6G ) . These data indicate that the nature of the antigenic peptide itself does not seem to determine the protective effect . We conclude that location of a subdominant peptide at GRA6 C-terminus dramatically enhanced its immunogenicity , changed the epitope hierarchy and had beneficial repercussions for parasite control .
In this study , we have identified the molecular bases underlying the marked immunodominance of a CD8 T cell response that controls the intracellular T . gondii parasite . Rather than peptide affinity for MHC I and naïve T cell frequency , we find that immunodominance is determined by the location of the epitope within the antigenic precursor . The endeavor to characterize natural T cell antigens from T . gondii has started only recently [17] , [20] , [26] , [27] but it has provided much needed tools to better understand T cell immunity to this widespread opportunistic pathogen . We report here that the 3 known Ld-restricted responses follow an immunodominance hierarchy . At chronic stage , GRA6II-specific CD8 T cells were between 10-fold ( in the spleen ) and 30-fold ( in the brain ) more abundant than CD8 T cells specific for the 2nd dominant epitope: IF9 derived from ROP7 . Response to the 3rd dominant epitope , SM9 derived from GRA4 , was hardly detectable . Remarkably , we did not observe immunodomination by the GRA6II dominant epitope . Immunodomination refers to situations in which the T cell response to a given epitope is inhibited by T cells specific for another , more dominant , epitope [2] . This phenomenon has been reported during infection by simian immunodeficiency virus [28] and by Trypanosoma cruzi , another protozoan parasite phylogenetically related to T . gondii [29] . A mechanism commonly proposed to explain immunodomination is elimination of APCs by the dominant cytotoxic T cells . Perhaps immunodomination does not occur here because , as compared to IFN-γ production , perforin-mediated cytolysis by CD8 T cells plays only a limited role during T . gondii infection [30] . The absence of immunodomination also suggests that accessibility of peptide-loaded APCs for T cells is not limiting . This may be because T . gondii is able to invade and be presented on MHC I by many cell types , even non-professional APCs [31] . Another major conclusion is that during chronic stage , subdominant responses could not compensate and provide efficient parasite control in the absence of the GRA6II dominant peptide . These data designate GRA6 as a strain-specific component which determines chronic parasitemia and is targeted by adaptive immunity . This is in contrast to already known T . gondii virulence factors which mostly interfere with innate processes , such as ROP16 which interferes with STAT transcription factors [32] , ROP18 which disarms immunity-related GTPases involved in host defense [33] , [34] ) or GRA15 which promotes NF-κB activation [35] . Of note , GRA6 is among the 20 most polymorphic genes in the T . gondii genome and many polymorphisms are located in its C-terminal region ( see ToxoDB . org and Fig . S1 in Text S1 ) . Beyond the 3 prototypic strains , sequence polymorphisms in GRA6 have been characterized in more exotic strains ( or haplogroups ) [36] . These atypical strains either express HF10 , HY10 or alternative versions of the decamer peptide with distinct polymorphisms . Interestingly , in chronically infected humans , some of these variations are specifically recognized by natural antibodies that are used as a tool to serotype the parasite [37] , [38] . Given that GRA6 C-terminus is targeted both by humoral and cellular responses , we speculate that selective pressure by adaptive immunity may have contributed in shaping GRA6 polymorphisms . By exploring the possible causes of HF10 immunodominance , we were able to rule out two possible explanations . We found no positive correlation of immunodominance hierarchy with peptide affinity for Ld or naïve T cell frequency . The numbers of naïve HF10- and IF9-specific T cells fall within the previously reported range of 15 to 1500 naïve CD8 T cells per mouse [39] but the number of SM9-specific cells ( 4300 ) may look unusually high . Although the exact reason remains unclear , this may be related to a large amount of positive selecting ligands available for this population . In agreement with this idea , an F1 H2bxd mouse strain gives half the value measured with the B10 . D2 H-2d strain ( data not shown ) . Remarkably , we found an inverse correlation between size of the naïve population and magnitude of the parasite-specific response . This latter result may seem paradoxical in the light of other situations , such as peptide immunization [23] or viral infection [7] , [9] , [10] , [24] , where size of the naïve pool was a good predictor of immunodominance . However , there are known exceptions to this rule [12] . Here , the low abundance of HF10-specific precursors may facilitate their expansion by limiting interclonal competition . Alternatively , the TCRs used by HF10-specific CD8 T cells may have a high affinity for the HF10-Ld complex , which could promote stronger signaling and proliferation . These hypotheses remain to be investigated . Our central finding is that C-terminal position of the epitope within GRA6 plays a crucial role for immunodominance . It is illustrated by the fact that the weakly immunogenic GRA4 epitope elicited strong SM9-specific responses when grafted at GRA6 C-terminus . Intriguingly , the internal position of SM9 gave rise to a lower , but substantial , CD8 response whereas we did not detect any HF10-specific T cells when HF10 was placed at the same internal position ( Fig . S7 in Text S1 ) . The bases for such a different outcome are unknown but they may lie in different epitope-dependent processing efficiencies or compensatory effects of the high frequency of SM9-specific T cell precursors counteracting the less favorable position for processing . Most notably , addition of residues to GRA6II C-terminus greatly impaired presentation . It is theoretically possible that the C-terminal flanking sequences abrogated HF10 presentation by altering the vacuolar trafficking of GRA6 and/or its membrane insertion . We think it is unlikely because 1 ) we found that transport of GRA6II-HA was undistinguishable from transport of total GRA6 and 2 ) presentation of the C-terminal SM9 peptide could occur efficiently in vitro and in vivo . Rather than regulating global GRA6 trafficking , we favor the idea that additional C-terminal sequences impair processing . Epitope-flanking sequences are indeed known to positively or negatively affect protease cleavage capacity and generation of the final peptide , with clear consequences on immunodominance [4] , [6] , [40] , [41] , [42] . Using minigenes , it was shown that the nature of C-terminal flanking residues profoundly impacts excision of the processed peptide [42] . In the context of a full-length viral protein , single changes to the epitope-flanking residues dramatically reduced presentation [41] and it was later proposed that the subdominant nature of certain peptides bearing appropriate consensus motifs might result from suboptimal C-terminal sequences [40] . Finally , it is interesting to note that the influence of the flanking motifs may differ whether the antigen is presented by the direct MHC I pathway or by cross-presentation [43] . In our case , we observed a dramatic impact of the absence or presence of C-terminal residues on presentation , suggesting a key role for antigen processing in modulating immunodominance . A systematic screening of C-terminal extensions may be useful to precisely define the rules that govern processing of GRA6 C-terminus . Since GRA6 behaves as an integral transmembrane protein in the vacuole [44] , the importance of the C-terminus could be related to the topology of GRA6 membrane insertion . One possibility is that GRA6 C-terminal domain is displayed in the cytosol and thus potentially accessible to host proteases . This mechanism was proposed for antigens from the intravacuolar bacteria Chlamydia trachomatis that are inserted in the surrounding membrane of the bacteria-containing vacuole [45] , [46] . This hypothesis remains to be tested . Alternatively , unfolded GRA6 may access the cytosol thanks to the recruitment of host endoplasmic reticulum components on the parasitophorous vacuole , as proposed for the soluble OVA model antigen [47] , [48] . In any case , we consider it likely that access of GRA6 to the MHC I pathway is less efficient than in the situation of a viral antigen directly synthesized by the host cell translation machinery . Consequently , any parameter that would facilitate processing ( e . g . being at C-terminus ) may become the determining factor for the presentation outcome . To our knowledge , this is the first evidence that C-terminal position can be positively correlated with immunodominance . Given the variety of parameters that can influence immunodominance , a remaining question is the degree of peculiarity of our current findings with respect to other antigens . A recent study interrogating the Immune Epitope Database ( www . iedb . org ) for a positional bias of viral epitopes reported that epitopes from both ends of a protein tended to be underrepresented [49] . An indirect way to assess the general relevance of the C-terminal position would be to transfer subdominant epitopes to the C-terminus of their respective antigens ( e . g . GRA4 , ROP7 ) and evaluate the impact on CD8 responses . Future studies , not only with T . gondii but also with other intracellular parasites , should shed light on the general relevance of this position . During T . gondii infection in vivo , two scenarios of MHC I presentation could co-exist . On the one hand , phagocytosed parasite material may be processed by bystander cells present in the vicinity of infected cells [50] . On the other hand , parasite proteins may be directly presented by actively infected cells [51] . Our work shows that antigen access to the MHC I pathway and efficient processing are the limiting factors that control immunodominance . Beyond amino acid mutations within the peptide sequence , modifying the epitope position may provide the parasite with a strategy to manipulate how it is detected by CD8 T cells . Understanding the features that make certain peptides immunogenic will shed light on the strategies used by parasites to interact with their host immune system .
In the US , animal studies were carried out in accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and in compliance with the guidelines of the Institutional Animal Care and Use Committee ( IACUC ) of the University of California . The animal protocols were approved by the IACUC of the University of California , Berkeley ( Animal Use Protocol # R057-0913BR ) and of the University of Pittsburgh , PA ( Protocol # 1210130 ) . In France , animal studies were carried out under the control of the National Veterinary Services and in accordance with European regulations ( EEC directive 86/609 dated 24 November 1986 ) . The protocol was approved by the Regional Ethics Committee from the Midi-Pyrénées Region ( Approval # MP/01/29/09/10 ) . C57BL/6J ( B6 ) , BALB/c and B10 . D2-Hc1 H2d H2-T18c/nSnJ ( B10 . D2 ) mice were purchased from The Jackson Laboratory ( Bar Harbor , ME , USA ) . B6xDBA/2 F1 ( B6D2 ) mice were purchased from Charles River ( France ) . For all experiments , sex and age-matched mice were used . Mice were handled with the approval of local ethics committees . Except for the CEP+GRA6II-HA clone which was a gift from J . Saeij ( Cambridge , MA , USA ) , all transgenic parasites were generated from the parental CEP . ΔHXGPRT . GFP . Luc strain [22] . Tachyzoites were maintained by passage on confluent monolayers of human foreskin fibroblasts ( HFF ) . For infections , parasites were harvested , filtered through 3 µm and 105 tachyzoites were injected intraperitoneally in 100 µl PBS . For in vitro expression of antigenic sequences , all C-terminally extended GRA6II sequences were cloned into the pcDNA1 vector containing the pcDNA1-embedded 3′UTR . Plasmids used for T . gondii transfection were derived from the pGRA . HA . HPT vector , a gift from J . D . Dunn and J . Boothroyd ( Palo Alto , CA , USA ) . More details on the construction of the plasmids are given in the Supporting Protocol S1 in Text S1 . L cells were triple transfected using a standard diethylaminoethyl dextran method with vectors coding for Ld , B7-2 and mutant GRA6 , as previously described [17] . For parasite transfections , 1 . 5×107 tachyzoites were electroporated with 50 µg of HindIII-linearized plasmid DNA and inoculated in 4 confluent HFF flasks in order to obtain up to 4 independent clones . The next day , 25 µg/ml mycophenolic acid and 50 µg/ml xanthine were added for selection . After 2 passages , resistant tachyzoites were cloned by limiting dilution and presence of the transgene was verified by PCR . For each construct , one clone that acquired resistance but no transgene was kept as HXGPRT+ control . RMA-S . Ld cells were a gift from T . Hansen ( St-Louis , MO , USA ) . RMA-S . Ld cells were incubated at 37°C , 5% CO2 for 8 h to saturate the culture medium with CO2 . The flask was sealed with parafilm and incubated at RT overnight . The next day , cells were washed with PBS and plated at 3×105 cells/well in a 96-W plate . Peptide was added to the cells in serial dilutions . The plate was incubated for 1 h at RT and 3 h at 37°C . Cells were stained with the 30-5-7 antibody ( specific for conformed , peptide-bound Ld ) and a phycoerythrine ( PE ) -coupled goat anti-mouse secondary antibody and analyzed by flow cytometry . HFFs were disrupted with a 23-G needle and tachyzoites were lysed in a lysis buffer containing 1% NP-40 , 10 mM Tris pH 7 . 4 , 150 mM NaCl , and protease inhibitors ( cOmplete EDTA-free , Roche ) for 30 min on ice . Lysates were centrifuged for 15 min at 15 , 000 g . Solubilized proteins were boiled and reduced for 5 min in SDS sample buffer , separated by electrophoresis on 12% polyacrylamide gels and transferred to nitrocellulose membranes . Immunologic detection was achieved using rabbit anti-GRA6 serum ( gift from L . D . Sibley , St-Louis , MO , USA ) , mouse anti-HA ( gift from D . Raulet , UC Berkeley , CA , USA ) or mouse anti-SAG1 ( clone TP3 , Santa Cruz ) followed by secondary horseradish-peroxidase-conjugated antibodies . Peroxidase activity was visualized by chemiluminescence . Mice were sacrificed 3 to 4 weeks after infection . Spleens were dissociated into single-cell suspensions in complete RPMI ( Invitrogen ) supplemented with 10% ( vol/vol ) FCS ( Hyclone ) . Samples were depleted of erythrocytes with ACK lysis buffer ( 100 µM EDTA , 160 mM NH4Cl and 10 mM NaHCO3 ) . Leukocytes from the brain were prepared as in [17] . In brief , brains were minced and digested for 1 h at 37°C with 1 mg/ml collagenase ( Sigma ) and 100 µg/ml DNAseI ( Roche ) in complete RPMI . Brain suspensions were filtered through 70-µm cell strainers and centrifuged for 10 min at 200 g . Cells were resuspended in 60% ( vol/vol ) Percoll ( GE Healthcare ) , overlaid on 30% ( vol/vol ) Percoll and centrifuged 20 min at 1 , 000 g . Infiltrating mononuclear cells were collected from the gradient interface and the remaining erythrocytes were lyzed with ACK lysis buffer . The number of infected splenocytes was determined by measuring the percentage of GFP+ cells by flow cytometry . Results from two samples with over 2×105 events collected per tube were averaged for each mouse . For cyst enumeration , the brain was homogenized over a 100 µm strainer and 5% of the entire brain was stained with fluorescein-conjugated Dolichos biflorus agglutinin ( Vector Laboratories ) . Cysts were counted using an inverted fluorescence microscope . For bioluminescence imaging , BALB/c mice were infected intraperitoneally with 105 tachyzoites of CEP expressing GRA6I or GRA6II or control CEP HXGPRT+ . Parasite burden was assessed using in vivo bioluminescence imaging as described previously [52] . Briefly , daily readings were performed using an IVIS Lumina II imaging system ( Caliper ) . Ten minutes prior to imaging , mice were injected intraperitoneally with 200 µL of 15 . 4 mg/mL D-Luciferin in PBS and anesthetized using 2% isoflurane . Dorsal and ventral images were acquired for 5 minutes and luminescence ( photons/s/cm2/sr , total flux expressed as photons/s ) was quantified using IgorPro Image Analysis Software ( Caliper ) . Bone marrow cells were obtained from mouse femurs and tibias . Primary BMDMs were differentiated for 7 days in Petri dishes with RPMI supplemented with 20% ( vol/vol ) FCS and 10% ( vol/vol ) colony-stimulating factor–containing culture supernatant ( purity , about 95% CD11b+ ) . Colony-stimulating factor-producing 3T3 cells were a gift from R . Vance ( UC Berkeley , CA , USA ) . BMDMs were infected for 24 h with γ-irradiated tachyzoites ( 120 Gy ) at various multiplicities of infection and used in antigen presentation assays . In all experiments , the proportion of infected ( GFP+ ) BMDMs was controlled by flow cytometry . B6D2 F1 mice were immunized subcutaneously with 100 µg synthetic SM9 peptide in complete Freund's adjuvant and boosted after 2 weeks . One week later , spleens were harvested and restimulated with 10 nM SM9 . Recombinant human IL-2 ( 50 U/ml ) and 5% T-stim ( both from BD Pharmingen ) were added after day 2 to support CD8 T cell proliferation . Four days after restimulation , responding cells were fused to the TCRαβ-negative lacZ-inducible BWZ . 36/CD8α fusion partner as described in [17] . Specificity of the resulting BDSM9Z hybridomas was tested by overnight incubation with peptide-pulsed or unpulsed Ld-transfected L cells . TCR-mediated stimulation of the BDSM9Z and the CTgEZ . 4 hybridomas [17] was quantified using a chromogenic substrate: chlorophenol red-β-D-galactopyranoside ( CPRG , Roche ) . Cleavage of the CPRG by β-galactosidase releases a purple product , which absorbance was read at 595 nm with a reference at 655 nm . Spleen and major lymph nodes from individual naïve B10 . D2 mice were harvested . Single cell suspension was stained with PE-labeled ( Prozyme ) SM9:Ld , IF9:Ld or HF10:Ld tetramers ( NIH tetramer facility ) . Tetramer enrichment was performed on each sample with anti-PE magnetic beads ( Miltenyi Biotec ) and each sample was stained with antibodies ( BD Biosciences ) for flow cytometry analysis . Total numbers of CD8α+tetramer+ T cells per mouse were determined as before [23] , [24] . For other stainings , surfaces were labeled according to standard procedures with flow cytometry buffer ( 3% ( vol/vol ) FCS and 1 mM EDTA in PBS ) . Intracellular IFN-γ was detected with the Cytofix/Cytoperm kit ( BD Pharmingen ) . DimerX H-2Ld:Ig ( fusion protein of H-2Ld and immunoglobulin; BD Biosciences ) was used according to the manufacturer's instructions and as described in [17] . All flow cytometry data were acquired on an XL Analyzer ( Coulter ) or a LSRII ( Becton Dickinson ) and were analyzed with FlowJo software ( Tree Star ) . Prism software ( GraphPad ) was used for statistical analyses . All P values were calculated with the two-tailed Mann-Whitney test ( nonparametric ) . | Toxoplasma gondii is a widespread intracellular parasite that can cause severe disease in immunocompromised individuals and lead to fetal abnormalities if contracted during pregnancy . Establishment of protective immunity relies on CD8 T cells , which recognize antigenic peptides presented by MHC class I molecules on the surface of T . gondii-infected cells . Intriguingly , while the proteome of T . gondii is large , CD8 T cell responses target a very limited set of peptides . These peptides can be ranked according to the magnitude of the associated CD8 response ( from immunodominant down to subdominant ) . Yet , little is known about the rules that define their immunogenicity and the hierarchy of the associated T cell responses . Using a panel of genetically modified T . gondii where the GRA6 dominant antigen was mutated , we show that the C-terminal location of the epitope within the source antigen is the critical parameter for immunodominance . Interestingly , when placed at the C-terminus of GRA6 , the subdominant status of an epitope can be overturned . Our results unravel the mechanisms that make parasite antigens accessible for the MHC I presentation pathway . They may help to ameliorate natural immune responses and improve vaccine design against intravacuolar pathogens . | [
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] | 2013 | Location of the CD8 T Cell Epitope within the Antigenic Precursor Determines Immunogenicity and Protection against the Toxoplasma gondii Parasite |
Natural killer cells provide an important early defense against viral pathogens and are regulated in part by interactions between highly polymorphic killer-cell immunoglobulin-like receptors ( KIRs ) on NK cells and their MHC class I ligands on target cells . We previously identified MHC class I ligands for two rhesus macaque KIRs: KIR3DL01 recognizes Mamu-Bw4 molecules and KIR3DL05 recognizes Mamu-A1*002 . To determine how these interactions influence NK cell responses , we infected KIR3DL01+ and KIR3DL05+ macaques with and without defined ligands for these receptors with SIVmac239 , and monitored NK cell responses in peripheral blood and lymphoid tissues . NK cell responses in blood were broadly stimulated , as indicated by rapid increases in the CD16+ population during acute infection and sustained increases in the CD16+ and CD16-CD56- populations during chronic infection . Markers of proliferation ( Ki-67 ) , activation ( CD69 & HLA-DR ) and antiviral activity ( CD107a & TNFα ) were also widely expressed , but began to diverge during chronic infection , as reflected by sustained CD107a and TNFα upregulation by KIR3DL01+ , but not by KIR3DL05+ NK cells . Significant increases in the frequency of KIR3DL01+ ( but not KIR3DL05+ ) NK cells were also observed in tissues , particularly in the gut-associated lymphoid tissues , where this receptor was preferentially upregulated on CD56+ and CD16-CD56- subsets . These results reveal broad NK cell activation and dynamic changes in the phenotypic properties of NK cells in response to SIV infection , including the enrichment of KIR3DL01+ NK cells in tissues that support high levels of virus replication .
Natural killer cells provide a critical early defense against viral pathogens by directly responding to infected cells without prior antigenic stimulation . This is accomplished through the integration of signals from activating and inhibitory receptors , which in primates include the highly polymorphic killer-cell immunoglobulin-like receptors ( KIRs ) [1 , 2] . KIRs contain two or three extracellular immunoglobulin-like domains ( 2D or 3D ) , and depending on whether they have long ( L ) or short ( S ) cytoplasmic tails , transduce either inhibitory or activating signals [1 , 2] . MHC class I molecules serve as ligands for the inhibitory KIRs [1 , 2] , and although the ligands for the activating KIRs are not as well defined , there is evidence that these receptors also recognize MHC class I molecules [3–5] . In the case of inhibitory KIRs , engagement of ligands on the surface of healthy cells normally suppresses NK cell activation; however , if these interactions are disrupted , for instance as a consequence of MHC class I downregulation by the HIV-1 Nef protein [6–8] , this inhibition is lost , triggering NK cell degranulation and the cytolysis of infected cells . The specificity of inhibitory KIRs is primarily determined by contacts with the α1 and α2 domains of their ligands . All HLA-B molecules and some HLA-A molecules can be categorized as either Bw4 or Bw6 allotypes depending on residues 77–83 of their α1 domains [9] . Whereas KIR3DL1 selectively binds to HLA-Bw4 ligands , no human KIRs are known to recognize HLA-Bw6 molecules . HLA-C molecules can likewise be classified as C1 or C2 allotypes on the basis of polymorphisms at positions 77 and 80 , which are recognized respectively by KIR2DL2 and KIR2DL3 or KIR2DL1 depending on the amino acid residues at these positions [10 , 11] . Consistent with crystal structures showing that KIRs contact HLA class I surfaces over C-terminal peptide residues [12–14] , peptides bound by MHC class I ligands can also influence these interactions [15 , 16] . KIR and HLA class I polymorphisms are associated with differences in the course of HIV-1 infection [17–19] . In HIV-1 infected individuals , KIR3DS1 and highly expressed KIR3DL1 alleles in combination with HLA-Bw4 alleles encoding isoleucine at position 80 ( HLA-Bw4-80I ) are associated with lower viral loads and slower courses of disease progression [17 , 20] . Accordingly , KIR3DS1+ and KIR3DL1+ NK cells preferentially expand in response to HIV-1 infection in HLA-Bw4-80I+ individuals [21] . In vitro studies have also shown that KIR3DS1+ NK cells can suppress HIV-1 replication in lymphocytes from HLA-Bw4-80I+ donors , but not from HLA-Bw6 homozygous donors [3] , and that KIR3DL1+ NK cells respond to HIV-1-infected cells that have downmodulated HLA-Bw4 ligands in a manner that reflects hierarchical differences in their education [22] . Additional studies have identified HIV-1 polymorphisms that suppress KIR2DL2+ and KIR2DL3+ NK cell responses to virus-infected or peptide-pulsed cells in vitro , suggesting that HIV-1 is under selective pressure in certain individuals to acquire changes in epitopes that stabilize HLA-C interactions with inhibitory KIRs as a mechanism of immune evasion [23–25] . Simian immunodeficiency virus ( SIV ) infection of the rhesus macaque is an important animal model for HIV-1 pathogenesis and AIDS vaccine development [26]; however , studies to address the role of NK cells in this system have been limited by immunogenetic differences between humans and macaques and a lack of defined ligands for macaque KIRs . Unlike humans , which have HLA-A , -B and–C genes , macaques and other Old world monkeys do not have a C locus [27 , 28] . Instead , these species have an expanded repertoire of A and B genes [27–30] . There are up to four Macaca mulatta ( Mamu ) -A genes and a highly variable number of Mamu-B genes on any given haplotype in the rhesus macaque [31 , 32] . Macaques accordingly lack KIR2DL/S genes that encode receptors for HLA-C , but have an expanded complement of highly polymorphic KIR3DL/S genes [33–37] . Phylogenetic and segregation analyses support the existence of 22 KIR genes in macaques [35 , 36 , 38]; however , as a consequence of the rapid pace of KIR evolution , only two of these genes ( Mamu-KIR2DL04 and -KIR3DL20 ) have recognizable human orthologs [1 , 2 , 39–41] . Thus , it is not possible to predict the ligands for macaque KIRs based on sequence similarity with their human counterparts . MHC class I ligands have nevertheless been identified experimentally for a few rhesus macaque KIRs [29 , 30 , 42] . Mamu-A1*002 , a molecule with a canonical Bw6 motif , was identified as a ligand for Mamu-KIR3DL05 ( KIR3DL05 ) [30] . Mamu-A1*002 and KIR3DL05 are respectively expressed by approximately 20% and 40% of Indian-origin rhesus macaques , and the binding of KIR3DL05 to Mamu-A1*002 is strongly influenced by SIV peptides [16 , 30] . Functional assays with primary NK cells also identified multiple Bw4 molecules as ligands for Mamu-KIR3DL01 ( KIR3DL01 ) . KIR3DL01 is the most polymorphic KIR in rhesus macaques and is expressed by 85–95% of animals of Indian origin [29 , 40] . Most rhesus macaques also have one or more Mamu-Bw4 alleles predicted to encode ligands for this receptor . Despite their coincidental similarity in nomenclature , rhesus KIR3DL01 and human KIR3DL1 are not orthologous gene products; however , their shared specificity for Bw4 ligands suggests that they may serve similar functions . In the present study , we investigated NK cell responses to SIV infection of KIR- and MHC class I-defined macaques . Twelve KIR3DL05+ macaques , of which half were Mamu-A1*002+ , eleven were KIR3DL01+ , and all but one were Mamu-Bw4+ , were infected with SIVmac239 , and longitudinal changes in NK cell subsets were monitored in peripheral blood and tissues . Infection with SIV broadly stimulated NK cell responses , resulting in significant increases in the number of NK cells in blood expressing markers of activation , proliferation and antiviral activity . Significant increases were also observed in the frequency of KIR3DL01+ NK cells in lymph nodes and gut-associated lymphoid tissues . These results reveal dynamic changes in the phenotypic and functional properties of NK cells in response to SIV infection and an enrichment of KIR3DL01+ NK cells at sites of early virus replication and CD4+ T cell turnover .
We previously identified MHC class I ligands for two rhesus macaque KIRs . We found that KIR3DL05 binds to Mamu-A1*002 , a common MHC class I molecule in the rhesus macaque with a Bw6 motif [30] , and that KIR3DL01 , which is among the most polymorphic and commonly expressed KIRs in rhesus macaques , recognizes MHC class I ligands with a Bw4 motif [29] . We further demonstrated that nearly a third of the SIV peptides bound by Mamu-A1*002 suppress the cytolytic activity of KIR3DL05+ NK cells by stabilizing this interaction [16] . To determine how these receptor-ligand interactions influence NK cell responses and the outcome of immunodeficiency virus infection , twelve KIR- and MHC class I-defined rhesus macaques were infected intravenously with SIVmac239 , and longitudinal changes in NK cells and viral loads were monitored in peripheral blood and lymphoid tissues . KIR3DL05+ macaques were initially identified by staining PBMCs with Mamu-A1*002 Gag GY9 tetramers as previously described [30] . Six Mamu-A1*002+ and six–A1*002- animals were then selected from this group on the basis of MHC class I genotyping ( Table 1 ) . Five of the Mamu-A1*002+ animals and three of Mamu-A1*002- animals were also positive for Mamu-A3*13 , which encodes another molecule identified as a ligand for KIR3DL05 ( Table 1 ) [42] . As a reflection of the high prevalence of KIR3DL01 in rhesus macaques , eleven of these animals expressed KIR3DL01 allotypes that could be detected by staining with the NKVFS1 antibody ( Table 2 ) [29] . Eleven of the animals were also positive for one or more Mamu-Bw4 alleles predicted to encode ligands for KIR3DL01 ( Table 1 ) . Complete KIR genotyping by next generation sequencing corroborated the presence or absence of KIR3DL01 and KIR3DL05 , and identified twenty-three novel KIR alleles in these animals ( Table 2 & S1 Table ) . SIV loads in plasma and lymphocyte counts in peripheral blood were monitored at weekly to monthly intervals after SIV inoculation . Absolute counts for NK and T cell subsets were determined by staining whole blood with antibodies to lineage-specific markers in a bead-based assay to adjust for sample volume ( S1 Fig ) . NK cells were defined as CD8+CD3- lymphocytes and verified by staining with an antibody to human NKG2A that cross-reacts with multiple NKG2 family members in macaques [43] . PBMCs were also stained in parallel with a separate panel that included antibodies ( or tetramer ) to KIR3DL01 , KIR3DL05 ( Mamu-A1*002 Gag GY9 tetramer ) , CD16 and CD56 . Absolute counts for NK cell subsets defined by the expression of KIR3DL01 , KIR3DL05 , CD16 and CD56 were calculated as a percentage of total NK cell counts at each time point . Longitudinal changes in lymphocyte counts for individual animals are shown in Fig 1 and summarized as mean cell counts for Mamu-A1*002+ versus–A1*002- animals in S2 Fig . Consistent with the variegated expression of KIRs , the majority of NK cells were KIR3DL01/05 double-negative ( KIR3DL01-05- ) and there was considerable animal-to-animal variation in the frequency of KIR3DL01+ and KIR3DL05+ NK cells . Prior to SIV inoculation , KIR3DL01+ cells constituted 12 . 8% ±4 . 7 ( 22 . 5±13 . 2 cells/μl ) , KIR3DL05+ cells constituted 8 . 7% ±1 . 4 ( 17 . 3±6 . 3 cells/μl ) and double-positive ( KIR3DL01+05+ ) cells constituted 0 . 58% ±0 . 21 ( 0 . 84±0 . 34 cells/μl ) of circulating NK cells . In response to SIV infection , sharp increases were observed in total NK and CD8+ T cell counts ( Fig 1A and 1B ) . These responses were reflected by significant increases in each of the KIR-defined NK cell subsets during acute infection ( weeks 1–4 ) , and were sustained by the KIR3DL05+ , KIR3DL01+05+ and KIR3DL01-05- subsets during chronic infection ( weeks 6–24 ) ( Fig 1D–1G ) . Phenotypic analyses previously defined CD16+CD56- and CD16-CD56+ NK cell populations in rhesus macaques that correspond to CD16+CD56dim and CD16-CD56bright populations in humans [44 , 45] . Similar to their human counterparts , CD16+CD56- ( CD16+ ) NK cells represent the predominant NK cell population in blood and have a higher capacity for cytolytic activity than the less frequent and less mature CD16-CD56+ ( CD56+ ) subset [44] . Macaques also have a CD16-CD56- NK cell population that is not found in humans , which appears to represent an intermediate in the differentiation of CD56+ NK cells into CD16+ NK cells [44–46] . Consistent with a previous cross-sectional study [44] , we observed significant increases in cell counts for the CD16+ subset during acute and chronic infection ( Fig 1H ) , and for the CD16-CD56- subset during chronic infection ( Fig 1J ) , whereas the CD56+ population remained unchanged ( Fig 1I ) . Unlike humans , which only express KIRs on functionally mature CD16+CD56dim NK cells , macaques express KIRs on CD16+ , CD56+ and CD16-CD56- NK cells [47] . Longitudinal comparisons revealed differences in the distribution of KIR3DL01 and KIR3DL05 on these subsets . Prior to SIV inoculation , KIR3DL01 and KIR3DL05 were both expressed at a higher frequency on CD16+ NK cells than on CD56+ or CD16-CD56- NK cells ( Fig 2 ) . Following SIV infection , the frequency of CD56+ and CD16-CD56- NK cells expressing KIR3DL05 rapidly increased , approaching similar percentages as the CD16+ subset during chronic infection ( Fig 2B ) . In contrast , the distribution of KIR3DL01 did not change during acute infection; however , the percentage of KIR3DL01+ CD16+ NK cells gradually declined coincident with an increase in the frequency of CD56+ and CD16-CD56- NK cells expressing this KIR during chronic infection ( Fig 2A ) . These changes are reflected by a decrease in the frequency of CD16-CD56- NK cells and an increase in the frequency of CD16+ NK cells lacking both KIR3DL01 and KIR3DL05 ( KIR3DL01-05- ) ( Fig 2C ) . These observations reveal differential changes in the proportion of CD16+ , CD56+ and CD16-CD56- NK cells expressing KIR3DL01 versus KIR3DL05 in response to SIV infection . NK cell education in humans increases the frequency of cells bearing KIRs that recognize HLA class I ligands , while decreasing cognate receptor levels on the cell surface [48–50] . We therefore analyzed the percentage NK cells expressing KIR3DL01 and KIR3DL05 , and the mean fluorescence intensity of surface staining for these receptors , with respect to the presence or absence of their MHC class I ligands . Neither the frequency nor the intensity of KIR3DL01 staining correlated with the predicted number of Mamu-Bw4 alleles ( Fig 3A and 3B ) . The frequency of KIR3DL05+ NK cells also did not correlate with the number of alleles encoding ligands for this receptor ( Mamu-A1*002 and/or–A3*13 ) ( Fig 3C ) ; however , KIR3DL05 staining did correlate inversely with the presence of these alleles ( Fig 3D ) . This correlation appears to be primarily driven by Mamu-A1*002 , since independent comparisons of KIR3DL05 staining for animals with or without these alleles revealed significantly lower KIR3DL05 levels in association with Mamu-A1*002 ( Fig 3F ) , but not Mamu-A3*13 ( S3A Fig ) . Additional analyses indicated that these patterns of KIR3DL01 and KIR3DL05 staining did not change in response to SIV infection . Comparisons of the frequency and intensity of KIR3DL01 staining at several time points before and after SIV infection did not reveal significant associations with the number of Mamu-Bw4 alleles ( S3B Fig ) . Similarly , whereas there was no difference in the frequency of KIR3DL05+ NK cells before or after SIV infection ( Fig 3E ) , KIR3DL05 staining was significantly lower for Mamu-A1*002+ animals prior to SIV inoculation and at multiple time points during acute and chronic infection ( Fig 3F ) . The absence of a correlation between KIR3DL01 staining and the number of Bw4 alleles in these animals may reflect incomplete knowledge of the ligands for this receptor , since several of the Mamu-Bw4 alleles listed in Table 1 were predicted to encode ligands for KIR3DL01 based on sequences in their α1 and α2 domains [29] , rather than on experimental verification . Furthermore , because next generation sequencing methods used for KIR and MHC class I genotyping cannot differentiate two or more alleles with the same sequence , these analyses do not account for possible differences in the copy number of KIR3DL01 or Mamu-Bw4 genes that may influence KIR3DL01 expression in some animals . In the case of KIR3DL05 , a dominant effect of Mamu-A1*002 on the expression of this receptor is consistent with the unusually high avidity of Mamu-A1*002 for KIR3DL05 [30] and with higher expression levels for Mamu-A1*002 than for ‘minor’ alleles of the Mamu-A3*13 locus [51] . Thus , the lower levels of KIR3DL05 staining detected on NK cells from Mamu-A1*002 animals suggest that this molecule may have a particularly strong effect on the education of KIR3DL05+ NK cells . Despite differences in KIR3DL05+ staining in Mamu-A1*002+ versus–A1*002- macaques , and changes in the frequency of NK cells expressing this KIR during acute infection , viral loads and CD4+ T cell counts did not differ for Mamu-A1*002+ versus -A1*002- animals ( Fig 1C , 1K and 1L ) . These comparisons therefore did not reveal an advantage to SIV replication in Mamu-A1*002+ macaques as a consequence of the presentation of inhibitory peptides to KIR3DL05+ NK cells , as suggested by cell culture experiments with sorted KIR3DL05+ NK cells [16] . Changes were observed in the expression of proliferation and activation markers on circulating NK cells in response to SIV infection that correspond to changes in absolute NK cell counts . The percentages of NK cells expressing Ki-67 as a marker for proliferation , and CD69 or HLA-DR as activation markers , were determined for CD16+ , CD56+ , CD16-CD56- , KIR3DL01+ , KIR3DL05+ and -KIR3DL01-05- NK cells ( S4 Fig ) , and used to calculate absolute counts for each subset . Since differences in NK cell counts were not observed for Mamu-A1*002+ versus–A1*002- animals , data from all eleven KIR3DL01+ animals and all twelve KIR3DL05+ animals were analyzed together . Significant increases were detected in the number of cells expressing Ki-67 , CD69 and HLA-DR during acute and chronic infection ( Fig 4 ) . The most significant increases were observed for CD16+ NK cells ( Fig 4A , 4C and 4E ) , which represent the largest and most functionally mature NK cell population in peripheral blood . Increases in the number of cells expressing Ki-67 were particularly evident during acute infection ( Fig 4A and 4B ) , indicating that changes in NK cell counts during the first three weeks of SIV infection are probably due , at least in part , to cell proliferation . Higher numbers of KIR3DL01+ , KIR3DL05+ and KIR3DL01-05- NK cells expressing CD69 and HLA-DR further indicate broad NK cell stimulation ( Fig 4D and 4F ) . Thus , changes in NK cell activation and proliferation parallel increases in cell numbers during acute and chronic SIV infection . To assess the potential of NK cells to home to tissues , PBMCs were stained with a panel that included antibodies to the mucosal homing receptor α4β7 as an indicator of trafficking to the intestinal mucosa , the chemokine receptor CCR7 as a marker for trafficking to peripheral lymphoid tissues and the chemokine receptor CXCR3 as a marker for trafficking to sites of inflammation . The numbers of CD16+ , CD56+ , CD16-CD56- , KIR3DL01+ , KIR3DL05+ and KIR3DL01-05- NK cells expressing each of these markers were calculated from absolute counts as described above . Relatively few circulating NK cells expressed CCR7 or CXCR3 , and with the exception of a modest increase in CXCR3+ KIR3DL01-05- NK cells during acute infection , significant changes for these markers were not detected ( S5 Fig ) . Significant increases were , however , observed in the frequency of CD16+ and CD16-CD56- NK cells expressing α4β7 , particularly during chronic infection ( Fig 4G ) . Among the KIR-defined subsets , the KIR3DL01-05- population exhibited the greatest increase in the percentage of α4β7+ cells , with significant increases in the frequency of α4β7 also detectable for KIR3DL01+ cells during acute infection and KIR3DL05+ cells during chronic infection ( Fig 4H ) . The potential antiviral activity of circulating NK cells was assessed by staining for functional markers of degranulation and cytokine release . PBMCs were incubated overnight , with and without stimulation with MHC class I-deficient 721 . 221 cells , and in the presence of an antibody to CD107a as a marker for degranulation . The cells were then stained the following day with reagents to differentiate KIR3DL01+ and KIR3DL05+ NK cells and for intracellular accumulation of TNFα ( S6 Fig ) . Because of variations in cell viability as a result of overnight incubation , these markers were analyzed as percentages of their respective NK cell populations rather than calculating absolute cell counts . In accordance with the especially broad and potent “missing self” stimulus provided by the 721 . 221 cell line , the overall magnitude of CD107a and TNFα upregulation was much higher in response to incubation with 721 . 221 cells than in the absence of these cells; however , differences in the expression of these markers were not detectable among KIR3DL01+ , KIR3DL05+ and KIR3DL01-05- NK cell subsets ( Fig 5A and 5B ) . We therefore focused on NK cell responses without 721 . 221 cells , occurring as a result of in vivo and/or ex vivo activation by SIV-infected cells , as a more physiological reflection of antiviral activity . In the absence of 721 . 221 cells , CD107a and TNFα were strongly upregulated on KIR3DL01-05- NK cells during acute and chronic infection ( Fig 5C and 5D ) . Although CD107a expression on KIR3DL01+ and KIR3DL05+ NK cells was delayed relative to the KIR3DL01-05- population , significant increases in both CD107a and TNFα were also detected for these subsets during acute infection ( Fig 5C and 5D ) ; however , while these responses were sustained for KIR3DL01+ NK cells , the percentage of KIR3DL05+ NK cells expressing CD107a and TNFα declined to baseline levels during chronic infection ( Fig 5C and 5D ) . Hence , these results reveal functional differences in degranulation and cytokine release that suggest greater antiviral activity for KIR3DL01+ NK cells than for KIR3DL05+ NK cells . Lymphocytes were isolated from lymph node and colorectal biopsies prior to SIV infection , and at two- and eight-weeks post-infection , to assess changes in the frequency of NK cells in these tissues . Compared to peripheral blood , NK cells constituted a relatively small percentage of lymphocytes in these tissue compartments . Before SIV infection , average NK cell frequencies in lymph nodes and gut-associated lymphoid tissues ( GALT ) were 1 . 2%±0 . 086 and 0 . 52%±0 . 065 , respectively , compared to 7 . 0%±0 . 96 in PBMCs . By eight-weeks post-infection , a small but highly significant increase in the percentage of total NK cells was detectable in lymph nodes ( Fig 6A ) . This increase was reflected by changes in the CD16+ , CD56+ and CD16-CD56- subsets . While the majority of NK cells in macaque lymph nodes are CD16-CD56- [44] , a significant increase in the frequency of CD16+ NK cells , and a corresponding decrease in the frequency of CD56+ cells , was observed in response to SIV infection ( Fig 6B ) . In contrast , the percentage of total NK cells in GALT did not change following SIV infection ( Fig 6A ) , and aside from a transient increase in the CD56+ population during acute infection , the proportions of CD16+ , CD56+ and CD16-CD56- cells also remained unchanged ( Fig 6C ) . To assess changes in the frequency of NK cells expressing KIR3DL01 and KIR3DL05 , the percentages of these cells were compared in lymph node and colorectal biopsies . Whereas SIV infection did not alter the frequency of KIR3DL05+ NK cells ( Fig 7A ) , significant increases in the frequency of KIR3DL01+ NK cells were observed in GALT during acute and chronic infection ( weeks 2 and 8 ) and in lymph nodes during chronic infection ( week 8 ) ( Fig 7B ) , which were mirrored by corresponding reductions in the frequency of KIR3DL01-05- NK cells in these tissues ( Fig 7C ) . Further analysis of KIR expression revealed that , similar to peripheral blood , KIR3DL01 and KIR3DL05 are expressed by a higher percentage of CD16+ NK cells than CD56+ or CD16-CD56- NK cells in lymph nodes ( Fig 7D and 7E ) ; however , the increased frequency of KIR3DL01+ NK cells appeared to reflect the upregulation of this receptor on CD56+ and CD16-CD56- NK cells ( Fig 7E ) . In the GALT , increases in the expression of KIR3DL01 ( Fig 7G ) , but not KIR3DL05 ( Fig 7F ) , on CD56+ and CD16-CD56- NK cells were more dramatic . Indeed , increases in the expression of KIR3DL01 on CD56+ and CD16-CD56- NK cells account almost entirely for the higher frequency of cells expressing this KIR after SIV infection ( 39 . 2%±7 . 6 and 24 . 9%±8 . 2 , respectively ) ( Fig 7G ) . Representative data showing the preferential upregulation of KIR3DL01 , but not KIR3DL05 , on NK cells of the GALT at week 2 post-infection , and that the majority of these KIR3DL01+ cells are either CD56+ or CD16-CD56- , is provided in Fig 8 .
KIR and HLA class I polymorphisms play a central role in the regulation of NK cell responses and can have a significant impact on the course of HIV-1 infection [17 , 19 , 23] . Studies to address the functional significance of KIR-MHC class I interactions in SIV-infected macaques have , however , been hampered by the lack of defined ligands for KIRs in non-human primates . To address this limitation , we and others have identified ligands for macaque KIRs [29 , 30 , 40 , 42 , 52] . We identified Mamu-A1*002 , a common MHC class I molecule expressed in approximately 20% of Indian-origin rhesus macaques , as a ligand for KIR3DL05 [30] , and found that nearly a third of the SIV peptides bound by Mamu-A1*002 suppress the cytolytic activity of KIR3DL05+ NK cells [16] . We further identified MHC class I molecules with a Bw4 motif as ligands for KIR3DL01 , which is the most polymorphic and commonly expressed KIR in rhesus macaques [29] . To determine how these receptor-ligand interactions influence NK cell responses and the ability to contain virus replication , we infected twelve KIR- and MHC class I-defined rhesus macaques with SIV and monitored NK cell responses and viral loads in peripheral blood and lymphoid tissues . As in humans , macaque KIRs are expressed in a variegated and stochastic fashion , which accounts for the presence of KIR3DL01 and KIR3DL05 on a fraction of total NK cells [29 , 30] . Unlike humans , however , which only express KIRs on functionally mature CD16+CD56dim NK cells , KIRs are expressed on all peripheral blood NK cell subsets in macaques [47] . KIR3DL01 and KIR3DL05 were accordingly detected on CD16+ , CD56+ and CD16-CD56- NK cells . Nevertheless , these KIRs are expressed on a higher percentage of CD16+ cells than CD56+ or CD16-CD56- cells , further supporting a functional correspondence between the CD16+ NK population in macaques and the dominant CD16+CD56dim population in human blood . The education of human NK cells is associated with an increase in the frequency of cells bearing KIRs that recognize self HLA class I ligands and a corresponding decrease in surface staining for those receptors [48–50] . Although neither the frequency of KIR3DL01+ or KIR3DL05+ NK cells , nor the intensity of KIR3DL01 staining , was associated with differences the number of MHC class I alleles predicted to encode ligands for these receptors , the level of KIR3DL05 staining correlated inversely with the presence of Mamu-A1*002 and -A3*13 . This correlation appears to be driven primarily by Mamu-A1*002 , since independent comparisons revealed significant differences in KIR3DL05 staining for Mamu-A1*002+ versus -A1*002- animals , but not for Mamu-A3*13+ versus -A3*13- animals; however , since KIR3DL05 staining was lower for Mamu-A3*13+ animals than for animals lacking both Mamu-A1*002 and -A3*13 , and most of the Mamu-A1*002+ animals were also -A3*13+ as a consequence of linkage disequilibrium between these alleles [53] , it is possible that Mamu-A3*13 may have a modest influence on KIR3DL05 levels that could be additive in combination with Mamu-A1*002 . Nevertheless , a more dominant effect of Mamu-A1*002 on KIR3DL05 staining would be consistent with the unusually high avidity of Mamu-A1*002 for KIR3DL05 [30] and with higher levels of Mamu-A1*002 expression than for gene products of the ‘minor’ Mamu-A3*13 locus [51] . Thus , lower levels of KIR3DL05 staining for Mamu-A1*002+ macaques suggest a pronounced effect of Mamu-A1*002 on the education of KIR3DL05+ NK cells . It should be noted , however , that the nature of NK cell education , or licensing , is not fully understood . It is possible that reduced levels of KIR3DL05 on NK cells from Mamu-A1*002+ animals reflect the binding of Mamu-A1*002 to KIR3DL05 on the same cells , similar to cis interactions previously described for MHC class I ligands of murine Ly49A [54] and human leukocyte Ig-like receptors ( LILRs ) [55 , 56] . Although to our knowledge cis interactions have not been reported for KIRs , it is tempting to speculate that high avidity interactions between Mamu-A1*002 and KIR3DL05 may sequester KIR3DL05 , thereby reducing the accessibility of this receptor for staining on the cell surface . Such interactions would not necessarily be inconsistent with the functional effects of KIR ligands on NK cell licensing , since the sequestration of inhibitory KIRs may reduce the threshold required for NK cell activation through these receptors . Although SIV peptides bound by Mamu-A1*002 were recently shown to suppress the cytolytic activity of Mamu-KIR3DL05+ NK cells , neither viral loads nor NK cell responses differed for Mamu-A1*002+ versus–A1*002- animals . Hence , these results did not reveal an advantage to SIV replication in Mamu-A1*002+ animals as a result of the presentation of inhibitory peptides to KIR3DL05+ NK cells , as in vitro assays with sorted primary NK cells might suggest [16] . However , these findings do not necessarily preclude a contribution of peptides to the evasion of NK cell responses . Rhesus macaques typically express six to thirteen different KIRs [36 , 40] , and the ligands for most of these receptors remain undefined . Macaque NK cells also express other more conserved inhibitory and activating receptors , such as CD94/NKG2 heterodimers and natural cytotoxicity receptors ( NCRs ) that may influence responses to viral infection . Thus , it is possible that the effects of Mamu-A1*002-bound viral peptides on KIR3DL05+ NK cells may have been obscured by other receptor-ligand interactions . Furthermore , while lower levels of KIR3DL05 on NK cells from Mamu-A1*002+ animals suggest a dominant effect of Mamu-A1*002 on the education of KIR3DL05+ NK cells , other MHC class I molecules may also serve as ligands for this receptor . Therefore , given the complexity of KIR and MHC class I immunogenetics in rhesus macaques , and our limited knowledge of receptor-ligand interactions in this species , it is perhaps not surprising that we did not detect gross differences in viral loads or NK cell responses as a result of peptide-dependent modulation of a single KIR ligand . The onset of adaptive immunity may also have complicated NK cell responses during chronic infection . Since previous studies have shown that the selection of CD8+ T cell escape variants in most of the SIV epitopes bound by Mamu-A1*002 generally occurs only after months of chronic infection [57–59] , CD8+ T cell escape is unlikely to have affected KIR3DL05+ NK cell responses or viral loads during acute infection; however , we cannot exclude the possibility that the emerge of escape variants may have contributed to variability in KIR3DL05+ NK responses during chronic infection . SIV infection nevertheless broadly stimulated NK cell responses . Within the first four weeks of infection , rapid increases in total NK cell counts , as well as cell counts for the CD16+ and CD16-CD56- populations , were observed in peripheral blood . In accordance with cross-sectional comparisons , these increases were maintained during chronic infection [44] . Similar changes were observed in NK cell populations defined by KIR3DL01 and KIR3DL05 . Increases in KIR3DL01+ , KIR3DL05+ and KIR3DL01+05+ NK cells during acute infection paralleled increases in total NK cells counts; however , while the number of KIR3DL05+ NK cells remained elevated during chronic infection , these increases were not sustained for the KIR3DL01+ subset . Longitudinal analyses revealed additional differences in the percentage of CD16+ NK cells in blood expressing KIR3DL01 versus KIR3DL05 . Whereas the percentage of CD16+ NK cells expressing KIR3DL05 was relatively unchanged , there was a gradual decrease in the frequency of KIR3DL01+ CD16+ NK cells after eight weeks of infection . Although this decrease was partially offset by increases in the frequency of CD56+ and CD16-CD56- cells expressing this KIR , the decline of the larger KIR3DL01+CD16+ population probably accounts for lower overall KIR3DL01+ NK cell counts in blood during chronic infection . Phenotypic analyses further revealed rapid increases in NK cell activation in response to SIV infection . Consistent with increases in circulating NK cell counts , the proliferation marker Ki-67 was highly upregulated during acute and chronic infection on KIR3DL01+ , KIR3DL05+ and KIR3DL01-05- NK cells . Similar increases were also observed in the number of NK cells expressing the activation markers CD69 and HLA-DR , and the mucosal homing receptor α4β7 . Markers of antiviral activity , including CD107a and TNFα , were likewise broadly upregulated during acute infection; however , the expression of these markers on KIR3DL01+ versus KIR3DL05+ NK cells diverged during chronic infection . Whereas CD107a and TNFα continued to be upregulated on KIR3DL01+ and KIR3DL01-05- cells , the percentage of KIR3DL05+ NK cells expressing these antigens declined to baseline levels by week six post-infection . These results reveal differential antiviral responses for KIR3DL05+ versus KIR3DL01+ NK cells , perhaps reflecting a greater role for the KIR3DL01+ subset in controlling SIV replication . Characterization of NK cell subsets in lymph nodes and the gastrointestinal mucosa revealed additional changes in response to SIV infection . Although NK cells constitute a relatively minor percentage of lymphocytes in these tissues , a highly significant increase in the frequency of total NK cells was detected in lymph nodes during chronic infection . Consistent with previous studies [60] , we found that the majority of NK cells in lymph nodes are CD16-CD56- , and of the smaller populations of CD16+ and CD56+ NK cells , the CD56+ subset is more prevalent in naïve animals . Following SIV infection , however , there was an inversion in the frequency of CD16+ versus CD56+ NK cells , with an accumulation of CD16+ NK cells and a corresponding decrease in the percentage of CD56+ NK cells . Significant increases were also detected in lymph node frequencies of KIR3DL05+ and KIR3DL01+ NK cells during acute and chronic infection , respectively . In gut-associated lymphoid tissues , the percentage of total NK cells did not change in response to SIV infection , and with the exception of a transient increase in the frequency of the CD56+ subset , the relative proportions of CD16+ , CD56+ and CD16-CD56- NK cells in this compartment also remained unchanged . However , dramatic changes were observed in the frequency of KIR3DL01+ NK cells . Significant increases in the percentage of KIR3DL01+ NK cells in GALT were detected during both acute and chronic infection . Surprisingly , most of these cells were CD56+ or CD16-CD56- , suggesting that this increase reflects the upregulation of KIR3DL01 on less mature NK cells rather than the accumulation of CD16+ NK cells expressing this KIR . Moreover , this response appears to be specific to KIR3DL01 , since similar increases were not observed for KIR3DL05+ cells . The enrichment of KIR3DL01+ NK cells in the gastrointestinal mucosa is intriguing , since the gut-associated lymphoid tissues are known to be a major source of HIV-1 and SIV replication and CD4+ T cell turnover [61–64] . Although rhesus KIR3DL01 and human KIR3DL1 are not orthologous , they may have similar functions; both recognize Bw4 ligands and are the most polymorphic KIRs of their respective species [29] . Moreover , the lysis of HIV-infected cells by KIR3DL1+ NK cells is primarily triggered by downmodulation of HLA-Bw4 ligands from the cell surface by the viral Nef protein [8 , 22] , and we previously demonstrated that Mamu-Bw4 molecules are efficiently downmodulated by SIV Nef [65] . Thus , increases in the frequency of KIR3DL01+ NK cells in the GALT may reflect an innate cellular response to especially high levels of SIV replication and Bw4 downmodulation in these tissues . To our knowledge , this study represents the first longitudinal analysis of NK cell responses in KIR- and MHC class I-defined macaques . Responses to SIV infection in peripheral blood were characterized by rapid increases in the CD16+ and CD16-CD56- populations , including KIR3DL01+ and KIR3DL05+ subsets . Markers of proliferation , activation and antiviral activity were widely expressed during acute infection , but began to diverge after four weeks , as indicated by sustained CD107a and TNFα upregulation by KIR3DL01+ NK cells , but not by KIR3DL05+ NK cells . Differential responses for KIR3DL01+ versus KIR3DL05+ NK cells were also evident in tissues . Whereas the percentages of KIR3DL05+ NK cells in lymph nodes and the gastrointestinal mucosa did not change , significant increases were observed in the frequency of KIR3DL01+ NK cells , especially in gut-associated lymphoid tissues . Thus , our results reveal broad NK cell activation and dynamic changes in multiple subsets in response to SIV infection , including an enrichment of KIR3DL01+ NK cells in mucosal tissues that represent major sites of ongoing virus replication and CD4+ lymphocyte depletion .
Twelve rhesus macaques ( Macaca mulatta ) of Indian origin , including six male and six female animals , were used in this study . Housing and care of the animals at the Wisconsin National Primate Research Center ( WNPRC ) were in compliance with the standards of the American Association for the Accreditation of Laboratory Animal Care and the University of Wisconsin Research Animal Resources Center ( UWRARC ) . Animal experiments were approved by the UWRARC ( protocol number G005496 ) and conducted in accordance with the principles described in the Guide for the Care and Use of Laboratory Animals [66] . Steps to improve animal welfare included environmental enrichment , such as foraging opportunities and manipulatable devices . Water was continuously available , commercial monkey chow was provided twice daily and fresh produce was supplied three times per week . Animals were sedated with ketamine HCl prior to the collection of blood and biopsy samples to minimize pain and distress associated with experimental procedures and were monitored twice daily by animal care and veterinary staff . Twelve KIR3DL05+ rhesus macaques , including six Mamu-A1*002+ and six–A1*002- animals , were selected for this study . These included eleven animals expressing allotypes of KIR3DL01 with aspartic acid at position 233 ( KIR3DL01 D233 ) [29] and excluded animals expressing the MHC class I alleles Mamu-A1*001 , -B*008 and–B*17 associated with spontaneous control of SIV replication [58 , 67–69] . KIR3DL05+ and KIR3DL01 D233+ macaques were identified by staining PBMCs with Mamu-A1*002 Gag GY9 tetramer and the NKVFS1 monoclonal antibody as previously described [29 , 30] . All animals were also genotyped by sequencing full-length KIR transcripts as recently described [70] . Briefly , RNA was isolated from PBMCs , and full-length KIR cDNA was sequenced using a PacBio RS II instrument with P6-C4 sequencing reagents . Sequences were identified and novel alleles were classified by comparison to previously reported alleles of rhesus macaque KIRs . GenBank accession numbers for newly identified KIR alleles are listed in S1 Table . MHC class I genotyping was performed using genomic DNA isolated from PBMC by sequencing a 150 bp region of exon 2 ( Illumina MiSeq system ) . Sequences were analyzed by comparison to an in-house database as previously described [71] . The MHC class I- and KIR-genotypes of each of the animals in this study are summarized in Tables 1 and 2 . Animals were infected intravenously with SIVmac239 . A vial of SIVmac239 challenge stock prepared in activated rhesus macaque PBMC was provided by Dr . Ronald Desrosiers , Miller School of Medicine , University of Miami . On the day of inoculation , the vial was thawed and diluted to 50 animal infectious doses per ml in sterile , serum-free RPMI . Within 30 minutes of preparation , a one ml dose of the virus dilution ( 7 . 8 pg p27 ) was administered to each animal under ketamine anesthesia through a 22 g catheter placed aseptically in the saphenous vein . Plasma was collected from blood drawn in tubes with EDTA as an anticoagulant and cryopreserved at -80°C . Virus was pelleted from 0 . 5 to 1 . 0 ml of plasma by ultracentrifugation for one hour at 20 , 000 x g . Viral RNA was extracted , reverse transcribed into cDNA , and quantified by real-time PCR using an assay based on amplification of a conserved SIV gag sequence as previously described [72] . Unless specified otherwise , all antibodies were purchased from BD Biosciences . All flow cytometry data were collected using a BD LSRII SORP and analyses were done with FlowJo 9 . 9 software ( TreeStar Inc . ) . A linear mixed-effect model was used for the analysis of longitudinal data . An individual macaque was included as a random-effect to account for correlation within subjects . The presence or absence of Mamu-A1*002 and the time points after infection were included as fixed-effects in the models . The three phases of the infection were naïve or pre-infection ( week 0 ) , acute infection ( weeks 1–4 ) and chronic infection ( weeks 5–24 ) . Two-sided p-values less than 0 . 05 were considered statistically significant . For the comparison of discrete data , Mann-Whitney U tests were performed using GraphPad Prism V6g . | Natural killer ( NK ) cells are an important cellular defense against viral pathogens , and are regulated in part by interactions between killer-cell immunoglobulin-like receptors ( KIRs ) on NK cells and MHC class I ligands on target cells . Using multi-parameter flow cytometry , we report the first longitudinal study of changes in the phenotypic and functional properties of NK cells in KIR- and MHC class I-defined rhesus macaques infected with simian immunodeficiency virus ( SIV ) . Our findings reveal broad NK cell activation and highly dynamic changes in the phenotypic properties of NK cells in response to SIV infection , including an enrichment of NK cells expressing KIR3DL01 in tissues that represent sites of high levels of virus replication . | [
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] | 2017 | KIR3DL01 upregulation on gut natural killer cells in response to SIV infection of KIR- and MHC class I-defined rhesus macaques |
Resistance-Nodulation-Division ( RND ) efflux pumps are responsible for multidrug resistance in Pseudomonas aeruginosa . In this study , we demonstrate that CpxR , previously identified as a regulator of the cell envelope stress response in Escherichia coli , is directly involved in activation of expression of RND efflux pump MexAB-OprM in P . aeruginosa . A conserved CpxR binding site was identified upstream of the mexA promoter in all genome-sequenced P . aeruginosa strains . CpxR is required to enhance mexAB-oprM expression and drug resistance , in the absence of repressor MexR , in P . aeruginosa strains PA14 . As defective mexR is a genetic trait associated with the clinical emergence of nalB-type multidrug resistance in P . aeruginosa during antibiotic treatment , we investigated the involvement of CpxR in regulating multidrug resistance among resistant isolates generated in the laboratory via antibiotic treatment and collected in clinical settings . CpxR is required to activate expression of mexAB-oprM and enhances drug resistance , in the absence or presence of MexR , in ofloxacin-cefsulodin-resistant isolates generated in the laboratory . Furthermore , CpxR was also important in the mexR-defective clinical isolates . The newly identified regulatory linkage between CpxR and the MexAB-OprM efflux pump highlights the presence of a complex regulatory network modulating multidrug resistance in P . aeruginosa .
Pseudomonas aeruginosa , a major pathogen associated with cystic fibrosis , is known for its intrinsic resistance to a wide range of antimicrobial agents and its ability to develop multidrug resistance following antibiotic therapy [1] . Resistance-Nodulation-Division ( RND ) efflux systems are largely responsible for intrinsic and acquired multidrug resistance in P . aeruginosa; genes encoding 12 RND efflux pumps have been identified in its genome [2 , 3] . Genes encoding RND efflux pumps are highly conserved in the genomes of many living organisms [4] . Recently , increasing attention has been focused on the physiological roles of efflux pumps relevant to the behaviour of bacteria in their natural ecosystems [4–6] . Accumulating evidence has demonstrated that efflux pumps are also important for processes of detoxification of intracellular metabolites , bacterial virulence in animal and plant hosts , cell homeostasis , and intercellular signalling [4] . Previously , we identified a novel MexT regulon , incorporating the MexEF-OprN efflux pump into a broader physiological context in P . aeruginosa [7] . MexT binding sites in the promoter regions of MexT regulon genes in P . aeruginosa are conserved in the promoter regions of orthologous MexT regulon genes in other Pseudomonas species . It is generally accepted that divergence of regulatory sites is slower than that of most non-coding regions among closely related species . This concept has been used to identify novel regulatory sites by comparing the promoter regions of orthologous RND efflux pump genes from closely related species [8] . The MexAB-OprM efflux pump plays a significant role in multidrug resistance in P . aeruginosa [2 , 3] . Overexpression of the mexAB-oprM operon was first identified in nalB-type P . aeruginosa strains , a phenotypic group showing multiple antibiotic resistance [9] . It is now known that two tandem promoters control expression of the mexAB-oprM operon in P . aeruginosa; the distal promoter is modulated by repressor MexR [10 , 11] , while the proximal promoter is modulated by repressor NalD [12] . A third repressor , NalC , indirectly modulates expression of the mexAB-oprM operon by controlling the expression level of ArmR , an anti-MexR protein [13–15] . Mutations causing defective forms of MexR , NalC , and NalD lead to overexpression of the mexAB-oprM operon and enhance multidrug resistance in P . aeruginosa [10–14] . In particular , mutations in mexR are the major genotypes associated with nalB-type strains and are often identified among clinical isolates [10–12 , 16 , 17] . In addition to MexR , NalC , and NalD , additional regulatory components have been shown to influence expression of the mexAB-oprM operon in P . aeruginosa . MexT , a LysR-type activator of RND efflux pump MexEF-OprN , exerts a negative regulatory effect on MexAB-OprM expression through an uncharacterized mechanism in nfxC-type P . aeruginosa strains [18] . RocA2 , a response regulator of the pilus assembly machinery cluster operon , also exerts a negative regulatory effect on MexAB-OprM expression , indicating a potential functional linkage between the MexAB-OprM efflux pump and biofilm formation [19] . BrlR , a biofilm-specific MerR-type regulator , activates MexAB-OprM expression through its binding to the promoter region during biofilm formation in P . aeruginosa [20] . AmpR , a LysR-type regulator of AmpC β-lactamase , also exerts a positive regulatory effect on MexAB-OprM expression by repressing MexR expression [21] . The existence of multiple regulatory components renders the mexAB-oprM operon subject to complex regulation in P . aeruginosa . As a response regulator , CpxR was first identified as an important regulator for protecting cell envelope and promoting cell survival in Escherichia coli [22–24] . Numerous studies have verified the role of CpxR in antibiotic resistance in pathogenic bacteria . In E . coli , overexpression of CpxR confers resistance to β-lactams in a drug-hypersusceptible mutant , in which AcrAB , a major efflux pump , was defective [25]; CpxR is involved in the defence response to aminoglycoside-induced oxidative stress [26 , 27]; it confers resistance to fosfomycin by directly repressing glpT and uhpT expression in enterohemorrhagic E . coli [28]; Induction of the CpxR pathway directly contributes to tolerance toward certain antimicrobial peptides , including polymyxin B and protamine [29 , 30] . In Salmonella typhimurium , CpxR also confers resistance to antimicrobial peptides protamine , magainin , and melittin through activation of two Tat-dependent peptidoglycan amidases [31]; moreover , it confers strong ceftriaxone resistance by modulating expression of STM1530 and ompD [32]; Laboratory-generated and clinical S . typhimurium strains lacking CpxR show reduced resistance to aminoglycosides and β-lactams [33] . In Klebsiella pneumoniae , CpxR is involved in multidrug resistance through direct promoter binding and activation of ompC and kpnEF [34 , 35] . In Vibrio cholerae , CpxR can activate expression of RND efflux pumps VexAB and VexGH , which can confer resistance to ampicillin [36] . Erwinia amylovora lacking CpxR show reduced resistance to β-lactams , aminoglycosides , and lincomycin [37] . Although CpxR is widely distributed in the genomes of various gamma-proteobacteria , its role in Pseudomonas species remains unknown . In this study , bioinformatics , biochemical , and genetic analyses identified a regulatory linkage between CpxR and multidrug efflux pump MexAB-OprM in P . aeruginosa . We show that CpxR activates mexAB-oprM expression by directly binding to the distal promoter and is important for multidrug resistance in nalB-type P . aeruginosa isolates under both laboratory and clinical conditions .
In order to unravel the regulatory networks responsible for modulating the expression of RND efflux pumps in P . aeruginosa , comparative genomic analysis was carried out to identify novel regulatory sites on the promoters of orthologous RND operons among different Pseudomonas species . In this case , we compared the promoter regions of the orthologous operons of mexAB-oprM , mexEF-oprN , and muxABC-opmB in 15 genome-sequenced Pseudomonas species . The results showed that , apart from the previously identified NalD repressor binding site on the mexA promoter [12] ( see S1A Fig ) , a well conserved DNA motif was identified on the muxA promoter ( Fig 1A ) . Interestingly , the motif contains a consensus binding site ( 5′-GTAAA- ( N ) 4-8-GTAAA-3′ ) for CpxR , a response regulator of the two-component system in E . coli [38] . The gene locus PA14_22760 has been annotated as cpxR in the genome of P . aeruginosa strain PA14 in the Pseudomonas database [39]; it encodes a protein with the highest BLASTP score ( 47% identity ) with E . coli CpxR among the ORFs of P . aeruginosa strain PA14 . As CpxR is a global regulator of the cell envelope stress response in E . coli [22 , 42] and might regulate the muxABC-opmB operon ( as shown in the inter-species analysis above; Fig 1A ) , we used its binding site ( 5′-GTAAA- ( N ) 4-8-GTAAA-3′ ) as a probe to perform intra-species analysis of the genome of P . aeruginosa PA14 . Because CpxR can exert its activity independent of the orientation of its binding site [38] , the existence of potential CpxR binding sites on both strands was assessed . The results showed that a number of genes possess the consensus CpxR binding site on their promoter regions ( S1 Table ) . Among such genes , PA14_22740 , which is adjacent to the cpxR locus in the genome of P . aeruginosa strain PA14 , encodes a small , putative periplasmic protein with two LTXXQ motifs ( S2 Fig ) , a canonical feature of the protein encoded by cpxP , the cognate target gene of CpxR in E . coli [43 , 44] . Furthermore , among P . aeruginosa strain PA14 genes , the protein encoded by PA14_22740 showed the highest BLASTP score ( 25% identity ) with E . coli CpxP protein . Thus , we annotated PA14_22740 as a cpxP gene in P . aeruginosa strain PA14 . Surprisingly , the promoter of mexAB-oprM in P . aeruginosa PA14 also contains a consensus CpxR binding site ( S1 Table and Fig 1B ) . The inter-species analysis showed that the conserved CpxR binding site is present on the promoter regions of the identified cpxP orthologues , similar to the case of the muxA orthologues ( Fig 1C , for the details see S1B Fig ) . In contrast , the presence of the CpxR binding site on the mexA promoter is unique to P . aeruginosa among the 15 Pseudomonas species for which the entire genome has been sequenced . Therefore , for the first time , by using comparative genomic analysis , we have found a potential regulatory linkage between CpxR and mexAB-oprM in P . aeruginosa . Since the newly identified CpxR binding site is located upstream of the distal promoter of mexA , which is known to be modulated by the MexR repressor , we further investigated the existence of the MexR binding site ( 5′-GTTGA- ( N ) 5-TCAAC-3′ , Fig 1B ) [40 , 41] in the promoter regions of mexA orthologues among Pseudomonas species . The results showed that the presence of the MexR binding site on the mexA promoter is unique to P . aeruginosa ( Fig 1C ) . In contrast , the NalD binding site is well conserved in the promoter region of each mexA orthologue ( Fig 1C and S1A Fig ) . In fact , the nalD orthologue ( ttgR ) locus is divergently linked to the mexA orthologue locus in the genomes of other Pseudomonas species , while the mexR locus is divergently linked to the mexA locus , which is completely separated from the nalD locus in the genome of P . aeruginosa ( Fig 1D ) . These observations indicate a species-specific coupling of multiple regulators ( CpxR and MexR ) with the MexAB-OprM efflux pump in P . aeruginosa . When the mexA , muxA , and cpxP promoters were fused with the lacZ reporter gene and their expression levels were monitored , we found that their activities were under the control of CpxR in P . aeruginosa . In particular , expression of the mexA , muxA , and cpxP promoters was strongly activated by the presence of ectopically expressed CpxR in PA14ΔcpxR ( Table 1 ) . In contrast , when the newly identified CpxR binding site on the mexA promoter was altered by site-directed mutagenesis ( mexApM1 , for details see Fig 1B ) , CpxR-dependent activation was completely abolished . When the conserved phosphorylation site ( the 52nd aspartate residue ) of CpxR was mutated to alanine ( CpxRD52A ) , the ectopically expressed CpxR could not activate expression of target promoters in PA14ΔcpxR ( Table 1 ) . As the stability of CpxRD52A is not altered in comparison with that of wild-type CpxR ( S3 Fig ) , these results suggest that phosphorylation of CpxR is important for its activity . The importance of phosphorylation in CpxR activation is further supported by the fact that the phosphorylated form of CpxR clearly bound to the target promoter region containing the intact conserved DNA binding site in a concentration-dependent manner in electrophoretic mobility shift assays ( EMSAs ) ( S4A and S4B Fig , from lane 2 to 5 ) . In contrast , such binding was abolished when an excess amount of unlabelled competitor DNA was present or the non-phosphorylated form of CpxR ( in the absence of acetyl phosphate ) was used in the assay ( S4A and S4B Fig , lane 6 and 7 respectively ) . When DNA fragments with a mutated CpxR binding site were used in the assay , no binding was observed ( S4C Fig ) . DNase I footprinting analysis further confirmed the location of the binding site of phosphorylated CpxR in the mexA promoter region ( Fig 2A and 2B ) . Taken together , these results demonstrate that CpxR binds directly to its conserved DNA binding site in a phosphorylation-dependent manner , and such binding is essential for CpxR-dependent activation of the target promoters in P . aeruginosa . The unique P . aeruginosa-specific regulatory linkage between CpxR and MexAB-OprM was demonstrated by similar experiments in an alternative host , Pseudomonas putida KT2440 . In this strain , consensus CpxR binding sites exist on the promoters of PP_4504 and PP_3585 , the orthologous genes of cpxP and muxA , respectively , but not on the promoter of ttgA , the orthologous gene of mexA . In P . putida KT2440 , ectopically expressed CpxR significantly activates expression of PP_4504 and PP_3585 , but does not alter expression of ttgA ( Table 1 ) . Therefore , among the Pseudomonas species analysed , CpxR-dependent activation of the promoter of mexAB-oprM is unique in P . aeruginosa . As CpxR can activate expression of mexAB-oprM and muxABC-opmB , the contributions of these genes to multidrug resistance in P . aeruginosa were investigated . Minimal inhibitory concentrations ( MICs ) of ciprofloxacin , ofloxacin , ceftazidime , cefsulodin , and aztreonam , but not amikacin , were increased at least 4-fold by ectopically expressed CpxR in PA14 and PA14ΔcpxR strains ( Table 2 ) in a manner dependent on MexA , but not MuxA . In this case , ectopically expressed CpxR failed to increase the MICs of the tested antibiotics in a mexA null-mutant PA14ΔmexA strain . In contrast , the MIC increases caused by the ectopically expressed CpxR were not altered in a muxA null-mutant PA14ΔmuxA strain ( Table 2 ) . These results indicate that CpxR activates expression of mexAB-oprM , which enhances multidrug resistance in P . aeruginosa . The newly identified CpxR binding site is located upstream of the distal promoter of mexA in P . aeruginosa . To determine which promoter ( distal or proximal ) is activated by CpxR , two mexA promoter-lacZ reporter systems were constructed . To monitor the expression of the distal promoter , the entire proximal promoter region was excluded in the mexApM2::lacZ construct; to monitor the expression of the proximal promoter , a key nucleotide within the -10 region of the distal promoter [45] was disrupted in the mexApM3::lacZ construct ( Fig 1B ) . Ectopically expressed CpxR strongly activated expression of mexApM2::lacZ , but not mexApM3::lacZ , in the PA14ΔcpxR strain ( Table 1 ) , indicating that CpxR is involved only in regulation of the distal mexA promoter , which is also directly regulated by the MexR repressor in P . aeruginosa . The possible interplay between CpxR and MexR on expression of the distal mexA promoter prompted us to investigate the involvement of CpxR in the multidrug resistance phenotype of nalB-type P . aeruginosa , which has been associated with defective MexR in laboratory and clinical isolates [13 , 16 , 17] . We monitored mexA expression levels in strains of various genetic backgrounds , including mexR null-mutant PA14ΔmexR , cpxR null-mutant PA14ΔcpxR , cpxR/mexR double-mutant PA14ΔcpxRΔmexR , and wild-type PA14 strains . The mexA expression level of the PA14ΔmexR strain was significantly higher than that of the wild-type PA14 strain ( Fig 3 ) , a result similar to that previously reported for nalB-type P . aeruginosa [12] . Moreover , the lack of CpxR in the PA14ΔcpxRΔmexR strain resulted in decreased mexA expression ( Fig 3 ) . To further confirm the regulatory influence of CpxR on expression of MexAB-OprM , the relative transcript level of mexB and protein level of MexA were investigated in the wild-type and mutant strains by quantitative real-time PCR and western blot analysis , respectively . The regulatory patterns of CpxR on the expression of the chromosomal genes were similar to that of the mexAp::lacZ reporter system in the tested strains ( Fig 3 ) . To evaluate the influence of changes in mexA expression on drug resistance , the MICs of antibiotics were determined . In the PA14ΔmexR strain , the MICs of the antibiotics were significantly increased in comparison with those of the parental PA14 strain . Moreover , a lack of cpxR led to decreased MICs in the PA14ΔcpxRΔmexR strain in comparison with those of the PA14ΔmexR strain . These results indicate that the MICs of antibiotics in the tested strains are correlated well with their expression levels of mexA ( Table 3 ) . A similar effect was observed when cpxR ( PA3204 ) was deleted in a mexR-deleted mutant of another standard laboratory P . aeruginosa strain , PAO1 ( PAO1ΔmexR ) , indicating that the observed effect was not specific to a particular P . aeruginosa strain ( Table 3 ) . Defective mexR is a genetic trait associated with the clinical emergence of multidrug resistance in P . aeruginosa during antibiotic treatment [16] . Previously , mexR defective strains were selected in vitro by plating susceptible P . aeruginosa strains on agar medium containing lethal levels of a fluorquinolone antibiotic ( ofloxacin or ciprofloxacin ) and a third-generation cephalosporin antibiotic ( cefsulodin or cefoperazone ) [10 , 11 , 46 , 47] . In this work , PA14 cells were plated on agar medium containing lethal levels of ofloxacin and cefsulodin antibiotics , after which 40 ofloxacin-cefsulodin resistant ( OCR ) colonies were randomly collected for further analysis . Among the selected colonies , five isolates exhibited significantly reduced expression levels of mexA , as well as reduced MICs of ciprofloxacin , ceftazidime , and aztreonam , when cpxR was deleted ( Table 4 ) . When the mexR sequences of the five isolates were analysed , PA14OCR16 , PA14OCR24 , PA14OCR28 , and PA14OCR32 were found to harbour defective mutations . The plasmid harbouring cpxR , but not cpxRD52A , complemented the phenotype , indicating that CpxR mediated the observed alteration in isolate PA14OCR16 ( Table 4 ) . The pattern of CpxR-dependent activation of mexAB-oprM expression and enhancement of multidrug resistance in the four OCR isolates with defective mutations in mexR was identical to that of the engineered PA14ΔmexR strain ( Table 3 ) , implying that CpxR might perform a common function in mexR-defective nalB-type P . aeruginosa strains . Interestingly , the fifth OCR isolate , PA14OCR36 , had an intact mexR gene . In this particular isolate , the expression level of cpxP was drastically increased with respect to those of mexR-defective isolates PA14OCR16 , PA14OCR24 , PA14OCR28 , and PA14OCR32 . In parallel , the mexA expression level of isolate PA14OCR36 was comparable to those of the mexR-defective strains ( Table 4 ) . Deletion of cpxR from isolate PA14OCR36 resulted in drastically decreased expression levels of cpxP and mexA . The plasmid harbouring cpxR , but not cpxRD52A , complemented the phenotype , indicating that CpxR is important for the observed alteration in cpxP expression in isolate PA14OCR36 ( Table 4 ) . Sequence analysis indicated the cpxR , nalC , and nalD genes of isolate PA14OCR36 were intact , suggesting that this isolate was distinct from constitutively active CpxR mutants or previously known nalC- or nalD-type mutants [12 , 14] . These results indicate that CpxR could override repression by MexR upon expression of the mexAB-oprM operon in isolate PA14OCR36 , a newly identified nalB-phenotype OCR isolate of P . aeruginosa . OCR isolate PA14OCR33 has a null mutation in the nalD locus and elevated mexA expression; mexA expression in PA14OCR33 is independent of CpxR , because deletion of cpxR did not alter the expression level of mexA in this strain ( Table 4 ) . This result confirms that CpxR plays no role in the regulatory influence of the NalD repressor on the expression of mexAB-oprM in P . aeruginosa . To evaluate the importance of CpxR under clinical conditions , we obtained P . aeruginosa clinical isolates from the Department of Microbiology , Chinese People’s Liberation Army General Hospital ( Beijing , China ) . Fifty independent clinical isolates exhibiting ciprofloxacin and ceftazidime resistance were analysed , among which three isolates , LAR005 , LAR023 , and LAR048 , exhibited significantly reduced expression levels of mexA , as well as reduced MICs of ciprofloxacin , ceftazidime and aztreonam , when cpxR was deleted ( Table 5 ) . When the mexR sequences of clinical isolates LAR005 , LAR023 , and LAR048 were analysed , each was found to harbour frame-shifted or nonsense mutations at different sites in the mexR coding region ( Table 5 ) . Taken together , these results indicate that CpxR plays an important role in modulating multidrug resistance in nalB-type P . aeruginosa isolates generated in the laboratory and collected in the clinic .
In this work , we applied comparative genomic analysis to illuminate the regulatory networks responsible for modulating RND efflux pump expression in P . aeruginosa . Similar comparative genomic analysis has been performed to identify novel regulons based on conserved DNA motifs on the promoter regions of potential target genes as binding sites of global regulators [7 , 48 , 49] . With the accumulation of whole-genome sequencing and transcriptomic data , comparative genomic analysis has become a powerful approach for identifying common or species-specific genetic regulatory networks among different species . Our work has demonstrated a novel regulatory linkage between CpxR and MexAB-OprM , an important efflux pump in P . aeruginosa . The significance of this regulatory linkage is several-fold: first , the regulatory influence of CpxR on RND efflux pump expression , even for pumps within the same orthologous group , could be very different among bacterial species . The direct regulatory influence of CpxR on expression of the mexAB-oprM orthologous operon is unique in P . aeruginosa ( see Fig 1 and Table 1 ) . Furthermore , it has been observed that the mdtABCD operon ( encoding a RND efflux pump ) possesses CpxR binding sites on its promoter region , while its expression is negatively regulated by CpxR in E . coli under conditions that activate the Cpx system [42] . In contrast , in this work , we demonstrate that the muxABC-opmB operon , an orthologue of the mdtABCD operon from E . coli ( see S2 Table ) , is directly activated by CpxR in P . aeruginosa ( see Table 1 ) . Second , given that CpxR is involved in positive regulation of RND efflux pump expression in Vibrio cholerae [36] and P . aeruginosa , bioinformatics studies predict that VexAB/VexGH from V . cholerae [36] and MuxABC/MexAB from P . aeruginosa belong to different orthologous groups ( for details , see S2 Table ) . Taken together , our observations suggest that the involvement of CpxR in regulating RND efflux pump expression may be evolutionarily divergent among bacterial species . Incorporation of the MexAB-OprM efflux pump into the CpxR regulon reinforces the physiological importance of this efflux pump in P . aeruginosa . Indeed , the MexAB-OprM efflux pump plays profound physiological roles in addition to its role in antibiotic resistance in P . aeruginosa , including quorum sensing signal trafficking [50] and mediating bacterial virulence in hosts [51–53] . These functions of MexAB-OprM suggest that the regulatory linkage between CpxR and MexAB-OprM might have other purposes in addition to its role in antibiotic resistance in P . aeruginosa . In this work , we have demonstrated that CpxR plays an important role in multidrug resistance by directly activating expression of mexAB-oprM in nalB-type P . aeruginosa isolates generated in the laboratory and collected in the clinic . Direct regulation of mexAB-oprM by CpxR suggests the existence of multiple pathways through which the expression level of the MexAB-OprM efflux pump might be elevated in P . aeruginosa . The Cpx system is involved in the cellular response to misfolded membrane proteins in E . coli [22] and V . cholerae [54] . Recently , several works have demonstrated the existence of a resistome in the genome of P . aeruginosa consisting of a broad array of genes belonging to different functional families , which give rise to decreased susceptibility to antibiotics when they are mutated [55–59] . Interestingly , a number of genes encoding membrane proteins belong to the resistome [58 , 59] . Future studies should assess whether CpxR is activated in response to misfolded membrane proteins as a means of determining whether it contributes to resistome expression in P . aeruginosa . Unlike the cpxRA operons in E . coli and V . cholerae , the cpxR locus is not directly linked to the sensor kinase gene locus in the genome of P . aeruginosa . Characterization of the signalling mechanism underlying the newly identified regulatory linkage between CpxR and MexAB-OprM , as well as identification of candidate CpxA sensors in P . aeruginosa , is underway . The combined effects of various signals mediated by multiple regulators , including CpxR and MexR , on MexAB-OprM expression will be understood in a broader physiological context in the near future .
For the determination of putative orthologous proteins , a primary BLASTP search in a given genome was conducted for the gene with the highest similarity . Next , additional searches for conserved functional motifs were conducted based on the literature when appropriate . Sequence retrieval and BLASTP searches related to whole-genome sequenced Pseudomonas species/strains were conducted using the Pseudomonas database ( http://www . pseudomonas . com ) [39] , whereas other species/strains were analysed using the KEGG database ( http://www . genome . jp/kegg/ ) . Sequence similarity was determined using the online Pairwise alignment tool ( http://www . ebi . ac . uk/Tools/psa/emboss_water/ ) . The intergenic regions containing the promoters of orthologous RND efflux pump operons were retrieved from 15 whole-genome-sequenced Pseudomonas species: P . aeruginosa PAO1 , P . aeruginosa PA14 , P . fluorescens Pf0-1 , P . fluorescens SBW25 , P . syringae pv . phaseolicola 1448A , P . syringae pv . syringae B728a , P . entomophila , P . putida GB-1 , P . putida KT2440 , P . mendocina ymp , P . mendocina NK-01 , P . stutzeri A1501 , P . stutzeri ATCC 17588 , P . brassicacearum , and P . fulva . For the inter-species analysis , conserved DNA motifs were obtained by alignment of the intergenic regions preceding the orthologous RND efflux pump operons using the MEME suite of online software [60] . For the intra-species analysis for putative CpxR binding sites , an online DNA motif search programme ( http://www . pseudomonas . com/replicon/setmotif ) was used to scan the entire genome of P . aeruginosa PA14 entering GTAAAN ( 4 , 8 ) GTAAA as the query form . Generation of gene-locus-deleted P . aeruginosa strains was conducted using a method described previously [61] . For each gene , an upstream region including the start codon ( longer than 500 bp ) and a downstream region containing the stop codon ( longer than 500 bp ) were PCR-amplified and linked together . The resulting fragment was cloned into the suicide plasmid pEX18Tc . A fragment containing the FRT gentamicin-resistance ( Gm ) cassette from plasmid pPS856 was then inserted between flanking regions of the plasmid . The gene locus of each P . aeruginosa strain was then replaced with the plasmid by double-crossover homologous recombination . The Gm-resistance marker in the chromosome was removed by introducing plasmid pFLP2 , which carries the Flp recombinase gene . Correct deletion in the constructed mutant was verified by PCR using primers that bound to flanking chromosomal regions of the fragments cloned into pEX18Tc . All DNA primers used in this study are listed in S3 Table . The promoter region of each gene was PCR-amplified and TA-cloned into the pEASY-T1 vector ( TransGen , China ) . Site-directed mutagenesis was performed using a protocol described previously [62] . Disruption of the CpxR binding site on the mexA promoter ( mexApM1 ) was performed by altering the 5′-GTAAACCTAATGTAAA-3′ sequence to 5′-GTAAACCTAATACAAA-3′ . Exclusion of the entire proximal promoter of mexA ( up to 162 bp from the mexA ATG codon ) was performed by PCR-amplifying the distal promoter only ( mexApM2 ) . Disruption of the -10 region of the mexA distal promoter ( mexApM3 ) was performed by altering the 5′-TATTTT-3′ sequence to 5′-TGTTTT-3′ . Once confirmed by sequencing , the promoter regions were subcloned into the broad-host , low-copy-number plasmid pMP190 [63] . The resulting plasmids were introduced into Pseudomonas strains by conjugal transfer from E . coli donor strain ST18 [64] . For the β-galactosidase assays , cells were grown overnight in Muller Hinton broth ( Oxoid ) supplemented with appropriate antibiotics , after which they were diluted 1:50 in 5 mL of fresh medium in 50-mL culture flasks at 37°C ( 30°C for P . putida KT2440 ) with mixing at 150 rpm . Cells were recovered during the logarithmic growth phase ( OD600 = 0 . 5–1 . 2 ) . β-galactosidase assays were performed as described by Miller [65] . The results are expressed as the mean values from two independent experiments with triplicate samples . In order to control in trans CpxR expression , the lacIq-tacP region was PCR-amplified using the pME6032 plasmid [66] as a template and cloned into broad-host plasmid pBBR1MCS5 to replace the original constitutively expressed lac promoter [67] . Next , the CpxR and CpxRD52A in trans expression systems ( pCpxR and pCpxRD52A ) were constructed using the altered plasmid . IPTG ( 0 . 2 mM ) was added to induce CpxR overexpression . It was noted that the basal level of CpxR expression in the absence of IPTG was sufficient to complement the cpxR deletion mutants in P . aeruginosa . The plasmid used to express the N-terminal His6-tagged CpxR proteins was constructed by PCR-amplifying the CpxR coding sequence and cloning it into pET28a ( Novagen ) . The plasmid was transformed into E . coli expression host strain BL21 ( DE3 ) and grown to an OD600 of 0 . 8 at 37°C with vigorous shaking in 1 L of LB medium containing kanamycin ( 50 μg/mL ) . The cells were then induced with 1 mM IPTG and allowed to express overnight at 22°C , after which they were harvested by centrifugation . The resulting pellet was resuspended in 10 mL of pre-cooled buffer A ( 20 mM Tris-HCl , 200 mM NaCl , 1 mM imidazole , pH 8 . 0 ) and centrifuged at 4°C for 10 minutes at 3 , 500 rpm , after which the pellet was resuspended in 60 mL of pre-cooled buffer A . The cells were disrupted by sonication at 180 W for 8 minutes . The debris and membranes were removed by centrifugation at 4°C for 60 minutes at 15 , 000 rpm . The soluble fraction was passed through a 0 . 2-μm filter and loaded onto a 5-mL nickel column which was previously washed with 10 column volumes of ddH2O and equilibrated with 10 column volumes of buffer A . CpxR proteins were eluted with a mixture of buffer A and buffer B ( 20 mM Tris-HCl , 200 mM NaCl , 500 mM imidazole , pH 8 . 0 ) , in which the proportion of buffer B was gradually increased from 0% to 100% . The flow speed was 1 mL/min during the elution process . The protein was collected when its protein peak appeared . The CpxR protein solution was desalted and concentrated to a final volume of 1 . 5 mL in buffer C ( 20 mM Tris-HCl , 200 mM NaCl , pH 8 . 0 ) . The concentration of CpxR protein was determined by the Bradford method . CpxR protein was stored in buffer C supplemented with 50% glycerol at -80°C . Purified N-terminal His-tagged CpxR proteins were phosphorylated using acetyl phosphate ( AP ) as previously described [68] . Briefly , 1 . 6 μM of purified CpxR was incubated with 50 mM AP in a reaction buffer containing 100 mM Tris-HCl ( pH 7 . 4 ) , 10 mM MgCl2 , and 125 mM KCl at 30°C for 2 hours . The mobility shift assay was carried out using the 2nd generation DIG Gel Shift Kit ( Roche ) . Briefly , 165 bp DNA fragments of the promoter region of mexAp and mexApM1 , 140 bp DNA fragments of the promoter region of cpxPp were PCR-amplified , after which 150 nM of purified PCR product was DIG-labelled according to the manufacturer’s instructions . The binding reaction was carried out with different concentrations of phosphorylated CpxR ( as great as 160 nM ) and 0 . 2 nM DIG-labelled DNA fragments at 37°C for 30 min . The samples were separated by electrophoresis on 6% native polyacrylamide gels , transferred to Hybond-N blotting membranes ( Amersham ) , and visualized by chemiluminescence . The promoter region of mexA ( 165 bp ) was TA-cloned into the pEASY-Blunt-simple vector ( TransGen , China ) . For the preparation of fluorescent FAM labelled probes , the promoter region of mexA was PCR-amplified with Dpx DNA polymerase ( TOLO Biotech , Shanghai ) from the above-mentioned plasmid using primer pairs M13F-47 ( FAM ) /M13R-48 and M13R-48 ( FAM ) /M13F-47 . The FAM-labelled probes ( 322 bp ) were purified by the Wizard SV Gel and PCR Clean-Up System ( Promega , USA ) and quantified using the Take3 Micro-Volume Plate function of BioTek Synergy Neo Multi-Mode Reader . DNase I footprinting assays were performed as previously reported [69] . Briefly , 400 ng of the probe ( final concentration of 50 nM ) was incubated with 1 . 5 μg of phosphorylated CpxR ( final concentration of 1 . 5μM ) in a total volume of 40 μL . After incubation for 30 min at 25°C , 10 μL of a solution containing approximately 0 . 015 units of DNase I ( Promega ) and 100 nmol of freshly prepared CaCl2 was added . Following incubation for 1 min at 25°C , the reaction was stopped by adding 140 μL of DNase I stop solution ( 200 mM unbuffered sodium acetate , 30 mM EDTA , and 0 . 15% SDS ) . Samples were extracted with phenol/chloroform and precipitated with ethanol , after which the pellets were dissolved in 30 μL of Milli-Q water . The preparation of the DNA ladder , electrophoresis , and data analysis were performed as described before [69] , except that a GeneScan-LIZ500 size standard ( Applied Biosystems ) was used . An overnight culture ( approximately 16 h ) was diluted 1:100 in Mueller–Hinton broth and grown to the logarithmic growth phase ( OD600 = 0 . 4~0 . 6 ) . Total RNA was extracted from 500 μL of cultured cells using the RNAprep Bacterial Kit ( TianGen , China ) . Residual genomic DNA was digested by RQ1 RNase-Free DNase ( Promega , USA ) . RNA samples were quantified using the Take3 Micro-Volume Plate function of a BioTek Synergy Neo Multi-Mode Reader . cDNA was synthesized from 1 μg of total RNA using TransScript cDNA Synthesis SuperMix ( TransGen , China ) according to the following procedure: after annealing of the RNA sample and the random hexamer primer for 5 min at 65°C , reverse transcription was carried out for 2 min at 25°C and 55 min at 42°C , followed by reverse transcriptase inactivation for 5 min at 70°C . An Opticon2 Real-time PCR system ( Bio-Rad , Hercules , CA , USA ) and SuperReal Premix SYBR Green Plus ( TianGen , China ) were used to perform quantitative PCR on a 1-μL sample of diluted cDNA ( 1:10 ) according to the following procedure: one denaturation cycle for 15 min at 94°C and 40 amplification cycles for 10 s at 94°C , annealing for 20 s at 60°C , extension for 20 s at 72°C . Control samples without reverse transcriptase confirmed the absence of contaminating DNA in any of the samples . The housekeeping gene rpsL was used as the internal reference gene . Relative expression of mexB was calculated according to the 2−ΔΔCT method [70] from three independent experiments . Primers for mexB and rpsL were designed as previously reported [17 , 71] For the western blot detection of CpxR protein in PA14ΔcpxR containing pCpxR or pCpxRD52A , an overnight culture was diluted 1:100 in Mueller–Hinton broth and grown to the logarithmic growth phase ( OD600 = 0 . 8–1 . 2 ) . Total protein was extracted using a Bacterial Protein Extraction Kit ( CWBiotech , China ) . Next , 5 μg of total protein and 10 ng of purified His-tagged CpxR were resolved in 10% SDS-polyacrylamide gels and transferred electrophoretically to PVDF membranes ( Millipore , USA ) . Electrophoretic transfer of proteins was carried out for 1 h at 4°C with 200 mA of constant current . The blotted membranes were subsequently blocked in phosphate-buffered saline containing 0 . 1% ( vol/vol ) Tween-20 ( PBST ) and 5% ( wt/vol ) skim milk ( Difco ) for 60 min . The membranes were incubated with primary anti-CpxR rabbit polyclonal antibodies ( 1:10000 ) in PBST containing 5% ( wt/vol ) skim milk at 37°C for 60 min , after which they were washed three times ( 5 min each ) with PBST and three times ( 5 min each ) with PBS . The membranes were incubated with secondary goat anti-rabbit IgG antibodies conjugated to horseradish peroxidase ( HRP ) in PBST containing 5% ( wt/vol ) skim milk , after which they were washed three times ( 5 min each ) with PBST and three times ( 5 min each ) with PBS . All washes were carried out at room temperature with agitation . Substrates for HRP were obtained from the Amersham ECL Western Blotting Detection Kit ( Amersham ) and used according to the manufacturer’s instructions . The enzymatic activity of HRP was detected using a 4200 Chemiluminescence Analyzer ( Tanon , China ) . To allow detection of MexA protein by western blotting , cell membrane proteins were isolated by ultracentrifugation . Briefly , an overnight culture was diluted 1:100 in Mueller–Hinton broth and grown to the logarithmic growth phase ( OD600 = 0 . 4–0 . 6 ) . After sonication , total cell membrane protein was extracted from 40 mL of cultured cells using a Bacterial Membrane Protein Isolation Kit ( Tiandz , Inc . , China ) according to the manufacturer’s instructions . The ultracentrifuged cell membrane protein pellets were resuspended in 100 μL of H2O . The concentrations of the membrane proteins were quantified using the Take3 Micro-Volume Plate function of a BioTek Synergy Neo Multi-Mode Reader . SDS-PAGE and immunoblotting for cell membrane proteins ( 10 μg of each sample ) were performed as described above , except for the following: SDS ( 0 . 1% ( wt/vol ) ) was included in the blotting buffer , the transfer was carried out for 16 h at 4°C with 25 mA constant current , and anti-MexB rabbit polyclonal antiserum ( 1:10000 ) was used as the primary antibody . Band intensity was quantified in three independent experiments using Image-pro Plus version 6 . 0 . The MIC of each antibiotic was determined on Muller Hinton agar by the two-fold dilution method . Mueller-Hinton agar plates containing serial twofold dilutions of each antibiotic ( from 0 . 03125 to 32 μg/mL for ciprofloxacin and ofloxacin , from 0 . 125 to 128 μg/mL for the other antibiotics ) were prepared . Overnight bacterial cultures were diluted 1:100 in fresh Mueller-Hinton broth , grown to the mid-logarithmic phase ( OD600 of 0 . 4–0 . 6 ) , harvested , and washed in PBS . The Mueller-Hinton agar plates were spotted with 3 μL of the diluted bacterial suspensions ( approximately 104 cfu ) . The MIC was defined as the concentration at which bacterial growth was completely inhibited after incubation at 37°C for 24 hours . Ciprofloxacin , ofloxacin , and amikacin were purchased from Bio Basic Inc . Ceftazidime was purchased from Sigma-Aldrich . Cefsulodin was purchased from TOKU-E ( Japan ) . Aztreonam was purchased from Selleck . P . aeruginosa PA14 cells ( approximately 4 × 109 cells ) grown overnight in LB broth medium were plated on LB agar containing 1 . 2 μg/mL ofloxacin and 2 . 4 μg/mL cefsulodin . After incubation at 37°C for 72 hours , resistant colonies appeared at a frequency of approximately 10−7 . Fifty independent P . aeruginosa clinical isolates ( LAR001–LAR050 ) characterized as amikacin-sensitive ( MIC≤ 2 . 0 μg/mL ) , ciprofloxacin-resistant ( MIC range , 1 . 0–16 μg/mL ) , and ceftazidime-resistant ( MIC range , 4 . 0–64 μg/mL ) were obtained from the Department of Microbiology , Chinese People’s Liberation Army General Hospital ( Beijing , China ) . | Pseudomonas aeruginosa is one of the major pathogens associated with cystic fibrosis and multidrug resistant P . aeruginosa has been listed as the Top 10 antibiotic resistance threats in the US CDC report ( http://www . cdc . gov/drugresistance/biggest_threats . html ) . Drug efflux systems play a major role in multidrug resistance in P . aeruginosa . Currently , the regulatory networks modulating efflux pump expression are not fully understood . Here , we demonstrate that CpxR , a potentially multifaceted regulator , is directly involved in regulation of expression of MexAB-OprM , the major efflux pump in P . aeruginosa . The newly identified activator CpxR plays an important role in modulating multidrug resistance in nalB-type laboratory and clinical isolates . This work provides insight into the complex regulatory networks modulating multidrug resistance in P . aeruginosa . | [
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] | 2016 | CpxR Activates MexAB-OprM Efflux Pump Expression and Enhances Antibiotic Resistance in Both Laboratory and Clinical nalB-Type Isolates of Pseudomonas aeruginosa |
Olfactory ensheathing cell ( OEC ) transplantation is a candidate cellular treatment approach for human spinal cord injury ( SCI ) due to their unique regenerative potential and autologous origin . The objective of this study was , through a meta-epidemiologic approach , ( i ) to assess the efficacy of OEC transplantation on locomotor recovery after traumatic experimental SCI and ( ii ) to estimate the likelihood of reporting bias and/or missing data . A study protocol was finalized before data collection . Embedded into a systematic review and meta-analysis , we conducted a literature research of databases including PubMed , EMBASE , and ISI Web of Science from 1949/01 to 2014/10 with no language restrictions , screened by two independent investigators . Studies were included if they assessed neurobehavioral improvement after traumatic experimental SCI , administrated no combined interventions , and reported the number of animals in the treatment and control group . Individual effect sizes were pooled using a random effects model . Details regarding the study design were extracted and impact of these on locomotor outcome was assessed by meta-regression . Missing data ( reporting bias ) was determined by Egger regression and Funnel-plotting . The primary study outcome assessed was improvement in locomotor function at the final time point of measurement . We included 49 studies ( 62 experiments , 1 , 164 animals ) in the final analysis . The overall improvement in locomotor function after OEC transplantation , measured using the Basso , Beattie , and Bresnahan ( BBB ) score , was 20 . 3% ( 95% CI 17 . 8–29 . 5 ) . One missing study was imputed by trim and fill analysis , suggesting only slight publication bias and reducing the overall effect to a 19 . 2% improvement of locomotor activity . Dose-response ratio supports neurobiological plausibility . Studies were assessed using a 9-point item quality score , resulting in a median score of 5 ( interquartile range [IQR] 3–5 ) . In conclusion , OEC transplantation exerts considerable beneficial effects on neurobehavioral recovery after traumatic experimental SCI . Publication bias was minimal and affirms the translational potential of efficacy , but safety cannot be adequately assessed . The data justify OECs as a cellular substrate to develop and optimize minimally invasive and safe cellular transplantation paradigms for the lesioned spinal cord embedded into state-of-the-art Phase I/II clinical trial design studies for human SCI .
Cellular transplantation strategies applying different cellular sources have been tested in experimental spinal cord injury ( SCI ) models as a possible treatment to propagate functional recovery [1–3] . Restoration of function after SCI remains one of the most formidable challenges in regenerative medicine , and the development of effective treatments is an unmet medical need . Pioneering studies by Ramón-Cueto and Nieto-Sampedro [4] reported the purification of olfactory ensheathing cells ( OECs ) to study regenerative paradigms and were followed by seminal work from the Raisman group , who showed that OEC transplants were populated by host axonal fibers after SCI , associated with neurological recovery [5] . Besides promoting neurite growth , subsequent studies unraveled guidance , neuroprotective , angiogenetic , phagocytic , localized immune modulatory , and remyelination properties as underlying neurobiological effector mechanisms [6–9] . OEC transplantation restored salutatory nerve conduction of sensory axons [10] and fostered the recovery of locomotion after SCI [11–13] . Conceptually , OEC transplantation can be classified as a bridging , non-relay approach [14] . The viability as autologous graft and genetic stability of OECs supports their feasibility for clinical translation . OECs comprise a unique and highly specialized cell type , physiologically located at the border between the central nervous system ( CNS ) and peripheral nervous system ( PNS ) , since the olfactory system is part of both the PNS and CNS ( Fig 1 ) . After olfactory nerve injury , olfactory receptor neurons located in the olfactory epithelium ( PNS ) can extend axons that enter the olfactory bulb ( CNS ) , thus representing a highly specialized aspect of physiological axonal regeneration in the mammalian CNS [6–9] . This regenerative ability is attributed to a distinct growth-enabling cellular environment composed of OECs , which can be derived from peripheral ( mucosal OECs ) or central sites ( olfactory bulb ) . Findings from studies supporting the effect of OEC transplantation were challenged by the landmark “Facilities of Research–Spinal Cord Injury ( FOR-SCI ) replication” project [17] , which confirmed some axonal outgrowth but failed to reproduce an effect on locomotor outcome after OEC transplantation [18] . Importantly , replication alone is an informative tool for deciphering the value for translation . Nevertheless , the probability of replicating “true” findings itself is low ( replication power ) . Mathematically , the chance to obtain a significant result and , hence , “reproduce” data is just about 50% in case of a p-value of about 0 . 05 [19 , 20] . A related challenge for the predictive value is inflated effect sizes ( “Delta inflation” [21] ) due to missing data and inappropriate power calculations , which lead to underpowered trials that are unable to confirm or reject a null hypothesis [22] . Omission of negative data renders an objective prioritization of experimental interventions for translation toward clinical trials difficult . The fragile and resource-intense translational path depends on objective measures , and its efficiency will be dependent on a bi-directional dialogue [23] . Meta-analyses based on systematic reviews contribute to this dialogue , as they are able to monitor for missing data and inflated effect sizes [24–26] . First comprehensive approaches to structured appraisal of in vivo evidence for cell-based therapies in SCI have been proposed [27] . Since the discovery of the effect of OECs on propagating neurite growth , various OEC cell preparations ( either olfactory mucosa or olfactory bulb derived ) have been developed and tested . Given the conflicting results of these experiments , we investigate the in vivo evidence for the efficacy of OEC transplantation after traumatic experimental SCI , applying a systematic review and meta-analysis with the DerSimonian and Laird random effects model . We use meta-regression to determine the influence of different OEC transplantation paradigms on estimates of efficacy . We use Funnel plotting , Egger-regression , and the trim-and-fill method to delineate the occurrence and impact of publication bias . We set out to integrate heterogeneity and missing data as important challenges for clinical translation .
The literature search identified 1 , 830 publications , of which 1 , 754 were excluded in the first instance ( Fig 2A: 283 excluded duplicates , 1 , 188 excluded based on abstract ) . Seventy-six studies were selected for further investigation , of which a final number of 49 met the prespecified inclusion criteria . Nine studies were excluded because they reported combined interventions ( “cocktails” ) , three studies did not measure behavioral outcome , five studies had an inappropriate outcome scale , one study was a duplicate , one study was a review , one study did not transplant OECs , a further one did not include a control group , and six studies could not be taken into account due to statistical inconsistencies . Sixty-two experiments reporting outcome in 1 , 164 animals contributed to our final analysis ( Fig 2A ) . All studies were done in rats , and all OEC transplantations were applied to the intradural compartment . The animals were subject to five different modalities of experimental SCI; 17 studies used transection ( 23 experiments ) , 17 studies used contusion ( 20 experiments ) , six used hemisection ( seven experiments ) , six used photochemical injury ( six experiments ) , and four used compression ( six experiments ) . All experiments lesioned the spinal cord at thoracic levels T8-13 . OECs were either transplanted immediately after injury ( 32 experiments ) or delayed ( 30 experiments ) , the longest post-SCI interval being 270 days . Most of the experiments used the Basso , Beattie , and Bresnahan [BBB] score [16] for evaluation of functional recovery ( 53 experiments ) . The BBB score is a 21-point open field locomotor rating score that categorizes stepping , paw placement , fore- and hindlimb coordination , tail position , joint movement , hindlimb movement , trunk position , and stability [16] . The overall analysis ( Fig 2B ) revealed a distinct difference in reported outcome using the BBB score compared to other scores , including directed forepaw reaching ( three experiments ) , kinematic analysis ( one experiment ) , beam walking ( one experiment ) , horizontal rope walking ( one experiment ) , tape removal ( one experiment ) , tape sensing ( one experiment ) , and spontaneous vertical exploration ( one experiment ) ( BBB: effect size [ES] 20 . 2%; others: ES 50 . 3% ) ( Fig 2C ) . We therefore excluded studies in which scores other than the BBB-score have been assessed exclusively from the subsequent analysis ( Figs 3 and 4 ) . Overall , OEC transplantation after traumatic SCI improved locomotor recovery by 23 . 6% ( 95% CI 17 . 8–29 . 5; I2 = 96 . 4% , 62 experiments , 1 , 164 animals ) when including various test paradigms ( Fig 2B ) . In experiments reporting BBB measures only , the effect was 20 . 3% ( 95% CI 14 . 4–26 . 1; I2 = 96 . 7% , 53 experiments , 1 , 027 animals ) . Meta-regression showed several aspects of study design that were significantly associated with different observed efficacies: The median score in the quality checklist was 5 ( IQR 3–5 ) , relating to a maximum score value of 9 . All studies were published in peer-reviewed journals , in which 28 studies reported blinded assessment of outcome and 25 did not . Only 17 studies ( 32% ) out of 53 reported random allocation to treatment or control group . None of the studies made use of sample size calculation , and only one used blinded induction of injury . None of the single study quality items accounted for a significant amount of heterogeneity . The combined 9-point item quality score revealed a trend for the relationship between effect size and study quality , but showed no statistical significance . Studies with lower-quality score items correspond with higher effect sizes , whereas higher quality studies associate with lower effect size ( e . g . , quality score 5: 22 . 5% [95%CI 13 . 6–31 . 3] , quality score 2: 32 . 3% [95%CI 13 . 1–51 . 5] ) . We assessed the dataset for missing data by applying funnel plotting and Egger regression analysis ( Fig 4 ) . Publication bias is a major source of missing data and attributed to unpublished studies mostly reporting negative results . Orthodox preclinical systematic review cannot assess this dilemma properly , as only published results are included , leading consequently to the risk of inflated effect sizes ( so-called file drawer problem ) . Trim and fill analysis was used to identify theoretically missing experiments based on the funnel plot’s symmetry . Only one missing experiment was imputed , suggesting that present publication bias for this cohort was only minor , adjusting the effect size only marginally from 20 . 3% to 19 . 2% ( Fig 4A ) . The finding of modest publication bias was confirmed by Egger regression ( Fig 4B ) . Although there is reason to believe that studies at risk of bias might give overstatements of efficacy , it is also plausible that such studies may give less precise estimates . Therefore , in a post hoc analysis , we investigated whether studies with a higher quality score ( i ) were closer to the overall calculated effect size or ( ii ) had higher precision ( 1/SE ) ( Fig 4B ) . The modified “traffic light” funnel plot implies that studies with higher-quality scores are closer to the overall corrected calculated effect size than studies of lower quality . Moreover , high-quality studies ( ≥5 ) appeared to be associated with a higher ( averaged ) precision compared with lower-quality studies ( <5 ) . Together , the “traffic light” funnel plot provides a visual tool to identify study clusters of high quality and precision that may indicate the true effect size is below the adjusted effect size of the trim-and-filled funnel plot .
This systematic review and meta-analysis of preclinical animal literature identified 49 studies of olfactory ensheathing cell transplantation reporting 23 . 6% improvement of functional outcome . Data from 1 , 164 animals comprising 62 experiments were stratified for neurobiological and study characteristics , which accounted for significant proportion of in-between study heterogeneity . Motor-score sub-analysis was confined to studies including assessments of neurobehavioral recovery applying the most commonly used BBB-score characterized by smaller effect sizes ( 20 . 3% ) compared to other measures of motor recovery ( Tarlov scale and others ) . Statistical techniques to detect and correct for publication bias revealed a slight overestimation of effect sizes due to theoretically missing experiments resulting in a corrected estimated efficacy of 19 . 2% . Besides minimizing variability to enable the most accurate reproduction ( e . g . , FORESCI Initiative [17] ) , an alternative approach is the deliberate incorporation of heterogeneity in multiple modeling characteristics in order to evaluate the impact of study differences with regards to the effect size . Bulb- and mucosa-derived OECs differ substantially , for example , with regards to their proliferative ability over time [28 , 29] . OECs derived from the olfactory bulb provided a larger effect on locomotor recovery compared with OECs derived from mucosa . The transplantation of OEC as purified and cultured cells reported better outcome compared to animals receiving blocks of olfactory tissue , which are less likely to integrate . Similar effects after clinically relevant contusion , compression , and transection SCI support the robustness , irrespective of model differences , such as axons being fully cut or just severed or the amount of bleeding due to injured arteries and veins . The purity of the OEC transplants is most commonly determined by immunocytochemical markers ( p75NTR , S100 , GFAP ) , but there is no stringent standard for the OEC preparation [28 , 30] . Of note , we observed no significant impact of the purity assessment with regards to the elicited locomotor recovery , but obvious trends . It is assumed that all OEC cultures may contain a small proportion of Schwann cells ( e . g . , trigeminal derived ) until proven otherwise or other critical cells able to augment anatomical and functional recovery after SCI [30] . Immediate OEC transplantation within 30 min after injury was superior to subacute ( 1–2 wk ) and delayed application ( 4–8 wk ) , indicative of a neuroprotective effect linked to a distinct therapeutic time window , which might be different in humans . Surprisingly , the benefits of early transplantation were not overruled by an anticipated elevated neurotoxic risk due to injection into oedematous tissue , which might amplify the extracellular pressure imposed on spared spinal neurons and axons after SCI . The invasive removal of distal axonal stumps and formed scar tissue ( “wound refreshing” ) resulted in worsened effect sizes , supporting the functional relevance of scar-associated neuronal circuitry either being spared or formed de novo after SCI [31] . In addition , the inherent ability of OECs to reorganize the glial scar ( expression of growth promoting matrix molecules and proteolytic enzymes ) might render surgical scar resection unnecessary . Despite migratory properties and the ability to intermingle with host scar-forming cells , the localization of transplantation matters . Injection to rostral and caudal parenchyma juxtaposed to the lesion site is significantly more effective than to the lesion core . This is in line with the proposed underlying neurobiological mechanisms such as neuroprotection and the propagation of neuroplasticity and remyelination , which do not apply to the pan-necrotic lesion core . Multiple injections with smaller volumes are associated with elevated locomotor effects compared to a reduced number of injections , which in turn require larger cell numbers and injection volumes . Considering technical aspects of transplantation , it appears that neurotoxicity generated by injection into the spinal cord itself is comparatively low ( likely as long as no vessel is affected ) and depends largely on the amount of volume being injected . The results favour multiple injections over large cell deposits through singular injection ( declining benefit-risk ratio with increasing injection volume ) . The data also suggest that the toxicity of invasive transplantation is largely determined by the injected volume and the attributable hydrodynamic dissection pressure [32] . In brief , ( i ) a volume over 3 μl per injection , ( ii ) a total transplantation volume exceeding 12 μl , and ( iii ) an OEC concentration higher than 180 , 000 cells/μl are associated with reduced locomotor outcome . The dose–response relationship between the amount of transplanted OECs and effect size confirms the biological plausibility . With dose escalation above 180 , 000 cells , increasingly neurotoxic effects start to predominate , with a ceiling to the beneficial effect likely due to elevated injection volumes ( hydrodynamic dissection pressure ) . This ceiling effect is further aggravated by a limited engraftment of cells per tissue unit [33] . In terms of efficacy , autologous OECs did not differ from xenogenous sources . The parameters obtained here from animals provide a framework for scaling for spinal transplantation for translation into paradigms of human SCI . A recent meta-analysis summarized 10 treatment series reporting on 1 , 193 chronic SCI patients and demonstrated an improvement rate of 39% on the American Spinal Injury Association ( ASIA ) impairment scale after chronic SCI [34] and OEC transplantation . However , it is noteworthy that all publications had low methodological quality and were lacking appropriate control groups . The mortality associated with full anesthesia and surgery was 0 . 35% ( n = 2 out of 566 ) . Reported adverse effects included fever , mild anemia , syringomyelia , cerebrospinal fluid leakage , aseptic meningitis , and sensory and motor deterioration . The safety being claimed in some reviews summarizing experimental OEC mode of action was challenged by a recent clinical case report identifying an intramedullar tumor formation 8 y after spinal mucosal OEC transplantation presenting with progressive pain [35] . The tumor required surgical resection and was identified as being composed of respiratory epithelium , submucosal glands with goblet cells , and mucosal mass . The risk of delayed tumor formation points toward a low ability to attack and remove transplanted cells of ectopic origin . Given the mortality attributable to anaesthesia and surgery per se , complications due to the transplantation and the risk of delayed tumor formation even when applying autologous cells render the long-term safety in human SCI uncertain and needs to be integrated in a benefit–risk ratio calculation . Experimental OEC SCI transplantations investigated here do not cover corresponding long-term observational time windows . The following limitations apply to this study . Our analysis depends on the validity of locomotor recovery scales used , which has been questioned [36] . However , the applied outcome measures scales are in widespread use and are considered the most informative , commonly used neurobehavioral element to assess outcome in modelling SCI . For this reason , it is unlikely that a preclinical intervention would be approved for translation if it did not result in improvements in the BBB score . Furthermore , all studies reported exclusively rat models of SCI . Large animal models are of translational relevance but are limited as being frequently underpowered and applying widely heterogeneous outcome measures and could therefore not be included . The funnel plot test is only one way to visualize missing data and can be debated [37] . We therefore included the individual experimental quality and animal number in the funnel plot , leading to a “traffic light” modification . In summary , systematic reviews and meta-analysis provide a rather broad estimate of efficacy .
Due to the unique properties of fostering a plethora of neurobiological repair mechanisms including axon sprouting , myelination , and neuroprotection , autologous OECs are considered a candidate cellular source for transplantation into the lesioned spinal cord . This systematic meta-analysis reports a substantial overall effect of OEC transplantation after experimental SCI . The data suggests more effective OEC transplantation paradigms , ( i ) when being derived from the olfactory bulb compared with mucosa-derived OECs , ( ii ) when injected to rostral-caudal parenchyma compared with the injury epicenter , and ( iii ) when being fractionated , allowing for smaller injection volumes . Invasive surgical resection of the fibrotic scar at the lesion site should be avoided . Publication bias was minimal and affirms the translational potential in terms of preclinical efficacy . Safety issues cannot be addressed here sufficiently , given that rodent models rarely study long-term follow-up . Therefore , late complications in humans cannot be addressed sufficiently . At present , the ideal cell for transplant-mediated CNS repair has not been identified . Based on validated efficacy , OECs qualify as autologous cell source to elucidate and optimize cell invasive transplantation paradigms after human SCI as a component for escalated treatment concepts , e . g . , by applying phase II trials with informative secondary endpoints aiming to characterize treatment responders and patients prone to develop transplantation-associated complications .
To identify animal studies describing the effect of OEC transplantation on neurobehavioral recovery after traumatic experimental SCI , the following search terms were used for PubMed , EMBASE , and ISI Web of Science ( search conducted October 2 , 2014 ) : ( olfactory OR olfactory bulb OR olfactory lamina propria OR olfactory ensheathing cells ) AND ( spinal cord injury OR hemisection OR contusion injury OR dorsal column injury OR complete transection OR corticospinal tract injury ) . Search results were limited to animals . The animal filter was modified for the search in PubMed [39] . Two investigators ( R . W . and J . R . ) independently assessed the search results . Studies were included if they reported the effects of OEC transplantation in animal models after spinal cord injuries , including contusion , compression , hemisection , transection , and photochemical injury . We included SCI experiments comparing functional outcome in a group of animals receiving OEC treatment with a control group receiving no treatment or vehicle treatment . Non-traumatic models of SCI were excluded , as well as studies reporting only combined treatments . For studies to be included they had to report the number of animals for each group , the mean effect size , and its standard deviation or standard error of the mean . We extracted details of individual study characteristics from each publication , and when a single publication reported more than one experiment , these data were extracted and treated as independent experiments . Study characteristics were extracted , including the gender and breed of the animals , time route and dose of transplantation , anaesthetic , and method of injury , as well as adjuvant treatment . Functional outcome was assessed for each experiment . Where data were expressed in text , numerical values were extracted . Where the outcome was expressed graphically only , Universal Desktop Ruler ( Version 3 . 6 , AVPsoft ) was used to visually extract the data points . Only the final time point of the assessment of functional recovery was included . As an assessment for “risk of bias , ” we assessed the methodological quality of each study using a modified 9-point item quality checklist , adapted from the CAMARADES ( Collaborative Approach to Meta Analysis and Review of Animal Data from Experimental Studies ) quality checklist [40]: ( i ) reporting of a sample size calculation , ( ii ) control of animals’ temperature , ( iii ) use of anaesthetics other than ketamine ( because of its marked intrinsic neuroprotective activity ) , ( iv ) randomized treatment allocation , ( v ) treatment allocation concealment , ( vi ) blinded assessment of outcome , ( vii ) publication in a peer reviewed journal , ( viii ) statement of compliance with regulatory requirements , and ( ix ) statement of potential conflicts of interest . This quality checklist overlaps substantially with the ARRIVE ( Animal Research: Reporting In Vivo Experiments ) guideline for reporting animal research [41] . A normalized effect size ( ES ) for each comparison was calculated , defined as the improvement of outcome in the treatment group compared with that in the control group with reference to the outcome of an untreated , unlesioned , “sham” animal . The attributed size of the control group was adjusted if a single control group was compared to more than one treatment group . We used DerSimonian and Laird random effects model meta-regression to calculate an overall estimate of effect size . The analysis was stratified according to the method of injury , type of treatment , time of application , number of transplanted cells , quality assessment score , adjuvant treatment , time of assessment , medium used for cell culture , transplant origin , assessment of OEC purity , details of surgical procedure , and type of anaesthetic . Meta-regression was used to determine how much heterogeneity can be explained by study design characteristics . Random effects meta-regression was conducted by taking into account both within-study and between-study variance using STATA13 with a significance level of p < 0 . 05 . In the regression model , the variance in the dependent variable that is accounted for by covariates is used to calculate an adjusted R2 , a measure of how much residual heterogeneity is explained by the model [38] . Figures were drawn using SigmaPlot ( Systat Software Inc , Version 12 ) . | Spinal cord injury converts into a debilitating disease affecting millions of chronic patients worldwide . Despite increased molecular knowledge over the last decades , no causal pharmacological or cellular therapy has proven effective so far . Due to their unique regenerative capabilities and their autologous origin , olfactory ensheathing cells ( OECs ) constitute an appealing candidate for topical cell transplantation . In contrast to few and heterogeneous experimental reports of OEC transplantation after spinal cord injury in humans , a considerable number of preclinical studies have been conducted applying OEC transplantation in rodent models . We set out to conduct a systematic review and meta-analysis to assess preclinical efficacy of OEC transplantation . We detected a significant overall increase of functional neurological recovery in animals after OEC transplantation compared to the control group . This effect was not distorted by publication bias . We identified several specific hallmarks of the cell transplantation procedure that determine the effect size of the transplantation . Our findings delineate conditions for optimized OEC transplantation into lesioned spinal cords and its relevance for effective translation to human trials . | [
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] | 2016 | Olfactory Ensheathing Cell Transplantation in Experimental Spinal Cord Injury: Effect size and Reporting Bias of 62 Experimental Treatments: A Systematic Review and Meta-Analysis |
Mammalian Nrf2-Keap1 and the homologous Drosophila CncC-dKeap1 protein complexes regulate both transcriptional responses to xenobiotic compounds as well as native cellular and developmental processes . The relationships between the functions of these proteins in xenobiotic responses and in development were unknown . We investigated the genes regulated by CncC and dKeap1 during development and the signal transduction pathways that modulate their functions . CncC and dKeap1 were enriched within the nuclei in many tissues , in contrast to the reported cytoplasmic localization of Keap1 and Nrf2 in cultured mammalian cells . CncC and dKeap1 occupied ecdysone-regulated early puffs on polytene chromosomes . Depletion of either CncC or dKeap1 in salivary glands selectively reduced early puff gene transcription . CncC and dKeap1 depletion in the prothoracic gland as well as cncCK6/K6 and dKeap1EY5/EY5 loss of function mutations in embryos reduced ecdysone-biosynthetic gene transcription . In contrast , dKeap1 depletion and the dKeap1EY5/EY5 loss of function mutation enhanced xenobiotic response gene transcription in larvae and embryos , respectively . Depletion of CncC or dKeap1 in the prothoracic gland delayed pupation by decreasing larval ecdysteroid levels . CncC depletion suppressed the premature pupation and developmental arrest caused by constitutive Ras signaling in the prothoracic gland; conversely , constitutive Ras signaling altered the loci occupied by CncC on polytene chromosomes and activated transcription of genes at these loci . The effects of CncC and dKeap1 on both ecdysone-biosynthetic and ecdysone-regulated gene transcription , and the roles of CncC in Ras signaling in the prothoracic gland , establish the functions of these proteins in the neuroendocrine axis that coordinates insect metamorphosis .
Cellular responses to many xenobiotic compounds , including various toxins and pharmacological agents , are controlled by mammalian Nrf2 and Keap1 , and by the homologous Drosophila CncC and dKeap1 proteins [1] , [2] , [3] . The Nrf2-Keap1 complex has multiple effects on carcinogenesis . Nrf2-deficient mice have increased susceptibility to chemical carcinogens , potentially because of defective activation of cytoprotective genes in response to carcinogen exposure [4] . Mutations in Nrf2 and Keap1 that are predicted to disrupt their interactions are found in many human cancers , suggesting that Nrf2 interactions with Keap1 counteract cancer progression [1] , [5] . Conversely , the deletion of Nrf2 suppresses pancreatic and lung tumorigenesis in a mouse model with constitutively active K-RasG12D expression [6] . The mechanisms whereby Nrf2 promotes tumorigenesis in conjunction with K-RasG12D are not known . Nrf2 and Keap1 are investigated as potential targets for therapeutic interventions in cancer , neurodegenerative diseases and developmental disorders [1] , [7] . Nrf2 ( NF-E2-Related Factor 2 ) is a bZIP family transcription factor that can bind to genes whose transcription is induced by xenobiotic compounds [1] . Keap1 ( Kelch-like ECH-Associated Protein 1 ) is a Kelch family protein that can interact with the N-terminal region of Nrf2 , and inhibits the activation of many genes activated by Nrf2 [8] . Studies in cultured mammalian cells indicate that Keap1 is predominantly localized to the cytoplasm [9] , where it promotes Nrf2 degradation and inhibits its accumulation in the nucleus [8] , [10] , [11] , [12] . Studies of the Drosophila homologues of Nrf2 and Keap1 have provided insights into the functions of these protein families in adult flies . The Drosophila cap‘n’collar locus encodes CncC , which contains a bZIP domain homologous to that of Nrf2 and N-terminal DLG and ETGE motifs homologous to those that mediate Nrf2 interaction with Keap1 [13] ( Figure 1A ) . Drosophila dKeap1 contains Kelch repeats homologous to those that mediate Keap1 interaction with Nrf2 as well as a sequence motif that is required for mammalian Keap1 export from the nucleus [3] , [10] . Overexpression of CncC and depletion of dKeap1 in adult flies activates the transcription of many genes that protect cells from xenobiotic compounds , whereas dKeap1 overexpression represses their transcription , indicating that the functions of these protein families in the xenobiotic response are conserved between mammals and Drosophila [2] , [3] . Several lines of evidence suggest that CncC and dKeap1 also affect cell proliferation and development . CncC overexpression and dKeap1 depletion inhibit intestinal stem cell proliferation , and counteract the proliferative effects of environmental stress in these cells [14] . Loss of function mutations in cncC and dKeap1 cause larval lethality [3] , [15] . The genes regulated by CncC and dKeap1 during larval development had not been established . Elucidation of the relationship between CncC and dKeap1 functions in xenobiotic responses and in development is important to define how the transcription regulatory functions of CncC and dKeap1 are regulated in response to intrinsic and extrinsic stimuli . In Drosophila and in other holometabolous insects , the onset of metamorphosis is triggered by an increase in the level of the endocrine hormone ecdysone [16] , [17] . Ecdysone is synthesized in the prothoracic gland ( PG ) by a series of cytochrome P450 enzymes [18] . The expression of these ecdysone-biosynthetic genes and the timing of pupation are regulated by Ras signaling in response to prothoracicotropic hormone ( PTTH ) binding to the Torso receptor [19] , [20] . Ecdysone facilitates the onset of metamorphosis by regulating transcription in many tissues , including the salivary glands where ecdysone-regulated transcription is manifest by puffs at specific polytene chromosome loci [21] . The transcription factors that bind to the ecdysone biosynthetic gene promoters and activate their transcription have remained unknown . In the work presented here , we found that CncC and dKeap1 occupied the classical ecdysone-regulated puffs on polytene chromosomes . Depletion of CncC or of dKeap1 in salivary glands reduced ecdysone-regulated gene transcription . Depletion of CncC or of dKeap1 in the PG as well as cncC and dKeap1 loss of function mutations reduced ecdysone biosynthetic gene transcription in larvae and in embryos , respectively . The reduced ecdysteroid levels caused by CncC and by dKeap1 depletion in the PG delayed pupation and suppressed the premature pupation caused by constitutive Ras signaling . These observations establish roles for CncC and dKeap1 in transcriptional programs in different tissues that coordinate metamorphosis .
To investigate if the subcellular localization of CncC was regulated by dKeap1 in the manner that has been reported for mammalian Nrf2 and Keap1 , we determined the distributions of CncC and dKeap1 . Both CncC and dKeap1 immunoreactivity were predominantly nuclear in Drosophila salivary gland cells ( Figure 1B , Figure S1A ) . Likewise , ectopic CncC and dKeap1 fused to fluorescent proteins were enriched within the nuclei of live salivary gland cells ( Figure 1B , Figure S1A ) . CncC and dKeap1 were also present in the nuclei of prothoracic gland , imaginal disc and gut cells , though the proportions that were localized to the nucleus varied in different tissues ( Figure 1C , Figure S1B ) . The intensity of anti-dKeap1 immunoreactivity was markedly reduced in dKeap1EY5/EY5 mutant larvae , and the bands corresponding to endogenous dKeap1 and CncC were not detected by immunoblotting of extracts from dKeap1EY5/EY5 and cncK6/K6 mutant larvae , demonstrating the specificity of these antibodies ( Figure S1C , S1D ) . These observations establish that both endogenous as well as ectopically expressed CncC and dKeap1 were localized to the nuclei in many different tissues , in contrast to the predominantly cytoplasmic localization observed for Keap1 and Nrf2 in many cultured mammalian cell lines . To establish if CncC and dKeap1 bound to specific chromatin loci , we visualized their occupancy on polytene chromosomes by immunostaining . Anti-CncC and anti-dKeap1 antibodies recognized overlapping sets of loci , including a majority of the classical ecdysone-regulated early puffs on polytene chromosomes ( e . g . 2B , 74EF , 75B , 63F , and 25B ) ( Figure 1D ) . Anti-CncC antibodies also recognized several loci that were not detected by anti-dKeap1 antibodies ( e . g . 22B and 97B ) and vice versa ( e . g . 50C and 94C ) . CncC and dKeap1 occupied many non-puff loci , and did not occupy all puffs , indicating that their occupancy was not controlled solely by chromatin decondensation . Ectopically expressed CncC and dKeap1 fusion proteins occupied loci that overlapped those occupied by endogenous CncC and dKeap1 , though they also occupied additional loci ( Figure 1D ) . Few other sequence-specific DNA binding proteins have been identified that bind to ecdysone-regulated puffs [22] , [23] , [24] . The overlapping sets of loci occupied by endogenous and ectopic CncC and dKeap1 , as detected by several different antibodies , corroborate the specificity of CncC and dKeap1 binding at these loci . To test if CncC and dKeap1 regulated transcription of the early puff genes that they occupied on polytene chromosomes , we investigated the effects of CncC as well as dKeap1 depletion in salivary glands on transcription of ecdysone-regulated genes . Expression of an shRNA that targets CncC [3] under the control of either the 71B-GAL4 or the Sgs3-GAL4 driver reduced the levels of almost all of the ecdysone-regulated early puff and glue gene transcripts examined ( Figure 2A ) . In contrast , transcription of most of the late puff genes that were not prominently occupied by CncC or dKeap1 was not affected by CncC depletion ( Figure 2A ) . 71B-GAL4 directs expression throughout salivary gland development and in imaginal discs [25]; Transcription directed by Sgs3-GAL4 is detected only in late 3rd instar salivary glands [22] , establishing that the change in transcription of ecdysone-regulated genes was due to CncC depletion in salivary glands . Expression of a different shRNA that targets all Cnc isoforms also reduced the levels of all of the early puff and glue gene transcripts examined ( Figure 2A ) . The cncC-RNAi transgene had no detectable effects on transcription in larvae that lacked a GAL4 driver ( Figure S2A ) . Expression of an shRNA that targets dKeap1 [3] under the control of the Sgs3-GAL4 driver also reduced the levels of almost all of the ecdysone-regulated early puff and glue gene transcripts examined , but had no effect on most of the late puff gene transcripts ( Figure 2B ) . In contrast to the concordant effects of CncC and dKeap1 depletion on ecdysone-regulated early puff gene transcription , CncC versus dKeap1 depletion had opposite effects on transcription of the gstD1 and gstE1 xenobiotic response genes ( Figure 2A , 2B ) [3] , [26] . To examine if CncC and dKeap1 depletion affected early puff gene transcription through indirect mechanisms , we measured the levels of ecdysone receptor subunit transcripts and ecdysteroids . CncC and dKeap1 depletion in the salivary glands had no effect on the levels of the ecdysone receptor ( EcR ) or ultraspiracle ( usp ) transcripts in the salivary glands ( Figure 2A , 2B ) . CncC depletion in the salivary glands also had no effect on the level of 20-hydroxyecdysone ( 20E ) in the larvae ( Figure S2B ) . There was no detectable effect on the size or the morphology of the salivary glands , or on the time of pupation . CncC and dKeap1 therefore likely regulated transcription of the ecdysone-regulated genes directly by binding to these loci . The effects of CncC and dKeap1 on ecdysone-regulated gene transcription in salivary glands , the arrested development of cncK6/K6 and dKeap1EY5/EY5 mutant larvae , and the presence of both CncC and dKeap1 in prothoracic gland nuclei prompted us to investigate their roles in ecdysone biosynthetic gene transcription . We investigated the effects of CncC and dKeap1 depletion in the prothoracic gland ( PG ) on ecdysone biosynthetic gene transcription . We measured the levels of the neverland ( nvd ) , spookie ( spok ) , phantom ( phm ) , disembodied ( dib ) , shadow ( sad ) , and shade ( shd ) transcripts in the brain complexes of larvae that expressed the shRNA targeting CncC or dKeap1 in the PG . Expression of the shRNA targeting CncC under the control of either the 5015-GAL4 or the phm-GAL4 driver reduced the levels of all ecdysone biosynthetic gene transcripts that are expressed exclusively in the PG ( Figure 3A , Figure S3A ) . 5015-GAL4 directs expression in the PG , the salivary glands and the lymph gland [27]; phm-GAL4 directs expression in the PG and at low levels in the wing and leg discs of 3rd instar larvae [28] . Expression of the shRNA targeting CncC also reduced Sad immunoreactivity in the PG ( Figure 3C , Figure S3B ) . Expression of the shRNA targeting dKeap1 under the control of the phm-GAL4 driver reduced the levels of nvd , spok , phm , but not the levels of dib and sad in the brain complex ( Figure 3A ) . CncC depletion in the PG therefore reduced transcription of all known ecdysone biosynthetic genes that are selectively expressed in the PG , and dKeap1 depletion reduced transcription of a subset of these genes . To determine the specificity of the reduction in ecdysone biosynthetic gene transcription upon CncC or dKeap1 depletion in the PG , we examined transcription of shd , which is expressed throughout the brain , and start1 , which is expressed predominantly in the PG [29] . The levels of shd and start1 transcripts in the brain complex were not reduced by CncC or dKeap1 depletion in the PG ( Figure 3A , Figure S3A ) . Expression of the shRNAs targeting CncC or dKeap1 also did not alter the size , morphology or the number of nuclei in the PG ( Figure 3C , Figure S3B and S3C ) . It is therefore unlikely that the effects of CncC or dKeap1 depletion on ecdysone biosynthetic gene transcription were caused by a disruption of PG development . To examine if CncC or dKeap1 affected ecdysone biosynthetic gene transcription at a different stage of development , we examined the effects of the cncK6 and dKeap1EY5 loss of function mutations on transcription of these genes in late embryos . The levels of nvd , spok , dib , sad , and shd transcripts were lower in cncK6/K6 homozygous than in cncK6/+ heterozygous embryos ( Figure 3B ) . Likewise , the levels of nvd , spok , phm , dib , and sad transcripts were lower in dKeap1EY5/EY5 homozygous than in dKeap1EY5/+ heterozygous embryos , whereas the level of shd transcripts was higher in the homozygous than in heterozygous embryos ( Figure 3B ) . The moderate effects of the cncK6 and dKeap1EY5 loss of function mutations on ecdysone biosynthetic gene transcription and the consequent lack of complete developmental arrest during embryogenesis could be due to maternal deposition of CncC and dKeap1 mRNA or proteins in the egg . The effects of these mutations on the levels of ecdysone biosynthetic gene transcripts in embryos corroborate the effects of CncC and dKeap1 depletion on transcription of these genes in the PG . In contrast to the concordant effects of the cncK6 and dKeap1EY5 loss of function mutations on ecdysone biosynthetic gene transcription , these mutations had opposite effects on transcription of the gstD1 xenobiotic response gene ( Figure 3B ) . We were not able to determine the effects of CncC or dKeap1 depletion on the level of gstD1 in the PG since gstD1 is expressed throughout the brain . The cncK6 and dKeap1EY5 mutations could affect transcription of the ecdysone biosynthetic genes through several mechanisms , including direct binding to the promoters and indirect effects on other transcription factors . To test if CncC and dKeap1 bound to the ecdysone biosynthetic genes , we measured CncC and dKeap1 occupancy at their promoter regions in late embryos using ChIP analysis . CncC and dKeap1 occupancy were observed at the phm , shd , dib and sad genes at levels that were comparable to their occupancy at the dKeap1 and gstD1 genes ( Figure 3D ) . Their occupancy was higher near the sad promoter compared to flanking regions ( Figure S3D ) . No CncC occupancy above background and only low dKeap1 occupancy was observed at the Rp49 , Actn3 , and Gapdh1 housekeeping genes . CncC and dKeap1 are therefore likely to regulate ecdysone biosynthetic gene expression directly by binding to their promoter regions . Defects in ecdysteroid biosynthesis in the PG can delay pupation and increase the size of the pupae [19] . We investigated if the reduction in ecdysone biosynthetic gene transcription caused by CncC or dKeap1 depletion affected the timing of pupation by altering larval ecdysteroid levels . Expression of the shRNA targeting CncC under the control of the phm-GAL4 or the 5015-GAL4 driver extended the average time between third instar molting and pupation by 40–125% ( Figure 4A ) . Expression of a different shRNA targeting all Cnc isoforms under the control of the phm-GAL4 driver also delayed the time of pupation ( Figure 4A ) . The cncC-RNAi transgene alone had no detectable effect . The mean size of the pupae formed by larvae that expressed the shRNA targeting CncC in the PG was larger than the mean size of the pupae formed by control larvae ( Figure 4B ) , indicating that the delayed pupation was not a secondary consequence of a reduced rate of larval growth . Some larvae continued to grow and formed giant semi-pupae ( Figure S4A ) . To evaluate the role of ecdysteroid levels in the delayed pupation , we measured the level of 20E in the larvae . Expression of the shRNA targeting CncC in the PG delayed the rise in 20E after third instar molting ( Figure 4C , Figure S4B ) . To establish if the reduced level of 20E was the cause of the delay in pupation , we added 20E to the food for the larvae that expressed the shRNA targeting CncC in the PG . Supplementation with 20E shortened the time between third instar molting and pupation in these larvae by almost 50% , restoring their time of pupation nearly to that of wild-type larvae ( Figure 4A ) . Expression of the shRNA targeting dKeap1 under the control of the phm-GAL4 driver extended the average length of the larval stage by 4 days ( Figure 4D ) . The mean size of the pupae formed by larvae that expressed the shRNA targeting dKeap1 in the PG was larger than the mean size of the pupae formed by control larvae ( Figure 4E ) . Taken together , these results establish that CncC and dKeap1 affected the time of pupation through their effects on ecdysone biosynthetic gene transcription and on the level of 20E . We examined the functions of CncC in relation to the Ras signaling pathway , which controls the timing of pupation in response to prothoracicotropic hormone ( PTTH ) binding to the Torso receptor [19] . Constitutively active RasV12 expression in the PG causes early pupation and a smaller pupal size [20] ( Figure 5A ) . Moreover , deletion of Nrf2 in mice suppresses the lung and pancreatic tumorigenesis caused by constitutively active K-RasG12D expression [6] . We determined the effect of CncC depletion in combination with RasV12 expression in the PG on the time of pupation and on pupal size . When the shRNA targeting CncC was co-expressed with RasV12 in the PG , the premature pupation was suppressed and the pupae were restored to nearly normal size ( Figure 5A , 5B ) . CncC depletion in the PG not only suppressed premature pupation caused by RasV12 expression , but delayed pupation relative to wild type larvae , suggesting that CncC was required for both ectopic and endogenous Ras signaling . We further examined if CncC depletion affected the consequences of constitutive Ras signaling for pupal development . Most of the animals that expressed RasV12 alone arrested at early pupal stages with no detectable eye pigmentation or wings ( Figure 5C , Figure S5A ) . In contrast , co-expression of the shRNA targeting CncC with RasV12 enabled a majority of the pupae to develop to late stages , and some to eclose and produce adult flies ( Figure 5C , Figure S5A ) . It is unlikely that CncC depletion affected RasV12 expression in the PG since CncC depletion did not alter the level of rasV12 transcripts in salivary glands ( Figure 5E ) . The genetic interactions between CncC depletion and RasV12 expression suggest that CncC mediated the regulation of pupation by the Ras signaling pathway . To determine if Ras signaling affected CncC binding to chromatin , we investigated if RasV12 expression in salivary glands affected endogenous CncC occupancy on polytene chromosomes . RasV12 expression increased both the number of loci occupied by CncC and the level of CncC occupancy at most loci , but did not affect the level of cncC transcripts in salivary glands ( Figure 5D , Figure S5B ) . RasV12 expression reduced CncC binding at some loci ( Figure 5D ) . Ras signaling therefore regulated both the efficiency and the specificity of CncC binding to chromatin . To establish if Ras signaling and CncC affected gene transcription in concert , we examined the effects of ectopic RasV12 and CncC expression on transcription of genes at two of the loci where RasV12 expression affected CncC occupancy in salivary glands ( Figure 5D , lower panels ) . Both RasV12 as well as CncC fusion protein expression activated transcription of these genes ( Figure 5E ) . Conversely , CncC depletion by shRNA expression counteracted the activation of these genes by RasV12 expression . RasV12 expression had selective effects on the transcription of genes at these loci since the transcription of other CncC target genes , including gstD1 and gstE1 , was not detectably affected by RasV12 expression ( Figure 5E ) . These results suggest that Ras signaling regulated CncC transcriptional activity by altering its occupancy at selected target genes .
Both CncC and dKeap1 depletion reduced transcription of ecdysone-regulated early puff genes . These loci were occupied by both CncC and dKeap1 , suggesting that CncC and dKeap1 activated transcription of these genes in concert . Similarly , transcription of most ecdysone biosynthetic genes was reduced by both CncC and dKeap1 depletion as well as by the cncK6/K6 and dKeap1EY5/EY5 loss of function mutations in larvae and embryos , respectively . The ecdysone biosynthetic genes were also occupied by both CncC and dKeap1 in embryos , suggesting that CncC and dKeap1 activated their transcription in concert . In contrast , CncC and dKeap1 depletion as well as the cncK6/K6 and dKeap1EY5/EY5 mutations had opposite effects on transcription of the gstD1 and gstE1 xenobiotic response genes in salivary glands and in embryos , respectively . Similarly , opposite effects of CncC and dKeap1 on transcription of other xenobiotic response genes have been previously reported in adult Drosophila [2] . CncC and dKeap1 therefore regulated transcription of different classes of genes through distinct mechanisms . Whereas xenobiotic response genes are regulated by antagonistic effects of dKeap1 on transcription activation by CncC , ecdysone biosynthetic and response genes were activated by concerted chromatin binding by CncC and dKeap1 . Chromatin binding by dKeap1 as well as its cooccupancy and cooperation with CncC have potential implications for Keap1 function and its effects on Nrf2 activity in mammalian cells . Keap1 can shuttle into the nucleus in some cells [10] , [12] , and could bind chromatin in association with Nrf2 or other interaction partners . The effects of CncC and dKeap1 depletion on ecdysone biosynthetic gene transcription and on the timing of pupation indicate that CncC and dKeap1 are important components of the transcription regulatory circuit that controls ecdysone biosynthesis ( Figure 6 ) . Many parts of the neuro-endocrine signaling axis that induces ecdysone biosynthesis have been characterized [19] , [20] , [30] , [31] . Previous studies had not identified the transcription factors that bind and regulate ecdysone biosynthetic genes . dSmad2 depletion in the PG reduces ecdysone biosynthetic gene transcription and inhibits pupation . dSmad2 depletion also reduces torso and InR transcription , and RasV12 or InR co-expression in combination with dSmad2 depletion restores both ecdysone-biosynthetic gene transcription as well as pupation [30] . It is therefore likely that dSmad2 affects ecdysone production indirectly by altering Torso or Insulin signaling . In contrast , CncC depletion suppressed the premature pupation caused by RasV12 expression in the PG , and RasV12 expression in salivary glands altered the loci occupied by CncC on polytene chromosomes . These results , together with CncC occupancy and regulation of ecdysone biosynthetic genes in embryos , suggest that CncC mediated the effects of Ras signaling in the PG on pupation by regulating ecdysone biosynthetic gene transcription . CncC and dKeap1 also regulated transcription of the early ecdysone-inducible genes in the salivary gland . CncC and dKeap1 binding at ecdysone-inducible early puffs , and the absence of effects of CncC depletion on ecdysone receptor subunit or on late puff gene transcription indicate that CncC and dKeap1 regulated early puff gene transcription directly . The functions of CncC and dKeap1 in regulation of genes that control both ecdysteroid synthesis as well as the transcriptional responses to this hormone place CncC-dKeap1 complex at the nexus of a regulatory network that that coordinates the onset of insect metamorphosis ( Figure 6 ) . The discovery that CncC and dKeap1 coordinate Drosophila metamorphosis has identified novel functions of Nrf2-Keap1 family proteins in normal cellular processes and development . The regulation of both metamorphosis and xenobiotic responses by CncC and dKeap1 suggests that these processes either share a common evolutionary ancestry , or that they are mechanistically or functionally interrelated . Most of the ecdysone biosynthetic genes encode cytochrome P450 class oxidoreductases [18] . P450 class oxidoreductases are also key mediators of the metabolic detoxification of many xenobiotic compounds [32] . The genes that were regulated by CncC and dKeap1 in the salivary and prothoracic glands during larval development and those that are regulated by CncC and dKeap1 in adult flies [2] , [3] were mostly non-overlapping . Among the genes identified in this study , only nvd among the ecdysone biosynthetic genes and Sgs5 among the ecdysone-regulated genes were detected by microarray analysis of transcripts induced by CncC expression in adult flies [2] . Thus , the effects of CncC and dKeap1 on the transcription of most of the genes that controlled the onset of metamorphosis were restricted to specific tissues and stages of development . The functions of CncC and dKeap1 in both hormonal regulation of development and in responses to toxic compounds and environmental stress could represent a mechanism that controls development in response to environmental conditions . Imaginal disc damage inhibits PTTH synthesis , resulting in reduced ecdysone synthesis and a delayed pupation [33] . Modulation of TOR signaling in the PG regulates ecdysone biosynthetic and ecdysone-regulated gene transcription and the timing of pupation [31] . Activation of TOR signaling in the PG suppressed the pupation delay caused by larval starvation , indicating that TOR signaling affected developmental timing in response to nutrient stress . Nutrient restriction and heat stress alter 20E and juvenile hormone levels in the ovaries , arresting oogenesis [34] . The interaction between CncC and dKeap1 could mediate responses to both external as well as endogenous signals that modulate developmental progression . Future studies of the effects of environmental stresses on the developmental functions of CncC and dKeap1 will test this hypothesis . The premature pupation and developmental arrest caused by constitutively active RasV12 expression were suppressed by CncC depletion in the PG . Similarly , the lung and pancreatic tumorigenesis caused by constitutive K-RasG12D expression are suppressed by Nrf2 deletion in mice [6] . K-RasG12D expression can cause a two-fold increase in Nrf2 transcription , but the significance of this change in Nrf2 transcription for tumorigenesis has not been established . RasV12 expression in Drosophila did not alter the level of CncC transcription , but increased the overall level of CncC binding to chromatin , shifted the loci occupied by CncC on polytene chromosomes , and activated genes at those loci in concert with CncC . These results suggest that Ras signaling can regulate the functions of CncC/Nrf family proteins by altering their target gene specificities or transcriptional activities . The mechanisms whereby Ras regulated CncC occupancy remain to be determined , but are likely to include phosphorylation as the MAPK pathway has been proposed to regulate both Nrf2 and the C . elegans homologue of CncC [35] , [36] , [37] . The relationships between the roles of CncC and dKeap1 in Drosophila metamorphosis and the functions of their mammalian homologues in development remain to be elucidated . Two of the mammalian homologues of CncC , Nrf1 and Nrf2 , appear to have partially overlapping functions during mouse development [38] , [39] , [40] , [41] . Genome-wide analyses have identified many genes occupied by Nrf2 that have no known functions in the xenobiotic response [42] , [43] . Although ecdysteroids are unique to invertebrates , steroid hormones have central roles in many aspects of mammalian physiology . Nrf2 can mediate the 1α , 25-dihydroxyvitamin D3-induced differentiation of acute myeloid leukemia cells through multiple mechanisms , including VDR/RXRα transcription [44] . Further studies of the mechanisms of action of CncC/Nrf and dKeap1/Keap1 family proteins in different phyla are required to establish the evolutionary relationships among these proteins and their functions in development and disease .
Plasmids encoding CncC , CncB , and dKeap1 fused to intact fluorescent proteins and fluorescent protein fragments were constructed as described in supplemental materials and methods . Transgenic Drosophila lines carrying these expression constructs were generated by microinjection in the w1118 background . The transgenic lines carrying UAS-cncC-RNAi , UAS-dKeap1-RNAi and UAS-rasV12 transgenes were as described [3] , [20] . The transgenic line carrying UAS-cnc-RNAi expressed an shRNA that targets all of the Cnc . Double transgenic lines were produced by crosses with Sgs3-GAL4 , 71B-GAL4 , 5015-GAL4 , phm-GAL4 and tub-GAL4 driver lines [19] , [22] , [25] , [27] . To minimize external sources of stress , all studies were conducted with larvae and embryos maintained at 25°C with the exception for larvae carrying the UAS-cnc-RNAi and UAS-dKeap1-RNAi transgenes , which were maintained at 29°C to improve the efficiencies of CncC and dKeap1 depletion . Homozygous and heterozygous embryos carrying the cncK6 and dKeap1EY5 alleles were identified by using the Dfd-YFP marker . Anti-CncC and anti-dKeap1 antisera were raised against proteins encompassing residues 88–344 of CncC and residues 620–776 of dKeap1 fused to GST . The antigens were immobilized and used for affinity purification of the antibodies . Polytene chromosome spreads isolated from the salivary glands of early wandering 3rd instar larvae were prepared and immunolabeled as described in supplemental information . Salivary glands , brain complexes ( including brain and prothoracic gland ) , and imaginal discs were isolated from early wandering 3rd instar larvae and were immunolabeled as described in supplemental information . mRNA was isolated from the salivary glands and brain complexes of early wandering 3rd instar larvae as well as embryos , and was quantified by RT-qPCR . The relative transcript levels were calculated by assuming that they were proportional to 2−Cp , and were normalized by the levels of Rp49 transcripts . For ChIP analysis , chromatin was isolated from dechorionated embryos , sheared by sonication , and precipitated using the antisera indicated . The precipitated DNA was quantified by qPCR . Newly molted 3rd instar larvae or newly hatched 1st instar larvae were collected and transferred into vials . The number of white prepupae ( WPP ) was scored every 12 hours or 24 hours . To determine the effect of 20E feeding on pupation , the larvae were grown on feeding plates topped with yeast paste containing 0 . 5 mg/ml 20E . 20E was extracted from larvae and white pre-pupae and was quantified using an enzyme immunoassay kit ( Cayman Chemical ) . Detailed experimental procedures and references are included in Text S1 , Table S1 , Table S2 . | Human Nrf2-Keap1 and the fruit fly CncC-dKeap1 protein complexes function both in response to foreign chemicals and in development . We found that CncC and dKeap1 control fruit fly development by regulating the production and actions of the principal hormone that controls the transformation of larvae into pupae . In hormone-responsive cells , CncC and dKeap1 bound to the genes that are activated by the hormone . When the amount of CncC or dKeap1 in these cells was reduced , the genes were not activated efficiently . When the amount of CncC or dKeap1 was reduced in the organ where the hormone is made , the genes whose products make the hormone were not activated efficiently . Because less hormone was made , it took longer for the larvae to turn into pupae , and the resulting pupae were bigger . Reduction of the amount of CncC intercepted previously identified signals for pupation . Nrf2 is required for the same signals to cause cancer in mice . The effects of CncC and dKeap1 both on genes that control hormone production and on genes that are switched on by the hormone in different organs indicate that they have multiple roles in the transformation of fruit fly larvae into pupae . | [
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] | 2013 | Regulation of Drosophila Metamorphosis by Xenobiotic Response Regulators |
Long-term potentiation ( LTP ) and long-term depression ( LTD ) are widely accepted to be synaptic mechanisms involved in learning and memory . It remains uncertain , however , which particular activity rules are utilized by hippocampal neurons to induce LTP and LTD in behaving animals . Recent experiments in the dentate gyrus of freely moving rats revealed an unexpected pattern of LTP and LTD from high-frequency perforant path stimulation . While 400 Hz theta-burst stimulation ( 400-TBS ) and 400 Hz delta-burst stimulation ( 400-DBS ) elicited substantial LTP of the tetanized medial path input and , concurrently , LTD of the non-tetanized lateral path input , 100 Hz theta-burst stimulation ( 100-TBS , a normally efficient LTP protocol for in vitro preparations ) produced only weak LTP and concurrent LTD . Here we show in a biophysically realistic compartmental granule cell model that this pattern of results can be accounted for by a voltage-based spike-timing-dependent plasticity ( STDP ) rule combined with a relatively fast Bienenstock-Cooper-Munro ( BCM ) -like homeostatic metaplasticity rule , all on a background of ongoing spontaneous activity in the input fibers . Our results suggest that , at least for dentate granule cells , the interplay of STDP-BCM plasticity rules and ongoing pre- and postsynaptic background activity determines not only the degree of input-specific LTP elicited by various plasticity-inducing protocols , but also the degree of associated LTD in neighboring non-tetanized inputs , as generated by the ongoing constitutive activity at these synapses .
Synaptic plasticity , i . e . long-lasting activity-dependent changes in synaptic transmission , is widely considered to be a critical neural mechanism for memory storage . Computational models are being increasingly used to understand the precise activity rules that govern the induction and persistence of synaptic plasticity . However , the most recent models of plasticity based on the precise timing of pre- and postsynaptic spikes ( i . e . , spike-timing-dependent plasticity or STDP ) typically ignore the fact that in vivo [1 , 2] , and also sometimes in vitro [3] , the studied neurons exhibit an ongoing spontaneous spiking with frequencies reaching up to 10 Hz that has the potential to profoundly influence plasticity outcomes . The first synaptic plasticity theory that explicitly took into account ongoing neuronal activity was the BCM theory [4] . A key element of this BCM theory is a whole-cell variable termed the modification threshold , the tipping point at which the presynaptic activity either leads to long-term depression ( LTD ) or long-term potentiation ( LTP ) of synaptic efficacy . A second key element is the theory’s postulate that the average ongoing level of background activity dynamically sets the position of the LTD/LTP tipping point in such a way that potentiation is favored when background postsynaptic cell firing is low on average and , vice versa , depression is favored when the postsynaptic activity is high on average . The BCM model has been used to account for experimental findings of experience-evoked plasticity in the developing visual [4 , 5] and adult somatosensory cortices in vivo [6 , 7] . The proposal of a modifiable plasticity threshold foreshadowed the concept of metaplasticity [8] , developed to account for the abundant experimental evidence that prior neural activity can change the state of neurons and synapses such that the outcome of future synaptic plasticity protocols is altered [9] . In contrast to the BCM model , the standard STDP rule and its numerous modifications [10] as they are applied in simulations do not currently take into account background activity . A first rigorous attempt to bridge STDP with the BCM theory was made by Izhikevich and Desai [11] , who showed that nearest spike STDP interactions led to a fixed LTD/LTP frequency threshold . However , according to the BCM theory , the position of the LTD/LTP threshold is not fixed but depends on the average postsynaptic activity ( which is in turn proportional to the average presynaptic input activity ) . Thus , in the spirit of metaplasticity and the BCM theory , we have proposed that the magnitudes of LTP and LTD in the STDP rule are not constant but dynamically change from moment to moment as a function of the previous average postsynaptic spiking [12] . This line of thinking is in accordance with recent phenomenological equations for synaptic plasticity that use a rapidly changing LTD amplitude [13–15] or LTD window [16] based on average postsynaptic activity . Granule cells in the hippocampal dentate gyrus have the intriguing ability to exhibit heterosynaptic plasticity when studied in vivo . Here , high-frequency stimulation ( tetanization ) of one set of synapses leads not only to LTP in that tetanized input but also LTD in a neighboring non-tetanized set of synapses [17–20] . We brought together the concepts of metaplasticity ( effectively a fast BCM-like homeostasis ) and STDP into a unified theoretical framework to model this as yet unexplained heterosynaptic plasticity phenomenon in the dentate gyrus of freely moving rats [12] . In that model , homosynaptic LTP occurred as a consequence of tetanization delivered to the medial perforant path input to a dentate granule cell , and LTD appeared simultaneously in the neighboring lateral perforant path synapses , as in experiments , but only if the model included ongoing spontaneous spiking for the lateral path inputs to drive the LTD mechanism under conditions of a transiently altered modification threshold that favored LTD . A direct prediction for experiments from this model was that blocking the spontaneous spiking of the lateral perforant path would also block the LTD . This prediction was tested and confirmed in anesthetized rats [21] . Based on the same STDP-BCM plasticity model , we were able to account for the absence of LTD using low-frequency stimulation paradigms in the dentate gyrus in vivo [22] . The above-mentioned computational studies used a highly simplified model of a dentate gyrus granule cell innervated by two representative excitatory synapses , one for medial path and one for lateral path , and modeled using the Izhikevich simple spiking neuron with parameters corresponding to a regularly spiking neuron [23 , 24] . This model does not have dendrites , and thus we refer to it as a “point model” of a neuron . In spite of the successful account of the STDP-BCM plasticity model in modeling complex homo- and heterosynaptic plasticity phenomena in the dentate gyrus , important questions remain . Namely , will the plasticity model work also for a more biophysically realistic model of the granule cell with dendrites , featuring kinetics of all relevant membrane ion channels and multiple input synapses ? Moreover , will our plasticity rule be able to reproduce the observed differential experimental effects of varying the frequency and temporal patterns of perforant path tetanization on the magnitude of synaptic plasticity ? Here , we will pay particular attention to a recent experimental study of freely moving rats [25] , in which different tetanization frequencies and temporal patterns applied to the medial path led to surprisingly different magnitudes of medial path LTP and , concurrently , LTD in the lateral path . Thus , although a 400 Hz delta-burst stimulation ( 400-DBS ) administered to the medial path produced robust LTP and LTD , as previously described [20] , unexpectedly the commonly used 100 Hz theta-burst stimulation ( 100-TBS ) [26 , 27] generated virtually no LTP or LTD . However , this theta-burst protocol could be converted into an LTP/LTD-inducing one by increasing the intraburst frequency from 100 to 400 Hz ( 400-TBS ) . We show that our relatively simple STDP plasticity rule with fast BCM homeostasis / metaplasticity can reproduce the Bowden et al . [25] pattern of results when implemented in a compartmental granule cell model with realistic biophysics . The model thus gives insight into the computations that granule cells are making , as driven by plasticity-inducing synaptic events arising from both tetanization and background activity .
To simulate heterosynaptic plasticity with a more complex type of model neuron , we adopted a published , multicompartmental and biophysically realistic computational NEURON model of a dentate gyrus granule cell , albeit with reduced morphology ( Fig 1A and Methods; [28 , 29] ) . This nine-section , 125 compartment granule cell model received input from 150 medial path synapses and 150 lateral path synapses , distributed in appropriate zones across the two dendritic branches ( Fig 1A ) . This model was endowed with a synaptic plasticity mechanism containing a presynaptically centered nearest-neighbor implementation of the STDP rule with fast homeostasis ( metaplasticity ) , the same as used previously for the point model [12] and described in the Methods . The multicompartmental granule cell model included the biophysics of the main ion channels in the dendrites and soma . Action potentials were generated in the soma and back-propagated along the dendrites , both electrotonically as well as due to the action of dendritic sodium and calcium ion channels . Thus the granule cell model took into account all of the complex spatio-temporal integration of EPSPs in the dendrites that are evoked by the spontaneous activity , the experimental high-frequency stimulation ( HFS ) protocol , and the back-propagating postsynaptic spikes . In addition , we employed a realistic simulation of the granule cell spontaneous input activity according to in vivo data that show a significant peak around 8 Hz for both the medial and lateral pathways [2] . For the initial series of simulations , the dendritic voltage threshold for the postsynaptic event detection at a synapse for local STDP implementation was -37 mV . To implement the global metaplasticity mechanism , the somatic voltage threshold for action potential detection for BCM homeostasis calculations was 0 mV . First , we modelled the 400-DBS protocol ( Fig 1A ) that has been used in many experiments for reliably inducing LTP and concurrent LTD [20 , 25] . This protocol was implemented together with simulated ongoing presynaptic spontaneous activity at the mean rate of 8 Hz , phase-locked on average for the medial and lateral path synapses . In contrast to the point model , however , we introduced a random jitter for the presynaptic inputs ( noise parameter = 0 . 05 ) . Thus , each synapse received presynaptic spikes at slightly different times from other synapses , which is a more realistic account of the ongoing spontaneous activity in vivo . In another difference from the point-neuron model [12] , we made no assumption regarding HFS affecting the ongoing presynaptic firing . Thus the presynaptic spontaneous activity pattern was maintained throughout the whole course of simulation: before , during and after HFS . Fig 1B ( top panel ) illustrates the evolution of mean medial path and lateral path weights in response to 400-DBS of all 150 medial path synapses for an optimal set of STDP parameters , i . e . Ap = 0 . 003 , Ad = 0 . 001 , τp = 20 ms and τd = 70 ms , α = 2500 ( see Methods ) . This protocol generated robust LTP ( 23 . 0 ± 2 . 3% , n = 3 simulations ) at the medial path synapses and even stronger LTD ( -38 . 5 ± 1 . 3% ) concurrently at the lateral path synapses . To make the model even more comparable to experiments , we also generated simulations whereby only 60% of the medial path synapses received the 400-DBS protocol . This latter choice was made because we considered it likely that in real-life experiments the electrical stimulation only engages a proportion of the input axons for a given pathway . The synapses chosen were randomly distributed spatially along the dendritic termination zone of the medial path inputs , and the same STDP parameters were employed . As shown in Fig 1B ( middle panel ) , this protocol again gave a robust LTP at the tetanized synapses ( 45 . 3 ± 4 . 4% , n = 3 simulations ) at the medial path synapses and now weaker albeit still robust LTD ( -30 . 6 ± 1 . 5% ) at the lateral path synapses . To investigate the relationship between localization of the synapse and magnitude of plastic change , the spatial distribution of the final weight for each of the 300 total synapses from a single simulation is plotted in Fig 1C . Nearly all synapses that received only the spontaneous activity showed LTD , whereas medial path synapses that received HFS potentiated . The degree of LTP or LTD did not depend on the distance from the soma ( Pearson’s correlation coefficient r < 0 . 05 ) . It is noticeable that when 100% of the medial path synapses received HFS , the LTD had a larger absolute magnitude than the LTP . This is because the depressed lateral path synapses lie more distally on the granule cell dendrites than the medial path synapses , and due to greater electrotonic decay of the voltage from these distal synapses , the absolute change in synaptic weight needs to be greater than for the proximal potentiated synapses if a homeostatic restoration of firing rate to baseline levels is to be achieved . On the other hand , when only 60% of the medial path synapses were tetanized , not only all of the lateral path synapses but also the remaining 40% non-tetanized medial path synapses were depressed . With more total synapses being depressed , then the absolute average degree of depression of lateral path synapses could be less than the absolute average potentiation of the tetanized synapses and still maintain homeostasis . The relative balance of LTP and LTD in the 60% tetanization condition fits well with experimental observations [20 , 25] . When the number of tetanized medial path synapses was reduced to only 20% , however , the balance between LTP and LTD shifted too far in favour of LTP , compared to the experimental data ( S1 Fig ) . It is worth noting that , as predicted by the point model and shown in experiments [21] , lateral path LTD was absent when spontaneous activity on this pathway was turned off during and after medial path HFS ( Fig 1B , bottom panel ) . This is because the LTD only occurs in the model when the STDP rule is triggered by lateral path ongoing spontaneous activity . We next tested whether the ineffectiveness of the 100-TBS protocol ( Fig 1A ) in eliciting synaptic plasticity in experiments could be reproduced in the compartmental model . Indeed , when using the same parameters as optimal for the DBS protocol , this TBS protocol produced only weak LTP ( 5 . 3 ± 0 . 3% , n = 3 ) of the tetanized medial path synapses ( 60% tetanization protocol ) and an equally weak concurrent LTD of the non-tetanized lateral path synapses ( -6 . 5 ± 0 . 3% , Fig 2A ) . These changes were much smaller in magnitude than for 400-DBS , as observed in experiments [25] . Similar effects were seen when 100% of the medial path synapses were tetanized . In the experiments , LTP and concurrent LTD induction could be largely rescued simply by increasing the pulse frequency within a theta-burst from 100 to 400 Hz , keeping all other parameters the same . Accordingly , we assessed the synaptic weight evolution for the 400-TBS protocol in the compartmental model . We observed a recovery of LTP for the tetanized medial path synapses and of LTD for the non-tetanized lateral path and medial path synapses , when tetanizing 60% the medial path synapses ( Fig 2B ) . The 400-TBS protocol produced mild LTP ( 25 . 0 ± 2% , n = 3 ) of the tetanized medial path synapses ( 60% tetanization protocol ) and mild concurrent LTD of the non-tetanized lateral path synapses ( -20 . 7 ± 1 . 6% ) , as seen in experiments [25] . Similar effects were seen when tetanizing 100% of the medial path synapses . Fig 3 summarizes the plasticity results ( 60% medial path tetanization ) using the compartmental model , and compares them with the experimental data obtained by Bowden et al . [25] , using the parameter set described above . It is important to consider , however , whether the qualitative or quantitative match with the experimental data is dependent on these specific parameters , or whether the STDP-BCM model has more generality . Accordingly we systematically varied the level of noise , τd , τp , the initial ratio of Ap / Ad and the dendritic threshold voltage for detecting the postsynaptic event for STDP pairing . The results are given as averages from 10 runs ( ± S . D . ) . To speed up these simulations , we reduced number of segments to 9 , i . e . 1 for soma , 2 for the granule cell layer , 2 for the inner molecular layer , 2 for the middle molecular layer and 2 for outer molecular layer parts of the dendrites . First we investigated the effect of the level of noise in the ongoing input spontaneous spiking . Noise = 0 means perfectly synchronized 8 Hz spiking at all synapses , while Noise = 1 means all synapses receive random , Poisson-distributed spiking with an average frequency of 8 Hz . Noise values ranging from 0 . 05 to 1 . 0 all provided excellent matches with the experimental data ( S2 Fig ) . For the case of Noise = 0 . 01 and 0 , the two pathways either both potentiated or both depressed , but this could be prevented by adjusting the starting conductances for both the medial and lateral path synapses . Overall , the model was very robust with respect to the asynchrony of the ongoing background activity . Intriguingly , the model performed better in the presence of noise than in the absence of it , even when there was completely random synaptic activity . Also of interest was the dependence of the model on time constant values ( τp and τd ) for the plasticity windows , as in experiments they can vary substantially across brain regions , cell types and the preparations used [30] . As commonly observed experimentally , we found that τd needed to be somewhat greater than τp , with the best match to the experimental data seen with τd = 70 ms and τp = 20 ms ( S3 Fig ) . Quantitatively poor results were obtained with pairings of 30/20 ms and 70/40 ms ( τd and τp , respectively ) , although qualitatively the patterns of LTP and LTD outcomes across the three HFS conditions remained similar across the parameter values used ( S3 Fig ) . For the optimal pairing of τd = 70 ms and τp = 20 , we also found that the starting amplitudes of potentiation ( Ap ) and depression ( Ad ) in a 3:1 ratio gave a result matching the experimental data , while a ratio of 1:1 gave a qualitatively and quantitatively poor match ( S4 Fig ) . As a final test of model robustness , we compared the effects of various synaptic voltage thresholds for triggering the STDP calculations , specifically -30 mV , -33 mV , -37 mV , and -40 mV . We found that -40 mV gave a very similar result to that obtained with our standard -37 mV threshold , but that setting the threshold at -30 mV distorted the outcome relative to the experiments ( S5 Fig ) , as it was common for the back-propagating spike to fail to reach this threshold for the bulk of the lateral path synapses . Taking the results of all these simulations together , however , the BCM-STDP plasticity rule was robust across a wide range of parameters in its ability to reproduce the experimental data , when implemented in a multicompartmental model of a granule cell . The multicompartmental model of granule cell , with realistic active and passive properties , was able to reproduce different experimental results arising from the various experimental protocols . This afforded us the opportunity to examine the firing properties of the granule cell model in response to HFS in order to ascertain biophysical explanations for the protocol dependence of the different plasticity outcomes . Of particular interest to us was the integration of postsynaptic action potential firing , which is the key parameter for BCM-based adjustment of the STDP parameters Ap ( potentiation-amplitude ) and Ad ( depression-amplitude ) . The integrated spike count 〈c〉 ( factored by a constant α ) for the first protocol , 400-DBS , is shown in the left panel of Fig 4A . The right panel shows the corresponding dynamic changes in Ap and Ad as these latter parameters are scaled by 〈c〉 . For this protocol , after the initial period of stabilization , time-averaged 〈c〉 remained steady and was only mildly increased during HFS of the medial path , because each burst of 10 presynaptic pulses ( at 400 Hz ) generated only 2–4 postsynaptic spikes due to action potential refractoriness ( Fig 5 , upper panel ) , and therefore both Ap and Ad barely changed . Thus it can be concluded that the DBS protocol was sufficient to instigate LTP of the medial path using relatively unchanged STDP parameters . Close inspection of the development of LTP and LTD shows that LTD began to arise only after LTP had begun to develop ( Fig 6 ) , indicating that the LTD in the lateral path was occurring as a consequence of LTP induction in the medial path . Lateral synapses were depressed because the spontaneous activity of the potentiated medial path started controlling postsynaptic spiking , and so the lateral path activity was participating less in the LTP-inducing pre-post order than in the LTD-inducing post-pre order . When we examined the spiking response to the 100-TBS paradigm , the time-averaged 〈c〉 was greatly increased during tetanization ( Fig 4B ) . Close inspection of the voltage traces ( Fig 5 , middle panel ) showed that for this protocol , almost every presynaptic volley caused a postsynaptic spike . Thus , even though this protocol has only 4 pulses per burst , by spiking the postsynaptic cell 4 times for each burst , this protocol actually generated more spikes than the DBS protocol . And since the bursts also come more frequently ( 5 Hz ) than for DBS ( 1 Hz ) , the integrated spike count rises even more dramatically . Surprisingly , despite this increase in postsynaptic spiking , LTP induction was very weak using the 100-TBS protocol . This counterintuitive result is explained by the fast homeostatic nature of our learning rule which generates a rapid and dramatic decrease in Ap and corresponding increase in Ad in response to the increased spiking ( Fig 4B ) , and these effects together considerably braked the induction of LTP in the medial path . The increase in Ad does not lead to a larger LTD , however , because its induction occurs mainly to balance the degree of LTP induction . To confirm that the change in Ap contributes to braking LTP , we turned off the homeostatic adjustment of Ap , and found that the average degree of LTP indeed increased from 5 . 3 ± 0 . 3% to 36 . 8 ± 0 . 9% , as predicted . If the enhanced spiking during 100-TBS , and resultant change in Ap and Ad , were responsible for observing less LTP compared to 400-DBS , than we can predict that LTP should be rescued for the theta-burst protocol when the intra-burst frequency is increased from 100 Hz to 400 Hz . This should reduce somatic spiking due to action potential refractoriness , reduce 〈c〉 , and thus dampen the homeostatic changes in Ap and Ad . This is exactly what happened for the 400-TBS protocol ( Figs 4C and 5 , bottom panel ) . The dentate granule cell is electronically compact , and therefore it is not clear whether active back-propagation of the action potential is needed for all the required STDP interactions to take place , especially for the lateral path synapses located on the distal dendrites . To address this issue , we turned off all the voltage-dependent sodium and calcium channels in the dendrites , and ran our model with the standard parameter set ( 60% medial path HFS ) for the three tetanization protocols . For the medial path synapses , the degree of LTP was increased for all three protocols , but the qualitative relations between them was preserved ( Fig 7 ) . STDP ( and LTP ) occurs in the MPP synapses without action potential backpropagation ( when dendritic sodium and calcium channels are blocked ) because DBS and TBS induced sustained depolarization through electrotonic conduction which is able to cross the local plasticity threshold ( Fig 7A and 7B ) . This is in line with recent experimental observations of LTP induction in the absence of somatic action potentials ( [31] , see also [32] ) . Similarly , turning off somatic sodium channels during HFS did not prevent LTP induction but it worsened the match with experiments ( the model failed to produce balanced LTP and LTD ) since the metaplasticity mechanism in the model needs somatic spikes to get activated . In contrast to LTP , when dendritic channels were inactivated , no significant LTD appeared on the lateral path synapses for any of the HFS protocols ( 7C ) . This can be explained by the failure of the passively conducted APs to breach the -37 mV threshold for STDP in the outer molecular layer , in contrast to what happens in the middle molecular layer ( Fig 7B ) . Thus , some degree of active propagation is necessary for the triggering of STDP in the lateral path synapses , at least for the voltage threshold for STDP that we have used . Recent theoretical results indicate that stable activity in neural networks requires a homeostatic rule with a fast detection ( seconds to minutes ) of firing rate changes [14] . Therefore , we wanted to test whether fast integration of spikes , which is a key part of our BCM-like metaplasticity rule , is needed to account for the experimentally determined pattern of LTP and LTD results . When we slowed down the integration of spikes from 1 min to 10 min , the degree of plasticity changes still occurred in the order 400-DBS > 400-TBS > 100-TBS , but the quantitative match with data from Bowden et al . [25] was much worse ( Fig 8C ) . In particular , the 100-TBS protocol generated unrealistically large LTP and LTD ( Fig 8B and 8C ) . The reason for this is that slow BCM homeostasis leads to a smaller rise in integrated spike count and thus a smaller decrease in Ap and a smaller increase in Ad in response to the increased spiking during 100-TBS ( c . f . Figs 4B and 8A ) . The match was even worse for an integration time of 20 min , and also for the extremely fast integration time of 0 . 1 min ( Fig 8C ) . We conclude that fast ( but not too fast ) BCM-like metaplasticity is important for best explaining the different plasticity outcomes from different HFS patterns .
Our biologically realistic simulations have shown that ongoing background activity is a key determinant of the degree of long-term potentiation and especially long-term depression in the dentate gyrus . All HFS protocols were implemented together with simulated ongoing presynaptic spontaneous activity at the mean rate of 8 Hz . This spontaneous spiking was phase-locked only on the average for the medial and lateral path synapses , as we introduced random jitter across a wide range for all the presynaptic inputs . Thus , each synapse received presynaptic spikes at somewhat different times from other synapses , which is a more realistic account of the spontaneous activity in vivo . This ongoing spontaneous input activity can explain why the heterosynaptic LTD is routinely seen in the dentate gyrus in vivo , when the hippocampal circuitry is intact [18–20] while absent in the dentate gyrus in vitro , when the input is severed [35] . We would like to point out that in neocortical slices too a substantial spontaneous activity can be present [3] and that may be why the heterosynaptic LTD can happen [36] . To summarize the operation of the compartmental granule cell model , medial path HFS causes the granule cell to repeatedly spike , and due to the causal pre-post order , the medial path synapses thus potentiate . Then , as the strengthened medial path spontaneous activity begins to assume control of the cell spiking , the lateral path spontaneous activity moves more into the LTD zone of the STDP windows . Concurrently , the spiking caused by HFS homeostatically adjusts the depression amplitude upward for some time , enhancing LTD induction . While a concomitant drop in potentiation amplitude also occurs , this is not sufficient to prevent LTP induction in the medial path with 400 Hz protocols . However , the 100 Hz-TBS gives poor LTP and thus poor LTD because TBS is so good at firing granule cells that this causes the potentiation amplitude parameter to markedly decline , braking LTP induction . This happens due to the BCM part of our STDP rule which causes that the increased postsynaptic spiking ( evoked by 100-TBS ) leads to a decrease in the potentiation amplitude parameter Ap and this reduces the induction of LTP in the medial path . The difference in firing of the granule cell in combination with the homeostatic STDP-BCM plasticity rule is the main cellular mechanism which explains the difference in plasticity outcomes ( 400-DBS > 400-TBS > 100-TBS ) . The difference in firing arises due to action potential refractoriness which depends on sodium and potassium channels in the soma of the granule cell . In the published point model [12] , the postsynaptic event setting the timing for STDP weight changes is the postsynaptic spike . In our new morphological ( multicompartmental ) model , it is the threshold dendritic voltage at any synapse location that would register the occurrence of a back-propagating action potential ( bAP ) for the purpose of STDP calculations . For most of the above simulations , we used -37 mV as that threshold membrane voltage , although other voltages could be used , as long as the distal dendrites could be driven to this threshold voltage by an action potential [37] . Thus STDP could be exhibited by the full extent of synapses on the dendritic tree [38 , 39] . It is noteworthy however that the postsynaptic event can result from summation of the local EPSPs and/or any bAP crossing certain threshold value . Thus , it is an interesting aspect of using -37 mV as the local STDP threshold that sometimes this threshold was crossed by synchronous synaptic activity even in the absence of a somatic action potential . This was particularly true during 400 Hz bursts , when the soma could not follow the rate of presynaptic activity , but the synchrony of afferent input was sufficient for the synaptic response to cause the membrane potentiation to rise higher than -37 mV . The extent to which this influenced the degree of LTP and LTD is not clear . However , since LTD induction lagged behind the LTP induction , it remained the case that post-HFS back-propagation of the action potential from the soma was the critical dendritic event for at least these STDP calculations . The necessity of having a postsynaptic threshold for a postsynaptic event in our model is in line with the hypothesis of Lisman and Spruston [40] that long-lasting synaptic modification may be protected by multiple thresholds that have to be crossed before weights can be persistently modified . First of these thresholds would be a critical dendritic depolarization that has to be reached as a result of temporal and/or spatial integration of EPSPs and back-propagating APs [40] . Kovalchuk et al . [41] showed that –30 mV is the voltage where the NMDA-mediated Ca2+ influx is maximal in the dendritic spines of CA1 cells . Since we have only one synaptic threshold for both LTD and LTP , a value slightly below -30 mV seems reasonable as a calcium increase is required also for induction of LTD , although we found -33 mV to be an effective threshold as well . Clopath et al . ’s synaptic plasticity model [13] has two voltage thresholds , one for LTD and one for LTP , but the value they used for hippocampal cells were very similar , i . e . -41 mV and -38 mV , respectively . A critical parameter of the model that affects BCM calculations is the length of the cell-firing integration period . Our model was the most stable and robust when using a relatively fast integration period of one minute . Here we briefly discuss a potential biochemical mechanism for this relatively fast homeostasis . When the amplitude of the postsynaptic Ca2+ signal falls above ( or below ) a certain threshold , then active synapses manifest LTP or LTD , respectively [34 , 42] . Many intracellular proteins contribute to these processes , with Ca2+-calmodulin-dependent protein kinase II ( CaMKII ) being a key element in LTP induction and possibly also in LTD induction [43 , 44] . But since CaMKII is generally inactivated in a relatively short timeframe ( ∼1 min ) after transient synaptic stimulation , other biochemical pathways must play important roles in the maintenance LTP [45] . The timeframe of CaMKII inactivation ( ∼1 min ) is particularly relevant to our putative homeostatic mechanism . This inactivation is caused by increased intracellular Ca2+ that leads to autophosphorylation of CaMKII , thereby converting the enzyme to a Ca2+-independent ( autonomous ) form . That is , even if there is a presynaptic signal that repeatedly opens NMDARs and further increases intracellular calcium , the relative magnitude of further LTP would be smaller than at the beginning of the synaptic stimulation . The opposite might be the case for the magnitude of LTD , because the enzymatic pathways leading to LTD are not inhibited anymore . Thus , we speculate that the level of Ca2+-independent CaMKII might be related to the fast dynamics of Ap and Ad ( see also [33 , 46] ) . Metaplasticity is defined as the activity dependent and persistent change in neuronal state that shapes the direction , duration and/or magnitude of future synaptic change [8 , 9] . A typical computational implementation of synaptic metaplasticity theory is the BCM-like model of synaptic plasticity in which the sign and magnitude of plasticity , as well as the position of the sliding modification threshold , are governed by the level of postsynaptic activity averaged over some past . The BCM-like sliding modification threshold serves a homeostatic function by producing cell-wide changes that keep synaptic plasticity within a working dynamic range and flexible . This has the net effect of keeping both LTP and LTD readily available to respond to future changes in correlated presynaptic and postsynaptic activity [47] . In our model , we implemented BCM-like metaplasticity , in which the metaplastic state affects all synapses across the cell . However we do not exclude other metaplastic mechanisms that take place on a semi-global or even local level [47] . Zenke et al . [14] carried out simulations of large balanced networks with a homeostatic triplet STDP rule , in which the timescale of homeostasis was on the order of seconds ( from 3 to 25 s; see also [15] ) . The moving average of the postsynaptic spike count was used to dynamically adjust only the LTD amplitude . Thus in their simulations with metaplastic triplet STDP , the amount of LTD varied homeostatically as a function of the moving average of the postsynaptic firing rate ( see also [13] ) . The difference in metaplastic equations between Zenke et al . [14] and our model is that our model assumes that the amplitude for LTP is also metaplastically regulated , as we do not find any experimental justification for why only the amplitude for LTD should be metaplastically modified and also metaplastic LTP and LTD amplitudes gave us a better homeostatic control . We have found that combining STDP and BCM-like rules with spontaneous activity can replicate the outcomes of three separate in vivo experiments in a biologically realistic ( i . e . morphological ) granule cell model . The new model is a significant improvement on the previous point neuron model [12] , which required some unrealistic assumptions in order to fit the data . Models have value not only in helping to understand complex mechanisms that may be difficult to assess experimentally , but also generate predictions that feed back to the experiments for validation purposes , and further understanding of the biology . From our model , we can make the following experimentally testable predictions: 1 ) that blocking the dendritic voltage-dependent sodium and calcium channels during and after HFS will disrupt heterosynaptic LTD of lateral path synapses but leave homosynaptic medial path potentiation intact; 2 ) that fast ( but not too fast ) metaplasticity is necessary to achieve a good match with experimental data; 3 ) that spiking by the granule cells should be higher during 100-TBS than for the other two protocols; 4 ) that if HFS activates a higher percentage of medial path fibers , the average amplitude of medial path potentiation gets smaller but the average amplitude of lateral path depression gets larger; 5 ) that decreasing spontaneous activity will lead to stronger homosynaptic LTP but weaker heterosynaptic LTD; 6 ) that altering the history of granule cell firing will dynamically change the size of STDP windows for LTP and LTD . Whereas predictions 5–6 arise both from the point and compartmental model , predictions 1–4 arise specifically from the compartmental model . Testing some of these predictions no doubt would require in vitro techniques , which might require adapting the procedures in order to permit spontaneous activity in the perforant path axons so that heterosynaptic LTD can be elicited .
We used the NEURON simulation program ( version 7 . 3 , [48 , 49] ) . Compartmental simulations were performed using an established active model of the hippocampal dentate gyrus granule cell with detailed biophysical properties previously published by Aradi and Holmes [28] and modified by Santhakumar et al . [29] . Simulation files were downloaded from the ModelDB database at http://senselab . med . yale . edu/modeldb/ , accession No . 51781 . The model granule cell comprised five distinct sections [29] i . e . soma , granule cell layer dendritic section , proximal , middle , and distal dendritic sections . The total number of segments was 125 , i . e . one segment for soma ( length L = 16 . 8 um ) , 2x5 segments for the two granule cell layer dendritic sections ( L = 50 um ) , 2x19 segments for the two inner molecular layer dendritic sections , the middle molecular layer dendritic sections which contain the medial path synapses , and the outer molecular layer dendritic sections which contains the lateral path synapses ( each section had L = 150um ) [50] . The sections contained nine voltage-activated channels: sodium , fast and slow delayed rectifier potassium , A-Type potassium , T- , N- , L-Type calcium , calcium-dependent SK- and BK-channels [29] . Parameters for passive and active properties were taken from Santhakumar et al . [29] . The channels were modelled using biologically realistic densities and kinetics . The model granule cell was able to reproduce firing behavior and basic physiological values ( resting membrane potential , input resistance , membrane time constant , action potential threshold and amplitude , afterhyperpolarization , spike frequency adaptation and sag ratio ) determined from electrophysiological measurements [29 , 51] . Excitatory synaptic conductance changes were simulated using the sum of two exponential functions: rise time 0 . 2 ms; decay time 2 . 5 ms; reversal potential 0 mV . The peak synaptic conductance ( representing synaptic weight ) was modified according to the plasticity rule described in the next subsection and implemented in the custom-written NEURON . mod file . To simulate synaptic plasticity , we employed the STDP rule modified to incorporate metaplasticity ( fast homeostasis ) as in [12] . For the STDP rule we used a pair-wise formula experimentally documented in hippocampal granule cells [38] . Taking into account the theoretical results with respect to the spike interaction scheme and relationship to the BCM theory [23] , we simulated the so-called presynaptic centered nearest-neighbor implementation of STDP [52] . That is , for each presynaptic spike , only two postsynaptic spikes are considered; the one that occurs before and the one that occurs after the presynaptic spike , i . e . : w ( t+ δt ) = w ( t ) ( 1 + Δwp − Δwd ) ( 1 ) We had two main reasons for choosing a nearest-neighbor STDP rule instead of an all spike interaction rule . 1 . Izhikevich and Desai [11] have shown that whereas all-to-all spike pairing rule fails to make STDP compatible with classical BCM formulation of LTP/LTD , nearest-neighbor pairing rule is able to link STDP to classical LTP/LTD ( see e . g . their Fig 1D and 1F ) . In other words , a classical BCM-like LTP/LTD curve ( BCM LTP/LTD frequency threshold ) emerges only from nearest-neighbor STDP rules but not from all-to-all spike interactions . Thus , since our LTP/LTD model is based on STDP and BCM , we had to use a nearest-neighbor STDP rule . 2 . Our biological justification for using a nearest-neighbor STDP rule is that neurons do not seem to possess plausible biophysical mechanisms which would allow them to store all preceding spikes in their memory for tens of minutes . Izhikevich and Desai [11] mention an additional biological reason to consider only nearest-neighbor pairs: the most recent backpropagating spike overrides the effect of all the earlier spikes by resetting the membrane voltage in the dendrite . In the pair-wise implementation of STDP , the presynaptic spikes that precede ( follow ) postsynaptic spikes within a certain time window produce long-term strengthening ( weakening ) of synapses , respectively . Thus , the positive and negative synaptic changes , Δwp and Δwd are calculated according to formula: Δwp ( Δt ) = Ap exp ( −Δt / τp ) if Δt > 0 ( 2 ) Δwd ( Δt ) = Ad exp ( Δt / τd ) if Δt < 0 ( 3 ) where Δt = tpost—tpre , is due to a single pre- and postsynaptic pair [38] . Parameters Ap and Ad determine the amplitude of synaptic change , which occurs when Δt is close to zero , while τp and τd determine the time windows over which synaptic changes can occur . In the compartmental model , however , tpost is the time when the postsynaptic voltage at the site of synapse crosses a dendritic voltage threshold of -37 mV , which is an important parameter of our model . Thus , in the compartmental model we should rather speak about pre-post event-pairing timing ( ETDP ) rule , in which the presynaptic event is a presynaptic spike , but the postsynaptic event is the local voltage crossing a given threshold . This threshold is the same for synaptic depression and potentiation , and may correspond to a membrane voltage at which the magnesium block is removed from NMDARs , as we know that both LTD and LTP in granule cells are NMDAR-dependent . Benuskova and Abraham [12] proposed that the amplitudes of positive and negative synaptic changes , Ap and Ad , are not fixed , but instead they dynamically change as a function of the average of the postsynaptic spiking activity over some recent past ⟨c⟩ such that at each time instant t , i . e . : Ap ( t ) = Ap ( 0 ) / 〈c〉 ( 4 ) Ad ( t ) = Ad ( 0 ) 〈c〉 ( 5 ) Positive constants Ap ( 0 ) and Ad ( 0 ) are initial amplitude values for synaptic potentiation and depression , respectively . Eqs ( 4 ) and ( 5 ) simply mean the amplitude for LTP gets smaller and the LTD amplitude gets larger when the average postsynaptic activity is high . The opposite is true for a low average postsynaptic activity . Then , it is easier to potentiate the synapses than to weaken them due to an expanded amplitude for LTP and shrunken amplitude for LTD . The new values of Ap ( t ) and Ad ( t ) are updated at each iteration based on the current value of the average activity ⟨c⟩ . Average postsynaptic activity ⟨c⟩ is calculated as an integral: 〈c〉=ατ∫−∞tc ( t′ ) exp ( − ( t−t′ ) τ ) dt′ ( 6 ) with c ( t’ ) = 1 or 0 if the postsynaptic spike is present or absent at time t , respectively , τ is the integration period , and α is the scaling constant . We used the value of τ = 60s for all the simulations and α = 2500 when the integration step was 0 . 2 ms and α = 5000 when it was 0 . 1 ms . The averaged postsynaptic activity ( Eq 6 ) expresses the weighted average of the postsynaptic spike count , with the most recent spikes entering the integral with bigger weight than previous ones . The integral can be replaced with a discrete sum [14] , but we numerically calculated the above integral in our code . We did not employ any weights renormalization , but we did set a hard upper bound of 100% synaptic weight change . 100% change was however reached only when the lateral spontaneous spiking was turned off . The rationale for using the spike count for ( Eq 6 ) comes from experiments of Abraham et al . [20] , in which antidromic spikes ( with NMDA receptors blocked ) were sufficient to increase the threshold for subsequent LTP induction by HFS . The particular integral ( Eq 6 ) was inspired by the calculation of the dynamic position of the LTD/LTP threshold in the plasticity model of the visual [5] and somatosensory cortices [6 , 7] . Medial and lateral synaptic inputs were activated by presynaptic spikes generated by independent spike generators ( NEURON’s built-in point process NetStim ) . In NEURON , the inter spike interval ( ISI ) of spiking activity is generated according to the following formula: ISI = ( 1− noise ) ISI0 + negexp ( −noiseISI0 ) ( 7 ) ( www . neuron . yale . edu/neuron/static/docs/help/neuron/neuron/mech . html#NetStim ) . When the noise is zero , the ISI is equal to the initial value ISI0 and spiking activity is fully periodic with the period equal to ISI0 . When noise is equal to one , the spiking train is random corresponding to the homogeneous Poisson distribution . When noise is between zero and one , the spiking activity is quasi-periodic with the mean frequency of <1 / ISI> . In our NEURON code , each synapse gets an independent train of spikes with ISI0 = 125ms and noise = 0 . 05 . The 400 Hz-DBS protocol consisted of five trains of 10 pulses delivered at 400 Hz at a delta ( 1 Hz ) interburst frequency repeated 10 times at 1 min intervals [25] . The 100 Hz-TBS had 10 trains of 4 pulses delivered at 100 Hz and a theta ( 5 Hz ) interburst frequency repeated eight times at 10 s intervals . The 400 Hz-TBS was a “hybrid” of the two of these two protocols involving 10 trains at TBS ( 5 Hz ) but using 400 Hz intraburst frequency , repeated eight times at 10 s intervals . All HFS protocols were superimposed upon the ongoing spontaneous spiking as described above ( S2 Fig ) . | The vast majority of computational studies that model synaptic plasticity neglect the fact that in vivo neurons exhibit an ongoing spontaneous spiking which affects the dynamics of synaptic changes . Here we study how key components of learning mechanisms in the brain , namely spike timing-dependent plasticity and metaplasticity , interact with spontaneous activity in the input pathways of the neuron . Using biologically realistic simulations we show that ongoing background activity is a key determinant of the degree of long-term potentiation and long-term depression of synaptic transmission between nerve cells in the hippocampus of freely moving animals . This work helps better understand the computational rules which drive synaptic plasticity in vivo . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | A Voltage-Based STDP Rule Combined with Fast BCM-Like Metaplasticity Accounts for LTP and Concurrent “Heterosynaptic” LTD in the Dentate Gyrus In Vivo |
The eukaryotic genome is packaged as chromatin with nucleosomes comprising its basic structural unit , but the detailed structure of chromatin and its dynamic remodeling in terms of individual nucleosome positions has not been completely defined experimentally for any genome . We used ultra-high–throughput sequencing to map the remodeling of individual nucleosomes throughout the yeast genome before and after a physiological perturbation that causes genome-wide transcriptional changes . Nearly 80% of the genome is covered by positioned nucleosomes occurring in a limited number of stereotypical patterns in relation to transcribed regions and transcription factor binding sites . Chromatin remodeling in response to physiological perturbation was typically associated with the eviction , appearance , or repositioning of one or two nucleosomes in the promoter , rather than broader region-wide changes . Dynamic nucleosome remodeling tends to increase the accessibility of binding sites for transcription factors that mediate transcriptional changes . However , specific nucleosomal rearrangements were also evident at promoters even when there was no apparent transcriptional change , indicating that there is no simple , globally applicable relationship between chromatin remodeling and transcriptional activity . Our study provides a detailed , high-resolution , dynamic map of single-nucleosome remodeling across the yeast genome and its relation to global transcriptional changes .
The eukaryotic genome is compacted into nucleosomal arrays composed of 146-bp DNA wrapped around a core histone octamer complex [1] . The location of nucleosomes affects nearly every cellular process requiring access to genomic DNA , but it is not well understood how nucleosomes are positioned and remodeled throughout any genome . Mapping nucleosome positions using DNA microarrays covering 4% of the yeast genome has shown that a majority of assayable nucleosomes were well positioned [2] . Computational analyses incorporating structural mechanics of nucleosome associated DNA [3–5] and comparative genetics [6] have predicted nucleosome positions in the yeast genome . However , experimental validation and comparison with available in vivo data show that intrinsic signals in genomic DNA determine only 15%–17% of nucleosome positioning above what is expected by chance [3 , 4] . In vivo nucleosome positions are influenced by the presence of numerous ATP-dependent remodelers , and the transcriptional machinery [7 , 8] . Recently , chromatin immunoprecipitation ( ChIP ) -sequencing technology was used to map the positions of nucleosomes containing the variant H2A . Z histone across the yeast genome [9] . H2A . Z nucleosomes are enriched at promoters; therefore , this study mapped about 10 , 000 nucleosomes . Tiling arrays have been recently used to catalog the positions of nucleosomes at 4–5-bp resolution across the yeast genome and their repositioning by chromatin remodelers [10 , 11] . However , dynamic changes in individual nucleosome positions in response to physiological perturbations that cause global transcriptional reprogramming have not yet been examined on a genomic scale in any organism . To map the location of individual nucleosomes on a genomic scale and at high resolution , we used ultra-high–throughput sequencing methodology ( Solexa/Illumina ) to sequence the ends of nucleosome-associated DNA . Our approach enabled us to map individual nucleosomes nominally at single-nucleotide resolution . Nucleosome density and stability at promoters and over coding regions were correlated specifically with transcription rate rather than absolute transcript levels . Two different modes of chromatin remodeling were associated with transcriptional regulation . Gene activation was mainly accompanied by the eviction of one to two nucleosomes from the promoter , and gene repression was mainly accompanied by the appearance of nucleosomes with varying stability over the promoter . Our work constitutes the first study of dynamic single-nucleosome remodeling in response to transcriptional perturbation across an entire eukaryotic genome .
We used micrococcal nuclease to isolate mononucleosome-associated DNA from yeast cells before and after a physiological perturbation ( heat shock for 15 min ) that causes genome-wide transcriptional changes , and sequenced the ends of the fragments . Only uniquely aligning reads were used to define the ends of nucleosomal DNA . After aligning sequence reads to the genome , we defined nucleosome peaks by first using a Parzen window probability estimation of read densities , then defining a peak of width 146 bp around the centers of appropriately spaced maxima in the density function ( Materials and Methods ) . Our approach yielded nucleosome positions at single-nucleotide resolution . We calculated a score for the position and stability of each nucleosome , which were normalized to account for differences in sequencing depth . Scores in the range of 0 . 2 to 0 . 25 and higher indicated nucleosomes whose positions often matched in the two independent biological samples and , hence , indicated bona fide nucleosomes; nucleosomes below this threshold were defined by too few reads to be discernable above background . At a score cutoff of 0 . 25 , we defined the locations of 49 , 043 nucleosomes in normally growing cells and 52 , 817 nucleosomes in heat-shocked cells . Assuming that two adjacent nucleosomes cannot be closer than 200 bp , altogether about 73% of the yeast genome is covered by a positioned nucleosome . Since only uniquely aligning reads were used in our analysis , and the yeast genome contains an appreciable fraction of repeated sequence elements , we estimate that about 78% of the genome is covered by positioned nucleosomes . We assessed the quality and accuracy of our nucleosome sequencing data by examining the nucleosomes known to be positioned at the PHO5 promoter . The yeast PHO5 promoter is repressed during growth in rich media by specifically positioned nucleosomes flanking a short , hypersensitive region containing a binding site for the transcription factor Pho4 [12] . These nucleosomes were evident in the alignment of our raw sequence reads , and their precise positions calculated by our analysis algorithm corresponded to the known positions of these nucleosomes . The positions of these three nucleosomes did not vary in the two independent biological samples before and after heat shock , as this perturbation does not affect the PHO5 promoter ( Figure 1A ) . Quantitative real-time PCR ( qPCR ) for the three nucleosome peaks and three troughs ( linker regions ) identified by sequencing provided independent experimental verification of these nucleosome positions and the fact that their positions did not change in the two samples ( Figure 1B ) . At individual promoters where transcription is activated by heat shock , the raw data traces and our inferred nucleosome peaks showed that nucleosomes were displaced at the promoter after the perturbation ( Figure 1C ) . Conversely , at promoters that are repressed , positioned nucleosomes appeared after the perturbation ( Figure 1D ) . The genome-wide nucleosome positions we identified experimentally correspond well with individual nucleosomes mapped on chromosome III as well as nucleosome-bound sequences isolated in previous studies [2 , 4] ( see Figure S1 and Table S1 ) . While this manuscript was in preparation , a catalog of nucleosome positions in yeast was published [13] . Our mapped nucleosome positions also agree well with this recent study ( Figure S1 and Table S1 ) . Thus , our mononucleosome preparations and the high-throughput sequencing assay recapitulated bona fide in vivo nucleosome positions and rearrangements . Low-resolution analysis using PCR microarrays has shown that promoters are nucleosome-poor relative to coding regions [14 , 15] . In accord with these findings , we found that both the number and the stability of nucleosomes were significantly lower at promoters than over coding regions ( p < 2 . 2 × 10−16 ) . We plotted the average nucleosome profile over all yeast genes to get an idea of how individual nucleosomes were distributed in relation to promoters and coding regions . Several features of chromatin organization were evident from this plot ( Figure 2A ) . First , as noted before , promoters showed a lower probability of nucleosomes as compared to coding sequences . Second , the apparent nucleosome-free region immediately upstream of the transcription start site ( TSS ) is only approximately the width of a single nucleosome . Third , there is a strongly positioned nucleosome , likely an H2A . Z-containing nucleosome , that marks the start of the transcribed region immediately downstream of the TSS [9] . Fourth , positioned nucleosomes continue at periodic intervals downstream of the TSS , with decreasing probabilities . These characteristics of nucleosome positioning corroborate results based on mapping nucleosomes across a single yeast chromosome [2] . We obtained nearly identical results in the independent heat-shocked cells ( Figure S2 ) . Interestingly , we also observed a strongly positioned nucleosome at the 3′ end of the coding region followed by a relatively nucleosome-free region , which has not been noted before . This 3′ nucleosomal mark does not reflect the boundary of a downstream promoter , because it was evident even at the 3′ end of convergently transcribed genes lacking another promoter immediately downstream of their 3′ end ( Figures 2B and S2 ) . This 3′ end chromatin feature was not biased towards convergently transcribed genes , but we noted a modest association with genes that were expressed at low levels and with long genes ( unpublished data ) . Our data also established that although the internucleosomal linker length could vary widely , the linker length is commonly about 30 bp in the yeast genome ( Figure S3 ) . Although our whole-genome data revealed stereotypical distribution patterns of nucleosomes around promoters , we reasoned that the average profile might conceal several distinct nucleosome occupancy profiles with distinct relationships to transcriptional activity or promoter sequence characteristics . To reveal such distinctions , we performed k-means clustering of the nucleosome peak profiles around all yeast promoters . Indeed , several classes of nucleosome profiles were now evident ( Figure 2C ) . There was no significant distinction between these different promoter classes with respect to either their occupancy by the general transcription factor TBP or their absolute transcript levels ( Figure S4 ) . However , there were biases among the clusters with respect to their representation of TATA box–containing and TATA-less promoters , as well as their transcription rates . In general , promoter classes containing a strongly positioned nucleosome were enriched for TATA-less promoters and had lower transcription rates , and conversely , the cluster containing poorly positioned nucleosomes was enriched for TATA-containing promoters and had higher transcription rates [16] ( Figure 2C ) . We ascertained that promoters that appeared to be largely devoid of positioned nucleosomes were not artificially caused by our exclusion of ambiguous sequence reads . The average nucleosome occupancy profiles for TATA-less and TATA-containing promoters , considered separately , showed that the absence of a consensus TATA element in the promoter was indeed correlated with the stereotypical genome-wide nucleosome profile ( Figures 2D and S2 ) . This distinction was not due to the lower number of TATA-containing promoters ( unpublished data ) . Correspondingly , genes with low transcription rates showed stronger nucleosome positioning as compared to genes with higher transcription rates ( Figure 2E ) . Visual inspection of nucleosome profiles before and after heat shock indicated that the positions of the majority of nucleosomes were closely maintained despite the genome-wide transcriptional perturbation ( Figure S1 ) . In general , individual nucleosome positions in each of the promoter classes were largely unchanged in cells after heat shock ( Figure 2C ) . Approximately 65% of all positioned nucleosomes throughout the genome in normally growing cells were within 30 bp of their positions in heat-shocked cells . At a score cutoff of 0 . 25 , less than 10% of the nucleosomes were displaced more than 100 bp after heat shock ( Table S1 ) . In addition to the promoter nucleosome classes , we also observed strong , periodically positioned nucleosomes located over the transcribed regions of most genes in the genome . This periodicity was evident when we aligned all coding regions to the first nucleosome downstream of the TSS and ranked all these genes by a nucleosome positioning periodicity ( NPP ) score applied to the coding region ( Figure 3A; Materials and Methods ) . There was no correlation between NPP and steady-state transcript levels ( unpublished data ) . However , genes with a high NPP score , which had strongly positioned nucleosomes over the coding region , were transcribed at significantly lower rates than genes with a low NPP score ( Figure 3B ) . Correspondingly , genes that were transcribed at low rates showed well-positioned periodic nucleosomes over the coding region relative to genes transcribed at higher rates , which showed weaker nucleosome positioning over the coding region ( Figure 3C ) . Overall , the stereotypical positioning of nucleosomes over coding regions and promoters is consistent with the notion that nucleosome positions in the yeast genome are not random , but rather , are strongly encoded intrinsically through a combination of DNA sequence composition and binding of other proteins . Analysis of DNA sequences associated with nucleosomes has indicated that nucleosome positions are intrinsically encoded in DNA [4 , 6 , 13] . However , it is not clear to what extent DNA sequence governs nucleosome positions compared to other factors that might also contribute to nucleosome positioning across the genome . One possibility is that when a nucleosome is strongly positioned at one site by virtue of DNA sequence , immediately adjacent nucleosomes are “stacked” against it and therefore show little sequence dependence . In particular , the regular array of nucleosomes we observed over coding regions could reflect sequence-dependent positioning of an H2A . Z nucleosome at the 5′ end of the array corresponding to the TSS , but with the remainder being positioned relative to the first one in a sequence-independent manner . To test this idea , we examined the sequence dependence of successive nucleosome positions in the strongly positioned nucleosomal arrays over the coding region . We first generated a profile of the AA/TT dinucleotide frequency for the sequences associated with the strongest positioned nucleosomes at the first position shown in Figure 3A . Like the profile generated from computational predictions of nucleosome positions [4 , 6] , our profile shows a repeating pattern with an approximate periodicity of ten nucleotides , indicative of the rotational positioning of the nucleosome over a preferred sequence ( Figure 3D ) . Although the information content of our measured dinucleotide profile is modest , it is significantly different from the same dinucleotide profile measured over randomly selected DNA sequences from the genome ( Figure 3D ) . We then measured the average correlation between our nucleosome sequence profile and the same dinucleotide profile for the set of sequences associated with all nucleosomes in each of the positions in the regular array of coding region nucleosomes . As expected , the first position showed the strongest correlation to the positioning sequence , but in general , successive nucleosome positions in the arrays showed lower , but significant , correlations to the positioning DNA profile ( Figure 3E ) . Thus , although the underlying DNA sequence as measured by the dinucleotide profile makes only a modest contribution to the positioning of nucleosomes , in general this contribution is maintained to a large extent even when nucleosomes are adjacent to another well-positioned nucleosome in the coding region . In order to examine how dynamic remodeling of individual nucleosomes was globally related to dynamic changes in transcription after the physiological perturbation , we generated nucleosome remodeling profiles for all promoters ( Materials and Methods ) . A positive value in the remodeling profile at a given promoter position indicated that there was a nucleosome covering the position during normal growth , but was depleted or evicted upon heat shock . A negative value indicated the opposite , namely , the appearance of a more strongly positioned nucleosome following heat shock . We grouped the remodeling profiles by k-means clustering and visualized specific patterns of nucleosomal changes at the promoter . We first analyzed remodeling profiles for promoters that were activated at least 2-fold and promoters that were repressed at least 2-fold by heat shock ( Figure 4A and 4B ) . Two well-defined groups ( Group 2 and 4 ) of activated genes contained promoters in which a single nucleosome that covered the promoter during normal growth was evicted upon heat shock , making the promoter more accessible for binding by transcription factors or the general transcription machinery ( Figure 4A ) . Of these , Group 2 showed a significant enrichment for targets of the activator Msn4 ( p = 0 . 02 ) [17] Promoters in Group 1 had a nucleosome-free region between the TSS and −200 bp both before and after heat shock . This group showed a significant enrichment for targets of the transcriptional activator Hsf1 ( p < 0 . 02 ) . Group 3 showed enrichment for the remodeler Swi5 ( p = 0 . 002 ) . The difference in nucleosome profiles between Group 1 and Group 2 genes and the differential enrichment of the two major stress transcription factor targets points to two distinct modes of action by these activators . Hsf1 is constitutively bound to many heat shock gene promoters [18] . The nucleosome profiles of Group 1 promoters , which showed enrichment for Hsf1 targets , suggest that Hsf1 binding induces eviction of the nucleosome covering the promoter or precludes its occupancy over this region . On the other hand , Msn4 target promoters ( enriched in Group 2 ) had a nucleosome covering the promoter during normal growth . Our data suggest that translocation of Msn4 into the nucleus upon heat shock [19] and its occupancy of the promoter results in eviction of the nucleosome , and thus facilitates activated transcription . Genes repressed more than 2-fold after heat shock could also be clustered into four major groups based on their nucleosome remodeling profiles ( Figure 4B ) . Group 2 repressed genes had a nucleosome-free region between −200 and −100 bp upstream of the TSS during normal growth , which was covered by the appearance of a single nucleosome after heat shock . Group 3 repressed genes were characterized by the appearance of a single nucleosome between −125 and +50 bp relative to the TSS after heat shock . Group 1 and Group 4 repressed genes had subtle differences between themselves and between normally growing and heat-shocked cells . They both had a nucleosome-free region between −200 and −100 bp regardless of the transcriptional status of the genes . The enrichment of transcription factor targets in these four groups based on data from the yeast functional regulatory network [20] and transcription factor ChIP-microarray ( ChIP-chip ) [17 , 21] is tabulated in Table S2 . Group 3 was significantly enriched for the targets of Rap1 , Sfp1 , Fhl1 , Gcn5 , and Esa1 , all of which are factors mediating the transcription of ribosomal protein genes during normal growth [22–25] . Consistent with this , ribosomal protein genes were significantly enriched in Group 3 ( p = 2 . 6 × 10−5 ) . In addition , Group 1 was significantly depleted for targets of all the above-mentioned transcription factors , and was also significantly depleted for ribosomal protein genes ( p = 6 . 5 × 10−4 ) . In order to quantitate whether distinct modes of nucleosome remodeling were generally used for gene activation and repression , we calculated a nucleosome remodeling score for both nucleosome eviction and nucleosome appearance ( Materials and Methods ) . Activated genes showed significantly higher nucleosome eviction than nucleosome appearance , whereas repressed genes showed significantly higher nucleosome appearance than eviction ( Figure 4C ) . Although these general trends are expected , we noted that if we clustered remodeling profiles based on more distal promoter regions ( −400 to −200 bp upstream of the TSS ) , we did observe several apparent nucleosome appearance events at activated promoters ( Figure S5 ) . At some promoters , nucleosome eviction proximal to the promoter could occur in conjunction with nucleosome appearance more distally , as would be expected for translational repositioning of nucleosomes . Since ribosomal protein genes form one of the most prominent classes of genes that are transcriptionally repressed by heat shock , we analyzed nucleosome changes at their promoters separately . Ribosomal protein genes were clustered into three classes based on the presence or absence of a well-positioned nucleosome between −50 and +100 bp in normally grown cells , and the nucleosome score . Upon heat shock , we observed the appearance of medium- to high-scoring nucleosomes between −200 and +100 bp of almost all of these ribosomal protein genes in the three groups ( Figure 4D ) . This analysis of nucleosomal changes at the promoters of the most strongly regulated genes indicates that chromatin remodeling events accompanying transcriptional regulation are restricted to a small number of discrete patterns involving one or two nucleosomes , rather than encompassing a larger domain around the promoter . We also clustered the nucleosome remodeling profiles for genes whose expression did not change appreciably by the physiological perturbation ( less than 1 . 2-fold change ) . Surprisingly , we still observed similar specific patterns of single-nucleosome remodeling events at many of these promoters , indicating that specific nucleosome events are not universally associated with transcriptional changes ( Figure 4E ) . Nucleosome positioning can influence the accessibility of the core promoter as well as binding sites for sequence-specific transcriptional regulators [10 , 26] . About 90% of the sites occupied by transcription factors on chromosome III under normal growth conditions were depleted of nucleosomes [2] . Examination of single-nucleosome remodeling at promoters that were activated or repressed by heat shock in our data revealed instances where the accessibility of the TSS and of experimentally defined transcription factor binding sites was indeed affected by remodeling . For example , at the UBC4 promoter , which is activated by heat shock , three moderately positioned nucleosomes covering two distinct Hsf1 binding sites as well as the TSS were evicted , whereas a single , well-positioned nucleosome appeared between the two Hsf1 binding sites ( Figure 5A ) . Conversely , at the RPL17B promoter , which is repressed by heat shock , one well-positioned nucleosome appeared after heat shock to cover the TSS and a low-confidence proximal Rap1 binding site . Interestingly , another moderate nucleosome upstream was evicted , exposing a higher confidence distal Rap1 binding site as well as an Fhl1 site ( Figure 5B ) . Such eviction and appearance of nucleosomes at adjacent sites could either reflect translational repositioning or independent events; our experiments cannot distinguish between these two possibilities . Based on these observations and other computational predictions of whole-genome nucleosome positions [4] , we hypothesized that chromatin remodeling upon transcriptional perturbation could result in changes in the accessibility of the functional binding sites of stress-related transcription factors . To test this hypothesis , we measured the change in accessibility of transcription factor binding sites upon heat shock , by comparing the overlap between functional binding sites for transcription factors measured by ChIP-chip [17] and nucleosome positions before and after heat shock ( Figure 6 ) . Of the 101 factors tested , 46 had fewer than 20 functional binding sites each in the genome , and we therefore excluded them from this analysis . The remaining 55 transcription factors could be stratified into three classes based on the change in accessibility of the functional binding sites after heat shock: factors whose binding sites showed an increase in accessibility after heat shock ( Figure 6A ) , factors whose binding sites showed no significant change in accessibility ( Figure 6B ) , and those that showed decreased accessibility after heat shock ( Figure 6C ) . As hypothesized , most of the transcription factors involved in mediating the stress response belonged to the first group . The functional binding sites for several key stress-related transcription factors such as Hsf1 , Msn2 , Msn4 , and Aft2 showed some of the strongest increases in accessibility because of nucleosome repositioning upon heat shock . In addition , binding sites for transcription factors Abf2 and Cbf1 , which are involved directly or indirectly in chromatin remodeling [27 , 28] , showed increased accessibility . Surprisingly , we also observed increased accessibility for transcription factors involved in ribosomal protein gene transcription such as Rap1 and Fhl1 ( see Figure 5B for an example ) . These two transcription factors continue to occupy ribosomal gene promoters even during transcriptional repression [29 , 30] , raising the possibility that their occupancy of the promoter under such conditions , facilitated by the increased chromatin accessibility that we observed , could be related to a repressive function . Transcription factors whose binding sites did not show a significant change in accessibility were mainly those involved in the regulation of genes in metabolic pathways .
We have mapped the dynamic remodeling of most nucleosomes in the yeast genome during a transcriptional perturbation using a combination of micrococcal nuclease digestion , isolation of mononucleosome associated DNA and Solexa sequencing . Using a Parzen window–based approach , which is a generally applicable method to analyze all similar datasets derived from ultra-high–throughput sequencing , we defined the dynamic remodeling of approximately 50 , 000 nucleosomes at single-nucleotide resolution in normally growing cells and in cells that were transcriptionally perturbed by heat shock for 15 min . Our study independently confirms expectations about nucleosomal positioning based on previous smaller scale and lower resolution studies , but also reveals novel features about chromatin structure and transcriptional activity , especially given that previous studies have not examined the dynamic repositioning of nucleosomes in response to genome-wide transcriptional reprogramming . Our results showed that in addition to a positioned nucleosome at the TSS , genes in general tend to also contain a well-positioned nucleosome at the 3′ end of the coding region . Yeast genes are thus demarcated by a well-positioned nucleosome at each end of their transcribed regions , with a nucleosome-free gap just beyond . This could potentially reflect chromatin organization that facilitates RNA polymerase initiation as well as termination . Most coding regions also showed strongly and regularly positioned nucleosomes , although the strength of the nucleosome positioning was weaker in genes transcribed at high rates . Interestingly , the first well-positioned boundary nucleosome downstream of the TSS , which is likely to be an H2A . Z variant–containing nucleosome based on previous studies [9] , showed similar stability in genes transcribed at high and low rates ( Figure 3B ) , suggesting that this chromatin landmark is important for demarcating promoters . Upon transcriptional perturbation , the majority of nucleosomes did not change positions , either at promoters or within coding sequences ( Figures 2 and 3 ) . Gene-specific remodeling was restricted to the discrete eviction , appearance , or repositioning of one or two nucleosomes localized to promoters . Remodeling events at genes that were activated or repressed upon heat shock could be classified into distinct patterns , indicating that there is no simple rule for nucleosome remodeling at promoters to activate and repress genes . Thus , although activation was generally and quantitatively associated with nucleosome eviction and transcriptional repression with nucleosome appearance ( Figure 4C ) , there were cases in which strongly positioned nucleosomes appeared at activated promoters ( Figures 5 and S5 ) . Translational repositioning of nucleosomes would seem like eviction and appearance at different spots in the same promoter . These observations suggest that nucleosome remodeling at promoters is not a trivial consequence of transcriptional activity appearing as overall openness of chromatin at activated promoters and obstruction at repressed promoters , but rather , that the precise placement of individual nucleosomes at promoters mechanistically regulates transcription by modulating access of trans-acting factors to specific sites . In addition to chromatin remodeling specifically at regulated promoters , many promoters however showed dynamic single-nucleosome remodeling during the physiological perturbation even in the absence of any resulting transcriptional change ( Figure 4E ) , indicating that selective , activity-specific remodeling was accompanied by a certain number of background , nonspecific remodeling events . We speculate that these background single-nucleosome remodeling events poise promoters for rapid future transcriptional activity , by either assembling partial preinitiation complexes [31] , or by exchanging core histones with one or more histone variants [32] . A recent study showed that nucleosomes are globally positioned by Isw2 acting at the boundary between genes and intergenic regions , and that some of the Isw2-dependent remodeling occurs independent of transcription [11] . Therefore , the background remodeling seen in the absence of transcriptional changes in our study could potentially reflect nonspecific remodeling by ISW-like complexes . We classified transcription factors into three classes based on change in accessibility of their binding sites upon transcriptional perturbation . All the prominent stress-related transcription factors belonged to the category showing a strong increase in accessibility upon transcriptional perturbation . In addition , we found that Rap1 and Fhl1 binding sites showed an increase in accessibility even though the majority of their target genes , namely the ribosomal protein genes , showed a decrease in transcription upon heat-shock stress . When transcription of the ribosomal protein genes is repressed by heat shock , osmotic shock , or inhibition of the TOR pathway by rapamycin , it is known that Ifh1 leaves the promoter , but Rap1 and Fhl1 remain bound [30] . It is possible that Rap1 and Fhl1 play a role in recruiting chromatin remodelers to bring about a repressive chromatin structure at the ribosomal protein genes . Previous studies have indicated that the primary discriminant between a functional and a nonfunctional transcription factor binding site in vivo is the presence of stably positioned nucleosomes covering the latter [4 , 9] . Our results above indicate that superimposed on this , there is a second mode of regulation at functional binding sites of stress-related transcription factors brought about by a stimulus-dependent remodeling of one or two nucleosomes , making the site more accessible for stable binding of transcription factors . Alternatively , binding of the transcription factor ( s ) could result in the remodeling of nucleosomes via the help of chromatin remodelers . The work described here is the first study of genome-wide dynamic nucleosome remodeling events at single-base resolution . More such studies in yeast and higher eukaryotes will shed light on the relationship between epigenetic changes at high resolution and the global regulation of gene expression .
Yeast S288C cultures were grown in rich medium and subjected to 15-min heat shock as described previously [18 , 33] . At the end of 15 min , control and heat-shocked cells ( 200 ml each ) were treated with formaldehyde to a final concentration of 1% for 30 min . The reaction was stopped by adding glycine to a final concentration of 125 mM , and cells were harvested by centrifugation . Cells were washed 2× in PBS and resuspended in 20 ml of Zymolyase buffer ( 1 M sorbitol , 50 mM Tris [pH 7 . 4] , and 10 mM β-mercaptoethanol ) . Cells were spheroplasted by treating with 25 mg of 20T Zymolyase , and incubated for 40 min at 30 °C with shaking at 200 rpm . The remainder of the steps were carried out using a modified protocol described in [2] . Briefly , cells were spun down , washed 1× with 5 ml of Zymolyase buffer , and resuspended in 2 ml of NP buffer ( 1 M sorbitol , 50 mM NaCl , 10 mM Tris [pH 7 . 4] , 5 mM MgCl2 , 0 . 075% NP 40 , 1 mM β-mercaptoethanol , and 500 μM spermidine ) . CaCl2 was added to a final concentration of 3 mM , and micrococcal nuclease digestions were carried out at concentrations ranging from 100 U/ml to 600 U/ml for 10 min at 37 °C . The reactions were stopped by adding 100 μl of 5% SDS and 50 mM EDTA . A total of 3 μl of 20 mg/ml proteinase K was added to each tube , and incubated at 65 °C overnight . The DNA was purified by phenol-chloroform-isoamyl alcohol ( 25:24:1 ) extraction , and precipitated using ethanol . The DNA was treated with DNase-free RNase , re-extracted with phenol-chloroform-isoamyl alcohol , precipitated with ethanol , and resolved on a 1 . 25% agarose gel alongside a 100-bp ladder . The mononucleosome size band ( approximately 150–200 bp ) was excised and purified using the Invitrogen Pure-Link quick gel extraction kit . The purified DNA was sequenced using Solexa sequencing technology . S288C cells from 50-ml cultures before and after heat shock at 39 °C for 15 min were resuspended in 8 ml of AE buffer ( 50 mM sodium acetate [pH 5 . 2] , 10 mM EDTA , 1 . 7% SDS ) . RNA extraction , cDNA labeling , and microarray manufacture and hybridizations were done as described previously [18 , 20 , 33] . For absolute expression analysis , sheared genomic DNA was labeled with Cy3 , and cDNA was labeled with Cy5 . For relative expression-change analysis , cDNA from heat-shocked cells was labeled with Cy5 , and cDNA from normally grown cells was labeled with Cy3 . The labeled cDNAs were mixed and hybridized onto DNA microarrays for 12–16 h . The arrays were washed , dried , and scanned with a Axon 4000B scanner ( Molecular Devices ) . Cy5/Cy3 ratios were quantitated using GenePix Pro software and analyzed using Acuity microarray informatics software after filtering to exclude bad spots . Primer pairs used in Figure 1 were designed to cover three peaks and three troughs in the promoter of PHO5 just upstream of the known Pho4 binding and DNaseI hypersensitive site [12] . Control primers used for normalization were designed in the region between YCR023C and YCR024C . qPCR was performed using SYBR green chemistry on an ABI 7900 instrument . Enrichment of target loci in the ChIP sample relative to sonicated genomic DNA was calculated for both unstressed cells and cells subjected to heat shock . Solexa sequencing reads were mapped back to the Oct 2003 yeast genome assembly obtained from the Saccharomyces Genome Database ( SGD ) ( http://www . yeastgenome . org/ ) and only reads that mapped uniquely to the genome were considered in the majority of our analysis . We generated 514 , 803 and 1 , 036 , 704 uniquely aligning reads for the normal and heat-shock growth conditions , respectively . Reads mapping to the plus and minus strands were processed separately . Reads were clustered using a Parzen window–based approach . Essentially , a Gaussian kernel was centered on each base pair in the genome , and a weighted score was calculated at that position . The mean of the Gaussian was taken as the position under consideration , with the standard deviation ( smoothing bandwidth ) set at 20 bp . Each read contributed to the mean position based on its kernelized distance from the mean . The weighted score indicated the likelihood of finding an edge of the nucleosome at the position . Thus , the entire genome was converted into a likelihood landscape that was further processed to find local maxima ( Figure S6 ) . These maxima were then treated as centers of a cluster . Membership of a read in a cluster was based on its relative contribution to the weighted score of the center . The number of reads assigned to a cluster was defined as the unweighted score of that cluster . We reasoned that a stable nucleosome would be expected to result in a denser clustering of the reads than an unstable one . The denser clustering of the reads results in better concordance of the unweighted score to the weighted score . Hence , each cluster was assigned a stability score that was calculated as the ratio of the unweighted score to the weighted score . Nucleosomes were identified as a plus cluster followed by a minus cluster within 100–200 bp . The nucleosome score was calculated as a sum of the plus and minus cluster unweighted scores . The nucleosome stability score was calculated as a weighted average of the individual stability scores of the participating clusters . Whole-genome maps for unstressed and stressed cells were filtered to exclude nucleosomes that had a normalized score less than 0 . 2 ( see normalization procedure below ) . For each nucleosome in unstressed cells , the distance to the nearest nucleosome after heat shock was calculated . These data are reported in Table S1 . Similar analysis was used to determine the overlap between nucleosome positions determined in this study and those from previous studies [2 , 4] . Reads equal in number to those we obtained from normal and heat-shocked cells were selected at random from the yeast genome assembly Oct 2003 , and peak finding was done as described . This process was iterated 20 times . The average maximum score obtained in the simulations was used as a scaling factor to normalize nucleosome peak scores for cells grown at 30 °C . Normalization was done by dividing nucleosome peak scores by the scaling factor . We then calculated a scaling factor for the heat-shock data by multiplying the scaling factor for the 30 °C data by the ratio of the median peak scores for 39 °C to the peak scores for 30 °C . This was done to correct for differences in sequencing depth for the two samples , thus enabling quantitative comparison of nucleosome profiles across the two conditions . The upstream −600 bp to downstream +1 , 000 bp of each uncharacterized and verified ORF in SGD was binned at 10 bp , and nucleosomes were mapped to each bin . The zero point was the TSS . A nucleosome was said to map to a given bin if it completely overlapped with the 10-bp bin . Each bin was assigned the score of the overlapping nucleosome . In the cases where our algorithm detected overlapping positions for a nucleosome , and more than one nucleosome mapped to a single bin , the bin was assigned the highest score . Genes were separated into TATA-containing or TATA-less [34] , and the average nucleosome profiles were generated for each group by averaging the scores for the bin across all the genes ( 973 and 4 , 382 promoters , respectively ) . Genes were similarly separated into the top 500 or bottom 500 with respect to transcription rates [16] , and average profiles were plotted for these classes . The NPP score was generated by calculating the similarity of the experimentally derived nucleosome profile over the coding region of every gene to an artificially generated profile where six nucleosomes of score 1 . 0 were regularly placed with 30-bp linker lengths . In general , genes with well-positioned nucleosome had profiles that were most similar to the synthetic profile and hence , had a high NPP score . The first ( +1 ) nucleosome downstream of the TSS is adjacent to a gap and is likely to be more strongly sequence dependent for positioning than a nucleosome that is flanked by other nucleosomes . We therefore derived AA/TT profiles from the sequence underlying the first nucleosome . To derive high-confidence sequence profiles , we aligned all genes to the first nucleosome as shown in Figure 3A . We selected all +1 nucleosomes with a score ≥0 . 9 for the input set . Since nucleosomes show a dyad symmetry in terms of positioning over DNA , the reverse complement of each sequence in the input set was also included before calculating the profile . We calculated frequency profiles for the dinucleotides AA and TT , and summed and smoothed them using a 3-bp moving average . This high-confidence AA/TT profile was then correlated with the AA/TT profiles derived from all nucleosomes at the +1 , +2 , +3 , and +4 positions . Genes that did not a have 200-bp–long promoter region were excluded for this analysis . For all of the genes that passed this filter , the difference between the nucleosome scores in normally grown cells and cells after heat shock was calculated bin-wise from −400 bp upstream to +200 bp downstream of the start codon . For the plots and clusters shown in Figure 4A and 4B , we then created subsets of these data that included either genes that were activated by at least 2-fold , or genes that were repressed at least 2-fold by heat shock . For the cluster in Figure 4E , we selected remodeling profiles that showed a difference in nucleosome score of at least 0 . 5 between the two growth conditions at three or more positions in the promoter , and also selected genes whose expression did not change by more than 1 . 2-fold . To calculate the remodeling score , a seven-bin window , corresponding to a distance of 70 bp ( approximately half of a nucleosome ) , was scanned along each profile , and the individual bin scores were averaged for each window . The maximum window score in the positive direction across the entire profile was assigned as the remodeling score for nucleosome eviction while a similar maximum in the negative direction was assigned as the remodeling score for nucleosome appearance . Transcription factor motifs were mapped across the entire genome using position-weight matrices derived from [17] using Patser [35] at a p-value cutoff of 0 . 01 . These were considered the putative binding sites while the functional ( “true” ) binding sites were derived from published ChIP-chip data [17 , 18 , 36] . A functional motif was considered to be occupied , and therefore not accessible , if it overlapped with a nucleosome that had a score ≥ 0 . 5 . The occupancy of the ChIP-chip binding sites was compared to that of the putative motif binding sites , and a hypergeometric distribution was used to calculate p-values . This analysis was done with data from both normal and heat-shock conditions . To calculate the significance of the change in binding site occupancy upon heat shock , the p-values for the heat-shock nucleosome data were divided by the p-values derived from the normal condition data . | The eukaryotic genome is packed in a systematic hierarchy to accommodate it within the confines of the cell's nucleus . This packing , however , presents an impediment to the transcription machinery when it must access genomic DNA to regulate gene expression . A fundamental aspect of genome packing is the spooling of DNA around nucleosomes—structures formed from histone proteins—which must be dislodged during transcription . In this study , we identified all the nucleosome displacements associated with a physiological perturbation causing genome-wide transcriptional changes in the eukaryote Saccharomyces cerevisiae . We isolated nucleosomal DNA before and after subjecting cells to heat shock , then identified the ends of these DNA fragments and , thereby , the location of nucleosomes along the genome , using ultra-high–throughput sequencing . We identified localized patterns of nucleosome displacement at gene promoters in response to heat shock , and found that nucleosome eviction was generally associated with activation and their appearance with gene repression . Nucleosome remodeling generally improved the accessibility of DNA to transcriptional regulators mediating the response to stresses like heat shock . However , not all nucleosomal remodeling was associated with transcriptional changes , indicating that the relationship between nucleosome repositioning and transcriptional activity is not merely a reflection of competing access to DNA . | [
"Abstract",
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"Results",
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"Materials",
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] | [
"molecular",
"biology",
"genetics",
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] | 2008 | Dynamic Remodeling of Individual Nucleosomes Across a Eukaryotic Genome in Response to Transcriptional Perturbation |
The promise of personalized cancer medicine cannot be fulfilled until we gain better understanding of the connections between the genomic makeup of a patient's tumor and its response to anticancer drugs . Several datasets that include both pharmacologic profiles of cancer cell lines as well as their genomic alterations have been recently developed and extensively analyzed . However , most analyses of these datasets assume that mutations in a gene will have the same consequences regardless of their location . While this assumption might be correct in some cases , such analyses may miss subtler , yet still relevant , effects mediated by mutations in specific protein regions . Here we study such perturbations by separating effects of mutations in different protein functional regions ( PFRs ) , including protein domains and intrinsically disordered regions . Using this approach , we have been able to identify 171 novel associations between mutations in specific PFRs and changes in the activity of 24 drugs that couldn't be recovered by traditional gene-centric analyses . Our results demonstrate how focusing on individual protein regions can provide novel insights into the mechanisms underlying the drug sensitivity of cancer cell lines . Moreover , while these new correlations are identified using only data from cancer cell lines , we have been able to validate some of our predictions using data from actual cancer patients . Our findings highlight how gene-centric experiments ( such as systematic knock-out or silencing of individual genes ) are missing relevant effects mediated by perturbations of specific protein regions . All the associations described here are available from http://www . cancer3d . org .
With the body of genomic and pharmacologic data on cancer growing exponentially , the main bottleneck to translate such information into meaningful and clinically relevant hypothesis is data analysis [1]–[3] . While numerous methods have been recently applied to the analysis of such datasets [4] most of them , particularly those dealing with mutation data [5] , use a protein-centric perspective , as they do not take into account the specific position of the different mutations within a protein [6] , [7] . Such approaches have been proven useful in many applications; however , they cannot fully deal with situations in which different mutations in the same protein have different effects depending on which region of the protein is being altered [8] . This idea can be easily explained by the fact that most proteins are modular , consisting of several distinct domains and/or functional regions , which we collectively call PFRs ( protein functional regions ) here . For instance , a receptor tyrosine kinase , such as EGFR , has two PFRs - an extracellular region , which is responsible for the interaction with the ligand or with other receptors , and an intracellular kinase domain , which in turn is responsible for the phosphorylation of its substrates . A phenotype , such as the response towards a drug , can be influenced by alterations of proteins at the whole-protein level ( changes in expression , deletion or epigenetic silencing of a gene ) , but also changes , such as mutations , modifying only the extracellular or the kinase domains . More importantly , even though it is likely that each of the three types of alterations ( whole-protein , only in the extracellular region or only in the kinase domain ) will have different consequences [9] , only those involving the whole protein have been studied . To explore how perturbations of specific PFRs in different proteins might influence the sensitivity of cancer cell lines towards specific drugs we developed a novel algorithm called e-Drug . This algorithm analyses patterns of mutations in functional regions within each protein in the human proteome and identifies those associated with changes in the activity of anticancer drugs . Our definition of PFRs includes protein domains , both those present in Pfam database and those predicted to exist using our in-house tools , and intrinsically disordered regions . Similar approaches focusing on Pfam protein domains have been used previously to study the molecular mechanisms underlying the pleiotropy of certain genes , especially those related to Mendelian disorders [10] , [11] , and cancer [12]–[14] . In the context of the analysis of drug-related data , PFRs have been mainly used to study phenomena such as polypharmacology or the structural details underlying interactions between drugs and domains [15] , [16] . However , to the best of our knowledge , such PFR-centric analyses have ever been used to study cancer pharmacogenomic datasets .
The e-Drug analysis protocol introduced here is illustrated in Fig . 1 on the example of the ERBB3 protein and the c-Met inhibitor PF2341066 . Some of the many functional relationships of this protein include physical interactions ( with EGFR , NRG1 and JAK3 ) or phosphorylations ( by CDK5 or ERBB3 itself ) . Each of these relationships can be mapped to a specific PFR within ERBB3 . For example , the N-terminal EGF receptor domains ( shown in red in Fig . 1 ) mediate the interactions with EGFR and NRG1 , whereas ERBB3's kinase domain ( shown in blue in Fig . 1 ) interacts with JAK3 and phosphorylates other ERBB3 molecules . When using the protein level analysis , cell lines with mutations in ERBB3 do not show any bias in the activity of PF2341066 , leading to a wrong conclusion that mutations in this protein do not influence the sensitivity towards this drug . However , the PFR level analysis shows that cell lines with mutations in the first receptor domain are resistant to treatment with the PF2341066 inhibitor , while those with mutations in any other PFRs of this protein , such as the kinase domain of the second receptor domain , do not show any specific behavior . Following this protocol , we have identified 171 statistically significant PFR-drug associations ( p<0 . 05 in the comprehensive , multistage significance analysis as described in the Methods Section and S1 Supporting Material ) . The full list is provided in the S1 Table and is available on-line from a newly developed resource at http://cancer3d . org [17] . We have found some cases where two PFRs from the same protein are associated with different drugs . For example , the MSH6 protein contains 3 different PFRs associated with 3 different drugs ( Fig . 2 ) . Mutations in the N-terminal IDR are associated with increased AEW541 activity , while mutations in the connector ( PF05188 ) and ATPase ( PF00488 ) domains are associated with higher Lapatinib and RAF265 activities , respectively . Given that MSH6 has been recently shown to be involved in pathways related to the repair of DNA-double-strand breaks [18] , the association identified here between mutations in MSH6's ATPase domain , as well as other PFRs in PAXIP1 or TP53 , and the activity of RAF265 suggest that the DNA-damage response pathway might have a role in modulating the activity of this drug . The best examples of the advantages of studying mutation effects on individual PFRs are those where mutations in different regions of the same protein are associated with the same drug but in opposite directions . This is the case of PIK3CA and the IGF1R inhibitor AEW541 . Using e-Drug we found that mutations in the p85 binding domain ( PF02192 ) decrease the activity of the AEW541 while mutations in the PIK accessory domain ( PF00613 ) are associated with increased activity of the same drug ( Fig . 3 ) . Mutations in different regions of PIK3CA are known to be oncogenic through different molecular mechanisms [19] , which could also explain the opposite effects in AEW541 sensitivity observed for these two domains . To find features that could explain the different responses to AEW541 depending on the PIK3CA domain mutated , we used proteomics data from The Cancer Proteome Atlas [20] . We focused our analysis on IRS1 expression levels as well as Akt phosphorylation status in the cell lines with mutations in the two PIK3CA domains , because these proteins are immediately up and downstream from PIK3CA , respectively ( Fig . 3 ) . Cell lines with mutations in the PIK accessory domain did not have changes in the phosphorylation levels of Akt at neither T308 ( p>0 . 34 ) nor S473 ( p>0 . 07 ) , but did have higher IRS1 expression ( p<0 . 05 ) . These results agree with recent data showing that the E545K mutation in PIK3CA enhances its interaction with IRS1 [21] . Since IRS1 mediates the interaction between IGF1R and PIK3CA , this increased interaction with IRS1 ( and therefore dependence on interaction with receptor tyrosine kinases such as IGF1R ) could explain why cell lines with mutations in PF00613 are more sensitive to IGF1R inhibition ( Fig . 3 ) . On the other hand , cell lines with mutations in the p85 binding domain showed higher Akt phosphorylation levels at both T308 ( p<0 . 01 ) and S473 ( p<0 . 02 ) , and also had lower IRS1 protein levels ( p<0 . 01 ) . Since Akt is one of the main PIK3CA effectors , a possible interpretation of these results is that cell lines with mutations in the p85-binding domain have intrinsically active PIK3CA phosphorylation activity , regardless of its interaction with receptor tyrosine kinases such as IGF1R . In this scenario , inhibiting IGF1R with AEW541 would have little effects , as these cells are already signaling downstream towards Akt ( Fig . 3 ) . Finally , given recent concerns about pharmacogenomic data using cell lines [22] we compared these results to those obtained from data on human tumors analyzed by TCPA ( n = 2229 ) . We confirmed all the tumors with mutations in PF02192 have higher levels of Akt phosphorylation at both T308 and S473 . The same samples also have lower IRS1 levels than those with no mutations in PF00613 or no mutations at all . Tumor samples with mutations in PF00613 , on the other hand , have higher IRS1 levels and no changes in Akt phosphorylation status . Since we had been able to confirm the hypothetical molecular mechanisms underlying the PFR-drug associations between AEW541 and PIK3CA in tumor samples , we wondered whether we could also predict survival of actual cancer patients using the PFRs identified in the CCLE data . To that end , we used clinical data from patients whose tumors have been analyzed by The Cancer Genome Atlas ( TCGA ) groups [23] to find patients that had been treated with drugs included in the CCLE . Since most of these drugs are still under clinical research , there were sufficient data only to analyze two types of drugs: Paclitaxel ( n = 778 ) and the topoisomerase inhibitors Irinotecan and Topotecan ( n = 188 ) . We used genomic data of the patients to find those who had mutations in PFRs that are associated to increased resistance towards these drugs in our analysis ( Fig . 4 ) . While we found no differences in patients treated with paclitaxel ( p>0 . 9 ) , patients that had mutations in PFRs associated with resistance to Topoisomerase inhibitors had worse outcomes ( p<0 . 01 ) than those with mutations in other regions of the same proteins or no mutations in these proteins at all . Interestingly , the mutation status of the whole proteins that contain the associated PFRs cannot predict the outcome of the patients ( p>0 . 9 ) , suggesting that only mutations in the specific PFRs , but not in other regions of the same proteins , confer resistance to topoisomerase inhibitors . One of the possible mechanisms for a PFR to be associated with differential drug activity is that the protein itself directly interacts with the drug of interest . To explore this hypothesis , we compared the set of proteins associated with each drug at both whole-protein and individual PFR levels , to the set of drug targets as identified by the STITCH database [24] . Of the 19 drugs that had at least one known target , only AZD6244 had its associated proteins and PFRs enriched with its targets , as mutations in two of the five genes known to code for proteins interacting directly with the drug , BRAF and KRAS , are also associated with differential activity for this drug ( p<0 . 005 ) . Expanding our search by varying the STITCH interaction score , including proteins that interact with compounds that have similar structures to the drugs included in the analysis ( Tanimoto score >0 . 70 ) or to proteins interacting with the drug targets also did not show any statistically significant associations ( S4 Fig . ) . We did a gene set enrichment analysis using GO annotations downloaded from Uniprot to understand the shared functions and relationships of the proteins and regions associated with changes in drug activity , ( Fig . 5 ) . Several groups of GO terms identified in this analysis , such as those related to signaling cascades ( extracellular and intracellular signaling ) , signal transduction ( kinase activity or protein phosphorylation ) , or protein binding , suggest that these genes might be involved in either the same pathways as the actual drug targets or similar pathways that might have some level of redundancy . Other GO terms , such as apoptosis , regulation of cell proliferation , or response to hypoxia , are functions known to play a role in drug resistance and carcinogenic potential of cells . Another group of GO terms identified in our analysis are those associated with the cytoskeleton . Given that most of the drugs analyzed in this study ( 17 out of 24 ) are kinase inhibitors , this was an unexpected observation . However , there is some evidence of the relationship between cytoskeleton proteins and the activity of kinase inhibitors in the literature . For example , many receptor tyrosine kinases , such as EGFR , HER2 , IGF1R , or FGFR , undergo internalization upon ligand binding . Moreover , interactions between Erlotinib and MYO2 or MYH9 have been described , and a MYH9 inhibitor synergizes with EGFR inhibitors to induce apoptosis in cells carrying the drug-resistant mutation T790M [25] .
Identifying biological features that correlate with the activity of anticancer drugs has been the subject of a significant and growing research focus in recent years . However , most of these efforts do not take into account the modular nature of proteins and focus on perturbations at the whole-protein level . Such analyses are likely to miss cases in which the location of the mutation within the protein influences its effects . Here we have described what is , to the best of our knowledge , the first systematic analysis of drug activity associations that distinguishes between different functional regions within proteins . We have shown that by focusing on specific PFRs we can find 171 associations between mutations in specific protein regions and changes in the activity of anticancer drugs . These associations could not have been identified by protein-centric approaches , as cell lines carrying mutations in other PFRs of the same protein ( i . e . perturbing regions that mediate other functions ) are not associated with any , with different or sometimes the opposite drug phenotypes . Cases in which the same gene is associated with different drugs through different PFRs , as in the case of MSH6 and the kinase inhibitors Erlotinib , AEW541 , Lapatinib , and RAF265 can provide insights into the mechanisms of the drug pleiotropy of a given gene , aiding in further experiments . A variation of this category is the association between PIK3CA and the AEW541 , where mutations in different PFRs can have opposing effects in terms of the activity of the drug . We have also shown the practical value of the PFR-drug associations discovered here on the independent data from the TCGA consortium . Specifically , we have shown that patients from the TCGA harboring mutations in regions associated with resistance to the drugs used to treat them have lower survival rates than patients with mutations in the very same genes but in regions not showing any association to the activity of such drugs . This result not only provides evidence to the significance of our approach , but it also argues in favor of the value of drug activity data collected using cell lines ( at least in the case of the CCLE ) , an issue that has recently drawn significant attention [22] and that will probably require substantial work in the near future in order to be solved . Another interesting result of the analysis presented here is that the proteins identified in our analysis as modulating sensitivity of cancer cells to drugs , are distinct from the actual targets of these drugs nor interact directly with them . This observation suggests that these genes modify drug activity through indirect interactions . For example , mutations in genes related to the cytoskeleton ( a subset enriched in the genes identified in our analysis ) might alter the potency of kinase inhibitors by changing the trafficking pattern of receptor tyrosine kinases . This is one of the most unexpected findings in our analysis . While more analyses and direct experimental verifications of these correlations are needed , such relations may suggest targets for therapies sensitizing cancer cells to chemotherapy with specific drugs . Overall , this work expands the number of correlations between cancer somatic mutations and drug activity , thus increasing the information we can extract from every dataset . Focusing on PFRs , corresponding to protein domains or IDRs , provides better statistical results than analysis of individual mutations and allows to identify correlations in cases where different effects cancel out and thus are missed on the whole gene analysis level . At the same time , it provides more details about the mechanism of the drug resistance than the analysis on the gene level . Increasing the number and details of features that predict the activity of anticancer drugs has important consequences in the field of personalized medicine , as it increases accuracy in stratifying patients into groups that require different treatment regiments and can suggest drug combinations as exemplified for EGFR and MYH9 . One interesting direction of work that we have not been able to address refers to the interaction between multiple drug activity modifiers . Now that we have been able to extend the catalog of alterations that alter a cell's sensitivity towards a drug using our PFR-centric approach , what happens when there are multiple such alterations in the same cell line or patient ? Do they cancel each other if they have opposite effects ? Do they synergize if they point towards the same direction ? Most attempts to answer these challenging questions are based on machine learning approaches [5] which , given the multidimensional nature of the problem , seems to be the most natural approach . However , simple methods based on naively counting the presence or absence of specific alterations , such as our own analysis of TCGA clinical data for Irinotecan and Topotecan presented here or analyses based on synthetic lethal interaction networks [4] , seem to also have some predicting power . Regardless of the specific approach , these are questions that will need to be answered in order to achieve the promise of personalized medicine . Another generalization of our results is that data obtained using gene knockouts , silencing RNAs , or other technologies that completely abolish the activity of individual proteins might miss more subtle effects caused by modifications of specific PFRs . Finally , we would like to emphasize that , just like the analyses at the protein level is not limited to the identification of features that correlate with drug activity , the analysis of PFR perturbations can be useful when looking for features associated with any phenotype .
We have used the CCLE dataset , which includes the mutation profiles of 1 , 668 genes in 906 human cancer cell lines and drug activity data for 24 different anticancer compounds . We focused our analysis on missense mutations , as truncating mutations can sometimes be misleading when performing the analysis in terms of functional regions . For example , when analyzing a protein that contains two different domains , if a truncating mutation happens in the first domain , it is not obvious whether the functional consequences of the mutation are caused by the fact that the first domain is altered or that the second domain is missing . We mapped the missense mutations reported by CCLE from their genomic coordinates to every protein coding isoform from ENSEMBL using the Variant Effect Predictor tool [26] . From the original 42 , 603 genomic-point mutations in 1 , 668 genes , we obtained 156 , 817 protein missense mutations in 9 , 311 proteins . The CCLE contains data on the drug activity of 24 different compounds in 479 cell lines from 8-point dose-response curves . These curves are adjusted to a logistical-sigmoidal function and described by 4 different variables: the maximal effect level ( Amax ) , the drug concentration at half-maximal activity of the compound ( EC50 ) , the concentration at which the drug response reached an absolute inhibition of 50% ( IC50 ) , and the activity area , which is the area above the dose-response curve . In our analysis we have used only the activity area because , according to the CCLE , it captures simultaneously both variables of drug activity: its efficacy and its potency . We defined protein functional regions as domains or intrinsically disordered regions . We decided to include intrinsically disordered regions because these can also contain important functional regions such as phosphorylation sites or regions that regulate or mediate protein interactions [27] . To identify protein domains , we retrieved , for each protein isoform , annotated Pfam domains from ENSEMBL . We have also included a set of 1 , 300 novel potential domains identified by AIDA , an algorithm based on iterative recognition of domains by homology recognition algorithms with various sensitivities [28] . We used Foldindex [29] to predict intrinsically disordered regions for each protein , including in our analysis those regions with a predicted unfolded score below –0 . 1 . Finally , we mapped the different mutations of each cell line to these protein features , giving us a total of 30 , 798 altered regions in 906 cell lines . These regions are divided into 19 , 918 Pfam domains and 10 , 880 intrinsically disordered regions . Note that the features can overlap , as the predictions were performed independently and there is no reason why , for example , an intrinsically unfolded region cannot overlap with ( or even be located within ) a Pfam domain . Note also that these numbers refer to PFRs in all known protein isoforms according to ENSEMBL v72 . While the results for all these PFR-Drug pairs can be browsed at http://www . cancer3d . org [17] , in this manuscript we only discuss results obtained for the largest isoform of each protein ( S1 Fig . ) . A similar protocol to assign protein functional regions was used in our previous publication on identifying domain cancer drivers [13] . As explained above , e-Drug looks for PFRs that , when mutated , correlate with drug activity of the different drugs . We divided the cell lines into those that have a coding missense mutation in the region being studied and those that do not . We then performed a Wilcoxon test comparing the drug activity of each compound in the two groups and kept those with a p-value below 0 . 01 . Finally , for those gene regions associated to a certain drug , the activity of the cell lines mutated in the region of interest was compared to the activity of cell lines mutated in other regions of the gene . By doing this , regions that are significantly different from the rest of the gene were identified . In this case , since the number of cell lines in both groups is lower and fewer tests were performed , a significance threshold of 0 . 05 instead of 0 . 01 was established . We considered as true positives those PFR that passed both thresholds and that are not in proteins that show an association ( p<0 . 01 ) with the same drug at the whole-protein level ( S1 Fig . ) . Note that the analysis is performed independently for each PFR . In the case that a protein contains two overlapping regions , e-Drug will handle each one of them independently and return their corresponding results . One of the problems that arise when analyzing PFRs instead of whole proteins is that the statistical power of the sample decreases significantly , as ( I ) there are less cell lines with mutations in the individual regions and ( II ) the number of correlations being tested increases , making multiple-testing corrections more stringent . To overcome these limitations and decrease the number of false positives among our associations we require three different thresholds for an association to be considered positive ( see Fig . 1 and S1–S3 Figs . ) . First , the p value of comparing the activity of the drugs between cell lines with mutations in the PFR against those without them has to be below 0 . 01 . This left us with 350 potential PFR-drug pairs identified in the CCLE data . Then , we repeated the analysis at the protein level and removed all the pairs that were also identified there ( p<0 . 01 , n = 102 , Fig . 1f ) . Finally , for the remaining 248 pairs , we compared the drug activity of the cell lines with mutations in the PFR against cell lines with mutations in other regions of the same protein . Expression data for 461 different proteins in 93 cancer cell lines was downloaded from the TCPA website on 12/11/2013 . Cell line names used in TCPA were manually mapped to CCLE when automated mapping was not possible . In order to find proteins with altered expression or phosphorylation levels in cell lines with mutations in PFRs of interest cell lines , we grouped them according to the mutation status of such PFRs and compared the expression levels in each group using a Wilcoxon test . To find proteins whose expression correlated with the activity of anticancer drugs we performed a Pearson correlation test using R . We have downloaded both , clinical and mutation data , for the 3 , 205 tumors described in the pan-cancer analysis of the TCGA . We then filtered out data from patients that had not been treated with any of the drugs included in the CCLE . Since most drugs included in the CCLE are still in under clinical research , we only had enough patients to analyze 2 different drugs: paclitaxel ( n = 778 ) and Irinotecan ( n = 58 ) . Each of these subsets of patients have then been classified in 3 groups: those that have a mutation in a PFR that , according to our analysis , increases resistance to the drug used to treat them , those with mutations in other regions of the same genes and those with no mutations in these genes . We have limited our analysis to gene-regions associated with lower drug activity because there are more such regions as compared to regions associated with increased activity . As a result very few patients in the TCGA dataset carry mutations in the former type of regions and have been treated with the matching drug . The survival analysis has been performed using the “Survival” package for R . It would be natural to expect that proteins that are associated with drug phenotypes might be enriched in either drug targets or their partners . To test this hypothesis , we downloaded the STITCH database that contains information on protein–chemical interactions . We then retrieved for each drug its known protein interactions and compared the overlap of proteins on this list with the proteins that showed an association with that same drug according to our analysis with the Fisher test . We performed the analysis using three different thresholds for the protein-drug interaction score as reported in STITCH: 700 , 800 and 900 . We also extended the analysis to ( a ) proteins interacting with drug targets ( according to either HPRD , BioGRID , MINT , or DiP ) and to ( b ) proteins that bind chemicals with a similar structure . We defined these drug-like chemicals as those that have a Tanimoto 2D similarity score with the drug over 0 . 70 . We calculated the Tanimoto scores with the R package RCDK . | There is increasing evidence that altering different functional regions within the same protein can lead to dramatically distinct phenotypes . Here we show how , by focusing on individual regions instead of whole proteins , we are able to identify novel correlations that predict the activity of anticancer drugs . We have also used proteomic data from both cancer cell lines and actual cancer patients to explore the molecular mechanisms underlying some of these region-drug associations . We finally show how associations found between protein regions and drugs using only data from cancer cell lines can predict the survival of cancer patients . | [
"Abstract",
"Introduction",
"Results",
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] | [
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] | 2015 | Analysis of Individual Protein Regions Provides Novel Insights on Cancer Pharmacogenomics |
Wolbachia inherited intracellular bacteria can manipulate the reproduction of their insect hosts through cytoplasmic incompatibility ( CI ) , and certain strains have also been shown to inhibit the replication or dissemination of viruses . Wolbachia strains also vary in their relative fitness effects on their hosts and this is a particularly important consideration with respect to the potential of newly created transinfections for use in disease control . In Aedes albopictus mosquitoes transinfected with the wMel strain from Drosophila melanogaster , which we previously reported to be unable to transmit dengue in lab challenges , no significant detrimental effects were observed on egg hatch rate , fecundity , adult longevity or male mating competitiveness . All these parameters influence the population dynamics of Wolbachia , and the data presented are favourable with respect to the aim of taking wMel to high population frequency . Challenge with the chikungunya ( CHIKV ) virus , for which Ae . albopictus is an important vector , was conducted and the presence of wMel abolished CHIKV dissemination to the saliva . Taken together , these data suggest that introducing wMel into natural Ae . albopictus populations using bidirectional CI could be an efficient strategy for preventing or reducing the transmission of arboviruses by this species .
Aedes albopictus is a medically important invasive mosquito that has expanded from Asia into many new regions of the tropics and also into temperate regions of the Americas and Europe; it transmits a number of arboviruses including dengue and chikungunya [1] . All known wild populations of Ae . albopictus are naturally superinfected at very high frequency with two strains of the maternally inherited bacterium Wolbachia pipientis , called wAlbA and wAlbB [2] , [3] . Recently , we reported the generation of an Ae . albopictus line transinfected with the wMel strain of Wolbachia from Drosophila melanogaster , which proved unable to transmit dengue virus in lab challenges and induced complete bidirectional CI when crossed with a naturally wAlbA plus wAlbB ( wAlbA/B ) -infected line [4] . The wMel strain , and the related wMelPop [5] have also been shown to strongly inhibit viral replication in Aedes aegypti , as well as causing chronic immune upregulation in that naturally Wolbachia-uninfected host [6]–[8] . If wMel is to be used to reduce the dengue transmission capacity of wild Ae . albopictus , a critical factor is whether mosquitoes infected with this strain incur major fitness penalties . It has previously been reported that the natural wAlbA/B strain superinfection actually provides a fitness advantage in the form of increased longevity and fecundity compared to an uninfected line [9] , [10]; given that wMel is not specifically adapted to Ae . albopictus and vice versa , significant fitness costs compared to wildtypes are possible . The over-replicating wMelPop strain variant is virulent and can approximately halve the lifespan of both its D . melanogaster and Ae . aegypti hosts [11] , [12] , and a wMelPop transinfection in Ae . albopictus produced a greatly reduced egg hatch from intra-strain matings [13] . Population dynamic mathematical models have emphasised the importance of fitness parameters in Wolbachia-based population replacement strategies [14] , [15] . It is therefore very important to gain a comprehensive understanding of any fitness consequences of replacing wAlbA/B with wMel in Ae . albopictus , including longevity of both male and female mosquitoes , fecundity and egg fertility rates for females and relative mating competitiveness for males . Chikungunya virus ( CHIKV ) is viewed as an important potential emerging or re-emerging pathogen in many parts of the world [15] , [16] . There have been a number of recent epidemics where Ae . albopictus has been incriminated as a major vector or as the sole vector , for example in the Indian Ocean ( La Reunion ) , Italy , southeastern France , central Africa , and the Kerala region of India [17]–[24] . It has been of particular concern that two viral adaptations , single amino acid substitutions in the envelope glycoproteins ( E1-A226V which arose around 2005 and E2-L210Q identified in 2009 ) , have significantly improved the efficiency of dissemination and transmission by Ae . albopictus [25]–[28] . Therefore another major aim of this study was to assess whether the wMel-transinfected line shows any impairment in its ability to transmit CHIKV , in light of the strong inhibition of dengue virus that has already been observed in this line [4] , and that fact that a wMelPop Wolbachia transinfection in Ae . aegypti has been shown to produce an inhibitory effect with respect to CHIKV [7] .
The Uju . wMel line [4] was generated by microinjection of wMel from D . melanogaster into a Wolbachia-uninfected ( cured ) Ae . albopictus strain UjuT . Uju . wt was generated by introgression of the natural wAlbA/B infection of Ae . albopictus from the Ascoli strain ( Italy ) into UjuT background for four generations [4] followed by a further three here; the resulting line was >99% UjuT nuclear background and contained both wAlbA and wAlbB . All Ae . albopictus colonies in Oxford were maintained at 27°C and 70% relative humidity on a 12-h light/dark cycle . Females were blood fed at six days post eclosion , and aspirated into small tubes for individualized laying two days later . Eggs were matured for five days before being counted to give fecundity data and then hatched in deoxygenated water in small plastic vials . Second instar larvae were counted to give hatch rate . Uju . wMel female egg hatch was lower than for wildtype colony controls in early generations , likely as a result of variable Wolbachia density causing CI effects . Therefore , between G6 and G12 selection for high hatch was applied to make the Wolbachia density more uniform , by discarding approximately one quarter of broods which showed the lowest hatch rates . Longevity was assessed in 30 cm×30 cm×30 cm BugDorm cages ( MegaView Ltd . , Taiwan ) containing one hundred male and one hundred female mosquitoes ( which were sexed as pupae ) . The mosquitoes were provided with moist cotton and supplied with sucrose ad libitum . A blood meal was provided at seven days , and subsequently every fourteen days; oviposition substrates were provided three days after blood feeding . Dead mosquitoes were counted and removed every four days . To assess developmental times , eggs were hatched and larvae reared at 100 larvae/litre and given 1 mg liver powder per larva per day; the number of adults eclosing per day was counted until all pupae eclosed . Male mating competitiveness was assessed using three independent replicates of 50 male Uju . wMel : 50 male Uju . wt : 50 females ( either Uju . wMel or Uju . wt ) , sexed at the pupal stage . Adults were allowed to emerge in Bugdorm cages and were left to mate for ten days . Females were then blood fed , and two days later were aspirated into small plastic vials for individualized laying . Eggs were hatched five days after laying; given the complete CI ( zero egg hatch ) observed in both directions in crosses between wildtype and wMel-transinfected parents [4] , the male parent was scored according to whether or not embryos hatched – hatching indicating that both contained the same Wolbachia strain . Spermathecae were dissected and examined under a dissecting microscope for the presence of stored sperm for all females that produced eggs with zero hatch , and data from any females without sperm were disregarded . CHIKV E1-226V was isolated from a patient on La Reunion Island in November 2005 [23] . This strain is characterized by a substitution of alanine by valine at position 226 of the E1 envelop glycoprotein in a region which is responsible for fusion of viral and cellular membranes within the endosome [29] . This anonymized strain was provided by the French National Reference Center for Arboviruses at the Institut Pasteur . Viral stocks were produced on Ae . albopictus C6/36 cells that were infected at a multiplicity of infection ( MOI ) of 0 . 1 PFU ( plaque forming units ) /cell for 48 h at 27°C . Stocks were constituted after two passages on C6/36 cells , titrated on Vero cells , and stored at −80°C until use . Mosquito adults were starved for 24 hours and then allowed to feed on artificial blood-meals consisting of a virus suspension ( 1∶3 ) , washed rabbit erythrocytes ( 2∶3 ) , and 5 mM ATP . The blood-meal was provided in glass feeders covered with a pig intestine membrane and maintained at 37°C . The titer of the blood-meal was 107 . 5 PFU/mL . Females placed in plastic boxes were allowed to feed for 15 min . Engorged females were sorted on ice , transferred in cardboard containers , provided with sugar solution and maintained in BSL-3 insectaries at 28°C until day 7 post-infection ( in line with previous experiments showing that viral titres gradually decreased after day 7 post infection [30] ) . . To evaluate transmission capacity , 35–50 females were anaesthetized on ice , their wings and legs were removed to induce stress and force salivation . The proboscis was inserted into a 20 µL tip containing 10 µL of foetal bovine serum . After 45 minutes , saliva was collected with the medium and expelled under pressure into a 0 . 5 mL tube containing 40 µL of DMEM . Samples were later titrated by focus fluorescent assay [31] on Ae . albopictus C6/36 cells . The transmission rate was estimated as the percentage of mosquitoes with infectious saliva among tested mosquitoes . Saliva samples were titrated by focus fluorescent assay on C6/36 Aedes albopictus cell culture . Cells were grown to confluent monolayers in 96-well plates and infected with 10-fold serial dilutions of virus . After incubation at 28°C for 3 days , plates were stained using hyper-immune ascetic fluid specific to CHIKV ( provided by the French National Reference Center of Arboviruses at the Institut Pasteur ) as the primary antibody and a conjugate anti-IgG mouse ( Bio-Rad ) as the secondary antibody . The focus fluorescent units were counted with a fluorescent microscope and the titer was calculated as FFU/saliva .
The egg hatch rate and the fecundity per single gonotrophic cycle observed in the wMel-transinfected Ae . albopictus line Uju . wMel [4] were not significantly different to the natural wAlbA/B-superinfected strain Uju . wt ( hatch rate , p = 0 . 369 , Wilcoxon rank sum test; fecundity , p = 0 . 738 , Wilcoxon rank sum test ) ( figure 1A & B ) . This is consistent with observations in other novel transinfections over time [32]–[34] . Additionally , the fecundity of Uju . wt was significantly higher than UjuT ( p = 0 . 028 , Wilcoxon rank sum test ) , supporting previous reports of increased fecundity in the presence of the natural Ae . albopictus Wolbachia superinfection compared to a cured Wolbachia-uninfected line [9] , [10] . As with all characterization experiments presented here , the three lines used ( UjuT , Uju . wMel and Uju . wt ) contained over 99% identical nuclear background , following introgression with UjuT males , in order to control for any genetic differences between the lines that may have arisen through bottlenecking . The fecundity of the Uju . wMel line contrasts with the significant fecundity reduction previously observed for the wPip transinfection of Ae . albopictus [35] , and the hatch rate is much higher in the wMel transinfected line than that previously observed for a wMelPop strain transinfection in Ae . albopictus , which averaged in the 10–20% range [13] . Neither wMel nor the natural wAlbA/B infections appear to have an effect on the longevity of female Ae . albopictus in these lab cage conditions ( figure 2A ) . However , the longevity of Uju . wMel males was significantly increased compared to both the uninfected UjuT ( p = 0 , log rank test ) and the naturally superinfected Uju . wt ( p = 0 , log rank test ) ( figure 2B ) . The average male lifespan was increased from 33 . 4 days ( UjuT ) or 33 . 6 days ( Uju . wt ) to 52 . 5 days ( Uju . wMel ) . The reason for this increase in male lifespan is unknown . The lack of a significant difference in longevity in either sex between Uju . wt and UjuT contrasts with a previous report [9] . This difference between studies could have been a result of the seven generations of introgression that were used here to homogenize genetic backgrounds . No difference was found between the larval/pupal development times of Uju . wMel and Uju . wt; the mean time taken to eclose from egg hatch was 10 . 85±1 . 18 days ( n = 505 ) and 10 . 87±1 . 52 days ( n = 951 ) for Uju . wMel and Uju . wt respectively ( p = 0 . 931 Log rank test; error represents one standard deviation ) . Male mating competitiveness of Uju . wMel males was compared to that of the naturally infected Uju . wt males using three independent replicates of 50 male Uju . wMel : 50 male Uju . wt : 50 females ( either Uju . wMel or Uju . wt ) . Hatching eggs from an individual female indicated that a compatible mating had occurred with a male with the same Wolbachia strain , whilst no hatch indicated an incompatible cross ( data from females with empty spermathecae were disregarded ) . No significant difference was found between the competitiveness of the wildtype and transinfected males under these conditions ( p = 0 . 652 , Chi-squared analysis using a likelihood framework ) ( figure 3 ) . The effect of wMel on CHIKV inhibition was assessed by allowing adult females to feed on blood-meals containing a virus suspension , washed rabbit erythrocytes and 5 mM ATP . After 7 days , 35–50 females were anaesthetized on ice and salivation was induced . The saliva was titrated by focus fluorescent assay on Ae . albopictus C6/36 cells . CHIKV was present in 52% and 44% of Uju . wt and UjuT individuals' saliva , whilst no CHIKV was found in any Uju . wMel saliva ( figure 4A ) , demonstrating complete viral inhibition caused by wMel . No significant difference was observed between the viral titers found in Uju . wt and UjuT ( p = 0 . 4129 , Wilcoxon rank sum test ) ( figure 4B ) , suggesting that the presence/absence of wAlbA/B has no effect on CHIKV . This supports previous research showing no overall significant difference in CHIKV titers between wAlbA/B infected and uninfected Ae . albopictus [36] .
In many regions of the world the most important disease transmission risk associated with Ae . albopictus is increasingly chikungunya [16] , [17] , and the relative importance of its transmission role compared to Ae . aegypti is probably greater for this disease than for dengue [18]–[23] . The data presented here provides a clear indication that the introduction to high frequency of the wMel transinfection in wild populations of Ae . albopictus should prevent it from acting as a chikungunya vector in those populations , in addition to a likely impact on the transmission of dengue . The strong inhibition of CHIKV by Ae . albopictus is noteworthy in light of the fact that the viral strain used possesses the envelope glycoprotein E1-A226V mutation , which has greatly increased the transmission potential by Ae . albopictus [25]–[28] . It would be useful in future studies to conduct challenges with other viral strains , particularly one that carries the E2-L210Q mutation , although this does not produce as strong a positive effect on viral fitness in Ae . albopictus as the E1-A226V variant does [27]; thus the expectation is that the degree of inhibition of other CHIKV variants produced by wMel would be at least as strong as that observed here . It was recently reported that the inhibition of CHIKV also occurs in the presence of wMel transinfection in Ae . aegypti [37] , and thus the use of Wolbachia offers considerable promise for future preventative control measures against this emerging disease . The mechanisms of viral resistance conferred by Wolbachia have yet to be fully elucidated . It has been suggested that a reactive oxygen species ( ROS ) -dependent activation of the Toll pathway , and subsequent increased expression of antimicrobial peptides , is responsible or plays a major role in wAlbB-transinfected Ae . aegypti [38] . However , no evidence of increased antimicrobial peptide expression has been found in Uju . wMel [4] . These experiments included qRT-PCR of both a defensin and a cecropin , which were both highlighted as important effectors in Ae . aegypti infected with wAlbB [38] . Whilst the involvement of immune genes has not been ruled out , other possible pathogen inhibition mechanisms include direct production of ROS by Wolbachia , as observed for other bacteria [39] , and competition for resources such as cholesterol between the host , the bacterium and the virus [7] . Interestingly , high experimental CHIKV titers have previously been shown to produce a reduction in density of wildtype wAlbA/B Wolbachia in Ae . albopictus [36] , which supports the hypothesis of resource competition between them . It will be important , with respect to the effectiveness of disease control strategies , to examine whether 100% blockage of viral transmission by wMel is maintained after many generations of carriage of this Wolbachia strain . We previously reported that Uju . wMel is fully bidirectionally incompatible with a wAlbA/B wildtype line , which provides a mechanism for contained replacement of the naturally occurring wildtype Wolbachia strains in natural populations , since the two could not stably co-exist within a population as long as they are freely interbreeding , and the majority strain would replace the minority [4] . A mathematical model [15] provides an example of the introduction of Wolbachia into discrete seasonal populations of mosquitoes . It is clearly undesirable to release large numbers of biting females to achieve a population majority in a target population . However , by releasing a high percentage of males ( the majority of adult males emerge before the females and pupae can also be separated into sexes by size ) and beginning releases at the start of the rainy season , a relatively modest program of releases over a period of months should be sufficient to take a Wolbachia strain that produces complete bidirectional CI with wildtypes to fixation in a population if there are no major fitness costs of the transinfection under field conditions . At the same time there would be the benefit of suppressed overall population size of biting females due to the releases of incompatible males . Once established at 100% frequency , the transinfection would be resistant to moderate levels of immigration of wildtypes from surrounding populations , since immigrating wildtype females would be incompatible with a majority of the males they encountered . The fitness parameters that have been measured here are all favorable with respect to the aim of introducing the wMel transinfection into natural Ae . albopictus populations for the purpose of disease prevention . The wMel-transinfected males were fully competitive with wildtype males in the simple cage mating assays employed . Extrapolations to what will occur under natural conditions must be made with caution . Semi-field contained greenhouse trials would give a better approximation to nature for longevity and mating competitiveness assays than insectary cages . Ultimately replacement dynamics can be fully assessed in open field trials; the relative effects on fitness of wMel in the greater genetic heterogeneity of wild populations is another unknown . Inn general however , small-cage lab assays have been found to be a reasonably reliable indicator of field success in the Aedes release programs conducted to date [40] , [41] . Detailed examinations of the relative effects of Wolbachia in Ae . albopictus on other life history parameters , particularly using different larval rearing conditions and examining effects on adult size , developmental time and larval and adult competitiveness , and also lifetime measures of fecundity , would also be useful future laboratory-based studies . Even if significant reductions in the mating competitiveness of wMel-infected males occurred under field conditions , this could be abrogated by a sufficiently high ratio of released males to wild type males . Various scenarios can be envisaged where the long-term effectiveness of such a Wolbachia-based strategy for disease control might gradually become compromised , such as the occurrence and spread of viral mutations that escape the inhibition , but such mutations are by no means inevitable . Based on all the data collected so far , the use of wMel offers a promising new method to prevent or reduce dengue and chikungunya transmission by Ae . albopictus . | The tiger mosquito Aedes albopictus is an invasive disease vector whose range has expanded throughout the tropics , and some temperate regions , in recent decades from its native South East Asia . It is an important vector of human viruses including dengue and chikungunya; in recent years a mutation has been detected in chikungunya virus that specifically increases transmission efficiency by Ae . albopictus , causing concern that epidemics of this disease will become more widespread and severe . Here we show that when transinfected with a strain of the symbiont Wolbachia called wMel , originating in fruitflies , the ability of the mosquito to transmit the mutated chikungunya virus was abolished in lab experiments . Furthermore , the wMel strain was shown to produce no detectable fitness costs in this new host , examining numbers of eggs produced , egg hatch , lifespan , and male mating competitiveness compared to wildtypes . This is encouraging with respect to developing the system for use in the control of dengue and chikungunya viruses . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
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] | [
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] | 2013 | A Wolbachia wMel Transinfection in Aedes albopictus Is Not Detrimental to Host Fitness and Inhibits Chikungunya Virus |
The repeat region of the Plasmodium falciparum circumsporozoite protein ( CSP ) is a major vaccine antigen because it can be targeted by parasite neutralizing antibodies; however , little is known about this interaction . We used isothermal titration calorimetry , X-ray crystallography and mutagenesis-validated modeling to analyze the binding of a murine neutralizing antibody to Plasmodium falciparum CSP . Strikingly , we found that the repeat region of CSP is bound by multiple antibodies . This repeating pattern allows multiple weak interactions of single FAB domains to accumulate and yield a complex with a dissociation constant in the low nM range . Because the CSP protein can potentially cross-link multiple B cell receptors ( BCRs ) we hypothesized that the B cell response might be T cell independent . However , while there was a modest response in mice deficient in T cell help , the bulk of the response was T cell dependent . By sequencing the BCRs of CSP-repeat specific B cells in inbred mice we found that these cells underwent somatic hypermutation and affinity maturation indicative of a T-dependent response . Last , we found that the BCR repertoire of responding B cells was limited suggesting that the structural simplicity of the repeat may limit the breadth of the immune response .
Malaria caused by Plasmodium falciparum causes the deaths of around 430 , 000 people each year [1] . The most advanced vaccine candidate for malaria is the RTS , S/AS01 vaccine which consists of a truncated version of the sporozoite-surface circumsporozoite protein ( CSP ) , packaged in a Hepatitis C core virus-like particle delivered in AS01—a proprietary liposome based adjuvant [2] . Phase II and Phase III clinical trials have repeatedly demonstrated that the vaccine gives around 50% protection against clinical malaria in field settings for the first year following vaccination [3] . The bulk of protection is attributed to antibodies targeting the CSP repeat epitope included within the vaccine , with some contribution from CD4+ T cells [4] . It is still unclear why the antibody response to CSP is only partially protective . We lack structural information about how neutralizing antibodies bind to CSP and knowledge on the breadth and nature of the B cell response elicited . Antibodies to CSP were first identified as potential mediators of protection following seminal studies that showed that immunization with irradiated sporozoites could induce sterile protection against live parasite challenge [5 , 6] . In the early 1980s , monoclonal antibodies ( mAbs ) isolated from mice immunized with sporozoites were found to be capable of blocking invasion of hepatocytes [7] and directly neutralizing parasites by precipitating the surface protein coat ( a process known as the circumsporozoite reaction ) [8] . These antibodies were then used to clone CSP , one of the first malaria antigens identified [8 , 9] . The N- and C-terminal domains of CSP from all Plasmodium species are separated by a repeat region , which was the target of the original mAbs [9–11] . In the 3D7 reference strain of P . falciparum , the CSP repeat has 38 repeats: 34 asparagine-alanine-asparagine-proline ( NANP ) -repeats interspersed with 4 asparagine-valine-aspartate-proline ( NVDP ) repeats that are concentrated towards the N-terminus [12] though different isolates can contain slightly different numbers of repeats [13] . One of the most effective P . falciparum sporozoite neutralizing antibodies identified in these early studies was 2A10 which can block sporozoite infectivity in vitro [7] and in in vivo mouse models utilizing rodent P . berghei parasites expressing the P . falciparum CSP repeat region [14 , 15] . While CSP binding antibodies have been shown to be able to neutralize sporozoites and block infection , it has also been proposed that CSP is an immunological “decoy” that induces a suboptimal , but broad , T-independent immune response due to the CSP repeat cross-linking multiple B cell receptors ( BCRs ) [16] . However , it remains unknown if the repetitive regions of CSP can cross-link multiple BCRs as they are not as large as typical type-II T-independent antigens [17] . Moreover , the ability to induce a T-independent response does not preclude a T-dependent component to immunity as well: various oligomeric viral surface proteins can induce both short-lived T-independent responses and subsequent affinity matured IgG responses [18 , 19] . Furthermore , the very little published data on the sequences of CSP binding antibodies does not convincingly support activation of a broad B cell repertoire: a small study of five P . falciparum CSP mouse monoclonal antibodies ( mAbs ) identified some shared sequences [20] . In humans , a study that generated mAbs from three individuals who received RTS , S found that the three antibodies studied had distinct sequences though these all used similar heavy chains [21] . We therefore set out to test the hypothesis that the CSP repeat can bind multiple antibodies or BCRs and drive a T-independent immune response . To do this we undertook a comprehensive biophysical characterization of the 2A10 sporozoite-neutralizing antibody that binds to the CSP repeat . We found that this antibody binds with an avidity in the nano-molar range which was unexpected as previous studies using competition ELISAs with peptides predicted a micro-molar affinity [22 , 23] . Strikingly , isothermal titration calorimetry ( ITC ) , structural analyses , and mutagenesis-validated modeling revealed that the CSP repeat can be bound by around six antibodies suggesting that the repeat may potentially crosslink multiple BCRs on the surface of a B cell . However , analysis of CSP-specific B cells revealed that CSP-specific B cells can enter Germinal Centers ( GCs ) and undergo affinity maturation contradicting the notion that the response to CSP is largely T-independent . Moreover , we found that the BCR repertoire of CSP-binding B cells is quite limited which may restrict the size and effectiveness of the immune response .
We began our analysis by performing isothermal titration calorimetry ( ITC ) to understand the interaction between 2A10 and CSP . For ease of expression we used a recombinant CSP ( rCSP ) construct described previously which was slightly truncated with 27 repeats compared to 38 in the 3D7 reference strain [12 , 24] . ITC experiments were run on the purified 2A10 antibody and the purified 2A10 antigen-binding fragment ( FAB ) fragment to test the thermodynamic basis of the affinity of 2A10 FAB towards CSP . Experiments were also performed on the 2A10 FAB fragment with the synthetic peptide antigen ( NANP ) 6 , which is a short segment of the antigenic NANP-repeat region of CSP ( Table 1; Fig 1 ) . The binding free energies ( ΔG ) and dissociation constants ( KD ) were found to be -49 . 0 kJ/mol and 2 . 7 nM for the full 2A10 antibody with CSP , -40 kJ/mol and 94 nM for the 2A10 FAB with CSP , and -36 . 4 kJ/mol and 420 nM for the 2A10 FAB with the ( NANP ) 6 peptide . Surprisingly , we did not observe a typical 1:1 antibody/FAB domain:antigen binding stoichiometry ( Table 1 ) . We found that each ( NANP ) 6 peptide was bound to by ~2 FAB fragments ( 2 . 8 repeats per FAB domain ) . With the rCSP protein we observed that ~11 FAB fragments could bind to each rCSP molecule , ( 2 . 5 repeats per FAB domain . Finally , when the single-domain FAB fragment is replaced by the full 2A10 antibody ( which has two FAB domains ) , we observe binding of 5 . 8 antibodies per rCSP molecule ( 4 . 7 repeats per antibody ) . Therefore all complexes exhibit approximately the same binding stoichiometry of two FAB fragments/domains per ~5 repeat units . These results suggest that the antigenic region of CSP constitutes a multivalent antigen and that repeating , essentially identical , epitopes must be available for the binding of multiple FAB domains . It is not possible to separate affinity from avidity in this system , although it is apparent that there is a substantial benefit to the overall strength of binding between the antibody and antigen through the binding of multiple FAB domains . The FAB:rCSP complex and the 2A10:rCSP complex had similar enthalpy and entropy of binding ( Table 1 ) , but the 2A10:rCSP complex had a lower overall ΔG binding , corresponding to a lower dissociation constant ( 2 . 7 nM vs . 94 nM for FAB:rCSP ) . The observation that this antibody-antigen ( Ab-Ag ) interaction is primarily enthalpically driven is consistent with the general mechanism of Ab-Ag interactions [25] . It is clear that the dissociation constant ( Kd ) of a single FAB domain to the ( NANP ) 6 peptide is substantially higher ( 420 nM ) , and that the avidity , the accumulated strength of the multiple binding events between the FAB domains of the antibody and the CSP repeat , is the basis for the lower Kd value observed in the 2A10:rCSP complex . Thus , the characteristic repeating pattern of the epitope on the CSP antigen allows multiple weak interactions with 2A10 FAB domains to accumulate , which yields a complex with a high avidity dissociation constant in the low nM range . To better understand the molecular basis of the multivalent interaction between 2A10 and rCSP , we performed structural analysis of the components . Previous work indicated that the NANP-repeat region of CSP adopts a flexible rod-like structure with a regular repeating helical motif that provides significant separation between the N-terminal and the C-terminal domains [26] . Here , we performed far-UV circular dichroism ( CD ) spectroscopy to investigate the structure of the ( NANP ) 6 peptide . These results were inconsistent with a disordered random coil structure ( S1 Fig ) . Rather , the absorption maximum around 185 nm , minimum around 202 nm and shoulder between 215 and 240 nm , is characteristic of intrinsically disordered proteins that can adopt a spectrum of states [27] . The lowest energy structures of the ( NANP ) 6 repeat were predicted using the PEP-FOLD de novo peptide structure prediction algorithm [28] . The only extended state among the lowest energy structures that was consistent with the reported spacing of the N-and C-terminal domains of CSP [26] , and which presented multiple structurally similar epitopes was a linear , quasi-helical structure , which formed a regularly repeating arrangement of proline turns ( Fig 2A ) . The theoretical CD spectrum of this conformation was calculated ( S1 Fig ) , qualitatively matching the experimental spectra: the maximum was at 188 nm , the minimum at 203 nm and there was a broad shoulder between 215 and 240 nm . To investigate the stability of this conformation , we performed a molecular dynamics ( MD ) simulation on this peptide , which showed that this helical structure could unfold , and refold , on timescales of tens of nanoseconds , supporting the idea that it is a low-energy , frequently sampled , configuration in solution ( S1 Movie , S2 Fig ) . We also observed the same characteristic hydrogen bonds between a carbonyl following the proline and the amide nitrogen of the alanine , and the carbonyl group of an asparagine and a backbone amide of asparagine three residues earlier , that are observed in the crystal structure of the NPNA fragment [29] . Thus , this configuration , which is consistent with previously published experimental data , is a regular , repeating , extended conformation that would allow binding of multiple FAB domains to several structurally similar epitopes . To better understand the interaction between the 2A10 and the ( NANP ) -repeat region , we solved the crystal structure of the 2A10 FAB fragment in two conditions ( S1 Table ) , yielding structures that diffracted to 2 . 5 Å and 3 . 0 Å . All of the polypeptide chains were modeled in good quality electron density maps ( Fig 2B ) , except for residues 134–137 of the light chain . This loop is located at the opposite end of the FAB fragment to the variable region and not directly relevant to antigen binding . The 2 . 5 Å structure contained a single polypeptide in the asymmetric unit , whereas the 3 . 0 Å structure contained three essentially identical chains . Superposition of the four unique FAB fragments from the two structures revealed that the variable antigen binding region is structurally homogeneous , suggesting that this region might be relatively pre-organized in the 2A10 FAB . This is consistent with the observation that antibodies typically undergo relatively limited conformational change upon epitope binding [25] . Indeed , a recent survey of 49 Ab-Ag complexes revealed that within the binding site , the heavy chain Complementarity Determining Region ( CDR ) -3 was the only element that showed significant conformational change upon antigen binding and even this was only observed in one third of the antibodies [30] . Attempts to obtain a crystal structure of a complex between 2A10 FAB and the ( NANP ) 6 peptide were unsuccessful; unlike binary Ab-Ag interactions , in which the Ab will bind to a single epitope on an antigen and produce a population of structurally homogeneous complexes that can be crystallized , in this interaction we are dealing with an intrinsically-disordered peptide , the presence of multiple binding sites on the peptide , and the possibility that more than one 2A10 FAB domain can bind the peptide . Therefore it is difficult to obtain a homogeneous population of complexes , which is a prerequisite for crystallization . Attempts to soak the ( NANP ) 6 peptide into the high-solvent form of 2A10 FAB , in which there were no crystal packing interactions with the binding-loops , caused the crystals to dissolve , again suggesting that the heterogeneity of the peptide and the presence of multiple epitopes produces disorder that is incompatible with crystal formation . Although it was not possible to obtain a crystal structure of the 2A10- ( NANP ) 6 peptide complex , the accurate structures of the 2A10 FAB fragment , the ( NANP ) 6 peptide , and the knowledge that antibodies seldom undergo significant conformational changes upon antigen binding [30] , allowed us to model the interaction , which we tested using site directed mutagenesis . Computational modeling of Ab-Ag interactions has advanced considerably in recent years and several examples of complexes with close to atomic accuracy have been reported in the literature [31] . Using the SnugDock protein-protein docking algorithm [31] , we obtained an initial model for binding of the peptide to the CDR region of the 2A10 FAB fragment ( Fig 2C ) . We then performed , in triplicate , three 50 ns MD simulations on this complex to investigate whether the interaction was stable over such a time period ( S2 Movie , S3 Fig ) . These simulations confirmed that the binding mode that was modeled is stable , suggesting that it is a reasonable approximation of the interaction between these molecules . To experimentally verify whether our model of the 2A10 FAB: ( NANP ) 6 peptide interaction was plausible , we performed site directed mutagenesis of residues predicted to be important for binding . Our model predicted that the interaction with ( NANP ) 6 would be mainly between CDR2 and CDR3 of the light chain and CDR2 and CDR3 of the heavy chain ( Fig 2C ) . In the light chain ( Fig 3A and 3B ) , Y38 is predicted to be one of the most important residues in the interaction; it contributes to the formation of a hydrophobic pocket that buries a proline residue and is within hydrogen bonding distance , via its hydroxyl group , to a number of backbone and side-chain groups of the peptide . Loss of this side-chain abolished binding . Y56 also forms part of the same proline-binding pocket as Y38 , and loss of this side-chain also resulted in an almost complete loss of binding . R109 forms a hydrogen bond to an asparagine residue on the side of the helix; mutation of this residue to alanine results in a partial loss of binding . Y116 is located at the center of the second proline-binding pocket; since loss of the entire side-chain through an alanine mutation would lead to general structural disruption of the FAB fragment , we mutated this to a phenylalanine ( removing the hydroxyl group ) , which led to a significant reduction in binding . Finally , S36A was selected as a control: the model indicated that it was outside the binding site , and the ELISA data indicated that had no effect on ( NANP ) n binding . Within the heavy chain ( Fig 3C and 3D ) , mutation of N57 to alanine led to complete loss of binding , which is consistent with it forming a hydrogen bond to a side-chain asparagine but also being part of a relatively well packed region of the binding site that is mostly buried upon binding . T66 is located on the edge of the binding site and appears to provide hydrophobic contacts through its methyl group with the methyl side-chain of an alanine of the peptide; mutation of this residue resulted in a partial loss of binding . Interestingly , mutation of E64 , which is location in an appropriate position to form some hydrogen bonds to the peptide resulted in a slight increase in binding , although charged residues on the edge of protein:protein interfaces are known to contribute primarily to specificity rather than affinity [32] . Specifically , the cost of desolvating charged residues such as glutamate is not compensated for by the hydrogen bonds that may be formed with the binding partner . Y37 is located outside the direct binding site in the apo-crystal structure; the loss of affinity could arise from long-range effects , such as destabilization of the position of nearby loops . In general , the effects of the mutations are consistent with the model of the interaction . The binding mode of the FAB fragment to the ( NANP ) 6 peptide is centered on two proline residues from two non-adjacent NANP-repeats ( Fig 3A and 3C ) . These cyclic side-chains are hydrophobic in character and are buried deeply in the core of the FAB antigen binding site , into hydrophobic pockets formed by Y38 and Y56 of the light chain and the interface between the two chains . In contrast , the polar asparagine residues on the sides of the helix are involved in hydrogen bonding interactions with a number of polar residues on the edge of the binding site , such as N57 of the heavy chain . Due to the twisting of the ( NANP ) 6 repeat , the binding epitope of the peptide is 2 . 5–3 alternate NANP repeats , with a symmetrical epitope available for binding on the opposite face ( Fig 4A ) . Thus , this binding mode is consistent with the stoichiometry of the binding observed in the ITC measurements , where we observed a stoichiometry of two 2A10 FAB fragments per ( NANP ) 6 peptide . To investigate whether this binding mode was also compatible with the indication from ITC that ~10 . 7 2A10 FAB fragments , or six antibodies ( containing 12 FAB domains ) could bind the CSP protein ( Table 1 ) , we extended the peptide to its full length . It is notable that the slight twist in the NANP helix results in the epitope being offset along the length of the repeat region , thereby allowing binding of ten 2A10 FAB fragments ( Fig 4B ) . Six 2A10 antibodies can bind if two antibodies interact by a single FAB domain and the other four interact with both FAB domains . The observation that the FAB fragments bind sufficiently close to each other to form hydrogen bonds also explains the observation from the ITC that the complexes with rCSP , which allow adjacent FAB fragment binding , have more favorable binding enthalpy , i . e . the additional bonds formed between adjacent FAB fragments further stabilize the complex and lead to greater affinity ( Table 1 ) . Thus , the initially surprising stoichiometry that we observe through ITC appears to be quite feasible based on the structure of the NANP-repeat region of the rCSP protein and the nature of the rCSP-2A10 complex . It is also clear that the effect of antibody binding to this region would be to prevent the linker flexing between the N- and C-terminal domains and maintaining normal physiological function , explaining the neutralizing effect of the antibodies . We next set out to determine the implications of our structure for the B cell response to CSP . Because the CSP protein could conceivably cross-link multiple B cell receptors ( BCRs ) we hypothesized that the B cell response might be T-independent . As a tool to test this hypothesis we used ( NANP ) n-based tetramers to identify and phenotype antigen specific B cells in mice immunized with P . berghei sporozoites expressing the repeat region of the P . falciparum CSP ( P . berghei CSPf ) [15] . The tetramers are formed by the binding of 4 biotinylated ( NANP ) 9 repeats with streptavidin conjugated phycoerythrin ( PE ) or allophycocyanin ( APC ) . To validate our tetramer approach , mice were immunized with either P . berghei CSPf or another line of P . berghei with a mutant CSP ( P . berghei CS5M ) that contains the endogenous ( P . berghei ) repeat region , which has a distinct repeat sequence ( PPPPNPND ) n . ( NANP ) n-specific cells were identified with two tetramer probes bound to different conjugates to exclude B cells that are specific for the PE or APC components of the tetramers which are numerous in mice [33] . We found that mice immunized with P . berghei CSPf sporozoites developed large tetramer double positive populations , which had class switched ( Fig 5A and 5B ) . In contrast , the number of tetramer double positive cells in mice receiving control parasites was the same as in unimmunized mice; moreover these cells were not class switched and appeared to be naïve precursors indicating that our tetramers are identifying bona-fide ( NANP ) n-specific cells ( Fig 5B and 5C ) . Further analysis of the different populations of B cells showed that most B cells present at this time-point were GL7+ CD38- indicating that they are GC B cells in agreement with results from a recent publication [34] ( Fig 5B and 5D ) . Given that T cells are required to sustain GC formation beyond ~3 days these data indicate that a T-dependent response can develop to CSP following sporozoite immunization [35] . Our previous data showing GC formation among ( NANP ) n specific B cells was indicative of a T-dependent response . To determine whether there might also be a T-independent component to the B cell response we immunized CD28-/- mice as well as C57BL/6 controls with P . berghei CSPf radiation attenuated sporozoites ( RAS ) and measured serum ( NANP ) n specific antibody by ELISA and the B cell response using our Tetramers . CD28-/- mice have CD4+ T cells but they are unable to provide help to B cell responses [36] . Interestingly 4 days post immunization there were comparable IgM and IgG anti- ( NANP ) n responses in the CD28-/- mice and control animals ( Fig 6A ) , indicative of a T-independent component to immunity . However by day 27 post immunization there was no detectable IgM or IgG antibody specific for ( NANP ) n in the CD28-/- mice suggesting the T-independent response is short-lived . We further analyzed ( NANP ) n specific B cell responses using our tetramers , in particular examining the number and phenotype ( plasmablast vs GC B cell ) of activated IgD- Tetramer+ cells ( Fig 6B ) . In agreement with our antibody data , similar numbers of antigen specific B cells were seen at 4 days post immunization in the CD28-/- and control mice and most of these cells were plasmablasts ( Fig 6C ) . However by 7 days post immunization the number of antigen specific cells declines in the CD28-/- mice as the T dependent GC reaction begins to predominate . Thus CSP on the surface of sporozoites is able to induce short-lived T-independent B cell response , but subsequently T-dependent responses predominate . We wanted to know if to induce a T-independent response it was necessary for CSP to be presented on the surface of the sporozoite or if free rCSP was sufficient . We found that indeed rCSP could induce a T-independent response as evidenced by similar IgM and IgG levels and IgD-Tetramer+ responses 4 days post immunization in control and CD28-/- mice ( Fig 6D and 6E ) . Finally we were concerned that there may be some residual CD4+ T cell help in the CD28-/- mice so we performed experiments in which we used the antibody GK1 . 5 to deplete CD4+ T cells [37] . In agreement with our previous data we found that sporozoites ( live or RAS ) and rCSP induced IgM responses in CD4 depleted mice , though we were unable to detect a significant IgG response ( S4 Fig ) . We also detected primed antigen specific B cells in GK1 . 5 treated mice following RAS or rCSP immunized mice 4 days post-immunization , albeit at lower levels than in mice treated with isotype control antibodies ( S4 Fig ) . Overall our data with GK1 . 5 depleted mice support our results in the CD28-/- model . Our ability to identify and sort ( NANP ) n specific B cells with our tetramers also allows us to examine the repertoire of antibodies that can bind the ( NANP ) n by sequencing the BCRs of the identified cells . While the repeat structure of CSP has been hypothesized to induce a broad polyclonal response based on data that the CSP repeat can absorb most of the sporozoite binding activity of human sera from immune individuals [23 , 38] , an alternative hypothesis is that the antigenically simple structure of the repeat epitope might only be recognized by a small number of naïve B cells . We therefore sorted ( NANP ) n-specific cells 35 days post immunization of BALB/C mice with sporozoites . We performed this analysis in BALB/C mice as this is the background of mice from which the 2A10 antibody was derived . We then prepared cDNA from the cells and amplified the heavy and kappa chain sequences using degenerate primers as described previously [39 , 40] . Heavy and kappa chain libraries were prepared from 4 immunized mice as well as from 3 naïve mice from which we bulk sorted B cells as controls . We obtained usable sequences from 3 of the 4 mice for both the heavy chain and kappa chain . Analysis of the heavy chain revealed that in each mouse 3 or 4 V regions dominated the immune response ( Fig 7A ) . The V regions identified ( IGHV1-20; IGHV1-26; IGHV1-34 and IGHV5-9 ) were generally shared among the mice . As a formal measure of the diversity of our V region usage in the ( NANP ) n specific cells and the bulk B cells from naïve mice we calculated the Shannon entropy for these populations . This analysis formally demonstrated that the diversity of the antigen specific B cells was significantly lower than the diversity of the repertoire in naïve mice ( Fig 7B ) . We further found that each V region was typically associated with the same D and J sequences even in different mice . For example , IGHV1-20 was typically associated with J4 , IGHV5-9 with J4 while in different mice IGHV1-34 was variously paired with J1 or J4 ( Fig 7C ) . Similar results were obtained for the kappa chain with the response dominated by IGKV1-135; IGKV5-43/45; IGKV1-110; IGKV1-117 and IGKV14-111 ( Fig 7D and 7E ) . The V regions were typically paired with the same J regions even in different mice ( Fig 7F ) , for example IGKV5 . 43/45 was typically paired with IGKJ5 or IGKJ2 and IGKV1-110 was typically paired with IGKJ5 , although IGKV1-135 was typically more promiscuous . One limitation of our high throughput sequencing approach is that the degenerate primers only amplified ~70% of the known IGHV and IGKV sequences in naïve mice , suggesting that we may not capture the full diversity of the response . However , comparison with the 5 published antibody sequences ( S2 and S3 Tables ) that include IGHV-1-20 , IGKV5-45 and IGKV1-110 reveals that we are likely capturing the bulk of the antibody diversity . Together these data suggest that the number of B cell clones responding to CSP may be limited , potentially reducing the ability of the immune system to generate effective neutralizing antibodies . Finally we were interested in knowing if the GC reaction we could see following sporozoite immunization was inducing higher affinity antibodies . We therefore examined our deep sequencing data to determine if CSP-specific antibodies had undergone somatic hypermutation ( SHM ) that would be indicative of B cells specific for CSP entering the GC . Taking advantage of the fact that our kappa chain primers capture the entire V-J sequences of the antibodies we sequenced we asked: 1 ) if the kappa chains shared between immune animals differed from the germline ( providing evidence of SHM ) and 2 ) if the mutations were conserved between different mice indicative of directed selection . Analysis of the reads from the kappa chains of the three immune mice showed that these had a much higher degree of mutation than bulk B cells from naïve mice , demonstrating SHM in the CSP-specific antibodies ( Fig 8A ) . We further examined each specific common kappa chain in turn ( IGVK1-110; IGKV1-135; IGVK5-43/45 ) comparing the sequences obtained from naïve B cells and ( NANP ) n specific cells in immune mice . This analysis showed that while , as expected , sequences from naïve mice contained few mutations , the sequences from immune mice had much higher levels of SHM . Importantly mutations were found to be concentrated in the CDR loops , and were frequently shared by immunized mice providing strong circumstantial evidence for affinity maturation ( Fig 8B; data for IGVK1-110 only shown ) . To directly test if CSP-binding antibodies undergo affinity maturation we expressed the predicted germline precursor to the 2A10 antibody ( 2A10 gAb ) in HEK293T cells . We identified the predicted germline precursors of the 2A10 heavy and light chains using the program V-quest [41] ( S5 and S6 Figs ) . This analysis identified the heavy chain as IGHV9-3; IGHD1-3; IGHJ4 and the light chain as IGKV10-94;IGKJ2 , with the monoclonal antibody carrying 6 mutations in the heavy chain and 7 in the light chain . The 2A10 gAb had considerably lower binding in ELISA assays compared to the 2A10 mAb itself ( Fig 8C ) , indicative that affinity maturation had taken place in this antibody . To determine the relative contribution of mutations in the heavy and light chain to enhancing binding we also made hybrid antibodies consisting of the mAb heavy chain and the gAb light chain and vice versa . Interestingly mutations in the light chain were almost entirely sufficient to explain the enhanced binding by the mAb compared to the gAb ( Fig 8C ) . To identify the specific mutations that were important we introduced the mutations individually into the gAb light chain construct . We prioritized mutations that were shared with the 27E antibody which has previously been found to be clonally related to 2A10 having been isolated from the same mouse and which shares the same germline heavy and light chains as the 2A10 mAb [20] . We found that two mutations ( L114F and T117V ) in the CDR3 of the light chain appeared to account for most of the gain in binding ( Fig 8C ) . The effect of these antibodies appeared to be additive rather than synergistic as revealed by experiments in which we introduced these mutations simultaneously ( Fig 8D ) . A further mutation close to the light chain CDR2 ( H68Y ) also caused a modest increase in binding . As expected mutations in the heavy chains appeared generally less important for increasing binding though M39I , N59I and T67F all gave modest increases in binding ( Fig 8E ) . Collectively our data suggest that CSP repeat antibodies can undergo SHM in GCs resulting in affinity maturation , however the antibody response may be limited by the number of naïve B cells that can recognize and respond to this antigen .
Here we provide an analysis of the structure of a Plasmodium falciparum sporozoite-neutralizing antibody ( 2A10 ) . Having obtained this structure we further modeled the binding 2A10 with its antigen target , the repeat region of CSP , and provide a thermodynamic characterization of this interaction . Finally , we used novel tetramer probes to identify and sort antigen specific B cells responding to sporozoite immunization in order to measure the diversity and maturation of the antibody response . We found that the avidity of 2A10 for the rCSP molecule was in the nanomolar range , which was much higher than the affinity previously predicted from competition ELISAs with small peptides [22 , 23] . This affinity is a consequence of the multivalent nature of the interaction , with up to 6 antibodies being able to bind to each rCSP molecule . Our model suggests that to spatially accommodate this binding the antibodies must surround CSP in an off-set manner , which is possible due to the slight twist in the helical structure that CSP can adopt . It is notable that the twisted , repeating arrangement of the CSP linker is the only structure that would allow binding in the stoichiometry observed through the ITC . We further found that the diversity of the antibody repertoire to the CSP repeat was limited , perhaps due to the relative simplicity of the target epitope . However , these antibodies have undergone affinity maturation to improve affinity , potentially allowing protective immune responses to develop . Using ITC we determined the dissociation constant of 2A10 for rCSP to be 2 . 7 nM , which is not unusual for a mouse mAb . However it is a tighter interaction than that predicted from competition ELISAs , which predicted a micro-molar affinity [22 , 23] . However , these competition ELISAs were performed with short peptides rather than rCSP . Indeed , when we performed ITC with a short peptide and FAB fragments we too obtained a dissociation constant in the micro-molar range ( 0 . 42 μM ) . The difference between the FAB binding to the peptide and the tight interaction of the antibody binding to full length CSP appears to be driven by a high avidity , multivalent interaction . There is also additional enthalpic stabilization ( per FAB domain ) in the 2A10:CSP complex , although this is partially offset by the increased entropic cost associated with combining a large number of separate molecules into a single complex . One caveat of these data is that we used a slightly truncated repeat in our recombinant CSP , however it is likely that longer repeats will have further stabilization of the interaction that could result in even higher affinity interaction between CSP and binding antibodies . The mechanism of sporozoite neutralization remains unclear , however our structural data may provide some insights . Repeat specific antibodies can directly neutralize sporozoites ( without complement or other cell mediators ) in the circumsporozoite reaction [8 , 42] . Morevoer FAB fragments alone are sufficient to block invasion [42 , 43] . However , it is well established that activation of complement and cell mediated immunity is important for the action of blood stage-specific antibodies [44 , 45] . It has also been suggested that the CSP repeat might act as a hinge allowing the N-terminal domain to mask the C-terminal domain which is believed to be important for binding to and invading hepatocytes [10] . Cleavage of this N-terminal domain is therefore required to expose the C-terminal domain and facilitate invasion [10] . Antibody binding as observed here may disrupt this process in several ways , either by opening the hinge to induce the premature exposure of the C-terminal domain . Alternatively since the repeat region is directly adjacent to the proteolytic cleavage site , anti-repeat antibodies might function by sterically hindering access of the protease to CSP , thus preventing sporozoite invasion of the hepatocyte . One possible consequence of the requirement for mutivalency to increase the avidity of the antibody , is that antibodies with different binding modes may interfere with each other limiting their effectiveness . Our results uncovering how neutralizing antibodies bind to CSP has several implications for understanding the development of the immune response to CSP . Notably the finding that the CSP molecule can be bound by multiple antibodies/B cell receptors raises the possibility that this molecule can indeed crosslink multiple BCRs and potentially act as a type-II T independent antigen [17] . We find that indeed there is a T-independent component to the response to CSP , though T cells are required to sustain the immune response beyond day 7 . As such the response to CSP appears follow a similar process to that seen for several oligomeric viral entry proteins , which induce a mix of T-independent and T-dependent responses [18 , 19] . It maybe that T-independent responses are driven by the density of CSP molecules on the sporozoite surface; however , rCSP can also induce a small T-independent response . This suggests that the CSP protein alone is sufficient to crosslink multiple BCRs on the B cell surface which is consistent with our structural model . Interestingly , the RTS , S/AS01 vaccine based on that contains 18 CSP repeats and does appear to induce high titers of anti-CSP antibodies which initially decline rapidly and are then more stable [4 , 46] . This may be consistent with the induction of a short-lived a type-II T-independent plasmablast response ( accounting for the initial burst of antibodies ) , followed by a T-dependent response ( which may be the basis of the more sustained antibody titers ) . The relative contributions of short-lived antibody production and long-term B cell memory to protection is an area for future investigation . The finding of a limited repertoire in the BCR sequences specific for the ( NANP ) n repeat contradicts previous suggestions that the response to CSP might be broad and polyclonal [38] . One explanation for this limited antibody diversity is that the antigenic simplicity of the CSP repeat region limits the range of antibodies that are capable of responding . A prior example of this is the antibodies to the Rhesus ( Rh ) D antigen . The RhD antigen differs from RhC by only 35–36 amino acids , resulting in the creation of a minimal B cell epitope [47] . The repertoire of antibodies that can bind this epitope are accordingly limited and mainly based on the VH3-33 gene family [48] . Another potential explanation for a limited antibody repertoire could be that the ( NANP ) n repeat shares structural similarity with a self-antigen as is speculated to happen with meningococcus type B antigens [49] , however it is not clear what this self-antigen might be . One potential outcome of this finding is that if each B cell clone has a finite burst size this may limit the magnitude of the overall B cell response . One area for future investigation is to determine the binding modes and sporozoite neutralizing capacities of other antibodies in the response . It is clear that not all CSP-repeat binding antibodies have the same capacity for sporozoite neutralization [7] . As such the finding of a limited repertoire of responding B cells may lead to the possibility that some people have holes in their antibody repertoires limiting their ability to make neutralizing antibodies . This may explain why , while there is a broad correlation between ELISA tires of antibodies to the CSP repeat and protection following RTS , S vaccination , there is no clear threshold for protection [4] . While our work has been performed with mouse antibodies , there are major similarities between mouse and human antibody loop structure [50] . The main difference between the two species is the considerably more diverse heavy chain CDR3 regions that are found in human antibodies [51] . Consequently , this leads to a much larger number of unique clones found in humans compared to mice . However , the number of different V , D and J genes and the recombination that follows are relatively similar between humans and mice [52] . From our data it can be observed that while the BCR repertoire was restricted in the V gene usage , these different V gene populations were represented in multiple unique clones , suggesting that increasing the number of clones is unlikely to substantially increase V-region usage . Our analysis was performed on inbred mice which may also limit repertoire diversity , however studies on the human IGHV locus reveal that in any given individual ~80% V region genes are identical between the maternal and paternal allele i . e . heterozygosity is not a major driver of human V region diversity [53 , 54] . It is notable that all 4 human monoclonal antibodies described to date from different volunteers share the use of the IGHV3-30 gene family [21 , 22] , suggesting that in humans as well as mice there may indeed be a constrained repertoire of responding B cells . Overall our data provide important insights into how the antibody response to CSP develops . Our results also help explain why relatively large amounts of antibodies are required for sporozoite neutralization and suggest that the ability to generate an effective B cell response may be limited by the very simplicity of the repeat epitope . These data support previous suggestions that CSP may be a suboptimal target for vaccination . However , we also find that CSP binding antibodies can undergo somatic hypermutation and reach high affinities . This suggests if we can develop vaccination strategies to diversify the repertoire of responding B cells and favor the GC response it may be possible to generate long-term protective immunity targeting this major vaccine candidate antigen .
All animal procedures were approved by the Animal Experimentation Ethics Committee of the Australian National University ( Protocol numbers: A2013/12 and A2016/17 ) . All research involving animals was conducted in accordance with the National Health and Medical Research Council's ( NHMRC ) Australian Code for the Care and Use of Animals for Scientific Purposes and the Australian Capital Territory Animal Welfare Act 1992 . BALB/C , C57BL/6 or CD28-/- [55] mice ( bred in-house at the Australian National University ) were immunized IV with 5 x 104 P . berghei CS5M sporozoites expressing mCherry [56] or 5 x 104 P . berghei CSPf sporozoites dissected by hand from the salivary glands of Anopheles stephensi mosquitoes . Mice were either infected with live sporozoites and then treated with 0 . 6mg choloroquine IP daily for 10 days or immunized with irradiated sporozoites ( 15kRad ) . For immunization with rCSP , 30ug rCSP was emulsified in Imject Alum according to the manufacturer’s instructions ( ThermoFisher Scientific ) and delivered intra-peritoneally . All mice received only a single immunization in these experiments . To deplete CD4+ T cells mice were treated with two doses of 100ug GK1 . 5 antibody on the 2 days prior to immunization ( BioXCell ) ; control mice received an irrelevant isotype control antibody ( LTF2; BioXCell ) . Single cell preparations of lymphocytes were isolated from the spleen of immunized mice and were stained for flow cytometry or sorting by standard procedures . Cells were stained with lineage markers ( anti-CD3 , clone 17A2; anti-GR1 , clone RB6-8C5 and anti-NKp46 , clone 29A1 . 4 ) antibodies to B220 ( clone RA3-6B2 ) , IgM ( clone II/41 ) , IgD ( clone 11-26c2a ) , GL7 ( clone GL7 ) , CD38 ( clone 90 ) , CD138 ( clone 281–2 ) and ( NANP ) 9 tetramers conjugated to PE or APC . Antibodies were purchased from Biolegend while tetramers were prepared in house by mixing biotinylated ( NANP ) 9 peptide with streptavidin conjugated PE or APC ( Invitrogen ) in a 4:1 molar ratio . Flow-cytometric data was collected on a BD Fortessa flow cytometer ( Becton Dickinson ) and analyzed using FlowJo software ( FlowJo ) . Where necessary cells were sorted on a BD FACs Aria I or II machine . Single cell suspensions from the spleens of immunized mice were stained with ( NANP ) n tetramers and antibodies to B cell markers as described in the supplementary experimental procedures . Antigen specific cells were sorted on a FACS ARIA I or II instrument prior to RNA extraction with the Arturus Picopure RNA isolation kit ( Invitrogen ) and cDNA preparation using the iScript cDNA synthesis kit ( BioRad ) . BCR sequences were amplified using previously described heavy and kappa chain primers including adaptor sequences allowing subsequent indexing using the Nextera indexing kit ( Illumina ) . Analysis was performed in house using R-scripts and the program MiXCR as described in supplementary experimental procedures . Variants of the 2A10 antibody were expressed in HEK293 T cells ( a kind gift of Carola Vinuesa , Australian National University ) as described in the supplemental experimental procedures . Binding to the CSP repeat was tested by ELISA and ITC using standard techniques as described in the supplementary experimental procedures . Statistical analysis was performed using Prism6 ( GraphPad ) for simple T tests and one-way ANOVAs from single experiments . Where data were pooled from multiple experiments , analysis was performed using linear mixed models in R version 3 . 3 . 3 ( R foundation for Statistical Computing ) . Linear mixed models are a regression analysis model containing both fixed and random effects: fixed effects being the variable/treatment under examination , whilst random effects are unintended factors that may influence the variable being measured . If significance was found from running a linear mixed model , pair-wise comparisons of the least significant differences of means ( LSD ) was undertaken to determine at which level interactions were occurring . Statistical significance was assumed if the p-value was < 0 . 05 for a tested difference . ( ns = not significant , * = p < 0 . 5 , ** = p < 0 . 01 , *** = p < 0 . 001 , **** = p < 0 . 0001 ) . Sequence data generated in this paper is deposited at the NCBI sequence read archive ( SRA ) with accession number SRP092808 as part of BioProject database accession number PRJNA352758 . Atomic coordinates and related experimental data for structural analyses are deposited in the Protein Data Bank ( PDB ) with PDB codes 5SZF and 5T0Y . | Vaccines aim to protect by inducing the immune system to make molecules called antibodies that can recognize molecules on the surface of invading pathogens . In the case of malaria , our most advanced vaccine candidates aim to promote the production of antibodies that recognize the circumsporozoite protein ( CSP ) molecule on the surface of the invasive parasite stage called the sporozoite . In this report we use X-ray crystallography to determine the structure of CSP-binding antibodies at the atomic level . We use other techniques such as isothermal titration calorimetry and structural modeling to examine how this antibody interacts with the CSP molecule . Strikingly , we found that each CSP molecule could bind 6 antibodies . This finding has implications for the immune response and may explain why high titers of antibody are needed for protection . Moreover , because the structure of the CSP repeat is quite simple we determined that the number of different kinds of antibodies that could bind this molecule are quite small . However a high avidity interaction between those antibodies and CSP can result from a process called affinity maturation that allows the body to learn how to make improved antibodies specific for pathogen molecules . These data show that while it is challenging for the immune system to recognize and neutralize CSP , it should be possible to generate viable vaccines targeting this molecule . | [
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] | 2017 | T-dependent B cell responses to Plasmodium induce antibodies that form a high-avidity multivalent complex with the circumsporozoite protein |
Genetical genomics is a strategy for mapping gene expression variation to expression quantitative trait loci ( eQTLs ) . We performed a genetical genomics experiment in four functionally distinct but developmentally closely related hematopoietic cell populations isolated from the BXD panel of recombinant inbred mouse strains . This analysis allowed us to analyze eQTL robustness/sensitivity across different cellular differentiation states . Although we identified a large number ( 365 ) of “static” eQTLs that were consistently active in all four cell types , we found a much larger number ( 1 , 283 ) of “dynamic” eQTLs showing cell-type–dependence . Of these , 140 , 45 , 531 , and 295 were preferentially active in stem , progenitor , erythroid , and myeloid cells , respectively . A detailed investigation of those dynamic eQTLs showed that in many cases the eQTL specificity was associated with expression changes in the target gene . We found no evidence for target genes that were regulated by distinct eQTLs in different cell types , suggesting that large-scale changes within functional regulatory networks are uncommon . Our results demonstrate that heritable differences in gene expression are highly sensitive to the developmental stage of the cell population under study . Therefore , future genetical genomics studies should aim at studying multiple well-defined and highly purified cell types in order to construct as comprehensive a picture of the changing functional regulatory relationships as possible .
Genetical genomics uses quantitative genetics on a panel of densely genotyped individuals to map genomic loci that modulate gene expression [1] . The quantitative trait loci identified in this manner are referred to as expression quantitative trait loci , or eQTLs [2] . Most genetical genomics studies that have thus far been reported have analyzed single cell types or compared developmentally unrelated and distant cell types [3]–[8] . Here , we report the first application of genetical genomics to study eQTL dynamics across closely related cell types during cellular development . We show results that discriminate between eQTLs that are consistently active or “static” and those that are cell-type–dependent or “dynamic . ” We used the hematopoietic system as a model to analyze how the genome of a single stem cell is able to generate a large variety of morphologically and functionally distinct differentiated cells . Differentiation of hematopoietic stem cells towards mature , lineage-committed blood cells is associated with profound changes in gene expression patterns . The search for differentially expressed genes , most notably for those transcripts exclusively present in stem cells and not in their more differentiated offspring , has been successful and has provided valuable insight into the molecular nature of stem cell self-renewal [9]–[12] . Yet , complementary approaches were needed to elucidate the dynamic regulatory pathways that are underlying the robust differentiation program leading to blood cell production . We describe a genetic analysis of variation in gene expression across four functionally distinct , but developmentally related hematopoietic cell populations . Our data reveal complex cell-stage specific patterns of heritable variation in transcript abundance , demonstrating the plasticity of gene regulation during hematopoietic cell differentiation .
We evaluated genome-wide RNA transcript expression levels in purified Lin−Sca-1+c-Kit+ multi-lineage cells , committed Lin−Sca-1−c-Kit+ progenitor cells , erythroid TER-119+ cells , and myeloid Gr-1+ cells , isolated from the bone marrow of ∼25 genetically related and fully genotyped BXD – C57BL/6 ( B6 ) X DBA/2 ( D2 ) – recombinant inbred mouse strains [13] . In this study , we exploit the fact that the purified cell populations are closely related , sometimes just a few cell divisions apart on the hematopoietic trajectory . The Lin−Sca-1+c-Kit+ cell population contains all stem cells with long-term repopulating ability , but also includes multipotent progenitors that still have lymphoid potential . Although long-term repopulating stem cells are known to only make up a fraction of the Lin−Sca-1+c-Kit+ population , for simplicity we will refer to this population as stem cells . The Lin−Sca-1−c-Kit+ cell population does not contain stem cells and lymphoid precursors , but does include common progenitors of the myeloid and erythroid lineages [14] . Finally , TER-119+ cells and Gr-1+ cells are fully committed to the erythroid and myeloid lineages , respectively . Unsupervised clustering of the most varying transcripts demonstrated that each of the four cell populations could easily be recognized based on expression patterns across all four cell types ( Figure 1 and Table S1 ) . We observed strong and biologically significant variation in gene expression during hematopoietic differentiation , independent of mouse strain . However , the genetical genomics strategy , in which we focus on inter-strain gene expression differences , allows for a far more comprehensive understanding of the genetic regulatory links underlying this variation . QTL mapping of gene expression traits allows us to identify eQTLs; genomic regions that have a regulatory effect on those expression traits . Two types of eQTLs can be distinguished , i . e . , those that map near ( less than 10 Mb from ) the gene which encodes the transcript ( local ) and those that map elsewhere in the genome ( distant ) [15] . Together , local and distant eQTLs constitute a genome-wide overview of the gene regulatory networks that are active in the cell type under study . The strongest eQTLs were found for genes that were expressed only in mouse strains carrying one specific parental allele , suggesting that local regulatory elements are distinct between the two alleles . Cases of such allele-specific expression included H2-Ob and Apobec3 . These transcripts were only detectable in strains that carried the B6 allele of the gene ( see Figure S1A , S1B ) . A global view of heritable variation in gene expression indicated that the strongest eQTLs are not associated with the most highly expressed genes , and that for most probes the expression difference between the B6 and D2 alleles is small ( see Figure S1C , S1D ) . Since the focus of this project is to study the influence of cellular differentiation state on regulatory links , we used ANOVA to distinguish between “static” eQTLs that show consistent genetic effects across the four cell types and “dynamic” eQTLs that are sensitive to cellular state ( i . e . , eQTLs that have a statistically significant genotype-by-cell-type interaction ) . We further partitioned dynamic eQTLs into different categories on the basis of their dynamics along the differentiation trajectory . The first eQTL category comprises genes that have static eQTLs across all four cell types under study . Variation in Lxn expression is shown as a representative example ( Figure 2A , left panel ) . Lxn expression has previously been shown to be higher in B6 stem cells compared to D2 stem cells , and to be negatively correlated with stem cell numbers [16] . In our dataset Lxn showed clear expression dynamics ( it was most highly expressed in stem cells ) , and was indeed more strongly expressed in cells carrying the B6 allele , but the expression difference between mice carrying the B6 or D2 allele remained constant across all cell types . In total , we identified 365 probes that displayed a static eQTL at threshold p<10−6 ( FDR = 0 . 02 ) . Among the 268 locally-regulated probes in this category was H2-D1 . The histocompatibility gene H2-D1 is known to be polymorphic between B6 and D2 mice , and would therefore be expected to be in the static eQTL category . The remaining 97 probes mapped to distant eQTLs , i . e . , their heritable expression variation was affected by the same distant locus in all four cell types ( Table 1 ) . All probes that belonged to the static eQTL category are graphically depicted in an eQTL dot plot displaying the genomic positions of the eQTLs compared to the genomic positions of the genes by which the variably expressed transcripts were encoded ( Figure 2A , right panel ) . Whereas in this plot local eQTLs appear on the diagonal , distant eQTLs appear elsewhere . In general , as has been reported before in eQTL studies , transcripts that were locally regulated showed strong linkage statistics . Not surprisingly , the statistical association between genotype and variation in transcript abundance for those transcripts that were controlled by distant loci was weaker . These genes are likely to be controlled by multiple loci , each contributing only partially to the phenotype , thereby limiting their detection and validation in the current experimental sample size . A list of all transcripts with significant static eQTLs is provided in Table S2 . The second eQTL category comprises genes that have dynamic eQTLs across all four cell types under study . In total , we identified 1283 eQTLs ( p<10−6 , FDR = 0 . 021 ) that showed different genetic effects in different cell types , indicating that eQTLs are highly sensitive to cellular differentiation state ( Table 1 ) . Within this dynamic eQTL category , the first four subcategories are composed of eQTLs that were preferentially active in only one of the four cell types we analyzed ( Figures 2B–2E ) . For example , Slit2 mapped to a strong eQTL that was active only in stem cells . Slit2 mRNA was only detected in the most primitive hematopoietic cell compartment in those BXD strains that carried the D2 allele at rs13478235 , a SNP that mapped 629 kb away from the Slit2 gene ( Figure 2B , left panel ) . Slit2 encodes an excreted chemorepellent molecule that is known to be expressed in embryonic stem cells [17] , to be involved in neurogenesis [18] and angiogenesis [19] , and to inhibit leukocyte chemotaxis [20] . We found a total of 140 genes that have eQTLs that are preferentially/selectively active in stem cells ( Figure 2B , right panel , largest symbols , Table 1 ) . These 140 genes included well-known candidate stem cell genes such as Angpt1 , Ephb2 , Ephb4 , Foxa3 , Fzd6 , and Hoxb5 . Interestingly , many transcripts with as yet unknown ( stem cell ) function were transcriptionally affected by stem-cell-specific eQTLs . Candidate novel stem cell genes include Msh5 , and Trim47 , in addition to a large collection of completely unannotated transcripts . A total of 45 , 531 , and 295 eQTLs were found to be preferentially/selectively active in progenitors , erythroid cells , and myeloid cells , respectively ( Table 1 ) . Very distinct patterns of cell-type–specific gene regulation emerged when these eQTLs were visualized in genome-wide dot plots ( Figures 2C–2E ) . Using genome-wide p-value thresholds of p<10−6 , we identified 53 distantly-regulated transcripts in stem cells , 13 in progenitor cells , 400 in erythroid cells , and 132 in myeloid cells . In erythroid and myeloid cells most of these transcripts mapped to relatively few genomic loci; these trans-bands are statistically significant , as assessed by a permutation approach taking expression correlation into account ( see Materials and Methods ) [21] . Typically , transcripts mapping to a common marker showed a directional bias towards either B6 or D2 expression patterns . In addition to the relatively simple eQTL dynamics that we have thus far illustrated , more complex eQTL dynamics were also detected using this approach . For example , Rpo1-2 is a transcript that shows a strong local eQTL in the two non-committed lineages included in our study , but shows a much weaker genetic effect in erythroid and myeloid cells ( Figure 2F ) . Whereas in mice carrying the B6 allele of Rpo1-2 the overall expression of the gene decreased substantially during differentiation of progenitor to erythroid cells , in mice carrying the D2 allele expression slightly increased . This observation hints at complex regulatory mechanisms underlying the expression of this gene . Full lists of genes in each dynamic eQTL subcategory described thus far are supplied in Table S2 . Additional subcategories and their exact definitions are explained more extensively in the Materials and Methods section , and complete results of all dynamic eQTLs are available in Table S3 . eQTL dynamics can be caused by transcription factors being switched on/off upon cellular differentiation , or by a transcription factor showing changed specificity due to variations in regulatory input . We found that most ( >75% ) of the dynamic eQTLs are active in only one of the four cell types under study ( Figure 3A ) . A more detailed analysis revealed that in the majority of cases the genes with a cell-type–specific eQTL were also most highly expressed in that particular cell type ( Figure 3B ) . Next , we explored whether we could find transcripts that were regulated by distinct eQTLs in different cell types ( see Materials and Methods ) . Such eQTL “swapping” would indicate major changes in transcriptional regulatory networks . We could find no evidence for such cases . However , given our limited population size we have a low power to detect multiple eQTLs , so swapping eQTLs may still exist but remain undetected in our experimental setting . It has been described that not all local eQTLs in genetical genomics experiments reflect actual expression differences between mouse strains , but rather indicate differential hybridization caused by polymorphisms in the sequences recognized by the probes [22] . For this reason , we divided both the static and dynamic eQTL categories in local and distant eQTLs , and indicated the number of probes that hybridized to sequences that are known to contain polymorphisms ( Figure 3C ) . As expected , the static eQTL category contained a higher number of such potential false local eQTLs . If these false positive eQTLs could be removed , the relative abundance of dynamic eQTLs would be higher , indicating that our study may even conservatively underestimate the level of eQTL dynamics .
We found that many eQTLs are highly sensitive to the developmental state of the cell population under study . Even when the purified cells were only separated by a few cell divisions , eQTLs demonstrated a remarkable plasticity . Furthermore , we provide evidence that the cell-stage-sensitivity of eQTLs is often intertwined with gene expression variation during development . We did not identify target genes that were regulated by distinct eQTLs in different cell types , suggesting that large-scale changes within transcriptional regulatory networks are not common . The fact that eQTLs appear to be highly cell-type–dependent highlights the importance of using well-characterized purified cell types in eQTL studies . In particular , eQTL studies of physiological or disease processes [23]–[26] should target the relevant cell type as precisely as possible , i . e . they should use cells or tissues directly involved in the patho-physiological process . This could even mean that several different cell types need to be separately studied , in particular if developmental trajectories are affected [27] . Using unfractionated bone marrow cells , we would have missed many of the diverse and dynamic patterns that we uncovered here , both at the expression level and at the genetic regulatory level . Even so , the four cell populations that we studied are still heterogeneous and further subfractionation of these populations based on different sets of markers would have resulted in even more precise regulatory maps . Many genetical genomics experiments have used highly heterogeneous samples , in which mRNA from a variety of different cell types was pooled [4] , [5] , [28]–[31] . In such mixed samples it is usually impossible to ensure that the contribution of individual cell types to the mixture is the same across samples . As a result , important parts of the variation in gene expression could arise from different sample compositions . For example , if in whole brain samples a heritable morphological or developmental trait leads to an increased size of some brain regions , this can cause apparent hotspots for transcripts that are specific for those particular regions . Our data provide a valuable tool for studying the exact consequences of sample heterogeneity on eQTL mapping: a further study could simulate a collection of samples made of computed mixtures of different hematopoietic cells in defined proportions . Clearly , cell purification strategies are essential to identify those cell-type–specific eQTLs that would otherwise be “masked” in heterogeneous cell populations . Therefore , future genetical genomics studies should be realized on as many cell types or cellular differentiation states as possible , and ideally even on the scale of individual cells . All data presented in this paper were deposited in the online database GeneNetwork ( http://www . genenetwork . org ) , an open web resource that contains genotypic , gene expression , and phenotypic data from several genetic reference populations of multiple species ( e . g . mouse , rat and human ) and various cell types and tissues [32] , [33] . It provides a valuable tool to integrate gene networks and phenotypic traits , and also allows cross-cell type and cross-species comparative gene expression and eQTL analyses . Our data can aid in the identification of candidate modulators of gene expression and/or phenotypic traits [34] , and as such can serve as a starting point for hypothesis-driven research in the fields of stem cell biology and hematology .
All animal experiments were approved by the Groningen University Animal Care Committee . Female BXD recombinant inbred mice were originally purchased from The Jackson Laboratory and housed under clean conventional conditions . Mice were used between 3 and 4 months of age . Bone marrow cells were flushed from the femurs and tibias of three mice and pooled . After standard erythrocyte lysis , nucleated cells were stained with either a panel of biotin-conjugated lineage-specific antibodies ( containing antibodies to CD3e , CD11b ( Mac1 ) , CD45R/B220 , Gr-1 ( Ly-6G and Ly-6C ) and TER-119 ( Ly-76 ) ) , fluorescein isothiocyanate ( FITC ) -conjugated antibody to Sca-1 and allophycocyanin ( APC ) -conjugated antibody to c-Kit , or with biotin-conjugated TER-119 antibody and FITC-conjugated antibody to Gr-1 . After being washed , cells were incubated with streptavidin-phycoerythrin ( PE ) ( all antibodies were purchased from Pharmingen ) . Cells were purified using a MoFlo flowcytometer ( BeckmanCoulter ) and were immediately collected in RNA lysis buffer . Lineage-depleted ( Lin− ) bone marrow cells were defined as the 5% of cells showing the least PE intensity . Total RNA was isolated using the RNeasy Mini kit ( Qiagen ) in accordance with the manufacturer's protocol . RNA concentration was measured using a Nanodrop ND-1000 spectrophotometer ( Nanodrop Technologies ) . The RNA quality and integrity was determined using Lab-on-Chip analysis on an Agilent 2100 Bioanalyzer ( Agilent Technologies ) . Biotinylated cRNA was prepared using the Illumina TotalPrep RNA Amplification Kit ( Ambion ) according to the manufacturer's specifications starting with 100 ng total RNA . Per sample , 1 . 5 µg of cRNA was used to hybridize to Sentrix Mouse-6 BeadChips ( Illumina ) . Hybridization and washing were performed by ServiceXS according to the Illumina standard assay procedures . Scanning was carried out on the Illumina BeadStation 500 . Image analysis and extraction of raw expression data were performed with Illumina Beadstudio v2 . 3 Gene Expression software with default settings and no normalization . The raw expression data from all four cell types were first log2 transformed and then quantile normalized as a single group . For cluster analysis we retained only genes having a minimal fold change of 2 ( difference of 1 in log2 scale ) in either direction in mean expression on the transition from Lin−Sca-1+c-Kit+ to Lin−Sca-1−c-Kit+ and on the transition from Lin−Sca-1−c-Kit+ to TER-119+ or to Gr-1+ . This filter reduced the dataset to 876 probes . We then computed the distance matrix for this group of probes , using the absolute Pearson correlation . Using this distance matrix , we applied the hierarchical clustering algorithm . From the resulting tree , 8 different clusters emerged from a manually chosen threshold . We then submitted each of these clusters to DAVID to identify enriched functional annotations [35] . The expression data of the four cell types were firstly corrected for batch effect and then analyzed separately by the following ANOVA model:where yi is the gene's log intensity on the ith microarray; μ is the mean; Qi is the genotype effect under study; and ei is the residual error . Next , expression data of the four cell types were combined and analyzed by a full ANOVA model including the cell type effect ( CT ) and the eQTL×CT interaction effect:where yij is the gene's log intensity at the ith microarray ( i = 1 , …n ) and jth cell type; CTj is the jth cell type effect; ( Q×CT ) ij is the interaction effect between the ith eQTL genotype and jth cell type , and eij is the residual error . The batch effect was included as one of the factors . For each probe , we performed a genome-wide linkage analysis to identify the two markers that showed the most significant main QTL effect and interaction effect , respectively . We defined an eQTL as local if it was located within less than 10 Mb from the gene . All other eQTLs were considered distant . The ANOVA yields significance p-values for the main QTL effect Qi and the interaction effect ( Q×CT ) ij for each probe at each marker . A small p-value for the interaction effect indicates that the eQTL effect is different between the cell types . This significant difference can be due to very diverse patterns , with different biological interpretations . It is therefore necessary to classify interaction eQTLs based on these patterns . To achieve this classification , for every interaction eQTL we evaluated the strength of the effect in each cell type by calculating the difference between the mean expression of both genotypes . The cell type for which the effect was the strongest was labeled “High . ” The cell type whose effect was most different from the strongest effect was labeled “Low . ” The remaining two cell types were assigned to the group they resembled most closely . This classification allowed us to define 14 categories of interaction eQTLs . Additionally , we identified eQTLs that have a consistent effect across all four cell types . This category of consistent eQTLs consists of all probes satisfying the following three conditions: the gene has a significant main effect Qi at marker m; for the same marker m , the interaction ( Q×CT ) ij is not significant; the mean eQTL effect across cell types has a coefficient of variation smaller than 0 . 3 . We permuted the strain labels in the genotype data 100 times , maintaining the correlation of expression traits while destroying any genetic association . Then we applied the full ANOVA model and stored the genome-wide minimum p-value for each transcript . Based on the resulting empirical distribution of p-values , we estimated that a threshold of −log10p = 6 corresponds to a false discovery rate [36] of 0 . 02 for the main QTL effect . The 99 . 9th percentile of the number of significant eQTLs per marker ( i . e . , the minimum size of statistically significant “eQTL hotspots” ) is 28 . We estimated the residuals of the full ANOVA model after fitting all factors up to the main QTL effect at each marker for each transcript [37] . Then we permuted the strain labels and applied the ANOVA model yij = Qi + CTj + ( Q×CT ) ij + eij to the permuted residuals at each marker for each transcript and stored the genome-wide minimum p-value . Based on 100 permutations and the resulting empirical distribution of p-values , we estimated that a threshold of −log10p = 6 corresponds to a false discovery rate of 0 . 021 for interacting QTL effect . The 99 . 9th percentile of the number of significant eQTLs per marker ( i . e . , the minimum size of statistically significant “interaction hotspots” ) is 8 . Swapping eQTLs are those transcripts that show one eQTL in one cell type , but another eQTL in another cell type . From the full model mapping described above , we obtained 1283 transcripts with a significant interaction effect between genotype ( first marker ) and cell type . After taking into account the genetic and interaction effects of the first marker , we scanned the genome excluding the region of the first marker ( window size = 30 cM ) and tested if there was a significant interaction effect between genotype and cell type and whether this new interaction effect was classified in a different cell type category ( see above Classification of eQTLs ) , which would indicate a swapping eQTL . This means , for each transcript , a two-marker full model mapping was applied using the following model:where yij is the gene's log intensity at the ith microarray ( i = 1 , …n ) and jth cell type; CTj is the jth cell type effect; Q*i and ( Q*×CT ) ij are the main genotype effect at first marker and interaction effect between cell type and the genotype effect at this marker , where the first marker is defined as the marker with maximal interaction effect from previous one-marker full model mapping; Qi is the genotype effect of the second marker; ( Q×CT ) ij is the interaction effect between the ith genotype and jth cell type , Qi*Qi is the epistasis effect and eij is the residual error . All raw data were deposited in the NCBI Gene Expression Omnibus ( GEO , http://www . ncbi . nlm . nih . gov/geo/ , accession number GSE18067 ) . All processed data were deposited in the GeneNetwork ( http://www . genenetwork . org ) [32] , [33] . | Blood cell development from multipotent hematopoietic stem cells to specialized blood cells is accompanied by drastic changes in gene expression for which the triggers remain mostly unknown . Genetical genomics is an approach linking natural genetic variation to gene expression variation , thereby allowing the identification of genomic loci containing gene expression modulators ( eQTLs ) . In this paper , we used a genetical genomics approach to analyze gene expression across four developmentally close blood cell types collected from a large number of genetically different but related mouse strains . We found that , while a significant number of eQTLs ( 365 ) had a consistent “static” regulatory effect on gene expression , an even larger number were found to be very sensitive to cell stage . As many as 1 , 283 eQTLs exhibited a “dynamic” behavior across cell types . By looking more closely at these dynamic eQTLs , we show that the sensitivity of eQTLs to cell stage is largely associated with gene expression changes in target genes . These results stress the importance of studying gene expression variation in well-defined cell populations . Only such studies will be able to reveal the important differences in gene regulation between different cell types . | [
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] | 2009 | Expression Quantitative Trait Loci Are Highly Sensitive to Cellular Differentiation State |
The Asian tiger mosquito , Aedes albopictus , is an important vector of dengue , chikungunya and Zika viruses and is a highly invasive and aggressive biter . Established populations of this species were first recognised in Australia in 2005 when they were discovered on islands in the Torres Strait , between mainland Australia and Papua New Guinea . A control program was implemented with the original goal of eliminating Ae . albopictus from the Torres Strait . We describe the evolution of management strategies that provide a template for Ae . albopictus control that can be adopted elsewhere . The control strategy implemented between 2005 and 2008 targeted larval habitats using source reduction , insect-growth regulator and pyrethroid insecticide to control larvae and adults in the containers . However , the infrequency of insecticide reapplication , the continual accumulation and replacement of containers , and imminent re-introduction of mosquitoes through people’s movement from elsewhere compromised the program . Consequently , in 2009 the objective of the program changed from elimination to quarantine , with the goal of preventing Ae albopictus from infesting Thursday and Horn islands , which are the transport hubs connecting the Torres Strait to mainland Australia . However , larval control strategies did not prevent the species establishing on these islands in 2010 . Thereafter , an additional strategy adopted by the quarantine program in early 2011 was harborage spraying , whereby the vegetated , well shaded resting sites of adult Ae . albopictus were treated with a residual pyrethroid insecticide . Inclusion of this additional measure led to a 97% decline in Ae . albopictus numbers within two years . In addition , the frequency of container treatment was increased to five weeks between treatments , compared to an average of 8 weeks that occurred in the earlier iterations of the program . By 2015 and 2016 , Ae . albopictus populations on the two islands were undetectable in 70–90% of surveys conducted . Importantly , a comprehensive surveillance network in selected strategic areas has not identified established populations of this species on the Australian mainland . The program has successfully reduced Ae . albopictus populations on Thursday Island and Horn Island to levels where it is undetectable in up to 90% of surveys , and has largely removed the risk of mainland establishment via that route . The vector management strategies adopted in the later years of the program have been demonstrably successful and provide a practical management framework for dengue , chikungunya or Zika virus outbreaks vectored by Ae . albopictus . As of June 2016 , Ae . albopictus had not established on the Australian mainland and this program has likely contributed significantly to this outcome .
The Asian tiger mosquito , Aedes albopictus , is a major public health concern . It is a vector of dengue , chikungunya and Zika viruses , and a potential vector of a wide range of other arboviruses [1–6] . Furthermore , Ae . albopictus is also considered one of the most significant nuisance mosquito species due to its high relative abundance and its aggressive day-biting behavior in peridomestic locations including backyards , leisure parks and gardens [7] . The combination of biting nuisance and vector status of Ae . albopictus underscores the importance of control of this species [8 , 9] . An unprecedented global expansion of Ae . albopictus has occurred in the last four decades [10–12] . Although originally largely confined to the forests of south-east Asia , this highly invasive mosquito has readily adapted to diverse environmental conditions in urban and rural areas of both tropical and temperate regions [6 , 12] . The movement of Ae . albopictus internationally has been mostly facilitated by the trade in used tires and other goods that are infested with eggs or larvae [13 , 14] . Once introduced , Ae . albopictus has spread rapidly to occupy large areas of almost every region or country in which it had become established , including Florida [15] , New Jersey [9] and Texas [16] in the USA , Spain [17] , Italy [18] , Cameroon [19] , Papua New Guinea [20 , 21] and many others . In Australia , Ae . albopictus was first detected on Masig ( Yorke ) Island in the Torres Strait in April 2005 [22] . The Torres Strait is a section of the eastern Arafura Sea approximately 150 km wide that separates the northernmost Australian mainland from the Western Province of Papua New Guinea ( PNG ) ( Fig 1 ) . It has at least 100 small islands , of which 17 are inhabited . A more detailed description of the islands is given by Ritchie et al . [22] . After the initial discovery of Ae . albopictus , a delimiting survey detected the species on nine other inhabited islands of the Torres Strait [22] . It was immediately recognised that the appearance of Ae . albopictus in the Torres Strait could lead to its subsequent establishment on the Australian mainland . A number of predictive models and expert reviews suggested that this mosquito could establish and survive in many populated regions of Australia , including those where Aedes aegypti does not occur [23 , 24] , and this would extend the risk of dengue , chikungunya and Zika transmission to these areas . Consequently , a control program funded by the Australian Government Department of Health was established in late 2005 to eliminate Ae . albopictus populations in the Torres Strait , and therefore reduce the risk of the mosquito being introduced to the mainland . The current paper describes the evolution of control strategies implemented during the 11 years that the program has been in operation . We discuss the challenges and limitations faced by the program , before outlining how it evolved into a very successful campaign , which has so far contained the infestation to the Torres Strait , protected that region’s major population centre , and mitigated the likelihood of Ae . albopictus colonizing the Australian mainland from that source .
The control program , under the name Aedes albopictus Eradication Program ( AAEP ) , was launched in late 2005 with the goal of eliminating Ae . albopictus from the Torres Strait [25] . A Technical Advisory Group of experts was established to regularly review efficacy of the program and make recommendations on its strategic direction . The program consisted of distinct surveillance and control components , and comprised a team of nine field staff based in Cairns , in north Queensland on the Australian mainland ( Fig 1 ) . The team travelled by air for two weeks of field work at a time , during which 2–4 islands were targeted . After the initial Ae . albopictus delimitation surveys across the Torres Strait in May 2005 [22] , the islands were surveyed again in 2006 soon after the AAEP commenced , and at least once a year until September 2008 . Surveys were also conducted on the islands of Masig , Poruma , Warraber , Erub and Mer in early 2009 . Generally , frequency of control and surveillance visits was variable among islands , ranging between one and four per year including the dry season , depending on Ae . albopictus apparent densities and the size of the community . On the islands , the team conducted source reduction , whereby any containers that could hold water and potentially support larval development were removed , destroyed , placed under cover , or treated with pellets or briquettes of the insect growth regulator s-methoprene . The s-methoprene was applied to smaller containers as pellets ( 40 g/kg a . i . ; ProLink Pellets Mosquito Growth Regulator , Wellmark International , USA ) , at a rate of 1 pellet/L of estimated container volume . Larger containers , such as rainwater tanks and wells , were treated with ProLink XR Briquets ( 18 g/kg a . i . ) applied at 1 briquet/5000 L water . Containers that could not be removed had their interior surfaces also sprayed with the residual pyrethroid , bifenthrin ( Bistar 80SC , 80g/litre a . i . , FMC Pty Ltd , Murarrie ) to kill adults that come in contact with them [26] . Samples of larvae were collected from infested containers for species identification . Larvae were morphologically identified initially using the taxonomic keys of Rueda [27] . Due to overlapping morphology between larvae of Ae . albopictus and the endemic species , Ae . scutellaris , larvae suspected to be Ae . albopictus were submitted for identification using molecular methods [28 , 29] . As part of the monitoring of mosquito populations on each island , the team also conducted human-bait sweep-net sampling for adult Ae . albopictus at potential harborage sites in the vicinity of residential properties and adjacent vegetation fringes . This is the quickest way to detect the presence of Ae . albopictus , and had been used since the delimiting surveys of 2005 [22] . Collectors with long-sleeved shirts and long trousers worked in pairs and spent five minutes at each site during daytime , collecting all mosquitoes flying around each person without allowing the mosquitoes to land or probe . Mosquito density was expressed as number of mosquitoes per collector per site [30] . An education and awareness campaign was conducted by the vector control field staff with support from the local Environmental Health Worker ( EHW ) stationed on each island . The EHW also facilitated yard access in the community . Residents were encouraged to routinely dispose of discarded water-holding containers , empty the non-essential water from domestic and peri-domestic temporary containers , or maintain mosquito-proof screens on permanent containers with essential water ( e . g . rainwater tanks ) around their homes . The campaign was supplemented with messages on local radio and in newspapers , as well as in schools . By mid-2008 , Ae . albopictus populations still persisted on all infested islands because the elimination plan had become untenable due to logistical challenges associated with the need to repeat treatments on a more frequent basis and to the probable re-invasion of mosquitoes via the island network and traffic from PNG [28] . It was decided to change strategy , and focus predominantly on protecting the two inner islands of Thursday Island ( population 3 , 100 with 650 properties ) and Horn Island ( population 700 with 170 properties ) . These are the major population , administrative and transport centres of the Torres Strait islands , and are the origin of almost all of the passenger and freight movements to the mainland . For that reason , they are considered to form the most likely regional origin for any mainland Ae . albopictus invasion . Every year at least 460 vessels , mostly carrying cargo , sail from these islands to the mainland , especially to Cairns and Seisia . Establishment of Aedes albopictus on these destinations is highly likely once introduced , because both locations have environments which already support high populations of container-inhabiting mosquitoes such as Ae . aegypti and Ae . notoscriptus . In the Seisia and nearby communities , Ae . scutellaris is also widespread and it has generally similar ecological preferences to Ae . albopictus . Another pathway of incursion exists with at least 1 , 470 flights to Cairns from Horn Island annually . Both seaport and airport areas on Horn Island are bordered by bushland which is highly suitable as Aedes albopictus habitat , and potential larval habitats have repeatedly been identified in and around the port premises . Consequently , any significant populations of Ae . albopictus thriving in these areas would most likely lead to incursion and establishment on the Australian mainland . At the time of adoption of the new strategy , known as a cordon sanitaire , the two islands were still free from Ae . albopictus . The insecticide for container treatment was changed to the pyrethroid lambda-cyhalothrin ( Demand , 25g a . i . /L , Syngenta Crop Protection , North Ryde ) to provide greater residual activity , particularly for larval control [31 , 32] , although frequency of wet season retreatments was still variable , sometimes with more than eight weeks between treatments . Despite this re-focus of the AAEP , Ae . albopictus was discovered on Horn Island in March 2010 and on Thursday Island in December 2010 , during yard inspections . The cordon sanitaire then changed from a strategy of exclusion to one of population suppression on these two islands , in order to minimise the pressure for a potential incursion to the mainland . Starting from January 2011 , the vector suppression efforts on Thursday Island and Horn Island , which had relied mainly on larval control , were supplemented with harborage spraying , targeting adults [30] . A limited field study on a backyard in China [33] had demonstrated that application of lambda-cyhalothrin to peri-domestic vegetation significantly reduced Ae . albopictus numbers for several weeks . Harborage spraying on Thursday Island and Horn Island involved the application of lambda-cyhalothrin to well-shaded vegetation below 2m height and leaf litter on the ground in locations identified as actual or potential resting sites for adult Ae . albopictus . Treatments were confined to backyard bushes and ~3-5m swath of fringing vegetation adjacent to residential and commercial properties . In general , the total treated area was less than 0 . 02% and 0 . 5% of the vegetated land area of Horn Island and Thursday Island respectively , minimising the impact on non-target fauna . This became a major additional component of the operations from that point forward . Between 2011–2012 , harborage sprays were applied using a backpack mist-blower ( Stihl SR420 ) , but in 2013–2014 a tractor-mounted 200 L tank was used with a 50m long hose connected to a handheld lance fitted with a cone nozzle . From January 2015 , the tractor-mounted tank was replaced by a more convenient high-pressure truck-mounted spray unit ( Fig 2 ) ( QuikSpray , QuikCorp Pty Ltd , Australia ) with a handheld lance . The spray unit had been modified to replace its spray-gun with a longer lance and valve component from a pneumatic sprayer ( B&G Equipment Company , Jackson , GA ) for better direction of spray onto and under targeted foliage . In all cases , insecticide was diluted according to the label at 160 ml per 10 litres of water and applied almost to the point of run-off . As a method to further protect port areas , lethal tire piles , consisting of 7–10 insecticide-treated car tires , were placed strategically ~20–50 m apart around seaport storage areas and airport buildings with the aim of attracting and killing gravid mosquitoes . Disused car tires are known to be attractive to container-inhabiting mosquito species [13 , 32] . The tires were filled with water , treated with s-methoprene pellets , and sprayed internally with lambda-cyhalothrin ( Demand 25g a . i . /L ) . Treatments were repeated every 5–6 weeks in the wet season . Property inspection , source reduction , container treatment , and larval surveillance were undertaken concurrently across all commercial and private residences on Thursday Island and Horn Island . For each inspection cycle , larval densities were expressed as number of positive containers per 100 houses ( Breteau Index ) for Ae . albopictus and Ae . aegypti . The interval between yard inspections ( and reapplication of lambda-cyhalothrin to containers ) was set at five weeks during wet seasons , with effect from January 2011 . Completion of surveys sometimes overlapped between calendar months , and for graphing and analytical purposes , the data were allocated to the month that had more sampling days within a survey cycle . For adult surveillance , the teams conducted sweep-net sampling on 150 selected potential harborage sites on Thursday Island and 80 sites on Horn Island . This represented a doubling of the previous number of sites starting January 2011 to ensure comprehensive coverage within and around the community areas following the first detection of Ae . albopictus on Thursday Island in December 2010 . Completion of an operational cycle ( control and surveillance across the two islands ) during the wet season took 2–3 weeks . The following cycle would begin 2–3 weeks later . Consequently , each island was treated at five-week intervals . One dry season cycle was also conducted for 2 weeks between July and November , and primarily focused on yard inspections and treatment or disposal of potential , as well as perennial , larval habitats such as flower-pots , vases and rainwater tanks . Water-storage tanks and rainwater tanks were generally treated with s-methoprene briquettes if they were not effectively screened , but in 2015 and 2016 there was more focus on repairing or destroying the tanks where possible . Harbourage treatment was generally not conducted during the dry season because adult mosquitoes were undetectable at this time , and there are operational challenges due to the frequent strong winds typically experienced on the islands during that season , which may contribute to excessive spray drift . The effectiveness of the control operations was assessed by comparing mosquito populations on Thursday Island and Horn Island with those on Hammond Island , an inner island where Ae . albopictus was known to occur and for which control had never been conducted . Hammond Island is just 1 km from Thursday Island , and connected by a local ferry service and frequent local boat movements . It has a similar terrain and ecology to Thursday Island . Sweep-net surveys on Hammond Island were conducted on 10–15 selected sites with suitable potential habitat for Ae . albopictus on at least five occasions to coincide with wet season surveys on Thursday Island and Horn Island between 2012 and 2015 . To ensure that Ae . albopictus had not established on the north Queensland mainland , a long-term monitoring program was established in the Northern Peninsula Area ( NPA ) starting in January 2011 . It consisted of 30 sticky ovitraps [34] strategically located throughout the communities of Seisia , New Mapoon , Bamaga , Umagico and Injinoo ( Fig 1 ) . The traps were checked and serviced weekly by a resident health technician in 2011 and 2012 before logistical issues with transport and time constraints forced discontinuation of the weekly trapping program . In addition , vector control officers from the Cairns team performed yard inspections and sweep-net surveys in the NPA communities over a two-week period once a year between February and May from 2011 to 2016 . On every occasion , a total of 700 properties was inspected and sweep-net sampling was conducted on 110 selected sites across the five communities . Other Ae . albopictus monitoring activities on the mainland of Australia occurred in the high-risk Ae . aegypti-infested tropical towns of Cairns and Townsville . These are the cities that receive much of the air and sea traffic that originates in the Torres Strait and parts of southeast Asia where Ae . albopictus is widespread . The monitoring involved up to180 mosquito traps in selected residential and industrial areas being checked weekly for more than seven years . The traps included sticky ovitraps assembled from locally-acquired materials or Gravid Aedes traps ( GAT ) [35] and Biogents Sentinel ( BGS ) traps ( Biogents AG , Germany ) . Additional BGS traps and sentinel tire traps for larval sampling were deployed at the Cairns port areas where vessels from the Torres Strait moored . These have been monitored weekly by biosecurity personnel for at least the last 15 years . Furthermore , ad hoc surveys were conducted each year as part of dengue control interventions in Cairns , Townsville and surrounding areas .
The number of outer islands found to be infested with Ae . albopictus rose from 10 in 2005 to 13 in 2006 , with detections on Badu , Boigu and Moa islands . Periodic sweep-net surveys conducted up to 2009 revealed spatial and temporal variability of Ae . albopictus populations among the islands , with the highest adult densities recorded on Masig , Warraber , Mabuiag , Erub and Ugar ( Fig 1 ) . Larvae of Ae . albopictus were detected through yard inspections on all 13 outer islands where adults had been detected . There were no detections of Ae . albopictus larvae or adults on Saibai Island and Hammond Island during this period . Overall , the control efforts were not able to provide sustained suppression of Ae . albopictus on the infested islands . Furthermore , genetic studies conducted on populations from the Torres Strait , Papua New Guinea and Indonesia suggested that if the species was eliminated , it would soon be re-introduced from an infested location [28] . Before Ae . albopictus was detected on Horn Island and Thursday Island , survey results in 2009 and 2010 showed that the islands had pre-existing populations of other container-inhabiting species , including Ae . aegypti , Ae . scutellaris and Culex quinquefaciatus . Thursday Island had high densities of Ae . aegypti ( BI ≥ 55 in March 2010 ) and had experienced dengue outbreaks due to this species previously [36 , 37] . During the first wet season after initial detections of Ae . albopictus infestations on TI and HI , the populations of this species increased very rapidly ( Fig 3–Fig 6 ) . Densities on both islands were highest in January-March 2011 ( Fig 3 and Fig 5 ) , with BI’s up to 21 and adult densities up to 0 . 9 per collector per sampling site ( Fig 4 and Fig 6 ) . When control efforts were intensified , starting in 2011 with the adoption of the harborage spraying strategy as an additional control tool , the BI’s declined by more than 80% in the 2012 wet season and by 90% in the 2013 wet season . With sustained suppression effort , only one positive container was found on TI and two on HI throughout the 2015 and 2016 wet seasons in which there had been 10 cycles of island-wide house to house yard inspections . Comparison of mean number of Ae . albopictus-positive containers detected on Thursday Island during peak wet-season conditions ( January-April ) , showed statistically significant differences between year 2011 and each of the years 2012–2016 ( Independent T-tests; p<0 . 05 , df = 7 ) . The vector control team generally identified and treated at least 3 , 000 potential and actual larval habitats during each cycle of yard inspections across the two islands . Containers encountered included buckets , tires , wheel-barrows , watering cans , pot-plant bases , bird-baths , bowls , pots , portable cooler boxes , ice-cream containers , take-away food containers , plastic sheets , tarpaulins , buckets of ornamental plant cuttings , building materials , lawn-mower catchers , drums , drain sumps , fence posts , disused household appliances such as washing machines and fridges , boats and discarded car bodies . Natural larval habitats , such as coconut shells , bromeliads and palm fronds were also recorded . General observations indicated no consistent reduction in the number of containers recorded during yard inspections . In many cases , larger items , such as boats , car bodies and disused appliances were always found at the same places as before , and items that often reappeared after removal included palm fronds , buckets , discarded take-away food containers and items of household rubbish . In 2015 , most of the car bodies and some of the disused appliances were removed from the community through the efforts of the Torres Shire Council . Interestingly , the impact of the control program on containers positive for Ae . aegypti ( Fig 7 ) was not as appreciable as it was on Ae . albopictus , most likely because none of the control strategies specifically targeted the adult resting sites of Ae . aegypti , and the surviving adults potentially utilised cryptic larval habitats . Occasional surveys over several years on nearby Hammond Island showed that Ae . albopictus was well-established by February 2012 , and would have most likely invaded the island at some point between 2009 and 2011 . Densities of Ae . albopictus averaged 5–8 adults per collector per site in five-minute sweep-net collections on each survey conducted during the wet season between 2012 and 2015 ( Fig 8 ) . In contrast , densities on TI and HI in the same months were less than 0 . 1 per collector per inspected site . This difference demonstrated the considerable impact of the suppression program on TI and HI . There was no detection of Ae . aegypti on Hammond Island . The mainland surveys in the NPA between 2011 and 2016 detected a variety of species which have been recorded from the area previously , including Ae . aegypti , Ae . notoscriptus , Ae . scutellaris , Ae . tremulus , Ae . vigilax , Cx . quinquefaciatus , Cx . annulirostris , Verrallina spp . and Tripteroides spp . Previously , there had been a single detection of Ae . albopictus in the NPA , when larvae were found in a small container in the New Mapoon community in 2009 during yard inspections conducted in response to a local dengue importation [38] . The container was removed , together with other potential larval habitats during the dengue response . This was an isolated incident and there have been no further detections on the NPA .
One of the key outcomes of the AAEP is that , although Ae albopictus has been widely established in the Torres Strait for over 10 years , it does not appear to have spread from there and , as of June 2016 , no established populations of Ae . albopictus have been discovered anywhere on the mainland of Australia . The Torres Strait to mainland route remains a major threat for Ae . albopictus importation , given that containers or equipment that have spent time outdoors exposed to rain , could be carrying desiccation-tolerant eggs [13 , 39] , and can then be transported to the mainland as returning construction or maintenance equipment or as personal effects . However , mosquito suppression at the main transit hubs of TI and HI appears to have been an effective strategy in reducing this risk . The cordon sanitaire strategy is an integrated approach composed of harborage spraying , source reduction , insecticide treatment of containers , lethal tire piles , mosquito population monitoring and public awareness campaigns supported by local authorities and local media . The consistently low densities of Ae . albopictus on TI and HI recorded from March 2011 onwards have demonstrated that harborage spraying with lambda-cyhalothrin appears to be the most important component of the intervention program . Smaller-scale field studies in backyard scenarios elsewhere have also shown considerable reductions in Ae . albopictus numbers after application of residual pyrethroids to vegetation [33 , 40 , 41] . Other studies have also reported that this method is even more efficacious when coupled with intensive source reduction . For example , in the Caribbean , a control program on Grand Cayman Island managed to effectively suppress Ae . albopictus densities between 1997–1999 , restricting the BI to below 0 . 65 in George Town , when control methods included both house-to-house source reduction and residual application of lambda-cyhalothrin to nearby vegetation and walls [42] . However , when strategies were changed due to financial constraints in 1999 to concentrate primarily on larval control , the BI rose more than ten times to 6 . 9 within the following two years , and the infested area more than doubled in the same period . Considerable effort in the Torres Strait was made to empower the local communities to eliminate containers through public announcements , communication via the media and with direct communication with householders . However , the continued presence of many established and new potential mosquito-containers on properties showed that the public was possibly overwhelmed by the amount of effort required in keeping the yards free of water-holding containers , especially in the wet season when rain readily fills any exposed containers . In parts of New Jersey , public education was similarly found to be insufficient in motivating residents to significantly reduce backyard mosquito-larval habitats [43] . However , public education in the Torres Strait was still partly successful , because the majority of residents co-operated with the AAEP control teams and allowed them access to their properties . While access restrictions ( residents not home during working hours , vacant and locked properties or access denial from residents ) can sometimes make it difficult to inspect and treat potential habitats [16 , 44] , this was not a major issue on Thursday Island and Horn Island because the AAEP personnel had excellent advocacy support from local government leaders . The program also performed under a well-publicised legislative arrangement in which control personnel were authorised to enter yards , even if residents were not home . However , the communities were aware of the benefits of such a program , so there were less than 1% refusals , and thus , legislative powers rarely had to be enforced . Although the precipitous decline of Ae . albopictus on TI and HI can be attributed to the integrated control strategies of the AAEP , it is important to acknowledge that natural ecological factors contributed to that downward pressure , at least seasonally . The Torres Strait experiences low rainfall and humidity between June and November ( Fig 3 ) , and Ae . albopictus populations can fall to relatively very low levels . That respite during the dry season has formed a key part of the overall strategy and kept annual control costs down . However , in the absence of any control program , Ae . albopictus can recover quickly during the wet season and can reach very high densities ( as evidenced by mosquito collections on Hammond Island ( Fig 8 ) and the other outer islands ) . While the results demonstrate the success of the Ae . albopictus suppression program on TI and HI , they also indicate that the threat of reinvasion from other infested islands remains high . There is still a risk that Ae . albopictus may be introduced via another mainland port from other international ports . For instance , within the last five years this species has been intercepted at international airports in Perth , Melbourne and Darwin , as well as seaports in Townsville , Darwin , Perth , Melbourne , Cairns and Brisbane . The interceptions involved adults caught in surveillance traps at the ports and sometimes larvae , pupae or adults detected on vessels or cargo arriving from places like Indonesia and PNG [45 , 46] . This is not surprising , as there had been several similar incidents at these and other ports well before Ae . albopictus infested the Torres Strait islands [46–48] . In response to an interception at the port area , it is standard practice for emergency mosquito control and surveillance operations to be conducted immediately within 1 km of the port . All responses have been successful so far , with no evidence of establishment after treatment [22 , 25] . An importation into Melbourne in 2012 was particularly concerning as it occurred via a shipment of Dracaena spp . ( lucky bamboo ) to a nursery [25] . Fortunately , the infestation was confined to a quarantine-approved premise and there was no evidence that it had breached this containment . As of June 2016 there was no evidence to suggest Ae . albopictus has become established on the Australian mainland . Although the original objective of the AAEP was the elimination of Ae . albopictus from all of the Torres Strait islands , for a number of reasons , this became untenable given the limited funding available , which limited the human resources available and the amount that could be spent on logistics ( airfares to and between the islands , accommodation for the project workers , etc ) , and resulted in a low frequency of surveillance and control visits to each island . Working in the outer islands presented further logistical problems , such as the lack of accommodation , and the costs and delays in transporting insecticides and equipment by sea . Furthermore , population genetics studies [28] indicated that there was a high potential for reinvasion due to sea traffic between islands , and also from PNG and the Indonesian archipelago . Thus , the implementation of a cordon sanitaire on the two inner islands offered the most effective utilisation of limited resources . The limited impact in suppressing the populations on the outer islands in the first three years of the program appears to have been the result of using larval control as the primary control strategy , and insufficient frequency of insecticide reapplications . Although Ae . albopictus is strongly associated with peri-domestic environments [49] , house-to-house source reduction and container applications of insecticides have limited impact as a sole intervention method , due to the ubiquitous and often cryptic larval habitats of this species [50] and , in the case of the Torres islands , the high rate of container replacement . In the Torres Strait , high-set water-storage tanks and rainwater tanks , as well as subterranean sites , are difficult to access and treat , whilst refuse , such as take-away containers , is continuously being produced and replaced , providing new untreated habitats for Aedes spp . In the successful Ae . aegypti elimination programs in the Northern Territory of Australia , it was concluded that at least a 6-week reapplication of residual insecticides to containers was necessary , in light of the effective length of insecticide activity and the continual production of new containers [26 , 32] . Therefore , the adoption of lambda-cyhalothrin as the primary insecticide for application in containers was more effective , as the insecticide would last at least 6 weeks and possibly up to 9 weeks [32] , compared to bifenthrin , which remains active for only approximately 2 weeks as a larvicide [31] . There were a number of methods employed for eliminating Ae . aegypti during the incursions into the Northern Territory , including application of s-methoprene , and residual pyrethroids to containers , which targeted both larvae and ovipositing adults , household bleach to kill eggs , and treatment of adult harborage areas adjacent to houses . It was concluded that the use of one method alone would not have led to success [26] . The suppression strategies of the AAEP did not have a pronounced effect on Ae aegypti populations on TI . This is probably because harborage spraying targets the vegetated resting sites of Ae . albopictus , and is not as relevant to the control of Ae . aegypti with its more endophilic , domestic resting behaviours ( i . e . patio furniture , garden sheds and especially the interior of houses [51] . Furthermore , cryptic larval habitats utilised by Ae . aegypti , such as un-located subterranean disused septic tanks and wells , may have enabled the greater survival of this species during the harsh dry season , before proliferation during the wet season . The evolution of the control programme from one of elimination to that of a cordon sanitaire approach , coupled with introduction of the harborage spraying component , has resulted in the successful suppression of Ae . albopictus populations on the primary transport hubs of the Torres Strait . This has reduced the risk of this species being introduced onto the Australian mainland via this route . This integrated strategy provides a template that can be followed for control of this species on the Australian mainland , should it be intercepted or become established . Indeed , recent interceptions of this species in Cairns , from origins other than the Torres Strait , have utilised strategies implemented during the AAEP . This strategy also provides a practical solution for effective management of dengue , chikungunya or Zika outbreaks in areas where Ae . albopictus is the primary vector . Indeed , the strategy was used in March 2016 to control a dengue outbreak on Erub and Badu Islands , leading to a rapid decline in cases and cessation of transmission [52] . The islands had dense populations of Ae . albopictus with no detection of Ae . aegypti , and this further demonstrated the potential public health risk due to Ae . albopictus if the species were to spread to southern parts of Australia where dengue vectors do not currently exist . | Aedes albopictus is a disease vector and biting nuisance of major public health concern . Established populations of Ae . albopictus were first recognised in Australia in 2005 after they were discovered on islands in the Torres Strait . Consequently , a control program was established in the same year to eliminate Ae . albopictus populations in the Torres Strait in order to reduce the risk of disease , as well as to prevent the mosquito from spreading to the mainland of Australia . In 2009 , the goal of the program changed from elimination to quarantine ( cordon sanitaire ) focusing mainly on the inner islands of Thursday Island and Horn Island , which are the major population , administrative and transport centres linking the Torres Strait region to the Australian mainland . The cordon sanitaire strategy involved an integrated approach composed of harborage spraying , source reduction , insecticide treatment of containers , lethal tire piles , mosquito population monitoring and public awareness campaigns . Strategic improvements in management techniques led to a 97% decline in Ae . albopictus numbers on the two islands between 2011 and 2012 . By 2015 , the program had successfully reduced Ae . albopictus populations on Thursday Island and Horn Island to levels where the species was frequently undetectable , and had largely removed the risk of mainland incursion via that route . In 2016 the improved management strategies were also adopted to successfully control a dengue outbreak in which Ae . albopictus was the implicated vector on two outer islands of the Torres Strait . | [
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] | 2017 | Holding back the tiger: Successful control program protects Australia from Aedes albopictus expansion |
Radiation-attenuated Plasmodium sporozoites ( RAS ) are the only vaccine shown to induce sterilizing protection against malaria in both humans and rodents . Importantly , these “whole-parasite” vaccines are currently under evaluation in human clinical trials . Studies with inbred mice reveal that RAS-induced CD8 T cells targeting liver-stage parasites are critical for protection . However , the paucity of defined T cell epitopes for these parasites has precluded precise understanding of the specific characteristics of RAS-induced protective CD8 T cell responses . Thus , it is not known whether quantitative or qualitative differences in RAS-induced CD8 T cell responses underlie the relative resistance or susceptibility of immune inbred mice to sporozoite challenge . Moreover , whether extraordinarily large CD8 T cell responses are generated and required for protection following RAS immunization , as has been described for CD8 T cell responses following single-antigen subunit vaccination , remains unknown . Here , we used surrogate T cell activation markers to identify and track whole-parasite , RAS-vaccine-induced effector and memory CD8 T cell responses . Our data show that the differential susceptibility of RAS-immune inbred mouse strains to Plasmodium berghei or P . yoelii sporozoite challenge does not result from host- or parasite-specific decreases in the CD8 T cell response . Moreover , the surrogate activation marker approach allowed us for the first time to evaluate CD8 T cell responses and protective immunity following RAS-immunization in outbred hosts . Importantly , we show that compared to a protective subunit vaccine that elicits a CD8 T cell response to a single epitope , diversifying the targeted antigens through whole-parasite RAS immunization only minimally , if at all , reduced the numerical requirements for memory CD8 T cell-mediated protection . Thus , our studies reveal that extremely high frequencies of RAS-induced memory CD8 T cells are required , but may not suffice , for sterilizing anti-Plasmodial immunity . These data provide new insights into protective CD8 T cell responses elicited by RAS-immunization in genetically diverse hosts , information with relevance to developing attenuated whole-parasite vaccines .
Plasmodium infections are a global health crisis resulting in ∼300 million cases of malaria each year and ∼1 million deaths [1] , [2] , [3] , [4] , [5] . At present , there are no effective licensed anti-malarial vaccines . Most vaccines under clinical evaluation are only partially protective and , for unknown reasons , immunity rapidly wanes [6] . Thus , development of an effective malaria vaccine that provides long-term protection remains an important goal to improve global health . Immunization with radiation-attenuated sporozoites ( RAS ) is the only documented means to induce sterilizing protection in both humans [7] , [8] and rodents [9] and , importantly this approach is under evaluation in clinical trials [10] . Studies with inbred mouse strains reveal a prominent and often essential role for CD8 T cells in RAS-induced protection [11] . However , RAS-immune inbred mice also exhibit substantial differences in resistance to challenge with Plasmodium berghei ( Pb ) or P . yoelii ( Py ) sporozoites , two major models of experimental malaria that are thought to differ in virulence . Despite decades of research , the precise characteristics of protective memory CD8 T responses following RAS-vaccination remain poorly understood . One reason for this relates to the limited number of defined CD8 T cell epitopes derived from rodent species of Plasmodia . BALB/c mice mount H-2Kd-restricted CD8 T cell responses against single defined circumsporozoite ( CS ) protein-derived epitopes from either Pb or Py and these epitopes can be targets of protective CD8 T cells [12] , [13] . However , despite evidence that non-CS antigens can also be targets of protective immunity [14] , [15] , there are few additional Plasmodium-specific epitopes identified from antigens other than CS in BALB/c mice , and no identified protective epitopes in H-2b C57BL/6 ( B6 ) mice . Thus , the paucity of epitope information for these parasites has contributed to our incomplete understanding of the specific quantitative and qualitative characteristics of RAS-induced CD8 T cell responses in inbred mice that are relatively easy ( BALB/c ) or difficult ( B6 ) to protect against Plasmodium sporozoite challenge [11] . Moreover , we recently showed that the threshold of memory CD8 T cell responses to the Pb-CS epitope ( monospecific responses ) required for sterilizing immunity against sporozoite challenge was extremely large [16] . Importantly , it is unknown whether a more diverse memory CD8 T cell response generated by whole parasite based RAS vaccination will decrease the threshold number of memory cells required for protection . This issue is of great relevance to translation of the attenuated whole parasite vaccines to humans . The identification and characterization of infection- or vaccination-induced , antigen-specific CD8 T cell populations has historically required defined antigenic peptide determinants with known MHC restriction . However , specific activation markers can be used to track effector , but not memory , CD8 T cell responses to viral vaccines in humans in the absence of defined antigenic determinants or known MHC-restriction [17] . We recently described an alternative surrogate actiation marker approach , relying on concurrent downregulation of surface CD8α and upregulation of CD11a ( α-chain of LFA-1 ) on effector and memory antigen-specific CD8 T cells responding to bacterial and viral-infections in mice [18] . Herein , we apply this surrogate marker approach to identify and longitudinally track the total CD8 T cell response following RAS-immunization in rodents . This surrogate marker approach allowed us for the first time to evaluate CD8 T cell responses and protective immunity to RAS-immunization in both inbred and outbred hosts . Collectively , our data show that despite broadening the number of antigenic targets through whole-parasite vaccination , extraordinarily large numbers of memory CD8 T cells are required , but not always sufficient , to protect the host against liver-stage Plasmodium infection . These data provide fundamentally new insight into protective CD8 T cell responses elicited by RAS-immunization in genetically diverse hosts , information with relevance to developing attenuated whole-parasite vaccines to protect humans .
Relative resistance after RAS-vaccination of both rodents and humans is commonly studied by sporozoite challenge 1–2 weeks following the last immunization [8] , [11] , [19] , [20] and thus evaluates immunity mediated by recently stimulated T cell populations . Herein , we wished to examine RAS-induced protection only after stable memory immune responses have been generated . Thus , we challenged RAS-vaccinated mice >60 days post-immunization , when numerically and phenotypically stable memory CD8 T cell populations are established following acute infections [21] . At this memory time point , a single Pb-RAS vaccination protected 100% of BALB/c mice , but failed to protect any B6 mice against homologous Pb sporozoite challenge , whereas one Py-RAS vaccination had minimal ( BALB/c , 10% ) or no ( B6 , 0% ) protective efficacy against homologous Py sporozoite challenge ( Figure 1A ) . These data demonstrate both mouse strain and Plasmodium species-dependent protection after single RAS-immunization of mice challenged at a bona fide memory time point . To examine the protective CD8 T cell response elicited by RAS-vaccination , we applied our recently described surrogate activation marker approach , based on downregulation of CD8α and upregulation of CD11a ( CD8αloCD11ahi ) [18] , to identify RAS-induced CD8 T cells . We chose to focus our initial analyses on peripheral blood ( PBL ) so that individual mice could be analyzed longitudinally . Importantly , long-term longitudinal analyses of naïve mice in our colony reveal that the circulating CD8αloCD11ahi T cell pool remains low ( 2–3% of all circulating CD8 T cells ) and stable for >250 days ( data not shown ) . For vaccinated mice , the fraction of CD8αloCD11ahi T cells in the PBL was determined prior to , and at various intervals after , immunization with 2×104 Pb-RAS in individual animals . We detected substantial increases in the frequency of CD8αloCD11ahi T cells in the blood of vaccinated mice at 7 and 61 days ( effector and memory time points , respectively ) post-immunization ( Figure 1B , left column as an example ) . Interestingly , only 16±3% of Pb-RAS-induced effector ( day 7 ) CD8 T cells in BALB/c mice are specific for the known H-2Kd-restricted CS252–260 epitope and , importantly , all of these defined antigen-specific CD8 T cells are found in the CD8αloCD11ahi population ( Figure 1B , right columns ) . Moreover , the fraction of CS252–260-specific CD8 T cells among the CD8αloCD11ahi population consistently remains ∼16% throughout the memory phase of the response ( day 61 ) ( Figure 1B , right columns ) . Based on previous studies showing that T cell responses against diverse epitopes are coordinately regulated [22] , these data further support that the surrogate activation marker approach identifies true RAS-induced , Plasmodium-specific CD8 T cells . Thus , ∼85% of Pb-RAS-induced CD8 T cells in BALB/c mice are reactive against epitopes from undefined antigens . Similar results were obtained for the CS280–288 epitope after single immunization of BALB/c mice with Py-RAS , although the fraction of CS280–288-specific memory CD8 T cells in the circulating CD8αloCD11ahi compartment was only ∼7% ( data not shown ) . To further demonstrate specificity of the surrogate activation marker approach , we determined that the increase over baseline ( PBL analyzed before immunization ) in the fraction of circulating CD8αloCD11ahi T cells 5–7 days after immunization depended on the immunizing dose of Pb-RAS in both BALB/c and B6 mice ( Figure 1C and E ) and was not observed in mice immunized with an equivalent suspension of irradiated salivary gland homogenates from non-infected mosquitoes ( Figure 1D and F ) . Thus , the CD8αloCD11ahi T cell response is specific for Plasmodium-antigens and not mosquito salivary gland antigens . Moreover , CD8 T cell responses in the blood of RAS-immune B6 and BALB/c mice were representative of CD8αloCD11ahi responses in the spleen and liver , both in terms of frequency ( Figure 2A ) and total number ( Figure 2B ) . Finally , we addressed specificity at the memory stage by transferring sort purified CD8αhiCD11alo ( naïve ) or CD8αloCD11ahi ( memory ) cells from day 78 RAS-immune B6 mice ( CD45 . 2 ) into CD45 . 1 hosts . Only the population of transferred CD8αloCD11ahi T cells underwent secondary expansion after RAS-immunization of the recipient mice ( Figure S1 ) . Thus , the CD8αloCD11ahi phenotype cells present at memory time points after RAS-immunization are Plasmodium-specific ( Figure S1 ) . As a composite , these data demonstrate that the changes in frequency of circulating CD8αloCD11ahi T cells in individual RAS-immunized mice reflects the distribution of parasite-specific effector and memory CD8 T cells in peripheral tissues and can be used to evaluate the total CD8 T cell response to RAS-immunization prior to sporozoite challenge . We next examined the magnitude and kinetics of total CD8 T cell responses in the PBL of BALB/c and B6 mice following Pb- or Py-RAS vaccination ( Figure 3A and B , respectively ) , using an immunizing dose of RAS ( 2×104 sporozoites ) that fell within the linear range of the CD8 T cell response in both inbred mouse strains ( Figure 1C , E ) . We observed substantial increases in the frequency of CD8αloCD11ahi T cells in the PBL of all groups , which peaked 6 days after RAS-immunization , followed by contraction and the formation of numerically stable primary ( 1° ) memory populations ( Figure 3A , B ) . Importantly , although B6 mice are more susceptible to sporozoite challenge following a single Pb- or Py-RAS immunization compared to BALB/c mice [11] ( Figure 1A ) , and CD8 T cells are necessary to mediate protection in Pb-RAS immune mice ( Figure 3C ) , Pb- or Py-RAS vaccination of B6 mice induced 1° effector and memory CD8 T cell responses that were ∼2-fold higher ( p<0 . 0001 ) than observed in BALB/c mice ( Figure 3A , B ) . Thus , our surrogate activation marker approach revealed that the susceptibility of single RAS-immunized B6 mice to homologous Pb or Py challenge is not due to a diminished total anti-Plasmodial CD8 T cell response . RAS remain infectious to host hepatocytes , but are unable to undergo differentiation into blood stage merozoites [23] , [24] . Interestingly , persistence ( up to 6 months ) of radiation-attenuated parasites was reported in the livers of RAS-vaccinated rats [25] and persistence of attenuated parasites has been hypothesized to underlie the long-term protective capacity of RAS-induced memory CD8 T cells [25] , [26] . To address this hypothesis , we treated BALB/c mice with 60 mg/kg primaquine on days 5 and 6 following Pb-RAS-vaccination to eliminate persisting parasites . In contrast to previous studies [25] , [26] , we found that primaquine treatment did not decrease protection against sporozoite challenge at a memory time point ( Figure 3E ) . Consistent with this result , primaquine treatment at these time points did not reduce RAS-specific circulating CD8 T cell frequencies ( Figure 3D , E ) . In parallel , we verified the efficacy of primaquine ( route , dose , schedule ) by treating naive BALB/c mice 24 and 48 hrs following challenge with 1000 infectious sporozoites . Primaquine treatment effectively stopped the development of blood stage infection in 100% ( 5/5 ) mice , whereas 5/5 vehicle-treated mice developed patent blood stage parasitemia . Thus , following the induction of CD8 T cell responses via Pb-RAS-vaccination of BALB/c mice , the persistence of attenuated parasites in the liver does not regulate the stability or protective capacity of the RAS-induced memory CD8 T cell populations . Short-interval ( every 2–3 weeks ) booster RAS-immunizations improve protection against sporozoite challenge of mice [11] , [27] , [28] although the impact on the Plasmodium-specific CD8 T cell compartment is unknown . Additionally , the impact of long-interval boosting , as generally employed in human vaccines , on RAS-induced protection at a secondary memory time point is unknown . Thus , we examined the effect of homologous RAS-boosting on bona fide memory CD8 T cell populations . Booster immunization at memory time points ( 60–80 days after initial priming ) with Pb-RAS in B6 mice or Py-RAS in BALB/c mice induced secondary expansion of CD8αloCD11ahi T cells ( Figure 4A , C ) . Surprisingly , the peak secondary response did not exceed the magnitude of the peak primary response to initial priming . Still , booster immunization resulted in a doubling of the secondary ( 2° ) memory CD8 T cell populations in both mouse strains ( Figure 4B , D ) . Importantly , we observed 100% protection in Pb-RAS-vaccinated B6 mice and Py-RAS-vaccinated BALB/c mice following sporozoite challenge at 2° memory time points after boosting ( days 168 and 154 , respectively ) ( Figure 4B , D ) , which remained wholly CD8 T cell-dependent ( Figure S2 ) . Of note , homologous Pb- or Py-RAS-boosting enriched the fraction of CS252–260- or CS280–288-specific CD8 T cells ( to ∼30% and 15% , respectively ) within the CD8αloCD11ahi compartment compared to single immunized BALB/c mice ( Figure 1B and Figure S3 ) . Importantly , these secondary CS252–260- or CS280–288-specific memory CD8 T cells are also found exclusively in the CD8αloCD11ahi compartment ( Figure S3 ) . Thus , homologous Pb-RAS or Py-RAS boosting of B6 or BALB/c mice , respectively , doubles the frequency of circulating RAS-specific 2° memory CD8 T cells and affords CD8 T cell-dependent sterilizing immunity against a stringent sporozoite challenge . Moreover , enrichment of the CS-specific responses in RAS-boosted BALB/c mice suggests that although ∼85–95% of the total initial CD8 T cell response targets antigens of undefined specificity , the CS-specific response in BALB/c mice dominates the recall response . These data for the first time reveal the effect of homologous RAS boosting on bona fide memory CD8 T cell responses , and further demonstrate that antigen-specific 2° memory CD8 T cell populations are also accurately identified using the CD8αloCD11ahi surrogate activation marker approach . Consistent with the results described above , homologous boosting of Py-RAS immune B6 mice also doubled ( on average ) the frequency of RAS-induced 2° memory CD8 T cells ( Figure 4E , F ) . However , these mice exhibited only modest ( 40% ) protection against sporozoite challenge at a 2° memory time point ( day 154 ) ( Figure 4F ) . Interestingly , a second booster immunization with Py-RAS resulted in a sustained increase in the frequency of CD8αloCD11ahi T cells , which now represented on average ∼40% of the CD8 T cell compartment of the PBL at day 215 ( Figure 4E , F ) . However , even this extreme commitment of Py-RAS-induced tertiary ( 3° ) memory CD8 T cells did not improve protection when these mice were challenged at a 3° memory time point ( day 215 ) ( Figure 4F ) . Thus , we could not achieve substantial levels ( >70–80% ) of protection against Py sporozoite challenge in B6 mice boosted every 60–70 days and challenged 60 days after the last boost . This contrasts sharply with reports that examine protective immunity following short interval boosting ( every 2–3 weeks ) followed by challenge ∼14 days after the last boost [11] , [29] . One clear difference between these two immunization regimens is the substantial role for CD4 T cells in protection after short-interval boost and challenge approaches in B6 mice [11] , [29] , whereas we could detect no role for CD4 T cells in protection against Pb sporozoite challenge of B6 mice , or against Py challenge of BALB/c mice in our long-interval prime-boost approach ( Figure S2 ) . These disparate results strongly suggest that the timing of RAS-immunization and sporozoite challenge significantly influences both the composition and protective capacity of the RAS-induced cellular response . Indeed , we are currently evaluating quantitative and qualitative characteristics of the total CD8 T cell response and protection following short-interval , prime-boost RAS vaccination and challenge , as well as evaluating surrogate activation marker approaches to specifically identify antigen-experienced CD4 T cells . We show that BALB/c and B6 mice fall on opposite ends of the spectrum regarding their ability to resist sporozoite challenges at memory time points following either Pb- or Py-RAS long-interval prime-boost vaccination . Indeed , many studies [14] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] employ BALB/c mice to evaluate whole-attenuated parasite vaccine-induced protective immunity , and it is unclear how these data model CD8 T cell responsiveness and protective immunity in outbred populations , such as humans , following RAS-vaccination . Thus , we next turned our attention toward analyses of the CD8 T cell response in outbred Swiss Webster mice . Due to the lack of information on MHC alleles and antigens in outbred populations , this analysis was only made possible through development of the CD8αloCD11ahi surrogate activation marker approach [18] . On the population level ( N = 30 mice ) , the kinetics and magnitude of Py-RAS-induced CD8 T cell responses of outbred mice mirrored those observed in inbred mice ( Figure 5A ) . However , and in striking contrast to the inbred mice , the initial CD8 T cell response following Py-RAS-vaccination in outbred mice was not uniform and varied widely , both in magnitude and day of the peak ( Figure 5C–F ) . Consistent with this , outbred mice also exhibited more variability in the magnitude of the 1° memory ( Figure 5G ) and 2° memory ( Figure 5H ) CD8 T cell response , compared to inbred mice . Similar to what was observed in both BALB/c and B6 mice singly vaccinated with Py-RAS , Swiss Webster mice challenged with Py sporozoites at a 1° memory time point ( day 79 ) were not efficiently protected ( Figure 5B ) . However , boosting Swiss Webster mice with Py-RAS resulted in a doubling ( on average ) of sporozoite-specific 2° memory CD8 T cells , and 80% of these mice were protected against a sporozoite challenge on day 154 ( Figure 5B ) . Thus , CD8αloCD11ahi surrogate markers can be used to identify and longitudinally track protective CD8 T cell responses in outbred mice following RAS prime-boost vaccination . In addition , these data show that despite the variability in magnitude of initial RAS-induced CD8 T cell response of outbred hosts , homologous boosting increases the secondary memory CD8 T cell population and protective immunity against sporozoite challenge . While protection of RAS-vaccinated mice using the long-interval ( >60 days ) , prime-boost scenario described above is CD8 T cell dependent ( Figure S2 ) , RAS-immunization also elicits a strong sporozoite-specific antibody response [29] , [38] . To determine whether differences in the Py-RAS-induced sporozoite-specific antibody response correlated with relative resistance or susceptibility to Py sporozoite challenge , we analyzed serum from individual BALB/c , B6 and Swiss Webster mice for sporozoite-specific IgG titers at each memory time-point . Importantly , an examination of anti-sporozoite titers at the secondary memory time point ( day 154 ) , where significant protection was achieved in BALB/c and Swiss Webster but not B6 mice ( Figures 4D and 5B , respectively ) , revealed no clear correlation between IgG titers and protection from sporozoite challenge ( Figure S4A ) . Moreover , antibody titer was not significantly different between 2° memory BALB/c mice ( 100% protected ) and 3° memory B6 mice ( 20% protected ) ( P = 0 . 0789 , Figure S4B ) , or between individual protected and non-protected B6 mice ( P = 0 . 4484 , Figure S4C ) . Thus , reduced anti-sporozoite IgG antibody titers do not appear to explain the enhanced susceptibility of RAS-vaccinated B6 mice to sporozoite challenge . We next examined potential qualitative differences in phenotype and specific functional attributes of protective and non-protective memory CD8 T cells . The nature of our longitudinal analyses precluded the collection of large quantities of blood from individual immunized mice . Thus , the small clinical sample limited our initial analyses to a key subset of markers that distinguish “central memory” ( TCM; CD62Lhi , CD27hi ) from “effector memory” ( TEM; CD62Llo , CD27lo ) CD8 T cell populations [39] , [40] , and a marker associated with memory CD8 T cell survival ( IL7 receptor α chain , CD127 ) [41] . At 60 days post-immunization most RAS-induced memory CD8 T cells in each mouse strain , vaccinated with either Pb- or Py-RAS , expressed a TEM phenotype ( CD62Llo , CD27lo , CD127lo , data not shown ) . However , to more directly address potential relationships between RAS-induced memory CD8 T cell phenotype and protection we evaluated expression of the same markers after Py-RAS boosting of BALB/c and Swiss Webster mice ( both protected ) and B6 mice ( not protected ) . The most striking difference between non-protective 2° memory CD8 T cells in B6 mice and protective memory CD8 T cells in BALB/c and Swiss Webster mice was the differential expression of CD62L ( Figure 6A ) . Forty percent ( on average ) of Py-RAS-induced 2° memory cells in B6 mice expressed the CD62Lhi TCM phenotype ( Figure 6A ) . In contrast , representation of the CD62Lhi TCM phenotype among Py-RAS-induced 2° memory cells in BALB/c and Swiss Webster mice was reduced 3-fold or 4-fold , respectively ( Figure 6A ) . A similar trend was observed for the CD27hi phenotype ( Figure 6B ) , while no correlation between CD127 expression and protective capacity was observed ( Figure 6C ) . Thus , non-protective 2° memory CD8 T cells in Py-RAS boosted B6 mice exhibit a more TCM phenotype , expressing significantly higher levels of CD62L and CD27 relative to protective 2° memory CD8 T cells in BALB/c or Swiss Webster mice ( Figure 6A , B ) . As a complimentary approach , we next performed a series of adoptive transfer studies in order to more clearly identify and directly compare RAS-induced 2° memory CD8 T cells in BALB/c and B6 mice . We transferred 8×104 Py-RAS-induced , CD8αloCD11ahi 1° memory ( d78 ) CD8 T cells into allelically disparate BALB/c and B6 recipients , which were subsequently immunized with Py-RAS to generate populations of endogenous 1° memory and allelically marked 2° memory parasite-specific CD8 T cells . One month after the booster immunization , we performed extensive phenotypic and functional analyses of the donor-derived , 2° memory CD8 T cells in each B6 and BALB/c recipient mouse . Surface expression of many markers , such as CD25 , CD69 , CD43glyco ( Figure 6D and Figure S5 ) were indistinguishable between these populations . In addition , we found no statistically significant differences in expression of integrins ( β1 , β2 , β7 , αM , αX , αE , α4 , α5 or α6 ) or inhibitory receptors ( PD-1 , LAG-3 , 2B4 , CD160 , KLRG-1 or CTLA-4 ) on RAS-induced , 2° memory CD8 T cells in B6 and BALB/c mice ( data not shown ) . However , in line with our initial observations , RAS-induced , non-protective 2° memory CD8 T cells in B6 mice exhibit a more TCM-like phenotype , relative to BALB/c mice , characterized by relatively higher proportions of CD27 and CD62L expressing cells ( Figure 6D and Figure S5 ) . We also observed significantly higher CD122 and CD127 expression on RAS-induced , 2° memory CD8 T cells in B6 mice ( Figure 6D and Figure S5 ) . Collectively , our phenotypic analyses support the notion that a TEM phenotype among RAS-induced , parasite-specific CD8 T cells strongly correlates with protection against liver stage Plasmodium infection . To examine specific functional attributes of RAS-induced memory CD8 T cells in BALB/c and B6 mice we relied on polyclonal TCR cross-linking to trigger the ex vivo induction of Granzyme B and inflammatory cytokine expression by allelically marked , 2° memory CD8 T cells . We found that similar fractions of BALB/c and B6 2° memory CD8 T cells expressed Granzyme B in response to dose-titrations of plate-bound anti-CD3ε ( Figure 6E ) . Moreover , we observed equivalent IFN-γ production ( % positive and MFI ) by RAS-induced CD8αloCD11ahi 2° memory CD8 T cells in BALB/c and B6 mice ( data not shown ) . Interestingly , however , a significantly higher fraction of B6 2° memory CD8 T cells co-expressed TNF-α and IFN-γ relative to BALB/c 2° memory CD8 T cells , the majority of which expressed IFN-γ alone ( Figure 6G ) . Collectively , these data show that a TEM phenotype , but neither Granzyme B nor polyfunctional cytokine expression , correlates with protective anti-Plasmodial liver stage immunity mediated by RAS-induced memory CD8 T cells . We previously reported that the numerical threshold for protection of BALB/c mice against Pb-sporozoite challenge mediated solely by memory CS252–260-specific CD8 T cells is exceedingly high ( >1% of PBL ( refs [16] , [42] ) , or >8% of CD8 T cells , Figure 7 ) . One explanation for the enormously high threshold for sterilizing protection in that scenario is that protective memory CD8 T cells recognize only a single antigenic determinant from P . berghei . On the other hand , RAS-vaccination has been shown to elicit protective CD8 T cells targeting non-CS antigens [14] , [15] and our data are consistent with the majority of RAS-induced CD8 T cells targeting non-CS antigens ( Figure 1B and Figure S3 ) . Thus , broadening the number of antigens ( i . e . additional parasite-derived proteins that may be more efficiently processed or presented compared to CS ) through whole parasite RAS-vaccination may lower the numerical requirements for protective immunity mediated by memory CD8 T cells . However , when we tabulated memory CD8 T cell responses in groups of RAS-vaccinated inbred and outbred mice that resisted sporozoite challenge , we identified similarly extreme numerical relationships between circulating memory CD8 T cell responses and protective anti-sporozoite immunity ( Table 1 ) . This is most evident for RAS-induced protective immunity against P . yoelii , which replicates faster in the liver [43] and exhibits a lower ID50 [32] compared to P . berghei , and thus , may better mimic the virulence of P . falciparum in humans . For example , anti-Py memory CD8 T cell responses representing ∼9% of CD8 T cells in BALB/c mice and ∼19% of CD8 T cells in Swiss Webster mice confer protection against a Py-sporozoite challenge , and responses exceeding 40% of CD8 T cells failed to efficiently protect B6 mice . Thus , the numerical requirements for sterilizing immunity following Plasmodium RAS-vaccination are extraordinarily high , regardless of whether the protective pool of memory CD8 T cells react to a single antigenic determinant after subunit vaccination , or whether the CD8 T cell response is directed against a broader set of antigenic determinants after whole-parasite immunization .
Although a critical protective role for CD8 T cells in RAS-immune mice was established more than 25 years ago , the characteristics of the protective CD8 T cell response remained essentially undefined due to the lack of defined Plasmodium epitopes . Here , we used surrogate activation markers to identify and longitudinally track RAS-induced CD8 T cell populations in the blood of individual hosts . This approach enabled us to describe specific quantitative and qualitative characteristics of memory CD8 T cell populations that mediate protection against sporozoite challenge . Moreover , the surrogate activation marker approach allowed us to monitor RAS vaccine-induced CD8 T cell responses in individuals within outbred populations of mice , without a priori knowledge of MHC alleles or parasite-specific antigenic determinants . These latter analyses revealed that , despite variability to the initial immunization , prime-boost RAS-vaccination effectively enhances parasite-specific memory CD8 T cell responses and affords sterilizing protective immunity among individuals of an outbred population . Collectively our studies show that independent of genetic background , extremely high frequencies of RAS-induced memory CD8 T cells are required , but may not always suffice for sterilizing anti-Plasmodial immunity , information directly relevant to ongoing efforts to translate attenuated whole-malaria parasite vaccines to humans . We previously reported that an extraordinarily large frequency of circulating CS-specific memory CD8 T cells generated by subunit vaccination is required to protect BALB/c mice against a stringent Pb sporozoite challenge [16] . In that study , memory CD8 T cell populations were generated such that they only targeted a single antigenic determinant derived from the parasite CS protein , CS252–260 . Herein we report the striking observation that diversifying the targets of the CD8 T cell response through whole-attenuated-parasite vaccination only minimally ( if at all ) reduces the numerical requirements for memory CD8 T cell-mediated protective immunity . For example , we show that following Pb-RAS vaccination of BALB/c mice ( the scenario in which protection is easiest to achieve ) resistance to sporozoite challenge at a memory time point is associated with ≥4% of the CD8 T cell compartment exhibiting the antigen-experienced phenotype ( CD8αloCD11ahi ) . Thus , the magnitude of the RAS-induced , poly-specific memory CD8 T cell response associated with protection against P . berghei challenge is only ∼2-fold lower than the mono- ( CS252–260 ) -specific memory CD8 T cell response ( ∼8% of the CD8 T cell compartment ) . Additionally , protection against Pb is associated with even larger memory CD8 T cell responses in B6 and outbred Swiss Webster mice ( 11% and 12% , respectively ) . Further , resistance to P . yoelii has even more extreme requirements , with protection associated with RAS-induced memory CD8 T cell responses exceeding ∼9 or ∼19% of the CD8 T cell compartment in BALB/c and Swiss Webster mice , respectively . Thus , our data demonstrate that poly-specific memory CD8 T cell-mediated sterilizing immunity to sporozoite challenge , regardless of the relative virulence of the Plasmodium species , requires commitment of a substantial fraction of the entire CD8 T cell compartment . This extreme numerical requirement is perhaps not surprising given the extraordinarily low ratio of Plasmodium-infected cells to total hepatocytes in the mammalian host following challenge with physiological numbers of sporozoites ( ∼1000 ) . Indeed , the gold standard readout for protection against sporozoite challenge is sterilizing immunity , or the prevention of blood stage infection . From a conservative perspective , this level of protection requires that each of a maximum of 1000 infected hepatocytes ( among >108 or >1011 total hepatocytes in the mouse or human liver , respectively ) be targeted through direct CTL activity or indirectly via the release of cytokines by parasite-specific memory CD8 T cells in order to prevent the development of blood stage infection . Thus , each RAS-induced memory CD8 T cell must surveil an extremely large number of hepatocytes in order to identify all cells that harbor parasites . The exceedingly low number of infected cells among the whole liver ( needle in the haystack [16] ) , coupled with the fact that every single infected cell must be successfully targeted to prevent blood stage infection , is the likely explanation for why the numerical requirements for memory CD8 T cell-mediated , anti-Plasmodial liver stage immunity are so high . While it is unclear why commitment of nearly 40% of the CD8 T cell compartment to the anti-Plasmodial memory CD8 T cell response is insufficient to effectively protect B6 mice , our studies extend the literature [11] , [20] by showing that host genetics play a significant role in determining the outcome of sporozoite challenge following RAS-vaccination at bona fide memory time points . We were unable to detect CS-specific CD8 T cell responses in RAS-vaccinated B6 mice ( data not shown ) , which could account for reduced protection . However , experiments have consistently shown that RAS-immune B10 . D2 mice are equally as difficult to protect as B6 and B10 mice [11] , [20] . Importantly , B10 . D2 mice express the same MHC genes as BALB/c mice and thus are able to mount CD8 T cell responses against the defined CS epitopes . Thus , vaccine-induced CD8 T cell responses against the defined immunodominant CS determinant are not sufficient for protection , underscoring the role of non-MHC-linked genes in regulating RAS-induced , anti-liver stage immunity . Another hypothesis to explain the dramatic susceptibility of hyper-Py-RAS-immune B6 mice is that critical phenotypic or functional attributes of the memory CD8 T cell response , that differ in B6 and BALB/c , regulate protective liver stage immunity . Although we found no differences in granzyme B , IFN-γ , TNF-α or IL-2 production by BALB/c- compared to B6-derived , RAS-induced memory CD8 T cells we did observe differential expression of key molecules that differentiate TCM from TEM populations . RAS-induced memory CD8 T cell populations in B6 mice consistently exhibited elevated proportions of CD62LhiCD27hi populations ( TCM phenotype ) compared to the predominantly TEM populations found in BALB/c and Swiss Webster mice . These data demonstrate a clear correlation between the expression of the TEM ( CD27loCD62Llo ) phenotype of secondary memory CD8 T cells and the ability of RAS-immune BALB/c and Swiss Webster mice to resist sporozoite challenge . The reason ( s ) for the difference in memory phenotype between RAS-immune B6 and BALB/c or/Swiss Webster mice are unknown . Interestingly , a recent report using a sensitive in vivo assay suggests that CS antigen persists for long periods of time after RAS immunization [44] . These studies were carried out only in BALB/c mice due to reagent availability . Given the potential of prolonged antigen encounter to influence memory T cell phenotype , it will be of interest to determine if antigen fails to persist in B6 mice and accounts for the altered T cell phenotype and reduced protection after RAS-immunization . Finally , it should be noted that , due to the enormous numbers of memory CD8 T cells required for sterilizing immunity , adoptive transfer studies to compare the per cell protective capacity of CD8αloCD11ahi memory populations from RAS-immune BALB/c and B6 have not succeeded Although it may be possible to measure reductions in parasite liver burden in CD8 T cell-recipient mice using quantitative PCR , this generally requires challenging mice with supraphysiological doses of sporozoites , a scenario that we wished to avoid . In addition , our interests are focused on the properties of memory CD8 T cells that result in sterilizing immunity and it is currently unclear how reduction in parasite burden after high dose challenge models complete elimination of infected hepatocytes . Clearly many other characteristics of the RAS-induced memory CD8 T cell response may contribute to protective immunity and warrant further investigation . Importantly , our surrogate activation marker approach should allow for detailed , prospective characterization of RAS-induced memory CD8 T cell responses in individual hosts , so that potential links between specific memory CD8 T cell attributes and protection can be evaluated . The identification of additional factors that correlate with or determine CD8 T cell-mediated protective immunity following RAS-vaccination should provide key insight into the pathways of protective CD8 T cell-mediated immunity elicited through whole-parasite vaccination . Our data highlight the utility of the surrogate activation marker approach for understanding Plasmodium-induced CD8 T cell responses in genetically diverse populations by providing a framework with which the field can begin to address several additional critical knowledge gaps . First , use of outbred rodents to evaluate vaccine-induced responsiveness and protective immunity is much more likely to mimic responses in genetically diverse humans or non-human primates . Identifying and characterizing individual-to-individual variability in response to vaccination should provide additional critical information that will complement data obtained through studying the highly overlapping responses in genetically identical , inbred rodent populations . Second , our studies provide a framework with which to optimize whole parasite immunization . Identifying ways to enhance potentially suboptimal delivery routes , vaccine doses or schedules , based on a quantitative and qualitative assessment of the total CD8 T cell response , will significantly improve efforts to optimize RAS-vaccination , or any other candidate vaccine delivery approaches . Lastly , the surrogate activation marker approach now permits direct comparisons between RAS and the genetically attenuated parasite ( GAP ) vaccines . Recent work has shown that such genetically attenuated Plasmodium parasites , harboring defined mutations in one or more key genes required for full liver stage differentiation , afford CD8 T cell-dependent protective immunity in rodents [33] , [45] . Moreover , it has been shown that the targeted gene ( s ) precisely control the point during liver stage development that the GAP arrests [31] , [45] , [46] , [47] . Whether early arresting or late arresting GAP-vaccine candidates differentially impact the protective characteristics of the CD8 T cell response is unknown . Given the potential safety advantage of GAP vaccination , these will be critically important questions that can now be directly addressed using surrogate activation markers to identify vaccination-induced effector and memory CD8 T cell responses .
All animal studies and procedures were approved by the University of Iowa Animal Care and Use Committee , under PHS assurance , Office of Laboratory Animal Welfare guidelines . Specific pathogen-free BALB/c , C57BL/6 , and Swiss Webster mice were purchased from the National Cancer Institute ( NCI ) and housed at the University of Iowa animal care unit at the appropriate biosafety level . Female Anopheles stephensi mosquitoes infected with either P . berghei ( NK65 ) or P . yoelii ( 17XNL ) were purchased from the New York University insectary . P . berghei and P . yoelii sporozoites were isolated from the salivary glands of infected A . stephensi mosquitoes . Sporozoites were attenuated by exposure to 200 Gy ( 20 , 000 rads ) . Mice were immunized with 200 to 100 , 000 RAS i . v . Boosted mice received 20 , 000 RAS no less than 60 days apart . In some experiments mice were injected with 60 mg/kg primaquine ( Sigma-Aldrich , St . Louis , MO ) i . p . on days 5 and 6 following RAS immunization . Subunit immunizations were performed as previously described [16] . Briefly , BALB/c mice were primed via tail vein injection of 1×106 splenic dendritic cells coated with peptides corresponding to CS252–260 of P . berghei ( DC-CS252–260 ) . Seven days later , mice were boosted with 2×107 CFU of recombinant Listeria monocytogenes expressing the CS252–260 determinant as a secreted minigene ( LM-CS252–260 ) . P . berghei and P . yoelii sporozoites were isolated from the salivary glands of infected A . stephensi mosquitoes . Immunized and naïve age-matched mice were challenged with 1000 sporozoites i . v . Thin blood smears were performed 10 days after sporozoite challenge . Parasitized red blood cells were identified by Giemsa stain and oil-immersion ( 1000× ) light microscopy . Protection is defined as the absence of blood stage parasites . At least 10 fields ( ∼10–15 , 000 red blood cells ) were examined for each mouse designated as protected . Protected mice were subsequently rechallenged following T cell depletion to verify that protection was CD8 T cell-dependent . RAS vaccine-induced CD8 T cell populations were identified by staining spleen or liver single cell suspensions , or peripheral blood following lysis of red blood cells , with anti-CD8α clone 53–6 . 7 ( eBioscence , San Diego , CA ) and anti-CD11a ( LFA-1α ) clone M17/4 ( eBioscience ) antibodies . Sporozoite-specific CD8 T cells were phenotyped by staining cells with anti-CD27 clone LG . 7F9 , anti-CD43 clone 1B11 , anti CD62L clone MEL-14 , anti-CD127 clone A7R34 , anti-CD25 clone PC61 , anti-CD69 clone H1 . 2F3 , anti-CD44 clone IM7 , anti-CD122 clone 5H4 antibodies , all from eBioscience . In some experiments , 8×104 Py-RAS-induced primary memory cells from CD90 . 2+ BALB/c or CD45 . 2+ B6 mice were adoptively transferred to naïve , congenic ( CD90 . 1+ or CD45 . 1+ ) recipients . One day following transfer , recipients were boosted with 1×105 Py-RAS . Thirty-three days later , splenocytes were stimulated ex vivo in anti-CD3ε-coated wells . BALB/c and B6 donor cells were identified by CD11ahiCD8αloCD90 . 2+ or CD11ahiCD8αloCD45 . 2+ surface staining and further characterized by intracellular staining for IFN-γ , TNF-α and IL-2 , or Granzyme B . CS252–260- and CS280–288-specific CD8 T cells were identified by incubating peripheral blood leukocytes with Kd/CS252–260-APC labeled tetramers or Kd/CS280–288-APC labeled tetramers , respectively . Cells were then stained with anti-CD8α , anti-CD90 . 2 and anti-CD11a . Following subunit immunization , the frequency of circulating CS252–260-specific CD8 T cells was determined by ex vivo intracellular cytokine staining for IFN-γ following a 5 . 5 hour incubation with brefeldin A in the presence or absence of CS252–260 peptide-coated P815 cells were used as antigen presenting cells . Cells were analyzed using a BD FACSCanto and data was analyzed using FLOWJO Software ( Tree Star , Inc , Ashland , OR ) . All animals were pre-bled prior to RAS vaccination to establish individual background circulating CD8αloCD11ahi T cell frequencies . Immunized mice were injected with 0 . 4 mg i . p . rat IgG , anti-CD4 ( clone GK1 . 5 ) , or anti-CD8 ( clone 2 . 43 ) antibodies on day −3 and day −1 prior to challenge with sporozoites . Depletion was verified by analyzing CD4 ( clone RM4-5 ) and CD8 ( clone 53-6 . 7 ) T cell populations in the blood of individual mice prior to challenge . In each case , the relevant population represented <0 . 5% of the PBL . The serum sporozoite-specific antibody titer from immunized mice was determined by the indirect fluorescent antibody test ( IFAT ) . Sporozoites were air dried on a multiwell microscope slide ( Cel-Line Thermo Scientific ) and blocked with 1% BSA/PBS . Sporozoite-specific IgG antibodies were detected by incubating with Cy3-conjugated goat anti-mouse IgG ( Jackson Immunoresearch Laboratories ) . Titers are expressed as the inverse of the lowest dilution of serum that retained immunoreactivity against air-dried sporozoites . | Plasmodium infections are a global health crisis resulting in ∼300 million cases of malaria each year and ∼1 million deaths . Radiation-attenuated Plasmodium sporozoites ( RAS ) are the only vaccines that induce sterilizing anti-malarial immunity in humans . Importantly , “whole parasite” anti-malarial RAS vaccines are currently under evaluation in clinical trials . In rodents , RAS-induced protection is largely mediated by CD8 T cells . However , the quantitative and qualitative characteristics of RAS-induced protective CD8 T cell responses are unknown . Here , we used surrogate markers of T cell activation to reveal the magnitude and kinetics of Plasmodium-specific CD8 T cell responses following RAS-immunization in both inbred and outbred mice . Our data show that , independent of host genetic background , extremely large memory CD8 T cell responses were required , but not always sufficient for sterilizing protection . These data have broad implications for evaluating total T cell responses to attenuated pathogen-vaccines and direct relevance for efforts to translate attenuated whole-Plasmodium vaccines to humans . | [
"Abstract",
"Introduction",
"Results",
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] | [
"infectious",
"diseases/protozoal",
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] | 2010 | Extreme CD8 T Cell Requirements for Anti-Malarial Liver-Stage Immunity following Immunization with Radiation Attenuated Sporozoites |
Isogenic bacterial populations can consist of cells displaying heterogeneous physiological traits . Small regulatory RNAs ( sRNAs ) could affect this heterogeneity since they act by fine-tuning mRNA or protein levels to coordinate the appropriate cellular behavior . Here we show that the sRNA RnaC/S1022 from the Gram-positive bacterium Bacillus subtilis can suppress exponential growth by modulation of the transcriptional regulator AbrB . Specifically , the post-transcriptional abrB-RnaC/S1022 interaction allows B . subtilis to increase the cell-to-cell variation in AbrB protein levels , despite strong negative autoregulation of the abrB promoter . This behavior is consistent with existing mathematical models of sRNA action , thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation . Importantly , we show that the sRNA-induced diversity in AbrB levels generates heterogeneity in growth rates during the exponential growth phase . Based on these findings , we hypothesize that the resulting subpopulations of fast- and slow-growing B . subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions .
In their natural habitats , bacteria constantly adapt to changing environmental conditions while simultaneously anticipating further disturbances . To efficiently cope with these changes , intricate interlinked metabolic and genetic regulation has evolved [1] . This complex regulatory network includes the action of small regulatory RNAs ( sRNAs ) [2] . sRNAs are a widespread means for bacterial cells to coordinate ( stress ) responses by fine-tuning levels of mRNAs or proteins , and they have been studied in great detail in Gram-negative bacteria [3] . Regulation by some sRNAs takes place by short complementary base pairing to their target mRNA molecules , for instance in the region of the ribosome-binding site ( RBS ) to inhibit translation or trigger mRNA degradation . In Gram-negative bacteria many of these sRNA-mRNA interactions are mediated by the RNA chaperone Hfq [4] . However , the Hfq homologue in the Gram-positive model bacterium Bacillus subtilis has no effect on the regulation of the eight sRNA targets reported in this species so far [5–7] . Owing to the complexity of sRNA regulation , only a relatively small number of studies have focused specifically on the physiological necessity of sRNA-target interactions . This is again particularly true for Gram-positive bacteria , such as B . subtilis , despite the fact that many potential sRNAs have been identified [8 , 9] . Within a bacterial population , genes and proteins can be expressed with a large variability , with high expression levels in some cells and low expression levels in others [10] . Examples of expression heterogeneity in B . subtilis are the extensively studied development of natural competence for DNA binding and uptake and the differentiation into spores [11–13] . In both cases , expression heterogeneity is generated by positive feedback loops , and results in bistable or ON-OFF expression of crucial regulators [14] . Distinctly from bistability , proteins can also be expressed with large cell-to-cell variability . This variation in expression levels , or noise , can originate from intrinsic or extrinsic sources [15 , 16] . Extrinsic noise is related to cell-to-cell fluctuations in numbers of RNA polymerase , numbers of genome copies , or numbers of free ribosomes . Conversely , intrinsic noise is caused by factors directly involved in the transcription or translation of the respective gene or protein . Interestingly , particularly noisy genes are often found to be regulators of development and bacterial persistence [12 , 17 , 18] . Because of the importance of noise in protein expression , cells have evolved mechanisms to regulate the noise levels of at least some proteins [10] . Reducing noise levels has been suggested as an important explanation why many transcriptional regulators in bacteria ( 40% in E . coli [19] ) autorepress the transcription of their own promoter ( i . e . negative autoregulation ( NAR ) ) . AbrB is a global transcriptional regulator in Gram-positive bacteria , including the important human pathogens Bacillus anthracis and Listeria monocytogenes [20 , 21] . B . subtilis AbrB positively regulates some genes when carbon catabolite repression ( CCR ) is relieved [22] , and negatively regulates the expression of over two hundred genes in the exponential growth phase [23] . Transcription of abrB is negatively autoregulated by binding of AbrB tetramers to the abrB promoter [24 , 25] . Upon entry into stationary phase , abrB transcription is repressed via increasing levels of Spo0A-P and AbrB is inactivated by AbbA [26 , 27] . The resulting AbrB depletion is consequently followed by activation of AbrB repressed genes , which are often important for stationary phase processes . Notably , because of its role in the elaborate sporulation and competence decision making network [26 , 27] , AbrB has mainly been studied in the context of entry into stationary phase while much less is known about its exact role in the exponential growth phase . We selected putative sRNAs from a rich tiling array dataset of 1583 potentially regulatory RNAs [9] . This selection was made for evolutionary conserved putative sRNAs with a high expression level on defined minimal medium . Deletion strains of these putative B . subtilis sRNAs were subsequently tested for growth phenotypes . One sRNA—RnaC/S1022—stood out since the mutant strain displayed a strongly increased final optical density on minimal medium with sucrose as the sole carbon source . The present study was therefore aimed at determining how RnaC/S1022 influences the growth of B . subtilis . Inspection of consistently observed predicted RnaC/S1022 targets indicated that the aberrant growth phenotype could relate to elevated AbrB levels . Here we show that , under certain conditions , B . subtilis employs RnaC/S1022 to post-transcriptionally modulate AbrB protein expression noise . The observed noise in AbrB protein levels is remarkable , because the abrB gene displays low transcriptional noise consistent with its NAR . Importantly , the sRNA-induced noise in the AbrB protein levels generates growth rate heterogeneity in the exponential phase .
RnaC/S1022 was first identified in a systematic screening of B . subtilis intergenic regions with an oligonucleotide microarray [28] . RnaC/S1022 is located in between yrhK , a gene of unknown function , and cypB , encoding cytochrome P450 NADPH-cytochrome P450 reductase ( also known as yrhJ ) . We tested the conservation of the B . subtilis RnaC/S1022 sequence with BLAST analysis against a set of 62 Bacillus genomes , and found evolutionary conservation in a clade of the phylogenetic tree including 19 B . subtilis , Bacillus atrophaeus , and Bacillus amyloliquefaciens genomes ( Fig . 1A and S1 Fig . for extensive alignments ) . Within these 19 genomes , the 5’ and 3’ ends of the RnaC/S1022 sequence are conserved , but the core sequence is disrupted in all 9 B . amyloliquefaciens genomes ( S1 Fig . ) . Notably , the RnaC/S1022 from B . atropheus 1942 seems to represent an in-between form of RnaC/S1022 that mostly resembles the RnaC/S1022 sequences from the B . amyloliquefaciens sp . genomes . Therefore , an alignment of only the RnaC/S1022 sequences from the 9 remaining B . subtilis genomes was used to predict the RnaC/S1022 secondary structure using the LocARNA tool [29] ( Fig . 1B , S1 Fig . ) . These analyses predict RnaC/S1022 to fold into a stable structure with a Gibbs free energy for the sequence shown in Fig . 1B of −38 . 5 kcal/mol , as calculated with RNAfold [30] . RnaC/S1022 was recently included in a screen for possible functions of conserved putative sRNAs identified by Nicolas et al . [9] that are highly expressed on M9 minimal medium supplemented with different carbon sources . Here , the RnaC/S1022 mutant stood out , because it consistently grew to a higher optical density ( OD ) in M9 minimal medium supplemented with sucrose ( M9S ) than the parental strain ( Fig . 1C ) . To distinguish effects on the growth rate and growth yield , lin-log plots of these growth profiles are presented in S2 Fig . , which show that the growth rate was only slightly influenced by the RnaC/S1022 deletion while the growth yield was strongly increased . Compared to M9S , the growth phenotype was less pronounced in M9 with glucose ( M9G ) . Since transcription of RnaC/S1022 is exclusively regulated by SigD [28] , we also tested a sigD mutant for growth under these conditions . Interestingly , the ΔsigD mutant displayed similar growth characteristics as the ΔRnaC/S1022 mutant ( Fig . 1C ) . Differential growth and increased competitiveness were previously reported for a sigD mutant [31] , and our observations suggest that in some conditions the increased final OD of the ΔsigD strain is partly due to deregulation of RnaC/S1022 . We wondered whether deregulation of an sRNA target was responsible for the remarkable growth phenotype observed for the ΔRnaC/S1022 mutant and decided to perform exploratory target predictions using TargetRNA [32] . Predicting sRNA targets can be successful , but target verification is complicated by the large number of false-positively predicted targets . We argued that additional information about the likelihood of a true target could be obtained by determining whether the predicted interaction is conserved over evolutionary time . To identify predicted RnaC/S1022-target interactions that are conserved , a bioinformatics pipeline was established that predicts sRNA targets in genomes in which the RnaC/S1022 sequence is conserved . Since we were interested in finding true B . subtilis sRNA targets , we only considered targets also predicted in B . subtilis , and these are listed in S1 Table . This analysis reduced the number of considered RnaC/S1022 targets to 47 ( from 147 predicted targets for TargetRNA_v1 predictions with P value ≤ 0 . 01 on the B . subtilis 168 genome ) . These 47 predicted targets included seven sporulation-related genes ( phrA , spoVAD , spoIIM , spoIIIAG , cotO , sspG , spsI ) . The sigma factor sigM was also consistently predicted but , since a sigM mutant strain only displays a growth phenotype under conditions of high salinity [33] , this seemed unrelated to the observed growth phenotype of the ΔRnaC/S1022 mutant on M9 medium . In addition , two consistently predicted targets are involved in cell division ( racA and ftsW ) , but we observed no specific cell-division abnormalities of the ΔRnaC/S1022 strain by live-imaging microscopy . Furthermore , the TCA cycle genes citB and citZ were predicted targets and tested by Western blot analysis , but no deregulation was observed . The last consistently predicted target of initial interest was the gene for the transition state regulator AbrB ( Fig . 1D ) . Reviewing the literature on abrB pointed us to an interesting observation where a spo0A mutant was reported to display increased growth rates on media similar to our M9 medium [22] . Furthermore , it had been reported that AbrB has an additional role in modulating the expression of some genes during slow growth in suboptimal environments [34] , which we argued could also be relevant to the M9S growth condition . Since abrB is a consistently predicted target of RnaC/S1022 ( Fig . 1D ) , we checked whether the presence of this sRNA coincides with the presence of the abrB gene . Indeed , abrB is conserved in 53 out of 62 available Bacillus genomes , and RnaC/S1022 is present in 19 of these 53 genomes ( Fig . 1A ) . In addition , we identified no genomes that contain RnaC/1022 but lack the abrB gene ( Fig . 1A ) . Accordingly , we hypothesized that RnaC/S1022 might be a regulator of AbrB . The combined clues from bioinformatics analyses and literature suggested that the growth phenotype of the ΔRnaC/S1022 mutant could relate to elevated AbrB levels . To test whether AbrB levels are indeed altered in this mutant , we performed Western blot and Northern blot analyses . This indeed revealed a strong trend towards higher AbrB protein and mRNA levels in the RnaC/S1022 mutant and for cells grown in M9G or M9S this effect was statistically significant ( Fig . 2 ) . Importantly , the growth phenotype as well as AbrB protein and mRNA levels returned to wild-type ( wt ) by ectopic expression of RnaC/S1022 under control of its native promoter from the amyE locus ( Fig . 1C and 2 ) . We also tested the effects of a Δspo0A mutation by Western and Northern blot analyses . Interestingly , the combined deletion of RnaC/S1022 and spoOA seemed to lead to a further increase in the AbrB protein and mRNA levels compared to the already elevated levels in the spo0A mutant background . Lastly , we observed a three-fold reduced natural competence of the ΔRnaC/S1022 mutant , which is expected when the AbrB levels are elevated [35] ( S3 Fig . ) . To test whether the AbrB levels were directly dependent on RnaC/S1022 levels , we placed the RnaC/S1022 complementation cassette in the amyE locus of the parental strain and used Western and Northern blotting to measure AbrB protein and mRNA levels . These analyses showed a trend towards reduction of both the AbrB protein and mRNA levels in cells grown on M9G and M9S , which would be consistent with elevated RnaC/S1022 expression and increased abrB regulation ( Fig . 2 ) . Since the amount of AbrB was apparently correlated to the amount of RnaC/S1022 , this suggested a stoichiometric relationship between these two molecules . Before testing whether there could be a direct interaction between RnaC/S1022 and the abrB mRNA , we decided to investigate the fate of the abrB mRNA in the presence or absence of RnaC/S1022 . For this purpose , we assayed the levels of the abrB mRNA at different time points after blocking transcription initiation with rifampicin in the RnaC/S1022 mutant strain and in the strain with two chromosomal copies of RnaC/S1022 . This analysis showed that the abrB mRNA level decreased significantly faster in the presence of RnaC/S1022 than in its absence ( S4 Fig . ) . In case of a direct interaction between RnaC/S1022 and the abrB mRNA , the observed difference could relate to an RnaC/S1022-triggered degradation of the abrB mRNA . Alternatively , this difference could be due to an RnaC/S1022-precluded protection of the abrB mRNA by elongating ribosomes [36] . The apparently stoichiometric relationship between AbrB and the sRNA RnaC/S1022 is suggestive of a direct sRNA—target interaction . The predicted interaction region in B . subtilis 168 spans a region from the RBS of abrB ( -10 ) until 19 bp after the start of the abrB ORF of which the strongest consecutive stretch of predicted base-pair interactions are present from +7 bp till +19 bp ( left top panel in Fig . 3 ) . In addition , only this region within the abrB-encoding sequence is part of the conserved predicted interaction region in B . atrophaeus and B . subtilis spizinenzii ( Fig . 1D ) . It has been reported that loop-exposed bases of sRNAs are more often responsible for regulation than bases in stems [37] . Two predicted loop regions of RnaC/S1022 are complementary with the predicted abrB interaction region ( one of two basepairs and one of seven basepairs; bases 51–52 and 57–63 in Fig . 1B and 3 ) . We therefore decided to introduce a point-mutation by a U to A substitution in the predicted 7-bp loop of RnaC/S1022 encoded by plasmid pRM3 and a compensatory mutation in a plasmid pRM15-borne truncated abrB-gfp reporter construct ( abrBtrunc-gfp ) . Strains containing different combinations of the respective plasmids were grown on M9G and assayed by Flow Cytometry ( FC ) in the exponential growth phase . Cells containing one of the abrBtrunc-gfp constructs in combination with the empty pRM3 plasmid displayed a unimodal distribution in GFP levels ( Fig . 3 , lower panels ) . However , when the wt abrBtrunc-gfp was assayed in combination with the wt RnaC/S1022 , a bimodal distribution in AbrBtrunc-GFP levels was observed , including a new peak of lowered fluorescence intensity ( Fig . 3 , top left ) . Interestingly , a unimodal fluorescence distribution was found when the wt abrBtrunc-gfp construct was combined with point-mutated RnaC/S1022* ( Fig . 3 , middle left ) or the mutated abrB*trunc-gfp with the wt RnaC/S1022 ( Fig . 3 , top right ) . In the case of the point-mutated abrB*-gfp construct , however , a bimodal fluorescence distribution was only observed when this construct was combined with the mutated RnaC/S1022* ( Fig . 3 , middle right ) . This implies that a direct mRNA-sRNA interaction takes place between abrB and RnaC/S1022 . Studying the condition-dependency of sRNA expression can give clues to its function and targets . To obtain high-resolution expression profiles , we constructed an integrative RnaC/S1022 promoter-gfp fusion [38] . As expected , the presence of this PRnaC/S1022-gfp fusion caused GFP fluorescence in wild-type cells , but not in cells with a sigD mutation ( Fig . 4D ) . Next , a live cell array approach was used to compare the PRnaC/S1022-gfp activity with that of another SigD-dependent promoter , Phag , which drives flagellin expression . These promoter fusion strains revealed that the expression of hag was consistently ∼4 fold higher than that of RnaC/S1022 ( Fig . 4 ) , which is in agreement with previously published expression data [9] . On LB medium , the expression of both RnaC/S1022 and hag peaked in the late exponential and transition phase , while on both tested minimal media the peak in expression occurred in early exponential phase ( Fig . 4 ) . This higher RnaC/S1022 expression level in the exponential phase on M9 relative to that in LB is in concordance with the stronger effect of ΔRnaC/S1022 on AbrB levels , as indicated by the Northern and Western blot analyses . Experimental methods that measure average protein levels in a population obscure possible cell-to-cell variation . To further study the cell-to-cell variation of AbrB-GFP in the exponential growth phase ( as observed in Fig . 3 ) , we therefore employed a full-length translational abrB-gfpmut3 fusion that was integrated into the chromosome via single cross-over ( Campbell-type ) recombination . Specifically , this integration resulted in a duplication of abrB where one full-length copy of abrB was expressed from its own promoter and fused in-frame to gfp , while the downstream abrB copy was truncated lacking the start codon required for translation [39] . In this AbrB-GFP strain all AbrB monomers have a C-terminally attached GFP molecule . While AbrB-GFP still localized to the nucleoid ( S5 Fig . ) , this AbrB-GFP strain displayed a somewhat reduced growth rate on media where AbrB is required for rapid growth . Since the translational abrB-gfp fusion is chromosomally integrated at the abrB locus , this system is insensitive to fluctuations in noise levels by plasmid copy number variation and its chromosomal location in the division cycle . We first used the AbrB-GFP fusion to test whether B . subtilis Hfq might have an effect of the RnaC/1022-abrB interaction . Consistent with previous studies on other sRNA targets of B . subtilis [5–7] , the direct RnaC/S1022-abrB regulation was found to be independent of Hfq since comparable FC profiles for AbrB-GFP expression were obtained for the parental strain and the hfq deletion mutant ( S6 Fig . ) . Next , we analyzed all AbrB-GFP strains by FC in the exponential phase on both LB and M9G . Noise measurements were not performed on M9S because of the strong growth difference between the parental and ΔRnaC/S1022 strains on this medium ( Fig . 1C ) . We observed that the difference between cells expressing AbrB-GFP at the highest level and those at the lowest level was large ( Fig . 5A ) . This means that AbrB-GFP is expressed with high noise ( quantified as the coefficient of variation; CV% ) . Interestingly , we observed lower AbrB-GFP noise in strains lacking RnaC/S1022 and , crucially , the presence of an additional genomic RnaC/S1022 copy further increased AbrB-GFP noise . Remarkably , increased RnaC/S1022 levels only reduced the minimal expression level of the distribution while not affecting the maximum AbrB-GFP expression level ( Fig . 5A ) , which is consistent with the data presented in Fig . 3 . There was a statistically significant positive linear correlation between RnaC/S1022 levels ( 0 , 1 or 2 genomic copies ) and AbrB-GFP noise ( on LB for pooled data points from Δspo0A and parental backgrounds R2 0 . 48 , P-value <0 . 001 , and M9G R2 0 . 43 , P-value <0 . 001 ) ( Fig . 5B ) . Direct statistical comparisons between AbrB-GFP noise levels at different sRNA levels also revealed significant changes ( Fig . 5 ) . The noise increase therefore seems correlated to the level of RnaC/S1022 . Notably , this relation was also observed for noise measurements in a spo0A deletion background , even though the mean AbrB-GFP expression was between 1 . 37 and 2 . 32 fold ( for LB and M9G respectively , μ = 1 . 79 ) higher in Δspo0A strains . This suggests that RnaC/S1022 has a specific role in noise modulation of AbrB-GFP . After observing that RnaC/S1022 specifically increases AbrB-GFP expression noise , we aimed to elucidate the origin of this AbrB-GFP noise . Three possibilities for noise generation by an sRNA are conceivable . Firstly RnaC/S1022 could have an additional indirect effect on abrB expression , leading to noisy expression from the abrB promoter and subsequent propagation of this noise to the AbrB protein level . Secondly , RnaC/S1022 may itself be expressed either in bimodal fashion or with high noise . The third possibility would be an AbrB-dependent repression of the RnaC/S1022 promoter and subsequent repression of AbrB protein levels by RnaC/S1022 . This double negative repression would correspond to positive feedback on the AbrB protein level , and positive feedback is a known source of expression heterogeneity [40] . To study the distribution of the abrB promoter , we integrated the pBaSysBioII plasmid [38] directly behind the Spo0A binding site in the promoter region of abrB [41] , resulting in a single-copy promoter fusion at the native genomic locus ( PabrB; -41bp of the abrB start codon ) . This location was selected to include the effect of AbrB autorepression and Spo0A ( -P ) repression , while excluding RnaC/S1022 regulation . We observed no bimodal or particularly noisy expression of this abrB promoter fusion , showing that transcription from the abrB promoter is homogeneous in the exponential phase ( Fig . 5C ) . Of note , bimodal or noisy expression of PabrB would have been surprising since transcription of abrB is autorepressed and it is generally found that this NAR reduces the noise of promoter expression [42 , 43] . Interestingly , the expression from the abrB promoter rises with increasing levels of RnaC/S1022 . This observation can be explained by AbrB autorepression and noise . There are more cells with low AbrB levels when the levels of RnaC/S1022 are increased . On average , this will lead to lowered repression of the abrB promoter , leading to a higher level of expression ( but not more noise ) from the abrB promoter ( Fig . 5C ) . This higher expression from the abrB promoter is apparently compensated for at the protein level by the elevated regulation of RnaC/S1022 ( Fig . 2 and 5B ) . Since we observed only a slight increase in abrB promoter noise specific to RnaC/S1022 ( Fig . 5C ) , the hypothesis that AbrB-GFP noise promotion originates from an additional effect of RnaC/S1022 on the abrB promoter can be rejected . A second possibility of noise promotion by RnaC/S1022 is that it is itself expressed with large noise similar to the SigD-dependent hag gene [44] . In this case , large cell-to-cell variation in sRNA levels would only lead to regulation in cells that have above-threshold sRNA levels , and this could generate the variation in AbrB-GFP levels . We tested this at the promoter level by FC analysis of the integrative RnaC/S1022 promoter-gfp fusion ( PRnaC/S1022; Fig . 4 ) and found this promoter fusion to be homogenously expressed with a tight distribution of GFP levels ( CV% of 64% for the M9G condition; Fig . 4D ) . Furthermore , we argued that the relatively low expression of PRnaC/S1022 could result in threshold-level regulation where the sRNA is only involved in regulating abrB in cells with above-threshold levels of RnaC/S1022 . However , this is not consistent with the observation of further increased noise levels in cells with two genomic copies of RnaC/S1022 ( Fig . 5B ) . We therefore consider the possibility of AbrB noise promotion via heterogeneous expression of RnaC/S1022 unlikely . It cannot be excluded , however , that variation in the levels of RnaC/S1022 might be introduced further downstream , for instance via mRNA degradation , or via regulation by a dedicated RNA chaperone . The third option would be a double-negative feedback loop consisting of sRNA repression of AbrB levels and AbrB repression of sRNA levels , which would ultimately lead to an increase in AbrB protein expression noise . This would thus depend on repression of the RnaC/S1022 promoter by AbrB , in addition to the confirmed negative regulation of abrB by RnaC/S1022 . Together this would lead to a decrease in AbrB protein levels in cells that start with below-threshold AbrB levels . First of all , we found no indication for AbrB binding sites in the region upstream of RnaC/S1022 in the dataset of Chumsakul et al . , where the binding sites of AbrB were mapped genome-wide [24] . To test whether the RnaC/S1022 promoter is indeed not directly controlled by AbrB , or possibly under indirect control of AbrB , we deleted the abrB gene from the above-mentioned PRnaC/S1022 promoter fusion strain . As expected , the RnaC/S1022 promoter activity levels were not detectably affected by the abrB deletion ( S7 Fig . ) , showing that it is unlikely that there is a negative feedback loop consisting of AbrB-dependent RnaC/S1022 repression and RnaC/S1022-dependent abrB repression . Since the experimental data presented above pointed to a direct role of the RnaC/S1022 sRNA in AbrB protein noise promotion , we wondered whether this possibility is consistent with mathematical models of sRNA regulation . To verify this , we considered a simple model of RNA regulation with two independently transcribed RNA species ( sRNA and mRNA ) [45–47] . In this model , these molecules are synthesized with constant transcription rates αs and αm , respectively . Translation of mRNA into protein Q , and the degradation of sRNA , mRNA , and protein molecules were modeled as linear processes that occur with rates δ . βs , βm , and γ , respectively . The sRNA-mRNA duplex formation was assumed to be an irreversible second-order process that occurs with a rate κ . In the model , molecules in the sRNA-mRNA duplex were removed from the dynamical system . A summary of all reactions and the master equation used in the model can be found in Fig . 6 . We first implemented model parameters used in an earlier sRNA modeling study by Jia et al . [47] ( Set I; Fig . 6A and D ) . Of note , these parameters were essentially the same as those of Levine et al . [45] . In all cases the sRNA transcription rate ( αs ) was a free variable to capture the effect of 0 , 1 , or 2 genomic copies of RnaC/S1022 . In addition , for each set of parameters we included two possible αm values to model the effect in the Spo0A deletion strain where the abrB transcription rate ( αm ) is approximately two-fold higher than in the parental strain ( as determined with PabrB-gfp ) . Varying extrinsic noise in the abrB transcription rate had no effect on the general modeling outcome ( S8 Fig . ) and the intermediate αm CV% level of 40% was selected for plots in the main text . After running the model with parameters from Set I , we observed that model-predicted protein noise strongly increased with increasing αs . This trend of increasing protein noise with increasing sRNA transcription rates was similar to what we observed for the genomic AbrB-GFP fusion ( Fig . 5A and B ) . Importantly , doubling αm ( two-fold higher mRNA transcription rate ) resulted in a more gradual noise increase with increasing αs , just as was observed in the Δspo0A mutant with the AbrB-GFP fusion ( Fig . 5 ) . We next sought to determine the effect of changing modeling parameters on the modeling outcome , because the selected mRNA half-life of ∼35 min ( βm 0 . 02 ) in parameter Set I would only be relevant for a subset of mRNA molecules as shown experimentally by Hambraeus et al . [48] ( with a relation between these of mean lifetime from Fig . 6A * ln 2 = half-life ) . We therefore constructed a second set of modeling parameters ( Set II ) , which gave the mRNA and sRNA species a half-life of ∼3 . 5 min ( βm and βs 0 . 20 ) while keeping protein half-life at ∼35 min . In addition , αm was increased from 2 transcripts per minute to 4 per minute , and δ was doubled to 2 synthesized proteins per minute . Although the maximum noise level from these Set II simulations was markedly different , it again clearly showed the trend of increasing protein noise with increasing sRNA transcription rates . We can therefore conclude that the modeling results robustly support the idea that sRNA regulation can generate noise at the protein level . This noise would be induced locally at the level of mRNA degradation or translation initiation , and the corresponding fluctuations would subsequently be propagated to the protein level . Recently , the theoretical background of this concept was also reported by Jost et al . , who stated that such behavior is especially expected when the levels of the srRNA and the mRNA are approximately equal [49] . Altogether , our experimental data and the modeling approach are consistent with the view that RnaC/S1022 is an intrinsic noise generator for AbrB-GFP at the post-transcriptional level . After defining the experimental and theoretical framework for noise promotion by the RnaC/S1022 sRNA , we wondered what the physiological relevance of this regulation might be . Since we and others ( Fig . 1A; [22] ) have reported an effect of AbrB levels on the growth of B . subtilis , a growth-related function seemed obvious . We therefore tested whether AbrB levels are a direct determinant of growth rate and yield under the relevant conditions . To do this , we placed the abrB gene under control of an isopropyl ß-D-1-thiogalactopyranoside ( IPTG ) -inducible promoter in the amyE locus using plasmid pDR111 [50] , and subsequently deleted the abrB gene from its native locus in this strain . We first verified the IPTG-dependent expression of AbrB from this construct by growing the parental strain , the ΔabrB strain , and the ΔabrB amyE::abrB strain on LB medium and , in the case of the ΔabrB amyE::abrB strain , the medium was supplemented with increasing IPTG concentrations . Subsequently , AbrB production was assessed by Western blot analysis ( Fig . 7A ) , which showed that AbrB production in the ΔabrB amyE::abrB strain was indeed IPTG-dependent . Notably , the abrB mutant strain displays a growth phenotype on LB medium , but this is only apparent in the late exponential growth phase [51] . To analyze the effect of differing AbrB levels on growth under conditions that are more relevant for the RnaC/S1022—abrB interaction , we grew the same strains on M9G , which was supplemented with differing amounts of IPTG for the ΔabrB amyE::abrB strain . As shown in Fig . 7B , the abrB deletion mutant did not grow in this medium . Importantly however , IPTG-induced expression of abrB in this mutant repaired the growth phenotype in a dose-dependent manner . This shows that the AbrB levels determine the growth rate and yield when cells are cultured on M9G . We next aimed to unravel the effect of sRNA-induced AbrB heterogeneity on growth . This requires the tracking of cells with low and high AbrB-GFP levels over time . To do this , we performed a live imaging experiment with the Δspo0A AbrB-GFP strain either containing zero sRNA copies due to the ΔRnaC/S1022 mutation , or two genomic copies due to the insertion of an additional RnaC/S1022 copy in amyE . The Δspo0A background was used to elevate AbrB-GFP levels and thereby to facilitate fluorescence measurements . Cells were pre-cultured in M9G as was done for the FC measurements and applied to agarose pads ( at OD600 ∼0 . 15 ) essentially as was described by Piersma et al . [52] . From these experiments , and consistent with FC data in Fig . 5 , it was apparent that there was a larger variation in AbrB-GFP levels in the strain with two genomic RnaC/S1022 copies , compared to the strain lacking RnaC/S1022 ( Fig . 8B; S1 Movie ) . In addition , this variation in AbrB-GFP levels was correlated to the variation in growth rates ( quantified as the specific cell length increase ) observed during the first 20 min of each live imaging run ( Fig . 8A ) . We excluded the possibility that this growth rate difference was dependent on the position on , or quality of the slide . Instead , it was solely linked to the cellular level of AbrB-GFP ( Fig . 8C; S1 Movie ) . Notably , in the two example cells from Fig . 8C ( S1 Movie ) AbrB-GFP levels gradually increase in the cell with a low start level ( i . e . high level of sRNA repression ) , which would be consistent with a gradual reduction in RnaC/S1022 expression on this solid agarose medium ( S9 Fig . ) . However , our experimental setting determines the effect of AbrB-GFP on growth before this reduction in RnaC/S1022 becomes relevant ( e . g . the first 5 pictures , or 20 min ) ( Fig . 8A ) . Beyond this , the increase in AbrB-GFP levels observed later ( >150 min ) in the live imaging experiment seems coupled to a concomitant increase in growth rate ( S9 Fig . ) . This is again consistent with the positive correlation of AbrB-GFP levels with growth rate . Interestingly , while we observed a few cells switching their AbrB-GFP expression state from high to low , the AbrB-GFP levels were generally stable throughout a cell’s lineage . Combined , these analyses show that the RnaC/S1022-induced heterogeneity in the AbrB-GFP expression levels generates diversity in growth rates within the exponential phase of growth .
In this study we show that B . subtilis employs the RnaC/S1022 sRNA to post-transcriptionally regulate AbrB and that this regulation results in increased heterogeneity in growth rates during the exponential phase of growth . RnaC/S1022 is the third sRNA in B . subtilis for which a direct target has been reported and this study reveals the value of evolutionary target predictions to identify true sRNA targets for this species . The observed growth rate heterogeneity induced by RnaC/S1022 is conceivably of physiological relevance since slowly growing bacterial cells are generally less susceptible to antibiotics and other environmental insults than fast growing cells [53–55] . Specifically , it was noted for hip strains of E . coli that slowly growing cells within a population will develop into persister cells when challenged with ampicillin [17] . Notably , in this system , the initial heterogeneity in growth rates was reported to be dependent on the HipAB toxin-antitoxin module [56] . Analogously , it is conceivable that a B . subtilis toxin-antitoxin module under negative AbrB control could be responsible for the heterogeneity observed in the present study . Another perhaps more likely possibility is that low AbrB levels cause the premature activation of transition- or stationary phase genes , thereby slowing down growth and causing premature stationary phase entry . AbrB has also been implicated in the activation of some genes when CCR is relieved [22 , 23] , and this could be related to the stronger growth phenotype observed on M9S compared to M9G . However , the AbrB level also determines growth rates on M9G ( this study; [22] ) , when CCR is active and AbrB is not known to have an activating role [22] . The initially observed growth phenotype of the ΔRnaC/S1022 mutant can be explained by the present observation that AbrB is an important determinant for growth on M9 medium , and that RnaC/S1022 regulation of AbrB is specifically linked to increasing AbrB noise . Specifically , the absence of RnaC/S1022 will reduce the number of cells expressing AbrB at a low level . Growth of the ΔRnaC/S1022 population will therefore be more homogeneous and , when inspected as an average , the population will enter stationary phase later than the parental strain . Beyond the mechanism of AbrB-mediated growth regulation , we show that noisy regulation of a growth regulator can also cause heterogeneity in growth rates . This suggests that the AbrB noise level has been fine-tuned in evolution , possibly as a bet-hedging strategy to deal with environmental insults . Two other questions addressed by this study are the origin of AbrB expression noise , and the likely reason why this noise is generated at the post-transcriptional level . The origin of AbrB expression noise via triggering of abrB mRNA degradation and/or inhibition of abrB translation fits the definition of an intrinsic noise source where the absence of RnaC/S1022 reduces the number of sources for intrinsic noise by one , and therefore results in lower protein expression noise . This specific noise-generating capacity of sRNA regulation might be due to the specific kinetics of the RnaC/S1022- abrB mRNA interaction . It is currently unclear whether this feature of sRNA-mediated regulation can be extended to other sRNA-mRNA pairs . Specifically , subtle consequences of sRNA regulation , such as noise generation , may have been overlooked in previous studies due to the use of plasmid-encoded translational fusions with fluorescent proteins expressed from strong non-native promoters as reporters . We therefore expressed all RnaC/S1022 and AbrB-GFP constructs from their native genomic location , from their native promoters , and assayed the effects in the relevant growth phase . NAR of AbrB seems to be the answer to the second question why noise is generated post-transcriptionally and not at the promoter level . AbrB’s NAR is important for its functioning in the stationary phase sporulation network [26 , 27] and is therefore likely a constraint for evolutionary optimization of AbrB expression in the exponential phase , which is the growth phase addressed in this study . In turn , NAR is a clear constraint on noise generation since it is generally believed to dampen noise [42 , 43] . Consistent with this view , we observed only a slight increase in PabrB promoter noise upon increasing AbrB protein noise , suggesting that AbrB NAR is responsible for minimizing promoter noise . Besides reducing noise , NAR has been implicated in decreasing the response time of a genetic circuit , linearizing the dose response of an inducer , and increasing the input dynamic range of a transcriptional circuit [19] . Individually , and in combination , these mechanistic aspects of NAR could explain why NAR is such a widespread phenomenon in transcriptional regulation . Besides this , the idea that AbrB and AbrB NAR are more widely conserved than RnaC/S1022 would be in line with the idea that AbrB expression in B . subtilis 168 has become fine-tuned by an additional regulator , which has evolved later in time . Lastly , on a more general note , the inconsistency between the abrB promoter and AbrB protein noise measurements make it clear that it is premature to draw conclusions about homogeneity or heterogeneity of protein expression when only data is gathered at the promoter level , especially for genes under a NAR regime . In conclusion , we have identified a novel direct sRNA target in the important B . subtilis transcriptional regulator abrB . Specifically , we provide functionally and physiologically relevant explanations for the evolution of the noise-generation aspects of this regulation in generating heterogeneity in growth rates . This noise is induced at the post-transcriptional level due to AbrB NAR . Based on our present observations , we hypothesize that the resulting subpopulations of fast- and slow-growing B . subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions .
E . coli and B . subtilis strains and plasmids used in this study are listed in S2 Table and oligonucleotides in S3 Table . E . coli TG1 was used for all cloning procedures . All B . subtilis strains were based on the trpC2-proficient parental strain 168 [1] . B . subtilis transformations were performed as described previously [57] . The isogenic RnaC/S1022 mutant was constructed according to the method described by Tanaka et al . [58] . pRMC was derived from pXTC [59] by Circular Polymerase Extension Cloning ( CPEC ) [60] with primers ORM0054 and ORM0055 using pXTC as PCR template and ORM0056 to circularize this PCR fragment in the final CPEC reaction . In this manner , the xylose-inducible promoter of pXTC was replaced with the AscI Ligation Independent Cloning ( LIC; [61] ) site from pMUTIN-GFP [39] . As a consequence , pRMC carries a cassette that can be integrated into the amyE locus via double cross-over recombination , allowing ectopic expression of genes in single copy from their native promoter . RnaC/S1022 was cloned in pRMC under control of its native promoter as identified by Schmalisch et al . [28] , and the subsequent integration of RnaC/S1022 into the amyE locus via double cross-over recombination was confirmed by verifying the absence of α-amylase activity on starch plates . The LIC plasmid pRM3+Pwt RnaC/S1022 , which is a derivative of plasmid pHB201 [51] , was used to express RnaC/S1022 under control of its native promoter . For IPTG-inducible expression of abrB , the abrB gene was cloned into pDR111 [50] , and subsequently placed in the amyE locus via homologous recombination . Deletion alleles were introduced into this and other strains by transformation with chromosomal DNA containing the respective mutations . The RnaC/S1022 , hag and abrB promoter gfp fusions were constructed at the native chromosomal locus by single cross-over integration of the pBaSysBioII plasmid [38] . A minimum of three clones were checked to exclude possible multi-copy integration of the plasmid . Lysogeny Broth ( LB ) consisted of 1% tryptone , 0 . 5% yeast extract and 1% NaCl , pH 7 . 4 . M9 medium supplemented with either 0 . 3% glucose ( M9G ) or 0 . 3% sucrose ( M9S ) was freshly prepared from separate stock solutions on the day of the experiment as previously described [9] . For live cell imaging experiments , the M9 medium was filtered through a 0 . 2 μm Whatman filter ( GE Healthcare ) . Strains were grown with vigorous agitation at 37°C in either Luria LB or M9 medium using an orbital shaker or a Biotek Synergy 2 plate reader at maximal shaking . Growth was recorded by optical density readings at 600 nm ( OD600 ) . For all growth experiments , overnight B . subtilis cultures in LB with antibiotics were diluted >1:50 in fresh prewarmed LB medium and grown for approximately 2 . 5 hours . This served as the pre-culture for all experiments with cells grown on LB medium . For experiments with cells grown on M9 medium , the LB pre-culture was subsequently diluted 1:20 in pre-warmed M9 medium and incubated for approximately 2 . 5 hours , which corresponds to mid- or early exponential growth . This culture then served as the pre-culture for experiments with cells grown on M9 medium . When required , media for E . coli were supplemented with ampicillin ( 100 μg ml−1 ) or chloramphenicol ( 10 μg ml−1 ) ; media for B . subtilis were supplemented with phleomycin ( 4 μg ml−1 ) , kanamycin ( 20 μg ml−1 ) , tetracyclin ( 5 μg ml−1 ) , chloramphenicol ( 10 μg ml−1 ) , erythromycin ( 2 μg ml−1 ) , and spectinomycin ( 100 μg ml−1 ) or combinations thereof . In order to find predicted targets co-conserved with RnaC/S1022 , we used the 62 Bacillus genomes available in Genbank ( as of January 31 , 2013 ) . On each of these genomes a BLAST search ( Blastn v2 . 2 . 26 with default parameters ) was conducted with the B . subtilis 168 RnaC/S1022 sequence as identified in Nicolas et al . [9] . Genomes where a homologue of RnaC/S1022 ( E-value < 0 . 001 ) was found were then subjected to TargetRNA_v1 search with extended settings around the 5’UTR ( −75 bp; +50 bp around the start codon and additional command line arguments “-z 250 -y 2 -l 6” ) using as query the sequence of the first high-scoring-pair of the first BLAST hit in that particular genome . A bidirectional best hit criterion ( based on Blastp v2 . 2 . 26 with default parameters and E-value cut-off 0 . 001 ) was used to compare the predicted targets in each genome with the predicted targets in the reference B . subtilis 168 genome ( Genbank: AL009126-3 ) . The data was tabulated and subsetted for B . subtilis 168 genes predicted for RnaC/S1022 in 8 or more genomes . The Bacillaceae phylogenetic tree was computed based on an alignment of the rpoB gene BLAST result from the same set of genomes mentioned above . RpoB was reported to be a better determinant of evolutionary relatedness for Bacillus species than 16S rRNA [62] . Cultures grown on LB , M9G , or M9S were sampled in mid-exponential growth phase ( OD600 0 . 4–0 . 6 ) and were directly harvested in killing buffer and processed as previously described [9] . Northern blot analysis was carried out as described previously [63] . The digoxigenin-labeled RNA probe was synthesized by in vitro transcription with T7 RNA polymerase and an abrB specific PCR product as template . 5 μg of total RNA per lane was separated on 1 . 2% agarose gels . Chemiluminescence signals were detected using a ChemoCam Imager ( Intas Science Image Instruments GmbH , Göttingen , Germany ) . Western blot analysis was performed as described [64] using crude whole cell lysates . To prepare lysates , cell pellets were resuspended in LDS-sample buffer with reducing agent ( Life technologies ) , and disrupted with glass beads in a bead beater ( 3 x 30 sec at 6500 rpm with 30 sec intermittences ) . Before loading on Novex nuPAGE 10% Bis-Tris gels ( Life technologies ) , samples were boiled for 10 min and centrifuged to pellet the glass beads and cell debris . Equal OD units were loaded on gel and the intensity of the AbrB band was corrected with the intensity for the unrelated BdbD control . Data from Northern blots and Western blots were quantified ImageJ software ( available via http://rsbweb . nih . gov/ij/ ) . Rifampicin ( Sigma Aldrich ) was added to 100 ml of exponentially growing M9G culture to a final concentration of 150 μg/ml from a 100x stock solution in methanol stored at −20°C . Just before the rifampicin addition and at 1 , 2 , 4 , 6 , 8 and 10 min after rifampicin addition , 10 ml of cells were harvested in killing buffer as described previously [9] . Cell pellets were washed once with 1 ml killing buffer and frozen in liquid nitrogen . RNA was extracted according to the hot phenol method as described previously [63] . Quantitative PCR was performed as described by Reilman et al . [51] . The Ct value corresponds to the PCR cycle at which the signal came above background . We analyzed the four mRNA decay time-series ( two strains and two replicates ) with a non-linear model of mRNA concentration described in [65] that aims at capturing initial exponential decay followed by a plateau . The rate of the initial decay is supposed to correspond to the physiological degradation of the mRNA . In contrast , the final plateau can be contributed by several factors , such as background noise in measurement , a stable subpopulation of molecules , or a higher stability of the mRNAs at the end of the dynamic . In our context , we assumed that the mRNA concentration is proportional to 2-Ct and thus we fitted ( with the nls function of the R package stats ) the model-Ct ( ti ) = log2 ( A* ( α1exp ( -γ1ti ) +α2 ) ) + εi , for i = 1…7 ( ti = 0 , 1 , 2 , 4 , 6 , 8 , 10 min ) with A>0 , α1>0 , α2>0 , γ1>0 , α1+α2 = 0 and εi a Gaussian white noise . The estimates of the γ1 parameters of the first model were compared between the two genetic backgrounds ( 0 genomic copies vs . 2 copies of RnaC/S1022 ) with a student t-test after a log-transformation to stabilize the variance . For the 2-copy background , we also examined a second model that involves two exponential decay terms as would for instance arise when two sub-populations of mRNAs with distinct degradation rates coexist . It writes-Ct ( ti ) = log2 ( A* ( α1exp ( -γ1ti ) +α2exp ( -γ2ti ) +α3 ) ) + εi with A>0 , α1>0 , α2>0 , α3>0 , γ1>γ2>0 , and α1+α2+α3 = 0 . For each pair of background and model , we plotted a “consensus” line whose parameters were obtained from the geometric mean between the two replicate experiments . Promoter activity was monitored every 10 min from cells grown in 96-well plates in a Biotek® Synergy 2 plate reader . Promoter activity was computed by subtracting the fluorescence of the previous time-point from that of the measured time-point ( as in Botella et al . [38] ) . Moving average filtering ( filter function in R with filter = rep ( 1/5 , 5 ) was applied for smoothing of the promoter activity plots . Cultures grown on LB , M9G , or M9S were sampled in mid-exponential growth phase OD600 0 . 4–0 . 5 and were directly analyzed in an Accuri C6 flow cytometer . The number of recorded events within a gate set with growth medium was 15 , 000 . The coefficient of variation ( i . e . relative standard deviation ) ( CV%; standard deviation / mean * 100% ) was used as a measure of the width of the distribution , or protein/promoter expression noise . To inspect co-localization of AbrB-GFP with the nucleoid , cells were cultured until the exponential growth phase , pelleted by centrifugation , resuspended in 400μl phosphate-buffered saline ( PBS ) containing 1μl 500 ng/μl 4' , 6-diamidino-2-phenylindole ( DAPI ) , and incubated for 10 min on ice . After this , the cells were washed once with PBS and slides were prepared for microscopy . Live imaging analysis was conducted on aerated agarose cover slips as described previously [52] . Segmentation , calculation of Feret diameter , and auto-fluorescence correction for every microcolony were performed with ImageJ also as described by Piersma et al . [52] . Subsequent computations and plotting was done with R . The specific cell length ( Feret diameter ) increase per hour was computed as follows: ( ( cumulative Feret diameter at t20 min / number of cells at t0 min ) – ( cumulative Feret diameter at t0 min / number of cells at t0 min ) ) / ( ( t20 min—t0 min ) / 60 min ) . Noise promoting dynamics by sRNA regulation was modeled in a stochastic simulation model [45–47] . The considered reactions , employed parameters , and the master equation are listed in Fig . 6 . The master equation was numerically integrated by employing an in-house developed implementation of the Gillespie algorithm [66] for each combination of model parameters . The stochastic simulations were started without any molecules and were run until a quasi-stationary state was reached . To capture the inherent stochasticity of the model we performed , for each set of model parameters , 50 x 10 , 000 simulation replicates ( i . e . 500 , 000 in total ) . This can be interpreted as 50 experiments involving 10 , 000 cells each . Mean , standard deviation , and the median was computed for every molecular species in the population of 10 , 000 cells . | Bacterial cells that share the same genetic information can display very different phenotypes , even if they grow under identical conditions . Despite the relevance of this population heterogeneity for processes like drug resistance and development , the molecular players that induce heterogenic phenotypes are often not known . Here we report that in the Gram-positive model bacterium Bacillus subtilis a small regulatory RNA ( sRNA ) can induce heterogeneity in growth rates by increasing cell-to-cell variation in the levels of the transcriptional regulator AbrB , which is important for rapid growth . Remarkably , the observed variation in AbrB levels is induced post-transcriptionally because of AbrB’s negative autoregulation , and is not observed at the abrB promoter level . We show that our observations are consistent with mathematical models of sRNA action , thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation . Since a low growth rate can be beneficial for cellular survival , we propose that the observed subpopulations of fast- and slow-growing B . subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Small Regulatory RNA-Induced Growth Rate Heterogeneity of Bacillus subtilis |
Breast cancer is the second largest cause of cancer death among U . S . women and the leading cause of cancer death among women worldwide . Genome-wide association studies ( GWAS ) have identified several genetic variants associated with susceptibility to breast cancer , but these still explain less than half of the estimated genetic contribution to the disease . Combinations of variants ( i . e . genetic interactions ) may play an important role in breast cancer susceptibility . However , due to a lack of statistical power , the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer . Thus , many genetic interactions , particularly among novel variants , remain understudied . Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs , where members of the same pathway share similar patterns of genetic interactions . Based on this key observation , we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations . We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts . Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts . The discovered interactions implicated the glutathione conjugation , vitamin D receptor , purine metabolism , mitotic prometaphase , and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk . Notably , while many of the pathways identified by BridGE show clear relevance to breast cancer , variants in these pathways had not been previously discovered by traditional single variant association tests , or single pathway enrichment analysis that does not consider SNP-SNP interactions .
Cancer , like many common diseases , is influenced by a variety of genetic and environmental factors . With the rise of inexpensive genotyping technologies , the portion of risk due to inherited genetic variants has been measured with unprecedented resolution . A recent comprehensive study reported excess familial risk for 20 of 23 cancer types with an overall heritability estimate of 33% [1] . This varied across different cancer types , from prostate cancer and breast cancer on the high end with estimated heritabilities of 57% and 31% respectively , to head and neck cancers on the low end with an estimated heritability of 9% [1 , 2] . This study concluded that for most cancers , our risk is at least partially influenced by the genes we inherit . As with other heritable diseases , there has been substantial interest in identifying specific genetic loci that increase or decrease an individual’s risk for specific cancers . Over the past decade , genome-wide association studies have been the primary strategy for discovering such loci , and indeed , have been successful at identifying a large number of single-nucleotide polymorphisms ( SNPs ) with statistically significant association to a variety of diseases including cancer [3–7] . However , for most diseases , there remains a large disparity between the disease risk explained by the discovered loci and the estimated total heritable disease risk based on familial aggregation [8–13] . For example , for breast cancer , there have been approximately 100 risk loci identified to date through genome-wide association studies , but the combination of these loci explains only approximately one-third of the genetic contribution to breast cancer risk [1] , a scenario that is typical across many diseases . There are a variety of explanations for this phenomenon , commonly referred to as “missing heritability . ” For example , one explanation is that disease risk is modulated by a large number of loci , each having a relatively small effect [8–12 , 14] . Alternatively , it has been proposed that rare variants , which are not measured by most microarray-based genotyping platforms , may be responsible [8–12 , 14] . Yet another possible explanation for our inability to explain the genetic component of disease is genetic interactions between combinations of common and/or rare loci [10 , 11 , 13 , 15 , 16] . Genetic interactions describe combinations of two or more genetic variants whose combined contribution to a phenotype cannot be explained by their independent effects [13 , 17 , 18] . In principle , genetic interactions can also be discovered through genome-wide association analysis by measuring the associations between specific combinations of variants and the disease phenotype . However , in practice , the large number of possible combinations introduces both computational and fundamental statistical challenges . For a typical genotyping array , computing associations for all possible pairs ( e . g . 1011 for 500k SNPs ) is a daunting computational task . While there have been efficient and scalable computational tools developed for this purpose [19–22] , even when association tests can be computed , statistical power is too limited to support genome-wide discovery of SNP-SNP interactions [13] . We recently developed a novel method , called BridGE , for discovering genetic interactions from genome-wide association studies [23] . The approach was designed based on key insights from reverse-genetic interaction screens in model organisms where it has been observed that genetic interactions frequently cluster into highly structured motifs [24–27] . More specifically , genetic interactions often cluster into coherent groups that connect or bridge across two distinct pathways . In other words , if variants in two different genes , each belonging to a different pathway , result in a genetic interaction , then any pairwise combination of deleterious SNPs in genes annotated to the two pathways should exhibit a similar interaction phenotype . We refer to this type of genetic interaction structure as a “between-pathway” model [28] . The BridGE approach leverages this idea to explicitly search for coherent sets of SNP-SNP interactions within GWAS cohorts that connect groups of genes corresponding to characterized pathways or functional modules . Although many pairs of loci do not have statistically significant interactions when considered individually , interactions can be collectively significant if there is an enrichment of SNP-SNP interactions between two functionally related sets of genes ( Fig 1A ) . The method imposes prior knowledge of pathway membership to exploit the expected between-pathway topology of genetic networks [23] . Because the number of hypothesis tests performed for all possible between-pathway combinations is substantially less than the number of tests for all possible SNP pairs ( ~105 as compared to ~1011 ) , this enables us to extract statistically significant pathway-level interactions that can be associated with either increased or decreased risk of disease . In this study , we describe the application of our BridGE method to breast cancer as part of the “Up for a Challenge—Stimulating Innovation in Breast Cancer Genetic Epidemiology” ( U4C ) competition . Breast cancer is the second largest cause of cancer death among women in the U . S . with approximately 40 , 000 deaths annually [29] . GWAS studies have been quite successful at identifying a number of susceptibility loci for breast cancer in a variety of populations [30–37] , but as described earlier , the known loci still explain only a limited portion ( ~one-third ) of the measured heritability [1] , suggesting that there are new genetic factors to be discovered . The U4C challenge presented a unique opportunity to apply our new method to several different breast cancer cohorts representing more than five different ethnic populations and enabled a detailed analysis of how genetic interactions vary across different patient populations . We describe new pathway-level genetic interactions discovered across four U4C studies ( six independent cohorts ) ( Table 1 ) [38–42] . Our approach can discover between-pathway interactions , as described above , as well as within-pathway interactions , which are pairwise combination of SNPs in genes annotated to the same literature-curated pathway . We also describe the identification of pathways that participate in many interactions without exhibiting a specific local structure ( i . e . “hub-pathways” ) . Independent discoveries from each cohort are discussed along with replication analysis where the proper cohorts exist . We conclude with a consensus analysis of genetic interactions , which revealed a set of new pathways that are associated with breast cancer across multiple cohorts .
We applied our recently developed method , BridGE , to explicitly search for pathway-level genetic interactions from genome-wide association study ( GWAS ) data [23] . The details of our method are described in our companion paper [23] , but a brief overview is provided as part of this study ( Methods ) . In general , BridGE takes as input human genotypes from matched disease/control groups , typical of that used for GWAS , together with a set of pathways as defined by curated functional standards ( e . g . KEGG[43] , Reactome [44] , Biocarta[45] ) . The method then searches for instances of three different pathway-level models of genetic interactions , all motivated by analysis of genetic interactions in yeast [24–27 , 46]: ( 1 ) between-pathway model ( BPM ) ( Fig 1A and 1C ) , ( 2 ) within-pathway model ( WPM ) ( Fig 1B and 1C ) , and ( 3 ) hub pathways ( PATH ) ( Fig 1C ) . Between-pathway interactions occur when two pathways impinge on a common function required to maintain a healthy ( non-disease ) state . Because the two pathways can functionally compensate for each other , the disease phenotype only occurs when both pathways are perturbed in the same individual . Under the within-pathway model , a single genetic variant partially disables a pathway’s function but , when combined with another deleterious variant affecting the same pathway , complete loss of pathway function results and leads to a disease state . Pathway hubs correspond to pathways with frequent modifier effects where the target loci are not necessarily functionally coherent as under the between-pathway model , and are identified by the BridGE algorithm as pathways that involve SNPs with an elevated number of SNP-SNP interactions . Specifically , BridGE tests each pathway-level interaction structure to assess enrichment for SNP-SNP interactions based on three statistics ( χ2global , χ2local and pperm for BPM and WPM ) ( See Methods ) [23] . BridGE also implements multiple disease models ( based on the assumption that the alleles increasing susceptibility to the disease are recessive , dominant or additive ) [23] and discovers interactions associated with both increased and decreased risk of the disease of interest . We first applied our BridGE approach to the BPC3 and CGEMS cohorts ( phs000812 and phs00147 , respectively ) . These cohorts are both comprised of European Americans with genotypes measured using a common array platform ( Illumina HumanHap550 , Table 1 ) , which provides a robust basis for replication analysis . We note that despite a common patient ethnic group , distinct disease populations are represented . The BPC3 cohort is comprised exclusively of women with ER-negative breast cancer while the CGEMS cohort consists of women with invasive , post-menopausal breast cancer . Previous studies suggest both unique and overlapping risk factors for ER negative and other breast cancers [47] . In addition to detailed analysis of the two European cohorts described above , we also applied BridGE to four additional cohorts , for a total of six cohorts: MCS ( JPN , LTN , AA ) and SBCGS ( CHN ) ( Table 1 ) . The JPN cohort was genotyped using the Illumina Human 1M platform , and thus , to facilitate comparison between the JPN and CHN cohorts , we used the imputed SNPs in the CHN cohort ( Affymetrix 6 . 0 platform ) to ensure enough common SNPs across two cohorts for BridGE analysis . For the CHN cohort , we attempted discovery both from the original genotypes as well as the imputed profiles . Results on the JPN cohort were originally reported in our companion paper [23] , but are analyzed in the context of the other cohorts discussed here . BridGE was applied to discover between-pathway ( BPM ) , within-pathway ( WPM ) , and pathway hub ( PATH ) interactions independently from three out of four additional cohorts ( Table 2 , MCS AA cohort is omitted as it did not yield significant discoveries ) . Indeed , we were able to find genetic interactions in three of the four additional cohorts , although the number of interactions identified varied across cohorts as did the corresponding model ( BPM , WPM , or PATH ) ( S6 Table , S7 Table and S8 Table ) . Notably , the SBCGS CHN cohort , the largest of all cohorts we analyzed , produced a large number of discoveries ( S7 Table ) . For example , at an FDR of 0 . 25 , we discovered 37 distinct BPMs , 1 WPM , and 1 PATH interaction . Several of these involved DNA repair pathways . In particular , the base excision repair pathway ( Reactome ) was involved in 6 of the 39 genetic interactions we discovered and included interactions with other pathways such as an adipocytokine signaling pathway ( KEGG ) , ubiquitin-mediated proteolysis ( KEGG ) , and a renal cell carcinoma gene set ( KEGG ) ( S7 Table ) . Because some of the most well-known risk factors for breast cancer , e . g . BRCA1/BRCA2 , PALB2 , and ATM , are involved in DNA repair [37 , 68] , the prominence of this pathway is not surprising . Our finding suggests that these pathways are frequent modifiers in this population . Most of the significant genetic interactions discovered across the five cohorts were unique to each cohort , suggesting that the strongest genetic interactions are distinct in each population and may reflect the broad set of ethnicities represented by these cohorts . However , we reasoned that there may also be common genetic interactions underlying breast cancer risk across diverse populations , and that if we performed joint discovery across these diverse cohorts , we may be able to detect such universal risk factors . We anticipated that pathway-level interactions with moderate significance in individual cohorts that were consistently identified across multiple populations would be highly significant when analyzed together . Applying this principle , we extended our BridGE approach to enable joint analysis of between- , within- , and hub-pathway interactions across multiple cohorts . Significance of BPMs , WPMs , or PATH interactions with support across multiple datasets was assessed through resampling of the pathway-level statistics from 10 sample permutations ( case-control label permutations ) we ran for all five cohorts ( see Methods for details ) . Indeed , this analysis identified a set of BPM , WPM and PATH genetic interactions with significant support across multiple different cohorts ( Fig 4 , S5A , S5B , S5C and S5D Table ) . For example , for BPM interactions , at a stringent joint significance threshold ( p ≤ 1 × 10−5 , see Methods for details ) , we identified 17 BPMs with support in multiple cohorts , which was significantly more than random expectation based on a permutation-derived null distribution ( p = 0 . 02 , S5D Table , see Methods for details ) . Similar analysis for WPM and PATH interactions suggested greater than expected coherence across the cohorts as well ( S5D Table ) . We visualized the complete set of BPM interactions as a network to explore the relationship between the discovered interactions ( Fig 5 ) . Several interesting breast cancer-relevant pathways emerged as part of this analysis , including a vitamin D receptor pathway that appeared to act as a consensus interaction hub by connecting several significant BPMs with support from at least two cohorts each . This hub’s existence suggests that the vitamin D receptor pathway is an important modifier of breast cancer risk . All of these interactions with the vitamin D receptor pathway were associated with protective effects ( decreased risk of disease ) and included interactions with integrin signaling and the toll-like receptor signaling pathway ( Fig 4A ) . Vitamin D is a secosteroid hormone , and several previous studies have explored the potential protective effect of vitamin D levels on breast and other cancers [69 , 70] . Interestingly , despite substantial interest , studies on the protective effects of vitamin D in cancer have produced mixed results [69] . Our observation that the vitamin D receptor pathway participates in many genetic interactions may suggest that only specific subsets of patients will benefit from increased dosage of vitamin D , which is consistent with these findings . These interactions were primarily supported in the MCS LTN and SBCGS CHN cohorts . Another pathway , the glutathione conjugation pathway ( Reactome ) ( Fig 4 and Fig 5 ) , emerged as the single strongest consensus pathway interaction hub ( PATH ) associated with increased breast cancer risk , with support in three of the five cohorts examined ( MCS LTN , MCS JPN , and SBCGS CHN ) . Several between-pathway interactions were associated with the glutathione conjugation pathway in the consensus analysis as well . With additional cohorts , these discoveries could be assessed for replication beyond our consensus analysis , which would further increase confidence . Glutathione S-Transferases ( GSTs ) comprise a large and conserved family of enzymes that catalyze conjugation of reduced glutathione ( GSH ) to a variety of substrates [71] . GST-mediated conjugation of glutathione often leads to formation of less reactive products and , as a result , GSTs play an important protective role in the detoxification of toxins and reactive oxygen species produced as a result of oxidative stress [71] . Non-enzymatic roles have also been reported , whereby GSTs modulate specific cell functions through physical interaction with specific proteins and lipids in a GSH independent manner [71] . Based on their broad enzymatic and non-enzymatic functions , GSTs have been identified as important targets for anti-inflammatory and anti-tumor drug therapies [71] . Indeed , several GST isoenzymes have been associated with various forms of cancer . For example , the GSTM family of isoforms has been the focus of more than 500 studies examining associations between GSTM genotypes and various malignancies . One of these studies suggested that homozygous deletion of GSTM1 is associated with protective effects against breast cancer [72] while other studies proposed that GSTM1 null alleles have a modest effect on lung cancer [73] . Polymorphisms in another GST isoenzyme , GSTP1 , have also been shown to modify response to chemotherapy in patients with colorectal cancer and multiple myeloma [74 , 75] , and GSTP1 was shown to influence risk of acute myeloid leukemia in patients successfully treated for breast cancer , non-Hodgkins lymphoma , Hodgkins and ovarian cancer [76] . Furthermore , human tumor cell lines can overexpress GSTP1 , GSTA and GSTM isoenzymes [77] . In fact , GSTP1 overexpression is considered a major cancer biomarker that can influence both disease development and treatment [77] . For example , GST overexpression can lead to enhanced GSH conjugation and inactivation of chemotherapeutic agents [71] as well as aberrant regulation of cell growth and apoptosis signaling pathways caused by direct binding and sequestration different protein and hormone ligands [71 , 77–82] . Indeed , our systematic analysis to identify between-pathway interactions involving the glutathione conjugation pathway revealed a clear relationship between GSTs and cancer-related signaling pathways ( Fig 6B and 6C , S10 Table ) . Given the discovery of the glutathione conjugation pathway as a pathway interaction hub ( PATH ) from our consensus analysis , we performed a full analysis of the three relevant cohorts to focus on discovering significant between-pathway interactions that specifically involved the glutathione conjugation pathway . By focusing on just this pathway , we further reduced the number of hypothesis tests to improve our power to discover specific pathways interacting with glutathione conjugation . This approach was successful for two of the three cohorts ( MCS LTN , SBCGS CHN ) and produced 17 and 77 interactions at FDR ≤ 0 . 25 ( S10 Table ) , respectively . Strikingly , 3 of these BPMs were independently discovered in both cohorts: regulation of PGC-1a , toll-like receptor 9 cascade and response to E . coli infection ( Fig 6B and 6C ) . One of these pathways , PGC1A ( also called PPARGC1A ) regulates the activity of numerous transcription factors that regulate cell growth and proliferation . These transcription factors include PPARγ ( Peroxisomal Proliferator-Activated Receptor γ ) , PPARa ( Peroxisome proliferator-activated receptor alpha ) , GR ( glucocorticoid receptor ) , THR ( thyroid hormone receptor ) and estrogen receptors ( Biocarta , [83] ) . Unsurprisingly , variants in PPARGC1A , PPARGC1B , PPARγ and PGC1a have been associated with familial as well as alcohol-related breast cancer risk [84 , 85] . In addition , PPARγ is upregulated in colon and breast cancer cells [86 , 87] and relationships between PGC1a expression levels in breast tumors and clinical outcome have also been reported [88 , 89] . Importantly , a mechanistic link between the PGC1a pathway and the glutathione conjugation pathway is well established , supporting the interactions we discovered and suggesting increased breast cancer risk in patients carrying variants in both of these pathways [71] . Specifically , PPARγ is activated by binding to its ligand 15-deoxy-Δ-prostaglandin J2 ( 15d-PGJ2 ) , a potent cyclopentanone [78] . 15d-PGJ2 biosynthesis requires GST [71] and , in addition to its production , GST also regulates 15d-PGJ2 activity by directly binding to both GSH-conjugated and unconjugated forms of 15d-PGJ2 and sequestering the ligand in the cytosol away from its nuclear target , PPARγ [78 , 81] . Indeed , stable expression of GST in a breast cancer cell line inhibited PPARγ-dependent gene expression [78] . Other studies have also shown that GST can modulate the activity of various signaling and metabolic pathways in a similar manner suggesting that sequestration by GST may represent a general mechanism for regulating pathway function [71 , 77 , 79 , 80 , 82] . Such a regulatory role is consistent with our discovery of the glutathione conjugation pathway as a pathway interaction hub ( PATH ) ( Fig 5A ) and its interactions with a substantial number of pathways known to control cell growth and proliferation . Another interaction involving glutathione conjugation included the Toll-like receptor 9 ( TLR9 ) pathway ( Fig 6B ) . TLR9 is known to control the innate immune response by detecting foreign DNA from microbial or other sources [90] , And has been extensively studied in the context of breast cancer [91] . TLR9 expression has been measured in normal epithelial cells of the mammary gland as well as epithelial cancer cells and fibroblast-like tumor cells [91] . TLR9 has also been shown to have prognostic significance , specifically in triple negative breast cancers where low TLR9 expression is associated with shorter disease-free-specific survival . The link between TLR9 and glutathione conjugation is unclear , but the established relevance of both pathways to breast cancer suggests that this interaction is worth further study . In general , the independent discovery of several genetic interactions involving the glutathione conjugation pathway across multiple cohorts suggests that it likely acts as a common modifier for other risk factors . We described the application of our recently developed method , BridGE , to several breast cancer cohorts . We found significant discoveries across 5 of the breast cancer cohorts examined , suggesting that genetic interactions indeed play a role in determining breast cancer risk . Our approach leverages the key observation from reverse genetic screens in yeast , which observed that genetic interactions often form dense clusters in which they bridge across two pathways , or connect pairs of genes within the same pathway . This observation about local structure prevalent in the yeast genetic interaction network provides a powerful basis for discovering interactions in human populations . Our results here demonstrate that this approach can shed new light on risk factors for breast cancer . We note that many of the pathways involved in genetic interactions reported here are novel and have never been implicated as genetic risk factors for cancer . For example , if we consider only BPM , WPM or PATH interactions passing a conservative cutoff of FDR < 0 . 05 , we discovered a total of 25 pathways across the five cohorts ( S11A Table ) . Based on the dbGaP GWAS catalog , traditional univariate analyses have identified 172 distinct SNP variants associated with breast cancer ( p ≤ 1 . 0 × 10−5 ) from published GWAS . Mapping these variants to nearby genes and then to pathways reveals that only 9 of the 25 pathways involved in the genetic interactions reported here include a gene for which a SNP had previously been reported , suggesting that the remaining 16 of 25 have not been previously implicated through germline genetic analysis . Thus , despite their clear relevance to breast cancer , the majority of genetic interactions reported here represent novel mechanisms underlying genetic risk of breast cancer relative to previous studies of single variants . Interestingly , the BridGE-discovered pathways also cover many of the previously reported SNPs . Of the 172 unique GWAS SNPs mentioned above , 47 can be mapped to our collection of 833 pathways and 34 of these 47 ( 72% ) map to at least one of the set of BridGE-discovered pathways ( FDR ≤ 0 . 25 ) , suggesting that most pathways linked to previously identified SNPs from single locus analysis are also involved in genetic interactions . There were a large number of pathway-level interactions unique to individual cohorts we examined , suggesting that interactions can be contributed by a broad range of mechanisms and likely vary substantially across different human populations . We did , however , find evidence for a core set of interactions with support across multiple populations . Specifically , significant interactions involving glutathione conjugation , vitamin D receptor , purine metabolism , mitotic prometaphase , and steroid hormone biosynthesis pathways were discovered across different cohorts , suggesting these pathways may act as important general modifiers of breast cancer . There are several other interesting directions for future work based on the results presented here . First , one of the main inputs of the BridGE method is the definition of pathways , for which it then discovers genetic interactions . Of course , the quality and utility of the genetic interactions discovered depend on the quality of the input pathway definitions . We expect that there are several pathways highly relevant to breast cancer that are not yet well-understood or at least not well-captured by current pathway databases . As these pathway definitions improve , the BridGE approach will improve in terms of its power in discovering interactions . In the context of breast cancer , there is a wealth of functional genomic data ( e . g . gene expression profiles ) that could directly inform the definition and further refinement of pathways . Leveraging these unbiased data to improve the input pathways before running BridGE would be worthwhile . Another limitation of the BridGE approach is the resolution of the discovered interactions . The genetic interactions reported in this study were all discovered at the pathway level ( i . e . between or within-pathways ) . The premise of the method , and indeed the reason we are even able to discover genetic interactions , is that while power to detect individual pairs of SNPs with disease association is low , these associations can be discovered at the pathway level . Because of this , it is typically difficult to pinpoint individual SNPs or combinations of SNPs for further investigation . For example , for the steroid hormone biosynthesis-AML gene set interaction , the BPM was discovered on the BPC3 cohor and replicated on the CGEMS cohort . However , the overlap in the individual SNP-SNP interactions supporting these BPMs in the different cohorts was relatively small . This likely reflects both the fact that our power for detecting the actual SNP-SNP interactions underlying the association is limited as well as the fact that the actual SNPs contributing interactions between these pathways can be highly heterogeneous . Our analysis of the glutathione conjugation pathway discovery provides some hints at how to approach this challenge . Once BridGE identified the glutathione conjugation pathway as a risk factor in several cohorts , we computed the density of SNP-SNP interactions connecting each gene in the pathway . This did highlight substantial differences in the SNP-SNP interaction density contributed by each gene , providing some clues as to which individual SNPs have the strongest contributions to the pathway-level trend ( Fig 6A ) . Consensus analysis of consistent SNP-level interactions across independent cohorts , much like we performed at the pathway level , could also be an effective strategy for narrowing the focus to individual variant combinations . In general , improved methods for further dissecting pathway-level genetic interactions to identify individual SNPs or pairs of SNPs responsible for a pathway-level interaction would be of interest . Finally , another direction worth further investigation is analysis of the clinical relevance of the discovered interactions . We expect that , at least in some cases , the genetic interactions predisposing individuals to breast cancer will influence the prevention , progression , and optimal treatment of the disease . Application of our method to large cohorts with the corresponding clinical information and development of predictive modeling approaches that leverage both pathway-level and SNP-level information from the discovered genetic interactions to model clinical features will be a focus of future work .
The details of the BridGE method are described in our separate paper [23] , but we provide a brief overview of the approach here . Because there is not enough power to detect individual SNP-SNP interactions from most GWAS studies , based on the observation from yeast reverse genetic screen that genetic interactions often form dense clusters bridging across two pathways , or connect pairs of genes within the same pathway , we developed a method to specially search for pathway-level interactions . More specifically , BridGE searches for three different structures: Between-pathway model ( BPM ) : Between-pathway interactions occur when two pathways impinge on a common function required to maintain a healthy ( non-disease ) state; because the two pathways can functionally compensate for each other , the disease phenotype only occurs when genetic perturbations occur in both pathways in the same individual . Within-pathway model ( WPM ) : Under the within-pathway model , a single genetic variant partially disables a pathway’s function and additional partial loss of function variants affecting the same pathway result in a complete loss of pathway function , leading to a disease state . Hub pathway model ( PATH ) : Pathway hubs correspond to pathways with frequent modifier effects where the target loci are not necessarily functionally coherent as under the between-pathway model . Briefly , the BridGE approach involves the following five main components [23]: ( 1 ) Data processing consisting of sample quality control , adjustment for population structure between the cases and controls to avoid false discoveries due to population stratification , and control for linkage disequilibrium ( LD ) by pruning the full set of SNPs into an unlinked subset , as LD could otherwise result in spurious BPM or WPM substructures . ( 2 ) Construction of SNP-SNP interaction networks based on SNP pair-level genetic interactions scored under different disease model assumptions ( additive , recessive , dominant or combinations of recessive and dominant models ) . ( 3 ) A low-confidence , high-coverage interaction network is derived by applying a lenient threshold to the SNP-SNP interaction network . ( 4 ) Pairs of pathways from predefined gene sets are tested for BPM or WPM enrichment of SNP-SNP pair interactions with a chi-squared test . The observed density is evaluated for significance based on comparisons to the global density ( χ2global ) , the marginal interaction density of the two pathways ( χ2local ) , as well as a permutation test ( pperm ) conducted by randomly shuffling the SNP-pathway assignment ( e . g . 100K~200K times ) . ( 5 ) Pathway-level statistics are assessed for significance after correction for multiple hypothesis testing . Each pathway-level interaction can be associated with either increased risk of disease ( risk interaction: pairs of minor alleles linking two pathways are more frequent in the diseased population ) or decreased risk of disease ( protective interaction: pairs of minor alleles linking two pathways are more frequent in the control population ) . So , for example , for the between-pathway model ( BPM ) the number of hypothesis tests evaluated by BridGE is two times the number of all pair-wise pathway-pathway interactions . A sample permutation strategy ( e . g . permutation of the case-control labels 10 times ) is used to estimate the false discovery rate accounting for multiple hypotheses testing . Further details of our methods are described in [23] . For each dataset analyzed , we followed these steps to perform quality control: ( 1 ) we used a standard PLINK ( Purcell , et al . 2007 ) procedure to remove individuals with more than 5% missing values , and remove SNPs with more than 5% missing values , less than 5% minor allele frequency , or failed Hardy-Weinberg equilibrium test at 1 . 0E-6; ( 2 ) we checked relatedness among individuals , and for any pair of individuals that had a proportion IBD score greater than 0 . 2 , one of them was removed from the study; ( 3 ) we removed subjects that were identified as population outliers based on multidimensional scaling ( MDS ) analysis after combining the study data with HapMap phase III data [92]; ( 4 ) we ensured balanced population structure between the cases and controls by matching each case with a control ( implemented in PLINK with "—cluster—cc–mc 2" ) . All datasets analyzed were processed with these steps . Additional steps unique to each cohort are included in the sections that follow . We applied the BridGE method to six different cohorts derived from four GWAS breast cancer studies ( BPC3 phs000812 , CGEMS phs000147 , MCS phs000517 , and SBCGS phs000799 ) . Specifically , we tested pathway-level interactions ( BPM , WPM and PATH ) for 6 different cohorts ( EUR812 , EUR147 , JPN517 , LTN517 , AA517 , and CHN799 ) , and for the CHN799 cohort , we used the both imputed and non-imputed data , independently . Details of the procedure used are described in the sections that follow . For each dataset , we first ran pilot runs to find a proper set of parameters to be used for a full BridGE run . Specifically , we tested the four disease models ( additive , recessive , dominant , or combinations of recessive and dominant models ) with different network thresholds by performing a small number of SNP permutations ( 10 , 000 ) ( SNP-pathway assignment was randomly permuted ) , and estimated which combination of disease model and network density cutoff was the most sensitive for each dataset . Based on the pilot results , a recessive/dominant combined disease model was chosen for BPC3 ( network density = 0 . 06 ) , SBCGS ( network density = 0 . 04 ) for both imputed and non-imputed version; a dominant model was chosen for CGEMS ( network density = 0 . 04 ) , MCS JPN cohort ( network density = 0 . 04 ) , and MCS LTN cohort ( network density = 0 . 02 ) . For the MCS AA cohort , the pilot run suggested that we were unlikely to discover pathway-level interactions , so we did not apply a full BridGE run on this dataset in order to focus our computational resources on analysis of other cohorts . For all BridGE runs , we used supercomputing resources provided by the Minnesota Supercomputing Institute . As described in [23] , for the discovery of between-/within-pathway ( BPM/WPM ) interactions , three metrics are used to measure the significance of the density of SNP-SNP interactions: χ2global and χ2local are chi-square tests to measure whether the observed SNP-SNP interaction density between two pathways , or within a pathway , is significantly higher than expected globally ( the overall network density ) , and locally ( the marginal density of SNP-SNP interactions for any SNPs linked to genes in either of the two pathways ) . Additionally , a permutation test in which SNP labels are randomly reassigned is used to derive a third measure of significance ( pperm ) . These permutations are used to establish a null distribution for χ2global and χ2local for each between-/within- pathway interaction . Finally , a false discovery rate is estimated for the entire set of between- or within-pathway interactions based on sample permutations in which the entire process is repeated under permutations of the case-control labels ( χ2global , χ2local and pperm ) [23] . For the hub pathway interactions ( PATH ) , a one-tailed rank-sum test was used to test if the SNPs linked to each pathway show significantly more interactions than non-pathway SNPs , in terms of interaction degree . The sample permutation and SNP label permutation procedure is same as the between- and within-pathway interaction discovery . We reported all significant BPM , WPM or PATH interactions with FDR≤0 . 25 . The summary table ( Table 2 ) shows that all five of the datasets have significant BPM , WPM or PATH level interactions . Since many of the pathways overlap with each other , the total number of discoveries can be inflated by the fact that many overlapping pathway-pathway interactions reflect the several overlapping pathways . Thus , we also report the number of discoveries after filtering for redundancy among the pathway interactions [23] , and the information on overlap is included in our supplemental files . Detailed discovery information for each cohort can be found in ( S1 Table , S4 Table , S6 Table , S7 Table , S8 Table and S9 Table ) . All technical details of the BridGE method are described in [23] . For significant pathway-level interactions identified from any of the two European cohorts ( BPC3 , CGEMS ) , we performed replication analysis . The disease models used for full interaction discovery in the BPC3 and CGEMS were different based on trends observed in the pilot runs ( see details above ) , so for the replication analysis , we used the disease model with the discovery cohort and ran BridGE with 1000 SNP label permutations for all candidate pathway-level interaction . Ten sample permutations were also run for the replication cohort , just as in the discovery cohort . Significant discoveries were tested for validation with two different approaches . The first approach checked for replication of the individual pathway-level interactions . For each pathway-level interaction ( e . g . BPM , WPM , or PATH ) , we measured all three significance scores ( χ2global , χ2local and pperm ) on the replication cohort and tested whether they met a nominal significance criteria ( p ≤ 0 . 05 ) . Of the discoveries from the BPC3 cohort , one of the 18 significant pathway-pathway interactions , the BPM connecting the steroid hormone biosynthesis pathway to an AML gene set , was significant ( p ≤ 0 . 05 ) by all three measures . Two additional BPMs were significant ( p ≤ 0 . 05 ) by two of the three significance measures ( amine compound SLC transporter gene set and Na ( + ) - and Cl ( - ) -dependent neurotransmitter transporters ) when tested for replication in the CGEMS cohort ( S2 Table ) . In addition to testing for replication of individual pathway-level interactions , we further investigated if the total number of replicating interactions among the entire set of discoveries was higher than expected by chance . This “set-level” replication analysis was done by resampling of the same amount of pathway-level statistics from all pairwise pathway interactions . Further details on replication procedures are described in [23] . Based on the NHGRI-EBI GWAS catalog[63] , there are 172 SNP variants ( mapped to 134 genes ) reported with strong association ( p≤1 . 0 x 10−5 ) with breast cancer susceptibility . To measure the extent to which our approach produced new pathway-level insights about breast cancer susceptibility , we evaluated how many pathways in our collection were implicated basted on these 172 risk loci , and how many pathways discovered by BridGE analysis were novel relative to this set derived from traditional GWAS single variant analysis . Of 172 SNPs linked to known breast cancer risk loci , 47 of these SNPs could be mapped to our collection of 833 pathways . Then we collected all pathways that were identified by BridGE in any of the breast cancer cohorts analyzed here with a conservative FDR cutoff ( FDR ≤ 0 . 05 ) , which yielded a total of 25 unique pathways either from significant BPM , WPM or PATH discoveries . Among these pathways , 9 were in common with the pathways already implicated by at least one known breast cancer risk locus . Thus , our analysis of genetic interactions by BridGE has implicated 16 new pathways ( FDR ≤ 0 . 05 ) as playing a role in breast cancer susceptibility . We listed all unique pathways resulted from the less stringent FDR cutoff ( FDR ≤ 0 . 25 ) in S11A Table . Although we found that many pathway-level interactions discovered by BridGE were relevant to breast cancer , the most significant pathway-level interactions discovered from each cohort were relatively unique , suggesting that the strongest genetic interactions in each population are distinct . However , we observed that interactions discovered in one cohort often exhibited strong signals in additional cohorts even though they did not meet the stringent threshold required for discovery significance in a single cohort . Thus , we developed a modified version of BridGE to enable joint discovery of pathway-level interactions across cohorts to enable the discovery of these moderately significant , but consistent interactions . More specifically , we first ran the standard version of BridGE on 4 different GWAS datasets ( 6 total cohorts ) , and we summarized each pathway-level interaction based on its permutation p-values ( pperm ) across all cohorts . We then selected pathway-level interactions that were nominally supported by at least two cohorts , for which we required that all test scores ( χ2global , χ2local and pperm for BPMs and WPMs , degree rank-sum test and pperm for PATH ) be nominally significant ( p ≤ 0 . 05 ) . For each surviving pathway-level interaction , we computed the geometric mean of the p-values of all individual cohorts that met the nominal significance requirement . These criteria produced a total of 3930 consensus between-pathway interactions ( BPM ) , 76 within-pathway interactions ( WPM ) , and 59 hub pathway interactions ( PATH ) , which were sorted based on the aggregate p-value ( see S5 Table ) . The most significant between-pathway interactions ( geometric mean ≤ 5 . 0 × 10−5 ) , within-pathway interactions ( geometric mean p ≤ 5 . 0 × 10−3 ) , and hub pathway interactions ( geometric mean p ≤ 5 . 0 × 10−3 ) are visualized in Fig 4 . To evaluate the statistical significance of the discovered consensus interactions , we used the 10 random sample permutation results from each cohort . We repeated the same procedure described above 100 times , but each time , selecting the results from one of the 10 randomly permuted sample labels from each cohort , and generated consensus p-values for these random results . We applied several cutoffs to the consensus p-values ( geometric mean ) ( 5 . 0 × 10−5 , 1 . 0 × 10−4 , 5 . 0 × 10−4 , 1 . 0 × 10−3 , 5 . 0 × 10−3 , 1 . 0 × 10−2 , and 5 . 0 × 10−2 ) and counted how many of interactions from the real consensus table met the cutoff relative to the permuted results to derived an empirical p-value for the BPM , WPM and PATH consensus observations independently . For BPMs , our analysis suggested the real consensus results were significantly larger than expected at the chosen cutoffs ( 5 . 0 × 10−5 , 1 . 0 × 10−4 , 5 . 0 × 10−4 ) ( p < 0 . 02 ) . For WPM and PATH , we tested geometric mean p-value cutoffs of ( 1 . 0 × 10−3 , 5 . 0 × 10−3 , 1 . 0 × 10−2 , and 5 . 0 × 10−2 ) . The WPM consensus interaction set was significantly larger than expected by a consensus p-value cutoff of 0 . 05 ( p = 0 . 05 ) . The PATH consensus interaction set was significantly larger than expected by chance ( p ≤ 0 . 05 ) with consensus p-value cutoffs of ( 1 . 0 × 10−3 , 5 . 0 × 10−3 , 1 . 0 × 10−2 ) . Detailed information for all consensus interactions is reported in ( S5A–S5D Table ) . From the consensus interaction analysis , we identified the glutathione conjugation pathway as a major source of genetic interactions in multiple cohorts . As a PATH hub interaction , glutathione conjugation was deemed significant in MCS LTN ( LTN517 ) and SBCGS ( CHN799 ) ( FDR ≤ 0 . 25 ) and was also nominally significance in MCS JPN ( JPN517 ) . Given this strong support across different cohorts , we further investigated this pathway . | Susceptibility to breast cancer is partially encoded in our genomes , but despite the development of new genomic technologies over the past decade , we are still not able to accurately predict disease susceptibility from genome sequences . One reason for this gap is that we lack methods for finding combinations of genome variants that lead to disease . Extensive studies in model organisms have experimentally constructed millions of double mutants to study genetic interactions and have defined the basic principles by which genes combine to cause phenotypes in an organism . One powerful outcome of these studies in model systems is that genetic interactions frequently form highly organized patterns that can be used as a basis for improved detection of them in humans . We developed a novel computational approach based on this principle for identifying pathway-level interactions that contribute to breast cancer disease risk . Applying this method to six different groups of breast cancer patients , we identified a core set of pathways , including glutathione conjugation , vitamin D receptor , purine metabolism , mitotic prometaphase , and steroid hormone biosynthesis . These pathways are well-supported across multiple cohorts and may contribute to breast cancer susceptibility . | [
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] | 2017 | Pathway-based discovery of genetic interactions in breast cancer |
Rice is a facultative short-day plant ( SDP ) , and the regulatory pathways for flowering time are conserved , but functionally modified , in Arabidopsis and rice . Heading date 1 ( Hd1 ) , an ortholog of Arabidopsis CONSTANS ( CO ) , is a key regulator that suppresses flowering under long-day conditions ( LDs ) , but promotes flowering under short-day conditions ( SDs ) by influencing the expression of the florigen gene Heading date 3a ( Hd3a ) . Another key regulator , Early heading date 1 ( Ehd1 ) , is an evolutionarily unique gene with no orthologs in Arabidopsis , which acts as a flowering activator under both SD and LD by promoting the rice florigen genes Hd3a and RICE FLOWERING LOCUST 1 ( RFT1 ) . Here , we report the isolation and characterization of the flowering regulator Heading Date Repressor1 ( HDR1 ) in rice . The hdr1 mutant exhibits an early flowering phenotype under natural LD in a paddy field in Beijing , China ( 39°54'N , 116°23'E ) , as well as under LD but not SD in a growth chamber , indicating that HDR1 may functionally regulate flowering time via the photoperiod-dependent pathway . HDR1 encodes a nuclear protein that is most active in leaves and floral organs and exhibits a typical diurnal expression pattern . We determined that HDR1 is a novel suppressor of flowering that upregulates Hd1 and downregulates Ehd1 , leading to the downregulation of Hd3a and RFT1 under LDs . We have further identified an HDR1-interacting kinase , OsK4 , another suppressor of rice flowering under LDs . OsK4 acts similarly to HDR1 , suppressing flowering by upregulating Hd1 and downregulating Ehd1 under LDs , and OsK4 can phosphorylate HD1 with HDR1 presents . These results collectively reveal the transcriptional regulators of Hd1 for the day-length-dependent control of flowering time in rice .
The ability of plants to reproduce during the appropriate season enables them to adapt to environmental changes in day length and temperature and requires precise monitoring of environmental and endogenous signals [1–5] . These external and internal signals comprise a complex regulatory network that includes the aging , autonomous , vernalization , photoperiod , gibberellin , and ambient temperature pathways [6 , 7] . This network allows plants to grow at different latitudes and altitudes and during different seasons [3 , 8 , 9] . The basis for this complex network is the control of photoperiod-dependent flowering , which involves processes such as day-length measurement in leaves , the generation of mobile signals called florigens , the transport of florigens from leaves to the shoot apex , and the perception of florigens at the shoot apical meristem to initiate floral evocation [10 , 11] . The molecular mechanisms that regulate plant flowering time via the photoperiodic pathway have been extensively studied in Arabidopsis , a long-day plant ( LDP ) , and in rice , a short-day plant ( SDP ) [2 , 6 , 11–13] . In Arabidopsis , the GIGANTEA ( GI ) -CONSTANS ( CO ) -FLOWERING LOCUS T ( FT ) transcriptional regulatory pathway has been characterized . GI integrates signals from photoreceptors and the circadian clock [14] and , as a regulator of transcription , activates CO , which in turn promotes FT expression . FT functions as a florigen and coordinates with SUPPRESSOR OF OVER-EXPRESSION OF CONSTANT 1 ( SOC1 ) to promote flowering [15 , 16] . Rice is a facultative SDP . Thus , short-day conditions ( SDs ) promote flowering in rice plants , and multiple flowering genes prevent flowering under long-day conditions ( LDs ) [3 , 13] . Phylogenetic analyses and functional studies have revealed that the rice orthologs of GI , CO , and FT are OsGI , Heading date 1 ( Hd1 ) , and Heading date 3a ( Hd3a ) / RICE FLOWERING LOCUS T1 ( RFT1 ) , respectively . The pathway regulating flowering time is conserved in Arabidopsis and rice , but the functions of specific genes differ [17–22] . Similar to Arabidopsis GI , OsGI upregulates Hd1 , which suppresses flowering under LDs but promotes flowering under SD by influencing the expression of Hd3a [18 , 19 , 23–25] . Hd3a is also modulated by Early heading date 1 ( Ehd1 ) , which encodes a B-type response regulator that functions independent of Hd1 [26] . Ehd1 is an evolutionarily unique gene that does not have any ortholog in Arabidopsis and that functions as a flowering activator under both SDs and LDs by promoting the rice florigensHd3a and RFT1 [20 , 21 , 26] . Several flowering regulators involved in this Ehd1 pathway have been identified [27–38] . Conversely , OsMADS51 , which is controlled by OsGI , serves as a flowering activator upstream of Ehd1 under SD [27] . OsID1/Ehd2/RID1 , hereafter referred to a as Early heading date 2 ( Ehd2 ) , and Early heading date 3 ( Ehd3 ) promote flowering under both SDs and LDs by inducing the expression of Ehd1 [28–30] . OsMADS50 and OsMADS56 antagonistically regulate LD-dependent flowering in rice [31 , 32] . OsLFL1 ( Oryza sativa LEC2 and FUSCA3 Like 1 ) , an Ehd1 promoter-binding B3-type transcription factor , inhibits Ehd1 expression to repress rice flowering only under LD [33] . Ehd1 and Hd3a are suppressed by Ghd7 ( Grain number , plant height and heading date 7 ) only under LDs , resulting in the late flowering of rice plants under LD [34] . DTH8 ( QTL for days to heading on chromosome 8 ) , a major quantitative trait locus ( QTL ) that regulates grain productivity , plant height , and heading date in rice , is also a strong repressor of Ehd1 and accounts for late flowering in rice under LDs [35 , 36] . Ehd3 , which encodes a plant homeodomain finger-containing protein , is a critical promoter of rice flowering both under SDs and LDs by regulating the expression of Ehd1 [37] . Early heading date 4 ( Ehd4 ) encodes a novel CCCH-type zinc-finger protein that promotes flowering only under natural LDs ( NLD ) [38] . Although several regulatory factors have been characterized in Hd1-dependent and Ehd1-dependent pathways in rice , whether there is an uncharacterized regulatory factor involved in these two pathways in rice is unknown . In this study , we reported the identification of an early flowering rice mutant , and the cloning of HDR1 ( Heading Date Repressor 1 ) , a novel gene encoding a 210-amino-acid protein with a molecular weight of ~23 kD . HDR1 encodes a ubiquitous protein in plants , and the moss ortholog PpSKI encodes a putative kinase ligand that is important for the recognition of and adaptation to conditions of limited energy during plant development [39 , 40] . Expression and functional analyses of HDR1 revealed that it activates Hd1 and represses Ehd1 , thereby downregulating the florigen genes Hd3a and RFT1 to postpone rice flowering . An HDR1-interacting kinase protein encoded by OsK4 was also identified , which functions to phosphorylate HD1 to regulate flowering time in rice .
We previously reported the development and characterization of T-DNA-mutagenized rice populations [41–43] . An average of 2 . 8 copies of T-DNA and 2 . 1 copies of newly transposed Tos17 were integrated into the rice genome during transformation process . Systematic characterization of the mutant populations led to the identification of early and late flowering mutants , and the sequences flanking the T-DNA/Tos17 insertion sites in the mutants were amplified [41–43] . One mutant , named hdr1 , flowered approximately 30 days earlier than wild-type ( WT , Oryza sativa var . Nipponbare ) plants following growth under NLDs ( Fig 1A ) in a paddy field in Beijing , China ( 39°54'N , 116°23'E ) . The phenotypes of WT and hdr1 plants were assessed under natural-day field conditions ( NDs ) in Beijing and under LDs ( 14 h of light/10 h of dark ) and SDs ( 10 h of light/14 h of dark ) conditions in a controlled growth chamber . Under NDs and LDs , the hdr1 plants flowered approximately 30 days earlier than the WT plants , whereas no significant difference was observed under SDs ( Fig 1B ) . These data suggested that HDR1 might participate in the regulation of photoperiodic flowering in rice . A genetic analysis revealed that a newly transposed copy of Tos17 that was inserted into the 3'-untranslated region ( UTR; 22 nt after the stop codon ) of a rice gene ( LOC_Os02 g55080 in the TIGR database [http://rice . plantbiology . msu . edu] ) co-segregated with the early flowering phenotype of hdr1 . HDR1 expression was analyzed using quantitative real-time PCR ( qPCR ) in WT and hdr1 plants using two pairs of primers based on the sequences of exon 2 ( Q-p1 and Q-p2 ) and the 3’-UTR ( Q-p3 and Q-p4 ) . Primers Q-p1 and Q-p2 indicated that HDR1 expression was dramatically decreased in hdr1 , and expression was only very weakly detected using primers Q-p3 and Q-p4 , indicating that a low level of HDR1 mRNA was produced in the hdr1 plants ( Fig 1C ) . The mature hdr1 plants were slightly shorter and had similar numbers of tillers ( Fig 1D and 1E ) . No obvious difference in panicle length , 500-grain weight , or yield per plant was detected , indicating that the fertility levels of the mutant and WT plants were similar ( Fig 1F–1H ) . Notably , the leaf-emergence rate of hdr1 was similar to that of WT under both SDs and LDs ( Fig 1I and 1J ) , indicating that the early flowering phenotype was not caused by an increase in growth rate . To verify that the early flowering phenotype we observed was caused by the disruption of HDR1 , we performed a complementation experiment using a genomic DNA construct that included the promoter , coding , and terminator regions of HDR1 and a full-length HDR1 cDNA construct driven by the HDR1 promoter . In total , 204 positive T0 lines were obtained and confirmed by PCR ( 69 lines for the genomic construct and 135 lines for the full-length cDNA construct ) . Of these , 142 lines ( 44 for the genomic construct and 98 for the cDNA construct ) exhibited a flowering time of 20–28 days later than hdr1 , similar to WT ( in a paddy field in Beijing , China . From these lines , 30 complemented T1 plants ( 15 for the genomic construct and 15 for the cDNA construct ) were randomly selected to produce homozygote plant for further analysis; these plants exhibited a normal flowering times compared with WT plants . Flowering time was subsequently measured in WT , hdr1 , and complemented plants under different photoperiods . The hdr1 plants flowered approximately 4 weeks earlier than the WT and complemented plants under LDs ( 14 h of light/10 h of dark ) during the natural growing season ( Beijing , China ) ( Fig 2A–2D ) . Slightly early flowering was also observed under SDs ( Fig 2D ) . These data indicate that both constructs complemented the early flowering phenotype of the mutant completely ( S3 Table ) . To further investigate the function of HDR1 in rice flowering time , we generated HDR1 RNA interference ( RNAi ) and overexpression transgenic plants . The HDR1-RNAi T0 plants exhibited an early flowering phenotype similar to that of the hdr1 mutant; in 118 PCR-confirmed T0 plants , we observed 32 lines that flowered 1 to 4 weeks earlier than WT . Furthermore , two independent T2 transgenic lines ( HDR1-RNAi-3 and HDR1-RNAi-8 ) exhibited early flowering under NDs and LDs , along with a dramatic reduction in HDR1 expression levels ( Fig 2E–2G ) . However , late flowering was not observed in paddy field-grown overexpression lines . Moreover , careful observation of plants under NDs , SDs and LDs revealed similar flowering times among the WT and transgenic overexpression plants ( S1A–S1C Fig ) . Taken together , our data indicated that HDR1 is a negative regulator of flowering time under LDs in rice . HDR1 in rice encodes an ortholog of PpSKI , a putative kinase ligand in moss [39 , 40] . HDR1-like proteins are ubiquitous in monocots , dicots , and moss , implying an ancient origin for this protein . Subsequent phylogenetic analysis revealed three corresponding main subgroups in monocots ( e . g . , rice , maize , sorghum , barley , millet , and wheat ) , eudicots ( e . g . , soybean , chickpea , grape , cucumber , tomato , potato , watermelon , almond tree , rape , apple tree , and Arabidopsis ) , and mosses and ferns ( Physcomitrella patens and Selaginellamoellendorffii , respectively ) . The Amborellatrichopoda homolog of HDR1 formed a single clade consistent with its unique evolutionary line of flowering plants that diverged very early ( ~130 million years ago ) from all other extant species of flowering plants ( Fig 3A ) . We also noted that the C-terminus of the HDR1-like family is highly conserved in different species ( S2 Fig ) . Upon HDR1 expression in rice leaf protoplasts , the in-frame HDR1 cDNA::GFP fusion protein was specifically localized in the nucleus ( Fig 3B–3E ) . To investigate the expression profile of HDR1 in rice , we examined HDR1 expression levels in several rice tissues under LDs . Higher HDR1 expression was detected in leaves and floral organs ( Fig 3F ) , in accordance with the previously described expression patterns of several other flowering-time genes [19–38] . Furthermore , genomic HDR1fragments ( including 2 kb of the promoter and part of the coding region ) were fused in frame to the β-GLUCURONIDASE ( GUS ) gene to generate rice transgenic plants expressing the fusion protein . Histochemical staining revealed that HDR1 was strongly detected in leaves compared with other tissues ( Fig 3H–3K ) . Many flowering genes display a diurnal expression pattern [19–38] . We therefore examined the daily rhythmic expression pattern of HDR1 transcripts under SDs and LDs . Under both conditions , HDR1 expression showed a diurnal expression pattern in leaves . The transcript level began to increase after dusk , reaches a peak before dawn , and damps rapidly thereafter under both SD and LD ( Fig 3G ) . The early flowering of hdr1 compared with WT under LDs but not SDs is similar to the phenotypes of a previously described Hd1 knock-down mutant [23–25] and Ehd1-overexpressing plants [26] . Hd1 and Ehd1 are two pivotal parts of the network that controls flowering in rice [13]; therefore , Hd1 and Ehd1 expression were studied in WT and hdr1 plants under LDs and SDs . Hd1 rhythmic expression was significantly decreased , whereas Ehd1 expression was significantly increased , under LDs but not SDs in hdr1 compared with WT plants ( Fig 4A and 4B ) . These results suggested that HDR1 might participates in the Hd1 and Ehd1 pathways to control flowering time in rice under LDs . Previous work showed that Hd1 can repress Ehd1 expression [44]; therefore , it is possible that the down-regulation of Ehd1 is due to altered Hd1 transcription . The expression of a non-functional allele of Hd1 or low-level expression of Hd1 in plants causes high-level expression of the florigen gene Hd3a , indicating that Hd1 represses Hd3a expression and delays flowering under LDs [18 , 25] , whereas Ehd1 increases Hd3a and RFT1 expression to promote flowering under LDs [26] . To confirm whether the decreased expression of Hd1 and increased expression of Ehd1 in hdr1 are responsible for the up-regulation of Hd3a and RFT1 and the subsequent promotion of flowering in rice , we examined the rhythmic expression patterns of Hd3a and RFT1 in mutant and WT plants . Under SDs , Hd3a and RFT1 expression remained unchanged between hdr1 and WT ( Fig 4A ) . However , Hd3a and RFT1 expression increased significantly compared with the levels in WT in the light and were maintained at low levels in the dark under LDs ( Fig 4B ) . These results were consistent with the conclusion that the two FT-like genes Hd3a and RFT ensure flowering in rice under LDs [13 , 22] . HDR1 rhythmic expression in hd1-mutant and ehd1 plants was also analyzed . HDR1 expression did not differ significantly in hd1-mutant plants compared with WT under LDs ( S3A Fig ) . The lack of change in HDR1 expression in the hd1 background and the significant suppression of Hd1 expression in an hdr1 background under LDs demonstrates that HDR1 is an upstream regulator of Hd1 . HDR1 expression was not also significantly affected in ehd1 plants , indicating that Ehd1 might encode a downstream component of HDR1 ( S3A Fig ) . OsGI [18 , 19 , 23–25] , OsPhyB [45] , Ehd2 [28–30] , Ehd3 [37] , Ehd4 [38] , Ghd7 [34] , DTH8 [35] , OsMADS50 [31] , and OsMADS51 [27] are important genes under the control of Hd1 and Ehd1 expression under LDs . We next investigated whether these regulatory genes are down- or up-regulated by HDR1 . The expression levels of OsGI , OsphyB , Ehd2 , Ehd3 , Ehd4 , Ghd7 , DTH8 , OsMADS50 , and OsMADS51 in the hdr1 background were similar to WT under LDs ( S3C Fig ) . When we tested the expression levels of HDR1 in osgi , osphyb , ghd7 , dth8 , osmads50 , osmads51 , ehd2 , ehd3 and ehd4 mutants or nonfunctional NIL , the qPCR assay did not indicate down or up-regulation of these flowering-time regulation genes ( Fig 4B ) . The expression data indicated that HDR1 function as flowering regulator , independent of OsGI , OsphyB , hd2 , Ehd3 , Ehd4 , Ghd7 , DTH8 , OsMADS50 , and OsMADS51 . In addition , a yeast two-hybrid assay did not indicate any direct association between HDR1 and these flowering regulators to regulate flowering in rice ( S4B Fig ) . We further carried out a yeast one-hybrid assay to investigate whether HDR1 can bind the promoter regions of Hd1 and Ehd1 . However , HDR1 could not bind the promoter regions of Hd1 and Ehd1 ( S8 Fig ) . HDR1 encodes a homolog of the ligand of the SNF1/AMPK/SnRK1 kinase PpSKI in moss [40] . Several kinase genes belonging to the SnRK family have been identified in plants , including Arabidopsis and rice [46–49] . A yeast two-hybrid ( Y2H ) library , which consisted of different developmental stages leaf and floral tissues , was screened to identify interacting kinase of HDR1 in rice . Using HDR1 as bait , multiple independent fragments for many interacting proteins were identified . Among them , 5 independent clones that fulfilled the criteria of interaction with HDR1 were isolated and sequenced . The different sequences of these 5 clones match to be a gene OsK4 ( LOC_Os08 g37800 [TIGR] ) . A protein with 97 . 84% similarity to OsK4 , named OsK3 ( Os03 g0289100 [TIGR] ) , also exists in the rice genome ( S5A Fig ) . Firstly , we conducted yeast two-hybrid assays to confirm direct interaction of OsK4 and OsK3 with HDR1 . Indeed , OsK4 and OsK3 both directly interacted with HDR1 ( Fig 5A , S5B Fig ) . Detailed assays were also performed to clarify the role of the different exons of HDR1 in interacting with OsK4 in the Y2H system , which revealed that exon1 and exon2 , but not exon3 , strongly interacted with OsK4 . Given the evolutionary conservation of the C-terminus of HDR1 , these data indicated that the N-terminus of HDR1 is important for interacting with OsK4 ( Fig 5B and S2 Fig ) . In addition , we also noted that hdr1 exhibited late germination , similar to that reported for osk3 and osk4 mutants ( S6 Fig and [48] ) . To investigate the subcellular localization of the HDR1 and OsK4 interaction in vivo , bimolecular fluorescence complementation ( BiFC ) was employed to confirm direct interaction between HDR1 and OsK4 using Arabidopsis protoplasts . As expected , YFP fluorescence was only detected in Arabidopsis protoplasts co-transfected with pSAT1-nVenus-C-HDR1 and pSAT1-cCFP-N-OsK4 , not two negative controls of pSAT1-nVenus-C-HDR1/pSAT1-cCFP-N and pSAT1-nVenus-C/pSAT1-cCFP-N-OsK4 , confirming the physical interaction of HDR1 and OsK4 in vivo ( Fig 5C ) . Next , we conducted co-immunoprecipitation ( Co-IP ) experiments to verify the interaction between HDR1 and OsK4 . For this purpose , we generated transgenic lines expressing FLAG-HDR1 driven by Actin1 promoter could rescue hdr1 phenotype ( S10 Fig ) , and utilized an OsK4 antibody that could specifically recognize the OsK4 protein ( S7 Fig ) . We found that anti-FLAG ( recognizing FLAG-HDR1 ) could efficiently immunoprecipitate OsK4 , revealing that OsK4 associated with HDR1 ( Fig 5F ) . Collectively , these data revealed that HDR1 strongly interacts with OSK4 . To investigate the function of OsK4 in rice flowering , we generated OsK4- and OsK3-RNAi plants . The RNAi lines were first identified using a GUS activity assay ( the vector pTCK309 contains a β-GLUCURONIDASE ( GUS ) gene derived by the 35S promoter within the T-border , which enables the convenient identification of transgenic plants ) , and then confirmed by qPCR ( Fig 6A ) . The OsK4-RNAi lines flowered approximately 2 weeks earlier than WT , whereas no early flowering plants were observed among the OsK3-RNAi lines ( Fig 6B and 6C ) . We further constructed the hdr1 OsK4-RNAi double mutant , and found it flowered similarly with hdr1 mutant in LDs ( Fig 6D ) . The early flowering of the hdr1 and OsK4-RNAi plants coupled with the interaction between HDR1 and OsK4 raised the question of whether a similar regulatory pathway involving HDR1 and OsK4 operates in rice under LD . qPCR analyses were conducted under LDs and SDs , but the upregulation of Ehd1 and downregulation of Hd1 were detected only under LD , confirming that OsK4 exhibited the same pattern of regulation as HDR1 . Similarly , the increase in Ehd1 and steep decrease in Hd1 caused the upregulation of the florigensRFT1 and Hd3a was observed only under LD ( Fig 7A ) . OsGI , OsPhyB , Ehd2 , Ehd3 , Ehd4 , Ghd7 , DTH8 , OsMADS50 and OsMADS51 were also investigated in OsK4-RNAi plants to check for an alternative regulatory pathway . The two RNAi lines exhibited no obvious differences in expression compared with WT plants ( Fig 7B ) . To clarify the relationship between HDR1 and OsK4 , we first analyzed the localization and expression of OsK4 and HDR1 in hdr1 or OsK4-RNAi transgenic plants . In hdr1 , the OsK4-GFP fusion protein localized to the nucleus ( S9A–S9D Fig ) , in accordance with a previous report [48] . To investigate whether a lack of HDR1 function affects the OsK4 protein level , we performed western blotting to measure OsK4 protein levels in hdr1 and WT , and found no differences in the levels of OsK4 ( Fig 8A and 8B ) . Two transgenic lines were used to determine the HDR1-GFP fusion protein location . HDR1 was exclusively localized to the nucleus , as in WT ( S9E–S9L Fig ) . Interestingly , HDR1 protein levels were dramatically decreased in OsK4-RNAi plants ( Fig 8C and 8D ) . The low HDR1 levels in Osk4-RNAi lines may due to the low HDR1; therefore , we examined the HDR1 gene rhythmic expression pattern in WT and the OsK4-RNAi lines . The expression rhythm of HDR1 was not changed in OsK4-RNAi ( Fig 8E ) . We also found that overexpression of HDR1 could rescue the early flowering phenotype of OsK4-RNAi plants ( Fig 8F ) . Given our findings that HDR1 and OsK4 did not directly interact with HD1 or EHD1 ( S4A Fig ) , but that HDR1 acted as an upstream regulators of Hd1 and Ehd1 and could form a complex with OsK4 , we explored the possibility of a physical interaction between HDR1-OsK4 and HD1 or EHD1 . We performed Y3H assays with HD1 or EHD1 fused to the GAL4 activation domain ( AD ) , and OsK4 fused to the GAL4-binding domain ( BD ) along with HDR1 co-expression . We found that , indeed , HDR1 and OsK4 together could interact with HD1 , not EHD1 ( Fig 9A ) . To further verify that HDR1 and OsK4 interacted to HD1 , a Co-IP assay was carried out . We found that HDR1 and OsK4 effectively precipitated HD1 when FLAG-HDR1 and OsK4 were input together , indicating that HD1 is part of the HDR1-OsK4 complex ( Fig 9B ) . It was reported that the HD1 ortholog CO in Arabidopsis could be phosphorylated [50] . We found accordingly that two forms of HD1 protein migrated differently after electrophoresis in WT , but none HD1 protein was detected in hd1 ( Fig 9C ) . To examine whether phosphorylation of HD1 contributes to these different forms , nuclear protein extracts of WT were incubated with λ phosphatase . After incubation of protein phosphatase , the slower-migrating form of HD1 was no longer detected ( Fig 9D ) . The direct interaction of HDR1 and OsK4 with HD1 in vivo raise the question of whether OsK4 phosphorylates HD1 , as well as the role of HDR1 in the process of HD1 phosphorylation . Firstly , we performed western blot to verify phosphorylated HD1 level in WT , hdr1 and OsK4-RNAi plants . Two forms of HD1 protein migrated differently after electrophoresis in WT and mutant lines , and less phosphorylated HD1 protein was detected in hd1 and OsK4-RNAi lines ( Fig 9E ) . To verify the phosphorylation of the HD1 , we carried out an in vitro phosphorylation assays . Using the recombinant purified proteins GST-OsK4 , FLAG-HDR1 and HIS-HD1 incubated with [γ-32P] ATP , autoradiogram indicated only in the presence of HDR1 , OsK4 could phosphorylate HD1 ( Fig 9F ) . We further confirmed that OsK4 immunoprecipitated from Flag:HDR1 plants could phosphorylate HD1 in vivo ( Fig 9G ) . All the data gave clues that HDR1was necessary for the phosphorylation of HD1 by OsK4 .
Rice is known as a short-day plant , and most rice cultivars may be induced to flower more rapidly under SDs than LDs . There are at least two independent flowering pathways , Hd1 and Ehd1 , that regulate floral transition in rice [13 , 21] . The Hd1 pathway is conserved between rice and Arabidopsis , but the Ehd1 pathway is unique to rice [21] . Our results reveal that HDR1 shows a similar diurnal expression pattern with Hd1 and Ehd1 , which accumulates after dusk , reaches a peak before dawn , and damps rapidly thereafter under both SD and LD ( Fig 3G ) . The expression pattern implicated HDR1 in photoperiodic control of flowering through the regulation of Hd1 and Ehd1 . Upon the loss of HDR1 function , Hd1 rhythmic expression was significantly decreased under LDs but not SDs , whereas Ehd1 expression was significantly increased ( Fig 4A and 4B ) . Previous studies have indicated that Hd1 may function to repress Ehd1 expression [44] . The results of the present study indicated that Hd1 expression is suppressed , and Ehd1 expression is promoted by HDR1 under LDs ( Fig 4B ) . Thus , HDR1 might integrate different pathways in the regulation of flowering , in which Hd1 is upregulated by HDR1to repress Ehd1 expression , and may also be independently involved in the Ehd1 pathway . Several genes that control the Hd1 and Ehd1 pathway have been identified and studied , including OsGI , Ehd2 , Ehd3 , Ehd4 , Ghd7 , DTH8 , OsMADS50 and OsMADS51 [27–38] . In an effort to explore the relationship between HDR1 and these known regulators , we first examined whether the expression levels of these genes were altered in the relevant mutants . The expression levels of HDR1 and these genes remain unchanged in WT and mutants under LD ( Fig 4C and S3C Fig ) . We also observed the nuclear localization of HDR1 which suggests this protein may function to form nuclear complexes with regulatory factors to control flowering time . However , HDR1 did not associate with these flowering regulators ( S4B Fig ) . These results suggested that HDR1 is a novel regulator of flowering time , independent of OsGI , Ehd2 , Ehd3 , Ehd4 , Ghd7 , DTH8 , OsMADS50 , and OsMADS51 ( Fig 10 ) . However , HDR1 was unable to bind the Hd1 and Ehd1 promoters ( S8 Fig ) , indicating that there are other components to media the binding of this complex or Hd1 and Ehd1 are likely indirect targets . HDR1 is ubiquitous in plants , including mosses , implying its ancient origin . The only reported homolog is PpSKI [40] , which encodes a ligand of the kinase SnRK1 in moss . The deletion of PpSKI increases gametophore formation and reduces protonemal growth under low-light conditions but not under normal light conditions in moss [40] . However , HDR1 functions in floral transition in rice , and disruption of HDR1 shows early flowering . Moreover , the PpSKI-interacting protein SnRK1 , which is a Snf1-related protein kinase found in yeast , animals , and plants , is required for necessary metabolic changes in plants during dark hours [39] . In plants , Snf1-related kinases participate in the regulation of many physiological processes , including the cell cycle , meristem development , and pathogen responses [49] . As predicted , HDR1 act as a Snf1-related kinase interactor 2 , and associated protein OsK4 , a Snf1-related kinase has been identified [39] . We postulated that this complex may function to phosphorylate target proteins . To test this hypothesis , we first analyzed the localization and expression of OsK4 and HDR1 in hdr1 or OsK4-RNAi transgenic plants to clarify the relationship between HDR1 and OsK4 . In hdr1 , the OsK4-GFP fusion protein localized to the nucleus ( S9A–S9D Fig ) , in accordance with a previous report [48] . Interestingly , the HDR1 protein levels were dramatically decreased in OsK4-RNAi plants ( S9E–S9L Fig ) . The fact that disrupting OsK4 function leads to effect steady-state levels of HDR1 expression suggests that OsK4 functions to affect the protein levels of HDR1 . These results collectively indicated that the early flowering phenotype in OsK4-RNAi plants might be regulated by a decrease in HDR1 protein levels . Our study indicated that HDR1 and OsK4 regulate Hd1 expression at transcriptional level . Protein interaction analyses support that HDR1-OsK4 directly interacts with HD1 at protein level . These data suggested that might exist a feedback way to regulate Hd1 , The likely regulating model could be found in some important functional gene in plants . Auxin response factors ( ARFs ) , NPH4/ARF7 and ARF19 , could bind auxin response promoter elements and mediate gene transcription [51] . Auxin induces the ARF19 gene , and NPH4/ARF7 and ARF19 together are required for its expression , suggesting NPH4/ARF7 and ARF19 may activate ARF19 in a positive feedback loop [51] . Hd1 like ARF19 , as above described , is regulated in a positive feedback way needed HDR1-OsK4 functionally . As mentioned above , OsK4 and HDR1 act as a Snf1-related kinase and a Snf1-related kinase interactor 2 , respectively . Recently studies indicated that the protein kinase CK2 ( Hd6 ) might control Hd1 activity through the phosphorylation of an unknown interacting factor to control flowering time in rice [50] . We thought that HDR1-OsK4 might also phosphorylate HD1 , although the expression level of Hd1 is regulated by HDR1 and OsK4 . As expected , HDR1-OsK4 could form nuclear complexes with HD1 in vivo , and phosphorylate HD1 ( Fig 9C ) . The HDR1 is key component of the complex to assist phosphorylation of HD1 . Taken together , these findings suggest that HDR1-OsK4 acts to downregulate Hd1/Ehd1 expression and phosphorylate Hd1 to prevent early flowering in LDs , conferring a photoperiodic response . AtSnRK1 , the OsK4 ortholog in Arabidopsis , is an atypical AMPK . AtSnRK1 family members comprise a catalytic α subunit and non-catalytic β and γ subunits , and multiple isoforms of each subunit type exist , giving rise to various isoenzymes [52] . This suggested that HDR1 might functionally act as non-catalytic β or γ subunits in rice . It is reported that the SnRK1 kinase complex are necessary to phosphorylate regulated transcription factors [46 , 49] . Hence , the phosphorylation of HD1 needs not only catalytic subunit OsK4 , but also subunits such as HDR1 presenting . It has not reported that phosphorylated transcription factors prior to combine the kinase complex or not [46 , 49] , whether HDR1 can only interact with phosphorylated Hd1 needed further illuminated later .
The Tos17-tagged mutant hdr1 was identified from our T-DNA insertion rice mutant library ( O . sativa L . spp . japonica cv . Nipponbare ) [41–43] . Screening for flowering time mutants was carried out by planting the mutant materials under natural LD conditions in the experimental field at the Chinese Academy of Agricultural Sciences in Beijing ( 39°54'N , 116°23'E ) , China , from May to October of each year . The agricultural traits of mutant and WT plants were recorded in three growing seasons . Rice plants were also grown in a growth chamber under LD ( 14 h light/10 h dark ) and SD ( 10 h light/14 h dark ) with a light intensity of 800 μmol•m-2•s-1 . To analyze the diurnal expression patterns of flowering genes and flowering time , the materials were first grown under NLD for 30–40 days and then transferred to LD or SD for 10 days . Genomic DNA flanking the Tos17 left border was cloned using PCR-based genome walking with nested specific primers according to Peng , with modifications [42] . The modified primers were TB1 , TB2 , AP1 and AP2 . The products were sequenced using TB2 as the sequencing primer . The Tos17 insertion site in HDR1 was identified by BLAST searches of the rice genome ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) using the rescued flanking sequences . Two constructs were used for complementation testing . For the cDNA construct , the full-length HDR1 cDNA ( amplified using primer pair HDR1-cp3-F and HDR1-cp3-R , S1 Table ) was cloned into the binary vector pCam2300K , which was modified from pCambia2300Actin1 carrying the 1 . 8-kb HDR1 promoter ( amplified using primer pair HDR1-cp1-F and HDR1-cp2-R , S1 Table ) , to generate pHDR1::HDR1 . For the genomic construct , a 5 . 1-kb fragment containing the promoter , coding , and terminator regions of HDR1 was amplified from genomic DNA ( using the primer pair HDR1-cp1-F and HDR1-cp1-R , S1 Table ) and then cloned into the vector pCam2300K . The two constructs were introduced into Agrobacterium tumefaciens strain AGL1 by electroporation and then transformed into hdr1 mutant calli . The regenerated plants were grown in a paddy field at the CAAS in Beijing , China [42] . The genotype of each transgenic plant was determined by PCR . DNA encompassing 113 bp of the 5' UTR of HDR1 , 150 bp of the 5' UTR of OsK4 and 160 bp of the 5' UTR of OsK3 were amplified using specific primer pairs ( S1 Table ) and inserted into pTCK309 using the SacI , SpeI , KpnI , and BamHI sites in inverted orientations . The construct was transferred to A . tumefaciens strain AGL1 by electroporation and then introduced into WT rice calli . The regenerated plants were grown in a paddy field at the CAAS in Beijing . Early flowering RNAi transgenic plants were selected and analyzed . To generate pActin1::Flag:HDR1 , the coding region of HDR1 was amplified using a specific primer pair ( S1 Table ) and inserted into pCamabia1307 using the XbaI and SalI sites . The plasmid containing Flag-HDR1 was digested with XbaI and SalI and then inserted into pCambia2300Actin1 using the XbaI and SalI sites . The construct was introduced into hdr1calli through A . tumefaciens-mediated transformation , and the transgenic plants were grown in a paddy field and analyzed by comparing the flowering time with WT plants . The full-length coding sequence of Hdr1 or OsK4 was PCR-amplified from cDNA using the primers pairs HDR1-GFP-fw and HDR1-GFP-rv or OsK4-GFP-fw and OsK4-GFP-rv ( S1 Table ) and cloned into pAN580 to generate p35S:: HDR1:GFP or p35S::OsK4:GFP . The fusion plasmid and pMcherry ( p35S::RFP , as a control ) were transiently co-expressed in rice leaf protoplasts by PEG ( polyethylene glycol ) as previously described [38] . The protoplast cell layers were then examined by laser scanning confocal microscopy [Leica TCS SP2; Wetzlar , Germany] . GFP fluorescence was imaged using excitation with the 488-nm argon laser line and a 505–530-nm band-pass emission filter . Total RNA was extracted using the RNeasy Plant Mini Kit ( QIAGEN , Hiden , Germany ) from various rice plants ( WT , hdr1 , hd1 , ehd1 , OsK4-RNAi , OsK3-RNAi , HDR1-overexpressing and various flowering relating genes [38] ) . For mutant or near isogenic lines of flowering time genes , penultimate leaves were harvested around the reported peak expression level of each gene during the 24-h photoperiod according to Gao ( Penultimate leaves were harvested around the reported peak expression level of each gene during the 24-h photoperiod at dawn for OsPhyB , Ehd1 , Ehd2 , Hd3a , RFT1 and Ehd4; 3 h after dawn for Ghd7; 8 h after dawn for Ehd3 , OsMADS50 , OsMADS51 and DTH8; and immediately after dusk for OsGI and Hd1 [38] ) . Total RNA ( 2 μg ) from each sample was reverse-transcribed with an oligo ( dT ) primer and Ace RT Enzyme [Toyobo , Osaka , Japan] according to the manufacturer’s instructions . The PCR conditions were as follows: preincubation at 94°C for 2 . 5 min , followed by 30 cycles of 94°C for 20 s , 52°C for 20 s , and 72°C for 30 s . qRT-PCR was performed on an iQ5 Multicolor Real-Time PCR Detection System [Bio-Rad] with SYBR Green Real-Time PCR Master Mix ( Life Technologies ) . The rice Ubiquitin1 gene was selected as an internal standard . The fold change of the transcript level of a gene of interest to that of UBQ1 is calculated as 2-ΔCt [ΔCT = CT ( gene of interest ) —CT ( UBQ ) ] . The primer pairs for other gene amplifications are specified in S2 Table . GUS assays were carried out as described previously [43] . Rice tissues were incubated in GUS staining solution ( 50 mM sodium phosphate , pH 7 . 0 , 10 mM EDTA , 0 . 1% Triton X-100 , 1 mg/ml X-gluc , 0 . 1 mM potassium ferricyanide , and 10% methanol ) at 37°C for 10–12 h , followed by washing with 70% ethanol . The stained tissues were imaged under a zoom stereo microscope [Nikon SMZ1000; Tokyo , Japan] . The protein-coding regions of HDR1 and OsK4 were amplified using gene-specific primers ( S1 Table ) . Then , the PCR products were ligated into pSAT1-nVenus-C and/or pSAT1-cCFP-N using the EcoRI and XhoI/SalI sites to produce pSAT1-nVenus-C-HDR1 , pSAT1-cCFP-N-OsK4 , pSAT1-nVenus-C-OsK4 , and pSAT1-cCFP-N-HDR1 . All constructs were verified by sequencing . Plasmid pairs were transfected into Arabidopsis protoplasts as described previously , using pairs of each construct and the empty vector as negatives control [53] . The cells were then examined by laser scanning confocal microscopy ( Leica TCS SP2 ) . YFP fluorescence was imaged by excitation with the 515-nm argon laser line and a 535–565-nm band-pass emission filter . The protein-coding regions of HDR1 , OsK3 , OsK4 , Hd1 , Ehd1 , Hd3a , RFT1 , Ehd2 , Ehd3 , Ehd4 , OsMADS50 , OsMADS51 , OsGI , OsPhyB , DTH8 and Ghd7 were amplified using gene-specific primers ( S1 Table ) . Then , the PCR products were fused into the activation domain ( AD ) vector pGADT7 and/or the DNA-binding domain ( BD ) vector pGBKT7 using the EcoRI and XhoI/SalI sites . All constructs were verified by sequencing . The constructs were then transformed into Saccharomyces cerevisiae strain AH109 [BD Biosciences , Palo Alto , CA , USA] according to the manufacturer’s protocol . No detectable self-activation was observed for each construct on SD selective medium ( SD-His-Leu + 5 mM 3-AT or SD-His-Trp + 5 mM 3-AT ) . Yeast cells were spotted on SD media lacking leucine , tryptophan , histidine and adenine . The 2 . 2-kb and 4 . 2-kb promoter regions of Hd1 and Ehd1 were amplified ( S1 Table ) , and the fragments were then cloned into the pLacZi vector using EcoRI and XhoI sites to generate pLacZip-Hd1 and pLacZip-Ehd1 . The resulting plasmids were used to transform yeast cells , followed by secondary transformation with pGBKT7-HDR1 and selection according to the manufacturer’s manual [PT1031-1 , CLONTECH Laboratories Inc . , Mountain View , CA] . Full-length of OsLFL1 cDNA was amplified ( S1 Table ) and then inserted into plasmid pGBKT to generate pGBKT-OsLFL1 as a positive control , as OsLFL1 binds the Ehd1 promoter region . Plasmids were co-transformed into the yeast strain YM4271 . Yeast cell were spotted onto SD/His- plates and grown for 2 days . Then , the yeast cells were transferred onto SD/His-/Leu- 45mM 3-AT plates with X-gal ( 5-bromo-4-chloro-3-indolyl-b-D-galacto-pyranoside ) for blue color detection . HDR1 was amplified ( S1 Table ) and digested with EcoRI and BamHI , OsK4 was amplified and digested with BamHI and PstI , and the PCR fragments were then introduced into the pBridge vector using the EcoRI and PstI sites , generating pBridgeHDR1-OsK4 . Then , the vector sequence was verified by DNA sequencing , and the constructs were used to transform yeast cells . The resulting strains were co-transformed with the AD fusion vector pGADT7-HD1 and pGADT7-EHD1 . Transformants were selected on medium according to the manufacturer’s instructions ( PT3212-5; CLONTECH Laboratories Inc . , Mountain View , CA , USA ) . Plasmids were co-transformed into the yeast strain AH109 . Yeast cells were spotted and grown on SD medium without leucine , tryptophan , histidine and adenine . Total proteins were extracted from six-week-old rice seedlings in RIPA buffer ( 50 mM Tris-HCl , pH 7 . 2 , 150 mM NaCl , 10 mM MgCl2 , and 1 mM phenylmethylsulfonyl fluoride , protease inhibitor mixture [Sigma , St . Louis , MO , USA] ) , and the supernatant was collected . The total protein concentration was determined using a Bradford protein assay kit ( Bio-Rad ) . Proteins were detected using anti-FLAG [Sigma , St . Louis , MO , USA] , anti-HDR1 , anti-HD1 or anti-OsK4 [kindly supplied by Dr . Liu , BIG , CAS] antibodies at 1:4000 dilution and visualized with an enhanced chemiluminescence ( ECL ) reagent ( GE Healthcare ) . The HIS-HDR1 fusion protein were overexpressed in Escherichia coli ( strain BL21 ) and purified to generate multi-clone antibodies . Frozen cells were extracted with 100 μl of extraction buffer ( 50 mM Tris-HCl . pH 7 . 6 , 15 mM MgCl2 , 0 . 1 M KCl , 0 . 25 M sucrose , 10% glycerol , 1 mM phenylmethylsulfonyl fluoride , protease inhibitor mixture [Sigma , St . Louis , MO , USA] , and 14 mM β-mercaptoethanol ) . After centrifugation of the sample at 15 , 000 x g for 10 min , the supernatant was sampled , and the protein concentration was measured . The amount of protein form each extract was measured according to the manufacturer’s instructions [Laboratories Inc . , Mountain View , CA , USA] . For Co-IP assays , total protein extracted from FLAG:HDR1 transgenic or none-transgenic and OsK4-RNAi transgenic or none-transgenic was mixed with purified 2mg HIS-HD1 and anti-FLAG antibody . The mixture was incubated overnight with Magna Protein A Magnetic Beads [Millipore , Temecula , California , USA] at 4°C in 1000 μl of binding buffer ( 20 mM Tris-HCl , pH 7 . 6 , 2 . 5 mM β-mercaptoethanol , and 0 . 1 M NaCl ) . After incubation , the beads were washed five times with washing buffer ( 20 mM Tris-HCl , pH 7 . 5 , 500 mM NaCl , and 0 . 5% Triton X-100 ) . After washing , 40 μl of 1× SDS-PAGE sample buffer was added , the mixture was denatured , and the sample was loaded on a 10% SDS-PAGE gel . Proteins were detected using a horseradish peroxidase ( HRP ) -conjugated anti-FLAG [Sigma , St . Louis , MO , USA] , anti-HD1 or anti-HIS antibody , and then visualized with an ECL reagent ( GE Healthcare ) . Protein phosphorylation experiments were carried out based on previously described [54] . Briefly , for in vitro phosphorylation of rice HD1 by OsK4 , purified GST-OsK4 , FLAG-HD1 and/or HIS-HDR1 fusion proteins in reaction buffer ( 50 mM HEPES-KOH ( pH 7 . 5 ) , 10 mM MgCl2 , 5 mM MnCl2 , 1 mM DTT , 0 . 1 mM ATP , and 10 mCi of [γ-32P]ATP ) . The mixture was allowed to react for 30 min at 30°C . The phosphorylation of proteins was taken autoradiogram using a phosphor imager after SDS-PAG electrophoresis . Immunoprecipitated proteins from FLAG-HDR1 plant bound to the protein A beads were also subjected to kinase assays . Each reaction contained 10μl of beads in reaction buffer as described above . The mixture was allowed to react for 30 min at 30°C . Radioactive signals were detected using a phosphor imager . An HD1 phosphorylation assay was also conducted using anti-HIS antibodies . | In rice , flowering time affects the potential yield , the growing season and regional adaptability . Change in day length is a key seasonal cue for regulating flowering time in rice , a facultative short-day ( SD ) plant . The photoperiodic pathway of rice contains the evolutionarily conserved Hd1-Hd3a module , which is homologous to the CO-FT module in the long-day ( LD ) plant Arabidopsis . In this work , we cloned a novel gene , HDR1 , that activates Hd1 and represses Ehd1 , thereby down-regulating the florigen genes Hd3a and RFT1 to postpone rice flowering . A protein associated with HDR1 , OsK4 , was also identified , and the resulting complex can interact with HD1 to phosphorylate HD1 . We conclude that HDR1 is a novel transcriptional regulator of Hd1 that plays a crucial role in regulating flowering time via the photoperiodic pathway in rice . | [
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] | 2016 | The Oryza sativa Regulator HDR1 Associates with the Kinase OsK4 to Control Photoperiodic Flowering |
Ubiquitylation is fundamental for the regulation of the stability and function of p53 and c-Myc . The E3 ligase Pirh2 has been reported to polyubiquitylate p53 and to mediate its proteasomal degradation . Here , using Pirh2 deficient mice , we report that Pirh2 is important for the in vivo regulation of p53 stability in response to DNA damage . We also demonstrate that c-Myc is a novel interacting protein for Pirh2 and that Pirh2 mediates its polyubiquitylation and proteolysis . Pirh2 mutant mice display elevated levels of c-Myc and are predisposed for plasma cell hyperplasia and tumorigenesis . Consistent with the role p53 plays in suppressing c-Myc-induced oncogenesis , its deficiency exacerbates tumorigenesis of Pirh2−/− mice . We also report that low expression of human PIRH2 in lung , ovarian , and breast cancers correlates with decreased patients' survival . Collectively , our data reveal the in vivo roles of Pirh2 in the regulation of p53 and c-Myc stability and support its role as a tumor suppressor .
Protein ubiquitylation is essential for a broad spectrum of cellular processes including nuclear export , endocytosis , transcriptional regulation , DNA damage repair and proteasomal degradation [1] . Impairment of the ubiquitylation process has been associated with a variety of human diseases including autoimmunity , immunodeficiency , inflammation and cancer [2] , [3] . One well established role for ubiquitylation in cancer is its function in regulating the stability and function of the tumor suppressor p53 and the oncogene c-MYC , two proteins with major roles in human malignancies [2]–[4] . Monoubiquitylation of p53 serves to signal its nuclear export as well as its mitochondrial translocation , while p53 polyubiquitylation targets it for proteasomal degradation . Remarkably , p53 is targeted for ubiquitylation by several ubiquitin E3 ligases including Pirh2 ( Rchy1 ) , Mdm2/Hdm2 , Cop1 , E6/E6AP , ARF-BP1 , Synoviolin and by atypical E3 ligases including E4F1 [5] . Similar to p53 , ubiquitylation is critical for the regulation of the function and stability of c-MYC , an oncoprotein frequently overexpressed in various human cancers including breast and ovarian cancer [6] , [7] . The ubiquitin ligases FBW7 , SKP2 and ARF-BP1/HectH9 have been shown to mediate c-MYC ubiquitylation . While c-MYC polyubiquitylation by FBW7 leads to its proteasomal degradation , its polyubiquitylation by ARF-BP1 increases its transcriptional activity . c-MYC ubiquitylation by SKP2 has been shown to mediate both its transactivation activity and proteasomal degradation [2] , [6] . Interestingly , some of the E3 ligases important for the regulation of c-Myc also regulate p53 function . SKP2 negatively regulates p53 by suppressing its acetylation by p300 while ARF-BP1 suppresses p53 through its polyubiquitylation and proteolysis [8] , [9] . Furthermore , FBW7 loss of function has been reported to attenuate p53 activity [10] . The identification of multiple E3 ligases that regulate the function and stability of p53 and c-MYC and the possible involvement of some of these E3 ligases in the regulation of both p53 and c-Myc have raised questions regarding the physiological functions of these E3 ligases . Despite the identification of Pirh2 as an E3 ligase that polyubiquitylates p53 in cell culture assays , its in vivo functions remain unknown . Here we report that mice deficient for Pirh2 are viable but display elevated levels of p53 and apoptosis in response to DNA damage . We also demonstrate that c-Myc is a novel Pirh2 interacting protein and that Pirh2 regulates c-Myc expression levels by mediating its polyubiquitylation and proteolysis . Consistent with this novel Pirh2 function , mice mutant for Pirh2 show elevated levels of c-Myc and increased risk for plasma cell hyperplasia , gammaglobulinemia , and tumorigenesis . In accordance with the role of p53 in suppressing c-Myc oncogenesis , its inactivation considerably increases spontaneous cancer susceptibility of Pirh2 deficient mice . We also report that the expression of PIRH2 is reduced in various human cancers and that lower levels of PIRH2 expression correlate with decreased survival of patients with lung , breast or ovarian cancer . Collectively , these data support a role for Pirh2 in the in vivo regulation of p53 functions , demonstrate its negative regulation of the oncoprotein c-Myc , and support its role as a novel tumor suppressor .
Although p53 is a substrate for ubiquitylation by several E3-ligases , to date only Mdm2 is known to be required for p53 regulation in vivo as its deficiency leads to embryonic lethality in a p53-dependent manner [11]–[13] . To characterize the physiological functions of Pirh2 , an E3 ligase previously reported to polyubiquitylate p53 and regulate its stability [14] , we generated Pirh2 mutant mice ( Figure S1A and S1B ) . Western blot analysis indicated loss of Pirh2 protein in Pirh2−/− cells ( Figure S1C ) . By contrast to Mdm2 deficiency [11]–[13] , loss of Pirh2 did not affect embryonic development . Pirh2−/− mice were born in a Mendelian ratio , were fertile and did not present any apparent developmental defects compared to their wildtype ( Wt ) littermates . Analysis of total cell numbers as well as the different cell subpopulations in thymus , spleen , lymph nodes ( LN ) and bone marrow ( BM ) of 6 to 8 week-old Pirh2−/− mice revealed no significant differences compared to Wt littermates ( Figure S1D and S1E ) . In addition , [3H] thymidine incorporation assay indicated that loss of Pirh2 had no significant effect on the in vitro proliferation of T- and B-cells from 6 to 8 week-old Pirh2−/− mice ( Figure S1F ) . These data indicate that Pirh2 is dispensable for embryonic and postnatal development . p53 is essential for several cellular processes including apoptosis , cell cycle , senescence and metabolism and its stability is regulated by various proteins including the E3 ligases Mdm2 , Cop1 , and ARF-BP1 [5] , [15] . To determine the role Pirh2 plays in the in vivo regulation of p53 , we examined p53 levels and functions in Pirh2 deficient mice and cells . Splenocytes and thymocytes from Pirh2−/− and Wt mice were irradiated ex-vivo , and the levels of p53 expression and activation were determined by Western Blot analysis . Untreated Pirh2−/− cells displayed mildly increased expression of p53 compared to Wt controls ( Figure 1A ) . However , untreated Pirh2−/− cells , similar to Wt controls , displayed no detectable expression of the p53 transcriptional targets p21 , bax , and Puma ( Figure 1A ) . We examined the effect of Pirh2 deficiency on irradiation ( IR ) induced p53 expression and have observed that irradiation of Pirh2−/− splenocytes resulted in higher expression levels of p53 and its targets p21 , bax , and Puma compared to Wt controls ( Figure 1A and 1B ) . Consitent with the reported increased transcription of pirh2 in response to p53 activation in mouse embryonic fibroblasts ( MEFs ) [14] , Pirh2 mRNA was found to increase by 1 . 5 fold at 1 to 4 h post irradiation of splenocytes ( Figure S2A ) . To examine the in vivo effects of Pirh2 loss on the expression of p53 , Pirh2−/− mice and their Wt littermates were subjected to whole-body irradiation ( 6 Gγ ) and their tissues were collected at different time points post-irradiation ( 0–12 h ) . Immunohistochemistry ( IHC ) examination of p53 expression indicated that while p53 was undetectable in spleen , thymus , intestinal crypts and liver from untreated Pirh2−/− and Wt mice , the frequency of cells expressing p53 in these organs was significantly higher post-IR of Pirh2−/− mice compared to Wt controls ( Figure 1C and 1D , Figure S2B–S2D ) . We next examined the effect of PIRH2 deficiency on P53 level using human RKO cells in which PIRH2 was knocked down by a tetracycline-inducible system [16] . Similar to murine cells , deficiency of PIRH2 in RKO cells only slightly increased P53 level under untreated conditions , but this increase was more pronounced post-IR ( Figure S2E ) . Phosphorylation of p53 is critical for its stability and functions [17] . For instance , in response to DNA damage , p53 is phosphorylated on Serine 15 ( p53-S15 ) by a number of kinases including ATM , DNA-PK and ATR and phosphorylation of this p53 site , together with others , is important for its stability and functions [17] . To further examine the effect of Pirh2 deficiency on p53 stability and function , we examined by Western blotting the level of p53-S15 in Pirh2−/− and Wt cells . While , the level of p53-S15 was similarly low in untreated Pirh2−/− and Wt splenocytes , it was significantly higher in irradiated Pirh2−/− cells compared to Wt controls ( Figure S3A ) . Similarly , IHC analysis indicated no differences in the levels of p53-S15 in spleen of untreated Pirh2−/− mice and Wt controls; however a marked increased expression of this phosphorylated form of p53 was observed post whole-body irradiation of Pirh2−/− mice compared to Wt controls ( Figure S3B ) . We next examined the effect of PIRH2 expression on the level of S15-p53 using H1299 cells transiently transfected with expression vectors for MDM2 or PIRH2 and either wildtype p53 , p53 S15A or p53 S15D . The presence of Alanine ( A ) at residue 15 of p53 prevents p53 phosphorylation at this site while the presence of Asparatic acid ( D ) at this site mimics its constitutive phosphorylation . Western blot analysis indicated that overexpression of MDM2 or PIRH2 decreased the level of wildtype p53 ( Figure S3C ) . Consistent with previous data , overexpression of MDM2 was able to decrease the stability of p53 S15A and p53 S15D [17]–[19] . Interestingly , while overexpression of PIRH2 resulted in decreased expression of both forms of p53 , its effect was more pronounced on p53 S15D . Collectively , these data support a role for Pirh2 in the regulation of p53 . p53 plays central roles in IR-induced apoptosis; therefore we have examined the effect of Pirh2 deficiency on the levels of apoptosis under untreated and DNA damage conditions . Splenocytes and thymocytes from Pirh2−/− and Wt mice were either untreated or irradiated ( 6 Gγ ) and their apoptotic levels examined using the AnnexinV/PI assay ( Figure 2A ) . We also examined the level of apoptosis in thymus , spleen and intestinal crypts from whole-body irradiated ( 6 Gγ ) mice using TUNEL assay and cleaved caspase 3 IHC ( Figure 2B and 2C , Figure S3D and S3E ) . Our data indicated no increased level of spontaneous apoptosis in the absence of Pirh2; however consistent with the elevated IR-induced expression of p53 and its proapototic target genes Bax and Puma in the absence of Pirh2 , IR-induced cell death was significantly elevated in Pirh2−/− cells and mice compared to Wt controls . These data demonstrate that Pirh2 deficiency results in elevated IR-induced apoptosis . In our search for novel Pirh2 interacting proteins we identified interaction of Pirh2 and c-Myc . We observed that human PIRH2 and c-MYC reciprocally co-immunoprecipitate upon overexpression in HEK293 cells ( Figure 3A ) . Endogenous interaction of murine Pirh2 and c-Myc was confirmed in NIH3T3 cells where immunoprecipitation of Pirh2 pulled down c-Myc and immunoprecipitation of c-Myc pulled down Pirh2 ( Figure 3B ) . We next examined whether PIRH2 and c-MYC interaction was direct and characterized the domains involved in this interaction . We performed Glutathione S-transferase ( GST ) pull-down assays using purified GST or His-fusion proteins of the full length or fragments of PIRH2 and c-MYC . These pull-down assays indicated interaction of PIRH2 amino acids ( aa ) 138–261 with the N-terminus of c-MYC ( aa 120–160 ) that contains MYCboxII ( MBII ) but not with N-terminus of c-MYC ( aa 1–69 ) that contains MYCboxI ( MBI ) ( Figure 3C and 3E , Figure S4A ) . Notably , MBII ( aa 128–143 ) is important for c-MYC proteasomal degradation , transcriptional repression and activation and for controlling c-MYC transformation ability [20] . Pull-down experiments using His-C-terminus of c-MYC ( aa 353–434 ) demonstrated the interaction of this region of c-MYC with the N-terminus of PIRH2 ( aa 1–137 ) ( Figure 3D and 3E ) . These data reveal c-MYC as a novel interacting protein for PIRH2 and indicate the importance of different domains of c-MYC and PIRH2 for this interaction . Polyubiquitylation of c-Myc is critical for the regulation of its stability and functions [2] , [6] . Our observation that c-MYC interacted with the E3 ligase PIRH2 raised the possibility that it might be a target for PIRH2 mediated ubiquitylation . We examined this hypothesis in HEK293T cells that overexpress HA-Ubiquitin ( Ub ) , human c-MYC , and/or either human full length PIRH2 or a PIRH2 lacking the ring finger domain ( PIRH2ΔR ) . 48 h post-transfection , immunoprecipitation followed by Western Blot analysis revealed high-molecular-weight c-MYC species where full length PIRH2 , but not PIRH2ΔR , were overexpressed in the presence of c-MYC and HA-Ub ( Figure 4A ) . Anti-HA immunoblot of c-MYC immunoprecipitates confirmed that the observed high-molecular-weight c-MYC species in the presence of the full length PIRH2 were indeed due to its polyubiquitylation . Our data also demonstrated the requirement for the RING finger domain of PIRH2 for its polyubiquitylation of c-MYC ( Figure 4B ) . Similarly , we examined the ability of PIRH2 to polyubiquitylate T58A c-MYC as phosphorylation of c-MYC at this site is critical for its polyubiquitylation by FBW7 . Consitent with our finding that MBI that contains T58 is dispensible for the interaction c-MYC-PIRH2 , PIRH2 was able to ubioquitylate T58A mutant c-MYC ( Figure S4B ) . We also performed in vitro ubiquitylation assays and observed that PIRH2 directly polyubiquitylates c-MYC ( Figure 4C and 4D ) . We next examined the role of Pirh2 in the ubiquitylation of the endogenous c-Myc using Pirh2−/− and Wt B-cells . Anti-IgM activated B-cells were pre-treated for 3 h with the proteasome inhibitor MG132 , and c-Myc immunoprecipitates were examined for their levels of ubiquitylation using anti-c-Myc and anti-ubiquitin antibodies . Pirh2 deficiency resulted in reduced level of both high-molecular-weight and polyubiquitylated forms of c-Myc ( Figure 4E and 4F ) . Taken together , these data identified c-Myc as a novel substrate for Pirh2-mediated polyubiquitylation . Since c-Myc polyubiquitylation can control its turnover , we examined the steady state levels of c-Myc in Wt and Pirh2−/− MEFs in the presence of the protein biosynthesis inhibitor cycloheximide ( CHX ) . These studies indicated that while less than 40% of c-Myc remained in Wt MEFs 1 h post-CHX treatment , near 80% of c-Myc remained in Pirh2−/− MEFs ( Figure 5A ) . Similarly , knockdown of human PIRH2 in RKO cells resulted in increased c-MYC stability ( Figure 5B ) . Because Pirh2 interacted with c-Myc and mediated its polyubiquitylation and proteolysis , we examined the effect of its deficiency on the level of c-Myc protein . We first assessed by Western blotting the expression of c-Myc in B-cells from young ( 6–8 weeks old ) Pirh2−/− mice and their Wt littermates . c-Myc protein level was significantly increased in naïve and in anti-IgM activated Pirh2−/− B-cells compared to Wt controls ( Figure 5C ) . Immunohistochemistry analysis of the expression level of c-Myc protein in spleen from young Pirh2−/− and Wt mice also showed increased c-Myc level in the absence of Pirh2 ( Figure 5D ) . Quantitative real time PCR analysis of cDNA from Pirh2−/− and Wt splenocytes indicated that despite the increased protein expression of c-Myc in Pirh2−/− cells , c-Myc mRNA abundance was not significantly affected compared to Wt controls ( Figure 5E ) . However , examination of c-Myc transcriptional targets indicated that the RNA expression level of E2f1 , Ccna2 , Ccnd2 and Brca1 was significantly increased while Mcl-1 RNA expression was repressed in accordance with the elevated levels of c-Myc in Pirh2−/− cells ( Figure 5E ) . Increased level of Ccnd2 protein was also observed in splenocytes from young Pirh2−/− mice compared to Wt littermates ( Figure 5F ) . Western blot examination of c-Myc expression in various tissues from young Pirh2−/− mice and Wt littermates indicated increased c-Myc level in the absence of Pirh2 , with the highest increase observed in spleen , testis and mammary glands ( Figure 5F and 5G ) . The tumor suppressor p19ARF has been shown to be upregulated in response to high , but not to low , levels of deregulated c-Myc [21] . Using Western blotting we evaluated the level of p19ARF in cells and tissues from young Pirh2−/− mice and their Wt littermates . The level of p19ARF protein was found elevated in Pirh2−/− activated B-cells and in tissues that express high levels of c-Myc ( e . g . spleen , testis , mammary glands ) ( Figure 5C , 5G and 5J ) . We next examined whether the loss of human PIRH2 would also lead to increased level of c-MYC protein . Western blot analysis indicated that similar to Pirh2 deficiency in mice , knock down of the human PIRH2 in RKO cells significantly increased the protein expression level of c-MYC ( Figure 5H ) . Conversely , overexpression of human PIRH2 in RKO cells downregulated their protein level of c-MYC ( Figure 5I ) . Since Skp2 and Fbw7 mediate c-Myc polyubiquitylation and proteasomal degradation [2] , [6] , [22] , [23] , we examined their expression levels in Pirh2−/− cells and Wt controls . Western blot analyses indicated no difference in the expression levels of Skp2 in spleen , liver and kidney from Pirh2−/− and Wt mice , while Fbw7 expression was barely detectable in these organs independently of the genotypes ( Figure 5J ) . We next sought to examine whether PIRH2-c-MYC complex may also contains SKP2 or FBW7 . While Western blotting analysis of whole cell lysate of RKO cells showed that these cells expressed PIRH2 , SKP2 and FBW7; PIHR2 immunoprecipitation from these cells pulled down c-MYC , but failed to pull down SKP2 or FBW7 ( Figure S4C ) . Therefore SKP2 and FBW7 are not part of the PIRH2-c-MYC complex . Collectively , these data demonstrate that Pirh2 deficiency leads to increased level of c-Myc protein and that Pirh2 regulates c-Myc stability . PIRH2 was reported to be overexpressed in lung and prostate cancer [24] , [25]; however , to date no human pathologies have been associated with its decreased expression . To assess whether reduced Pirh2 expression associates with cancer , we examined PIRH2 expression in primary human cancers using microarray studies that associated mRNA levels to patient outcome . Median dichotomization was used to identify patients with high- and low-PIRH2 expression levels . We found that lower levels of PIRH2 mRNA were associated with reduced patient survival in breast cancer [26] , [27] ( Figure 6A ) , ovarian cancer [28] , [29] ( Figure 6B ) and squamous cell carcinomas of the lung [30] , [31] ( Figure 6C ) . The expression level of PIRH2 mRNA was also surveyed in microarray studies of human cancer using the Oncomine database [32] . Expression of PIRH2 mRNA was significantly reduced in ovarian clear cell adenocarcinoma [33] , adult germ cell tumors [34] and invasive bladder cancer [35] compared to controls ( Figure S5 ) . These data indicate the high prognostic significance of PIRH2 expression for patients with these tumor types . Deregulated c-Myc has been associated with a number of diseases including plasma cell hyperplasia , gammaglobulinemia , kidney failure and cancer development [7] , [36]–[38] . Monitoring of cohorts of Pirh2 mutant and Wt mice indicated that both Pirh2−/− and Pirh2+/− mice had reduced lifespan compared to Wt littermates ( Figure 7A ) . Necropsy of Pirh2 mutant mice indicated that these mutants developed a lymphoproliferative disorder characterized by plasma cell hyperplasia . 100% of sick Pirh2 mutants presented splenomegaly while lymphadenopathy was observed in 40% and 20% of sick Pirh2−/− and Pirh2+/− mice respectively ( Figure 7B and 7C ) . Hematoxylin and Eosin ( H&E ) staining of spleen and lymph nodes from sick Pirh2 mutant mice , together with in-touch blood smears , illustrated the presence of plasma cells with large basophilic cytoplasm , eccentric nuclei with clock face appearance and occasional multiple Russell bodies ( Figure 7D–7G ) , all typical features of well differentiated plasma cells . FACS analysis and IHC staining for CD138 , a marker for plasma cells , indicated increased proportion of CD138+B220− cells in the spleen of sick Pirh2−/− mice ( Figure S6A and S6B ) . Infiltration of these plasma cells was evident in regional lymph nodes and kidneys ( Figure 7E and 7G ) . Pirh2−/− plasma cell perivascular infiltrates to non lymphoid organs displayed elevated levels of c-Myc that was accompanied by increased proliferation as indicated by their level of Ki67 ( Figure S7A ) . Consistent with these data and the increased expression of c-Myc in Pirh2−/− splenocytes , activated T-cells or B-cells from Pirh2−/− mice displayed higher proliferation rates compared to Wt controls ( Figure S7B ) . In addition these Pirh2−/− activated cells also displayed increased apoptosis compared to Wt controls ( Figure S7C ) . As the cytokine interleukin-6 ( IL-6 ) is important for the differentiation and survival of plasma cells [39] , and Pirh2−/− mice develop a plasma cell disorder , we next examined by ELISA the serum level of IL-6 in Pirh2−/− mice and their controls . We observed elevated levels of IL-6 in the serum of sick Pirh2 mutant mice compared to Wt littermates ( Figure S6D ) . Macroscopic analysis of sick Pirh2−/− and Pirh2+/− mutant mice indicated the presence of pale kidneys in about 40% of the cases . In order to examine the cause for this kidney defect , H&E staining was performed on kidneys from Pirh2 mutants and Wt littermates . Since infiltration of plasma cells was observed in kidneys from Pirh2 mutant mice ( Figure 7G ) , we investigated whether the kidney defects of these mice would be associated with immunoglobulin ( Ig ) deposits . Staining of kidney sections with anti-Ig antibodies indicated the presence of glomerular Ig deposition in Pirh2−/− and Pirh2+/− mice but not in age matched Wt littermates ( Figure S6C ) . In accordance with the plasma cell hyperplasia and the elevated level of glomerular Ig deposition observed in Pirh2 mutant mice , ELISA analysis of serum Ig levels of 10 to 12 month-old Pirh2−/− mice indicated that these mice suffered gammaglobulinemia . Significantly elevated levels of IgG1 , IgG2b and IgA were observed in the serum of Pirh2−/− mice compared to their Wt littermates ( Figure S6E ) . While plasma cells in sick Pirh2 mutant mice showed several histological and morphological features of malignancy , their lack of clonality as assessed by rearrangement of Ig loci , and the polyclonal gammaglobulinemia of Pirh2 mutant mice prevented us from characterizing the disease as a plasma cell neoplasm . These data indicate that Pirh2 deficiency results in plasma cell hyperplasia , gammaglobulinemia , glomerular Ig deposition and kidney failure that likely contribute to the premature death of Pirh2 mutant mice . Overexpression of ubiquitin ligases including MDM2 , COP1 and ARF-BP1 has been observed in human cancer [40]–[42] , while downregulation of others such as FBW7 or Rnf8 promotes tumorigenesis [2] , [43] , [44] . Monitoring of cohorts of Pirh2 mutant mice indicated that in addition to plasma cell hyperplasia and kidney failure , approximately 25% of Pirh2−/− and 17% of Pirh2+/− sick mice developed solid tumors including sarcomas , liver , testes , mammary and lung tumors ( Figure 8C and 8D , Figure S8A–S8D and Table S1 ) . P53 is the most frequently inactivated tumor suppressor in human cancer [45] , and its loss facilitates c-Myc induced oncogenesis [46] . To examine the effect of p53 deficiency on development and tumorigenesis of Pirh2 mutant mice , we crossed Pirh2−/− and p53−/− mice and generated Pirh2−/−p53−/− mice . Double mutant mice were born in a Mendelian ratio and showed no developmental defects . Remarkably , Pirh2−/−p53−/− mice had a dramatically reduced lifespan compared to p53−/− and Pirh2−/− mice with all double mutants dying before 126 days of age compared to 230 days for p53−/− littermates ( Figure 8A ) . Necropsy examination of Pirh2−/−p53−/− mice indicated that 60% of them died due to their tumor burden , while the cause of death remained unknown for the remaining mutants . The tumor spectrum of Pirh2−/−p53−/− mice was significantly altered compared to Pirh2−/− and p53−/− mutants and consisted of adenocarcinomas , sarcomas , thymomas and T-cell lymphomas ( Figure 8B , 8E and 8F; Table S2 ) . Examination of cells and tissues from healthy Pirh2−/−p53−/− mice indicated that these mice , similar to Pirh2−/− mice , exhibited elevated levels of c-Myc ( Figure 8G and 8J ) . Western blot analysis also indicated elevated levels of c-Myc in Pirh2−/− tumors ( Figure 8H and 8I ) ; however the majority of these tumors did not show elevated levels of p19ARF ( Figure 8I ) . In addition , similar to Pirh2−/− tumors , c-Myc levels were also elevated in Pirh2−/−p53−/− tumors ( Figure 8J ) . These data support Pirh2 function as a tumor suppressor and demonstrate that tumorigenesis of Pirh2 deficient mice is exacerbated by the loss of p53 .
Ubiquitylation plays critical roles in the regulation of stability and function of p53 and c-Myc , two of the most important tumor suppressors and oncogenes , respectively . Several E3 ligases have been shown to regulate p53 stability [5]; however , the physiological functions of most of these E3 ligases have not yet been addressed . Mdm2 is well accepted as the master regulator for p53 stability and has been demonstrated for its requirement for embryonic and postnatal development [11]–[13] , [47] . Previous studies also indicated that while Mdm2 is required for p53 stability due to its central role in the polyubiquitylation and degradation of p53 [13] , [17] , Mdm2 independent regulation of p53 degradation also exists . In this study we report that in contrast to Mdm2 deficiency , absence of Pirh2 did not affect embryonic development and only mildly affected p53 steady state levels . However , in response to DNA damage and despite the presence of other E3 ligases for p53 ( e . g . Mdm2 ) , Pirh2 deficiency resulted in higher p53 levels in several tissues . Although , we cannot exclude a tissue specificity for the increased IR-induced p53 expression in the absence of Pirh2 , collectively our current data indicate a role for Pirh2 in the regulation of p53 stability in response to DNA damage and support a model in which different E3 ligases regulate p53 turnover in a developmental , tissue , stress or time specific manners . The oncogene c-MYC is frequently deregulated in human cancer [7] and similar to p53 , its stability and function are regulated by a number of posttranslational modifications including ubiquitylation . SKP2 , FBW7 and ARF-BP1 have been demonstrated to mediate c-MYC ubiquitylation [2] , [6] . In this study we identified c-MYC as a novel interacting protein for PIRH2 and demonstrated that this interaction requires both the N- and C-terminal domains of PIRH2 as well as the MBII and the C-terminal domain of c-MYC . Similar to its interaction with PIRH2 , c-MYC interaction with SKP2 also involves both its MBII and C-terminal domain [22] , [23] . In this study , we also demonstrate that PIRH2 polyubiquitylates c-MYC and that this c-Myc polyubiquitylation controls c-MYC stability and proteolysis . Pirh2 deficient murine cells and tumors , and human RKO cells knocked down for PIRH2 , displayed significantly elevated levels of c-Myc protein , supporting that ubiquitylation control of c-Myc turnover is not only mediated by Skp2 and Fbw7 , but also involves Pirh2 . Our in vitro data showing that PIRH2 interacts with c-MYC and mediates its polyubiquitylation strongly support a direct role for this E3 ligase in c-MYC polyubiquitylation and degradation . The embryonic lethality of Fbw7 mutant mice [48] and the postnatal developmental defects of Skp2 mutants [49] , [50] contrast with the normal embryonic and postnatal development observed with Pirh2 mutants . These developmental differences , together with the differential requirement for these E3 ligases for cancer suppression [2] , [43] , highlight the complex in vivo functions of these E3 ligases . Although c-Myc protein level was elevated in all examined tissues of Pirh2−/− mice , spleen , testis and mammary glands displayed the highest levels of c-Myc . In agreement with the upregulation of p19ARF in response to high levels of deregulated c-Myc [21] , spleen , testis and mammary glands of Pirh2−/− mice displayed increased p19ARF expression . However , p19ARF expression was not increased in Pirh2−/− tissues ( e . g . lung , liver ) that only displayed a moderate elevation of c-Myc expression . As c-Myc induced p19ARF expression results in the sequestration of mdm2 in the nucleolus and the subsequent activation of p53 [51] , it is possible that increased levels of c-Myc and p19ARF in Pirh2−/− mice might contribute to their higher IR-induced p53 responses compared to control littermates . However , our observation that liver of Pirh2−/− mice display elevated IR-induced p53 expression compared to Wt controls whithout any detectable level of p19ARF , supports that increased IR-induced p53 expression in Pirh2 mutants can take place independently of p19ARF . Further biochemical and genetic studies are required to determine whether the increased expression of c-Myc and p19ARF in Pirh2 deficient cells affects their p53 responses . Our finding that Pirh2 polyubiquitylates c-Myc and mediates its proteolysis , and the elevated c-Myc level associated with Pirh2 deficiency support the possibility that deregulated c-Myc in Pirh2−/− mice may increase their cancer susceptibility . Indeed , Pirh2 mutant mice displayed increased predisposition to develop tumors including sarcomas , testis , mammary and lung tumors . In accordance with the role p53 plays in suppressing c-Myc induced oncogenesis [46] , Pirh2−/−p53−/− mice showed a markedly increased cancer predisposition compared to single mutants . The accumulation of c-Myc in Pirh2 mutant mice is also consistent with their elevated risk for developping plasma cell hyperplasia , gammaglobulinemia and kidney failure [36]–[38] . Human PIRH2 has been reported to be overexpressed in lung and prostate cancer [24] , [25] . However , the human PIRH2 locus at 4q21 is lost in a diverse subset of solid tumors including epithelial tumors of the breast and lung [52] , [53] . We report in this study that PIRH2 expression is downregulated in various human cancers and that lower PIRH2 expression correlates with decreased survival of patients with lung squamous cell carcinomas , breast or ovarian cancer . Interestingly , Pirh2 mutant mice also displayed increased risk for lung and breast cancer . The ability of the E3 ligase Pirh2 to negatively regulate IR-induced p53 and c-Myc steady state level , together with the increased risk for Pirh2 mutant mice to develop various pathologies , including cancer , all highlight the importance of this novel tumor suppressor and demonstrate the requirement for its tight regulation .
E14K embryonic stem cells were electroporated with the linearized targeting construct for Pirh2 ( Figure S1 ) and Pirh2fl2-3-neo ES clones identified by PCR and Southern blotting . Pirh2Δ2-3 ES clones lacking exon 2 and 3 and the Neomycin resistance cassette were obtained following transient transfection of two targeted Pirh2fl2-3-neo ES clones with CMV-Cre recombinase . Pirh2Δ2-3 ES clones were used to derive two independent lines of mutant mice ( referred here as Pirh2 mutants ) . All mice were in 129/C57BL/6 genetic background and were maintained in the animal facility of the Ontario Cancer Institute in accordance with the established ethical care regulations of the Canadian Council on Animal Care . P53 mutant mice [54] were obtained from Taconic . MEFs were derived according to standard procedures . HEK293T cells , RKO cells and PIRH2-KD RKO clone #11 in which PIRH2 can be knocked down by a tetracycline-inducible system [16] , have been also used . Single cell suspensions from thymi , spleens , lymph nodes and bone marrow of Wt and Pirh2 mutant mice were stained with the following antibodies against the cell surface markers: CD4 , CD8 , CD3 , CD43 , CD95 , CD138 , IgM , IgD , B220 , and Thy1 . 2 ( ebioscience and Pharmingen ) . Stained cells were analyzed by flow cytometry using the FACScan and Cellquest software . Activation of T and B-cells was performed as previously described [55] . Cells were harvested and total RNA isolated with Trizol ( Gibco ) . cDNA was generated using Invitrogen kit . SYBR green kit from Applied Biosystems was used for Real-time PCR . The following oligonucleotides were used: Actin ( 5′AACAGGAAGCCCATCACCATCTT3′ and 5′GCCCTTCCACAATGCCAAAGTT3′ ) , IL-6 ( 5′TAGTCCTTCCTACCCCAATTTCC3′ and 5′TTGGTCCTTAGCCACTCCTTC3′ ) , c-Myc ( 5′ATGCCCCTCAACGTGAACTTC3′ and 5′CGCAACATAGGATGGAGAGCA3′ ) , Ccna2 ( 5′GCCTTCACCATTCATGTGGAT3′ and 5′TTGCTGCGGGTAAAGAGACAG3′ ) , Ccnd2 ( 5′GAGTGGGAACTGGTAGTGTTG3′ and 5′CGCACAGAGCGATGAAGGT3′ ) , E2f1 ( 5′CAGAACCTATGGCTAGGGAGT3′ and 5′GATCCAGCCTCCGTTTCACC3′ ) , Mcl-1 ( 5′AAAGGCGGCTGCATAAGTC3′ and 5′TGGCGGTATAGGTCGTCCTC3′ ) , p21 ( 5′CCTGGTGATGTCCGACCTG5′ and 5′CCATGAGCGCATCGCAATC3′ ) , Puma ( 5′CCTGGGTAAGGGGAGGAGT3′ and 5′AGCAGCACTTAGAGTCGCC3′ ) , Brca1 ( 5′AAGGAGCCCGTGTGCTTAG3′ and 5′TTGCCCTAGATGTGTTGTCTTTT3′ ) , and Pirh2 ( 5′CAGACTTGTGAAGACTGTAGCAC3′ and 5′ CGAAGATTCGTGGTTAGGCAT3′ ) . PCR were performed on the Applied Biosystem 7900HT Fast Real-Time PCR system . Recombinant purified full length PIRH2 and its fragments were incubated with His-c-MYC fusion proteins in an assay buffer containing PBS ( pH 7 . 4 ) , 100 mM NaCl , 1 mM Benzamidine , 0 . 5 mM PMSF and 5 mM βME in a 1∶2 molar ratio at 4°C overnight . Then , proteins were incubated with the 100 µl of glutathione-Sepharose beads ( GE Healthcare ) for an additional 2 h . The mixture was transferred to a microcolumn and was extensively washed with the assay buffer . Bound proteins were eluted with 30 mM reduced glutathione and detected by SDS-PAGE and Coomassie staining Western blot analysis was performed as previously described [55] . The following antibodies were used: c-Myc ( C33 and N262; Santa Cruz ) , Ccnd2 ( Santa Cruz ) , PIRH2 ( Santa Cruz and BETHYL laboratories ) , SKP2 ( Santa Cruz ) , FBW7 ( Sigma ) , p19ARF ( Novus and Calbiochem ) , and anti-β-actin ( Sigma ) . HEK293T cells were transfected with expression plasmids encoding human PIRH2 ( pcDNA3-hPIRH2 ) and c-MYC ( pcDNA3-c-MYC ) using the calcium phosphate method . After 48 h , the cells were lysed in a solution containing 50 mM Tris-HCl ( pH 7 . 4 ) , 150 mM NaCl , 1% Triton-X , 1 mM phenylmethylsulfonyl fluoride , 400 µM Na3VO4 and protease inhibitor cocktail tablet ( Roche ) . The cell lysates from transfected HEK293T and non transfected NIH3T3 were centrifuged at 14 , 000×g for 10 min at 4°C , and the resulting supernatant was incubated with anti-c-MYC antibody ( N-262 ) for 2 h at 4°C . Protein G-Sepharose ( Amersham ) was added to the mixture and the sample rotated overnight at 4°C . The resin was separated by centrifugation , washed four times with ice-cold lysis buffer , and then boiled in SDS sample buffer . Immunoblot analysis was performed with the anti-hPIRH2 ( BETHYL laboratories ) or anti-c-MYC antibody ( mouse monoclonal antibody; 9E10 ) , horseradish peroxidase-conjugated antibodies to mouse or rabbit immunoglobulin G ( 1∶10 , 000 dilutions , Cell Signaling ) and an enhanced chemiluminescence system ( ECL , Amersham ) . Immunoprecipitation of endogenous PIRH2 from RKO cells was performed using anti-PIRH2 antibody from Santa Cruz ( FL261 ) . pcDNA3-hPIRH2 was transfected in RKO cells using the calcium phosphate method . HEK293T cells were transfected with expression plasmids encoding the full length or a mutant lacking the ring finger domain ( ΔR ) of human PRIH2 together with Wt or T58A c-MYC and HA-tagged ubiquitin by the calcium phosphate method . After 48 h , the cells were lysed in a modified RIPA buffer as described earlier . The cell lysates were centrifuged at 14 , 000×g for 10 min at 4°C , and the resulting supernatant was incubated with anti-c-MYC antibody ( N262 ) for 2 h at 4°C . Protein G-Sepharose was added to the mixture , which was then rotated overnight at 4°C . The resin was separated by centrifugation , washed four times with ice-cold lysis buffer , and then boiled in SDS sample buffer . Immunoblot analysis was performed with the anti-ubiquitin ( FK2; BIOMOL international ) or anti-c-MYC antibody ( 9E10 ) , horseradish peroxidase-conjugated antibodies to mouse or rabbit immunoglobulin G and ECL . Splenocytes from Wt and Pirh2−/− 8 week-old mice were cultured in the presence or absence of anti-IgM ( 20 µg/ml ) and/or MG132 ( 20 µM; calbiochem ) for 3 h . Cells were then lysed in a modified RIPA buffer as described earlier and cell lysates immunoprecipitated with anti-c-Myc antibody ( N262 ) . The immunocomplexes were denatured in Laemmli's sample buffer ( 25 mM Tris pH 8 . 3 , 192 mM glycine , 0 . 1% SDS ) to dissociate contaminant proteins associated with c-MYC . c-MYC proteins were reimmunoprecipitated ( 2nd IP ) with anti-c-Myc antibody . The resin was separated by centrifugation , washed four times with ice-cold lysis buffer , and then boiled in SDS sample buffer . Immunoblot analyses were performed with the anti-ubiquitin ( FK2 ) or anti-c-Myc antibody ( 9E10 ) , horseradish peroxidase-conjugated antibodies to mouse immunoglobulin G and ECL . Human ubiquitin activating enzyme E1 and 6xHis tagged ubiquitin were purchased from Boston Biochem . The ubiquitylation reaction was performed in a volume of 20 µL in a buffer of 50 mM Tris pH 7 . 6 , 5 mM MgCl2 , 2 mM ATP , 2 mM DTT . The reaction mixture typically contained E1 ( 50 ng , Calbiochem ) , UBE2D2/UbcH5b ( 100 ng ) , Ubiquitin ( 5 µg , Sigma ) , Pirh2 ( 1 µg ) and full-length c-Myc ( 0 . 5 to 1 µg ) . After incubation at 30°C for 1 . 5 h , the reactions were stopped by addition of SDS-PAGE sample buffer and resolved on 7 . 5–10% SDS-PAGE gels . Ubiquitylated proteins were visualized and evaluated by Western Blot using anti-c-MYC ( N262 ) and anti-Ub antibody ( U0508 , Sigma ) . Tissues and tumors fixed in buffered formalin were processed for paraffin-embedded sectioning at 5 µm , and stained with H&E ( Fisher ) . IHC was performed using anti-c-Myc ( C19 , Santa Cruz ) , anti-cleaved caspase 3 ( Cell Signaling ) or Ki67 ( Dako ) . TUNEL staining was performed using an in situ cell death detection kit ( Boehringer Mannheim ) . Microarray data from public studies were downloaded from GEO [26]–[30] . All data was log2 transformed and normalized according to the original authors , with the exception of the lung adenocarcinoma dataset , whose pre-processing has been described elsewhere [56] . Unadjusted Cox proportional hazards models were fit in the R statistical environment ( v2 . 6 . 2 ) using the survival package ( v2 . 34 ) . P-values were calculated using the Wald test . Correlation analyses used Pearson's correlation as implemented in R ( v2 . 6 . 2 ) . Scatter- and box-plots were generated using the lattice package ( v0 . 17–4 ) . Long-term ( >7 years ) vs . short-term survival groups in ovarian cancer dataset [29] were analyzed using a t-test with Welch's adjustment for heteroscedascity . We have used all 25 long-term and all 30 short-term survival samples available from the raw data . Analyzing only strictly long- and strictly short-term survival samples ( n = 23 and n = 27 respectively ) does not change the average expression and standard error; it only reduces the significance to p = 0 . 002162 . Additional data were considered from Oncomine Research Edition v . 3 . 6 , with statistical analysis as described previously [32] . | Tumor suppressors and oncogenes play critical roles in cancer development . The tumor suppressor p53 and the oncogene c-MYC are among the most frequently deregulated genes in human cancer , and their ubiquitylation mediated by several E3 ligases is critical for their turnover and their functions . P53 has been shown to be ubiquitylated by Pirh2; however , the physiological significance of this modification of p53 remains unknown . In this study we have generated mice deficient for Pirh2 and have observed that loss of Pirh2 results in a higher level of p53 and cell death , especially in response to radiation . Remarkably , we also identified that Pirh2 interacts with c-Myc and mediates its polyubiquitylation and degradation . c-Myc accumulates in the absence of Pirh2 and this accumulation is accompanied by increased tumorigenesis of Pirh2-deficient mice . We also report that dual deficiency of Pirh2 and p53 synergizes cancer development . Examination of the expression level of PIRH2 in human cancers indicated that its lower expression level associates with poor survival of patients with lung , ovarian , or breast cancers . Collectively , these data indentify Pirh2 as a novel tumor suppressor involved in the regulation of both p53 and c-Myc . | [
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"biology"
] | 2011 | Role of Pirh2 in Mediating the Regulation of p53 and c-Myc |
The protozoan parasite Theileria inhabits the host cell cytoplasm and possesses the unique capacity to transform the cells it infects , inducing continuous proliferation and protection against apoptosis . The transforming schizont is a multinucleated syncytium that resides free in the host cell cytoplasm and is strictly intracellular . To maintain transformation , it is crucial that this syncytium is divided over the two daughter cells at each host cell cytokinesis . This process was dissected using different cell cycle synchronization methods in combination with the targeted application of specific inhibitors . We found that Theileria schizonts associate with newly formed host cell microtubules that emanate from the spindle poles , positioning the parasite at the equatorial region of the mitotic cell where host cell chromosomes assemble during metaphase . During anaphase , the schizont interacts closely with host cell central spindle . As part of this process , the schizont recruits a host cell mitotic kinase , Polo-like kinase 1 , and we established that parasite association with host cell central spindles requires Polo-like kinase 1 catalytic activity . Blocking the interaction between the schizont and astral as well as central spindle microtubules prevented parasite segregation between the daughter cells during cytokinesis . Our findings provide a striking example of how an intracellular eukaryotic pathogen that evolved ways to induce the uncontrolled proliferation of the cells it infects usurps the host cell mitotic machinery , including Polo-like kinase 1 , one of the pivotal mitotic kinases , to ensure its own persistence and survival .
The apicomplexan parasites Theileria annulata and T . parva are transmitted by ticks and cause severe lymphoproliferative disease in cattle in large areas of Africa , the Middle East , and Asia . The pronounced pathology and high mortality are linked to the ability of Theileria to stimulate the uncontrolled proliferation of the cells it infects , inducing a phenotype typical of tumor cells . T . parva infects predominantly T- and B-lymphocytes , whereas T . annulata targets B-lymphocytes and macrophages/monocytes . Theileria-transformed cells proliferate independently of antigenic stimulation or exogenous growth factors . Parasitized cells become resistant to apoptosis [1]–[3] and acquire the capacity to invade and multiply in non-lymphoid as well as lymphoid tissues ( reviewed in [4]–[6] ) . In buffalo , the natural host of Theileria , and in domestic animals that survive infection , the parasite persists for years , resulting in a carrier state . Theileria parasites differ from other Apicomplexan parasites , such as Plasmodium and Toxoplasma , in that they do not reside in a parasitophorous vacuole . Shortly after entry , the invading sporozoite dissolves the surrounding host cell membrane . Free in the cytosol , the sporozoite immediately associates with host cell microtubules ( MTs ) and differentiates into the schizont stage of the life cycle [7] , a multinucleated syncytium that maintains the host cell in a transformed state . Significant progress has been made understanding how this parasite manipulates its host . To achieve transformation , the parasite induces the activation of host cell signaling pathways that control cell proliferation and survival . This includes signaling pathways that regulate G1 to S transition , resulting in host cell DNA replication [6] , [8] . Transformation is entirely dependent on the presence of the parasite in the cytoplasm , however , and upon killing of the parasite by treatment with a specific theilericidal drug , cells lose the transformed phenotype , stop proliferating [9] , and reacquire sensitivity to apoptosis . Theileria-infected cells can be cultured indefinitely in vitro , and in established cultures , more than 95% of the cells harbor the parasite . Parasite and host cell DNA replication is asynchronous , with the schizont predominantly undergoing DNA synthesis and nuclear division as the host cell enters mitosis [10] . Schizonts are strictly intracellular and , to maintain the host cell transformed phenotype , the organism must be passed on to both daughter cells each time the host cell goes through mitosis and cytokinesis ( M phase ) . Viruses that transform their host cells have evolved mechanisms to guarantee their persistence in proliferating cells . In the case of retroviruses , this involves integration into the host cell genome ( reviewed in [11] ) . DNA viruses such as Kaposi's sarcoma-associated herpesvirus , Epstein-Barr virus , or papillomavirus have evolved a conserved strategy to ensure genome segregation during mitotic division that involves tethering episomal viral genomes to mitotic chromosomes using virus-encoded proteins ( reviewed in [12] ) . How can Theileria , as a large and complex eukaryotic syncytium , persist in a continuously dividing host cell ? Early microscopic observations have provided first clues for an involvement of the host cell mitotic apparatus [13] . However , the kinetics and molecular mechanism underlying the interaction between the schizont and its host cell during mitosis have not yet been investigated in detail . The regulation of mitosis and cytokinesis involves a range of mitotic kinases , motor proteins , and MT structures that undergo extensive reorganization to coordinate diverse functions such as chromosome segregation and cell division . In line with their multiple functions , regulatory proteins are subject to extensive spatio-temporal regulation , translocating between different structures where they fulfill specific functions . The mitotic spindle , which forms during early mitosis , consists of astral and interpolar MTs as well as kinetochore fibers that attach to condensed chromosomes . Cells are only allowed to exit mitosis when all chromosomes are correctly aligned on the mitotic spindle . At this point , Cdk1/cyclin B is inactivated , and during the ensuing anaphase , kinetochore MTs shorten to deliver the sister chromatids towards the poles . In the zone between the separating sets of sister chromatids ( spindle midzone ) , a specialized structure , the central spindle , forms , consisting of bundles of antiparallel MTs with overlapping plus ends . By recruiting a specific set of regulatory proteins , including mitotic kinases such as Polo-like kinase 1 ( Plk1 ) and Aurora B , as well as activators of the RhoA GTPase , the central spindle provides an important signaling platform that determines the plane of cleavage furrow formation and cytokinesis . For detailed information on central spindle assembly and cytokinesis , the reader is referred to recent reviews [14]–[16] . Plk1 has a broad range of functions during different stages of cell division and is subject to complex spatial and temporal control ( reviewed in [17]–[19] ) . Plk1 is degraded at the end of M phase and protein levels remain low during G1 . Levels increase when cells enter S phase , accumulating strongly during G2 . As the cell prepares for mitosis , Plk1 can be found localized to centrosomes and a first accumulation at centromeres can be observed . Upon progression through prometaphase and metaphase , Plk1 associates with spindle poles and kinetochores . The choice of Plk1 docking partners is regulated by Cdk1 [20] , and upon Cdk1 inactivation at anaphase , Plk1 is released from kinetochores and recruited to the newly forming central spindle . Finally , during cytokinesis , Plk1 is found localized to the midbody . By phosphorylating different interacting partners , Plk1 contributes to a number of events linked to cytokinesis such as contractile ring formation and cleavage furrow ingression [17] , [21]–[23] . The application of RNAi-mediated Plk1 knock-down or Plk1 inhibition using dominant negative mutants proved highly valuable to dissect the early functions of Plk1 in mitosis . However , important new insights into the role of Plk1 during cytokinesis only became possible with the recent development of specific chemical tools that allow the rapid and complete inactivation of Plk1 at precise time-points during mitosis , and without interfering with earlier functions [24]–[27] . Armed with these new tools , we analyzed how the Theileria schizont interacts first with the mitotic spindle and subsequently with the central spindle during host cell M phase . We show that the parasite establishes a close interaction with both structures and found that its association with the central spindle depends on catalytically active Plk1 . The latter associates with the schizont surface in a biphasic manner and recruitment is negatively regulated by host cell Cdk1 .
To monitor the interaction of the schizont with de novo synthesized MTs , T . annulata-transformed cells were exposed to nocodazole , a drug that inhibits MT polymerization . After 16 h of treatment , mitotic cells lacking MTs were arrested in prometaphase because of spindle checkpoint activation . Within minutes of nocodazole removal , new bundles of MTs formed that aligned closely with the parasite surface , stained with anti-TaSP1 , a commonly used schizont surface marker ( Figure 1A ) [28] . The appearance of multiple small MT asters early upon nocodazole release is most likely not due to parasite-induced MT nucleation as such asters could also be observed in uninfected bovine control cells ( Figure S1A ) . At metaphase , the parasite was found oriented symmetrically towards both spindle poles , straddling the chromosomes assembled in the metaphase plate . At anaphase , spindle midzone MTs , located between the separating sister chromatids , were aligned longitudinally along large sections of the parasite as dense MT bundles ( Figure 1B ) . As the cleavage furrow ingressed , central spindle MTs , including those associated with the parasite , became compacted and , during cytokinesis , both chromosome sets and the parasite were equally distributed between the two daughter cells ( Figure 1C ) . TaC12 cells are of macrophage origin and in order to allow a morphological comparison , different stages of mitosis/cytokinesis as observed in a bovine control cell line of macrophage origin ( BoMac ) or cells that no longer contain the parasite are presented in Figure S1B and C . The accumulation of host cell MT bundles at the schizont surface does not require bipolar spindles as it could also be observed in cells treated with monastrol , a small-molecule inhibitor of the mitotic kinesin Eg5 that induces the formation of monopolar half-spindles ( Figure S2 ) [29] . In monastrol-treated cells the parasite is less mobile compared to untreated cells , facilitating live imaging of MT interactions with the parasite surface . A kymograph analysis suggested that host cell astral MT bundles appear to be stably associated with the schizont surface ( Figure S2 ) . In previous work , we demonstrated that T . parva and T . annulata can aggregate the host cell kinases IKK1 and IKK2 at its surface , activating a signaling pathway that promotes survival of the transformed host cell [30] . Using immunofluorescence microscopy , we investigated whether this might also apply to mitotic kinases . In unsynchronized cultures of T . annulata-transformed cells , Plk1 was found to localize to the surface of the schizont in approximately 30% of cells . This was most striking in cells in G2 and also in cells undergoing anaphase ( Figure 2A ) . Intriguingly , during prometaphase and metaphase , Plk1 was consistently absent from the schizont surface . The association of Plk1 with the parasite during different phases of the cell cycle is presented in Figure S3 . Plk1 was not detectable in cells in G1 . During G2 , when Plk1 is abundantly expressed , prominent labeling of the schizont surface could be observed , coinciding with the time at which Plk1 started to accumulate at host cell centromeres . Binding to the schizont was maintained until nuclear accumulation of cyclin B1 and nuclear envelope breakdown became apparent during prophase ( unpublished data ) . Once cells reached prometaphase and metaphase , Plk1 localized predominantly to the spindle poles and kinetochores but was not associated with the schizont . With the onset of anaphase , Plk1 re-accumulated on the parasite surface . In cells progressing to telophase , Plk1 association with the parasite was largely restricted to the section of the schizont that is incorporated into the central spindle . To analyze Plk1 recruitment to the parasite surface in more detail , we used different synchronization protocols . The different synchronization strategies used in this study are depicted schematically in Figure S4A . In a first set of experiments , Theileria-infected cells were synchronized in early S phase using a thymidine block . Cells were released from the arrest and Plk1 association with the schizont monitored by immunofluorescence microscopy as cells progressed towards G2 and M phase ( Figure S5 ) . The percentage of cells containing Plk1-binding schizonts increased progressively as they advanced through G2 and then decreased as they entered mitosis . Progression into M phase was monitored by immunoblot analysis of lysates , prepared at each time point , using antibodies specific for phospho-histone H3 . Immunoblot analysis also showed that reduced association of Plk1 with the parasite as cells proceeded into mitosis was not due to declining Plk1 levels as these continued to increase during the course of the experiment . In cells synchronized in prometaphase by treatment with nocodazole , Plk1 could not be detected on the parasite ( Figure 2B , left panels ) confirming our observation made in unsynchronized cultures . The lack of Plk1 binding could not be attributed to the lack of MTs as identical observations were made in cells synchronized in prometaphase by treatment with monastrol ( Figure 2B , right panels ) . We next defined at which stage after metaphase the Plk1-parasite interaction was reinstated . By blocking the degradation of cyclin B using the proteasomal inhibitor MG132 , inactivation of the Cdk1/cyclin B complex can be prevented , thus synchronizing cells in a metaphase-like state ( see scheme Figure S4A ) [26] . To follow progression through anaphase , telophase and exit from M phase , the degradation of cyclin B1 and securin , and the disappearance of the phospho-histone H3 epitope was monitored by immunoblot . Consistent with earlier observations , no parasite-associated Plk1 could be detected in metaphase-arrested cells ( Figure 2C ) . Upon release from metaphase arrest , Plk1 associated with the parasite surface as soon as anaphase started . Binding was lost as cells completed cytokinesis and entered interphase/G1 . The capacity to induce transformation of the mammalian host cell is restricted to the schizont stage of Theileria and host cell proliferation ceases when the schizont differentiates to the next life cycle stage in a process called merogony [31] , [32] . Figure S4B provides an overview of the mammalian stages of the Theileria life cycle . Merogony can also be induced in vitro by exposure to heat shock [31] or treatment with chloramphenicol [33] . Merogony is an asynchronous stochastic process that occurs in individual cells over a period of 4 to 10 d . Upon induction , the number of cells harboring parasites expressing the differentiation marker TamR1 gradually increased , reaching up to 45% . This was accompanied by a pronounced reduction in the number of cells expressing Plk1 , including cells containing the transforming schizont with Plk1 located to its surface ( from typically 30% in normal cultures to <10% ) . The reduction in the number of cells expressing Plk1 likely reflects cell cycle arrest in G1 or G0 . As parasites proceeded into merogony , they lost the capacity to bind Plk1 ( Figure S6 ) . In 13% of the cells , scattered , but weak , Plk1 binding to parasites in early stages of differentiation could still be observed ( an example is shown in Figure S6 , middle row ) . No Plk1 binding could be detected when parasites had completed differentiation . Taken together , these data show that host cell Plk1 interacts with the surface of the parasite in a biphasic manner and that this is restricted to the transforming stage of the life cycle . The pattern of Plk1 binding to the schizont surface correlated inversely with the spectrum of Cdk1/cyclin B activity . While Cdk1-mediated phosphorylation can create docking sites for Plk1 [34] , [35] , in other cases Cdk1 prevents binding . Plk1 can also “self-prime” , however , and the choice of Plk1 docking partners through the course of mitosis and cytokinesis is thus controlled by the activation state of Cdk1 and that of Plk1 itself [20] , [36] , [37] . Cdk1 activity is required to maintain the mitotic state . Unscheduled inactivation of Cdk1 during mitosis induces a cytokinesis-like process that takes place before chromosome alignment and proper chromatid segregation has occurred ( Figure S4A ) [38] . As shown in Figure 2B , Plk1 is not associated with the schizont in Theileria-transformed cells synchronized in prometaphase . Blocking Cdk1 activity by treatment with the chemical inhibitor RO-3306 , however , induced the immediate accumulation of Plk1 on the schizont surface ( Figure 3A and B ) . This also occurred in the presence of nocodazole , indicating that Plk1 recruitment to the parasite surface does not require mitotic MTs . In both cases , the association induced by Cdk1 inhibition was transient and downregulated within 30 min . As Plk1 binding to substrates can result from self-priming [20] , [36] , we investigated the requirement of Plk1 catalytic activity for Plk1 binding to the parasite surface in more detail . TaC12 cells synchronized in S-phase were released from thymidine block and cultured in the presence or absence of the specific Plk1 inhibitor BI-2536 [39] , [40] at concentrations from 100 nM up to 1 µM . In agreement with the described role of Plk1 in early mitosis [27] , [39]–[42] , cells released from S-phase in the presence of BI-2536 accumulated in G2/M and underwent prometaphase-like mitotic arrest , whereas control cells progressed through mitosis into G1 ( shown for BI-2536 at 100 nM; Figure 4A ) . Accumulation in prometaphase upon BI-2536 treatment was also observed in unsynchronized TaC12 cultures ( Figure S7A ) . In BI-2536 cells that were still in G2 , Plk1 was readily detected at the parasite surface , and this was even more marked at higher doses of BI-2536 ( Figure 4B ) . In agreement with our observations described above , Plk1 association with the parasite was strongly reduced in those cells that had arrested in “prometaphase” ( Figure 4C and D ) . When Cdk1 was inhibited in cells arrested in “prometaphase , ” Plk1 immediately reaccumulated at the parasite surface; this also occurred when BI-2536 was added at concentrations as high as 1 µM ( Figure 4C and D ) . In the presence of BI-2536 , Plk1 interaction with the parasite could still be detected in the majority of the cells after 30 min , whereas Plk1 was absent from the parasite in cells treated only with Cdk1 inhibitor at that time point ( Figure 4C ) . Importantly , in the presence of BI-2536 , the pronounced ectopic cleavage furrow formation observed upon Cdk1 inhibition was inhibited ( Figure S7F ) , confirming the reported role for Plk1 in regulating furrow ingression ( see also below and Figure S12 ) [21] , [22] , [24]–[27] . We also determined whether Plk1 recruitment to the parasite during normal anaphase required Plk1 activity . In the presence of BI-2536 , cells released from metaphase arrest entered anaphase but failed to undergo furrow ingression . In these cells , Plk1 was found associated with the parasite surface ( Figure 4E ) . In further control experiments , BI-2536 was found to exert the same inhibitory effects in TaC12 cells as described in other systems; this included failure to maintain bipolar spindles ( Figure S7B and C ) , cytokinetic failure ( not shown ) , and the accumulation of binucleate cells ( Figure S7D and E ) . Although residual Plk1 activity can never be completely excluded , our findings indicate that Plk1 docking to the parasite surface can occur in the presence of Plk1 inhibitor concentrations that potently block several physiological functions of Plk1 in the cell . Together , these data indicate that Cdk1 negatively regulates Plk1 association with the schizont surface and that Plk1 binding does not appear to require catalytic activity . To determine which region of Plk1 is responsible for binding to the schizont surface , different myc-tagged forms of Plk1 were transiently expressed in T . annulata-transformed cells . Immunofluorescence analysis showed that myc-tagged Plk1 localized to the parasite surface ( Figure 5 ) . When the N-terminal kinase domain ( KDom ) and the C-terminal Polo-box domain ( PBD ) were expressed separately , only the latter showed binding . H538 and K540 are important residues in the PBD that are required for phospho-ligand binding [35] , [43] . Mutation of these residues to alanine completely abrogated PBD binding to the parasite , indicating that a functional PBD with an intact phosphopeptide recognition domain is required for Plk1 interaction with the parasite . To exclude the potential participation of endogenous Plk1 in the creation of PBD docking sites on the parasite surface , cells transfected with PBD constructs were treated with BI-2536 ( Figure 6 ) . Inhibition of Plk1 activity did not interfere with myc-PBD , myc-PBDH538A/K540A , or kinase dead full-length Plk1 ( myc-Plk1K82R ) binding . This was also observed when doses as high as 1 µM were used ( Figure S8 ) , confirming that recruitment is , in all likelihood , independent of Plk1 activity . Plk1 is closely involved in central spindle function and in helping to determine the site of contractile ring assembly and furrow ingression , ultimately resulting in cytokinesis ( see reviews [14] , [15] and references therein ) . To test to what extent the schizont is linked to these processes , precocious anaphase and cytokinesis-like contractility were induced in prometaphase-arrested cells by Cdk1 inhibition . As cells proceeded to “anaphase” , de novo synthesized MTs assembled as central spindle-like structures at the schizont surface ( Figure 7A ) . Even though normal sister chromatid separation did not take place , cells attempted cleavage ( Figure S9 ) . RhoA , the main regulator of actin dynamics , was found to accumulate at the cell cortex in a narrow zone in the immediate vicinity of the parasite ( Figure 7B ) . Interestingly , cleavage furrows occurred almost invariably at sites where MT bundles had assembled on the parasite surface ( Figure 7C , Figure S9 ) , and the position of host cell chromosomes had little or no influence on this process . These findings indicate that by forming a foundation for Plk1-enrichment and MT polymerization the schizont may help focus MT-associated cytokinesis signals known to stimulate contractile ring assembly and furrow ingression . Prompted by the findings above , we analyzed the central spindle-like structures associated with the parasite in more detail . In bona fide central spindles , anti-tubulin antibodies do not label the zone where antiparallel MT bundles overlap , most likely because of epitope masking caused by the accumulation of central spindle components such as PRC1 , centralspindlin , and the chromosomal passenger complex [15] . PRC1 is a major substrate of Plk1 that accumulates in the midzone during anaphase where it participates in central spindle MT bundling . Aurora B , a member of the chromosomal passenger complex , and Plk1 both translocate from kinetochores to the central spindle . There , Plk1 is recruited by PRC1 [20] and Mklp2 [36] and contributes to anaphase spindle elongation and the regulation of cytokinesis [21] , [22] , [24]–[27] . To avoid potential side effects caused by nocodazole treatment and chemical inhibition of Cdk1 , experiments were carried out using cells synchronized in metaphase by MG132 , as these possess an intact mitotic spindle and undergo normal anaphase . In addition , to exclude the possibility that colocalization of the central spindle-like structures with the parasite surface merely occurred by apposition induced by furrow contraction , we made use of the myosin II inhibitor blebbistatin , which blocks contraction of the cleavage furrow without affecting mitosis or assembly of the contractile ring [44] . Upon removal of MG132 , cells progressed to anaphase and central spindle-like structures assembled on the parasite ( Figure 8 ) . The midzone components Plk1 , Aurora B , PRC1 , and Cyk-4/MgcRacGAP all localized to the centre of parasite-associated central spindle-like structures ( arrowheads ) . Cyk-4/MgcRacGAP is a RhoGAP and member of the centralspindlin complex , which is required for central spindle assembly and also recruits the RhoGEF Ect2 to the central spindle ( see review [15] and references therein; [21] , [22] ) . Similar observations were made with cells in which precocious anaphase was induced by Cdk1 inhibition ( Figure S10 ) . Together , our findings indicate that the central spindle-like structures assembling at the surface of the schizont resemble bona fide central spindles . We next tested whether central spindle association with the parasite surface is dependent on Plk1 catalytic activity . In cells released from metaphase in the presence BI-2536 , central spindle formation at the parasite surface was strongly reduced . Instead , central spindles formed in the centre of the cell , independent of the position of the parasite ( Figure 9 ) . Plk1 did not localize to the middle section of the central spindles ( Figure 9A , filled arrowheads ) in BI-2536-treated cells but , as observed before , accumulated on the parasite surface ( Figure 9A , open arrowheads ) . Identical results were obtained using a second , structurally unrelated Plk1 inhibitor , BTO-1 , indicating that the effects observed with BI-2536 are not off-target effects ( Figure S11A ) . In Figure 9B , whole cells are displayed by maximum intensity projection of confocal sections . MT staining confirms that , when Plk1 was inhibited by BI-2536 or BTO-1 , central spindle assembly was not linked to the location of the parasite in the cytoplasm , and central spindle MT bundles failed to associate with the parasite surface . To facilitate monitoring the effect of Plk1 inhibition on the relative position of parasite and central spindles in more detail , cells were stained with an antibody that recognizes PRC1 , an intrinsic marker of central spindles ( Figure S11B ) . Whereas central spindles located at the parasite surface could be observed in >90% of control cells , this was reduced to <20% in cells treated with BI-2536 or BTO-1 ( Figure 9C ) , reflecting a marked decrease in the parasite's affinity for central spindles . Interestingly , BI-2536 did not prevent the interaction of the schizont with mitotic spindle MTs emerging from the spindle poles , and such interactions could still be observed during anaphase . This indicates that Plk1 activity is only required for interaction with the central spindle . Although our observations so far are consistent with the hypothesis that schizont interaction with mitotic and central spindle MTs is required for its distribution over the two daughter cells—and thus for parasite maintenance , they do not provide functional evidence to that extent . During late anaphase , the schizont can still be found associated to host cell MTs emanating from the spindle poles ( see for instance Figures 1B , 2A , and 8 , 9 ) , and in contrast to schizont interaction with central spindle MTs , this process does not require Plk1 catalytic activity . We tested whether preventing the interaction of the schizont with MTs emanating from the spindle poles or with central spindle MTs interfered with schizont segregation during cytokinesis . Treatment with nocodazole after the onset of anaphase can result in the disassembly of astral MTs , whereas midzone MTs remain differentially stable [45]–[47] . This allowed us to separate the contribution of astral MTs in positioning the parasite from that of central spindle MTs . T . annulata-transformed TaC12 cells were released from metaphase arrest for 60 min and then exposed for 20 min to nocodazole . This resulted in the disassembly of astral MTs and the parasite was found in close association with the central spindle ( Figure 10A , control + noc ) . As described [46] , in the absence of astral MTs , furrow ingression was often asymmetric as was also reflected by the predominantly unilateral accumulation of RhoA ( Figure 10B ) . Whereas parasite division between the forming daughter cells per se was not affected , the schizont was distributed less evenly than in control cells . This was monitored by measuring the distribution of parasite surface area on either side of the cleavage furrow ( Figure 10C ) . When added during metaphase , BI-2536 ( and other Plk1 inhibitors ) potently block cleavage furrow ingression [22] , [24] , [26] . When Plk1 is inhibited as cells enter anaphase , however , central spindle formation and cleavage furrow ingression can occur , but cells fail to complete cytokinesis [27] . In TaC12 cells , these conditions are met when BI-2536 is added 15 min after release from MG-132-induced metaphase arrest ( Figure S12 ) . While BI-2536 treatment prevented the “incorporation” of the parasite into the central spindle , distribution between the forming daughter cells occurred with the same efficiency as observed for control cells ( Figure 10A , BI-2636 and Figure 10C ) . Importantly , however , in cells that did not contain astral spindle MTs , schizont segregation was severely impeded when parasite-central spindle interaction was blocked by BI-2536 treatment ( BI-2536+ Noc ) , and in many cases , the schizont remained completely sequestered on one side of the cleavage furrow . These data strongly indicate that both astral and central spindle MTs help facilitate parasite distribution between the two daughter cells during cytokinesis . Taken together , these findings underpin the notion that the interaction with host cell astral and central spindle MTs is essential for proper segregation over the two daughter cells .
The biphasic pattern and the way in which Plk1 interacts with the parasite surface are intriguing . Plk1 binding to the parasite can first be observed during host cell G2 as the cell prepares for mitosis . During early mitosis , when Cdk1 becomes fully activated , Plk1 dissociates from the parasite surface to re-accumulate at the onset of anaphase , when Cdk1 is inactivated by the anaphase promoting complex/cyclosome . The inverse correlation between Cdk1 activity and Plk1 interaction with the schizont and the fact that pharmacological inhibition of Cdk1 induces rapid binding of Plk1 to the parasite clearly point toward Cdk1 as a negative regulator . This is reminiscent of Cdk1's known role as a spatio-temporal regulator of Plk1 . Depending on the cell cycle stage , location , and kinase substrate , Cdk1 can either create Plk1 binding sites or prevent Plk1 binding ( reviewed in [17] ) . For instance , it has been shown that , in metaphase , Cdk1 prevents Plk1 binding to PRC1 by phosphorylating a site adjacent to the Plk1 docking site [20]; this block is lifted upon Cdk1 inactivation at anaphase . By analogy , it would be possible that Cdk1-mediated phosphorylation of the parasite surface prevents Plk1 from docking . Mammalian Plk1 lacks Cdk1 phosphorylation sites , and a direct modification of Plk1 by Cdk1 can therefore be excluded . It is also conceivable that Plk1 binding to the parasite involves one or more additional proteins , of host or parasite origin , forming a complex that can only bind in the absence of Cdk1 activity . Transfection experiments revealed that Plk1 binds to the schizont through its PBD . Catalytically inactive Plk1 and the PBD alone both bound to the parasite in cells treated with BI-2536 , confirming that Plk1 itself is , in all likelihood , not the priming kinase . This is underpinned by the finding that , in the presence of either BI-2536 or BTO-1 , increased—rather than reduced—Plk1 binding to the parasite could be observed . Similar results were obtained using a third , unrelated Plk1 inhibitor . On the other hand , PBD mutants lacking H538/K540 , required for electrostatic interactions with the negative charges of phospho-S/T groups , did not bind to the schizont surface , indicative of the involvement of a phosphate group in Plk1 docking . The fact that neither Plk1 nor Cdk1 appear to function as the priming kinase points toward the involvement of another serine/threonine kinase . Although unusual , this is not without precedent; for instance , calmodulin-dependent kinase has been shown to create Plk1 binding sites in meiosis [58]–[60] . Alternatively , it is conceivable that Plk1 binds to a moiety that mimics a Plk1 binding site , which is only accessible when Cdk1 is inactive . Plk1 binding to a substrate without priming phosphorylation has been observed in Drosophila for Polo binding to the MT-associated protein Map205; in this case the PBD was found to be required , but not sufficient , for interaction [61] . The nature of the schizont surface protein that provides a docking site for Plk1 is presently not known . Considering the complexity of the interactions between parasite and host cell during mitosis , the participation of several schizont proteins is plausible , and experiments are presently underway to address this topic . While many aspects of central spindle assembly are still enigmatic , a picture is emerging , indicating that central spindles show a high degree of self-organization ( see review [15] and references therein ) . It has been shown that the combined presence of PRC1 , centralspindlin , and the chromosomal passenger complex suffices to induce the robust bundling of central spindle MTs . All three complexes required for self-organization can be detected in parasite-associated central spindles . The fact that central spindles can be detected at the parasite surface within a very short time after induction of precocious anaphase ( within 10 to 20 min of Cdk1 inactivation ) suggests that central spindles either assemble in situ or interact with the schizont immediately after they are formed . In cells in which furrow ingression was blocked by treatment with the myosin II inhibitor blebbistatin , newly formed central spindles were found almost invariably in association with the parasite surface . When Plk1 was inhibited , however , central spindles assembled independently of the position of the schizont . Parasite interaction with astral spindle MTs emanating from the spindle poles , on the other hand , was clearly not affected . Astral spindle MTs have themselves been implicated in central spindle assembly ( see reviews [15] , [62] , [63] and references therein ) . One plausible explanation could therefore also be that a subpopulation of MTs emanating from the spindle poles form stable interactions with the parasite surface where they subsequently self-organize into central spindles in a Plk1-dependent manner . Whichever the mechanism , we propose that by interacting with astral MTs emerging from both spindle poles , the schizont is aligned strategically spanning across metaphase chromosomes arranged at the equator of the cell . This interaction in all likelihood also ensures that the parasite remains positioned correctly as the chromosome masses separate during anaphase , at which time a central section of the parasite interacts with midzone MTs that form the central spindle in preparation of the ensuing cytokinesis . MTs have been reported to be the main structural constituent of the spindle apparatus required for induction of cell cleavage [64] . The formation of a contractile ring required for cytokinesis depends on the focused localization of myosin II at the cortex of the cell and coordinated activation of the small GTPase RhoA . Astral MTs contribute by spatially coordinating cortical myosin recruitment generating a region of high contractility at the cell equator [65]–[67] , and together with central spindle MTs , they localize RhoA to the cell cortex [68] . The central spindle is thought to provide the platform for RhoA activation . Once recruited to PRC1 , Plk1 acts to promote the localization of the RhoGEF , Ect2 , to the central spindle by phosphorylating Cyk-4 ( MgcRacGAP ) , which functions as Ect2 anchor and activator . This way , a signaling platform is created that triggers RhoA activation in a narrow zone overlaying the central spindle [68]–[70] , regulating the onset of division [21] , [22] . In normally dividing Theileria-transformed cells , the parasite is almost always found symmetrically partitioned between the separating daughter cells with its middle section “incorporated” into the central spindle and the midbody . Despite its size , the presence of the parasite does not appear to disturb cytokinesis . It is conceivable that by accumulating host cell central spindles at its surface , containing the signaling molecules required for RhoA activation , an uninterrupted interaction between the central spindle and cell cortex is guaranteed allowing flawless furrow ingression at the equator of the cell and unperturbed abscission . Abscission , the final event in cytokinesis leading to two separate cells , involves vesicle transport and membrane fusion ( reviewed in [14] ) . The centralspindlin complex , found first at the central spindle and subsequently at the midbody , regulates not only acto-myosin ring contraction but also vesicle transport to the cleavage furrow , required for abscission [71] . Our microscopic observations show that , during telophase , a short central section of the parasite is first trapped in the midbody as a narrow tube ( see Figure 1C ) and is subsequently included in the process of abscission . It is not known whether parasite-own structures provide specific cues in preparation of its abscission or whether such signals derive from the host cell . Whichever , once incorporated into the central spindle/midbody , the parasite does not affect host cell central spindle function or abscission , and from this point onward , schizont cytokinesis appears to be a passive process that is largely controlled by the host cell . This is supported by the fact that , in the absence of host cell abscission , independent parasite division does not take place . In summary , we propose a two-step model for the division of Theileria schizont between the separating daughter cells , involving first the mitotic spindle and subsequently the central spindle ( Figure 11 ) . As the host cell enters mitosis , the schizont binds newly forming MTs that emanate symmetrically from the spindle poles , allowing the schizont to position itself so that it spans the equatorial region of the mitotic cell where host cell chromosomes assemble during metaphase . This step is independent of Plk1 activity as it also takes place in the presence of potent Plk1 inhibitors . During anaphase , the schizont becomes closely associated with central spindles assembling in the midzone between the separating chromosomes . In contrast to the first step , this interaction requires catalytically active Plk1 . By “hijacking” the central spindle , an important spatial regulator of cleavage furrow formation , the schizont is strategically positioned to be included in the plane of cell division at each host cell cytokinesis , without disturbing the process . Thus , while different transforming viruses either integrate into the host cell genome or target mitotic chromosomes to ensure persistence [12] , the transforming protozoan Theileria evolved to single out the mitotic apparatus that mediates chromosome separation and cytokinesis . Considering the schizont is strictly intracellular [13] and its presence crucial for the constitutive activation of the signaling pathways that drive proliferation and protection against apoptosis ( reviewed in [6] , [8] ) , we posit that this process is essential , not only for parasite persistence but also for the exponential expansion of the parasite population . In a more general context , there is mounting evidence from studies on protein-protein interactions that pathogens have evolved to target host proteins that function as hubs ( those involved in many interactions ) or bottlenecks ( proteins central to many pathways ) [72] . In earlier work we provided evidence that Theileria hijacks IKK , a central regulator of many NF-κB activation pathways [30] . By scavenging Plk1 , a key regulator of mitosis , Theileria provides a second striking manifestation of this evolutionary process .
Theileria annulata ( TaC12 ) -infected macrophages were cultured in Leibovitz 15 medium ( Gibco ) supplemented with 10% foetal calf serum ( FCS , Amimed ) , 10 mM Hepes pH 7 . 2 ( Merck ) , 2 mM L-glutamine ( Gibco ) , 70 µM β-mercaptoethanol ( Merck ) , and antibiotics ( Lonza ) . The SV40-transformed cell line of Theileria-uninfected bovine macrophages ( BoMac ) was cultured in DMEM Glutamax medium ( Gibco ) supplemented with 10% FCS and antibiotics . Plasmids encoding myc-tagged versions of human Plk1 ( Figures 5 , 6 , and S8 ) , including full-length wild-type Plk1 , full-length kinase dead Plk1 ( K82R ) , wild-type kinase domain ( aa 1–330 ) , wild-type PBD ( aa 326–603 ) , and mutant PBD ( H538A/K540A ) , were previously described [43] . Cells were transfected using Lipofectamine 2000 ( Invitrogen ) following the manufacturer's recommendations . TaC12 cells stably expressing mRFP-α-tubulin ( Figure S2 ) were transfected with the plasmid pmRFP-C1 ( a kind gift by Daniel Gerlich , ETH Zürich ) and selected using 2 mg/ml G418 ( Alexis ) . Stocks of the inhibitors BI-2536 ( a kind gift by Boehringer Ingelheim , and partly purchased from Axon Medchem ) , BTO-1 , blebbistatin ( Sigma ) MG132 , monastrol , and RO-3306 ( Alexis ) were prepared in DMSO . DMSO was added at the appropriate concentration to all control samples . Cells were washed in serum-supplemented medium when transferred to another medium . To test the binding of ectopically expressed versions of Plk1 in the presence of Plk1 inhibitor , cells were treated with 100 nM ( Figure 6 ) or 1 µM BI-2536 ( Figure S8 ) immediately after transfection and incubated for 8 h . For the MT re-polymerization assay ( Figure 1A ) , cells were arrested in prometaphase by addition of 0 . 1 µg/ml nocodazole ( Biotrend ) for 16 h and harvested by shake-off followed by 30 min treatment with 3 µg/ml nocodazole . These cells were then released in drug-free medium for up to 120 min . For synchronous release from S arrest , cells were treated with 4 mM thymidine ( Sigma ) for 24 h and transferred into drug-free medium for up to 12 h ( Figure S5 ) . To demonstrate the absence of Plk1 from the parasite surface during early mitosis , cells were arrested in prometaphase by 16 h treatment with 0 . 1 µg/ml nocodazole or 100 µM monastrol and harvested by shake-off ( Figure 2B ) . For the chemical induction of precocious cytokinesis , cells were arrested in prometaphase by 16 h treatment with 0 . 1 µg/ml nocodazole , harvested by shake-off , washed , and then treated with medium lacking nocodazole but containing 10 µM RO-3306 for up to 60 min ( Figures 3A , 7A–C , S9 ) . Alternatively , prometaphase cells were kept in the presence of nocodazole and treated with RO-3306 plus 3 µg/ml nocodazole ( Figure 3A , B ) . To test the requirement of Plk1 catalytic activity for its binding to the parasite surface as well as the effect of Plk1 inhibition on ectopic furrowing ( Figures 4B–D , S7F ) , cells were synchronized in S phase by thymidine block ( see above ) , washed , and immediately treated with 0 . 1 µg/ml nocodazole or BI-2536 ( 100 nM or 1 µM ) for 15 h . Cells arrested in prometaphase were collected by shake-off and fixed or washed ( nocodazole-blocked cells only ) and treated with 10 µM RO-3306 or kept in the presence of BI-2536 and additionally subjected to 10 µM RO-3306 for 10–30 min . To prevent cleavage furrow ingression , RO-3306-treatment was done in the presence of 100 µM blebbistatin ( Figure S10 ) . For synchronous release into anaphase , cells were arrested in prometaphase with 0 . 1 µg/ml nocodazole , harvested by shake-off , transferred into medium containing 20 µM MG132 for 2 h , and finally released from metaphase arrest for 80 min in drug-free medium ( Figure 2C ) or medium containing 100 µM blebbistatin ( Figures 8 , 9A–C , S11A , B ) . For anaphase-specific inhibition of Plk1 , these cells were additionally treated with 100 nM or 1 µM BI-2536 or 20 µM BTO-1 during washout ( S12A ) , or 15 min after MG132 washout ( Figures 4E , 9A–C , S7D and E , S11A and B , S12A–C ) . To investigate the role of astral and central spindle MTs for parasite positioning ( Figure 10A–C ) , cells were released from metaphase arrest as described above in the presence of 100 nM BI-2536 ( added 15 min after MG132 washout ) or inhibitor-free medium . After 60 min release the medium was supplemented with 10 µM nocodazole and cells were incubated for 20 min at 37°C . To test the effect of BI-2536 on spindle maintenance , metaphase-synchronized cells were additionally treated with 100 nM BI-2536 for 160 min ( Figure S7B and C ) . For the elimination of the parasite from TaC12 cells ( Figure S1A and B ) , cultures were grown 4 d in the presence of 100 ng/ml of the theilericidal drug BW720c [73] . Cells were then subjected to a MT re-polymerization assay as described above . Chemical induction of merogony ( Figure S6 ) was done as previously described [33] . Briefly , cells were cultivated in the presence or absence of 50 µM chloramphenicol ( Sigma ) for 10 d . Synchronous entry into M phase ( Figure S5 ) and progression from metaphase to anaphase ( Figure 2C ) were monitored by immunoblot analysis of protein extracts prepared in RIPA buffer ( 50 mM Tris-Hcl pH 7 . 5 , 1% NP-40 , 0 . 25% Na-deoxycholate , 150 mM NaCl , 1 mM EDTA , 1 mM PMSF , 1× Roche Complete protease inhibitor cocktail , 1 mM NaF , 1 mM Na3VO4 ) . Primary antibodies were mouse monoclonal anti-cyclin B1 ( clone GNS-11 , Pharmingen ) , anti-Plk1 ( clone 35–206 , Calbiochem ) , anti-securin ( clone DCS-280 , MBL ) , anti-α-tubulin ( clone DM1A , Sigma ) , as well as rabbit polyclonal anti-phospho-Ser10 histone H3 ( Upstate ) and anti-histone deacetylase 1 ( Santa Cruz ) . Interphase cells were grown on coverslips , and cells harvested by mitotic shake-off were seeded on poly-L-lysine coated coverslips ( Sigma ) . Samples were fixed with 4% paraformaldehyde in PBS for 10 min at room temperature ( for Aurora B , c-myc , Plk1 , TamR1 , TaSP , and α-tubulin staining ) , with methanol for 10 min at −20°C ( for Cyk-4 and PRC1 staining ) or with 10% trichloroacetic acid on ice for 15 min ( for RhoA staining ) . Cells were subsequently permeabilized in 0 . 2% Triton X-100 ( prepared in PBS ) for 10 min at room temperature . Antibody incubations were done in PBS containing 10% heat-inactivated FCS , DNA was stained with DAPI ( Molecular Probes ) , and cells were mounted using Glycergel ( Dako ) . Widefield microscopy was done on a Nikon Eclipse 80i microscope equipped with a Retiga 2000R CCD camera ( Qimaging ) using 60× and 100× Plan Apo objectives ( Nikon ) and Openlab 5 software ( Improvision ) . For confocal microscopy , a Leica TCS SP2 system was used , equipped with an acousto-optical beam splitter , a 63× Plan Apo objective ( Leica ) , and Leica confocal software . Images were processed using Photoshop ( Adobe ) or Imaris ( Bitplane ) software . To measure the distribution of the parasite in dividing cells after nocodazole wash-in ( Figure 10A–C ) , whole cells were stained for TaSP1/α-tubulin and recorded using z-stacks . Two-dimensional projections were generated and the parasite area on each side of the cleavage furrow was measured using Openlab software . The following antibodies were used: mouse monoclonal anti-Aurora B ( AIM-1 , clone 6 , BD Transduction Laboratories ) , anti-c-myc ( clone 9E-10 , Santa Cruz ) , anti-Plk1 ( clone 35–206 , Calbiochem ) , anti-Rho A ( clone 26C4 , Santa Cruz ) , anti-α-tubulin ( clone DM1A , Sigma ) and 1C12 , which detects the T . annulata schizont surface ( kindly provided by Brian Shiels , University of Glasgow ) , as well as rabbit polyclonal anti-PRC1 ( kindly provided by Francis Barr , University of Liverpool ) , anti-TamR1 ( Brian Shiels , University of Glasgow ) [74] , and the schizont surface marker anti-TaSP ( kindly provided by Jabbar Ahmed , Borstel Research Center ) [28] , goat polyclonal anti-Cyk-4 ( MgcRacGAP , Abcam ) , and rat monoclonal anti-α-tubulin ( Abcam ) . Appropriate ( isotype-specific ) secondary antibodies conjugated with either Marina Blue , Alexa-Fluor 488 , Alexa-Fluor 594 , or Texas Red ( Molecular Probes ) were used . To generate kymographs of TaC12 cells stably expressing mRFP-α-tubulin ( Figure S2 ) , cells were synchronized in prometaphase using monastrol ( see above ) and kept in the presence of the drug during time-lapse imaging . Fluorescence was recorded at 30 s intervals over a period of 20 min and kymographs were generated from these data with a width of 5 pixels using NIS Elements imaging software ( Nikon ) . Immediately after imaging , cells were fixed and stained ( see above ) to identify the position of the parasite in the previously recorded cells . To determine the percentage of cells displaying furrow ingression after release into anaphase in the presence of 100 nM BI-2536 ( S12A ) , cells were synchronized and drug-treated as described above and observed by time-lapse imaging in 10 min intervals over a period of 4 h . Time-lapse imaging was done using a TE2000E-PFS microscope ( Nikon ) equipped with a Plan Fluor 20× , 60× objective ( Nikon ) , Orca ER CCD camera ( Hamamatsu ) , and incubation chamber ( Life Imaging Services ) . To test the effect of BI-2536 on cell cycle progression of TaC12 cells ( Figure S7A ) , unsynchronized cultures were cultured in the presence of 100 nM BI-2536 or the equivalent volume of DMSO for 20 h . To monitor the effect of Plk1 inhibition on progression of M phase in TaC12 cells ( Figure 4A ) , cultures were synchronized in S phase as described above and released for 20 h in the presence of 100 nM or 1 µM BI-2536 . Cell suspensions were fixed in 80% ethanol at −20°C o/n followed by treatment with 200 µg/ml RNaseA in PBS at 37°C for 30 min . Finally , cells were stained in DAPI staining solution ( 100 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM CaCl2 , 0 . 5 mM MgCl2 , 0 . 1% NP-40 , 3 µM DAPI ) , and cellular DNA content was measured using a BD LSR II and BD FACS Diva software ( Becton-Dickinson ) . | As part of their survival tactics , intracellular parasites often resort to cunning mechanisms to manipulate the cells they inhabit . Theileria , an important and particularly artful parasite of cattle in the tropics , transforms parasitized cells ( that is , it induces continuous proliferation and protection from apoptosis—a state reminiscent of tumor cells ) . As a large , strictly intracellular syncytium , the transforming Theileria schizont cannot exit from the infected cell to invade other target cells . How then does the parasite ensure that each daughter cell , generated upon host cell division , remains infected and transformed ? Our data show that the parasite co-opts the mitotic apparatus of the host cell and Plk1 , a host protein kinase with a central regulatory role in mitosis and cytokinesis . As the host cell enters mitosis , the schizont binds to the microtubules that emanate symmetrically from the two spindle poles . This microtubule binding positions the schizont so that it spans the equatorial region of the mitotic cell where host cell chromosomes assemble . Then , as sister chromatids start to separate , the schizont associates with Plk1 and the central spindle that assembles between the separating chromosomes , with the activity of Plk1 presumably coordinating progression through mitosis with proper schizont positioning . This alignment with the central spindle positions the schizont to be included in the plane of cell division at the onset of cytokinesis , thus ensuring faithful passage of a Theileria schizont on to each daughter cell . | [
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] | 2010 | The Transforming Parasite Theileria Co-opts Host Cell Mitotic and Central Spindles to Persist in Continuously Dividing Cells |
Human T-cell leukemia virus type 1 ( HTLV-1 ) is an etiological agent of several inflammatory diseases and a T-cell malignancy , adult T-cell leukemia ( ATL ) . HTLV-1 bZIP factor ( HBZ ) is the only viral gene that is constitutively expressed in HTLV-1-infected cells , and it has multiple functions on T-cell signaling pathways . HBZ has important roles in HTLV-1-mediated pathogenesis , since HBZ transgenic ( HBZ-Tg ) mice develop systemic inflammation and T-cell lymphomas , which are similar phenotypes to HTLV-1-associated diseases . We showed previously that in HBZ-Tg mice , HBZ causes unstable Foxp3 expression , leading to an increase in regulatory T cells ( Tregs ) and the consequent induction of IFN-γ-producing cells , which in turn leads to the development of inflammation in the mice . In this study , we show that the severity of inflammation is correlated with the development of lymphomas in HBZ-Tg mice , suggesting that HBZ-mediated inflammation is closely linked to oncogenesis in CD4+ T cells . In addition , we found that IFN-γ-producing cells enhance HBZ-mediated inflammation , since knocking out IFN-γ significantly reduced the incidence of dermatitis as well as lymphoma . Recent studies show the critical roles of the intestinal microbiota in the development of Tregs in vivo . We found that even germ-free HBZ-Tg mice still had an increased number of Tregs and IFN-γ-producing cells , and developed dermatitis , indicating that an intrinsic activity of HBZ evokes aberrant T-cell differentiation and consequently causes inflammation . These results show that immunomodulation by HBZ is implicated in both inflammation and oncogenesis , and suggest a causal connection between HTLV-1-associated inflammation and ATL .
Human T-cell leukemia virus type 1 ( HTLV-1 ) infects to mainly CD4+ T cells [1] , and the provirus is known to exist in effector/memory T cell and regulatory T cell ( Treg ) subsets [2 , 3] . HTLV-1 induces clonal expansion of infected cells and consequently causes a malignancy of CD4+CD25+ T cells , adult T-cell leukemia ( ATL ) [1] . This virus also gives rise to inflammatory diseases including HTLV-1 associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) , HTLV-1 uveitis ( HU ) , dermatitis , and HTLV-1-associated bronchoalveolitis ( HABA ) —diseases which are characterized by infiltration of T cells into the lesions [4–7] . In addition , the incidence of several infectious diseases , e . g . , infective dermatitis [8] and strongyloidiasis [9] , is higher in HTLV-1 carriers than uninfected individuals , suggesting the presence of HTLV-1-mediated cellular immunodeficiency . These findings indicate that HTLV-1 modifies the immunophenotypes of T cells in the host , and these diseases are induced or promoted by aberrant action of infected T cells . Importantly , some clinical observations imply that in HTLV-1-infected subjects , inflammation accelerates ATL development [10 , 11] , although a molecular basis connecting inflammation to leukemogenesis has not yet been elucidated . In order to understand the causal link between them , suitable animal models are necessary . The HTLV-1 provirus encodes several regulatory/accessory genes in its pX region [12] . Among them , tax and HTLV-1 bZIP factor ( HBZ ) , which are encoded in the plus- and minus-strand of the pX region respectively , are thought to be important in pathogenesis . HBZ is the only viral gene that is genetically conserved and constitutively expressed in ATL cells [13] , whereas Tax is often inactivated by transcriptional silencing or genetic mutations [14 , 15] . Moreover , HBZ-transgenic ( HBZ-Tg ) mice that express HBZ in CD4+ T cells develop systemic inflammatory diseases , cellular immunodeficiency , and T-cell lymphomas , suggesting that HBZ plays important roles in HTLV-1-mediated pathogenesis [16 , 17] . In HBZ-Tg , the number of CD4+CD25+ T cells and effector/memory CD4+ T cells are increased as same as ATL cases [3] . Considering the similarities between phenotypes of HBZ-Tg mice and the clinical features of HTLV-1-infected individuals , the HBZ-Tg mouse model is useful for investigating the mechanisms of pathogenesis by HTLV-1 . We reported previously that the number of induced Tregs ( iTregs ) was increased in HBZ-Tg mice through upregulation of Foxp3 , which is a master gene of Tregs [18] . On the other hand , expression of Foxp3 in HBZ-expressing iTregs is easily lost , whereupon these cells convert to IFN-γ-producing cells that are called exFoxp3 cells [19] . We hypothesized that the increase in iTregs and the concurrent induction of IFN-γ-producing cells are implicated in HBZ-mediated pathogenesis in vivo . In this study , we focused on the significance of IFN-γ in HBZ-induced inflammation and lymphoma , and established HBZ-Tg/IFN-γ knock out ( KO ) mice . The incidence of dermatitis was significantly lower in HBZ-Tg/IFN-γ KO mice than HBZ-Tg mice , and importantly , HBZ-Tg/IFN-γ KO mice developed no T-lymphomas . In addition , since the intestinal microbiota have important roles in the development and proliferation of iTregs [20] , we generated germ-free ( GF ) HBZ-Tg mice to evaluate the impact of the intestinal microbiota on the increase in Tregs . Even in aseptic circumstances , HBZ-Tg mice developed dermatitis and had the same pattern of T-cell immunophenotypes as specific pathogen free ( SPF ) HBZ-Tg mice , suggesting that HBZ causes inflammation in a cell intrinsic manner . We also found that the severity of dermatitis correlates with the development of lymphoma in HBZ-Tg mice . These results suggest a close link between inflammation and oncogenesis in HBZ-Tg mice , and demonstrate the important role of IFN-γ in the molecular mechanism of HBZ-mediated pathogenesis .
In order to analyze the impact of IFN-γ on HBZ-mediated pathogenesis , we crossed HBZ-Tg mice with IFN-γ KO mice to establish HBZ-Tg/IFN-γ KO mice ( S1 Fig ) [21] . We found that some HBZ-Tg mice developed dermatitis at only 8 weeks of age , and 90% of HBZ-Tg mice developed dermatitis within 2 years ( Fig 1A and 1B ) , and these results are consistent with our previous observations [16] . In contrast , HBZ-Tg/IFN-γ KO mice did not suffer from dermatitis until 19 weeks or older , and after 2 years , only 50% of these mice had developed the skin disease ( Fig 1B ) . To evaluate the presence of systemic inflammation , we performed histological analysis of multiple organs from ten mice of each genotype at 24 weeks of age . The analysis revealed that 30% of HBZ-Tg mice showed infiltration of lymphocytes into the skin at the time point of analysis , whereas no HBZ-Tg/IFN-γ KO mice showed any abnormalities ( Fig 1C and Table 1 ) . Our previous study also showed that HBZ-Tg mice which became moribund had lymphomas [16] . Surprisingly , we found that 30% of HBZ-Tg mice had already developed lymphomas in spleen and lymph nodes at 24 weeks of age—earlier than we had guessed—and more importantly , the severity of inflammation correlated with lymphoma development ( Fig 1D and Table 1 ) . In contrast , no HBZ-Tg/IFN-γ KO mice had lymphoma . These data strongly suggest that IFN-γ has an important role in inflammation and lymphoma caused by HBZ , and that inflammation might accelerate oncogenesis in HBZ-expressing T cells . The numbers of Foxp3+CD4+ T cells and effector/memory T cells are increased in HBZ-Tg [16] . To evaluate the influence of IFN-γ on CD4+ T cells , we performed flow cytometry and compared the patterns of T-cell subsets between HBZ-Tg and HBZ-Tg/IFN-γ KO mice . CD4+ T cells from HBZ-Tg/IFN-γ KO mice expressed Foxp3 at similar level to that of HBZ-Tg mice ( Fig 2A and 2B and S2 Fig ) . Likewise , the effector/memory population was increased in HBZ-Tg/IFN-γ KO mice ( Fig 2A and 2B and S2 Fig ) , indicating that these changes in CD4+ T-cell subset populations in HBZ-Tg mice are independent of IFN-γ production and not directly correlated with the inflammatory phenotypes of the HBZ-Tg mice . Next , we analyzed the production of inflammatory cytokines . Splenic T cells from 24-week-old mice were stimulated by phorbol myristate acetate ( PMA ) /ionomycin and the expression of IL-17 , TNF-α , IL-2 , IL-4 and IFN-γ in CD4+ T cells was evaluated by flow cytometry . IFN-γ production was clearly increased in HBZ-Tg mice . Production of IL-17 and IL-2 were also increased in both HBZ-Tg and HBZ-Tg/IFN-γ KO mice ( Fig 2C and 2D and S2 Fig ) . These findings show that loss of IFN-γ does not affect the production of these inflammatory cytokines by HBZ-expressing CD4+ T cells . Recently , it has been reported that iTregs are most abundant in the colonic mucosa in mice , and that the number of mucosal Tregs is remarkably decreased in germ-free mice , indicating that the gut microbiota has important roles in the development and proliferation of iTregs [20] . Since both HBZ-Tg and HBZ-Tg/IFN-γ KO mice demonstrate increased numbers of iTregs , we asked if the microbiota affected HBZ-mediated iTreg expansion and subsequent inflammation as an extrinsic factor . In order to analyze the impact of microbiota on HBZ-mediated pathogenesis , we generated the germ-free ( GF ) HBZ-Tg mice , which are genetically the same as the HBZ-Tg mice we reported previously [16] . Contrary to our expectation , these GF HBZ-Tg mice were phenotypically no different than regular HBZ-Tg mice maintained in SPF conditions . The GF HBZ-Tg mice started developing skin inflammation as early as 9 weeks of age , and 16 of 28 ( 57% ) GF HBZ-Tg mice suffered from dermatitis by 18 weeks of age ( Fig 3A ) . Regarding the phenotypes of T cells , there were no significant differences between GF and SPF HBZ-Tg; the number of both effector/memory T cells and Tregs were higher than those in nontransgenic littermates , and the production of IFN-γ was upregulated in HBZ-Tg in both settings ( Fig 3B and 3C and S3 Fig ) . These results imply that the intrinsic activity of HBZ is more important than the intestinal microbiota in influencing the immune modulation , inflammation , and lymphomas observed in HBZ-Tg mice . In a previous study , we showed that a chemokine receptor , CXCR3 , was highly expressed on HBZ-Tg CD4+ T cells and that most cells that had migrated into inflammatory lesions were CXCR3 positive [18] . CXCR3 is expressed in IFN-γ-producing Th1 cells [22] . Thus we hypothesized that the reduction of inflammation in HBZ-Tg/IFN-γ KO mice might correlate with reduced CXCR3 expression on their CD4+ T cells . We compared CXCR3 expression levels between HBZ-Tg and HBZ-Tg/IFN-γ KO mice , and found that HBZ-Tg/IFN-γ KO mice expressed high levels of CXCR3 on CD4+ T cells despite of the absence of IFN-γ ( Fig 4A ) . Furthermore , we carried out chemotaxis assay to evaluate the function of CXCR3 expressed on CD4+ T cells of HBZ-Tg and HBZ-Tg/IFN-γ KO mice . Murine recombinant CXCL10 , which is a major ligand of CXCR3 , was used as a chemoattractant [22] . CD4+ T cells were purified from HBZ-Tg and HBZ-Tg/IFN-γ KO mice , and these cells were placed in the upper chambers . The lower chambers were filled with media containing 200 or 500 ng/mL CXCL10 or control media . The migration capacity of CD4+ T cells from HBZ-Tg/IFN-γ KO mice was similar as that from HBZ-Tg mice ( Fig 4B ) . From these results , we conclude that CXCR3 was inducible and functional in HBZ-Tg/IFN-γ KO mice . Next , we evaluated the importance of CXCL10 in disease development in HBZ-Tg mice , since CXCL10 is one of the chemokines induced by IFN-γ [23] . To do this , we established HBZ-Tg/CXCL10 KO mice [24] ( Fig 5A ) . HBZ-Tg/CXCL10 KO mice developed dermatitis beginning at 12 weeks old ( Fig 5B and 5C ) . At 24 weeks of age , about 80% of the mice had developed dermatitis ( Fig 5C ) . Histological analysis revealed that HBZ-Tg/CXCL10 KO mice also developed inflammation in several other organs ( Table 2 ) . In addition , HBZ-Tg/CXCL10 KO mice showed increases in the numbers of Tregs and effector/memory fraction compared to WT mice ( Fig 5D ) . All phenotypes of HBZ-Tg/CXCL10 KO mice we analyzed were quite similar to those of HBZ-Tg mice . We thus concluded that the CXCR3/CXCL10 axis was not related to pathogenesis in our HBZ-Tg mouse model . Although CD4+ T cells from HBZ-Tg mice and HBZ-Tg/IFN-γ KO mice were similar in their migratory responses to CXCL10 , their abilities to infiltrate tissues in vivo may differ , because the HBZ-Tg/IFN-γ KO mice did not develop dermatitis to the same degree that the HBZ-Tg mice did . Therefore we looked for chemokine receptors or adherent molecules that are highly expressed on T cells in HBZ-Tg but not HBZ-Tg/IFN-γ KO mice . As shown in Fig 6A , most of the molecules studied were highly expressed on CD4+ T cells of both HBZ-Tg and HBZ-Tg/IFN-γ KO mice compared with wild type littermates . However , we found that the chemokine receptor CCR9 was upregulated only in HBZ-Tg mice ( Fig 6B ) , suggesting that upregulation of CCR9 is involved in inflammation mediated by HBZ and IFN-γ . In order to identify further cellular genes implicated in HBZ/IFN-γ-mediated inflammation , we performed DNA microarray analysis . We extracted RNA from CD4+ T cells of WT , HBZ-Tg , IFN-γ KO , and HBZ-Tg/IFN-γ KO mice and evaluated the profiles of gene expression . According to the result of microarray , we picked up several genes that were expressed higher in HBZ-Tg than HBZ-Tg/IFN-γ KO , and validated their expression profiles by quantitative RT-PCR . Among these genes , we further looked for the genes that are overexpressed in human ATL cases . Finally , we identified Neo1 , Il1f9 , Fgfr4 , Hip1 , Iklf2 , and Nrxn3 that met these criteria ( Fig 7A and 7B ) . Interestingly , human homologues of these genes were upregulated especially in the aggressive form of ATL ( Fig 7B ) . They are likely to be divided into 2 groups by the pattern of the expression in healthy donor cells . One contains the genes which expression is unchanged or reduced in phytohaemagglutinin ( PHA ) -stimulated cells compared with resting cells , such as NEO1 , NRXN3 , IKZF2 , and HIP1 . In contrast , IL1F9 and FGFR4 belong to another group in which their transcription are enhanced by PHA , suggesting that they are inducible by potent mitogenic stimulation even in normal T cells . These genes were generally overexpressed in HTLV-1-transformed and ATL cell lines although there were several exceptions ( S1 Table ) . Interestingly , it has been reported that most of them are aberrantly expressed in several types of cancer cells , suggesting that they are associated with the linkage between chronic inflammation and oncogenesis in HTLV-1-infected subjects .
Persistent inflammation is widely recognized as a tumor-promoting factor in many cancers , and it is estimated that about 15% of human malignancies are associated with chronic inflammation and infection [25] . For example , inflammatory bowel diseases , such as ulcerative colitis , are associated with colon cancer [26] . Chronic gastritis caused by Helicobacter pylori [27] and chronic hepatitis caused by hepatitis B virus or hepatitis C virus [28] are implicated in development of gastric cancer and hepatocellular carcinoma ( HCC ) , respectively . In these solid tumors , infiltrating immune cells are thought to produce cytokines , chemokines , and growth factors that induce the proliferation of tumor cells [25] . In addition , those inflammatory cells produce reactive oxidative species resulting in genetic instability [29] . Activation of the TNF-α or the NF-κB pathway is important especially in the development of HCC [30] and colon cancer [31] . In the case of HTLV-1 infection , the virus itself dysregulates the functions of CD4+ T cells , modifies T-cell subsets , and triggers clonal expansion of infected cells . HTLV-1 causes both inflammation and a malignant disease , but a precise mechanism crosslinking these diseases was not clarified . Several clinical observations have suggested the correlation between HTLV-1-associated inflammatory diseases and ATL . It was reported that the frequency of ATL development in HTLV-1-infected patients with diffuse pan-bronchiolitis was significantly high among all HTLV-1 carriers [10] . In addition , the abundance of certain HTLV-1-infected clones is increased in HTLV-1 carriers with strongyloides and infective dermatitis [11] , implying that these inflammatory diseases increase the risk of ATL development . In this study , we found T-cell lymphomas only in HBZ-Tg mice with dermatitis , and severity of inflammation tended to correlate with lymphoma development , suggesting that inflammatory signals induced by HBZ accelerate oncogenic processes . Since there is no immune reaction against HBZ in these mice , HBZ triggers inflammation only by its intrinsic action . This idea is compatible with the findings that , even in a germ-free environment , the number of Tregs was increased in HBZ-Tg mice and they developed systemic inflammation the same as under SPF conditions . These results suggest that the inflammatory phenotypes of HBZ-Tg mice are caused by an inherent function of HBZ , and that HBZ-mediated inflammation promotes oncogenesis in HBZ-expressing CD4+ T cells . In addition , we show here that IFN-γ is an important molecule in the pathogenesis by HBZ . IFN-γ is conventionally recognized as a cytokine that acts in host defense against various pathogens and tumor rejection . IFN-γ is secreted by mainly activated CD4+ T cells ( Th1 cells ) , cytotoxic CD8+ T lymphocytes , and natural killer cells , and has cytostatic/cytotoxic effects by inducing cell-mediated immune responses [32] . IFN-γ primarily activates the JAK/STAT signaling pathway through interaction with IFN-γR1 , and induces the transcription of primary response genes such as IRF family genes . Many of these primary response genes encode transcription factors that induce a lot of secondary response genes to react to the stimulation . Previous studies showed that blockade of IFN-γ/IFN-γR signaling in mice compromised rejection of tumors by the immune system , indicating that IFN-γ functions in immune surveillance against tumors [33–35] . On the other hand , under certain circumstances , IFN-γ is also known to have a protumorigenic function involving proliferative and anti-apoptotic signals in tumor cells [32] . In this study , we found that knocking out of IFN-γ significantly decreased the incidence of inflammation and malignant lymphoma in HBZ-Tg mice , indicating that IFN-γ plays a supportive role in the development of both types of diseases caused by HBZ . To understand how IFN-γ contributes to HBZ-associated pathogenesis , we looked for cellular factors differentially expressed in CD4+ T cells of HBZ-Tg compared with HBZ-Tg/IFN-γ KO mice . These genes are thus implicated in pathogenesis mediated by HBZ and IFN-γ together . CCR9 is an intestine oriented chemokine receptor [36] . This upregulation is consistent with our observation that massive infiltration of lymphocytes was observed in HBZ-Tg mice [18] . We also identified several cancer-related genes which are overexpressed in both HBZ-Tg and ATL patients . NEO1 encodes a cell surface protein that belongs to the immunoglobulin superfamily . It has been reported that overexpression of NEO1 in gastric cancer is involved in cell proliferation and migration [37] . IL1F9 , also known as IL36gamma , is an IFN-γ-inducible gene that has been reported to activate NF-κB and MAPK signaling in human T cells [38] . FGFR4 encodes a member of the fibroblast growth factor receptor family , and implicated in the tumorigenesis of many types of cancers , such as HCC , prostate cancer , breast cancer , pancreatic cancer [39–43] . IKZF2 encodes a member of the Ikaros family of zinc-finger proteins , Helios , which is mainly expressed in T cell . A recent study showed that aberrant isoforms of IKZF2 are dominantly expressed in ATL cells , and function in T-cell proliferation and survival [44] , suggesting that HBZ might dysregulate the expression pattern of IKZF2 in ATL cells . HIP1 is also overexpressed in several cancer tissues like breast cancer and possesses the oncogenic properties through BCL-2 and NF-κB pathways [45] . Taken together , it is possible that HBZ and HBZ-mediated inflammation induce these factors and subsequently trigger transformation in a part of HTLV-1-infected cells . In order to clarify the significance of each factor in HBZ-mediated pathogenesis , further experiments will be required . Interestingly , previous studies on Tax , which is another oncoprotein of HTLV-1 , showed that transgenic mice expressing Tax under control of the granzyme B promoter developed LGL leukemia , and knocking out of IFN-γ in this strain enhanced the tumor formation [46 , 47] , suggesting that IFN-γ has the opposite effect on Tax-mediated oncogenesis that it has on HBZ-mediated oncogenesis . In these Tax-Tg mice , IFN-γ was shown to have an anti-angiogenic effect by suppressing the transcription of VEGF [47] . HBZ and Tax regulate several signaling pathways in opposite manners [1] , suggesting that IFN-γ may differentially regulates the effects of HBZ and Tax on HTLV-1-infected cells or HBZ and Tax may regulate IFN-γ in opposite way , in response to the cellular context . In HAM/TSP patients , IFN-γ-producing cells are increased in a CD4+FoxP3- subpopulation , and suggested to have a role in the pathogenesis of this inflammatory disease [48 , 49] . A recent study showed that HTLV-1-infected cells in the cerebrospinal fluid expressed IFN-γ and CXCR3 , and its ligand CXCL10 was expressed in astrocytes upon stimulation with IFN-γ , leading to an IFN-γ-CXCL10-CXCR3 inflammatory loop [50] . In our HBZ-Tg mice , however , CXCL10 is not associated with inflammation , since loss of CXCL10 didn’t affect the development of inflammatory diseases . In addition , the upregulation of CXCR3 observed in HBZ-Tg mice was independent of IFN-γ . Therefore CD4+ T cells from HBZ-Tg/IFN-γ KO mice still expressed high levels of CXCR3 , and could react to its ligand . According to these observations , CXCL10/CXCR3 is unlikely to have strong effects on inflammation induced by HBZ . Indeed , the expression of several other adherent molecules and chemokine receptors such as CCR4 , CD29 , and CD49d , also showed the same pattern as CXCR3 ( Fig 6 ) . Induction of these molecules is mediated by HBZ , but not associated with IFN-γ , suggesting that these molecules might be involved in the inflammation that occurred late in HBZ-Tg/IFN-γ KO mice . Further studies are needed to test this hypothesis . In conclusion , we showed that IFN-γ , which is secreted by Th1-like cells such as exFoxp3 cells , has important roles in HBZ-mediated inflammation . HBZ increases the number of Tregs in a cell intrinsic manner , and consequently induces IFN-γ in vivo . Importantly , inflammation is closely linked to the development of malignant lymphomas in HBZ-Tg mice . This is the first report showing the relationship between the immunomodulating function of HBZ and oncogenesis that might explain the clinical observations of ATL development in HTLV-1-infected subjects with chronic inflammations .
C57BL/6J mice were purchased from CLEA ( Tokyo , Japan ) . Transgenic mice expressing the spliced form of the HBZ gene under control of the mouse CD4 promoter have been described previously [13 , 16] . B6 . 129S7-Ifnγtm1Ts/J ( Ifnγ-/- ) [21] and B6 . 129S4-Cxcl10tm1Adl/J ( Cxcl10-/- ) [24] mice were purchased from The Jackson Laboratory ( CA , USA ) . Mice used in this study were maintained under SPF conditions unless otherwise specified . GF HBZ-Tg and wild type mice were reconstituted from frozen embryos and reared at the Central Institute for Experimental Animals ( Kawasaki , Japan ) . GF mice aged 18 weeks were transferred to Kyoto University , and analyzed within 24 hours . HTLV-1-transformed cell lines ( MT-2 and MT-4 ) , ATL cell lines ( MT-1 , ED , TL-Om1 , ATL-43T+ , and ATL-55T+ ) were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum ( FBS ) and antibiotics at 37°C under a 5% CO2 atmosphere . For IL-2-dependent cell lines ( ATL-43T+ and ATL-55T+ ) , recombinant human IL-2 ( 100 U/ml ) was added in the culture media . Peripheral blood mononuclear cells ( PBMCs ) of ATL patients and healthy donors were collected by Ficoll-Paque PLUS ( GE Healthcare ) . To obtain PHA-stimulated cells , PBMCs were treated with 10μg/ml PHA ( Sigma ) for 3 days . The following antibodies were used for flow cytometric analysis of mouse lymphocytes: Anti-CD3e ( 145-2C11 ) , CCR5 ( C34-3448 ) , IFN-γ ( XMG1 . 2 ) , IL-2 ( JES6-5H4 ) , IL-17 ( TC11-18H10 ) , CD29 ( Ha2/5 ) , CD49d ( 9C10 ) , and CD162 ( 2PH1 ) antibodies were purchased from BD Pharmingen . Anti-CD4 ( RM4-5 ) , CD8 ( 53–6 . 7 ) , CD44 ( IM7 ) , CD62L ( MEL-14 ) , CXCR3 ( CXCR3-173 ) , CCR4 ( 2G12 ) , and TNF-α ( MP6-XT22 ) antibodies were from Biolegend . Anti-CD25 ( pc61 ) , Foxp3 ( FJK-16s ) , CCR9 ( eBioCW-1 . 2 ) , and IL-4 ( 11B11 ) antibodies were from eBioscience . Anti-CCR10 antibody ( 248918 ) was purchased from R&D systems . In order to stain cytokines , splenocytes were stimulated with 50ng/mL PMA ( Nakarai Tesque ) , 1μg/mL ionomycin ( Nakarai Tesque ) and a protein transport inhibitor , BD Golgi plug ( BD Pharmingen ) for 4 hours before harvesting cells . After cell surface staining , cells were fixed and permeabilized with Fixation/Permeabilization working solution ( eBioscience ) and intracellular antigens were stained . Flow cytometric analysis was carried out using a FACS Verse with FACSuite software ( BD Biosciences ) and Flow Jo ( FlowJo , LLC ) . Mouse tissues were fixed in 10% formalin in phosphate buffer ( Nakarai Tesque ) and then embedded in paraffin . Hematoxylin and eosin staining was performed according to standard procedures . Images were captured using a Provis AX80 microscope ( Olympus ) equipped an OLYMPUS DP70 digital camera , and detected using a DP manager system ( Olympus ) . Mouse CD4+ T cells were isolated from splenocytes by CD4 T lymphocyte enrichment Set-DM ( BD Biosciences ) and resuspended in RPMI containing 0 . 1% BSA . To evaluate migration activity , a Transwell insert ( 3 . 0um ) ( CORNING ) was used . The lower chamber was filled with chemotaxis medium containing mouse recombinant CXCL10 ( R&D systems ) . One million cells were added into the upper chamber . The chamber was incubated for 1 hour at 37C and 5% CO2 . Cells that migrated towards CXCL10 were counted using Flow cytometry . CD4+ T cells were isolated from WT , HBZ-Tg , IFN-γ KO and HBZ-Tg/IFN-γ KO mice as described above and lysed in TRIzol ( Life Technologies ) . Total RNAs were extracted from these lysates with Direct-zol RNA MiniPrep ( Zymo Research ) . RNA quality was checked using Agilent 2100 Bioanalyzer ( Agilent Technologies ) . Microarray experiments were carried out with SurePrint G3 Mouse GE 8x60K ( Agilent Technologies ) according to manufacturer’s instructions . Data was analyzed with GeneSpring GX software ( Agilent Technologies ) . Splenocytes harvested from WT , HBZ-Tg , IFN-γ KO , and HBZ-Tg/IFN-γ KO mice and human PBMCs obtained from ATL patients and healthy donors were lysed with TRIzol reagent , and RNA was extracted as described above . cDNAs were synthesized from 1μg of total RNAs using random primers and SuperScript III Reverse Transcriptase ( Life Technologies ) . The expression levels of candidate genes were quantified by the StepOnePlus real time PCR system ( Life Technologies ) using FastStart Universal SYBR Green Master ( Roche ) . Relative expression levels of each gene were calculated by the delta delta Ct method [51] . The sequences of primers used in this study are listed in S2 Table . Human NRXN3 was quantified using Taqman Gene Expression Assays ( Applied Biosystems , Hs01028186_m1 ) . Animal experiments were performed in strict accordance with the Japanese animal welfare bodies ( Law No . 105 dated 19 October 1973 modified on 2 June 2006 ) , and the Regulation on Animal Experimentation at Kyoto University . The protocol was approved by the Institutional Animal Research Committee of Kyoto University ( Permit numbers are D13-02 , D14-02 , and D15-02 ) . Experiments using clinical samples were conducted according to the principles expressed in the Declaration of Helsinki , and approved by the Institutional Review Board of Kyoto University ( Permit numbers are G310 and G204 ) . ATL patients provided written informed consent for the collection of samples and subsequent analysis . | HTLV-1 is a retrovirus which causes a cancer , ATL , and inflammatory diseases of several tissues , such as the spinal cord , eye , skin , and lung . Although these HTLV-1-mediated malignant and inflammatory diseases are recognized as distinct pathological entities , an increased number of HTLV-1 infected cells and enhanced migration/infiltration of infected cells into the lesions are common features of these diseases . Indeed , several clinical observations have suggested a causal link between inflammation and ATL ( see Discussion ) . In order to investigate this issue , appropriate animal models are indispensable . Among HTLV-1-encoded regulatory/accessory proteins , HTLV-1 bZIP factor ( HBZ ) is thought to be critical to HTLV-1-mediated pathogenesis . We previously reported that HBZ transgenic ( HBZ-Tg ) mice which express HBZ in CD4+ T cells developed both systemic inflammation and T-lymphomas , indicating that they are suitable to evaluate the link , if any , between these phenomena . In this study , we generated several new genetically engineered strains by modifying HBZ-Tg mice , and found that IFN-γ is an accelerator of HBZ-induced inflammation . Importantly , we show that the incidence of inflammation is correlated with that of lymphomagenesis in HBZ-Tg . These findings indicate that modification of T-cell machinery by HBZ is closely associated with both HTLV-1-associated inflammatory diseases and ATL . | [
"Abstract",
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"Results",
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"and",
"Methods"
] | [] | 2015 | Interferon-γ Promotes Inflammation and Development of T-Cell Lymphoma in HTLV-1 bZIP Factor Transgenic Mice |
A complex system of multiple signaling molecules often produce differential gene expression patterns in animal embryos . In the ascidian embryo , four signaling ligands , Ephrin-A . d ( Efna . d ) , Fgf9/16/20 , Admp , and Gdf1/3-r , coordinately induce Otx expression in the neural lineage at the 32-cell stage . However , it has not been determined whether differential inputs of all of these signaling pathways are really necessary . It is possible that differential activation of one of these signaling pathways is sufficient and the remaining signaling pathways are activated in all cells at similar levels . To address this question , we developed a parameter-free method for determining a Boolean function for Otx expression in the present study . We treated activities of signaling pathways as Boolean values , and we also took all possible patterns of signaling gradients into consideration . We successfully determined a Boolean function that explains Otx expression in the animal hemisphere of wild-type and morphant embryos at the 32-cell stage . This Boolean function was not inconsistent with three sensing patterns , which represented whether or not individual cells received sufficient amounts of the signaling molecules . These sensing patterns all indicated that differential expression of Otx in the neural lineage is primarily determined by Efna . d , but not by differential inputs of Fgf9/16/20 , Admp , and Gdf1/3-r signaling . To confirm this hypothesis experimentally , we simultaneously knocked-down Admp , Gdf1/3-r , and Fgf9/16/20 , and treated this triple morphant with recombinant bFGF and BMP4 proteins , which mimic Fgf9/16/20 and Admp/Gdf1/3-r activity , respectively . Although no differential inputs of Admp , Gdf1/3-r and Fgf9/16/20 signaling were expected under this experimental condition , Otx was expressed specifically in the neural lineage . Thus , direct cell–cell interactions through Efna . d play a critical role in patterning the ectoderm of the early ascidian embryo .
In animal embryos , cell-cell interactions directed by secreted and membrane-bound signaling ligands play an important role in establishing specific gene expression patterns . There are 16 ectodermal cells in the animal hemisphere of the 32-cell embryo of the ascidian , Ciona intestinalis , and all 16 have the potential to express Otx upon induction ( Fig 1A ) . Four signaling molecules , Fgf9/16/20 , Admp ( anti-dorsalizing morphogenetic protein; a signaling molecule belonging to the BMP subfamily in the TGFβ superfamily ) , Gdf1/3-r [formerly called Gdf1/3-like and renamed according to the nomenclature guideline recently published [1]] , and Efna . d ( formerly EphrinA-d ) , cooperatively regulate Otx expression in four cells , which give rise to neural cells [2–4] . Fgf9/16/20 activates Otx expression through the ERK pathway , which is antagonized by Efna . d [5 , 6] . Admp and Gdf1/3-r negatively regulate Otx expression by inducing the binding of the effector transcription factor Smad to an Otx enhancer ( Fig 1B and 1C ) . The observation that Otx expression expands throughout the ectoderm upon knockdown of Efna . d or double-knockdown of Admp and Gdf1/3-r [4] indicates that these three genes are essential for differential expression of Otx within the ectodermal cells and patterning the ectoderm . On the other hand , another study indicated that a differential input of Fgf9/16/20 signaling could direct differential Otx expression in the ectoderm [7] . Thus , it has not yet been established which of these factors is critical for patterning of the ectoderm of normal embryos . In other words , it has not been determined whether differential inputs of all of these signaling pathways are really necessary . For instance , it is possible that differential activation of one of these signaling pathways is sufficient and the remaining signaling pathways are activated in all cells at similar levels . Because our previous experiments [4] did not necessarily give an answer to this question , we took an advantage of theoretical analysis in the present study . Although quantitative models have successfully simulated molecular gradients for embryonic patterning in other model systems [8–10] , it is difficult to precisely determine parameters for signaling gradients and kinetics of signaling molecules in the ascidian embryo . Boolean functions provide an alternative , because inputs and outputs are treated as binary values , and parameters that are difficult to determine are not used . In previous studies , Boolean functions have successfully explained how combinations of different transcription factors determine specific gene expression patterns [11–15] . Here we report determination of a Boolean function for Otx expression in the 32-cell embryo of Ciona intestinalis . This function reveals how individual cells sense signaling inputs and which signaling is the limiting factor for patterning the ectoderm .
Here we introduce a method for determining a Boolean function of gene expression directed by extracellular signals within a population of equivalent cells . Before formalizing neural induction of the ascidian embryo , we first considered a Boolean function describing a simple hypothetical biological system illustrated in Fig 2A . This system consists of two cells , I and II , and two signaling molecules , a and b . Cells I and II initially express the same set of transcription factors , and are therefore equivalent . After a sufficient period of time , gene o is activated only in cell I but not in cell II under control of signaling molecules a and/or b . Hence , the Boolean function for the expression of gene o is represented by [Xo = F ( Xa , Xb ) ] , where Xo represents expression of gene o , and Xa and Xb represent the signaling states of a and b . Inputs and outputs are considered in binary space . If a signal sufficiently activates its intracellular pathway , it is represented as ‘1’ , and otherwise as ‘0’ hereafter . Because there are 4 ( = 22 ) possible combinations of input signaling states in each cell in this hypothetical system , there are 16 ( = 4×4 ) possible states in the whole system ( Fig 2B ) . These states , which are represented as ( Xa , Xb ) , are called "sensing patterns" hereafter , because they represent how individual cells sense individual signaling inputs . In this hypothetical system , signaling molecule a comes from the upper side of Fig 2A , and signaling molecule b comes from the lower side . Obviously , eleven sensing patterns are incompatible with the following two simple principles , which we call Rule 1 and Rule 2 . We next applied this logic to the signaling system that induces the neural marker gene Otx in the neural lineage ( a6 . 5 and b6 . 5 ) of the 32-cell Ciona embryos ( Fig 1A ) . Previous studies revealed that four signaling molecules , Admp , Efna . d , Fgf9/16/20 and Gdf1/3-r , are directly involved in inducing Otx expression in two pairs of cells ( a6 . 5 and b6 . 5 ) within 16 equivalent ectodermal cells ( eight pairs of cells ) in the animal hemisphere ( Fig 1A–1C ) [2–4] . The activities of these signaling pathways are denoted by the binary variables , Xadmp , Xefn , Xfgf , and Xgdf , and the expression of Otx ( Xotx ) is represented by a Boolean function , Xotx = F ( Xadmp , Xefn , Xfgf , Xgdf ) . In this biological system , there are four binary input variables and eight pairs of equivalent cells , and the number of possible sensing patterns is 4 , 294 , 967 , 296 [= ( 24 ) 8] . As in the case of the hypothetical biological systems , we first screened individual sensing patterns with Rule 1 and Rule 2 . During the 32-cell stage , the Ciona embryo dynamically changes its shape . Therefore we tried to determine sensing patterns and Boolean functions for three early 32-cell embryos , for which geometric data were obtained in a previous study [7] ( S3 Table; S4A–S4C Fig ) . In these analyses , only geometric data were different from the first analysis for the mid-to-late 32-cell embryo . We obtained the same four sensing patterns from each of these three virtual embryos ( S4D Fig ) . Three of them were the same as the ones obtained from the mid-to-late 32-cell embryo , and were compatible with the same Boolean function as that obtained from the mid-to-late 32-cell embryo . The remaining one , sensing pattern 4 , was slightly different , and was compatible with eight Boolean functions , one of which was the Boolean function compatible with the other three sensing patterns ( S4D and S4E Fig ) . Even under sensing pattern 4 , the Boolean functions compatible with this sensing pattern indicated that Efna . d is a critical factor for patterning the ectoderm . Sensing pattern 4 showed that Efna . d signaling and Gdf1/3-r signaling are not sufficiently active in a6 . 5 and b6 . 5 , implying that these two factors are candidates for a factor for patterning the ectoderm . However , all eight Boolean functions compatible with sensing pattern 4 indicated that Gdf1/3-r signaling cannot pattern the ectoderm under this sensing pattern , because F ( Xadmp = 1 , Xefn = 0 , Xfgf = 1 , Xgdf = 0 ) and F ( Xadmp = 1 , Xefn = 0 , Xfgf = 1 , Xgdf = 1 ) gave the same result , Xo = 1 . Therefore , even if Otx expression in early 32-cell embryos is directed by a Boolean function different from the one in the mid-to-late 32-cell embryo , our analysis indicated that Efna . d is the limiting factor for patterning the ectoderm of the 32-cell embryo . The above prediction that Efna . d is the limiting factor for patterning the ectoderm of the 32-cell embryo was consistent with our observation in a previous study that Otx expression is expanded throughout epidermal cells of Efna . d morphants [4] . However , it has not been determined whether differential inputs of Fgf9/16/20 , Admp , and Gdf1/3-r are really unnecessary for patterning the ectoderm . To test this , we used triple morphants of Fgf9/16/20 , Admp , and Gdf1/3-r . First we confirmed our previously result that Fgf9/16/20/Admp/Gdf1/3-r morphants do not express Otx in the animal hemisphere [4] ( Fig 4A ) . Next , we incubated Fgf9/16/20/Admp/Gdf1/3-r morphants in sea water containing recombinant bFGF and BMP4 proteins , which mimic Fgf9/16/20 and Admp/Gdf1/3-r activity [4] . The bFGF concentration was determined empirically on the basis of our previous study [4]; Otx is expressed on average in two cells of Fgf9/16/20 morphants incubated with 1 ng/mL of bFGF [4] . In this experimental condition , in which no gradients of bFGF and BMP4 within embryos were expected , Otx was expressed predominantly in the neural lineage , as in control embryos ( Fig 4B; Table 1 ) . Although relatively weak Otx expression in the epidermal lineage was observed only in a very small number of embryos , cells with neural fate almost always expressed Otx in these embryos , and ectopic expression was also observed in one unperturbed embryo ( Table 1 ) . In addition , as expected in our previous study [4] , Otx expression was observed in the neural and epidermal lineages of quadruple morphants of Fgf9/16/20 , Admp , Gdf1/3-r , and Efna . d incubated in sea water containing recombinant bFGF and BMP4 proteins ( Fig 4C; Table 1 ) , while injection of the same amount of a control morpholino oligonucleotide did not affect Otx expression ( Fig 4D; Table 1 ) . Thus , as predicted by the theoretical model , a difference in strength of Efna . d signaling , which is known to attenuate ERK activation [5 , 6] , can evoke specific Otx expression without differential inputs of Fgf9/16/20 , Admp and Gdf1/3-r , even if differential inputs of these factors might contribute to specific Otx expression in normal embryos .
We determined a Boolean function for Otx expression in the animal hemisphere of the mid-to-late 32-cell ascidian embryo , based on a theoretical analysis using data obtained in previous studies [2–4 , 7 , 18] and in this study . We found that three sensing patterns of signals are compatible with this Boolean function . It is possible that the 32-cell-embryo normally takes only one of these sensing patterns . However , because the Boolean function indicates that Otx is specifically expressed in the neural lineage under either of these sensing patterns , the choice of sensing patterns by 32-cell embryos might not be strictly determined . In other words , these sensing patterns might represent fluctuations of signaling and robustness of this system . The cis-regulatory module of Otx for the expression in the neural lineage contains multiple Ets-binding sites and Smad-binding elements ( SBEs ) [3 , 4] ( Fig 1B ) . Ets is positively regulated by Fgf9/16/20 signaling and negatively regulated by Efna . d signaling . SBEs are responsive to signaling of Admp and Gdf1/3-r , and negatively regulate the expression of Otx in the neural lineage . The Boolean function in Fig 3D indicates that Fgf9/16/20 and Efna . d work positively and negatively . It also indicates that Admp and Gdf1/3-r have a redundant function , because these two molecules are interchangeable . Thus , although no particular cis-regulatory mechanism was assumed in the present study , the cis-regulatory module is not inconsistent with the Boolean function that we revealed in the present study . Our theoretical method does not use quantitative parameters that cannot be easily measured , such as the kinetics of individual signaling molecules . Instead , we only use expression patterns of signaling molecules and geometrical configurations of individual cells within the embryo . The former was determined by in situ hybridization [3 , 18] , and the latter was determined by computation of a series of confocal images [7] . Although activities of signaling pathways were treated as binary values , gradients or differential inputs of signaling molecules were taken into consideration . For this purpose , we assumed that the area of the contact of a cell with its surrounding cells that express a ligand is correlated with the strength of signaling . This is the case at least for Fgf9/16/20 [7] , and it will be hard to imagine cases in which this assumption is inappropriate in early ascidian embryos with the following two reasons . First , our assumption also takes into consideration a case in which diffusion is very fast and no gradient is formed . Second , if an antagonist altered the activity of a signaling molecule within the embryo , this molecule could be considered as an additional signaling molecule . However , no genes for known antagonists for Fgf9/16/20 , Admp , and Gdf1/3-r are expressed from the zygotic genome at or before the 32-cell stage [18] . The sensing patterns of individual cells in normal embryos showed that cells that do not sense Efna . d signaling above a threshold level give rise to neural cells , whereas cells that sense sufficient levels of Efna . d signaling give rise to epidermal cells . A previous study indicated that a differential input of Fgf signaling can differentiate ectodermal cells to neural cells under some experimental conditions and Fgf signaling is thought to be transmitted stronger in neural cells [7] . The present study does not necessarily rule out a possibility that a differential input of Fgf signaling contributes to patterning of the ectoderm in a normal embryo . A differential input of Fgf signaling will indeed contribute to patterning of the ectoderm in a normal embryo with the following three reasons: ( 1 ) Fgf signaling might be stronger in neural cells than in epidermal cells [7] ( Fig 3A ) ; ( 2 ) Efna . d signaling attenuates the ERK pathway activated by Fgf9/16/20 [5 , 6] ( Fig 1B ) ; ( 3 ) a differential level of activation of the ERK pathway controls the expression of Otx [2–4] . However , our results indicate that a differential input of Efna . d is essential for the initial patterning of the ectoderm at the 32-cell stage in a normal embryo . Secreted molecules often form continuous gradients , which are used for patterning of animal embryos [19] . Our result indicates that concentration gradients of Fgf9/16/20 , Admp and Gdf1/3-r , or differential inputs of them , are not required , although these molecules are required for establishing the proper expression pattern of Otx . Efna . d is a membrane-bound protein , and therefore cannot form a continuous gradient as secreted molecules do . Cells located near the animal pole are surrounded by ectodermal cells , and are therefore expected to receive a stronger Efna . d signal . On the other hand , cells located in the periphery of the animal hemisphere are not completely surrounded by ectodermal cells , and are therefore expected to receive a weaker Efna . d signal . This is reminiscent of the differentiation of inner cell mass and trophectoderm of mammalian embryos . The fate choice between them mainly depends on Hippo signaling , which is thought to be activated through direct cell–cell interaction as Efna . d signaling [20 , 21] . In the early animal embryo , cell–cell interaction through direct contacts may provide a more robust system for creating sharp boundaries of gene expression .
The contact areas of individual animal blastomeres of the 32-cell embryo with cells expressing Admp , Efna . d , Fgf9/16/20 and Gdf1/3-r were calculated using four different 3D-virtual embryos , which were reconstructed virtually from several series of confocal images [7] and the expression patterns of these genes [3 , 18] . Given the delay between gene expression and protein translation , we assumed that cells descended from cells expressing a ligand gene at the 16-cell stage would express the encoded protein at the 32-cell stage [4] . The contact surfaces of individual animal blastomeres of the 32-cell embryo with anterior vegetal cells expressing Fgf9/16/20 were previously calculated [7] . We recalculated the contact surfaces of individual animal blastomeres of the 32-cell embryo with all cells expressing Fgf9/16/20 using geometrical data [7] , in our previous study [4] and the present study . The contact surfaces of individual animal blastomeres with cells expressing Efna . d for one early 32-cell embryo were also calculated previously [4] . In the present study , we calculated the contact areas of individual cells with cells expressing Admp and Gdf1/3-r ( S1 and S3 Tables ) for three early 32-cell embryos and one mid-to-late 32-cell embryo . The files we used were downloaded from the Aniseed database [22] ( http://www . aniseed . cnrs . fr ) , and the file names are shown in S1 and S3 Tables . Note that we ruled out autocrine effects of Efna . d , because it is a GPI-anchored membrane protein . C . intestinalis ( type A ) adults were obtained from the National Bio-Resource Project for Ciona . The morpholino oligonucleotides for Fgf9/16/20 , Admp , Gdf1/3-r , and Efna . d used in this study were those used in our previous study [4] . These morpholino oligonucleotides were designed to block translation . We also used a standard control MO ( 5’-CCTCTTACCTCAGTTACAATTTATA-3’ ) purchased from Gene Tools , LLC . DIG-RNA probes for whole-mount in situ hybridization were synthesized by in vitro transcription with T7 RNA polymerase as described previously [18] . Human recombinant bFGF ( Sigma ) and BMP4 ( HumanZyme ) were used at concentrations of 1 ng/mL and 100 ng/mL , respectively . Identifiers for genes examined in the present study are as follows: CG . KH2012 . C2 . 125 for Fgf9/16/20 , CG . KH2012 . C3 . 716 for Efna . d , CG . KH2012 . C2 . 421 for Admp , CG . KH2012 . C4 . 547 for Gdf1/3-r , and CG . KH2012 . C4 . 84 for Otx . | It is often difficult to understand a complex system of multiple signaling molecules in animal embryos only with experimental procedures . Although theoretical analysis might solve this problem , it is often difficult to precisely determine parameters for signaling gradients and kinetics of signaling molecules . In the present study , we developed a parameter-free method for determining a Boolean function for understanding a complex signaling system using gene expression patterns of signaling molecules and geometrical configurations of individual cells within the embryo . In the ascidian embryo , four signaling ligands , Ephrin-A . d ( Efna . d ) , Fgf9/16/20 , Admp , and Gdf1/3-r , coordinately induce Otx expression in the neural lineage at the 32-cell stage . In addition to determining a Boolean function , our method determined sensing patterns , which represented whether or not individual cells received sufficient amounts of the signaling molecules . The sensing patterns predicted that differential expression of Otx in the neural lineage is primarily determined by Efna . d , but not by differential inputs of Fgf9/16/20 , Admp , and Gdf1/3-r . We confirmed this prediction by an experiment . As a result , we found that only Efna . d signaling pathway is differentially activated between ectodermal cells and the remaining signaling pathways are activated in all ectodermal cells at similar levels . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | A Boolean Function for Neural Induction Reveals a Critical Role of Direct Intercellular Interactions in Patterning the Ectoderm of the Ascidian Embryo |
Protozoan parasites of the genus Leishmania cause a large spectrum of clinical manifestations known as Leishmaniases . These diseases are increasingly important public health problems in many countries both within and outside endemic regions . Thus , an accurate differential diagnosis is extremely relevant for understanding epidemiological profiles and for the administration of the best therapeutic protocol . Exploring the High Resolution Melting ( HRM ) dissociation profiles of two amplicons using real time polymerase chain reaction ( real-time PCR ) targeting heat-shock protein 70 coding gene ( hsp70 ) revealed differences that allowed the discrimination of genomic DNA samples of eight Leishmania species found in the Americas , including Leishmania ( Leishmania ) infantum chagasi , L . ( L . ) amazonensis , L . ( L . ) mexicana , L . ( Viannia ) lainsoni , L . ( V . ) braziliensis , L . ( V . ) guyanensis , L . ( V . ) naiffi and L . ( V . ) shawi , and three species found in Eurasia and Africa , including L . ( L . ) tropica , L . ( L . ) donovani and L . ( L . ) major . In addition , we tested DNA samples obtained from standard promastigote culture , naturally infected phlebotomines , experimentally infected mice and clinical human samples to validate the proposed protocol . HRM analysis of hsp70 amplicons is a fast and robust strategy that allowed for the detection and discrimination of all Leishmania species responsible for the Leishmaniases in Brazil and Eurasia/Africa with high sensitivity and accuracy . This method could detect less than one parasite per reaction , even in the presence of host DNA .
Leishmaniases are a major worldwide public health problem and manifest themselves as a spectrum of diseases that may be exacerbated by other infections , such as human immunodeficiency virus . According to the World Health Organization , these diseases are endemic in 98 countries on 5 continents , with more than 350 million people at risk [1 , 2] . Clinically , Leishmaniases can be broadly divided as either cutaneous or visceral , but neither form is exclusively linked to a particular species . Although cutaneous manifestations of the diseases are not life threatening , these manifestations can result in obstruction or destruction of the pharynx , larynx and nose in their final stages [2] . The visceral form is the most severe form , characterized by fever , loss of weight , splenomegaly , hepatomegaly , lymphadenopathies and anaemia , often with fatal outcomes if not timely treated [3] . The severity of the disease and its therapeutic responses are variable and depend on the patient’s immune response , the Leishmania species and even the parasite strain [4] . In this scenario , the development of optimized protocols for discriminating between the different Leishmania species is extremely useful and important in clinical management and treatment . The ability to evaluate the most appropriate species-specific treatments also supports the elucidation of the mechanisms of action of new drugs and the establishment of new species-specific treatment protocols . Furthermore , the identification of these parasites allows the generation of important data for clinical , epidemiological and ecological studies . There are very few publications addressing a Leishmaniasis diagnosis using a High Resolution Melting ( HRM ) analysis , a methodology that detects differences in the nucleotide composition of a specific real-time PCR product . The method is based on thermodynamic differences in the dissociation curve profiles of amplicons generated from real-time PCR . The generated curves are specific signatures that identify polymorphisms due to small differences in nucleotide composition . In spite of the paucity of papers on the HRM method , some workers have already used it to discriminate Leishmania using targets against 7SL RNA [5 , 6] , haspb [7] , the rRNA ITS sequence [8 , 9] , the rRNA ITS sequence coupled to hsp70 [10 , 11] and a FRET-based assay using MPI and 6PGD [12] . Amongst several targets described for Leishmania identification , the heat-shock protein 70 coding gene ( hsp70 ) has proven to be useful in identifying many species of different geographical origins [13–17] . In this work , we propose a more efficient protocol using HRM analyses targeting the hsp70 sequence for the discrimination of seven Brazilian Leishmania species , as well as three Eurasian and African species . This methodology was validated with DNA from reference strains , experimental infections in mice , human clinical samples and naturally infected phlebotomine sand flies .
Promastigotes of L . ( L . ) tropica ( MHOM/SU/60/OD ) , L . ( L . ) donovani ( MHOM/IN/80/DD8 ) , L . ( L . ) infantum chagasi ( MCER/BR/1981/M6445 ) , L . ( L . ) major ( MHOM/IL/81/Friedlin ) , L . ( L . ) amazonensis ( MHOM/BR/1973/M2269 ) , L . ( L . ) mexicana ( MNYC/BZ/62/M379 ) , L . ( L . ) lainsoni ( MHOM/BR/81/M6426 ) , L . ( V . ) braziliensis ( MHOM/BR/1975/M2903 ) , L . ( V . ) guyanensis ( MHOM/BR/1975/M4147 ) , L . ( V . ) naiffi ( MDAS/BR/1979/M5533 ) and L . ( V . ) shawi ( MCEB/BR/84/M8408 ) were grown at 25°C in M199 medium with 10% fetal bovine serum ( Life Technologies , Carlsbad , CA , USA ) . Procyclic forms of Trypanosoma cruzi ( Y strain ) and T . brucei ( 427 strain ) were grown at 28°C in liver-infusion-tryptose medium and SDM-79 , respectively , with 10% fetal bovine serum ( Life Technologies ) . Human DNA , FMUSP-IOF-2016 , obtained from USP Medical School , was used in specificity tests . DNA samples from reference strains were purified by a salting-out procedure using an adaptation of the protocol described by Miller et al . 1988 [18] . Approximately 2 . 5 x 109 promastigotes in stationary growth culture were centrifuged at 3000 x g for 10 min at 25°C . The cells were resuspended in 6 mL of lysis buffer ( 10 mM Tris-HCl , pH 7 . 4; 400 mM NaCl; 2 mM EDTA ) and lysed by the addition of 600 μL of 10% SDS . After overnight digestion with 1 mg of proteinase K at 37°C , 2 mL of saturated NaCl solution was added to lysate , and then , the lysate was vigorously mixed for 15 seconds and centrifuged for 15 minutes at 25°C for the removal of precipitated proteins . Two volumes of cold absolute ethanol were added to the supernatant , and the precipitated DNA was washed with 70% ethanol and resuspended in 1 mL of TE buffer ( 10 mM Tris , pH 7 . 4; 1 mM EDTA ) . DNA from samples obtained from fresh humans biopsies , collected by doctors at Clinical Hospital of Medical Faculty USP , or fixed and paraffin-embedded samples from the collection of Instituto Evandro Chagas , ( Belem-Para ) were used in accordance to the norms established by the National Committee of Ethics in Research ( Comissão Nacional de Ética em Pesquisa , CONEP/CNS ) , resolution 196/96 with the approval of the Ethics in Research Committees of the Institutions of origin ( CAPPesq no . 0804/07 , IEC n° . 0029/2007 ) . Fresh experimentally infected BALB/c mice samples of L . ( L . ) amazonensis or L . ( V . ) braziliensis were obtained 6 weeks after infection when the animals were sacrificed and tissues were collected and DNA was obtained as described below; the procedures involving the use of BALB/c mice had the approval of the Ethical Committee for use of Animals of Biomedical Sciences Institute of University of São Paulo ( CEUA-ICB-USP ) , under protocol #145 of October 20th , 2011 , according to Brazilian Federal Law 11 . 794 of October 8th 2008 . DNA from infected phlebotomines captured in nature were purified using the commercial DNeasy Tissue & Blood kit ( QIAGEN , Hilden , Germany ) , according to the manufacturer´s manual . Paraffin-embedded samples were prepared according to de Lima et al . 2011 [19] . The DNA concentration was measured by spectrophotometry . Initially , we amplified the hsp70 234 bp fragments for all species analyzed in this study using the primers described by Graça et al . [17] . The alignment of the nucleotide sequence of those fragments was used to design primers for HRM analysis . Oligonucleotides used in the PCR assays to amplify a 144 bp fragment of hsp70 ( amplicon 1 ) were hsp70C reverse , previously described by Graça et al . 2012 [17] , and a new forward oligonucleotide designed and named hsp70F2 ( 5’–GGAGAACTACGCGTACTCGATGAAG–3’ ) . For the amplification of a 104 bp fragment of hsp70 ( amplicon 2 ) specific to the species from the L . ( Viannia ) subgenus , the oligonucleotides hsp70F1 ( 5’–AGCGCATGGTGAACGATGCGTC–3’ ) and hsp70R1 ( 5’–CTTCATCGAGTACGCGTAGTTCTCC–3’ ) were designed . The hsp70 amplicon sequences are shown in Fig 1 and indicate the position of the primers . Conventional PCR reactions were performed on a Mastercycler Gradient Thermocycler ( Eppendorf , Hamburg , Germany ) with TopTaq Master Mix ( QIAGEN ) in a final volume of 25 μL with 200 nM of each primer and 50 ng of genomic DNA as a template . The thermal cycling conditions were as follows: an initial denaturation step of 94°C for 5 min , followed by 40 cycles of denaturation at 94°C for 1 min , annealing at 60°C for 30 sec and extension at 72°C for 30 sec , with a final extension at 72°C for 10 min . Real-time PCR reactions were performed using MeltDoc Master Mix for HRM with the fluorophore SYTO9 ( Life Technologies ) in a final volume of 20 μL with 200 nM of each primer and 50 ng of genomic DNA . The real time amplification conditions were as follows: an initial denaturation step at 94°C for 5 min , followed by 40 cycles of denaturation at 94°C for 30 sec and annealing/extension at 60°C for 30 sec , with the acquisition of fluorescent signals at the end of each extension step , followed by the dissociation curve for HRM analysis in Thermocycler PikoReal96 ( Thermo Fisher Scientific , Walthman , MA , USA ) . The 234 bp hsp70 fragment produced by conventional PCR , as described by Graça et al . 2012 [17] , from each Leishmania species used in this study was purified and cloned into a pGEM-T vector using the pGEM-T Easy Vector System ( Promega , Madison , WI , USA ) and E . coli SURE competent cells . The recombinant plasmids from at least three colonies were purified , and they were sequenced with T7 and SP6 primers and the BigDyeTerminator v3 . 1 Cycle Sequencing Kit ( Applied Biosystems , Foster City , CA , USA ) . The sequencing was performed on an ABI 3130 XL Platform ( Life Technologies ) . Recombinant plasmids containing the hsp70 target were linearized with ScaI . The plasmid copy number was calculated considering the molar mass concentration , and a serial dilution on a tenth proportion was used to produce standard curves for each quantification test . The quality parameters for the standard curves were obtained by PikoReal Software ( Thermo Fischer Scientific ) analysis , including the correlation coefficient , linear dynamic range and PCR efficiency . HRM assays were performed at the end of each real-time PCR . The amplicon dissociation analysis was performed by capturing fluorescence signals in 0 . 2°C intervals and holding for 10 seconds in each range of the melting curve ( between 60°C to 95°C ) . The acquisition of fluorescence data and the construction of dissociation profiles were performed using PikoReal96 software . HRM software normalizes melting curves relatively to values from pre- and post-melting point assigned as 100% and 0% , respectively . Then the software determines the normalized difference that means the signal-to-noise ratio difference of each sample versus a user-defined sequence that can be any . The call efficiency is the benchmark measured in percentage of the similarity between two dissociation profiles using fluorescence and Tm values as parameters . The software performs a paired comparison between the profile of the sample of unknown identity and each standard and chooses the standard that has the closest value . The “call” identity refers to the designation allotted to the sample being identified based on that of the closest standard . The graphs containing the means and standard deviations of the Tm values obtained by the HRM analyses were made in GraphPad PRISM v . 6 . 02 software .
The hsp70 sequences deposited in GenBank for L . ( L . ) tropica ( FN395025 . 1 ) , L . ( L . ) donovani ( AY702003 . 1 ) , L . ( L . ) infantum ( HF586351 . 1 ) , L . ( L . ) major ( HF586346 . 1 ) , L . ( L . ) amazonensis ( EU599090 . 1 ) , L . ( L . ) mexicana ( EU599091 . 1 ) , L . ( L . ) infantum chagasi ( FN395036 . 1 ) , L . ( V . ) braziliensis ( GU071173 . 1 ) , L . ( V . ) guyanensis ( EU599093 . 1 ) , L . ( V . ) lainsoni ( GU071174 . 1 ) , L . ( V . ) naiffi ( GU071183 . 1 ) and L . ( V . ) shawi ( GU071177 . 1 ) were used for oligonucleotide design . DNA from all Leishmania reference strains analyzed in this study was used as templates in conventional PCR , and the amplicons were cloned and sequenced to confirm the sequences to those deposited in GenBank . The obtained hsp70 amplicon sequences were then aligned , and we chose regions containing polymorphic sites to be used in HRM methodology ( Fig 1 ) . The two pairs of oligonucleotides depicted in the alignment produced the two expected PCR fragments for all Leishmania reference strain DNA used as a template . The 144 bp amplicon 1 is the PCR product used in the amplification of all Leishmania species . The 104 bp amplicon 2 was produced by the oligonucleotide pair designed for species of the L . ( Viannia ) subgenus ( Figs 1 , S1 and S2 ) . The average and standard deviation of the melting temperature ( Tm ) of each amplicon was determined in duplicate from three independent experiments using 50 ng of DNA as a template from each reference species . The melting profiles and obtained Tm values of hsp70 amplicon 1 for all species studied are presented in Figs 2 and 3 and Table 1 . For a reliable discrimination , we calculated the dispersion of Tm values and only considered differences in Tm values exceeding 0 . 3°C ( Fig 2 ) . The standard curves for the quantification assays using the cloned target showed good linear correlations ( 0 . 99 for all curves ) and efficiencies varying from 92 , 37 to 97 . 23% for all tested species , in the range of 101 to 107 copies ( S3 Fig ) . Moreover , to evaluate the specificity/sensitivity of hsp70 amplicon 1 as a target , HRM assays were performed using genomic DNA from the seven references species of Leishmania in proportions of 1:1 or 1:100 in relation to a human reference DNA ( FMUSP-IOF-2016 ) , and the call identification agreed 100% with the reference samples , even in samples where the call efficiency was approximately 75% ( Table 2 ) . To test if the initial amount of target DNA caused a variation in the Tm , serial dilutions containing 50 ng to 50 fg ( DNA amount corresponding to 5 . 0 x 105 to 0 . 5 of parasite ) of Leishmania DNA from reference strains were used as a template to produce both hsp70 amplicon 1 ( Fig 4A ) and hsp70 amplicon 2 ( Fig 4B ) . The Tm variation obtained for both amplicons in each species showed that some species presented a fluctuation of Tm values that overlapped with other species . In the case of overlapping Tm values for amplicon 1 , a sequential discrimination can be performed by HRM analysis of amplicon 2 . This amplicon is specific for the L . ( Viannia ) subgenus species , allowing the segregation of two patterns that group L . ( V . ) guyanensis , L . ( V . ) lainsoni and L . ( V . ) shawi with Tm = 83 . 92 ± 0 . 04°C or L . ( V . ) naiffi and L . ( V . ) braziliensis with Tm = 84 . 39 ± 0 . 04°C ( Figs 4 and 5 ) . The Ct values obtained in the amplification curves of amplicon 2 , using DNA of all Leishmania studied indicated that the reactions were at least 5 orders of magnitude more specific to Leishmania ( Viannia ) species than for the L . ( Leishmania ) species ( Figs 5C and S2 ) , confirming that amplicon 2 can be used to discriminate L . ( Viannia ) from the L . ( Leishmania ) species . Moreover , using the information on the geographical origin of the samples associated with the HRM analysis of hsp70 amplicon 2 allowed for the discrimination between L . ( L . ) donovani and L . ( L . ) infantum chagasi; among L . ( L . ) major , L . ( L . ) amazonensis , L . ( L . ) mexicana and L . ( V . ) lainsoni . DNA from uninfected mouse , human , or Trypanosoma cruzi and T . brucei were used as templates and compared to the standardized positive range of Tm values for the tested Leishmania species . No cross-reactivity was detected . For these controls , characteristic Tm values ( T . cruzi: 83 . 08 ± 0 . 07°C and T . brucei: 83 . 91 ± 0 . 06°C ) or no amplification was observed ( mouse and human ) ( S4 Fig ) . The HRM analysis of hsp70 amplicon 1 obtained with DNA from other Leishmania isolates also used as reference strains resulted in a 100% correlation with the Tm values of the reference species used in this study ( Table 3 ) . Some of those strains represent isolates obtained from different geographical regions in Brazil , and experimentally corroborated the identification through the HRM protocol for possible polymorphisms . The intra-specific variability was further assessed by the in silico analysis of polymorphism of 186 hsp70 entries from L . ( L . ) tropica , L . ( L . ) donovani , L . ( L . ) infantum , L . ( L . ) major , L . ( L . ) amazonensis , L . ( L . ) mexicana , L . ( V . ) lainsoni , L . ( V . ) braziliensis , L . ( V . ) guyanensis , L . ( V . ) naiffi , L . ( V . ) shawi , L . ( V . ) peruviana , L . ( V . ) panamensis , L . ( L . ) aethiopica , L . ( L . ) martiniquensis and L . siamensis . All the sequences were aligned to include the regions of amplicons 1 and 2 . The aligned sequences were then examined for polymorphisms among species as well as among strains of the same species . We then calculated the percentage of similarity and estimated the theoretical Tm value of both amplicons ( S1 Table ) . If we assume that the nucleotide differences that we detected are real polymorphisms and not sequencing errors then we can see from S1 Table that the differences in the theoretical Tm values of each species results in the same discriminatory pattern . Of the 186 strains analyzed , only two strains of L . infantum , MCAN/IR/96/LON-49 and LEM75/zymodeme1 , presented a theoretical Tm value whose difference was higher than 0 . 3°C . We cannot rule out the possibilities that this difference is in fact a real one , due to sequencing errors or reflects different taxa . In the absence of bona fide samples we also determined the theoretical Tm of amplicons 1 and 2 ( S1 Table ) of two Leishmania species found in America , L . ( V . ) peruviana and L . ( V . ) panamensis , that occur outside Brazil . The obtained data indicated that these two species could be differentiated from the others L . ( Viannia ) species by the coupled HRM analysis of the two amplicons . The theoretical Tm value of the African L . ( L . ) aethiopica , potentially allowed the discrimination from L . ( L . ) donovani , L . ( L . ) infantum and L . ( L . ) major , but not from L . ( L . ) tropica ( S1 Table ) . The enriettii complex members L . ( L . ) martiniquensis and L . siamensis presented identical theoretical Tm values . To validate the HRM protocol for different types of sample preparations , sixteen DNA obtained from real biological samples , like fresh tissue from hamster inoculated with infected sample from human or dog cases; cell culture of the human isolated strain; human fresh tissue; human paraffin-embedded tissue; tissues from experimentally infected BALB/c mice and naturally infected phlebotomines , that had been previously tested in our laboratory by sequencing of SSU rDNA [20] or by discriminatory PCR targeting g6pd [21] , were submitted to HRM analysis . The results obtained presented a correlation with the results obtained with the other targets ( Table 4 ) .
The establishment of optimized protocols for the detection and identification of the aetiological agents of Leishmaniases are extremely useful tools in a clinical context . Identifying the species can lead to species-specific treatment protocols to promote a better efficacy of treatment , assessing the need for patient follow up as well as the development and understanding of the mode of action of potential new drugs . Several methodologies targeting different genomic or mitochondrial DNA have been described in the past 20 years , and PCR is currently the preferred method in studies involving the detection and identification of Leishmania . These methodologies have been developed by designing primers that exploit species-specific sequence polymorphisms in different targets , such as kDNA [22] , the SSU rDNA gene [20 , 23] , the glucose-6-phosphate dehydrogenase gene ( g6pd ) [21 , 24] , rDNA internal transcribed spacers ( ITSs ) [25] , hsp70 [13–17] and cysteine proteinase B gene ( cpb ) [7 , 26] . However , none of these methods represents a gold standard because the targeted polymorphisms were unsuitable for simple and direct identification protocols . These PCR analyses involved the use of multiple targets requiring a combination of several primers creating the need of running more than one reaction to identify a single sample . The multiplex PCR that uses several pair of primers in one reaction and restriction fragment length polymorphism analysis ( RFLP ) of PCR products both need of a subsequent DNA fractionation by gel electrophoresis . These procedures require experienced operators to interpret the results , besides the risk of laboratory contamination with amplicons , due to the manipulation of PCR product . Another way to exploit DNA polymorphisms is the determination of the C+G composition of PCR products from conserved regions by calculating the Tm of the amplicon in a melting curve . HRM methodology has been successfully used for Leishmania identification using different targets , such as the 7SL RNA gene that discriminated L . tropica , L . major and species that cause visceral Leishmaniases in clinical samples [5 , 6] . Additionally , using the same target , researchers determined that rodent Ctenodactylus gundi is a potential host of L . tropica in Tunisia [5] . Polymorphisms on haspb ( Hydrophilic Acylated Surface Protein B gene ) analyzed by HRM allowed the differentiation of strains of L . ( L . ) donovani from distinct regions of East Africa [7] . In Southeastern Iran , the rRNA ITS sequence incriminated Phebotomus sergenti as a natural vector of L . ( L . ) tropica [10] , or the discrimination between L . ( L . ) tropica and L . ( L . ) infantum in Turkey [9] . HRM analysis of the ITS-1 rRNA region discriminated L . ( L . ) major , L . ( . L ) tropica , L . ( L . ) aethiopica and L . ( L . ) infantum in samples from Middle East , Asia , Africa and Europe [8] . The combination of two targets , hsp70 and the rRNA ITS1 sequence , using the absolute HRM values allowed for the discrimination of six American Leishmania species [11] and MPI/6PGD-FRET PCR distinguished L . ( V . ) braziliensis from L . ( V . ) peruviana [12] . Here , we described an algorithm using HRM methodology for the rapid detection and discrimination of Leishmania species circulating in Brazil and Eurasia/Africa ( Fig 6 ) . We used the sequence coding for hsp70 , but in order to obtain a discriminatory PCR product , we designed the primers to encompass a region that was no larger than 144 bp and that had relevant polymorphisms for HRM analysis , that is , shifts of AT base pairs to CG or vice-versa . Moreover , to be effective , the total amount of polymorphisms was taken into account , and compensatory changes were avoided . Using these criteria , we obtained two PCR products: amplicon 1 and amplicon 2 . Using the algorithm described in Fig 6 , the analysis of the produced melting profiles of amplicon 1 for the Brazilian species allowed for the discrimination of L . ( L . ) i . chagasi , L . ( L . ) amazonensis/L . ( L . ) mexicana/L . ( V . ) lainsoni , L . ( V . ) braziliensis/L ( V . ) guyanensis , L . ( V . ) naiffi and L . ( V . ) shawi using differences in the Tm of at least 0 . 3°C . For Eurasian samples , amplicon 1 produced values with the same 0 . 3°C interval to discriminate L . ( L . ) tropica from L . ( L . ) major and from L . ( L . ) donovani/L ( L . ) infantum , but these two species cannot be discriminated from each other ( Fig 2 ) . The occurrence of an overlap in the Tm value for the Brazilian species L . ( L . ) amazonensis and L . ( L . ) lainsoni after a positive reaction of amplicon 1 can be solved by a positive reaction of amplicon 2 . This amplicon sequence is specific for Leishmania ( Viannia ) species , so L . ( L . ) amazonensis will not be amplified and L . ( V . ) lainsoni will present the corresponding Tm value ( Fig 5 ) . The occurrence of an overlap in the Tm value for the American species L . ( L . ) amazonensis and L . ( L . ) mexicana can be solved by amplicon 1 sequencing because this amplicon is not identical , but there are two mismatches ( position 82 A to G and position 100 G to T in L . ( L . ) amazonensis and L . ( L . ) mexicana , respectively ( Fig 1 ) , that are compensatory in the melting profile . It is interesting to note that these two species are very closely related . Uliana et al . [23] distinguished L . ( L . ) amazonensis from L . ( L . ) mexicana by SSU rDNA , but Castilho et al . [21] also failed to distinguish these species by g6pd because the region of the g6pd sequence that was used is identical in the two species . It is also interesting that Hernandez et al . [11] , using a larger amplicon ( 337 bp ) of hsp70 , succeeded in differentiating L . ( L . ) mexicana from L . ( L . ) amazonensis; however , Fraga et al . [13] failed to distinguish these two species using RFLP in another region of hsp70 . However , when the complete nucleotide sequence of the hsp70 PCR fragment of 1268 bp is used , the discrimination between the two species can be achieved [27] . These problems once again emphasize that one gene or a particular sequence of a gene is not reliable to define a species or plot its phylogeny . Recently , Real et al . [28] showed that L . ( L . ) mexicana and L . ( L . ) major had , respectively , 5 and 7 species-specific orthologous gene families , while L . ( L . ) amazonensis had 23 different gene families . Moreover , the geographical parameter can also be used; Uliana et al . used SSU rDNA polymorphism to show that these species present a characteristic distribution in Latin America that correlates to monoclonal antibody profiles [29] . The Tm overlap for Eurasian species occurred for L . ( L . ) donovani and L . ( L . ) infantum , which presented identical sequences for amplicon 1 . Again , the geographical origin of the sample can be used because L . ( L . ) donovani is more frequently found in India and East Africa and presents anthroponotic behavior . L . ( L . ) infantum is found in Africa , China and the Mediterranean and shows zoonotic behavior [30] . However , the two species can be discriminated by multilocus enzyme electrophoresis ( MLEE ) or multilocus microsatellite typing ( MLMT ) [30] . Recently , the haspb coding region was initially used in a classical PCR coupled to RFLP [31] , while the gene coding for cpb was used as a target in conventional PCR [7] . We propose to use the latter in case of doubt between the two species ( Fig 6 ) . The in silico analysis of amplicon 1 and 2 from other Leishmania species from America or from Eurasia/Africa , also indicated the potentiality of the hsp70 HRM protocol to discriminate L . ( V . ) peruviana , L . ( V . ) panamensis and L . ( L . ) aethiopica/ L . ( L . ) martiniquensis/L . siamensis from L . ( L . ) donovani and L . ( L . ) major but not from L . ( L . ) tropica . It is interesting to note that the ITS-HRM analysis applied to L . ( L . ) tropica L . ( L . ) aethiopica , L . ( L . ) infantum , L . ( L . ) major and L . ( L . ) donovani [8] presented exactly the same degree of resolution of the hsp70 HRM described here . We also noticed that the initial amount of template DNA influenced the Tm determination ( Fig 4 ) . This Tm variation could be important in cases where the Tm values are in the same range and can lead to a misidentification if the reference sample is at a different concentration . This is the case for L . ( L . ) amazonensis and L . ( L . ) lainsoni . However , as has been previously explained , the use of hsp70 amplicon 2 allowed for the discrimination between these two species . The two other species that presented an overlapping Tm range depending on the initial amount of DNA were L . ( V . ) braziliensis and L . ( V . ) guyanensis , which could be discriminated by the use of an HRM analysis on the same amplicon 2 . In fact , when we applied the protocol described here to other Leishmania isolates , the obtained “call” ( the identification of the problem sample in relation to the reference samples ) presented a 100% correlation with the reference strains ( Table 3 ) . The test of sixteen samples consisting of fresh hamster tissue from animals injected with human or dog biopsy macerates , fresh or paraffin embedded human biopsies , tissues of experimentally infected BALB/c mice or even naturally infected phebotominae , produced identification “calls” comparable to the identification results using SSU rDNA sequencing or g6pd PCR ( Table 4 ) , showing that the source of the sample as well as its conservation do not interfere in the HRM protocol . Moreover , the use of HRM protocol is easier than the use of SSU rDNA and/or g6pd PCR , since those methods require either sequencing of the product or three or more distinct PCRs followed by gel electrophoresis analysis . Overall , the hsp70 HRM protocol described herein accurately and sensitively identified Leishmania species that are important in the majority of cases of Leishmaniases in the Brazil and Eurasia . The test is simple and rapid , and its use in the clinic or in research samples has many advantages , such as a lower total cost for the identification of a sample and other characteristics that facilitate its application . There is no need for sequencing or gel fractionation to analyze the product , thus avoiding laboratory contamination with PCR products because these products are discarded without being manipulated . It also reduces the need for trained personnel to analyze the fractionation profile of an electrophoretic gel or sequencing data to provide a result . Also the HRM assay presents a possibility of quantifying parasites present in samples because it is a real-time PCR-based technique . Moreover , the whole process can be automated because the analyzer software will produce the “call” result by comparing the tested samples to the reference sample identities , which must always be included in the reactions . In conclusion , the protocol described herein is a low cost , reliable , easy to apply , potentially automated procedure that is a good alternative for the detection , quantification and identification of Leishmania species in biological and clinical samples . | The different clinical forms of the Leishmaniases range from cutaneous to visceral infections and are caused by organisms belonging to the genus Leishmania . Controversy over the validity of different molecular methods to correctly identify a species hinders the association of a given species with different clinical forms , complicating the prognosis and the development of suitable treatment protocols . A correct identification leads to a better understanding of the action and consequent development of new drugs and immunological reactions . It also provides important information about the relationship of each species with its hosts ( humans , animal reservoirs and sandflies ) in different geographical areas and ecological situations , helping to design control strategies . Today , PCR is the most commonly used method for Leishmania identification , but even though several targets have been described , no simple and direct protocol has emerged . In this paper , we coupled hsp70 real-time PCR with the determination of amplicon melting profiles in order to explore polymorphic regions by HRM analysis . This methodology yielded discriminatory melting temperature ( Tm ) values for Brazilian and Eurasian/African Leishmania species . The protocol has proven to be 100% reliable with both clinical and experimental samples . The major advantage of the presently described method is that it is simple , less expensive , highly sensitive and easily automated . | [
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] | 2016 | High Resolution Melting Analysis Targeting hsp70 as a Fast and Efficient Method for the Discrimination of Leishmania Species |
Depression is a serious mental disorder that affects a person’s mood , thoughts , behavior , physical health , and life in general . Despite our continuous efforts to understand the disease , the etiology of depressive behavior remains perplexing . Recently , aberrant early life or postnatal neurogenesis has been linked to adult depressive behavior; however , genetic evidence for this is still lacking . Here we genetically depleted the expression of huntingtin-associated protein 1 ( Hap1 ) in mice at various ages or in selective brain regions . Depletion of Hap1 in the early postnatal period , but not later life , led to a depressive-like phenotype when the mice reached adulthood . Deletion of Hap1 in adult mice rendered the mice more susceptible to stress-induced depressive-like behavior . Furthermore , early Hap1 depletion impaired postnatal neurogenesis in the dentate gyrus ( DG ) of the hippocampus and reduced the level of c-kit , a protein expressed in neuroproliferative zones of the rodent brain and that is stabilized by Hap1 . Importantly , stereotaxically injected adeno-associated virus ( AAV ) that directs the expression of c-kit in the hippocampus promoted postnatal hippocampal neurogenesis and ameliorated the depressive-like phenotype in conditional Hap1 KO mice , indicating a link between postnatal-born hippocampal neurons and adult depression . Our results demonstrate critical roles for Hap1 and c-kit in postnatal neurogenesis and adult depressive behavior , and also suggest that genetic variations affecting postnatal neurogenesis may lead to adult depression .
Depression is the most common mental disorder and a leading cause of disability around the world [1 , 2] . In the US , the lifetime prevalence for major depression is estimated to be as high as 16 . 2% [3] . There are a variety of symptoms associated with depression , including anhedonia , depressed mood , fatigue , helplessness , and other cognitive and metabolic abnormalities [4 , 5] . Despite its wide influence , the causes of depression have not been made clear , nor have we established effective and long-lasting treatments for it . To gain insight into its etiology , twin studies were conducted to determine whether genetics could play a role in depression . The results revealed that genetic factors account for about 40% of the risk of developing depression , with the remaining 60% being due to environmental factors [6] . Despite various genetic and environmental causes of depression , there must be some common pathways that lead to depressive symptoms . It has been long proposed that deficiency in serotonin ( 5-HT ) level may underlie depression as selective serotonin reuptake inhibitors ( SSRIs ) , the most frequently used antidepressant drugs , work through enhancing extracellular levels of 5-HT [7 , 8] . Furthermore , synaptic dysfunction [9 , 10] , hyperactivity of the hypothalamic-pituitary-adrenal ( HPA ) axis [11] , and expression changes or polymorphisms of brain-derived neurotrophic factor ( BDNF ) may also be associated with depression [12] . Recently , hippocampal neurogenesis has emerged as an attractive theory of depression [13 , 14] , largely because many antidepressants are known to enhance hippocampal neurogenesis [15–18] , and ablating adult neurogenesis reduces some of the behavioral effects of antidepressants [19] . Of note , many of the common theories of depression such as those aforementioned have also been closely linked to adult neurogenesis [20–23] , further accentuating its importance in depressive behavior . Although much of the focus in depression research has been on adult neurogenesis , postnatal neurogenesis , which occurs early in life , is also important as an increasing number of studies found that it is capable of influencing adult depressive behavior [24–27] . Nevertheless , most if not all of these studies used either chemicals or maternal separation to induce a decline in postnatal neurogenesis . To our knowledge , a genetic model for investigating the relationship between postnatal neurogenesis and adult depression is still lacking . Huntingtin-associated protein a ( Hap1 ) is an intriguing neuronal-enriched protein that interacts with several disease-related proteins , including huntingtin and Ahi1 , whose mutations cause Huntington's disease ( HD ) and Joubert syndrome , respectively [28 , 29] . Mounting evidence shows that Hap1 mediates the intracellular transport of several neurotrophic factors and their receptors to support neuronal function and survival , which may require a concerted effort with huntingtin [30–37] . The involvement of Hap1 in BDNF/TrkB trafficking [33–35] may be especially important as BDNF is the most abundant neurotrophic factor in the nervous system and is essential for neuronal activity and the survival of animals . Our recent work revealed that Hap1 regulates early postnatal hypothalamic neurogenesis by stabilizing BDNF/TrkB signaling and that this hypothalamic neurogenesis is critical for the postnatal growth and survival of mice [32] . Earlier work found that neuronal deficiency of the Hap1-binding protein Ahi1 leads to a depressive-like phenotype in adult mice [38] . In the current study , we found that selective depletion of Hap1 expression at postnatal ages also led to adult depressive-like behavior , which was associated with reduced postnatal neurogenesis in the hippocampus . Moreover , we discovered that this decreased neurogenesis is mediated by a different mechanism involving c-kit , a receptor for stem cell factor ( SCF ) that is also expressed in neural progenitor or stem cells in the rodent brain [39–41] . c-kit is downregulated in Hap1 KO mouse hippocampus because its stability requires Hap1 . Our results also demonstrate that overexpression of c-kit in the postnatal hippocampus can augment hippocampal neurogenesis and alleviate adult depression , suggesting a new mechanism by which Hap1 regulates postnatal neurogenesis and consequent adult depressive behavior .
Since mice with neuronal deficiency of the Hap1-interacting protein Ahi1 show depressive phenotypes [38] , we were interested to see whether Hap1 KO mice would display similar phenotypes and whether Hap1 deletion at different ages would affect behavioral outcomes . To test these questions , we used the Cre-LoxP system via tamoxifen ( TM ) induction to induce Hap1 deletion at different postnatal stages ( Fig 1A ) . Substantial depletion of Hap1 protein expression was achieved in all KO groups as analyzed by western blot ( S1 Fig ) . Two to three months after the TM induction , we subjected the Hap1-depleted mice to the forced swim test ( FST ) and tail suspension test ( TST ) . These tests showed that early postnatal ( Fig 1B and 1C ) , but not late postnatal ( Fig 1D and 1E ) or adult ( Fig 1F ) , depletion of Hap1 resulted in significant increases in immobility , which is considered depressive-like behavior . Since the relationship between decreased or altered locomotion and depression has been suggested by previous clinical studies [42–45] , we also tested locomotor activity in these mice and found that depletion of Hap1 at an earlier age also resulted in lower locomotor activity in adult mice compared to controls ( Fig 1G ) . To further strengthen the idea that early postnatal Hap1 depletion leads to an adult depressive phenotype , we crossed floxed-Hap1 mice with camk2a-Cre transgenic mice and obtained Hap1 conditional KO mice in which Hap1 is deleted in camk2a-expressing neurons ( Fig 2A ) . Based on camk2a-promoter activities , Hap1 expression was diminished early postnatally to varying extents in the forebrain areas , with the greatest reduction seen in the cortex and hippocampus ( Fig 2B ) . As camk2a-Hap1 KO mice still maintained a fairly high level of hypothalamic Hap1 expression , which controls feeding and growth [46] , we only saw a mild decrease in the body weight gain of these mice at the postnatal stage ( Fig 2C ) . The KO mice displayed a mild decrease in body weight during postnatal life , but the body weight difference between KO and control mice at 4 months old was indiscernible . To see whether camk2a-Hap1 KO mice would behave similarly in the depression tests as the induced Hap1 KO mice , we performed the FST and TST , which demonstrated that camk2a-Hap1 KO mice at 2-month old indeed displayed depressive-like behavior ( Fig 2D ) . To rule out the possibility that our Hap1 KO mice had any physical impairment that might result in poor performances in depression tests , as well as a locomotor activity assay , we did a rotarod test on both P1 KO and camk2a-Hap1 KO mice; we found no such impairment in either of the KO groups ( S2A Fig ) . Instead , P1 KO mice displayed enhanced performance , which very likely could be the result of their smaller body size allowing them to more readily balance and cling on to the rod . Imipramine , a tricyclic antidepressant , is used in the treatment of major depression and can rapidly reduce depressive phenotypes in mice [38 , 47] . To examine whether this drug could rescue the depressive phenotype in Hap1-depleted mice , we delivered imipramine via intraperitoneal injection ( i . p . ) and found that it substantially increased the mobility of camk2a-Hap1 KO mice in both the FST and TST , more so than it did to the control mice ( S2B Fig ) . As a result , the mobility of the mice after treatment was not significantly different between the genotypes ( S2B Fig ) . To further validate the depressive phenotype , we assessed anhedonia in camk2a-Hap1 KO mice using the sucrose preference test . Anhedonia , or an inability to experience pleasure , is considered the core feature of major depression [48] . In rodents , the sucrose preference test is used widely to assess anhedonia based on the finding that depressed human patients have higher hedonic responses to sucrose solutions than controls [49] . We found that camk2a-Hap1 KO mice displayed lower preference to 1% sucrose solution than controls , as measurements of fluid intake indicated that , although water intake was not different between the genotypes , camk2a-Hap1 KO mice consumed significantly less sucrose solution and total fluid ( Fig 2E ) . Collectively , these results support a depressive-like phenotype caused by Hap1 depletion . Hippocampal neurogenesis occurs abundantly after birth and remains active in adulthood . As the involvement of hippocampal neurogenesis in adult depression has been widely demonstrated [13] , we next examined whether Hap1 also regulates hippocampal neurogenesis . Thus , we first injected BrdU into P6 Hap1 P1 KO mice and controls , and analyzed BrdU incorporation 24 hours later . BrdU immunostaining and stereological quantification clearly showed a decrease in proliferating cells in the hippocampal DG ( Fig 3A and 3B ) , indicating that early postnatal Hap1 expression may positively regulate hippocampal neurogenesis . We then performed the same assay on Hap1 P1 KO and P21 KO mice at P34 and found that , while Hap1 depletion at P1 still had an effect on DG neurogenesis at P34 , late postnatal Hap1 KO ( P21 KO ) could no longer influence hippocampal neurogenesis ( Fig 3C and 3D ) . Since camk2a-Hap1 KO mice started to deplete Hap1 weakly at the late embryonic stage and much more potently after birth [50 , 51] , we went on to assess BrdU incorporation in camk2a-Hap1 KO mice to see whether the observed adult depressive phenotype in these mice was associated with a decrease in postnatal neurogenesis . We found that camk2a-Hap1 KO mice also had a reduced number of BrdU+ cells in the DG ( Fig 3E ) , further supporting an important role for early postnatal Hap1 expression in hippocampal neurogenesis . Neural progenitor cells ( NPCs ) , when undergoing differentiation , can give rise to both neuronal and glial cells . Because of the decreased number of proliferating NPCs as indicated by the number of BrdU+ cells , we also wanted to know whether the differentiation of NPCs was perturbed by the early postnatal loss of Hap1 . To explore this , we injected BrdU into Hap1 P1 KO and control mice at P6 and sacrificed them 4 weeks later so that NPCs , which incorporated BrdU at P6 , would differentiate into either mature neurons or glia . Co-immunostaining of BrdU with NeuN , a marker for mature neurons , or GFAP , a marker for mature astrocytes , revealed that P1 deletion of Hap1 significantly affected neuronal differentiation , as the ratio of NeuN+/BrdU+ cells in P1 KO mice ( 53% ) is significantly lower than in controls ( 70% ) ( Fig 3F , left panel ) . In contrast , the ratio of GFAP+/BrdU+ cells among BrdU+ cells was increased in P1 KO mice ( Fig 3F , right panel ) . Because Hap1 expression is not seen in mature glial cells [52 , 53] , we believe that this increase is unlikely to be due to the direct effect of Hap1 on glial cell differentiation , but rather is a reflection of decreased neuronal differentiation in P1 KO mice . Since neural activities and signal transduction are very different between developing and mature brains , many genes are expected to play differential roles at these two stages . Whether Hap1 plays differential roles in neurogenesis in postnatal and adult brains has gone unexplored . As the hippocampus is a brain region known to be highly responsive to stress [21] , we wanted to test the idea that adult-expressed Hap1 may respond to stress in the regulation of hippocampal neurogenesis and animal behavior . To this end , we used Hap1 P21 KO mice , which did not present gross phenotypes , including deficits in neurogenesis , and subjected them to 7-day repeated restraint stress . P21 KO mice did not exhibit impaired hippocampal neurogenesis under normal conditions; however , after repeated stress , they showed a significant reduction in hippocampal neurogenesis , much greater than in control mice ( Fig 4A and 4B ) . Repeated restraint stress is well known to suppress hippocampal neurogenesis , but does not lead to apparent depressive behavior in WT rodents [54–57] . We thought that a certain level of neurogenesis must be maintained in order for animals to battle against the potential behavioral changes caused by stress . Because the lack of Hap1 caused hippocampal neurogenesis to drop to an abnormally low level following stress , it is possible that this reduction might undermine the ability of animals to cope with stress . Using the forced swim test ( FST ) , we found that after repeated restraint stress , Hap1 P21 KO mice displayed a marked increase in immobility compared to non-stressed Hap1 KO and stressed control mice ( Fig 4C ) , suggesting that adult expression of Hap1 is involved in the maintenance of hippocampal neurogenesis in response to certain types of stress , including restraint stress , thereby protecting animals against stress-induced depressive-like behavior . Our results indicate that early postnatal Hap1 deletion reduces hippocampal neurogenesis and leads to an adult depressive-like phenotype . To find a molecular target for Hap1-regulated hippocampal neurogenesis , we prepared P1 WT and Hap1 KO mouse hippocampal lysates and performed mass spectrometry analysis to look for potential targets of Hap1 whose levels were significantly altered between WT and KO samples . Among all the proteins identified , we found that c-kit , or mast/stem cell growth factor receptor , showed nearly 50% down-regulation in Hap1 KO hippocampus ( Fig 5A ) . A previous study indicated that c-kit is expressed in neuroproliferative zones of the rat brain , and in vivo administration of its ligand , stem cell factor ( SCF ) , increases neurogenesis in these regions [40] . We first confirmed the mass spectrometry result by immunofluorescent staining ( Fig 5B ) and western blot analysis ( Fig 5C and 5D ) of c-kit , which revealed that c-kit expression in Hap1 KO hippocampus is indeed decreased . Then , we looked at c-kit levels in Hap1 P1 KO and adult KO hippocampal tissues . As compared with controls , c-kit expression was significantly lower in P1 KO , but not adult KO , hippocampus , suggesting that c-kit down-regulation could be associated with the decreased hippocampal neurogenesis and adult depressive-like phenotype displayed in P1 KO mice ( S3A and S3B Fig ) . Our previous results indicate that Hap1 controls postnatal hypothalamic neurogenesis by stabilizing TrkB protein level [32] . To find out whether the same mechanism also underlies Hap1-mediated regulation of postnatal hippocampal neurogenesis , we assessed TrkB levels in germline Hap1 KO as well as P1 and adult KO hippocampal tissues , and found that neither of these KO tissues showed a reduction in TrkB level ( Fig 5C , 5D , S3A and S3B Fig ) , suggesting that Hap1 may regulate hippocampal neurogenesis through different signaling molecules , such as c-kit . We next examined c-kit expression levels by western blot in various tissues from camk2a-Hap1 KO and control mice . Besides the hippocampus , which showed a significant reduction in c-kit level , we also found a trend of decreased c-kit expression in the striatum and hypothalamus of camk2a-Hap1 KO mice ( Fig 5E ) . Notably , c-kit is highly expressed in the hippocampus compared to other tissues examined ( Fig 5E ) , which lends credence to the finding that SCF/c-kit signaling regulates neurogenesis . To look for more evidence that c-kit activation and neurogenesis are perturbed in camk2a-Hap1 KO mouse hippocampus , we assessed the levels of phosphorylated c-kit ( pc-kit ) , a proliferating cell marker , ki67 , a neuroblast or immature neuron marker , DCX , and a mature neuron marker , NeuN , via western blot analysis ( Fig 5F and 5G ) . The results showed that all these proteins were significantly reduced , indicating that loss of Hap1 affects c-kit activation by down-regulating its expression level , compromising postnatal hippocampal neurogenesis in camk2a-Hap1 KO mice . We next looked at the expression level of c-kit in the hippocampus at different ages and found that , very similar to Hap1 , the c-kit expression level peaks at the postnatal stage ( Fig 6A ) , which supports a role for both proteins in the regulation of postnatal neurogenesis . To see whether Hap1 directly promotes the c-kit level in hippocampal neurons , we first examined whether these two proteins are expressed in the same neurons . Therefore , we cultured primary hippocampal neurons from P1–P2 WT pups and co-stained these neurons at DIV5 with antibodies for Hap1 and c-kit . We saw that Hap1 and c-kit were coexpressed in most neurons in this in vitro system ( Fig 6B ) . To examine their expressions in vivo , we also performed co-immunofluorescent staining of Hap1 and c-kit in P12 WT mouse brain sections and found that Hap1 and c-kit were partially coexpressed in the subgranular zone of the DG , a region where new neurons are born ( Fig 6C ) . To examine which type of cells in the DG express c-kit , we stained WT mouse brain sections from P7–P15 with antibodies for c-kit and an array of cell type-specific markers . We found that during the early postnatal stage , c-kit was expressed in NPCs , immature neurons , and mature neurons , as there was co-immunostaining of c-kit with nestin , DCX , or NeuN in the same cells ( S4A Fig ) . In mature neurons , c-kit expression was found mainly in GAD67-expressing GABAergic interneurons , but not in prox1-expressing granule cells ( S4A Fig ) . We also saw a similar pattern of expression for Hap1 at this age ( S4B Fig ) . Therefore , it is possible that Hap1 and c-kit may directly regulate the proliferation and/or differentiation of hippocampal NPCs at an early postnatal stage , or that they might be involved in GABAergic control of hippocampal neurogenesis , a phenomenon reported previously [58–61] . In 1-month old mouse hippocampus , however , c-kit expression was largely restricted in GAD67-expressing GABAergic interneurons ( S4A Fig ) . Since Hap1 could not be detected in NPCs in adult rat hippocampus , and more than 60% of Hap1-immunoreactive cells express GABA [62] , it is likely that Hap1 and c-kit may mediate GABAergic control of adult hippocampal neurogenesis , which could serve as a buffer mechanism against stress-induced behavioral changes . Hap1 is known to stabilize internalized membrane receptors via its trafficking function between endosomes and lysosomes [29 , 31 , 32 , 63–65] . To verify whether Hap1 directly regulates c-kit levels intracellularly , we measured the stability of transfected c-kit in Neuro2A cells . We found that knocking down endogenous Hap1 expression via siRNA substantially diminished the stability of c-kit ( Fig 6D ) , indicating that direct intracellular regulation of c-kit levels by Hap1 is at least part of the mechanism by which Hap1 stabilizes c-kit . We next asked whether increasing the expression of c-kit in the hippocampus of camk2a-Hap1 KO mice could rescue the neurogenesis defect and adult depressive-like behavior displayed by these mice . To this end , we designed adeno-associated virus ( AAV ) expressing c-kit under the synapsin-1 promoter and validated the successful infection of the virus and expression of c-kit both in Neuro-2a cells ( Fig 7A ) and mouse hippocampus , which was stereotaxically injected with AAV-c-kit at P3 ( Fig 7B ) . The overexpression of c-kit in the hippocampus resulted in increased BrdU incorporation at P18 for both camk2a-Hap1 KO and control mice ( Fig 7C and 7D ) , suggesting that postnatal hippocampal neurogenesis can be modulated by altering the c-kit level . Furthermore , behavioral examination using the FST ( Fig 7E ) and TST ( Fig 7F ) showed that P3 AAV-c-kit injection in the hippocampus had a significant effect on mouse depressive-like behavior compared with control virus . Although c-kit overexpression slightly reduced the immobility of control mice in both tests , this overexpression in the hippocampus was able to significantly improve the performance of camk2a-Hap1 KO mice to a greater extent , thus partially ameliorating the adult depressive-like phenotype in these mice . As the virus directed the expression of c-kit in the hippocampus , the improved behavioral phenotype is very likely a specific result of the enhanced hippocampal neurogenesis by c-kit overexpression . Taken together , these results indicate that suppressed c-kit expression indeed accounts for both the neurogenesis defect and adult depressive behavior in mice lacking Hap1 , and upregulation of c-kit or Hap1 levels could be considered as therapeutic options .
Since its identification as the first known interacting protein of huntingtin , Hap1 has been investigated in a number of studies for its role in both physiological and disease contexts . It has become evident that the neuronal expression of Hap1 is essential for early postnatal survival of mice , as germline Hap1 KO results in an early postnatal lethal phenotype [30 , 66]; however , whether Hap1 depletion induced at different ages has differential effects on adult animal behaviors has gone unexplored . Using an inducible Hap1 KO mouse model , we were able to demonstrate that early postnatal Hap1 depletion leads to reduced neurogenesis in the hippocampus and depressive-like behavior in adult mice . Our studies thus demonstrate for the first time that genetic induction of defective postnatal neurogenesis can lead to adult depressive-like behavior . Depression is a major mental disorder that affects hundreds of millions of people worldwide [67] . Due to its wide spectrum of symptoms , many genetic and environmental factors may trigger the onset of a depressive episode . These episodes are normally observed in adults , but are also seen in childhood and adolescence , although the latter cases are more difficult to diagnose and often go overlooked [68] . However , the importance of early life experience to adult depression is accentuated by the fact that , in the most severe cases of adult depression , some form of abuse was experienced in childhood [69] . It is therefore apparent that environmental factors in early life can contribute significantly to adult depression; whether genetic mutations also play a role in predisposing people with unpleasant early life experiences or even without such experiences to depression remains largely unknown . Our results using induced Hap1 KO mice show that early loss of a gene involved in neurogenesis can indeed contribute to the etiology of depression , suggesting that early genetic diagnosis could possibly help with prediction of and early intervention for depression , or likely other forms of adult-onset mental disorders . Hippocampal neurogenesis has emerged as an appealing theory to explain depression; however , a causal relationship between hippocampal neurogenesis and depression has not been established [70 , 71] . Because neurogenesis happens more dynamically in the early postnatal brain than adult brain and since Hap1 regulates postnatal hypothalamic neurogenesis [32] , we hypothesized that Hap1 may also regulate postnatal hippocampal neurogenesis , and the reduction of which could contribute to adult depressive behavior . We found that Hap1 deletion at P1 significantly reduced hippocampal neurogenesis at the early postnatal stage and caused depressive-like behavior in adults . Since these P1 KO mice also showed severe growth retardation and reduced survival , we used camk2a-Hap1 KO mice as another model for the study , which deletes Hap1 mainly in the cortex and hippocampus from early postnatal life . These mice could survive and grow normally , yet exhibited a postnatal neurogenesis defect as well as adult depressive behavior , suggesting that reduced postnatal neurogenesis is more likely to be a cause of the depressive phenotype . In addition , we also found that loss of Hap1 reduces the expression of c-kit , which is present in progenitor cells for hippocampal neurogenesis . It should be pointed out that in conditional Hap1 KO mice , Hap1 depletion , which is mediated by Cre under the control of camk2a promoter , is not restricted to the hippocampus . To explore whether Hap1 depletion in the postnatal hippocampus plays a pivotal role in adulthood depression , we used stereotaxic injection to overexpress c-kit in P3 mouse hippocampus and found that this overexpression can augment hippocampal neurogenesis and mitigate the depressive phenotype caused by the loss of Hap1 , further supporting a causal role of reduced postnatal hippocampal neurogenesis in adult depression in our Hap1-deficient mice . All these findings provide evidence of a new role for Hap1 in adult depressive behavior . Our earlier study suggested that Hap1 is an essential gene for postnatal survival , but might be dispensable for adults [32] . However , mice used for those experiments were raised in standard housing conditions , which are far different from all the stress and threats animals would experience in the wild . In our case , although late postnatal or adult Hap1 depletion did not lead to overt phenotypes , its expression could be needed under certain stress conditions . Despite a lack of depressive phenotype caused by ablation of adult hippocampal neurogenesis , stress is found to down-regulate adult hippocampal neurogenesis , and adult-born hippocampal neurons are required for the regulation of the hypothalamic–pituitary–adrenal ( HPA ) axis in buffering stress-induced depressive behaviors [14 , 21 , 72] . Thus , an investigation of whether adult Hap1 expression influences hippocampal neurogenesis and depressive behavior under stressful conditions was of great interest to us . We found that 7-day repeated restraint stress was able to diminish adult hippocampal neurogenesis in P21 KO mice in which Hap1 depletion occurred after the postnatal period . This finding suggests that Hap1 expression may be required for adult hippocampal neurogenesis under stress and that its loss at least increases susceptibility to stressors in adulthood . Since postnatal neurogenesis is necessary for the maturation of the central nervous system after birth , its reduction might affect the connectivity of certain critical neural circuitries , rendering animals susceptible to depression later in their lives . It is also possible that adult-expressed Hap1 plays an important role in maintaining a proper level of hippocampal neurogenesis and thus the hippocampal neural circuitry , which is needed for animals to buffer against stress-induced behavioral changes . Hap1 is known to interact with a number of endocytic receptors in a way that their degradation is inhibited and levels are stabilized [29–32 , 63–65] . These receptors could be important for neurogenesis or other neuronal functions that are involved in maintaining and regulating hippocampal connectivity and circuitry in response to environmental stress . We conducted mass spectrometry analysis and found that c-kit , a protein that has been suggested to play a role in hippocampal neurogenesis and synaptic potentiation , as well as hippocampal-dependent learning and memory [40 , 73 , 74] , was downregulated in Hap1 KO hippocampus . Since c-kit also undergoes ligand-induced internalization [75 , 76] , it is likely that Hap1 functions to stabilize endocytic c-kit as it does to those other receptors . Moreover , Hap1 and c-kit both peak their expression levels in the hippocampus at early postnatal stage , and show a continued decrease in expression with age . Although this alteration is in line with the age-dependent decline in neurogenesis , it remains to be investigated whether increasing Hap1 expression or its mediated signaling , e . g . , SCF/c-kit pathway in the adult brain can be beneficial to adult neurogenesis and reducing aging-related phenotypes [77 , 78] . We found that Hap1 and c-kit are coexpressed in NPCs , immature neurons , and GABAergic interneurons in the DG during early postnatal life , though they become more restricted in interneurons later in life . Such different distributions could account for the differential roles of Hap1 in postnatal and adult neurogenesis and associated behavioral phenotypes . It is also possible that during the postnatal stage , Hap1 and c-kit regulate the proliferation and differentiation of a subpopulation of DG NPCs into interneurons , and once differentiated , they remain expressed in these interneurons , which can also regulate neurogenesis as reported previously [58–60] . As their expressions decrease significantly with age , Hap1 and c-kit might become part of the cellular machinery in response to stress or injuries as supported by the previous finding on c-kit [41] . The differential roles of Hap1 in postnatal and adult life are not unexpected as Hap1 is a multifaceted protein that interacts with different partners . Its association with other proteins and the resulting functions may be cell-type dependent and also depend on posttranslational modulations that can be cell-type and age-dependent . Whether Hap1 regulates c-kit function differently during postnatal and adult life requires further investigations . The neurogenesis and behavioral rescue in camk2a-Hap1 KO mice via c-kit overexpression in the hippocampus suggests that c-kit-mediated signaling pathways are important for postnatal hippocampal neurogenesis and adult depressive behavior . In conclusion , we have demonstrated a novel role for Hap1 in the regulation of postnatal neurogenesis and adult depressive-like behavior and provided the first genetic model that relates postnatal neurogenesis to adult depression . Our findings may help us better understand the mechanisms of depression , as well as identify potential therapeutic interventions .
All animal studies were performed in compliance with IACUC ( Institutional Animal Care and Use Committee ) at Emory University . Dulbecco’s modified Eagle’s medium ( DMEM ) , Neurobasal-A , B27 , GlutaMAX-1 , D-Hank’s , and fetal calf serum ( FCS ) were obtained from Life Technologies . Trypsin , poly-D-lysine , BSA , BrdU , imipramine , cycloheximide were all from Sigma . Cell culture dishes , coverslips , plates , and flasks were purchased from Corning and Nunc , Inc . Guinea pig antibody to Hap1 was generated in our laboratory [28 , 30] . Rabbit anti-c-kit and phosphorylated-c-kit ( Cell Signaling ) , rabbit anti-NeuN , mouse anti-NeuN , GFAP , nestin , GAD67 , prox1 ( all from Millipore ) , goat anti-DCX and guinea pig anti-DCX ( Santa Cruz and Millipore ) , rat anti-BrdU ( Accurate Chemical ) , rabbit anti-Ki67 ( Thermal ) , mouse anti-calretinin ( BD Transduction Laboratories ) , mouse anti-tubulin ( Sigma ) were used for western blot and immunofluorescent staining . Dilutions of the primary antibodies used can be found in S1 Table . HRP-tagged or fluorescent secondary antibodies were obtained from Jackson ImmunoResearch Laboratories . Mice were housed in the Division of Animal Resources at Emory University on a 12-h light ( 7 am-7 pm ) /dark ( 7 pm-7 am ) cycle . All procedures and husbandry were in accordance with the NIH Guide for the Care and Use of Laboratory Animals . Generation of germline Hap1 KO mice ( C57BL/6/black Swiss ) , floxed-Hap1 mice ( C57BL/6/SV129 ) , and TM-inducible Cre-ER ( C57BL/6/CBA ) /floxed-Hap1 mice was described in our previous studies [30 , 32 , 79] . Transgenic mice expressing Cre under the control of the mouse calcium/calmodulin-dependent protein kinase II alpha ( camk2a ) promoter ( C57BL/6/BALB/C ) were kindly provided by Dr . Stephen Warren ( Emory University ) . Camk2a-Hap1 KO mice were generated by crossing the floxed-Hap1 mice with camk2a-Cre transgenic mice . Control mice were floxed-Hap1 mice without transgenic Cre , and were all littermates of the KO mice . Mouse brain tissues or harvested cells were lysed in ice-cold RIPA buffer ( 50 mM Tris pH 8 . 0 , 150 mM NaCl , 1 mM EDTA pH 8 . 0 , 1 mM EGTA pH 8 . 0 , 0 . 1% SDS , 0 . 5% DOC , and 1% Triton X-100 ) containing Halt protease inhibitor cocktail ( Thermol Scientific ) and phosphatase inhibitors . The lysates were incubated on ice for 30 min , sonicated , and centrifuged at top speed for 10 min . The supernatants were subjected to SDS-PAGE . The proteins on the gel were transferred to a nitrocellulose membrane , which was then blocked with 5% milk/PBS for 1 h at room temperature . The blot was incubated with primary antibodies in 3% BSA/PBS overnight at 4°C . After 3 washes in PBS , the blot was incubated with HRP-conjugated secondary antibodies in 5% milk/PBS for 1 h at room temperature . After 3 washes in PBS , ECL Prime ( GE Healthcare ) was then used to detect immunoreactive bands on the blot . A detailed list of the primary antibodies used can be found in S1 Table . For immunofluorescent staining of cultured neurons , neurons were washed once with PBS , and fixed with 4% paraformaldehyde ( PFA ) for 10 min . Fixed cells were washed 3 times with PBS , and then blocked with 3% BSA + 5% normal donkey serum/PBST ( 0 . 2% Triton X-100 in PBS ) for 1 h at room temperature . Primary antibodies were diluted in blocking buffer and incubated with the cells at 4°C overnight followed by 3 washes with PBS and incubation with fluorescent secondary antibodies and nuclear dye . After 3 washes , the cells were ready for examination using a Zeiss ( Axiovert 200M , Germany ) microscope with a digital camera ( Orca-100; Hamamatsu Photonics , Bridgewater , NJ ) and the Openlab software ( Improvision , Lexington , MA ) . Immunofluorescent staining of brain sections was performed as described previously [29 , 38] . Briefly , mice were deeply anesthetized , perfused with saline followed by 4% PFA fixation . Brains were postfixed overnight in the same fixative , and switched to 30% sucrose at 4°C . After completely sunk , brains were sectioned at 15 μm ( 40 μm for BrdU staining ) with a cryostat at −19°C and mounted onto gelatin-coated slides . The tissues on the slides were washed , blocked , and immunostained with antibodies using the same method described above for cultured cells . For BrdU immunostaining , sections were first treated with 2 N HCl for 30 min at 37°C and then neutralized with 0 . 1 M sodium borate ( pH 8 . 5 ) for 15 min at room temperature . A detailed list of the primary antibodies used can be found in S1 Table . TM induction in mice was performed as described previously [32] . Briefly , TM ( Sigma T5648 ) was dissolved in ethanol at 20 mg/ml and stored at -20°C before use . To induce Hap1 depletion , a calculated amount of TM was mixed with corn oil , and ethanol was removed by a vacuum centrifuge . 1 . 1 mg or 2 . 2 mg TM per 40 g body weight was used to inject P1 ( subcutaneous ) or P10 ( i . p . ) mouse pups for 3 consecutive days . For mice at P15 or older , 4 mg TM per 40 g body weight was used for i . p . injection for 5 consecutive days . Both TM-inducible Cre-ER/floxed-Hap1 mice or camk2a-Hap1 KO mice and their control littermates were given TM injections at the same time . For BrdU injection into Hap1 P1 KO mice and controls , mice at P6 were i . p . injected with 50 mg/kg body weight BrdU . The animals were perfused and fixed 24 hours later for the analysis of NPC proliferation , or 4 weeks later for the analysis of neural differentiation . For BrdU injection into camk2a-Hap1 KO or P21 KO mice and controls , mice at P33 ( or P17 for AAV-c-kit rescue experiment ) were i . p . injected with 50mg/kg body weight BrdU . Twenty four hours later , the mice were perfused and fixed for analyzing the number of proliferating cells . Stereological cell counting and quantification were performed as described in our previous study [32] . Briefly , to quantify BrdU+ cells , the optical-fractionator method implemented in Stereo Investigator 9 . 03 . 2 ( MicroBrightField , Magdeburg , Germany ) was used . One-in-six 40-μm serial sections covering the entire hippocampal region were stained to visualize and quantify BrdU+ cells in the DG . A minimum of 3 mice from each genotype and 8 sections from each mouse brain were examined for comparisons . The volume of the DG and the total number of BrdU+ cells in the DG were calculated by Stereo Investigator software . The total number of BrdU+ cells was then divided by the volume to yield cell density presented as the number of BrdU+ cells per mm3 . For estimation of the ratios of NeuN+/BrdU+ and GFAP+/BrdU+ cells among BrdU+ cells , sections were co-stained for BrdU and either NeuN or GFAP . Each BrdU+ cell counted was also examined for the presence of NeuN or GFAP labeling , and the double-positive cells were marked and quantified separately . The densities of the double-positive cells were divided by those of the total BrdU+ cells to yield the ratios . Counting of the cells was performed under the 40× lens in a Zeiss AX10 microscope . In multiple experiments , conditional Hap1 KO mice and their control littermates ( age indicated in figure legends ) were placed individually into a round opaque plastic cylinder ( 18 cm in height , 15 cm in diameter ) filled with water ( 25°C ) at a depth of 12 cm . Immobility time , defined as floating or the absence of active behaviors , such as swimming or struggling to escape , was measured . Slight movements of the feet and tail necessary to keep the head above water were excluded as mobility . Each mouse was measured for 6 min by a trained observer who was kept blind to the genotypes of the mice and drug treatment . No pretest training of mice was performed . As for FST , conditional Hap1 KO mice and their control littermates ( age indicated in figure legends ) were used for TST in multiple experiments . The mice were suspended by taping the tail ( ~1 cm from tip of tail ) to a horizontal bar at a height of 40 cm from the table surface for 6 min . The trial was conducted for a duration of 6 min , and the immobility time was recorded manually via stopwatch by a trained observer who was blind to the genotypes of the mice examined . Mice were considered immobile when they hung passively and motionlessly without escape-oriented behaviors . Motor activity was evaluated using Rotamex ( Columbus Instruments ) . Two-month old Hap1 P1 KO or camk2a-Hap1 KO mice and their control littermates were trained on a rotating rod at a speed of 5 rpm for three 5-min trials on 3 consecutive days . Testing was performed on the fourth day . During the test , the rotating rod was gradually accelerated to 40 rpm over 5 min . Latency to fall from the rotarod was recorded in 3 trials , and the average of the 3 trials was used for each mouse . Tricyclic antidepressant imipramine ( 30 mg/kg , sigma ) was freshly made in saline and i . p . injected into 4-month old camk2a-Hap1 KO or control mice 30 min before FST or TST . Saline injection was used as vehicle treatment . One-year old Hap1 P21 KO and control mice were subjected to repeated restraint stress by placement for 4 h per day for 7 consecutive days in ventilated 50 ml conical tubes . After each stress session , mice were immediately returned to their home cages . Control mice were housed in separate cages from the stressed mice , and were deprived of food and water but otherwise untouched during each session . Twenty four hours after the final session , mice were evaluated by FST . For neurogenesis analysis , BrdU ( 100mg/kg body weight ) was i . p . injected before the stress session on each of the last 3 days . Twenty four hours after the final session , mice were perfused and fixed , their brains were then sectioned for BrdU immunostaining . Locomotor activity was measured using an automated system ( San Diego Instruments , La Jolla , CA ) with photobeams that record ambulations ( consecutive beam breaks ) . Two-month old Hap1 P1 KO and control mice were individually placed in the chambers under 12-h light-dark cycle with free access to food and water . Mice were allowed 4 h to acclimate to the new environment before recording . Activities were recorded every 30 min for 24 h . The sucrose preference test was conducted as previously described with modification [21] . Briefly , 2-month old camk2a-Hap1 KO and control mice were individually housed with free access to food and two weighed bottles of liquid: one filled with water , the other with 1% sucrose solution . The positions of the two bottles were balanced across animals . After 3 days of acclimation , both bottles were removed and weighed at 12 pm , and then put back in reversed positions at 7 pm . The bottles were weighed again in 1 h for an acute test , and again on the next morning for an overnight test . Sucrose preference was calculated as ( Δweightsucrose ) / ( Δweightsucrose + Δweightwater ) × 100 . Hippocampal dissection and neuronal cell culture were performed as previously described [80] . Briefly , hippocampi were dissected from P1 WT mice and placed in a sterile 35-mm petri dish containing ice-cold Hanks’ balanced salt solution ( HBSS , Ca2+-and Mg2+-free ) , chopped into 1 mm3 pieces by microscissors , and digested with 0 . 125% ( w/v ) trypsin at 37°C for 25 min . The enzymatic activity was terminated by adding DNaseI ( 200 U/ml final concentration ) and heat-inactivated FCS ( 20% final concentration ) into the solution . The tissue was then dissociated by triturating through a fire-polished Pasteur pipette , spun down and washed twice with culture medium ( Neurobasal-A supplemented with B27 and GlutaMAX-1 ) . After resuspension in culture medium , 2 × 105 cells per well were plated onto 6-well plates . Neurons that were cultured for 5 days in vitro ( DIV ) were fixed by 4% PFA and used for immunofluorescent staining . Adenoviral Hap1-specific and scramble siRNAs were prepared in our previous study [81] . Viral stocks were adjusted to 1X108 viral particles/μl before use . Neuro2A cells were incubated with adenoviral Hap1 siRNA at a multiplicity of infection of 50 . Twenty-four h after infection , the virus-containing medium was removed , and the c-kit plasmid was transfected for another 48 h before performing the protein stability assay . Mouse c-kit cDNA was subcloned into a pAAV-MCS vector ( Cell Biolabs ) . The human synapsin-1 promoter sequence was inserted into the construct to replace the original promoter in the vector . AAV-c-kit ( Serotype 9 ) was packaged and amplified by the viral vector core at Emory University . AAV-GFP ( Serotype 9 , SignaGen Laboratories ) under the same promoter was used as a control . A camk2a-Hap1 KO or control mouse pup at P3 was placed in a latex sleeve and immersed up to the neck in crushed ice and water ( 2–3°C ) for 7–10 min . The pup was then placed on an ice pack ( 3–4°C ) and stabilized on a platform while being injected with 0 . 5 μl of AAV-c-kit or AAV-GFP viral particles ( 1X1012 particles/ml ) into each side of the hippocampus ( 1 . 5 mm lateral from the sagittal suture , 2 mm rostral to the lambda , and 2 mm below the skull ) over 2 min from a 5-μl Hamilton syringe and 33-gauge needle . The needle was kept still for another 2 min before withdrawal . The surgical field was illuminated with fiber optic to minimize inadvertent and uncontrollable warming . The pup was then transferred into a clean cage placed on a heat pad ( 33°C ) with nest for 30 min to recover from hypothermia before returning to the home cage . We used 4–6 pups per experimental group . All data are expressed as mean ±SEM . The statistical significance was determined by two-tailed Student’s t-tests or two-way ANOVA followed when appropriate by post hoc t-tests using GraphPad Prism 5 . 0 software . A value of p<0 . 05 was considered statistically significant . | Although the majority of the neurons in the brain are generated during embryonic stage , new neurons are continuously being produced postnatally , and at a much lower rate in adulthood . As postnatal neurogenesis is a key component of the brain maturation process that creates dynamic ‘wirings’ in the brain necessary for an individual to grow , learn , and cope with the external world , attenuated postnatal neurogenesis may affect an individual’s mental stability , rendering a higher susceptibility to depression later in life . In the current study , we genetically ablated the expression of huntingtin-associated protein 1 ( Hap1 ) in mice at various ages or in selective brain regions , and found that early loss of Hap1 significantly reduces postnatal hippocampal neurogenesis , and leads to adult depressive-like behavior . We also found c-kit as an effector to mediate the neurogenesis defect and adult depressive-like phenotype in mice lacking Hap1 . The results provide the first genetic evidence to demonstrate the importance of postnatal neurogenesis in adult depression , and may offer new avenues in the prevention and treatment of depression . Our study also has potential implications to other adult-onset mental disorders . | [
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] | [] | 2015 | Postnatal Loss of Hap1 Reduces Hippocampal Neurogenesis and Causes Adult Depressive-Like Behavior in Mice |
Inflammasomes are cytosolic multi-protein complexes that initiate immune responses to infection by recruiting and activating the Caspase-1 protease . Human NLRP1 was the first protein shown to form an inflammasome , but its physiological mechanism of activation remains unknown . Recently , specific variants of mouse and rat NLRP1 were found to be activated upon N-terminal cleavage by the anthrax lethal factor protease . However , agonists for other NLRP1 variants , including human NLRP1 , are not known , and it remains unclear if they are also activated by proteolysis . Here we demonstrate that two mouse NLRP1 paralogs ( NLRP1AB6 and NLRP1BB6 ) are also activated by N-terminal proteolytic cleavage . We also demonstrate that proteolysis within a specific N-terminal linker region is sufficient to activate human NLRP1 . Evolutionary analysis of primate NLRP1 shows the linker/cleavage region has evolved under positive selection , indicative of pathogen-induced selective pressure . Collectively , these results identify proteolysis as a general mechanism of NLRP1 inflammasome activation that appears to be contributing to the rapid evolution of NLRP1 in rodents and primates .
Mammals have evolved multiple mechanisms to detect microbes in order to initiate immune responses during infection . While both harmless and pathogenic microbes are detected , pathogens generally induce robust responses sufficient to mediate their elimination , whereas commensals trigger milder responses that do not generally produce immunopathology . One family of pattern recognition receptors that can discriminate between pathogens and commensals is the nucleotide-binding domain ( NBD ) and leucine-rich repeat ( LRR ) containing ( NLR ) protein family [1–4] . NLRs are cytosolic proteins that can be activated upon pathogen access to the host cell cytosol [5] . Pathogens employ a variety of virulence factors , such as toxins and secretion systems , to access the cytosol , resulting in NLR activation [6] . By contrast , commensals do not generally encode these virulence factors . Upon activation , several NLRs have been shown to form a scaffold , termed an inflammasome , which recruits and activates the Caspase-1 protease ( CASP1 ) [7] . Active CASP1 is required for the cleavage and release of the cytokines IL-1β and IL-18 , and also initiates a lytic and inflammatory cell death known as pyroptosis . The molecular mechanisms by which different NLRs are activated in response to pathogen stimulation are not completely understood . In one well-characterized mechanism of NLR activation , members of the NAIP subfamily of NLRs have been shown to bind directly to specific bacterial ligands such as flagellin [8–10] . Upon ligand binding , NAIPs co-associate with a different NLR member , NLRC4 , to form an inflammasome complex that recruits and activates CASP1 and ASC . However , most NLRs do not appear to utilize the simple receptor-ligand activation mechanism utilized by NAIPs . For example , the NLRP3 inflammasome appears to respond to potassium efflux [11] , but the underlying molecular basis for this response remains unknown . Mouse NLRP1B is another NLR that does not appear to be activated by a receptor-ligand type mechanism . Instead , NLRP1B variants from certain inbred mouse strains , e . g . , BALB/c and 129 , can be activated by the lethal factor ( LF ) protease that is produced and secreted by Bacillus anthracis , the causative agent of anthrax [12] . Together with protective antigen ( PA ) , LF forms a bipartite toxin , Lethal Toxin ( LeTx ) . The role of PA is to form a translocation channel that delivers LF into the host cell cytosol , where LF hampers the host immune response by cleaving and inactivating most MAP kinase kinases [13 , 14] . In addition to cleavage of MAPKKs , which appears to promote anthrax virulence , LF also directly cleaves NLRP1B proximal to its N-terminus [15] , which is both necessary and sufficient [16] for NLRP1B inflammasome formation and CASP1 activation . Activation of NLRP1B-dependent inflammasome responses appears to contribute to host defense via a mechanism requiring IL-1β and neutrophils [17 , 18] . Together , these results suggest NLRP1B can function as a sensor of bacterial proteases , similar to other immune responses that are specifically activated by virulence factors [19–21] . Interestingly , other members of the NLRP1 gene family do not appear to be activated by LF . NLRP1B is highly polymorphic in mice [12] , and only two of five identified alleles have been shown to respond to LF . The allele found in C57BL/6 ( B6 ) mice fails to respond to LF , but it remains unclear if this is because NLRP1BB6 is not cleaved by LF , or because NLRP1BB6 has lost inflammasome functionality . In addition , it is not clear what stimuli might activate NLRP1A , the other known functional murine NLRP1 paralog . A previous study identified a mouse carrying a missense gain-of-function mutation in NLRP1A ( Q593P ) that exhibits spontaneously active inflammasome responses [22] , but the mechanism of wild-type NLRP1A activation is unclear . Recently , several groups have provided evidence that NLRP1B in mice and NLRP1 in rats can respond to Toxoplasma gondii infections [23–25] . However , the mechanism by which T . gondii activates NLRP1 is unknown . Interestingly , it has also been shown that NLRP1 can function as a metabolic sensor that is activated by reduced intracellular levels of ATP [26 , 27] . Thus , it remains unclear whether proteolysis represents a unique activation mechanism limited to certain rodent NLRP1 isoforms , or is instead a general mechanism that governs NLRP1 activation in diverse species . The mechanism of activation of the human ortholog of NLRP1 is controversial and has been suggested to be distinct from that of mouse NLRP1B . Human NLRP1 was the first NLR shown to assemble into a multi-protein inflammasome complex [7] , but in that study , NLRP1 was activated spontaneously in cellular lysates . Thus , the mechanism by which NLRP1 is activated in response to a specific stimulus was not addressed . Similar to mouse NLRP1BB6 , human NLRP1 is not responsive to anthrax LF [28] . One study observed that muramyl dipeptide ( MDP ) , a fragment of peptidoglycan found in bacterial cell walls , stimulated the oligomerization of NLRP1 in a cell-free system [29] . However , other studies raise doubts that MDP is a specific agonist for NLRP1 [30–33] . In particular , it has been difficult to dissociate a direct role for MDP in NLRP1 activation from the known ability of MDP to prime inflammasome expression via the NOD2 sensor . Importantly , genetic data demonstrating a requirement for NLRP1 in the inflammasome response to MDP is currently lacking . The overall architecture of human and rodent NLRP1 proteins is similar , including conservation of a four domain module , NBD-LRR-FIIND-CARD , which comprises the majority of the protein . One important structural difference between human NLRP1 and rodent NLRP1 isoforms is the presence of an N-terminal Pyrin domain ( PYD ) in human NLRP1 that is absent from rodent NLRP1 isoforms . In other NLRs , the PYD plays an essential function in recruiting CASP1 via a PYD/CARD-containing adaptor protein called ASC . Thus , it was originally unclear whether N-terminal cleavage would activate human NLRP1 , since this cleavage would disrupt or remove the PYD . However , all NLRP1 proteins also contain a C-terminal Caspase Activation and Recruitment Domain ( CARD ) . Although the initial description of the inflammasome suggested that the CARD was insufficient to bind CASP1 directly [7] , more recent data suggest that the CARD rather than the PYD is the domain in human NLRP1 that recruits and activates CASP1 [29 , 34 , 35] . Interestingly , mutations in the PYD of human NLRP1 have recently been shown to lower the threshold for spontaneous NLRP1 activation by destabilizing the PYD [36] , suggesting that the PYD may play a role in maintaining NLRP1 in its inactive state . Given the mechanism of activation of mouse NLRP1B inflammasome , these findings raise the possibility that human NLRP1 could be activated by proteolytic cleavage resulting in the removal of an auto-inhibitory PYD . In this study , we address directly whether proteolytic cleavage is a general mechanism for NLRP1 activation in rodents and humans . We took advantage of our ability to reconstitute NLRP1 inflammasomes individually in heterologous cells that lack endogenous inflammasomes to determine whether N-terminal cleavage is sufficient for NLRP1 activation . Inflammasome reconstitution avoids confounding problems of interpretation that have arisen when using cells such as macrophages , which can form multiple inflammasomes . Consistent with previous observations , we find that LF cleaves and activates only some alleles of mouse NLRP1B . But interestingly , we observed that induced N-terminal proteolysis of the B6 isoforms of rodent NLRP1B and NLRP1A is sufficient to cause inflammasome activation . Moreover , consistent with a general role for proteolytic activation , the human NLRP1 ortholog could also be activated via direct proteolysis in a specific N-terminal linker region between the PYD and NBD domains . Evolutionary analysis demonstrates that this linker region is evolving rapidly under positive selection , suggestive of pathogen-driven evolution to diversify to detect novel protease virulence factors . Taken together , our results provide a plausible and general molecular mechanism for activation of NLRP1 and suggest that host immunity proteins may evolve toward recognition by bacterial proteases to engage in evolutionary arms races with pathogens .
NLRP1B is highly polymorphic in mice , and five different allelic variants have been identified [12] . Of these five variants , only two respond to LF to form an inflammasome to promote pyroptosis . Although the NLRP1B variant from B6 mice ( NLRP1BB6 ) is expressed [37] , it is not responsive to lethal toxin ( LeTx ) , and indeed , it is unclear if NLRP1BB6 can even form an inflammasome . Alignment of the amino acid sequence of NLRP1BB6 to the LF-responsive NLRP1B129 isoform reveals considerable variation between alleles , with only 84 . 6% amino acid identity . Interestingly , most of these polymorphisms occur in the N-terminal portion of the protein ( Fig 1A ) , suggesting they may affect N-terminal proteolysis by LF . We therefore first investigated if LF cleaves the B6 isoform of NLRP1B . In our studies , we decided to reconstitute a functional NLRP1 inflammasome response in transfected HEK 293T cells . Reconstituted systems have previously been used to study NLRP1 [16 , 26 , 35 , 38] and provide several advantages: ( 1 ) 293T cells lack other inflammasomes; thus , we avoid the confounding effects other inflammasomes have had on prior studies of endogenous NLRP1 , e . g . , in THP-1 cells; ( 2 ) A reconstituted system allows us to easily express modified NLRP1 alleles ( e . g . , point mutants , GFP fusions , epitope-tagged alleles , etc . ) that are critical to our studies; ( 3 ) A reconstituted system provides a uniform genetic background for testing various NLRP1 alleles , and thus allows for direct comparisons across alleles; ( 4 ) By titrating the amount of plasmid used in transfections , our reconstituted system also permits control over the levels of inflammasome gene expression . This latter point is critical because overexpression of NLRs tends to lead to their spontaneous activation . This can be useful to establish that a given NLRP1 protein is potentially functional ( i . e . , properly folding ) , especially when a physiological stimulus to activate a given NLRP1 isoform is unknown . However , for our studies of NLRP1 activation by proteolysis , it is also possible to express NLRP1 at lower levels ( below the threshold for spontaneous activation ) , as we describe below . Thus , 293T cells were transfected with expression plasmids encoding either GFP-HA-NLRP1B129 or GFP-HA-NLRP1BB6 , along with expression plasmids encoding CASP1 and IL-1β to reconstitute a functional inflammasome . All NLRP1 proteins contain a FIIND ( Function-to-find Domain ) , which is an auto-processing domain that spontaneously cleaves itself to generate two polypeptides that are believed to remain non-covalently associated in the mature NLRP1 protein [34 , 35 , 39] . For reasons that remain unclear , FIIND auto-processing is essential for the function of both rodent and human NLRP1 [34 , 35] . As expected , we observed that transfected NLRP1BB6 appears as two bands on SDS PAGE , implying that the protein is properly folded and that FIIND auto-processing can occur . Surprisingly , expression of NLRP1BB6 promoted significant amounts of IL-1β processing even in the absence of LF . This suggests that , despite numerous polymorphisms , NLRP1BB6 is able to form an inflammasome when over-expressed ( Fig 1B ) . However , the large amount of cleaved IL-1β observed in cells expressing NLRP1BB6 was unexpected , and was much greater than the basal activity of the 129 allele , despite comparable expression and FIIND processing for both isoforms . Importantly , LeTx treatment did not further increase the amount of cleaved IL-1β ( p17 ) in cells expressing the B6 allele , though LeTx did stimulate IL-1β processing by cells expressing NLRP1B129 ( Fig 1B ) . Cleavage of the known LeTx target MAP kinase MEK2 served as an additional positive control for LeTx addition ( Fig 1C ) . We did not observe LeTx-dependent cleavage of the B6 isoform , but did observe the expected cleavage of the 129 isoform . In order to reduce the levels of spontaneous activation seen in cells expressing NLRP1BB6 , we titrated the amount of expression plasmid used in the transfections . We observed a dose-dependent decrease in the amount of spontaneous IL-1β processing under these conditions ( Fig 1B ) , but were still unable to reveal LeTx-induced inflammasome activity . We conclude that NLRP1BB6 is capable of forming an inflammasome , but it is not cleaved nor activated by LF , and exhibits weaker intrinsic auto-inhibition that can be overcome under conditions of overexpression . While NLRP1BB6 is neither cleaved nor activated by LF , we hypothesized that it might nevertheless be cleaved and activated by unknown proteases with specificities different than the LF protease . Following a strategy we used previously with NLRP1B129 [16] , we tested this hypothesis by engineering a tobacco etch virus ( TEV ) protease cleavage-site in the NLRP1BB6 isoform . The TEV site was inserted in NLRP1BB6 at a position corresponding to the site at which NLRP1B129 is cleaved by LF . We then transfected cells with reduced amounts of expression plasmids encoding the NLRP1BB6-TEV allele ( insufficient to produce spontaneous inflammasome formation ) along with plasmids encoding TEV or LF protease , and assessed inflammasome formation via detection of cleaved IL-1β by immunoblot . Interestingly , TEV protease , but not LF protease , was able to induce significant IL-1β processing in cells expressing the NLRP1BB6-TEV isoform ( Fig 1D and Fig 1E ) . The TEV-dependent activation of NLRP1BB6-TEV was comparable in magnitude to that seen with NLRP1B129-TEV , or to the LF-dependent activation of wild-type NLRP1B129 . Consistent with the lack of cleavage observed upon LeTx treatment ( Fig 1B ) , the WT B6 isoform was neither cleaved nor responsive to LF over-expression ( Fig 1C ) . Similarly , as a further specificity control , over-expression of Dengue virus NS2B/NS3 protease , which is known to target host cell substrates [40] had no effect on NLRP1B129 activation ( S1 Fig ) . The Dengue protease was clearly active as it underwent auto-proteolytic cleavage required for its activity . These results are consistent with a model in which NLRP1B activation occurs through specific protease recognition and cleavage . We next assessed why NLRP1BB6 is not cleaved by LF . NLRP1BB6 exhibits three amino acid substitutions in the region corresponding to the LF-cleavage site in NLRP1B129 ( Figs 1A and S2A ) , and we hypothesized that these polymorphisms might explain the differential LF proteolysis of the two isoforms . We mutated these three residues in the 129 allele to match the ones found in the B6 allele , and then tested the sensitivity of the resulting proteins to LF cleavage . 129/B6-A ( L39P/R41G ) and 129/B6-B ( M47T ) exhibited little to no reduction in LF-dependent cleavage and inflammasome responsiveness ( S2B Fig ) . Combining the two sets of mutations to generate 129/B6-C resulted in a protein with a partially reduced level of LF-cleavage . A previous paper examining cleavage of a polypeptide encoding the first 118 amino acids of NLRP1B129 indicated that K38 and K44 were necessary for susceptibility to cleavage by LF [41] , but we found that mutating both residues simultaneously in the context of the full-length protein did not affect sensitivity of NLRP1B129 to cleavage ( S2C Fig ) . This indicates that K38 and K44 residues are incomplete determinants of LF sensitivity . Our results suggest that in addition to cleavage site polymorphisms , polymorphisms outside the immediate vicinity of the cleavage site might also contribute to differential proteolysis of the two isoforms . This conclusion is consistent with what is known about the mechanism of MAPKK recognition by LF ( see Discussion ) . B6 macrophages also express a transcript for NLRP1A [12 , 37] , but no exogenous stimulus has been identified that can activate NLRP1A . Since B6 macrophages do not respond to LF , it has been presumed that NLRP1AB6 is not activated by LF . Comparison of the primary amino acid sequences of NLRP1AB6 versus NLRP1BB6 again reveals numerous N-terminal polymorphisms ( Fig 2A ) . However , it remained unclear whether NLRP1AB6 is cleaved by LF and whether NLRP1AB6 could form a functional inflammasome in response to proteolysis . We therefore generated a construct to express the reference sequence for NLRP1AB6 found in NCBI ( NM_001004142 . 2 ) . We first tested if high levels of expression of NLRP1AB6 are sufficient to produce a functional inflammasome in the 293T reconstituted system described above . Overexpression of NLRP1AB6 under a strong ( CMV ) promoter indeed led to substantial IL-1β processing , comparable to the amount induced by overexpression of NLRP1B129 ( Fig 2B ) . Consistent with the lack of responsiveness of B6 macrophages to LeTx , LeTx did not enhance the amount of IL-1β processing in cells expressing NLRP1AB6 , whereas cells expressing NLRP1B129 were responsive to LeTx as expected . To test directly whether LF can cleave NLRP1AB6 , we fused GFP-HA to the N-terminus of NLRP1A as described above for NLRP1B . Co-expression of GFP-HA-NLRP1AB6 and LF did not result in detectable N-terminal cleavage , which is robustly seen with NLRP1B129 ( Fig 2C ) . In order to test whether proteolysis is sufficient to activate NLRP1AB6 , we engineered a TEV-protease site into NLRP1A at a position corresponding to the site of LF cleavage in NLRP1B129 ( Fig 2A ) . In these experiments , we transfected less NLRP1 plasmid and used a plasmid encoding a weaker ( LTR ) promoter to reduce spontaneous activation . Interestingly , specific proteolysis of NLRP1AB6-TEV induced significant IL-1β processing , comparable to the cleavage-induced IL-1β processing observed with wild-type NLRP1B or NLRP1B129-TEV ( Fig 2C ) . Importantly , this TEV-induced activation correlated with N-terminal cleavage of NLRP1AB6 , and was not observed with NLRP1 forms that lack the TEV-protease site . The LF cleavage sites identified in rodent NLRP1 variants do not appear to be conserved in human NLRP1 ( hNLRP1 ) , and treatment of human macrophages with LeTx does not lead to inflammasome activation [28] . In addition , human and primate NLRP1 variants contain an N-terminal Pyrin domain ( PYD ) , which is shared with numerous mammals , but has been lost in the recently duplicated rodent NLRP1 variants ( S3A and S3B Fig ) . The PYD is connected to the NBD via an inter-domain linker >100 amino acids in length that has no predicted secondary structure ( S4 Fig ) . Although hNLRP1 is not cleaved by LF , both hNLRP1 and rodent NLRP1 contain a predicted unstructured region directly N-terminal to the NBD . A hypothetical cleavage event within this region of hNLRP1 would liberate a C-terminal fragment containing the NBD-LRR-FIIND-CARD domains that would resemble the active cleaved form of rodent NLRP1 . We therefore hypothesized that the unstructured inter-domain linker region in hNLRP1 might be sensitive to proteases and that proteolysis could lead to NLRP1 activation . One difficulty with the hypothesis that hNLRP1 is activated by proteolytic cleavage within the inter-domain linker region is that such a cleavage event would result in removal of the N-terminal PYD . An initial report suggested that the PYD was necessary for ASC and CASP1 co-recruitment by hNLRP1 [7] , but recent reports suggest that the C-terminal CARD is sufficient for CASP1 activation and that the PYD fulfills an auto-inhibitory function in hNLRP1 [34 , 36] . We first decided to test the roles of the PYD and N-terminal cleavage in hNLRP1 activation in our reconstituted system . We assembled a human NLRP1 cDNA encoding the reference sequence isoform 1 ( NM_033004 . 3 ) and expressed this cDNA in 293T cells with human CASP1 , ASC , and IL-1β expression vectors to reconstitute a fully human NLRP1 inflammasome . As a positive control , we also generated an expression construct encoding a variant of hNLRP1 deleted of its LRR domain , as ΔLRR mutants of NLR proteins are typically constitutively activated [10 , 31 , 38 , 42] . In addition , we generated a construct lacking the N-terminal PYD to test its necessity for inflammasome function . When overexpressed at levels that promote spontaneous activation , wild-type , ΔLRR and ΔPYD hNLRP1 all induced IL-1β cleavage ( Fig 3A and 3B ) , confirming that the PYD is dispensable for inflammasome function , and thus , proteolysis of the N-terminal PYD domain could conceivably result in an active hNLRP1 protein . To assess whether N-terminal cleavage of hNLRP1 is sufficient to induce inflammasome activation , we engineered three different TEV-protease sites ( T1-T3 ) into the inter-domain linker ( Figs 3C and S4 ) . We tested the ability of TEV protease to activate these NLRP1 variants , when expressed at lower levels ( LTR promoter ) to reduce spontaneous activation . TEV-induced cleavage at T1 and T2 resulted in significant IL-1β processing that depended on the TEV-protease sites , as TEV did not induce IL-1β processing by the wild-type version of hNLRP1 ( Fig 3D ) . Cleavage of hNLRP1 resulted in a level of activation comparable to that seen with the constitutively active ΔLRR mutant . Interestingly , TEV-induced cleavage at T3 did not result in IL-1β processing , indicating that cleavage must occur within a specific region to induce hNLRP1 inflammasome activation . The PYD is present in most NLRP1 orthologs , with mice and rats representing the main exceptions among mammals ( S3 Fig ) . Our observation that the PYD is not essential for human NLRP1 activation raises the important question of why the PYD has been conserved in most mammalian NLRP1 isoforms . We hypothesized that perhaps the PYD is necessary for maintaining auto-inhibition of NLRP1 , and that proteolysis might remove this auto-inhibition . To assess whether the PYD is specifically required to maintain NLRP1 auto-inhibition , we generated a series of expression constructs in which various portions of the N-terminus of hNLRP1 , including the PYD , was replaced by GFP ( Fig 3E ) . Surprisingly , an hNLRP1 mutant in which the PYD was replaced by GFP was still auto-inhibited , and was still able to be activated by proteolysis at T1 or T2 ( Fig 3F ) . These results suggest that the PYD is neither specifically required for auto-inhibition nor for activation of hNLRP1 . The above data suggest that human NLRP1 could act as a sensor of bacterial proteases . Such a model predicts that the region that is cleaved by bacterial proteases for activation might need to rapidly and recurrently evolve to become an effective substrate for novel bacterial proteases . To address the plausibility of this hypothesis , we investigated the evolutionary history of primate NLRP1 with specific focus on the inter-domain linker . A previous analysis indicated that NLRP1 , at a whole gene level , exhibits a strong overall signature of positive ( ‘diversifying’ ) selection [43] as measured by an excess of amino-acid altering mutations over what would be expected by the neutral theory of molecular evolution [44] . Such a signature of positive selection is often observed in immune related genes as a consequence of an evolutionary ‘arms race’ with pathogens [45] . To pinpoint the regions of NLRP1 that were diversifying under positive selection , we analyzed sequences of NLRP1 from 11 primate genomes ( S1 Table ) using two methods for detection of positive selection ( PAML [46] and PARRIS [47] ) . These analyses confirmed that primate NLRP1 has a very high likelihood of having evolved under positive selection ( Fig 4A; PAML M7 vs M8 p<0 . 0001; PARRIS p<0 . 05 ) . Interestingly , a strong signature of positive selection is evident when the inter-domain linker region between the PYD and the NBD ( residues 81–321 in human NLRP1 ) is analyzed alone ( without consideration of the rest of NLRP1 ) indicating that residues in this region have recurrently evolved under positive selection ( specific codons detailed in S2 Table ) . Removal of this region from the analyses does not eliminate the signature of positive selection ( Fig 4A ) , suggesting that additional regions of the protein ( e . g . , the LRRs ) have also evolved under positive selection ( specific codons detailed in S3 Table ) . These additional regions may also contribute to protease recognition , or alternatively , may be evolving in response to distinct selective pressures ( e . g . , pathogen-encoded inhibitors ) . Codon-based analyses of the N-terminal linker region from 20 divergent primate NLRP1 genes identified several individual codons that exhibit statistically ( posterior probability >0 . 9 ) significant signatures of positive selection ( Fig 4B and 4C ) . These data strongly suggest that the inter-domain linker region plays an important role in NLRP1 function and that it has undergone repeated pathogen-driven selection to diversify in amino acid sequence . Consistent with these data in primates , we see evidence for positive selection acting on the N-terminus of mouse NLRP1 alleles based on a pairwise comparison of Nlrp1a and Nlrp1b ( S3C Fig ) . These results , combined with the importance of this region in protease-mediated NLRP1 activation in both rodents and primates , are consistent with a model in which the linker region is evolving rapidly to acquire the ability to be cleaved by—and thus detect—pathogen-encoded proteases of differing sequence specificities . Since the above data imply that rodent and human NLRP1 can be activated by a conserved mechanism involving N-terminal proteolysis , we decided to evaluate other reported differences between human and mouse NLRP1 . ATP binding to human NLRP1 has been reported to be required for inflammasome oligomerization and CASP1 activation [29] . By contrast , a requirement for ATP binding is not exhibited by mouse NLRP1B . Instead , prevention of ATP binding by mutation of the conserved Walker A motif appears to lead to a constitutively active mouse NLRP1B [26] ( Fig 5A ) . We decided to test the effect of mutating the Walker A motif ( K340A and K340R ) in human NLRP1 . Surprisingly , but consistent with observations with mouse NLRP1B , Walker A mutations did not abrogate the ability of hNLRP1 to promote IL-1β processing mediated by hNLRP1 ( Fig 5B ) . This further indicates that fundamental aspects of the mechanism of NLRP1 activation are conserved between rodents and humans .
The recent finding that the lethal factor ( LF ) protease activates NLRP1B129 via direct proteolytic cleavage [16 , 41] has raised the question whether proteolysis is a general mechanism of NLRP1 activation . Although multiple allelic and paralogous variants of NLRP1 have been described , no clear agonist for most of these variants has been discovered , and for some variants it remains unclear whether they exhibit inflammasome function at all . Human NLRP1 has been proposed to be activated by muramyl dipeptide ( MDP ) , a fragment of bacterial peptidoglycan [29] , suggesting that the mechanisms of hNLRP1 and rodent NLRP1 activation might be entirely distinct . In contrast , we show here that proteolysis can act as a common activator of diverse NLRP1 variants from mice and humans . We first evaluated the sensitivity of NLRP1A and NLRP1B of B6 mice to cleavage by the LF protease , and found that neither NLRP1AB6 nor NLRP1BB6 were cleaved by LF . This result directly accounts for the prior observation that B6 macrophages do not form an inflammasome when they are treated with lethal toxin ( LeTx ) [12] . However , the reason why neither of these paralogs are cleaved by LF is harder to discern . We found that conversion of the cleavage site in NLRP1B129 to the sequence present in NLRP1BB6 only had a modest effect on cleavage of NLRP1B129 . These data suggest that other regions of NLRP1B contribute to the specificity of LF for NLRP1B129 versus NLRP1BB6 . This hypothesis is consistent with the way in which LF recognizes its MAPKK substrates [13] , which is dictated not only by the sequence of the cleavage-site itself but also by a C-terminal region termed the LFIR [48] . Similarly , we suspect that another domain of NLRP1B might contribute to its interaction with LF . Despite the inability of LF to cleave NLRP1AB6 or NLRP1BB6 , we were able to demonstrate that proteolytic cleavage is sufficient to activate both isoforms . Although identification of a gain-of-function mutation previously suggested that NLRP1AB6 can form an inflammasome , our data provide the first evidence that NLRP1BB6 is likewise able to activate Caspase-1 ( CASP1 ) . In fact , we unexpectedly observed that NLRP1BB6 exhibits high basal inflammasome activity , as compared to NLRP1B129 , when overexpressed in 293T cells . This spontaneous activity is unlikely to be physiologically relevant since endogenous NLRP1B in macrophages is likely expressed at levels significantly below the threshold that produces spontaneous activation . Taken together our data suggest that both NLRP1AB6 and NLRP1BB6 can be activated by a conserved cleavage-dependent mechanism , but these NLRP1 paralogs appear to have evolved to respond to proteases or stimuli other than LF . Since Bacillus anthracis is not known to exert a major selective pressure on natural rodent populations , the loss of responsiveness of NLRP1 to LF might not incur a significant fitness cost , especially if the loss of responsiveness to LF was accompanied by the acquisition of responsiveness to a more significant pathogen . Our data demonstrate that NLRP1AB6 and NLRP1BB6 are potentially activated by proteolysis , and suggest that identification of cognate proteases will be an important future avenue for research . Although human NLRP1 was the first NLR described to form an inflammasome [7] , its proposed mechanism of activation has been controversial , and surprisingly distinct from that of mouse NLRP1B . In the initial study , it was claimed that the PYD mediates ASC and CASP1 co-recruitment [7] . However , a recent study disputed this conclusion , reporting that the PYD was dispensable for CASP1 activation or ASC binding [34] . Moreover , the observation that mutations in NLRP1 that destabilize the PYD result in spontaneous NLRP1 inflammasome activation suggest that the PYD may play a role in maintaining NLRP1 in an inhibited state [36] . Our results augment these prior findings and suggest that cleavage of the PYD releases NLRP1 from its auto-inhibited state . Unexpectedly , we were able to replace the PYD with an unrelated protein fold ( GFP ) without loss of NLRP1 auto-inhibition . This suggests that the PYD might mediate auto-inhibition of NLRP1 via a mechanism involving steric hindrance rather than specific intramolecular contacts with the PYD . This model might also explain why rats and mice could have evolved to lose the PYD altogether , as an unrelated N-terminal sequence appears sufficient to replace the PYD and maintain auto-inhibition . Our results do not rule out the possibility that the PYD has additional functions beyond auto-inhibition . Indeed , recent studies have shown that some plant NLRs acquire accessory domains , termed ‘integrated decoys’ , that exist primarily to serve as the target of pathogen-encoded virulence factors [20 , 21] . For example , some plant pathogens encode virulence factors that disrupt the function of WRKY-family transcription factors , e . g . , by acetylation . In response , certain plant NLRs have apparently acquired WRKY domains , not for the purpose of mediating transcription , but instead to serve as ‘decoys’ that allow the NLR to detect WRKY disruption . Modification of the WRKY domain in the NLR by the virulence factor was shown to result in NLR activation and anti-pathogen responses . By analogy , it is conceivable that the function of the PYD in NLRP1 is to function as a decoy that detects pathogen-encoded virulence factors that target PYD-containing proteins . In such a scenario , the PYD would not need to have any intrinsic signaling function , but would merely need only to resemble a PYD sufficiently enough to be subject to pathogen modification and attack . Disruption of the PYD ( by proteolysis , or perhaps by other modifications ) could then lead to NLRP1 activation and initiation of host defense Regardless of the above speculation , and despite previous indications to the contrary , our results suggest that the mechanism of human NLRP1 activation closely resembles that of rodents . Like mouse NLRP1 , we find that full-length hNLRP1 auto-processes its FIIND domain , and that FIIND auto-processing appears to be required for inflammasome activity , in agreement with a previous report [34] . Our results conflict with those of the Reed group [29] , which utilized a hNLRP1 variant that lacks exon 14 , an exon that encodes part of the FIIND domain essential for NLRP1 activity [34] . Faustin et al . had also previously observed a requirement for the ATP-binding Walker A motif for NLRP1 oligomerization and CASP1 activation , whereas mouse NLRP1B Walker A mutants were previously found to be spontaneously active [26] , a result we confirm here . Because Faustin et al had utilized a variant of NLRP1 lacking exon 14 , we decided to re-evaluate the effect of the Walker A mutation in the context of the full-length human NLRP1 in cells . Consistent with what had previously been observed with mouse NLRP1 , we found that an intact Walker A motif is dispensable for hNLRP1 inflammasome formation . Thus , human NLRP1 exhibits more structural and functional similarity to mouse NLRP1 than previously appreciated . Importantly , this similarity extends even to the mechanism of NLRP1 activation , as we were also able to show that N-terminal proteolysis is sufficient to activate hNLRP1 , just as we observed for all mouse NLRP1 variants tested . Thus we propose that proteolysis can be a general mechanism of NLRP1 activation in diverse species , but the proteases that activate NLRP1 may differ for different NLRP1 variants . Our results do not rule out the possibility that additional conserved mechanisms for NLRP1 activation may also exist [26] . Genome-wide evolutionary analyses have previously shown that NLRP1 exhibits a strong signature of positive selection [43] . Positive selection is commonly observed in immune-related defense genes , driven by their participation in an evolutionary ‘arms race’ with pathogens [45] . Under this ‘arms race’ model , pathogens evolve mechanisms to evade or disable recognition by the immune system , which in turn counter-selects for variation in host defense genes to re-establish pathogen recognition . Many examples of this form of co-evolution have been observed with viruses and host defense pathways [45] . Indeed , we were previously able to identify signatures of positive selection in a different sub-family of NLRs called the NAIPs [49] . Interestingly , the region of NAIPs undergoing positive selection was the same region implicated in the specific detection of bacterial ligands . In our analysis of NLRP1 evolution in primates , we were able to extend previous analyses by identifying specific amino acid positions that showed strong evidence of positive selection . Many of the positively selected codon positions we identified mapped to the inter-domain region between the PYD and the NBD . Remarkably , this linker region is the same region in which we found that proteolysis can activate human NLRP1 . Secondary structure predictions also indicate that this region is a large unstructured coil that spans more than 100 residues past the PYD ( S2 Fig ) , facilitating protease sensitivity . An unstructured linker may also be relatively evolutionarily unconstrained and could therefore be free to alter amino acid sequences to acquire the ability to be cleaved by novel proteases . Considering the high level of sequence diversification in this linker region within primates and rodents , it is not surprising that the same LF protease that activates mouse NLRP1B does not activate other NLRP1 proteins . Rather , we expect that each NLRP1 linker has evolved to be recognized and cleaved by a unique protease ( or set of proteases ) from pathogens that are specific to a particular host . Consistent with the idea that mammalian hosts can detect the proteolytic activity of pathogen-encoded virulence factors , it was recently reported that the SpeB protease of group A Streptococcus can be detected through direct cleavage of IL-1β [50] . Taken together , our results definitively establish direct N-terminal proteolysis as a sufficient signal to activate all NLRP1 variants we tested , including mouse NLRP1AB6 , mouse NLRP1BB6 , and human NLRP1 . It will obviously be of great interest to identify proteases , other than anthrax LF , that are physiological activators of NLRP1 isoforms . One possibility is that NLRP1 may be activated by self-encoded ( rather than pathogen-encoded ) proteases . However , the signature of positive selection we observe in the linker/cleavage region implies that a host-pathogen arms race is likely driving the rapid evolution of NLRP1 . Unless the specificity of self-encoded proteases is also rapidly evolving , they would not be expected to drive rapid evolution of the protease target site in NLRP1 . Thus we tend to favor the idea that NLRP1 variants evolved to recognize diverse pathogen-encoded proteases . However , identification of these proteases may be technically challenging since the pathogens producing them may have long been driven into extinction or , alternatively , evolved proteases with specificities that avoid cleaving NLRP1 . Nevertheless , our results establish that a unified proteolytic mechanism underlies activation of NLRP1 variants in diverse species , and provide a basis for understanding the rapid evolution of this family of cytosolic immunosensors .
The B6-NLRP1B allele was amplified by PCR from a plasmid with a cDNA sequence representing BC141354 ( Thermo Fisher Scientific Biosciences ) using primers 1+2 ( S4 Table ) , and sub-cloned into CMSCV-IRES-hCD4 using XhoI and NotI sites . For the N-terminal GFP fusion , B6-NLRP1B was amplified using primers 3+2 , and subcloned into MSCV-GFP-MCS into the NotI site . A TEV site was added to the B6 allele using QuikChange Mutagenesis as described in [16] with primers 4+5 . The 129 allele LF cleavage site was mutated with primers 6+7 , 8+9 , or 12+13 to generate 129/B6-A , 129/B6-B and K38A/K44A respectively . Then 129/B6-A was sequentially mutated with primers 10–11 to generate 129/B6-C . An NLRP1A cDNA template was obtained from Source BioScience ( BC156396 . 1 ) . This sequence was amplified and cloned with primers 18+19 into pcDNA3 . 1-Myc-HisA using the BamHI and NotI sites . This original sequence contains three splicing differences when compared to the reference sequence NM_001004142 . Two of these differences result in inclusion of two extra exons not found in the reference sequence . These exons were sequentially deleted using primer pair 22+23 , followed by primer pair 24+25 . The third difference was a missing 3’ exon . To correct this difference , we used SOE PCR [51] to insert the missing exon . PCR Fragment 1 was amplified with primers 29+26 , and PCR fragment 2 was amplified with primers 27+19 . PCR Fragment 2 was extended at its 5’ end with primers 28+19 to form fragment 3 . Fragments 1 and 3 were mixed , allowed to anneal to each other , and extended and amplified with primer 29+19 to form fragment 4 . Fragment 4 was digested with EcoRI and NotI , and was used to replace the EcoRI and NotI fragment released from the original NLRP1A-pCDNA that had been already corrected for the first two exons . The final NLRP1A resembling the reference sequence was amplified with primers 20+21 . The 5’end of this fragment was extended with primers 17+21 , and then sub-cloned into MSCV-GFP-MCS into the NotI site . A TEV-site was inserted by Quikchange using primers 30+31 . A human NLRP1 cDNA template was obtained from the American Type Culture Collection ( ATCC ) ( I . M . A . G . E . Clone ID: 5756099 ) , which contains a sequence resembling transcriptional variant 5 ( NM_001033053 . 2 ) . This cDNA is missing the last two 3’ exons that are found in transcript variant 1 ( NM_033004 . 3 ) and are predicted to encode the CARD , a domain necessary for signaling . We modified the sequence of transcript variant 5 to add these last two exons to match variant 1 . The last exon was amplified by PCR with primers 40+33 and gDNA from THP-1 cells ( ATCC ) to form fragment 1 . The N-terminal fragment 2 was amplified with primers 39+32 . Fragments 1 and 2 were mixed , annealed , extended , and then amplified with primer 32+33 . The final PCR product was cloned into pcDNA3 . 1-Myc-HisA with KpnI and XhoI . The entire length of the newly modified ORF was sequenced , and a point mutation was identified and modified with primers 34+35 to resemble the reference sequence . A ΔPYD variant was amplified with primers 38+33 and cloned back into pcDNA3 . 1-Myc-HisA . The LRR was deleted , TEV-sites were inserted , and Walker A mutations were modified via Quikchange using primers 36+37 , 41+42 , 43+44 , 45+46 , and 47+48 respectively . GFP N-terminal fusions were constructed by amplifying hNLPR1 with primers 49 , 50 , 51 , and 52 at the 5’end and primer 53 at the 3’end , and these fragments were cloned MSCV-GFP-MCS at the NotI site . A human ASC expression plasmid was obtain through the Addgene repository ( plasmid #41553 ) [52] . A human IL1B ORF was amplified from cDNA made from a human microglial cell line stimulated with LPS ( gift from Kaoru Saijo at UC Berkeley ) using primers 60+61 . The amplified DNA was cloned into pcDNA3 . 1-V5 . A plasmid encoding human CASP1 ( NM_033292 . 3 ) was obtained from the Dietrich Lab ( Harvard Medical School ) . The NS2B/NS3 coding sequence from Dengue virus type 3 isolate D3/H/IMTSSA-MART/1999/1243 was synthesized ( IDT ) with 5’ XhoI and 3’ NotI sites for sub-cloning into the plasmid pQCXIP , resulting in the addition of a 5’ HA tag . Full-length primate NLRP1 gene sequences were collected from public databases ( see S1 Table for accession numbers ) . To augment the publicly available NLRP1 sequences , we amplified and sequenced the 5' region of NLRP1 ( corresponding to the linker region between the PYD and NBD ) from RNA isolated from three additional primate cell lines ( Coriell Institute for Medical Research ) ( Mandrill: PR00399 , saki monkey: PR00239 and howler monkey: PR00708 ) by RT-PCR using primers pNLRP1fwd pNLRP1rev shown in S4 Table . These additional sequences have been deposited to Genbank ( S1 Table ) . All sequences were aligned based on their translated sequence and alignments were manually curated in Geneious [53] . Phylogenetic trees were generated using PhyML [54] . Maximum likelihood evolutionary analyses for positive selection were performed using PAML [46] or the PARRIS [47] package implemented at datamonkey . org ( http://www . datamonkey . org/ ) . Reported p-values compare the log likelihood values for models that disallow or allow for codons to evolve under positive selection . Specific codons that have evolved under recurrent positive selection with a posterior probability of >0 . 90 were identified using PAML [46] or the FUBAR [55] package implemented at datamonkey . org . Sliding window analyses were performed using K-estimator [56] . HEK 293T ( ATCC ) cells were grown in complete media ( DMEM , 10% FBS , 100 U/ml Penicillin , 100 μg/ml Streptomycin , and supplemented with 2mM L-glutamine ) . HEK 293T cells were seeded the day prior to transfection at a density of 1 . 5x105 cells/well in a 24-well plate with complete media . DNA complexes were made with Lipofectamine 2000 ( Invitrogen ) according to manufacturer’s instructions and overlaid on cells for 24–36 hours prior to analysis . Amounts of transfected DNA were normalized with empty vector when necessary . Lethal toxin ( LeTx ) , comprised of recombinant E . coli-expressed His-tagged lethal factor ( LF ) and His-tagged protective antigen ( PA ) , was the kind gift of Bryan Krantz [57] . In some experiments , instead of exposing cells to LeTx protein , cells were instead transfected with a cDNA for LF , as indicated in the figure legends . Cells were lysed in RIPA buffer supplemented with 1mM PMSF and 1× Complete Protease Inhibitor Cocktail ( Roche ) . Lysates were spun at max speed in an Eppendorf microfuge at 4°C for 20 min and supernatants were mixed with 6× Laemmli sample buffer . To detect full length and FIIND processed NLRP1B , lysates were incubated at room temperature for 15min prior to SDS-PAGE . To analyze all other proteins , including the N-terminally cleaved form of NLRP1B , samples were boiled for 10min prior to separation . SDS-PAGE was performed with Novex 10% and 12% BisTris gel system according to manufacturer’s instructions ( Invitrogen ) . Separated proteins were transferred to Immobilon-FL PVDF membranes . Membranes were blocked with Odyssey blocking buffer ( Licor ) . The following antibodies were used for the following antigens: HA mAB 3F10 ( Roche ) , MEK-2 SC-13115 ( Santa Cruz ) , MYC mAb 9E10 ( Clonetech ) , IL-1β AF-401-NA ( R&D systems ) . Secondary antibodies anti-rat , mouse and goat were all conjugated to Alexa Fluor-680 ( Invitrogen ) . | Hosts and their pathogens often engage in evolutionary ‘arms races’ , iterative cycles of adaptation , in which each opponent evolves strategies to overcome the other . For example , the anthrax bacterium overcomes the host immune response by producing lethal factor , a proteolytic enzyme that specifically cleaves and inactivates host immunity proteins called MAP kinases . Rodents counteract this strategy by producing a sensor protein called NLRP1 that is cleaved by anthrax lethal factor . Upon cleavage , NLRP1 activates a potent anti-bacterial immune response that compensates for the loss of the MAP kinase response . Humans also produce NLRP1 , but human NLRP1 is neither cleaved nor activated by lethal factor . Thus , the mechanism of human NLRP1 activation and its function in immunity remains unknown . In our study , we show that human NLRP1 , like rodent NLRP1 , can be activated by proteolytic cleavage . Interestingly , evolutionary analysis supports the hypothesis that primate NLRP1 is rapidly evolving to be cleaved by ( and thereby detect ) pathogen-encoded proteases . Our results elucidate a general mechanism for NLRP1 activation and suggest that host immunity proteins may evolve toward recognition by bacterial proteases to engage in evolutionary arms races with pathogens . | [
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] | 2016 | Functional and Evolutionary Analyses Identify Proteolysis as a General Mechanism for NLRP1 Inflammasome Activation |
A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex ( MHC ) class I and II binding affinities . Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods . Despite the recent advances made in generating high-quality MHC-eluted , naturally processed ligandome , the reliability of new predictors on these epitopes has yet to be evaluated . This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available , untested data of both synthetic and naturally processed epitopes . 32 human leukocyte antigen ( HLA ) class I and 24 HLA class II alleles are included in the blind test set . Artificial neural network ( ANN ) -based approaches demonstrated better performance than regression-based machine learning and structural modeling . Among the 18 predictors benchmarked , ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions , respectively , on binding/non-binding classification ( Area Under Curves = 0 . 911 ) . NetMHCpan4 also demonstrated comparable predictive power . Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan . The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data . However , the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders . When used on naturally processed MHC-ligands , tools that have been trained on elution data ( NetMHCpan4 and MixMHCpred ) shows better accuracy than pure binding affinity predictor . The variability of false prediction rate is considerable among HLA types and datasets . Finally , structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches . With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics , a unbiased view for establishing best practice of T-cell epitope predictions is presented , facilitating future development of methods in immunogenomics .
The increasing wealth of immunogenomic information generated by next-generation sequencing ( NGS ) technologies is boosting the application of cancer immunotherapy that takes full advantage of individual’s adaptive immunity by administrating personalized cancer vaccines . [1–3] An essential step in provoking adaptive immunity , delivered by the activated CD8+ or CD4+ T cells , is the recognition of T cell receptor ( TCR ) to T cell epitopes . [4] As sequence repertoire for potential TCR-recognizing epitopes , whole exome or transcriptome from pathogens or tumor cells can be analyzed by bioinformatics pipelines to identify vaccine candidates . [5 , 6] Among various processes related to antigen presentation , the binding of antigen peptides to MHC proteins is considered to be the major determinant . Therefore , computational predictors that identify MHC-binding peptides in a high-throughput fashion are critical . [7–9] In principle , these predictors utilize availability of the large-scale peptide-MHC binding affinity matrix from experimental measurements , to train machine learning ( ML ) -based classifiers to distinguish MHC-binders from non-binders . [10] While all serving the purpose of MHC-binding prediction in general , the increasing method variations among these tools , in combination with the emerging new types of experimental data , render it necessary to rationally select the best approach , especially for the potential applications in cancer vaccine design . Immune Epitope Database ( IEDB ) hosts a series of ML-based tools , each trained on specific dataset of experimental peptide-MHC binding affinity matrix . [10] These different tools encompass two common approaches of ML ( Table 1 ) , namely , linear regression ( LR ) and artificial neural network ( ANN ) . LR predicts peptide-MHC binding affinity by fitting the weight matrix that relates peptide sequence to end-point binding affinity value . Depending on the specific parameters used , such as whether regularization of weight matrix is included during training stage , tools utilizing LR can be further categorized into naïve position-specific scoring matrix ( PSSM ) [11] and stabilized matrix method ( SMM ) [12] . The inclusion of regularization terms , in general , helps to prevent overfitting of LR weight matrix on training set . For MHC class I epitope prediction , SMM is widely adapted , including smm[12] , smmpmbec[13] , and PickPocket[14] . For MHC class II , IEDB-hosted tools also contain those applying naïve PSSM , including comblib[15] and tepitope[16] . The applicability of LR approaches to predict MHC-binding relies largely on the assumption that the contribution of individual residues to the overall binding affinity is linear in nature . While it has been shown that certain types of amino acids are predominant at MHC-anchoring positions of peptide epitopes[17] , the correlation between neighboring residues was also demonstrated to affect MHC-binding . Therefore , ANN presents as a better approach to capture the non-linear relationship between peptide sequence and MHC-binding affinity , compared to LR . [18 , 19] In ANN , the contribution of residue type of peptides to MHC-binding is simulated by one or more hidden layers . [20] These nodes essentially add extra features in addition to the input peptide sequences and are able to comprehend the contribution of intrapeptide residue-residue interactions to the binding affinity . IEDB tools utilizing ANN include ann ( NetMHC3 . 4 ) and NetMHC4 for MHC class I prediction[18 , 21] , and nn_align ( NetMHCII2 ) for MHC class II prediction[7 , 22] . To overcome the low reliability resulted by a lack of sufficient training data for specific human leukocyte antigen ( HLA ) alleles that encode MHC proteins , ANN pan-allele tools have also been developed , such as NetMHCpan and NetMHCIIpan . [23] These approaches perform imputation to obtain MHC-binding affinity of untrained allele , on the basis of neighboring MHC bearing the highest sequence similarity . Pan-allele methods exhibited comparable classification accuracy between common HLA alleles and rare alleles that were not yet trained in allele-specific approaches . Despite the cross-validation results reported previously for different approaches and tools , their prediction power is eventually determined by the performance on predicting “blind” dataset , that is , data that have never been exposed to the predictors . Benchmarking the trained predictors against blind peptide-MHC binding data can provide necessary metrics needed . While such effort has been attempted as an automated process on the IEDB server[19] ( ~ 7000 peptides across 42 HLA alleles with experimental IC50 available , since 2014 ) , the evaluation metric reported only contains the ranking score of each tool , lacking other detailed metrics such as correlation of absolute binding affinities and accuracy of predicting strong binders , which are critical for precise epitope selection . Also , allele-specific accuracy is not reported . Furthermore , the benchmarking on emerging , high-quality mass spectrometry ( MS ) peptide is yet available . The same gaps also apply to the MHC class II benchmarking . [24] In this paper , we aim to deliver a systematic and quantitative benchmarking of a spectrum of MHC class I and II binding predictors , using blind binding affinity dataset collected from both IEDB consortium as well as independent studies . With respect to the application of MHC-binding prediction in vaccine development , a significant gap of capacity is the lack of knowledge on the correlation between the MHC binding affinity and the immunogenicity of peptides . [25 , 26] While ML-based predictors are capable of selecting potent MHC-binders , the selected sequences can only be presented to TCR if they are truly generated by proteasome cleavage and transported to MHC within antigen-presenting cells ( APCs ) . [27 , 28] The accuracy in predicting these two processes is limited by the volume of high-quality training set . For the same reason , accurately predicting immunogenic T cell epitopes by trained ML framework has also been a daunting task . [29] Previous attempts to filter epitope-based vaccine candidates by solely relying on MHC-binding prediction have discovered that a majority of predicted binders were non-immunogenic . [30 , 31] Therefore , evaluating the accuracy of different binding affinity predictors in identifying naturally processed T cell epitopes is of critical relevance to their applications in neoantigen and vaccine prescreening . Besides ML-based predictors , protein structural modeling taking advantage of high-resolution crystallographic data has emerged as an informative alternative , not only to predict peptide-MHC binding affinity , but also to guide understanding on the immunogenicity of peptide-MHC complex . [30 , 32] The development of structure-based prediction of peptide-MHC binding by peptide-protein docking algorithms has enabled enhanced sampling of peptide-protein binding landscape . [33 , 34] Structure-based predictions can potentially complement ML-based approaches by providing high-resolution peptide-MHC structure , which allows the further assessment on the TCR interaction and the immunogenicity of the predicted epitope . Hence , the structural modeling approach presents an opportunity for predicting the T cell epitope immunogenicity . The aforementioned gaps in current knowledge formulate several key queries of this paper . We firstly introduced the test set and evaluate the prediction performance of MHC class I and II tools on the blind test set . The tools include published IEDB methods , MixMHCpred[17] , and mhcflurry , as well as our development of pan-type I HLA epitope prediction version [20 , 35] . To provide a comprehensive understanding of prediction reliability , we performed evaluation metrics covering the prediction accuracy of binder classification and binding affinity ranking . We then benchmarked particular user cases , including understanding allele-specific performance and recapturing absolute binding affinity of strong MHC-binders . We also focused on evaluating the reliability of MHC-binding predictors to recover T cell-presenting epitopes using the dataset of naturally processed and eluted peptides . In addition , we demonstrated the performance of structure-based approach Rosetta FlexPepDock for peptide-MHC binding affinity prediction and explored its possible usage to improve T-cell epitope identification towards better identification power on T cell immunogenicity .
8 MHC Class I and 6 MHC Class II binding prediction methods hosted on IEDB Analysis Resource Server[10] were benchmarked ( Table 3 ) . Sequence submission was performed through RESTful API . mhcflurry[20] , NetMHC4[18] , NetMHCpan3[23] , and MixMHCpred were locally installed on Linux server as stand-alone binary executables . NetMHCpan4 benchmarking was conducted on the website interface hosted by DTU Bioinformatics . Based on the ML principle utilized , these tools can be divided into two general categories: LR-based binding score matrix and ANN approaches . PickPocket[14] is the only tool that is trained on features reflecting sequence space of MHC proteins rather than binding peptides . Pan-allele methods , including NetMHCpan2 . 8 and NetMHCpan3 , utilize a nearest neighbor classification to assign untrained allele in quest to a trained allele based on similarity in binding pocket sequence . While other web-based prediction tools are not included in the current benchmarking , mainly due to lack of disclosed corresponding training datasets , their principles can be fitted into the aforementioned two types of ML approaches . Therefore , we are confident that the tools benchmarked in this study represent a comprehensive landscape of commonly applied MHC binding prediction algorithms . Python package of mhcflurry has been pulled from the original code repository . The original version uses 9mer peptide as input feature with sparse matrix encoding of sequence . To incorporate the sequence of class I MHC , the input dimension has been extend to 43mer to create mhcflurry_pan ( S6 Fig ) . The 34mer putative MHC binding pocket information was extracted from NetMHCpan3 , which covers 3725 HLA types . Two branches of mhcflurry_pan were developed: 1 ) the modified ANN was trained on all available alleles of HLA binding data ( mhcflurry_pan ) ; and 2 ) the modified ANN was trained on all available alleles of HLA binding data , except leaving out the HLA type being tested ( mhcflurry_pan_LOO ) . The accuracy of these approaches at both cross-allele and individual allele levels were benchmarked against mhcflurry ( Figs 1 and 2 ) . The final version of mhcflurry_pan is available at: https://github . com/juvejones/mhcflurry_pan . FlexPepDock protocol in Rosetta3 . 5[44 , 45] was implemented into the workflow of predicting binding of 9-mer peptides to MHC Class I proteins utilizing existing high-resolution crystal structure of peptide-MHC complexes . The protocol resembles the ones introduced previously with the addition of comparative modeling as the initial step for building bound peptide backbone based on template structure . [45 , 46] As a result , we were able to obtain a good conformational sampling of peptide with a low number of decoys ( 1000 ) . [34] Starting from a peptide-MHC complex as the template , rotamer libraries of the peptide were firstly built , then incorporated for the following comparative modeling step . Needle package[47] was used to create the pairwise alignment profile between the target peptide sequence and the template required for comparative modeling . Through comparative modeling , backbone conformation of the target peptide was generated that resembles the template , providing a centroid-mode initial sampling of the peptide-MHC binding conformation . [46] The refinement step generated 1000 structures , which were optimized from the centroid-mode and provided high-resolution side chain packing structures within MHC pocket . Finally , clustering of the 1000 structures was performed based on 2 . 5 Å root-mean square distance ( RMSD ) cutoff . The lowest scoring model within the largest structural cluster was identified as the globally energy-minimized peptide-MHC binding complex . The reweighted scores , comprised of MHC protein energy , peptide energy , and peptide-MHC interfacial energy , were transformed into ( 0 , 1 ) scale by establishing the highest and lowest scoring peptides in each allele as upper and lower boundaries , respectively . The resulted Rosetta Score for each peptide was aggregated and associated to the experimental binding affinity array . Visualization of the complex structures was made by VMD 1 . 9 . 3 . [48] Receiver operating characteristic ( ROC ) curve and area under curve ( AUC ) were employed to benchmark the performance of binary classification between binders and non-binders , using a commonly applied cutoff of IC50 = 500 nM for MHC class I[49] and 1000 nM for class II[50] . Alternatively , we also evaluated HLA allele-specific IC50 cutoff based on established study of binding repertoire size , by using the specific IC50 value corresponding to identifying at least 75% of binder peptides of each HLA . [51] To further identify the performance of the prediction tools to distinguish strong binders from weak binders , volume under surface ( VUS ) was used to calculate the capacity to correctly classify a group of three peptides into strong binder , weak binder , and non-binder types on the basis of IC50 cutoffs of 50 and 500 nM: VUS=∬{Pr{t−≤T≤t+}}dxdy x=Pr{T≤t−}=F− ( t− ) , y=Pr{T>t+}=G+ ( t+ ) D00={0≤x≤1 , 0≤y≤G+ ( F−−1 ( x ) ) } where t− and t+ indicate lower and upper cutoffs respectively , Pr indicates corresponding distribution probability , and D00 indicates integral space The calculation of VUS also introduces the specificity ( SPE ) measure of correctly assigning a peptide to strong binder . Spearman’s ranking correlation coefficient ( SRCC ) between predicted and measured binding affinities was calculated to evaluate the reliability of binding predictions to correctly rank out stronger MHC-binding peptides . R-squared values were generated by linear regression of predicted IC50 to experimental IC50 . Error estimation was performed based on 95% confidence interval . All analysis and corresponding data visualizations were implemented using R scripts . In particular , package ROCR[52] was used for ROC and AUC analysis and DiagTest3Grp[53] was used for VUS analysis . Note that MixMHCpred does not directly output binding affinity , so the ‘Max_score’ was used as the predicted affinity strength for calculating accuracy and correlations .
The half maximal inhibitory concentration ( IC50 ) characterizes the effectiveness of a peptide in substituting a high affinity molecule for binding to MHC and represents the binding affinity of that peptide . The threshold of IC50 = 500 nM or 50 nM selects peptide binders or strong binders , respectively , to MHC and can be used to identify T cell epitopes . The accuracy of the predictors can thus be judged by the correctness of classifying peptides into binders and non-binders based on experimental results . For MHC I-peptide binding prediction , mhcflurry exhibits the best binary classification performance with AUC = 0 . 911 ± 0 . 010 . ANN-based approaches with the most recent versions ( mhcflurry , NetMHC4 , NetMHCpan4 , and NetMHCpan3 ) on average perform better than LR-based ones , including PickPocket , smm , and smmpmbec ( Fig 1 ) . Compared with LR , the ability of ANN to adapt weights of the hidden layer to capture the complex interactions between MHC-binding residues has been suggested previously . [43] In addition , ANN generally performs better on the task of regularization , leading to less overfitting on the training set . When comparing different versions of the same tool , newer versions ( NetMHC4 and NetMHCpan3/4 ) generally outperform older ones ( NetMHC3 . 4 and NetMHCpan2 . 8 ) , with the exception of NetMHCpan4 versus NetMHCpan3 . The improvement is likely a result of updated training set . On the other hand , since NetMHCpan4 was developed with the specific aim of improving the prediction of MS-derived , MHC-eluted peptides , the lack of better accuracy compared to NetMHCpan3 on binding affinity dataset is not surprising . Note that AUC is independent of the cutoff chosen for binder versus non-binder classes , therefore providing the overall robustness with respect to accurately selecting MHC-binders from the peptide pool generated by all tumor somatic mutations . ROC curve also contains information about false-alarm rate in the positive class space defined by a given cutoff , which is the ratio of false positive rate ( FPr ) to true positive rate ( TPr ) . For the high ANN performers such as mhcflurry and NetMHC4 , high TPr ( > 80% ) can be achieved with relatively low FPr ( ~ 10% , Fig 1C ) . MixMHCpred demonstrates interesting behavior . While the overall AUC ( 0 . 842 ± 0 . 020 ) and the FPr at TPr = 80% ( ~ 22% ) do not rank high among the methods , it attains second lowest FPr at TPr = 90% . As an important criterion for selecting reliable MHC-binding prediction , low FPr directly contributes to downsized and efficient experimental validation cycle . In this regard , mhcflurry represents a preferable tool as it achieves lowest FPr at either TPr = 80% or 90% ( Fig 1B and 1C , orange ) , and MixMHCpred is also viable at TPr = 90% ( Fig 1C , lime ) . The preferable performance of mhcflurry encourages us to develop its pan-HLA version , mhcflurry_pan , on the basis of its open source ANN code ( see Methods for details ) . Extended by the dimension of input sequence feature to 43mer , our version of mhcflurry_pan has achieved comparable predictive power of MHC-I epitopes in comparison with NetMHCpan4 , with AUC = 0 . 873 ± 0 . 012 ( Fig 1 ) , with median AUC across alleles = 0 . 931 ( Fig 2 ) . Specificities of predicting both binder ( < 500 nM ) and strong binder ( < 50 nM ) are in the same statistical range with the original HLA-specific mhcflurry . We also tested the performance of mhcflurry_pan when trained by leave-one-out ( mhcflurry_pan_LOO ) approach , in which the corresponding binding data of HLA type in-test were not included . The accuracy of mhcflurry_pan_LOO maintained for a certain subset of alleles but lowered predictive power for others , with median AUC = 0 . 790 . We note that this precision reaches similar level compared to the most recent version of NetMHCpan4 . [54] Overall , mhcflurry_pan has achieved sound accuracy to facilitate the prediction of epitopes on untrained class I HLA types , which is critical for the study of tumor immunogenicity and design of vaccine epitopes across broad cancer patient populations . In addition to conventionally used AUC criteria for differentiating MHC binder from non-binder , we introduce VUS ( Volume Under Surface ) as a measure for an additional classification of strong MHC binders that have IC50 < 50 nM ( see Methods for details ) . This cutoff has been used empirically to further filter out peptides that may detach from MHC under instable thermal conditions . Fig 3 shows that mhcflurry still outperforms others with respect to three-class classification , followed by mhcflurry_pan and NetMHC4/NetMHCpan3 . The ability of these tools to identify strong binders is also illustrated by specificity for predicting peptides with < 50 nM affinity ( SPE , Fig 3B ) . For this benchmarking , mhcflurry_pan has the highest value 87 . 3% TPr on identifying peptides that have binding affinity < 50 nM . NetMHC4 , NetMHCpan3 , and NetMHCpan4 all have the second highest value ( 0 . 836 ) . For predicting the affinity ranking of peptides to MHC , we calculated SRCC and R-squared for the value pairs of experimental-predicted ( Fig 3C and 3D ) . SRCC was employed commonly in previous studies to measure the correlation of affinity ranking between predictions and experimental values , proven valuable in selecting vaccine candidate epitopes . In addition to SRCC , we also calculated R-squared here to benchmark how close predicted affinities match experimental data . Both data indicate mhcflurry delivers the best correlation between predicted and experimentally measured binding affinities . A SRCC value of 0 . 761 ± 0 . 015 for mhcflurry demonstrates that mhcflurry is reliable with respect to ranking strong binders above weak binders when applied to epitope identification . On the other hand , the linear correlation between the predicted and measured absolute binding affinities , as indicated by R-squared , may not be satisfying . Even the best-in-class mhcflurry ( R-squared = 0 . 641 ) can predict experimentally IC50 correctly only to a certain extent . Since the threshold for designating strong MHC binder peptides are only arbitrarily chosen at either 50 or 500 nM , the deficiency of these ML-based predictions tools at providing correct absolute binding affinity is likely to impact the identification of MHC-binding epitopes . A further confounding factor is that these threshold values were shown to be only applicable to certain alleles , while alleles with varied population frequency exhibiting different affinity ranges of binding repertoire . [51] While our data show that allele-specific cutoff generally does not alter the prediction accuracy in terms of AUC ( S2 Fig ) , this still impacts the way to interpret true or false prediction rate at specific cutoff . The correct prediction of absolute peptide-MHC binding affinity is equally , if not more , significant compared with the binary classification task . For MixMHCpred , the prediction score does not directly correspond to binding affinity due to the different type of HLA-ligandome training data . Therefore , the comparison with other predictors on SRCC and R-squared is not straightforward . In addition , the cut-point between ‘strong’ and ‘weak’ HLA-binders is not well-defined for MixMHCpred training data , leading to declined VUS and SPE ( Table 1 ) . Nonetheless MixMHCpred appears to be a valid candidate for binary classification of MHC-ligand . Figs 1 and 3 demonstrate that mhcflurry is a superior choice for predicting 9-mer MHC I-binding epitopes , which is also in accordance with recent automated benchmarking results hosted on IEDB server . [19] We note that one advantage of mhcflurry for end-users is the Python API that enables tunable ANN training parameters , such as number of hidden neurons and dropout probability . In particular , setting dropout probability allows user-defined ANN to prevent overfitting on the training set . In this benchmarking , a relatively larger number of hidden neurons ( 64 ) and dropout rate of 0 . 1 were assigned to capture effectively the non-linear weight matrix relating 9-mer peptide sequence to binding affinity at a low degree of overfitting . While most MHC class I binding peptides are 9-mers , the length of MHC class I ligands may vary ( 3 . 4% 8-mer , 44 . 4% 9-mer , 29 . 9% 10-mer and 28 . 7% longer in IEDB database ) . Therefore , we also conducted analysis to compare the accuracy of class I MHC-ligand predictors between 9-mer and 10-mer testing data . All methods benchmarked for 9-mer dataset were considered expect mhcflurry and mhcflurry_pan . The highest AUC was obtained on consensus ( 0 . 968 ) and the second highest one was obtained on NetMHC4 ( 0 . 965 ) . Overall the classification accuracy is encouraging as reflected by the ROC curves ( Fig 4A ) . Among the 11 methods , six even demonstrated statistically significant higher accuracy for 10-mers than corresponding testing data for 9-mers ( Fig 4B ) . Similar observation can be made on SRCC result as well . Taken AUC and SRCC together into consideration , NetMHC3 . 4 , NetMHC4 , smm , and consensus methods demonstrated consistently more reliable prediction for the HLA alleles tested . As shown by Figs 5 and 6 , ANN-based approach nn_align ( NetMHCII2 ) exhibits a significant advantage of accuracy over other tools regarding the binding prediction of MHC class II epitopes . nn_align delivers an AUC value of 0 . 911 ± 0 . 004 , with 80% TPr reached at the expense of ~ 13% FPr ( Fig 5C ) . Further examination of ROC curves at high TPr range suggests that other two popular methods , including NetMHCIIpan and smm_align , have also been able to achieve < 20% FPr when reaching TPr of 80% . nn_align also achieves the highest VUS , SRCC and R-squared , with comparable performance to the best MHC class I prediction tools . The only exception in the metrics is SPE ( 0 . 671 for nn_align versus 0 . 836 for NetMHCpan3 ) . This deficiency can be a result of the test set composition , as only 12 . 3% are strong MHC II binders ( IC50 < 50 nM ) , while 38 . 8% are strong MHC I binders . In other words , MHC II binding prediction tools have a higher chance of falsely omitting strong binders , especially when the size of positive data available is small . Trailing nn_align on accuracy , NetMHCIIpan serves as a good alternative approach when the allele of interest is not yet trained by nn_align . In the present study , we also employed the ANN framework of mhcflurry to train allele-specific MHC class II predictors using the same training set as of nn_align . For nn_align , smm_align , and NetMHCIIpan , this preference was determined a priori by Gibbs sampling of existing MHC II-binding sequences . [55] However , in contrast to these methods , class II mhcflurry did not consider separately the two 3-mer peptide flanking regions and the 9-mer binding core . [7] In other words , the binding core and the flanking regions bear the same weight as training input features . Instead , the 15-mer sequences were directly used as if all residues were assigned to binding region . The substandard performance of mhcflurry-MHC class II predictors ( Fig 5 , AUC = 0 . 740 ) indicates that this strategy has difficulty in capturing the correct sequence-binding affinity relationship . Evidently , the proper training to determine such relationship by ANN requires explicit consideration on the sequence matrix of flanking and binding core regions of peptides . Despite the large sequence space of peptides bound to MHC proteins , the binding motif to a specific MHC protein is often characteristic . For example , the anchoring residues of MHC class I-binding 9-mer peptides exhibit heave amino acid preference at the 2nd and 9th position . [17 , 56] For this reason , even the simplistic PSSM approach can often predict the binding preference of peptides to MHC proteins with high accuracy , given a sufficient training set . However , in LR-based PSSM approaches such as smm and PickPocket , the contribution of each amino acid to overall MHC binding affinity is assumed to be independent . [13] Given evidence showing that pair-wise interactions between neighboring residues of MHC-binding peptides also influence the binding behavior[57] , it is expected that ANN performs superiorly in terms of learning such complex features and handling regularization . This hypothesis is confirmed by our benchmarking on both MHC I and II binding prediction tools . Allele-specific binding prediction performance , measured by AUC for binary classification and by SRCC for affinity ranking , was consolidated as shown in S1 Fig . S2 Fig and S3 Fig show the ROC curves of individual MHC class I and II allele . The grey blocks on the heatmap suggest alleles that are not available for that particular method . At current state , allele-specific mhcflurry and pan-allele methods , including NetMHCpan2 . 8 , NetMHCpan3 , and PickPocket , accept the widest range of HLA alleles . For HLA type II , the training set of nn_align encompasses the most allele types . The effect of training data size is examined by dividing HLA alleles into three groups , each with different sizes of MHC-binding peptide repertoire . For MHC class I , training data size has no significant impact on the performance of binary classification , as shown by lack of difference on heatmap across cyan , yellow and purple regime ( S1 Fig ) . The same observation can be made based on boxplots of AUC ( S1 Fig ) . Alleles with training set that has larger than 2500 peptides tend to achieve affinity ranking predictions better than alleles with binding repertoire smaller than 500 peptides . In contrast , larger training size for class II cases does not necessarily lead to better performance of neither binary classification ( AUC ) or affinity ranking ( SRCC ) . This conclusion holds true in the case of allele-specific binding affinity threshold as well ( S1 Fig ) . This result suggests that while gaining more training data can potentially increase the accuracy of affinity ranking on specific alleles , significant improvement on the performance of identifying MHC-binding epitopes by binary classification is less likely expected . Our data shows that a dataset larger than 500 points may be sufficient enough as the training set for ML predictions . Depending on different cross-validation schemes employed , the size of allele-specific MHC-affinity array in pursuit needs to encompass both training and cross-validation sets , especially for the practical application of training new learning network for rare alleles . While ANN-based approaches have given satisfying performance on the classification of MHC binders , the prediction on accurate absolute binding affinity has been less scrutinized . As already shown by Figs 4D and 6D , the linear correlation between predicted and measured IC50 values does not adequately support the prediction as a good indicator for absolute binding affinity of untrained sequences . In practical applications , a strong binder threshold of IC50 = 50 nM is often used to further identify the most potent epitopes; therefore we plotted and fitted the regression between predicted and measured IC50 at the stronger binder regime for MHC class I and II test data ( Fig 7 ) . As suggested by the decreased R-squared , the correlation is deteriorated than the whole IC50 range ( Figs 4D and 6D ) . A fair amount of points which represent measured strong MHC-affinity were incorrectly predicted to be non-binders or weak binders ( Fig 7 , points below grey dashed and dotted lines ) . The FNr of strong binder classification , as shown in Fig 7 , suggest the high risk of false filtering when applying 50 nM as cutoff for peptide binders . Furthermore , MHC I binding predictors actually generated diverge predictions for peptides that have highly similar measured affinity ( Fig 7 , yellow arrows . ) Previous studies have reported that the sequences identified from MHC-peptide binding predictions often resulted in a sparse immunogenic space . [31 , 42] Clearly , the false negative rate and the weak correlation between measured and predicted affinities of these predictors can be key sources of errors . While these predictors can identify the sequence pool of strong binders relatively well by classification , prioritizing antigen candidates by ranking the predicted MHC binding affinities may not be appropriate . One caveat to recognize is the sensitivity or resolution of experimental binding assays in measuring high affinity zone , which leads to the inaccuracy in training the predictors . Therefore , extra caution is required when applying the predictors , such as applying the predicted relative ranking score instead of the affinity for minimizing the discrepancy between predicted and experimental absolute affinity values . While MHC-binding is postulated to be the most important step for antigen processing and presentation , subsequent steps involving proteasome cleavage of proteins and transporter-associated processing ( TAP ) contribute together to determine the final peptidome displayed on APC surface . Methods such as NetChop[58] and NetCTL[59] were devised to train ANN to predict such events . Due to the smaller amount of high-quality data available for training , however , these predictions can hardly achieve the same level of classification reliability compared to binding prediction tools such as NetMHC . Thus in practical applications of antigen identification , often MHC-binding prediction is relied solely upon . Recent efforts have emerged for developing the reliable prediction of naturally APC-presented peptides , by training ANN on a set of MHC-eluted peptide sequences that were obtained from high-resolution and high-throughput liquid chromatography-mass spectrometry ( LC-MS ) experiments . [17 , 43 , 54 , 60 , 61] Here we assessed the ability of currently benchmarked tools to identify MHC-eluted peptides correctly . The test sets include three MS-derived datasets from recent studies ( Methods ) . [62] Across the three datasets , we obtained varied accuracy for NetMHC4 , NetMHCpan4 , and MixMHCpred methods ( Figs 8 and S4 ) . We considered the primary cutoff using ranking score , in that the binding affinity threshold ( IC50 ) should not be applicable in the scenario of predicting elution probability . In addition , similar to the heterogeneous nature of binding repertoire , the MHC-eluted repertoire will mostly likely vary in size and affect the allele-specific cutoff . Overall , NetMHCpan4 achieves better accuracy compared to NetMHC4 , which is expected as it considered elution data in addition to binding affinity data for training . Due to the fact that the negative class in Abelin and Sternberg testing sets are synthetic peptides randomly drawn from MS peptidome database , their binding affinities are also very low , leading to high accuracy of both NetMHCpan4 as well as NetMHC4 ( Fig 8A and S4 Fig ) . In contrast , the negative class in Dana Farber testing set is composed of predicted MHC binders . In this case , NetMHCpan4 overall achieves lower false prediction rates than NetMHC4 . Similar trend can also be observed when using predicted binding affinity/elution score as cutoff ( S4 Fig ) . MixMHCpred also has improved accuracy than NetMHC4 on the three testing data when comparing prediction score using a putative cut-point of 0 . 5 . Its low false prediction rates are also close to NetMHCpan4 , with the improvement of zero false discovery rate ( FDr ) on B4403 allele and zero false negative rate ( FNr ) on A0301 allele . When considering all the prediction tools on Dana Farber data , NetMHCpan4 performs the best across six HLA types ( S5 Fig ) , which is likely due to that the dataset has already been included in the training set . One potential caveat of binding affinity predictors for tasking eluted peptide prediction is the relative high FDr , in that MHC-eluted peptides identified by MS experiments were often observed to have much smaller repertoire size than MHC-binding peptides . This caveat is also demonstrated here , with FDr reaching as high as 54% ( Fig 8B , HLA-B0702 in Dana Farber testing set ) . While FNr is generally lower than FDr , significant allelic variability was also observed , with FNr reaching 30% for certain HLA types . Considering that the repertoire size of actual MHC-presented epitopes is often limited , the misclassification of 3 out 10 epitopes can be critical during experimental validations . These observations demonstrate the variability of MHC-binding predictors when used for classifying antigen presentation , as shown by the non-trivial FPr and FNr . We note that , due to the inherit difference in repertoire sequences of MHC-binding and MHC-presenting peptides , it is reasonable for prediction methods trained on one type of data to perform sub-optimal on another type . In general , ML-based MHC-binding prediction tools are capable of achieving decent AUC values for classifying eluted versus non-eluted antigen-processed peptides , especially with the recent development of NetMHCpan4 and MixMHCpred . Nevertheless , the FNr and FDr should be taken into rigorous account in a cancer vaccine prediction pipeline . We applied structure-based peptide-protein docking protocol FlexPepDock to model the binding of 9-mer peptides to MHC class I proteins . Fig 9A shows the ROC curves and AUC benchmarking classification by Rosetta Score . The accuracy of the binary classification is at the lower spectrum end when put in line with ML-based approaches . In order to achieve a TPr of 80% , the Rosetta predictor commits about 50% FPr or higher for most alleles . Consistent with previous modeling study , while FlexPepDock displays as an operating predictor for differentiating MHC binders of certain alleles , it suffers from the insufficiency of backbone conformational sampling . In the current FlexPepDock protocol , although backbone structure was optimized before side chain refinement , the flexibility of MHC-binding epitope backbone made searching of the vast conformational space exhaustively challenging . Thus , the accuracy of final optimized model still largely depends on the template crystal structure chosen . [32] However , the peptide-MHC complex structure rendered by FlexPepDock still provides useful information at 3-D level . We extracted the conformation and energy of two HLA-A0201 binding epitopes from modeling results and analyzed the peptide-MHC binding pocket ( Fig 9B and 9C ) . Rosetta FlexPepDock correctly ranked FLSHDFTLV ( FLS ) ( exp . IC50 = 1 nM , Rosetta score = -784 . 357 ) above FLGGTPVCL ( FLG ) ( exp . IC50 = 238 nM , Rosetta score = -765 . 261 ) as stronger binder . The binding conformations for both peptides are stabilized by the fitting of leucine ( L , B2 and B9 ) or valine ( V , B9 ) side chains into the hydrophobic pocket on MHC surface ( Fig 9B and 9C , blue labels ) . These two amino acids were confirmed to be the primary anchoring residues for epitope binding to A0201 . [17 , 32] On the other hand , we found that stronger binder FLS may not necessarily be an ideal candidate for eliciting immunogenicity , in that its 5th and 6th residues , aspartic acid and phenylalanine respectively , actually pose side chains towards the MHC pocket as well ( Fig 9B ) . In contrast , slightly weaker binder FLG poses 5th and 6th residues threonine and proline preferably outwards , making their contact with T-cell receptor more energetic favorable ( Fig 9C ) . [29 , 63 , 64] In this case , positive epitopes from binding affinity-based prediction may not entirely conform to antigens that promote strong T-cell receptor binding and immunogenicity . Structural modeling-based approaches such as Rosetta FlexPepDock , hence , provide an alternative avenue to complement the analysis pipelines towards identifying vaccine candidates .
Our data suggests that while current MHC-binding predictors achieve high accuracy on classifying MHC-binders and non-binders , their performance on delivering precise binding affinities are inferior . This problem is almost intrinsic to ML-based approaches: the effect of the most dominant features on the weight matrix is penalized by regularization intentionally to achieve better generalization on those blind test data with less dominant features . [65] While this setting is designed to solve the classification problem , it limits the extent of recovering the absolute binding affinity by regression prediction . One source of the inaccuracy roots in the loss of sensitivity of experimental assays at either very high or low binding affinity regimes . Another related error may come from imbalance of training data across different affinity tiers . As a consequence , epitope candidates for subsequent experimental validation selected by ranking the predicted binding affinities may not necessary reflect the in vivo affinity values . Molecular modeling-based technique can calculate peptide-MHC binding free energy with high fidelity to experimental value . [66] Due to the limitation on computational cost , this solution would be applied only as a downstream , detailed analysis for structural interaction between peptides and MHC . [30] Imprecise affinity prediction can also lead to inferior classification performance for certain alleles , in that arbitrary affinity thresholds are often used in practice ( i . e . , 500 nM for binders and 50 nM for strong binders ) . Such thresholds were shown to underestimate MHC-binding peptide repertoire for rare HLA alleles . [51] To alleviate this limitation , percentage rank , instead of binding affinity , has been introduced to rank epitope candidates . Increasing the prediction accuracy of absolute binding affinity for ML-based approaches remains to be a major direction of improvement . In the current study , it is demonstrated that majority of the methods are able to attain comparable prediction performance between MHC class I 9-mer and 10-mer ligands . This observation is in concordance with previous testing on these methods , which employ strategies of designing gap and insertion[18] , or different training matrices for mapping different length[17] . We note that expanding the benchmarking to other length of MHC-ligands or immunogenic peptides is of significant relevance for understanding length preference of T-cell epitopes , which will be considered in our future work . The MHC-peptidome repertoire used to train binding affinity prediction tools has included a substantial amount of artificially synthesized peptides . While this inclusion has largely enhanced the sequence space of potential MHC-binders , it also generated bias in training set . The predictions from these tools lead to a skewed population of binding motifs that do not necessarily consider APC endogenous processing and TAP . As seen from our benchmarking of IEDB tools , the precision of the predictors in identifying naturally processed MHC-binders is suboptimal compared to predicting binding affinity . Mass spectrometry ( MS ) of “pull-down” experiment , taking advantage of MHC-specific antibodies , yields a relatively unbiased sampling of naturally processed endogenous peptides , and can correct the training set bias introduced by synthetic sequences . Moreover , it has been reported that a considerable amount of peptides eluted from MHC molecules are in fact proteasome-spliced sequences . [27] MS-based naturally processed MHC-peptidome is expected to facilitate the discovery of such non-canonical MHC-binding motifs as well . Increasing number of studies have focused on generating such MS-based MHC-peptidome dataset[43 , 67 , 68] , while a large fraction of the results are scattered and yet to be included as new training set . Incorporating large-scale MS-based MHC-peptidome data with existing binding affinity and antigen-processing predictors[17 , 42 , 43 , 54] , as demonstrated by our benchmarking of NetMHCpan4 and MixMHCpred versus NetMHC4 , is capable to improve the identification accuracy of naturally processed epitopes . Overall , the integration of multiple data sources in the antigen presentation and T-cell interaction pathways should be considered for efficient identification of vaccine candidates . For example , current MS-based MHC-peptidome data still lack good concordance with peptides predicted to be strong MHC binders . Our benchmarking indicates that binding affinity predictors have varied accuracies on MS-identified peptides , suggesting potential bias can be introduced when analyzing original MS data with predicted affinity filtering . For example , the Sternberg dataset was filtered by NetMHC4 prediction to reduce non-binding negatives and therefore is subject to bias when benchmarked on this predictor . A recent study has alleviated such issue with MHC-peptidome deconvolution[17] , which also provides future direction to assess the soundness of different predictors on unbiased MHC-peptidome testing sets . In addition to curating high-quality MHC-peptidome data , considerable upgrading of prediction power can potentially be gained by combining feature descriptors related to MS data , such as protein abundance , to fully realize the advantage of ANN approach[42] . This strategy offers a path towards a deep understanding of antigen presentation process . The ultimately goal of MHC-binding and other antigen presentation predictions is to identify peptides for eliciting adaptive immune response . In particular , the success of cancer immunotherapy has opened a venue for applying personalized cancer vaccine based on individual’s HLA allele types and tumor profile . Tumor-associated antigens or neoantigens are prominent candidates for cancer vaccine . In silico prediction methods are expected to prioritize antigens based on their potentials to elicit T cell responses , yet only a small fraction of predicted candidates turned up to be immunogenic in many case studies . [26] One reason is the lack of database reporting the relationship between epitope sequences and the associated T cell immunogenicity . Current high-throughput approach using tetramer staining of tumor infiltrating lymphocytes[69] has only produced a limited amount of affinity matrix as training set . As shown in this paper , alternative approach using structure-based modeling may be used to predict TCR-peptide-MHC interaction without prior knowledge . However , compared to the interactions between peptide epitope and MHC , the recognition of TCR to peptide-MHC is much more complex , due to the lack of well-defined binding groove at TCR protein surface . This complexity raises a tremendous amount of computational burden in practice . Despite these difficulties , we argue that the current prediction algorithms are necessitated to evolve towards T cell epitope prediction , in order to transform personalized cancer vaccine and biomarker development into practices that are approachable beyond research laboratory . In this study , we performed a systematic and quantitative benchmarking of popular MHC class I and II-binding prediction methods by using a comprehensive evaluation metrics . We also developed mhcflurry into a pan-HLA prediction approach to facilitate its application on HLA types with insufficient training data . For MHC class I , mhcflurry ( AUC = 0 . 911 ) and consensus ( AUC = 0 . 968 ) was demonstrated to be the best binary classifier on 9-mers and 10-mers , respectively . mhcflurry also achieved the best for multi-class classification and relative affinity ranking . Pan-HLA version of mhcflurry have the best accuracy in identifying strong MHC I-binders ( IC50 < 50 nM ) . For MHC class II , nn_align/NetMHCII2 ( AUC = 0 . 911 ) constantly outperformed other tools on all evaluation standards . The current binding prediction tools have achieved tremendous accuracy with respect to categorical classification of strong MHC-binders from the test set , demonstrating the advances made by large-scale synthetic peptide-MHC binding dataset and state-of-the-art ML approaches . On the other hand , important lessons have also been learnt as to the deficiency of current algorithms in predicting absolute binding affinity . With respect to identifying naturally processed MHC-peptidome using predicted ranks , variability of accuracy has been observed between different testing data , while the newly developed tool NetMHCpan4 displayed good performance compared to conventional binding affinity predictors . The contrast between results on MHC-binding and MHC-elution epitopes presents a new view of best practice in T-cell epitope prediction . In addition , we conducted extensive benchmarking of structure-based peptide-MHC binding prediction by Rosetta FlexPepDock , demonstrating the usage and weakness of structural modeling for antigen identification . Our benchmarking results provided an overall guideline regarding the predictive capacity of MHC-binding predictors and the potential directions of improvement for their applications in personalized cancer vaccine design and development . | Computationally predicting antigen peptide sequences that elicit T-cell immune response has broad and significant impact on vaccine design . The most widely accepted approach is to rely on machine learning classifier , trained on large-scale major-histocompatibility complex ( MHC ) -binding peptide dataset . Because of the constant development of machine learning algorithms and expanding training data , providing comprehensive benchmarking of existing algorithms on blind testing dataset is important for recognizing the pros and cons of different algorithms and providing guidelines on specific applications . Here we present a study of such benchmarking by characterizing on a wide array of accuracy metrics , highlighting the best-in-class algorithms as well as their limitations . The rising concept that “naturally presented” antigen epitopes are more likely to generate effective T-cell immune response has led us to also consider the accuracy of these machine learning algorithms on predicting naturally presented peptides . We demonstrate that recent advance in incorporating high-quality naturally presented peptide data from mass spectrometry experiments has improved the accuracy . Our benchmarking of machine learning predictors for MHC-binding and MHC-naturally presented antigen peptides contributes to establishing best practice of computational T-cell epitope analysis , which also has implication in tumor neoantigen-based cancer vaccine discovery . | [
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] | 2018 | Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes |
The brain uses its intrinsic dynamics to actively predict observed sensory inputs , especially under perceptual ambiguity . However , it remains unclear how this inference process is neurally implemented in biasing perception of ambiguous inputs towards the predicted percepts . The process of perceptual inference can be well illustrated by the phenomenon of bistable apparent motion in the Ternus display , in which subjective perception spontaneously alternates between element motion ( EM ) and group motion ( GM ) percepts depending on whether two consecutively presented frames are grouped over time or not . The frequency of alpha-band oscillations has long been hypothesized to gate the temporal window of perceptual grouping over time . Under this hypothesis , variation in the intrinsic alpha frequency should predict perceptual outcome of the bistable Ternus display . Moreover , we hypothesize that the perception system employs this prior knowledge on intrinsic alpha frequency to resolve perceptual ambiguity , by shifting perceptual inference towards the predicted percepts . Using electroencephalography and intracranial recordings , we showed that both between and within subjects , lower prestimulus alpha frequencies ( PAFs ) predicted the EM percepts since the two frames fell in the same alpha cycle and got temporally integrated , while higher PAFs predicted the GM percepts since the two frames fell in different alpha cycles . Multivariate decoding analysis between the EM percepts with lower PAFs and the GM percepts with higher PAFs further revealed a representation of the subsequently reported bistable percept in the neural signals shortly before the actual appearance of the second frame . Therefore , perceptual inference , based on variation in intrinsic PAFs , biases poststimulus neural representations by inducing preactivation of the predicted percepts . In addition , enhanced prestimulus blood-oxygen-level–dependent ( BOLD ) signals and network dynamics in the frontoparietal network , together with reduced prestimulus alpha power , upon perceiving the EM percepts suggest that temporal grouping is an attention-demanding process .
The brain is increasingly being understood as engaged in probabilistic unconscious perceptual inference to actively predict and explain observed sensory inputs , which helps to resolve ambiguity in sensory signals [1–6] . Therefore , our perception of the world is not simply based on our sensory inputs . Instead , what we perceive is heavily altered by contextual information [7–10] and expectations [11–14] . Besides context and expectation , intrinsic neural oscillatory signatures and organization status of the brain dramatically modulate the perceptual outcome of ambiguous stimuli [15–19] . However , it remains unclear how perceptual inference employs intrinsic brain activity to bias the perception of ambiguous sensory information towards predicted percepts with the progress of time . The process of perceptual inference can be well illustrated by the phenomenon of bistable apparent motion in the Ternus display [20 , 21] , in which subjective perception spontaneously alternates between spatial and temporal grouping interpretations of a constant ambiguous dynamic visual scene ( Fig 1 ) . The human brain adopts two major strategies of perceptual grouping to achieve perceptual coherence along the spatial and temporal dimension , despite the ever-changing visual inputs and the resulting fragmentary nature of the retinal image across space and time [22–26] . Spatially , grouping local visual elements into a holistic percept allows us to perceive scenes and objects as a whole rather than a meaningless collection of unconnected parts [27–29] . Temporally , successive discrete visual events unfolding in time could be grouped based on temporal proximity to perceive the stability of object identity and location [24 , 30 , 31] . In the classical Ternus display ( Fig 1A ) , two horizontally spaced disks appear at shifted locations in two successive frames . Depending on the interframe interval ( IFI ) , observers typically report two distinct percepts [32 , 33] . Temporal grouping is explicitly dominant at short IFIs: the central overlapping disks between the two frames are temporally integrated as one single disk , and the visual persistence of the central overlapping disk makes the lateral disk in frame 1 appear to jump across the central disk , i . e . , element motion ( EM ) ( Fig 1A , upper panel; S1 Video ) . On the other hand , spatial grouping is explicitly dominant at long IFIs: the two disks within each frame are spatially grouped and perceived as moving together as a group , i . e . , group motion ( GM ) ( Fig 1A , lower panel; S2 Video ) . Most critically , when the IFI reaches a certain psychophysical threshold , the Ternus display becomes ambiguous/bistable: the report of EM ( temporal grouping ) versus GM ( spatial grouping ) percepts randomly fluctuates on a trial-by-trial base , resulting in comparable proportions of GM and EM percepts ( Fig 2A ) [24 , 33] . Interestingly , the typical transition IFI threshold between temporal and spatial grouping in the Ternus display occurs around a time window of approximately 100 ms [33 , 34] ( see Fig 2A and 2B ) , which corresponds to the average cycle of occipital alpha-band oscillations , with peak frequencies ranging between 8 and 13 Hz ( i . e . , 70- to 120-ms cycle ) . The alpha oscillations , as one of the most predominant oscillations in the visual system , are considered as one underlying mechanism of perceptual cycles by gating the transient temporal windows of perception [35–39] . Accordingly , accumulating evidence shows that the phase of ongoing alpha oscillations reflects cyclic shifts of neuronal excitability [40–42] and predicts not only behavioral performance [15 , 43–45] but also a variety of subsequent neural signals related to stimulus processing [16 , 46 , 47] . Besides the phasic effects , the peak frequency of alpha-band oscillations predicts reaction times ( RTs ) [48] and variations in temporal resolution of perception [36 , 49–51] . The phasic and frequency effect of alpha oscillations lead to the long-standing hypothesis that the alpha cycle provides the discrete temporal window of perceptual grouping: whether two stimuli are integrated into a single percept or segregated into separate events depends on whether they fall in the same cycle of the alpha oscillation [52 , 53] . In terms of the Ternus paradigm , if the two frames fall in the same alpha cycle , they will be temporally integrated , resulting in the EM percepts; if the two frames fall in different alpha cycles , they will be segregated temporally , and spatial grouping will take place separately in the two frames , resulting in the GM percepts . Especially when the sensory inputs become ambiguous at the transition IFI threshold ( Fig 1B ) , we hypothesize that the intrinsic prestimulus alpha frequency ( PAF ) plays a critical role in determining whether the two frames are grouped over time or not , which accordingly affects the codependent spatial grouping process . Specifically speaking , lower PAFs ( i . e . , longer prestimulus alpha cycles , Fig 1B , upper panel ) allow the two consecutively presented frames to fall in the same alpha cycle , resulting in the EM percepts , while higher PAFs ( i . e . , shorter prestimulus alpha cycles , Fig 1B , lower panel ) allow the two frames to fall in different alpha cycles , resulting in the GM percepts . We thus predict that the PAF can affect the outcome of bistable perceptual grouping in the Ternus paradigm , with higher PAFs preceding the bistable GM than EM percepts . Furthermore , we hypothesize that perceptual inference employs the intrinsic PAFs to predict the perceptual outcome in the bistable Ternus display . Specifically , the brain generates predictions towards the GM percepts according to higher PAFs since the higher PAFs make it more possible for the two frames to fall in different alpha cycles . On the other hand , the brain generates predictions towards the EM percepts according to lower PAFs since the lower PAFs make it more possible for the two frames to fall within the same alpha cycle . Under the framework of perceptual inference , combining the specific prediction and forthcoming inputs , perceptual inference towards one specific percept will be made [54–56] . The perceptual inference is efficient if it is consistent with the subsequently perceived percept but inefficient if inconsistent . We thus predict that the efficiency of the perceptual inference may bias neural representations of the perceived percepts with the progress of time . In particular , the efficient perceptual inference may induce the corresponding representation pattern underlying the predicted percepts even before the actual presentation of the stimuli . Alternatively , if perception was based solely on sensory inputs , one would assume that neural representations underlying the integrated percepts are induced only after the actual presentation of the stimuli . To distinguish between the above hypotheses , we adopted electroencephalography ( EEG ) in healthy adults and intracranial recordings in epileptic patients and used multivariate decoding techniques on the EEG data to further probe the representational content of neural signals in a time-resolved manner .
In the EEG ( n = 17 ) , intracranial ( n = 4 ) , and functional magnetic resonance imaging ( fMRI ) ( n = 18 ) experiments , participants were asked to report the perceived EM versus GM percepts after viewing 1 ) the explicit EM stimuli with the short IFI , 2 ) the explicit GM stimuli with the long IFI , and 3 ) the bistable stimuli with the transition IFI threshold ( Fig 1 ) . The transition IFI threshold , at which equal proportions of EM and GM trials were reported , was determined individually for each participant prior to the main experiment ( Fig 2A; see Materials and methods ) . The individual IFI threshold for each participant , estimated by a psychometric function fitted to the participant’s responses at each of the seven IFIs ( see details in Materials and methods , Figs 2A and S1 ) , was shown in Fig 2B for the EEG and fMRI experiments , respectively . A two-sample t test showed no significant difference between the two experiments in terms of the group mean IFI threshold , t ( 33 ) < 1 . In the two explicit conditions , the mean accuracy rates in the explicit EM and explicit GM condition were comparable and both above 85% in both the EEG , t ( 16 ) < 1 , and the fMRI experiment , t ( 17 ) < 1 ( Fig 2C ) . The accuracy rate of the explicit trials was taken as an indicator of whether a participant could indeed clearly discriminate between the two different types of percept . Our data indicated that the participants could clearly distinguish the two explicitly different percepts at the short versus long IFIs . RTs , however , were significantly slower in the explicit EM than the explicit GM condition in both the fMRI experiment ( t ( 17 ) = 3 . 06 , p < 0 . 01 ) and the EEG experiment ( t ( 16 ) = 3 . 73 , p < 0 . 005 ) ( Fig 2E ) . In the bistable condition , there was no significant difference between the bistable EM and the bistable GM conditions in terms of both the rates of choice ( EEG experiment: t ( 16 ) < 1 , fMRI experiment: t ( 17 ) < 1 ) ( Fig 2D ) and the RTs ( EEG experiment: t ( 16 ) < 1 , fMRI experiment: t ( 17 ) < 1 ) ( Fig 2E ) . In addition , behavioral performance of the four epileptic patients with depth electrodes showed similar patterns as the healthy participants ( see S1 Table ) .
By using EEG , intracranial recordings , and fMRI , we investigated how the frequency of alpha-band oscillations acts as the critical neural dynamics that accommodate the temporal and spatial grouping during ambiguous perception in the Ternus paradigm and , more importantly , how the brain makes predictions , based on intrinsic alpha frequency , to resolve perceptual ambiguity . At the behavioral level , comparable task performance/judgment difficulty was revealed between the bistable temporal and spatial grouping condition ( Fig 2D and 2E ) . Therefore , any neuronal difference between the two bistable conditions cannot be attributed to differences in judgment difficulty . At the neural level , both within and between subjects , peak prestimulus frequency of alpha oscillations in the occipitoparietal regions predicted the bistable temporal versus spatial grouping ( Figs 3 and 4 ) . Moreover , efficient perceptual inference , based on spontaneous variance in the intrinsic PAFs , induced a representation of the subsequently reported bistable percept in the neural signals before the actual appearance of the second frame , indicating a preactivation of the subjectively perceived bistable percepts ( Fig 5 ) . Based on the above observations , we propose that the alpha frequency gates the time window for perceptual grouping and perceptual inference based on intrinsic alpha frequency biased poststimulus neural representations by inducing preactivation of the predicted percepts . In addition , the reduced prestimulus alpha power ( Fig 6A ) , together with enhanced prestimulus blood-oxygen-level–dependent ( BOLD ) activity and network dynamics in the frontoparietal network ( Fig 6C and 6D ) , predicted bistable EM rather than GM percepts . It has been proposed that perception is discrete and cyclic in a manner of perceptual cycles [15 , 16 , 45 , 61 , 62] . Accumulating recent evidence showed that perceptual performance depends on the frequency of the critical rhythm at around the onset time of stimuli [50 , 51 , 63] . A higher frequency of the brain oscillations should be equivalent to a faster frame rate of discrete perception and vice versa . Accordingly , lower alpha frequency was reported to be associated with poorer temporal resolution [50 , 51] , as if the slower frame rate of perception made two successive flashes more likely to fall within the same frame and thus be perceived as one [61] . In the present Ternus paradigm , the intrinsic alpha frequency determines whether the two frames are integrated over time ( i . e . , EM ) or not ( i . e . , GM ) . Specifically , when the alpha frequency is relatively slow ( i . e . , longer alpha cycles ) to cover both spatially and temporally segregated information segments , temporal grouping between the frames dominates over spatial grouping , resulting in the EM percepts ( Fig 1B , upper panel; Fig 3C and 3D ) . On the other hand , when the alpha frequency is relatively high ( i . e . , shorter alpha cycles ) to cover only spatially segregated information segments , spatial grouping with the frames dominates , resulting in the GM percepts ( Fig 1B , lower panel; Fig 3C and 3D ) . The intracranial data further confirmed the above alpha frequency effect in distributed visual areas including both the dorsal and ventral visual stream , such as the primary and secondary visual areas in the lingual gyrus , higher-order areas in the fusiform gyrus , the lateral occipital cortex ( LOC ) , the middle temporal gyrus ( MT ) , and the IPS ( Fig 4 ) . The dorsal occipitoparietal areas , such as the inferior IPS , have been associated with perceptual integration of multiple elements and object representations [64 , 65] . The ventral visual areas , such as LOC , have been found to be involved in object recognition [66] . Moreover , it has been well documented that the MT area is highly responsive to visual motion and codes highly specialized representations of visual information [67–69] , which is putative for generating apparent motion percepts [70 , 71] . The present results further suggest that the alpha frequency effect is a ubiquitous property of the visual system , which is involved in representing coherent object motion percepts . It has been revealed that neural oscillations could create temporal windows that favor the communication between neurons [72 , 73] . The common alpha frequency effect in distributed visual systems may drive the communication between neuronal groups in these areas to effectively encode and organize the dynamic visual inputs and induce coherent apparent motion percepts . Please note , for both the present EEG and the intracranial results , a very small within-subject variance in the PAF ( about 0 . 1 Hz in the EEG data , Fig 3D , and about 0 . 2 Hz in the intracranial data , Fig 4 ) was associated with qualitatively different perceptual percepts . Based on our hypothesis , at the within-subject level , the most critical factor that causes different perceptual outcomes is the perceptual inference built through the intrinsic alpha frequency , but not the absolute alpha frequency per se . It has been suggested before that the perceptual inference is very sensitive to subtle changes in the intrinsic brain states [74 , 75] . Therefore , in the present study , a slightly lower alpha frequency could be enough to induce a perceptual inference towards the EM percept , while a slightly higher alpha frequency could be enough to induce an inference towards the GM percept . In addition , the small within-subject frequency effect is consistent with previous studies showing small frequency modulations [51 , 58] . Technically speaking , this small effect might result from the fact that the alpha frequency data derived from the EEG and intracranial signals reflect the summed activity of both the task-relevant and the task-irrelevant neuronal populations . Therefore , the observed effect could be attenuated by the noises from the task-irrelevant neuronal populations [51] . The generative models of perceptions commonly consider the brain as an unconscious inference machine that uses hidden states to predict observed sensory inputs [55 , 76] . Although there have been some detailed theories on the neural basis of the underlying computations of this system [77] , it still lacks direct empirical evidence about how the brain uses its intrinsic states to build up specific prediction signals for perceptual inference . In the present study , even with the sensory inputs ( the two frames with a threshold IFI ) being kept constant in the bistable condition , the subjective perception varies between the EM and GM percepts on a trial-by-trial base , thus suggesting fluctuations in prior predictions . Please note , the definition of prediction in the present study stands for “internal model’s prediction” under the general perceptual inference framework [54 , 74] , which is different from the term of “top-down prediction” manipulated in the field of cognitive neuroscience and psychology [78 , 79] . The former one represents the priors in the Bayesian framework and includes any factors that can provide prior information [6 , 55]: the perceptual prediction based on intrinsic PAFs in the present study is an example of this type of prediction . On the other hand , the latter term of “top-down prediction” is associated with the top-down control mechanisms in the higher-order brain areas , which is not directly supported by the data in the present study . The present Ternus display puts the brain under the explicit contextual information that there are two possible apparent motion percepts , i . e . , GM versus EM . Moreover , the alpha peak frequency , which provides the critical window for perceptual integration , is widely considered as one putative marker of an individual’s intrinsic state [80 , 81] . Therefore , perception is able to employ the current intrinsic alpha frequency to build prior probability of predictions about the most possibly perceived apparent motion percepts ( Fig 5A , left panel ) . The perceptual inference is efficient if it is consistent with the perceptual outcome , i . e . , in the high PAF GM trials and the low PAF EM trials; the inference is inefficient if it is inconsistent with the perceptual outcome , i . e . , in the high PAF EM trials and the low PAF GM trials ( Fig 5A , right panel ) . Our results showed that the peak alpha frequency not only predicted the outcome of bistable perceptual grouping ( Figs 3C , 3D and 4 ) but also modulated the fidelity of neural representations of the integrated percepts ( Fig 5 ) . Compared to the inefficient inference , neural representations of the bistable EM versus GM percepts could be more robustly decoded under the efficient inference ( Fig 5D , 5E and 5F ) , suggesting that the efficient inference based on intrinsic PAFs enhanced the fidelity of neural representations of the predicted percepts . More interestingly , under the efficient inference , the neural signals evoked by the actual presentation of the second frame could be readily read out from the neural signals even before the presentation of the second frame ( Fig 5F and 5G ) , suggesting a preactivation of the predicted percepts . These results thus fundamentally advance our mechanistic understanding on how the alpha frequency builds up specific prediction signals for perceptual inference: perceptual predictions on the spatially versus temporally integrated percepts are generated based on variation in the intrinsic PAFs , which induces preactivated neural representations that resemble the neural representations evoked by the actual stimuli . Please note , since time 0 in the decoding analysis was relative to the presentation of frame 2 , the significant 0–100 ms poststimulus time window of the decoding analysis ( Fig 5F ) corresponds to about 100 ( threshold IFI ) –200 ms ( threshold IFI + 100 ms ) relative to the actual presentation of frame 1 . It is thus possible that , about 150–200 ms after the presentation of frame 1 , the participants have already generated conscious perceptual experiences of the predicted percepts under the modulation of perceptual prediction [14 , 82] . However , since the whole significant poststimulus time window still involves relatively early processing phases around 100–150 ms after the presentation of frame 1 , one alternative interpretation is that the present relatively early poststimulus time window might reflect early neural mechanisms such as the iconic memory [83] . Since the explicit EM percepts are observed at the short IFI while the explicit GM percepts are observed at the long IFI ( Figs 1A and 2A ) , and since the bistable EM percepts involve higher alpha frequency than the bistable GM percepts ( Fig 3D ) , it is possible that shorter time frames and accordingly faster temporal processing are involved in the EM percepts , which may demand more efficient communication of information through the brain . It has been correspondingly suggested that attention facilitates fast temporal processing [84–86] . Therefore , one hypothesis is that the bistable EM percepts may require more frontoparietal attentional network involvement than the bistable GM percepts . Alternatively , in contrast to EM , which is more a temporal matching , GM is more a gestalt/global matching of objects , which ignores retinotopic correspondence in favor of object-based grouping [32 , 87 , 88] . Such high-level , nonretinotopic , gestalt grouping of GM might be expected to require more frontoparietal involvement as opposed to the occipital regions , which might be sufficient for short-lived , retinotopically organized grouping [89] . Our fMRI results provide supporting evidence to the former hypothesis: both increased prestimulus neural activity ( Fig 6C ) and increased prestimulus network dynamics ( Fig 6D ) in the frontoparietal network predicted the subsequent bistable EM ( temporal grouping ) , rather than GM ( spatial grouping ) , percepts . Moreover , the enhanced frontoparietal activations and DMN deactivations during the bistable EM trials ( Fig 6B ) indicated that bistable temporal grouping was more attention-demanding than bistable spatial grouping [90 , 91] . Consistent with the fMRI results , the prestimulus alpha power results ( Fig 6A ) also supported this conclusion by showing a lower prestimulus alpha power in the bistable EM than GM trials . Since it has been well documented that alpha power is an effective indicator of the level of attention engaged in a certain cognitive task ( the higher the alpha power , the lower the level of attention ) [81 , 92–94] , the lower prestimulus alpha power during the bistable EM trials indicated higher level of attention . Therefore , the fMRI results , together with the prestimulus alpha power results , suggested that temporal grouping is more attention-demanding than spatial grouping in the Ternus paradigm . To further understand the more general role of alpha frequency in perceptual grouping across both space and time rather than just see the specific effect with a single variant of the Ternus paradigm , future experiments with paradigms examining perceptual grouping at different levels of complexity and with regard to different visual attributes are still needed . In terms of the Ternus paradigm per se , the present study only focused on the temporal window of perceptual integration and the effect of perceptual inference , while there are other possible interpretations of this specific illusion , such as alternations between object versus group processing [32 , 33] , between the use of top-down predictions ( for example , trial history ) [95] , and between the use of different reference frames [96–98] . Moreover , other frequency-band oscillatory activities , such as the theta-band oscillations that have been suggested to be implicated in the perception of apparent motion [63] and temporal integration [99] , might be involved in the present phenomenon as well , but the current research methods may not be sufficient enough to detect these significant theta effects . For example , in the intracranial experiment , since most of the implanted electrodes of the four patients were in the posterior brain regions with few electrodes in the higher-order areas , it remains unknown whether there is a significant theta effect in the higher brain areas , such as the frontal cortex [100 , 101] . To summarize , by adopting a Ternus display in which subjective perception fluctuates between temporally versus spatially integrated percepts , we showed that the occipitoparietal alpha frequency defines a temporal window for perceptual integration . Moreover , in the situation of efficient perceptual inference , neural representations of the predicted percepts based on the alpha frequency were preactivated before the actual presentation of the critical stimuli . Therefore , perceptual inference employs PAF-induced predictions to resolve perceptual ambiguity .
All the participants gave their informed consent prior to the experiment in accordance with the Declaration of Helsinki . The fMRI , the EEG , and the patient experiments were all approved by the Ethics Committee of School of Psychology , South China Normal University ( 06202015_TernusCQ ) . The placement of the depth electrodes was based solely on the clinical needs for the treatment of the patients and was thus independent of the purpose of the present study . This study did not add any invasive procedure to the intracranial recordings . All the participants were at least 18 years old and gave their written informed consent prior to the experiments . Nineteen adult participants ( 12 females , mean age of 19 . 6 years old ) took part in the EEG experiment . Another group of 20 adult participants ( 12 females , mean age of 23 . 4 years old ) took part in the fMRI experiment . Two participants in the EEG experiment were discarded because of excessive eye movement artifacts . One participant in the fMRI experiment was discarded because of low accuracy ( less than 70% ) in the explicit conditions , and another participant was discarded because of the excessive head movements during the scanning . Therefore , 17 participants in the EEG experiment and 18 participants in the fMRI experiment were included for further analysis . Additionally , four adult patients ( two males , mean age of 24 years old ) undergoing intracranial recordings with stereotactically implanted multilead electrodes ( Guangdong Sanjiu Brain Hospital , China ) for epilepsy treatment participated in the present study . Although the anatomical locations of the electrodes were different in each patient , we included the patients whose electrodes were implanted in the occipital and parietal regions . Patients who had destructive lesions such as tumor or encephalomalacia were excluded . All the participants were right-handed , with normal or corrected-to-normal visual acuity . Visual stimuli consisted of two consecutively presented frames of stimuli ( frame 1 and frame 2 ) , and each frame was presented for 30 ms ( Fig 1 ) . There was a blank period between the two frames , i . e . , the IFI . The IFI could be either explicitly short at 50 ms or explicitly long at 230 ms or at the transition threshold , which was specific for each subject based on pre-experiment psychophysics . Each frame contained two horizontally arranged black disks ( 1 . 6° of visual angle in diameter ) on a gray background . The center-to-center spatial distance between the two disks was 3° of visual angle . The two frames shared one common disk location at the center of the display . The location of the lateral disk of the first frame , either on the left or the right side of the shared central disk , was always opposite to the lateral disk of the second frame ( Fig 1 ) . Specifically speaking , frame 1 with left and central disks and frame 2 with right and central disks induced rightward apparent motion; frame 1 with right and central disks and frame 2 with left and central disks induced leftward apparent motion . The same set of stimulus parameters was adopted for the fMRI , the EEG , and the patient experiments . Depending on the IFI and participants’ online judgments in the bistable trials , there were four types of experimental trials: 1 ) the explicit EM trials ( “Explicit EM” ) with the short IFI of 50 ms; 2 ) the explicit GM trials ( “Explicit GM” ) , with the long IFI of 230 ms; 3 ) the bistable trials with the threshold IFI , which were judged by the participants as the EM trials ( “Bistable EM” ) ; and 4 ) the bistable trials with the threshold IFI , which were judged by the participants as the GM trials ( “Bistable GM” ) . To specify the 50% threshold of IFI for the bistable condition for each individual subject , we asked each participant to perform a psychophysical pretest before the main experiment . Prior to the psychophysics test , participants were shown demos of the explicit EM and GM conditions and performed a practice block with only explicit EM and GM trials until the accuracy reached no less than 95% . During the formal psychophysics test , the first frame was presented for 30 ms . After a variable IFI ( seven levels: 50 , 80 , 110 , 140 , 170 , 200 , or 230 ms ) , the second frame was presented for 30 ms as well . Participants were asked to perform a two-alternative forced choice ( 2AFC ) task in which they had to choose between the EM and the GM percept . For each IFI condition , the percentage of GM reports ( i . e . , “1 –percentage of EM reports” ) was collapsed over the leftward and rightward motion directions . The seven data points ( one for each IFI ) were fitted into a psychometric curve using a logistic function [102] . The transition IFI threshold , i . e . , the point at which EM and GM were reported with equal possibility , was calculated by estimating the 50% performance point on the fitted logistic function for each participant [102] . The individual transition threshold derived from the psychophysics test was then used as the IFI in the bistable trials of the subsequent main experiment . Differently from the EEG and fMRI experiment , in the intracranial experiment , an adaptive staircase procedure [103] was adopted to find the individual IFI threshold at which 50% of the stimuli were perceived as GM . Participants were instructed to fixate at a central fixation throughout the experiment without moving their eyes . The experimental task was to discriminate the two types of motion by pressing two prespecified buttons on the response pad using the thumb of each hand , respectively . The mapping between the two response buttons and the two types of apparent motion percept was counterbalanced between participants . In each trial , the first frame was presented for 30 ms , and after a variable IFI ( 50 ms , 230 ms , or the individual IFI threshold ) , the second frame was presented for another 30 ms . The fMRI experiment consisted of 440 trials in total , including 80 explicit EM trials , 80 explicit GM trials , 160 bistable trials , and 120 null trials . The null trials , in which only the central fixation cross was presented , were used as the implicit baseline . The participants were asked to rest for a short period of time ( 11 s , i . e . , five repetition times [TRs] ) after every 6 minutes’ task performance , which made three short periods of rest in total . During the three short rest periods , the scanner kept running , and a visual instruction “rest” was presented on the center of the screen throughout . One TR after the disappearance of the “rest” instruction , the behavioral task resumed . The EEG experiment consisted of four blocks , and each block included 40 explicit EM trials , 40 explicit GM trials , and 80 bistable trials , which were intermixed randomly , resulting in 640 experimental trials in total . A rest break was allowed between blocks . For the fMRI and EEG experiment , each trial was followed by a time interval that was selected randomly among 2 , 000 , 2 , 250 , 2 , 500 , 2 , 750 , and 3 , 000 ms . In the intracranial experiment , there were four blocks of 80 trials ( 320 trials in total ) , 10% of which were explicit EM and GM trials . The intertrial interval varied randomly between 1 . 5 and 2 . 5 s . In all the three experiments , the temporal order of all the trials was randomized for each participant individually to avoid potential problems of unbalanced transition probabilities . All participants completed a training section of 5 min before the recording . EEGs were continuously recorded from 64 Ag/AgCl electrodes ( 10–20 System ) with BrainAmp DC amplifiers ( low-pass = 100 Hz , high-pass = 0 . 01 Hz , and sampling frequency = 500 Hz ) . The vertical electro-oculogram was recorded by one electrode under the participants’ left eyes . All the electrode impedances were kept below 5 kΩ . Signals were referenced online to the unilateral mastoid . Offline processing and analysis were performed using EEGLAB [104] and customized scripts in MATLAB ( The MathWorks , Natick , MA , USA ) . Data were down-sampled to 160 Hz , rereferenced to the average reference , epoched from –800 ms before the first frame to 1 , 000 ms after the first frame for the subsequent alpha frequency analysis , and re-epoched from –500 ms to 500 ms relative to the presentation of the second frame for the decoding analysis . Trials containing visually identified eye movements or muscle artifacts were excluded manually . Visually identified noisy electrodes were spherically interpolated . Ten to 13 semirigid , multilead electrodes were stereotactically implanted in the four participants , respectively . All the electrodes have a diameter of 0 . 8 mm and contain 10–16 2-mm–wide and 1 . 5-mm–apart contacts . The precise anatomical location of each contact was identified by coregistering each participant’s postimplantation CT with the preimplantation 3D T1 image , using rigid affine transformations derived from FSL’s FLIRT algorithm [105] . Intracranial recordings were conducted using commercial video–intracranial monitoring system . The data were bandpass filtered online from 0 . 1 to 300 Hz and sampled at 1 , 000 Hz , using a reference contact located in the white matter . For the offline analysis , recording signals were down-sampled to 500 Hz . Contacts in the epileptogenic zones were excluded from further analyses . Each contact was rereferenced with respect to its direct neighbor , i . e . , bipolar montage , to achieve high local specificity by removing effects of distant sources that spread equally to adjacent sites through volume conduction . All the data were epoched from –800 to 1 , 000 ms relative to the presentation of the first frame . A Siemens 3T Trio system with a standard head coil at Beijing MRI Center for Brain Research was utilized to obtain T2*-weighted echo-planar images ( EPIs ) with blood oxygenation level-dependent contrast . The matrix size was 64 × 64 mm3 , and the voxel size was 3 . 4 × 3 . 4 × 3 mm3 . Thirty-six transversal slices of 3-mm thickness that covered the whole brain were acquired sequentially with a 0 . 75-mm gap ( TR = 2 . 2 s , TE = 30 ms , FOV = 220 mm , flip angle = 90° ) . There was a single run of functional scanning , including 524 EPI volumes . The first five volumes were discarded to allow for T1 equilibration effects . Data were preprocessed with Statistical Parametric Mapping software SPM12 ( Wellcome Department of Imaging Neuroscience , London , UK; http://www . fil . ion . ucl . ac . uk ) . Images were realigned to the first volume to correct for interscan head movements . The mean EPI of each participant was then computed and spatially normalized to the MNI single-participant template using the “unified segmentation” function in SPM12 . This algorithm is built on a probabilistic framework that enables image registration , tissue classification , and bias correction to be combined within the same generative model . The resulting parameters of a discrete cosine transform , which define the deformation field necessary to move individual data into the space of the MNI tissue probability maps , were then combined with the deformation field transforming between the latter and the MNI single participant template . The ensuing deformation was subsequently applied to individual EPI volumes . All images were thus transformed into standard MNI space and resampled to 2 × 2 × 2 mm3 voxel size . The data were then smoothed with a Gaussian kernel of 8-mm full-width half-maximum to accommodate interparticipant anatomical variability . Data were high-pass filtered at 1/128 Hz and analyzed with a general linear model ( GLM ) as implemented in SPM12 . Temporal autocorrelation was modeled using an AR ( 1 ) process . For the behavioral data in the EEG , intracranial , and fMRI experiment , omissions and trials with RTs 3 standard deviations ( SDs ) away from the mean RT in each condition were first excluded from further analysis . For the calculation of accuracy rates in the two explicit conditions , the explicit trials at the short IFI with a judgment of GM and the explicit trials at the long IFI with a judgment of EM were considered as incorrect trials , which were discarded and excluded from further analysis . For both the EEG and fMRI experiment , paired t tests were performed to test the difference in the accuracy rates between the two types of explicit trials , the proportions of EM and GM trials in the bistable condition , and the mean RTs for the two explicit and the two bistable conditions , respectively . For all the electrodes in all the participants , a power spectrum ( from 5 Hz to 30 Hz ) was obtained through a Fast Fourier Transform ( FFT ) of all the trials ( from –800 to 0 ms relative to the presentation of the first frame ) . An amplitude topographic map of the most prominent frequency band in the power spectrum was obtained . For each participant , the individual peak alpha frequency was determined as the value corresponding to the maximum peak frequency from the 800 ms of data prior to the presentation of the first frame within the 8–13 Hz range for the selected posterior electrodes . The Pearson product–moment correlation between the individual alpha frequency and the individual IFI threshold obtained from the psychophysical procedures was then calculated . Instantaneous PAF and alpha power were analyzed using the methods and code developed by Cohen [58] . We chose only one electrode , which showed the strongest alpha amplitude among all the occipital electrodes in the posterior ROI for each participant , to calculate the PAF and the alpha power [51] . To avoid contaminations by the poststimulus signals , only the prestimulus period ( from –800 to 0 ms ) of the EEG signals were extracted , and all the poststimulus period signals ( starting from 0 ms ) were excluded . Furthermore , to avoid edge artifacts at the stimulus onset due to filtering , the prestimulus signals of each bistable trial were copied , flipped from left to right , and appended to the right side of the original data . These epochs were filtered between 8 and 13 Hz with a zero-phase , plateau-shaped bandpass filter with 15% transition zones . Phase angle and amplitude time series were extracted from the filtered data with a Hilbert transform . The alpha power was obtained by calculating the square of the amplitude . For the frequency calculation , the temporal derivative of the phase angle time series describes how phase changes over time and thus corresponds to the instantaneous frequency in Hz ( when scaled by the sampling rate and 2π ) . Since noises in the phase angle time series can cause sharp , nonphysiological responses in the derivative , the instantaneous frequency was filtered with a median filter with an order of 10 and a maximum window size of 400 ms: data were median filtered ten times with 10 time windows ranging from 10 to 400 ms prior to averaging across trials . Since this analysis considers changes only in the instantaneous phase of the data , it is mathematically independent from the amplitude of the oscillation , except where amplitude is equal to zero and phase is undefined . Subsequently , the instantaneous PAFs were averaged across bistable EM and GM trials , respectively . Multivariate decoding techniques were further adopted to investigate how the PAF affects the representation contents of the bistable EM and GM percepts with the progress of time . For each participant , we first calculated the instantaneous alpha frequency for each time point in the prestimulus window ( from –800 ms to 0 relative to the onset of frame 2 ) of each bistable trial , based on the one chosen electrode with the maximal alpha amplitude . Subsequently , statistical tests ( paired t test ) between the bistable EM and bistable GM conditions were performed at the group level . The significant time points were further selected as the time points of interest , and the PAF for each trial was determined by averaging the instantaneous alpha frequency across these significant time points ( –570 to –350 ms relative to the presentation of frame 2; see S3 Fig ) . Subsequently , amplitude data epochs of all the bistable trials ( –400 to 400 ms relative to the presentation of frame 2 ) , right after the preprocessing steps and without any further processing steps ( no spectral analysis applied ) , were sorted according to the calculated PAF of each trial and half split into the high PAF and the low PAF trial sessions . The bistable GM trials in the high PAF session and the bistable EM trials in the low PAF session were selected as the two types of trials in the efficient inference condition; the bistable EM trials in the high PAF session and the bistable GM trials in the low PAF session were selected as the two types of trials in the inefficient inference condition . Please note , the PAF of each bistable trial was used only as an indicator to categorize the bistable trials into the efficient versus inefficient conditions in a post hoc way but was never used as the actual data fed into the subsequent decoding analysis . To exclude potential confounds caused by different number of trials upon comparing different conditions , we matched the trial count in the above four types of trials by randomly selecting a subsample of trials from the conditions with more trials . We then applied a multivariate linear discriminant analysis to characterize the temporal dynamics that discriminated between the subjectively perceived bistable EM versus GM percepts for the efficient and inefficient inference condition , respectively . Classifications were based on the regularized linear discriminant analysis to identify a projection in the multidimensional EEG data , x , that maximally discriminated between the two representations across all stimulus levels . Each projection is defined by a weight vector , w , which describes a one-dimensional projection y of the EEG data y=∑iwixi+c , with i summing over all channels and c a constant . The regularization parameter was optimized in preliminary tests and kept fixed for all the analyses . The decoding analysis was performed in a time-resolved manner by applying it to each time point sequentially , resulting in an array of classifiers , for example , w ( t1 ) , w ( t2 ) , w ( t3 ) and so on . To improve the signal-to-noise ratio , the data were first averaged within a time window of 50 ms centered around the time point of interest . This process could introduce some contaminations from the poststimulus signals to the prestimulus signals around the stimuli onset: the signal at time 0 contains the information within –25 to 25 ms . However , the abovementioned contaminations can influence the prestimulus signals about 25 ms at most . Subsequently , the classifier performance was assessed not only at the time point used for training ( for example , classifier w ( t1 ) was tested at t1 , w ( t2 ) was tested at t2 , and so on ) but also on data from all the other time points ( for example , classifier w ( t1 ) was tested on all the time points t1 , t2 , t3 , and so on ) . The performance of the classifier was quantified using the receiver operator characteristic ( ROC ) , based on leave-one-out cross-validation within each participant . The above procedure resulted in a ( training time ) × ( decoding time ) temporal generalization matrix per condition . We first extracted the averaged alpha amplitude during the prestimulus period ( –800 to 0 ms relative to the first frame ) for each contact in the same manner as for the EEG analysis ( using FFT ) . The first 10 contacts with the highest alpha amplitude ( 8–13 Hz ) were then selected as ROIs for each patient . Subsequently , we adopted similar methods and procedures as in the EEG analysis to calculate the prestimulus instantaneous frequency for the bistable EM and GM trials for each contact , which was subsequently averaged across the 10 contacts . The difference between two conditions was statistically tested using nonparametric cluster-based permutation tests , which were implemented in customized scripts in MATLAB ( The MathWorks ) . Specifically speaking , paired t tests were first calculated between the two conditions , for example , the temporal generalization matrices for the efficient versus inefficient inference conditions . Elements that passed a threshold value corresponding to a p-value of 0 . 05 were marked , and neighboring marked elements were identified as clusters . Cluster-based correction was applied when multiple time points were tested ( Figs 3D , 4 and 5D–5G ) : data were first randomly shuffled 1 , 000 times ( 500 times in the decoding analysis ) ; for each shuffle , the count of suprathreshold samples within a cluster was used to define the cluster size; and the largest cluster size was entered into a distribution of cluster sizes [106] , which was expected under the null hypothesis . Clusters in the real data were considered as statistically significant only if they exceeded the size of 95th percentile of the null distribution of clusters , at α = 0 . 05 . At the individual level , the GLM was used to construct a multiple regression design matrix . The four experimental conditions were modeled as regressors of interest: explicit EM , explicit GM , bistable EM , and bistable GM . The four types of event were time locked to the onset of the first frame in each trial by a canonical synthetic hemodynamic response function and its first-order time derivative with an event duration of 0 s . In addition , all the omission trials and the outlier trials in which RTs were outside of the mean RT ± 3 SD were modeled separately as another regressor . The six head movement parameters derived from the realignment procedure were also included as confounds . Parameter estimates were subsequently calculated for each voxel using weighted least-square analysis to provide maximum likelihood estimators based on the temporal autocorrelation of the data . No global scaling was applied . For each participant , simple main effects for each of the four experimental conditions were computed by applying appropriate “1 0” baseline contrasts , that is , experimental conditions versus implicit baseline ( null trials ) . The four first-level individual contrast images were then fed into a within-participants ANOVA at the second group level employing a random-effects model ( flexible factorial design in SPM12 including an additional factor modeling the subject means ) . In the modeling of variance components , we allowed for violations of sphericity by modeling nonindependence across parameter estimates from the same subject and allowing unequal variances between both conditions and participants using the standard implementation in SPM12 . We were particularly interested in the differential neural activity between the two types of bistable trials ( bistable EM versus bistable GM ) . Areas of activation were identified as significant only if they passed a conservative threshold of p < 0 . 005 , family-wise error ( FWE ) corrected for multiple comparisons at the cluster level , with an underlying voxel level of p < 0 . 005 , uncorrected [107] . To investigate how the prestimulus neural activity predicted the outcome of bistable perceptual grouping , a new GLM model was estimated . Given that the ITI was jittered between 2 , 000–3 , 000 ms and one-third of all the trials were null trials , the prestimulus periods of all the experimental trials were long enough and adequately jittered for the present statistical analysis on prestimulus neural activity . In the new GLM model , four types of new events were time locked to the time points after the participants made their responses in the preceding trials ( “Trials N-1” ) of the four types of experimental trials , i . e . , the prestimulus preparation period of the current trial ( “Trials N” ) . All the outliers , errors , and missed trials and trials preceded by outliers and errors were separately modeled as another regressor . In this way , parameter estimates in each of the four newly defined critical neural events indicate the height of prestimulus preparation neural activity prior to the actual presentation of the explicit EM , the explicit GM , the bistable EM , and the bistable GM stimuli . Brain regions of activation were identified as significant only if they passed a conservative threshold of p < 0 . 005 FWE correction for multiple comparisons at the cluster level , with an underlying voxel level of p < 0 . 005 , uncorrected . Since the left IPS exhibited specific selectivity towards the bistable EM percepts during both the pre- ( Fig 6C ) and the poststimulus ( Fig 6B ) period , we used the left IPS as the seed region to perform the PPI analysis , focusing on the prestimulus period . For the PPI analysis , prestimulus neural activity ( time locked to the responses in “Trials N-1” ) in the left IPS was used as the physiological factor and the contrast of “bistable EM versus bistable GM” as the psychological factor . For each participant , the neural contrast of “bistable EM versus bistable GM” was first calculated in the individual level GLM . Subsequently , each participant’s individual peak voxel in the left IPS was determined as the maximally activated voxel within a sphere of 16-mm radius ( i . e . , twice smoothing kernel ) around the coordinates of the peak voxel from the second-level group analysis ( Fig 6B ) . Individual peak voxels from every participant are located in the same anatomical structure ( left IPS MNI coordinates: x = –33 ± 6 , y = –37 ± 7 , z = 42 ± 6 ) . Next , the left IPS time series in every participant were extracted from a sphere of 4-mm radius around the individual peak voxels . The PPI term was created for each participant by multiplying the deconvolved and mean-corrected BOLD signal in the given ROI ( i . e . , the physiological variable ) with the psychological variable of interest ( i . e . , “bistable EM versus bistable GM” ) . After convolution with the HRF , mean correction , and orthogonalization , three regressors ( the PPI term , the physiological variable , and the psychological variable ) were entered into the GLM to reveal areas in which neural activations were predicted by the PPI term , with the physiological and the psychological regressors being treated as confounding variables . The PPI analysis was first carried out for each participant and then entered into a random-effects group analysis . Statistical significance was set to p < 0 . 005 , uncorrected at the voxel level , with the cluster extent exceeding 100 voxels . | Our subjective perception of the external world is constantly shaped not only by sensory inputs but also by the real-time internal status of our own brain . Upon facing an ambiguous sensory input , our brain employs its intrinsic dynamics to bias perception towards the predicted percept , resolving in this manner perceptual ambiguity . However , it remains poorly understood how this process is implemented in the brain . Using electroencephalogram and functional MRI in healthy participants and intracranial recordings in epileptic patients , we show that neural rhythms in the alpha frequency ( 8–13 Hz ) —one of the predominant neural oscillations in the visual system—gate the time window for perceptual grouping: two consecutively presented frames in a constant ambiguous dynamic visual scene will be temporally integrated if they fall in the same alpha cycle but spatially integrated if they fall in different alpha cycles . Moreover , our brain employs the real-time speed of its intrinsic alpha oscillations to actively predict the most possibly observed percepts by inducing preactivation of neural representations that resemble the one evoked by the actual stimuli , even before it is presented . | [
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] | 2019 | Perceptual inference employs intrinsic alpha frequency to resolve perceptual ambiguity |
A small percentage of women with cervical HPV infection progress to cervical neoplasia , and the risk factors determining progression are incompletely understood . We sought to define the genetic loci involved in cervical neoplasia and to assess its heritability using unbiased unrelated case/control statistical approaches . We demonstrated strong association of cervical neoplasia with risk and protective HLA haplotypes that are determined by the amino-acids carried at positions 13 and 71 in pocket 4 of HLA-DRB1 and position 156 in HLA-B . Furthermore , 36% ( standard error 2 . 4% ) of liability of HPV-associated cervical pre-cancer and cancer is determined by common genetic variants . Women in the highest 10% of genetic risk scores have approximately >7 . 1% risk , and those in the highest 5% have approximately >21 . 6% risk , of developing cervical neoplasia . Future studies should examine genetic risk prediction in assessing the risk of cervical neoplasia further , in combination with other screening methods .
Cervical cancer remains a major cause of female mortality worldwide , particularly in developing countries that have limited screening programs [1] . Only a small fraction ( ~1% ) of women with cervical human papillomavirus ( HPV ) infection go on to develop cervical neoplasia [2] , and the factors determining risk of progression are incompletely understood . In the current study we used the hypothesis-free genome-wide association study ( GWAS ) approach to identify genetic variants associated with cervical neoplasia . These variants may underlie disease mechanisms and point to genetic markers of progression to cervical neoplasia . A genetic contribution to the risk of HPV-associated cervical neoplasia is supported by several lines of evidence . A family segregation study suggested that shared genes account for 27% of cervical cancer heritability [3] . Also , persistent HPV infections are associated with the two genetic conditions: epidermodysplasia verruciformis caused by mutations in the EVER1 and EVER2 genes [4] , and WHIM syndrome , associated with mutations in CXCR4 [5] . Furthermore , genetic associations have been reported with HLA loci in cervical cancer in several studies using HLA typing and genome-wide association study ( GWAS ) approaches . Specifically the haplotype HLA-B*0702-DRB1*1501/HLADQB1*0602 is associated with increased disease risk , and reduced risk is associated with alleles of the haplotype HLA-B*1501/HLA-DRB1*1301/HLA-DQA1*0103/HLA-DQB1*0603 [6–10] . Resolving which alleles on these haplotypes are primarily associated with cervical pre-cancer and cancer is challenging due to the complex and extensive linkage disequilibrium that occurs across the major histocompatibility complex ( MHC ) . Recently it has been suggested that the haplotypic associations with HLA-B*0702 and HLA-DRB1*1501/HLA-DQB1*0602 are largely driven by allelic variation in the MICA gene ( rs67841474 ) and the effects of a SNP nearby HLA-DRB1 that affects HLA-DRB1 expression ( rs9272143 ) [9 , 11] . Non-MHC associations with cervical cancer have also been reported with polymorphisms in a large number of genes from candidate gene studies , including IRF3 , TLR2 , EXO1 , CYBA , XRCC1 and FANCA [12] , OAS3 , SULF1 , IFNG , DUT , DMC1 , GTF2H4 and EVER1/2 [13] , ERAP1 , LMP7 and TAP2 [14] , TP53 [15] , TERT [16] and IL17 [17] . However , none of these findings have achieved genome-wide levels of significance , and as yet no non-MHC locus has been robustly associated with cervical pre-cancer or cancer . GWAS have been reported for cervical cancer in Scandinavian [9] , Chinese [18] and Japanese cohorts [19] . Two non-MHC associations were detected in the Chinese study ( rs13117307 at chromosome 4q12 within the gene EXOC1; rs8067378 at chromosome 17q12 upstream of the gene GSDMB ) . We report here a GWAS aiming to define genetic susceptibility to HPV-associated cervical neoplasia .
After quality control filters were applied , a total 2866 cases and 6481 controls remained . Details of these and the cohorts from which they originated are provided in Table 1 . These were genotyped or imputed for 10 , 863 , 230 SNPs . Using logistic regression including 4 principal components as covariates , genome-wide association testing was performed . The genomic inflation factor ( 1000 ) was 1 . 02 ( Q-Q plot S1 Fig ) . Genome-wide significant association was observed for multiple SNPs across the MHC on chromosome 6p21 . 3 ( Figs 1 and 2 ) . Considering the MHC region in more detail , an analysis of SNPs , imputed HLA alleles and amino-acid constituents of HLA alleles was performed . We also directly compared imputation of HLA alleles from the GWAS data to high resolution HLA genotypes , and found strong concordance between imputed two-digit HLA types and variants ( 97 . 6%-99 . 4% ) , and slightly lower concordance for four-digit resolution ( 95 . 8–98 . 7%; Table 2 ) . The strongest associated SNP , rs9271858 ( OR = 7 . 44 , P = 5 . 20 × 10−15 ) , lies in the MHC Class II region near HLA-DQA1*0102 . This SNP is in strong linkage disequilibrium with rs9272143 ( r2 = 1 ) . Conditioning this signal for the previously reported MICA5 . 1 and HLA-DRB1-eQTL associations at rs67841474 and rs9272143 , residual MHC association remained ( HLA-B*0702 , OR = 1 . 22 P = 2 . 39 × 10−5; HLA-B*1501 , OR = 0 . 62 P = 1 . 11 × 10−9; HLA-DQB1*0602 , OR = 1 . 29 P = 2 . 49 × 10−6; HLA-DRB1*1501 , OR = 1 . 28 P = 8 . 61 × 10−6 ) . Our analysis identifies two independent risk HLA-haplotypes , HLA-DRB1*15/HLA-DQB1*0602/HLA-DQA1*0102 and HLA-DRB1*0401/HLA-DQA1*0301 . Within each haplotype the HLA alleles are in strong LD ( Fig 3 ) , whereby conditioning on any one of the HLA alleles controls for the association signal at the other alleles . The strongest risk HLA-associations are seen with HLA-DQB1*0602 ( OR = 1 . 44 , P = 4 . 46 × 10−12 , Table 3 ) and HLA-DRB1*1501 ( OR = 1 . 43 , P = 5 . 55 × 10−12 ) . HLA-DQB1*0602 is in positive linkage disequilibrium with the HLA Class II risk alleles HLA-DRB1*1501 ( r2 = 0 . 93 ) and HLA-DQA1*0102 ( r2 = 0 . 64 ) , and in lower linkage disequilibrium with the HLA Class I risk alleles HLA-C*0702 ( r2 = 0 . 25 ) and HLA-B*0702 ( r2 = 0 . 28; Fig 3 ) . Controlling for the association at HLA-DQB1*0602 or HLA-DRB1*1501 controls for the association at each of the other HLA Class I or II alleles in these linkage disequilibrium blocks ( P > 0 . 05 ) , whereas controlling for either or both of HLA-B*0702 or HLA-C*0702 leaves strong residual association at both HLA-DQB1*0602 and HLA-DRB1*1501 ( OR>1 . 3 P<1 . 5 × 10−5 all analyses ) . This indicates that the primary association of this haplotype is best tagged by HLA-DRB1*1501/HLA-DQB1*0602/HLA-DQA1*0102 , and that the associations of HLA-B*0702/HLA-C*0702 are likely to be due to linkage disequilibrium rather than themselves being disease-causative . Moving along the x axis of Fig 3 , the second block of risk associations are seen at HLA-DRB1*0401/HLA-DQA1*0301 . In the uncontrolled analysis , nominal associations were observed for each allele in Table 3 ( HLA-DQA1*0301 ( OR = 1 . 16 , P = 2 . 80 × 10−4 ) , HLA-DRB1*0401 ( OR = 1 . 24 , P = 7 . 98 × 10−5 ) ) . Conditioning on the HLA-DQB1*0602 risk linkage disequilibrium block strengthens these associations ( HLA-DQA1*0301 to OR = 1 . 27 , P = 2 . 4 × 10−7 and HLA-DRB1*0401 to OR = 1 . 25 , P = 4 . 7 × 10−7 ) . If both risk and protective alleles are used to condition ( i . e . , additionally including HLA-DQB1*0603 and HLA-B*15 ) , the residual association with HLA-DQA1*0301 and HLA-DRB1*0401 becomes stronger still ( both at P<1 × 10−8 ) . These two alleles are in moderate linkage disequilibrium with one another ( r2 = 0 . 49 ) , but not with HLA-DQB1*0602 , HLA-DQB1*0603 or HLA-B*15 ( r2<0 . 05 , all comparisons ) . Controlling for either HLA-DRB1*0401 or HLA-DQA1*0301 controls for association at the other allele ( residual association P > 0 . 05 ) , but significant associations remain at HLA-DQB1*0602 , and HLA-B*15 ( residual associations OR>1 . 5 P<5 × 10−15 ) . Controlling for HLA-DRB1*0401 , HLA-DQB1*0602 , HLA-DQB1*0603 , and HLA-B*15 , no HLA allele or MHC SNP shows any association ( P > 0 . 005 ) . This indicates that HLA-DQA1*0301 and HLA-DRB1*0401 tag a further independent association for cervical neoplasia , and that no further major risk MHC associations remain . The strongest protective association was seen with HLA-B*15 ( OR = 0 . 64 , P = 1 . 56 × 10−9 ) , driven primarily by the HLA-B*1501 allele which makes up 90% of HLA-B*15 alleles in this dataset and is itself strongly associated with cervical neoplasia ( OR = 0 . 63 , P = 4 . 44 × 10−9 ) . HLA-C*0303 which is in moderate LD with HLA-B*15 ( r2 = 0 . 27 ) also shows protective association ( OR = 0 . 66 , P = 9 . 28 × 10−7 ) . Conditioning on HLA-B*15 completely controls for the association at HLA-C*0303 ( OR = 0 . 80 , P = 0 . 03 ) , whereas conditioning on HLA-C*0303 leaves residual association at HLA-B*15 ( OR = 0 . 7 P = 2 . 04 × 10−5 ) , implying that the causative association is with HLA-B*15 . Reduced risk is also observed with the HLA Class II alleles HLA-DRB1*13 ( OR = 0 . 62 , P = 2 . 87 × 10−8 ) , HLA-DQB1*0603 ( OR = 0 . 63 , P = 4 . 17 × 10−8 ) , and HLA-DQA1*0103 ( OR = 0 . 63 , P = 3 . 36 × 10−8 ) . These three HLA Class II alleles are in strong positive linkage disequilibrium with one another , but not with the protective HLA Class I alleles ( Fig 3 ) . Controlling for the association at HLA-DQB1*0603 , no residual association is seen at HLA-DQA1*0103 ( OR = 0 . 77 P = 0 . 35 ) , and only minor association is seen at HLA-DRB1*13 ( OR = 0 . 77 P = 0 . 002 ) , but residual association remains at HLA-B*15 ( OR = 0 . 68 P = 1 . 17 × 10−7 ) and HLA-C*0303 ( OR = 0 . 71 P = 4 . 53 × 10−5 ) . This indicates that there are separate protective associations with the HLA Class II haplotype HLA-DRB1*13/HLA-DQB1*0603/HLA-DQA1*0103 , and the HLA Class I allele HLA-B*15 , and that other protective allelic associations are likely to be due to linkage disequilibrium with these associated variants . In unconditional analyses , associations with P<10−6 are observed with HLA-DRB1 amino acid positions -25 , -16 , -1 , 11 , 12 , 13 , 32 , 70 , 71 , 96 , 133 , 142 , and 149 ( relative to the reference HLA-DRB1 sequence ) . At HLA-DQB1 , associations at P<10−6 are seen with amino acid position 9 and 30 , and at HLA-DQA1 at amino acid positions 24 , 41 , and 130 . The strongest association observed in all analyses of SNPs , amino-acids and HLA types was with amino-acid position 71 in HLA-DRB1 ( with possible amino acids K , A , E , R , KA , KE , or KR , P = 1 . 25 × 10−17; Table 4 , Fig 2A ) . The amino-acids at this locus have a gradient of association with cervical neoplasia risk , with the presence of alanine being associated with increased risk of cervical neoplasia ( OR = 1 . 42 , P = 1 . 44 × 10−11 ) , and of glutamic acid with reduced risk of the disease ( OR = 0 . 67 , P = 2 . 57 × 10−11; S1 Table ) . Controlling for the association with HLA-DRB1 amino acid 71 controls for the association ( P > 0 . 0005 ) with all the HLA Class II alleles except HLA-DRB1*0401 ( P = 3 . 29 × 10−4 ) and HLA-DQA1*0501 ( P = 4 . 17 × 10−4; Table 4 ) . Similarly , controlling for amino-acid position 13 in HLA-DRB1 controls for all associations at imputed HLA Class II amino acids ( conditioned P > 10−3 ) , with the exceptions of HLA-DQA1*0103 ( conditioned P = 9 . 32 × 10−5 ) and HLA-DRB1*13 ( conditioned P = 2 . 54 × 10−4; Table 4 ) . Conditioning on both HLA-DRB1 amino acid 13 and 71 controls for all HLA Class II ( but not Class I ) allele and amino acid associations ( P > 0 . 03 for all HLA Class II alleles and amino acids; Fig 2B ) . This suggests that these two amino-acids are of functional importance in the mechanism by which HLA Class II alleles influence cervical neoplasia risk . The side chains of these amino acids are part of pocket 4 of HLA-DRB1 , which is defined by positions 9 , 13 , 70 , 71 , 74 and 78 . Serine ( position 13 ) and glutamic acid ( position 71 ) are associated with reduced risk of cervical neoplasia , whilst histidine/arginine ( position 13 ) and alanine ( position 71 ) are associated with increased risk ( S2 Table ) . Genome-wide significant HLA Class I association remains after conditioning on HLA-DRB1 amino acids 13 and 71 , with the strongest associated allele being HLA-B*15 , for which strong residual association is seen ( P = 7 . 97 × 10−10 ) . The strongest amino-acid association in this analysis is with the amino-acid 156 in HLA-B ( P = 9 . 82 × 10−10 ) . Controlling for the association of this amino acid , only minor residual HLA or MHC SNP association is observed ( P > 0 . 0005; Table 4 , Fig 2C ) .
This study demonstrates that host genetic variants are major determinants of HPV-associated cervical neoplasia . It confirms the strong association of the MHC with cervical neoplasia , and specifically identifies the amino acid positions within both HLA Class I and Class II alleles . These findings are consistent with roles for both CD4 and CD8 T-lymphocytes in disease pathogenesis , given the known role of these genes in presentation of antigen to these cell types . Both risk and protective associations were seen , providing evidence that some alleles have greater roles in relation to particular HPV genotypes and histological subtypes . Furthermore , these findings at least partially explain the differential association of HPV16 and HPV18 with cervical squamous cell carcinoma compared with adenocarcinoma . Overall , three haplotypes , HLA-DRB1*15/HLA-DQB1*0602/HLA-DQA1*0102 , HLA-B*0702/HLA-C*0702 , and HLA-DRB1*0401/HLA-DQA1*0301 , were associated with increased risk of both HPV16 and HPV18-associated cervical cancer , and for the development of both squamous cell carcinoma and adenocarcinoma . Conditional analysis indicated that risk was primarily driven by the HLA Class II alleles HLA-DRB1*1501/HLA-DQB1*0602/HLA-DQA1*0102 , with the HLA Class I associations with HLA-B*0702/HLA-C*0702 being due to linkage disequilibrium . The haplotype HLA-DQA1*0301/HLA-DRB1*0401 was independently associated with increased disease risk though with a smaller effect size compared with the HLA-DRB1*1501/HLA-DQB1*0602/HLA-DQA1*0102 haplotype . Perhaps because of lower power due to its smaller effect size , no association was observed between the HLA-DQA1*0301 or HLA-DRB1*0401 and specific cervical cancer histologic types or HPV DNA types . An independent HLA Class I haplotype tagged primarily by HLA-B*15 was strongly associated with reduced risk of squamous cell carcinoma and HPV16-associated cervical cancer but had only marginal association with adenocarcinoma ( P = 0 . 039 ) and no association with HPV18-associated cervical cancer ( P = 0 . 95 ) . HPV18 is more prevalent in adenocarcinoma than in squamous cell cancers , and this result whilst limited in power raises the possibility that the difference in HPV type distribution between histologic types is partly due to host genetic factors rather than purely arising from differences in tissue tropism and pathogenicity of HPV16 and HPV18 . Recent cervical cancer sequencing studies have demonstrated a high carriage rate of deletions involving HLA-B providing further evidence that HLA-B is directly involved in cervical cancer risk or pathogenesis [29] . Once mutational profiles of sufficient tumours of different histological type are reported , it will be interesting to use this data to test the hypothesis that HLA-B mutations differentially predispose to different histological types of cervical cancer . In addition to the strong reduced risk of cervical neoplasia associated with HLA-B*15 , the haplotype HLA-DRB1*1301/HLA-DQB1*0603 ( and in some analyses HLA-DQA1*0103 ) was associated with reduced risk of squamous cell cancer , adenocarcinoma , and HPV16- and HPV18-related cervical neoplasia . The HLA-DRB1*1301/HLA-DQA1*0103/HLA-DQB1*0603 haplotype has previously been associated with protection from oral and oropharyngeal cancer , particularly in HPV-positive cases ( OR = 0 . 23 , P = 1 . 6 × 10−6 ) [28] . This is of particularly interest given that HPV16 is the most common HPV genotype associated with oropharyngeal cancer . To further assess the signals from the HLA region , we examined risk of cervical neoplasia at the amino acid level . We observed that the HLA Class II haplotype associations are driven by carriage of particular amino acids at HLA-DRB1 positions 13 and 71 . The main risk haplotype , HLA-DRB1*1501/HLA-DQB1*0602/HLA-DQA1*0102 , carries the risk amino-acid alanine on HLA-DRB1*1501 at HLA-DRB1 position 71 and the risk amino acid arginine at position 13 . The secondary risk haplotype HLA-DRB1*0401/HLA-DQA1*0301 carries the risk amino-acid histidine on HLA-DRB1*0401 at HLA-DRB1 position 13 , but at position 71 carries the amino acid lysine which is of neutral effect ( see S1 and S2 Figs ) . In contrast , the main protective haplotype HLA-DRB1*1301/HLA-DQB1*0603/HLA-DQA1*0103 carries the protective amino-acid glutamic acid on HLA-DRB1*1301 at position 71 , and at amino acid position 13 also carries a protective serine . These amino-acids belong to different classes and have varying charges and hydrophobicity . These charge differences at these keys position within pocket 4 of the HLA-DRB1 the peptide binding groove may interact with putative HPV peptides that are permissive for or protect against development of infections that lead to cervical cancer . The HLA Class I associations observed are driven by the identity of the amino acid at position 156 in HLA-B . At this position the protective HLA-B*15 allele has tryptophan while other HLA-B alleles have either arginine , leucine or aspartic acid . The amino acid at position 156 in HLA-B is not in the peptide-binding grove , but this particular amino acid position has previously been shown to be associated with persistent viral infection [30] . The classical allele HLA-B*35 has two subtypes HLA-B*3501 and HLA-B*3508 that differ only at amino acid position 156 ( HLA-B*3501 leucine , HLA-B*3508 arginine ) , yet these HLA-B*3501 subtypes have strikingly different peptide affinities to antigens produced by the cytomegalovirus , indicating that amino-acid sequence variation outside of the peptide binding pockets can have significant effects on antiviral immunity [30] . Further studies will be required to determine the relationship between these amino-acid associations and the ability of HLA-DRB1 and HLA-B to present HPV epitopes . Nonetheless , these findings are likely to be of use in design of peptide vaccines for HPV-associated neoplasia . Previous reports have suggested that the primary MHC associations with cervical neoplasia are with the MICA5 . 1 allele and an eQTL SNP for HLA-DRB1 ( rs927214 ) , and that the associations of HLA-B*0702 and the HLA-DRB1*1501/HLA-DQB1*0602/HLA-DQA1*0102 haplotype are secondary to the MICA5 . 1 allele and rs927214 [11] . This study finds no evidence to support these suggestions , with no association observed at either MICA5 . 1 or rs927214 , having controlled for the association of the classical HLA loci . In analyses conditioning on either or both of MICA5 . 1 and rs927214 , residual association was still seen with classical HLA loci , including the HLA-B*0702 and HLA-DRB1*1501/HLA-DQB1*0602/HLA-DQA1*0102 haplotypes . This indicates that the MICA5 . 1 allele and rs927214 are not primarily associated with cervical neoplasia . It is not clear why the findings from this study are different from those previously reported , and further evaluation in a pooled analysis or in other ethnic groups is warranted . Our analysis indicates that common variant non-MHC loci contribute 36% of the liability of the disease . Cervical cancer has previously been shown to have significant familiality , indicating that either shared genetic or environmental factors are involved in disease predisposition [3 , 31–33] . Twin and family studies have indicated that the heritability of cervical cancer is 22–64% [3 , 31 , 34 , 35] . According to a structural equation modelling study , the heritability of invasive cervical cancer ( 22% ) was higher than that of in situ cancer ( 13% ) , while childhood shared environment contributed more to the in situ type ( 13% vs 3% ) [36] . In the current study , removing CIN2 cases did not affect the common genetic variant contribution to disease risk . Unlike these studies , our study design is not subject to shared socio-behavioural or environmental effects within families , which are known to be significant in cervical cancer [3 , 35] and may influence heritability estimates . The GCTA method , however , only measures the component of genetic variation related to liability that is captured by the genotypes studied . As this study does not have power to address low frequency or rare genetic variants , the contribution of such variants to cervical cancer liability is not included in our analysis . We demonstrate here that there is substantial but as yet unidentified non-MHC contribution to cervical cancer , suggesting that larger , more powerful , studies are likely to identify further genetic susceptibility factors for this disease . Consistent with the high heritability of the disease , our analysis shows that genetic risk scoring studies have potential value in identifying women at high risk of the disease . The positive predictive value of the cases with a genetic risk score in the top 10% was 7 . 1% ( SD = 0 . 93% ) , and in the top 5% was 21 . 6% ( SD = 4 . 8% ) , compared with the risk of cervical neoplasia in HPV carriers of 1% . This suggests that genetic risk screening could be of value in identifying some women with very high risk of developing cervical neoplasia . Women in the lower 50% of genetic risk scores have ≤0 . 6% ( SD = 0 . 027% ) chance of developing cervical cancer , assuming a prevalence of cervical neoplasia of 1% among HPV exposed women . The informativity of low genetic risk scores did not significantly increase in those with more extreme low risk scores , with women in the lower 10% of the genetic risk score distribution having 0 . 54% ( SD = 0 . 043% ) chance of developing cervical cancer . These values may vary depending on the proportion of women who have been HPV infected who progress to cervical neoplasia , which is not well defined in different populations . This suggests that genetic risk scores may have clinical value in determining women at high risk of cervical neoplasia , but not in identifying women at sufficiently low risk to be of clinical value . In conclusion , this study has demonstrated strong association of MHC haplotypes with increased and reduced risk of HPV-associated cervical cancers , with findings implicating both HLA Class I and Class II loci . These associations are driven by the identity of amino-acids at positions 13 and 71 in HLA-DRB1 and 156 in HLA-B . No non-MHC associations were identified , but strong common variant heritability was demonstrated , indicating that host genetic variation is a major determinant of the likelihood of cervical neoplasia in HPV affected women . Further research is indicated in the potential for genetic risk score analysis in combination with other measures to identify a subset of women at particularly high risk of cervical neoplasia .
Case and control sets are described in Table 1 . Phenotypic information was collected for grade , histology , and HPV genotype where available from the contributing studies . Cancers were histologically classified as either adenocarcinoma , squamous cell carcinoma or other ( Table 1 ) . HPV DNA typing performed as part of the original studies was summarized into four groups , those with HPV16 ( but not HPV18 ) , those with HPV18 ( but not HPV16 ) , those with neither HPV16 nor HPV18 , or those carrying both HPV16 and HPV18 ( S3 Table ) . All studies were conducted among majority European descent populations . All case samples were genotyped at the University of Queensland Diamantina Institute using Illumina Human660-Quad BeadChips . Controls were either genotyped by the Wellcome Trust Sanger Centre ( WTCCC2 cohort ) [27] , or Erasmus University , Rotterdam ( Umea cohort ) , using Illumina Human610-Quad BeadChips . Bead intensity data were processed and normalized for each sample and genotypes called within participating studies using BeadStudio . Genotype results derived from the two different genotyping chips were combined and the GWAS QC was performed using PLINK [37] . Standard quality control measures were applied including identification and exclusion of cryptic related samples ( 112 Umea controls , 97 cases ) , exclusion of samples with an outlying heterozygosity rate ( >0 . 37 or < 0 . 32 ) or excess missingness ( >5%; 67 Umea controls , 104 cases ) . SNPs with Hardy-Weinberg equilibrium P-values <10−7 , or minor allele frequencies <5% were excluded . Population stratification was accessed using Shellfish ( http://www . stats . ox . ac . uk/~davison/software/shellfish/shellfish . php ) ; after the removal of regions of long range LD the sample set was first spiked with HAPMAP samples to remove ethnic outliers ( 18 Umea controls , 44 cases ) , and then the principal components were recalculated using the remaining samples . Four principal components were used as covariates to control for population stratification . Considering previously associated SNPs from candidate gene or GWAS studies , where the exact SNP was neither genotyped nor imputed in the current study , a proxy SNP with high LD ( r2>0 . 9 ) with the original report was sought . Where no such proxy SNP was available , the SNP with the most significant association at the locus/candidate gene was reported . The genotype data for SNPs that were common between the datasets were imputed using Impute2 using 1000 Genomes Phase 3 reference data , and association testing performed using SNPTEST [38] . Imputed loci with quality score <0 . 6 were excluded from the association testing . Detailed investigation of the MHC region and HLA loci was performed using SNP2HLA , an analysis package that performs HLA allele and amino acid imputation from SNP data , and association analysis [39] . HLA amino acid imputation was performed using a reference panel from the Type 1 Diabetes Genetics Consortium ( n = 5 , 225 ) . Loci imputed by SNP2HLA with r2<0 . 5 were excluded and samples where the allele dosage at any HLA type exceeded 2 . 5 were removed ( an additional 25 cases and 67 controls ) . To assess the accuracy of HLA-allele imputation , previously reported findings from 501 cases that had had HLA-B , -C , -DRB1 and -DQB1 DNA-based direct genotyping performed to four digit resolution in one of the studies included in the GWAS were compared with imputed data [7] . Association and conditional logistic regression analysis of the MHC region was performed using the SNP2HLA dosage files using PLINK and custom R scripts . Study power was calculated using Genetic Power Calculator [40] . The reference sequence for HLA-DRB1 used was GenBank Access number AB829523 . 1 , and for HLA-B was GenBank Accession number AB826450 . Assuming a population prevalence of cervical neoplasia among HPV-infected women of approximately 1% [2] and that the controls were not screened for HPV infection ( as was the case in this study ) , the study has >95% power to detect loci with minor allele frequency = 0 . 1 , D’ = 0 . 9 , with an additive odds ratio of 1 . 4 or more , at a genome-wide significance threshold of P<5 × 10−8 , or an odds ratio of 1 . 3 or more at a suggestive significance threshold of P<5 × 10−5 . The proportion of variance in risk of developing cervical pre-cancer or cancer captured by the SNPs genotyped and imputed in this study was determined using the Genome-wide Complex Trait Analysis ( GCTA ) method . This uses the available SNP data to assess the degree of relatedness of cases compared with healthy controls to assess heritability in non-familial datasets [41] . Genetic risk scores were calculated for each individual using the adaptive MultiBLUP algorithm [42] using only genotyped SNPs in common between all SNP arrays where the missing rate was less than 5% , the frequency was greater than 2% and the Hardy Weinberg P-value for the unaffected individuals was > 1e-7 ( n = 277 , 670 SNPs ) . A conservative approach was adopted whereby the cohort was divided into independent training and test sets ( rather than using a cross-validation approach ) so that a training ( 1341 cases , 3217 controls ) and test ( 3218 controls , 1342 cases ) sets was used . The training set was then used to calculate the scoring matrix , which included control of cryptic relatedness using the kinship matrix ( near identical results were obtained by using the 4 principal components to control for population stratification in the training and test data ) . This MultiBLUP algorithm first selects regions based on a P-values threshold ( option—sig1 ) obtained using the training cohort . Within these regions all SNPs with a significances threshold greater than a second P-value threshold ( option—sig2 ) are considered by the algorithm which then controls for the LD structure . The P-value thresholds were optimized by choosing a range of values between 10−7 and 5 × 10−3 for sig1 and 0 . 001 and 0 . 05 for–sig2; the resulting weighted predictors were applied to the test cohort to obtain per sample scores from which the AUC was obtained . Thresholds within these ranges provided AUC ranging from 0 . 66 to 0 . 68 , with the peak AUC at sig1 = = 5e10−4 and sig2 = 0 . 02 of AUC = 0 . 68 , that included 35 regions and 692 predictors ( SNPs ) , 234 of these within the MHC regions . An example Manhattan plot of the LRT P-values for this training set are provided in supplementary figures . Using these final sig1 and sig2 parameters we repeated the training and scoring procedure 10 times using random permutations of samples in the training and test sets to obtain standard deviations ( SD ) in the predictions . The average AUC was 0 . 678 ( SD = 0 . 008 ) with an average of 32 regions ( SD = 4 ) identified in the training examples . Positive and negative predictive values were then calculated using standard methods [43] for all 10 iterations and the mean predictive values and their standard deviat but at position 71 carries the amino acid lysine ion calculated . | Around 1% of women with cervical human papillomavirus ( HPV ) infection progress to cervical cancer . Previous studies had indicated that a person’s genetic makeup could predispose to HPV-associated cervical cancer , and that some of the genes likely to be involved include the immune-related human leukocyte antigen ( HLA ) genes among the major histocompatibility complex ( MHC ) . However , it has been difficult to determine which alleles might be associated with cervical pre-cancer or cancer due to the complex and high level of co-inheritance of MHC alleles . Here , we performed a genome-wide association study that assessed the correlation of genetic variants among those with cervical cancer and healthy controls . We show that host genetics is a major determinant of HPV-associated cervical cancer , with 36% of liability due to common genetic variants in the population , and identify both risk and protective HLA alleles . Our study was also sufficiently powerful to identify particular residue variants on a number of the immune-related proteins that provide risk or protection , providing further insight into the biological basis for cervical cancer development . Our findings could lay the foundation for screening for people at increased risk of developing cancer following HPV infection , and aid in the treatment and prognosis of cervical cancer . | [
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"genetics"
] | 2017 | Defining the genetic susceptibility to cervical neoplasia—A genome-wide association study |
The single-celled cotton ( Gossypium hirsutum ) fiber provides an excellent model to investigate how human selection affects phenotypic evolution . To gain insight into the evolutionary genomics of cotton domestication , we conducted comparative transcriptome profiling of developing cotton fibers using RNA-Seq . Analysis of single-celled fiber transcriptomes from four wild and five domesticated accessions from two developmental time points revealed that at least one-third and likely one-half of the genes in the genome are expressed at any one stage during cotton fiber development . Among these , ∼5 , 000 genes are differentially expressed during primary and secondary cell wall synthesis between wild and domesticated cottons , with a biased distribution among chromosomes . Transcriptome data implicate a number of biological processes affected by human selection , and suggest that the domestication process has prolonged the duration of fiber elongation in modern cultivated forms . Functional analysis suggested that wild cottons allocate greater resources to stress response pathways , while domestication led to reprogrammed resource allocation toward increased fiber growth , possibly through modulating stress-response networks . This first global transcriptomic analysis using multiple accessions of wild and domesticated cottons is an important step toward a more comprehensive systems perspective on cotton fiber evolution . The understanding that human selection over the past 5 , 000+ years has dramatically re-wired the cotton fiber transcriptome sets the stage for a deeper understanding of the genetic architecture underlying cotton fiber synthesis and phenotypic evolution .
Ever since Darwin's time , biologists have recognized that human domestication of wild plants and animals offers promising opportunities for enhancing our understanding of the evolutionary process . As highlighted in recent reviews [1] , [2] , comparisons among wild and domesticated forms of crop plants often lead to insights into the genetic architecture and developmental mechanisms that underlie traits subjected to strong directional human selection . The power of this approach is magnified by the recent advent of high-throughput “omics” technologies , which hold promise for leading us to an eventual systems-level understanding of phenotypic change . Domesticated forms of cultivated species differ from their wild counterparts in numerous traits , particularly those subjected to intentional directional selection , e . g . , loss of seed dormancy , larger and/or more fruits , determinate growth , annualized habit , and earlier flowering . Insights into the evolution of this “domestication syndrome” [3] are made possible by comparative studies of wild and domesticated representatives of individual cultivated species [1] , [2] . Upland cotton ( Gossypium hirsutum L . ) is the most important domesticated fiber plant in the world , accounting for more than 90% of global cotton production . Originally native to the northern coast of the Yucatan peninsula in Mexico , upland cotton is widely cultivated in over 50 countries in both hemispheres [4] . The trait for which cotton was initially domesticated is the remarkably elongated , single-celled epidermal trichomes , or hairs , that cover the cottonseed surface ( colloquially termed “fibers” ) . These seed hairs vary greatly in length , color , strength , and density among the myriad wild , semi-domesticated , feral and modern annualized forms that collectively comprise the species G . hirsutum . In truly wild G . hirsutum trichomes are short ( typically <1 . 5 cm ) , coarse , and are various shades of tan to brown . Gossypium hirsutum was initially domesticated at least 5000 years ago , and following millennia of directional selection , domesticated forms now produce long , strong , and fine white fibers along with a dramatically enhanced fiber yield . In addition to this increase in fiber length , strength , and quality , the domestication process brought about other morphological transformations , including decreased plant stature , earlier flowering , and loss of seed dormancy . Gossypium hirsutum is an allotetraploid containing two diverged sets of chromosomes , “A” and “D” , which became reunited in a common nucleus as a result of a hybridization event approximately 1–2 million years ago ( mya ) . This merger of an African/Asian , A genome ( similar to modern G . arboreum ) and an American , D genome ( much like modern G . raimondii ) gave rise to a new allopolyploid lineage that diversified into five species ( AD1 to AD5 ) [4] , [5] . Considering the importance of polyploidy as a major evolutionary process in plants and its prevalence in all flowering plants [6] , comparative analyses of wild and domesticated cottons may provide new perspectives about how human selection affects duplicated genes in allopolyploids . In addition , many important crops , such as alfalfa , potato , wheat , soybean , and cabbage , are obvious polyploids , so studying gene expression in allopolyploid cotton has the potential to offer novel insights on the role of polyploidy in crop evolution ( e . g . , Bao et al . [7] ) . To date , and despite its importance to understanding molecular mechanisms governing fiber development , there have been only a handful studies of global gene expression in G . hirsutum , using expressed sequence tags ( ESTs ) [8] , [9] and microarrays [10]–[14] . In addition , most have focused on comparisons of modern , annualized G . hirsutum and its fiberless/lintless mutants . The single notable exception is the study of Rapp et al . , who used microarrays to compare truly wild and domesticated G . hirsutum [13] . Notably , Rapp et al . explored global gene expression patterns in wild and domesticated G . hirsutum cotton fibers across five temporal/developmental time points , and found that about one quarter of all genes examined exhibited expression changes during domestication , indicating massive alteration of the cotton fiber transcriptome by domestication and crop improvement [13] . However , a limitation of the study of Rapp et al . is that they employed only one accession representing each of the wild and domesticated gene pools , raising the possibility that some of the observed differential expression might simply reflect expression variation that is unconnected to the evolutionary transformation of interest [13] . Also , the microarray methodology relies on less precise probe/target hybridization , is subject to high background noise , and has a narrower range of gene expression quantification , in comparison to profiling using RNA-Seq data [15] . Finally , the genome sequence for G . raimondii only recently became available [16] , providing deeper annotation and better discrimination among homologs ( and homoeologs ) , and hence enhanced power to decipher gene expression level changes across the whole genome . Here , to gain insight into the evolutionary genomics of cotton domestication , we conducted comparative transcriptome profiling of developing cotton fibers from multiple accessions of wild and domesticated G . hirsutum using RNA-Seq data . Two developmental stages were studied , 10 and 20 days post anthesis ( dpa ) , representing key stages of primary cell wall growth and the transition to secondary cell wall growth , respectively [17] , [18] . By examining gene expression levels digitally , we found that approximately one-third of the genes in the genome are expressed in cotton fiber regardless of lineage , accession , and developmental stages . Notably , nearly 5 , 000 genes are diagnosed as being differentially expressed as a consequence of cotton fiber domestication . These data suggest that human selection has reprogrammed the transcriptome on a massive scale , and that part of this rewiring entails a reallocation from stress response pathways toward fiber growth .
We profiled the transcriptome during development in wild and domesticated cottons using two developmental stages , 10 and 20 dpa . Both wild and domesticated cottons showed more gene up-regulation than down-regulation during the transition from 10 to 20 dpa , e . g . , 782 vs . 362 in domesticated cottons ( Figure 1 ) . However , three times as many genes ( 3 , 487 vs . 1 , 144 ) were differentially expressed during development in wild cottons compared to domesticated cottons ( Figure 1 ) . This pattern is also supported by the MDS plot , which showed less variation between the two developmental stages of domesticated cottons compared to wild cottons ( Figure S1 ) . Our results with respect to developmental variation differ from those of Rapp et al . [13] , where differential expression was observed at 2 . 6-times as many genes in domesticated cotton relative to a wild accession ( 5 , 851 vs . 2 , 207 with 1 . 5-fold change; Table S1 ) . However , our results parallel those from a second domesticated allopolyploid , G . barbadense , using either microarrays [22] or RNA-Seq data ( M . J . Yoo et al . , unpublished data ) . To clarity the difference between the two studies , we reanalyzed our data using the same accessions used in Rapp et al . [13] . The inclusion of TX2094 and YUC as two biological replicates resulted in 10 times as many DE genes compared to a single accession analysis ( TX2094 vs . “TX2094+YUC” = 436 vs . 4 , 520; Table S1 ) and a 30% increase in DE genes relative to the “All accession” analysis ( 4 , 520 vs . 3 , 487; Table S1 ) . As for domesticated cottons , since we have only one replicate for TM1 , we included a second domesticated cotton for comparison , which resulted in a 17∼21% increase compared to the “All accession” analysis ( Table S1 ) . These results suggest that the observed conflict between the two studies likely is explained by technical differences among platforms and the reference genome used . As expected based on our understanding of primary and secondary cell wall biosynthesis in cotton [17] , [18] , the two developmental stages were clearly differentiated by the expression patterns of genes involved in cell wall biogenesis . Cellulose synthase ( CesA ) genes , such as CesA4 , CesA6 , CesA7 , and CesA8 were more up-regulated at 20 than 10 dpa in wild cottons , while they exhibited less differential expression during fiber development in domesticated cottons ( Figure S2A , B ) . Among them , four CesA genes were highly expressed at 20 dpa in domesticated G . hirsutum , consistent with a previous report [23] , but only CesA8 , a homologue of GhCesA1 , was up-regulated at 20 dpa compared to 10 dpa in domesticated cottons ( Figure S2A , B ) . Cellulose synthase-like ( Csl ) genes , particularly CslA and CslC , responsible for glucomannan and xyloglucan synthesis , respectively [24] , were up-regulated at 10 dpa in both wild and domesticated accessions ( Figure S2A , C ) . Additional differentially expressed genes related to cell wall biogenesis exhibited various patterns during fiber development . For example , β-galactosidase was up-regulated at 10 dpa , while β-1 , 3-glucosidase and β-xylosidase were up-regulated at 20 dpa; these two enzymes are thought to function in hydrolyzing galactan , glucan , and xylogucan , respectively , into monosaccharides , such as glucose , which can be further processed to either cellulose or pectin [25]–[27] . Xyloglucan endotransglycosylase ( XTH ) genes , which encode proteins involved in xyloglucan breakdown and subsequent rejoining with different acceptor chains , showed variable expression patterns during development ( Figure S3 ) . For example , XTH5 and XTH28 were up-regulated at 10 and 20 dpa , respectively , in both wild and domesticated cottons . Genes related to pectin synthesis , for example , UDP-D-glucuronate-4-epimerase , β-galactosidase , and pectate lyase , were also up-regulated at 10 dpa relative to 20 dpa in both wild and domesticated accessions . However , UDP-glucose-6-dehydrogenases , which oxidize UDP-glucose into UDP-glucuronate , were up-regulated at 10 dpa of domesticated cottons , but down-regulated in wild cottons . Since the foregoing genes represent only a small portion of the total number of differentially expressed genes , we further investigated the difference in development between wild and domesticated cottons using functional analyses ( see below ) . To investigate transcriptomic changes in cotton fiber that distinguish wild from domesticated cottons , and hence reflect the presumptive effects of human selection , we compared the gene expression patterns of wild and domesticated cottons from multiple accessions . A total of 4 , 946 ( 13 . 2% ) genes were differentially expressed between wild and domesticated cottons ( Figure 1 ) , approximately evenly split between genes that were differentially up- and down-regulated between these two pools . However , nearly three times as many genes were differentially expressed at 10 relative to 20 dpa , a result that at least partially mirrors the data in Rapp et al . [13] ( 1 . 7-fold more genes differentially expressed at 10 dpa relative to 20 dpa ) ; the two studies differ in that there was a greater bias toward up-regulation in domesticated than in wild cotton in the earlier study . However , if strict criteria for differential expression are applied , such as RPKM≧5 and more than 2-fold change ( All accessions ( RPKM> = 5 ) and Rapp et al . in Table S2 ) , the two studies yield similar results; for example , about 60% of differentially expressed genes at both developmental time points were up-regulated in domesticated cottons relative to wild cottons , although it looks like there are more differentially expressed genes at 20 dpa in domesticated cottons than in wild cottons in Rapp et al . [13] compared to this study ( not statistically significant; P = 0 . 1106 ) ( Table S1 ) . A single accession analysis resulted in an extremely small number of DE genes ( <1% of the genes in the reference genome ) , perhaps due to the lack of biological replicates , while inclusion of two TX2094 samples in RNA-Seq data showed more DE genes compared to multiple accession analysis ( ( TX2094+YUC ) −TM1 vs . All accession = 3 , 609 vs . 2 , 910 at 10 dpa , 3 , 299 vs . 1 , 339 at 20 dpa; Table S2 ) . These results suggest that variation among biological replicates plays an important role in analyzing RNA-Seq data; that is , more biological replicates from one accession increase the power of DE gene detection ( see the previous section ) . However , at the same time , including multiple accessions facilitates discovery of DE genes across multiple accessions ( e . g . , ( TX2094+TX665+TX2095 ) − ( TM1+CRB250+cascot7 ) vs . All accession = 1 , 254 vs . 3 , 581 at 10 dpa , 876 vs . 1 , 365 at 20 dpa; Table S2 ) . We evaluated whether the effects of human selection were biased with respect to the genomic distribution of the effected loci . To do this we tabulated differentially expressed genes by chromosome , and then calculated an expectation based on a null hypothesis of equal distribution , calibrated by the number of genes in each scaffold . This analysis revealed that chromosomes 8 and 1 were differentially targeted during domestication at 10 and 20 dpa , respectively ( red text in Table S3 ) . With respect to the latter observation about chromosome 1 , the results reflect a putative nuclear mitochondrial DNA ( NUMT ) sequence block ( Figure S4 ) [16] that contained an unexpectedly high number of up-regulated genes at 20 dpa in both “domestication” ( wild vs . domesticated ) and “development” ( 10 vs . 20 dpa ) contrasts . For example , during domestication 36 of 84 differentially expressed genes on chromosome 1 were included in this NUMT block and 12 genes are found to be mitochondrial genes , including eight NADH dehydrogenase and four cytochrome-c-related genes . The comparison of wild and domesticated cottons highlights the fact that the transcriptome of developing cotton fibers was highly altered by five thousand years or more of domestication and crop improvement . To explore this complexity , we first investigated genes previously inferred to be involved in fiber initiation , elongation , and secondary wall biosynthesis ( reviewed in [28] ) . Interestingly , most of the genes involved in the first ( initiation ) and third ( secondary wall biosynthesis ) of these stages were up-regulated in wild cottons compared to domesticated cottons at 10 and 20 dpa , respectively ( Table S4 ) . In contrast , many genes involved in fiber elongation were highly up-regulated in domesticated cotton compared to wild cottons at 10 dpa , while several genes from this same developmental stage were up-regulated in wild cottons at 20 dpa , encoding annexin , actin depolymerizing factor , FASCICLIN-like arabinogalactan-protein , and tubulin alpha-2 chain ( Table S4 ) . Considering expression levels of these genes , differentiation between wild and domesticated cottons was greater during fiber elongation , which is also true at the global transcriptome level , as reported here . Among the 3 , 581 genes differentially expressed between wild and domesticated cotton at 10 dpa , we tabulated the most highly up-regulated genes in wild or domesticated cottons , relative to their counterparts , with respect to fold change ( with RPKM≧50 ) . This analysis reveals that many genes involved in fiber elongation were over-expressed in domesticated cottons , including profilin 1 , HXXXD-type acyl-transferase family protein , expansin A8 , beta-6 tubulin , FASCICLIN-like arabinogalactan 9 ( FLA9 ) , and 3-ketoacyl-CoA synthase 2 ( KCS2 ) ( Table 3 ) . KCS genes are involved in fatty acid elongation and are known to be highly expressed during fiber elongation [29]–[31] . In this study , nine and three of 27 KCSs were up-regulated in domesticated cotton compared to wild types at 10 and 20 dpa , respectively ( Figure S5 ) . Notably , five of nine differentially expressed KCSs showed A-homoeolog expression bias , while the other four exhibited no bias ( Table S5 ) . CesA and Csl play critical roles in cell wall biosynthesis [17] , [18] , [23] and were differentially expressed between wild and domesticated cottons . Five of 18 CesA , five of seven CslC and three of six CslD genes in the reference genome were up-regulated in domesticated cotton at 10 dpa ( Figure 2 ) . FLAs have been classified into four groups [32] , but the function of only a few FLAs are known . For example , in Arabidopsis , FLA4 or SOS5 ( At3g36550 ) plays a role in cell expansion [33] , and several FLA homologues of G . hirsutum were highly expressed during fiber elongation [34] . We also observed several FLA homologues that were up-regulated in domesticated cottons compared to wild cottons at 10 dpa , including two SOS5 homologues and three of four AtFLA7 homologues ( Figure S6 ) . Profilin ( PRF ) and its partners ( e . g . , actin , tubulin , and villin ) , which play an important role in actin polymerization [7] , [35] , were also up-regulated in domesticated cotton , and their expression levels were high , except for villins ( Figure S7 ) . Consistent with Bao et al . [7] , PRF1 exhibited the highest expression differences between wild and domesticated cottons , and PRF3 and PRF4 were up-regulated in domesticated cottons relative to wild cottons ( Figure S7 ) . However , the other two PRF genes were not differentially expressed ( cf . ref [7] ) , a conflict perhaps explained by the larger number of accessions studied here . As for ACTIN ( ACT ) , there were two main clades of Gossypium ACTs , ACT1/3/4/11/12 ( clade I ) and ACT7 ( clade II ) ( Figure 3 ) . These two clades are distinct in their gene expression patterns; members of ACT1/3/4/11/12 generally are expressed in reproductive organs , such as pollen , pollen tubes , and ovules of Arabidopsis , while ACT7 is expressed in vegetative tissues , including root hairs and trichomes along with ACT2 and ACT8 [36]–[39] . In the present study , at 10 dpa nine and two ACT genes were up-regulated in domesticated and wild cottons , respectively ( Figure 3 ) . Interestingly , all of the genes closely related to Arabidopsis ACT7 were commonly and highly up-regulated in domesticated cottons , except Gorai . N017400 . Previously identified GhACT1 , which was shown to participate in fiber elongation [40] , showed the highest similarity to Gorai . 007G063600 ( probably GhACT2; see Table S4 ) included in the ACT7 clade . Notably , Gorai . 007G063600 seems to be duplicated; its duplicate , Gorai . 007G063700 , was also up-regulated in domesticated cottons with expression levels similar to that of Gorai . 007G063600 ( Figure 3 ) . Other ACT genes , which are members of clade I or which are up-regulated in wild cottons , exhibited relatively low expression levels compared to ACT7 homologues . In contrast to some key genes observed to be up-regulated under domestication , in wild cottons some genes involved in phenylpropanoid metabolism , such as flavonoid biosynthesis and anthocyanin biosynthesis , were highly up-regulated at both developmental time points compared to their counterparts in domesticated cottons ( Figure 4 ) . For example , PHENYLALANINE AMMONIA LYASE 1 ( PAL1 ) exhibited 5 . 6 times higher expression than in domesticated cottons at 10 dpa , and other genes involved in this pathway showed similar patterns ( Figure 4B ) . In addition , some MYB transcription factors ( TFs ) were also up-regulated in wild cotton compared to domesticated cotton ( Figure 5 , Figure S8 ) . In particular , half of the 23 differentially expressed MYB TFs in wild cottons were related to the phenylpropanoid pathway , as noted earlier [16] , and their up-regulation was observed at both developmental stages ( Figure S8 ) . At 20 dpa , similar sets of genes were differentially expressed , but their expression levels were relatively lower compared to those observed in 10 dpa ( e . g . , Figure S3 , S4 , S5 , S6 , S7 , S8 ) . Many of the genes up-regulated in domesticated cottons were related to protein synthesis ( see below ) or were found in a putative nuclear mitochondrial DNA ( NUMT ) sequence block ( Table S3; see above ) . In addition and importantly , 14–19% of the differentially expressed genes encode unknown proteins , in agreement with previous reports [13] , [21]; these genes become obvious targets for future functional analysis , to discover their roles in cellular development and in evolution . Among the 2 , 830 TFs that are annotated in the cotton genome , fewer than 10% were differentially expressed between wild and domesticated cottons . Specifically , 266 ( 184 vs . 82 up-regulated in wild vs . domesticated , or the reverse ) and 132 ( 100 vs . 32 up-regulated in wild vs . domesticated , or the reverse ) were differentially expressed at 10 and 20 dpa , respectively . Among these , only 48 TFs were expressed at the level of RPKM≧50 , indicating that the majority of TFs are not highly expressed in fibers . Of these 48 highly expressed TFs , 27 and 16 were up-regulated in wild and domesticated cottons relative to their counterparts , respectively . Five genes were not differentially expressed including GLABRA2 ( GL2 ) and MYB60 which have been functionally studied . GL2 regulates cell wall-related gene expression ( CeSA5 and XTH17 ) during root development in Arabidopsis [41] , while MYB60 is involved in stomatal regulation and root growth under drought stress in grapevine [42] and Arabidopsis [43] , or repressing anthocyanin biosynthesis in lettuce [44] . As for TFs up-regulated in wild cottons relative to domesticated cottons , three TFs families were the most commonly represented , including homeobox , MADS and MYB TFs ( Figure 5 , Table S6 ) . This is consistent with previous studies on the importance of MYBs in fiber development [45]–[48] , but over-representation of MADS genes has not previously been reported for cotton fibers . MADS genes that were differentially expressed were related to carpel ( e . g . , AGAMOUS , SHATTERPROOF1 , SEPALLATA ) and seed development ( SEEDSTICK ) ; results here suggest the possibility that these genes have found a new role in fiber development . In domesticated cottons , three TFs , C3H , TCP , and trihelix , were the most commonly represented classes among the 48 TFs ( Figure 5 , Table S6 ) ; this includes a TF that recently has been identified as important for fiber development in both G . barbadense [49] and G . hirsutum [50] . Gorai . 007G036800 , a homologue of GhTCP14 [50] , was up-regulated in domesticated cottons relative to wild cottons , supporting its relatedness to fiber elongation . To evaluate whether specific biological processes were enriched in representation by either development or domestication , two different functional analyses were performed , the Singular Enrichment Analysis ( SEA ) and the Parametric Analysis of Gene set Enrichment ( PAGE ) . Although SEA and PAGE deploy different strategies , both methods yielded similar results . Thus , we present only SEA results here ( Table S7 ) , to highlight some of the differences between wild and domesticated cottons . In general , during development more biological processes were differentially regulated in wild cottons than domesticated cottons ( wild vs . domesticated = 71 vs . 1 biological processes ( P ) in Table S7A ) , as expected based on the degree of differential expression found in comparison of two developmental time points in wild and domesticated cottons ( Figure 1 ) . For example , at 10 dpa in wild cottons , genes related to lipid metabolism were enriched , including fatty acid biosynthetic process , very-long-chain fatty acid ( VLCFA ) metabolic process , sterol biosynthetic process , and steroid biosynthetic process , and secondary metabolites biosynthesis process was also up-regulated , including phenylpropanoid , coumarin , flavonoid , and anthocyanin biosynthesis processes ( Table S7A ) . In addition , gibberellic acid ( GA ) mediated signaling pathway was also over-represented at 10 dpa in wild cottons , noting that GA is required for fiber initiation and elongation [51] , [52] . At 20 dpa in wild cottons , in addition to in cell wall organization or biogenesis , genes involved in response to abiotic and biotic stimuli , such as water deprivation , organic substance , chemical and hormone stimuli were over-represented relative to 10 dpa ( Table S7A ) . In domesticated cottons , there was no biological process enriched between 10 and 20 dpa based on PAGE analysis ( data not shown ) , while SEA results indicated that genes related to lipid metabolic process were up-regulated at 10 dpa compared to 20 dpa ( Table S7A ) . In the domestication contrast , SEA results showed that more biological processes were up-regulated in domesticated cottons than in wild cottons ( 123 in domesticated cottons vs . 49 in wild cottons; Table S7B ) . Many up-regulated genes in wild cottons relative to domesticated cottons at 10 dpa were related to protein-DNA complex assembly , nucleosome assembly , response to disaccharide stimulus , and secondary metabolite synthetic processes , such as anthocyanin , flavonoid , and phenylpropanoids ( Table S7B ) . Consistent with this result , cellular components , such as chromosome and nucleosome , and molecular function of transcription factor activity were highly enriched in wild cottons ( Table S7B ) . At 20 dpa of wild cottons , genes involved in cell wall macromolecule metabolism and amine catabolism were up-regulated relative to domesticated cottons , suggesting that secondary cell wall synthesis is active in wild cottons . Amine catabolism involves protein degradation , which may generate nitrogen-containing compounds for secondary metabolite synthesis . In fact , most genes related to amine catabolism were associated with phenylpropanoid biosynthesis , for example , 4-coumarate:CoA ligase 1 ( 4CL1 ) , PAL1 , cinnamyl alcohol dehydrogenase ( CAD ) , and cinnamoyl CoA reductase 1 ( CCR1 ) . On the other hand , domesticated cottons were defined by fiber elongation-related processes ( e . g . , vesicle-mediated transport , actin cytoskeleton organization , and cellulose metabolism ) at 10 dpa and energy generation and protein synthesis at 20 dpa ( Table S7B ) . For example , many genes differentially expressed at 20 dpa of domesticated cottons compared to wild cottons were involved in RNA elongation , cellular respiration along with oxidative phosphorylation , and protein synthesis ( translation ) . Also , some genes involved in fatty acid biosynthesis were up-regulated , perhaps to facilitate membrane growth and turnover during fiber elongation and maturation [53] . To explore whether there is a bias in usage of parental gene copies ( homoeologs ) during development and domestication , a phenomenon termed homoeolog expression bias [54] , [55] , the relative contribution of homoeologs to total gene expression was investigated . An average of 17 , 800 genes had homoeolog-specific reads , of which 17 . 5 to 53 . 5% showed unequal ( biased ) expression in any one case ( Table 4 ) . Notably , by far the highest percentage of genes showing biased homoeolog contributions to the transcriptome was at 10 dpa for domesticated cottons , a rate nearly twice that observed at 20 dpa . In addition , more genes at 10 dpa than at 20 dpa exhibit homoeolog bias in all comparisons ( Table 4 ) . Considering the entire data sets , which include all genes having a minimum number of homoeolog-specific reads ( RPKM≧1 ) , there is no global bias in homoeolog expression in either wild or domesticated cottons; that is , despite appreciable gene-level bias , the number of genes that exhibit either At or Dt bias ( where the lower case t designates homoeolog in the allopolyploid ) are approximately equal ( balanced homoeolog bias , sensu Grover et al . [54] ) . This same result also characterizes most other comparisons . To assess how homoeolog usage is affected during development and by domestication , we compared the same homoeolog from two different developmental stages or from the two pools of wild vs . domestication cottons . During cotton fiber development from 10 dpa to 20 dpa , we observed more homoeolog expression change in wild cottons than in domesticated cottons ( 4 , 358 of 22 , 012 ( 19 . 8% ) in wild vs . 2 , 110 of 19 , 974 ( 10 . 6% ) in domesticated; Table 5 ) although there is no difference in DE genes between wild and domesticated cottons ( Table 5 ) . There were more homoeolog changes at 10 dpa than at 20 dpa ( 3 , 350 of 20 , 994 ( 16 . 0% ) at 10 dpa vs . 2 , 433 of 21 , 230 ( 11 . 5% ) at 20 dpa; Table 5 ) as a result of domestication . However , we observed balance in most comparisons; for example , there are similar numbers of At and Dt down- or up-regulation during fiber development in wild cottons ( down-regulation of At vs . Dt = 290 vs . 321 , up-regulation of At vs . Dt = 320 vs . 336; Table 5 ) . When combined with the results from Table 4 ( described above ) , we infer that homoeolog modulations ( both expression and change ) were balanced in cotton fiber regardless of development and domestication .
Cotton fiber development involves an extraordinarily complex biology regulated by multiple and diverse pathways and transcriptional regulatory networks . In this study , we generated global transcriptome profiles of developing cotton fibers from multiple accessions of wild and domesticated G . hirsutum . Using RNA-Seq , we determined that at least one-third and likely about half of the genes ( depending on the RPKM threshold ) in the cotton genome are expressed in developing fibers . This number is consistent with previous estimates of the fiber transcriptome diversity generated for G . hirsutum cv . TM1 and G . arboreum , notwithstanding the technical differences among studies [20] , [21] . It is striking that the genic diversity in the transcriptome of fibers , which are single cells , is comparable to that of entire young leaves of G . hirsutum ( Table 2 ) [55] , which are far more complex organs comprising multiple different cell types and with varying cellular specializations and diverse metabolic roles . This comparison justifies the perspective that the cotton fiber transcriptome is extraordinarily rich and that it is subject to complex transcriptional regulation during fiber development . One of the justifications for the experimental design used in the present study was to attempt to account for expression variation that might occur within wild and within domesticated G . hirsutum and hence account for this variation to strengthen inferences about the differences between these groups . Accordingly , we selected multiple accessions within each pool . For the two developmental stages studied here , 10 and 20 dpa , we estimate that , respectively , 3 . 1% ( 1 , 144 ) and 9 . 3% ( 3 , 487 ) of the duplicate gene pairs in the tetraploid cotton genome were differentially expressed between 10 and 20 dpa in domesticated and wild cottons , respectively . Importantly , when we reanalyze our RNA-Seq data , restricting our attention to the same two accessions as used in Rapp et al . [13] , TM1 ( domesticated ) and TX2094 ( wild ) , we observe about a 40% increase in the number of differentially expressed genes ( 6 , 908 vs . 4 , 946 ) ( Table S2 ) . These data indicate that inclusion of multiple accessions narrowed the differences between the two pools “wild” and “domesticated” , boosting confidence in inferences regarding the effects of human selection , and in the process identifying expression variation arising from other causes . In addition to bolstering the notion that the fiber transcriptome is highly diverse and dynamic , the results presented here include deep and rich data sets that can be mined for clues regarding processes of cellular development and those that have been most strongly affected by human-mediated directional selection under domestication . The data also provide new information on homoeolog usage and biases in a polyploid cell type . Each of these topics is discussed in more detail in the following . A body of work has established that cotton fiber development consists of stages that are well-defined temporally , i . e . , fiber initiation ( 0–3 dpa ) , primary cell wall synthesis and elongation ( 3–15 dpa ) , transition to secondary cell wall growth ( 15–20 dpa ) , secondary wall biosynthesis ( 20–40 dpa ) , and maturation ( 40–50 dpa ) [18] . Our observation of relatively little differentiation between 10 and 20 dpa in domesticated cottons suggests that the fiber primary elongation developmental program had continued to 20 dpa , consistent with a previous study based on fiber growth curves [56] . In domesticated cotton , the rates of fiber growth and maturation were the highest between 10 and 15 dpa , but extended up to 30 dpa ( 20 days of fiber elongation ) , while fibers of wild cottons elongated fastest between 15 and 20 dpa ( 5 days of elongation ) [56] , [57] . In particular , cotton fiber from wild G . hirsutum already reached >90% of maturation around 20 dpa , indicating early termination of fiber elongation , and likely entry into the transition phase leading to secondary wall synthesis . Our transcriptome profiling results showed that gene expression patterns were significantly more differentiated between 10 and 20 dpa in wild cottons , perhaps reflecting this subtle temporal shift in the fiber developmental program . Based on fiber growth curve analyses , fiber elongation appears modest until 15 dpa in wild cottons , yet is almost complete by 20 dpa [56] . Thus , 10 dpa from wild cottons represents an early stage of fiber elongation in wild , relative to domesticated , G . hirsutum , while by 20 dpa fibers from wild cotton likely have completed primary cell wall synthesis and have entered the transition to secondary wall synthesis . This inference is also supported by expression patterns of genes previously reported from gene-by-gene surveys; wild cottons showed more up-regulations of genes related to fiber initiation and secondary wall biosynthesis at 10 and 20 dpa , respectively , compared to domesticated cottons ( Table S4 ) . This difference in developmental timing might account for the three-fold increase in the number of differential expressed genes between the two developmental stages of wild cottons relative to domesticated cottons ( Figure 1 ) . Previous studies support this interpretation of a period of prolonged fiber elongation under domestication , and in parallel in different domesticated cotton species . Notably , similar conclusions have been reached for diploid domesticated cotton , G . arboreum [58] , and in a second domesticated allopolyploid cotton , G . barbadense [59] . In addition , Hu et al . showed , in a recent , high-throughput iTRAQ proteomic analysis , that the proteome of domesticated cotton during early fiber elongation ( 5–10 dpa ) was similar to that of later developmental stage of wild cottons ( 10–20 dpa ) [59] . Thus , it seems that human domestication may have induced parallel prolongations and developmental shifts on the fiber elongation period in both diploid and allopolyploid species , as evidenced by growth curve analysis [56] , and both transcriptomic and proteomic analyses [58] , [59] . These studies , as well as light microscopy observations [13] which demonstrate that wild and domesticated G . hirsutum share similar timing and morphology of early wall thickening , point to the need to develop a deeper understanding of the underlying developmental programs and architecture of fiber growth and evolution . In a recent metabolic profiling study [12] of a lintless mutant and its wild type ( WT ) G . hirsutum relative , 487 metabolites identified from nine developmental time points clearly differentiated the metabolomic profiles of the lintless mutant from that of WT cotton during elongation , but there was no clear differentiation between the two forms during fiber initiation ( −3 to 3 dpa ) . This suggests that the short period of fiber elongation in the lintless mutant , where fiber cells become arrested at about 6 mm of linear growth , resembles , to a certain extent , the wild representatives of the domesticated species . Considering the evolutionary , morphological transformation of fiber from lintless in wild to linted in domesticated cottons [4] , prolonged fiber elongation was a key innovation for longer fiber , which is apparent at the transcript , protein , and metabolite levels . Additional insight into the nature of this developmental shift will probably arise from further integrated studies of various “omics” , combined with a denser sampling of developmental time points . Analysis of differential expression showed that wild cottons deployed a higher number of biological processes compared to domesticated cottons , for example SEA results showed there were 77 vs . 1 in wild vs . domesticated cottons , respectively ( Table S7A ) . In particular , as consistent with little differentiation between 10 and 20 dpa in domesticated cottons , only one biological pathway , lipid metabolic process , was over-represented at 10 dpa relative to 20 dpa . However , in wild cottons , different metabolic pathways were over-represented in the DE gene sets that characterize development , including fatty acid biosynthesis and secondary metabolite biosynthesis at 10 dpa and cell wall organization and biogenesis at 20 dpa ( Table S7A ) . Interestingly , the GA mediated signaling pathway was enriched at 10 dpa in wild cottons . Considering that GA is required for fiber growth [52] and shows the highest level in 10 dpa fibers [51] , up-regulation of this pathway indicates active fiber elongation at 10 dpa compared to 20 dpa in wild cottons . For example , GAST1 PROTEIN HOMOLOG 4 ( GASA4 ) was known to promote GA response and regulate redox status in Arabidopsis [60] , and two cotton homologues ( Gorai . 006G017000 , Gorai . 012G054200 ) were highly expressed and up-regulated at 10 dpa compared to 20 dpa in wild cottons . On the other hand , “response to stress” pathways were enriched in 20 dpa wild cottons , thus , genes related to stress were up-regulated , including those in the dehydrin family protein ( EARLY RESPONSIVE TO DEHYDRATION 10; Gorai . 002G119600 ) , senescence-associated genes ( Gorai . 012G124700 ) , late embryogenesis abundant like 5 ( Gorai . 002G119600 ) , and cold-regulated 47 ( Gorai . 009G189500 ) . These genes are known to be expressed in response to abiotic stress , such as high salinity , drought , and cold in Arabidopsis [61]–[63] , implying that wild cottons are utilizing stress-response pathways at 20 dpa . This inference was also supported by up-regulation of many ROS genes ( see below ) , suggesting that up-regulation of stress-related gene expression in wild cottons could have resulted in halting fiber elongation and promoting the transition to secondary wall biosynthesis . Many genes related to phenylpropanoid biosynthesis were up-regulated during fiber development in wild cottons ( Figure 4A ) with expression levels that were significantly higher at 10 than at 20 dpa ( Figure 4B ) , in agreement with previous studies [64] , [65] . The same differential regulation characterized wild vs . domesticated cottons at both developmental time points ( discussed further below ) . Wild and domesticated cottons exhibited no differences in homoeolog utilization at both expression ( Table 4 ) and change ( Table 5 ) . This is consistent with Yoo et al . [55] and Rambani et al . [66] which showed an overall equal usage of both homoeologs in young leaf and petal tissues of both TX2094 and Maxxa , respectively . These results suggest that domestication has not affected the utilization of homoeologs from the two co-resident genomes of allopolyploid cotton . However , further study is required to evaluate whether this equal usage of homoeologs was derived from vertical inheritance of progenitor A and D genome conditions , of if instead trans-acting regulatory factors overwhelmed pre-existing , evolved cis- and trans- differences that accumulated during evolutionary divergence of the two progenitor diploids Over the course of several thousands of years of domestication and selection , the short , coarse , and brown fibers of wild G . hirsutum were transformed into the long , strong , and fine white fibers that characterize modern upland cultivars . Recent large-scale transcriptomic and proteomics analyses have begun to reveal some of the molecular underpinnings of this remarkable morphological modification [12] , [13] , [20]–[22] , [58] , [59] , [67] . Here we tried to build on this initial insight into the effects of the domestication and plant improvement process by generating transcriptomes from multiple accessions , thus permitting gene expression changes resulting from domestication to be isolated from those arising from other causes . The wild and domesticated cottons selected exhibit the typical fiber characteristics of their respective pools in fiber color and fiber length ( Figure 1 ) , and they also were highly differentiated with respect to their transcriptomes ( Figure S1 ) . One key result is the observation of greater gene expression differentiation between wild and domesticated cottons at 10 than 20 dpa ( Figure 1 ) , consistent with previous studies [11] , [13] . These were partitioned almost equally toward either wild or domesticated cottons ( Figure 1; dom vs . wild = 1 , 839 vs . 1 , 736 ) . However , if we consider only the more highly expressed genes , i . e . , those with a RPKM≧5 , twice as many genes were up-regulated in domesticated cottons compared to wild cottons at 10 dpa ( dom vs . wild = 1 , 476 vs . 733 ) , implicating a ramping up of cellular machinery involved in primary wall synthesis and perhaps down regulation of other pathways ( see below ) . A corollary implication is that the majority of up-regulated genes in the wild cottons compared to domesticated cottons at 10 dpa were expressed at a lower level ( RPKM<5 ) . Similar trends were observed in the 20 dpa comparison , but with less imbalance and smaller absolute numbers ( dom vs . wild = 349 vs . 302 with RPKM≧5 ) . Gene enrichment analyses indicate that specific biological processes were enriched as a consequence of domestication . In particular , the combined significance of functional suggestions become apparent when one considers carbohydrate and fatty acid metabolism with respect to glycolysis , cell wall component biosynthesis , the pentose phosphate pathway , and phenylpropanoid biosynthesis [68]–[70] . In domesticated cottons , carbon resources appear to be more heavily invested in cell wall component biosynthesis , such as cellulose and matrix polysaccharides , as well as energy production through glycolysis ( Figure 6 ) . In addition , acetyl-CoA , a product of glycolysis , is linked to synthesis of VLCFAs that are precursors for phospholipids and sphingolipids , essential components of plasma membranes [70] . VLCFAs accumulate preferentially in elongating fibers and KCSs , the rate-limiting enzyme in biosynthesis of VCLFAs [71] , are also up-regulated during fiber elongation [29]–[31] . In this study , we also observed several KCSs that were highly expressed in domesticated cottons at 10 dpa ( Figure S5 ) . Notably , five of nine KCSs differentially expressed exhibited A-homoeolog expression bias ( Table S5 ) , implying that domestication process could have selected maternal parental copy only . Further study on the genome scale is required to elucidate whether this phenomenon is stochastic or linked to specific pathway ( s ) . Other genes related to this pathway were also up-regulated in domesticated cotton compared to wild cottons , including beta-ketoacyl reductase ( KCR ) , fatty acid hydroxylase ( CER3/WAX2 ) , fatty acid reductase ( CER4/FAR3 ) , lipid transport protein ( LTP ) , and ATP-binding cassette transporter ( WBC1 ) ( Figure 6 ) . Noteworthy , CesA , CslC and CslD genes were up-regulated in domesticated cottons at 10 dpa only , while CesA genes were up-regulated in wild cottons at 20 dpa ( Figure 2 ) . This indicates that CslC and CslD genes have become up-regulated by domestication early in fiber development . Other fiber elongation-related genes , e . g . , profilin and its partners , were also up-regulated in domesticated cottons at 10 dpa ( Figure 3 , Figure S7 ) , including members of one sub-clade of ACT7 ( Figure 3 ) . In Arabidopsis , ACT7 is a vegetative actin , along with ACT2 and ACT8 , ( the latter two have no obvious homologs in cotton ) , and are involved in root growth and epidermal cell specification [39] . Here , we observe two sub-clades of ACT7 in cotton , one up-regulated in wild cottons , while the other is up-regulated in domesticated cottons ( Figure 3 ) . Thus , the domestication process may have recruited enhanced utilization of one sub-clade of ACT7 for greater fiber elongation . In wild cottons , nucleotide biosynthesis and phenylpropanoid biosynthesis were enriched , based on differential expression . In particular , many genes related to phenylpropanoid biosynthesis were up-regulated during fiber development and domestication in wild cottons ( Figure 4A ) , and their expression levels were much higher at 10 than 20 dpa ( Figure 4B ) , in agreement with previous studies [64] , [65] . Notably , phenylpropanoids , particularly flavonoids , are known to inhibit fiber elongation [64] , but protect cells from abiotic and biotic stresses [72] . The involvement of flavonoids in fiber processes has been shown in many studies at the transcript , protein , and metabolic levels [12] , [13] , [20] , [64] , [73] . Tan et al . [64] , in particular , showed that the flavonoid naringenin negatively regulates fiber development and that higher levels of naringenin accumulate in short , brown fibers . This up-regulation of genes related to phenylpropanoid biosynthesis as well as nucleotide biosynthesis is illustrated in the model suggested in Figure 6 , presented within the conceptual framework of carbon/nitrogen balance . For optimal growth and development , carbon and nitrogen metabolism need to be tightly coordinated [74] . Based on the presumed function of the differentially expressed genes , more C compound related pathways are enriched in domesticated cottons relative to wild cottons , as reflected in the greater allocation to cell wall component synthesis ( e . g . , cellulose , VCLFA ) , energy generation through glycolysis , and amino acid synthesis ( Figure 6 ) . In turn , these biological processes might lower C/N , giving rise to less accumulation of anthocyanin . In contrast , nitrogen related pathways were enriched in wild cottons relative to domesticated cotton , as represented by nucleotide biosynthesis and phenylpropanoid biosynthesis . These two pathways can redirect carbon flow to nitrogen metabolism by diverting glucose-6-phosphate ( G6P ) into the pentose phosphate pathway or phosphoenolpyruvate ( PEP ) to the phenylpropanoid pathway ( Figure 6 ) . It may be , therefore , that the domestication process reallocated carbon resources toward carbohydrate and fatty acid metabolism . This speculation is also supported by a comparative metabolomics survey [12] of a lintless mutant and its wild type progenitor; the lintless mutant exhibited up-regulation of genes related to nitrogen compound metabolism along with accumulation of nitrogen compounds , compared to its WT . This phenomenon remains to be demonstrated at the metabolic level in wild and domesticated cottons . Perhaps related to the above are differences in the deployment of stress-response pathways . For example , GAST1 protein homolog 1 ( GASA1; Gorai . 010G004400 ) , involved in diverse developmental programs and stress responses [75] , was highly up-regulated in wild cottons compared to domesticated cottons at 20 dpa . GASA genes have been reported to promote cell elongation in petunia flower [76] , [77] or arrest cell elongation in gerbera [78] and strawberry [79] . Possibly , up-regulation of GASA1 in wild cottons at 20 dpa implies a negative regulation of cotton fiber elongation and/or modulation of stress response . Analyses of 176 genes related to the reactive oxygen species ( ROS ) -scavenging network [80] support the possibility of greater ROS sensitivity of wild cottons at 20 dpa; for example , during development 27 ROS genes were differentially expressed in wild cottons ( 7 vs . 20 genes = 10 vs . 20 dpa ) , while there were only 8 ROS genes identified in domesticated cottons , and also more ROS genes were up-regulated in wild than in domesticated cottons . ROS plays different roles depending on concentrations and context; ROS at low concentrations are involved as secondary messengers in several plant hormone responses , including seed germination , lignin biosynthesis , programmed cell death , and osmotic stress , while at high concentrations ROS are known to cause oxidative damage to proteins , lipids , and DNA [81] . It has been suggested that proper regulation of ROS homeostasis is necessary for cotton fiber elongation [28] , [58] . For example , many ROS genes were up-regulated in parallel in domesticated diploid and polyploid cottons during early fiber elongation ( 2 dpa ) [82] , but only a few genes were investigated during fiber elongation , including ascorbate peroxidase ( APX ) [83] , copper/zinc superoxide dismutase ( CSD ) [17] , and peroxidase ( POX ) [84] , which were all up-regulated in domesticated cottons relative to wild cottons at 10 dpa ( Table S4 ) . H2O2 accumulated at low levels during early elongation and peaks at 20 dpa in domesticated cottons [83] , [85] , but its levels have not been examined in wild accessions . Interestingly , recent analysis of transcriptomic profiles of a lintless mutant compared to its wild type G . hirsutum at 8 and 12 dpa showed higher expression levels of genes related to stress-response processes [12] . Up-regulation of stress-related genes in the lintless mutant and wild accessions of G . hirsutum relative to domesticated cottons suggests elevated levels of ROS in mutant and wild cotton fibers . It would be interesting to carefully evaluate the levels of different ROS molecules during development in wild vs . cultivated cotton under controlled conditions . Although the transcriptomic data presented here are extraordinarily complex , as is usually the case in comparative profiling experiments , the data allow a speculative scenario to emerge from a consideration of the different classes of genes and pathways that are enriched under domestication . Specifically , we raise the suggestion that initial domestication of G . hirsutum , followed by several millennia of improvement and breeding , resulted in a shift or reallocation of resources from stress-related pathways in wild cottons to greater growth in domesticated forms . We envision that the reallocation and accompanying divergence in multiple pathways led to a prolonged period of fiber elongation , which at maturity are recognized now as the long , white , and fine fibers of modern cotton commerce . This scenario should become testable using a combination of forward genetic tools combined with advanced segregating populations ( e . g . , isogenic introgression lines ) , in conjunction with genomic , transcriptomic , proteomic , and metabolomic profiling . This systems approach holds the promise of improving our understanding of the evolutionary modification of a remarkable single-celled structure , while simultaneously providing clues to advance cotton breeding objectives .
Four wild and five domesticated G . hirsutum were selected for fiber transcriptome profiling based on their geographic origins and cotton fiber traits ( Table 1; Figure 1 ) . Wild cottons were originally from Yucatan , Mexico [86] , while domesticated cottons were from four major cotton cultivation areas , i . e . , Plains , Delta , and eastern and western U . S . A . Between three and twenty ovaries were collected for domesticated and wild cottons , respectively , from two developmental stages , 10 and 20 dpa , and were immediately dissected to harvest ovules , which were snap-frozen in liquid nitrogen until extraction . RNA was extracted using either a hot borate/lithium chloride procedure [87] or a CTAB extraction protocol [88] , then purified by the RNeasy Plant Mini Kit ( Qiagen , Stanford , CA , USA ) . Purified RNAs were quantified and qualified with Agilent 2100 Bioanalyzer ( Agilent , Santa Clara , CA , USA ) . After mRNA purification using the MicroPoly ( A ) Purist kit ( Ambion , Austin , TX , USA ) , RNA-Seq libraries were constructed with NEBNext mRNA Sample Prep Master Mix Set 1 following the manufacturer's suggestion ( New England Biolabs , MA , USA ) . The constructed libraries , indexed with six nucleotide sequences , were pooled together with equimolar amounts and were sequenced on the Illumina HiSeq 2000 sequencer with 100 base reads at the Genomics Core Facility at the University of Oregon . Short read sequences were deposited in the NCBI Sequence Read Archive ( SRA ) with a study number SRP017061 . Raw reads were sorted into the correct accession according to their indexed nucleotides . After trimming the indexed sequences , reads were filtered based on the quality scores ( Q = 20 ) and read length ( ≧17 bp ) with a fastx tool kit ( http://hannonlab . cshl . edu/fastx_toolkit/index . html ) . Fastq formatted reads were mapped to the reference genome ( Cotton D V2 . 0; 37 , 505 genes ) [16] using GSNAP [89] . Reads with SNP information between A and D genome progenitors were parsed into A or D homoeolog-specific bins ( At or Dt ) using PolyCat ( http://bioinfo3 . pgml . uga . edu/polyCat/upload . html ) [90] . Before identifying differentially expressed genes in each comparison of domestication ( wild vs . domesticated G . hirsutum ) and development ( 10 vs . 20 dpa ) , we examined the sample relations based on a multidimensional scale ( or principal coordinate ) using the edgeR package ( ver . 2 . 0 . 5 ) in R software ( ver . 2 . 16 . 0 ) [91] . If one sample shows a large distance from the others , that sample was removed for computing differential expression . The DESeq package ( ver . 2 . 1 . 0 ) was used to detect differentially expressed genes in each contrast of domestication and development [92] , and differential expression was defined when a gene showed at least 1 . 5-fold change with RPKM≧1 ( RPKM: Reads Per Kilobase of gene model per Million mapped reads ) [19] in all biological replicates of either wild or domesticated G . hirsutum . Also , to evaluate whether specific chromosomes or chromosome regions were selected during domestication and development , we investigated the distribution of differentially expressed genes on the 13 chromosomes in the haploid diploid cotton genome . For homoeolog-specific read counts , expression bias was evaluated using Fisher's exact test of the edgeR package . The distribution of p-values was controlled for a false discovery rate ( FDR ) by the BH method [93] at α = 0 . 05 . Homoeolog-specific reads were analyzed as described in Yoo et al . [55] , and differential expression was delimited by 1 . 5-fold expression changes with RPKM≧1 in either At or Dt reads across all biological replicates . In addition , we traced homoeolog expression changes during development and by domestication via comparing homoeolog expression patterns in each contrast . For example , At reads at 10 dpa were more down-regulated or highly expressed in domesticated cottons than in wild cottons , this expression change was tabulated as reflecting down- or up-regulation of At , respectively , during domestication . For several gene families where some members are known to be involved in fiber development , we examined expression patterns of individual paralogs and ( homoeologs ) based on their phylogenetic relationships . Sequences were annotated by homology search against Arabidopsis thaliana and aligned via Clustal W [94] . Phylogenetic trees were constructed using MEGA 5 . 05 with a default option of Maximum Parsimony [95] , and majority rule consensus trees were constructed . To explore the nature of the biological pathways that were altered by domestication or that change during development , differentially expressed genes in each contrast were analyzed by SEA tool of agriGO ( http://bioinfo . cau . edu . cn/agriGO/index . php ) which performs GO term enrichment in one set of genes by comparing it to a reference list using fold changes [96] . For SEA , we used genes identified as differentially expressed ( RPKM≧5 in either wild or domesticated cottons ) in each contrast , with multi-test adjustment of the Benjamini-Yekutieli method ( FDR<0 . 05 ) [97] , and a minimum 5 mapping entries . | Ever since Darwin biologists have recognized that comparative study of crop plants and their wild relatives offers a powerful framework for generating insights into the mechanisms that underlie evolutionary change . Here , we study the domestication process in cotton , Gossypium hirsutum , an allopolyploid species ( containing two different genomes ) which initially was domesticated approximately 5000 years ago , and which primarily is grown for its single-celled seed fibers . Strong directional selection over the millennia was accompanied by transformation of the short , coarse , and brown fibers of wild plants into the long , strong , and fine white fibers of the modern cotton crop plant . To explore the evolutionary genetics of cotton domestication , we conducted transcriptome profiling of developing cotton fibers from multiple accessions of wild and domesticated cottons . Comparative analysis revealed that the domestication process dramatically rewired the transcriptome , affecting more than 5 , 000 genes , and with a more evenly balanced usage of the duplicated copies arising from genome doubling . We identify many different biological processes that were involved in this transformation , including those leading to a prolongation of fiber elongation and a reallocation of resources toward increased fiber growth in modern forms . The data provide a rich resource for future functional analyses targeting crop improvement and evolutionary objectives . | [
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] | 2014 | Comparative Evolutionary and Developmental Dynamics of the Cotton (Gossypium hirsutum) Fiber Transcriptome |
Few studies have evaluated the association between quality of life ( QoL ) and functional activity limitations ( FAL ) of leprosy patients as determined by the Screening of Activity Limitation and Safety Awareness scale ( SALSA ) . To identify the association between FALs and the QoL of patients during and post leprosy treatment . Cross-sectional survey of 104 patients with leprosy followed in specialist reference centres in Sergipe , Brazil , between June and October 2014 . QoL was evaluated using the World Health Organization-QoL-BREF ( WHOQoL-BREF ) questionnaire . The SALSA scale was used to measure FALs . Low SALSA scores were present in 76% of patients . QoL scores were lower for the physical and environmental domains , with median ( interquartile range ( IQR ) ) scores of 53 . 6 ( 32 . 1–67 . 9 ) and 53 . 1 ( 46 . 9–64 . 8 ) , respectively . There was a statistical association between increasing SALSA scores and lower QoL as measured by the WHOQoL-BREF . Functional limitations are associated with lower QoL in leprosy patients , especially in the physical and environmental WHOQoL-BREF domains .
Leprosy is still a neglected public health problem with at least 200 , 000 new cases diagnosed annually worldwide . The highest prevalence occurs in low and middle income countries such as India , Brazil , Myanmar , Madagascar , Nepal , and Mozambique [1] . This chronic and insidious infection affects and impairs the skin and peripheral nerves and results in significant physical disability . This chronic and insidious infection affects and impairs the skin and peripheral nerves and results in significant physical disability . The clinical and pathological presentation of leprosy is determined by the immunological response to Mycobacterium leprae and the capacity of the host to develop an effective cell mediated immunity . In addition , leprosy-specific reactions are also a major cause of disability [2 , 3] . These reactions are called type 1 and 2 , with type 1 essentially being a reversal reaction or ‘upgrading’ of the cell mediated immunity to M . leprae antibodies . These reactions are characterised by a marked increase in delayed type hypersensitivity and type 1 helper T lymphocyte cytokines . Type 2 reactions in turn are considered the result of immune complexes attracting granulocytes and complement activation with the selective activation of cytokines . An estimated three million people exhibit leprosy-related impairments worldwide [4] , generating severe social stigma and isolation , relationship and psychological problems and decreased ability to work [4 , 5] . The prevalence of disabilities due to leprosy varies among countries . Brazil has increased its detection of new cases with physical disability at diagnosis . In 2001 , there was a 17 . 8% proportion of grade 1 physical disability and 6% of grade 2 . In 2008 , the proportion of grade 1 was 20 . 7% and grade 2 was 7 . 7% [6] . Functional activity limitations ( FALs ) associated with leprosy are well described . The main risk factors to develop FALs are the presence of leprosy reactions , presence of affected nerves , multibacillary leprosy and delay in diagnosis and/or treatment [7] . However , little is known about the interaction between these FALs and the quality of life ( QoL ) . QoL is a broad concept including physical and psychological health , personal independence , social relationships , personal beliefs and the interaction of these factors with the environment . The term QoL incorporates the multidimensional nature and perception of overall quality of life but often is quoted as the impact of an illness or injury on the quality of life [8] . Currently , the main focus of rehabilitation centres is to prevent and treat physical impairment and to improve the QoL of patients . Therefore it is essential to assess how functional limitations affect the QoL of these patients to inform the development of interventions . This study aimed to describe the relationship of FALs and the QoL of patients with a diagnosis of leprosy in an endemic area of Brazil .
This was a cross-sectional survey to describe the FALs and QoL of patients attending two leprosy reference centres in Sergipe State , Northeast Brazil . Eligible criteria for inclusion in this study were patients >15 years old with diagnosis of leprosy and in MDT treatment or in treatment post-discharge for leprosy reactions . Patients with diabetes , excess alcohol consumption , known to be infected with the Human Immunodeficiency Virus , or with mental or physical conditions interfering with the assessment were excluded . All consecutive patients attending ( a ) the University Hospital Clinic and ( b ) the Leprosy and Tuberculosis Reference Centre , in Aracaju , Sergipe State , from June to October 2014 were enrolled . After obtaining written informed consent to participate , participants were interviewed using a structured questionnaire that included demographic and clinical information ( leprosy classification , leprosy reactions and disability grade ) and an evaluation of their FALs and QoL . In addition , the clinical records of the patients and the database from the Sistema de Informação de Agravos de Notificação ( SINAN ) were reviewed to confirm the diagnosis and the presence of leprosy reactions . The SINAN is a national database containing information for all leprosy patients in Brazil . Patients were classified using the World Health Organisation ( WHO ) leprosy classification as having paucibacillary ( PB ) ( ≤5 skin lesions and/or only one affected nerve trunk ) or multibacillary ( MB ) leprosy ( >5 skin lesions and/or >1 affected nerve trunk ) . Leprosy reactions were defined as episodes characterized by acute inflammation of skin lesions or nerves ( type 1 ) and/or the appearance of inflamed cutaneous nodules with/without neuritis ( type 2 ) [2] . The WHO disability classification was used . In this classification , grade 0 indicates no disability; grade 1 loss of sensibility in the eyes , hands and/or feet without visible deformity and grade 2 the loss of sensitivity and visible deformities [9] . QoL was assessed using the Portuguese version of the WHO-QoL-BREF ( WHOQoL-BREF ) questionnaire [10] ( S1 and S2 Texts ) . The WHOQol-BREF is an international cross-culturally comparable QoL assessment instrument . This instrument is subdivided into physical , psychological , social relationships and environmental domains and each item is rated on a scale from 0 to 5 , with higher scores indicating better QoL [8 , 11] . Functional Activity Limitations were measured using the Portuguese version of the Screening of Activity Limitation and Safety Awareness ( SALSA ) scale ( S1 and S2 Texts ) . The scale measures activity limitations and risk awareness in patients who have or have had a disease with peripheral neuropathy , as in leprosy . The scale includes assessment of the eyes , hands ( skills and labour ) , feet ( mobility ) and self-care . SALSA scores range from 10 to 80 , with 10–24 allocated to patients without significant limitations; 25–39 for mild limitations and 40–49; 50–59 and 60–80 for moderate , severe and very severe limitations , respectively . The risk awareness score ranges from 0 to 11 , with higher scores indicating greater awareness of the risks involved in daily life activities [7 , 12] . All questionnaires were completed by the interviewers in a quiet private place . The two interviewers were trained members of the team . We chose to interview the participants because patients with leprosy are often poor and have low educational level . When a respondent did not understand the meaning of a question , the interviewer re-read the question and did not explain the sentence with other words . The interviewers were not involved in the treatment of patients . Categorical variables were described using frequencies and percentages . The WHO analysis syntax for SPSS was used to calculate the four WHOQoL-BREF domain scores . Scores were standardised to a 4–20 scale , and domain scores were converted to a 0–100 scale as per the WHOQoL guidelines [11] . Pearson’s Chi-square or Fisher Exact Tests were used to compare the categorical variables association with the FALs . FALs were dichotomized as present ( when there any limitation ) or absent ( without limitation ) . DG 1 and DG 2 were considered as disability for statistical analysis . The normal distribution of the scores was verified using the Kolmogorov-Smirnov test and most WHOQoL-BREF and SALSA scores had skewed distributions . We therefore used nonparametric tests to verify the significance of the distributions between the study variables . Kruskal-Wallis’s test was used to assess differences between measurements of the WHOQoL-BREF domains by the SALSA categories . When Kruskal-Wallis’s test was significant , we performed multiple comparisons using the Dunn's test ( post-hoc test ) to determinate differences between the groups . Spearman’s Rho correlations were used to describe the relationship between SALSA and WHOQoL-BREF domain scores . P values <5% were considered statistically significant . The study was approved by the Human Research Ethics Committee of Federal University of Sergipe ( CAAE: 31078114 . 3 . 0000 . 5546 ) . All investigation has been conducted according to the Declaration of Helsinki . Informed consent written was obtained from the participants . Parents or guardians provided written informed consent before enrolling their children in the study .
One hundred and six patients were selected and invited to participate ( S1 Table ) . Two patients who did not understand the WHOQoL-BREF questionnaire were excluded . Of the 104 patients included , 56 ( 53 . 8% ) were male; their median ( IQR ) age was 48 . 0 ( 37 . 2–58 . 0 ) years old and the median ( IQR ) schooling was 5 . 0 ( 3–10 ) years . Twenty ( 19 . 3% ) participants were receiving multidrug therapy ( MDT ) for leprosy at the time of the interview and 84 ( 80 . 7% ) were receiving post-discharge treatment for leprosy reactions . There was no significant difference between the mean ages of patients receiving MDT or post-discharge treatment for leprosy reactions . Eighty-six ( 82 . 7% ) participants had MB and 18 ( 17 . 3% ) PB leprosy at the time of diagnosis . Twenty ( 19 . 2% ) patients had leprosy-related deformities ( Grade 2 ) ( Table 1 ) . The median ( interquartile range ( IQR ) SALSA score was 31 . 0 ( 25 . 0–41 . 5 ) points , with 25 ( 24% ) patients having no significant FALs , 52 ( 50% ) mild , 9 ( 8 . 7% ) moderate , 6 ( 5 . 8% ) severe and 12 ( 11 . 5 ) very severe FALs . There was an association between the presence of disabilities and FALs ( p = 0 . 001 ) . The median ( IQR ) SALSA score was higher in patients with MB leprosy than PB leprosy [33 ( 25 . 8–44 . 3 ) versus 25 ( 22 . 0–31 . 5 ) , p = 0 . 02] . There was no difference in the SALSA score by sex ( p = 0 . 10 ) or leprosy reaction ( p = 0 . 20 ) . The median ( IQR ) scores for the WHOQoL-BREF domains were: 53 . 6 ( 32 . 1–67 . 9 ) for physical , 62 . 5 ( 50 . 0–75 . 0 ) for psychological , 70 . 8 ( 58 . 3–75 . 0 ) for social and 53 . 1 ( 46 . 9–64 . 8 ) for the environment domains ( Table 1 ) . Table 2 shows the WHOQoL-BREF scores by SALSA categories . There was a significant difference among the distribution of the SALSA categories into physical ( χ2 = 45 . 6; p<0 . 001 ) and environmental ( χ2 = 24 . 9; p<0 . 001 ) domains . Pairwise comparisons between SALSA categories were made using the Dunn’s test . Patients with moderate ( 0 . 001 ) , severe ( 0 . 008 ) and very severe ( <0 . 001 ) limitations had lower physical domain scores . Patients with severe ( 0 . 004 ) and very severe ( 0 . 001 ) limitations had lower environmental domain scores ( Table 3 ) . Increasing SALSA scores were associated with decreasing WHOQoL-BREF scores for the physical ( r = -0 . 68; p<0 . 001 ) , psychological ( r = -0 . 28; p = 0 . 003 ) , social ( r = -0 . 21; p = 0 . 03 ) and environmental ( r = -0 . 47; p<0 . 001 ) scores .
This study describes that patients with leprosy have FALs and that their presence , as assessed by SALSA , is associated with low QoL . In Brazil , treatment and post-discharge follow-up of cases is routinely performed in primary health care centres and only cases with complications are referred to the centres of reference . Recent studies from Brazil of leprosy patients attending primary health care settings [7 , 13] have reported a FALs prevalence between 24% and 58% of cases and thus the higher prevalence observed is likely due to our participants being selected from reference centres . Patients with leprosy had lower physical and environmental domain outcomes , which is in agreement with other Brazilian studies evaluating the QoL of people with leprosy sequelae [11 , 14] . In Bangladesh , patients undertaking leprosy treatment had lower scores in the psychological and physical domains but not in the environmental domain [15] and in India the lower values were reported in the social and environmental domains [16] . The differences between out study and the Asian reports therefore can be explained by the epidemiological context such as the characteristics of patients being enrolled in the studies and differences in the cultural and socioeconomic context , including the availability of long term rehabilitation and support programs . Increased FAL was associated with decreased QoL in the four domains , with greater impairment in the physical and environmental domains . Deformities have multiple impacts on leprosy patients because they cause both functional limitations and a decreased perception of physical health . This is consistent with a study in India observing that physical domain scores were lower in deformed than in non-deformed patients [15] . The QoL scores in the environmental domain were associated with the presence of FALs with a strong inverse association between the prevalence of leprosy and income , education and social inequity [17] . Patients reported lower scores for ‘financial resources’ , ‘information available’ , ‘leisure activities’ and ‘satisfaction with transport’ , which is consistent the socioeconomic level of most leprosy cases . Furthermore , the low scores in the environmental domain suggest difficulties in accessing healthcare services , despite diagnostics , care and treatment being available free of charge . The psychological and social domains were inversely associated with FALs , as reported by others [4 , 5 , 15 , 18] , although the Spearman Rho test showed a weak correlation between the FALs and these domains . Perhaps , the higher scores in the psychological and social domains than the physical and environment domains were due to the availability of supportive social networks and psychology services enhancing the capacity of patients to cope with the disease [11] . It is also important to note that we only included patients receiving treatment and those who had leprosy reactions . It would be important to document the FALs and QoL of patients who received leprosy treatment and have not experience leprosy reactions , as this group may have a very different QoL than the patients reported here . The low QoL of patients with leprosy and leprosy reactions is associated with the presence of FALs , especially in the physical and environmental domains . Despite the increase in access to diagnosis and appropriate treatment using MDT in recent decades , patients with significant activity limitations may face reduced QoL . Fortunately , QoL and FALs can be improved by early treatment and rehabilitation interventions and functional limitations should be diagnosed early and monitored to assess the impact of treatment and rehabilitation . | Leprosy is still a neglected public health problem . Leprosy causes disability and functional limitations ( FALs ) if not treated early . We describe the functional activity and quality of life ( QoL ) of adults with leprosy attending two reference centres in Sergipe , Brazil . Patients with leprosy had low QoL , which was associated to the degree of FALs . Despite increased access to diagnosis and modern multidrug leprosy therapy , leprosy is still strongly associated with FALs and low QoL . We stress here the importance of public health efforts to diagnose the disease early and to provide supportive systems for patients receiving treatment in endemic areas . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Functional Activity Limitation and Quality of Life of Leprosy Cases in an Endemic Area in Northeastern Brazil |
Innate immune recognition is classically mediated by the interaction of host pattern-recognition receptors and pathogen-associated molecular patterns; this triggers a series of downstream signaling events that facilitate killing and elimination of invading pathogens . In this report , we provide the first evidence that peroxidasin ( PXDN; also known as vascular peroxidase-1 ) directly binds to gram-negative bacteria and mediates bactericidal activity , thus , contributing to lung host defense . PXDN contains five leucine-rich repeats and four immunoglobulin domains , which allows for its interaction with lipopolysaccharide , a membrane component of gram-negative bacteria . Bactericidal activity of PXDN is mediated via its capacity to generate hypohalous acids . Deficiency of PXDN results in a failure to eradicate Pseudomonas aeruginosa and increased mortality in a murine model of Pseudomonas lung infection . These observations indicate that PXDN mediates previously unrecognized host defense functions against gram-negative bacterial pathogens .
The lung is exposed to a constant barrage of inhaled harmful agents and microorganisms . Several layers of defense in the normal lung help prevent infection from inhaled or aspirated microorganisms . These include the mechanical filtering of particles that occur in the nasal airway , the trapping of particles in mucus and mucociliary clearance . Respiratory epithelial cells also secrete surfactant proteins , antimicrobial peptides and complements; all of these secreted proteins are important in innate immunity [1 , 2] . In addition , alveolar macrophages , neutrophils , lymphocytes and circulating antibodies participate in the clearance of microorganisms from the lung [3] . Innate immune responses are classically initiated by recognition of pathogens through host pattern-recognition receptors ( PRRs ) [4 , 5] . The interaction between PRRs and the specific pathogen-associated ligands , named pathogen-associated molecular patterns ( PAMPs ) activates the downstream signaling events and host defense mechanisms to eliminate invading pathogens [4 , 5] . Recognition of the PAMPs allows the host immune system to distinguish infectious pathogens from the host . Four families of PRRs have been identified , the Toll-like receptors ( TLRs ) , nucleotide-binding and oligomerization domain ( NOD ) -like receptors ( NLRs ) , retinoic acid inducible gene-1 ( RIG-1 ) like receptors ( RLRs ) and C-type lectin receptors ( CLRs ) [6] . Members of these families contain at least one of nine highly conserved protein domains such as leucine-rich repeats ( LRRs ) and immunoglobulin-like ( Ig ) domains [6] . These domains are crucial for recognizing PAMPs within invading pathogens . Common bacterial PAMPs include lipopolysaccharide ( LPS ) , peptidoglycan ( PGN ) , bacterial flagellin and lipoteichoic acid [7] . The heme-containing peroxidase ( hPx ) family is known to participate in host defense [8 , 9] . Myeloperoxidase ( MPO ) , the proto-enzyme of hPx family , has been extensively investigated [8 , 9] . MPO was thought to be the only peroxidase capable of generating HOCl under physiological conditions [8] . It has been proposed that MPO binds to pathogens as a result of its higher cationic surface charge ( pI = 9 . 3 ) in the acidic environment of the phagosome [10 , 11]; however , this interaction is non-specific and any molecule with anionic surface charge , including but not limited to pathogens , may potentially interact with MPO with its attendant risk of collateral damage . Lactoperoxidase ( LPO ) is secreted into some body fluids including milk , saliva and mucus of airway , but not plasma and alveolar lining fluid; its physiological role is to prevent microbial growth at these mucosal surfaces [12 , 13] . However , the presence of hPx with bactericidal activity in the lower airways and alveolar regions has not been demonstrated . Vascular peroxidase-1 ( VPO1 ) is a newly-identified member of the hPx family in mammals [14] . The ortholog of VPO1 in Drosophila is known as peroxidasin ( PXDN; as accepted by HUGO Gene Nomenclature Committee ) [15] . In this article , the official name of PXDN is used instead of the alias , VPO1 . PXDN catalyzes generation of hypohalous acids and kills bacteria in vitro [16 , 17] . Homozygous mutation in PXDN causes developmental defects including congenital cataract , corneal opacity and glaucoma [18] , abnormalities which are also present in PXDN mutant mice [19] . Unlike the classic hPx’es ( MPO , eosinophil peroxidase , LPO and thyroid peroxidase ) , the expressions of which are restricted to specific cells or tissues , PXDN is more ubiquitously expressed [14] . PXDN is found at high circulating levels in human and mouse plasma , approximately 1 . 1 μM and 2 . 6 μM , respectively [20] . We previously reported that PXDN is the second mammalian hPx capable of catalyzing the oxidation of chloride in the presence of H2O2 to generate HOCl [16] . Interestingly , PXDN is unique among members of the hPx family [14]; it has additional domains in its N-terminus that include five LRRs and four Ig C2 domains ( Fig 1A ) . LRRs and Ig domains are highly conserved protein domains and important in innate immune pattern recognition . However , the physiological function of PXDN in host defense is unclear . In the current study , we have identified a novel dual-function activity of PXDN , with its N-terminus recognizing LPS and its C-terminus mediating bactericidal killing , utilizing both in vitro and in vivo approaches . This is the first study , to our knowledge , demonstrating a critical role for PXDN in host defense of the lung .
Our data reveal that PXDN is able to generate hypohalous acids and kills bacterial in vitro [16 , 17] . However , the precise mechanism of bactericidal activity of PXDN is unknown . PXDN contains specific N-terminus with five LRRs and four Ig C2 domains , and is predicted in protein-protein interaction and/or protein-pathogen interaction . Its C-terminus , which mainly consists of the peroxidase domain , is responsible for the generation of hypohalous acids [14] . We hypothesize that the N-terminus of PXDN binds to GN bacteria and facilitates bacterial killing by the peroxidase activity at the C-terminus . We sub-cloned full-length PXDN ( FL-PXDN ) as well as specific constructs containing LRRs and/or Ig C2 domains of PXDN into expression plasmids harboring His-tag ( Fig 1A ) . These recombinant proteins were expressed either in E . coli or human HEK293 cells , and purified using HisPur resin . We first mixed recombinant FL-PXDN with Pseudomonas aeruginosa ( P . aeruginosa ) ; bacterial suspensions were spun down by centrifugation . PXDN was detected in the bacterial pellets ( Fig 1B ) , and a dose-dependent increase of peroxidase activity was verified in these fractions ( Fig 1C ) . Additionally , we assessed whether endogenous PXDN in circulating plasma from healthy human volunteers can bind to P . aeruginosa and E . coli . Live P . aeruginosa and E . coli bacterial suspensions were mixed with human plasma . After centrifugation , PXDN was co-precipitated with bacteria , suggesting direct interaction between bacteria and plasma PXDN ( Fig 1D ) . Unlike MPO , the pI value of PXDN is near neutral ( pI = ~7 . 0 ) ; thus , the interaction of PXDN with bacteria is unlikely to be due to its surface charge . Ceruloplasmin is a ferroxidase enzyme present in circulating plasma at a concentration of 20 to 60 mg/dL ( 1 . 5–4 . 5 μM ) [21] . We utilized this enzyme as an internal control to verify the specificity of the interaction of bacteria with plasma PXDN . As expected , ceruloplasmin did not bind to P . aeruginosa while minimal binding to E . coli was detected ( S1 Fig ) . We further evaluated whether specific regions in PXDN mediates binding to P . aeruginosa and E . coli . Similarly , recombinant truncated peptides of PXDN 29-250aa and PXDN 251-609aa were incubated with bacteria , respectively . These recombinant peptides were able to bind to P . aeruginosa and E . coli in a dose-dependent manner ( Fig 1E ) . The lower molecular weight bands ( approximate 35 kDa ) observed in the PXDN 251-609aa group likely represent degradation products of the PXDN 251-609aa peptide ( Fig 1E ) . Together , these data demonstrate that PXDN interacts with GN bacteria via its N-terminus . Next , we determined the mechanisms of PXDN binding to P . aeruginosa and E . coli . LPS , cell-surface polysaccharide , maintains outer membrane integrity and mediates host-pathogen interactions . LPS comprises O-antigen , core polysaccharides and lipid A . LPS from E . coli and P . aeruginosa has the same general structure . Lipid A and the proximal region of oligosaccharides are relatively conserved , while O-antigen is highly variable in composition and structure [22] . We hypothesized that LPS serves as a PAMP that directly binds the LRRs and/or Ig domains of PXDN . To determine whether LPS binds to PXDN , we performed surface plasmon resonance ( SPR ) assays . As shown in Fig 2A , both the LRRs and Ig domains of PXDN were able to bind LPS . The association constants of LRRs and Ig domains of PXDN to LPS are 2 . 4 x 103 and 1 . 4 x 105 , respectively . We further queried which component of LPS is responsible for the interaction with PXDN . E . coli LPS structure is determined by a set of genes that encode biosynthesis of LPS components at various stages ( Fig 2B ) [23] . For example , a mutation in the rFaC gene causes formation of LPS consisting of only lipid A and 3-deoxy-D-manno-oct-2-ulosonic acid ( KDO ) , with an absence of the O-antigen and most of the core polysaccharides . We carried out bacterial binding experiments of FL-PXDN and truncated PXDN with the various mutants of E . coli LPS . FL-PXDN and the truncations of PXDN 29-250aa and PXDN 251-609aa bound to several LPS variants ( Fig 2C ) . Importantly , both full-length and truncated forms of VPO-1 were able to bind to the LPS variant that only contains lipid A ( rFaC mutant ) . Lipid A was able to inhibit PXDN binding to E . coli ( Fig 2D ) . Since gram-positive ( GP ) bacteria lack of LPS , we determined whether the N-terminus of PXDN binds specifically to GN bacteria , but not to GP bacteria . Binding assays with the GP bacteria , Staphylococcus aureus ( S . aureus ) , were carried using truncated PXDN 29-250aa and 251-609aa ( the experiment was performed concurrently with the studies of GN bacteria , as shown in Fig 1E ) . In contrast to P . aeruginosa and E . coli , the N-terminal domains of both LRRs and Ig domains did not bind to S . aureus ( Fig 2E ) . Taken together , these data strongly support that the N-terminal domains of PXDN bind to LPS of GN bacteria . We explored whether bacterial binding induces PXDN activation . LPS was added to reaction mixtures containing PXDN , H2O2 and 3 , 3’ , 5 , 5’-tetramethylbenzidine ( TMB ) , and oxidation products were measured as an index of PXDN-dependent peroxidase activity . PXDN activity increased in a dose-dependent manner with the addition of LPS ( Fig 3A ) . Maximal PXDN activity occurred at an LPS concentration of 40 μg/mL; the induced activation was ~2 . 8 fold higher than control without LPS ( Fig 3A ) . Higher concentrations of LPS inhibited PXDN activity ( Fig 3A ) . Further , live P . aeruginosa and E . coli at varying colony formation units ( CFUs ) were added to the reactions . Stimulation of PXDN activation was found to be dependent on the number of CFUs , with PXDN activity increasing approximately 3 . 7 and 4 . 2 folds by P . aeruginosa and E . coli , respectively , at 106 CFUs ( Fig 3B and 3C ) . Live bacteria in the absence of PXDN are incapable of oxidizing TMB ( S2 Fig ) . Interestingly , live E . coli did not activate MPO and LPO , which are not known to have specific interacting domains with LPS or bacteria ( S3A and S3B Fig ) . Thus , both LPS and live bacteria are capable of activating PXDN , but not MPO or LPO . Since the hPx family plays an important role in host defense [8 , 9] , we further investigated whether PXDN in the presence of H2O2 is able to kill bacteria , similar to the MPO/H2O2 system in phagocytes [8 , 24] and the LPO/H2O2 system at mucosal surfaces [25] . Bacterial suspensions of P . aeruginosa were incubated with H2O2 and halide anion ( chloride , bromide or thiocyanate , a pseudohalide ) , in the absence or presence of PXDN ( 0 . 2 to 1 μM ) . Bactericidal activities were assessed by survival rates of P . aeruginosa relative to control ( without PXDN ) . Survival of P . aeruginosa was dose-dependent , with 0 . 2 μM PXDN inducing ≥ 50% killing effect under these conditions ( Fig 3D ) . Physiological concentrations of PXDN ( ~1 . 1 μM ) [20] completely killed the bacteria in the presence of physiological concentrations of Cl- or Br- and 10 μM H2O2 ( Fig 3D ) . PXDN was less efficient in killing P . aeruginosa in the presence of 10 μM H2O2 and 100 μM of SCN- . This is likely explained by the lower oxidizing potency of the product , HOSCN . However , PXDN ( 0 . 5 μM ) , in the presence of 10 μM H2O2 and 500 μM SCN- , completely killed P . aeruginosa ( S4 Fig ) . Neither truncated PXDN 29-250aa nor PXDN 251-609aa had the capacity to kill P . aeruginosa ( Fig 3D ) . Similar capacity for E . coli killing was observed with FL-PXDN ( Table 1 ) . Interestingly , anti-lipid A antibody was able to significantly inhibit bacterial killing by PXDN ( Fig 3E ) , consistent with the observation that PXDN binds to lipid A ( Fig 2D ) . In addition , we evaluated bacterial killing of sera . Our data indicate that sera from PXDN-deficient mice is diminished capacity of bacterial killing , comparing with that from WT mice ( Fig 3F ) , supporting a role of circulating PXDN in host defense . We further examined whether PXDN is capable of killing GP bacteria , which lack LPS and do not directly bind PXDN ( Fig 2E ) . We carried out the bacterial killing experiments in the presence of H2O2 and Cl- . Two different lots of recombinant FL-PXDN were utilized; both lots were unable to kill S . aureus and methicillin-resistant Staphylococcus aureus ( MRSA ) , while their capacity to kill E . coli was maintained ( Figs 3G and S5 ) . Together , these data provide strong evidence that binding of PXDN to LPS of GN bacteria is essential for bacterial killing . The mammalian lung is endowed with multiple mechanisms of host defense that protect the mucosal epithelial barrier from pathogens , although a role for PXDN has not been established . We investigated the expression and distribution of PXDN in the mammalian lung and its potential role in lung host defense . First , high levels of PXDN were detected in bronchoalveolar lavage fluid ( BALF ) from normal human volunteers , whereas only trace amounts of MPO were detected ( Fig 4A ) . Second , PXDN expressed in alveolar epithelial cells of WT mice , but not in PXDN-deficient mice ( Fig 4B , dark brown ) . We further determined the expression of PXDN in type II alveolar epithelial cells ( AECs ) . Primary type II AECs and fibroblasts were isolated from lungs of C57BL/6 mice using previously described methods [26] . PXDN was expressed in type II AECs , but not in fibroblasts ( Fig 4C ) . PXDN expression in lung epithelial cells is also supported by the data of proteomic profiling and RNA-seq profiling ( https://lungmap . net/ ) . Interestingly , PXDN expression in AECs was induced by LPS in a dose-dependent manner ( Fig 4D ) . We ascertained whether type II AECs are capable of PXDN-mediated P . aeruginosa killing . PXDN was expressed and activated by adding hematin ( 1 μg/mL ) and sodium butyrate ( NaBu , 5 mM ) , since the combination of hematin and NaBu induces PXDN expression and enhances PXDN activity [14] . After stimulation for 24 h , cells and medium were separated for evaluation of PXDN-mediated P . aeruginosa killing . AECs as well as the corresponding supernatants significantly induced P . aeruginosa killing ( Fig 4E ) . 4-aminobenzoic acid hydrazide ( ABAH ) , an inhibitor of hPx enzymes , and catalase , which reduces H2O2 to water , inhibited P . aeruginosa killing ( Fig 4F ) . These studies demonstrate that lung epithelium is a source of PXDN which mediates H2O2-dependent bactericidal activity , a previously unrecognized host defense function of this enzyme . To determine whether PXDN mediates critical host defense functions in vivo , we employed a murine model of GN bacterial pneumonia in wild-type and PXDN mutant mice ( PXDNmhdakta048 ) [19] . P . aeruginosa , a leading cause of GN bacterial pneumonia in humans [27] , were intra-tracheally injected into mice to induce acute lung infection . PXDN-deficient mice had markedly diminished survival , with 100% mortality at 24 h following infection while wild-type mice had 44% and 22% survival at 24 and 48 h , respectively ( Fig 5A ) . Wild-type mice that survived acute infection at 48 h appeared to recover completely and remained healthy for several days . The high mortality in PXDN-deficient mice was associated with increased bacterial burden , as evidenced by CFUs of P . aeruginosa in lung tissues ( Fig 5B ) . PBS treatment did not result in death in either strain of mice , and bacteria were absent in the lungs of these mice ( Fig 5A and 5B ) . Intratracheal injection with sublethal dose of P . aeruginosa ( 3 x 106 CFUs/mouse ) showed increased bacterial burden in the lungs of PXDN-deficient mice ( Fig 5C ) . In the liver and spleen , fewer bacterial CFUs were detected from both WT and PXDN-deficient mice; the bacterial number detected from the spleen from PXDN-deficient mice was significantly higher than that from WT mice ( Fig 5D ) . The infected lungs of PXDN-deficient mice revealed more severe tissue injury and neutrophil infiltration , while uninfected lungs showed normal structure ( Fig 5E ) . Together , these studies provide compelling data to support a critical host defense function of PXDN , specifically against GN bacterial pneumonia .
The lung is a uniquely vulnerable organ with a very thin , delicate epithelial lining , abundant blood flow , and a vast surface area . The lung resides at the interface of the body and environmental exposures to inhaled or aspirated pathogens . Thus , the lung is an important organ in host defense . Multiple layers of defense in the normal lung are involved in innate immune functions . Loss of one or more of these host defense mechanisms increases the susceptibility of the lung to infections . PXDN is a newly identified hPx , an enzyme family that plays an important role in host defense . Its physiological function is largely unknown , although recent studies implicate this gene in basement membrane synthesis [28 , 29] . The present study , for the first time , identifies PXDN as a novel host defense enzyme in the lung with selectively for recognizing and directly killing GN bacteria . The N-terminus of PXDN , which contains five LRRs and four Ig domains , selectively binds to LPS while the C-terminus of PXDN containing the peroxidase domain kills GN bacteria via generation of hypohalous acids . An enzyme containing the molecular structure of both pattern-recognition domain and scavenger domain suggests evolutionary conservation of a dual-function protein capable of both pathogen recognition and killing , expanding our current view of innate immunity . The original theory of innate immune pattern recognition is based on the interaction between host PRRs and specific PAMPs , and the activation of downstream signaling events and host defense mechanisms [4 , 5] . On the other hand , many effectors including complement system , antimicrobial peptides , and lysozymes may bind to important bacterial molecules and directly kill pathogens . For example , neutrophils possess bactericidal permeability-increasing protein ( BPI ) in their azurophilic granules . BPI is structurally related to LPS binding protein , and avidly binds LPS in GN organisms to directly kill them by compromising membrane integrity [30] . Peptidoglycan recognition proteins ( PGRPs ) are innate immune molecules present in insects , mollusks , echinoderms , and vertebrates . Mammals have four PGRPs . One mammalian PGRP , PGLYRP-2 , is an N-acetylmuramoyl-L-alanine amidase that hydrolyzes bacterial peptidoglycan and reduces its pro-inflammatory activity . The three remaining PGRPs kill bacteria by interacting with cell wall peptidoglycan [31] . Our data support the uniqueness of PXDN , among the known mammalian hPx’es , based on its structural characteristics that allows for its dual-function in pathogen recognition and killing . PXDN is found in circulating plasma at a concentration of 1 . 1 ± 0 . 6 μM in humans and 2 . 6 ± 0 . 6 μM in mouse , approximately ~1000-fold higher than MPO [20] . Although its peroxidase activity is ~5–10% of MPO activity [14] , the net activity of PXDN may be 50–100 folds higher than MPO in plasma . The high expression of PXDN in alveolar epithelium and in bronchoalveolar lavage fluid suggests that this enzyme may mediate critical host defense functions and mucosal immunity . The ability of PXDN to directly bind GN bacterial pathogens allows for more targeted activation and killing without collateral damage to surrounding tissues . The concept of targeted killing is further supported by the finding that LPS induces PXDN activation , a phenomenon has not been reported for other members of the hPx family . Our data reveals that LPS is able to activate PXDN by ~4-fold . Thus , the bactericidal activity of PXDN to GN bacteria is great increased in a selective and targeted manner . The mechanism of activation of PXDN by LPS may be due to LPS-mediated conformation changes of PXDN since the appropriate conformation is critical for the catalytic activity of hPx enzymes [8] . The activation of PXDN by LPS has important implications for enhanced bactericidal activity , while limiting damage to host tissues . P . aeruginosa is a pathogen responsible for a variety of severe infections , including acute lower respiratory tract infections in both immunocompetent and immunocompromised hosts , as well as chronic respiratory infections in select patient populations such as those with cystic fibrosis [32] . High incidence , infection severity and increasing resistance characterize P . aeruginosa infections , highlighting the need for new therapeutic options . Our findings of PXDN killing of GN bacteria , including P . aeruginosa , support therapeutic interventions involving PXDN to augment host defense against such infections . In summary , PXDN is a novel host defense enzyme in the lung with dual function in pathogen recognition and killing . Further studies are required to determine its role in systemic immunity . The unique structural and functional characteristic of PXDN expands our current understanding of mucosal innate immunity , and has important implications for novel therapeutic strategies .
C57BL/6 and PXDNmhdakta048 mutant mice ( male and females , 8–12 week-old ) were used in the study . PXDNmhdakta048 mice were in C57BL/6 background [19] . Unless otherwise stated , mice were fed with normal chow diet . The protocol of animal study was approved by the Institutional Animal Care and Use Committee of the University of Alabama at Birmingham with approval number 20223 . The animal care and use are adhered to the regulations and guidelines of International Association of Assessment and Accreditation of Laboratory Animal Care , Office of Laboratory Animal Welfare and the United State Department of Agriculture . Recombinant FL-PXDN , PXDN 29-250aa or PXDN 251-609aa was added into 100 μL PBS containing 4 x 108 live P . aeruginosa strain K or E . coli K12 . The mixture was incubated at RT for 1 h . Bacteria were spun down at 3099 x g for 5 min . The cells were washed twice by 5 x initiating volume of PBS . Bacteria were lysed in 2 x SDS-PAGE loading buffer and the lysates were subject to immunoblot analysis using anti-His antibody . Protein bands were visualized by chemiluminescence . Negative controls contained only PXDN or bacteria . Quantitative analysis was carried out by using ImageJ software ( The National Institute of Health ) . In some experiments , LPS-deficient E . coli stains were utilized . LPS-deficient E . coli strains and their parent strain K12 BW25113 were from The Coli Genetic Stock Center at Yale University . In bacteria and plasma PXDN binding assay , 4 x 108 E . coli or P . aeruginosa in 50 μL PBS were mixed with 50 μl of human plasma . Control groups were cells or plasma alone . In some experiments , lipid A ( Sigma-Aldrich Cat . #L5399 ) was used . In brief , indicated amount of lipid A was mixed with 150 nM of recombinant PXDN in 600 μL PBS . The mixture was shacked at RT for 15 min . Then 2 x 108 live E . coli K12 cells were added into the mixture and incubated at RT for additional 30 min . Bacterial pellets were obtained and subject to immunoblot analysis . Analyses were carried out using a Biacore-T200 instrument ( GE-Healthcare ) at 22°C in PBS . Recombinant PXDN 29-250aa or PXDN 251-609aa ( 50 μg/mL ) were captured onto an NTA Sensor Chip ( GE-Healthcare ) , respectively . LPS ( 2 μM ) was injected over each surface , as well as over a blank surface . Full kinetics was carried out by flowing a serial concentration ( range from 0 . 25 to 10 . 0 μM ) of LPS over the chip . Binding data ( Ka , Kd and KD ) were collected and analyzed by using the BIAevaluation software ( Biacore ) . All measurements were conducted in triplicate . Rate constants of SPR were calculated as following . Association rate: d[AB]/dt = ka·[A]·[B] Dissociation rate: -d[AB]/dt = kd·[AB] Equilibrium dissociation constant: KD = kd/ka = [A]·[B]/[AB] hPx as indicated was added into 100 μL of TMB solution ( TMB Liquid Substrate System , Sigma-Aldrich ) , which contains H2O2 . Reaction mixture was incubated at RT for 30 min . TMB oxidation was recorded at absorbance 650 nm . In some experiments , LPS ( Sigma-Aldrich Cat #L2880 , from E . coli 055:B5 ) or GN bacteria were mixed with 400 nM/heme of recombinant PXDN at RT for 30 min . 20 μL of mixture was added into 100 μL TMB solution . After 30 min , absorbance at 650 nm was recorded . P . aeruginosa strain K and E . coli K12 cells were incubated in 50 mM phosphate buffer ( pH 6 . 2 ) containing 140 mM NaCl , 10 μM H2O2 , and indicated amounts of PXDN at 37°C for 1 h . Cell mixtures were plated on LB agar plates , followed by incubation at 37°C overnight . In control experiments , only H2O2 ( 10 μM ) or Cl- ( 140 mM ) was present . The CFUs were counted and relative survival rates were calculated as CFUs in the experimental group divided by CFUs in the control group . Other halide anions ( bromide , iodide and thiocyanate ) in addition to Cl- were used as indicated . In some experiments , anti-lipid A antibody was used for inhibition of bacterial killing by PXDN . GP bacteria were also utilized in some bacterial killing experiments . 100 μL reaction mixtures contained 50 μL serum from C57BL/6 or PXDN-deficient mouse and 50 μL PBS containing P . aeruginosa and 50 μM H2O2 . After incubation at 37° for 1h , the mixture was plated on LB agar plates and incubated at 37° for overnight . Colonies were counted . Chemiluminescent dye L-012 is a sensitive substrate for measuring heme-containing peroxidase activity . It generates chemiluminescence once oxidation . In present study , 20 μM ( final concentration ) was added into 100 μL of bacterial suspension containing bound hPx and 20 μM of H2O2 . Chemiluminescent light at 450 nm was immediately recorded by a luminometer ( Molecular Devices , Sunnyvale , CA ) . Mouse lung tissue was harvested and fixed in 10% formalin . The tissue sections ( 5 μm ) were prepared , and Haemotoxylin and Eosin ( H&E ) staining was performed at the Comparative Pathological Laboratory at the University of Alabama at Birmingham . The Conventional immunohistochemistry ( IHC ) was carried out by using anti-PXDN antibody ( 1:600 ) . Images were taken using BZ-X710 All-in-One Fluorescence Microscope ( Keyence Corporation of America , Itasca , IL , USA ) . Mouse primary lung type II AECs were isolated as described in [26] with slight modification . In brief , 4–5 mice were euthanized with CO2 . Blood was exsanguinated by clipping abdominal aorta . Trachea and lungs were carefully exposed and lungs were perfused through puncture of right ventricle with 10 mL of sterile PBS until lungs clear of blood . 18G catheter was inserted into trachea . 1 mL of dispase II ( 5 U/mL ) per mouse was instilled into the lung; then 1% warm low melting agarose was instilled . Lungs were carefully removed and incubated in dispase II solution for 45 min . Lung tissue was minced with scissors until the consistency of jelly . Minced lungs were incubated with 5 mL per mouse of DNase I ( 42 U/mL ) /DMEM solution for 10 min; then the suspension was filtered through successive filters ( 100 μm , 35 μm and 15 μm ) . Biotin-conjugated anti-CD32/16 ( BD Biosciences , Cat . #BD553143 , 15 . 6 μl per mouse ) and Biotin-conjugated anti-CD45 ( BD Biosciences , Cat . #BD553078 , 36 μl per mouse ) were added into the filtered suspension and incubated at 37°C for 30 min with gentle shake . The filtered suspension was centrifuged and cells were re-suspended in complete media ( 5 mL/mouse ) . 1 mL of streptavidin-magnetic beads ( Promega , Cat . #8452 ) was added into the cells and the mixture was incubated for 30 min . The media containing unbound cells were carefully removed and placed into culture dish . The cells were then incubated at 37°C overnight . Next day , the media containing non-adherent cells were transferred into 50 mL-conical tube . The remaining adherent cells were fibroblast . The cell suspension in 50-mL tube was centrifuged at 344 x g for 10 min at 4°C . The cell pellet was re-suspended in complete media . Cells were placed into fibrinogen coated plates . These cells were type II alveolar cells , generally with ~95% purity , evaluated using anti-surfactant Protein C antibody . All steps were carried out aseptically . The conventional immunoblotting assay was carried out using anti-PXDN affinity-purified polyclonal antibody ( against residues 49–63 of human PXDN ) [20] , anti-MPO polyclonal antibody ( CALBIOCHEM , Cat #475915 ) or anti-His antibody ( Qiagen ) . BALF samples were centrifuged at 400 x g for 10 min at 4°C . Resultant supernatants were portioned and stored at -80°C for analysis . BALF was concentrated by Centricon ( ~10x ) . Human plasma and purified MPO ( Cat . #MY167 , Elastin Products Company , Owensville , MO ) were as positive controls for PXDN and MPO , respectively . In some experiments , the primary lung type II AECs were stimulated with 2 ng/mL of TGF-β or LPS ( 0 . 1 or 0 . 5 μg/mL ) for 24 hrs . Cell lysates were subject to conventional immunoblotting by using anti-PXDN or anti-MPO antibody . β-actin was used as loading control . AECs were cultured in 12-well plate in DMEM ( Life Technologies , Inc . ) supplemented with 10% FBS ( Life Technologies , Inc . ) without antibiotics . Cells were incubated at 37°C in 5% CO2 until 70% confluence . Cells were serum-starved for 16 hrs . Some cells were induced by addition of TGF-β1 ( 2 ng/mL ) or NaBu ( 5 mM ) /hematin ( 1 μg/mL ) for 24 h to increase the PXDN expression . In some experiments , ABAH ( 100 μM ) or catalase-polyethylene glycol ( PEG-cat , 200 U/mL ) was added . Cells and medium were separated for evaluation of the bacterial killing , respectively . 100 μL supernatant of medium were incubated with 4 x 104 P . aeruginosa cells containing 10 μM H2O2 at 37°C for 1 h . AECs ( ~5 x 105 ) were added 1 mL fresh DMEM plus 4 x 104 P . aeruginosa cells and 10 μM H2O2 and incubated at 37°C for 1 h . The mixture was plated on LB agar plates followed by incubation at 37°C overnight . The CFUs were counted and relative survival rate was calculated as CFUs in the experimental group divided by CFUs in the control group . P . aeruginosa strain K ( a gift of Dr . Jean-Francois Pittet at the Department of Anesthesiology , UAB ) was used for acute infections . Inoculums for mouse infections will be prepared as previously described with modification [33] . In Brief , bacteria from LB agar plate were inoculated in 5 mL of LB broth at 37°C with shaking ( 175 rpm ) . After 16–18 h of incubation at 37°C , the stationary-phase bacteria were pelleted , washed 3 times with 15 mL of sterile in PBS , and re-suspended in 3 mL sterile PBS . This stock will be diluted in sterile PBS to give an appropriate titer . Female and male C57BL/6 mice or PXDN mutant mice at 8–12 week-old ( 9-11/group ) were used . The mice were briefly anaesthetized with ketamine-xylazine ( 100 mg/kg ketamine and 6 mg/kg xylazine ) via intraperitoneal . The mouse was laid on a board with head elevated at 45° . 30 μL of PBS containing 3 . 0 x 106 or 7 . 0 x 106 CFUs of P . aeruginosa strain K was intratracheally instilled with a 29 G gauge needle . Mice were allowed to recover for 30–60 min prior to being returned to the cage . 50–100 mg of lung , liver or spleen was aseptically removed from mice with cervical dislocation at 20 h after instillation . The tissue was weighed and homogenized for bacteria clearance analysis . Homogenized tissue was washed with 4 mL sterile PBS . The tissue suspension was filtered through 100 μm Nylon mesh ( Fisher Scientific , Cat #22363549 ) to remove tissue debris . The filtered suspension was centrifuged at 3099 x g at 4°C for 10 min to pellet the bacteria . The pellet was re-suspended in sterile PBS ( 10 μL/mg tissue ) . The bacterial resuspension was serial dilution . 50 μL of sample was plated on LB agar plates ( triplicate ) and the plates were incubated at 37°C for 18 h . Bacterial colonies were counted for analysis . Data were shown as means ± SD , unless otherwise indicated . Quantitative variables were compared by means of Student's t-test for two groups or ANOVA for multiple groups . A value of P < 0 . 05 was considered significant . | Multicellular organisms have evolved diversified host defense mechanisms for survival against invading pathogens . Of the mechanisms , the recognition of pathogens is classically mediated by the interaction of host and pathogen , which triggers a series of downstream responses to eliminate pathogens . Proteins that both selectively and directly interact with and kill pathogens are not well identified . In current study , we have determined the dual-function mechanisms of PXDN -mediated bacteria killing . We provide the first evidence for a novel role of PXDN in directly binding to gram-negative bacteria and mediating bactericidal activity . PXDN is highly expressed in the lung and secreted into epithelial lining fluid of the lung , and is induced by LPS and TNF-α . PXDN mutant mice reveal impaired lung host defense in acute lung infection model of P . aeruginosa . PXDN is a new class of bactericidal enzyme with dual function of recognizing and killing pathogens . This finding of an enzyme with dual function has important implications for new conceptual understanding of the innate immunity as well as for therapeutic development . | [
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] | 2018 | Peroxidasin contributes to lung host defense by direct binding and killing of gram-negative bacteria |
Gene duplication facilitates functional diversification and provides greater phenotypic flexibility to an organism . Expanded gene families arise through repeated gene duplication but the extent of functional divergence that accompanies each paralogous gene is generally unexplored because of the difficulty in isolating the effects of single family members . The telomere-associated ( TLO ) gene family is a remarkable example of gene family expansion , with 14 members in the more pathogenic Candida albicans relative to two TLO genes in the closely-related species C . dubliniensis . TLO genes encode interchangeable Med2 subunits of the major transcriptional regulatory complex Mediator . To identify biological functions associated with each C . albicans TLO , expression of individual family members was regulated using a Tet-ON system and the strains were assessed across a range of phenotypes involved in growth and virulence traits . All TLOs affected multiple phenotypes and a single phenotype was often affected by multiple TLOs , including simple phenotypes such as cell aggregation and complex phenotypes such as virulence in a Galleria mellonella model of infection . No phenotype was regulated by all TLOs , suggesting neofunctionalization or subfunctionalization of ancestral properties among different family members . Importantly , regulation of three phenotypes could be mapped to individual polymorphic sites among the TLO genes , including an indel correlated with two phenotypes , growth in sucrose and macrophage killing . Different selective pressures have operated on the TLO sequence , with the 5’ conserved Med2 domain experiencing purifying selection and the gene/clade-specific 3’ end undergoing extensive positive selection that may contribute to the impact of individual TLOs on phenotypic variability . Therefore , expansion of the TLO gene family has conferred unique regulatory properties to each paralog such that it influences a range of phenotypes . We posit that the genetic diversity associated with this expansion contributed to C . albicans success as a commensal and opportunistic pathogen .
Changes in gene copy number provide a rapid mechanism of adaptation to new or different environments by utilizing available functional sequences to cope with altered conditions . Gene duplication commonly arises through errors in DNA replication or sister chromatid recombination to produce a second identical gene copy [1–3] . The presence of functionally redundant genes loosens evolutionary constraints on the two paralogs and allows them to mutate through genetic drift [4] . As this process is repeated , gene duplication can lead to gene family expansion , which provides significant evolutionary fodder on which selection can act to promote adaptation . Following gene duplication , the replicated sequence can either be lost or retained to serve a redundant or new function in the organism . In most cases , one of the paralogs is inactivated by deleterious mutations , thereby restricting further evolution of the other gene duplicate [5 , 6] . However , if a mutation in a duplicated gene provides a selective advantage , both paralogs may be retained as they contribute separately to fitness of the organism [7–9] . Accumulated polymorphisms between gene duplicates can lead to subfunctionalization in which each gene performs a separate function that previously existed within the ancestral gene or neofunctionalization where one of the paralogs evolves a novel function and the other retains the ancestral function . Most studies of gene duplication and divergence rely on comparison of two paralogs to assess the selective pressures that operated following gene duplication because it provides a more simplified context for analysis [5 , 10–13] . Such copy number variants may have arisen through small scale or whole genome duplication [14–17] . Although the evolutionary outcomes of gene duplication resulting from whole genome duplication have been studied extensively [18–22] , small scale duplications are much more common , with copy number variation in some genes occurring at rates up to 1 . 7x10-4 duplications per cell division , far exceeding the basal point mutation rate [23] . The evolutionary fate of genes following small-scale duplication is driven largely by genomic context [24–26] , gene dosage and protein complex formation [27–29] , as well as by gene expression level [28 , 30] . Yet , the evolutionary trajectories of gene families that encode many paralogous sequences remain largely unexplored . Subtelomeres , or telomere-associated sequences , are genomic regions of linear chromosomes that separate the telomeric repeats from chromosome-specific sequences . These regions typically harbor a mixture of duplicated genes and repetitive sequences that often resemble fragments of mobile genetic elements [31 , 32] . Subtelomeric regions evolve rapidly and are characterized by extensive genetic turnover due , in part , to the presence of these repetitive sequences [33 , 34] . Frequent recombination , elevated mutation rates via acquisition of single nucleotide polymorphisms ( SNPs ) and insertions/deletions ( indels ) , and the constant processes of gene duplication and gene disruption contribute to the rapid evolution of subtelomeric regions [25 , 35–37] . Consequently , subtelomeres are often the most dynamic regions of the genome [25 , 38 , 39] , with profound changes detectable over time scales readily achieved via experimental evolution studies [36] . Expanded gene families commonly reside within subtelomeric regions and are characterized by extensive copy number variation and a rapid accumulation of mutations that can alter their expression , structure , or function [40] . As a result , gene families that reside within the subtelomeres are typically under strong selection and are associated with species-specific lifestyles that promote organismal success [40–43] . For example , the MAL , MEL , and SUC genes in S . cerevisiae allow cells to utilize different carbon sources ( maltose , melibiose , and sucrose , respectively ) , and fluctuate in copy number depending on the available growth substrate [40 , 44 , 45] . In this way , the subtelomeric genes contribute to phenotypic plasticity and rapid adaptation to nutrient availability across diverse environments . The Candida clade of species includes mammalian commensals that are closely related to other Saccharomycotina but did not undergo a whole genome duplication event [46 , 47] . Of these , C . albicans is the most clinically prevalent species for humans because it is a common commensal also capable of causing debilitating mucosal infections as well as life-threatening systemic infections [48 , 49] . The success of C . albicans is due , in part , to its ability to occupy and persist in a range of commensal host niches including the gastrointestinal tract ( pH 7 . 4–8 , 37–40°C ) , the oral cavity ( pH 6 . 3–7 . 4 , 33–35°C ) , and the anaerobic colon [50 , 51] . The organism often breaches these mucosal niches and becomes bloodstream-borne , especially in hosts with compromised immunity . Progression of disease is dependent upon host immunity as well as a battery of fungal virulence attributes including the ability to transition between different cell morphologies , to resist stresses within the host including oxidative and cell wall damage , and to evade immune system components [52–57] . The expansion of several gene families involved in virulence traits distinguishes C . albicans from other Candida clade species , and thus may have a role in elevated C . albicans virulence . Expansion of the ALS , SAP , and LIP gene families in C . albicans increases the functional capacity of adhesins , proteases , and lipases , respectively , which have known roles in pathogenesis [58–60] . The most dramatic gene expansion occurred within the telomere-associated ( TLO ) gene family , which has fourteen copies in C . albicans , two copies in the most closely-related C . dubliniensis species , and a single copy within all other Candida species [61 , 62] . In C . albicans , these genes are typically the penultimate gene on each chromosome arm [63 , 64] . The fourteen TLO genes were classified into three clades ( α , β , and γ ) based on sequence variation that clusters towards the 3’ end of the gene . TLO genes display ~97% nucleotide identity within a clade and 82% identity between clades ( when excluding indels ) , yet the three Tlo clades differ in localization to different cellular compartments and in transcript abundance [63 , 64] . All TLOs encode a conserved Med2 domain found in the Med2 component of the tail subunit of the Mediator complex . Accordingly , Tloα and Tloβ clade members are functional components of the C . albicans Mediator complex [65 , 66] . Mediator functions as a major transcriptional regulator that recruits RNA polymerase II to specific promoters through interaction with transcription factors [67 , 68] . It is unclear if TLO expansion has led to functional diversification in C . albicans and how continued evolution to produce diverse sequences affects functional specialization of the TLO genes . More broadly , it is not known how gene family expansion beyond a few members shapes the functional specificity of individual members within the amplified gene family . Here we investigated the role of individual TLO genes across a breadth of biological functions relevant to virulence and to growth under different nutrient conditions . Induced expression of individual TLO genes using a Tet-ON approach altered a range of phenotypes including complex interactions such as virulence . In most cases , a phenotype was affected by more than one TLO gene , but this effect was not simply a function of TLO clade or phylogenetic relatedness . Two phenotypes were associated with specific changes to the Tlo protein sequence at the C-terminal end of the Med2 domain . Furthermore , different evolutionary pressures appear to be operating on the TLO gene family , with most polymorphisms encoding synonymous changes in the Med2 region and a vast excess of non-synonymous changes occurring within the gene/clade-specific 3’ end . Thus , expansion of the TLO gene family is associated with functional diversification , with significant evidence of selection operating on regions and specific sites within the genes .
Previous experiments have assessed aggregate information on TLO function as part of C . albicans Mediator [66] or for a select few TLOs under relatively isolated conditions [62] . Yet , retention of the recently expanded TLO gene family across multiple sequenced isolates of C . albicans , despite the high frequency with which it diverges [36] , suggests that individual family members likely provide a selective advantage [69] . To test this hypothesis , we constructed strains in which the expression of individual TLO genes could be manipulated through a regulatable promoter , via the Tet-ON inducible expression system designed for use in C . albicans [70] . The Tet responsive promoter ( pTET ) was targeted to the endogenous locus of individual TLO genes where it replaced one of the native promoter alleles ( Fig 1A ) . Integration of the targeting construct produced an in-line inducible expression system in which transcription is activated upon addition of doxycycline ( +Dox ) and repressed when no Dox ( -Dox ) is present . In the absence of Dox , only the TLO allele lacking the pTET promoter is expressed . Repeated transformations were performed to produce a series of strains with each strain containing a single Tet-inducible TLO gene ( S1 Table ) . Ultimately , we isolated inducible strains for all TLOs with the exception of TLOα1 and TLOα10 . The strains harboring Tet-regulated TLOs were then tested for expression in the presence and absence of doxycycline . Primers unique to each TLO gene [64] were used to determine the total transcript abundance for individual TLOs . Addition of doxycycline to the parental SC5314 strain did not produce any consistent alteration on collective TLO gene expression ( p = 0 . 371 ) ( Fig 1B ) . Integration of the Tet-regulated promoter at TLO genes reduced the native expression levels of most targeted loci ( Fig 1B , S2 Table ) , consistent with loss of expression of the regulated allele in the absence of Dox . Induction of the pTET-TLO allele by addition of Dox increased transcript abundance significantly for regulated TLO genes ( p = 0 . 034 ) ( Figs 1B and S1 ) . Thus , integration of a Tet-regulatable promoter at individual TLO loci allows each TLO gene to be manipulated and assessed for phenotypic contributions individually . Tlo proteins are incorporated into Mediator , which modulates the expression of a large proportion of the encoded genome [67 , 68 , 71] . Mediator has previously defined regulatory roles in carbon utilization during growth [72 , 73] , with MED2 playing a specific role in gluconeogenesis [74] . To identify alterations in growth that may result from changes in TLO expression , doubling times were calculated for all TLO inducible strains across a range of nutritional environments . When grown in in rich media conditions ( peptides and carbohydrates ) with dextrose as the primary carbon source , induction of five different TLO genes ( TLOα12 , TLOβ2 , TLOγ8 , TLOγ11 , and TLOγ13 ) increased the observed doubling times , indicating a reduced growth rate relative to uninduced expression of the same strains ( Fig 2A ) . Cells grown with sucrose as the primary carbon source displayed a wider range of doubling times for Tet-induced TLO genes , with all strains showing a similar trend towards slower growth ( Fig 2B ) . Six TLOs increased doubling times when induced during growth in sucrose with two of these genes also having increased doubling times in dextrose . Little effect on growth rates was observed when cells were cultured in fructose-containing media ( Fig 2C ) . Inclusion of maltose as the primary carbon source had the opposite effect ( Fig 2D ) : most strains grew more rapidly ( lower doubling times ) . Three strains ( TLOα9 , TLOγ11 , and TLOγ13 ) had significantly faster growth on maltose under inducing conditions . Importantly , addition of Dox to the parental strain had no significant effect on doubling time across the assayed growth condition . These data suggest that there is a complex interplay between growth rates , carbon sources , and the expression of different constellations of TLO genes . Although Tet-induced TLO expression affected growth rates across a range of carbon sources in rich media , regulated expression had little effect on growth rates in nutrient-poor media ( Spider , YP , or sorbitol ) with the notable exception of growth on YP media ( 0 . 3% yeast extract , 0 . 5% peptone ) , in which growth rates increased for strains with induced expression for six of eight TLOs ( S2 Fig ) . The six Tet-regulated TLOs that influenced growth in YP included genes that had no effect in YP media supplemented with different sugars , suggesting that the nutrients other than carbon source , such as those in yeast extract , had a different and perhaps stronger effect than did the different carbon sources in rich medium . To determine if altered expression of the TLO genes played a role in response to other stress conditions , we tested growth in the presence of a variety of physiological stresses using spot dilution assays in which the doxycycline used to regulate TLO expression had no effect on growth . Similarly , induced TLO expression had little effect on growth under several physiological stresses , including growth on synthetic complete defined ( SCD ) medium at 30°C , 37°C , pH 4 . 0 , pH 8 . 0 , or in the presence of 100μg/mL Calcofluor White ( S3 Fig ) . However , induced expression of TLOα3 and TLOα9 provided a growth advantage relative to –Dox in the presence of 1M NaCl , suggesting that these two alpha-clade TLOs confer some resistance to high salt conditions ( Fig 2E ) . By contrast , under oxidative stress conditions , TLOs from the gamma-clade provided an advantage in 2mM H2O2 ( Fig 2F ) . All strains failed to grow well at higher oxidative concentration ( 6mM H2O2 ) and induction of any single TLO did not rescue growth ( S3C Fig ) . Induction of TLOα3 expression revealed a growth advantage in the presence of hydroxyurea ( HU ) , a DNA damaging agent ( S4 Fig ) . Conversely , TLO induction had more prominent effects in response to methylmalonyl sulfonate ( MMS ) , a different DNA damaging agent . Induction of TLO genes both increased resistance to MMS , as was seen with TLOα9 , and reduced resistance with induction of TLOγ4 and TLOγ11 ( Fig 2G ) . Thus , TLO genes may influence survival under a range of stress conditions but they appear to play a more prominent role in carbon utilization . Preliminary observations of prepared overnight cultures indicated that cells expressing inducible TLOs ( supplemented with Dox ) were more flocculant because they settled more rapidly when left undisturbed , compared to SC5314 +Dox . A more quantitative analysis of flocculation , in which optical density ( OD600 ) of vortexed cells was monitored at 15 min intervals , found that induction of all Tet-regulated TLO strains flocculated faster than SC5314 +Dox ( Fig 3A ) . Furthermore , induction of TLO expression resulted in faster flocculation relative to the–Dox condition for half of the assayed TLO genes ( Fig 3B ) . Increased flocculation may result from changes in cell size and/or cell aggregation . Neither introduction of the pTET promoter nor induction of TLO expression for any strain caused a noticeable change in cell size ( S5 Fig ) . Conversely , induced TLO expression significantly altered cell aggregation . Whereas SC5314 formed aggregates composed of roughly equal numbers of cells in the presence or absence of Dox , induction of TLO expression significantly decreased aggregate size for seven of twelve TLOs ( Fig 3C and 3D ) . Reduced aggregate size would be expected to decrease the degree of flocculation , because larger aggregates should settle more rapidly . This suggests that additional factors likely contribute to the enhanced cell settling phenotype in Tet-induced TLO strains . Filamentous growth can contribute to flocculation by both increasing cell size and altering the surface properties of C . albicans cells , such that they adhere to one another more readily [75 , 76] . To test the degree to which filamentous growth affected flocculation , we performed solid agar adhesion-invasion assays for all Tet-regulated TLO strains . No tight adhesion to solid YPD or Spider media at 30°C was detected for any of the strains under any condition ( S6 Fig ) . However , Tet-induced TLO expression did influence the degree of agar invasion as measured by observable hyphal density and/or prevalence . Induction of TLOγ7 and TLOγ8 decreased and increased , respectively , the extent of agar invasion on YPD at 30°C ( Fig 4A and 4D ) . On solid Spider media at 30°C , increased agar invasion occurred for strains containing four regulated TLO genes ( TLOα9 , TLOγ8 , TLOγ13 , and TLOγ16 ( Fig 4A and 4D ) . An alternative approach to assess filamentous growth is to measure a modified M score [69] , which quantifies the relative abundance of filamentous growth within a colony’s mass . After 7 days of growth on either YPD or Spider media in the presence of absence of Dox , colonies were imaged and the degree of filamentous growth was measured . Custom scripts assisted in these measurements that differentiate radial filamentous regions of the colony ( green ) from the central colony body ( red ) ( Fig 4B ) . This script also accounts for colonies that fail to produce any significant filamentation ( blue ) in the overall filamentous growth score . As with agar invasion , addition of Dox to SC5314 parental cells did not induce a change in filamentous growth . In contrast , TLOα9 +Dox increased and TLOγ4 +Dox decreased filamentous growth ( Figs 4C , 4D and S7A ) . Of note , TLOγ4 was scored as ‘hypofilamentous’ upon Tet induction relative to the uninduced condition because it was hyper-filamentous in the–Dox conditions relative to wildtype levels of filamentous growth in the presence of Dox . Induction of TLO expression on Spider media did not alter filamentous growth for any of the assayed strains ( S7B Fig ) . Thus , Tet-regulated expression of TLO genes affected filamentous growth in a condition-dependent manner influenced by nutrient , carbon source , stress , and potentially other environmental conditions . In C . albicans , biofilms require both the adhesion of yeast cells to the substrate at the base of the biofilm and subsequent filamentous growth to form an interwoven hyphal mat that accounts for much of the biofilm biomass [77] . Biofilm formation on silicone implanted devices is clinically relevant because it can seed disseminated infection and complicate patient treatment [49 , 78 , 79] . To assess biofilm formation , we used a simplified in vitro system in which cells were incubated with silicone elastomer squares and allowed to form communities for approximately 3 days ( Fig 5A ) . TLO expression was induced overnight , prior to incubation on the silicone substrate , and was discontinued during the process of biofilm formation . Tet-regulated induction of two TLO genes , TLOα34 and TLOα9 , reduced biofilm mass in the absence of induction compared to the parental SC5314 strain ( S8A and S8B Fig ) . When induced in the presence of Dox , TLOα3 and TLOα34 increased biofilm mass while TLOγ16 decreased biofilm mass significantly relative to SC5314 ( S8A and S8C Fig ) . Three genes affected biofilm biomass when induced . TLOα3 and TLOα34 increased biofilm mass and TLOγ16 reduced biofilm mass when induced ( Fig 5B ) . Transcript levels of both TLOα3 and TLOγ16 in the Tet-regulated strain increased dramatically during biofilm production compared to growth in liquid YPD ( Fig 5C ) . Dox-induction of TLO expression yielded a small increase in TLOα3 transcript abundance and a sharp decrease in TLOγ16 transcript abundance relative to their–Dox levels ( p = 0 . 013 ) , which mirrors the change in biofilm production following induction . Thus , integration of the Tet-inducible expression system at specific TLOs altered phenotypes independent of induction of TLO expression with Dox . Additionally , biofilm formation is a complex phenotype involving multiple processes; accordingly , TLOs involved in biofilm production did not completely overlap with those contributing to filamentation or cell-cell adhesion . Recent work has highlighted a role for the Mediator tail subunit in resistance to azole class antifungal drugs [80–82] , but involvement by TLO ( Med2 in Mediator ) was not specifically addressed . Tet-regulated TLO strains incubated overnight with or without Dox were plated onto solid agar and allowed to grow in the presence of a 25 μg fluconazole disc . After two days of growth , the size of the zone of inhibition appeared similar across most strains and induction conditions . The susceptibility phenotype ( size of the zone of inhibition ( ZOI ) ) of two induced TLOs , TLOα3 and TLOα34 , decreased and increased , respectively , when induced compared to the uninduced state ( Fig 6 ) . Changes in resistance due to regulated TLO expression were relatively minor , typically altering the size of the ZOI by no more than 15% . No alterations to fluconazole tolerance ( measured by the fraction of growth inside the ZOI [83] ) were apparent for any strain in the presence of absence of Dox and the rate of change in growth ( slope ) differed for only a single TLO , TLOγ11 , in the presence of Dox ( S9 Fig ) . Thus , expression of a few TLO genes , one telomeric and one located far from the telomeres had some effects on azole drug resistance , although this effect was neither broadly conserved among TLOs nor profound . To more directly test the role of TLOs in virulence , Tet-regulated TLO strains were co-incubated with RAW 264 . 7 macrophages in vitro at an MOI of two following logarithmic phase growth in the presence or absence of Dox . After 16 hours , LDH release from infected cultures was measured to quantify macrophage survival ( Fig 7A ) . C . albicans cells with induced expression of TLOα34 , TLOα9 , TLOα12 , or TLOγ11 resulted in more macrophage death compared to the uninduced cells of the isogenic strain ( Fig 7B ) . To test virulence with an in vivo model , we infected Galleria mellonella , a model for disseminated candidiasis , with C . albicans [69] . Larvae were infected with overnight cultures of C . albicans cells that had been induced or not induced with Dox and larval survival was monitored during the infection . Induction of three TLOs , TLOα34 , TLOγ4 and TLOγ7 , altered the morbidity of infected Galleria worms . Of these , Tet-induced expression of two genes , TLOα34 or TLOγ4 , significantly increased lethality ( Fig 7C ) , while induction of TLOγ7 reduced virulence compared to the uninduced state ( Fig 7D ) . Thus , individual TLO genes , when induced , have different effects on virulence attributes such as macrophage lysis and G . mellonella viability . Taken together , the above results reveal that TLO genes evolved varying degrees of influence on different virulence traits of C . albicans . A heat map displaying all significant associations of individual TLO expression ( +Dox vs . –Dox ) with each assayed phenotype reveals that there are few conserved functions shared by most of the TLO genes ( Fig 8 ) . Induced TLO expression promoted unidirectional changes in a number of phenotypes such as cell aggregation , growth at 30°C , and macrophage killing . Yet , a number of phenotypes can change in either direction upon induction of specific TLO genes . Thus , TLO gene family members have shared and unique modes of transcriptional regulation . This suggests a complex pattern of genotype-phenotype associations due to evolution and inheritance of TLO genes in C . albicans . To better visualize the relationship between TLO genes in controlling phenotypic traits , hierarchical clustering was performed using the phenotypic data for all Tet-induced loci . Comparison of phenotypic scores ( Fig 8 ) in all pairwise combinations for the TLO genes served as the basis for calculated relative distance ( S10 Fig ) . One major branch-point separated the TLOs into two main clusters , which were each composed of a mixture of TLOα and TLOγ genes ( Fig 9A ) . This suggests that the functions acquired by different Tlo proteins are not clade-specific . Yet , replotting the data for each TLO using principal components analysis ( PCA ) assigned 25 . 5% and 19 . 6% of the variation among the data set to PC1 and PC2 , respectively ( Fig 9B ) . Interestingly , this approach separated the TLOγ genes into two clusters on either side of the main TLOα genes cluster and TLOβ2 . The two TLOγ groups separated primarily along PC1 with TLOγ4 and TLOγ11 being lower on PC1 . A single gene , TLOα9 , remained an outlier . This suggests that a mixture of clade-associated and TLO-specific features produce the functional variation observed among Tet-regulated TLO strains . The sequence of TLO genes can be separated into roughly two halves , an N-terminal Med2 domain and a C-terminal gene/clade-specific region [64] . While the Med2 domain is responsible for the association of Tlo with the Mediator complex , the function of the C-terminal region is less clear and may interact with specific transcription factors to recruit RNA polymerase II through Mediator [65 , 84] . To map individual phenotypes to specific polymorphisms that differ between members of the TLO gene family , we focused only on the Med2 domain , as the gene/clade specific region sequence diverged too much to allow individual substitutions to be analyzed across all TLO clades . Within the first 315 nucleotides ( nt ) of the genes , encompassing the Med2 domain , seven polymorphisms could be correlated relative to 15 phenotypes that were altered upon TLO induction . Three phenotypes mapped to specific sites within the Med2 domain . Doubling time in YPD rich media associated specifically with a synonymous polymorphism ( A or G ) at nucleotide 201 ( p = 0 . 034 ) ( Fig 10A ) . Two traits , the ability to lyse macrophages and the growth rate in YPS mapped to polymorphisms at nucleotide positions 303 to 306 near the end of the Med2 domain ( p = 0 . 025 ) . This polymorphic site includes synonymous G to A transition at position 303 together with a three nucleotide CGT indel beginning at position 304 , which alters the coding sequence by introducing an arginine . Many other positions in the clade/gene specific region of TLOs may affect phenotypic properties of C . albicans , but the high prevalence of indels following the Med2 domain precludes a systematic analysis . Comparing variants in TLO sequences to the phylogenetic tree allows a reconstruction of the mutational history of the TLO gene family during evolution . To identify mutations that arose during gene family expansion , two closely-related TLOs ( i . e . , TLOγ5 and TLOγ13 ) were compared to build a common ancestral sequence that occupied the node connecting those two genes ( S11A Fig ) . This process was repeated until all nodes were connected through reconstructed sequences . This reconstruction identified 146 unique mutations that arose during TLO expansion , with most polymorphisms clustered towards the 3’ end of the gene in the gene/clade-specific domain ( S10B Fig ) . Importantly , the ratio of non-synonymous to synonymous mutations was highest in the gene/clade-specific region; the Med2 domain harbored a significantly higher frequency of synonymous than non-synonymous SNPs . This suggests that different evolutionary pressures are operating on the TLO sequence: purifying selection acting on infrequently maintained SNPs has promoted sequence identity of the Med2 domain , while positive selection has diversified the gene/clade-specific domain . This implies that all of the Tlo proteins continue to function through their interaction with Mediator . Sequencing of TLOα34 , the one TLO gene not located at a telomere , identified multiple polymorphisms relative to the genome reference sequence , including nine SNPs ( four of them non-synonymous substitutions that produced significant amino acid changes ( A502D , V509D , V511D , and L535S ) and two insertions/deletions ( indels ) within the 3’ gene/clade-specific domain of TLOα34 compared to the Assembly 21 ( A21 ) sequence ( S12 Fig . , S3 Table ) . Together with an eighteen nucleotide insertion and three nucleotide deletion , these mutations suggest that rapid TLO evolution is not limited to those genes found within subtelomeres . TLO sequences within a clade had relatively neutral selection coefficients ( mean Ka/Ks of 0 . 76 and 1 . 25 for TLOα and TLOγ intra-clade diversity , respectively ) , which increased dramatically to Ka/Ks = 5 . 33 between TLOβ2 and TLOα-clade ancestral sequences and to Ka/Ks = 2 . 45 between TLOα/β and the TLOγ ancestral sequences ( S11A Fig ) . The average selection coefficient for all TLOs is higher than for other Candida expanded gene families , including the serine aspartyl proteases ( SAPs ) in C . albicans ( Ka/Ks = 1 . 70 ) , the EPA adhesins in C . glabrata [85] ( Ka/Ks = 1 . 41 ) , and other expanded gene families in C . albicans ( S11B Fig ) . Thus , it appears that selection has propelled divergence within the TLO gene family that appears to have had phenotypic consequences on TLO function .
Evolutionary studies of functional divergence following gene duplication commonly analyze variation between two paralogous sequences , to facilitate direct comparison . The degree to which extensive gene family expansion associates with continued functional diversification remains largely unexplored due to the complex nature of assessing individual family members for specific phenotypes . Here , we performed functional analysis of 12 of the 14 C . albicans TLO genes and found that different TLOs regulate distinct phenotypic properties to different degrees . Each TLO affected multiple phenotypes and most phenotypes were affected by multiple TLO genes when individual TLO genes were induced for expression . The TLO gene family has undergone extensive genotypic evolution with a significant proportion of variation occurring within the gene/clade-specific 3’ end , which is experiencing significant positive selection for acquired mutations . Furthermore , phenotypic variation in three traits could be mapped to specific polymorphic sites in the TLO gene family , suggesting specific mutational events following gene duplication lead to diverse functions . The TLO gene family encodes a highly similar set of interchangeable protein subunits , yet individual genes affect distinct sets of biological functions . For example , induction of TLOα9 altered outcomes in seven phenotypic assays ranging across cell growth , filamentous growth , stress responses , and interactions with macrophages . Additionally , most TLO genes caused a mixture of phenotypic outcomes when induced , suggesting that a single TLO likely affects the differential expression of a significant number of downstream genes to produce the observed phenotype . Indeed , the two C . dubliniensis TLO homologs each regulate a large combination of unique and overlapping gene sets that promote exclusive and overlapping phenotypes [86] . We assume that incorporation of a particular Tlo protein into the Mediator complex may shift the relative expression of a distinct set of genes and thereby modulate a particular phenotypic response . The resulting phenotypic plasticity has the potential to confer a repertoire of available Mediator ‘types’ that could operate as a primary driver of TLO expansion . Thus , retention of divergent TLOs would act as a bet-hedging mechanism by which shifts in the incorporation of certain Tlo proteins would provide greater adaptability during changes in growth conditions or new host niches . Induced expression of individual TLOs provided the most direct route to assess paralog function . Regulated transcription of candidate genes can overcome difficulties in phenotypic expression due to compensation and redundancy but introduces its own caveats such as toxicity , pathway overload , stoichiometric imbalance , and promiscuous interactions with non-physiological targets when a gene is overexpressed [87] . Thus , overexpression can produce phenotypes that are not directly attributable to the gene of interest but other affected cellular processes [88–90] . Indeed , genetic analysis using an inducible deletion or overexpression system in C . albicans found disagreements between the two approaches that may reflect these effects [91] . Furthermore , the strength of induced expression in C . albicans can alter observed phenotypes [92 , 93] . Aberrant phenotypes produced by induced TLO expression were mitigated , in part , by a lack of noticeable toxicity and association with known pathways . Previous studies demonstrated that Tlo proteins exist in excess of other Mediator components as a free Tlo pool [65 , 94] , which suggests inherent stoichiometric imbalance with regards to Mediator . The Tlo incorporated into Mediator also appears quite plastic as multiple Tlos have been biochemically purified from the complex [65] . It is possible that induced TLO expression leads to target promiscuity either through Mediator’s role in transcriptional regulation or the Mediator tail’s role in chromatin remodeling [95] . However , the excess of Tlo protein found in C . albicans favors a model in which induced expression alters the relative availability of the regulated Tlo to be incorporated into Mediator and regulate expression of gene sets through interaction with different transcription factors although target promiscuity may occur . Most tested phenotypes were affected by multiple different TLO genes . Nearly all TLOs had a similar effect on cell aggregation; in contrast , regulation of growth in YPS and macrophage killing fell primarily on specific TLO clades . In most cases , at least two separate TLO genes affected each phenotype altered by TLO induction and different subsets of TLO genes modulated most of the phenotypes ( Fig 8 ) . Furthermore , different genes that regulated the same phenotype displayed both enhancing and suppressing effects , indicating that the evolution of individual TLO genes as well as TLO clades likely influences the phenotypic consequences of induced expression . 15 of 22 phenotype assays detected a phenotype associated with induction of at least one or two of the 12 TLO genes tested . Six of the seven phenotypes that were not affected corresponded to different environmental stresses such as pH and high temperature , and other stresses typically were significantly affected by only one or two Tet-induced TLO genes , suggesting that the TLO genes may not have a prominent role in stress responses . Previous expression profiling of SC5314 grown in a range of stress conditions that overlap with those tested here ( pH 4 . 0 , pH 8 . 0 , Calcofluor white , etc . ) supports this hypothesis: TLO genes did not display significant expression changes in a range of stress conditions [96] . This contrasts sharply with Med2 and other tail components of Mediator in S . cerevisiae that have integral roles in the regulation of general stress response pathways [97 , 98] . On the other hand , overlapping contributions to a given phenotype among TLO genes may reduce the phenotypic effect of inducing a single gene or result in minor effects that are difficult to distinguish . Therefore , contributions of individual TLOs to some phenotypes may be underscored or missed entirely . A notable exception is exposure to MMS , which methylates DNA and leads to DNA replication fork stalling . Mutants of the single copy of MED2 in S . cerevisiae display defects in viability following DNA damage analogous to the results here seen with induction of certain TLOs ( TLOα9 , TLOγ4 , and TLOγ11 ) [99 , 100] . We suggest that this may reflect a broader role for MED2 and Mediator in DNA regulation and repair rather than transcriptional regulation in response to extracellular stress . In contrast to the role in stress response , induction of a diverse set of TLO genes affected growth rates in a range of carbon sources but did not significantly alter growth in minimal media . In some cases , the same gene ( i . e . , TLOα9 ) produced opposite effects when grown in media supplemented with two different disaccharide sugars . The C . albicans genome encodes 20 different hexose transporters that are regulated through complex signaling networks that remain to be fully elucidated [101 , 102] . Modulation of these pathways by TLO genes could have important phenotypic consequences on carbon source utilization , which in turn could have major effects on C . albicans biology and host interactions [103–105] . Of note , induction of TLO gene expression altered growth rates either by unidirectionally increasing or decreasing doubling times for any single carbon source . Similarity in the metabolic response to induced expression of disparate TLO genes across clades suggests a conserved role for Med2 subunits that may have existed prior to TLO expansion . Induction of TLO expression also had a profound impact on filamentous growth and biofilm formation in different media contexts . Different TLO genes promoted or suppressed filamentous growth and biofilm formation although the TLOs involved varied depending on the conditions used . Thus , TLOs whose induction increased filamentous growth did not promote biofilm formation and vice versa . The critical step of cell-cell adhesion decreased when most TLOs were induced , although correspondence with decreased biofilm mass was only seen for Tet-induced expression of TLOγ16 . Interestingly , expression of TLOγ16 decreased following induction and biofilm formation whereas TLOα3 increased in expression and biofilm formation after induction , suggesting individual TLOs may affect different components of the regulatory circuits controlling biofilm formation that can , in turn , affect their own expression [106] . Thus , the cumulative steps of cell adhesion and filamentous growth are not necessarily additive for biofilm formation . Since biofilm formation can proceed without induction of the filamentous growth transcriptional circuit and filamentous growth does not necessarily yield biofilms , the two processes are not entirely codependent [77 , 107 , 108] . Modulation via expression of different Tlo proteins may play a direct role in promoting different aspects of biofilm formation , including adhesion , filamentous growth , and substrate invasion . Indeed , Mediator components have fundamental roles in cell state transitions that are analogous to yeast-hyphal differentiation in other organisms [109–111] , suggesting a conserved role for the complex in defining cell type-specific expression . In C . albicans , Mediator and the tail module , which includes Med3 and Med2/Tlo , function within the phenotypic switch between the ‘sterile’ white and mating-competent opaque states [66] . Thus , TLOs have the potential to control cell state transitions broadly across the breadth of cell types described for C . albicans [55 , 112–114] and , more specifically , for adhesion and the yeast-hyphal transition important for flocculation and biofilm formation . Induced expression of single TLO genes had surprising effects on complex phenotypes such as immune cell survival and virulence . Unexpectedly , Tet-induced expression of a single member of the large paralogous TLO gene family increased the ability of C . albicans to kill macrophages and G . mellonella hosts . While many C . albicans mutants have defects in virulence ( reviewed in [53] ) , attributing a given mutation to virulence traits can be complicated by general fitness defects . In contrast , TLOs that regulated pathogenicity had equivalent or slightly reduced fitness when induced despite also displaying increased virulence . This suggests that TLOs modulate genes specific to virulence properties that do not significantly influence growth and filamentous growth processes under the assayed conditions in vitro . The subtelomeric position of the TLO genes exposes them to significantly elevated frequencies of expression variability and genome change relative to other regions of the genome . Variability in TLO expression is observed in these strains consistent with previous reports although this expression plasticity is somewhat dampened [115] , presumably due loss in variation of the regulated allele . The observed variation in expression may act to promote variation in available Mediator ‘types’ by altering the available Tlo pool over time [115] . This could explain some of the phenotypic variability in these assays , as the relative abundance of the regulated Tlo in the total cellular pool is being altered but is still competing with other Tlos for incorporation into Mediator to produce an observable effect . The subtelomeric context of TLOs may also account for , in part , the large number of polymorphisms that distinguish each TLO gene among its paralogous sequences . Yet TLOα34 , the only non-telomeric TLO gene , underwent significant sequence evolution as well , indicating an underlying chromosomal or genetic feature that contributes to this process . Additionally , it is reasonable to assume selection has acted on the TLO gene family during expansion . Extensive sequence variation exists among TLO family members and there is strong evidence of positive selection during their evolution , especially at the major nodes that separate the three TLO clades . Thus , a large number of SNPs differentiate the TLO clades and these SNPs often produces a change to the protein sequence . Interestingly , different selective pressures appear to be operating across the TLO gene sequence . Purifying selection across the Med2 domain likely reflects the continued requirement for integration within Mediator [116] , whereas positive selection operates on the variable 3’ end of the gene that has evidence of encoding a transcriptional activation domain ( TAD ) [94 , 116] . Variation within the TAD could provide a mechanism for recruitment of different transcription factors . Emergence of SNPs within TLO sequences to produce allelic variants may further differentiate function within a single gene although we have identified few heterozygous SNPs within chromosomal homologs of single TLO genes . This may be a consequence of frequent recombination between chromosome homologs in the subtelomeres that rapidly fixes heterozygous positions through gene conversion or break-induced replication [33 , 36] . Importantly , an indel position at the end of the Med2 domain was associated with growth in sucrose and macrophage interaction , demonstrating that variant positions may be under selection for specific phenotypes . Numerous indels within the gene/clade-specific region complicates further analysis of variant positions in the 3’ end of the TLOs . Yet , it is tempting to speculate that divergent evolution of the TLO sequence , especially within the TAD , affects phenotypic plasticity among the Tet-regulated strains by affecting the expression of different sets of target genes . Thus , expansion of the Med2-domain containing TLOs in C . albicans led to sequence variation that results in phenotypic variation to promote a highly adaptive lifestyle .
Strains of Candida albicans used in this study are listed in S1 Table . Strains were grown on YPD agar at 30°C unless otherwise noted . For induction of the tetracycline-inducible system , cultures were grown overnight in 3 mL of YPD liquid media with constant agitation in the presence ( induced ) or absence ( uninduced ) of 50 μg/mL of doxycycline . Saturated cultures were then prepared for individual experiments using their respective protocols . Strains were transformed by standard lithium acetate transformation procedures as described previously through multiple rounds of transformation [117] . For integration of the tetracycline-inducible system at the endogenous TLO locus , the tetracycline-responsive promoter , the reverse tetracycline transactivator ( rtTA ) , and the nourseothricin resistance marker ( SAT1 ) were amplified from plasmid pNIM6 [70] using primers ALO110 and ALO111 . Primer sequences are listed in S2 Table . These primers target this amplicon to the native TLO locus corresponding to a direct integration at the ATG start codon . The integration site was determined by polymerase chain reaction ( PCR ) using primers ALO108 and ALO109 , corresponding to the pTET promoter and downstream in the TLO coding sequence , respectively . In some cases , additional sequencing was required to specify the TLO targeted by integration . Amplification of the integration site with ALO108 and primers ALO225 , ALO226 , and ALO227 , which bind farther downstream within the clade-specific region , were used identify integration at specific TLO genes for clades α , γ , and β , respectively . RNA was collected from 2x106 cells grown for four hours in liquid YPD medium in the presence or absence of 50 μg/mL of doxycycline . Cells were removed from the medium and RNA isolated using the MasterPure Yeast RNA Purification Kit ( EpiCentre , Madison , WI ) according to the manufacturer’s instructions . Subsequently , 1 μg of RNA was used to synthesize cDNA using oligo- ( dT ) 18 and Superscript III reverse transcriptase ( Thermo Scientific , Waltham , MA ) . cDNA was assayed for genomic DNA contamination using intron-spanning primers , ALO30 and ALO31 , for ribosomal protein large subunit 6 ( RPL6 ) and only cDNA lacking genomic contamination was used for qRT-PCR ( S4 Table ) . qRT-PCR was performed with PowerUp SYBR Green ( Applied Biosystems , Foster City , CA ) using an Applied Biosystems QuantStudio 3 qPCR machine and analyzed with the QuantStudio Design and Analysis Software package version 1 . 4 . 2 . Primers used are listed in S4 Table . Quantification of individual TLO genes was assessed relative to ACT1 . The comparative Rq method was used measure expression levels . Experiments for each gene were performed a minimum of three biological replicates in technical duplicates . Overnight cultures were grown in 300 μL YPD liquid medium with or without 50 μg/mL doxycycline . Cultures were diluted 1:2000 into the appropriate growth medium with continued application or lack thereof of doxycycline . Optical density was measured every 15 minutes for 18–48 hours at 30°C shaking at 250rpm using an AccuSkan FC plate reader ( Fisher Scientific , Hampton , NH ) . The polynomial measurement of the curve was used to derive doubling times . These experiments were completed with a minimum of three biological replicates with two technical replicates each . Overnight cultures were grown in YPD liquid medium in the presence or absence of 50 μg/mL of doxycycline . Cultures were vortexed and diluted to an initial optical density ( OD600 ) of 2 . 0 in a channeled cuvette . OD600 readings were taken at the start of the assay and every 15 minutes on a ThermoFisher NanoDrop One ( Fisher Scientific , Hampton , NH ) for a total of 210 minutes to plot cell settling . These experiments were completed with six biological replicates . Overnight cultures were grown in YPD liquid medium in the presence or absence of 50 μg/mL of doxycycline . Cultures were diluted 1:2 in a total volume of 100 μL YPD liquid . An aliquot was visualized across a minimum of 10 random fields of view using a Leica DM750 with an attached Leica MC170HD digital camera ( Leica , Wetzlar , Germany ) . The number of cells per aggregate was tallied across all fields of view and plotted as average for each induced TLO gene with standard error . Two biological replicates at a minimum were performed per strain . Overnight cultures were grown in YPD liquid medium in the presence or absence of 50 μg/mL of doxycycline . Cells from each overnight culture were counted by hemocytometer and plated at a concentration of 100 cells per plate onto solid YPD and Spider medium . These plates were grown at 30°C for 7 days and imaged using a BioRad ChemiDoc XRS+ imaging system ( BioRad , Hercules , CA ) . Images were processed by the visual analysis tool MIPAR v1 . 4 . 1 ( MIPAR , Worthington , OH ) and scored using the following formula: Filamentation score = 100 * ( Cf ) * ( 0 . 8 ( Rh/Ry ) + 0 . 2 ( Sw ) ) . Cf is the proportion of filamenting cells , Rh is the radius of the hyphal halo , Ry is the radius of the yeast colony , and Sw is the score for colony wrinkling . Three biological replicates were performed at a minimum per strain . Overnight cultures were grown in YPD liquid medium in the presence or absence of 50 μg/mL of doxycycline . Cells were counted with a hemocytometer and plated at a concentration of 100 cells per plate onto solid YPD and Spider medium . These plates were grown at 30°C for 5 days and imaged prior to rinsing as described above for filamentation . A steady stream of water was run over the plate to remove non-adherent colonies and imaged . Remaining colonies were then rubbed off with a gloved finger and imaged to assess agar invasion . Three biological replicates were performed at a minimum per strain . Biofilm production and measurement was performed as outlined in ( Nobile et al , Cell , 2012 ) . Briefly , silicone squares were pre-treated overnight in 12 well tissue culture plates with 2 mL of adult bovine serum . Wells were washed with PBS and 2 mL of Spider medium was added to each well . Overnight cultures were grown in YPD liquid medium in the presence or absence of 50 μg/mL of doxycycline . Cells were introduced at an OD600 of 0 . 5 to each well and incubated for 90 minutes at 37°C , shaking at 120 rpm . Silicone squares were then removed with sterile forceps , rinsed in a separate PBS wash well , and transferred to a new well with 2 mL of Spider media . Cultures , now adhered to the silicone squares , were incubated for 60–65 hours at 37°C , shaking at 120 rpm . After incubation , media was gently pipetted from the wells and plate was left to dry on benchtop , slightly ajar , for 24 hours . Produced biofilm was then scraped off and weighed . Four biological replicates were performed at a minimum per strain . Overnight cultures were grown in YPD liquid medium at 30°C in the presence or absence of 50 μg/mL of doxycycline . Cell density was determined using OD600 and cultures were adjusted to an OD600 of 1 . 0 in 1 mL ddH20 . These dilutions were used as a base for five sequential ten-fold dilution done in a 96 well plate . Or each stress condition , 5 μl of each dilution was spotted to the appropriate prewarmed agar plates including a synthetic complete defined ( SCD ) medium plate absent any stressor as a control for growth . Plates were then incubated at 30°C unless otherwise indicated and imaged at 24 hours and 48 hours . C . albicans macrophage killing was assessed by using the CytoTox96 nonradioactive cytotoxicity assay ( Promega , Madison , WI ) . RAW 264 . 7 macrophages were seeded at 2 . 5 x 104 cells per well in a 96 well plate in RPMI supplemented with 10% fetal bovine serum ( FBS ) and incubated overnight at 37°C and 5% CO2 . Overnight C . albicans cultures were grown in YPD liquid medium at 30°C in the presence or absence of 50 μg/mL of doxycycline . These overnight cultures were then diluted 1:20 and grown for 3 hours into logarithmic phase growth in YPD medium with or without DOX . Log phase C . albicans cultures were then washed with PBS three times , inoculated into macrophages at a multiplicity of infection ( MOI ) of 2 , and incubated overnight at 37°C and 5% CO2 . To assess macrophage killing , plates containing C . albicans infected macrophage were centrifuged at 250 x g for 5 minutes and 10 μL from each well was transferred to a new plate . The transferred solution was diluted 1:5 with 40 μL of PBS and assayed using the Promega CytoTox Assay Kit according to the manufacturer’s instructions . The abundance of lactate dehydrogenase ( LDH ) release was calculated according to the manufacturer’s protocol . Galleria mellonella infections were carried out using previously described protocols ( Fuchs et al . 2010 ) . In brief , overnight cultures were grown in YPD liquid medium at 30°C in the presence or absence of 50 μg/mL of doxycycline . Cultures were washed 3 times in 5 ml sterile PBS . Cell density was quantified through hemocytometer . Cell suspensions ( ~2 . 5x10^5 CFUs ) in a 10 μl volume of sterile PBS were injected into the terminal pro-leg of G . mellonella larvae ( Vanderhorst Wholesale , www . snackworms . com ) using a 26 G , 10 μl syringe ( Hamilton , No . 80300 ) ( n = 30 larvae per TLO ) . Dilutions of cell suspensions were plated onto YPD agar and CFUs counted to confirm inoculum . After infection , G . mellonella larvae were incubated at 37°C for 7 days . G . mellonella larvae were scored daily for signs of death ( immobility and darkened pigmentation ) . The Log-rank ( Mantel-Cox ) test was used for statistical analysis of survival curves . Overnight cultures were grown in YPD liquid medium at 30°C in the presence or absence of 50 μg/mL of doxycycline . Cells for each strain were cultured overnight in YPD at 30°C in the presence or absence of 50 μg/mL of doxycycline . Optical density measurements were used to dilute the cultures to 0 . 04 OD/ml ( 800 , 000 cells/ml ) and 70 μL plated onto solid YPD agar . Inoculated plates were left for one hour to dry and a single 25 μg fluconazole disc ( Liofilchem , TE , Italy ) was placed in the center of the plate . Cells were allowed to grow for 48 hours at 30°C and images taken using a BioRad ChemiDoc XRS+ imaging system ( BioRad , Hercules , CA ) . Drug resistance was quantified using the diskImageR program which allows for analysis of drug response parameters [83] . Alignment of TLO sequences was performed using the Multiple Sequence Comparison by Log Expectations ( MUSCLE ) [118] . A phylogenetic reconstruction was produced using maximum likelihood in MEGA7 . Phenotypic correlations between TLOs were produced by converting significant phenotypic changes across all assays into either a 1 , 0 , or -1 , indicating an increased , unchanged , or decreased phenotype , respectively . A dendrogram was constructed from this matrix using Euclidean distances in R ( v3 . 4 . 2 ) [119] . Principal components were constructed and visualized using the pca3D package . Statistics were performed using Microsoft Excel or R ( v3 . 4 . 2 ) developed by the R Development Team [119] . Statistics were performed with a Student’s t-test unless otherwise annotated . | Gene duplication is a rapid mechanism to generate additional sequences for natural selection to act upon and confer greater organismal fitness . If additional copies of the gene are beneficial , this process may be repeated to produce an expanded gene family containing many copies of related sequences . Following duplication , individual gene family members may retain functions of the ancestral gene or acquire new functions through mutation . How functional diversification accompanies expansion into large gene families remains largely unexplored due to the difficulty in assessing individual genes in the presence of the remaining family members . Here , we addressed this question using an inducible promoter to regulate expression of individual genes of the TLO gene family in the commensal yeast and opportunistic pathogen Candida albicans , which encode components of a major transcriptional regulator . Induced expression of individual TLOs affected a wide range of phenotypes such that significant functional overlap occurred among TLO genes and most phenotypes were affected by more than one TLO . Induced expression of individual TLOs did not produce massive phenotypic effects in most cases , suggesting that functional overlap among TLO genes may buffer new mutations that arise . Specific sequence variants among the TLO genes correlated with certain phenotypes and these sequence variants did not necessarily correlate with sequence similarity across the entire gene . Therefore , individual TLO family members evolved specific functional roles following duplication that likely reflect a combination of inherited function and new mutation . | [
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] | 2018 | Functional diversification accompanies gene family expansion of MED2 homologs in Candida albicans |
Defects in the genes encoding the Paf1 complex can cause increased genome instability . Loss of Paf1 , Cdc73 , and Ctr9 , but not Rtf1 or Leo1 , caused increased accumulation of gross chromosomal rearrangements ( GCRs ) . Combining the cdc73Δ mutation with individual deletions of 43 other genes , including TEL1 and YKU80 , which are involved in telomere maintenance , resulted in synergistic increases in GCR rates . Whole genome sequence analysis of GCRs indicated that there were reduced relative rates of GCRs mediated by de novo telomere additions and increased rates of translocations and inverted duplications in cdc73Δ single and double mutants . Analysis of telomere lengths and telomeric gene silencing in strains containing different combinations of cdc73Δ , tel1Δ and yku80Δ mutations suggested that combinations of these mutations caused increased defects in telomere maintenance . A deletion analysis of Cdc73 revealed that a central 105 amino acid region was necessary and sufficient for suppressing the defects observed in cdc73Δ strains; this region was required for the binding of Cdc73 to the Paf1 complex through Ctr9 and for nuclear localization of Cdc73 . Taken together , these data suggest that the increased GCR rate of cdc73Δ single and double mutants is due to partial telomere dysfunction and that Ctr9 and Paf1 play a central role in the Paf1 complex potentially by scaffolding the Paf1 complex subunits or by mediating recruitment of the Paf1 complex to the different processes it functions in .
Gross chromosomal rearrangements ( GCRs ) , such as translocations and deletions , are common in many cancers [1] . DNA repair and DNA damage signaling defects that cause increased rates of accumulating GCRs in model systems like Saccharomyces cerevisiae have been identified in sporadic tumors and in inherited cancer predisposition syndromes , suggesting that increased genome instability plays a role in the development of some cancers [2–7] . In addition to defects in DNA metabolism [8 , 9] , defects in transcription are also a source of genome instability . How transcriptional defects cause GCRs is not completely understood , but collisions with the replication machinery , formation of RNA:DNA hybrids , and/or transcription-associated homologous recombination ( HR ) are potential mechanisms [10 , 11] . Recently we identified CDC73 in a large-scale screen for genes that suppress the formation of GCRs in S . cerevisiae [6] . CDC73 encodes a subunit of the Paf1 complex , and CDC73 has been previously implicated as playing a role in maintaining the stability of yeast artificial chromosomes , chromosome transmission fidelity , and suppression of direct repeat HR [12–14] . The Paf1 complex , which is comprised of Paf1 , Cdc73 , Rtf1 , Ctr9 , and Leo1 , binds to and modifies the activity of RNA polymerase during transcription [15–20] . This complex has been implicated in a variety of processes , including transcription elongation , mRNA 3’-end maturation , histone methylation and ubiquitination , expression of normal levels of telomerase RNA TLC1 and maintenance of normal telomere lengths [16 , 21–24] , and is conserved among eukaryotes [25] . Somatic mutations in CDC73 in humans are associated with breast , renal , gastric , and parathyroid cancers [26–28] , and germline mutations in CDC73 cause the cancer susceptibility syndrome hyperparathyroidism-jaw tumor syndrome ( HPT-JT ) [29 , 30] . In addition , a small fraction of familial Wilms tumor cases have been attributed to germline mutations in CTR9 [31] . However , little is known about how CDC73 and CTR9 function as tumor suppressors , particularly since mutations in the genes encoding the other members of the Paf1 complex have not yet been linked to the development of cancer . Here we have investigated how the Paf1 complex acts to suppress genome instability with the goal of shedding light on how the human homolog of CDC73 may function as a tumor suppressor . We have found that PAF1 , CDC73 , and CTR9 play the most important roles in suppressing the accumulation of GCRs among the genes that encode subunits of the Paf1 complex . Strains with CDC73 defects appear to have perturbations in telomere maintenance that result in increased GCR rates and that these defects result in synergistic increases in GCR rates when combined with defects in TEL1 and YKU80 , which cause other types of defects in telomere maintenance that also result in increased GCR rates . Deletion analysis identified a 105 amino acid region of Cdc73 that was necessary and sufficient for its incorporation into the Paf1 complex , nuclear localization , and Cdc73 function . These analyses enhance our understanding of how Cdc73 , as a subunit of the Paf1 complex , suppresses genome instability , and provide insights into how its human homolog may function as a tumor suppressor .
Because we previously identified CDC73 as a genome instability suppressing ( GIS ) gene [6] , we tested if other genes encoding subunits of the Paf1 complex suppressed the formation of GCRs selected in the duplication-mediated GCR ( dGCR ) assay ( Fig 1A ) . The cdc73Δ , ctr9Δ , and paf1Δ single mutations caused the largest increases in dGCR rate ( 9–22 fold ) , and the leo1Δ and rtf1Δ single mutations caused small increases in the dGCR rate ( 3–4 fold; Fig 1B; S1 Table ) . As will be discussed in detail below , we found that the cdc73Δ mutation caused a synergistic increase in the dGCR rate when combined with yku80Δ or tel1Δ mutations ( Fig 1B ) and tested if the yku80Δ or tel1Δ mutations synergized with deletions of other Paf1 complex genes . Similar to the effects of the single mutations , the cdc73Δ , ctr9Δ , and paf1Δ mutations caused strong synergistic increases in the dGCR rate when tested in combination with either a yku80Δ or tel1Δ mutation whereas the rtf1Δ and leo1Δ mutations did not cause a synergistic increase in the dGCR rate in combination with either a yku80Δ or tel1Δ mutation relative to the respective single mutations ( Fig 1B; S1 Table ) . Interestingly , the mutations that caused the strongest increases in GCR rates in these experiments , cdc73Δ , paf1Δ and ctr9Δ , caused the largest decreases in telomere lengths and TLC1 levels along with causing strong defects in telomere gene silencing ( see below ) , whereas the mutations that caused little if any increases in GCR rates in these experiments , rtf1Δ and leo1Δ , also caused the smallest decreases in telomere lengths and TLC1 levels [32] . Given the differences in the roles of the Paf1 complex subunits in suppressing the accumulation of GCRs , we also tested if transcriptional elongation defects , which are caused by Paf1 complex defects [33–35] , might correlate with the increased GCR rates in mutant strains . We measured transcriptional elongation defects that result in sensitivity to 6-azauracil , which depletes cellular rGTP levels [36] . Deletion of PAF1 or CTR9 caused strong sensitivity to 6-azauracil , deletion of CDC73 caused weaker sensitivity , and deletion of RTF1 or LEO1 caused no sensitivity ( Fig 1B; S1A Fig ) . These results are in accord with the results of studies employing other transcriptional elongation assays [33–35]; however , it should be noted that the magnitude of the effect caused by defects affecting the Paf1 subunits , including Cdc73 , varies between the transcriptional elongation assays used , and 6-azauracil sensitivity assays can show strain-to-strain variation [37] . Strains with deletions of PAF1 or RTF1 have defects in the silencing of telomere-proximal genes ( CDC73 , CTR9 and LEO1 were not tested ) [23] , which has been termed the telomere position effect ( TPE ) [38] and deletions in PAF1 , CTR9 , RTF1 , and to a lesser extent CDC73 , but not LEO1 cause defects in the histone H3 modifications required for gene silencing including TPE [23 , 39–41] . To determine if TPE defects correlated with increased GCR rates , we measured TPE by monitoring the survival of strains with a telomere-proximal URA3 gene in the presence of 5-fluoroorotic acid ( 5FOA ) , which is toxic to strains expressing URA3 . Deletion of PAF1 and RTF1 caused the greatest loss of TPE ( Fig 1B; S1A Fig ) , whereas milder TPE defects were observed in cdc73Δ and ctr9Δ strains , and no TPE defect was observed in the leo1Δ strain . The stronger TPE defects caused by the paf1Δ and rtf1Δ mutations are consistent with the known role of Paf1 and Rtf1 in the specific recruitment of histone modifiers [23 , 39] . To verify that the TPE defects in the cdc73Δ strain were due to loss of telomere silencing and not due to induction of ribonucleotide reductase , which accounts for the apparent TPE defect in pol30-8 and cac1Δ strains [42] , we tested the 5FOA sensitivity of the cdc73Δ strain in the presence of sublethal concentrations of hydroxyurea ( HU ) , which rescues the TPE in pol30-8 and cac1Δ strains [42] . Consistent with the results in the absence of HU , growth on 5FOA-containing plates was not restored by addition of HU ( S1B Fig ) . We did not test the deletion of the other Paf1 complex genes because their role or lack of a role in transcriptional silencing is well established [23 , 39–41] and because paf1Δ and ctr9Δ strains are HU sensitive [37] . The data presented here along with published data [32] suggest that PAF1 plays important roles in genome stability , transcriptional elongation , telomere silencing , maintaining TLC1 levels , and telomere length maintenance . CDC73 has an important role in genome stability , maintaining TLC1 levels , telomere length maintenance and a lesser but detectable role in telomere silencing but little if any role in transcriptional elongation . CTR9 has important roles in genome stability , transcriptional elongation , maintaining TLC1 levels , and telomere length maintenance , and a role in telomere silencing that was similar to that observed for CDC73 . RTF1 has the most important role in telomere silencing , but plays little if any role in genome stability and transcriptional elongation , and lesser roles in maintaining TLC1 levels , and telomere length maintenance . And LEO1 plays little if any role at all in the Paf1 complex functions considered here and only a modest role in maintaining TLC1 levels and telomere length maintenance . These observations suggest a model for the complex in which Paf1 facilitates the functions of the other subunits potentially by mediating recruitment of the complex to the different processes it functions in ( Fig 1C ) , consistent with the results of coimmunoprecipitation experiments in S . cerevisiae and binding assays performed with human homologs [22 , 43 , 44] . Since PAF1 and CDC73 played the largest roles in suppressing GCRs and the cdc73Δ mutation caused fewer additional defects , we sought to understand how the Paf1 complex suppresses genome instability by focusing on CDC73 . We crossed strains containing the dGCR assay and a cdc73Δ mutation or an rtf1Δ mutation as a control to a 638-strain subset of the S . cerevisiae deletion collection that contained deletions of known GIS genes and cooperating GIS ( cGIS ) genes [6 , 45] . The resulting haploid double mutant strains were scored by patch tests for the increased accumulation of CanR 5FOAR papillae that are a measure of the formation of GCRs relative to the single mutant strains ( Fig 2A ) . Forty-three mutations caused increased strain patch scores when combined with the cdc73Δ mutation ( Fig 2B ) ; potential suppressive interactions were not investigated as slow growth phenotypes can also cause reduced strain patch scores . Selected interactors causing increased patch scores were verified by quantitative fluctuation assays ( S2 Table ) . Almost none of the mutations that caused increased scores when combined with the cdc73Δ mutation interacted with an rtf1Δ mutation ( Fig 2B ) , consistent with the more modest effects of rtf1Δ on GCR rates ( Fig 1B ) . Among the CDC73 interactors were 7 genes ( YKU70 , YKU80 , TEL1 , MRC1 , NUP60 , RAD6 , and VPS20 ) in which mutations cause shorter telomeres [46–48] . Combined with reports that cdc73Δ strains have reduced levels of the telomerase RNA TLC1 [32] , these results suggested that defects in telomere homeostasis could be responsible for some of the strong interactions . To extend these results , we focused on YKU80 , YKU70 , and TEL1 because the role of these genes in telomere homeostasis is better understood than the other 4 genes . A cdc73Δ mutation showed synergistically increased patch scores when it was combined with either yku70Δ or yku80Δ mutations , which disrupt the Ku complex and cause both shortened telomeres and non-homologous end joining ( NHEJ ) defects [49 , 50] . Quantitative rate measurements demonstrated that the cdc73Δ yku80Δ double mutant had a 162-fold increase in dGCR rate as compared to the 4- to 9-fold increase in dGCR rate seen for the respective single mutants ( Table 1 ) . In contrast , deletion of DNL4 , which encodes the DNA ligase required for NHEJ but not telomere maintenance , did not result in a synergistic increase in GCR rates when combined with the cdc73Δ mutation ( S2 Table ) suggesting that the increased GCR rates of the cdc73Δ yku70Δ and cdc73Δ yku80Δ double mutants do not involve a defect in NHEJ . Similarly , the cdc73Δ mutation showed a strong interaction with a tel1Δ mutation in the dGCR assays as measured by patch scores ( Fig 2A and 2B ) , and the cdc73Δ tel1Δ double mutant had a 236-fold increase in the dGCR rate ( Table 1 ) . TEL1 encodes a protein kinase involved in the DNA damage checkpoint that also plays a role in maintaining normal telomere lengths such that a tel1Δ mutation causes shortened telomere lengths [53 , 54] . In contrast , mutant strains that contained a cdc73Δ mutation in combination with defects in other checkpoint genes either did not have increased dGCR patch scores ( RAD9 , DUN1 , and RAD53 ) or only had small increases in dGCR patch scores ( MEC3 , RAD17 , RAD24 , and MEC1 ) , supporting the view that the genetic interaction between the cdc73Δ and tel1Δ mutations reflects the telomere maintenance defect caused by the tel1Δ mutation . The tel1Δ yku80Δ double mutant had a 28-fold increase in the dGCR rate ( Table 1 and S2 Table ) and the cdc73Δ tel1Δ yku80Δ triple mutant had a 2024-fold increase in the dGCR rate ( S2 Table ) , consistent with the hypotheses that loss of CDC73 , YKU80 , and TEL1 cause partial defects in different telomere maintenance pathways and that the increased GCR rates that result from combining mutations in these genes may reflect increased telomere maintenance defects . GCRs selected in the dGCR assay are most commonly generated by non-allelic HR between the DSF1-HXT13 region on the left arm of chromosome V ( chrV L ) and divergent homologies on chrIV L , chrX L , and chrXIV R [51] . PCR analysis of GCRs formed in the cdc73Δ , cdc73Δ tel1Δ and cdc73Δ yku80Δ dGCR strains showed that the distribution of GCRs were essentially the same as that from the wild-type strain , despite the >200-fold increase in GCR rate in some of the strains analyzed ( Fig 2C ) . As expected , introduction of the HR-defective rad52Δ mutation decreased the dGCR rates of the cdc73Δ tel1Δ and cdc73Δ yku80Δ double mutants by 45-fold and 25-fold , respectively ( S2 Table ) . In addition , the rad52Δ mutation shifted the spectrum of GCRs recovered in the cdc73Δ mutant to GCRs that were not formed by non-allelic HR ( Fig 2C ) . As observed in the dGCR assay , synergistic increases in GCR rates were also observed when the cdc73Δ mutation was combined with either the yku80Δ or tel1Δ mutation in strains containing either the unique sequence ( uGCR ) assay or the short homology GCR ( sGCR ) assay ( Table 1 ) . Since the sGCR assay selects for a somewhat broader diversity of types of GCRs including de novo telomere additions than the uGCR assay and is not dominated by a single type of GCR as compared to the dGCR assay ( summarized in Fig 1A ) , we used the sGCR assay to determine if the absence of CDC73 altered the distribution of the GCRs formed . We analyzed 1 parental strain and 11 independent GCR-containing isolates by paired-end next-generation sequencing for the wild-type strain , the cdc73Δ single mutant strain , and the cdc73Δ tel1Δ and cdc73Δ yku80Δ double mutant strains ( Fig 3; S3 and S4 Tables; S2–S8 Figs ) . In the wild-type sGCR strain , 46% of the GCRs analyzed ( 5 of 11 ) were produced by de novo telomere addition , 18% ( 2 of the 11 ) were produced by HR between the SUP53 tRNA gene introduced by the can1::PLEU2-NAT marker and another leucine tRNA gene , and 36% ( 4 of the 11 ) were produced by HR between the YCLWdelta5 fragment introduced by the can1::PLEU2-NAT marker and another Ty-related sequence ( Fig 3A; S5 , S9 and S10 Figs; S4 Table ) . The presence of both de novo telomere addition and HR-mediated GCRs among the GCRs selected in the sGCR assay is useful for characterizing mutations that alter the GCR spectra . Analysis of GCRs formed in the cdc73Δ , cdc73Δ tel1Δ , and cdc73Δ yku80Δ sGCR strains revealed that no de novo telomere addition GCRs were recovered when CDC73 was deleted ( Fig 3A; S6–S8 Figs; S4 Table ) . Remarkably , the majority of the GCRs selected in strains containing a cdc73Δ mutation were inverted duplications , and most of these contained a second breakpoint that was mediated by HR ( Fig 3G; S11 Fig ) . Inverted duplications mediated by hairpins were frequent in the cdc73Δ tel1Δ sGCR strain ( Fig 3H; S12 Fig ) , which is consistent with the previously observed increase in hairpin-mediated inverted duplications observed in the uGCR assay for the tel1Δ single mutant strain [52] . For inverted duplication GCRs , the initial inversion GCRs would be predicted to be dicentric , but in all cases identified here , these GCRs underwent additional rearrangements to generate stable monocentric chromosomes . These additional rearrangements commonly involved HR between repetitive elements on chrV L and other repetitive elements elsewhere in the genome , including an unannotated delta sequence on chrV R ( S13 and S14 Figs ) . All of the GCRs observed other than de novo telomere addition-mediated GCRs were different types of translocations; the rates of accumulating these translocations in the sGCR assay relative to the wild-type rate were increased 52-fold for the cdc73Δ single mutant , 242-fold for the cdc73Δ tel1Δ double mutant , and 460-fold for the cdc73Δ yku80Δ double mutant sGCR strains . Most GCR-containing strains contained a normal complement of chromosomes , except for one cdc73Δ yku80Δ GCR-containing strain that contained two copies of chrXVI ( S15 Fig ) . Taken together , the shift in the GCR spectra in sGCR strains lacking CDC73 is consistent with an underlying defect in telomere homeostasis as most mutations that result in high GCR rates result in increased levels of de novo telomere addition GCRs as long as functional telomerase is present [55] . Given the limits on the numbers of GCRs we can presently sequence , our analysis cannot definitively prove that de novo telomere addition GCRs are not formed when CDC73 is deleted , but does demonstrate that other types of GCRs , which are all different types of translocations , are selectively increased ( e . g . , the increase in the rate of de novo telomere additions in the cdc73Δ mutant relative to the wild-type is <5-fold compared to a 52-fold increase in the rate of translocations ) . The relative lack of de novo telomere addition GCRs among the GCRs selected in the sGCR assay in strains containing cdc73Δ mutations could indicate a complete failure of de novo telomere additions , as is observed with strains with deletions of YKU80 or genes encoding telomerase subunits [55] , or a partial defect that only decreases the efficiency of de novo telomere additions relative to other GCR-forming mechanisms , as is observed with tel1Δ strains [52 , 55] . We therefore combined the cdc73Δ mutation with a deletion of PIF1 , which causes a substantial increase in GCRs formed through an increase in de novo telomere additions due to decreased inhibition of telomerase at DSBs [56 , 57] , even under conditions where pif1 mutations potentially prevent the formation of GCRs mediated by break-induced replication [52 , 55] . Mutations inhibiting de novo telomere addition suppress the increased GCR rate caused by the pif1Δ mutation , whereas mutations causing only reduced efficiency of de novo telomere addition do not [52 , 55] . The cdc73Δ mutation partially suppressed the increased GCR rate caused by the pif1Δ mutation ( Table 1 ) , suggesting that the cdc73Δ mutation causes a substantial , but incomplete , defect in the formation of GCRs mediated by de novo telomere addition . This could be due to reduced levels of functional telomerase resulting from the partial reduction of TLC1 telomerase RNA levels observed in cdc73Δ mutants [32] . Deletions of CDC73 , YKU70 , YKU80 and TEL1 all result in shortened telomeres [32 , 46 , 47] . To investigate if cdc73Δ double and triple mutant strains have increased telomere defects in addition to increased GCR rates , we generated haploid single , double , and triple mutant strains containing different combinations of CDC73 , EXO1 , TEL1 and YKU80 deletions by crossing mutant strains to each other to generate fresh mutant haploid spore clones for telomere length analysis . Consistent with previous results [32 , 46 , 47] , telomere lengths were reduced in cdc73Δ , and to a greater extent in tel1Δ , and yku80Δ single mutant strains ( Fig 4A ) . Exo1 plays a role in resection of deprotected telomeres [58] and deleting EXO1 partially restored the shortened telomeres caused by the cdc73Δ , tel1Δ , and yku80Δ mutations; this is consistent with prior observations that exo1Δ yku80Δ double mutants have slightly longer telomeres than yku80Δ single mutants [59] . The tel1Δ yku80Δ , cdc73Δ yku80Δ , and cdc73Δ tel1Δ double mutant combinations all showed potential signs of additional telomere dysfunction compared to the respective single mutants , which included: ( 1 ) a telomere length that was shorter than seen in the respective single mutants ( cdc73Δ tel1Δ ) or potentially shorter than seen in the respective single mutants ( tel1Δ yku80Δ , which was previously reported [60] , and cdc73Δ yku80Δ ) ; and ( 2 ) a smeared telomere pattern ( tel1Δ yku80Δ and cdc73Δ yku80Δ ) , which was reminiscent of the telomere pattern seen in telomerase-defective post-senescent survivors that maintain their telomeres by alternative mechanisms [61] . Remarkably , the cdc73Δ tel1Δ yku80Δ triple mutant strain did not have a distinct telomere-containing band , but rather had only a smeared pattern , suggestive an even stronger telomere defect . The genetic interactions observed between the cdc73Δ , tel1Δ , and yku80Δ mutations resulting in increased telomere dysfunction mirrors the synergistic increases in GCR rates seen in strains containing combinations of these mutations . The cdc73Δ single mutant and the tel1Δ yku80Δ , cdc73Δ yku80Δ , and cdc73Δ tel1Δ double mutants all grow slowly and have evidence of telomere defects . We therefore investigated whether or not these strains would show evidence of crisis , escape from senescence and improved growth by serially restreaking the mutant strains on non-selective medium ( S16 Fig ) . To ensure that our serial restreaking procedure could detect senescence and recovery , we tested the tel1Δ yku80Δ double mutant strain and found it initially grew slowly but eventually recovered a wild-type growth rate as previously reported [62] ( not illustrated ) . In contrast , the slow growth of the cdc73Δ single mutant and the even slower growth of the cdc73Δ yku80Δ , and cdc73Δ tel1Δ double mutants showed only partial improvement in growth after 11 rounds of restreaking and never achieved wild-type growth rates . One possible explanation for this difference is that telomere maintenance-independent effects on transcription could also contribute to the slow growth phenotype caused by the cdc73Δ mutation . The telomere structures of these serially propagated strains were analyzed by Southern blot and the telomere species of the tel1Δ yku80Δ , cdc73Δ tel1Δ , cdc73Δ yku80Δ , and cdc73Δ tel1Δ yku80Δ strains were all observed to contain smeared telomere fragments ( Fig 4B ) ; this suggests that the telomeres in these mutants may be partially maintained by one of the RAD52-dependent telomerase-independent telomere maintenance pathways [61 , 63] . Consistent with this , the cdc73Δ tel1Δ rad52Δ and cdc73Δ yku80Δ rad52Δ triple mutants all had very short telomeres , but lacked the smeared pattern seen in the Southern blots ( Fig 4B ) . In contrast , we were unable to generate a cdc73Δ tel1Δ yku80Δ rad52Δ quadruple mutant by either PCR mediated gene disruption or by crossing different mutant strains to each other; this is consistent with a requirement of RAD52-dependent HR in the cdc73Δ tel1Δ yku80Δ triple mutant either for telomere maintenance or for the repair of some other type of spontaneous DNA damage in this triple mutant . Pulse field gel electrophoresis ( PFGE ) was used to analyze chromosomes from cdc73Δ single , double and triple mutant strains for the presence of aberrant sized chromosomes ( Fig 4C ) . The cdc73Δ , tel1Δ , and yku80Δ single mutant strains and the cdc73Δ tel1Δ , cdc73Δ yku80Δ , and tel1Δ yku80Δ double mutant strains had chromosomal banding patterns that were similar to that from the respective wild-type strain , although the double mutants showed more chromosomes with abnormal sizes despite being grown in the absence of any selection for chromosome rearrangements . The cdc73Δ tel1Δ rad52Δ and cdc73Δ yku80Δ rad52Δ triple mutants had increased numbers of chromosomes with abnormal sizes compared to the respective cdc73Δ tel1Δ and cdc73Δ yku80Δ double mutants . In contrast , no chromosome bands were visible when the cdc73Δ tel1Δ yku80Δ triple mutant was analyzed , which is consistent with reports that chromosomes from post-senescent survivors are unable to enter PFGE gels , likely because of the structure of the HR intermediates that act in telomere maintenance [61] . The aberrant chromosomes observed in this experiment were not studied further; however , the structures of GCRs selected in many of these mutant strains have been determined ( Fig 3 ) . We also investigated whether cdc73Δ single and double mutants with telomere defects had TPE defects . Consistent with previous results [64] , we found that deletion of YKU80 caused significant TPE defects relative to wild-type cells and hence a decreased ability to grow on plates containing 5FOA ( Fig 4D ) . In contrast , the cdc73Δ and tel1Δ single mutant strains had modest but easily detectible or no sensitivity to 5FOA , respectively ( Fig 4D , S1A Fig , S17A Fig ) . However , the cdc73Δ yku80Δ and cdc73Δ tel1Δ double mutant strains showed increased sensitivity to 5FOA , suggesting increased perturbation of the chromatin structure proximal to the telomeres , and hence loss of silencing in these double mutants . Consistent with a synergistic defect in TPE rather than an indirect effect due to induction of ribonucleotide reductase [42] , growth on 5FOA-containing plates was not restored by addition of HU ( S1B Fig , S17A Fig , S20B Fig ) . To test interactions between cdc73Δ and additional telomere homeostasis mutations , we measured the dGCR rates of strains containing a cdc73Δ mutation in combination with deletions of SIR2 , SIR3 , or SIR4 , which cause defects in TPE , telomere chromatin structure and , at least in the case of SIR3 and SIR4 ( SIR2 does not appear to have been tested ) also cause shortened telomeres [61 , 65] , but were missing from our screen as these genes are required for mating [61 , 66] . The single sir2Δ , sir3Δ and sir4Δ mutant dGCR rates were increased 6 to 8-fold relative to the wild-type dGCR rate , and the double mutation combinations with the cdc73Δ mutation resulted in a synergistic increase in dGCR rates that were 41 to 190-fold higher than the wild-type dGCR rates ( S2 Table ) . In contrast , only 9 of the 36 mutations tested ( including sir3Δ and sir4Δ ) that were known to cause shortened telomeres [46–48 , 67] resulted in synergistic increases in dGCR rates when combined with cdc73Δ ( S18 Fig ) . However , of the 27 mutations that did not interact , 3 mutations caused extremely high GCR rates and 1 mutation was in a Paf1 complex genes making it unlikely that interactions could be detected . Of the remaining 23 non-interacting mutations , many caused weak or inconsistent phenotypes ( lst7Δ was reported to cause both long and short telomeres ) , 20 were identified in only one of two genetic screens performed suggestive of causing weak or inconsistent phenotypes and in most cases have not yet been demonstrated as causing a defect in a specific aspect of telomere homeostasis such as defects in TPE . Moreover , the cdc73Δ mutation also caused a strong synergistic increase in the dGCR rate when combined with a deletion of EXO1 ( Table 1 ) . EXO1 encodes a 5’ to 3’ exonuclease that acts in different DNA repair pathways and is the primary nuclease that resects deprotected telomeres [68–70] . Unlike the case of the cdc73Δ mutation , combining the exo1Δ mutation with either a yku80Δ or a tel1Δ mutation did not cause synergistic increases in the dGCR rate ( S2 Table ) . Taken together , these data do not argue that the cdc73Δ mutation causes synergistically increased GCR rates in strain backgrounds that have short telomeres per se . Rather , the interaction of cdc73Δ with tel1Δ and yku80Δ may reflect an interaction between mutations that disrupt specific aspects of telomere structure including telomere chromatin structure [61 , 65] , nuclear localization of telomerase [71 , 72] , and/or telomerase recruitment to telomeres [73 , 74] . The data described above are consistent with a role for CDC73 in suppressing genome instability arising due to telomere dysfunction . This effect could be due to roles of CDC73 in promoting TLC1 transcription [32] or causing defects in transcriptional elongation that give rise to recombinogenic RNA:DNA hybrids ( R-loops ) [75–78] , particularly at the sites of long noncoding telomeric repeat containing RNA ( TERRA ) [79] . We measured the TLC1 levels in cdc73Δ , tel1Δ , and yku80Δ single and double mutant strains and found that the yku80Δ and cdc73Δ mutations caused a small and large decrease in TLC1 levels , respectively , as previously reported [32] and that the cdc73Δ tel1Δ and cdc73Δ yku80Δ double mutants had the same level of TLC1 as the cdc73Δ single mutant ( S17B Fig ) . Introduction of a plasmid expressing TLC1 into strains in the uGCR assay caused a statistically significant ~4-fold decrease in the GCR rate of the cdc73Δ tel1Δ double mutant and caused a small , but not statistically significant , decrease in the GCR rate of the cdc73Δ yku80Δ double mutant ( S5 Table ) . Consistent with the suppression results , the TLC1 expression plasmid caused 1 ) increased TLC1 levels in all strains tested , 2 ) increased the telomere lengths in the cdc73Δ single mutant and the cdc73Δ tel1Δ double mutant , and 3 ) potentially a small increase in telomere length in the cdc73Δ yku80Δ double mutant as evidenced by a modest increase in more slowly migrating telomere species ( S19 Fig ) . We also measured the TERRA levels in cdc73Δ , tel1Δ , and yku80Δ single and double mutant strains and found that these mutants did not significantly affect TERRA accumulation , except for an increase of the chrXV L TERRA in a yku80Δ single mutant ( S17C Fig ) . To test if the effects of cdc73Δ might be due to the accumulation of R-loops , we introduced a plasmid bearing RNH1 , which encodes S . cerevisiae RNase H1 , into uGCR assay strains . In contrast to TLC1 overexpression , the RNH1 plasmid did not substantially affect the uGCR rate of either the cdc73Δ tel1Δ double mutant or the cdc73Δ yku80Δ double mutant ( S5 Table ) . Taken together , these data suggest that the increased GCR rate caused by the cdc73Δ mutation may in part reflect an alteration in telomere structure caused by reduced telomerase activity due to reduced TLC1 levels . However , the synergistic increases in GCR rates seen in the cdc73Δ tel1Δ and cdc73Δ yku80Δ double mutants ( and potentially the ctr9Δ and paf1Δ double mutants ) is unlikely to be explained solely by reduced TLC1 levels as these double mutants have the same TLC1 levels as the cdc73Δ single mutant . Cdc73 , like other members of the Paf1 complex , has no known enzymatic activity [24] . The N-terminal region ( S . cerevisiae residues 1–229 ) lacks identifiable domains; whereas the C-terminal domain ( S . cerevisiae residues 230–393 ) has a conserved GTPase-like fold [41 , 80] and has been proposed on the basis of chemical crosslinking and cryo-electron microscopy to make direct interactions with the RNA polymerase II subunit Rpb3 [81] . We replaced the wild-type chromosomal copy of CDC73 with various CDC73 deletion mutations to gain insights into Cdc73 function ( Fig 5A; S20 Fig; S6 Table ) . We found that deletion of the C-terminal domain ( cdc73Δ230–393 ) resulted in wild-type dGCR rates , normal sensitivity to 6-azauracil and normal TPE . This result contrasts with a previous report suggesting that a cdc73Δ231-393-TAP construct causes increased sensitivity to 6-azauracil relative to wild-type CDC73 [41]; this difference may be due to the presence of the TAP tag in the previous study . On the other hand , deletion of the N-terminal region ( cdc73Δ2–229 ) , caused defects in all three assays that were similar to those caused by the cdc73Δ single mutation . Additional analysis of CDC73 ( Fig 5A ) defined a minimal deletion , cdc73Δ125–229 , that caused a similar fold-increase in the dGCR rate compared to that caused by the cdc73Δ single mutation ( 17 . 3-fold increase vs . 9 . 3-fold increase ) and caused a synergistic increase in the dGCR rate when combined with the yku80Δ mutation that was similar to that observed with the cdc73Δ mutation ( 98 . 1-fold increase vs . 162-fold increase ) . This minimal deletion also caused increased sensitivity to 6-azauracil and reduced TPE ( Fig 5A , S20A Fig ) as well as reduced TLC1 levels ( Fig 5B ) and shorter telomeres ( Fig 5C ) similar to that caused by the cdc73Δ single mutation; as before , addition of sublethal concentrations of HU to distinguish TPE from 5FOA-induced overexpression of ribonucleotide reductase verified that the cdc73Δ125–229 mutation , like the cdc73Δ mutation , caused TPE defects ( S20B Fig ) . As the effect of the cdc73Δ125–229 mutation could either have been due to loss of a functional region of Cdc73 or due to causing defects in folding Cdc73 , we generated a gene construct that encoded only residues 125–229 ( cdc73:125–229 ) . This gene construct , which encoded 105 residues from the center of Cdc73 , was sufficient to substantially restore Cdc73 functions in suppressing GCRs , maintenance of TLC1 levels , TPE , and telomere length homeostasis ( Fig 5 , S20 Fig ) . These results define a minimal functional Cdc73 construct , Cdc73:125–229 , and a minimal non-functional Cdc73 construct , Cdc73Δ125–229 . Residues 125–229 of Cdc73 precede the C-terminal GTPase domain and lie in a region that is predicted to be less ordered by IUPRED [82] ( S21A Fig ) and that has reduced conservation ( S21B Fig ) . Previous chemical crosslinking of the Paf1 complex bound to RNA polymerase II identified 22 crosslinks between Cdc73 and other Paf1 subunits of which 19 were between Cdc73 and Ctr9 , which is primarily composed of tetratricopetide repeat ( TPR ) domains [81] . Analysis of these data also revealed that the Cdc73 region containing residues 125–229 had 9 crosslinks to Ctr9 ( ~50% of Cdc73-Ctr9 crosslinks ) , 2 crosslinks to Leo1 , 2 crosslinks to Rpb11 , and 1 crosslink to Rpb2 ( S21C Fig ) . Together these data are consistent with the possibility that the TPR domains of Ctr9 bind to an unstructured Cdc73 peptide or Cdc73 alpha helices , rather than a folded Cdc73 domain , like other known TPR-peptide interactions [83] . To test for a direct Cdc73-Ctr9 interaction in the Paf1 complex , we tested the ability of Paf1 and Cdc73 to co-immunoprecipitate in a wild-type strain or strains with deletions of LEO1 , RTF1 , or CTR9 ( S21D Fig ) . Consistent with this hypothesis , the Paf1-Cdc73 interaction was lost in the ctr9Δ strain , whereas deletions of LEO1 and RTF1 had only modest effects on the Paf1-Cdc73 interaction . As the paf1Δ mutation causes increased dGCR rates similar to those caused by the cdc73Δ mutation , we sought to determine if the functional truncated Cdc73 proteins bound the Paf1 complex and if the defects associated with the minimal non-functional Cdc73Δ125–229 truncation were due to loss of Paf1 complex association or due to defects in other functions . We tested the ability of C-terminally Venus-tagged full-length Cdc73 , Cdc73Δ230–393 , Cdc73Δ2–124 , Cdc73Δ125–229 , or Cdc73:125–229 to co-immunopreciptate with C-terminally myc-tagged Paf1 , Rtf1 , Ctr9 , or Leo1; all tagged proteins were expressed from the respective chromosomal loci . Cell lysates from doubly tagged strains were prepared from log-phase cells and immunoprecipitated with anti-myc antibodies , and then probed by Western blotting using anti-GFP antibodies . Full-length Cdc73 co-immunoprecipitated with Paf1 , Rtf1 , Ctr9 , and Leo1 ( Fig 6A ) , although the interaction with Rtf1 appeared to be weaker than the interaction with the other Paf1 complex subunits , consistent with previous observations [22 , 43 , 84] . The functional Cdc73 truncations , Cdc73Δ230–393 , Cdc73Δ2–124 , and Cdc73:125–229 , all associated with Paf1 , Ctr9 , Leo1 , and Rtf1 ( Fig 6A ) . Reduced binding to Leo1 was observed with both the Cdc73Δ230–393 and Cdc73:125–229 truncations , suggesting that the C-terminus of Cdc73 may stabilize Leo1 in the complex . In contrast , the non-functional Cdc73 truncation , Cdc73Δ125–229 , had substantially reduced binding to each of the other Paf1 complex subunits; a low level of residual binding was only detected with Ctr9 and Leo1 . Thus residues 125–229 of Cdc73 appear to be necessary and sufficient for stable binding of Cdc73 to the Paf1 complex . The Paf1 complex has been localized to the nucleus in wild-type cells by immunofluorescence [85] , so we monitored the cellular localization of Cdc73 truncations . The wild-type and truncated forms of Cdc73 were C-terminally tagged with Venus , and functional versions of Cdc73 , including the minimal construct Cdc73:125–229 , localized to the nucleus ( Fig 6B ) , with a high ratio of nuclear to cytoplasmic fluorescence ( Fig 6C ) . In contrast , the non-functional Cdc73 truncation Cdc73Δ125–229 , which did not stably associate with the Paf1 complex , had diffuse localization in both the nucleus and the cytoplasm , but was still expressed at normal levels based on total cellular fluorescence ( Fig 6D ) . Thus , residues 125–229 of Cdc73 either include a nuclear localization signal or are necessary for binding to a Paf1 complex that is imported into the nucleus . Single mutant strains with deletions of PAF1 , CTR9 , RTF1 , and LEO1 appeared to have normal nuclear localization of a Cdc73-Venus fusion protein ( S22A Fig ) , although these mutations resulted in enlarged cells and abnormally elongated buds , as previously described [18 , 86] . Similarly , deletion of CDC73 did not prevent the nuclear localization of C-terminally Venus tagged Paf1 , Rtf1 , Ctr9 , or Leo1 ( S22B Fig ) , indicating that defects caused by the cdc73Δ mutation were not due to defects in the nuclear localization of other Paf1 complex subunits . Finally , the cdc73Δ mutation did not cause major changes in the cellular levels of the other Paf1 complex subunits as measured by Western blot ( S22C Fig ) . These localization data are consistent with the observation that all Paf1 complex subunits other than Cdc73 are predicted to contain nuclear localization signals ( S23 Fig ) ; this is different from that seen with human Cdc73 , which possesses a functional N-terminal nuclear localization signal [87] . Together , these data suggest that Cdc73 does not regulate the cellular localization of the Paf1 complex , but instead mediates the suppression of genome instability once the complex is already in the nucleus , potentially through contributions to overall complex stability or conformation .
Transcription , and defects in transcription including those that lead to the accumulation of R-loops , are becoming an increasingly well-appreciated source of genome instability [10 , 11] . Using a screen to identify genes that suppress the accumulation of GCRs , we found the loss of CDC73 results in increased rates of accumulating GCRs in three different GCR assays . We also found that a cdc73Δ mutation resulted in synergistic increases in GCR rates and in increased levels of telomere dysfunction when combined with either tel1Δ or yku80Δ mutations . This is reminiscent of the observation that tlc1Δ tel1Δ double mutants have synergistic increases in GCR rates relative to the respective single mutants , although they show delayed senescence and delayed loss of telomeres [88]; analysis of GCR rates and other telomere-related phenotypes in tlc1Δ yku80Δ double mutants was not possible as these double mutants cannot be propagated [59 , 89] . The fact that the cdc73Δ tel1Δ yku80Δ triple mutant appears to be highly defective in telomerase function and also shows a large synergistic increase in the rate of accumulating GCRs further suggests that telomere dysfunction is likely a hallmark of genome instability in cdc73Δ strains and that cdc73Δ , tel1Δ , and yku80Δ mutations all cause different defects that contribute to increased rates of accumulation of GCRs . A role for CDC73 in contributing to telomerase function is also consistent with our inability to observe GCRs formed by de novo telomere additions relative to the large increase in the levels of different translocation GCRs among the GCRs selected in the sGCR assay in cdc73Δ mutants . Consistent with the observation that cdc73 defects result in reduced levels of the TLC1 RNA component of telomerase [32] , overexpression of TLC1 partially suppressed the increased GCR rate of the cdc73Δ tel1Δ double mutant . In contrast , over-expression of RNase H1 , which degrades R-loops , did not suppress the increased GCR rate of the cdc73Δ tel1Δ double mutant . The absence of telomerase in S . cerevisiae results in shortening of telomeres and reduced rates of cell growth until telomerase negative cells undergo crisis and survivors emerge in which telomeres are maintained by one of two different HR-mediated telomere maintenance pathways [61 , 63] . These surviving cells do not have increased rates of accumulating GCRs , although additional genetic defects can result in synergistic increases in GCR rates in these telomerase negative cells [55] . One exception where telomerase defects alone result in increased GCRs is telomerase negative cells that have been stabilized by re-expression of telomerase during crisis before the shortened telomeres have started to be maintained by HR [90] . In addition , tel1Δ mutations , which by themselves result in shortened telomeres and small increases in GCR rates , can result in large increases in GCR rates when combined with mec1 or other mutations [91] . Under all of these conditions , the telomeres with altered structures fuse to the ends of broken chromosomes , and the resulting fusion chromosomes then appear to undergo breakage and additional rearrangement events [92]; these altered telomeres can also undergo telomere to telomere fusion [93] . The structural analysis presented here showed that the cdc73Δ mutation that causes an increased GCR rate and the cdc73Δ tel1Δ and cdc73Δ yku80Δ double mutation combinations that cause synergistic increases in GCR rates did not appear to cause the accumulation of either de novo telomere addition-mediated GCRs or GCRs mediated by fusion of altered telomeres to broken chromosome ends . Telomerase activity is likely reduced but not absent in cdc73Δ mutants [32] , which would explain the presence of telomeres that are shorter than normal and this is likely sufficient to result in modest increases in the rate of accumulating GCRs as well as the absence of de novo telomere addition-mediated GCRs . When a cdc73Δ mutation is combined with other mutations like tel1Δ and yku80Δ , which affect telomere maintenance in different ways and also cause shortened telomeres , there is an increased defect in telomere maintenance and an increased alteration of telomere chromatin structure as indicated by synergistic increases in TPE defects and a synergistic increase in the rate of accumulating GCRs . A hypothesis that explains the increased GCR rates and the spectrum of GCRs observed is that in these mutants reduced telomere maintenance combined with alterations in telomere chromatin structure results in a fraction of chromosome ends that escape protection and undergo extensive degradation ( Fig 7 ) . These degraded chromosome ends can then be processed by end joining to other DSBs , hairpin formation or short sequence-mediated HR resulting in the GCRs selected in the sGCR assay or longer sequence non-allelic HR resulting in the translocations selected in the dGCR assay . This mechanism also accounts for the increased GCR rates seen in paf1Δ and ctr9Δ single and double mutants analyzed as these mutations also cause telomere maintenance and telomere chromatin structure defects as evidenced by reduced TLC1 levels , short telomeres and TPE defects [32] . The lack of or limited increased GCR rates seen in rtf1Δ and leo1Δ single and double mutants is also accounted for by this mechanism as these latter mutations have smaller effects on TLC1 levels and telomere shortening [32] , and in the case of leo1Δ mutations , no defect in TPE reflective of alterations in telomere chromatin structure . The Paf1 complex promotes transcription elongation , 3’-end mRNA maturation , and histone modification [16 , 21–24] . Our results demonstrate that different subunits of the Paf1 complex subunits promote different Paf1 complex functions: ( 1 ) suppression of GCRs primarily requires Paf1 , Ctr9 , and Cdc73; ( 2 ) resistance to 6-azauracil inhibition of transcriptional elongation primarily requires Paf1 and Ctr9; and ( 3 ) silencing of telomere-proximal genes requires Cdc73 , Paf1 , Ctr9 and Rtf1 to differing degrees . The rather disparate effects of deleting genes encoding different Paf1 complex subunits observed here mirrors previous observations of different requirements for individual Paf1 complex subunits under different stress conditions [37] . Using an assay that detected chromosome loss and GCRs but did not distinguish between the two , a previous study showed that deletions of CDC73 and LEO1 , but not PAF1 , resulted in increased genome instability that could be suppressed by increased expression of RNase H1 [12] . The relatively important role of Paf1 in all of the Paf1 complex functions ( our results as well as in previous studies [37] ) is consistent with the idea that Paf1 functions by mediating recruitment of the other Paf1 subunits to the different processes they functions in . Alternatively , Paf1 may provide the major function of the Paf1 complex and may be recruited to different processes by different Paf1 complex subunits: Leo1 and Rtf1 bind RNA [94]; Rtf1 binds phosphorylated Spt5 , which is a component of TFIIS and binds the elongating RNA polymerase II complex [95 , 96]; and the Cdc73 C-terminal domain mediates binding to the phosphorylated C-terminal domain ( CTD ) of RNA polymerase II [97] . The importance of the interaction of Cdc73 with Paf1 is demonstrated by the deletion analysis of Cdc73 . The C-terminal domain and the N-terminal regions of Cdc73 were found to be dispensable for CDC73 function . However , the central 105 amino acid region ( residues 125–229 ) was necessary and sufficient to: ( 1 ) suppress the defects of cdc73Δ strains studied here; ( 2 ) mediate incorporation into the Paf1 complex; and ( 3 ) promote nuclear localization of Cdc73 . Remarkably , the C-terminal region , which binds the phosphorylated RNA polymerase II CTD [97] and contributes to suppression of Ty element expression [41] , was not required for any of the functions analyzed here . The dispensable nature of the Cdc73 C-terminal domain could be consistent with the redundancy of recruitment of the Paf1 complex to RNA polymerase II by either Cdc73 binding to the phosphorylated RNA polymerase II CTD or by Rtf1 binding to phosphorylated Spt5 [97] . This redundancy also explains the synergistic defect in 6-azauracil sensitivity caused by combining a deletion of the C-terminal domain of Cdc73 with loss of Rtf1 [41] . Moreover , the available data suggest that the central 105 amino acid region ( residues 125–229 ) of Cdc73 plays some previously unappreciated function in the Paf1 complex . Extensive chemical crosslinking between this region of Cdc73 and the TPR domain containing protein Ctr9 [81] and the requirement for Ctr9 for coimmunoprecipitation of Cdc73 with Paf1 suggest that the Ctr9-Cdc73 interaction recruits Cdc73 to the Paf1 complex . The fact we were unable to computationally predict a function-associated motif or domain structure within the central 105 amino acid region and that the N-terminus of S . cerevisiae Cdc73 up to residue 236 is highly sensitive to partial proteolysis [41] suggests the central 105 amino acid region of Cdc73 is likely unstructured in the absence of the Paf1 complex . This is consistent with the role of TPR domains in binding alpha-helices and unstructured peptides [83] . Together these data are also consistent with the fact that ctr9Δ mutations , like cdc73Δ mutations , also cause increased GCR rates , cause synergistic increases in GCR rates when combined with yku80Δ and tel1Δ mutations , have TPE defects , and defects in TLC1 expression [32] . Mutations in human CDC73 ( also called HRPT2 ) identified in cases of sporadic and hereditary parathyroid carcinomas appear to primarily be loss-of-function mutations including frameshifts , premature stop codons , and deletions that result in truncated proteins . In many cases , the heterozygous germline mutations observed are associated with events leading to loss-of-heterozygosity in tumors; however , some tumors appear to have amplification of the mutant copy of CDC73 , suggesting a dominant genetic phenotype [28–30 , 98–100] . The region of human Cdc73 ( also called parafibromin ) responsible for Paf1 complex binding [29] is in a region that is similar to the central 105 amino acid region in S . cerevisiae Cdc73 identified here , and at least some mutant versions of human Cdc73 seen in parathyroid carcinomas have lost their ability to interact with the Paf1 complex [101] . The 3 mutations in CTR9 found in Wilms tumor families comprised a nonsense mutation and 2 splice site mutations , all of which were consistent with causing loss-of-function [31] . Our results suggest that the CDC73 mutations seen in sporadic and hereditary parathyroid carcinomas and the CTR9 mutations found in Wilms tumor could cause increased genome instability; however , it is not currently known if these defects in human CDC73 and CTR9 cause genome instability and telomere dysfunction in human cells as observed here for the S . cerevisiae cdc73Δ mutation . Given the ability of the Paf1 complex to affect transcriptional elongation , RNA 5’ end maturation , and histone modification , inherited and sporadic CDC73 mutations and inherited CTR9 mutations in human cancers could have pleiotropic effects in which increased genome instability might not play the only role in carcinogenesis .
All S . cerevisiae strains used in this study were derived from S288c and were constructed by standard PCR-based gene disruption methods or by mating to strains containing mutations of interest ( S7 Table; [102 , 103] ) . GCR assays were performed using derivatives of RDKY7635 ( dGCR assay ) , RDKY7964 ( sGCR assay ) , and RDKY6677 , ( uGCR assay ) ( S7 Table; [6 , 51] ) . The Venus , mCherry and 9myc tags were amplified from pBS7 , pBS35 , and pYM19 , respectively [102 , 104] , inserted at the 3’ end of the indicated genes using standard methods . For determining GCR rates of strains transformed with the RNase H1 tet-off overexpression plasmid pCM184RNH1 ( a gift from Andrés Aguilera , Universidad de Sevilla , Seville , Spain [105] ) or the ADH1 promoter TLC1 overexpression plasmid pVL2679 ( a gift from Victoria Lundblad , Salk Institute ) , transformants were cultured overnight in complete synthetic medium ( CSM ) –Trp liquid media and plated onto either CSM–Trp medium or CSM–Arg–Trp medium supplemented with 1 g/L 5FOA and 60 mg/L canavanine . To test for transcription elongation defects , 6-azauracil ( Sigma-Aldrich ) was added to synthetic complete medium at a final concentration of 50 μg/ml . CDC73 , including 998 bp upstream and 536 bp downstream , was amplified by PCR using the primers 5’-CAC CGA ATT GCA AGC GCT TGC AAC TTG TTC TTT CTG TGC -3’ and 5’-GAA TTG CAA GCG CTC CCA TGG AAA TGA GAG AAG C-3’ ( AfeI cut site underlined ) and cloned into the pENTR/D-TOPO vector ( Thermo Fisher Scientific ) to generate pRDK1705 . The hygromycin B resistance gene was amplified from the plasmid pFA6a-hphNT1 with the primers 5’-GAA TTG CAA AGC TTC GGA TCC CCG GGT TAA TTA A-3’ and 5’-GAA TTG CAA AGC TTT AGG GAG ACC GGC AGA TCC G-3’ ( HindIII cut site underlined ) and inserted into pRDK1705 at a HindIII cut site located 693 bp upstream of the CDC73 start codon to make plasmid pRDK1706 . The cdc73 alleles were made in pRDK1706 using the GeneArt Site-Directed Mutagenesis kit ( Life Technologies ) to generate pRDK1708 ( cdc73Δ230–393 ) , pRDK1770 ( cdc73Δ2–229 ) , pRDK1771 ( cdc73Δ92–229 ) , pRDK1772 ( cdc73Δ2–91 ) , pRDK1781 ( cdc73Δ92–147 ) , pRDK1782 ( cdc73Δ148–229 ) , pRDK1784 ( cdc73:92–229 ) , pRDK1788 ( cdc73Δ2–124 ) , pRDK1789 ( cdc73Δ125–229 ) , and pRDK1790 ( cdc73:125–229 ) . These plasmids were integrated at the endogenous CDC73 locus by transformation with AfeI-digested plasmid DNA . Integrants were confirmed by PCR and Sanger sequencing . We crossed a strain containing the dGCR assay and a cdc73Δ or rtf1Δ mutation against 638 strains from the S . cerevisiae deletion collection and obtained haploid progeny by germinating spores generated from the resulting diploids , as previously described [6] . Systematically generated cdc73Δ double mutants and control haploid and diploid strains were screened by flow cytometry for DNA content to exclude diploid isolates . Briefly , 10 μL aliquots of overnight cultures grown in YPD were added to 190 μL of fresh YPD , and the cells were incubated in a 30°C shaker for 3 hours . Cells were washed , resuspended in 60 μL of dH2O , and fixed with 140 μL of cold absolute ethanol . Fixed cells were sonicated and resuspended in 150 μL of 50 mM sodium citrate with 1 mg/mL Proteinase K ( Sigma-Aldrich ) and 0 . 25 mg/mL RNase A ( Sigma-Aldrich ) and incubated at 37°C overnight . Treated cells were washed , resuspended in 100 μL of 50 mM sodium citrate containing 1 μM Sytox Green ( Life Technologies ) , and analyzed using a BDS LSR II flow cytometer at The Scripps Research Institute flow cytometry core facility . Data were analyzed using FlowJo v10 [106] . Patch tests for identifying systematically generated double mutants with increased GCR rates were performed as described [6] . GCR rates were determined using at least 14 independent cultures from 2 independent biological isolates of each strain using the fluctuation method as previously described [107] . Significantly different GCR rates were identified through analysis of the 95% confidence intervals . The t ( V;XIV ) and t ( V;IV or X ) homology-mediated translocation GCRs were identified by PCR , as previously described [51] . Multiplexed paired-end libraries were constructed from 5 μg of genomic DNA purified using the Purgene kit ( Qiagen ) . The genomic DNA was sheared by sonication and end-repaired using the End-it DNA End-repair kit ( Epicentre Technologies ) . Common adaptors from the Multiplexing Sample Preparation Oligo Kit ( Illumina ) were then ligated to the genomic DNA fragments , and the fragments were then subjected to 18 cycles of amplification using the Library Amplification Readymix ( KAPA Biosystems ) . The amplified products were fractionated on an agarose gel to select 600 bp fragments , which were subsequently sequenced on an Illumina HiSeq 2000 using the Illumina GAII sequencing procedure for paired-end short read sequencing . Reads from each read pair were mapped separately by bowtie version 2 . 2 . 1 [108] to a reference sequence that contained revision 64 of the S . cerevisiae S288c genome [109] , hisG from Samonella enterica , and the kanMX4 marker ( S3 Table ) . Reads are available from National Center for Biotechnology Information Sequence Read Archive under accession number: SRP107803 . GCR structures were determined using mapped reads using version 0 . 6 of the Pyrus suite ( http://www . sourceforge . net/p/pyrus-seq ) [52] . Rearrangements relative to the reference S288c genome were identified by analyzing the read depth distributions ( S5–S8 Figs ) , the discordantly mapping read pairs ( S2–S4 Figs; S4 Table ) , and/or extracting the sequences of the novel junctions ( S9–S13 Figs ) . Associated junction-sequencing reads , which were reads that did not map to the reference but were in read pairs in which one end was adjacent to discordant reads defining a junction , were used to sequence novel junctions . Most hairpin-generated junctions ( S12 Fig ) could be determined using alignments of junction-sequencing reads . For junctions formed by HR between short repetitive elements ( S9–S11 Figs ) and for problematic hairpin-generated junctions ( S12 Fig ) , the junction sequence could be derived by alignment of all reads in read pairs where one read was present in an “anchor” region adjacent to the junction of interest and the other read fell within the junction to be sequenced . Junctions indicated by copy number changes , discordant read pairs , and junction sequencing were identified with a high degree of confidence; however , previous analyses have indicated that even junctions inferred from only copy number changes can be experimentally verified at high frequency [52 , 92 , 110 , 111] . Analysis of the sequencing data identified all of the genetic modifications introduced during construction of the starting strains , such as the his3Δ200 deletion , ( S2–S4 Figs ) as well as the molecular features associated with the selected GCRs ( S5–S13 Figs; S4 Table ) . Several inverted duplications ( isolates 307 , 324 , and 331 ) with a YCLWdelta5/YELWdelta1 junction copied very little sequence in the vicinity of YELWdelta1 , and had an additional HR-mediated translocation between YELWdelta1 and an unannotated delta sequence on chrV R , which we term here “YERWdelta27” ( S14 Fig ) . Telomere Southern blots were performed using a modified version of a previously described protocol [112] . Genomic DNA was purified from 50 mL overnight cultures using the Purgene kit ( Qiagen ) . 5 μg of DNA was digested with XhoI ( New England Biolabs ) in a 50 μL reaction for 2 hr at 37°C . The reaction was stopped by adding 8 μL of loading buffer , and the samples were run on a 0 . 8% agarose gel in 0 . 5X TBE for 16 hr at 50 V . The DNA in the gel was transferred to Amersham Hybond-XL membranes ( GE ) by neutral capillary blotting , allowed to run overnight . The DNA was crosslinked to the membrane by UV irradiation in a Stratalinker ( Stratagene ) apparatus at maximum output for 60 seconds . Biotinylated TG probes were purchased from ValueGene . Probe hybridization was performed with ULTAhyb oligo hybridization buffer ( Life Technologies ) at 42°C for 1 hr . The membrane was then washed extensively and developed with a chemiluminescent nucleic acid detection kit ( Life Technologies ) and imaged with a Bio-Rad Imager . DNA plugs for PFGE were prepared as described [113] . Strains were grown to saturation in 50 mL of YPD at 30°C for 3 days . Cell counts were measured by optical density at 600 nm , and 7 . 5 x 108 cells from each strain were washed and resuspended in 200 μL of 50 mM EDTA , then mixed with 70 μL of 1 M sorbitol , 1 mM EDTA , 100 mM sodium citrate , 0 . 5% β-mercaptoethanol , 8 U/mL of zymolase . The cells were then mixed with 330 μL of liquefied 1% ultrapure agarose ( Bio-Rad ) to prepare multiple 80 μL plugs . The plugs were incubated in 15 mL conical tubes in 750 μL of 10 mM Tris pH 7 . 5 , 500 mM EDTA pH 8 , 1% β-mercaptoethanol for 16 hr at 37°C . The plugs were then incubated in 750 μL 10 mM Tris pH 7 . 5 , 500 mM EDTA pH 8 , 1% sodium N-lauryl sarcosine , 0 . 2% sodium dodecyl sulfate containing 2 mg/ml Proteinase K ( Sigma-Aldrich ) for 6 hr at 65°C . Finally , the plugs were washed in 50 mM EDTA pH 8 prior to resolving the chromosomes in a 1% agarose gel run in a CHEF ( clamped homogeneous electric field electrophoresis ) apparatus in chilled ( 14°C ) 0 . 5x TBE ( 89 mM Tris-borate , pH 8 . 3 , 25 mM EDTA ) . Electrophoresis was performed using a Bio-Rad CHEF-DRII apparatus at 6 V/cm , with a 60 to 120 s switch time for 24 h . The gels were stained with ethidium bromide and imaged . The TPE assay was constructed by transforming BY4742 ( MATalpha leu2Δ0 his3Δ1 ura3Δ0 lys2Δ0 ) with pADH4UCA ( [38] , a gift from Virginia Zakian , Princeton University ) digested with SalI and EcoRI . Integration of URA3 into ADH4 , which was verified by PCR , generated the strain RDKY8230 , and mutant derivatives were constructed by PCR-mediated gene disruption ( S7 Table ) . TPE was assayed by culturing strains overnight in YPD at 30°C followed by spotting 1 . 5 μL of 10-fold serial dilutions onto CSM , and CSM supplemented with 1 g/L of 5FOA ( CSM+5FOA ) . Plates were incubated at 30°C for 3 days before imaging . In some experiments , the plates also contained either 10 mM or 30 mM HU [42] . RNA isolation and qRT-PCR for TLC1 and TERRA RNA levels were performed using published techniques [114 , 115] . Cells were grown in YPD to an OD600 of 0 . 6 to 0 . 8 . 1 mL samples were used for RNA isolation with the RNeasy kit ( Qiagen ) , with on-column DNase I treatment using the RNase-Free DNase Set ( Qiagen ) . 1 μg RNA was reverse transcribed with the iScript cDNA Synthesis Kit ( Bio-Rad ) , which uses random primers . cDNA was diluted 1:10 with distilled H2O . qPCR was performed with 2 μL of the dilution in a final volume of 20 μL using the iTaq Universal SYBR Green Supermix ( Bio-Rad ) in a Bio-Rad CFX96 Touch Real-Time PCR Detection System . Reaction conditions: 95°C for 10 min , 95°C for 15 sec , 50°C for 1 min , 40 cycles . Primer concentrations and sequences were the same as previously described [115] . The μMACS anti-c-myc magnetic bead IP kit ( Miltenyi Biotec ) was used in immunoprecipitation experiments . Lysates were generated from strains in which one or two Paf1 complex genes in the S . cerevisiae strain BY4741 ( MATa leu2Δ0 his3Δ1 ura3Δ0 met15Δ0 ) were tagged with Venus or c-myc . Strains were grown to mid-log phase in 50 mL YPD , harvested , resuspended in 1 mL of the supplied lysis buffer , and incubated on ice for 30 minutes . Cells were lysed with the addition of 100 μL of glass beads and vortexed four times for 1 minute with cooling . Lysates were clarified at 14 , 000 rpm for 10 minutes at 4°C . Protein concentrations were determined using the DC Protein Assay ( Bio-Rad ) . For the input analysis , 500 μg of protein was trichloroacetic acid ( TCA ) precipitated , resuspended in 100 μL of 2x SDS gel loading buffer ( 100 mM Tris-Cl ( pH 6 . 8 ) , 4% SDS , 20% glycerol , 200 mM DTT , 0 . 2% bromophenol blue ) and 10 μL was used for Western Blotting . For the immunoprecipitation , 1000 μg of protein was incubated with 50 μL anti-c-myc MicroBeads ( Miltenyi Biotec ) for 30 minutes on ice , then passed through the μMACS separator column . The column was washed twice with 200 μL of lysis buffer , washed twice with 200 μL of wash buffer 1 , then washed once with 100 μL of wash buffer 2 . The column was then incubated with 20 μL of heated elution buffer for 5 minutes , before the proteins were eluted with 50 μL of heated elution buffer . Of the eluted volume , 12 μL was used for Western Blotting . Proteins were resolved on a 4–15% SDS-PAGE gel ( Bio-Rad ) and transferred overnight onto nitrocellulose membrane ( Bio-Rad ) . Venus-tagged proteins were detected with the rabbit monoclonal antibody ab290 ( Abcam , 1:2000 ) and myc-tagged proteins were detected with 71D10 rabbit monoclonal antibody ( Cell Signaling , 1:1000 ) . Horseradish peroxidase-conjugated goat anti-rabbit secondary antibody ( Jackson Laboratories , 1:5000 ) was used , followed by chemiluminescence detection with SuperSignal Femto Sensitivity Substrate ( Life Technologies ) and imaged with a Bio-Rad Imager . Venus-tagged protein levels were also detected using mouse monoclonal antibody B34 ( Covance , 1:1000 ) and mouse monoclonal anti-Pgk1 antibody ( ab113687 , Abcam , 1:5000 ) was used to detect Pgk1 as a loading control . Exponentially growing cultures were washed and resuspended in water before being placed on minimal media agar pads , covered with a coverslip , and sealed with valap ( a 1:1:1 mixture of Vaseline , lanolin , and paraffin by weight ) . Cells were imaged on a Deltavision ( Applied Precision ) microscope with an Olympus 100X 1 . 35NA objective . Fourteen 0 . 5 μm z sections were acquired and deconvolved with softWoRx software . Further image processing , including intensity measurements were performed using ImageJ . Intensity levels were quantified by taking the mean intensity in the nucleus , the cytoplasm , and a background measurement outside of the cell using a 3-pixel diameter circle . The ratio of background-subtracted nuclear fluorescence to background-subtracted cytoplasmic fluorescence was then calculated per cell . The total fluorescence was estimated by taking the background-subtracted nuclear fluorescence and adding it to 12 . 5 times the background-subtracted cytoplasmic fluorescence as an approximation of the ratio cytoplasmic to nuclear volume . | Maintaining a stable genome is crucial for all organisms , and loss of genome stability has been linked to multiple human diseases , including many cancers . Previously we found that defects in Cdc73 , a component of the Paf1 transcriptional elongation complex , give rise to increased genome instability . Here , we explored the mechanism underlying this instability and found that Cdc73 defects give rise to partial defects in maintaining telomeres , which are the specialized ends of chromosomes , and interact with other mutations causing telomere defects . Remarkably , Cdc73 function is mediated through a short central region of the protein that is not a part of previously identified protein domains but targets Cdc73 to the Paf1 complex through interaction with the Ctr9 subunit . Analysis of the other components of the Paf1 complex provides a model in which the Paf1 subunit mediates recruitment of the other subunits to different processes they function in . Together , these data suggest that the mutations in CDC73 and CTR9 found in patients with hyperparathyroidism-jaw tumor syndrome and some patients with Wilms tumors , respectively , may contribute to cancer progression by contributing to genome instability . | [
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] | 2018 | Cdc73 suppresses genome instability by mediating telomere homeostasis |
Memory phenotype ( CD44bright , CD25negative ) CD4 spleen and lymph node T cells ( MP cells ) proliferate rapidly in normal or germ-free donors , with BrdU uptake rates of 6% to 10% per day and Ki-67 positivity of 18% to 35% . The rapid proliferation of MP cells stands in contrast to the much slower proliferation of lymphocytic choriomeningitis virus ( LCMV ) -specific memory cells that divide at rates ranging from <1% to 2% per day over the period from 15 to 60 days after LCMV infection . Anti-MHC class II antibodies fail to inhibit the in situ proliferation of MP cells , implying a non–T-cell receptor ( TCR ) -driven proliferation . Such proliferation is partially inhibited by anti–IL-7Rα antibody . The sequence diversity of TCRβ CDR3 gene segments is comparable among the proliferating and quiescent MP cells from conventional and germ-free mice , implying that the majority of proliferating MP cells have not recently derived from a small cohort of cells that expand through multiple continuous rounds of cell division . We propose that MP cells constitute a diverse cell population , containing a subpopulation of slowly dividing authentic antigen-primed memory cells and a majority population of rapidly proliferating cells that did not arise from naïve cells through conventional antigen-driven clonal expansion .
Peripheral non-Treg CD4+ T cells are often divided into two major subpopulations that can be designated naïve-phenotype ( NP ) and memory-phenotype ( MP ) cells , respectively [1] . In the mouse , MP cells are characterized by the expression of high levels of CD44 and low levels of CD45RB; they lack Foxp3 and high levels of CD25 . MP cells may be either CD62L dull or bright [2] . It is generally assumed that MP cells constitute the aggregate of all antigen-specific memory cells; that is , of all cells that have expanded in response to antigenic stimulation . However , there are some reasons to question the concept that all MP cells are indeed foreign antigen-experienced cells . MP cells proliferate rapidly; estimates of their proliferative rates in lymph nodes range from 4% to 10% per day [2] , [3] . By contrast , T-cell receptor ( TCR ) transgenic [4] , [5] or polyclonal [5] , [6] CD4 T cells that had responded to immunization with cognate antigens or infection proliferate at <1% to 2 . 5% per day when examined after the initial expansion and contraction phases have been completed [7] . The proliferation of antigen-primed CD4 T cells is largely driven by cytokines rather than through TCR stimulation [8]–[14] . What drives the rapid , apparently spontaneous , proliferation of MP under normal conditions is unknown , although when transferred to lymphopenic recipients , their proliferation is burst-like ( i . e . , they divide multiple times in a relatively short period ) and appears to be driven by TCR-mediated stimulation . Understanding the proliferation of MP cells has also been of considerable interest among those studying lymphocyte dynamics in chronic infections , particularly with lentiviruses , where proliferative rates of human or macaque MP cells in HIV- or SIV-infected individuals are much greater than those of comparable cells from noninfected individuals [15] , [16] . Indeed , such rapid proliferation has been associated with the state of excessive inflammation that , in turn , has been regarded as a principal driver of the immunodeficiency of AIDS patients [17]–[19] . It has been suggested , on the basis of BrdU labeling and of measurement of Ki-67 expression in SIV-infected macaque CD4 T cells , that much of the proliferation of these MP cells represents recent burst-like divisions , presumably in response to antigenic stimulation , of cells that were undergoing the familiar pattern of clonal expansion and transition from central or effector memory populations to tissue-seeking effector cells [17] , [20] . Although this mode of proliferation appears to be the case for SIV-infected macaques and presumably HIV-infected humans , whether it explains the proliferation of MP cells in normal individuals has not been determined . Recognizing that MP CD4 T cells constitute a large and heterogeneous population , we repeated previous experiments establishing the differences in proliferative rates of MP cells from those of authentic antigen-experienced memory cells and also compared the behavior and frequency of MP CD4 T cells in conventional and germ-free ( GF ) mice . In order to understand whether the proliferation of MP cells in situ ( not in transfer models ) was driven by antigen and was burst-like or by cytokines and was stochastic , we treated mice with anti-MHC class II antibodies or with anticytokine antibodies . Further , we reasoned that if the expansion of MP cells was burst-like , it should have originated from a small number of precursors and thus proliferating MP cells should have a much more limited TCR sequence diversity than MP cells that were not dividing . Our results indicate that in situ MP cell division is driven largely by cytokines and not by TCR-mediated stimulation , that the diversity of the CDR3 regions of TCR β chains of particular VβJβ sets is similar in dividing and nondividing cells , and that conventional and GF MP cells are not distinguishable in either frequency , division rate , or , in a preliminary analysis , in sequence diversity . These results imply that the bulk of MP CD4 T cells in young adult mice differ in key respects from authentic antigen-driven memory cells .
To readdress the question of the relative proliferative rates of MP cells and antigen-specific memory cells , we first evaluated the use of Ki-67 as a measure of recent proliferation . C57BL/6 mice received BrdU in a single intraperitoneal ( IP ) injection and were humanely killed 24 h later or BrdU was administered in their drinking water and mice were humanely killed 3 d later . Figure 1A shows that 11% of CD44bright Foxp3− CD4 lymph node T cells evaluated 24 h after a single injection of BrdU were stained by an anti-BrdU antibody , confirming the rapid proliferative rate of these cells . All of these BrdU+ cells were Ki-67+ and , in addition , 25% of the CD44bright Foxp3− CD4 cells were Ki-67+/BrdU− , as anticipated , since Ki-67 is known to be expressed for a period of time after cells have completed their cycle . When we examined cells from the mice that had received BrdU for 3 d ( Figure 1A ) , we found that 35% of the cells were BrdU+ , reaffirming their rapid proliferative rate . The great majority of the Ki-67+ cells were BrdU+ , indicating that most cells do not retain Ki-67 expression for more than 3 d after their last division . Indeed , Pitcher et al . [21] reached a similar conclusion regarding Ki-67 expression as a result of analyzing the proliferation of PBMCs from SIV-infected macaques by simultaneous staining for BrdU and Ki-67 [21] . Accordingly , we used either Ki-67 or BrdU in different experiments; particularly , we have relied on Ki-67 in later experiments in which we examined TCR Vβ sequence diversity among dividing and nondividing MP cells . In those experiments , we also took advantage of the finding ( Figure 1A ) that Ki-67 mean fluorescence intensity ( MFI ) was highest in cells that had taken up BrdU during the previous 24 h . We then compared the frequency of Ki-67+ cells among splenic MP cells and antigen-specific ( tetramer+ ) CD4 T cells obtained 60 d after acute lymphocytic choriomeningitis virus ( LCMV ) infection ( Figure 1B ) . The frequency of tetramer+ CD4 T cells in LCMV-infected mice is greater among splenic CD4 T cells than lymph node CD4 T cells , so we limited our evaluation to tetramer+ cells from the spleen and compared them to splenic MP cells whose proliferative rates are somewhat less than those of lymph node MP cells . In this experiment , 17%±3% of the splenic MP cells were Ki-67+ . Among tetramer+ CD4 T cells ( 5 . 2% of the CD44bright CD4 T cells ) obtained from mice infected 60 d earlier , only 7%±2% were Ki-67+ . This finding implies that ∼2% of the tetramer+ cells divided each day , a frequency similar to the proliferative rates of antigen-specific memory CD4 T cells reported by others [5]–[7] . This difference in proliferative rates could be explained if MP cells have derived quite recently from NP cells and are still dividing relatively rapidly , while the tetramerpos memory cells induced by intentional immunization were examined 60 d after infection , and in experiments reported by others at least 40 d after infection/immunization , when their proliferative rates may have slowed considerably . If this were the case , we might anticipate that authentic memory cells would be dividing substantially more rapidly when studied relatively shortly after infection . We assessed the expression of Ki-67 in tetramerpos and tetramerneg CD44bright CD25− CD4 T cells 15 d after LCMV infection . At that time , 7 . 7% ( ±0 . 3% ) of the CD44bright cells were tetramerpos . Of these , 7 . 8%±1 . 9% were Ki-67+ compared to 18 . 9%±0 . 6% of the tetramerneg MP cells ( Figure 1C , results from one of three mice ) . This experiment indicates that one cannot account for the differences in the in situ proliferative rate of MP cells and of the antigen-driven memory cells on the basis of the more recent priming of the former than the latter . We did verify that tetramerpos cells examined 6 d after infection were essentially all ( 97% ) Ki-67+ , indicating that these cells had undergone rapid proliferation as a result of antigenic stimulation . As we will show later , it is highly unlikely that most of the Ki-67+ MP cells represent a population in the midst of its antigen-driven expansion from naive or memory precursors . The high proliferative rate of MP cells might be due to a distinct , small subpopulation dividing very rapidly while a large population divides slowly . We thought that explanation unlikely in view of the classic report by Tough and Sprent [2] that more than 60% of CD44bright CD4 T cells became BrdU+ during a labeling period of 30 d , implying that over that period of time the great majority of CD44bright CD4 T cells had divided at least once . We observed an even more rapid proliferation with ∼60% of MP ( CD44bright Foxp3− ) CD4 T cells having taken up BrdU in a 10-d labeling period ( Figure 1D ) , again arguing that the high proliferative rate of MP cells is not a property of a small subpopulation of these cells . The presence and proliferation of MP cells in GF mice needs to be considered in assessing the possible role of foreign antigens in stimulating the in situ proliferation of MP cells in normal animals . We reported that the proliferative rate of CD44bright CD25− cells in SW GF mice was ∼4% in 6 h and was no different from that of such cells from conventional SW mice [3] . This implies that the generation and proliferation of MP cells can be achieved in mice with very limited antigenic load . To examine this in greater detail and in the mouse strain that was being studied in our experiments , we injected BrdU into conventional and GF C57BL/6 mice and evaluated the frequency of BrdU+ cells 6 h later . BrdU+ cells constituted 4 . 7% of the GF CD44brightFoxp3− lymph node CD4 T cells and 5% of the same cells from conventional donors . The proportion of Ki-67+ MP lymph node cells was 38 . 7% in GF mice and 38% in conventional mice ( Figure 2A ) . The absolute numbers of total lymph node CD4 T cells , of CD44bright CD4 T cells and of Foxp3− CD44bright CD4 T cells were not different and thus there was no difference in the numbers of Ki-67+ or of BrdU+ cells . This finding was the case for both peripheral and mesenteric lymph node cells ( Figure 2B and 2C ) . Thus , numbers and proliferative behavior of MP cells is similar in mice with very limited antigen-exposure ( i . e . , GF mice ) to that in conventional mice , raising the possibility that a substantial proportion of MP cells in conventional mice may develop through a process other than foreign antigen-driven activation and expansion . One approach to evaluating the importance of antigen stimulation in T-cell dynamics is to determine whether proliferation can be blocked by anti-MHC class II antibodies [22] . To that end , we utilized FcγRγ−/− mice so that the anti-class II antibody would not block T-cell responses by elimination of antigen-presenting cells . In such mice , anti-class II antibodies powerfully inhibit antigen-specific in vivo responses . We transferred CD45 . 1 OT-2 cells to CD45 . 2 FcγRγ−/− C57BL/6 mice , treated the recipients with the anti-class II antibody Y3P ( 1 . 8 mg IP ) and 1 d later immunized them with an ovalbumin peptide plus LPS . BrdU was given to these mice in drinking water from the time of immunization and mice were humanely killed 3 d later . Mice treated with mouse immunoglobulin G ( IgG ) rather than Y3P showed expansion of the transferred cells . 68 . 5% of these cells were BrdU+ and 78 . 2% were Ki-67+ . By contrast , in the treated mice , there was no expansion of the transferred cells when compared to unimmunized mice and only 6% were BrdU+ and 12 . 5% Ki-67+ ( Figure 3A and 3B ) . In the same animals , the frequency and number of MP cells that were BrdU+ , Ki-67+ were not affected by treatment with Y3P ( Figure 3A and 3B ) . In a separate experiment , in which BrdU was administered to nonimmunized FcγRγ−/− mice for 6 h prior to humanely killing , Y3P had no effect on the frequency or numbers of BrdU+ or of Ki-67+ MP cells ( Figure 3C ) . Normal C57BL/6 mice were either untreated or received anti–IL-15 , anti–IL-7Rα , or anti–IL-2 antibody on day 1 and day 4 and were humanely killed on day 7 . There was no effect on total numbers of CD44bright cells in lymph nodes but the numbers of Ki-67+ cells was significantly reduced among recipients of anti–IL-7Rα ( Figure 4 ) , indicating that at least a portion of the proliferative response of MP cells depended on IL-7 , or conceivably , thymic stromal lymphopoietin ( TSLP ) . Neither anti–IL-15 nor anti–IL-2 had a significant effect . Transfer of MP CD4 T cells into Rag2−/− recipients results in burst-like proliferation such that the great majority of the cells present 6 to 7 d later have undergone 7 or more divisions , as judged by carboxyfluorescein diacetate succinimidyl ester ( CFSE ) dilution [5] , [23]–[26] . We carried out such an experiment and confirmed that the majority of the transferred CD4 T cells present 6 d later had undergone multiple divisions . Neither anti–IL-15 nor anti–IL-7Rα had any inhibitory effect , but Y3P almost completely inhibited proliferation ( Figure 5A ) , indicating that the expansion of cells in this lymphopenic setting required recognition of MHC and presumably of peptide/MHC complexes . If MP cells are normally undergoing proliferation bursts , we reasoned that the dividing cells would have originated from relatively small cohorts of cells that would each go through many divisions . This should have the effect that the dividing cells would have relatively limited sequence diversity compared to nondividing MP cells in the same individual . To test whether this thesis is correct in a system in which we could be confident that cells had undergone multiple cell divisions , we transferred 1 million CFSE-labeled CD44bright CD25− CD4 T cells into Rag2−/− C57BL/6 recipients . We humanely killed the mice 3 d after transfer so that the proportion of cells that had not divided would still be substantial; by 7 d , the cells that had divided multiple times completely dominate the distribution . We purified , by cell sorting , CD4+ CD44bright cells that had completely diluted their CFSE and those that had retained their full amount of CFSE , implying they had not divided . We limited our sequence analysis to one Vβ/Jβ set , Vβ2/Jβ1 . 1 . We chose Jβ1 . 1 since , as the most proximal Jβ , those TCRβ gene segments that have retained Jβ1 . 1 are very likely to be using it in their rearranged Vβ chain . Limiting the range of Vβs studied also allowed us to sample a larger fraction of the repertoire among those TCRs with a relatively small number of sequences than would have been the case had we tested all Vβs . Among 42 sequences of transferred MP CD4 T cells that had not divided ( CFSEhigh ) , 37 were unique; one sequence occurred three times , three occurred twice , and 33 but a single time ( Figure 5B ) . By contrast , among 42 sequences from MP CD4 T cells that had divided seven or more times ( CFSElow ) , there were only three unique sequences , occurring 12 ( CASSHDSKNTEVFFGKG ) , 14 ( CASSQEAGRGTEVFFGKG ) , and 16 ( CASSQRGGKGVFFGKG ) times , respectively . Interestingly , two of the sequences from the cells that divided multiple times were also found among the cells that had not divided , but in these cases they were represented only two or three times . This experiment validates our expectation that a cell population undergoing burst-like division should have a relatively limited repertoire that should be distinguishable from that of cells of a comparable phenotype that had not divided . It further indicates that only a subset of the MP cells undergo burst-like proliferation in a lymphopenic environment . To examine the repertoire complexity of proliferating MP cells in situ in comparison to that of nondividing MP cells , we took advantage of our observation that the MFI of Ki-67+ cells was highest among cells that had recently divided . We could not utilize pulse labeling with BrdU because the process of staining for BrdU expression requires the use of DNase , interfering with DNA sequence analysis . We sorted CD4+ , Foxp3− , CD44bright , Ki-67bright cells and CD4+ , Foxp3− , CD44bright , Ki-67negative cells and sequenced both Vβ2/Jβ1 . 1 and Vβ4/Jβ1 . 1 CDR3 segments from two individual mice ( Figure 6 ) . We obtained 320 Vβ2/Jβ1 . 1 sequences from the Ki-67negative cells of mouse 1 and 208 sequences from the Ki-67bright cells of this donor . We plotted sequences against their relative representation as indicated by their percentage frequency ( Figure 7 ) . We also listed the number of sequences that occurred one to five times or more than five times in the embedded table . Among the 320 sequences from the Ki-67negative MP cells , 133 were unique . Of these , 103 occurred only once or twice . However , some sequences were much more frequent . The sequence CASSRTGGNTEVFFGKG comprised almost 6% of the Vβ2/Jβ1 . 1 sequences from Ki-67 negative cells , and five other sequences constituted 3% or more of all the sequences . The pattern of sequence expression among the Ki-67bright cells was quite similar to that of the Ki-67negative cells ( Figure 7 ) . Of the 208 sequences , 138 were unique . Of these , 128 occurred once or twice . One sequence occurred ∼6% of the time . Interestingly , this was the same sequence ( CASSRTGGNTEVFFGKG ) that occurred most frequently among the Ki-67negative cells , suggesting that it represents a clone whose dividing and nondividing members reflect prior clonal expansion , possibly from naïve cells , rather than a process of ongoing burst-like proliferation . However , we cannot exclude the possibility that the high frequency of this sequence represents a late phase of a clonal expansion episode when some of the cells have already stopped dividing and have lost Ki-67 expression . We also sequenced Vβ4/Jβ1 . 1 CDR3 regions from the MP cells of the same mouse ( Figure 7 ) . In this case , we obtained 79 sequences from Ki-67negative cells and 90 sequences from Ki-67bright cells . The results were quite similar to those observed from the sequences of Vβ2/Jβ1 . 1 Ki-67negative and Ki-67bright cells . We had 56 unique sequences among those from Ki-67negative cells; of these , 54 occurred once or twice . Two sequences were more common , both representing more than 12% of the total sequences . Among the 90 Vβ4/Jβ1 . 1 sequences from the Ki-67bright cells , there were 53 unique sequences of which 48 occurred once or twice . One sequence ( CASSIFESIGKG ) constituted ∼25% of all the sequences; however , that sequence was one of the two that each constituted 12% of the sequences from the Ki-67negative cells , implying that these dividing cells may be accounted for by the over-representation of the same particular clone and may not represent an ongoing burst-like expansion . As stated above , we cannot rule out the possibility that this represents a just-completed burst in which some cells have already ceased dividing . There are three sequences that occurred three times and one that occurred four times among the Ki-67bright cells . Two of those had not occurred among the Ki-67negative sequences and thus could conceivably represent burst-like proliferation . However , the majority of the dividing Ki-67bright cells do not appear to have recently expanded from a precursor by multiple cell divisions . We sequenced Vβ2/Jβ1 . 1 and Vβ4/Jβ1 . 1 CDR3 regions from Ki-67negative and Ki-67bright cells from a second donor ( Figure 7 , mouse 2 ) . Although the results were generally similar , in this mouse , there were sequences among the Ki-67bright cells that occurred multiple times but which were not found among the Ki-67negative cells , suggesting they may represent burst-like proliferation . Indeed , among the Vβ2/Jβ1 . 1 sequences from mouse 2 , two sequences comprised 18% and 17% of the sequences but were only observed once among the Ki-67negative cells and two sequences observed in 5% of the Ki-67bright sequences and not in the Ki-67negative . However , even among the 77 Vβ2/Jβ1 . 1 Ki-67bright CDR3 sequences from this mouse , 31 were unique and 23 occurred only once or twice . Among the Vβ4/Jβ1 . 1 sequences , there was one from the Ki-67bright cells that occurred in 9% of the sequences but was not found among the Ki-67negative cells , suggesting that it may represent burst-like expansion . Here too , there were many unique sequences that occurred rarely . Of the 75 Vβ4/Jβ1 . 1 sequences from Ki-67bright cells , there were 47 unique sequences of which 43 occurred only once or twice . Furthermore , it should be pointed out that we occasionally observe a dominant sequence among those from Ki-67negative cells ( in this mouse , 50% of the sequences from KI-67negative are CASSFESIGKG ) that is not ( or is only infrequently ) represented in the sequences from the Ki-67bright cells . Overall , we conclude that the complexity of Vβ2/Jβ1 . 1 and Vβ4/Jβ1 . 1 CDR3 sequences from Ki-67bright cells cannot be distinguished from that of the Ki-67negative cells . Estimating the maximum percentage of Ki-67bright cells that could have been part of bursts from these data is nonetheless not simple . Taking the most inclusive view , it could be argued that all Ki-67bright sequences that are represented many times should be considered as having originated from burst-like clonal expansion during the period immediately before the mice were humanely killed . To obtain an estimate of the frequency of such events , we summed all the sequences that occurred four or more times in the Ki-67bright cells . In the four groups studied , there were 119 sequences among those that occurred four or more times . Since the total number of Ki-67bright sequences analyzed was 450 , this implies that ∼25% of the sequences may represent Ki-67bright cells that were part of burst-like clonal expansion . If we exclude those 88 sequences that occurred multiple times in both the Ki-67negative and Ki-67bright groups , then the proportion of dividing cells that are part of burst-like proliferation is ∼7% . Thus , the majority of dividing cells do not appear to be part of an ongoing process of burst-like clonal expansion from a limited number of precursors , which was the case when we examined the burst-like division of the Vβ2/Jβ1 . 1 MP cell population that occurred upon transfer to severely lymphopenic recipients . We also examined sequences from Ki-67negative and Ki-67bright Vβ2/Jβ1 . 1 CD44bright cells from lymph nodes of GF mice . Overall , the patterns of sequence distribution were remarkably similar to those of conventional mice ( Figure 8 ) . There were large numbers of unique sequences , most of which were represented only once or twice . There were some CDR3 sequences that did occur relatively frequently among the Ki-67bright cells and were also frequent among the Ki-67negative cells . In each mouse , one sequence was represented frequently among the Ki-67bright cells but was not observed among the Ki-67negative cells . In mouse one , it constituted ∼26% of the Vβ2/Jβ1 . 1CDR3 sequences from the Ki-67bright cells; in mouse 2 , it constituted 20% of the sequences . Thus , a considerable minority of the proliferation of the GF MP cells may have derived by burst-like expansion . While the sample size of sequences from the GF donors was relatively small , they showed substantial diversity in both the Ki-67negative and Ki-67bright cells , suggesting that in the GF mice , the CD44bright cells have not arisen by differentiation and expansion of cells with a very limited TCR repertoire .
MP CD4 T cells from normal mice ( i . e . , mice housed in specific pathogen-free facilities ) proliferate quite rapidly . BrdU labeling reveals that ∼10% of these cells from lymph nodes take up BrdU in a single day and more than 30% of MP cells are Ki-67+; on the basis of our estimate that the great majority of Ki-67+ cells have divided within the past 3 d , this implies that more than 30% of lymph node MP cells divide in 3 d , a result that is confirmed by more extended BrdU labeling . The frequency of Ki-67bright MP cells is somewhat less in the spleen . By contrast , NP cells take up BrdU at ∼0 . 1% per day and very few are Ki-67+ . What drives the rapid proliferation of the MP cells has been a matter of uncertainty . Some have concluded that their proliferation is driven by TCR engagement on the basis of transfer to lymphopenic hosts , where it is observed that by 7 d after transfer the majority of the surviving cells have divided seven times or more . Zamoyska and colleagues [25] and Leignadier et al . [27] have used a tetracycline/off system to delete TCR from mature T cells . In both instances , deleting TCR resulted in a substantial diminution in the proportion of CD44bright CD4 T cells that had gone through multiple divisions when transferred to lymphopenic recipients . Similarly , anti-MHC class II antibody blocked expansion of CD4 T cells introduced into neonatal recipients [23] , and we showed here that the rapid proliferation of MP CD4 T cells introduced into Rag2−/− recipients was completely inhibited by the anti-MHC class II antibody Y3P . Furthermore , Surh and colleagues reported that the rapid proliferation of CD4 T cells that occurred when these cells were introduced into scid mice was largely lost if the scid recipients were GF [28] . As a group , these observations clearly indicate that in severely lymphopenic settings , expansion of MP cells depends on TCR recognition of peptide/MHC complexes . The results we present here indicate that only a portion of the transferred MP cells undergo this striking proliferation . When we sequenced CDR3 segments from the Vβ2/Jβ1 . 1 TCRs 3 d after transfer of MP cells into Rag2−/− recipients , we found only three sequences among the rapidly dividing cells , whereas there were 37 sequences among those that had not divided , implying that the rapid proliferation was a property of a limited set of cells among the transferred MP population . This result is consistent with the observation that naïve CD4 T cells from most TCR transgenic donors fail to rapidly proliferate on transfer to lymphopenic recipients [1] , [24] , [29] and on our immunoscope analysis of TCR Vβ complexity in Rag2−/− recipients of numbers of CD4 T cells varying from 10 million to 10 , 000 [29] suggesting that only ∼3% of the transferred cells undergo rapid expansion . It is interesting that two of the sequences represented frequently in the dividing cells were also found in the nondividing population , implying that not all cells of the same specificity are stimulated in a lymphopenic environment . However , the results obtained by the study of transfer to lymphopenic environments do not appear to be a valid representation of the mechanisms underlying the rapid proliferation of MP cells in situ . Indeed , survival of MP cells in lymphocyte-sufficient settings has been reported to not require expression of TCRs . Furthermore , there is a large literature demonstrating that survival of antigen-specific memory cells arising during immunization does not require TCR engagement , but rather depends upon the availability of cytokines , particularly IL-7 and IL-15 [10]–[12] , [23] , [24] . However , we wish to point out that the analysis of antigen-specific memory cells emerging from intentional immunization may not necessarily represent what governs the proliferative behavior of MP cells . Indeed , both the work presented here and recent studies analyzing antigen-specific CD4 memory T cells at varying times after priming show that antigen-specific memory cells emerging from intentional immunization divide relatively slowly compared to MP cells . Lenz at al . [7] infected mice with LCMV . 50 d later , a 7-d exposure to BrdU resulted in only 15% of BrdU+ spleen cells among those capable of producing interferon gamma ( IFNγ ) in response to challenge with two different LCMV peptides . Purton et al . [5] transferred TCR transgenic SMARTA CD4 T cells , specific for an LCMV epitope , into C57BL/6 mice that were then infected with LCMV . 72 d after infection , 12% of the transgenic cells took up BrdU during a 5-d labeling period . Jenkins and colleagues [6] infected mice with Listeria monocyogenes expressing an ovalbumin peptide ( LM2W1S ) . 40 d after infection , the mice received BrdU for 14 d; among spleen and lymph node CD4 T cells capable of binding an ovalbumin tetramer , only 11 . 5% were BrdU+ . Our results are consistent with these reports . We infected C57BL/6 mice with LCMV . Fifteen and 60 d later , the frequency of Ki-67+ cells among tetramer+ cells was measured . At 15 d , 8% of the CD44bright tetramer+ cells were Ki-67+; at 60 d , ∼7% . Collectively , these studies indicate that after the expansion phase following immunization is complete , antigen-specific CD44bright CD4 T cells divide at a rate of <1% to ∼2 . 5% per day . The possibility that MP cells and authentic memory cells might represent distinct cell types , or rather that the MP pool contains both authentic memory cells and another cell population , was also suggested by our prior study in SW GF mice that showed that their MP CD4 T cells proliferated at a rate similar to MP cells from conventional donors [3] . We have examined this point in greater detail here in GF and conventional C57BL/6 mice and confirm that the proportion and absolute number of non-Treg CD44bright CD4 T cells from peripheral and mesenteric lymph nodes of GF mice are similar to those from conventional mice as are the proportion and number of proliferating MP cells . It should also be pointed out that prior studies of GF mice maintained on elemental diets ( i . e . , antigen-free mice ) had shown the presence of substantial numbers of activated CD4 T cells , equivalent in frequency to those in conventional mice [30]–[32] . While these studies were carried out before the availability of the reagents now used to classify MP cells , they strongly suggest that antigen-free mice have similar numbers of MP CD4 T cells as do conventional mice and thus support the concept that foreign ( including commensal ) antigens are not critical to the emergence of the majority of MP cells . Here we have shown that the in situ proliferation of MP cells is not inhibited by anti-MHC class II antibody , using a reagent that strikingly inhibits the proliferation of antigen-specific cells in response to antigen challenge and that blocks the rapid proliferation of MP cells transferred to lymphopenic recipients . Rather , we observe that anti–IL-7Rα antibody diminishes , but does not abolish , proliferation of MP cells , implying that IL-7 or TSLP plays a role in this proliferation . An alternative way to examine the issue of whether the rapid proliferation of these cells represents an antigen-driven response , during which one would anticipate that limited numbers of precursors give rise to bursts consisting of multiple divisions , is to examine the TCR sequence diversity of proliferating MP cells and to compare that to the sequence diversity of quiescent MP cells . If MP proliferation was primarily due to burst-like clonal expansion stimulated by exposure to antigen , it would be expected that the sequence diversity of the proliferating cells would be substantially less than that of the quiescent cells . Indeed , when we studied proliferating and nonproliferating MP cells in lymphopenic recipients , this is precisely what we observed . We examined CDR3 sequences from Vβ2/Jβ1 . 1 and Vβ4/Jβ1 . 1 MP CD4 T cells that were Ki-67bright or Ki-67negative . We chose to limit our study to these two TCR Vβ sets so that we could sample a larger proportion of these defined subrepertoires than we could with the same number of sequences of total CD44bright Ki-67bright cells . As an estimate of the number of cells under study , we used the following considerations . The total number of lymph node CD4 T cells is ∼8 million . ∼15% of these cells are CD44bright , or ∼1 . 2 million . Of these , ∼half are CD25+ , so that MP cells constitute ∼600 , 000; ∼5% express Vβ2 or Vβ 4 , or ∼30 , 000 . Of the Vβ2 or Vβ 4 expressing cells , ∼10% are Jβ1 . 1 , or ∼3 , 000 . The Ki67bright cells are ∼10% of the MP cells so that the total number of Ki-67bright Vβ2/Jβ1 . 1 or Vβ4/Jβ1 . 1 MP cells in all the lymph nodes of the animal is ∼300 . Thus , the maximum number of unique sequences would be 300 in this cell population; this would be substantially less if repetitive sequences existing among these cells , which would be anticipated on the basis of the likelihood that the generation of MP cells from naïve precursors involved clonal expansion . Thus , in our initial analysis , involving >200 sequences from the CD44bright Ki-67bright Vβ2/Jβ1 . 1 cells of mouse 1 , our sample , while not complete , is quite substantial . Even the samples of 70 to 80 sequences in the other cases are sufficient to provide useful information about complexity , as judged by our observations of multiply repeated sequences . A further point is our reliance on CDR3 sequences from the β chain of the TCR as a clonal marker . It is possible that we have overestimated the frequency of repeats since there may be occasions in which the same Vβ is used with different Vα's , but we suspect that in the vast majority of cases the CDR3 sequence of the TCR β chain is indeed a clonal marker . Our results indicate that the distribution of sequences in the Ki-67bright and Ki-67negative populations was not markedly different . If we made the assumption that any sequences that occurred many times among the Ki-67bright cells represented cells that had recently been in a burst-like expansion , then ∼25% of the Ki-67bright cells would be judged to be in such bursts . To obtain this estimate we arbitrarily assigned any sequence that occurred four or more times among the Ki-67bright cells to the set that occurred “many” times . However , this could easily be an overestimate depending on how one interprets those instances in which a similarly high frequency of the same sequence was found among Ki-67negative cells . On the one hand , this might reflect a large clone in which cell division occurred on a stochastic basis so that the frequency of the clone was similar among the dividing and nondividing cells . If we make this assumption , then the proportion of Ki-67bright cells that were in bursts becomes ∼7% . Alternatively , instances in which a sequence found in the Ki-67bright cells was equally ( or over-represented ) among the Ki-67negative cells may represent the late-phase of a burst episode in which a portion of the cells had already stopped dividing and lost Ki-67 expression but others continued to divide . Overall , we conclude that the sequence data are consistent with a minority of the Ki-67bright cells being part of burst; whether that minority is small or considerable cannot easily be determined . However , when taken together with the failure of anti-class II antibody to block proliferation of MP cells , it is reasonable to conclude that the proportion of Ki-67bright cells that are part of burst-like expansion is quite small . There may be circumstances in which clonal expansion/burst-like antigen-driven proliferation plays a much great role than is found among MP cells from normal mice . It has been argued that the dynamics of MP cells from SIV-infected macaques , in which proliferative rates are far higher than proliferative rates of MP cells from noninfected macaques , is best explained by multiple overlapping burst episodes of several cell divisions occurring within a brief period of time [17] , [18] . These cells , which exist in a highly inflammatory setting , may well show enhanced sensitivity to their cognate antigens or to self-peptide/MHC complexes . A detailed analysis of their sequence complexity and of the complexity of MP cells derived from chronically infected individuals or individuals that were in a state of chronic inflammation would help to clarify this point . As discussed above , we found that GF and conventional C57BL/6 mice have comparable numbers of MP CD4 T cells in their peripheral and mesenteric lymph nodes and these cells display similar proliferative rates . We sequenced CDR3 gene segments from Vβ2/Jβ1 . 1 Ki67bright and Ki-67negative MP cells of two GF mice and found that they , like the comparable cells from conventional mice , were very similar in their sequence diversity . One could argue that GF mice are not antigen free , although there are reports that antigen-free mice show normal numbers of “activated” CD4 T cells . Nonetheless , there can be little doubt that GF mice have a much reduced antigenic experience . If the MP cells of GF mice represent clonal expansions because of an extremely limited set of naïve cells responding to a comparably limited set of antigenic stimuli , it would be anticipated that the MP cells from GF mice would have a much more restricted repertoire than MP cells from conventional mice . While our limited number of sequences may not be sufficient to reach a definitive conclusion on this point , there does not appear to be any major difference in the degree of diversity of the Ki-67bright or Ki-67negative MP cells from the two sets of donors . On the basis of these observations , we propose that MP cells may be a more diverse population than had been considered and that only some of these cells may have emerged by exposure to conventional foreign antigens . The maintenance of most MP cells and their rapid proliferative rate appear to be largely dependent on cytokines . The origin of the infrequently occurring proliferation bursts remains to be clarified , but an obvious possibility is through recognition and response to self-peptides on competent antigen-presenting cells . It should be pointed out that diversity in CD8 T cells has also been described , with one population being designated “bystanders” and that such cells take on a memory phenotype in mice deficient in the transcription factor KLF2 , the signaling kinase itk , or the histone acetyltransferase CBP [33] . Whether such “bystander” CD8 cells bear a relationship to the rapidly dividing CD4 MP cells discussed here remains to be determined . Overall , one may ask what is the function of the large set of MP cells in normal mice ? We have proposed [23] , [34]–[37] that they represent a pool of cells capable of making a rapid effector response to cross-reactive antigens of pathogens during a period in which the naïve cells proliferate and differentiate . MP cells might play an even more important role in instances in which naïve cells are limiting and no “authentic” memory cells are specific for an introduced pathogen , such as might be the case in aged individuals . Devising models in which these cells are absent will be essential to testing their function . Finally , why proliferative rates of authentic memory and MP cells are different is not clear .
C57BL/6 ( B6 ) , B6 Rag2−/− , B6 FcγRγchain−/− , and OT-II CD45 . 1 mice were obtained from the National Institute of Allergy and Infectious Diseases ( NIAID ) contract facility at Taconic Farms . GF mice were maintained at the NIAID GF facility . All other mice were maintained under pathogen-free conditions in NIAID animal facilities . Mice infected with LCMV were inoculated IP with 2×105 PFU Armstrong strain . The care and handling of the animals used in our studies was in accordance with the guidelines of the National Institutes of Health ( NIH ) Animal Care and Use Committee . Y3P was obtained from Harlan Bioproducts . Antibodies to IL-15 ( 5H4 ) , IL-2 ( S4B6 ) , CD127 ( IL-7Rα; SB/14 ) , CD4 ( pacific blue; RM4-5 ) , Ki-67 ( PE; B56 ) , Vβ2 ( FITC; B20 . 6 ) , Vβ4 ( FITC; KT4 ) were purchased from BD Biosciences . Anti-CD44 ( Alexa-700; IM7 ) and anti-Foxp3 ( PE; NRRF-30 ) were purchased from eBioscience . The detection of BrdU was carried out according to instructions in the kit provided by BD Biosciences . The I-Ab-GP-66-77 tetramer that recognizes receptors for an immunodominant LCMV epitope was provided by the NIH tetramer facility ( Emory Vaccine Center ) . All flow cytometry analyses were performed using an LSR-II ( BD Biosciences ) . Inguinal , axillary , cervical , and mesenteric CD4 MP lymph node T cells were obtained by sorting on a FACSAria ( BD Biosciences ) . Purity was >99% CD4 , CD44+ or , depending on the experiment , CD4 , CD44+ , Foxp3− . In some experiments , sorted cells were labeled with CFSE ( Molecular Probes ) at a final concentration of 1 . 25 µmol and transferred IP into recipient mice . From 105 to 0 . 5×105 KI-67 negative and bright CD4 CD44bright , Foxp3− T cells were FACS-sorted into FCS . Cells were resuspended in lysis buffer ( 20 mmol Tris-HCl , pH 7 . 5 , 150 mmol NaCl ) with 4 µg/ml proteinase K ( Fermentas ) , incubated at 56°C for 50 min and then at 95°C for 10 min . Volumes were adjusted to 30 µl; 10 µl were used to amplify the Vβ2/Jβ1 . 1 and Vβ4/Jβ1 . 1 CDR3s with the following primers: Vβ2 5′ CAGTCGCTTCCAACCTCAAAGTTC′ or Vβ4 5′ CGATAAAGCTCATTTGAATCTTCGAATC and 3′ Jβ1 . 1 AGCTTTACAACTGTGAGTCTGGTTCCTTTACC using 35 PCR cycles of 45 s at 95°C , 45 s at 57°C , and 45 s at 72°C . The PCR products were cloned into the TOPO blunt end vector ( Invitrogen ) and bacteria were transformed . Single colonies were isolated and suspended into 10 µl of water . PCR was carried out on 3 µl of bacterial suspension from single colonies using the universal M13 primers . PCR products were sequenced by Agencourt ( Beckman Coulter ) using universal T3 primer . The rate of readable sequences was 70% to 80% . Lymph node cells were stained with anti-CD4 , anti-Vβ2 , or anti-Vβ4 followed by single-cell sorting into 96-well plates . Plates were heated to 95°C for 3 min followed by a first round PCR of 40 cycles ( 45 s 95°C , 45 s at 57°C , 45 s at 72°C ) using the 5′ Vβ2 or Vβ4 primers and a 3′ Jβ2 . 7 AGGCTCACGGTTTTAG primer . 3 µl of the first PCR product were subjected to a second PCR ( 25 cycle of 45 s at 95°C , 45 s at 57°C , 45 s at 72°C ) using the Vβ2 or Vβ4 primers and the 3′ Jβ1 . 1 primer . PCR products were obtained in eight and seven wells out of 96 for Vβ2 or Vβ4 , respectively , indicating a frequency for each of ∼10% . Analysis of the CDR3 sequences was performed using Matlab as a platform to generate a program to analyze the sequences . | The class of immune cells called CD4 T lymphocytes consists of two major cell types: naïve cells that have not yet participated in an immune response and memory cells , which are cells that have responded to antigen , expanded in number , and acquired new characteristics . These two cell types can be distinguished from one another because they display different cell surface marker proteins . In this paper , we argue that many—probably most—of the cells researchers generally characterize as memory cells on the basis of their surface markers are not authentic memory cells . True memory cells—the ones produced , for example , when we immunize a child against a disease—divide very slowly , whereas the bulk of the cells we generally characterize as memory cells divide very rapidly . Mice that have never been exposed to antigens have as many of these “memory-like” cells as normal mice have , implying that these cells arise by a process that does not require foreign antigen . Analysis of the sequence of the antigen recognition receptors on these “memory-like” cells indicates that their replication does not derive from a few cells or clones undergoing multiple rounds of proliferation , thus their division cannot be explained by conventional , antigen-driven clonal expansion . We conclude that this large population of “memory-like” cells has arisen by a mechanism independent of a response to foreign antigen , and that these cells may have a crucial biological function . | [
"Abstract",
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] | [
"immunology",
"biology"
] | 2011 | Memory Phenotype CD4 T Cells Undergoing Rapid, Nonburst-Like, Cytokine-Driven Proliferation Can Be Distinguished from Antigen-Experienced Memory Cells |
We provide a novel computational framework on how biological and artificial agents can learn to flexibly couple and decouple neural task modules for cognitive processing . In this way , they can address the stability-plasticity dilemma . For this purpose , we combine two prominent computational neuroscience principles , namely Binding by Synchrony and Reinforcement Learning . The model learns to synchronize task-relevant modules , while also learning to desynchronize currently task-irrelevant modules . As a result , old ( but currently task-irrelevant ) information is protected from overwriting ( stability ) while new information can be learned quickly in currently task-relevant modules ( plasticity ) . We combine learning to synchronize with task modules that learn via one of several classical learning algorithms ( Rescorla-Wagner , backpropagation , Boltzmann machines ) . The resulting combined model is tested on a reversal learning paradigm where it must learn to switch between three different task rules . We demonstrate that our combined model has significant computational advantages over the original network without synchrony , in terms of both stability and plasticity . Importantly , the resulting models’ processing dynamics are also consistent with empirical data and provide empirically testable hypotheses for future MEG/EEG studies .
Humans and other primates are remarkably flexible in adapting to constantly changing environments . Additionally , they excel at integrating information in the long run to detect regularities in the environment and generalize across contexts . In contrast , artificial neural networks ( ANN ) , despite being used as models of the primate brain , experience significant problems in these respects . In ANNs , extracting regularities requires slow , distributed learning , which does not allow strong flexibility . Moreover , fast sequential learning of different tasks typically leads to ( catastrophic ) forgetting of previous information ( for an overview see [1] ) . Thus , ANNs are typically unable to find a trade-off between being sufficiently adaptive to novel information ( plasticity ) and retaining older information ( stability ) , a problem known as the stability-plasticity dilemma . In recent years , a wide variety of solutions have been provided for this stability-plasticity dilemma . These solutions can broadly be divided in two classes . The first class is based on the fact that catastrophic forgetting does not occur when tasks are intermixed . Thus , one solution is to keep on mixing old and new information [2–5] . [3] suggested that new information is temporarily retained in hippocampus . During sleep ( and other offline periods ) , this information is gradually intermixed with old information stored in cortex . This framework inspired subsequent computational and empirical work on cortical-hippocampal interactions [6–8] . The second class of solutions is based on the protection of old information from being overwritten . Protection can occur , first , at the level of synapses . For example , [9] combined a slow and fast learning system , with slow and fast weights reflecting long- and short-time-scale contingencies , respectively . This allows the network to both extract stable regularities ( slow learning system ) and flexibly adapt to fast changes in the environment ( fast learning system ) . Another recent idea is to let synapses ( meta- ) learn their own importance for a certain task [10] , [11] . Weights that are very important for some task are not allowed to ( and thus protected from ) change . Hence , information encoded in those weights is preserved . Second , protection can also be implemented at the level of ( neural ) activation . The most straightforward approach to implement such protection is to orthogonalize input patterns for the relevant tasks [12] , [13] . Another approach to achieve protection at the level of neural activation , is gating . This means that only a selected number of network nodes can be activated . Because weight change depends on co-activation of relevant neurons [14] , [15] , this approach protects the weights from changing . For example , [16] proposes that in each of several tasks a ( randomly selected ) 80% of nodes is gated out , thus effectively orthogonalizing different contexts . They showed that synaptic gating allowed a multi-layer network to deal with several computationally demanding tasks without catastrophic forgetting . Crucially , it remains unknown how biological agents deal with this dilemma . The current study aims to provide a novel computational framework focused on biological agents that makes empirically testable predictions at MEG/EEG level . For this purpose , we combine two prominent principles of computational neuroscience , namely Binding by Synchrony [17–20] ) and Reinforcement Learning ( RL; [21] , [22] ) . In BBS , neurons are flexibly bound together by synchronizing them via oscillations . This implements selective gating ( e . g . , [23] ) in which synchronization enhances the communication between neuronal groups ( gates are opened ) and desynchronization disrupts the communication between neural groups ( gates are closed ) . In sum , BBS allows the model to flexibly alter communication efficiency on a fast time scale . By using RL principles , the model can learn autonomously when neurons need to be ( de ) synchronized . In the modeling framework , BBS binds relevant neural groups , called ( neural task ) modules , and unbinds irrelevant modules . This causes both efficient processing and learning between synchronized modules; and inefficient processing and absence of learning between desynchronized modules . The resulting model deals with the stability-plasticity dilemma by flexibly switching between task-relevant modules and by retaining information in task-irrelevant modules . An RL unit [24] uses reward prediction errors to evaluate whether the model is synchronizing the correct task modules . In order to test the generalizability of our framework , we apply it to networks containing modules that learn via three classic synaptic learning algorithms , namely Rescorla-Wagner ( RW; [15] , [25] ) , backpropagation ( BP; [26] ) and Restricted Boltzmann machines ( RBM; [27] ) . The RW algorithm [25] is one of the most well-known and basic supervised-learning algorithms in cognitive neuroscience . Here , on each trial , an error term is computed based on the discrepancy between a model-generated output pattern and some target output pattern . Learning consists of using this error term for finding a weight configuration that minimizes the average error across trials . This algorithm is typically fast and efficient for learning simple ( i . e . , linearly separable ) input-output associations . Hence , it has no problems with plasticity . However , while learning one set of input-output associations ( set B ) , the algorithm may unlearn another , currently irrelevant set of input-output associations ( set A ) . Thus , when set A becomes relevant again , it will have to relearn it . In sum , the RW algorithm does suffer from a lack of stability , but due to its high plasticity it might have only minor problems with respect to the stability-plasticity dilemma , especially when the learning rate is high . In this case , it might relearn the forgotten information ( set A ) so fast that also the stability problem is negligible . Nevertheless , the RW algorithm suffers from some severe limitations on the complexity of problems that it can solve . It is very efficient in dealing with linearly separable input-output associations , but cannot deal with more complex , not linearly separable , problems . This limitation of the RW algorithm is solved in BP [26] . Similar to RW , learning with BP consists of using the error term for finding a weight configuration that minimizes the average error across trials . Relative to RW , this algorithm is able to solve a much wider range of problems . In particular , it can also solve nonlinearly separable problems . It does this by adding hidden layers between input and output . For training the weights toward the hidden layers , BP propagates the error term backwards from output toward the hidden ( i . e . , deeper ) layers in the network . Crucially , sequential learning of input-output associations poses severe computational problems on the BP algorithm . Because the number of ( interdependent ) weights that should be adjusted to solve a problem is much higher , the algorithm learns much slower . Hence , if the learning rate is low , new learning can be very slow , causing a lack of plasticity . If the learning rate is very fast , on the other hand , this problem is mitigated but there is no stability in the model . This is because , similar to RW , the learning algorithm will adapt all available weights and therefore overwrite previous information . An algorithm that can also learn with hidden layers ( and thus solve more complex problems ) is RBM . Despite the algorithmic differences , RBM suffers from the same stability-plasticity dilemma as BP . To further illustrate the generality of our framework , S1 Text show that our framework can also be applied to networks with modules that learn via RBM . For brevity , the main text restricts attention to RW and BP . The full model consists of three units ( Fig 1A ) . The Processing unit contains a network consisting of a number of task-specific modules; the two learning algorithms ( RW or BP ) are implemented between modules of the Processing unit . In addition , RL and Control units together form an hierarchically higher network modeled after basal ganglia/primate prefrontal cortex [28] . The RL unit ( modeling ventral striatum/ anterior medial frontal cortex ( aMFC ) ) evaluates behavior . More specifically , it learns to assign a value to a specific task module ( how much reward it receives by using this module ) and compares this value with the externally received reward to compute prediction errors . Additionally , the RL unit has a Switch neuron ( see Fig 2A ) . This Switch neuron computes a weighted sum of negative prediction errors across trials . When this sum reaches a threshold of . 5 , it signals the need for a strategy switch to the Control unit ( see Methods for details ) . This Control unit drives neural synchronization in the Processing unit . One part of the Control unit ( modeling lateral frontal cortex ( LFC ) ) contains task neurons that point to task modules in the Processing unit [29]; another part ( modeling posterior medial frontal cortex ( pMFC ) ) synchronizes task modules based on those task neurons [30] . Crucially , LFC and pMFC both use prediction error information , but on different time scales . The pMFC uses prediction errors on a fast time scale to enhance control over the synchronization process as soon as a negative prediction error occurs . In contrast , the LFC uses prediction errors on a slow time scale to know when the task rule has changed and a switch of modules is needed . In order to drive neural synchronization between task modules in the Processing unit , we rely on the idea of binding by random bursts [30–32] . Here , applying positively correlated noise to two oscillating signals reduces their phase difference . In addition to implementing binding by random bursts , the current work also implements unbinding by random bursts . In particular , applying negatively correlated bursts increases the phase difference between oscillating signals and thus unbinds ( i . e . , dephases ) the two signals . We test our model on a ( cognitive control ) reversal learning task . Here , each hierarchically lower algorithm ( RW or BP; in the Processing unit ) sequentially learns different task rules . The relevant task rule changes across task blocks ( Fig 1B ) . The model must detect when task rules have changed , and flexibly switch between different rules without forgetting what has been learned before . We divide the task in six equally long task blocks . In the first three blocks , the model should learn three different new task rules ( rule A , B and C in blocks 1 , 2 and 3 respectively ) . In the second half , the model has to switch back to the previously learned rules ( rule A , B and C in blocks 4 , 5 and 6 respectively; see also Fig 1B ) . For the RW network , we use a one-dimensional task . Here , on each trial one out of three stimulus features is activated . For every task rule we link a stimulus feature to a response option . More specifically , in task rule A , feature 1 ( F1 ) is associated to response 1 ( R1 ) , feature 2 ( F2 ) to response 2 ( R2 ) and feature 3 ( F3 ) response 3 ( R3 ) . In task rule B , F1 is associated to R2 , F2 to R3 and F3 to R1 . Task rule C associates F1 to R3 , F2 to R1 and F3 to R2 . For the BP network , a multi-dimensional task is used . Here , on each trial multiple stimulus features are activated . More specifically , the task utilizes four dimensions . Every dimension has three features . One of the dimensions represents a cue that indicates which out of the other three ( stimulus ) dimensions is relevant on the current trial . In line with the one-dimensional task , the 3 stimulus features of each dimension are within each task rule linked to one response option . The one-dimensional task ( for RW ) consists of 360 trials; the multi-dimensional task ( for BP ) consisted of 3600 trials . For comparison , we divided each task sequence in 120 trial bins for analysis and plotting . Fig 2A illustrates the detailed model for both tasks . We compare our combined ( henceforth , full ) models with models that use no synchronization ( i . e . , only contain the Processing unit; called no-synchrony models ) . We evaluate plasticity as the ability to learn a new task; and stability as the interference from learning a new task toward performance on the old task ( see Fig 1B and Methods ) .
In Fig 4A , the accuracy evolution across all task blocks is plotted for both the full and no-synchrony RW model with a slow learning rate , β = . 2 , for the simple ( linearly separable ) task . The full model is marginally slower in learning new task rules . However , when the model needs to switch back to a previously learned rule ( task blocks 4–6 ) we observe a minor advantage for the full model in the first trials , since it does not have to relearn the task . A very different picture emerges for the complex ( nonlinearly separable ) task . Fig 4D shows the accuracy of the full and no-synchrony BP model . During the first task block , the no-synchrony and full model perform similarly . When the task rule switches for the first time ( i . e . , after the first task block ) , the drop in accuracy is slightly larger for the no-synchrony model than for the full model . This is caused by the fact that the no-synchrony model has to learn task rule B with weights that were pushed in a direction opposite to those that are optimal for task rule A . Instead , the full model switches to another task module and starts learning from a random weight space . A similar phenomenon occurs after the second rule switch . For the following task switches , the model has to switch back to rules it already learned before; it is here that the full potential of the full model emerges . The full model can switch back to a previous module , where all old information was retained . Instead , the no-synchrony model has catastrophically forgotten the first task rule and must hence relearn it . Fig 6 shows the overall accuracy , stability and plasticity of our full model and of the no-synchrony model for the two task structures discussed in the previous section ( 1- and 3-dimensional tasks ) . In order to gain more insight in how the model performance is affected by task complexity , we also show overall results for the BP model on a 2-dimensional task . Thus , we show results for tasks of increasing complexity , namely for 1 dimension ( RW model ) , 2 dimensions ( BP model ) and 3 dimensions ( BP model ) . Results of the RBM model are discussed in S1 Text . As a model of how the brain controls its own processing , we next aimed at describing the relation between our model and previous empirical data , and provide testable hypotheses for future empirical work .
Importantly , our model makes several predictions for empirical data . First , it predicts significant changes in the phase coupling between different posterior neo-cortical brain areas after a task switch . Here , we suggest that desynchronization may be important to disengage from the current task . Consistently , [55] found that strong desynchronization marked the period from the moment of disambiguation of ambiguous stimuli to motor responses . Additionally , Parkinson disease patients , often characterized by extreme cognitive rigidity , show abnormally synchronized oscillatory activity [56] . Thus , we suggest that neural synchronization between task-relevant brain areas is crucial for implementing task rules . Additionally , desynchronization is necessary for disengaging from a task . Second , we explored midfrontal theta-activation in the time-frequency domain by wavelet convolution . These analyses showed an increase of theta power after an error . This was caused by bursts that were sent from the RL unit as described in Eq ( 8 ) . Hence , the model predicts an increase of theta amplitude in the MFC after negative prediction errors in tasks where these prediction errors signal the need for increased cognitive control [34] , [35] , [37] . Third , we connected the model to research demonstrating theta/gamma interactions where faster gamma frequencies , which implement bottom-up processes , are typically embedded in , and modulated by , slower theta-oscillations , in order to implement top-down processes [40] , [57–59] . For this purpose , we considered coupling between pMFC theta phase and gamma amplitude in the Processing unit . Our model predicts a strong PAC increase in the first trial ( s ) after a task switch , which decays slowly after the switch . This reflects the binding by random bursts control process which is increased after task switches , and decays once a task rule is sufficiently implemented . Hence , the model predicts a strong coupling between frontal theta phase and posterior gamma amplitude when new task rules need to be implemented . The model contained several limitations , and consequently also possibilities for future extensions . First , the RL unit currently learns to assign a value to some task module . It can determine when a task switch occurred , and then make a binary switch assessment; to switch or not to switch to another task module . Thus , when the model realizes that the current task module/strategy is incompatible with the current task/environment , it has to change its behavior . It will attempt random strategies until an appropriate one is found . Learning when to switch can be considered as a type of meta-learning . However , the full model would benefit significantly from more advanced meta-learning mechanisms . Future work will address this issue by adding second level ( contextual ) features which allow the LFC to ( learn to ) infer which of multiple task modules should be synchronized . One useful application of such second level features would be task set clustering , which allows to generalize quickly over multiple contexts . Specifically , if a novel second-level feature becomes connected to an earlier learned task set ( in LFC ) , all the task-specific mappings of this task set would immediately generalize to the novel second-level feature . This is consistent with immediate generalization seen in humans [60–62] . Second , several parameters of the model were fixed , but might more generally be controllable ( learnable ) as well . For example , the time scale of the Switch neuron is controllable by the σ parameter in Eq ( 12 ) . In a very stable environment , a low σ is adaptive , which slows down the time scale , decreasing the weight of more recent prediction errors . Instead , if the environment is unstable , a less conservative strategy is in order ( high σ ) , in which case the model accumulates evidence across less trials in order to make a switch decision . Earlier models already described how switching between hypotheses could depend on environmental stability and noise [63]; such manipulation ( here , of parameter σ ) might be usefully implemented in future developments of the current model too . Third , although using negative prediction errors to modulate the control amplitude of the pMFC is efficient in the current context , this might not be ideal in more complex environments . Thus , another future challenge is broadening the control signal ( i . e . , beyond negative prediction errors ) that the model uses to optimally adapt to the environment’s reward and cost structure [45] . Fourth , the node architecture of neuronal triplets is an oversimplification of how oscillations are produced in the human brain . Several neural models propose that interacting excitatory ( E ) cells and inhibitory ( I ) cells generate oscillations [33] , [64] . These oscillatory neurons are grouped with stimulus-driven neurons in cortical columns; oscillatory neurons modulate the activation of the stimulus-driven neurons [65] . In the current model , these assumptions are implemented in the simplest way , namely where each column consists of just three neurons ( E , I , and x ) , and the oscillatory activity modulates the stimulus-driven activity . Furthermore , our implementation of processing within a neuronal triplet is perhaps biologically implausible , in the sense that the neuron that processes stimuli ( x ) is distinct from the neurons that generate the oscillations ( E , I ) which do not process any stimulus information . Future work will determine whether the current approach can be scaled to more biologically plausible architectures . Fifth , the model ignored some aspects of oscillatory dynamics . For instance , our model only implements neural synchronization between Processing unit neurons with the same ( gamma-band ) frequencies . This scenario might be unrealistic in a typically noisy human brain . However , the problem of noise can be efficiently solved by employing rhythmic bursts , such as the theta-frequency we implemented here . Specifically , one-shot synchronizing bursts would cause oscillations with ( slightly ) different ( gamma-band ) frequencies to gradually drift apart after the burst . With rhythmically paced bursts , the gamma oscillations have no time to drift apart since the next burst occurs before the drift becomes substantial . In line with this idea , previous work has demonstrated how the model can deal with gamma frequency differences of at least around 2% [30] . Moreover , one might wonder if it would be optimal to send bursts at a frequency much faster than theta , thus providing no opportunity for noisy oscillations to drift apart . However , the current work showed that accuracy of the model dramatically declines if the pMFC sends bursts at a faster frequency than theta . The reason is that bursts given by the pMFC to the Processing unit introduce noise to the system . This can be clearly observed in Fig 3C , in which there are short periods of irrelevant neuronal activation during the bursts . Hence , an optimal agent would want to limit the bursts as much as possible . Since these bursts are phase locked to the pMFC oscillation and rely on its amplitude , the model performs best with slower pMFC frequencies that are rapidly attracted ( high Damp ) towards a small amplitude ( low rmin ) . Again , the oscillations in the Processing unit of the current model all have the exact same frequency . When Processing unit activations do not have the same frequency , we thus conjecture that there is an optimal , intermediate ( theta ) bursting frequency , depending on the Processing unit ( gamma ) frequency . Future work should explore such an optimal balance between a Controller/ bursting ( theta ) frequency and a Processing ( gamma ) frequency in more noisy systems . Another aspect of oscillatory dynamics we ignored is that BBS may be more biologically plausible , and more efficient , with small inter-areal delays [66] . Future work will consider an additional ( meta- ) learning mechanism that learns to synchronize nodes with an optimal phase delay between task modules . The current work relies heavily on previous modeling work of cognitive control processes . For instance , in the current model the LFC functions as a holder of task sets which bias lower-level processing pathways [29] , [67] . It does this in cooperation with the MFC . Here , the aMFC determines when to switch between lower-level task modules . Additionally , also the amount of control/ effort that is exerted in the model is determined by the RL processes in the aMFC[44–46] . More specifically , negative prediction errors will determine the amount of control that is needed by strongly increasing the pMFC signal [42] . This is consistent with earlier work proposing a key role of MFC in effort allocation [44] , [45] , [68] . In the current model , the MFC , together with the LFC , functions as a hierarchically higher network that uses RL to estimate its own task-solving proficiency . Based on its estimate of the value of a module , and the reward that accumulates across trials , it evaluates whether the current task strategy is suited for the current environment . Based on this evaluation , it will decide to stay with the current strategy or switch to another . More specifically , the value learned by the RL unit acts as measure of confidence that the model has in its own accuracy . The model uses this measure of confidence to adjust future behavior , a process that has been labeled as meta-cognition [69] , [70] . This is in line with previous modeling work that described the prefrontal cortex as a reinforcement meta-learner [43] , [46–48] . One problem we addressed in this work was the stability-plasticity dilemma . As we described before , previous work on this dilemma can broadly be divided in two classes of solutions . The first class is based on mixing old and new information [2–5] . The second class is based on protection of old information . Our solution also exploited the principle of protection . Future work must develop biologically plausible implementations of the mixing principle too , and investigate to what extent mixing and protection scale up to larger problems . We provided a computationally efficient and empirically testable framework on how the primate brain can address the tradeoff between being sufficiently adaptive to novel information , while retaining valuable earlier regularities ( stability-plasticity dilemma ) . We demonstrated how this problem can be solved by adding fast BBS and RL on top of a classic slow synaptic learning network . RL is used to synchronize task-relevant and desynchronize task-irrelevant modules . This allows high plasticity in task-relevant modules while retaining stability in task-irrelevant modules . Furthermore , we connected the model with empirical findings and provided predictions for future empirical work .
As mentioned before and is shown in Fig 1A , our model consists of three units . First , the Processing unit includes the task-related neural network , which is trained with a classical learning rule ( RW , BP or RBM ) . On top of this classical network , an extra hierarchical layer is added consisting of two units [28] . The RL unit , adopted from the RVPM [24] , evaluates whether the Processing unit is synchronizing the correct task modules . This evaluation is used by the Control unit [30] to drive neural synchronization in the Processing unit . Thus , this hierarchically higher network allows the models to implement BBS in an unsupervised manner . We test our model on a reversal learning task [71] , [72] . We divide the task in six equally long task blocks . In the first three blocks , the model should learn three different new task rules ( rule A , B and C in blocks 1 , 2 and 3 respectively ) . In the second half , the model has to switch back to the previously learned rules ( rule A , B and C in blocks 4 , 5 and 6 respectively ) . For the RW network , we use a one-dimensional task . This task consisted of 360 trials . Here , on each trial one out of three stimulus features is activated . For every task rule we link a stimulus-feature to a response option . More specifically , in task rule A , feature 1 ( F1 ) is associated to response ( R1 ) , feature 2 ( F2 ) to response 2 ( R2 ) and feature 3 ( F3 ) response 3 ( R3 ) . In task rule B , F1 is associated to R2 , F2 to R3 and F3 to R1 . Task rule C associates F1 to R3 , F2 to R1 and F3 to R2 . All stimuli are presented equally often in random order . For the BP and RBM networks , a multi-dimensional task is used consisting of 3600 trials . In order to gain insight in how the complexity of the task affects our model , we implemented a task with two stimulus dimensions ( two-dimensional task ) and one with three stimulus dimensions ( three-dimensional task ) . For the RBM model , we only implemented the three-dimensional task . Every stimulus dimension has three features . In total , a task consists of N + 1 dimensions , in which N is the number of stimulus dimensions and the extra dimension is a cue dimension ( with N features ) , indicating which of the N stimulus dimensions is relevant on the current trial . On each trial one feature of every dimension is activated . In line with the one-dimensional task , the N = 3 task features of the stimulus dimensions are , within each task rule , linked to one response option . Again , in each block , each possible stimulus is presented equally often , in a random order . To test the generality of our findings , we varied the synaptic learning rate . This parameter was varied from 0 to 1 in 11 steps of . 1 . For each value , we performed 10 replications of the simulation . In every simulation , the strength of synaptic connections at trial 1 was a random number drawn from the uniform distribution , multiplied by half the bias value ( and 1 for the RW based model ) . The effects of other model parameters were already demonstrated in previous work [24] , [30] , but we again validated that the model shows qualitatively similar patterns when we varied some of the parameters . A table of all parameter values used in both the original simulations and parameter explorations is provided in the S1 Text . Specifically , we explored different frequencies ( C in Eqs ( 1 ) and ( 2 ) ) in the Processing unit and the pMFC . Additionally , we also explored the Damp and rmin parameters in the pMFC ( again Eqs ( 1 ) and ( 2 ) ) . For this simulation we used the RW model with β = . 2 . We fully crossed all parameter values for C , Damp and rmin . and performed 5 replications . In a separate set of explorations , we varied σ and α in the RL unit ( see Eqs ( 11 ) and ( 12 ) ) for both the RW and BP algorithm , for both a slow and a fast-synaptic learning rate ( β ) . Again , we performed 5 replications for each parameter combination . Results of the latter parameter exploration are described in the S1 Text . For the purpose of comparison , we divided the trials of the task for every model into 120 bins . For the RW model , bin size equals 3 trials; for the BP and RBM models , bin size equals 30 trials . We evaluate the performance of our model on several levels . First , we evaluate overall task accuracy . Second , we evaluate plasticity . For this purpose , we explore the performance of the model during the first 5 bins of the first 3 blocks . Hence , we test how quickly a model can learn a new task rule . Third , we evaluate stability . In particular , we explore the interference of learning other task rules in between two periods of performing the same task rule . For this purpose , we compare the accuracy during the first 5 trial bins of block 4 , 5 and 6 with the last 5 trial bins of block 1 , 2 and 3 . If the model saved what was learned , this difference should be zero . If the model displays catastrophic forgetting , it would have a negative stability score . Importantly , we also connect with empirical data and describe testable hypotheses for future empirical work . As a measure of phase synchronization between excitatory neurons in the Processing unit , we compute the correlation at phase lag zero . A correlation of 1 indicates complete synchronization and -1 indicates complete desynchronization . Phase-amplitude coupling ( PAC ) is computed as the debiased phase-amplitude coupling measure ( dPAC; [73] ) in each trial . Here , dPAC=|1h∑t=1hat× ( eiφt−Φ− ) | ( 14 ) in which Φ−=1h∑t=1heiφt ( 15 ) In these equations , t represents one time step in a trial , h is the number of time steps in a trial , a is the amplitude , φ is the phase of a signal , and i2 = -1 . In the current paper , we are interested in the coupling between the phase of the theta oscillation in the pMFC node of the Control unit and the gamma amplitude in the Processing unit . Phase was extracted by taking the analytical phase after a Hilbert transform . The gamma amplitude was derived as the mean of the excitatory phase code activation of all nodes in the Processing unit by at=1I∑i=1I|Eit| ( 16 ) with I being the number of nodes in the Processing unit , t referring to time and Ei being the respective excitatory phase code neuron . For all measures , we represent the mean value over Nrep = 10 replications and error bars or shades show the confidence interval computed by mean± 2× ( SD/√Nrep ) . Additionally , we evaluated the pMFC theta activation . More specifically , time–frequency signal decomposition was performed by convolving the signal of EpMFC by complex Morlet wavelets , ei2πfte−t2/ ( 2σ2 ) , where i2 = -1 , t is time , f is frequency , ranging from 1 to 10 in 10 linearly spaced steps , and σ = 4/ ( 2πf ) is the “width” of the wavelet . Power at time step t was then computed as the squared magnitude of the complex signal at time t and frequency f . We averaged this power over all simulations and all replications of our simulations . This power was evaluated by taking the contrast between the inter-trial intervals following correct ( 1 ) and error ( 0 ) reward feedback . Matlab codes that were used for both the model simulations and data analysis are available on GitHub ( https://github . com/CogComNeuroSci/PieterV_public ) . | Artificial and biological agents alike face a critical trade-off between being sufficiently adaptive to acquiring novel information ( plasticity ) and retaining older information ( stability ) ; this is known as the stability-plasticity dilemma . Previous work on this dilemma has focused either on computationally efficient solutions for artificial agents or on biologically plausible frameworks for biological agents . What is lacking is a solution that is both computationally efficient and empirically testable on biological agents . Therefore , the current work proposes a computational framework on the stability-plasticity dilemma that provides empirically testable hypotheses on both neural and behavioral levels . In this framework , neural task modules can be flexibly coupled and decoupled depending on the task at hand . Testing this framework will allow us to gain more insight in how biological agents deal with the stability-plasticity dilemma . | [
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] | 2019 | Learning to synchronize: How biological agents can couple neural task modules for dealing with the stability-plasticity dilemma |
The High Pathogenicity Island of Yersinia pseudotuberculosis IP32637 was previously shown to be horizontally transferable as part of a large chromosomal segment . We demonstrate here that at low temperature other chromosomal loci , as well as a non-mobilizable plasmid ( pUC4K ) , are also transferable . This transfer , designated GDT4 ( Generalized DNA Transfer at 4°C ) , required the presence of an IP32637 endogenous plasmid ( pGDT4 ) that carries several mobile genetic elements and a conjugation machinery . We established that cure of this plasmid or inactivation of its sex pilus fully abrogates this process . Analysis of the mobilized pUC4K recovered from transconjugants revealed the insertion of one of the pGDT4–borne ISs , designated ISYps1 , at different sites on the transferred plasmid molecules . This IS belongs to the IS6 family , which moves by replicative transposition , and thus could drive the formation of cointegrates between pGDT4 and the host chromosome and could mediate the transfer of chromosomal regions in an Hfr-like manner . In support of this model , we show that a suicide plasmid carrying ISYps1 is able to integrate itself , flanked by ISYps1 copies , at multiple locations into the Escherichia coli chromosome . Furthermore , we demonstrate the formation of RecA-independent cointegrates between the ISYps1-harboring plasmid and an ISYps1-free replicon , leading to the passive transfer of the non-conjugative plasmid . We thus demonstrate here a natural mechanism of horizontal gene exchange , which is less constrained and more powerful than the classical Hfr mechanism , as it only requires the presence of an IS6-type element on a conjugative replicon to drive the horizontal transfer of any large block of plasmid or chromosomal DNA . This natural mechanism of chromosome transfer , which occurs under conditions mimicking those found in the environment , may thus play a significant role in bacterial evolution , pathogenesis , and adaptation to new ecological niches .
Horizontal gene transfer ( HGT ) is a driving force for bacterial evolution , as it allows the dispersion of adaptive loci between closely related and also phylogenetically distant bacterial species . Well-characterized mobile genetic elements such as conjugative plasmids , transposons , Integrative conjugative elements ( ICE ) , pathogenicity islands ( PAI ) , or phages are associated with HGT of specific adaptive functions ( antibiotic resistance , virulence , metabolic pathways ) and participate to genome plasticity . However , exchanges of chromosomal regions that form the core genome and are not part of the mobile genetic pool are also observed . While their importance in bacterial evolution and speciation is now well established , the underlying mechanisms are often loosely described and remain hypothetical in many cases . The Gram-negative enteropathogen Yersinia pseudotuberculosis carries a PAI termed High Pathogenicity Island ( HPI ) [1] , which encodes the siderophore yersiniabactin [2] . The fact that this island is mobile within the genome of its host strain [3] , and is present and often conserved both in terms of genetic organization and nucleotide sequence in various bacterial genera such as Escherichia coli ( various pathotypes ) , Klebsiella or Citrobacter [4] , suggested that it may have retained its ability to be horizontally transmitted to new bacterial hosts . Indeed , we evidenced the transfer of the HPI between natural Y . pseudotuberculosis isolates [3] . This phenomenon was observed only when the bacteria were incubated at low temperature ( optimal at 4°C ) and in broth , and was more efficient in an iron-poor medium [5] . However , this transfer did not require the integration/excision machinery encoded by the HPI , was RecA-dependent in the recipient strain , and involved not only the HPI but also adjacent sequences encompassing at least 46 kb of chromosomal DNA [3] . Similar results were recently obtained for the HPI of natural Escherichia coli isolates , using a multi locus sequence typing approach . The E . coli HPI was found to have been acquired simultaneously with the chromosomal flanking regions of the donor strains [6] , indicating again that the island was transmitted as part of a larger chromosomal region . This phenomenon is not restricted to the HPI and to enterobacteria since it has been recently reported that movement of the Enterococcus faecalis PAI was invariably accompanied by transfer of flanking donor chromosome sequences [7] . The aim of this work was to characterize the mechanisms underlying horizontal chromosomal gene transfer in Y . pseudotuberculosis . We describe here a natural system of conjugative transfer , which may be used by a wide variety of bacterial species for gene exchanges , and which may represent a driving force for bacterial evolution .
Since we did not know whether the lateral transfer process previously observed was limited to the region encompassing the HPI or could involve any portion of the chromosome , two other loci ( ureB and or5076 ) were labeled with a spectinomycin ( Spe ) and trimethoprim ( Tmp ) resistance cassette , respectively . These two genes were chosen because , based on the IP32953 sequence , they are predicted to be separated from each other and from the HPI ( tagged with a kanamycin ( Kan ) cassette in the irp2 gene ) by at least 1 . 5 Mb of chromosomal DNA ( Figure S1 ) . Moreover , the ureB gene , which is part of the urease locus , and or5076 , encoding a putative toxin transporter [8] are not predicted to be involved in DNA transfer . After co-incubation of the donor 637-irp2K-ureBS-5076T and recipient 637ΔHPI-NalR strains ( Table 1 ) under conditions ( 4 days at 4°C in LB-αα' with shaking ) that we previously found to be optimal for HPI transfer [3] , recipient strains having acquired either the irp2K ( NalR , KanR , RifS ) , ureBS ( NalR , SpeR , RifS ) or or5076T ( NalR , TmpR , RifS ) antibiotic resistances were obtained . Acquisitions of the corresponding tagged loci were checked by PCR ( Figure S1 ) . Transfer frequencies were of the same magnitude for the three antibiotic-tagged loci ( ≈10−8 , Figure 1 ) . None of the transconjugants obtained had simultaneously acquired two of the antibiotic-tagged loci , indicating that the sizes of the chromosomal fragments transferred were inferior to 1 . 5 Mb . We previously showed that horizontal transfer of the HPI occurs only at low temperatures [3] . The same temperature dependency was observed for ureBS and or5076T: transfer of each of the three antibiotic-tagged loci was detected only when the donor and recipient strains were co-incubated at temperatures below 20°C ( Figure 1 ) , and was more efficient at 4°C than at 12°C ( ≥13 fold higher ) , as previously observed for irp2K . Therefore , distantly located chromosomal loci can be transferred with similar efficiencies and temperature regulations . Whether this transfer mechanism could also mediate horizontal transmission of episomal molecules was addressed by introducing the non-conjugative and non-mobilizable plasmid pUC4K ( KanR ) into the donor 637-RifR . Using the defined optimal growth conditions , transfer of pUC4K from the 637 ( pUC4K ) to the recipient 637ΔHPI-NalR was obtained and confirmed by PCR with primers 210A/210B ( Table S1 ) . This transfer occurred at a frequency of 2 . 3 ( ±0 . 4 ) ×10−7 , which is at least 10 times higher than that of chromosomal loci . Therefore , the process of DNA transfer is not limited to chromosomal DNA but can also involve plasmid molecules . Altogether our results demonstrate the existence of a mechanism that potentially allows transfer of any chromosomal or episomal DNA molecule at low temperature . This mechanism was thus named GDT4 ( for Generalized DNA Transfer at 4°C ) . The capacity of other Y . pseudotuberculosis strains to mediate GDT4 was studied by tagging the IP32953 and IP32777 strains with both a Kan and Spe cassettes inserted into the irp2 and ureB loci , respectively ( Table 1 ) . When these two recombinant strains were used as donors , no IP32637 transconjugants having acquired either irp2K or ureBS were obtained , indicating that GDT4 is not a property common to the entire Y . pseudotuberculosis species . Strain IP32637 has the peculiarity of harboring an extra high molecular weight ( ≥100 kb ) plasmid [9] . The role of this additional plasmid in chromosomal transfer was assessed by comparing GDT4 in IP32637 and its IP32637c plasmid-cured derivative [9] . Two tagged donor strains , 637c-irp2K and 637c-ureBS ( Table 1 ) , were generated and co-incubated with the 953-NalR recipient . No transconjugants were obtained , indicating a role of this plasmid in DNA transfer . The high molecular weight plasmid was thus designated pGDT4 . pGDT4 does not appear to be ubiquitous in the species Y . pseudotuberculosis as the genome sequences of IP32953 and of other Y . pseudotuberculosis strains available in databases did not evidence the presence of this plasmid . To get an insight into the frequency of pGDT4 carriage in this species , a 4 kb HindIII fragment of this episome , designated pGDT4 . seq was cloned into pUC18 , sequenced , and used to design primers ( 358A/B ) for PCR screening . The analysis of a panel of 39 Y . pseudotuberculosis strains of serotypes I to V ( Table S2 ) for the presence of the pGDT4 sequence identified two isolates ( IP32699 and IP30215 ) that gave a PCR product of the expected size ( Table S2 ) . Both strains contained high molecular weight episomes whose HindIII-digestion patterns yielded some restriction fragments with a size similar to those of pGDT4 , but the overall profiles of the three episomes were different ( data not shown ) . Therefore , the plasmids found in IP32699 and IP30215 probably share some regions with pGDT4 , but they are not identical to this plasmid . Since Yersinia pestis is a recent descent of Y . pseudotuberculosis [10] , we also screened by PCR a panel of 51 strains of Y . pestis belonging to the three classical biovars ( Antiqua , Medievalis and Orientalis ) for the presence of the pGDT4-borne sequence . None of the strains tested yielded an amplification product ( Table S2 ) , suggesting the absence of vertical or horizontal transmission of pGDT4 to Y . pestis . Plasmid analysis of transconjugants resulting from the co-incubation of the 637-irp2K-ureBS donor strain with the 637c-NalR recipient revealed that about half of them had acquired pGDT4 together with the chromosomal irp2 ( 8/20 ) or ureB ( 11/20 ) tagged region , thus indicating that pGDT4 is also transferable . To further study pGDT4 transfer capacity , the plasmid was labeled by allelic exchange of the pGDT4-4kb segment with a Tmp cassette . When the 637-irp2K-ureBS donor strain carrying the tagged pGDT4T was co-incubated with the 637-NalR recipient , transconjugants harboring pGDT4T were obtained with a frequency approximately 103 times higher than that of chromosomal genes ( Figure 2A and Table S3 ) . The transfer frequency of pGDT4T increased 200 fold when 953-NalR instead of 637-NalR was used as a recipient ( Figure 2B and Table S3 ) , indicating that properties inherent to the recipient cells may influence their capacity to take up pGDT4 . The difference in the ability of the two strains to acquire this plasmid could not be explained by a mechanism of surface exclusion , as the frequency of transfer of pGDT4T to IP32637 harboring or not harboring a resident pGDT4 was similar ( Figure 2B and Table S3 ) . Some unidentified intrinsic properties of the recipients such as a difference in their restriction/modification systems may be responsible for this difference . As observed with chromosomal DNA , no transfer of pGDT4T was detected when the bacteria were mated at temperatures ≥28°C ( Figure 2C and Table S3 ) . However , in contrast to chromosomal DNA [3] , pGDT4T was transferable on nitrocellulose filters , at frequencies similar to those observed in a liquid medium ( Table S3 ) , but again , only at low temperature ( Figure 2C ) . Therefore , transfer of pGDT4 is also temperature-dependent , but in contrast to chromosomal genes , it occurs at much higher frequencies , and both in liquid and on solid media . Since the presence of pGDT4 is required for transfer , we wondered whether its presence could confer GDT4 properties to a strain that is naturally unable to mediate chromosomal transfer . For this purpose , a 953-ureBS transconjugant that had acquired pGDT4 simultaneously with chromosomal genes was used as donor and co-incubated with a 637ΔHPI-RifR recipient . While the parental 953-NalR strain was unable to transfer chromosomal DNA , the 953-ureBS ( pGDT4 ) transconjugant gained the capacity to retransfer the acquired ureBS locus , though with a frequency 10 times lower ( 10−9 ) than that observed during the first transfer . These results further point at pGDT4 as a key element in the mechanism of chromosomal transfer . To determine whether pGDT4 could encode its own transfer machinery , the plasmid was sequenced ( EMBL accession number FM178282 ) . The schematic map of the 94 , 967 bp circular plasmid molecule is shown on Figure 3 . Of the 102 predicted coding sequences ( cds ) identified on pGDT4 , 74 had homologs in databases ( Table S4 ) . Four major functional groups of genes were delineated on pGDT4: Since pGDT4 carries a large set of genes predicted to be involved in conjugative transfer , we wondered whether this pGDT4-specific mobility function mediates GDT4 . To investigate this potential role , the Mpf function was inactivated by allelic exchange of a large portion of the pil region ( from pilL to pilV ) of pGDT4 with a Tmp cassette in 637 ( pUC4K ) . After co-incubation of the resulting strain 637 ( pUC4K , pGDT4Δpil ) with the 953-NalR recipient , no transconjugants having acquired pUC4K were obtained , indicating that the pilus-encoding region of pGDT4 is required for generalized DNA transfer . As the Mpf region is predicted to encode a conjugative machinery , GDT4 most likely occurs by a mechanism of conjugation . To rule out other possible mechanisms of transfer , DNAse was added to the medium during the co-incubation period . The transfer frequency of the irp2 locus from the donor 637-irp2K-ureBS to the recipient 637ΔHPI-NalR was not affected , arguing against an acquisition of naked DNA through a transformation process . Cell-free filtrates of the supernatant of the donor strain incubated with the recipient strain did not allow DNA transfer , suggesting the absence of transferable DNA released from the bacteria but protected from the action of a DNAse ( inside phage particles or membrane vesicles ) . These results argue against a transfer of DNA by transformation or transduction and further point at conjugation as the most likely mechanism . However , since this conjugative process was observed in liquid medium under agitation , we wondered whether a strong shaking of the culture would disrupt the pilus-mediated interactions between bacterial cells , and therefore decrease the transfer frequency . Surprisingly , when we increased the agitation of the medium containing the donor and recipient cells to 130 rpm ( which was vigorous under our experimental conditions ) , the frequency of transfer of the irp2 locus was not affected ( 0 . 9×10−8 ) . Electron microscopy analysis of IP32637 cells grown under conditions optimal for GDT4 did not reveal any pilus structures on the bacterial surface . In contrast , tightly aggregated bacilli that seemed to be connected by “bridges” were observed ( Figure 4 ) . We noted that after pUC4K transfer , the plasmid sizes of pUC4K in 10 different transconjugants were variable . This was confirmed after digestion of the 10 plasmids with NdeI , an enzyme that has a single restriction site in pUC4K . Three plasmids ( rpUC4K-1 to -3 ) had the expected pUC4K size , while the seven others ( rpUC4K-4 to -10 ) had a size superior to that of the original molecule ( data not shown ) , indicating that various types of rearrangements had occurred during plasmid transfer . Remarkably , a search for the potential transposition of pGDT4-borne IS ( ISYps1 , ISL3 , ISYps2 , ISNCY or ISYps3 ) on rpUC4K molecules by PCR ( primers described in Table S1 ) showed that all seven larger size recombinant plasmids ( rpUC4K-4 to -10 ) harbored ISYps1 . rpUC4K-5 to -9 had a size compatible with the acquisition of a single ISYps1 copy . Digestion with XhoI , an enzyme that cuts once in pUC4K and once in ISYps1 , yielded two restriction fragments , thus confirming the presence of a single ISYps1 copy . However , two distinct restriction profiles were observed , one for rpUC4K-5 and -8 and one for rpUC4K-6 , -7 and -9 ( data not shown ) , indicating the occurrence of different genetic rearrangements . Sequencing of the regions encompassing the ISYps1 insertion site in one recombinant plasmid of each group ( rpUC4K-5 and rpUC4K-6 ) demonstrated that the IS was inserted at two different sites , approximately 100 bp apart , and in opposite orientation ( Figure 5a and 5b ) . The insertion generated an 8 bp duplication of the target sequence: AAAATAGG in rpUC4K-5 and TATTTGAA in rpUC4K-6 . rpUC4K-4 had a size superior to that of the above five plasmids . XhoI digestion revealed the presence of two ISYps1 copies on this plasmid ( Figure 5c ) . To determine whether the region located between these two ISYps1 copies on rpUC4K-4 corresponded to a portion of pGDT4 , a PCR amplification of three pGDT4 genes ( the Ig-like domain , parF and traM ) , each located between two different ISYps1 copies on pGDT4 ( Figure 5 ) was performed . No positive signal was detected , suggesting that the region located between the two ISYps1 is a duplicated portion of pUC4K ( Figure 5c ) . The rpUC4K-10 plasmid was different from all others since the PCR analysis showed that it carries all five pGDT4-borne IS , as well as the Ig-like domain and parF genes ( but not traM , Figure 5d ) . rpUC4K-10 has thus most likely acquired the entire pGDT4 sequence located between the two ISYps3 transposons ( Figure 5 ) . Altogether , these results show that most pUC4K transfers generated a variety of genetic modifications that were systematically accompanied by the transposition of the pGDT4-borne ISYps1 element . ISYps1 belongs to the IS6 family , known to transpose through replicative transposition . This mode of transposition gives rise exclusively to replicon fusions ( cointegrates ) , in which the donor and target replicons are separated by two IS copies in direct orientation . The cointegrate can be subsequently resolved by recombination between the two IS copies [17] . To determine whether ISYps1 transposes through this mechanism , this IS was cloned into the suicide mobilizable vector pSW23T and introduced into a replication-permissive E . coli strain , yielding ω7249 ( pSWYps1 . 1 ) ( Table 1 ) . After mating of this donor strain with ω4826 , a non-replication permissive recA- recipient , ω4826::pSWYps1 . 1 transconjugants resulting from pSWYps1 . 1 integration into the recipient chromosome were obtained with a frequency of 8 . 5 ( ±0 . 4 ) ×10−6 ( which corresponds to the frequencies of both conjugation and transposition , Table S5 ) . Since the frequency of conjugation under these conditions was found to be 3 . 4 ( ±0 . 9 ) ×10−3 ( Table S5 ) , the transposition frequency of ISYps1 is thus approximately 2×10−3 . The genomic DNA of eight independent ω4826::pSWYps1 . 1 colonies were digested with HindIII ( which cuts once in pSW23T and not in ISYps1 ) , and hybridized with an ISYps1 probe . All eight clones harbored , as expected , two integrated copies of ISYps1 ( Figure S2 ) . Of note , all clones exhibited different hybridization profiles . To further determine whether association between ISYps1 and a conjugative plasmid allows cointegrate transfer , the IS was cloned on a non-mobilizable vector ( pSW23 ) and introduced into an E . coli strain carrying the conjugative plasmid R388 ( Table 1 ) . After mating of the resulting pi3 ( R388 , pSWYps1 . 2 ) donor strain with the ω4826 recipient that cannot sustain pSWYps1 . 2 replication , ω4826 ( R388::pSWYps1 . 2 ) transconjugants were independently selected on Cm ( pSWYps1 . 2 tag ) and Tmp ( R388 tag ) MH agar plates . CmR clones were found at a frequency of 9 ( ±3 ) ×10−5 ( Table S5 ) and were all TmpR , while in the absence of an ISYps1 carried on the pSW23 , no CmR transconjugants were obtained ( Table S5 ) . This further demonstrates that Yps1 drives the formation of cointegrates that can be subsequently transferred by conjugation . Under these conditions , R388 was transferred at a frequency of 2 . 2 ( ±0 . 7 ) ×10−1 ( Table S5 ) , indicating that transfer of these cointegrates occurs at high frequencies ( ≈2×10−5 ) . To further characterize these events , the plasmid profiles of five independent R388::pSWYps1 . 2 cointegrates were analyzed after restriction with MfeI ( 10 sites in R388 , one in pSWYps1 . 2 ) . All five pSWYps1 . 2 insertions were in different locations on R388 ( data not shown ) . The two MfeI junction fragments from one of these R388::pSWYps1 . 2 cointegrates were cloned into the EcoRI site of pUC18 and the precise cointegrate location was determined by sequencing . Transposition of pSWYps1 . 2 occurred in the orf5 cassette of the R388 integron [18] and led , as for the two pUC4K insertion events analyzed above , to an 8 bp duplication . The duplicated sequence ( GATCCGAG ) was different from the other two , further indicating the absence of a specific integration site . Our results thus demonstrate that ISYps1 is able to transpose into a variety of insertion sites by replicative transposition through cointegrate formation , mediating the transfer of potentially any piece of non-mobilizable DNA molecule .
We have evidenced a mechanism of HGT that convey the conjugative transfer or virtually any piece of chromosomal or plasmid DNA in a natural isolate of Y . pseudotuberculosis . This mechanism shares some characteristics with those previously described , but has several novel and unique properties . GDT4 is not observed at temperatures ≥20°C and its efficiency increases as the temperature decreases . Although some conjugative plasmids have been previously shown to be self-transferable at 14°C but not at 37°C [19] , [20] , to our knowledge no plasmid able to conjugate at 4°C has ever been described . Temperature-dependent plasmid transfer is primarily mediated by H-NS and Hha proteins , which can be both plasmid and/or chromosome encoded [20] . The pGDT4 sequence did not reveal any gene encoding such proteins , but it is known that chromosomally encoded Hha and YmoA ( equivalent to H–NS ) act as thermoregulators in Y . enterocolitica [21] . These proteins ( also encoded by the Y . pseudotuberculosis genome [8] ) may thus modulate pGDT4 transfer at cold temperatures . Interestingly , H-NS is an integral part of bacterial stress response pathways and its function is known to be sensitive to changes in environmental conditions such as temperature [22] , [23] . A cold stress could thus be a signal for the bacteria to transfer their genetic material by GDT4 . The low temperature and an iron-poor liquid environment may also induce changes in the bacterial membrane structure , as observed for the closely related organism Y . pestis , in which the transcription patterns of various genes encoding components of the bacterial membrane were modified during iron starvation [24] or growth at 10°C [25] . These modifications might facilitate the formation of pores through which chromosomal DNA could translocate . Indeed , large cell aggregates in which bacteria appeared to be connected by bridges were observed . Similar tight bacterial contacts , designated conjugative junctions [26] or conjugational junctions [27] have been observed during RP4 or F-mediated mating of E . coli , respectively . However , these physical properties do not seem to be pGDT4-mediated , as we also observed large bacterial aggregates and possibly intercellular channels with IP32953 , a strain that does not harbor this plasmid ( data not shown ) . These bacterial aggregates have some similarities with biofilms in which bacteria are also closely connected . Biofilm formation occurs under natural conditions in a variety of bacterial species [28] , including Y . pseudotuberculosis [29] . It could thus be hypothesized that GDT4 may take place between bacteria residing within biofilms in their natural ecological niches . This observation also suggests that acquisition of new functions , including virulence factors , by Y . pseudotuberculosis takes place in the environment rather than in a mammalian host . Another characteristic feature of GDT4 is that transfer of chromosomal DNA occurs only in a liquid medium . Like other conjugative processes , GDT4 requires a pilus-like mating system and a mating channel to occur , as demonstrated by the fact that inactivation of the pGDT4-borne pilus apparatus abolished this mechanism . While some plasmids transfer better on plates , some others encoding long flexible pili allow DNA transfer efficiencies of the same magnitude in liquid and on solid media [30] , [31] , and this applied to pGDT4 . In contrast , the absence of transfer of chromosomal DNA on agar was unexpected . Also unexpected was the fact that the efficiency of transfer in broth was not affected by a strong agitation , as opposed to recent findings showing that a vigorous shaking negatively affected the transfer of several conjugative plasmids , including the F' plasmid that encodes long and flexible pili [32] . Actually , growth in a liquid medium at low temperature could create the conditions optimal for GDT4 . Indeed , this environment might be more favorable for the formation of tight bacterial aggregates and inter-cellular bridges through which long stretches of chromosomal DNA could transit . pGDT4 also triggered the conjugative transfer of the non-mobilizable plasmid pUC4K . Similarly , transfer of the non mobilizable plasmid pBR325 by an RP4::miniMu mobilizing plasmid was previously observed [33] , but the mechanism underlying this genetic transfer was not characterized . cis-mobilization of non-mobilizable plasmid DNA can occur after integration of a conjugative plasmid into the genetic element to be transferred . Integration arises either by homologous recombination between identical elements , often two copies of the same IS located on each DNA molecule ( as for the Hfr formation in E . coli [34] ) , or through the formation of cointegrates mediated by specific transposons or ISs [35] . Integration of pGDT4 into pUC4K could not occur via homologous recombination as the two replicons do not share any common IS or identical DNA sequences . However , pGDT4 carries ISYps1 , an IS which is predicted to belong to the IS6 family ( http://www-is . biotoul . fr/is . html ) . ISYps1 is the second IS of the IS6-type identified in the genus Yersinia [36] . Members of this family have the capacity to create cointegrates by replicon fusion in the absence of a homologous IS on the target DNA [37] . Furthermore Tn3 , which also moves by replicative transposition , has been found to mediate cis-mobilization of non mobilizable plasmids by this mechanism [35] . The fact that several rpUC4K plasmids obtained after pGDT4-mediated transfer carried a copy of ISYps1 argues for a role of this IS in pGDT4 integration into its target . We have demonstrated that ISYps1 is indeed transposing through replicative transposition . ISYps1 has a low specificity of recognition of the target sequence , as attested by our observation that the three insertion sites sequenced ( two on pUC4K and one on R388 ) were different . This model also predicts the formation of cointegrates carrying two copies of the IS element , each flanking the sequence of the donor plasmid . We do have observed the formation of cointegrates between R388 and pSWYps1 flanked by the expected ISYps1 copies . A resolution step , which occurs through homologous recombination between the two IS copies , is then required to separate the donor and target replicons , leaving a single IS copy in the target and restoring the donor plasmid . The rpUC4K-5 to -9 molecules that were found to carry one ISYps1 copy are most likely the results of such a resolution event . The presence on one recombinant plasmid ( rpUC4K-10 ) of a portion of pGDT4 carrying ISYps1 , ISYps2 , ISYps3 , ISNCY and ISL3 indicates that additional , complex rearrangements involving the Tn3-like transposon ISYps3 can also occur . Finally , the existence in some transconjugants of pUC4K plasmids with a size identical to that of the original molecule could be the result of the resolution of cointegrates containing pUC4K concatemers . GDT4 thus represents a remarkable illustration and validation of the model of Tn3-mediated transmission of non conjugative plasmids proposed by Crisona et al . in the 1980's [35] . Following this model , the first step is the integration of pGDT4 into its target DNA by ISYps1-mediated replicon fusion during plasmid replication ( Figure 6 ) . As mentioned above , this generates a cointegrate which carries the two replicons separated on each side by an ISYps1 copy . This cointegrate then uses the conjugative machinery encoded by pGDT4 to promote its transfer to the recipient strain . The final step is the resolution of the cointegrate by homologous recombination between two ISYps1 copies or any other duplicated sequence present in the cointegrate . Since pGDT4 carries several ISYps1 , the resolved molecules have different sizes and DNA composition . GDT4 is also able to mediate the translocation of chromosomal DNA , most likely by integration into the bacterial chromosome and transfer in a Hfr-like manner . The Hfr mechanism is one of the earliest and best described examples of chromosomal transfer and is mediated by the F plasmid of E . coli [38]–[40] . F integrates stably into the E . coli chromosome through homologous recombination between IS copies present on both the F plasmid and the bacterial chromosome [38]–[42] to create Hfr strains , with transfer origins located at different chromosomal loci [43] , [44] . In contrast to the classical Hfr mechanism , integration of pGDT4 into the chromosome probably occurs , as in pUC4K , via the ISYps1-mediated replication fusion mechanism . At least three pieces of evidence support this hypothesis: ( i ) no IS element is shared by pGDT4 and the IP32637 chromosome , in contrast to what is expected for the Hfr mechanism , ( ii ) in Y . pseudotuberculosis , three distantly located chromosomal loci ( irp2K , ureBS and or5076T ) were transferred with similar frequencies , and ( iii ) in all eight E . coli transconjugants analyzed , pSWYps1 was inserted at different sites on the chromosome . ISYps1 thus appears to have a very low specificity of recognition , allowing its insertion at multiple sites on bacterial plasmids and chromosomes . After mobilization of the chromosomal fragment adjacent to the pGDT4 integration site and transfer to a recipient strain , following the Hfr-type transfer model , homologous recombination between the incoming DNA and the chromosome is expected to take place , leaving no trace of pGDT4 in the chromosome of the transconjugant . Our previous observation that RecA activity is necessary in the recipient , but not in the donor strain for chromosomal transfer [3] , and the results of the present study showing that pGDT4 is absent from some transconjugants that have acquired chromosomal genes further support this model of horizontal transfer . Our study thus validates the model proposed by Willets et al . in the 1980's for the mobilization of the E . coli chromosome via the formation of a cointegrate with the R68 . 45 plasmid during IS21 transposition [45] . Such cointegrate formations were widely used at that time to establish the genetic map of various bacterial species ( see for instance [46] , [47] ) . Most importantly our results show , without the need for heterologous plasmids like RP4 or R68 . 45 , that this type of chromosomal conjugative transfer may occur under natural conditions in wild type bacterial pathogens carrying endogenous plasmids . The capacity of wild type bacteria to naturally transfer large pieces of chromosomal DNA following the typical Hfr mechanism of homologous recombination between identical IS copies on the chromosome and the plasmids has been documented in a variety of bacteria , including extremophiles [48] , Gram-positive cocci [49] , and actinomycetes [50] . What we describe here is certainly a less constrained and more powerful mechanism , as it only requires the presence of an IS of the IS6 family on a conjugative replicon to generate cointegrates able to drive the horizontal transfer of any piece of DNA ( chromosomal or episomal ) . It is remarkable that a high density of IS is commonly observed on plasmids . For instance the Shigella plasmid pWR100 carries 93 copies of complete or truncated IS belonging to 21 different types [51] . Thus , more than being IS depository , this location may reflect the broad selective advantage brought by plasmid/IS associations as a chromosomal transfer device . Such a ‘genetic symbiosis’ , offers a means for the natural transfer of large blocks of genes conferring new metabolic properties or virulence functions . According to our model , GDT4 does not leave any signature in the recipient genome in most instances , and therefore its contribution to the numerous horizontal gene exchanges that shape bacterial genomes can hardly be quantified . However , according to the ISfinder database , approximately 5% of the known IS belong to the IS6 and Tn3 families , which use a replicative transposition mechanism . As they are found in all bacterial and archaeal phyla , the mechanism we describe here might be responsible for a substantial fraction of gene exchanges occurring among bacterial species . Remarkably , this mechanism of DNA transfer was optimal when the bacteria were grown under conditions ( low temperatures , iron poor medium , biofilm-like bacterial aggregates ) that might be close to those met by these microorganisms in their normal ecological niches . This natural GDT mechanism may thus play a significant role in bacterial evolution , genetic polymorphism , pathogenesis and adaptation to new environmental conditions .
Bacterial strains used in this study are listed in Table 1 and Table S1 . Wild type strains were taken from the collection of the Yersinia Research Unit ( Institut Pasteur ) . Bacteria were grown in LB ( Luria Bertani ) or MH ( Mueller Hinton ) medium for 24 h at 28°C ( Yersinia ) or 37°C ( E . coli ) with agitation , or for 48 h on LB or MH agar plates . When necessary , kanamycin ( Kan: 100 µg ml−1 ) , rifampicin ( Rif: 100 µg ml−1 ) , nalidixic acid ( Nal: 25 µg ml−1 ) , spectinomycin ( Spe: 50 µg ml−1 ) , tetracycline ( Tc: 15 µg ml−1 ) , chloramphenicol ( Cm: 25 µg ml−1 ) , trimethoprim ( Tmp: 20 µg ml−1 ) , thymidine ( dT: 0 . 3 mM ) or the iron chelator αα'-dipyridyl ( 0 . 2 mM , Sigma ) were added to the medium . Spe ( aadA ) or Tmp ( dfr ) non-polar cassettes were PCR-amplified using primers described in Table S1 , and pSW25 [52] or pGP704N-dfr [3] as templates , respectively . All allelic exchanges of chromosomal or plasmid genes by an antibiotic resistance cassette were done following the LFHR-PCR procedure [53] . The Spe and Tmp cassettes were introduced into the chromosomal ureB and or5076 genes , respectively , using primers that amplify upstream and downstream fragments of ureB and or5076 , as shown on Figure S1 and Table S1 . To label pGDT4 , the plasmid was digested with HindIII and a 4 kb fragment ( pGDT4-4kb ) was purified and cloned into pUC18 . Approximately 600 bp of each extremity of the cloned fragment were sequenced . These sequences were then used to design primers ( 358A/B and 359A/B , Table S1 ) that served for allelic exchange between the Tmp cassette and the target region of pGDT4 in strain 637-irp2K-ureBS . Correct insertion of the Tmp cassette was confirmed by PCR using primer pair 358A/359B . Mutagenesis of the pil region was done by replacing the pGDT4 region extending from pilL ( pGDT4_0086 ) to pilV ( pGDT4_0097 ) by a Tmp cassette , using primer pairs 773A/B and 774A/B ( Table S1 ) . The various antibiotic-tagged derivatives cured of pKOBEG-sacB were selected on sucrose plates . Optimal conditions for chromosomal DNA transfer in Y . pseudotuberculosis have been previously described [3] . Briefly , the donor strain ( usually RifR ) harboring chromosomal loci labeled with antibiotic cassettes and the recipient strain ( usually NalR ) were grown overnight in LB at 28°C with agitation . Equal amounts ( 5×106 ) of donor and recipient cells were mixed in 25 ml of LB-αα' and grown at 4°C with mild rotary agitation ( 80 rpm ) for 4 days . Donor and recipient bacteria were quantified on Rif and Nal plates , respectively , and transconjugants were selected on Nal plates containing the appropriate antibiotic . To ensure that the colonies were not spontaneous NalR mutants of the RifR recipient strain , the Rif susceptibility of the transconjugants was systematically checked . For every single DNA transfer experiment , 10 to 20 transconjugant colonies were analyzed by PCR for the acquisition of the corresponding antibiotic-tagged locus with primer pairs 233B/166 , 92A/322B and 348B/346A ( Table S1 ) as indicated on Figure S1 . When the transfer of the irp2K locus was analyzed , the acquisition of the entire HPI by the recipient strain was further checked with primer pairs A10/144A and A9/143B ( Figure S1 and Table S1 ) . The frequency of DNA transfer was determined as the number of NalR ( or KanR , SpeR , TmpR ) RifS transconjugants per RifR donor cells . To determine whether free DNA molecules in the medium could mediate GDT4 , the donor bacteria 637-irp2K-ureBS and the recipient 637ΔHPI-NalR were co-incubated in the presence of 100 U/ml of DNAse in the culture medium . The activity of the DNAse under these conditions was checked by adding 1 ug/ml of bacterial DNA to the culture medium and by observing that the added DNA was degraded . Transfer of pGDT4T was studied after incubation of the donor ( 637-irp2K-ureBS ( pGDT4T ) ) and various NalR recipient cells for four days at 4 , 28 or 37°C in liquid or solid media . On solid medium , 2×108 donor and recipient cells were mixed on a 0 . 45 µm nitrocellulose filter ( Millipore ) and at the end of the incubation period , the bacterial mixture was suspended in 1 ml of MH . Donor and recipient cells were quantified on MH-Rif and MH-Nal plates , respectively . Transconjugants having acquired pGDT4T were identified as NalR/TmpR/RifS colonies . In each transfer experiment , 10 transconjugants were analyzed by PCR for the presence of pGDT4T with primer pair 358A/346B ( Table S1 ) . Finally , the pGDT4T transfer frequency was calculated as the number of NalR/TmpR/RifS transconjugants per donor cells . One transconjugant resulting from the co-incubation of the 637-irp2K-ureBS donor strain with the 637c-NalR recipient was used to obtain a plasmid extract which contained only pGDT4 . Sequencing was performed using the whole genome shotgun strategy [54] . A 2–3 kb insert library was generated by random mechanical shearing of pGDT4 DNA and cloning into pcDNA-2 . 1 ( Invitrogen ) . Recombinant plasmids were used as templates for cycle sequencing reactions consisting in 35 cycles ( 96°C for 30 s; 50°C for 15 s; 60°C for 4 min ) in a thermocycler , using the Big dye terminator kit ( V3 . 1 , Applied Biosystems ) . Samples were precipitated and loaded onto a 96-lane capillary automatic 3700 DNA sequencer ( Applied Biosystems ) . In an initial step , 1000 sequences from the library were assembled into 5 contigs using the Phred/Phrap/Consed software [55] , [56] ( 8-fold sequence coverage ) . Consed was used to predict links between contigs . PCR products amplified from the pGDT4 template were used to fill gaps and to re-sequence low quality regions using primers designed by Consed . Physical gaps were closed using combinatorial PCR . The correctness of the assembly was confirmed by ensuring that the deduced restriction map was identical to the one obtained experimentally . The traX and traY genes fusion into a single traXY gene was checked by re-sequencing this locus on the original pGDT4 DNA preparation . ISYps1 , ISYps2 and ISYps3 designation were attributed by the ISfinder database ( http://www-is . biotoul . fr/ ) . The nucleotide sequence of pGDT4 has been submitted to the EMBL database under accession number FM178282 . Details and properties of the different ISYps characterized in this work are accessible through the ISFinder web site . Bacteria were negatively stained with 2% uranyl acetate onto glow discharged copper grids . The samples were observed in a Jeol 1200EXII and/or a JEM 1010 ( Jeol ) equipped with a Keenview camera ( Eloise ) at 80-kV accelerating voltage . Images were recorded with an Analysis Pro Software version 3 . 1 ( Eloise ) . The sequence corresponding to the ISYps1 copy carried on rpUC4K-6 , flanked by its 113 bp upstream and 138 bp downstream regions , was amplified using primers 1039/1040 and cloned as an EcoRI-BamHI insert into the suicide mobilizable vector pSW23T [52] , giving rise to pSWYps1 . 1 . This plasmid was then introduced into E . coli ω7249 [57] , a strain allowing pSW23T replication and conjugative transfer . Conjugation between this donor strain and E . coli ω4826 was performed as previously described [57] , The frequency of conjugation–transposition frequency was calculated as the number of CmR transconjugants ( ω4826::pSWYps1 . 1 ) per total number of recipients ( TcR ) . The conjugation frequency was established in parallel by conjugation from the same donor ω7249 ( pSWYps1 . 1 ) to a ω4826 pir+ derivative ( obtained through transformation with plasmid pSU38Δpir which expresses pir [52] ) . The frequency of illegitimate recombination of the pSW23T which can lead to CmR transconjugants was established by conjugation between donor ω7249 ( pSW23T ) and ω4826 , and found to be 4 . 6 ( ±1 . 7 ) ×10−8 . Genomic DNA from 8 independent ω4826::pSWYps1 . 1 colonies were extracted using QIAGEN Genomic Tips and buffer set , digested with HindIII , and hybridized with a probe internal to ISYps1 ( generated by PCR amplification with primers 1041/1044 and labeled with α-32P dCTP , using the Random Primed labeling kit ( Roche ) ) . The EcoRI-BamHI fragment carrying ISYps1 was transferred from pSWYps1 . 1 to the non-mobilizable version of pSW23 [52] , giving rise to pSWYps1 . 2 . This plasmid was then introduced into the E . coli pi3 pir+ strain that harbors the IncW conjugative plasmid R388 , which does not carry any IS ( GenBank BR000038 ) , giving rise to pi3 ( R388 , pSWYps1 . 2 ) . Conjugation of this donor strain with ω4826 yielded ω4826 ( R388::pSWYps1 . 2 ) . The frequency of cointegrate formation after mating was calculated as the number of CmR transconjugants per total number of recipients harboring R388 ( TmpR ) . The ability of R388 to form transferable cointegrates with pSW23 in the absence of ISYps1 was assessed in the same conditions by replacing pSWYps1 . 2 by pSW23 in the pi3 ( R388 ) donor , and found to be inferior to 10−9 . | All living species have the capacity to evolve in order to adapt to new and often hostile conditions . Horizontal gene transfer is a major route for rapid bacterial evolution . Some clearly identified mobile genetic elements ( plasmids , phages , etc . ) are by essence exchanged between bacteria . However , the mechanisms generating the bacterial core genome diversity are much less understood . In this study we have characterized in Y . pseudotuberculosis , a natural bacterial pathogen causing mesenteric lymphadenitis and enteritis , a mechanism of horizontal gene exchange that conveys the transfer of virtually any piece of chromosomal or plasmid DNA to a new bacterial host . This generalized mechanism of DNA transfer is optimal when the bacteria encounter conditions that might resemble those they met in their natural ecological niches . We demonstrate that this transfer mechanism is extremely powerful , as the presence on a conjugative replicon of an insertion sequence having a low specificity of insertion and transposing through replicative transposition is sufficient to drive the horizontal transfer of virtually any piece of chromosomal or episomal DNA . As such , this mechanism is much less constrained than the classical Hfr mechanism described in laboratory E . coli and could be used by a wide variety of bacterial species for gene exchange and evolution . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"biology"
] | 2012 | A Natural System of Chromosome Transfer in Yersinia pseudotuberculosis |
GspB is a serine-rich repeat ( SRR ) adhesin of Streptococcus gordonii that mediates binding of this organism to human platelets via its interaction with sialyl-T antigen on the receptor GPIbα . This interaction appears to be a major virulence determinant in the pathogenesis of infective endocarditis . To address the mechanism by which GspB recognizes its carbohydrate ligand , we determined the high-resolution x-ray crystal structure of the GspB binding region ( GspBBR ) , both alone and in complex with a disaccharide precursor to sialyl-T antigen . Analysis of the GspBBR structure revealed that it is comprised of three independently folded subdomains or modules: 1 ) an Ig-fold resembling a CnaA domain from prokaryotic pathogens; 2 ) a second Ig-fold resembling the binding region of mammalian Siglecs; 3 ) a subdomain of unique fold . The disaccharide was found to bind in a pocket within the Siglec subdomain , but at a site distinct from that observed in mammalian Siglecs . Confirming the biological relevance of this binding pocket , we produced three isogenic variants of S . gordonii , each containing a single point mutation of a residue lining this binding pocket . These variants have reduced binding to carbohydrates of GPIbα . Further examination of purified GspBBR-R484E showed reduced binding to sialyl-T antigen while S . gordonii harboring this mutation did not efficiently bind platelets and showed a significant reduction in virulence , as measured by an animal model of endocarditis . Analysis of other SRR proteins revealed that the predicted binding regions of these adhesins also had a modular organization , with those known to bind carbohydrate receptors having modules homologous to the Siglec and Unique subdomains of GspBBR . This suggests that the binding specificity of the SRR family of adhesins is determined by the type and organization of discrete modules within the binding domains , which may affect the tropism of organisms for different tissues .
The serine-rich repeat ( SRR ) glycoproteins of Gram-positive bacteria are an expanding family of microbial adhesins and virulence factors [1]–[6] . These proteins consist of a distinctive signal sequence and export-targeting region at the N-terminus , a short SRR region ( ∼50–170 amino acids ) , a ligand binding region , a second lengthy SRR region ( ∼400–4000 amino acids ) , and a cell wall anchoring motif at the C-terminus ( Fig . 1 ) [1] , [2] , [4] , [6]–[9] . The binding regions of the SRR glycoproteins contain significant sequence variation , which appears to account for their broad range of binding targets , including platelet membrane and salivary glycoproteins [1] , [4] , [9]–[11] , endothelial cells [12] , epithelial cells [13] , erythrocytes [14] , [15] , and keratins [16] , [17] . Expression of SRR proteins has been associated with increased virulence in several animal models of infection , including endocarditis [6] , [18] , meningitis [12] , pneumonia [5] , and bacteremia [7] , [19] . A number of bacterial surface components have been shown to mediate platelet binding in vitro , either by interacting directly with receptors on the platelet membrane , or via bridging molecules , such as fibrinogen [20]–[29] . The contribution of these interactions to virulence , however , has been assessed for relatively few of these adhesins . Previous studies have focused on the molecular basis for the SRR adhesin mediated binding of gram-positive bacteria to human platelets , and the role of this process in the pathogenesis of infective endocarditis . This interaction appears to be important for the attachment of blood-borne bacteria to platelets on the surface of damaged cardiac valves , thereby initiating infection . In addition , the subsequent deposition of platelets onto the infected endocardium may be due in part to bacterium-platelet binding , resulting in the formation macroscopic vegetations , which are the hallmark lesions of this disease [30] . Three of the SRR proteins ( GspB of Streptococcus gordonii strain M99 , Hsa of S . gordonii strain Challis , and SrpA of Streptococcus sanguinis strain SK36 ) bind human platelets through their interaction with glycocalicin , which is the carbohydrate-rich extracellular portion of the platelet membrane glycoprotein GPIbα [9] , [11] . While the specific carbohydrate receptor for SrpA has not yet been identified , GspB and Hsa recognize sialylated trisaccharides [1] , [11] , [31] , [32] . Dot blot assays using immobilized carbohydrates have demonstrated that GspB has high fidelity for sialyl-T antigen ( i . e . NeuAcα ( 2–3 ) Galβ ( 1–3 ) GalNAc ) , one of the major carbohydrates on GPIbα [11] , while Hsa binds to glycocalicin via either sialyl-T antigen or sialyllactose ( Neu5Acα ( 2–6 ) Galβ ( 1–4 ) Glc ) [31] , [32] . Binding of these SRR adhesins to platelets is a high affinity process , with the interaction between GspB and glycocalicin having a KD of 2 . 38×10−8 M [11] and appears to be a major factor in the pathogenesis of infective endocarditis , since the loss of GspB or Hsa expression results in a marked reduction in virulence [33] . Structural information can enhance the understanding of the determinants of binding specificity . Here , we report the high-resolution crystal structure of GspBBR , both alone and in complex with the disaccharide α-2 , 3-sialyl ( 1-thioethyl ) galactose , a precursor to synthetically-produced sialyl-T antigen . From these structures , we identified that a subdomain of GspBBR resembling mammalian sialic acid binding proteins binds to α-2 , 3-sialyl ( 1-thioethyl ) galactose . Site directed mutagenesis and in vivo studies in a rat model of infective endocarditis verified that this carbohydrate binding site within GspBBR mediates binding of S . gordonii strain M99 to sialyl-T antigen , the host carbohydrates of the platelet membrane receptor GPIbα , and intact platelets , and that this interaction is important for virulence . Our analysis of the structure further identified that GspBBR contains an unusually modular fold , prompting us to re-analyze the sequences of the SRR family of adhesins . We determined that other structurally uncharacterized SRR adhesins also contain modules within their binding regions , suggesting that particular subdomains may be included , removed , or interchanged , manifesting in the broad range of binding partners observed in the family .
We determined the structure of GspBBR to 1 . 4 Å resolution using the method of Multiwavelength Anomalous Dispersion from a single Dy3+ derivative ( Fig . 2 , Table 1 , Table 2 ) . GspBBR folds into an elongated rod , with dimensions of ∼130 Å×30 Å×30 Å . This rod is comprised of three apparently independently-folded subdomains arranged in a linear fashion , like beads on a string . The secondary structure of each of the three subdomains is predominated by β-strands . Interestingly , the first two subdomains are organized around core folds that resemble those found within the eukaryotic immunoglobulin ( Ig ) superfamily ( Fig . 3 ) . Ig-folds have previously been identified in prokaryotic proteins [34]–[41] , and it has been noted that some of these bacterial proteins with Ig-folding topologies contain sequence similarity to their eukaryotic counterparts , while the others lack the residues conserved in the core of eukaryotic proteins with Ig-folds . GspBBR has homology with bacterial proteins that do not contain detectable sequence similarity to eukaryotic proteins with Ig-folds , lacking even the cysteines that normally form a disulfide bond . Several topology variants of Ig-folds have been characterized . A structural homology search using the EMBL DaliLite server [42] identified that the N-terminal subdomain of GspBBR contains a folding topology , strand inserts , and inter-sheet angle reminiscent of the DE-variant of the Ig-fold [36] ( Fig . 3A–C ) . This Ig-fold topology is found in prokaryotic proteins and was first identified within the A-region of the Staphylococcus aureus CNA protein [35] . CNA belongs to the family of microbial surface components recognizing adhesive matrix molecules ( MSCRAMMs ) of Gram-positive pathogens [34]–[36] . Given its structural similarity , this N-terminal subdomain of GspBBR will be termed the CnaA subdomain . The highest structural similarity between the CnaA subdomain of GspBBR and any other structurally characterized protein is to the C-terminal subdomain of the binding region of SRR adhesin Fap1 from Streptococcus parasanguinis ( Fap1NR-β ) [43] . The RMS deviation of the structural alignment between the CnaA subdomain of GspBBR and the Fap1NR-β subdomain is 2 . 5 Å for 200 Cα atoms . Surprisingly , a structural homology search using EMBL DaliLite [42] identified that the second subdomain contained a topology and strand inserts reminiscent of the V-set Ig fold adopted by eukaryotic sialic acid binding immunoglobulin-like lectins [44]–[46] ( Siglecs; Fig . 3D–F ) . However , the C′ and C″ strands normally inserted into the V-set Ig-fold are instead replaced by a long loop inserted at the same location ( Fig . 3D–F ) . The RMS deviation of the structural alignment between the second subdomain of GspBBR and Siglec-5 is 2 . 9 Å for 210 Cα atoms despite only 6% sequence identity . Like GspB , Siglecs bind carbohydrate receptors . Accordingly , the second domain is termed the Siglec subdomain . This subdomain of GspBBR contained electron density consistent with a cation-binding site ( see Supporting Text S1 , Fig . S1 , Supporting Protocol S1 ) . This was rather unexpected since Siglecs themselves do not bind cations in the carbohydrate-binding domain . The seven-coordinate number suggests that under physiological conditions , this should be a Ca2+ binding site . We explored the role of Ca2+ and other cations for the binding of S . gordonii strain M99 to glycocalicin . However , metal depletion , metal substitution , and site directed mutagenesis of the residues coordinating the ion were all consistent with the cation not being essential for carbohydrate receptor recognition ( data not shown ) . While still predominated by β-strands , the C-terminal subdomain of GspBBR does not contain an Ig-fold . In fact , a structural homology search using DaliLite [42] did not identify any proteins of known structure with significant similarity to this C-terminal subdomain ( Fig . 2D ) . As a result , it will be termed the Unique subdomain . We sought to identify the details of the interaction between GspBBR and the host receptor sialyl-T antigen . Free sialyl-T antigen is a rare reagent that is not commercially available and is challenging to synthesize . Therefore , we developed a 4-step synthesis for α-2 , 3-sialyl ( 1-thioethyl ) galactose ( NeuAcα ( 2–3 ) ( 1-CH3CH2S ) Galβ ) ( see Supporting Protocol S1; Fig . S2 ) which is the disaccharide truncation of and a synthetic precursor to sialyl-T antigen ( NeuAcα ( 2–3 ) Galβ ( 1–3 ) GalNAc ) . To confirm that α-2 , 3-sialyl ( 1-thioethyl ) galactose binds to GspBBR , we assessed the ability of this disaccharide to inhibit the binding of S . gordonii to glycocalicin . In the presence of 44 mM α-2 , 3-sialyl ( 1-thioethyl ) galactose , binding of S . gordonii to glycocalicin was reduced by 90% ( Fig . 4A ) . Given the structural similarity to the native host receptor , this strongly suggests that α-2 , 3-sialyl ( 1-thioethyl ) galactose competes directly for the sialyl-T antigen binding site . We determined the co-crystal structure of α-2 , 3-sialyl ( 1-thioethyl ) galactose in complex with GspBBR . New electron density consistent with bound disaccharide ( Fig . 4B ) was apparent within a defined pocket ( Fig . 4C ) located at the N-terminus of the C strand and C-terminus of the F strand in the Siglec domain ( Fig . 3F ) of the Siglec subdomain ( Fig . 4D ) . While the similarity of the fold of the Siglec subdomain to mammalian Siglecs might predict a similar binding pocket , both the overall location of the α-2 , 3-sialyl ( 1-thioethyl ) galactose binding site on the domain and the specific contacts between GspBBR to the carbohydrate differ from that of structurally characterized mammalian Siglecs [44]–[46] ( Fig . 5 ) . In fact , in GspBBR a helix is positioned in the location where carbohydrates bind to Siglecs , precluding the use of a structurally similar binding site . Instead , the local secondary structure surrounding the α-2 , 3-sialyl ( 1-thioethyl ) galactose binding site resembles a β-grasp domain , which is a motif that commonly binds the α-2 , 3-linkage of sialic acid based multivalent carbohydrates [47]–[49] . In GspBBR , the backbone carbonyls of Ala506 and Arg449 , the Nη1 and Nη2 of Arg484 , the backbone amide nitrogen of Thr483 and the side chain hydroxyl of Tyr443 make direct contacts to the α-2 , 3-sialyl ( 1-thioethyl ) galactose disaccharide , while the side chain hydroxyl of Tyr485 makes water-mediated contacts ( Fig . 4B , Table 3 ) . A comparison of the structure of GspBBR determined with and without the disaccharide did not show significant structural changes localized within the binding pocket , which suggests that it is pre-formed . Thus , while the physiologically-relevant receptor trisaccharide sialyl-T antigen is a rare reagent , we can qualitatively suggest its binding location by modeling the third carbohydrate onto α-2 , 3-sialyl ( 1-thioethyl ) galactose . In our model , all three carbohydrates of the host receptor fit into a pre-formed binding pocket on the Siglec subdomain , with the third carbohydrate of sialyl-T antigen extending toward the Unique subdomain ( Fig . S3 ) . While disaccharide binding to GspBBR did not appear to induce obvious changes in conformations of side chains within the binding pocket , the interdomain angle between the CnaA and Siglec subdomains unexpectedly straightened by 40° ( Fig . 6; Video S1 ) . The change in orientation between the CnaA and Siglec subdomains involves rotation around a single hinge consisting of residues Lys398-Asp399-Thr400 , where the side chain Asp399 is one of the ion coordinating residues . To confirm that this crystallographically-identified α-2 , 3-sialyl ( 1-thioethyl ) galactose binding site is indeed important for GspB-mediated binding to GPIbα , we generated four isogenic variants of strain M99 with point mutations within the Siglec subdomain of GspB ( Table 4 ) . Three of these variants contained mutations within the crystallographically-identified binding site ( Y443F , R484E and Y485F ) , while a control mutation ( E401A ) was located within the Siglec domain , but away from the receptor binding site . Importantly , none of the point mutations affected surface expression of GspB ( Fig . 7A ) . Each of these isogenic variants had an 86% or greater decrease in binding to GPIbα in vitro , as compared with the parent strain ( p<0 . 001 ) ( Fig . 7B ) . This decrease is comparable to that of the gspB null mutant . In contrast , binding by the E401A variant was not significantly different from that of M99 ( p = 0 . 44 ) . We then selected the R484E mutation for more detailed study . The purified GST-GspBBR fusion protein harboring the R484E substitution exhibited a marked decrease in binding to biotinylated sialyl-T antigen ( Fig . 7C ) . The R484E substitution also resulted in reduced binding to human platelets in vitro as assessed by quantifying the amount of input inoculum bound to fixed platelets ( Fig . 7D ) . We next visualized platelets by Differential Interference Contrast ( DIC ) and fluorescence microscopy and quantified the number of platelets with surface-bound bacteria . In the presence of the DAPI-labeled PS846 ( M99ΔgspB ) or the PS2116 ( M99 gspbR484E ) strain , substantially fewer platelets had bacteria bound as compared to platelets in the presence of wild-type S . gordonii strain M99 ( Fig . 8 , Table 5 ) . It should be noted that while the platelet numbers were normalized when placed onto the cover slip , during the experiment the wild type bacteria seemed to form microscopic aggregates with the platelets ( data not shown ) but this was not observed when platelets were mixed with strains PS2116 or PS846 . This confirms the importance of this residue for carbohydrate binding . It has previously been demonstrated that loss of GspB expression results in a marked decrease in virulence , as measured by a rat model of infective endocarditis [18] . To assess whether binding via the Siglec subdomain to host carbohydrate receptors contributes to virulence , we examined the impact of the R484E substitution on the ability of S . gordonii strain M99 to produce endocardial infection . We first compared the relative virulence of M99 with strain PS2116 , ( M99 gspbR484E ) . Catheterized rats were simultaneously infected intravenously with 2×10−5 CFU of M99 and PS2116 . After 72 h , the animals were sacrificed and the relative levels of bacteria within tissues determined . Animals co-infected with M99 and PS2161 ( which carries the spec resistance marker just upstream of gspB ) served as controls . When assessed at the above time-point , animals co-infected with M99 and PS2116 had significantly reduced densities of the mutant strain within vegetations ( mean ± S . D . = 6 . 81±1 . 70 log10 CU/g . veg . ) as compared with parental strain M99 ( 7 . 47±1 . 69 log10 CFU/g . veg; P<0 . 02 ) . Loss of sialyl-T antigen binding was also associated with significantly reduced bacterial densities within kidneys and spleens ( P<0 . 02 and P<0 . 001 , respectively; Table 6 ) . In parallel studies , no differences were observed between the M99 and the control strain ( PS2161 ) , as measured by CFU per gram of tissue within vegetations , kidneys , or spleens ( data not shown ) . We also analyzed these findings by calculating a competition index , in which the ratio of M99 and PS2116 within tissues was normalized for the CFU of each strain within the inoculum . When analyzed by this approach , the densities of the GspB mutant strain PS2116 remained significantly reduced in all tissues , as compared with M99 ( P<0 . 002 ) , while the densities of the control strain were statistically similar . We then compared the relative virulence of PS2116 with PS846 ( M99ΔgspB ) . Infective endocarditis was produced as above , using an inoculum containing the two strains in a 1∶1 ratio . When assessed at 72 h post-infection , PS2116 and PS846 had similar densities of organisms within all tissues ( Table 6 ) . Of note , the levels of both strains with vegetations were markedly lower than those achieved by wild-type M99 in the above studies . Thus , these two strains appeared comparably attenuated in the setting of endocarditis , indicating that platelet binding by the Siglec domain may be the predominant GspB interaction contributing to virulence in endocarditis . While overall sequence analysis of members of the SRR family has identified unifying sequence trends ( Fig . 1 ) , sequence comparisons of the binding regions have shown little homology . A structural comparison between the three distinct subdomains of GspBBR ( Fig . 2A ) and the two distinct subdomains of the SRR adhesin Fap1 [43] immediately indicates that these family members adopt unrelated folds in their binding domains . Like GspBBR , Fap1NR appears to be composed of independently-folded subdomains; however its binding region only contains two modules whereas the binding region of GspBBR contains three . Intriguingly , Fap1NR contains a helical subdomain at its N-terminus ( Fap1NR-α ) that does not resemble any of the subdomains of GspBBR , and a CnaA subdomain at its C-terminus ( Fap1NR-β ) that resembles the N-terminal CnaA subdomain of GspBBR ( GspBBR-C ) . This suggested to us that members of the SRR family could undergo reorganization of the modules within their host binding regions , with particular modules or combinations of modules conferring specific properties . Accordingly , we re-analyzed the binding regions of selected members of the family using a new strategy , where we used BLAST [50] and ClustalW [51] to query the sequences of the binding regions of SRR adhesins with input sequences corresponding to subdomains of either GspBBR or Fap1NR , or of short regions ( ∼200 amino acids ) of sequences of structurally uncharacterized binding regions of SRR adhesins . These modified sequence comparisons strongly suggest that the binding regions of members of the SRR family have indeed evolved to contain modules ( Fig . 9 ) , and show several distinct groupings . For example , all three assessed SRR adhesins with carbohydrate binding partners contain the Siglec and Unique subdomains in tandem , strongly suggesting that the inclusion of these modules within the binding region confers lectin-like binding characteristics . These focused sequence alignments additionally identify that a CnaA subdomain may be common in SRR adhesins , appearing in three of the eight binding regions that we assessed . As opposed to the combination of the Siglec and Unique subdomains , which are associated with carbohydrate binding , each adhesin containing a CnaA subdomain may have a different binding specificity ( Fig . 9 ) . Interestingly , in the SRR adhesins that were assessed , the CnaA subdomains were always found paired with sequence for another subdomain . It is important to note that several of the binding regions of the SRR adhesins do not have significant sequence identity to any protein of known structure . For example , the binding region of S . agalactiae SRR1 ( SRR1GBS ) likely contains two subdomains , SRR1GBS-α at the N-terminus , which does not contain detectable sequence similarity to any protein of known fold , and SRR1GBS-β at the C-terminus , which has sequence similarity to MSCRAMMs and likely adopts a similar folding topology to CnaA . Likewise , no part of the binding regions of S . pneumoniae PsrP , S . aureus SraP , or S . epidermidis seSRRBR exhibits detectable sequence similarity to any known protein , but the latter two have high sequence similarity to each other .
SRR adhesins have been identified in a variety of Gram-positive pathogens , and have been implicated as virulence factors in a wide spectrum of infections [5] , [6] , [12] , [18] . The diversity of these infections ( e . g . , endocarditis , meningitis , pneumonia ) and the broad scope of their anatomic locations are consistent with the binding regions of the SRR proteins differing considerably in their selectivity . Indeed , although only a few ligands for this family of proteins have been identified , they range from carbohydrates ( such as sialyl-T antigen ) [32] to proteins ( such as keratins ) [16] , [17] . To date , however , the structural basis for this selectivity has been unknown . In several members of the family ( e . g . the SRR adhesins of S . gordonii and S . pneumoniae ) the domain between the two SRR regions mediates binding . However , for many of these SRR adhesins ( i . e . SraP of S . aureus ) , the host receptor has yet to be identified [6] , [43] , and the delineation of the binding region is assumed based upon sequence comparisons within the family . Among the best characterized of the SRR adhesins is GspB from S . gordonii , which has demonstrated binding affinity for the sialyl-T antigen carbohydrate decorating platelet glycoprotein GPIbα . Our crystal structure of the binding region , GspBBR , identified a modular organization with three subdomains ( Fig . 2A ) , two of which are organized around Ig-folds . Proteins containing Ig-folds are commonly found within the mammalian immune system , where they exhibit a variety of functions; however , Ig-folds are not uncommon within pathogens , where they act exclusively as virulence factors . The first characterized bacterial protein to contain an Ig-fold was PapD , a chaperone for the assembly of pili in E . coli [37] . The Ig-fold has been observed in several other types of virulence factors , including components of pili ( carbohydrate-binding fimbriae ) [41] , [52] , and in adhesins of varying specificity for host receptors , including MSCRAMMs [34]–[36] and invasins [38]–[40] . Co-crystallization of GspBBR with α-2 , 3-sialyl ( 1-thioethyl ) galactose identified a specific binding site for this disaccharide within the Siglec subdomain ( Fig . 4B–D ) , and the introduction of single point mutations within this region significantly reduced levels of binding of S . gordonii to glycocalicin . Moreover , the R484E substitution in GspB reduced platelet binding by M99 ( Fig . 7D , Fig . 8 , Table 5 ) , and had a marked reduction in binding of the GST-GspBBR fusion protein to sialyl-T antigen ( Fig . 7C ) , confirming the importance of the Siglec subdomain for carbohydrate binding . These results indicate that this binding pocket within the Siglec subdomain is required for binding to sialyl-T antigen , and that the interaction of this GspB subdomain with sialyl-T antigen is important for binding to platelets . When examined in a co-infection model of infective endocarditis , the isogenic variant of M99 expressing GspB-R484E ( strain PS2116 ) was also significantly reduced in virulence ( most notably a 78% reduction in bacterial levels within vegetations as compared with the parent strain ) . When PS2116 was tested with PS846 ( M99ΔgspB ) in this model , the strains had comparable but reduced densities within target tissues ( as compared with WT M99 ) , indicating that they were similarly reduced in virulence . These findings indicate that the predominant property of GspB contributing to the pathogenesis of endocarditis is its interaction with sialyl-T antigen . In previous in vivo studies , co-infection with M99 and PS846 yielded more pronounced differences between the strains , as measured by densities of organisms within target tissues [18] . This may reflect subtle experimental differences , such as inoculum size , but could also be due to residual binding activity of the mutated GspB . Of note , PS2116 had low but detectable levels of platelet binding in vitro . Similarly , the GST-GspBBR-R484E had measurable levels of binding to sialyl-T antigen , as compared to GST alone . This residual binding seen in vitro , presumably due to the other key residues identified by crystallography , may account for the residual virulence of the PS2116 strain . Alternatively , although ligands for GspB other than sialyl-T antigen have not been identified , it is conceivable that GspB has other interactions in vivo that contribute to virulence in this setting , beyond those mediating platelet binding . Upon binding of the α-2 , 3-sialyl ( 1-thioethyl ) galactose to GspBBR , a 40° interdomain angle straightening is observed between the CnaA and Siglec subdomains ( Fig . 6 , Video S1 ) . The interdomain hinge is centered at the ion binding site , making the interdomain angle change reminiscent of calcium dependent interdomain angle changes observed in C-cadherin , which also contains modules of Ig-fold topology [53] . There are several possibilities for the origin and role of this interdomain reorganization in GspBBR . It is first possible that GspBBR exhibits natural flexibility between these two domains , and that the difference in crystal contacts artifactually resulted in the straighter form of the protein being trapped in the co-structure of GspBBR with α-2 , 3-sialyl ( 1-thioethyl ) galactose . Intriguingly , however , an interdomain straightening is also observed in small angle x-ray scattering ( SAXS ) of the binding region of SRR protein Fap1NR upon lowering the pH to 5 . 0 , where Fap1NR has highest affinity for its host receptor [43] . Those experiments , performed in solution , are not limited by crystal contacts and suggest that interdomain straightening upon ligand binding could be a conserved feature of members of the SRR family . Physiological roles of interdomain straightening upon ligand binding include improved hydrodynamics in the cardiovascular system or oral cavity . The elongation upon ligand binding is perhaps more compellingly reminiscent of the interdomain straightening that occurs upon the binding of P-selectin and β3-integrin to their respective ligands [54] . These proteins have been suggested to form “catch bonds” in order to have increased affinity to their ligand upon the application of tensile force . Each of these possibilities for the observed straightening of GspBBR upon ligand binding is currently under investigation . While both Siglecs and GspBBR bind to carbohydrate receptors , the structural details of disaccharide binding to this novel bacterial Siglec bear little resemblance to the binding of sialic acid moieties to characterized mammalian Siglecs [44]–[46] ( Fig . 5 ) . Indeed , comparison of the binding sites reveals that the secondary structure of GspBBR has a helix that is found at the same location where 3′ sialyllactose binds to Siglec-5 ( Fig . 5A ) , thus eliminating the possibility of analogous carbohydrate binding . Instead , GspBBR appears to use a β-grasp domain to form the specific host receptor binding site , a domain found in another group of sialic acid binding proteins , staphylococcal superantigen-like proteins ( SSLs ) that forms a V-like cleft for carbohydrate binding [47]–[49] . Nevertheless , both mammalian Siglecs and GspBBR have binding pockets predominated by tyrosines and arginines . With a closer look at each binding site , in Siglecs , the sialic acid moiety of the binding partner makes a salt bridge with an arginine residue . This salt bridge is not present in the binding site of GspBBR despite the presence of R484 and its key role in binding; this arginine instead binds to the C6 hydroxyl and the pyranose oxygen of the 1-thioethylgalactose ( Table 3 ) . Importantly , the structure of GspBBR provides insight into the binding repertoire of other SRR proteins , and specifically highlights which modules mediate binding to host carbohydrate receptors . Homologues of GspB containing regions with high sequence similarity to the Siglec and Unique subdomains ( such as S . gordonii strain Challis Hsa and S . sanguinis SrpA ) are also demonstrated lectins . Supporting this sequence analysis , our co-crystal structure of GspBBR with α-2 , 3-sialyl ( 1-thioethyl ) galactose demonstrates that the first two carbohydrates of host receptor bind within a pre-formed pocket on the Siglec subdomain . Using this experimental co-structure as a starting point , we could model binding of the sialyl-T antigen . This model is consistent with the third carbohydrate of this trisaccharide binding to an extended lobe on this pocket . This region of the binding pocket is still within the Siglec subdomain but extends toward the Unique subdomain ( Fig . S3 ) . The structures of both GspBBR and Fap1NR identified a CnaA-like subdomain within the binding region , and our sequence analysis ( Fig . 9 ) additionally predicts a CnaA subdomain at the C-terminus of the binding region of S . agalactiae SRR1 ( SRR1GBS-β ) , an adhesin for keratin 4 [16] . As opposed to the Siglec subdomain , which contains an identifiable receptor binding site , the precise function of the CnaA subdomain is less clear . MSCRAMMs , such as CnaA from S . aureus , recognize peptides through the formation of a binding site between two Ig-like domains ( Fig . S4 ) . The bound peptide forms an additional strand that becomes a part of the Ig-fold . In contrast , GspBBR , Fap1NR , and SRR1GBS each apparently contain a single CnaA subdomain amongst the modules in the binding region ( Fig . 9 ) . Our modeling suggests that binding of a peptide between two domains in a manner analogous to binding of a peptide to other MSCRAMMs is not feasible . Indeed , an additional strand found in the subdomain of GspBBR occupies the peptide binding site of MSCRAMMs . ( Fig . S4 , Fig . 3C strand A′ ) . Nevertheless , in SRR adhesins , the CnaA subdomain is always found together with at least one other subdomain , suggesting that the function may either require or be tuned by the presence of a second subdomain . Indeed , recent studies on the binding region of Fap1 support this hypothesis . The structures of Fap1NR-α ( helical subdomain ) and Fap1NR-β ( CnaA subdomain ) identified a region of surface-exposed hydrophobic residues on each domain that is predicted to be contiguous based upon SAXS . Fap1 normally mediates binding of this bacterium to the oral cavity , and mutagenesis of these hydrophobic residues abrogated binding to an in vitro tooth model consisting of saliva-coated hydroxylapatite [43] . Our sequence analysis additionally predicts that the binding regions of other SRR adhesins contain modules that are structurally distinct from those identified in either GspBBR or Fap1NR . Two of these , S . aureus SraPBR and S . epidermidis seSRRBR , have high sequence identity to each other , but no detectable sequence similarity to any other SRR adhesin ( Fig . 9 ) . This strongly suggests that they bind a common , as yet unidentified , host receptor that is distinct from the carbohydrates and keratins recognized by other SRR adhesin family members . By comparison , neither S . pneumoniae PsrPBR , which has been demonstrated to bind keratin 10 , nor the N-terminal subdomain of S . agalactiae SRR1BR , which binds keratin 4 , have detectable sequence similarity to any currently available sequence ( Fig . 9 ) . Given this analysis , it is clear that a more detailed understanding of the binding characteristics of the SRR adhesin family will require that for each module type , the binding partner should be identified , and the structure of the binding region should be determined both alone and in complex with the appropriate host receptor .
All procedures involving rats were approved by the Los Angeles Biomedical Research Institute animal use and care committee , following the National Institutes of Health guidelines for animal housing and care . Platelets were collected using a protocol approved by the UCSF Committee on Human Research ( H1193-25513-07 ) and by the VUMC Human Research Protection Program IRB Committee ( 110364 ) . The DNA encoding residues 233–617 of GspB ( GspBBR ) was cloned into the pGEX vector containing an N-terminal glutathione S-transferase ( GST ) fusion tag as described [11] . This clone contains a single base pair change as compared to the deposited NCBI sequence that results in a serine at position 444 instead of an asparagine . The protein was expressed with E . coli BL21 Gold ( Stratagene ) in Luria Broth Medium as described [55] and purified using a GST affinity column ( GE Healthcare ) . The GST tag was removed from GspBBR using Factor Xa , and GspBBR was further purified using size exclusion chromatography on a Superdex 200 10/300 GL column ( GE Healthcare ) with buffer containing 20 mM Tris pH 7 . 4 [55] . All crystals of GspBBR were grown using the hanging drop vapor diffusion technique at 23°C [55] using 1 µl protein solution and 1 µl of reservoir solution equilibrated against 1 ml of the reservoir solution . Crystals of native GspBBR grew from two chemically distinct sets of conditions . The first set of conditions included GspBBR at a concentration of 6 mg/ml buffered in 20 mM Tris pH 7 . 4 and equilibrated against a reservoir solution containing 33% Jeffamine ED-2001 , 0 . 1 M HEPES pH 7 . 5 and 0 . 15 M KCl at 23°C . These crystals belonged to the primitive orthorhombic space group P212121 with unit cell dimensions a = 33 . 7 Å , b = 96 . 8 Å , c = 100 . 2 Å with α = β = γ = 90° . Prior to data collection , crystals were cryo-protected in a solution containing all of the chemical components of each reservoir solution and 15% glycerol then flash-cooled in liquid nitrogen . The dataset used for refinement merged to 1 . 4 Å resolution . The second type of native GspBBR crystals grew when the protein was equilibrated against a reservoir solution containing 25% polyethylene glycol 3350 , 0 . 1 M HEPES pH 7 . 5 , 0 . 15 M NH4CH3COO , and 10 mM spermidine and induced crystal growth from 10 mg/ml GspBBR buffered in 20 mM HEPES pH 7 . 5 at 18°C . These crystals also belonged to the orthorhombic space group P212121 , but had altered unit cell dimensions of a = 33 . 4 Å , b = 86 . 7 Å , c = 117 . 9 Å , α = β = γ = 90° . Prior to data collection , these crystals were flash cooled in liquid nitrogen without additional cryo-protectant . The best dataset from this crystal form merged to 2 . 0 Å resolution . Crystals of GspBBR in complex with the α-2 , 3-sialyl ( 1-thioethyl ) galactose disaccharide were grown using the hanging drop vapor diffusion method with 6 mg/mL GspBBR in buffer containing 1 mM α-2 , 3-sialyl ( 1-thioethyl ) galactose and 20 mM Tris pH 7 . 4 . The reservoir solution contained 8% PEG 3350 , 7 . 5 mM CoCl2 , 7 . 5 mM NiCl2 , 7 . 5 mM CdCl2 , 7 . 5 mM MgCl2 , and 0 . 1 M HEPES pH 7 . 5 . Crystals grew within two days and were cryo-protected in reservoir solution supplemented with 20% glycerol and flash-cooled in liquid nitrogen . The disaccharide co-crystals formed in the primitive monoclinic space group P21 with unit cell dimensions a = 69 . 9 Å , b = 34 . 0 Å , c = 83 . 4 Å with α = γ = 90° and β = 99 . 2° . The best data from this crystal form merged to 1 . 9 Å resolution . Crystals were assessed for diffraction quality at the Stanford Synchrotron Radiation Lightsource ( SSRL ) beamlines 9-2 , 11-1 , and 12-2 and the Life Sciences Collaborative Access Team ( LS-CAT ) beamlines ID-21-D/F/G . Datasets were collected using the beamlines , temperatures , wavelengths , and detectors listed in Table 1 and Table 2 . All data were processed using the HKL2000 [56] and CCP4 [57] suites of programs . A Dy3+ derivative was prepared by soaking pre-formed crystals of GspBBR in 1 mM DyCl3 for three days . Data were collected at three wavelengths near the Dy3+ L3 edge ( Table 2 ) . Dy3+ bound to a single site in the protein as determined using the SHELXD [58] subroutine in the program SHARP [59] . While HoCl3 also successfully derivatized GspBBR , non-isomorphism between crystals prevented the use of this second derivative in a traditional MIR calculation . The non-isomorphism was so severe that phases calculated in SHARP [59] only used data from a single DyCl3 soaked crystal and did not include a native dataset for reference . This process resulted in reasonable phasing statistics and an overall figure of merit of 0 . 83 at 2 . 0 Å resolution ( Table 2 ) . Phases were improved by solvent flattening in DM [60] which produced electron density maps of high quality ( Fig . 2B ) . Automated chain tracing was performed using PHENIX [61] , which was able to trace residues 251–316 and 327–601 , representing 94 . 7% of the model . This resulted in an initial Rcryst of 23 . 8% and Rfree of 25 . 5% . The lack of isomorphism between crystals prevented transfer of these initial coordinates to other data sets using a simple rigid body refinement . As a result , the model from the Dy3+ data set was transferred to the remaining data sets using the program PHASER [62] followed by rigid body refinement in CNS [63] . Each structure was subjected to alternate rounds of model building using the program COOT [64] and refinement using CNS [63] and REFMAC [65] . The coordinates for the α-2 , 3-sialyl ( 1-thioethyl ) galactose were built using CCP4i Sketcher [66] and PRODRG [67] . Refinement statistics for all final models are listed in Table 1 . Figures were created using PyMOL [68] , and inter-domain rotations were determined using DynDom [69] . Based upon work by Danifshesky and coworkers [70] , we developed a four-step synthesis for the sialyl-T antigen precursor , α-2 , 3-sialyl ( 1-thioethyl ) galactose ( see Supporting Protocol S1 ) . The correct synthesis of the disaccharide was verified by NMR ( Fig . S2 ) . The α-2 , 3-sialyl ( 1-thioethyl ) galactose was resuspended in water for all applications . Point mutations of gspB were introduced into the S . gordonii chromosome via a strategy that involved recombination by double cross-over between gspB codons 487–602 and a gene approximately 300 bp upstream . This approach ensured incorporation of only the intended mutation of gspB codons ranging from 399 to 485 , and avoided possible imprecise recombination within the SRR regions . As a first step , the 5′ end of gspB ( codons 1 to 486 along with 200 nts from a non-coding region upstream ) was replaced with a chloramphenicol resistance cassette as follows . A 0 . 5 kb segment of a gene of unknown function upstream of gspB was amplified using PCR . The product was digested with XhoI and ClaI and then cloned upstream of the cat gene in pC326 [71] . A segment spanning gspB codons 487 to 602 was then amplified using primers B487F and B602R , digested with SpeI and NotI , and cloned downstream of the cat gene . The resulting plasmid , pC326Δ5′B , was used to transform S . gordonii strain M99 as described [4] . One of the chloramphenicol-resistant transformants , ( M99 Δ5′gspB::cat ) , was selected for subsequent gene replacement . A series of plasmids was then constructed to facilitate the replacement of the 5′ end of gspB in M99 Δ5′gspB::cat . The XhoI-ClaI fragment from pC326Δ5′B was cloned upstream of the spec gene in pS326 [72] , and a 1 kb NsiI-SpeI fragment of gspB ( spanning codons 1 to 296 ) , was cloned downstream . A SpeI-NotI fragment spanning codons 296 to 602 was then cloned downstream of the NsiI-SpeI fragment . Point mutations in the resulting plasmid , pS326B602 , were generated by a two-stage PCR procedure . In the first stage , primer 25F along with a reverse gspB primer , or the corresponding gspB forward primer along with primer B602R , were used to amplify the upstream or downstream segments , respectively . The two PCR products were combined for the second stage and then amplified using primers 25F and B602R . The PCR product was digested with SpeI and NotI and then used to replace the corresponding segment of pS326B602 . The incorporation of only the intended change in any segment generated by PCR was confirmed by DNA sequence analysis . Plasmids were then used to transform M99 Δ5′gspB::cat , resulting in a replacement of Δ5′gspB::cat with a 5′gspB::spec variant . As a control , the wild-type gspB sequence along with the spec cassette ( pS326B602 ) was also crossed into the M99 Δ5′gspB::cat chromosome ( generating strain PS2161 ) . Transformants were selected on spectinomycin and scored for the loss of chloramphenicol resistance . Expression of the variant GspB proteins on the bacterial cell surface was verified by western blotting as described [72] . GST , GST-GspBBR and GST-GspBBR-R484E were purified from E . coli as described [55] . The purified proteins were diluted to 320 µM into DPBS , serial two-fold dilutions were made , and 50 µl of each dilution was added to wells of a 96-well microtiter plate . After incubating the plate overnight at 4°C , unbound proteins were removed by aspiration and wells were rinsed with 100 µl DPBS . Biotinylated sialyl-T antigen ( sialyl-T antigen conjugated to biotin via a polyacrylamide linker; GlycoTech Corporation ) was diluted to 50 µg/ml in DPBS containing 1× Blocking Reagent ( Roche ) , 50 µl was added to each well , and the plate was incubated for 2 h at RT with vigorous rocking . After removing unbound biotin-sialyl-T antigen , wells were rinsed three times with 100 µl DPBS , 50 µl of streptavidin-conjugated horseradish peroxidase ( 0 . 1 µg/ml in DPBS ) was added to each well and the plate was incubated for 1 h at 23°C . The wells were washed twice with 100 µl DPBS , and then 200 µl of a solution of OPD ( 0 . 4 mg/ml citrate-phosphate buffer ) was added to each well . The contents of the wells were mixed by gently vortexing the plate , and the absorbance at 450 nm was measured 20 min after the addition of the OPD substrate . Data were plotted as the means ± standard deviations , with n = 3 . The binding of S . gordonii to immobilized glycocalicin was performed as described previously [32] . In brief , strains were grown for 18 hr , washed twice with DPBS , sonicated briefly to disrupt aggregated cells , and then diluted to approximately 2×107 per ml . To determine whether α-2 , 3 sialyl ( 1-thioethyl ) galactose inhibited binding to glycocalicin , the washed and sonicated bacteria were diluted into DPBS or DPBS containing 44 mM α-2 , 3-sialyl ( 1-thioethyl ) galactose , pH 7 . 5 . The bacterial suspensions were then applied to wells of a microtiter plate that had been coated with glycocalicin ( 1 . 25 µg/well ) . After 2 h at room temperature , the unbound bacteria were removed by aspiration . Wells were washed three times with DPBS , and the bound bacteria were released by trypsinization . The number of input and bound bacteria were determined by plating serial dilutions of the bacterial suspensions on sheep blood agar plates , and binding was expressed as the percent of the input bound to glycocalicin . The binding of S . gordonii to immobilized human platelets was assessed as described previously [4] . Results of both assays are reported as the means ± standard deviations , with n = 6 . Differences in binding were compared by the unpaired t-test . S . gordonii strains M99 wild type , PS2116 , and PS846 were grown in 5 mL of Todd Hewitt Broth at 37°C without shaking for 18 hours . Cells were then vortexed to resuspend bacteria and spun down at 4000× g for ten minutes . The supernatant was removed and the bacteria washed twice with 5 mL of DPBS containing MgCl2 and CaCl2 . The bacteria were then resuspended in 5 mL of DPBS and sonicated briefly to disrupt aggregated clumps . To 1 mL of each cell suspension , 500 nM 4′ , 6-diamidino-2-phenylindole ( DAPI ) was added . Platelets were fixed using 1 . 6% paraformaldehyde . 500 µL of platelets were mounted on poly-L-lysine coated cover slips ( in a 6-well tray ) which was spun at 400× g for ten minutes in order to promote platelet adherence to the cover slips . Excess platelets were removed by washing with Tris Buffered Saline ( TBS ) and 1 mL of TBS was added to each well . 500 µL of the bacterial suspension was added to the platelets and the samples were rocked vigorously for 30 minutes at 23°C . Excess bacteria were then removed by washing three times with TBS . Each sample was mounted onto a slide for microscopy . Slides were imaged using Nikon TiE Inverted light microscope equipped with a Photometrics CoolSnap HQ CCD camera . Platelets were imaged with a 100×1 . 49NA objective using DIC optics and a DAPI filter cube . Image J software was used to create the contrast and composite images . A competition model of infective endocarditis was produced in Sprague–Dawley female rats ( 250–300 g ) as described previously [18] . In brief , the animals were anesthetized with ketamine ( 35 mg/kg ) and xylazine ( 10 mg/kg ) . A sterile polyethylene catheter was surgically placed across the aortic valve of each animal , such that the tip was positioned in the left ventricle . Catheters were left in place throughout the study . Catheterized animals were then infected intravenously ( IV ) with an inoculum containing 2×105 CFU of both strains ( i . e . , a 1∶1 mixture of i ) S . gordonii strain M99 and strain PS2116 ( 5′gspBR484E::spec ) , ii ) M99 and strain PS2161 ( 5′gspB::spec control strain ) , or iii ) PS2116 and strain PS846 ( M99 ΔgspB::pEVP3; CmR ) [18] , [73] . At 72 h post-infection , animals were sacrificed with thiopental ( 100 mg , intraperitoneally ) . Animals were included in the final analysis only if the catheters were correctly positioned across the aortic valve at the time of sacrifice , and if macroscopic vegetations were seen . All cardiac vegetations , as well as samples of the kidneys and spleens were harvested , weighed , homogenized in saline , serially diluted , and plated onto Todd Hewitt agar ( THA ) ( for the parental S . gordonii strain M99 ) and THA containing 100 µg/ml of spectinomycin ( for strains PS2116 and PS2161 ) or chloramphenicol 5 mg/ml ( for strain PS846 ) for quantitative culture , to determine the number of CFU/g of S . gordonii strains within tissues . After 48 h of incubation at 37°C , bacterial colonies were counted . The number of bacteria within tissues was expressed as the log10 CFU per gram of tissue . Differences between means were compared for statistical significance by the paired t-test , using p≤0 . 05 as the threshold for significance . The data were also analyzed by calculating a “competition index , ” which was defined as the ratio of S . gordonii strain M99 and strain PS2116 or PS2161 , as well as PS2116 and PS846 , within tissues for each animal , normalized by the ratio of organisms in the inoculum . The mean of the log10 normalized ratios was tested against the hypothesized ‘no effect’ mean value of 0 , as described previously , using a paired t-test . The coordinates and structure factors for S . gordonii strain M99 GspBBR have been deposited in the Research Collaboratory for Structural Bioinformatics Protein Data Bank with accession codes 3QC5 ( native 1 ) , 3QC6 ( native 2 , crystal form 2 ) , and 3QD1 ( α-2 , 3-sialyl ( 1-thioethyl ) galactose bound ) . | The binding of bacteria to human platelets is thought to be important for development of infective endocarditis , a life-threatening infection of the cardiovascular system . Streptococcus gordonii is a leading cause of endocarditis . This pathogen uses a protein called GspB to attach to carbohydrates on human platelets . While this binding interaction appears to be mediated by a specific , contiguous domain within GspB , little is known about the molecular details of the interaction between GspB and the carbohydrate receptors on its human host . We therefore determined the crystal structure of the region of GspB that binds to platelet carbohydrates , both alone and in complex with a synthetic carbohydrate receptor . Using this structure as a guide , we were able to produce three strains of S . gordonii that lacked the ability to bind to platelet carbohydrates . One of these isogenic variants was studied more in-depth and lacked the ability to bind to human platelets in vitro and was reduced in virulence when tested in vivo . These studies provide the first structural information detailing the molecular interactions between any serine-rich repeat adhesin and its host receptor , and identify how different , related adhesins may have evolved different specificities for host receptors . | [
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] | 2011 | A Structural Model for Binding of the Serine-Rich Repeat Adhesin GspB to Host Carbohydrate Receptors |
Listeria monocytogenes ( Lm ) uses InlA to invade the tips of the intestinal villi , a location at which cell extrusion generates a transient defect in epithelial polarity that exposes the receptor for InlA , E-cadherin , on the cell surface . As the dying cell is removed from the epithelium , the surrounding cells reorganize to form a multicellular junction ( MCJ ) that Lm exploits to find its basolateral receptor and invade . By examining individual infected villi using 3D-confocal imaging , we uncovered a novel role for the second major invasin , InlB , during invasion of the intestine . We infected mice intragastrically with isogenic strains of Lm that express or lack InlB and that have a modified InlA capable of binding murine E-cadherin and found that Lm lacking InlB invade the same number of villi but have decreased numbers of bacteria within each infected villus tip . We studied the mechanism of InlB action at the MCJs of polarized MDCK monolayers and find that InlB does not act as an adhesin , but instead accelerates bacterial internalization after attachment . InlB locally activates its receptor , c-Met , and increases endocytosis of junctional components , including E-cadherin . We show that MCJs are naturally more endocytic than other sites of the apical membrane , that endocytosis and Lm invasion of MCJs depends on functional dynamin , and that c-Met activation by soluble InlB or hepatocyte growth factor ( HGF ) increases MCJ endocytosis . Also , in vivo , InlB applied through the intestinal lumen increases endocytosis at the villus tips . Our findings demonstrate a two-step mechanism of synergy between Lm's invasins: InlA provides the specificity of Lm adhesion to MCJs at the villus tips and InlB locally activates c-Met to accelerate junctional endocytosis and bacterial invasion of the intestine .
Listeria monocytogenes ( Lm ) is a potentially deadly food-borne pathogen that colonizes the gastrointestinal tract of several mammalian species , and can also cause invasive disease and systemic spread if it crosses the intestinal epithelial barrier [1] . Lm evolved two major molecular invasion proteins , referred to here as invasins: Internalin A ( InlA , Internalin ) and Internalin B ( InlB ) [2] , [3] . These proteins promote internalization into nonphagocytic cells where Lm can grow in the cytosol as a facultative intracellular pathogen and directly spread to neighboring cells through actin-based motility [2]–[5] . Listerial invasion of the gastrointestinal tract requires InlA since deletion of the inlA gene makes Lm avirulent when given through the enteric route [6] . By contrast , inlA is dispensable for simulation of late-stage pathogenesis when bacteria are administered intravenously [6] . InlA binds the most distal extracellular domain of E-cadherin , a transmembrane epithelial cell-cell junction protein [7]–[9] . InlB , the second Lm surface protein involved in invasion , binds c-Met , a receptor tyrosine kinase ( RTK ) and the natural receptor for Hepatocyte Growth Factor ( HGF ) [2] , [10] . InlB promotes invasion of multiple mammalian cell types , and has been implicated in liver colonization after intravenous infection of mice [2] , [10]–[24] . Although InlB is not essential for fetoplacental infection , it was recently shown to act synergistically with InlA to promote fetoplacental infection of intravenously inoculated pregnant gerbils and transgenic mice expressing a humanized E-cadherin [20] , [25] , [26] . InlB is also known to function synergistically with InlA during invasion of cultured epithelial cells through an unknown mechanism [2] , [13] , [20] , [24] , [27]–[30] . Paradoxically , neither E-cadherin or c-Met are available on the apical or lumenal side of epithelia , thus it was puzzling to understand where Lm finds its receptors for invasion of the intestine [31]–[33] . We identified the cell extrusion zone at the tips of the intestinal villi as a novel site for gastrointestinal invasion where Lm uses InlA to bind E-cadherin for attachment and entry [30] . The intestinal epithelium is in a constant state of rapid renewal in a process that begins with stem cell division within the crypts , followed by maturation and migration of cells up to the tips of the intestinal villi . Once the oldest cells reach the villus tip , programmed cell death is triggered and individual dying cells are extruded into the lumen [34] , [35] . It has been estimated that 1400 cells are shed from each villus tip per day , which is ∼1011 cells per day from the human small intestine [34] . Surprisingly , this occurs without disruption of epithelial continuity because the surrounding cells constrict the dying cell and meet to form a new multicellular junction ( MCJ ) below the extruding cell [30] , [35]–[38] . In the process , the cells that form the MCJ may also remove and recycle the old junctions and adhesive contacts by endocytosis [35] . We showed that Lm takes advantage of extrusion for adhesion and invasion because MCJs transiently expose basolateral E-cadherin to the lumen of the intestine and at analogous sites in tissue culture [30] . Although a reasonable hypothesis , it is not known whether other basolateral proteins , like c-Met , are exposed to the apical side at MCJs . In contrast to what has been observed during infection of cultured cells , a role for InlB in the intestinal phase of infection could not be demonstrated previously [20] , [24] . However , several observations suggest that InlA and InlB may both function during infection of the gastrointestinal tract . First , the inlB gene is immediately downstream of inlA and is translated bicistronically with inlA [3] . The inlAB operon is upregulated when bacteria are in the intestinal lumen or under conditions simulating the gastrointestinal environment , indicating that InlB expression is temporally upregulated prior to bacterial invasion of intestinal tissue [39]–[42] . Finally , InlB promotes invasion of isolated intestinal epithelial cells when InlA-E-cadherin interactions are functional [20] . Thus , we hypothesized that InlB functions synergistically with InlA to promote Lm invasion of MCJs of the villus tip extrusion zone and that c-Met may be exposed to lumenal surfaces during cell extrusion . Until recently , it was not technically feasible to study the functions of InlA and InlB together in commonly utilized animal models since both proteins are ‘species specific’: InlA binds rabbit and guinea pig E-cadherin , but not rat and mouse E-cadherin; InlB activates mouse c-Met , but not guinea pig or rabbit c-Met [24] . The mouse is the predominant animal model for studying systemic Listeriosis and host immune responses following intraperitoneal or intravascular infection [43] . However , an understanding of the intestinal phase of infection has lagged behind , since mice are very resistant to enteric infection with Lm due to the absence of the InlA-E-cadherin interaction [6] , [9] . To study the intestinal phase of Listeriosis in the mouse , one strategy has been to develop transgenic mice that express a permissive E-cadherin [6] , [20] . Alternately , InlA was recently engineered to bind murine E-cadherin ( InlAm ) and is sufficient to reconstitute intestinal invasion after intragastric infection of mice [44] , [45] . In this study we constructed Lm strains that express InlAm with or without InlB to dissect the role of InlB in a mouse model of enteric infection . We also made strains that express green fluorescent protein ( GFP ) in order to perform co-infection studies where two strains that are differentially marked are mixed and inoculated together . Using this method we confirm that InlA is essential to invade the extrusion zone of the intestinal villus tips after oral infection , and establish a role for InlB working synergistically with InlA in colonization of the intestinal villi . Based on published cell biological experiments in non-polarized epithelial cells , we considered three nonexclusive hypotheses for InlB action at MCJs of villus tips . One is that InlB acts directly as an adhesion protein ( adhesin ) to promote Lm uptake , as suggested by experiments with endothelial cells [14] . A second is that InlB activates c-Met to promote cell-cell dissociation , as seen with recombinant HGF or InlB applied to small islands of cultured epithelial cells , thereby allowing access of Lm to E-cadherin at the basolateral surface [10] . Finally , we considered that InlB might promote Lm invasion by increasing endocytosis of junctional E-cadherin through c-Met activation as shown for HGF action on nonpolarized cells [46] , [47] . To study these possibilities on polarized epithelia we used Madin-Darby canine kidney ( MDCK ) cell monolayers grown on Transwell filters , a well-characterized model epithelium that is permissive for all aspects of the Listeria's intracellular life-cycle including InlA- and InlB-mediated invasion [30] , [48] . We discovered that InlB promotes invasion of the MCJs in polarized MDCK monolayers , but not by acting as an adhesin or increasing Lm attachment to E-cadherin across the junctions . Instead we find that InlB locally activates c-Met from the lumenal side to modulate the kinetics of invasion . Using endocytosis assays combined with confocal microscopy analysis , we show that both MCJs in tissue culture and the villus tip extrusion zone are naturally more endocytic than other regions of the epithelium and that InlB modulates this process . We propose that Lm has evolved a two-step mechanism to hijack and alter junctional remodeling for epithelial attachment and invasion . First Lm specifically target and adhere to the MCJs of the villus tips through apically exposed E-cadherin , and then they use InlB to accelerate the recycling of junction components to increase invasion at MCJs .
In order to study InlB and InlA in the same animal model we had to overcome the species specificity of each molecule . We chose to use an InlA mutation that is capable of binding murine E-cadherin ( InlAm ) [44] . In contrast to Lm expressing wild type InlA , Lm expressing InlAm are pathogenic to mice by enteric inoculation [44] . In the small intestine , InlAm promotes invasion through villous tissue but has no effect on passive bacterial uptake by Peyer's Patches [44] . We infected mice intragastrically with Lm that express InlAm and GFP ( WTm GFP ) to study Lm invasion of the intestinal villous epithelium . By culturing fecal pellets at different times after infection , we noted that peak shedding of the inoculum occurs by 3 hours . We therefore chose to examine the small intestine for evidence of bacterial invasion by direct visualization of tissue whole mounts within 4–6 h of infection . We find that WTm GFP invade the extrusion zone at the tips of the murine intestinal villi , similar to what we previously reported for Lm in a rabbit ileal loop model , and in accord with the observation by Wollert et al . that a similarly modified strain invades the murine intestinal epithelium [30] , [44] . Infected villus tips were most abundantly observed in tissue from the terminal ileum , in agreement with previous observations of enteric infection of permissive animals [49] , [50] . We found that that ingestion of Lm does not result in generalized invasion of all intestinal villi . Rather , we find that infection occurs at sporadic villus tips ( Figure 1 ) . We used 3D confocal microscopy analysis to characterize Lm invasion of villus tips within ∼1 cm2 tissue sections from the terminal ileum ( Figure 1 , Figure S1 , Video S1 ) . Intracellular Lm with polymerized actin comet tails are observed in villus tips by 4 hours after intragastric inoculation ( Figure 1A ) , only slightly longer than the time needed to generate actin-based motility in tissue culture ( ∼3 h ) [30] , [48] , [51] . Thus Lm rapidly traffic through the murine bowel and establish initial infection of villus tips . To examine the role of InlB in intestinal infection , we inoculated mice with either WTm GFP or an isogenic strain lacking inlB ( ΔinlBm GFP ) and examined the small intestine using confocal microscopy to determine the frequency of infected villi and the number of Listeria per infected villus tip . Both strains preferentially invade the terminal ileum and invade approximately the same number of villi ( N ) within a section of tissue by 5 hours post inoculation ( Figure S1 ) . However , mice infected with WTm GFP have approximately twice the number of Lm per villus tip than mice infected with ΔinlBm GFP ( Figure 1B–C , 3D rendered top panels , Figure S1 ) . Both strains are able to escape the endosome and replicate in the cytosol of enterocytes since they induce actin polymerization on the bacterial surface , as observed in Z-planes located below the apical brush border ( Figure 1B–C , lower panels ) . To control for variability between mice in intestinal transit , and thus more stringently examine whether InlB is involved in early colonization of the villus tips , we mixed the two strains at a 1∶1 ratio and performed co-infection experiments . In order to distinguish the two strains , we tagged them differentially with GFP and then counterstained them with anti-L . monocytogenes antibodies in red . Thus , the GFP expressing strain appears yellow ( or a combination of red and green ) in a merged image and the non-GFP expressing strain appears red ( Figure 1D–H ) . As shown in Figure 1D , in co-infections with WTm GFP and ΔinlBm , scattered villi are infected . In all co-infections , the villus tips infected with WTm GFP have significantly more intracellular bacteria than villus tips infected with ΔinlBm at 6 hours , even though the number of infected villi by each strain ( N ) was similar ( Figure 1E ) . We switched the strains in which GFP was expressed to control for possible variations in antibody staining or possible effects of GFP on bacterial colonization ( Figure 1F–H ) . As with the converse experiment , the presence of inlB significantly increases villus tip infection ( Figure 1H ) . The majority of bacterial plaques within each infected villus are probably clonal since we found only 1 villus tip with both red and yellow bacteria ( Figure 1G ) among 175 infected villi analyzed ( Figure 1E , 1H ) . To better understand how InlB promotes invasion of the villus tip extrusion zone , we studied the kinetics and mechanisms of Lm invasion in polarized epithelial cells ( Figure 2 ) . We used MDCK cells grown on Transwell supports to visualize and study events at multicellular junctions ( MCJs ) . Several clues of InlB function have been derived from studies using recombinant InlB , a genetically modified InlB that is covalently linked to the bacterial cell wall ( InlB-SPA ) , or InlB-coated beads interacting with non-polarized epithelia [10] , [11] , [19] , [24] , [52]–[61] . These studies indicate that InlB can bind and activate the basolateral c-Met receptor leading to clathrin-mediated internalization of c-Met . It is not known whether InlB functions for Lm invasion as a soluble or a bacterium-associated factor or how InlB reaches this receptor in an intact epithelium since c-Met is not usually exposed on the apical membrane of polarized epithelia [62] , [63] . Additionally , there are conflicting data regarding the role of InlB in intracellular replication [64] , [65] . We infected MDCK monolayers polarized on Transwell filters from the apical side with GFP-expressing wild type Lm ( WT ) or GFP-expressing inlB-mutant Lm ( ΔinlB ) and analyzed attachment , invasion and intracellular replication . Attachment to the apical surface is not affected by the absence of InlB as determined by recovered colony forming units ( CFUs ) from a 10-minute attachment assay ( Figure 2A ) . This is in agreement with our previous finding that InlA , rather than InlB , is the dominant adhesin for polarized cells [30] . Microscopic examination of the sites of attachment shows that ΔinlB also bind exclusively at intercellular junctions and preferentially at MCJs with the same specificity and frequency as WT ( [30] and see below ) . Since attachment was not affected by InlB , we studied its role in invasion following attachment by incubating adhered WT or ΔinlB with the epithelium for a period of 1 h , treating with gentamicin for 30 minutes to kill extracellular bacteria , and determining the number of viable intracellular bacteria . We find that InlB is important for efficient invasion since intracellular ΔinlB are significantly reduced compared to WT ( ∼35% , p<0 . 0001 , Figure 2A ) . At various time points during the 1 h infection , polarized MDCK monolayers were fixed and analyzed by confocal immunofluorescence microscopy with an inside-outside staining protocol that distinguishes attached extracellular bacteria from internalized bacteria . Both WT and ΔinlB invade polarized MDCK monolayers almost exclusively through MCJs , which represent only ∼2% of all available junctions ( Figure 2B , Figure S2 and [30] ) , however invasion by ΔinlB is delayed . By 20 minutes after adhesion , internalized WT bacteria are observed , while all ΔinlB remain extracellular . At each time point after attachment a greater proportion of WT than ΔinlB are internalized ( Figure 2B , 2C ) . Thus , InlB is dispensable for cell attachment in polarized epithelia but increases invasion once bacteria are associated with the cell surface . We also investigated the role of InlB in intracellular replication to determine whether the increase in internalized bacteria is solely due to an accelerated entry of WT bacteria or also due to increased replication within the cell . Polarized MDCK monolayers were infected with WT or ΔinlB at a multiplicity of infection ( MOI ) of 10 bacteria/cell . At various time points , the monolayers were fixed and analyzed by confocal immunofluorescence microscopy to quantify the replication rate of the intracellular bacteria ( Figure S2 ) . At each time point during infection WT plaques are greater in area and bacterial number than ΔinlB ( Figure S2 , Figure 2D ) . However , intracellular doubling times are essentially identical between the two strains ( WT Td = 1 . 25 h and ΔinlB Td = 1 . 26 h; comparison of fits ( k ) , p = 0 . 97; Figure 2D ) . Thus , InlB influences the rate of epithelial invasion at MCJs but is not involved in intracellular growth . Soluble InlB activates c-Met signaling when added to nonconfluent epithelia with exposed basolateral surfaces [10] , [11] , [19] . However , since c-Met is a basolateral protein not exposed on the apical side it is unclear whether the same occurs in polarized epithelia [31] , [66] . To test the role of c-Met on apical invasion of polarized epithelial cells , we pretreated the confluent polarized monolayers with SU11274 to inhibit c-Met signaling or DMSO as a control , then infected them with WT or ΔinlB through the apical compartment [67] . The kinase inhibitor reduces WT invasion to the level of ΔinlB invasion but has no significant effect on the invasion of ΔinlB ( Figure 2E ) . Thus , c-Met activation is required for InlB activity during apical invasion of the MCJs . Since c-Met is not readily available in the apical surface , we wondered whether InlB acts as a soluble factor or whether c-Met is activated locally at the MCJs after bacterial attachment . It has been suggested that InlB may function as a soluble and diffuse c-Met agonist since InlB is only loosely associated with the bacterial surface , and since recombinant InlB can mimic HGF by inducing cell membrane ruffling or cell scattering [10] , [19] , [52] , [62] , [68] . On the other hand , Lm invade cells through tight membrane invaginations without apparent changes of cell surfaces where bacteria are absent , suggesting that InlB associated with the bacterial surface mediates c-Met activation within close proximity to each individual bacterium [14] , [63] , [69] . We performed co-infections of polarized MDCK monolayers with a mixture of WT and ΔinlB and hypothesized that WT would rescue the defect of ΔinlB invasion if InlB acts as a soluble factor acting on all cells within the epithelium . We find that InlB does not act globally on the epithelium , since ΔinlB continue to exhibit a defect in invasion in the presence of WT in a mixed infection . The magnitude of the defect is the same in mixed as in separate infections ( Figure 2A , 2F ) and we obtained the same competition defect for ΔinlB at MOI ratios of 100∶1 , 10∶1 , or 1∶1 ( Figure 2F , Figure S3 ) . To further address whether c-Met activation is restricted to the immediate surrounding of individual bacteria , we tested whether the c-Met kinase inhibitor used in a mixed infection would reduce both WT and ΔinlB invasion , or alternately whether c-Met inhibition would selectively affect WT invasion . As in single infections , the c-Met kinase inhibitor reduced WT invasion to the level of ΔinlB in a mixed infection ( Figure 2F , Figure S3 ) . These results indicate that local c-Met activation by InlB at the MCJ is responsible for the increased invasion . Since InlB increases the rate of Lm internalization through activation of c-Met at MCJs , we also wondered whether MCJs are intrinsically different in their endocytic activity as compared to the rest of the apical surface . MCJs represent sites of recent or ongoing cell extrusion where the tight junctions ( TJs ) are being rapidly remodeled [30] , [35] . Additionally , we find that E-cadherin is remodeled through endocytosis during cell extrusion and MCJ formation ( Figure S4A ) . Thus , we hypothesized that MCJs may be more permissive to bacterial entry than other junctional sites because of greater endocytic potential . This is also suggested by the observation that Lm invasion through MCJs is more likely than invasion through other junctional sites of attachment: 26% of Lm associated with a polarized MDCK epithelium attach to epithelial junctions that are not a MCJ but invasion occurs almost exclusively at MCJs since 97% of intracellular foci of Lm originate at these sites , even in the absence of InlB ( Figure 2B , Figure S2 and [30] ) . To test whether endocytosis is naturally increased at MCJs , we added fluorescent dextran to the apical side of uninfected polarized MDCK monolayers for 30 minutes and determined whether uptake is greater at MCJs than through the rest of the apical surface ( Figure 3 ) . Puncta of internalized dextran are readily found in the cells making MCJs and the fluorescence intensity of dextran is higher than at non-multicellular junction ( Non-MCJ ) regions of the polarized monolayer ( Figure 3A , 3E ) . Interestingly , some internalized dextran at MCJs colocalizes with internalized E-cadherin as well as ZO-1 , a scaffolding protein associated with the TJs in polarized cells ( Figure 3E ) [70] . We observe similar puncta of endocytosed E-cadherin at MCJs in vivo at villus tips ( Figure S4B ) . Thus , significant endocytosis occurs specifically at MCJ sites in a polarized epithelium . In addition , E-cadherin , the receptor for Lm internalization , is naturally endocytosed at MCJs . We asked whether c-Met activation at MCJs could locally accelerate endocytosis since growth factor activation of RTKs has been shown to induce endocytosis of E-cadherin through either macropinocytosis or clathrin-mediated endocytosis [46] , [71] , [72] . We pretreated polarized MDCK cells from the apical side for 1 h with HGF or InlB prior to the addition of fluorescent dextran to the apical compartment . We find that both HGF and InlB significantly increase the amount of dextran endocytosed at MCJs ( p<0 . 001 ) , but not at non-MCJ regions compared to untreated cells ( Figure 3A–B , Figure S5 ) . To control for the specificity of this process we used a truncated InlB consisting of only the C-terminal GW domains ( GW[2]–[3] ) and this has no effect on endocytosis compared to untreated monolayers ( Figure S5 ) [15] . These results suggest that basolateral c-Met is made transiently accessible through the rapid junctional remodeling at MCJs . Puncta of endocytosed dextran were also co-localized with junctional proteins at MCJs in HGF and InlB treated monolayers ( Figure 3A , 3F ) . Increased endocytosis of dextran after HGF and InlB treatment is the product of an increase in the number of puncta of internalized dextran per MCJ ( Untreated versus InlB or HGF p<0 . 001 , Figure 3C ) and an increase in the amount of dextran internalized as determined by fluorescence intensity per punctum ( Untreated versus InlB p<0 . 05 , Untreated versus HGF p<0 . 01 , Figure 3D ) . This suggests that both the rate of endocytosis as well as the capacity of individual endocytic vesicles is increased by HGF or InlB . In nonpolarized cells , Lm invasion requires molecular machinery associated with clathrin-mediated endocytosis , including dynamin [59] , [73] . To test whether invasion of the MCJs is also dynamin-dependent , we pretreated polarized MDCK monolayers with either DMSO as a control or dynasore , an inhibitor of dynamin , and infected them with Lm ( Figure 4 ) [74] . Using inside-outside confocal microscopy analysis of monolayers infected for 1 h , we find that Lm invade control cells at MCJs , but cannot invade cells treated with dynasore ( Figure 4A ) . A second assay using gentamicin protection also confirmed this result . Lm were allowed to invade for a period of 1 h , the infected monolayers were treated with gentamicin for 30 minutes and the number of viable intracellular bacteria was determined . Compared to control cells , polarized cells treated with dynasore are significantly less permissive for Lm invasion ( Figure 4B–C; ∼13% DMSO , WT p<0 . 0001; ∼16% DMSO , ΔinlB GFP p<0 . 0001 ) To test whether the increased rate of apical endocytosis at MCJs is also a dynamin-dependent process , we pretreated polarized MDCK monolayers with dynasore or DMSO as a control prior to addition of fluorescent dextran . Pretreatment of polarized cells with dynasore inhibits nearly all endocytosis of dextran at multicellular junctions regardless of HGF or InlB treatment ( Figure 3F , 3G ) . Indeed , uptake at multicellular junctions is not significantly higher than uptake at non-multicellular junctions within monolayers treated with dynasore ( Figure 3G ) . These data suggest that InlB accelerates dynamin-dependent endocytosis at MCJs leading to an increase the rate of Lm uptake at these sites . Our tissue culture results suggested that the villus tip extrusion zone might also be permissive to Lm invasion because of an increased rate of endocytosis in vivo . We incubated fluorescent dextran in mouse ileal loops for 45 minutes and examined villus tips by confocal microscopy to test this hypothesis ( Figure 5 ) . Puncta of fluorescent dextran are readily found in the villus tip epithelium , but not the epithelium along the lateral sides of villi or crypt epithelium ( Figure 5A , 5B and data not shown ) . To test whether InlB promotes endocytosis at the villus tips , we incubated InlB with dextran in mouse ileal loops ( Figure 5D , 5E ) . Internalized puncta of dextran at MCJs are found associated with E-cadherin in both untreated and InlB treated villi ( Figure 5C , 5F ) . However , InlB significantly increases the amount of dextran endocytosis at the villus tips ( p<0 . 05 , Figure 5G ) by increasing the number of puncta of dextran per villus tip ( p<0 . 05 , Figure 5H ) and the amount of dextran per punctum ( p<0 . 005 , Figure 5I ) .
Epithelia are the first site of interaction between the host and a wide variety of invading pathogens and the intercellular junctions are crucial to maintain a tight seal between epithelial cells to prevent microbial invasion . It is interesting that diverse microbes have evolved strategies to usurp the epithelial junctions to mediate extracellular colonization , intracellular invasion or paracellular breach ( reviewed in [75]–[79] ) . Microbes that invade epithelial cells often use receptors for internalization that are part of the junctions or are basolateral proteins . For example , reoviruses bind JAM-A , coxsackie and adenovirus bind CAR , hepatitis C virus binds claudins and occludin , rotaviruses , Shigella flexneri and enteropathogenic Yersiniae bind integrins , α-herpesviruses bind Nectins , and Listeria monocytogenes ( Lm ) binds E-cadherin [7] , [80]–[97] . Although targeting of junction or basolateral proteins by invasive pathogens is a successful strategy , it is also seemingly paradoxical since these receptors are not normally localized at the apical surface . The study of Listeria pathogenesis in the gastrointestinal tract reveals that Lm has evolved to target a subset of intercellular junctions that have a natural and transient defect in cell polarity generated during the process of cell extrusion . First , we noted that Lm uses InlA to access E-cadherin as it becomes exposed at multicellular junctions ( MCJs , Figure 6A ) [30] . Our studies here of InlB suggest that the MCJ's are not only a natural site of local loss of polarity , but also that the normal process of junction renewal involves accelerated endocytic processes that can be hijacked and modulated by additional bacterial invasive factors ( Figure 6B ) . Why are MCJs inherently endocytic ? The formation and resolution of MCJs by cell extrusion requires junctional reorganization , changes in cell position , and changes in cell morphology [30] , [35] , [38] , [98] . There is increasing evidence that remodeling of adhesive contacts , including modification of junctional length or cell position within epithelia , requires endocytosis of adhesion molecules such as E-cadherin [99]–[104] . Furthermore , it was found that in cells neighboring extruding cells , large endosome-like structures contain tight junction ( TJ ) strands [35] . We also find that cells neighboring extruding cells internalize E-cadherin , a component of the adherens junction ( AJ ) , from the extruding cell while forming a MCJ ( Figure S4 , Figure 5B ) . Thus endocytosis at MCJs may be important to release adhesive contacts between the extruding cell and the rest of the epithelium , for removal of lumenally exposed basolateral and junctional proteins , and for redistribution of cell shape and position during cell extrusion [35] , [99]–[101] , [103]–[105] . It has been suggested that Lm adherence and invasion via E-cadherin is analogous to AJ assembly because of the similarity of their molecular requirements [28] , [63] , [106] , [107] . However , our model suggests that Lm invasion subverts junction disassembly , rather than assembly ( Figure 6 ) . This concept is supported by the fact that InlA binding results in tyrosine phosphorylation , ubiquitination and endocytosis of E-cadherin [27] . InlA binding to E-cadherin is sufficient for Listeria invasion , however modulation of endocytosis by InlB accelerates this process ( Figure 6B ) . We show that while InlB is dispensable for attachment , it synergistically promotes invasion of MCJs through activation of c-Met kinase signaling . Activation of cell signaling that results in endocytosis is a strategy utilized by other invasive microbes . For example , viruses like coxsackievirus , HIV , caposi's sarcoma-associated herpesvirus and adenovirus , and bacteria like Salmonella , Shigella , Brucella , Neisseria , Mycobacteria , Haemophilus and Legionella can trigger macropinocytosis or macropinocytosis-like processes [80] , [108]–[123] . In contrast , Lm utilizes a so-called ‘zipper-like’ mechanism of invasion/endocytosis distinct from macropinocytosis [14] , [69] . Other investigators have shown that Lm requires dynamin and other molecular components of clathrin-mediated endocytosis for efficient invasion of nonpolarized cells [55] , [59] , [73] . Furthermore , macropinocytosis is thought to be independent of dynamin and requiring an alternate pinchase [124] , [125] . It has been suggested that Lm hijacks the actin- and dynamin-dependent internalization of clathrin-coated paques , which are larger than clathrin-coated pits [126] , [127] . We also find that Lm invasion of a polarized epithelium through the MCJs requires functional dynamin even in the absence of InlB . Similarly , L . innocua expressing InlA , but not InlB , requires functional dynamin for invasion [128] . This further supports the notion that Lm subverts junction disassembly since both Lm invasion via E-cadherin and junction regulation via E-cadherin endocytosis require functional dynamin [102] , [129] . InlB has been shown to promote dynamin-dependent internalization of Listeria when the bacteria have access to the basolateral surface , and HGF similarly promotes internalization of E-cadherin when added to basal surfaces [59] , [71] . Although c-Met is not exposed on the apical surface of epithelia , we hypothesized that InlB could activate c-Met because of the local loss of cell polarity that occurs at MCJs . We find that apical treatment of polarized epithelia with either HGF or InlB increases apical endocytosis of dextran at MCJs ( Figure 6B ) . Interestingly HGF and InlB do not increase endocytosis at non-MCJ regions of the epithelia suggesting that c-Met , a basolateral protein like E-cadherin , is also only accessible from the apical side through the process of cell extrusion and MCJ formation [30] . We confirmed these results in vivo showing that purified InlB added from the lumenal side increases endocytosis of fluorescent dextran at the extrusion zone of the intestinal villus tip . We provide here the first evidence that InlB is involved in intestinal invasion . Other studies have failed to identify a role for InlB in the intestinal phase of infection [20] , [24] . However , the contribution of InlB to infection may have been difficult to discern at late time points because most studies utilize severe systemic disease as an endpoint of infection , or because of high variation in animal to animal infections . Additionally , other studies of enteric Listeriosis have used treatments that neutralize stomach acid . This may suppress expression of inlA and inlB , which are upregulated by an acid stress response [39]–[42] . In contrast , we did not alter the acid environment and also developed a coinfection assay that allows for precise quantification of Lm in the villus tips of the same animal at early time points . Although the effect of InlB for promoting invasion of the villus tips is not large , it is comparable in magnitude of the role of InlB for invasion of cultured epithelial cells . In addition , it is comparable in magnitude to the recently discovered role for InlB in placental invasion after intravenous infection , an experimental route that bypasses the gastrointestinal tract and prior cell invasion [20] . Our study has focused only on the role of InlB in modulating Lm invasion of a very specific site , the MCJ . Future investigation will address whether InlB affects the pathophysiology of gastrointestinal colonization and of invasive Listeriosis after oral infection . In summary , we have explored the mechanisms of Lm invasion of polarized epithelia , the first stage of an infection that can range from asymptomatic colonization , to self-limiting enteritis , to potentially deadly invasive and disseminated disease . Our mechanistic model demonstrates how two microbial invasins with different receptors and different adhesin properties can function cooperatively to promote invasion of the intestinal villus tips ( Figure 6 ) . The process of cell extrusion requires junctional remodeling and removal of adhesive contacts that allows the dying cell to detach from the epithelium ( Figure 6A ) . After the cell has been extruded , basolateral proteins from the old junction must be removed from above the newly formed TJ on the surrounding cells at the MCJ ( Figure 6A , 6B ) . As an evolutionary strategy , it is interesting that Lm targets junction remodeling and dynamin-dependent removal of E-cadherin from the cell surface as a mechanism of internalization rather than binding a more accessible , but more stable , apical receptor . This concept should be relevant to the study of other microbes that target junctional receptors . Without InlB , Lm invasion is less efficient . Without InlA , InlB does not provide adhesive strength for Lm to bind to the epithelium . Since activation of c-Met results in the co-endocytosis of both receptors , InlB has evolved to provide a local increase in junctional remodeling that allows for enhanced dynamin-dependent Lm internalization ( Figure 6B ) .
All animal experiments were performed in accordance to NIH guidelines , the Animal Welfare Act , and US federal law . Such experiments were approved by Stanford University's Administrative Panel on Laboratory Animal Care ( A-PLAC ) , which has been accredited by the Association of Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . All animals were housed in a centralized and AAALAC-accredited research animal facility that is fully staffed with trained husbandry , technical , and veterinary personnel . A stock of 5 µg/ml HGF in H2O 0 . 1% BSA was stored at −80°C until dilution at use ( Sigma-Aldrich , St . Louis , MO ) . InlB-His6 and a truncated variant containing only the terminal GW domains , GW[2]–[3]-His6 at ∼25 mg/ml in 10 mM sodium acetate pH 4 . 5 , 1 mM DTT , 0 . 5 mM EDTA were purified as described in [15] , [16] and stored −80°C until dilution at use . c-Met Inhibitor SU11274 and dynamin inhibitor dynasore ( [67] , [74]; Calbiochem , San Diego , California ) were stored in DMSO at −20°C until dilution at use . A stock of Neutral fixable Texas Red 10 kDa dextran ( Molecular Probes , Eugene , Oregon ) was stored at 25 mg/ml in DMEM at −20°C until dilution at use . The tRNAARG site-specific shuttle integration vectors pPL3 and pPL3e , which respectively confer chloramphenicol and erythromycin resistance to Listeria , and the L . monocytogenes ( Lm ) strain DH-L1039 , which expresses sGFP under the control of the Hyper-SPO1 promoter fused to the 5′ UTR of hly ( pHyperSPO1-hly5′UTR-sGFP ) , were the kind gifts of Dr . Darren E . Higgins ( Harvard University , Boston , Massachusetts ) [130] . pHyperSPO1-hly5′UTR-sGFP was PCR amplified from DH-L1039 genomic DNA with primers 37/33 ( Table 1 ) . SalI digested pHyperSPO1-hly5′UTR-sGFP was ligated with SalI digested pPL3 or pPL3e to generate pMP74 or pMP76 , respectively . inlA and inlAB were PCR amplified from WT Lm 10403S genomic DNA with primers 1/2 and 1/3 , respectively , as in [25] . inlA or inlAB were ligated with pCR4-BluntTOPO ( Qiagen , Valencia , CA ) and subjected to two rounds of Quickchange site-directed mutagenesis ( Stratagene , La Jolla , CA ) with primer pairs 47/48 and 49/50 to introduce S192N and Y369S mutations into inlA and generate the murinized variants inlAm or inlAmB ( Table 1 ) . inlAm or inlAmB were digested with BamHI and ligated with BamHI digested pPL3 , pPL3e , pMP74 or pMP76 . These constructs were transformed into SM10 ( λpir ) , and introduced to Lm by conjugative mating as described in [131] ( Table 2 ) . Integration was confirmed with primers NC16/PL95 as described in [131] ( Table 1 ) . Lm strains are listed in Table 2 . Lm were grown on BHI agar or in BHI broth ( BD/Difco , San Jose , California ) supplemented with streptomycin at 200 µg/ml , chloramphenicol at 7 . 5 µg/ml or erythromycin at 5 µg/ml , when appropriate . One-shot Top10 E . coli ( Invitrogen , Carlsbad , California ) , used for general cloning steps , was cultured in LB broth and on LB agar supplemented with kanamycin at 50 µg/ml or choramphenicol at 25 µg/ml , when appropriate . E . coli strain SM10 ( λpir ) was kindly provided by Dr . Denise Monack ( Stanford University , Stanford , California ) . E . coli SM10 ( λ pir ) , as the donor for bacterial conjugation , was cultured in LB supplemented with kanamycin at 30 µg/ml and chloramphenicol at 25 µg/ml , when appropriate . MDCK II , MDCK II E-cadherin-GFP and MDCK II E-cadherin-RFP cells were kindly provided by W . James Nelson ( Stanford University , Stanford , California ) [132] , [133] . Cells were maintained at 37°C in 5% CO2 atmosphere in DMEM ( Gibco , San Diego , California ) supplemented with 5% fetal bovine serum ( FBS , Gibco ) . For infection experiments , cells were trypsinized and seeded on 12 well polycarbonate tissue culture dishes or 12 mm polycarbonate tissue culture inserts ( Transwell filters; Costar , Cambridge , Massachusetts ) at a density of 106 cells/cm2 and supplemented with fresh media daily for 4 days . For experiments with inhibitors , DMEM 2 . 5 µM c-Met Inhibitor SU11274/0 . 15% DMSO was added to the monolayers 12 h prior to infection or DMEM 80 µM dynasore/0 . 1% DMSO was added to the monolayers 30 min or 1 h prior to infection . Lm infections ( multiplicity of infections , MOIs , of 1∶1 to 100∶1 ) and assays of attachment invasion were performed essentially as described in [30] . To assay for intracellular replication , polarized MDCK monolayers were infected with an MOI of 10 bacteria/cell for a 10 minutes to allow attachment and were then washed 4X with DMEM to remove unadhered bacteria . Six to ten plaques per time point were randomly found and imaged by 3D confocal microscopy without regard to size or bacterial number and subsequently analyzed for bacterial number from all acquired images . Prism software ( GraphPad , San Diego , California ) was utilized for construction of graphs and for statistical analysis of data . Student's t-test was used to compare two sample groups . ANOVA with Bonferroni's post-tests was used to analyze 3 or more sample groups . The competitive index ( C . I . ) of two strains was determined as C . I . = ( Stain A output/Strain B output ) / ( Stain A input/Strain B input ) . Lm cultures were grown at 30°C overnight in BHI without agitation , pelleted and resuspended in phosphate buffered saline ( PBS ) . Female 8-week old BALB/c mice ( obtained at 6–7 weeks from The Jackson Laboratory , Bar Harbor , Maine ) were food restricted overnight but allowed free access to water and inoculated with a feeding needle intragrastrically with a maximum volume of 200 µl . Mice were then immediately allowed free access to food and water . MDCK II or MDCK II E-cadherin-GFP cells were trypsinized and seeded on 12 mm polycarbonate Transwell tissue culture inserts at a density of 106 cells/cm2 and supplemented with fresh basal media daily for 5 days . The media was changed to plain DMEM 80 µM Dynasore/0 . 1% DMSO or DMEM 0 . 1% DMSO at −1:30 hours . A final concentration of 1 µg/ml InlB or GW[2]–[3] , or 0 . 1 µg/ml HGF was added at −1:00 h to the apical side and at time 0:00 1 mg/ml neutral fixable Texas Red 10 kDa dextran was added to the apical side for 30 minutes . Monolayers were washed 4X to remove extracellular dextran and monolayers were fixed and processed for immunofluoresence microscopy , as described in [30] . Confocal images were analyzed using Volocity software ( Improvision , Lexington , Massachusetts ) . To quantify and quantitatively describe intracellular fluorescent dextran puncta , an analysis script was designed to find objects within 5–100% fluorescence intensity , exclude objects less than 0 . 5 µm3 or greater than 100 µm3 and separate touching objects with an object size guide of 0 . 1 µm3 . The data were clipped to a square region of interest 50 µm×50 µm centered at a multicellular junction ( MCJ ) or at non-MCJ regions . BALB/c mice ( The Jackson Laboratory ) were fasted overnight prior to surgery but allowed free access to water . Anesthesia was induced by intraperitoneal injection with a mixture of ketamine ( 40 mg/kg ) and xylazine ( 4–5 mg/kg ) in water and the animal was kept on a 37°C pad for the duration of the procedure . For each mouse a midline laparotomy was performed to expose the bowel . The ileocecal junction was identified , and the ileum was ligated with a silk tie just proximal to the cecum . A second circumferential ligature was placed ∼4 cm proximal . A suspension of 2 . 5 mg/ml neutral fixable Texas Red 10 kDa dextran with or without 10 µg/ml InlB in dPBS was inoculated via a hypodermic needle into the loop ( ∼50 µl/cm ) . The intestine was returned to the abdominal cavity and the incision was closed with surgical staples . The mouse was kept under anesthetic for 45 minutes at which time the animal was euthanized and intestines were removed and fixed for whole-mount confocal microscopy imaging , as described in [30] . Confocal images were analyzed using Volocity software ( Improvision ) . To quantify and quantitatively describe intracellular fluorescent dextran puncta , an analysis script was designed to find objects within 5–100% fluorescence intensity , exclude objects less than 1 µm3 or greater than 20 µm3 . The data were clipped to region of interest surrounding each villus tip analyzed . Live-cell time-lapse microscopy was performed essentially as described in [134] . Confocal immunofluorescence microscopy was performed as described in [30] . Lm were detected by incubation of samples with biotin-conjugated rabbit anti-L . monocytogenes , all antigens ( YVS4207 , Accurate Chemical & Scientific Corp . , Westbury , NY; 1∶100 for tissue , 1∶600 for tissue culture ) . Tight junctions were detected by incubating samples with mouse anti-ZO-1 antibodies ( Zymed , South San Francisco , California; 1∶300 dilution ) . E-cadherin was detected with mAb anti-E-cadherin ( BD Transduction Labs , San Jose , California; 1∶600 dilution ) . Alexa-fluor conjugated streptavidin or Anti-IgG Alexa-fluor conjugated antibodies of appropriate species reactivity and fluorescence spectra were used for secondary detection ( Molecular Probes ) . An immunofluorescence inside/outside staining that distinguishes extracellular from intracellular L . monocytogenes was modified from [135] with appropriate antibodies for this study . All nuclei were visualized by incubating samples with TOPRO-3 ( Molecular Probes ) . F-actin was visualized by incubating samples with Alexa-fluor conjugated phalloidins ( Molecular Probes ) . | The anatomical context in which attachment and invasion factors find host receptors determines when and where microbes can colonize and invade . For example , Listeria monocytogenes ( Lm ) , a cause of human and animal food-borne disease , invades the villous epithelium only at the intestinal villus tips where dying cells are extruded from the epithelium . This is because Lm's receptor , E-cadherin , a cell-cell junction protein normally hidden from the intestinal lumen , becomes transiently exposed during the dramatic junctional disassembly and reorganization required for extrusion and maintenance of epithelial continuity . Here , we find that basolateral c-Met , a receptor tyrosine kinase used by Lm for invasion of cells in tissue culture , is also activated as a consequence of its exposure on the apical side at cell extrusion sites . Lm stimulates c-Met signaling once bacteria have attached to junctional E-cadherin . Furthermore , c-Met activation at cell extrusion sites induces uptake of E-cadherin , accelerating invasion of Lm . Thus , Lm not only utilizes the dynamic nature of junctional remodeling to attach to villus tips , but also hijacks signaling that controls junctional endocytosis as a mechanism of intestinal barrier breach . Other enteric microbes whose receptors are inaccessible from the lumen may also target remodeling junctions for attachment and invasion . | [
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Hybrid trials that include both clinical and implementation science outcomes are increasingly relevant for public health researchers that aim to rapidly translate study findings into evidence-based practice . The DeWorm3 Project is a series of hybrid trials testing the feasibility of interrupting the transmission of soil transmitted helminths ( STH ) , while conducting implementation science research that contextualizes clinical research findings and provides guidance on opportunities to optimize delivery of STH interventions . The purpose of DeWorm3 implementation science studies is to ensure rapid and efficient translation of evidence into practice . DeWorm3 will use stakeholder mapping to identify individuals who influence or are influenced by school-based or community-wide mass drug administration ( MDA ) for STH and to evaluate network dynamics that may affect study outcomes and future policy development . Individual interviews and focus groups will generate the qualitative data needed to identify factors that shape , contextualize , and explain DeWorm3 trial outputs and outcomes . Structural readiness surveys will be used to evaluate the factors that drive health system readiness to implement novel interventions , such as community-wide MDA for STH , in order to target change management activities and identify opportunities for sustaining or scaling the intervention . Process mapping will be used to understand what aspects of the intervention are adaptable across heterogeneous implementation settings and to identify contextually-relevant modifiable bottlenecks that may be addressed to improve the intervention delivery process and to achieve intervention outputs . Lastly , intervention costs and incremental cost-effectiveness will be evaluated to compare the efficiency of community-wide MDA to standard-of-care targeted MDA both over the duration of the trial and over a longer elimination time horizon .
DeWorm3 is a series of cluster randomized trials evaluating the feasibility of interrupting transmission of STH using biannual ( twice annually ) community-wide MDA targeting eligible community members of all ages . Additionally , DeWorm3 aims to assess the relative influence of community-wide MDA on STH prevalence and transmission intensity as compared to standard-of-care targeted MDA . The rationale , objectives , and design of the DeWorm3 Project clinical trial are described elsewhere in this supplement [11] . The effectiveness of community-wide MDA for STH is driven in part by epidemiological factors ( baseline disease prevalence and STH species distribution ) , intervention characteristics ( drug efficacy ) , systems factors ( health system strength ) , and social factors ( community member beliefs and preferences ) that influence intervention acceptability , penetration , and uptake . These factors are highlighted in the DeWorm3 theoretical model ( Fig 1 ) . The overall objective of DeWorm3 IS research is to evaluate these factors in order to develop and test a community-wide STH MDA model that is sustainable and scalable in STH-endemic areas . Specific research aims include:
Stakeholder analyses will be conducted in each DeWorm3 trial site at baseline ( pre-launch ) , following trial launch , and at study endline . The purpose of the stakeholder analysis is to identify the individuals who influence or are influenced by effective targeted MDA and community-wide transmission interruption efforts and understand how the network dynamics may influence study outcomes and future policy development . A social network analysis strategy will be used to characterize the relationships between stakeholders in order to ( 1 ) describe and map members of the network , ( 2 ) characterize the relationship between the network members , and ( 3 ) analyze the network using standard network measures such as density , centrality , and homophily [12] . Qualitative research will be conducted at trial baseline , midline , and endline to identify factors influencing implementation quality , feasibility , and sustainability by site . Qualitative research will utilize the Consolidated Framework for Implementation Research ( CFIR ) as a guide for data collection and analysis . The CFIR is a validated tool utilized in the United States and increasingly in low- and middle-income countries [14 , 15] . The CFIR is built upon a number of preexisting implementation theories to provide a comprehensive meta-theoretical framework of 39 “constructs” that influence implementation [16] . Application of the CFIR helps guide theory development and verify whether and why interventions work through the identification of core and modifiable intervention characteristics [17] . CFIR constructs are organized according to five major domains influencing implementation effectiveness including: ( 1 ) the intervention , ( 2 ) the inner setting , ( 3 ) the outer settings , ( 4 ) the individuals involved , and ( 5 ) the process of undertaking the intervention . The “intervention” is defined as the core characteristics of the planned intervention . The “inner and outer settings” comprise the contexts where the implementation activities will occur . The “individuals involved” are the agents of change , those who have power and influence to seek , experiment with , or evaluate interventions . Lastly , the implementation process describes an active progression towards attaining the outcome of the intervention described [18] . We have selected a subset of 21 constructs to assess according to the following salience criteria: ( 1 ) is the construct a potential barrier or facilitator to community-wide MDA for STH ? and ( 2 ) Would the construct exhibit heterogeneity across stakeholder groups or clusters ? Question guides are designed to address the targeted constructs and are tailored to the stakeholder level ( i . e . national , district , etc . ) . Baseline qualitative research will help refine CFIR construct selection for use during process and summative research as well . When a health system is preparing to implement a new intervention or to change standard practice , effective implementation of the change may be influenced by the degree to which stakeholders identify the system as “ready” to implement the change or practice . Within DeWorm3 , readiness implies that individuals feel empowered as members of the health system to contribute to the new interventions , they are confident in the flexibility and responsiveness of the system to adapt to the change , they are not threatened by the change , and they believe intervention is appropriate for achieving desired outcomes . With the repurposing of community-wide LF MDA platforms for STH transmission interruption and a transition from targeted to community-wide MDA , there will be a considerable need for systems adaptation and a multi-level and multi-faceted assessment of the government and its partners’ readiness to implement [22] . It is important to understand the factors that drive health system readiness to implement novel interventions such as community-wide MDA for STH both in order to contextualize observed trial outputs and outcomes as well as to provide evidence-based guidance in other settings regarding health system factors that should be in place prior to implementation [23] . Drawing from organizational readiness for change theory , DeWorm3 has developed a structural readiness assessment survey tool [24] . At baseline , the tool will be used to identify needs or conditions that can be targeted for effective change management . At study endline , the readiness tool will be used in a prognostic manner to evaluate health system readiness to sustain or scale-up the intervention [24] . Process mapping is a systems analysis approach to identifying the flow of inputs required to achieve optimal outputs , such as high MDA treatment coverage . Process mapping generates a systems-wide view of complex , interdependent components that can contribute to effective MDA programs with high coverage . Process mapping also helps build a shared understanding of how work is carried out and promotes common organizational goals , while simultaneously generating information regarding what aspects of the intervention are adaptable across settings and which are key determinants of intervention uptake . DeWorm3 will use two process mapping methods . The first is in-depth process mapping , which will take place annually in six randomly selected clusters over the duration of the trial . The second is routine workflow mapping , which will take place in each cluster during each round of MDA . The in-depth process mapping will help identify all activities that must take place in school or community-based MDA programs , ideal activity levels for achieving optimized outputs , actual activity achievements , and the discrepancy between ideal and actualized activities . Routine workflow tracking will provide information regarding key activity performance in each cluster , and how different activities may or may not be tied to reaching treatment coverage targets . Both of these strategies will identify contextually-relevant modifiable bottlenecks that may be addressed to improve the intervention delivery process and to achieve key intervention outputs . The financial and economic costs and incremental cost-effectiveness of community-wide and targeted MDA for STH will be ascertained at the end of the DeWorm3 trial ( following three years of the intervention and two years of surveillance ) . Using mathematical models , costs and cost-effectiveness will also be evaluated over a longer time horizon appropriate for an elimination scenario . Participants in individual interviews and focus groups will provide written informed consent . Participants who are not literate will sign with a thumbprint in the presence of an impartial witness . Parents of participating children will provide consent on behalf of their child; children will provide assent in accordance with national ethical guidelines . All qualitative data will be confidential and all names or identifying information will be encrypted or removed from transcripts to protect the identity of the participants and their associated institutions . Written consent is not required for participation in readiness surveys , as approved by ethical review committees . All readiness surveys will be anonymous to protect the identities of participants and their relevant institutions . Only the participant’s affiliated stakeholder group will be recorded . The IS research component of the DeWorm3 Project has been reviewed and approved by the Institut de Recherche Clinique au Bénin ( IRCB ) through the National Ethics Committee for Health Research ( 002-2017/CNERS-MS ) from the Ministry of Health in Benin , The London School of Hygiene and Tropical Medicine ( 12013 ) , The College of Medicine Research Ethics Committee ( P . 04/17/2161 ) in Malawi , Christian Medical College in Vellore , and KEM Hospital Research Centre Ethics Committee ( 1718 and 1719 ) . The study was also approved by The Human Subjects Division at the University of Washington ( STUDY00000180 ) . The DeWorm3 Project aims to generate additional evidence necessary to optimize the delivery of evidence based interventions tested within the DeWorm3 trials . Regardless of the trial outcomes , these data will contribute to the ability of policy makers and STH programs to deliver high-quality targeted or community-wide MDA at scale . We will utilize recognized dissemination frameworks to ensure that optimal dissemination routes are established early in trial implementation [32] . Results will be disseminated to community members and health workers in trial sites , Ministries of Health in endemic countries , funders , implementing partners , and policymakers at the World Health Organization . Embedding implementation science methods within the DeWorm3 trial provides an opportunity to study the mechanisms that contribute to acceptable , efficient , and effective community-wide and population targeted MDA . Stakeholder analysis , framework-based qualitative research , structural readiness assessments , process mapping , and cost-effectiveness research will generate the multidisciplinary evidence needed to identify best practices in implementation , core and adaptable components of the intervention across settings , and considerations for sustaining and scaling-up community-wide MDA for STH transmission interruption . Implementing a hybrid trial at such a large scale also provides an opportunity to evaluate opportunities to embed IS into clinical trial work across heterogeneous settings , which may be relevant for other areas of disease focus . | The DeWorm3 Project is a series of randomized clinical trials testing the feasibility of interrupting the transmission of soil-transmitted helminths . We have integrated implementation science research questions into the trials in order to optimize delivery of trial interventions as well as to speed the translation of study evidence into relevant policy and practice . DeWorm3 implementation science research will take place at baseline ( formative research ) , midline ( process research ) , and endline ( summative research ) . DeWorm3 will use stakeholder mapping and network analysis , qualitative data collection via individual interviews and focus groups , structural readiness surveys , process mapping , and economic evaluation methods to assess opportunities to maximize intervention effectiveness , evaluate the efficiency of the intervention relative to the standard-of-care , and identify strategies for sustaining , scaling , and replicating effective components of trial interventions . The implementation science research described in this protocol will be helpful to policy makers and program implementers who aim to use DeWorm3 findings to inform guidelines and routine programmatic activities . The DeWorm3 implementation science protocol is also relevant to researchers interested in incorporating implementation hypotheses into their own clinical research studies . | [
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] | 2018 | Evaluating the sustainability, scalability, and replicability of an STH transmission interruption intervention: The DeWorm3 implementation science protocol |
Aneuploidy represents the most prevalent form of genetic instability found in human embryos and is the leading genetic cause of miscarriage and developmental delay in newborns . Telomere DNA deficiency is associated with genomic instability in somatic cells and may play a role in development of aneuploidy commonly found in female germ cells and human embryos . To test this hypothesis , we developed a method capable of quantifying telomere DNA in parallel with 24-chromosome aneuploidy screening from the same oocyte or embryo biopsy . Aneuploid human polar bodies possessed significantly less telomere DNA than euploid polar bodies from sibling oocytes ( −3 . 07 fold , P = 0 . 016 ) . This indicates that oocytes with telomere DNA deficiency are prone to aneuploidy development during meiosis . Aneuploid embryonic cells also possessed significantly less telomere DNA than euploid embryonic cells at the cleavage stage ( −2 . 60 fold , P = 0 . 002 ) but not at the blastocyst stage ( −1 . 18 fold , P = 0 . 340 ) . The lack of a significant difference at the blastocyst stage was found to be due to telomere DNA normalization between the cleavage and blastocyst stage of embryogenesis and not due to developmental arrest of embryos with short telomeres . Heterogeneity in telomere length within oocytes may provide an opportunity to improve the treatment of infertility through telomere-based selection of oocytes and embryos with reproductive competence .
Gain or loss of an entire chromosome ( aneuploidy ) is the most common genetic cause of miscarriage and developmental delay in humans . Advanced maternal age is a well known risk factor and a reflection of the observation that aneuploidy primarily arises during meiosis of the maternal gamete , the oocyte [1] . It is also well established that a decline in fertility occurs as maternal age increases . Therefore , as women continue to delay their childbearing into the mid to late thirties , there has been a growth in the utilization of preimplantation genetic screening ( PGS ) to avoid aneuploid conceptions during the in vitro fertilization ( IVF ) -based treatment of infertility . PGS of aneuploidy has recently advanced to include the ability to screen for all 24 chromosomes [2]–[4] has revealed that aneuploidy of any and all chromosomes found in humans can be present at the preimplantation stages of human embryonic development [5] . A number of events have been proposed to play a role in the development of aneuploidy during maternal meiosis of the oocyte . These include inappropriate or lack of formation of chiasmata , which link homologous chromosomes to ensure proper alignment [6] , and late exit from the production line of oogenesis [7] . More recently , telomere dysfunction has been proposed as a phenomenon that unifies these and other events and as a general explanation for female reproductive senescence [8] . Indeed , the role of telomeres in maintaining chromosome stability was proposed over 70 years ago [9] and many studies have since demonstrated that excessive telomere shortening results in chromosome instability in somatic cells [10] . An animal model of telomere deficiency has also illustrated the importance of telomeres in germ cell chromosome stability [11]–[13] . In 4th generation telomerase knockout mice , oocytes develop abnormal spindles . Since spindle formation is a critical event in proper chromosome segregation , this observation suggests that telomeres may play a role in the development of oocyte aneuploidy in the human . However , the prevalence of aneuploidy as a result of spindle formation defects in the telomerase knockout oocytes or ensuing embryos has not been specifically measured . In addition , artificially inducing telomere shortening through genetic deletion of the telomerase gene in mice may not reflect naturally occurring events in human oogenesis or embryogenesis . In order to directly investigate whether telomere DNA is associated with development of aneuploidy in the preimplantation stages of human development , one must address several limitations . These include studying low quantities of DNA , obtaining access to normal human ooctyes and embryos , and developing the ability to simultaneously quantify telomere DNA and comprehensively diagnose aneuploidy from the same oocyte or embryo biopsy . The present study represents the first opportunity to overcome these hurdles . Much of this opportunity is related to the recent development of an accurate single cell SNP microarray based 24-chromosome aneuploidy screening methodology [2] . Since this technique involves the use of whole genome amplification ( WGA ) , excess DNA is available to simultaneously quantify telomere DNA . Moreover , since SNP microarray based 24-chromosome aneuploidy screening has been employed in a number of Institutional Review Board approved clinical trials [3] , [4] , DNA from the highest quality ( normal ) IVF derived human oocytes and embryos is also available for analysis of telomere DNA . Therefore , the objectives of the present study were to first validate a method to simultaneously assess 24 chromosome aneuploidy and telomere DNA content from human oocytes and embryos and then to test the hypothesis that telomere DNA content is associated with development of human embryonic aneuploidy .
A novel assay was developed and evaluated for the ability to accurately quantify relative amounts of telomere DNA from single or few cells after WGA . This was important to demonstrate the reliability of using existing 24-chromosome aneuploidy screened WGA DNA from oocyte and embryo biopsies . A multicopy Alu-Ya5 sequence was targeted as an endogenous control for telomere DNA quantity in order to accommodate differences in the number of input cells , the presence of aneuploidy , or possible single cell single locus PCR drop out within the reference and test samples . As expected , real-time PCR products using previously published primers for telomere DNA [14] , and SYBR Green-based detection , displayed a single peak upon dissociation curve analysis ( Figure 1A ) . This confirmed sequence specificity of amplification from both isolated genomic DNA and whole genome amplified DNA . In addition , the relative telomere DNA quantity observed in isolated genomic DNA and whole genome amplified DNA of various cell lines was similar to values calculated from the reported literature [15]–[19] ( Figure 1B ) . Most importantly , the Pearson correlation ( r2 ) between experimentally determined relative telomere DNA quantities from isolated genomic DNA and whole genome amplified DNA from the same cell lines was 0 . 97 , indicating that WGA faithfully represents the relative quantities of telomere DNA found in the original sample . A total of 18 polar bodies ( 9 euploid and 9 aneuploid ) from 9 IVF patients were evaluated for telomere DNA length ( Table 1 ) . The patient-specific variable of maternal age of the oocyte was controlled for by conducting paired analyses of telomere DNA quantity in sibling aneuploid and euploid polar body biopsies from oocytes derived from the same patient and IVF treatment cycle . Examples of results of aneuploidy screening are shown in Figure 2A . Aneuploid polar bodies displayed significantly lower quantities of telomere DNA than paired sibling euploid polar bodies ( −3 . 04 fold , P = 0 . 016 , Figure 3 ) . A total of 24 blastomeres ( 14 euploid and 10 aneuploid ) from 9 IVF patients were evaluated . Each patient provided at least one euploid and one aneuploid blastomere for paired analysis of relative telomere DNA length in sibling embryos ( Table 2 ) . Examples of results of aneuploidy screening are shown in Figure 2B . Again , all patient specific variables were controlled by comparing an aneuploid blastomere to a euploid blastomere from sibling embryos derived from the same patient and IVF treatment cycle . Samples were also evaluated by paired analysis for differences in embryo fragmentation , cell number , and morphological grade . Morphological characteristics were not significantly different between sibling aneuploid and euploid embryos in this study ( Table 2 ) . Photographs of each of the cleavage stage embryos included in this study are also shown in Figure 2C . Aneuploid blastomeres displayed significantly reduced telomere DNA quantity relative to their paired sibling euploid blastomeres ( −2 . 6 fold , P = 0 . 002 , Figure 3 ) . A total of 20 trophectoderm biopsies from sibling blastocyst stage embryos ( 10 aneuploid and 10 euploid ) from 10 IVF patients were evaluated ( Table 3 ) . Examples of results of aneuploidy screening for each of the trophectoderm samples are shown in Figure 2D . In addition to controlling for maternal and paternal age by sibling paired analysis of telomere DNA quantity , embryo morphology was controlled for by selecting samples with identical morphological grade [20] within each pair of blastocysts ( Table 3 ) . Aneuploid blastocyst trophectoderm displayed quantities of telomere DNA that were not significantly different from their sibling euploid blastocysts ( P = 0 . 340 , Figure 3 ) . One mechanism by which aneuploid blastocysts may develop telomere DNA length equivalent to the length observed in euploid blastocysts is through telomere DNA elongation between the cleavage and blastocyst stages of development ( Figure 4A ) . This phenomenon has been observed in animals [21] , [22] and recently by unpaired analysis in humans [23] . Telomere DNA was quantified in the 1st and 2nd polar body and a blastomere from each of 21 cleavage stage embryos , and in the 1st and 2nd polar body and a trophectoderm biopsy from each of 29 blastocyst stage embryos . These samples were selected since all polar body and embryo biopsies were found to be euploid . Telomere DNA in each 2nd polar body and each embryo biopsy was evaluated relative to the 1st polar body that was derived from the same oocyte ( Figure 4B ) . No significant differences in levels of telomere DNA were observed in 1st or 2nd polar bodies derived from the same oocyte ( 1 . 5-fold , P = 0 . 71 , and 1 . 2-fold , P = 0 . 32 , respectively ) . In addition , telomere DNA in blastomeres from cleavage stage embryos was not significantly different from levels found in the corresponding 1st polar body ( 2 . 0-fold , P = 0 . 14 ) . However , telomere DNA in trophectoderm biopsies from blastocyst stage embryos displayed a significant increase in quantity relative to the corresponding 1st polar body ( 5 . 7-fold , P = 5 . 5×10−10 ) . These results indicate that telomere DNA is elongated during development between the cleavage and blastocyst stage of development in the human . Another mechanism by which aneuploid blastocysts may develop telomere DNA length equivalent to the length observed in euploid blastocysts is through developmental selection ( Figure 4A ) . For this mechanism to be true , one would expect that embryos which fail to progress to the blastocyst stage ( developmental arrest ) would also possess significantly reduced telomere DNA length relative to embryos which successfully develop to the blastocyst stage ( developmentally competent ) . Forty-four paired analyses of developmentally arrested and competent embryos , that were otherwise developmental stage and ploidy matched , were evaluated for relative telomere DNA quantity ( Figure 4C ) . A significant difference in telomere DNA length between arrested and developmentally competent embryos was not observed ( P = 0 . 29 ) . This result indicates that aneuploid blastocysts do not develop levels equivalent to euploid blastocysts through selection against embryos with short telomeres .
An accurate method for simultaneous relative quantitation of telomere DNA content and 24 chromosome aneuploidy screening in single cells was developed . Key features of this assay include the use of a multicopy endogenous control target sequence to prevent differences in the number of cells , nuclear DNA content ( haploid bivalent , haploid univalent , diploid ) , the presence of aneuploidy , or single locus drop out from impacting the relative quantitation of telomere DNA in the test and reference samples . The use of a multicopy gene for normalization may also help to avoid issues with amplification bias from single cells since it represents an abundant target within the single cell and would be less susceptible to the well characterized single locus bias that is found after applying WGA [24] . The same concept may be true when applying WGA based amplification to evaluate telomere DNA since it also represents a multicopy sequence . It was also important to utilize DNA processing specific relative quantitation since an apparent decrease in telomere DNA content relative to the total amount of DNA was observed after whole genome amplification ( mean ΔCT of 22 . 5±1 . 3 ) compared to isolated genomic DNA ( mean ΔCT of 12 . 9±0 . 6 ) from the same cell lines . In addition , the paired nature of the relative quantitation was also important . For example , the paired sample study design controlled for all patient specific variables that may be associated with telomere DNA content including maternal age , paternal age , and genetic background . Paired analysis may also be important to avoid potential issues related to the use of the Alu-Ya5 sequence when normalizing telomere DNA content measurements since different individuals in a given population may possess natural variations in Alu-Ya5 copy number . The paired-sample design also controls for all unknown patient specific variables inherent in any study involving humans . In addition to controlling for differences that may be attributed to patient specific variables , this study also controlled for morphological characteristics of cleavage and blastocyst stage embryos . This was important since previous work indicated that embryo fragmentation was predictive of telomere DNA quantity [25] . The present study established that pairs of euploid and aneuploid samples were either not significantly different ( cleavage stage ) or specifically selected to have been given identical morphological grades ( blastocyst stage ) . With these parameters controlled within each paired analysis , significant difference in telomere DNA quantity was observed between the aneuploid and euploid polar bodies and cleavage stage blastomeres but not trophectoderm from the blastocyst . The correction of telomere DNA length through elongation between the cleavage and blastocyst stage of embryogenesis was also established in this study . Similar findings were obtained in animal studies [21] , [22] and in a recent study involving unpaired analyses of human embryos [23] . Most importantly , results of the present study demonstrate that telomere DNA length is associated with human aneuploidy development for the first time . The correlation between telomere length and aneuploidy during embryogenesis also corresponds with the previously identified and predominantly maternal meiotic origin of aneuploidy [1] , and with recent observations of genomic instability at the cleavage stage of development [26] . Interestingly , the relationship between telomere content and aneuploidy was maintained when embryonic cells were evaluated at the cleavage stage of development despite the presence of both maternal and paternal chromosome telomere DNA . However , the extent to which telomere content was decreased in aneuploid cleavage stage embryonic cells was slightly lower than that observed in polar bodies from the oocyte ( Figure 3 ) , which is consistent with the maternal origin of aneuploidy . Future studies may need to focus on whether there is an association between telomere DNA content and the cell division and parental specific origins of aneuploidy . This will require development of the ability to predict origins of aneuploidy in single cell quantities of DNA in a manner similar to that described for analysis of large quantities of DNA from products of conception [27] . Although rare , it is possible for compensation of meiosis I errors derived from premature separation of sister chromatids ( PSSC ) to lead to a euploid oocyte by segregation of the abnormality to the 2nd polar body [28] . However , all the aneuploid polar bodies evaluated in this study were only from oocytes which led to aneuploid embryos . Future studies might include evaluating whether decreased telomere length is differentially associated with aneuploidy from PSSC compared to nondisjunction . Since reduced recombination remains one of the well known risk factors in nondisjunction and development of aneuploidy in humans [6] , the strong association between telomere length and aneuploidy in oocytes and cleavage stage embryos in the present study might be the result of reduced meiotic recombination and impaired chromosome pairing and synapsis that have been observed in oocytes from telomere deficient mice [12] . In support of this is the observation that the decrease in oocyte telomere length reported in telomere deficient mice relative to wild type oocytes was approximately 0 . 4-fold ( 20/50 , as displayed in figure 5b of reference [12] ) , similar to the decrease of 0 . 43-fold observed in aneuploid relative to euploid polar bodies in the present study ( Figure 3 ) . Some studies also suggest that telomeres are directly involved in homologous chromosome pairing where synapsis may begin at the telomeres [29] . Further investigation into whether polar bodies and embryos with decreased telomere length also possess reduced numbers of recombination events may help support these findings . The observation that heterogeneity in telomere DNA content exists within oocytes and embryos derived from a single controlled ovarian stimulation ( COS ) cycle may represent a phenomenon relevant to improving reproductive medicine . If reduced telomere DNA length represents an intermediate event that precedes aneuploidy development , then telomere length may represent a useful marker of embryonic reproductive potential . Given the recent evidence that not all euploid embryos possess reproductive potential , that the age related decline in fertility is not entirely due to aneuploidy [3] , and the results of the present study , further investigation into the predictive value of telomere DNA length for ovarian reserve and reproductive potential and senescence using the methods developed in this study is warranted .
This study was conducted under Institutional Review Board approval from Western IRB ( Olympia , WA ) and with informed patient consent . Telomere DNA was amplified using “telg” and “telc” primers previously described by Cawthon et al [14] at a final concentration of 900 nM each . SYBR Green PCR Master Mix ( Applied Biosystems Inc . , Foster City , CA ) was used at the manufacturer's recommended concentration . In order to normalize input DNA , primers and a TaqMan probe for the multicopy Alu ( Ya5 family ) sequence were used as previously described [30] but with use of a FAM dye instead of VIC ( Applied Biosystems Inc . ) . A multicopy gene for normalization was used to avoid potential issues with a single copy gene locus dropout from single cell WGA , and the potential impact of aneuploidy on single copy gene copy number . TaqMan Alu primers and probe , and TaqMan Gene Expression Master Mix were used at the manufacturer's recommended concentrations ( Applied Biosystems Inc . ) . Both the telomere DNA SYBR Green and Alu DNA TaqMan assay reactions were performed in quadruplicate for each template DNA and in a final reaction volume of 5 µl in a MicroAmp Optical 384-Well Reaction Plate ( Applied Biosystems Inc . ) . Five ng of genomic DNA or 10 ng of WGA DNA template was used in each reaction . A 7900HT SDS real-time PCR instrument ( Applied Biosystems Inc . ) was used with the default cycling conditions and dissociation curve settings in the instrument control and data acquisition software ( SDS version 2 . 3 , Applied Biosystems Inc . ) . The default settings of RQ Manager version 1 . 2 data analysis software ( Applied Biosystems Inc . ) were used to assign a threshold cycle number to each reaction . Results were exported to Excel ( Microsoft Inc . , Redmond , WA ) for statistical analysis . MCF-7 , HeLa , A431 , Jurkat , and K562 cell line isolated genomic DNA was obtained from BioChain Inc . ( Hayward , CA ) and the expected telomere DNA length was estimated from previous reports [15]–[19] . More specifically , the average length in kilobases ( kb ) reported for each cell line was used as the numerator ( i . e . A431 = 3 . 0 kb; K562 = 6 . 5 kb; HeLa = 9 . 0 kb; and Jurkat = 11 . 5 kb ) , and the average length in the MCF-7 cell line was used as the denominator to calculate a ratio ( relative fold change quantity ) . The resulting relative fold change quantities served as a reference for analysis using the assay developed in the present study . Whole genome amplification ( WGA ) was performed on 35 picograms of genomic DNA using a GenomePlex Single Cell WGA4 Kit according to the manufacturer's instructions ( Sigma Aldrich Inc . , St . Louis , MI ) . WGA DNA was purified using GenElute PCR purification kit according to the manufacturer's instructions ( Sigma Aldrich Inc . ) . Purified WGA DNA was quantified using a Nanodrop 8000 spectrophotometer ( Nanodrop Inc . , Wilmington , DE ) . Relative quantitation ( fold change ) of telomere DNA content was determined using the comparative CT method [31] with telomere DNA representing the “gene of interest” and Alu DNA representing the “internal control” ( i . e . telomere DNA CT−Alu DNA CT = ΔCT ) . Cell line telomere DNA ΔCT values were evaluated with the MCF-7 cell line ΔCT as the reference control . Pearson correlation coefficients were calculated for fold change values experimentally determined from either genomic DNA ( n = 4 ) or WGA DNA ( n = 4 ) with respect to the expected fold change values calculated from telomere DNA lengths reported in the literature [15]–[19] . A Pearson correlation coefficient was also calculated for experimentally determined genomic DNA with respect to WGA DNA . It should be noted that the assay developed in this study cannot be used to determine an absolute measure of telomere length such as might be represented in kb units . The assay is only capable of determining the relative quantity of telomere DNA in one sample compared to another ( fold change ) . DNA from human polar bodies and embryo biopsy WGA was obtained as previously described [32] , [33] , and used for analysis of telomere DNA content . All samples were obtained under Institutional Review Board approval ( WIRB , Olympia , WA ) and with informed patient consent . Samples were specifically selected from cases where both an aneuploid and a euploid 1st polar body , 2nd polar body , or embryo biopsy WGA DNA sample was available from the same patient and IVF treatment cycle , and from embryos with similar morphology ( cell number , fragmentation , and grade ) , in order to allow for paired analysis . All polar bodies were biopsied from oocytes that led to embryos suitable for transfer by conventional morphological assessment . Euploid polar bodies used in this study were always associated with oocytes in which both the 1st and 2nd polar body was euploid . Likewise , aneuploid polar bodies used in this study were always associated with oocytes which produced an aneuploid embryo . Euploid embryo or polar body ΔCT values were used as the reference control for aneuploid embryo or polar body ΔCT values in patient specific pairs . Confirmation of normal distribution of aneuploid or euploid embryo or polar body telomere DNA quantities was evaluated by performing a Shapiro-Wilk W test . Telomere DNA length ( ΔCT ) in polar bodies , blastomeres , trophectoderm biopsies , and arrested embryos was compared for significance using a paired Student's t-test . Blastocyst stage embryo morphology was controlled by selecting samples with identical morphological grade [20] within each patient specific pair . Aneuploid and euploid cleavage stage embryo telomere DNA length ( ΔCT ) was compared using a mixed linear model with fixed effects terms from telomere DNA length ( ΔCT ) versus ploidy state , and a patient random effect term to account for between-patient variability . Cleavage stage embryo telomere DNA lengths were also tested in a mixed linear model against characteristics of morphology , including cell number , fragmentation rate , and embryo grade . Tests were performed with R's nlme package [34] , [35] . Telomere results were analyzed blind to aneuploidy status and vice-versa . Aneuploidy assignments were based on the use of a previously published method involving single nucleotide polymorphism ( SNP ) microarray based copy number analysis [2] . This method has had preclinical validation performed on randomized and blinded single cells from cell lines with previously well characterized aneuploid karyotypes , demonstrating 98 . 6% accuracy and no false positive aneuploidy diagnoses [2] . It has also had clinical validation performed through a prospective randomized non-selection clinical trial demonstrating 100% negative predictive value for the reproductive potential of human embryos [3] . SNP microarray data described in this study have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE25864 ( http://www . ncbi . nlm . nih . gov/geo ) . Twenty one sets of samples including the 1st polar body , 2nd polar body , and blastomere derived from the same oocyte , and 29 sets of samples inluding the 1st polar body , 2nd polar body , and trophectoderm , were evaluated . All samples were included on the basis of chromosomal normalcy as predicted using SNP microarray based analysis described above . Quantities of telomere DNA in the 2nd polar bodies , blastomeres , and trophectoderm were calculated relative to the corresponding first polar body from the same original oocyte . Forty four embryos that arrested at developmental stages equivalent to 44 developmentally competent sibling embryos were evaluated . Quantities of telomere DNA in arrested embryos were calculated relative to the developmentally competent embryos . | Human eggs ( oocytes ) are exceptionally prone to the erroneous acquisition of too few ( monosomy ) or too many ( trisomy ) chromosomes during development ( meiosis ) . In fact , this type of instability , termed aneuploidy , represents the most common genetic cause of miscarriage in pregnant women ( i . e . trisomy 16 ) and developmental delay in newborns ( i . e . Down syndrome from trisomy 21 ) . Although aneuploidy has become a growing problem for women as they delay childbearing into the late thirties , the underlying molecular etiology remains unknown . Since telomere DNA is known to protect the ends of chromosomes from degradation during cell division and is associated with aneuploidy in cancer cells in adults , we tested whether telomere DNA plays a role in aneuploidy development in human oocytes and embryos ( where aneuploidy is much more common ) . We demonstrate that telomere DNA deficiency is indeed associated with aneuploidy in oocytes and early preimplantation ( cleavage ) stage embryos . This association is reversed upon development to late preimplantation ( blastocyst ) stage embryos as a result of telomere DNA elongation . These results indicate that telomere DNA deficiency may cause inappropriate chromosome segregation during human oocyte cell division ( meiosis ) and may serve as a marker for oocytes and embryos that lack the ability to produce healthy children . | [
"Abstract",
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] | [
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] | 2011 | Telomere DNA Deficiency Is Associated with Development of Human Embryonic Aneuploidy |
Filamentous fungus Penicillium oxalicum produces diverse lignocellulolytic enzymes , which are regulated by the combinations of many transcription factors . Here , a single-gene disruptant library for 470 transcription factors was constructed and systematically screened for cellulase production . Twenty transcription factors ( including ClrB , CreA , XlnR , Ace1 , AmyR , and 15 unknown proteins ) were identified to play putative roles in the activation or repression of cellulase synthesis . Most of these regulators have not been characterized in any fungi before . We identified the ClrB , CreA , XlnR , and AmyR transcription factors as critical dose-dependent regulators of cellulase expression , the core regulons of which were identified by analyzing several transcriptomes and/or secretomes . Synergistic and additive modes of combinatorial control of each cellulase gene by these regulatory factors were achieved , and cellulase expression was fine-tuned in a proper and controlled manner . With one of these targets , the expression of the major intracellular β-glucosidase Bgl2 was found to be dependent on ClrB . The Bgl2-deficient background resulted in a substantial gene activation by ClrB and proved to be closely correlated with the relief of repression mediated by CreA and AmyR during cellulase induction . Our results also signify that probing the synergistic and dose-controlled regulation mechanisms of cellulolytic regulators and using it for reconstruction of expression regulation network ( RERN ) may be a promising strategy for cellulolytic fungi to develop enzyme hyper-producers . Based on our data , ClrB was identified as focal point for the synergistic activation regulation of cellulase expression by integrating cellulolytic regulators and their target genes , which refined our understanding of transcriptional-regulatory network as a “seesaw model” in which the coordinated regulation of cellulolytic genes is established by counteracting activators and repressors .
Cellulolytic fungi have an inherent characteristic of cellulose deconstruction and can be used for bioconversion of insoluble plant cell wall polysaccharides into fermentable sugars [1–3] . The highly efficient production of their extracellular hydrolytic enzymes and other synergistic proteins [2 , 4] , such as swollenin [5] , plays a key role in reducing the cost of the biorefinery process [4] . However , incomplete knowledge of transcriptional regulatory networks for cellulolytic fungi has hampered the systematic improvement of cellulase production . These cellulolytic system genes are coordinately but differentially regulated in various cellulase producers [2 , 6] . Further characterization and manipulation of the cellulase regulatory network’s components will allow the rational engineering of cellulolytic fungi for improved enzyme production . Transcriptional regulation of cellulolytic gene expression is central in controlling the carbohydrate hydrolysis process [6] , and several positive or negative transcriptional factors of these degradative pathways were identified , such as the regulators encoded by the creA/cre1/cre-1 [7–9] , xyr1/xlnr/xlr-1 [10–12] , aceI [13] , aceII [14] , ace3 [15] , clrB/clr-2/manR [16 , 17] , and bglR [18] genes . The overexpression of these activators or deletion of some repressors is efficient in enhancing the cellulase and hemicellulase expressions [19 , 20] . However , the degree of cellulase induction differentially responds to these diverse regulator abundances . The transcription factor CreA , an ortholog of Migl from Saccharomyces cerevisiae [21] , is a pivotal regulator mediating carbon catabolite repression ( CCR ) in filamentous fungi [7–9] , and its deletion results in the obvious increase of cellulase expression and secretion . A transcriptional regulatory cascade that controls the xylanolytic genes between CreA and XlnR is also built in Aspergillus niger in response to preferred carbon sources [22] . In addition , the cre1 deletion mutant shows a conidiation formation defect [23] . Two novel zinc binuclear cluster transcription factors ( CLR-1 and CLR-2 ) required for growth and enzymatic activity on cellulose were identified in Neurospora crassa [17] . The constitutive expression of clr-2 by the control of the promoter from ccg-1 is sufficient to drive cellulase gene expression when cultures are subjected to starvation [20] . In addition , the β-glucosidase regulator BglR and cellulase expression activator AceII were identified in Trichoderma reesei [18] , but their orthologous encoding genes were absent in the Penicillium oxalicum genome [24] . Currently , the abilities to tune the expression abundance of just one transcription factor , as noted above , have profound effects on cellulase expression in these cellulolytic fungi [18–20 , 25] . However , whether such a simple mechanism could operate in the context of cellulolytic regulator combinations , including these characterized and novel transcription factors , remains unclear . The P . oxalicum wild-type strain 114–2 was isolated from the soil in China more than 30 years ago [26] . A partially derepressed mutant JU-A10 , which shows cellulolytic activity that is more than three times higher than that of its parent strain 114–2 , was obtained after many rounds of mutagenesis and screening [26] . The mutant JU-A10 was further mutated to a cellulase hyper-producer JU-A10-T [26] and has been utilized in industrial processes for years . The clear genetic background provided by genome sequencing facilitated the rational improvement of these strains to enhance the expression of cellulolytic enzymes [24] . Currently , several structural genes associated with cellulase expression have been studied . The deletion of gene bgl2 ( encoding the major intracellular β-glucosidase ) [27] or PDE_01641 ( the ortholog of N . crassa NCU05137 ) [28] results in the increase of cellulase production in P . oxalicum . In addition , three cellodextrin transporters ( CdtC , CdtD , and CdtG ) were identified , and their overexpression obviously increases the extracellular cellobiohydrolase activities [29] . However , evidence from diverse cellulolytic fungi showed that engineering cellulolytic transcription factors might have more efficacy in upregulating cellulase expression than merely manipulating the expression of structural genes for the major cellulases [1 , 19 , 20 , 30] . Subsequent studies to identify several specific regulators and their roles in regulating cellulase gene expression were conceptually appealing in cellulolytic fungi . In this study , twenty transcription factors putatively involved in cellulase expression pathways were identified from a single-gene disruptant library . The single overexpression or deletion of these genes triggered cellulase expression to varying degrees , and synergistic and tunable cellulase expressions were observed in the combinations of the identified individual transcription factors . Furthermore , the responsiveness of the induction of cellulase expression by activator and relief from potential carbon catabolite repression to the internal signal cascades by the lack of the major intracellular β-glucosidase Bgl2 was also well-established . The suggested mechanisms of synergistic effects on cellulase expression might be general properties in cellulolytic fungi and broadly enable engineering strategies for the protein hyper-producers .
To decipher the transcriptional-regulatory network that governs cellulase expression in P . oxalicum , we first sought to identify the transcription factors ( TF ) that play roles in cellulolytic gene expression systematically . A total of 522 genes encoding sequence-specific regulators were predicted according to the protein sequence domain [24] . For the amplification of the flanking sequences of these TF-disrupting cassettes , primers were designed to meet the following criteria: GC-content 45%–60% , Tm: 50°C –60°C , and a length of 20 base pairs . The lengths of the 5’ and 3’ flanking regions ranged from 1 . 0 kb to 1 . 5 kb for each gene . The chimeric primers ( S1 Table ) for the amplification of upstream and downstream flanking fragments carried 25 bases of homologous sequence overlapping with the ends of ptra marker sequence [31] . A final fragment that contains target gene flanking sequences surrounding ptra was created by double-joint PCR [32] and transformed into the P . oxalicum Δpku70 mutant via protoplast transformation [33] . Pku70 and its homologs are involved in the non-homologous end joining ( NHEJ ) repair of double-strand breaks in diverse eukaryotes [32] . Considering the high homologous recombination frequency in the pku70 mutant ( NHEJ-deficient background ) [33] , we selected three colonies per gene from these resulting transformants . The conidia from these primary transformants were purified by repeating the mono-spore isolation twice on the pyrithiamine resistance plates to obtain homokaryotic knockout mutants . The transcription factor gene replacement with ptra was verified by PCR-based screening . We found that the use of unpurified final amplicon of deletion cassettes resulted in almost 90% success in deleting the targeting genes in the Δpku70 mutant . Finally , a transcription factor mutant set , which bears a single deletion for 470 transcription factor genes in P . oxalicum , was successfully constructed . The transcription factor deletion strains were screened and initially characterized for cellulose deconstruction on cellulose plates . According to the halos produced by the transformants on cellulose plates , 20 transcription factors that displayed putative roles in cellulase production were identified ( Table 1 ) . Twelve deletion strains exhibited increased cellulase activities and eight deletion strains exhibited decreased cellulase activities . These transcription factors represented negative and positive regulators of cellulose deconstruction , respectively . None of these transcription factors has been well characterized at the molecular level in P . oxalicum . Among these transcription factor genes , PDE_05999 , PDE_03168 , PDE_07674 and PDE_03964 were previously known and encoded as ClrB , CreA , XlnR and AmyR regulators , respectively . The strongest effects on cellulose deconstruction observed in ΔclrB , ΔcreA , ΔxlnR and ΔamyR mutants indicated that these three genes encode the major regulators of some lignocellulolytic enzymes ( Fig 1A ) . To clarify the mechanisms of lignocellulose deconstruction in P . oxalicum , we initially focused on the characterization of these central lignocellulolytic regulators ClrB , CreA , XlnR , and AmyR , and then identified their target genes involved in plant cell wall deconstruction . Up to date , mating assays in P . oxalicum have not been performed to remove the pku70 deletion through the crossing approach as T . reesei [34] and N . crassa [35] strains . We therefore constructed the corresponding mutants in the wild-type strain through the conventional transformation approach . Genomic DNA from putative transformants was analyzed by q-PCR ( quantitative-PCR ) and/or Southern blot ( S1 Fig ) , and these transformants in which a single copy integration at the only a transcription factor gene locus were selected and further characterized . The function of clrB ( PDE_05999 ) was identified independently in our lab . The regulator protein sequence has 39% ( BioEdit , E Value = 1e-131 ) of identity to the homolog of N . crassa and 56% of identify ( E Value = 0 ) to that of A . nidulans [17] ( Table 1 ) . The P . oxalicum clrB gene encodes a protein of 780 amino acid residues . Two introns of 76 and 64 nucleotides , which follow the characteristics of the clrB homolog in N . crassa , were identified [17] . The deduced transcription factor ClrB contains normal characteristics of Zn ( II ) 2Cys6 binuclear cluster DNA binding motif near the N-terminus ( residues 40–71 ) and the middle homology domain ( residues 351–453 ) that is related to fungal specific transcription factors , including XlnR/XYR1 [10 , 11] and yeast regulatory protein GAL4 [36] . In this study , several putative cellulolytic transcription factors , such as PDE_03268 , PDE_03964 , and PDE_09881 , also contain normal characteristics of these zinc binuclear cluster proteins ( Table 1 ) . To investigate the influence of ClrB on cellulase expression , we constructed a ΔclrB strain from P . oxalicum wild-type strain 114–2 ( CGMCC 5302 ) . The ΔclrB strain displayed significantly reduced growth on cellulose plate , but identical phenotype on glucose , xylan , or potato dextrose agar ( PDA ) plates relative to wild-type strain ( Fig 1A ) . The ΔclrB mutant exhibited dramatically reduced cellulase activities when compared with the wild-type strain ( Fig 1B–1D ) , similar to the recent findings with clr-2/clrB in N . crassa/A . nidulans [17] . Northern blot analysis was used to study the cellulolytic gene cbh1 ( PDE_07945 ) , eg2 ( PDE_09226 ) , and xyn1 ( PDE_08094 ) expressions in the wild-type and ΔclrB strains grown on cellulose . Fig 2A shows that the mRNA levels of cbh1 and eg2 in the ΔclrB mutant significantly decreased and could hardly be detected . As a result , a slight decrease in the xyn1 transcription level was observed in the ΔclrB strain compared with that in the wild-type control , while a low transient increase expression of xyn1 was observed at the fourth hour following the shift ( Fig 2A ) . The introduction of a wild-type copy of clrB at the clrB locus ( strain RclrB ) completely restored the growth defects of the ΔclrB mutant in cellulose , as well as the cellulolytic enzyme activities of culture supernatants ( Fig 1B–1D ) . These results demonstrated that ClrB might be in the central part of the transcriptional-regulatory network of cellulase expression by controlling the transcription of cellulolytic genes . The P . oxalicum genome contains approximately 10 , 000 genes and is predicted to encode 18 cellulases , 51 hemicellulases , and other cellulolytic enzymes involved in plant biomass degradation [24] . Therefore , to build a comprehensive picture by which P . oxalicum responds to cellulose , we adopted RNA-Seq to measure genome-wide mRNA abundances in the P . oxalicum wild-type strain and ΔclrB mutant when exposed to Vogel’s minimal medium containing 2% cellulose for 4 hours . The three biological replicates of each strains showed a high Pearson correlation ( S2 Fig ) . A total of 224 genes were differentially expressed between the ΔclrB and the wild-type strains on cellulose ( S2 and S3 Tables ) . Of these genes , 103 genes showed lower transcription levels in the ΔclrB mutant than in the wild-type strain ( S2 Table ) . These genes of decreased expression in clrB regulon were subjected to gene ontology enrichment analysis . Percentages of the genes distributed within each functional category are shown in Fig 3 . Among these downregulated genes , 24 genes encoding transporters were enriched , including PDE_00607 ( p = 7 . 99e-173 ) , encoding cellodextrin transporter CdtC [29] , and PDE_06576 ( p = 2 . 96e-58 ) , encoding putative maltose permease , which suggests that ClrB might also be involved in the cellodextrin and maltose metabolisms . In total , 32 genes encoding carbohydrate-active enzymes ( CAZymes ) were included , including 9 main cellulase genes and two of 11 β-glucosidases PDE_00579 ( p = 1 . 98e-109 ) and PDE_04251 ( p = 2 . 39e-42 ) ( S2 Table ) , which demonstrates that the genes involved in plant cell wall deconstruction were significantly enriched . Only 6 of the 51 hemicellulase genes showed obvious reduction in transcription levels in the absence of ClrB , including PDE_02101 ( p = 0 . 0033 ) , PDE_06649 ( p = 0 . 00046 ) , PDE_01302 ( p = 5 . 63e-12 ) , PDE_09710 ( p = 3 . 09e-09 ) , PDE_05998 ( p = 1 . 36e-37 ) , and PDE_06023 ( p = 1 . 87e-41 ) ( S2 Table ) . These data demonstrated that ClrB might play a significant role in activating cellulase gene expression but has differential regulatory effects on cellulolytic and xylanolytic genes in the early inducing phase on cellulose ( 4 hours post-transfer ) . In total , 121 genes showed higher transcription levels in the ΔclrB mutant than in the wild-type strain ( S3 Table ) . Among these upregulated genes , only 7 genes encoding CAZy proteins , including two predicted hemicellulase genes PDE_07585 ( p = 0 . 02 ) and PDE_08238 ( p = 0 . 013 ) , showed altered expressions . No cellulase , β-glucosidase , and xylanase genes were included . Genes in this set were enriched in oxidoreductase activity ( p = 8 . 1e-6 ) . To assess whether the overexpression of clrB enhanced the cellulase expression , two clrB overexpression recombinants were constructed under the control of its native promoter ( strain OEclrB ) and the A . nidulans gpdA promoter ( strain gpdA ( p ) ::clrB ) in P . oxalicum wild-type strain [37] , respectively . Both the OEclrB and gpdA ( p ) ::clrB strains showed varied halos on 1% cellulose plates ( Fig 1A ) and showed almost 2 . 5- and 4 . 1-fold increases in filter paper enzyme activity ( FPA ) , 2 . 5- and 4 . 0-fold increases in cellobiohydrolase ( pNPCase ) activity , and 8 . 7- and 16 . 5-fold increases in endoglucanase ( CMCase ) activity when grown on cellulose for 48 hours , respectively ( Fig 1B–1E ) . Northern blot analyses also showed that the mRNA levels of cbh1 and eg2 in the gpdA ( p ) ::clrB mutant were much higher than those in the wild-type strain on cellulose ( Fig 2A ) . To further test whether cellulase production was tightly responsive to clrB transcriptional abundance , we reconstructed the PDE_02864 ( p ) ::clrB expression cassette in which the clrB open reading frames and 3’ untranslated region were under the control of the novel promoter from the PDE_02864 encoding 40S ribosomal protein S8 . The gpdA ( p ) ::clrB-PDE_02864 ( p ) ::clrB strain was constructed and showed even higher cellulase expression than those in the single gpdA ( p ) ::clrB and PDE_02864 ( p ) ::clrB mutants on cellulose ( S3 Fig ) . These results demonstrated that dose effect of clrB transcriptional abundance is important for the high expression for cellulases , and tunable cellulase expression may be controlled by the ClrB concentration under cellulose conditions . In addition to ClrB , P . oxalicum XlnR is another cellulolytic activator in the Zn2Cys6 binuclear cluster motif superfamily that was identified previously along with the homologous N . crassa XLR-1 [12] and T . reesei XYR1 [10] . To demonstrate whether XlnR showed a differential role in cellulolytic gene expression regulation , the ΔxlnR and gpdA ( p ) ::xlnR mutants with constitutive expression for xlnR under the control of the A . nidulans gpdA promoter in the wild-type strain were constructed . The ΔxlnR mutant showed a slightly decreased growth on both cellulose and xylan media , but not on glucose or PDA plates ( Fig 1A ) . Northern blot results also showed that weak expressions for cbh1 and eg2 transcripts and invisible xyn1 were observed in the ΔxlnR strain compared with that in the wild-type strain in the cellulose-containing medium ( Fig 2A ) . These data indicated that P . oxalicum XlnR is a general transcription factor that regulates cellulase and xylanase expressions but not like T . reesei XYR1 , which is the essential regulator that governed both cellulolytic and xylanolytic gene expressions [10] . To further analyze whether synergistic or additive effects for these two major cellulolytic activators ClrB and XlnR existed , the clrB was also overexpressed in the gpdA ( p ) ::xlnR mutant , and the gpdA ( p ) ::clrB-gpdA ( p ) ::xlnR mutant that contains simultaneously overexpressed ClrB and XlnR was obtained . The gpdA ( p ) ::clrB-gpdA ( p ) ::xlnR strain showed 1 . 3- , 1 . 7- and 2 . 1-fold increased expressions in FPA , xylanase , and pNPCase activities compared with that in the gpdA ( p ) ::clrB strain after shift to cellulose for 96 hours ( Fig 4A–4C ) , but decreased production in the pNPGase activity ( Fig 4D ) . Conversely , lack of both ClrB and XlnR ( ΔclrB-ΔxlnR ) led to a greater abrogation of cellulase and xylanase expression than each absence mutation under cellulose growth conditions ( Figs 1B and 2A ) . These data revealed that ClrB and XlnR had additive effects on positively regulating the cellulase and hemicellulase gene expressions , and the high-abundance transcripts of ClrB and XlnR could facilitate the induction of cellulase expression under cellulose growth conditions . PDE_03168 ( CreA ) , as a homolog of the major carbon catabolite repressors in phylogenetically diverse fungi [7–9] , played a negative role in the degradation of plant cell wall polymers . In this study , the ΔcreA mutant of P . oxalicum consumed cellulose faster than the wild-type strain ( Fig 1A ) , similar to the findings in T . reesei cre1 [23] or N . crassa cre-1 deletion strains [38] . The ΔcreA strain grown on cellulose exhibited significantly increased cellulase activities compared with its parent strain , and showed almost 7 . 2- , 2 . 2- , 8 . 0- , and 4 . 4-fold increases in FPA , pNPCase , CMCase , and xylanase activities after shifting to cellulose for 96 hours , respectively ( Fig 1B–1E ) . The ΔcreA mutant exhibited higher steady state amounts of cbh1 and eg2 mRNA than that in the wild-type strain by Northern blot analysis ( Fig 2A ) and q-PCR experiments ( Fig 2B ) under cellulose growth conditions . However , the gpdA ( p ) ::creA mutant that contains an overexpression of creA showed significantly lower cellulase gene expression than that in the wild-type strain when grown on cellulose ( Fig 2B ) , which indicates that cellulolytic gene expression under CCR mediated by the CreA in P . oxalicum was responsive to the creA transcript abundances . Although the ΔcreA mutant produced higher cellulase expression than its parental strain P . oxalicum 114–2 , we could not entirely exclude the possibilities that the increase of cellulolytic enzyme in ΔcreA mutant might be the partial cause of the enhancement of cellulolytic activators for ClrB and XlnR . To test these hypotheses , q-PCR was performed and the expression levels for clrB and xlnR in the ΔcreA mutant showed near to that in the wild-type strain on cellulose but obvious increase under glucose-repressing conditions ( Fig 5A ) . These data indicated that CreA repressing the expression of clrB and xlnR was associated with the carbon source used in the medium , and CreA and ClrB , as well as CreA and XlnR might form a transcriptional cascade that regulates the cellulolytic gene expression in P . oxalicum . Previously , we reported that a cellulase hyper-producer P . oxalicum mutant JU-A10 , which bears a shift mutation at creA locus , has an extremely severe effect on morphology , including short and thicker hyphae [1] . The creA deletion in P . oxalicum wild-type strain 114–2 showed smaller colonies and reduced hyphae growth ( Fig 1A ) . Under the microscope , the ΔcreA mutant on glucose plates exhibited considerably robust hyphae ( Fig 5B ) . Thus , some hyphae morphology mutation in P . oxalicum mutant JU-A10 [1] might be specific results caused by the shift mutation at the creA location in the JU-A10 mutant by chemical mutagenesis . Similarly , the T . reesei Δcre1 mutant also displayed shorter and more robust hyphae than its parental strain [23] . We hypothesized that the morphology mutation caused by creA homologs may be general in filamentous fungi . However , whether hyphae morphology mutation was related to the increase in protein production and cellulase expression in these mutants containing deletion of creA needs to be further investigated in the future . Cellulase genes were responsive to both major opposing regulators ClrB and CreA . First , whether the low cellulase expression exhibited by the ΔclrB mutant could be recovered when knocking out the CreA encoding gene remains unknown . To bypass this problem , we deleted the creA in the ΔclrB strain and obtained the ΔcreA-ΔclrB mutant . Northern blot , presented in Fig 2A , shows that ΔcreA-ΔclrB mutant exhibited a slightly higher amount of cbh1 mRNA than that in the ΔclrB strain . The ΔcreA-ΔclrB mutant also showed 3 . 1- , 0 . 5- , 3 . 1- , and 1 . 8-fold increases in FPA , pNPCase , CMCase , and xylanase activities relative to wild-type strain after shifting to cellulose for 96 hours , respectively ( Fig 1B ) . In contrast to the ΔclrB mutant , the ΔcreA-ΔclrB strain produced visible halo in the cellulose medium plate when cultured for 9 days ( S4 Fig ) . These data indicated that the lack of CreA partially rescued the cellulase expression defect in the ΔclrB mutant . Conversely , the gpdA ( p ) ::creA-gpdA ( p ) ::clrB strain that contains simultaneous overexpressions of CreA and ClrB also showed increases in the transcription expression levels for most of the cellulase genes compared with that in the gpdA ( p ) ::creA mutant ( Fig 2B ) . The strong cellulase production observed in the ΔcreA and gpdA ( p ) ::clrB mutants indicated that these two genes encoded the major transcription factors that oppositely regulate cellulolytic gene expression . Although , the triple-mutant RE-10 ( Δbgl2-ΔcreA-gpdA ( p ) ::clrB ) was recently obtained [39] , it is still not known why high cellulase expression in which occurs . Therefore , we deleted creA in the gpdA ( p ) ::clrB strain to determine whether their functions in cellulase production were synergistic . To our knowledge , no reports have been described to perform this to determine their synergistic effects on cellulase induction expression in other cellulolytic fungi . Interestingly , the strain with the combination of creA deletion and overexpression of clrB displayed a much larger halo around its colony than that of the ΔcreA and gpdA ( p ) ::clrB mutants on cellulose plate ( Fig 1A ) . The differences in the FPA , pNPCase , CMCase , and xylanase activities were even more pronounced ( 11 . 6- , 11 . 6- , 58 . 6- , and 15 . 9-fold higher , respectively ) between the ΔcreA-gpdA ( p ) ::clrB and wild-type strains ( Fig 1B ) . The ΔcreA-gpdA ( p ) ::clrB mutant exhibited higher steady-state amounts of cbh1 , eg2 , and xyn1 mRNA than that of the ΔcreA and gpdA ( p ) ::clrB strains under cellulose growth conditions by Northern blot analysis ( Fig 2A ) . Q-PCR results also indicated that all three cellobiohydrolase and 10 endoglucanase genes showed strong synergistic increases in transcription levels with the exception of five endoglucanase genes for PDE_009267 , PDE_00698 , PDE_03711 , PDE_09014 , and PDE_06768 when grown on cellulose ( Fig 2B ) . In addition , three of the 11 β-glucosidase genes ( PDE_00579 , PDE_04251 , and PDE_04859 ) showed similar induction patterns as the above thirteen cellulase genes in the ΔcreA-gpdA ( p ) ::clrB mutant on cellulose ( S5 Fig ) . These data showed that the simultaneous overexpression of ClrB and lack of CreA have synergistic effects on cellulolytic and xylanolytic gene expressions . To build a comprehensive picture by which P . oxalicum responds to cellulose , the genome-wide mRNA abundance in P . oxalicum wild-type strain was first measured on Vogel’s medium with no carbon or 2% glucose for 4 hours as an alternative reference . A total of 581 genes were differentially expressed between Avicel and no carbon cultures at the fourth hour ( |log2 ( fold change ) | > 1 and probability ≥ 0 . 8 ) , 155 and 117 of which showed greater and lower expression levels on cellulose than that under either glucose growth or no carbon conditions , respectively ( S4 and S5 Tables ) . We refer to this gene set , including 272 genes , as a “cellulose regulon” for P . oxalicum . The cellulose regulon encompassed 10 of 18 predicted cellulase genes , 30 of 51 predicted hemicellulase genes , and 3 of 11 predicted β-glucosidase genes PDE_02736 ( p = 1 . 65e-07 ) , PDE_00579 ( p = 9 . 24e-174 ) , and PDE_04251 ( p = 1 . 02e-80 ) ( S4 and S5 Tables ) . To further elucidate the regulation mechanisms for the opposing regulators CreA and ClrB in the gene expression of P . oxalicum under cellulose growth conditions , three biological replicates of ΔcreA , ΔcreA-ΔclrB , and ΔcreA-gpdA ( p ) ::clrB mutants were also subjected to transcriptional profiling at the same condition . The three biological replicates of each mutant showed a high Pearson correlation ( S6 Fig ) and demonstrated the reliability of RNA-Seq . The gene ontology enrichment analyses for these 117 decreased genes of cellulose regulon showed no statistically significant results with the threshold ( FDR < 0 . 05 ) . The hierarchical clustering of expression patterns for the 155 increased genes of cellulose regulon in the wild-type strain and the ΔclrB , ΔcreA , ΔcreA-ΔclrB , and ΔcreA-gpdA ( p ) ::clrB mutants displayed four classes of genes with similar expression patterns . A heat map that depicts the relative expression levels of selected genes from groups 1 to 4 is shown in Fig 6A . Group 1 consisted of 91 genes ( S4 Table ) . Most of the genes in this set exhibited strong synergistic induction effects on transcriptional expression in the ΔcreA-gpdA ( p ) ::clrB mutant ( Fig 6A ) . In this cluster , we detected a significant enrichment of genes involved in hydrolase activities ( p = 1 . 5e-30 ) . This group included 9 of 18 cellulase genes ( PDE_00507 , PDE_09969 , PDE_07929 , PDE_07928 , PDE_05633 , PDE_09226 , PDE_07124 , PDE_07945 , and PDE_01261 ) , two β-glucosidase genes ( PDE_00579 and PDE_04251 ) , and three sugar transporter genes for cellodextrin transport-1 ( PDE_00607 ) , major facilitator superfamily ( MFS ) maltose transporter ( PDE_06576 ) and monosaccharide transporter ( PDE_04857 ) , all of which showed significantly low levels in the ΔcreA-ΔclrB strain versus ΔcreA mutant , but similar transcription activities between the ΔcreA-ΔclrB and ΔclrB mutants , which indicates that the expression levels for these enriched cellulolytic genes were repressed by CreA but ClrB dependent under inducing conditions . Cellodextrin transport-2 genes ( PDE_007257 and PDE_00753 ) were also CreA repressed and ClrB induced but not ClrB dependent . A total of 16 out of the 51 hemicellulase genes ( PDE_06649 , PDE_02101 , PDE_00016 , PDE_04478 , PDE_01302 , PDE_09278 , PDE_06023 , PDE_04182 , PDE_08094 , PDE_02514 , PDE_00752 , PDE_09710 , PDE_06067 , PDE_05998 , PDE_02418 , and PDE_07897 ) were enriched in this subset , and most of which were significantly repressed by CreA , but only four genes ( PDE_05998 , PDE_06023 , PDE_01302 , and PDE_09710 ) were ClrB induced . Noticeably , the expression levels for PDE_05998 ( beta-mannosidase ) and PDE_06023 ( beta-1 , 4-mannanase ) were ClrB dependent . Interestingly , three genes involved in starch degradation , encoding starch binding domain-containing protein ( PDE_01354 ) , glucoamylase ( PDE_05527 ) , and α-amylase ( PDE_01201 ) were also downregulated in the ΔclrB mutant and upregulated in the ΔcreA mutant , which suggests that the expression for these amylase genes were tightly co-regulated with the cellulase genes under cellulose growth conditions . A significant enrichment of genes ( PDE_09832 , PDE_04496 , and PDE_09715 ) involved in the sporulation process ( p = 8 . 2e-5 ) was also detected . In addition , 21 genes that encode hypothetical proteins were within this dataset ( 23% ) . The above data suggested that this cluster might represent the most central components associated with cellulose deconstruction , which indicates that this large synergistic activation by engineering CreA and ClrB might be specific to these cellulolytic target genes . Group 2 consisted of 27 genes ( S4 Table ) . The expression levels of this set were partially induced in the ΔclrB mutant and repressed in the ΔcreA mutant , but were significantly downregulated in the ΔcreA-gpdA ( p ) ::clrB strain compared with that in the wild-type strain ( Fig 6A ) . Although GO-term analyses revealed that no statistically significant results with the threshold ( FDR < 0 . 05 ) , genes that encode β-glucosidase ( Bgl1 , PDE_02736 ) , endo-β-1 , 4-glucanase ( PDE_09267 ) , and two β-xylosidases ( PDE_07334 and PDE_08037 ) were included in this dataset . Group 3 consisted of 23 genes ( S4 Table ) , and the expression levels of which were partially repressed in the ΔcreA and ΔclrB mutants , and were cumulatively repressed in the ΔcreA-ΔclrB mutant , but were partially recovered in the ΔcreA-gpdA ( p ) ::clrB mutant ( Fig 6A ) . The GO-term analyses of the dataset of 23 genes showed a significant enrichment in the carbohydrate metabolic process ( p = 3 . 6e-7 ) . Five hemicellulase genes that encode PDE_06306 , PDE_03572 , PDE_07080 , PDE_03573 , and PDE_08036 and one α-glucosidase ( PDE_00400 ) were also within this group . Group 4 consisted of 13 genes ( S4 Table ) . The group genes were slightly induced in the ΔclrB mutant and showed no induction in the ΔcreA mutant , but were significantly induced in the ΔcreA-ΔclrB mutant compared with that in the wild-type strain ( Fig 6A ) . One gene that encodes β-xylosidase ( PDE_00049 ) was within this dataset , and eight genes encoded hypothetical proteins . Secreted proteins are expected to play a crucial role in cellulose degradation because of the nature of cellulolytic system for the deconstruction of plant cell walls . However , whether the cellulolytic protein concentration in secretome potentially correlates with their relative mRNA levels , and how this induction stimulus under cellulose conditions causes P . oxalicum to manipulate its secretome to facilitate cellulose degradation remain unclear . Thus , extensive secretome surveys using label-free LC-MS/MS were conducted to analyze the secretomes under cellulose conditions systematically . Herein , a supernatant from 4-day old wild-type culture grown on cellulose was digested with trypsin and analyzed by LC-MS-MS . For this purpose , 157 nonredundant proteins ( P-value < 0 . 01 ) were identified based on a single or several peptide entries from the P . oxalicum protein database , the pI of which was concentrated in a pH range of 4–7 , and 86 proteins were predicted to be secreted based on SignalP computational analysis ( SignalP 4 . 1 Server , http://www . cbs . dtu . dk/services/SignalP/ ) ( S6 Table ) . Proteins with predicted activities on carbohydrates in the P . oxalicum wild-type secretome dataset existed , including 10 of 18 predicted cellulases , one β-glucosidase Bgl1 , and 10 of 51 predicted hemicellulases ( S6 Table ) . Subsequently , a question was posted with regard to the extent to which ClrB can be attributed to the regulation of extracellular protein abundances in the cellulolytic system . The total protein in the ΔclrB mutant culture supernatant was only 30% of that in the wild-type strain ( Fig 6B ) . To characterize the secretome changes in response to the crucial regulator ClrB further , a total of 104 predicted secreted proteins in the gpdA ( p ) ::clrB strain ( S6 Table ) and 61 predicted secreted proteins in the ΔclrB mutant ( S6 Table ) were identified after the shift to 2% cellulose for 4 days , respectively . These observations demonstrate that ClrB significantly increased the number of extracellular proteins on cellulose . The ΔclrB secretome dataset under cellulose growth condtions included only 5 of 18 predicted cellulases and 5 of 51 predicted hemicellulases in P . oxalicum genome . The gpdA ( p ) ::clrB secretome dataset included 13 of 18 predicted cellulases and 10 of 51 predicted hemicellulases . A comparison between the secretomes of ΔclrB and those of gpdA ( p ) ::clrB grown on cellulose showed that only 49 proteins overlapped ( S6 Table ) , including 5 of 18 cellulases and 4 of 51 hemicellulases . These data indicate that ClrB enhanced various cellulolytic enzymes , including their secretion strength . More importantly , the observed changes of cellulolytic proteins in ΔclrB and gpdA ( p ) ::clrB strains highly correlated with their corresponding mRNA abundances and broadly mirrored the ClrB-specific positive roles for the transcript expression of cellulolytic genes ( S6 Table ) . Concurrently , to evaluate the CreA-influenced extracellular proteins , we first used sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) to analyze the secretomes of ΔcreA and gpdA ( p ) ::creA cultures under cellulose growth conditions ( Fig 6B ) . The protein pattern of ΔcreA mutant showed more bands than that of gpdA ( p ) ::creA mutant on SDS-PAGE ( Fig 6B ) . The total protein concentration in ΔcreA mutant culture supernatant was 2 . 6-fold higher than that in the wild-type strain ( Fig 6B ) . We further investigated the extracellular proteins influenced by CreA by adopting the label-free LC-MS/MS analysis to identify and quantify the proteins . A total of 85 and 30 predicted secretion proteins in ΔcreA and gpdA ( p ) ::creA mutants were identified when grown on cellulose ( S6 Table ) , respectively . The SDS-PAGE and LC-MS/MS analysis results for culture secretomes revealed that protein secretion , including protein abundance and distribution , was dramatically repressed by CreA under cellulose growth conditions . The secretome of ΔcreA-gpdA ( p ) ::clrB mutant was investigated under cellulose conditions to further identify the regulon with ClrB and CreA synergistic effects at the secretome levels . The total amount of the secreted protein in ΔcreA-gpdA ( p ) ::clrB culture supernatant displayed a 7 . 5-fold increase compared with that in the wild-type strain ( Fig 6B ) . Label-free LC-MS/MS analysis was used , and 97 predicted secretion proteins were identified in ΔcreA-gpdA ( p ) ::clrB mutant ( S6 Table ) . These proteins included 10 of 18 cellulases , and 15 of 51 hemicellulases . No β-glucosidase was detected in ΔcreA-gpdA ( p ) ::clrB mutant . To assess differences more accurately in protein distribution , the secretomes from ΔclrB , gpdA ( p ) ::clrB , ΔcreA , gpdA ( p ) ::creA , and ΔcreA-gpdA ( p ) ::clrB mutants were combined to locate targets that were the basal components in P . oxalicum secretomes under cellulose growth conditions ( Fig 6C and S6 Table ) . In these datasets , we identified that 25 proteins overlapped ( S6 Table ) , including two cellobiohydrolases ( i . e . , PDE_07945 and PDE_07124 ) , three endoglucanases ( i . e . , PDE_09969 , PDE_07929 , and PDE_09226 ) , four hemicellulases ( i . e . , PDE_02101 , PDE_06023 , PDE_04182 , and PDE_08094 ) , two extracellular membrane proteins ( i . e . , PDE_08075 and PDE_02536 ) that contain common in fungal extracellular membranes domain , three amylases ( i . e . , PDE_01201 ( alpha-amylase Amy13A ) , PDE_01354 ( protein with starch binding domain ) , and PDE_09417 ( glucoamylase GluA/Amy15A ) ) , and five hypothetical proteins ( i . e . , PDE_03934 , PDE_06089 , PDE_07106 , PDE_09289 , and PDE_00667 ) . All these proteins existed in the wild-type strain secretome under the same culture condition ( S6 Table ) . After a 4-h shift from no carbon source , the transcription expression levels of 14 of these proteins increased in cellulose versus those in glucose ( S6 Table ) . The above data imply that β-glucosidase gene bgl2 ( PDE_00579 ) was the major object regulated by ClrB and CreA at the level of transcription ( S5 Fig ) . However , lack of CreA in the ΔclrB mutant could not recapitulate bgl2 expression level ( S5 Fig ) , suggesting that bgl2 expression level was strictly ClrB-dependent under induction conditions . Considering the upregulation of cellulolytic genes in Δbgl2 mutant [27] , we speculated that the expression levels for cellulolytic genes were further enhanced via the overexpression of clrB or deletion of creA in a bgl2 deletion background . In support of this hypothesis , we first constructed Δbgl2-gpdA ( p ) ::clrB and Δbgl2-ΔcreA mutants . The cellulase gene transcription levels and cellulase activities , which were greater than those in each single mutation strain on cellulose ( Fig 7A and 7B , and S7A and S7B Fig ) , as well as the Δbgl2-ΔcreA strains exhibited even more cellulase productions than Δbgl2-gpdA ( p ) ::clrB mutant on cellulose ( Fig 7A and 7B , and S7A and S7B Fig ) . These results suggest that the decrease of intracellular β-glucosidase activity may facilitate the transcriptional induction of cellulolytic genes . Cellulase gene expression depends on the presence of the inducers and on the positive regulation of activators in cellulolytic fungi [2] . Recent studies indicated that the constitutive expressions of N . crassa clr-2 [20] and T . reesei xyr1 [40] could recapitulate the response to cellulose when incubated without carbon . To assess whether the constitutive expression of clrB , deletions of bgl2 or creA , or combination of these genetic manipulations was sufficient for the induction of cellulase genes independent of inducers , the cellulase expression levels in Δbgl2 , gpdA ( p ) ::clrB , ΔcreA , Δbgl2-gpdA ( p ) ::clrB , Δbgl2-ΔcreA , and Δbgl2-gpdA ( p ) ::clrB-ΔcreA mutants were evaluated as opposed to the wild-type strain when cultures were shifted from a glucose medium to a carbon-free medium for 4 hours . The findings revealed that Δbgl2-gpdA ( p ) ::clrB mutant exhibited even more transcriptional abundances on carbon-free medium than on cellulose ( Fig 7C and S7C Fig ) , whereas the “starvation response” for cellulase expression also occurred in Δbgl2-ΔcreA and RE-10 mutants ( Fig 7C and S7C Fig ) . However , such a response was significantly low compared with that in Δbgl2-gpdA ( p ) ::clrB mutant ( Fig 7C and S7C Fig ) . Consistent with these results , the pNPCase and CMCase activities were more than 10-fold higher in Δbgl2-gpdA ( p ) ::clrB strain than those in each single mutant and wild-type strain under carbon-free conditions ( S8A and S8B Fig ) . The creA , amyR and xlnR transcription abundances in Δbgl2-gpdA ( p ) ::clrB , Δbgl2-ΔcreA , Δbgl2-gpdA ( p ) ::clrB-ΔcreA and wild-type strains were assayed to test whether CreA , AmyR and XlnR mediated the synergistic induction in Δbgl2-gpdA ( p ) ::clrB strain when subjected to starvation . The findings indicated that amyR had a 7 . 7-fold decrease , whereas creA had a 4 . 3-fold increase and clrB had a 17 . 5-fold increase in Δbgl2-gpdA ( p ) ::clrB mutant versus that in the wild-type strain ( Fig 7D ) . These data signify that AmyR may share a key role in the “starvation response” for cellulolytic genes and provide a novel insight into the cellulase gene regulatory mechanisms during energy abstinence . Given the dose-controlled or additive regulation of cellulase genes by ClrB and XlnR presented in gpdA ( p ) ::clrB-PDE_02864 ( p ) ::clrB , gpdA ( p ) ::xlnR , and gpdA ( p ) ::clrB-gpdA ( p ) ::xlnR mutants ( Fig 4A–4D and S3 Fig ) , and the synergistic transcriptional induction of cellulolytic genes in Bgl2-deficient background ( Fig 7A and 7B , and S7A and S7B Fig ) , we assessed whether the dose effects of ClrB and XlnR transcriptional abundance were feasible in further enhancing the cellulase expression in triple-mutant RE-10 [39] . We examined this hypothesis by reconstructing two overexpression cassettes ( i . e . , PDE_02864 ( p ) ::clrB-sur and PDE_02864 ( p ) ::xlnR-sur ) , in which the sur cassette ( conferring resistance to sulfonylurea ) was used as a resistance marker . These overexpression cassettes for clrB and xlnR were separately transformed into RE-10 [39] . The quadruple mutants RE-27 ( Δbgl2-ΔcreA-gpdA ( p ) ::clrB-PDE_02864 ( p ) ::clrB ) and RE-29 ( Δbgl2-ΔcreA-gpdA ( p ) ::clrB-PDE_02864 ( p ) ::xlnR ) were obtained , and their cellulase expression abilities were separately evaluated on cellulose and wheat bran media . Although all these experiments were performed in flasks , both RE-27 and RE-29 mutants showed more cellulolytic and xylanolytic enzyme activities and secretion abilities than RE-10 ( Fig 8A–8C and 8E , and S9A–S9F Fig ) . When grown on a medium with 2% of cellulose as a sole carbon source for 120 h , RE-27 mutant displayed 62 . 3% , 34 . 8% , 288 . 5% and 28 . 0% greater FPA , pNPCase activity , xylanase activity and total secreted protein level , but 26 . 3% lower pNPGase activity , respectively , than RE-10 ( S9A–S9E Fig ) . Similarly , the RE-29 mutant showed 55 . 3% , 44 . 1% , 255 . 2% and 20 . 6% greater FPA , pNPCase activity , xylanase activity and total secreted protein level , but 39 . 8% lower pNPGase activity , respectively ( S9A–S9E Fig ) . We also observed a significant decrease in amyR expression level for both RE-27 and RE-29 mutants as compared to the wild-type strain on cellulose by q-PCR ( Fig 8F ) . The findings further signify that AmyR may share a key role in the regulatory network for cellulolytic genes . When grown on a wheat bran medium , RE-27 mutant exhibited FPA ( 8 . 85±0 . 66 U/mL ) , CMCase activity ( 31 . 25±0 . 77 U/mL ) , xylanase activity ( 1341 . 97±172 . 94 U/mL ) , amylase activity ( 125 . 07±1 . 32 U/mL ) and total secreted protein concentration ( 16 . 40±1 . 08 g/L ) , and RE-29 mutant also displayed FPA activity ( 7 . 58±0 . 34 U/mL ) , CMCase activity ( 31 . 03±0 . 29 U/mL ) , xylanase activity ( 1285 . 93±11 . 12 U/mL ) , amylase activity ( 148 . 82±3 . 19 U/mL ) and total secreted protein concentration ( 16 . 16±0 . 72 g/L ) , respectively ( Fig 8A–8E ) . These data signify that the dose-controlled regulation mechanisms of the cellulolytic regulators are a promising strategy for cellulolytic fungi to develop enzyme hyper-producers via the RERN technology . In the above P . oxalicum cellulose regulon and basal secretome components , some enzymes involved in starch degradation were tightly associated with the cellulolytic protein expression on cellulose . The “starvation response” in Δbgl2-gpdA ( p ) ::clrB mutant also dramatically decreased at the amyR expression level under carbon-free conditions . Therefore , P . oxalicum amyR ( PDE_03964 ) , an Aspergillus oryzae amyR homolog [41] , was considered tightly associated with cellulolytic enzyme production . The strain with the deletion of amyR exhibited visible varying halos on cellulose and starch plates , as well as an identical phenotype on glucose relative to its parental strain ( Fig 9A ) . As such , this strain demonstrated its differential roles in amylase and cellulase expressions . Moreover , this condition suggests that ΔamyR mutant has no defects in glucose uptake , sensing , or metabolism . The FPA in an amyR knockout mutant was about 1 . 6-fold higher than that in the wild-type strain of P . oxalicum ( Fig 9B ) , while the amyR deletion reduced amylase under cellulose growth conditions ( Fig 9C ) . ΔamyR mutant also displayed higher amounts of cbh1 and eg2 mRNA than that in the wild-type strain according to the results of northern blot ( Fig 9D ) . Nonetheless , such mutant was deficient for transcribing the major glucoamylase gene gluA ( PDE_09417 ) when grown on cellulose . This observation implies that AmyR was the main activator for amylase expression , and it repressed cellulase expression in response to the utilization of cellulose sources . We constructed ΔamyR-gpdA ( p ) ::clrB and ΔamyR-ΔcreA mutants to investigate whether AmyR plays a negative role in the synergistic/additive transcriptional activation of cellulolytic genes . As predicted , both ΔamyR-gpdA ( p ) ::clrB and ΔamyR-ΔcreA mutants produced more cellulase activities than the strains that contain each individual mutation ( Fig 9B ) and showed higher transcription levels for cellulase genes under cellulose growth conditions ( Fig 9E and 9F ) . The additive regulation for cellulase gene expression also existed in ΔamyR-ΔxlnR and ΔamyR-Δbgl2 mutants on cellulose ( Fig 9G and S10A Fig ) . Therefore , the deletion of amyR in triple-mutant RE-10 ( Δbgl2-ΔcreA-gpdA ( p ) ::clrB ) might further enhance cellulase expression under cellulose conditions . This premise also holds true in RE-27 and RE-29 mutants . Correspondingly , we constructed ΔamyR-Δbgl2-ΔcreA-gpdA ( p ) ::clrB quadruple mutant ( strain RE-30 ) . However , the resulting strain RE-30 did not obtain greater cellulase expression than its parental triple-mutant RE-10 ( S10B and S10C Fig ) , but AmyR still contributed to the functions of activating amylase expression in RE-10 on cellulose ( S10D Fig ) . These results demonstrate that the deletions of amyR in RE-10 mutants were less effective for inducing cellulase expression than those in wild-type , gpdA ( p ) ::clrB , ΔcreA , and Δbgl2 strains on cellulose . The fact that RE-30 mutant produced cellulolytic enzymes near RE-10 implies that AmyR played a significantly different regulation activity for cellulase expression in RE-10 mutant than the wild-type strain under cellulose growth conditions . Considering the low expression of amyR in Δbgl2-gpdA ( p ) ::clrB mutant under carbon-free conditions ( Fig 7D ) , we hypothesized that the transcriptional abundance for amyR was also downregulated in RE-10 on cellulose . As predicted , we first observed a significant decrease in ΔcreA-gpdA ( p ) ::clrB strain in RNA-seq data ( RPKM: 124 . 5±5 . 8 in ΔcreA , 20 . 0±3 . 1 in ΔcreA-gpdA ( p ) ::clrB for amyR versus 211 . 1±2 . 3 in the wild-type ) . The q-PCR experiments also revealed that the amyR expression was synergistically downregulated in the RE-10 mutant ( Figs 7D and 10A ) . These data suggest that ClrB and CreA were supposed to participate in the control of the transcriptional response of amyR gene upon exposure to cellulose because the expression of amyR was decreased in gpdA ( p ) ::clrB and ΔcreA mutants , and increased in ΔclrB and gpdA ( p ) ::creA mutants ( Fig 10A ) . The deletion of bgl2 also resulted in the decreased expression of amyR , which may also facilitate the decreased expression of amyR in ΔcreA-gpdA ( p ) ::clrB ( Fig 10A ) . In other words , no differential expression for cellulase expression between RE-30 and RE-10 mutants may be tightly related to the dramatic deregulation of amyR in RE-10 ( Figs 7D and 10A ) . By contrast , the synergistic increase of cellulase induction in ΔcreA-gpdA ( p ) ::clrB and RE-10 mutants may be partially a consequence of the decreased expression of amyR . The extent to which and how AmyR is involved in cellulase expression regulated by ClrB and CreA is still uncertain . To gain insight into the molecular mechanism that underlies the AmyR-regulated cellulolytic gene expression on cellulose , we evaluated the global changes in ΔamyR mutants with three biological replicates by RNA-Seq . Consequently , 71% of the reads were mapped to the P . oxalicum 114–2 reference genome . The biological replicates of each ΔamyR mutant showed a high Pearson correlation ( S6D Fig ) and demonstrated the reliability of RNA-Seq . Given a |log2 ( fold change ) |>1 and probability≥0 . 8 as the threshold , we determined that 131 genes ( S7 Table ) were upregulated and 579 genes ( S8 Table ) were downregulated in response to the deletion of amyR compared with the wild-type , respectively . We then compared the RNA-Seq data for the 579 upregulated genes with that from the wild-type strain and ΔamyR , ΔclrB , ΔcreA , and ΔcreA-gpdA ( p ) ::clrB mutants . The hierarchical clustering of these genes revealed nine groups of genes with similar expression patterns ( S11 Fig ) . The expression levels for groups 1 and 2 increased in ΔcreA and ΔcreA-gpdA ( p ) ::clrB mutants . Group 1 consisted of 195 genes . Within this subset , the proteins with dolichyl-diphosphooligosaccharide-protein glycotransferase activity ( p = 3 . 0e-8 ) were enriched . Similarly , five genes in this group involved in starch degradation ( i . e . , PDE_04151 , PDE_09417 , PDE_05527 , PDE_01201 , and PDE_01354 ) were enriched . This case demonstrates that AmyR is the main activator for amylase gene expression . Group 2 composed of 122 genes . The GO-term analysis of these genes displayed a significant enrichment in the molecular function categories of the structural constituent of ribosome ( p = 2 . 6e-101 ) and rRNA binding ( p = 1 . 8e-5 ) . These results signify that AmyR may play an important role in the translation process ( p = 1 . 8e-38 ) . Groups 3 to 6 , 8 , and 9 showed no statistically significant results with cutoff ( FDR<0 . 05 ) via GO-term analyses . Group 7 contained 67 genes , in which 20 genes were enriched in the carboxylic acid metabolic process ( p = 9 . 02e-12 ) . We also compared the RNA-Seq data for the 131 upregulated genes with that from the wild-type strain and ΔamyR , ΔclrB , ΔcreA , and ΔcreA-gpdA ( p ) ::clrB mutants . The hierarchical clustering of these genes revealed four groups of genes with similar expression patterns ( Fig 10B ) . Group 1 included 84 genes ( S7 Table ) , which were induced in ΔclrB mutant but repressed in ΔcreA mutant , particularly in ΔcreA-gpdA ( p ) ::clrB mutant . The GO enrichment analysis revealed the induced expressions of the subsets of genes involved in the cellular amino acid metabolic ( p = 5 . 4e-10 ) and organic acid biosynthetic processes ( p = 1 . 6e-5 ) . One gene cluster ( from PDE_01212 to PDE_01220 ) encoding unclassified proteins was also involved in this dataset . Group 2 consisted of 22 genes that were induced in ΔcreA mutant ( S7 Table ) , especially in ΔcreA-gpdA ( p ) ::clrB mutant . The GO function annotation of this subset genes revealed that the genes for hydrolase activity ( p = 8 . 2e-12 ) constitute the largest group , including nine cellulase genes ( i . e . , PDE_07124 , PDE_07945 , PDE_05193 , PDE_05633 , PDE_09226 , PDE_07929 , PDE_00507 , PDE_07928 , and PDE_01261 ) , five hemicellulase genes ( i . e . , PDE_06649 , PDE_02101 , PDE_09278 , PDE_06023 , and PDE_08094 ) , β-glucosidase-encoding genes ( i . e . , PDE_00579 and bgl2 ) , swollenin ( i . e . , PDE_02102 ) , acetylesterase ( i . e . , PDE_05194 ) , ABC multidrug transporter ( i . e . , PDE_07165 ) , and formyltetrahydrofolate deformylase ( i . e . , PDE_07944 ) . This group also contained cellodextrin transport-1-encoding genes ( i . e . , PDE_00607 ) , a tetratricopeptide repeat protein ( i . e . , PDE_08095 ) , and a hypothetical protein ( i . e . PDE_06089 ) . Group 3 comprised 16 genes that were induced in ΔcreA mutant ( S7 Table ) , particularly in ΔcreA-gpdA ( p ) ::clrB and ΔamyR mutants . This gene set was categorized using GO terms . The results illustrated that the genes involved in the carbohydrate metabolic process ( p = 1 . 06e-6 ) were enriched , including hemicellulases-encoding genes ( i . e . , PDE_01302 , PDE_09710 , and PDE_05998 ) , endoglucanase ( PDE_09969 ) , β-glucosidase ( PDE_04251 ) , α-mannosyltransferase ( PDE_09901 ) , and α-xylosidase ( PDE_06944 ) . More importantly , a putative cellulose degradation regulator ( PDE_05883 ) was observed within this set . The transcriptional expression for PDE_05883 was ClrB-dependent and repressed by CreA and AmyR . Moreover , this expression showed an additive increase in ΔcreA-gpdA ( p ) ::clrB under cellulose conditions ( RPKM: 6 . 9±0 . 8 in ΔclrB , 35 . 3±1 . 4 in ΔcreA , 3 . 4±1 . 1 in ΔcreA-ΔclrB , 62 . 9±9 . 6 in ΔcreA-gpdA ( p ) ::clrB , and 51 . 4±3 . 8 in ΔamyR for PDE_05883 versus 16 . 1±0 . 6 in the wild-type strain ) . The variations in the expression levels of PDE_05883 in these mutants were further identified via q-PCR experiments ( Fig 10C ) . PDE_05883 encodes a conserved fungal Zn2Cys6 binuclear cluster domain with a significant amino acid homology with N . crassa cellulase essential regulator CLR-2 ( NCU08042 ) for cellulose degradation . PDE_05883 shares 37% identity with CLR-2 in N . crassa ( BioEdit , Expect = 5e-096 ) and a 45% identity with P . oxalicum ClrB ( BioEdit , Expect = 5e-0163 ) . Therefore , the gene of PDE_05883 was named as clrB-2 , and the corresponding protein was called ClrB-2 . The P . oxalicum closest homolog ClrB ( BioEdit , Expect = e-131 ) of N . crassa CLR-2 was identified in this study . The function of ClrB involved in cellulose degradation was also extensively characterized in the preceding discussion . Nonetheless , only limited information has been reported about the molecular mechanism of this ClrB-2 . In sum , the above findings provide an additional objective assessment of the role of AmyR in regulating cellulase expression . Likewise , the preceding analyses suggest that the combinatorial cross-regulation of ClrB , CreA , AmyR , and PDE_05883 defining a regulatory network of cellulase expression must be further characterized . Group 4 consisted of four genes that were repressed in ΔcreA mutant ( S7 Table ) , but were significantly induced in ΔcreA-gpdA ( p ) ::clrB and ΔamyR mutants . These genes involved β-1 , 6-glucanase-encoding genes ( PDE_02004 ) , succinate semialdehyde dehydrogenase ( PDE_05599 ) , 5-nitroimidazole antibiotic resistance protein ( PDE_08743 ) , and 4-aminobutyrate aminotransferase ( PDE_09301 ) . The results of the transcriptome analyses , Northern blot , and q-PCR experiments specified above demonstrate that the core cellulolytic genes are tightly regulated by ClrB , CreA , AmyR , and XlnR transcription factors under inducing conditions . The fine-tuned regulation mechanisms allowed us to hypothesize that these central transcription factors may directly bind to the promoters of their core targets . This case was expected because the CreA and XlnR homologs in T . reesei [42] have been determined to be capable of binding to cellulase gene promoters , corresponding to cbh1 in P . oxalicum . To support this hypothesis , GST-tagged ClrB ( GST-ClrB ) , CreA ( GST-CreA ) , and XlnR ( GST-XlnR ) binding domains were separately expressed in Escherichia coli and were purified . The nucleotide sequences of the putative target gene corresponded to 2 kb cbh1 promoter fragment . The abilities of the recombinant proteins to bind to cbh1 were assessed via electrophoretic mobility shift assay ( EMSA ) . When the concentration of GST-ClrB , GST-CreA , or GST-XlnR fusion proteins increased , slower migrating shifted bands were evident ( Fig 11A ) . However , no shifted band could be observed only with a high-concentration GST ( negative control ) . These findings signify that ClrB , CreA , and XlnR could directly bind to cbh1 promoter region , and the number of binding sites may be more than one . An important question is whether the direct interactions associated with these regulators play important roles in regulating their target genes by assembling into active transcription complexes , in addition to their direct binding to DNA segments , as observed in T . reesei [42 , 43] . To investigate this possibility , the full-length open reading frames ( ORFs ) of the transcription factors ClrB , CreA , AmyR , and XlnR were PCR amplified using cDNA from P . oxalicum 114–2 as the templates . All amplicons were cloned into plasmid pGAD-T7 and separately obtained fusion proteins ( i . e . , AD-ClrB , AD-CreA , AD-XlnR , and AD-AmyR ) . Similarly , the full-length creA , amyR , and xlnR were cloned into the partner plasmid pGBK-T7 and resulted in BD-CreA , BD-AmyR , and BD-XlnR . Protein–protein interaction assay was performed . The results showed that the strains with interactions between ClrB and AmyR , XlnR and ClrB , XlnR and AmyR , and XlnR and CreA could grow on synthetic drop-out ( SD ) plates that lack Leu , Trp , and His ( QDO , Clontech ) ( Fig 11B ) . Likewise , the results revealed that XlnR interacted directly with ClrB , CreA , and AmyR as well as with ClrB and AmyR in vitro .
In this study , a master transcription factor ClrB was exceptionally identified in P . oxalicum transcription factor mutant set screening . This key transcription factor positively regulated the cellulolytic gene expression , and its deletion strain exhibited dramatically reduced cellulase activities ( Fig 1B ) . However , this factor was not strictly required for xylanase gene expression . The homology search of P . oxalicum ClrB within some fungal proteomes showed that the homologs of ClrB ( i . e . , Clr-2 in N . crassa , ClrB in A . nidulans , and ManR in A . oryzae ) [16 , 17] were recently determined and were required to induce major cellulases , some major hemicellulases , and mannanolytic enzyme gene expression . The search of T . reesei protein databases via a Basic Local Alignment Search Tool using a ClrB/Clr-2 query revealed that a protein ( Trire2: 26163 ) with low sequence identity existed . However , the homology search of N . crassa Clr-2 within P . oxalicum proteome illustrated that the protein sequences for ClrB ( BioEdit , Expect = 5e-0163 ) and PDE_05883 ( BioEdit , Expect = 5e-096 ) have 45% and 37% identity with Clr-2 sequence [17] , respectively . These phenomena for two Clr-2 homologs in P . oxalicum proteome were not observed in N . crassa and T . reesei proteomes . This case suggests that differential-inducing mechanisms for cellulase expression may exist among cellulolytic fungi . The homologs of regulator XlnR were the most conserved in cellulolytic fungi [10–12] . However , P . oxalicum XlnR does not have the same transcriptional inducing ability for cellulolytic and xylanolytic genes as in others [10–12] . Significant differences in these regulatory patterns for cellulase genes were observed in P . oxalicum ΔxlnR and T . reesei Δxyr1 mutants [10] . The lack of XlnR homolog in T . reesei eliminated cellulase expression , but not in P . oxalicum ΔxlnR mutant ( Fig 2A and 2B ) . The deletion of P . oxalicum xlnR slightly reduced the transcript levels of some cellulases and abolished the major xylanase expression under induction conditions ( Fig 2A ) , which were similar to that in N . crassa Δxlr-1 mutant [12] . These findings suggest that the transcriptional regulation of lignocellulose-degrading enzymes mediated by XlnR homologs may be highly conserved in various filamentous fungi , but may also have interesting differences . CreA/CRE1/CRE-1 is a wide-domain master regulator of carbon metabolism identified in filamentous fungi [7–9] . This regulator allows an organism to utilize a preferred carbon source but hinders it from metabolizing complex carbon sources , including cellulose [7–9] . In this study , the function of CreA homologs in repressing cellulolytic and xylanolytic gene expressions was conserved among cellulolytic fungi ( Fig 2A and 2B ) . CreA homologs generally play an important role among cellulolytic fungi by linking CCR to developmental programs , including the conidia formation and hyphal morphology in T . reesei [23] and P . oxalicum ( Fig 5B ) . This study determined that some transcription factors ( i . e . , PDE_07199 , PDE_04095 , and PDE_08372 ) are involved in both developmental programs and cellulase induction expression ( Table 1 ) . These regulators are conserved in cellulolytic fungi ( Table 1 ) . Therefore , the possible existence of an intimate crosstalk among certain developmental processes , such as sporulation and cellulase production pathways , is mediated by some regulators in ascomycete fungi . By using RNA-seq data , we showed that expression of amyR was synergistically decreased in the ΔcreA-gpdA ( p ) ::clrB mutant . The lack of AmyR significantly induced cellulase expression and decreased the expression for amylase genes involved in starch degradation ( Fig 9A–9C ) . As such , AmyR may control the balance between starch and cellulose utilization by inducing and/or repressing cellulolytic and amylolytic gene expressions in P . oxalicum , respectively . The multiple-sequence alignment analysis showed that P . oxalicum AmyR shares a weak homology with N . crassa COL26 ( NCU07788 , 23% sequence identity , E value = 2e-038 by BioEdit ) [44] and T . reesei BglR ( Trire2: 52368 , 24% sequence identity , E value = 3e-031 by BioEdit ) [18] , but is highly homologous to T . reesei , a functionally uncharacterized Zn ( II ) 2Cys6-type fungal-specific transcription factor ( Trire2: 55105 , 38% sequence identity , E value = 9e-095 by BioEdit ) . Interestingly , the regulatory functions of AmyR gene were distinct from those of N . crassa COL26 [44] and T . reesei BglR [18] . During its initial response to cellulose , P . oxalicum ΔamyR mutant exhibited induction and did not decrease the cellulolytic gene expression as in N . crassa Δcol-26 mutant [44] . Moreover , N . crassa COL26 obviously repressed cre-1 transcription to promote the relief of CCR [44] , but the creA transcript abundance only slightly increased in P . oxalicum ΔamyR mutant . N . crassa Δcol-26 mutant exhibited a severe growth defect on glucose , but not in P . oxalicum ΔamyR mutant . Although both T . reesei BglR [18] and P . oxalicum AmyR mutants displayed an elevated cellulase expression under inducing conditions , they demonstrated distinct regulatory trends to β-glucosidase expression . This difference might be related to the functional studies of BglR in T . reesei mutant PC-3-7 containing bgl2 mutation and other uncharacterized mutations [18] . Nevertheless , T . reesei 55105 ( Trire2 ) , which is much more homologous to P . oxalicum AmyR than T . reesei BglR ( Trire2: 52368 ) , may be a candidate regulator involved in cellulase expression regulation . The cellulase expression in P . oxalicum is a highly coordinated process regulated by a suite of cellulolytic transcription factors ( i . e . , ClrB , CreA , XlnR , AmyR and ClrB-2 ) and other novel uncharacterized regulators . In this study , the cellulolytic regulators ClrB , XlnR , AmyR and ClrB-2 were significantly regulated at the transcriptional levels during their growth on glucose , but slightly at the early phase for under cellulose growth conditions ( Figs 5A and 12A ) . The CreA tightly regulated the expression of clrB , xlnR , amyR and clrB-2 in response to environmental carbon . These data suggested that CreA might have a cascade regulation because it repressed the activator genes for AmyR , ClrB , ClrB-2 and XlnR as well as the structural genes whose expression was upregulated by ClrB , XlnR and ClrB-2 ( Fig 12A ) . This “double-lock” regulation of cellulolytic genes mediated by regulator homologues in cellulolytic fungi might be general , which could facilitate the fast conversion of carbon metabolism from favored carbon sources to cellulose and hemicellulose utilization . This cascade regulation mechanism mediated by P . oxalicum CreA was similar to the pathway described Cre1-mediated double repression of xyr1 and xyn1 in H . jecorina [10] . Some similar situations with regard to the repression of the transcription of A . nidulans ethanol and xylan regulons have been previously reported [45 , 46] . The deletion of creA resulted in the slightly decreased expression of amyR ( Fig 10A ) , whose absence further led to the upregulation of cellulase genes ( Fig 12A ) . Similarly , the overexpression of ClrB led to a decreased expression of amyR , whose expression level was synergistically downregulated in RE-10 mutant , but increased in gpdA ( p ) ::clrB-PDE_02864 ( p ) ::clrB mutant ( Fig 10A ) . These data implied that regulatory function of ClrB and CreA on amyR expression may be important for cascade regulation for cellulolytic genes under cellulose growth conditions . Another key finding of this study is that the transcriptional expression of ClrB-2 , a novel regulator , is responsive to ClrB , XlnR , CreA and AmyR , which implied that ClrB-2 may mediate the cascade transcriptional regulation for cellulolytic genes by ClrB , XlnR , CreA and AmyR ( Figs 5A , 10C and 12A ) . We did not systematically determine how cellulolytic genes could transform in clrB-2 mutant yet . Nonetheless , the variable expression levels on cellulose in these regulator mutants suggest that ClrB-2 is one of the most interesting target in P . oxalicum cellulolytic regulatory networks . Whether ClrB , CreA , XlnR , and AmyR converge to exert their partial regulatory function via ClrB-2 on cellulolytic gene expression must be urgently elucidated . The synergistic and collective regulations of cellulase expression by cellulolytic regulators are still elusive and largely uncharacterized in cellulolytic fungi . Thus far , no research has systematically investigated whether or how the most central cellulolytic factors CreA and ClrB homologs perform the synergistic regulation of cellulase genes . In this study , ΔcreA-gpdA ( p ) ::clrB strain yielded strong synergistic effects on cellulase expression ( Figs 1A–1E , 2A and 2B ) . This observation indicates that the full induction of cellulase genes requires not only an exclusive inducer-induction and activation by positive regulators , but also the release of negative transcription factors . In accordance to these synergistic regulatory mechanisms in P . oxalicum , we constructed significantly higher cellulase hyper-producer RE-27 and RE-29 than the triple-mutant RE-10 ( Fig 8A and 8B ) . We believe that the RERN technology presented here will be a valuable contribution in transforming some non-industrial model species ( e . g . , N . crassa and A . nidulans ) into more industrially relevant species . A very interesting finding in this study is that the cellulase expression increased evidently accompany with the increase of the copy number and the efficiency of its promoter of clrB or xlnR gene ( Fig 8A and 8B , and S3A–S3D Fig ) , indicating that a tunable cellulase expression may be controlled by the activator concentration under cellulose conditions . These data signify that the cellulase expression is not only dependent on the presence of the activators ClrB and XlnR , but also severely dependent on their dose effects of ClrB and XlnR transcriptional abundances . The other issue that must be explored is how cross-correlations occur between the cellulolytic regulators . To evaluate the role of CreA in CreA-mediated repression of cellulase and hemicellulase gene expressions , we developed three possible hypotheses . a ) Putative CreA binding sites overlap with the putative ClrB or XlnR binding sites , and CreA could preferentially bind to the sites and/or block their binding to target promoters by competition . A recent study identified the presence of putative cis-regulatory elements recognized by both XYR1 and CRE1 and spaced in XYR1- and CRE1-dependent cellulase gene promoters [42] . b ) CreA could stably associate with ClrB or XlnR components to form a heterocomplex , thereby making ClrB and XlnR completely non-functional for the induction of cellulolytic and xylanolytic genes . In this study , CreA and XlnR were assumed to directly interact with each other according to the yeast two-hybrid assay ( Fig 11B and 11C ) . c ) The activity of transcription factor is influenced by intracellular protein post-translational modifications , such as phosphorylation . These modifications may influence the ability of the transcription factor to bind to its binding sites [47] . In sum , the findings presented above support our proposition that biological relevances may exist between CreA and ClrB as well as between CreA and XlnR , which tightly regulate the expression of cellulase and hemicellulase genes . Beta-glucosidases are the conserved components required in cellulose deconstruction , the number of which significantly varies among the genomes of cellulolytic fungi [24 , 48 , 49] . The lack of P . oxalicum intracellular Bgl2 contributes to the increase of cellulase expression [27] , but not in T . reesei [50] and N . crassa [51] . These findings have raised the question as to how Bgl2 mediates the carbon metabolism involved in signal cascades in relation to the regulation of cellulase gene expression , which may reflect the general trend of cellobiose/cellodextrins-induced cellulase expression in cellulolytic fungi [27 , 50 , 51] . Some β-glucosidases in T . reesei [50] and N . crassa . [51] have been observed to play important roles in balancing cellobiose production and metabolism in intra- and extra-cellular environments . Given the general phenomenon that cellobiose induces cellulase expression in cellulolytic fungi [27 , 50 , 51] , P . oxalicum Δbgl2-gpdA ( p ) ::clrB mutant showed a strongly elevated cellulase expression ( Fig 7A and 7B ) , which could be partially ascribed to the signal induction cascade mediated by cellobiose/cellodextrins from cellulose ( Fig 12A ) . The robust induction of cellulase expression in Δbgl2-ΔcreA mutant was remarkably greater than that in each deletion mutant on cellulose ( Fig 7A and 7B ) . This observation supports the premise that the CCR mediated by CreA increased when the major predicted intracellular β-glucosidase was absent under cellulose growth conditions . Although the lack of Cre-1 in N . crassa triple β-glucosidase mutant showed higher concentrations of secreted active cellulases than that in wild-type strain on cellobiose , it did not facilitate protein production and cellulase induction on cellulose [51] . In sum , ClrB positively regulated the transcriptional expression of bgl2 ( S5 Fig ) , but its deletion conversely enhanced the signal cascade activation regulation by ClrB and/or the repression regulation of CCR mediated by CreA ( Figs 7A , 7B and 12A ) . In addition , we also determined that the cellulase expression in Δbgl2-ΔamyR double mutant was further enhanced compared with each individual deletion strain ( S10A Fig ) . These results revealed that the functional regulations for cellulase expression by these cellulolytic regulators may be sensitive to inducers in intracellular environments . This finding implies that the combination of intracellular cellodextrin induction and redesigned cellulolytic transcription factor regulation in P . oxalicum may be general for the full induction of cellulase expression on cellulose . Many recent studies have attempted to robustly produce cellulases without inducers , but the molecular mechanism of cellulase induction under non-inducing conditions has remained elusive in diverse cellulolytic species [20 , 40] . Recently published data also showed that the misexpression of N . crassa clr-2 through Pccg-1 [20] and Ptcu1-driven expression of xyr1 [40] was sufficient for inducer-free cellulase expression . However , the cellulolytic gene transcript between P . oxalicum gpdA ( p ) ::clrB and wild-type strains had no obvious differences under non-inducing conditions . Such case was similar in the XlnR activator in the gpdA ( p ) ::xlnR strain under CreA-mediated CCR , and cbh1 transcription was still remarkably in low-level expression in the ΔcreA mutant on a carbon-free medium , which indicates the full induction requirement of cellulase gene expression , as evident in T . reesei [25] or N . crassa [51] . However , the cellulolytic gene transcription levels were obviously upregulated in the ΔcreA-gpdA ( p ) ::clrB mutant under repressing conditions . These findings implied that the lack of CreA contributed to the activating function for ClrB on cellulolytic genes even in glucose . Notably , the Δbgl2-gpdA ( p ) ::clrB strain exhibited an induction transcription of core cellulase genes for several orders of magnitude increase than the wild-type strain that shifted to a carbon-free medium ( Fig 7C and S7C Fig ) . No bulk of cellodextrins was apparently transported into the cell to induce cellulase gene expression when subjected to starvation , but the induction abilities of these core cellulase regulons in the Δbgl2-gpdA ( p ) ::clrB mutant under non-inducing conditions were comparable to under cellulose conditions ( Fig 7C and S7C Fig ) . The amyR expression level had a 7 . 7-fold decrease , clrB had a 17 . 5-fold increase , and clrB-2 had a 9 . 3-fold increase in the Δbgl2-gpdA ( p ) ::clrB mutant versus wild-type strain under carbon-free conditions ( Fig 7D ) , which implied that AmyR , ClrB , and ClrB-2 were possibly tightly involved in cellulase expression regulatory during energy abstinence , as well as that under cellulose growth conditions . These were novel findings that indicated the “starvation response” for cellulase genes in P . oxalicum and other diverse cellulolytic fungi . Cellulase formation apparently occurred because of consistent respective regulators , including the characterized or novel transcription factors identified in P . oxalicum in this context . Cellulase synergistic and dose-controlled regulatory systems mediated by diverse cellulolytic effectors were observed in the system mutation of this study for P . oxalicum regulators . Using this model ( Fig 12A ) , the accumulation of intracellular cellodextrins can trigger signaling cascades that include expression of cellulase genes repressed by CreA and AmyR and activated by ClrB and XlnR . However , our data also support that the transcriptional regulation for CreA , AmyR , ClrB and XlnR genes is a powerful part of the regulatory network of cellulase gene expression . In the early cellulolytic induction , ClrB functions to repress expression of amyR , whose expression level is activated by CreA and reduced in Bgl2-deficient background ( Fig 12A ) . Moreover , transcriptional expression for ClrB , AmyR , XlnR and ClrB-2 genes is also significantly repressed by CCR mediated by CreA in the presence of glucose ( Fig 12A ) . The data established ClrB as a focal point to regulate cellulase expression by integrating other regulators and the target genes of these regulators , which refined our understanding on transcriptional regulatory network as a “seesaw model” in which coordinated regulation of cellulolytic genes was established through activators and repressors counteraction ( Fig 12B–12D ) . These observations also suggested the hypotheses that the rational design of cellulase or high-value protein super producers might be guided in the future for the combinatorial effects of diverse cellulolytic effectors .
All the strains used in this study are listed in the S9 Table and are grown on Vogel’s medium that contain 2% glucose ( mass/volume percent ) , unless otherwise noted . The P . oxalicum wild-type strain 114–2 ( CGMCC 5302 ) was used as parental strain throughout this study . Hygromycin B , pyrithiamine , phosphinothricin , and sulfonylurea were added to the media with final concentrations of 200 , 300 , 1 . 6 , and 4 μg/mL used for transformant selection , respectively . Vogel’s 50x salts ( 1 , 000 mL ) was used: 125 g Na3Citrate•2H2O , 250 g KH2PO4 , 100 g NH4NO3 , 10 g MgSO4•7H2O , 5 g CaCl2•2H2O , 0 . 25 mg biotin , and 5 ml trace element solution ( 5 g Citric acid•H2O , 5 g ZnSO4•7H2O , 1 g Fe ( NH4 ) 2 ( SO4 ) 2•6H2O , 0 . 25 g CuSO4•5H2O , 0 . 05 g MnSO4•1H2O , 0 . 05 g H3BO3 , and 0 . 05 g Na2MoO4•2H2O , which were dissolved in distilled water; the resulting total volume was 100 mL ) . The wheat bran medium ( mass/volume percent ) was composed of corn cob residue ( 2 . 0% ) , Avicel ( 0 . 6% ) , wheat bran ( 4 . 6% ) , soybean cake powder ( 1 . 0% ) , ( NH4 ) 2SO4 ( 0 . 2% ) , NaNO3 ( 0 . 28% ) , urea ( 0 . 1% ) , KH2PO4 ( 0 . 3% ) , and MgSO4 ( 0 . 05% ) . Vogel’s medium that contained 1 M sorbitol was used in all the transformation experiments . Vogel’s medium with 2% Avicel was used as a sole carbon source to induce cellulase expression for cellulase activity assays , q-PCR , Northern blot , or RNA-seq analyses . The fluid mediums of the P . oxalicum strains were all cultivated in Erlenmeyer flasks at 30°C in constant light and 200 rpm agitation rate . The cultivation was performed on plates by adding agar in the fluid medium as a solidifying agent at 30°C in constant light . P . oxalicum protoplast prepared according to modified methods , as described by Gruber et al . [52] . Seven to eight PDA plates were prepared , and 50 μL of fresh spore solution was streaked out on every cellophane-covered PDA plate . The plates were incubated at 30°C for 11–12 hours . Three milliliters of this protoplasting solution [0 . 075 g lysing enzymes up to 25 mL solution A ( 1 . 2 M sorbitol and 0 . 1 M KH2PO4 , and pH = 5 . 6] were pipetted into a sterile petri dish , and one cellophane disc with freshly grown mycelium was added . Subsequently , 3 mL of protoplasting solution was readded . The petri dish was incubated at 30°C for 150 min . The final protoplast suspension was filtered into a sterile 50 mL centrifuge tube through a lens cleaning tissue in a glass funnel . The suspension was centrifuged for 10 min at 2000 rpm and 4°C in a swing-out rotor . The supernatant was cautiously decanted , and the pellet was resuspended in 10 mL of solution B ( 1 M sorbitol , 50 mM CaCl2 , 10 mM TrisHCl , and pH = 7 . 5 ) . The suspension was recentrifuged for 10 min at 2000 rpm and 4°C , and then the supernatant was cautiously decanted and the protoplasts were resuspended in 0 . 5 mL of solution B . The protoplasts were stored in ice . The following components: 200 μL protoplast suspension , 10 μL DNA fragment , and 50 μL of solution C ( 25% PEG 6000 , 50 mM CaCl2 , 10 mM TrisHCl , and pH = 7 . 5 ) , were added into a 10 mL centrifuge tube and were mixed gently , and then the mixture was incubated on ice for 20 min . Two milliliters of solution C ( room temperature ) were added to the transformation mixture , which was mixed gently . The mixture was incubated for 5 min at room temperature , and then 4 mL of solution B was added and was mixed gently . All the transformation mixture was added to 30 mL Vogel’s medium ( 55°C ) that contained hygromycin B , pyrithiamine , phosphinothricin , or sulfonylurea , and was then mixed shortly and was poured onto the bottom of the Vogel’s medium . Transformants would be visible for 3–4 days for hygromycin B , pyrithiamine , or phosphinothricin , and for 6–8 days for sulfonylurea at 30°C . The P . oxalicum genome is available in DDBJ/EMBL/GenBank ( under the accession number AGIH00000000 ) or at http://genome . jgi . doe . gov/Penox1/Penox1 . home . html . Transcription factors were identified and annotated according to InterPro IDs in the Fungal Transcription Factor Database [53] . The transcription factor deletion strains , where each coding region of the transcription factor was substituted with a selective marker ptra gene [31] , were constructed from the P . oxalicum strain Δpku70::hph [33] as follows . Deletion cassettes conferring resistance to pyrithiamine hydrobromide were constructed according to the double-joint PCR strategy [32] . Primer pairs for ( x ) -F1+ ( x ) -ptraR and ( x ) -ptraF+ ( x ) -R1 ( S1 Table ) were designed using the Primer 5 software , and the pairs were used to amplify the upstream and downstream fragments for 1000–1500 bp on either side of the encoding regions for each single target gene . The ptra selectable marker cassette was PCR-amplified from the pME2892 plasmid [54] with the PtraF1+PtraR1 ( S1 Table ) primer pair and was PCR product-purified . The upstream and downstream fragments contained 25 bp homology to the ptra cassette sequence . The primer pair ( x ) -F2+ ( x ) -R2 ( S1 Table ) was used to produce final deletion cassettes through double-joint fusion PCR [the program used to fuse the three fragments: 94°C 2 min for 10 cycles ( 94°C for 30 s , 58°C for 10 min , and 72°C for 3 min ) , 72°C 5 min] [32] . PCR experiments were performed in final volumes of 50|μL that contained 1 unit of Trans HIFI DNA polymerase , 0 . 2|mM dNTP , and 0 . 4|μM of each primer . The program used to amplify the fused knockout cassettes was as follows: one cycle of 94°C ( 120 s ) , 30 cycles of 94°C ( 30 s ) , 58°C ( 30 s ) , and 72°C ( 1 min for every 1 kb of amplified product ) , followed by a final 10|min at 72°C . All the PCRs were performed in a Bio-Rad DNA Engine Peltier Thermal Cycler . Each knockout cassette was independently transformed into P . oxalicum strain Δpku70::hph protoplasts [33] . The mature transformant conidia from the Vogel’s medium slant was streaked onto a Vogel’s medium plate that contained pyrithiamine hydrobromide . At least three Ptra-resistant colonies obtained from each assay were analyzed through diagnostic PCR to confirm the deletion using the primer pairs ( x ) -F1+ptraYZR or ptraYZF+ ( x ) -R1 ( S1 Table ) . Within the pair , the primer ( x ) -F1 or ( x ) -R1 ( S1 Table ) was located outside the transforming deletion fragment of the genome , and the primer ptraYZR or ptraYZF ( S1 Table ) was unique to the ptra sequence . These obtained strains constituted a stock of the P . oxalicum transcription factor deletion mutant . The complementation cassette that conferred resistance to hygromycin B was used to transform into the corresponding mutant to identify that the interesting phenotype ( s ) observed for the transcription factor gene deletion mutants was indeed caused by the deletion of a relevant gene . The upstream and downstream fragments contained 25 bp homology to the hph cassette sequence . clrB and amyR wild-type allele complementation cassettes were obtained by amplifying the upstream fragments ( encompassing the 1 . 5 kb promoter , the open reading frame , and 0 . 5 kb 3’ untranslated region ) using the primer pairs clrB-F1+clrBHPH-R and AmyR-F1+AmyRHPH-R ( S1 Table ) , respectively . The 1 . 5 kb downstream flanking segments of the 3’ untranslated region from the P . oxalicum genome DNA were amplified through the primer pairs clrBHPH-F+clrB-R1 and AmyRHPH-F+AmyR-R1 ( S1 Table ) . The 1 . 8|kb hph gene fragment was amplified from the pSilent-1 plasmid [55] with the primers Hphs-F and Hphs-R ( S1 Table ) . These three PCR fragments were ligated through Double-joint PCR and were amplified through the nest primer pairs clrB-F2+clrB-R2 and AmyR-F2+AmyR-R2 ( S1 Table ) . The resulting clrB and amyR complementation cassettes were transformed into ΔclrB::ptra and ΔamyR::ptra mutants , and the complementation strains RclrB and RamyR of these cassettes were obtained , respectively . The clrB promoter was replaced with the gpdA ( glyceraldehyde-3-phosphate dehydrogenase ) promoter from A . nidulans [37] . Moreover , 1 , 314 bp of the gpdA promoter was amplified from the plasmid pAN7-1 [56] using the primers PgpdA-F1 and PgpdA-R1 ( S1 Table ) . Subsequently , 2 , 008 bp ptra selectable marker cassette was PCR-amplified with the primer pair PtraF1+PtraR1 ( S1 Table ) and was PCR product-purified . Furthermore , 3 , 148 bp of the clrB open reading frame and 3’ untranslated region were amplified with the primer pair clrB-Fa+clrB-Ra ( S1 Table ) , and this fragment overlapped with the gpdA promoter and ptra fragment by 25|bp at the ends of this fragment . These 6375 bp PCR products were then ligated in the order of gpdA ( p ) ::clrB-ptra by splicing through double-joint PCR with the nest primers PgpdA-F2 and PtraR1 ( S1 Table ) . A similar strategy was used to construct the creA overexpression cassette under the influence of the P . oxalicum gpdA promoter . Moreover , 1749 bp of the gpdA promoter was amplified from the P . oxalicum 114–2 genome DNA through the primers PGP-F1 and PGP-R ( S1 Table ) . In addition , 1890 bp of the hph cassette was amplified from the plasmid pSilent-1 [55] with the primers Hphs-F and Hphs-R ( S1 Table ) . Moreover , 1743 bp of the creA open reading frame and 3’ untranslated region was amplified using the primer pair GPcre-F+GPcre-R ( S1 Table ) , and this fragment overlapped with the gpdA promoter and hph fragment by 25|bp at the ends of this fragment . These PCR products were then ligated in the order of gpdA ( p ) ::creA-hph by splicing through double-joint PCR with the primers PGP-F2 and HPH-R1 ( S1 Table ) . The resulting 5248 bp gpdA ( p ) ::creA-hph overexpression cassette was transformed into P . oxalicum wild-type and gpdA ( p ) ::clrB-ptra strain protoplasts , and the gpdA ( p ) ::creA and gpdA ( p ) ::creA-gpdA ( p ) ::clrB mutants were obtained , respectively . Similarly , the xlnR promoter was replaced with the gpdA promoter from A . nidulans . Moreover , 1314 bp of the gpdA promoter was amplified from the plasmid pAN7-1 [56] through the primer pair PgpdA-F1 and PgpdA-R1 ( S1 Table ) . In addition , 1890 bp of the hph cassette was amplified from the plasmid pSilent-1 [55] . The xlnR open reading frames and 3’ untranslated region were amplified with the primer pair XlnR-Fa+XlnR-RH2 ( S1 Table ) , and this fragment overlapped with the gpdA promoter and the hph fragment by 25|bp at the ends of this fragment . These PCR products were then ligated in the order of gpdA ( p ) -xlnR-hph by splicing through double-joint PCR with the primers PgpdA-F2 and Hphs-R1 ( S1 Table ) . The resulting 6675 bp gpdA ( p ) ::xlnR-hph overexpression cassette was transformed into gpdA ( p ) ::clrB strain protoplasts . The resulting gpdA ( p ) ::clrB-ptra , gpdA ( p ) ::creA-hph , and gpdA ( p ) ::xlnR-hph overexpression cassettes were used to transform P . oxalicum wild-type strain protoplasts . The gpdA ( p ) ::creA-ptra , gpdA ( p ) ::clrB-ptra , and gpdA ( p ) ::xlnR-hph overexpression mutants were selected on Vogel’s medium plate that contained hygromycin B or pyrithiamine hydrobromide . Double-joint PCR was performed to construct the creA knockout cassette , with the hph cassette flanked by 1 . 5 kb upstream ( CreA-F1+Crehph-R ) ( S1 Table ) and 1 . 5 kb downstream ( Crehph-F+CreA-R1 ) ( S1 Table ) of the creA ORF . Moreover , 1 . 8 kb of the hph cassette was amplified from the plasmid pSilent-1 [55] . The ΔcreA::hph final deletion cassette fragment was obtained through the primer pair ( CreA-F2+CreA-R2 ) ( S1 Table ) with the three fragments above used as a PCR template . The ΔcreA::hph cassettes were transformed into P . oxalicum wild-type , gpdA ( p ) ::clrB-ptra , and ΔclrB strain protoplasts . The selection of the ΔcreA , ΔcreA-gpdA ( p ) ::clrB , and ΔcreA-ΔclrB transformants was consistent with the previous study . The ΔxlnR cassettes conferred resistance to hygromycin B . The hph selectable marker cassette was PCR-amplified with the primers Hph-F1 and Hph-R1 ( S1 Table ) from the plasmid pAN7-1 [56] . The upstream and downstream flanking fragments were amplified with the primer pairs XlnR-F1+XyrHph-R and HphXyr-F+XlnR-R1 ( S1 Table ) , which contained 25 bp homology to the hph cassette sequence . The primer pair XlnR-F2 and XlnR-R2 ( S1 Table ) was used to produce final deletion cassettes through a double-joint fusion PCR . The resulting ΔxlnR::hph knockout cassette was used to transform wild-type protoplasts , and the P . oxalicum ΔxlnR mutant was obtained . The ΔxlnR::hph knockout cassette was used to transform the ΔclrB protoplasts , and then the ΔxlnR-ΔclrB mutant was obtained . The above gpdA ( p ) ::xlnR-hph overexpression cassette was used to transform the gpdA ( p ) ::clrB-ptra strain protoplasts , and the gpdA ( p ) ::clrB-gpdA ( p ) ::xlnR mutant was constructed . The upstream and downstream fragments of bgl2 ORF were amplified with the primer pairs Bgl2-F1+Bgl2hph-R and Bgl2hph-F+Bgl2-R1 ( S1 Table ) . Moreover , 1 . 8 kb of the hph cassette was amplified from the plasmid pSilent-1 [55] . The final Δbgl2::hph fragment was obtained through the primer pair Bgl2-F2+Bgl2-R2 ( S1 Table ) , with the three fragments above used as a PCR template . The Δbgl2::hph cassette was used to transform the protoplasts of the ΔcreA and gpdA ( p ) ::clrB mutants , and then the Δbgl2-ΔcreA and Δbgl2-gpdA ( p ) ::clrB mutants were obtained , respectively . PDE_02864 ( p ) ::clrB-hph overexpression cassettes that conferred resistance to hygromycin B was constructed . The hph selectable marker cassette was PCR-amplified with the primers Hphs-F and HPH-R1 from the plasmid pSilent-1 [55] . The PDE_02864 promoter , and clrB open reading frame and 3’ untranslated region were amplified with the primer pairs DF1+DClrB-R and ClrB-F+ClrBHPH-R ( S1 Table ) from the P . oxalicum genome DNA , respectively . The primer pair DF2+HPH-R1 ( S1 Table ) was used to produce the final PDE_02864 ( p ) ::clrB-hph overexpression cassette via double-joint fusion PCR . The resulting PDE_02864 ( p ) ::clrB-hph overexpression cassette was used to transform the wild-type and gpdA ( p ) ::clrB strain protoplasts , and then the PDE_02864 ( p ) ::clrB-hph and gpdA ( p ) ::clrB-PDE_02864 ( p ) ::clrB mutants were obtained , respectively . The upstream and downstream flanking fragments of the amyR encoding region were amplified with the primer pairs PDE_03964-F1+amyRHph-R and amyRHph-F+PDE_03964-R1 ( S1 Table ) , respectively . The final ΔamyR::hph fragment was obtained through the nest primer pair PDE_03964-F2+PDE_03964-R2 ( S1 Table ) with the three fragments ( amyR flanking sequences and hph encoding cassette ) used as templates via double-joint PCR . The ΔamyR::hph cassette was used to transform the gpdA ( p ) ::clrB-ptra protoplasts , and the ΔamyR-gpdA ( p ) ::clrB mutant was obtained . The ΔamyR::ptra ( ΔPDE_03964::ptra , from TF knock-out cassette set ) knockout cassette was used to transform the Δbgl2::hph , ΔcreA::hph , and ΔxlnR::hph mutants , and the ΔamyR-ΔcreA , ΔamyR-Δbgl2 , ΔamyR-ΔxlnR mutants were obtained , respectively . First , the ΔamyR::sur knockout cassette , and PDE_02864 ( p ) ::clrB-sur and PDE_02864 ( p ) ::xlnR-sur overexpression cassettes that conferred resistance to sulfonylurea were constructed . The sur selectable marker cassette was PCR-amplified with the primers Sur-F1 and Sur-R1 ( S1 Table ) from the plasmid pCB1536 [57] . The upstream and downstream flanking fragments for the ΔamyR-sur knockout cassette were amplified with the primer pairs PDE_03964-F1+amyRsur-R and amyRsur-F+PDE_03964-R1 ( S1 Table ) , which contained 25 bp homology to the sur cassette sequence , respectively . The primer pair PDE_03964-F2+PDE_03964-R2 ( S1 Table ) was used to produce the final deletion cassette ΔamyR-sur through a double-joint fusion PCR . The resulting ΔamyR-sur knockout cassette was used to transform the RE-10 ( Δbgl2-ΔcreA-gpdA ( p ) ::clrB ) protoplasts , and the P . oxalicum and RE-30 ( ΔamyR-Δbgl2-ΔcreA-gpdA ( p ) ::clrB ) mutants were obtained . The clrB promoter was replaced with the PDE_02864 ( encoding 40S ribosomal protein S8 ) promoter from P . oxalicum . The PDE_02864 promoter sequence was amplified with the primer pair DF1+DP-R . In addition , the clrB and xlnR open reading frames , and the 3’ untranslated regions were amplified with the primer pairs DClrB-F+ClrBsur-R and DXlnR-F+XlnRSur-R , respectively . The primer pair DF2+Sur-R1 ( S1 Table ) was used to produce the final PDE_02864 ( p ) ::clrB-sur and PDE_02864 ( p ) ::xlnR-sur overexpression cassettes via double-joint fusion PCR . The resulting PDE_02864 ( p ) ::clrB-sur and PDE_02864 ( p ) ::xlnR-sur overexpression cassettes were used to transform the RE-10 protoplasts , and then the RE-27 ( Δbgl2-ΔcreA-gpdA ( p ) ::clrB-PDE_02864 ( p ) ::clrB ) and RE-29 ( Δbgl2-ΔcreA-gpdA ( p ) ::clrB- PDE_02864 ( p ) ::xlnR ) mutants were obtained . Fungal genomic DNA was isolated , as described previously [58] . The Primer 5 software was used to identify the appropriate restriction enzymes for the Southern blot analysis of the gene replacement mutants . The fragments used for the probes were amplified with the primers presented in the S1 Table . A DIG-High Prime labeling kit was used to label the knockout cassette flank fragment probes . The homokaryons and integration patterns of the transforming cassettes in the genome were confirmed through Southern blot analysis , as described in the manipulations . Colony morphology and conidiation were analyzed after inoculating the Vogel’s medium plates that contained 2% glucose , 2% xylan , 2% starch , or 1% cellulose as sole carbon source or potato dextrose agar ( PDA ) medium at 30°C for 5 days . The halo sizes that varied among the P . oxalicum strains were measured on cellulose or starch plates by adding Triton X-100 to a final concentration of 0 . 5% . Iodine solution ( 6 g of KI , 0 . 6 g of I2 in 100 mL of H2O ) was used to indicate visualize starch-degrading colonies through the hydrolysis halo at room temperature for 10 min , which determined if the amylase expression was affected in the P . oxalicum strain . P . oxalicum hyphae and conidia were microscopically examined through lactophenol cotton blue staining ( 0 . 05 g cotton blue , 20 g phenol crystals , 40 mL glycerol , 20 mL lactic acid , and 20 mL distilled water ) . Cellulase was produced in a 500 mL flask that contained 100 mL of fluid medium through a two-step cultivation procedure . Strains were first grown at 30°C in 100 mL of medium that contained 2 g of glucose as a carbon source and were then regulated at pH 5 . 5 and 200 rpm for 20 hours . The cultures were collected through vacuum drum filtration during this second step , and 0 . 5 g vegetative mycelia was added to 100 mL of Vogel’s medium that contained 2% cellulose as carbon source or wheat bran medium at an initial pH of 5 . 5 at 30°C and 200 rpm . Culture supernatants ( crude enzyme ) were diluted with sodium acetate buffer solution ( SABF , 0 . 2 M , pH 4 . 8 ) . Enzymatic hydrolyses of the polysaccharides were also performed in SABF ( 0 . 2 M , pH 4 . 8 ) . The filter paper enzyme ( FPA ) , endoglucanase ( CMCase ) , xylanase , and amylase activities of the culture supernatants ( diluted samples ) were assayed using a DNS reagent ( 10 g 3 , 5-dinitrosalicylic acid , 20 g sodium hydroxide , 200 g sodium potassium tartrate , 2 . 0 g redistilled phenol , and 0 . 50 g sodium sulfite anhydrous per 1000 mL DNS reagent ) against Whatman No . 1 filter paper , carboxymethylcellulose sodium salt ( CMC-Na ) , xylan ( from beechwood ) , and soluble starch . CMC-Na , xylan , or starch was dissolved in SABF to a final concentration of 1% ( mass/volume percent , m/v % ) , and then the mixture was left overnight and was shaken well before using . The following components were added in a 2 . 0 mL reaction mixture: 0 . 5 mL diluted culture supernatants and 1 . 5 mL CMC-Na , xylan , or starch solution for CMCase , xylanase , or amylase activity assays , respectively; and 2 . 0 mL diluted culture supernatants and 50 mg Whatman No . 1 filter paper for FPA assay into 25 mL colorimetric tube . The mixture was mixed gently and the reaction mixture was incubated for FPA measurement in a 50°C water bath for 1 hour , for CMCase and xylanase activity measurements at 50°C for 30 min , and for amylase activity measurement at 40°C for 10 min . Three milliliters of DNS reagent were then added to stop the reaction . A blank tube ( with boiled crude enzyme ) was used as control to correct any reducing sugar present in the crude enzyme samples . The tubes were placed in boiling water for 10 min , 20 mL distilled water was added , 200 μL of reaction mixture was pipetted , and the absorbance was determined at 540 nm . The cellobiohydrolase ( pNPCase ) and β-glucosidase ( pNPGase ) activities were measured by using 4-Nitrophenyl β-D-cellobioside ( pNPC ) and 4-Nitrophenyl β-D-glucopyranoside ( pNPG ) as substrates , respectively . The pNPC or pNPG was dissolved in SABF to a final concentration of 1 mg/mL . Moreover , 50 μL of pNPC solution ( containing 1 mg/mL D-Glucono-δ-lactone ) or 50 μL of pNPG solution and 100 μL of diluted culture supernatants were mixed , and then the mixtures were incubated in a 50°C water bath for 30 min . The reaction was stopped by adding 0 . 15 mL of sodium carbonate solution ( 10% , m/v ) , then 200 μL of these reaction mixtures was pipetted , and the absorbance was measured at 420 nm . One unit of enzyme activity was defined as the amount of enzyme required to release 1 μmol of glycoside bonds of the substrate per minute under defined assay conditions . Independent triplicate cultures were sampled and analyzed . The total protein was determined using a Bradford assay kit according to the instructions of the manufacturer . Freshly harvested conidia of the wild-type strain or mutants were inoculated with 106 conidia/mL into 100 mL Vogel’s medium that contained 2% glucose , and then grown for 22 hours at 30°C . Mycelia were harvested via vacuum filtration , and then washed with Vogel’s medium without a carbon source , followed by 2 h growth in 100 mL Vogel’s medium without a carbon source . Subsequently , mycelia were harvested via vacuum filtration , and then transferred into Vogel’s medium that contained 2% cellulose for 4 , 8 , 22 , and 46 hours , with 2% glucose for 4 hours , or into a medium without any carbon source for 4 hours . Mycelia were harvested via vacuum filtration , and then immediately finely ground under liquid nitrogen , and then 1 mL of TRIzol reagent was added per 50–100 mg powder . The total RNA was isolated according to the instructions of the manufacturer . The total amount ( 2 μg ) of mRNA loaded was normalized by using rRNA as a loading control . The probes used for the Northern blot analysis were the partial cDNAs of cbh1 ( PDE_07945 ) , eg2 ( PDE_09226 ) , and xyn1 ( PDE_08094 ) that were cloned from the P . oxalicum genome DNA via PCR with primer pairs ( S1 Table ) . The probes were labeled using a DIG Northern Starter kit , and Northern blot analysis was performed according to the instructions of the manufacturer . Putative targets were validated through q-PCR . Each strain was cultured independently from the Northern blot and RNA-seq experiments . cDNA was synthesized from the total RNA by applying a reagent kit with a gDNA eraser according to the instructions of the manufacturer . The obtained cDNA was applied for quantitative reverse transcription-PCR experiments . All q-PCR amplification was performed in 20 μl total volume with 7 . 4 μl distilled water , 0 . 8 μl of each primer ( 10 mM ) , 10 μl SYBR Premix Ex TaqII , and 1 μl template cDNA through the following program [59]: 95°C for 2 min , 40 cycles at 95°C for 10 sec , and 30 s at 61°C . The fluorescence signal was measured at the end of each extension step at 80°C . A melting curve program with a temperature gradient of 0 . 1°C per second from 65°C to 95°C was performed . The corresponding primers are shown in the S1 Table . The quantity and copy number of each target gene were calculated using a standard curve . Six 10-fold serial dilutions of purified DNA template ( 0 . 5 kb–1 . 0 kb ) were prepared for the target genes to determine the standard curve of each target gene . The correlation coefficient ( R2 ) for each standard curve was verified to be 0 . 99 or greater . The transcriptional expression of transcription factor genes were measured by q-PCR , and their expression levels were normalized to wild-type . Gene expression levels for cellulase genes were measured by q-PCR using actin ( PDE_01092 ) as a control and normalized to expression levels by actin values/10000 . Three biological replicates were performed on the same 96-well plate by using cultures grown in parallel . Data processing and statistical analyses were performed using Microsoft Excel . A cDNA library prepared from mRNA was organized according to standard protocols . Quality control was implemented using the Real-Time PCR Systems . All the cDNA libraries were sequenced on the Illumina platform . Sequenced reads were mapped against predicted transcripts from the P . oxalicum 114–2 genome using the SOAP2 software for short oligonucleotide alignment ( http://soap . genomics . org . cn/soapaligner . html ) [60] . Transcript abundance ( Reads Per Kb per Million reads , RPKM ) [61] was estimated with RPKM = [# of mapped reads]/ ( [length of transcript]/1000 ) / ( [total reads]/10^6 ) . The differential gene expression was analyzed using DESeq software package [62] and NOIseq v2 . 10 ( http://www . bioconductor . org/packages/release/bioc/html/NOISeq . html ) with |log2 ( fold change ) |>1 and probability≥0 . 8 as thresholds [63] . The biological replicates used for RNA-seq were highly reproducible . These datasets ( RPKM ) were subjected to hierarchical cluster analysis using the software HCE3 . 5 ( http://www . cs . umd . edu/hcil/hce/ ) to determine the groups of genes with similar expression patterns for a different group of regulons . The Blast2go software v3 . 0 was used for the gene ontology analyses ( https://www . blast2go . com/ ) [64] . Secreted proteins were predicted using SignalP 4 . 1 ( http://www . cbs . dtu . dk/services/SignalP/ ) . Secondary metabolism gene clusters were identified using annotated proteins in P . oxalicum [24] . Protein sequence alignments were performed among the P . oxalicum , T . reesei , and N . crassa proteomes using the BioEdit Sequence Alignment Editor software ( http://www . mbio . ncsu . edu/BioEdit/bioedit . html ) . Culture supernatants were collected after shifting to cellulose for 96 hours by filtrating using 0 . 22 μm PES membrane , and then the supernatants were desalted with 10 kDa molecular cut-off membrane , and were precipitated by acetone and trichloroacetic acid ( 20:1 ) . The obtained protein powders were dissolved in denaturation buffer ( 0 . 5 M Tris-HCL , 2 . 75 mM EDTA , 6 M Guanadine-HCL ) , and were then reduced using 1 M DTT at 37°C for 1 hour . The following alkylation was performed using iodoacetamide for 2 hours away from light , and the samples were desalted and collected using a Microcon YM-10 Centrifugal Filter Unit . The obtained protein samples were digested thoroughly using trypsin for 12 hours , and these peptide mixtures were desalted with a ZipTip C18 column . These collected secretome samples were further separated on a C18-reversed phase column and then directly mounted on the electrospray ion source of a mass spectrometer . The peptides were subjected to nanoelectrospray ionization , followed by tandem mass spectrometry ( MS/MS ) in an LTQ Orbitrap Velos Pro coupled with high-performance liquid chromatography . Intact peptides were detected in the Orbitrap at 60000 resolution . Peptides were selected for MS/MS using a collision-induced dissociation operating mode with 35% normalized collision energy setting . Ion fragments were detected in the LTQ Orbitrap . A data-dependent procedure that alternated between one MS scan , followed by 10 MS/MS scans , was applied for the 10 most abundant precursor ions above the 5000 threshold ion count in the MS survey scan with the following Dynamic Exclusion settings: 2 repeat counts , 30 s repeat duration , and 120 s exclusion duration . An electrospray voltage of 2 . 2 kV was applied . For the MS scans , the m/z scan range was 350 Da to 1800 Da . Mass spectrometry data processing was performed using the Mass-Lynx software ( version 4 . 1 , Waters ) . LC-MS/MS analysis data were identified by searching the P . oxalicum protein database ( http://genome . jgi . doe . gov/Penox1/Penox1 . home . html ) . The DNA-binding domain of ClrB ( 1–163 amino acids ) was PCR-amplified from the P . oxalicum 114–2 genome DNA using the primers ClrB-RTF and ClrB-RTR ( S1 Table ) . The resulting amplicon was digested using EcoRI and BamHI , and then inserted into the expression vector pGEX-4T-1 at the GST downstream with corresponding restriction sites . The correct fusion plasmid was confirmed via nucleotide sequencing , and then transformed into E . coli BL21 ( DE3 ) . Parental and recombinant strains were cultured in a lysogeny broth medium and were induced by 0 . 05 mM isopropyl-β-D-thiogalactopyranoside ( IPTG ) at 30°C and 150 rpm for 8 hours to induce the GST alone and the GST-ClrB binding-domain production . Protein purification was performed by using Glutathione Sepharose 4B beads after cell ultrasonic decomposition according to the product manual . The protein concentration was then measured using a Bradford Kit according to the instructions of the manufacturer . A 2 kb promoter of PDE_07945 was then amplified from the P . oxalicum 114–2 genome DNA using the primers PDE_07945-F and PDE_07945-R ( S1 Table ) , and then purified using a gel extraction kit according to the instructions of the manufacturer . The DNA concentration was determined using a UV-Vis Spectrophotometer Q5000 . GST alone or GST-ClrB binding-domain was mixed in the binding buffer ( containing 100 mM Tris-HCl , 100 mM KCl , 10 mM EDTA , 2 . 5 mM DTT , and 20% Ficoll-400 , supplementing 1 μg Poly ( dI-dC ) to avoid unspecific binding ) with purified DNA ( 20 ng ) at room temperature for 10 min for electrophoretic mobility shift assay . The protein-DNA mix was then separated via gel electrophoresis , stained by ethidium bromide , and then visualized . The protein and DNA complex were retardant relative to free DNA . Binding reaction was performed by gradient increasing the protein content to avoid artificial results . GST alone was used as negative control . The Matchmaker GAL4 two-hybrid system 3 was used for yeast two-hybrid assays . Full-length ORFs of the transcription factors ClrB , CreA , AmyR , and XlnR were PCR-amplified using the P . oxalicum 114–2 cDNA as templates ( with primers listed in S1 Table ) . All the amplicons were cloned into the plasmid pGAD-T7 with the corresponding restriction sites , leading to AD-ClrB , AD-CreA , AD-XlnR , and AD-AmyR . Similarly , the full-length creA , amyR , and xlnR were cloned into the partner plasmid pGBK-T7 , which generated BD-CreA , BD-AmyR , and BD-XlnR , respectively . All the fused plasmids were confirmed via nucleotide sequencing . The plasmids pGAD-T7 or AD-X coupling with pGBK-T7 or BD-X in pair were co-transformed into S . cerevisiae AH109 and were cultivated on an SD medium without Trp and Leu for 3 days at 30°C . Protein–protein interaction assay was performed on SD plates without Leu , Trp , and His , and on YPD plates to avoid artificial results . pGBKT7-53/pGADT7-T and pGBKT7-Lam/pGADT7-T pairs were used as internal positive and negative controls , respectively . Moreover , AD-ClrB , AD-CreA , AD-xlnR , and AD-AmyR paired with an empty pGBK-T7 introduced into AH109 were used to eliminate false positive results . The RNA-seq data have been deposited in NCBI's Gene Expression Omnibus with accession number GSE69298 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE69298 ) . | Cellulolytic fungi have evolved into sophisticated lignocellulolytic systems to adapt to their natural habitat . This trait is important for filamentous fungi , which are the main source of cellulases utilized to degrade lignocellulose to fermentable sugars . Penicillium oxalicum , which produces lignocellulolytic enzymes with more diverse components than Trichoderma reesei , has the capacity to secrete large amounts of cellulases . Meanwhile , cellulase expression is regulated by a complex network involved in many transcription factors in this organism . To better understand how cellulase genes are systematically regulated in P . oxalicum , we employed molecular genetics to uncover the cellulolytic transcription factors on a genome-wide scale . We discovered the synergistic and tunable regulation of cellulase expression by integrating cellulolytic regulators and their target genes , which refined our understanding of transcriptional-regulatory network as a “seesaw model” in which the coordinated regulation of cellulolytic genes is established by counteracting activators and repressors . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Synergistic and Dose-Controlled Regulation of Cellulase Gene Expression in Penicillium oxalicum |
The IAPE ( Intracisternal A-type Particles elements with an Envelope ) family of murine endogenous retroelements is present at more than 200 copies in the mouse genome . We had previously identified a single copy that proved to be fully functional , i . e . which can generate viral particles budding out of the cell and infectious on a series of cells , including human cells . We also showed that IAPE are the progenitors of the highly reiterated IAP elements . The latter are now strictly intracellular retrotransposons , due to the loss of the envelope gene and re-localisation of the associated particles in the course of evolution . In the present study we searched for the cellular receptor of the IAPE elements , by using a lentiviral human cDNA library and a pseudotype assay on transduced cells . We identified Ephrin A4 , a GPI-anchored molecule involved in several developmental processes , as a receptor for the IAPE pseudotypes . We also found that the other 4 members of the Ephrin A family –but not those of the closely related Ephrin B family- were also able to mediate IAPE cell entry , thus significantly increasing the amount of possible cell types susceptible to IAPE infection . We show that these include mouse germline cells , as illustrated by immunohistochemistry experiments , consistent with IAPE genomic amplification by successive re-infection . We propose that the uncovered properties of the identified receptors played a role in the accumulation of IAPE elements in the mouse genome , and in the survival of a functional copy .
Mammalian genomes are filled with numerous copies of mobile genetic elements . Among them , endogenous retroviruses are the remnants of infectious retroviruses that once infected the germline of their host and have since then been transmitted from one generation to the other following a mendelian pattern ( reviewed in refs . [1]–[3] ) . After this initial insertion within the genome , some of these elements were recruited by their host and some of their open reading frames changed into “ordinary” cellular genes which now fulfil physiological functions , like the syncytin genes that are involved in the formation of the placenta [4]–[8] . However , the bulk of these elements still behave like transposons and increase their copy number after the initial invasion of the germline . While doing so , they can cause insertional mutagenesis , either by directly interrupting open reading frames or by inducing dysregulations of cellular genes ( reviewed in [3] ) . The amplification is thought to have initially proceeded via successive re-infections of the germline using a traditional extracellular infection route . However , the most successful families of elements ( with regards to their copy number ) identified so far have switched to a strictly intracellular amplification mechanism that does not require the viral particles to be exposed to the extracellular compartment and that makes them much more efficient ( reviewed in [9] , [10] ) . This switch in the amplification strategy is usually correlated to the loss of the envelope ( env ) gene that encodes the membrane glycoprotein responsible for the binding of the particle to a cellular protein used as a receptor , and a modification of the intracellular trafficking of the particles via an alteration of the N-terminal part of the structural Gag protein . These changes can be seen in pan-mammalian ERV-L elements , as well as in the mouse MusD and IAP ( Intracisternal A-type Particles ) elements [10]–[12] , and lead to an intracellularisation of the elements . During the process , the latter increase their amplification efficiency within their current host but completely loose their autonomy , being unable of re-infection and direct horizontal transfer , and thus cannot colonise new species anymore . The mouse IAPE family is particularly interesting in this respect: we previously demonstrated that it is the progenitor of the intracellular IAP elements [10] , which are probably the most successful and active family of retrotransposons in the mouse , being responsible for an estimated 10% of the de novo mutations occurring in laboratory animals . But at the same time IAPEs also survived as infectious elements , with an identified mouse endogenous proviral copy being able to produce fully functional particles that can re-infect a variety of cells from different species . The IAPE family is for this reason quite special , since the progenitors of the other widespread intracellularised elements have disappeared from the genome of their host . We wondered whether this specificity could be due to a particular tropism of the particles , and tested this hypothesis by searching for the IAPE cellular receptor .
To identify the protein used as a receptor by the IAPE endogenous retrovirus , we made use of a lentiviral cDNA library generated from human Huh7 cells ( see Methods ) . It was selected because Huh7 cells can be infected by retroviral pseudotypes carrying the IAPE Env ( data not shown ) and are therefore certain to express the IAPE envelope receptor . As schematised in Figure 1A , these cDNA were introduced into simian Vero cells , which are resistant to infection by the IAPE Env pseudotypes , using VSV-G lentiviral pseudotypes at a multiplicity of infection ( MOI ) of approximately 6 . The cells were grown for 3 days to allow expression of the transduced cDNAs before being submitted to two cycles of infection by IAPE Env pseudotypes containing a hygromycin resistance gene ( final MOI estimated to be more than 1 ) , or by control pseudotypes containing the hygromycin resistance gene ( hygroR ) but with no envelope proteins . After three days of amplification , the cells were submitted to hygromycin selection . Because of the high background due to the infection protocol used with the pseudotypes ( high MOI and spinoculation ) , we obtained a high number of hygroR clones , with no clear difference between the IAPE Env and no Env pseudotype-infected cell populations . This was not surprising since the theoretic frequency of cells transduced by a given cDNA ( approx . 1 out of 10 , 000 cells , assuming all genes are equally expressed ) is much lower that the background level of infection ( 1 cell out of 100–1000 ) . The clones were thus subjected to a second round of selection using IAPE Env pseudotypes encoding the red fluorescent protein mCherry that we applied directly on the original plates ( Figure 1A ) . The plates were then manually screened for red fluorescence to identify clones containing multiple ( >10 ) independent infection foci . 42 such clones were identified , grown individually and then assayed a third time for their susceptibility to infection with IAPE Env using GFP-containing pseudotypes . The 5 more infectable ones ( IAPE Env titre increased by 10–100 fold as compared to the parental Vero cell line ) were selected for further analysis of their Huh7-derived cDNA content . To characterise the cDNA present in the selected clones , we extracted their genomic DNA and total RNA and subjected them to PCR/RT-PCR using primers located within the lentiviral vector and surrounding the cDNA cloning sites . These PCR analyses showed that all the selected clones contained more than one cDNA , which was expected due to the high MOI used during the cDNA library transduction . In addition , the 5 clones can be split in two groups with regards to their cDNAs content , indicating that these 5 clones originated from only two initial transduction events . This is possible since the cells were amplified before they were infected with the IAPE Env pseudotypes . Most of the cDNAs we identified were not considered as potential receptors for the IAPE Env , either because they were truncated at their 5′ end and did not contain a complete ORF , or because they corresponded to soluble intracellular proteins . However , two of the clones contained a full-length cDNA of TMEM9 ( the gene for Transmembrane Protein 9 ) ( clones number 2 and 3 in Figure 1B ) , while the last three ( number 1 , 4 and 5 ) had been transduced with a cDNA of EFNA4 ( the gene for Ephrin A4 ) , both of which encode membrane-associated proteins . TMEM9 was the less likely candidate , as it is described as a strictly intracellular protein , associated with the endosomal membranes [13] ( see Figure 1B ) . In addition , qRT-PCR experiments indicated that it is already moderately expressed in the parental Vero cells and that its expression was only increased by approximately 4 times in one of the two clones containing its cDNA , with its level being mostly unchanged in the four other positive clones ( Figure 1C ) . Its ectopic expression in Vero cells , that we achieved either by transfection or by lentiviral transduction , did not render the cells susceptible to infection by the IAPE Env pseudotypes ( not shown ) , definitively demonstrating that TMEM9 is not a receptor for the IAPE Env . The other putative receptor we identified , Ephrin A4 , was a much better candidate . As with the other members of the ephrin A family , it is a plasma membrane protein , attached by a GlycosylPhosphatidylInositol ( GPI ) anchor , that can interact with a set of integral membrane proteins called EphA ( Ephrin A receptor proteins ) ( Figure 1B ) . In vivo , these interactions are widely used in developmental processes and axonal guidance ( reviewed in [14] , [15] ) . By qRT-PCR , we found that this gene is poorly ( if at all ) expressed in the Vero cell line and its transcript level is increased by 50–500 fold in the five clones we had identified as positive for IAPE Env infection ( Figure 1C ) . Using specific primers in a PCR reaction performed on genomic DNA , we checked that only three of these clones contained an EFNA4 cDNA , indicating that the other two ( that possess the TMEM9 cDNA ) had overexpressed their endogenous copy via an unknown mechanism . All these findings made it a promising candidate as an IAPE Env receptor . To test it directly , we overexpressed this gene in Vero and WOP ( a SV40-transformed murine fibroblast cell line ) cells , and tested if it made them more susceptible to infection by IAPE Env-bearing pseudotypes containing either the GFP or the LacZ reporter gene ( see scheme in Figure 2A ) . As shown in Figure 2B and C , these two cell lines are naturally resistant to infection by the IAPE Env pseudotypes . However , the same cells expressing Ephrin A4 ( via a lentiviral vector ) can be very efficiently infected by the IAPE Env pseudotypes . This effect is specific , since it did not make the Vero cells susceptible to infection by the Friend ecotropic Env pseudotypes , nor the WOP cells infectable by the syncytin-2 ( syn2 ) or xenotropic MLV ( Xeno ) Env pseudotypes . In these two cell lines , the titre of VSV-G pseudotypes was not modified , indicating that the gain in the IAPE Env titre is not due to a general increase in the susceptibility of these cell lines to lentiviral pseudotypes . This set of experiments indicated that the EFNA4 cDNA we identified in the Vero clones is responsible for their acquired susceptibility to IAPE Env pseudotypes . We then constructed an expression vector for a His-tagged soluble IAPE Env SU subunit , as previous studies with other retroviral envelopes had shown that such constructs can be used to stain cells expressing the cognate receptors [16] , [17] . In FACS experiments , the IAPE soluble SU protein could only label WOP cells that had been previously transduced with an expression vector for Ephrin A4 ( Figure 3A ) , whereas an irrelevant His-tagged SU protein ( derived from the syncytin1 envelope protein ) did not give any staining , consistent with these cells not expressing a functional receptor for syncytin1 . The specific staining observed with the IAPE soluble SU protein indicates that the acquired infectability of the Ephrin A4-expressing WOP cells by the IAPE pseudotypes is linked to the ability of these cells to bind the IAPE envelope protein . However this did not rule out that Ephrin A4 could act as an intermediate , inducing the expression ( or potentially altering the subcellular localisation ) of another protein that would be the “true” receptor . We thus set up the reverse experiment: 293T cells were transiently transfected by an expression vector for either the IAPE Env , or a control Env . Two days post transfection , these cells were stained using a soluble , Fc-tagged Ephrin A4 protein ( Ephrin A4-Fc ) or a control Fc protein , and a fluorescent secondary antibody directed against the Fc domain . The Ephrin A4-Fc fusion protein has been successfully used to label the natural ligands of Ephrin A4 [18] , and we used it to test if it would also bind the IAPE Env . As shown in Figure 3B , we could detect by FACS analysis a strong staining with the Ephrin A4-Fc protein in the IAPE Env expressing cells , whereas we did not get any staining with the control Fc protein in the IAPE Env expressing cells and the control amphotropic MLV ( Ampho ) Env expressing ones . Thus , expression of the IAPE Env by a cell increases the specific binding of Ephrin A4 . Finally , to definitely demonstrate a physical interaction between the 2 proteins , we performed a pull-down assay using the soluble recombinant version of the 2 proteins described above , together with control proteins ( see scheme in Figure 3C ) : IAPE or syncytin1 His-tagged SU proteins were mixed with the soluble Ephrin A4-Fc protein ( or controls ) and incubated for 1 hour at 37°C . The Fc-tagged proteins were then pulled down using protein A-agarose beads , the beads were washed and the presence of associated SU proteins that may have co-precipitated was tested by Western blot analysis using an antibody directed against the His tag . As shown in Figure 3C , the IAPE SU protein was efficiently pulled down only when incubated with the Ephrin A4-Fc protein . The syncytin1 control SU protein was never recovered , indicating that the observed interaction is specific . This clearly shows that the IAPE Env and Ephrin A4 proteins physically interact . Altogether , these results demonstrate that the Ephrin A4 protein is a receptor for the IAPE Env . Since the cDNA library we used to identify the receptor of the IAPE Env was made from Huh7 cells , we tested de facto the human version of this gene for its receptor activity . However , IAPE elements are murine endogenous retroviruses and , in the course of our study , we could not identify a mouse cell line that was susceptible to infection by IAPE Env-pseudotyped viruses , which could have indicated the IAPE family was behaving as a xenotropic retrovirus . We thus decided to test the mouse version of Ephrin A4 for its activity as a receptor , as well as the rat one since it is another species containing IAPE elements . The cDNAs of these two genes ( see Methods ) were cloned into a lentiviral vector that we used to transduce mouse WOP cells before challenging them with IAPE Env pseudotypes , as described previously . The results of this experiment are shown in Figure 4A . As expected for cells not expressing a functional receptor , the presence of IAPE Env on the pseudotyped particles does not notably increase the titre as compared to the no-Env control pseudotypes ( average fold increase in the untreated cells 1 . 6±0 . 5 ) . As expected , the fold increase observed for cells transduced with a control gene ( 1 . 1±0 . 3 ) is not significantly different ( unpaired Student test ) , unlike what is observed when the cells are transduced with the EFNA4 cDNAs ( fold increases: 231±151 , 16±7 and 16±10 for the human , mouse and rat EFNA4 genes , respectively; p<0 , 05 in all 3 cases ) . This indicates that the mouse and rat genes can be used as receptors by the IAPE Env , even though they are around ten times less efficient than the human version of Ephrin A4 . We ensured both by qRT-PCR ( not shown ) and staining for Ephrin A proteins ( Figure S1 ) that the expression levels of all constructs were similar and cannot account for the observed differences . To characterise further the interaction between the IAPE Env and Ephrin A4 proteins , we used the soluble His-tagged IAPE SU protein as a probe and stained WOP cells transduced with the different versions of the EFNA4 gene or a control . We were able to show that the lower receptor activity we observed with the rodent EFNA4 genes is linked to a marked decrease in their IAPE Env binding ( Figure S1 ) . As mentioned earlier , Ephrin A4 is a member of a multigenic family of proteins , the Ephrin As , that are all related GPI-anchored membrane proteins . We thus decided to test all five members for their activity as a receptor for the IAPE Env , as well as the three EFNBs genes , which are also related , but encode integral membrane proteins ( called Ephrin B proteins ) ( reviewed in [14] , [15] ) . We cloned all 8 mouse cDNAs , introduced them in a lentiviral vector and tested them as previously described . Figure 4B shows the IAPE Env pseudotype titres we measured on the transduced WOP cells , expressed as percentages of the values obtained with the human version of EFNA4 . As shown in the figure , none of the Ephrin B proteins can be used as a receptor by the IAPE Env , whereas all five Ephrin A members are functional ( see legend for statistical analysis ) . However , three of them ( Ephrin A1 , 3 and 4 ) show only limited receptor activity , whereas Ephrin A2 and Ephrin A5 are nearly as efficient as the human Ephrin A4 protein . As before , we checked that these differences are not due to variations in the Ephrin expression levels ( Figure S1 and qRT-PCR not shown ) . Finally , staining of the transduced cells with the soluble His-tagged IAPE SU protein ( Figure S1 ) indicated that the mouse Ephrin A2 and Ephrin A5 proteins have a higher affinity for the IAPE Env compared to the other members of the family for which we could not detect any specific binding . This could account for the differences we observed in their receptor activity . The experiments above indicate that each of the five members of the Ephrin A family can be used as a receptor by the IAPE Env in ex vivo experiments . In a previous study , we demonstrated that the IAPE elements behave like infectious retroviruses , i . e . they produce extracellular particles that can infect cells , but they are unable to undergo intracellular retrotransposition cycles , unlike the related IAP elements [10] . The mouse genome contains around 200 copies of this family , indicating that it replicated quite successfully during rodent evolution . We thus tried to detect expression of the Ephrin A proteins in the germline of mice , since their presence should be necessary to account for the amplification of the IAPE elements through successive re-infections of the germline . First , we quantified the expression of the five EFNA genes in a panel of mouse organs using qRT-PCR . We could detect a strong expression of some of these genes in the embryos and the adult brain , as expected for genes involved in developmental processes and axon guidance . We could also detect some expression for EFNA1 , 2 , 4 and 5 in the adult ovary , and for EFNA2 and 3 in the testis ( Figure S2 ) . We therefore performed immunohistochemistry experiments on cryosections of these two organs in order to confirm these data , using as a probe a recombinant EphA7-Fc protein ( a soluble form of EphA7 that can bind all-Ephrin A proteins , [14] ) , as previously described in ref . [19] . As shown in Figure 5 , we could see a specific staining on both organs ( panels A , B for the ovary , C , D for the testis ) , not obtained with the control “Fc-only” samples ( panels A and C ) . In the ovary , a strong staining was seen in the oocytes , as well as to a lower extent in some cells of the growing follicles . In the testis , we saw also a specific staining of some cells within the seminiferous tubules . Some of the staining was quite a distance within the tube ( panel D ) , in a location containing only germline cells since the somatic Sertoli cells are restricted to the periphery of the tubes . In both cases , the staining was strong and rather ubiquitous , which may be due to the fact that the probe can detect all Ephrin A proteins . We thus did a series of experiments using antibodies specific for Ephrin A2 or Ephrin A5 , which are the most efficient murine receptors for IAPE Env . As shown in Figure S3 , in the ovary each of these two antibodies gave a specific staining pattern , with Ephrin A2 being detected mostly in the oocytes and in the interstitial cells of the ovary stroma whereas Ephrin A5 was detected mostly in follicle cells , and at a lower level in the oocytes . This is consistant with the staining observed using the EphA7-Fc probe that stains all Ephrin A proteins , and confirms that efficient IAPE receptors are expressed in murine female germ cells . In the testis , the Ephrin A2 antibody gave a staining similar to that observed using EphA7-Fc , with spermatozoa stained as well as some dispersed cells inside the seminiferous tubules . The Ephrin A5 antibody gave a less intense signal , and was found mostly in spermatozoa . According to these data , the expression pattern of the Ephrin A proteins , found both in the oocyte and in male germline cells , is thus compatible with their role in the amplification of the IAPE family of endogenous retroviruses .
In this study , we set to identify the protein used by the endogenous retroviral family IAPE to enter its target cells . We used a well-established strategy aiming at complementing a refractory cell line with a cDNA library in order to identify genes able to render the cells sensitive to infection . With this method , we found that the human gene EFNA4 encodes a functional receptor , and that its mouse homolog , as well as the other members of the Ephrin A family , also function as receptors for IAPE . By using recombinant soluble proteins , we could further demonstrate a direct interaction between Ephrin A4 and IAPE Env , ruling out any artefact in the screening that could have led us to the identification of an inducer of the bona fide receptor gene . The Ephrin A proteins are all GPI-anchored membrane proteins that are used in vivo as ligands for the multigenic ephrinA receptor membrane proteins , and are involved in numerous biological processes ranging from axonal guiding to insulin regulation or immune processes ( reviewed in [14] , [15] , [20] ) . Over the years , a series of cellular proteins used by retroviruses as receptors have been identified ( reviewed in [21] , [22] and see Figure 6A ) . In most cases , the encoded proteins have been shown to contain several transmembrane domains . In other cases , the receptor possesses a single transmembrane domain , like the transferrin receptor that is used by the Mouse Mammary Tumor Virus ( MMTV ) [23] . But few occurrences of GPI-anchored proteins used as receptors have been reported: there is hyaluronidase 2 ( Hyal2 ) , the receptor of Jaagsiekte Sheep RetroVirus ( JSRV ) [24] , and one of the isoforms of TVA , the receptor used by the avian sarcoma and leukosis virus ( the other isoform being a single transmembrane protein ) [25] . The apparent oddity of the Ephrin A proteins as receptors , with their GPI-anchor , may mostly be due to the fact that the majority of the receptors identified so far are used by gammaretroviral and closely related envelope proteins . All of them share a common organisation ( reviewed in [2] , [26] , [27] ) : their TM subunit is particularly conserved , especially around the so-called immunosuppressive domain ( CKS17 ) that is followed by a CX6–7C ( C ) motif , and even if their SU subunits are less conserved , they still possess common features , including a CWLC ( consensus CXXC ) motif that is thought to be involved in SU-TM interaction through the TM CX6–7C ( C ) motif . This common structure may be a reason why they all recognise a same class of proteins , containing multiple membrane spanning domains ( Figure 6A ) . IAPE or JSRV Env belong to a far less described group of retroviral envelope proteins , whose only features shared with the gammaretroviral group are a furin SU-TM cleavage site ( R , X , R/K , R ) and the TM CX6–7C ( C ) motif [26] . No similarity between the 2 groups can be detected within the SU subunit . It is therefore likely that this second group of envelope proteins may have evolved to make use of a different subset of receptors ( Figure 6B ) . Another unusual feature of the IAPE envelope we uncovered in this study is its apparent loose recognition specificity , since all 5 mouse Ephrin A proteins can be used as a receptor . These proteins are all related , but they have evolved independently for millions of years , which has resulted in a significant divergence ( identity rate between 62 and 68% in amino acids ) . Accordingly , considering this rather low conservation , it was unexpected to observe that the five proteins are all functional receptors . However , this level of conservation is probably enough to maintain common structural features enabling recognition of all five proteins by a same molecule . This is supported by the natural ligands of the Ephrin A proteins , the EphA receptors , which show a broad specificity too: most of them can recognise all or most of the Ephrin A proteins , even though the affinity for one or the other Ephrin A can vary by more than one log as measured in ex vivo assays ( reviewed in ref . [14] ) . It is thus not so surprising to have the IAPE envelope showing the same sort of general family-wide recognition . It is also in agreement with the observation that the Nipah and Hendrah viruses can use both Ephrin B2 and Ephrin B3 proteins as their receptor , even if Ephrin B2 is preferred [28] , [29] . Interestingly we could not detect any similarity between the IAPE SU domain of the envelope ( responsible for the recognition of the receptor ) and the EphA proteins , suggesting that the ability to interact with the Ephrin A proteins was a de novo acquisition by the IAPE retroviral elements , and not the result of a recombination with a cellular copy of an EphA gene . On an evolutionary point of view , the IAPE use of the 5 Ephrin A proteins as receptors is interesting considering its endogenous status . Until now among the reported cases , only one or sometimes two different proteins can be used as a receptor by a given retroviral Env ( reviewed in [21] , [22] ) . With all Ephrin A proteins being functional receptors , the IAPE family seems rather unusual . However , it could be hypothesised that this feature has been a great asset for the amplification and survival of the family . First because of the very nature of the EFNA genes , which are involved in a series of developmental processes and are widely expressed ( reviewed in [14] , [20] and data in Figure 5 and Figures S2 and S3 ) , particularly very early in the development , when the germline is still readily accessible to viral particles . This high expression combined with the ability to infect through any of the five Ephrin A proteins suggests that the number of possible target cells is quite high . Second because , with these five proteins being functional receptors , it seems virtually impossible for the host to evolve so as to be protected against re-infection of its germline by IAPE viral particles . Such an escape mechanism is a common theme in host-pathogen interactions , and indeed has occurred recently in the mouse lineage , with most modern laboratory mice being protected from their own xenotropic endogenous proviruses thanks to a recent point mutation in their unique xpr1 receptor gene , which renders it non-functional for xenotropic virus entry ( reviewed in [30] ) . In the case of the IAPE family however , the inactivation of the five Ephrin A proteins as receptors while maintaining their physiological function is most certainly an impossible task , and this could explain why this family was so successful and is still maintained within the mouse genome concomitantly with its envelope-less strictly intracellular IAP progeny .
CMV-driven mammalian expression vectors for the IAPE Env ( IAPE D2* ) and the other envelope proteins used as controls have been described previously [10] , [31] , as well as the plasmids used to generate lentiviral HIV-1 pseudotypes [32] . New self-inactivating lentiviral vectors ( pSIN ) were derived from pHR'SIN-cPPT-SEW [33] by replacing the GFP open reading frame ( BamHI – NotI fragment ) by that of other genes: LacZ gene , hygromycin resistance gene , Cherry fluorescent protein encoding gene , cDNAs of EFNA and EFNB genes . The latter were obtained by RT-PCR performed on purified RNA extracted from mouse embryos ( mouse genes ) or the 208F cell line ( rat EFNA4 ) . The expression plasmids for the soluble Fc-tagged versions of Ephrin A4 , EphA2 and the Fc-only control were described previously [18] . The vector used to produce the soluble His-tagged SU IAPE env subunit was generated by replacing the TM domain in the IAPE Env expression plasmid by a VHRGSH6 sequence placed just downstream from the cleavage site which was changed into an AAAR sequence . The control plasmid encoding the soluble His-tagged SU of Syncytin1 was generated as described in ref . [17] . All fragments generated by PCR were sequenced to ensure that no mutation had been introduced during this step . The cDNA library was custom-made by Invitrogen using mRNA extracted from Huh7 cells , which was reverse-transcribed and cloned in the pLenti6/V5-Dest lentiviral vector . Total RNAs were extracted using the RNeasy extraction kit ( Qiagen ) and treated with DNase I ( Ambion ) . 1 µg was used for each RT reaction using the MLV reverse-transcriptase ( Applied Biosystems ) . Quantitative PCR was done using 5 µL of a 1/25 dilution of the cDNAs in a final volume of 25 µL by using SYBR Green PCR Master Mix ( Applied Biosystems ) . PCR was carried out using an ABI PRISM 7000 sequence detection system . The efficacy of the PCR reaction was checked for each primer pair using serial dilutions of a reference sample and found to be more than 90% . The transcript levels were normalized relative to the amount of RPLO transcripts using the ΔΔCt method . Samples were assayed in duplicate . 293T , WOP and Vero cells were grown at 37°C and 5% CO2 in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% heat-inactivated foetal calf serum ( Invitrogen ) , 100 µg/mL streptomycin , and 100 U/mL penicillin . Hygromycin selection was performed for two weeks using 400 u/mL hygromicin B ( Calbiochem ) . For transfection , 293T cells were seeded at approximately 20% confluence . The day after seeding , they were transfected using Fugene 6 ( Roche ) or JetPrime ( Polyplus transfection ) following the manufacturer instructions , except we used 3 µg total DNA per 6 cm dish ( this quantity was adapted proportionally to the dish surface when transfections were performed in different scales ) . The cells were washed and placed in fresh medium the day after transfection . For virus production , we used the following ratio for the plasmids: 8 . 91 ( HIV Gag-Pol expression vector ) : 1; pSIN lentiviral vector: 1 . 5; Env expression vector: 0 . 3 . The viral particles-containing supernatants were collected at day 3 post transfection , and passed through 0 . 45 µm filters before use . Infections were performed by adding viral supernatants to target cells in the presence of polybrene ( 8 µg/mL ) , and complemented with cyclosporine A ( 5 µM ) in the case of Vero cells to abrogate TRIM5α–mediated restriction of the HIV1-derived particles [34] . In some experiments , the infection rates were increased by subjecting the cells to spinoculation ( centrifugation for 2h30 at 1200 g , 25°C ) just after adding the viral supernatants ( this step also slightly increased the background infection level measured with the No Env Pseudotypes ) . Infection was detected three days post infection by staining the cells with X-Gal for the LacZ reporter gene , or by FACS for the GFP reporter gene . In the latter case , we used samples containing 5–15% GFP positive cells and calculated a viral titre from the volume of supernatant used for the infection and the number of target cells that were seeded . The 5–15% window was chosen to ensure we were still in the linear zone of the infection curve , where positive cells have only been infected by one particle . For the cell staining experiments , we used Fc-tagged soluble recombinant proteins ( Ephrin A4-Fc , EphA2-Fc and the control Fc-only , described in [18] ) and His-tagged soluble Env SU ( IAPE-His and Syncytin1-His ) as “probes” . These were produced by 293T cells in a serum-free medium ( OptiMEM , Invitrogen ) after transient transfection with a CMV-driven mammalian expression vector using Fugene6 ( Roche ) . The protein-containing supernatants were collected at day 2 post transfection and passed through 0 . 45 µm filters before use . Samples were analysed by Western blot to ensure the proteins were produced at similar levels in the supernatants . The cells to be stained ( 106 per sample ) were detached using PBS 5 mM EDTA , washed in PBS and incubated for 1 h at 37°C in neat supernatant containing the recombinant protein , washed twice in PBS , 2% FCS , 0 . 1% sodium azide and incubated for 30 min with a fluorescent antibody ( Alexa 488 Anti mouse IgG , Molecular Probes or Alexa 488 PentaHis antibody , Qiagen ) at 4°C . They were then washed 3 times in PBS , 2% FCS , 0 . 1% sodium azide , resuspended in PBS , 0 . 1% sodium azide and fixed with paraformaldehyde before FACS analysis . Fc-tagged and His-tagged soluble recombinant proteins were produced as described above . Protease inhibitors ( cOmplete protease inhibitors cocktail tablets , Roche ) were added to the supernatants containing the recombinant proteins . 700 µL of each supernatant ( His-tagged SU and Fc-tagged protein ) were mixed and incubated for 1 h at 37°C with gentle agitation . Then protein A-agarose beads ( Pierce , 20 µL packed beads per sample ) were washed twice in PBS and saturated in PBS , 0 . 5% BSA before they were added to each sample complemented with BSA ( 0 . 5% final concentration ) . After a 1 h incubation at 37°C with gentle agitation , the beads were washed 5 times in PBS , Tween 0 . 1% before 150 µL of Laemmli buffer containing 10% ß-Mercaptoethanol were added to each sample that was then boiled for 5 min . For Western blot analyses , reduced samples ( 5 µL of neat supernatant or 8 µL of the pull-down assay product ) denatured in Laemmli buffer or LDS loading buffer ( Invitrogen ) were subjected to SDS-PAGE using gradient precast gels ( Novex 4–12% Bis-Tris gels , Invitrogen ) . After migration , proteins were transferred onto a nitrocellulose membrane using a semi dry transfer system . His-tagged proteins were detected using the Penta-His HRP antibody ( Qiagen ) , and the Fc-tagged ones using the ECL sheep anti mouse IgG ( HRP-linked F ( ab′ ) 2 fragment , Amersham ) . The antibody directed against the IAPE Env has been previously described [10] , and the antibody used to detect the amphotropic MLV Env was a goat anti Rauscher Leukemia Virus gp70 [originally obtained from the National Cancer Institute , Frederick , MD] . 8–9 week old C57Bl/6 mice were used . The testis and ovaries were fixed by immersion in 4% paraformaldehyde in 0 . 1 M sodium phosphate pH 7 for 5 h , followed by 20% sucrose in PBS overnight . The tissues were embedded in Tissue-Tek OCT ( Sakura ) and sections were cut using a cryostat . Staining of total Ephrin A proteins was performed using the EphA7-Fc fusion protein ( stains all 5 Ephrin A proteins , see [14] ) or its control Fc-only protein ( 10–25 µg/mL , both purchased from R&D Systems ) essentially as described in ref . [19] , except that the blocking was performed in the presence of rat anti mouse CD16/CD32 used at a 1/50 dilution ( Pharmingen ) . Staining of specific Ephrin A proteins was done using either a goat anti mouse Ephrin A2 antibody ( R&D systems ) or a rabbit anti Ephrin A5 antibody ( Novus Biologicals ) , following the recommendations provided . Revelation was done using Alkaline Phosphatase-linked secondary antibodies ( donkey anti-Rabbit IgG and bovine anti-Goat IgG for Ephrin A2 and Ephrin A5 , respectively , Jackson ImmunoResearch ) and NBT/BCIP substrate ( 1-Step NBT/BCIP plus Suppressor , Thermo Scientific ) . The sequences we used as references for the cloned EFNA and EFNB genes are as follows: Hs_EFNA4: NM005227; Rn_ENFA4: NM001107692 , Mm_EFNA1: NM010107 , Mm_EFNA2: NM007909 , Mm_EFNA3: NM010108 , Mm_EFNA4: NM007910 , Mm_EFNA5: NM207654 , Mm_EFNB1: NM010110 , Mm_EFNB2: NM010111 , Mm_EFNB3: NM007911 . IAPE-D2* Env sequence can be deduced from the IAPE D2 provirus [10]: AC131339 , pos: 143356–35028 . Similar results were obtained using IAPE-D1 Env whose sequence can be deduced from AC123738 , pos: 161181–152862 . This study was carried out in strict accordance with the French and European laws and regulations regarding Animal Experimentation ( Directive 86/609/EEC regarding the protection of animals used for experimental and other scientific purposes ) . The protocol was approved by the Institut Gustave Roussy Animal Experiment Committee ( MENRT n° 26 ) . | In mammals , nearly half the genome is composed of reiterated scattered sequences . Some of them , called endogenous retroviruses , have a structure similar to that observed for the integrated form of infectious retroviruses . The current theory to account for their presence is that an infectious retrovirus once infected the germline of its host . This viral genome was then transmitted to the progeny and expressed from there , producing new infectious particles , which could re-infect new germline cells and thus increase the viral genomic copy number . However no evidence has yet been provided to support this model . In this study , we identify a family of five cellular proteins , the Ephrin As , as receptors for a model mouse family of endogenous retroviruses , the IAPE elements . We analyse their expression pattern and show that both the oocytes and some male germline cells express Ephrin A proteins and can thus be infected by IAPE particles . This finding strongly supports the current model of ERVs amplification . In addition , the IAPE envelope ability to use five different cellular receptors suggests that it might be impossible for the host to evolve a resistance against this viral element , and provides a clue on how the IAPE family survived so long in the mouse genome . | [
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] | 2011 | The Mouse IAPE Endogenous Retrovirus Can Infect Cells through Any of the Five GPI-Anchored EphrinA Proteins |
In the heart , electrical stimulation of cardiac myocytes increases the open probability of sarcolemmal voltage-sensitive Ca2+ channels and flux of Ca2+ into the cells . This increases Ca2+ binding to ligand-gated channels known as ryanodine receptors ( RyR2 ) . Their openings cause cell-wide release of Ca2+ , which in turn causes muscle contraction and the generation of the mechanical force required to pump blood . In resting myocytes , RyR2s can also open spontaneously giving rise to spatially-confined Ca2+ release events known as “sparks . ” RyR2s are organized in a lattice to form clusters in the junctional sarcoplasmic reticulum membrane . Our recent work has shown that the spatial arrangement of RyR2s within clusters strongly influences the frequency of Ca2+ sparks . We showed that the probability of a Ca2+ spark occurring when a single RyR2 in the cluster opens spontaneously can be predicted from the precise spatial arrangements of the RyR2s . Thus , “function” follows from “structure . ” This probability is related to the maximum eigenvalue ( λ1 ) of the adjacency matrix of the RyR2 cluster lattice . In this work , we develop a theoretical framework for understanding this relationship . We present a stochastic contact network model of the Ca2+ spark initiation process . We show that λ1 determines a stability threshold for the formation of Ca2+ sparks in terms of the RyR2 gating transition rates . We recapitulate these results by applying the model to realistic RyR2 cluster structures informed by super-resolution stimulated emission depletion ( STED ) microscopy . Eigendecomposition of the linearized mean-field contact network model reveals functional subdomains within RyR2 clusters with distinct sensitivities to Ca2+ . This work provides novel perspectives on the cardiac Ca2+ release process and a general method for inferring the functional properties of transmembrane receptor clusters from their structure .
Mechanical contraction of the heart occurs as a result of intracellular Ca2+ release in cardiac myocytes . L-type Ca2+ channels ( LCCs ) and a packed cluster of up to 100 Ca2+-sensitive Ca2+-release channels [1 , 2] , known as ryanodine receptors ( RyR2s ) , are co-located at discrete subcellular junctions within the cell ( Fig 1A ) . These Ca2+ release units ( CRUs ) are formed by deep invaginations of the cell membrane containing LCCs , known as transverse-tubules ( TTs ) , and the junctional sarcoplasmic reticulum ( JSR ) membrane , a cisternal sheet containing the RyR2s that wraps around the TT to form a narrow subspace ∼ 15 nm in width . During excitation-contraction coupling ( ECC ) , electrical stimulation increases the probability of LCC openings and influx of Ca2+ into these subspaces . Binding of Ca2+ to the closely-apposed RyR2s [3] increases their open probability and release of Ca2+ from JSR stores in a process known as Ca2+-induced Ca2+ release ( CICR ) . Further Ca2+ release from RyR2s activates surrounding RyR2s via a local rise in subspace Ca2+ concentration . Understanding this process is critical to our understanding of cardiac physiology in health and disease . Isolated release events known as Ca2+ “sparks” underlie the cell-wide release of Ca2+ that occurs on every heartbeat when the RyR2s are activated by the opening of voltage-sensitive Ca2+ channels . Ca2+ sparks are also observed in resting myocytes when initiated by the spontaneous opening of a single RyR2 that then probabilistically triggers Ca2+ release from the rest of the cluster . Here , the probability that an RyR2 is in an open state at any point in time will be referred to as its open probability , and the probability that a sufficiently large percentage of the RyR2s open for a spark to occur will be referred to as spark probability ( pS ) . Spark probability is an important physiological parameter that in part controls the frequency of sparks [4 , 5] . There is significant experimental evidence that not all junctional RyR2 openings result in Ca2+ sparks [6–9] . These non-spark openings may in part be attributed to non-junctional RyR2s located outside the release site [10 , 11] . However , mathematical modeling suggests that junctional RyR2 openings can fail to trigger Ca2+ sparks and are sufficient to account for the non-spark openings [4 , 5 , 12 , 13] . In support of this , recent experiments using Ca2+ nanosensors targeted to the release site have demonstrated locally elevated Ca2+ concentration in the subspace due to spontaneous junctional RyR2 openings [14 , 15] . This is also consistent with the observation that the majority of Ca2+ released via non-spark openings is extruded through the Na+/Ca2+ exchanger , which is localized near the junctions [15] . Therefore there is compelling evidence suggesting that Ca2+ spark initiation is likely a probabilistic process . Many studies have implicated Ca2+ sparks in heart disease . For example , spark frequency is increased in heart failure [7 , 16] , which is associated with decreased JSR Ca2+ content and thus impaired contractile function [17] . Ca2+ sparks may also cause spontaneous Ca2+ waves [18 , 19] that promote cellular arrhythmias [20] . Heterogeneity in the Ca2+ sensitivity of RyR2 clusters has also been implicated in the occurrence of arrhythmic Ca2+ alternans [21] . Therefore factors that influence Ca2+ spark probability are likely to be involved in mechanisms driving pathological cardiac dysfunction . Advancements in super-resolution imaging techniques have enabled the study of nanoscale receptor organization in a variety of cell types [22 , 23] . In cardiac myocytes , stimulated emission depletion ( STED ) microscopy has been applied to study TT remodeling in heart failure at nanometer resolution [24] . Within the cardiac Ca2+ release sites , RyR2s are known to form tightly-packed clusters from electron microscopy studies in vivo [1] ( Fig 1B ) , which is supported by the observation that the channels organize into packed lattices with ∼ 31 nm spacing in vitro [25] . Super-resolution imaging studies of RyR2 clusters using super-resolution microscopy in cardiac myocytes have revealed that they are heterogeneous in size and shape [2] . We recently developed a three-dimensional , biophysically-detailed model of cardiac Ca2+ release events , which we will refer to as the 3D spark model . Fig 1C shows example simulations , in which a single open channel either fails or succeeds to activate the remaining RyR2s . We obtained realistic RyR2 clusters obtained using STED microscopy and showed that the precise spatial arrangement of RyR2s critically influences spark probability [4] . Larger , more compact clusters exhibited higher spark probability than smaller , fragmented ones . Representing RyR2 clusters as a two-dimensional lattice , we found that the maximum eigenvalue ( λ1 ) of the lattice’s adjacency matrix predicts the Ca2+ spark probability of the cluster . Properties of the eigenvalues and eigenvectors of a graph’s adjacency matrix have been widely studied , and λ1 in particular is known to be a measure of the interconnectedness of the graph [26 , 27] . λ1 was found to be a more accurate predictor of spark probability than is the total number of channels ( n ) , which does not consider structural aspects of the cluster . Fig 1D shows the relationship between spark probability , n , and λ1 for two RyR2 clusters analyzed previously [4] . The two embedded cluster lattices have nearly equal spark probability , despite one being much larger than the other . This is because the larger lattice contained four empty spaces in the interior . However , the λ1 values were similar for these two clusters and therefore consistently correlated with spark probability . Here we present an analytical model of the Ca2+ release process and derive the relationship between λ1 and spark probability . The model is applied to realistic RyR2 clusters obtained using stimulated emission depletion ( STED ) microscopy . We found through an eigendecomposition that some RyR2 clusters possess functional subdomains with distinct sensitivity to Ca2+ . This work outlines a unique approach to understanding CICR and provides a theoretical framework for comparing the physiological function of protein clusters based solely on structural information .
In this section , we present results using a contact network ( CN ) model of RyR2 cluster activation where channels are coupled through local interactions with their neighbors . Contact network models are widely used to study the spread of disease due to contact between infected and susceptible individuals [28] . In our model , interactions instead arise from Ca2+-dependent activation due to local influx and diffusion of Ca2+ , which causes neighboring channels to open . For simplicity , we assume that the local Ca2+ concentration gradient near an open RyR2 declines rapidly enough in space such that only adjacent RyR2s interact [4 , 29 , 30] . Each channel transitions stochastically between open and closed states ( Fig 2A ) . If an RyR2 channel i has Yi ( t ) neighboring RyR2 channels that are open , its opening rate is βYi ( t ) , where β is a constant parameter . Therefore β is the RyR2 opening rate when one nearest neighbor RyR2 is open . Note that in the full biophysical 3D spark model , the RyR2 opening rate when all neighbors are closed is very small ( ∼ 9 × 10−7ms−1 ) . Therefore we have taken this rate to be zero in this formulation . The value of β is varied in our analyses . The RyR2 closing rate , δ , is assumed to be a constant 0 . 5ms−1 . Derivation of the model and parameters are given in Methods and Models . The CN model is able to capture RyR2 gating dynamics during the initiation phase of Ca2+ sparks . We used the Stochastic Simulation Algorithm of Gillespie [31] to simulate the stochastic CN model . Fig 2B shows traces of the number of open channels ( NO ) during representative simulations of spark initiation in the 3D spark and CN models for a 7 × 7 lattice cluster . A single RyR2 is opened at t = 0 , which then triggers openings of other channels . The CN model qualitatively reproduces channel gating behavior during the initiation of the spark . In the 3D spark model , Ca2+ sparks occur with greater than 95% probability if a minimum of four channels open . Therefore , we define this as the minimum number for successful spark initiation in both models . We also assume that each RyR2 in the cluster is equally likely to open spontaneously , and so the first open channel is chosen at random . The advantage of developing the CN model is that we can derive analytical relationships between the dominant eigenvalue of the RyR2 lattice’s adjacency matrix , λ1 , and spark probability . We show ( see Eq ( 9 ) in Methods and Models ) that for a deterministic mean-field approximation of the model , RyR2 open probability decays to zero when λ 1 < δ β . ( 1 ) This implies that , in the mean-field approximation , δ/β is a stability threshold for λ1 at which RyR2 activity switches from decay to growth . While it was not immediately clear how this threshold related to the behavior of the full stochastic CN model , we expected that the model would exhibit constant spark probability when λ1 = δ/β . That is , for a set of cluster structures each with a different value of λ1 , the spark probability would be consistent across clusters when each cluster’s opening rate was set to β = δ/λ1 . Fig 2C shows the spark probability for a collection of 107 RyR2 clusters obtained using STED microscopy ( see [4] for imaging methods ) . For each simulation , λ1 was computed for the cluster and β was set to the threshold value δ/λ1 . The range of spark probabilities across all clusters was narrow ( 0 . 14±0 . 0078 ) . This was also observed when using sub-threshold values β = 0 . 5δ/λ1 ( 0 . 029±0 . 0024 ) and a supra-threshold values β = 2δ/λ1 ( 0 . 28±0 . 012 ) . Therefore spark probability is constant when β is scaled inversely with λ1 . For comparison , we also plotted spark probability when β is set to a single value across all clusters ( Fig 2D ) . In this case , spark probability increased with λ1 in agreement with the 3D spark model ( see Fig 1D ) . The CN model was able to accurately predict Ca2+ spark probability for a range of cluster geometries . We estimated the spark probabilities for a collection of 15 RyR2 clusters obtained using STED microscopy . The value of β was adjusted until the spark probabilities in the CN model correlated with those of the 3D spark model ( Fig 2E ) . Maximal correlation was achieved for β = 0 . 115 ( R2 = 0 . 939 ) , which gives the value of δ/β = 4 . 35 . Note that the theoretical value of λ1 for any cluster is bounded above by the maximum number of channel neighbors ( 4 ) [26] . Consequently , δ/β = 4 . 35 > λ1 implies that the system is always sub-threshold for any cluster structure under normal physiological conditions . The CN model also predicts spark probability for different opening rates . To show this , we first estimated pS in the 3D spark model for a 7 × 7 cluster with the opening rate scaled by a constant factor . We then scaled β = 0 . 115 by the same factor and determined pS in the CN model . This was repeated for a range of scaling factors . Noting that the closing rates δ are the same in both models , we could directly compare pS in the two models by plotting it as a function of δ/β , where β is the scaled value . For the 3D spark model , β is the value used in the corresponding CN simulation . In both models , spark probability fell rapidly as δ/β approached λ1 from the left before decreasing gradually to the right of λ1 . This suggests that spark probability is more sensitive to RyR2 gating kinetics when the opening rate is elevated . From the data in this section , we concluded that the CN model is able to accurately predict pS over a range of opening rates and cluster geometries . Cardiac Ca2+ release is actively regulated under normal conditions and modulated in various diseases . To study this regulation , we expanded the CN model by deriving a simple model of Ca2+ diffusion between RyR2 Ca2+ sources . The parameter β was estimated using this diffusion model and a model of RyR2 gating . All parameters were taken from Walker et al . [4] , except for the effective Ca2+ diffusion coefficient ( dC ) , which was adjusted to give β = 0 . 115 as determined in the previous section . A number of signaling molecules regulate RyR2 channels , affecting their opening rate . This includes RyR2 phosphorylation by Ca2+/calmodulin-dependent protein kinase II ( CaMKII ) and protein kinase A ( PKA ) [32 , 33] and JSR Ca2+ concentration [34] . Channel gating can also be altered under oxidative stress [35] and by genetic mutations [36 , 37] . As shown in Fig 3A , δ/β is inversely proportional to the channel opening rate constant ( k+ ) , reflecting the increased Ca2+ spark frequency observed under such conditions [7 , 38 , 39] . Note that the closing rate is δ and therefore scales δ/β linearly . Increasing the unitary channel current ( iRyR ) resulted in a decrease in δ/β ( Fig 3B ) . This behavior is consistent with experimental evidence [40] , in which decreased iRyR resulted in lowered spark frequency . The CN model was also sensitive to parameters affecting the diffusion of Ca2+ ions in the release site subspace . Fig 3C shows the dependence of δ/β on dC . As dC increases , Ca2+ ions are more likely to escape the nanodomain around the open channel , thus decreasing spark probability . Uniformly increasing the distance between the open channel pore and neighboring Ca2+ binding site increased δ/β so as to decrease spark probability ( Fig 3D ) . In this section , we have used a simple diffusion model to probe the effects of perturbations to biophysical properties of the release site including the opening rate , unitary channel current , Ca2+ diffusion coefficient , and inter-channel spacing . The CN model suggests that minor modifications to these parameters can alter the stability of the system , thus leading to significant changes in spark probability . Up to this point , we have considered spark probability when each RyR2 is equally likely to open first . An emergent property of the 3D spark model was that the probability of a spark occurring varied with the choice of initiating RyR2 [4] . Channels closer to the epicenter of the cluster were more likely to trigger sparks because they have more possible combinations of first , second , third , etc . neighbors along which channel openings could propagate . Likewise , channels on the periphery of the cluster were less likely to trigger sparks . We derive a linear mean-field representation of the CN ( LCN ) model ( see Methods and Models ) to quantitatively study how spark probability depends on the position of the initiating RyR2 . The LCN model can be used to compute the expected number of open channels as a function of time . We reasoned that a greater expected number of open channels during the spark initiation phase would imply that sparks are more likely to occur and therefore would correlate with pS . Using the LCN model , we derived an expression for the expected number of open channels , E[NO] ( see Eq ( 15 ) ) , and computed its value for a collection of 15 RyR2 clusters . We find that E[NO] derived in the LCN model correlated with pS in the 3D spark model ( R2 = 0 . 934 , Fig 4A ) . Note that the equation for E[NO] is time-dependent , but the results were not sensitive to our choice of the time point t ( R2 = 0 . 933 and 0 . 923 at t = 4 and 12 ms , respectively ) . To further establish the relationship between E[NO] and pS , we compared E[NO] to λ1 for a broader collection of 107 clusters obtained from STED microscopy ( Fig 4B ) . A strong correlation between these variables was present , in contrast to the number of channels in the cluster , which was not consistently correlated with E[NO] . Recall that these conclusions were also drawn from the data of Fig 1D for a smaller collection of clusters . Taken together , these data suggest that λ1 and E[NO] are both accurate predictors of pS , while by itself the number of channels without regard to relative channel locations is not . The LCN model was used to compute the vector whose elements are the expected number of open channels given each possible initiating RyR2 . We denote this vector E [ n ¯ O ] ( see Eq ( 14 ) ) , where each element ( E [ n ¯ O ] ) j is the expected number of open channels given that channel j is opened initially . Note that our nominal value of δ/β is in the sub-threshold regime , which implies that E[NO] and E [ n ¯ O ] both decay in the LCN model ( see Eqs ( 15 ) and ( 14 ) ) . Fig 4C shows how ( E [ n ¯ O ] ) j was initially 1 , reflecting the first open channel , and decayed in time . This occurred at varying rates within an individual cluster , depending on the choice of initiating RyR2 . ( E [ n ¯ O ] ) j decayed more rapidly for channels j near the edge compared to those near the center , consistent with the lower peripheral spark probabilities estimated using the 3D spark model ( Fig 4D ) . This is because channels near the edge have fewer adjacent channels to trigger , and therefore it is less likely that a second channel will open before the first closes . Furthermore , the peripheral channels tend to have fewer second , third , etc . neighbors that can potentially be activated compared to central channels . We conclude that both the expected number of open channels in the LCN model is strongly correlated with spark probability . This fact will be used to further analyze spatial gradients in spark probability that depend on which RyR2 is opened initially . The LCN model can be decomposed into a set of independent eigenmodes by taking the similarity transform of the adjacency matrix: A = V D VT , where V is the modal matrix whose columns are formed by a set of orthonormal eigenvectors { v ¯ 1 , v ¯ 2 , . . . , v ¯ n } and D is a diagonal matrix of eigenvalues {λ1 , λ2 , … , λn} in descending order . Note that A is symmetric such that V−1 = VT . The ith eigenmode is defined by the pair λi-v ¯ i , in which λi determines the rate of decay of the eigenmode in time and the values ( v ¯ i ) j determine the membership of channel j in the eigenmode . We derived an expression for E [ n ¯ O ] as a weighted sum of the eigenvectors E [ n _ O ( t ) ] = ∑ i = 1 n b i ( t ) v _ i , ( 2 ) where the weights b i ( t ) = e ( β λ i − δ ) t u ¯ T v ¯ i , with u ¯ being the all-one-vector . A similar expression for E[NO] is given by E [ N O ( t ) ] = ∑ i = 1 n c i ( t ) , ( 3 ) where c i ( t ) = 1 n e ( β λ i − δ ) t ( u ¯ T v ¯ i ) 2 = 1 n b i ( t ) u ¯ T v ¯ i . Therefore E [ n ¯ O ] and E[NO] are essentially equal to weighted sums of the eigenmodes . The derivation of these equations can be found in Methods and Models . In the previous section , we presented further evidence of the relationship between λ1 and spark probability as well as intra-cluster spatial gradients in spark probability . A natural question to then ask is: does the dominant eigenvector ( v ¯ 1 ) corresponding to λ1 give us information about these gradients ? Furthermore , how significant are other eigenmodes ? The spatial distribution of E [ n ¯ O ] is shown for collection of 10 RyR2 clusters in Fig 5A . We further defined c i = c i ( t ^ ) / ∑ j c j ( t ^ ) at t ^ = 8 ms , which gives the fractional contribution of the ith eigenmode to N O ( t ^ ) . We decomposed these clusters into their eigenmodes and plotted the values of ci corresponding to the 8 greatest eigenvalues in Fig 5A . In most cases ( clusters ( 1 ) - ( 4 ) , ( 6 ) , ( 7 ) , ( 9 ) ) , c1 was the only large value , implying that the dominant eigenmode characterized the behavior of the LCN model . Clusters ( 5 ) , ( 8 ) , and ( 10 ) , however , exhibited another significant ci corresponding to a subdominant eigenmode . Examining the dominant eigenmode of each cluster , we found that for clusters characterized by only the dominant eigenmode , there was a single locus of elevated membership in the dominant eigenvector corresponding to the channels j with greatest values ( E [ n ¯ O ] ) j ( Fig 5C ) . Furthermore , the eigenmode’s spatial gradients resembled the full solution in Fig 5A . For clusters with a subdominant eigenmode ( ( 5 ) , ( 8 ) , ( 10 ) ) , however , the dominant eigenvector did not fully characterize the spatial gradients in E [ n ¯ O ] . For these clusters , the subdominant eigenmode accounted for areas of high E [ n ¯ O ] that were not included in the dominant eigenmode ( Fig 5D ) . In addition , the subdominant eigenmodes were insignificant in the other clusters . To quantitatively assess how well the dominant and subdominant eigenmodes characterize spark probability , we computed the correlation coefficients between the E [ n ¯ O ] and the dominant ( k = 1 ) , the sum of dominant and subdominant ( k = 2 ) , and sum of the dominant , subdominant , and a tertiary eigenmode corresponding to the third largest ci ( k = 3 ) ( Fig 5E ) . Clusters well-described by the dominant eigenmode generally yielded high ρ1 > 0 . 84 , indicating that v ¯ 1 was sufficient to characterize the spatial gradients in spark probability . For the clusters with significant subdominant eigenmodes , ρ1 was lower ( < 0 . 68 ) , and the second eigenmode was required to establish a correlation . Note that inclusion of the tertiary eigenmode did not greatly improve the correlation , suggesting that the first two eigenmodes were most significant . In this section , we characterized the intra-cluster spatial gradients in spark probability in terms of the eigenvalues and eigenvectors of the adjacency matrix . In the majority of cases , the dominant eigenmode λ1-v ¯ 1 was sufficient to approximate the gradients . Clusters ( 5 ) , ( 8 ) , and ( 10 ) of Fig 5 , however , possessed secondary subdomains of channels separated from the dominant subdomain by a bottleneck ( i . e . dumbbell-like morphology ) . These functional subdomains generally contained channels with lower spark probability than the dominant subdomain . This is consistent with Eq ( 2 ) , which indicates that these secondary subdomains are also characterized by a decay rate 1/λs > 1/λ1 and therefore would be expected to have lower spark probability . It is not clear how one can determine whether a cluster is characterized by a single eigenmode or dominant-subdominant pair of eigenmodes without performing the eigendecomposition computations . For example , comparing clusters ( 6 ) and ( 8 ) in Fig 5 , it is not immediately obvious why ( 8 ) requires both modes and ( 6 ) does not . To better understand this relationship , we progressively severed the connection between two functional subdomains at the top and bottom of cluster ( 6 ) . In Fig 6A , we removed channels from this cluster proceeding left to right along the row of channels indicated by the dashed line in the baseline cluster . A subdominant eigenmode emerged as the channels were removed . The dominant eigenmode remained in the lower subdomain , while the subdominant eigenmode formed in the upper region . Note the formation of two disjoint subclusters in cluster ( A4 ) , which have eigenmodes similar to when connected by a single channel in ( A3 ) . The formation of a secondary subdomain is further demonstrated by an increase in the value of ci for the subdominant eigenmode ( Fig 6B ) . In this example , the subdominant eigenmode appeared after removing only one channel and gradually became more prominent with the removal of additional channels . Therefore , the formation of a subdominant eigenmode can be quite responsive to reductions in the region dividing two possible subdomains , each distinguished by different propensities for sparks . We next investigated how sensitive spark probability is to small changes in lattice shape . Fig 6C shows a series of clusters in which only a single channel was removed from the original cluster . As expected , removing a channel along the upper edge as in cluster ( C1 ) where spark probability is low resulted in a small change in λ1 ( Δλ1 ) . Discarding a central channel as in cluster ( C4 ) resulted in the greatest change . One may expect that removing channels with the higher spark probability would cause a greater decrease in λ1 . However , the channel removed in cluster ( C2 ) corresponded to a greater element in E [ n ¯ O ] than the channel in ( C3 ) , yet the change in λ1 was less . This is because the channel in cluster ( C3 ) had greater membership in the dominant eigenmode . To illustrate this , we systematically removed each channel one at a time from the baseline cluster and calculated Δλ1 . Fig 6D shows that there was a consistent relationship between Δλ1 and the discarded channel j’s corresponding element of the dominant eigenvector ( v ¯ 1 ) j in the original cluster but not ( E [ n ¯ O ] ) j . Therefore element j of the dominant eigenvector determines the extent by which spark probability decreases when a single channel j is removed .
We have shown in previous work that the precise structure of RyR2 channel clusters influences properties of Ca2+ release [4] . In particular , the probability of a Ca2+ spark occurring when an RyR2 opens spontaneously depends strongly on the arrangement of the RyR2s in the subspace . This has implications for Ca2+ cycling in the cell , as Ca2+ spark probability controls the frequency of Ca2+ sparks and the excitability of the cluster [5] . An emergent property of this biophysically-detailed model was that λ1 is a strong predictor of Ca2+ spark probability . Here we presented a model similar to those used to study the spread of contagion , such as in disease epidemics [41] . In this model , a single RyR2 is opened initially , which increases the open probability of its neighbors via a local rise in Ca2+ concentration . After deriving a linearized mean-field formulation of the system , we showed that the open probability of the RyR2s is extinguished when λ1 < δ/β . Therefore λ1 governs a stability threshold for spark generation . In the stochastic model , spark probability was constant across all clusters when λ1 = δ/β . Therefore , if one compares any two different RyR2 clusters , the cluster with lower λ1 value would need a lower δ/β ratio ( i . e . greater RyR2 mean open time or opening rate ) to achieve the same spark probability as the other cluster . This explains why λ1 is correlated with Ca2+ spark probability . Cator and Van Mieghem derived a second-order CN model , with which they showed that the true threshold for the system to exhibit exponentially long transients is in fact bounded from above by λ1 [42] . Nevertheless , the first-order model presented here was sufficient to account for the relationship between λ1 and spark probability . It is known that the maximum eigenvalue of a graph’s adjacency matrix is related to the number of walks on the graph [26] . Specifically , if Wk is the number of possible walks of length k on a graph with n vertices , then W k ≈ n λ 1 k when k is large . Furthermore , Wk is proportional to ( u ¯ T v ¯ 1 ) 2 when k is large . Recall that this term also appears in the expression for c1 . It is no coincidence that Wk and E[NO] are related . Intuitively , a greater number of walks implies that there are more possible contiguous sets of RyR2s along which channel openings can propagate . This is essentially the underlying relationship between λ1 and Ca2+ spark probability . An eigendecomposition of the CN model further identified RyR2 subdomains characterized by different spark probabilities , as observed in the 3D spark model . Secondary subdomains with lower spark probability were found in clusters containing two groups of channels separated by central narrow regions ∼ 2 – 3 channels in width . This lends meaning to the eigenvectors of the model , which define the membership of the RyR2s to each functional subdomain . Interestingly , v ¯ 1 is a known measure of vertex centrality [43] , which means that the proportion of all possible walks of length k beginning at vertex j is ( v ¯ 1 ) j / ( u ¯ T v ¯ 1 ) when k is sufficiently large . This implies that the elements of v ¯ 1 indicate the relative number of lattice walks beginning at each channel . Our results suggest that this is approximately true for clusters characterized by the dominant eigenmode , as channels with greater values of ( v ¯ 1 ) j had higher spark probability . Because we consider the transient behavior of channel gating during a fixed time window , the assumption that k is large ( i . e . t is large ) may not hold , thus explaining why a subdominant eigenmode was observed for some clusters . Our results indicate that the system is near the threshold under normal conditions , as the ratio δ/β is close to the threshold λ1 . Therefore , small changes to β can greatly change the qualitative behavior of the system . Using a simple Ca2+ diffusion model , we determined that spark probability is sensitive to changes in biophysical parameters . RyR2 open probability is modulated by a variety of factors including phosphorylation [32 , 33] , JSR Ca2+ concentration [34] , oxidative stress [35] , and genetic mutations [36 , 37] . Most of these increase the opening rate of the channel and cause elevated Ca2+ spark frequency . Our recent work [4] and others [30 , 44] have shown that RyR2 regulation by JSR Ca2+ concentration is not necessary for spark termination . Rather , depletion of the JSR Ca2+ stores causes a sufficient decrease in unitary RyR2 current such that the channel openings are not sustained . This mechanism is supported in the present model as well , as shown by the sharp increase in δ/β as iRyR is decreased ( see Fig 3B ) , as it would be due to reduction of the Ca2+ concentration gradient from inside the JSR to the subspace when RyR2s open . A recent imaging study by Asghari et al . [45] observed regulation of RyR2 cluster structure . The authors reported RyR2s clusters in dense side-by-side lattices , as assumed in the present study , as well as checkerboard-like arrangements with greater spacing of ∼ 37 nm compared to the baseline of 31 nm . Increasing channel spacing uniformly caused an increase in δ/β to 6 . 3 at 37 nm ( see Fig 3D ) . Note that for any graph whose vertices have a maximum of m neighbors , λ1 < m [27] . Therefore λ1 < 4 for cluster lattices . This suggests that any cluster in the checkerboard arrangement would be unlikely to exhibit Ca2+ sparks in the absence of other changes . Interestingly , checkerboard spacing was observed upon channel phosphorylation or after decreasing the cytosolic Mg2+ concentration , both of which increase RyR2 open probability [33 , 34] . Therefore the increase in inter-channel spacing may counteract the effects of these conditions . We maintained focus on the relevance of cluster morphology to Ca2+ spark probability when a single RyR2 opens spontaneously . Ca2+ release can also be triggered following electrical excitation of the cell due to Ca2+ influx through apposing LCCs located on the transverse tubule . Note modeling studies suggest that coupling fidelity between LCCs and RyR2s is still strong despite low spark probability [4 , 5 , 13] . This is because although LCC mean open time is shorter ( ∼ 0 . 5 ms ) , unitary LCC current is approximately the same as the RyR2 , there are usually several LCCs per RyR2 cluster ( the ratio of RyR2s to LCCs is 4–10 [46] ) , and LCC openings are synchronized upon membrane depolarization to drive local buildup of Ca2+ . The study of sub-cellular structure using super-resolution techniques requires careful interpretation of the raw image data . In this study , we generated RyR2 cluster lattices based on fluorescence intensity using a uniform thresholding algorithm . Intensities at or above the 95th percentile were interpreted to represent the RyR2 positions over the entire image . To assess uncertainty in the results with respect to our choice of threshold , we analyzed a single set of STED images using both the 95th and 98th percentile thresholds . At the higher threshold , more of the fluorescence signal is filtered out and thus the clusters contain fewer RyR2s . This resulted in lower values of λ1 and decreased pS ( Fig 7A ) . The large differences in spark probability after using the higher threshold highlight the sensitivity of the model to the image processing methods . Nevertheless , there was still a strong correlation between pS and λ1 when using the higher threshold ( Fig 7B ) . Consequently quantitative prediction of spark probability applying λ1 requires consistent interpretation of super-resolution imaging data and in addition benefits from an incremental alteration of image analysis parameters if possible . We did not consider weaker interactions between RyR2s such as those between diagonally-adjacent neighbors . This results in an underestimation of the open probabilities . We also did not consider clusters with heterogeneous inter-channel spacing as observed in Asghari et al . [45] . We also only considered single connected clusters containing no gaps that divide the cluster into separate subclusters . We assumed that the Ca2+ concentration gradient surrounding an open RyR2 declines sufficiently rapidly such that a negligible Ca2+ concentration is sensed in nearby subclusters , and therefore spark initiation occurs independently . These limitations could be overcome by using a distance matrix or diffusion model as in [47] to compute inter-RyR2 Ca2+ coupling . In addition , the LCN model is known to deviate most from the exact model near the stability threshold ( δ/β ≈ λ1 ) [48] . Note it has been shown that the solution of the mean-field CN model is an upper-bound on the true probabilities [49] , and although higher-order approximations do exist [42] we chose the first-order mean-field approximation for its simplicity and analytical tractability . The CN model may also be applied to similar biological systems . It may be adapted to study Ca2+ release triggered by an LCC . The spark probability would be related to the coupling fidelity between LCCs and RyR2s . This model could be used to analyze the arrangement of LCCs as such experimental data become available . It may also be applied to future imaging studies to compare RyR2 cluster morphology to , for example , identify interspecies variability or remodeling in heart disease . For example , reduced RyR2 cluster sizes and fragmented JSR morphology have been observed in mouse models of catecholaminergic polymorphic ventricular tachycardia [50] . Inositol trisphosphate receptors ( IP3Rs ) located in the sarcoplasmic reticulum are known to aggregate into small clusters that exhibit similar release events known as Ca2+ “puffs , ” and recent work has implicated cluster size in release extent [51] and trigger probability [52] . The present models could be used to compare IP3R cluster geometries like those reported in a recent study [53] . In skeletal muscle , Ca2+ release is coordinated mainly by physical LCC-RyR1 and RyR1-RyR1 interactions [54] . Imaging studies have observed that RyR1 clusters in slow-twitch muscle fibres were typically smaller and more fragmented than in fast-twitch muscle [1 , 55] . The model presented here could be used to relate these observations to known differences in the Ca2+ release properties of these cell types . More generally , the model could be adapted to complement super-resolution imaging studies of a wide range of receptors that form similar supramolecular clusters in other cell types [22 , 23] . A general theoretical model has suggested that clusters of ligand-activated receptors behave cooperatively [56] . Examples from neurons include include synaptic microclusters of syntaxin 1 [57] , acetylcholine receptor complexes at the neuromuscular postynapse [58] , and rings of AMPA receptors found in spiral ganglion neurons [59] . Another example are immunoreceptors [60] , which form clusters to amplify signal initiation and transduction , perhaps by decreasing the effective dissociation constants of ligands and downstream effectors [61] . Furthermore , Greenfield et al . employed super-resolution techniques to investigate the spatial organization of receptors involved in bacterial chemotaxis [62] . These receptors form clusters in the cell membrane and , similar to RyR2s , exhibit cooperative interactions that enhance sensitivity to low chemical signals . This work presents a new perspective on cardiac calcium release and , more generally , highlights the relevance of subcellular variability in microdomains for the study of multi-scale biological systems .
All animal procedures were reviewed and approved by the Institutional Animal Care and Use Committee at University Medicine Göttingen Zentrale Tierexperimentelle Einrichtung ( ZTE ) . Animal sacrifice was applied as described in Wagner et al . [24] . Contact process models have been widely studied for their use in modeling disease and computer virus spread ( see Keeling and Eams [41] for a review ) . In the present work , the CN model represents the RyR2 channel gating of a cluster of n channels . We will restrict ourselves to clusters that are connected , i . e . there are no separate islands of channels . The model is composed of a set of n random variables Xi ( t ) = 1 if channel i is open at time t and 0 otherwise . If the channel is open , the probability that it closes within an infinitesimal time step dt is given by δdt , where δ = 0 . 5ms−1 is constant . If channel i is closed , it transitions into the open state in time dt with probability βYi ( t ) dt , where Yi ( t ) is the number of open adjacent channels . β is a constant given by β = k+ Cη , where k+ = 1 . 107 × 10−4 μM−ηs−1 is the opening rate constant , C is the local elevation of Ca2+ concentration caused by an open neighbor , and η = 2 . 1 is the Hill coefficient for Ca2+ binding [4] . The adjacency matrix A is defined as an n × n matrix , where element ( A ) ij = 1 if channels i and j are adjacent , and 0 otherwise . The number of open adjacent channels is then given by Yi ( t ) = ∑j ( A ) ij Xj ( t ) . Let pi ( t ) = P ( Xi ( t ) = 1 ) , the probability that channel i is open at time t , which obeys the equation [48] d p i ( t ) d t = β ( 1 - X i ( t ) ) ∑ j = 1 n ( A ) i j X j ( t ) - δ X i ( t ) . ( 4 ) The entire system can be more compactly represented as the matrix equation d p _ ( t ) d t = ( β diag { u _ - X _ ( t ) } A - δ I ) X _ ( t ) , ( 5 ) where p ¯ ( t ) = [ p 1 ( t ) , . . . , p n ( t ) ] , u ¯ is the all-one-vector , X ¯ ( t ) = [ X 1 ( t ) , . . . , X n ( t ) ] , and I is the identity matrix . The system is therefore described by a set of n coupled stochastic differential equations , whose solution is analytically intractable . We simulated the CN model using the Gillespie algorithm [31] . Spark probability in the CN model was estimated by running an ensemble of 10 , 000 simulations per data point . Here we incorporate a simple model of Ca2+ diffusion that relate the CN model to the Ca2+-based communication between RyR2s . We use the steady-state diffusion equation for a continuous point source in a semi-infinite volume to obtain the Ca2+ concentration sensed by a RyR2 neighboring a single open channel [63] C = i R y R 2 π z F d C r , ( 6 ) where iRyR = 0 . 15 pA is the unitary current of a single channel , z = 2 is the valency of Ca2+ , F is Faraday’s constant , dC is the effective diffusion coefficient of Ca2+ in the release site subspace , and r = 31 nm is the distance between the open channel pore and neighboring Ca2+ binding site . The diffusion coefficient for Ca2+ in the subspace is unknown , though estimates for dC in the cytosol range from 100 to 600 μm2s-1 [64] . Ca2+ buffering molecules , electrostatic interactions with the membrane , and tortuosity imposed by the large RyR2 channels can affect the motion Ca2+ ions [65] . In light of these factors , the value of dC was adjusted from 250 to 146 μm2s-1 to obtain the nominal value of β = 0 . 115 that yields accurate spark probabilities ( see Results ) . A common approach is to derive a mean-field approximation of the first moment of Xi ( t ) by assuming that the higher moments are equal to 0 [48] . This yields a set of non-linear ordinary differential equations d p _ ( t ) d t = ( β diag { u _ - p _ ( t ) } A - δ I ) p _ ( t ) , ( 7 ) where p ¯ ( t ) is now the vector of mean-field open probabilities . This non-linear system is difficult to analyze analytically [48] . We further simplify the model by linearizing the equations about p ¯ = 0 [28] d p _ ( t ) d t = ( β A - δ I ) p _ ( t ) . ( 8 ) We refer to this as the linearized mean-field CN ( LCN ) model , which is amenable to the tools of linear systems theory . Note that the system is stable if and only if the maximum ( dominant ) eigenvalue of β A − δ I , given by βλ1−δ , is less than 0 , or λ 1 < δ β , ( 9 ) where λ1 is the maximum ( dominant ) eigenvalue of A . Therefore , if λ1 < δ/β , the open probabilities in the LCN decay to 0 . Otherwise , p ¯ ( t ) is unbounded as t → ∞ . While physically meaningless , this result implies that the open probabilities increase when most channels are closed , or p ¯ ( t ) ≈ 0 ( near the origin of linearization ) . The eigendecomposition of A is given by A = V D V T , ( 10 ) where V is the modal matrix with columns formed by the orthonormal eigenvectors { v ¯ 1 , . . . , v ¯ n } of A , and D is a diagonal matrix of the eigenvalues {λ1 , … , λn} in descending order . Note that A is symmetric and therefore V−1 = VT . Combining Eqs ( 8 ) and ( 10 ) gives d p _ ( t ) d t = V ( β D - δ I ) V T p _ ( t ) , ( 11 ) which can be rewritten as the summation d p _ ( t ) d t = ∑ i = 1 n ( β λ i - δ ) v _ i v _ i T p _ ( t ) . ( 12 ) The solution of this system is given by p _ ( t ) = ∑ i = 1 n e ( β λ i - δ ) t v _ i v _ i T p _ ( 0 ) . ( 13 ) We refer to the eigenmodes as the eigenvalue-eigenvector pairs λi-v ¯ i . Note that p ¯ ( t ) is essentially a sum of the eigenmodes . If the initial probability distribution p ¯ ( 0 ) = α v ¯ i for some constant α , then p ¯ ( t ) ∝ v ¯ i for all t . In other words , the trajectory of the system will be entirely characterized by the ith eigenmode . In general , the contribution of the ith eigenmode is determined by the weight v ¯ i T p ¯ ( 0 ) and a time-dependent exponential factor with time constant 1/ ( βλi − δ ) . We define E [ n ¯ O ( t ) ] as the vector whose elements ( E [ n ¯ O ( t ) ] ) i give the expected number of open channels at time t given that channel i is open initially . This is computed by taking the sum of the elements of p ¯ ( t ) in Eq ( 13 ) E [ n _ O ( t ) ] = ∑ i = 1 n e ( β λ i - δ ) t ( u _ T v _ i ) v _ i . ( 14 ) We assume that in a resting RyR2 cluster , every channel experiences the same Ca2+ concentration and therefore is equally likely to initiate a spark . The expected total number of open channels when the first open channel is chosen randomly can be computed by setting p ¯ ( 0 ) to the uniform distribution and again summing over all elements of p ¯ ( t ) E [ N O ( t ) ] = 1 n ∑ i = 1 n e ( β λ i - δ ) t ( u _ T v _ i ) 2 . ( 15 ) | Many transmembrane receptors have been shown to aggregate into supramolecular clusters that enhance sensitivity to external stimuli in a variety of cell types . Advances in super-resolution microscopy have enabled researchers to study these structures with sufficient detail to distinguish the precise locations of individual receptors . In the heart , efforts have been successful in imaging calcium release channels , which are found in clusters of up to ∼ 100 in the sarcoplasmic reticulum membrane of cardiac myocytes . We showed in a recent study how the precise cluster structure affects the frequency of spontaneous release events known as calcium “sparks . ” Here we have developed an analytical model of calcium spark initiation that clearly illustrates how the structure controls spark likelihood . We then applied this model to a collection of channel cluster structures obtained using super-resolution microscopy , revealing spatial gradients in the functional properties of individual channels . This work provides insight into the calcium release process in the heart and a framework for evaluating functional heterogeneity in populations of receptor clusters using structural information alone . | [
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] | [] | 2015 | On the Adjacency Matrix of RyR2 Cluster Structures |
In the last decade , the number of emerging Flaviviruses described worldwide has increased considerably . Among them Zika virus ( ZIKV ) and Usutu virus ( USUV ) are African mosquito-borne viruses that recently emerged . Recently , ZIKV has been intensely studied due to major outbreaks associated with neonatal death and birth defects , as well as neurological symptoms . USUV pathogenesis remains largely unexplored , despite significant human and veterinary associated disorders . Circulation of USUV in Africa was documented more than 50 years ago , and it emerged in Europe two decades ago , causing massive bird mortality . More recently , USUV has been described to be associated with neurological disorders in humans such as encephalitis and meningoencephalitis , highlighting USUV as a potential health threat . The aim of this study was to evaluate the ability of USUV to infect neuronal cells . Our results indicate that USUV efficiently infects neurons , astrocytes , microglia and IPSc-derived human neuronal stem cells . When compared to ZIKV , USUV led to a higher infection rate , viral production , as well as stronger cell death and anti-viral response . Our results highlight the need to better characterize the physiopathology related to USUV infection in order to anticipate the potential threat of USUV emergence .
The recent Zika virus ( ZIKV ) outbreak has reminded us that the emergence of new viruses depends on multiple factors and is therefore extremely difficult to predict . Among potential emerging viruses , Usutu virus ( USUV ) has recently focused attention . USUV is an African mosquito-borne virus closely related to West Nile virus ( WNV ) that belongs to the Japanese encephalitis virus ( JEV ) serogroup in the Flavivirus genus ( Flaviviridae family ) [1] . USUV was discovered in 1959 from a mosquito of the Culex neavei species in South Africa and isolated by intracerebral inoculation of newborn mice [2] . The USUV genome is a positive , single-stranded RNA genome of 11 , 064–11 , 066 nucleotides with one open-reading frame encoding a 3434-amino-acid-residue polyprotein , which is subsequently cleaved into three structural ( core , membrane , and envelope ) and eight nonstructural ( NS1 , NS2A , NS2B , NS3 , NS4A , 2K , NS4B , and NS5 ) proteins [3–5] . USUV natural life cycle is similar to WNV: it involves birds as reservoirs and ornithophilic mosquitoes as vectors like the common Culex pipiens . Notably , USUV-infected mosquitoes were recently detected in several European countries [6 , 7] . Mammals including horses or wild boars , were described as accidental or dead-end hosts [8–10] . Most of the sequences from USUV strains isolated in Europe can be differentiated from the originally isolated USUV strain SAAR 1776 , whereas other sequences are closely related to the original isolated strain [11][12] . This suggests that USUV had been introduced in Europe several times from endemic areas in Africa , probably by migratory birds [12] . Interestingly , USUV has been detected in the wild ( i . e common blackbirds ( Turdus merula ) , waterfowls , raptors , greyland geese ( Anser anser ) , mallard ducks ( Anas platyrhynchos ) ) and domestic ( sentinel chickens ( Gallus gallus domesticus ) , canary ( Serinus canaria domestica ) ) birds in numerous European countries since 1996 [6 , 13–21] . Infected birds present severe neurological signs , often fatal , such as depression , incoordination and inability to fly [22] . These signs are associated with brainstem and cortical neuron necrosis [20 , 22] . In humans , USUV infection was described in Central African Republic and in Burkina Faso in 1981 and 2004 respectively , and associated with fever and skin rash [8] . Molecular and serologic evidences of USUV infection in Italian and German blood donor indicate that the virus is also silently circulating among asymptomatic humans in Europe and could thus be a concern for blood transfusions or organ transplants , as previously evidenced for the closely-related WNV [23–26] . Since 2009 , some neurological disorders such as encephalitis , meningitis and meningoencephalitis were found associated with USUV-infection in immunocompromised and immunocompetent patients [27–30] . Importantly , a retrospective study published in 2017 showed that USUV was the cause of previously unexplained encephalitis in Italy suggesting that neurological cases associated to USUV may be more common than previously thought [31] . Although USUV is an emerging pathogen and dispersed quickly in Europe , very little is known about its pathogenesis , biologic features and host spectrum . It is nonetheless described that USUV infection upregulates the cellular autophagic pathway [32] and can induce type 1 IFN production [33 , 34] . Susceptibility of adult wild type ( WT ) mice to USUV is limited [35] , whereas mice lacking the interferon type 1 receptor ( Ifnar1-/- ) are susceptible , as described for other flaviviruses such as ZIKV [36–39] . Experimental infections of 1-week-old suckling WT mice by intraperitoneal injection reproduced neurological signs such as depression , disorientation , paraplegia , paralysis and coma [40] . Spinal cord examination showed moderate neuronal death , especially in the ventral horns , and multifocal demyelination [40] . These observations highlight the urgent need for a clear understanding of the pathophysiological mechanisms involved in USUV infection , in particular in terms of neurovirulence and neuronal tropism . In this study , we used several cellular models to better understand USUV neuronal tropism and associated physiopathological effects . We demonstrated that USUV can efficiently infect murine mature neurons , astrocytes and microglia . We also compared the cellular effect of USUV with those of ZIKV , an emerging flavivirus that has been described to be link with neurological disorders including microcephaly and Guillain-Barré syndrome . ZIKV induces more rarely meningitis and encephalitis , and is thus consider to be less neuroinvasive than encephalitic flaviviruses in adults [41] . Here , we show that USUV infects human astrocytes more efficiently than ZIKV , reduces cell proliferation and induces stronger anti-viral response . Finally , we show that USUV strongly infect induced pluripotent stem cell ( IPSc ) -derived human neuronal stem cells ( NSCs ) and induces caspase-dependent apoptosis .
To better understand the involvement of USUV in neurological impairments , we first aimed to describe the cellular neurotropism of European strain of USUV ( Vienna , 2001 ) . Report of USUV-associated neuropathology in mice suggest that this animal model is pertinent to study USUV cellular interactions both in vivo and ex vivo [40 , 42] . To monitor viral replication in the murine central nervous system ( CNS ) , we first used acute hippocampus slices prepared from dissected brains from 6–7 day-old wild type ( WT ) mice . Two days post-isolation , USUV was applied ( 3x105 tissue culture infective dose 50% ( TCID50 ) per slice ) on top of the slices , which were further maintained in culture . 4 days post-infection ( dpi ) , slices were fixed , astrocytes , microglial cells and neurons labeled by GFAP , Iba1 and NeuN staining respectively and USUV antigens were observed using a pan-flavivirus antibody ( 4G2 ) that recognizes the envelope protein of several flavivirus [43] . Fig 1A shows that in mock-treated slices , no pan-flavivirus labeling was observed , whereas USUV-infected samples showed strong pan-flavivirus staining , indicating an efficient USUV infection . Co-labeling with neuronal- ( NeuN ) , astrocyte- ( GFAP ) and microglial- ( Iba1 ) specific antibodies with the pan-flavivirus antibody showed a broad tropism of USUV for brain cells ( Fig 1B and 1C ) . To confirm these observations ex vivo , we infected quasi-pure primary hippocampal neuron cultures with USUV at a multiplicity of infection ( MOI ) of 2 . Strong labeling was observed 4 dpi , with patches of viral proteins also found along neurites ( Fig 2A ) . Quantification showed that around 70% of cells with neuronal morphology were efficiently supporting the replication of USUV at 2 and 4 dpi ( Fig 2B ) . Supernatants from USUV-infected cells at 4 dpi were collected and applied to Vero cells to measure viral titer , quantified by tissue culture infective dose ( TCID ) 50 . Efficient viral replication was observed with viral titer quantified around 1 . 6×106 TCID50/ml while titer after inoculation was 1×104 TCID50/ml . Similarly to what we observed in hippocampal slices , we detected viral antigens not only in mature neurons ( labeled with NeuN , Fig 2C ) and in cell bodies but also in axons ( labeled with Tau , Fig 2D ) . Of note , neuronal damage was observed at late time post-infection , showing refringent cell bodies and neurite destruction in USUV-infected neurons at 8 dpi , suggestive of cellular toxicity ( Fig 2E ) . Moreover , as the culture contains sparse glial cells , we observed that astrocytes were also infected as co-labeling pan-flavivirus and GFAP could be observed ( Fig 2F ) . To study more precisely viral replication in glial cells , we took advantage of a culture of spinal glial cells that contains ~80% of astrocytes and ~20% of microglial cells . Four dpi , antigens were observed by immunofluorescence in GFAP- and Iba1-positive cells ( Fig 3A and 3B ) and viral titer of the supernatant was estimated as being around 9x106 TCID50/ml while titer after inoculation was 2×104 TCID50/ml . Altogether , these data suggest a broad neurotropism for USUV in the murine CNS , possibly associated with neuronal toxicity , which could be consistent with the neurological disorders observed in animals and human . Extrapolating data observed in mice concerning cellular tropism or pathologies to human diseases can be misleading . Thus to better understand how USUV affects cells of the human brain , we first used primary human astrocytes . This cellular type is one of the first to be activated in brain infection and/or inflammation and are often targeted by flaviviruses including ZIKV [43] and WNV [44] . Here , we aimed to compare astrocyte tropism/virulence of USUV and ZIKV , another Flavivirus recently described for its neurotropism and its ability to infect this cell type [45–49] . To this end , we infected astrocytes with USUV or ZIKV at a MOI of 2 . We first monitored whether both viruses were inducing cytopathogenic effects ( CPE ) that are often characterized by cells rounding up in the process of dying by apoptosis , pyroptosis or necrosis . Four dpi , USUV-infected astrocytes appeared sparsely populated compared to non-infected and ZIKV-infected , and showed little if any CPE ( Fig 4A ) . ZIKV did not appear to modulate cell proliferation or to trigger important CPE ( Fig 4A ) . Importantly , upon USUV infection , at MOI of 0 . 1 and 2 , we could not detect cell death , suggesting that apoptosis is not triggered in infected astrocytes ( S1 Fig ) . Because bright light pictures of USUV-infected astrocytes seem to indicate a possible defect in cellular proliferation , we next investigated whether cell division was affected by USUV infection . The nucleotide analog BrdU was applied to astrocytes infected or not by USUV , and was quantified at 1 , 3 and 6 dpi by ELISA . Compared to non-infected cells , USUV-infected astrocytes showed a decrease in proliferation from 3 dpi ( Fig 4B ) . Moreover , we detected 48% infected cells by USUV and 41% by ZIKV using immunofluorescence pan-flavivirus staining , ( Fig 4C and 4E ) , with a localization characteristic of the endoplasmic reticulum , a classical site for flavivirus replication ( Fig 4D ) . Finally , supernatants from USUV or ZIKV-infected astrocytes ( MOI 2 ) were collected at different time post-infection to measure growth kinetics ( Fig 4F ) . For USUV , a plateau in replication was observed between 24 h and 96 h post-infection , followed by a decrease in viral titer . However , ZIKV viral titer was significantly lower than USUV and the plateau lasted longer ( Fig 4F ) . Finally , the proteins AXL and DC-SIGN have been reported to act as cellular receptors for some flaviviruses , including ZIKV [50–52] . To monitor whether USUV is using these proteins to infect astrocytes , we performed a competition experiment by pre-incubating cells with either anti-AXL or anti-DC-SIGN antibodies prior to infection with USUV or ZIKV . By measuring viral titer 4 dpi , we confirmed that blocking AXL decreased ZIKV replication ( Fig 4G ) , while blocking DC-SIGN impaired to a less extent ZIKV replication in astrocytes ( Fig 4G ) . In contrast to ZIKV , USUV replication was not modulated by blocking either molecules , suggesting that USUV does not act through these specific cellular receptors to infect human astrocytes ( Fig 4G ) . The data demonstrate that USUV is not only more efficiently targeting human astrocytes than ZIKV but also may trigger deleterious effects by acting , at least partially , on cellular proliferation . Because both USUV and ZIKV efficiently infected primary human astrocytes , we next aimed at analyzing the modulation in the expression of genes involved in anti-viral responses . We used a PCR array consisting in 84 genes that are modulated in the interferon ( IFN ) response or the cellular pattern recognition receptors ( PRR ) among other mechanisms [43] ( S1 Table ) . To compare the anti-viral response elicited by flavivirus infection in astrocytes , mRNAs were collected from USUV- and ZIKV -infected astrocytes 4 dpi and subjected to retrotranscription . cDNA relative abundance was then analyzed by qPCR . Under these conditions , we found that 33 genes were significantly upregulated ( more than 2 fold ) by USUV in astrocytes ( Fig 5A–5C ) . Several cytokines and chemokines genes were found upregulated upon both USUV and ZIKV infections such as IFN-Β , TNF , IL12A , IL15 , IL6 , CCL5 , CXCL10 , CXCL8 OR CXCL11 . Moreover , gene expression of PRR genes such as Ddx58 ( RIG-I ) , Dhx58 ( LGP2 ) or Tlr3 were also upregulated by both viruses , whereas Tlr9 was only modulated by USUV ( Fig 5C ) . Importantly , in all cases , the upregulation of these antiviral genes was stronger following USUV than ZIKV infection , up to 100 times for the chemokines CCL5 , CXCL10 and for the IFN-Β ( Fig 5C ) . Other genes , such as the chemokine CCL3 , CD40 , CTSB and the transcription factors FOS and IRF7 were specifically upregulated in USUV infected astrocytes . Interestingly , other differences in the cellular responses triggered by USUV vs ZIKV were observed , in particular regarding the MAPK pathway ( MAP2K1 , MAP2K3 , MAP3K7 , MAPK1 , MAPK3 ) , the peptidyl-prolyl isomerases PIN1 and members of the inflammasome pathway such as Nlrp3 and Il1β that were preferentially modulated by ZIKV ( Fig 5C ) . These data highlight the strong induction of an antiviral response by USUV and suggest substantial differences in the cellular response process against flaviviruses in astrocytes . The recent ZIKV epidemic highlighted that developing brains can be highly sensitive to flavivirus infection [45–48 , 53 , 54] . Moreover , in the adult brain , specific niches such as the hippocampus are involved in adult neurogenesis and are potential targets for viral infections [53] . To study the potential tropism for USUV to human NSCs , we used IPSc-derived NSCs obtained according to standard methods [43] . NSCs were infected with USUV and ZIKV at a MOI of 2 and efficient USUV and ZIKV infectivity were detected 2 dpi using the pan-flavivirus antibody ( Fig 6A ) . Moreover , we detected 77% infected cells by USUV and only 21% by ZIKV using immunofluorescence pan-flavivirus staining ( Fig 6B ) . Production of infectious particles was observed as attested by viral titers of approximately 108 TCID50/ml obtained from supernatants of USUV-infected NSCs , which was significantly higher than ZIKV at 2 , 4 and 6 dpi ( Fig 6C ) . Interestingly , and in contrast to astrocytes , USUV-infected NSCs at 4 dpi showed round-up morphology and condensed nuclei , suggestive of an apoptotic cell death ( Fig 6D and 6E ) . Quantification of cell viability by trypan blue showed that ~80% of USUV-infected NSCs were indeed undergoing cellular death at 4 dpi ( Fig 6F ) . Similarly , activated-caspase 3 was observed by immunoblotting , confirming that USUV infection triggered apoptotic pathways in IPSc-derived NSCs ( Fig 6G ) . USUV-associated cellular death could be strongly decreased by pre-treating NSCs with the anti-apoptotic agent Z-VAD ( pan-caspase inhibitor ) prior to USUV infection . Indeed , NSCs treated with Z-VAD for 4 days concomitantly to USUV showed fewer apoptotic nuclei than USUV-infected cells without Z-VAD treatment ( Fig 6H ) . Altogether , these observations suggest that NSCs are strongly permissive to USUV infection and undergo cellular death by caspase 3-dependant apoptosis .
Despite its immune-privileged status , the CNS can respond efficiently to viral challenges . Following CNS infection , homeostasis can be altered as inflammation can arise in distinct anatomical regions causing inflammatory diseases such as myelitis , meningitis , encephalitis and meningoencephalitis that can severely affect human health and be associated with long-term sequelae . Symptoms and severity of the disorders caused by neurotropic viruses depend on several factors , including cell tropism , viral cytopathogenicity and the host immune response . Numerous flaviviruses have been described to access to the CNS and cause neuronal impairment [55] , in particular Dengue virus ( DENV ) , Japanese encephalitis virus ( JEV ) , Tick‑borne encephalitis virus ( TBEV ) , WNV and ZIKV , [56 , 57] . JEV , TBE and WNV are particularly neurovirulent and can cause encephalitis in humans . In contrast , DENV is more viscerotropic [58] and ZIKV appears to be less neuroinvasive in adults in comparison to the typical encephalitic flaviviruses [41] . Neurotropic viruses are continually emerging and are particularly problematic because they are often less adapted to their new host , and they can spread rapidly in the population and induce severe disorders , as experienced during the recent ZIKV epidemics . In this study , we report for the first time that USUV is able to infect efficiently a wide range of neuronal cells ex vivo ( mature neurons , astrocytes , microglia and IPSc-derived human NSCs ) associated with deleterious effects . The neurotropic ability of USUV was first suspected in birds since strong avian mortality has been described in Austria in 2001 [20] , whereas the ability of the virus to infect humans is known since 1981 with a case in Central African Republic [59] . In 2009 , a severe meningoencephalitis case related to USUV infection was reported in an immunocompromised Italian patient , demonstrating the zoonotic potential of USUV [27] . An additional case was also detected after orthotropic liver transplantation [28] . Later , a study in Croatia revealed also the ability of USUV to infect immunocompetent patients as three additional USUV neuroinvasive infections in humans ( meningitis and meningoencephalitis ) were described [29 , 30] . In 2016 , a retrospective study detecting USUV RNA and USUV antibodies in cerebrospinal fluid ( CSF ) and serum samples revealed the presence of anti-USUV antibodies in more than 6% of samples analyzed in Italy [60] . Moreover , the authors detected USUV RNA in eight cerebrospinal fluids ( CSFs ) . Among them , four patients were identified with neurological symptoms ( encephalitis and meningoencephalitis ) . This last study suggest that USUV infection in humans may be more common than previously thought , at least in specific areas , and highlights the need to better understand the pathophysiology of this virus . Neurons are a direct or indirect target for neurotropic flaviviruses . Mechanisms of neuronal injury after viral infection could be explained by several non-exclusive mechanisms such as direct cell death , destruction of infected cells in the CNS by cytotoxic T lymphocytes or neuronal cell death or dysfunction by bystander-infected cells . Notably , as USUV seems to infect different types of CNS cells ( neurons , microglia and astrocytes ) , we could expect a combination of mechanisms that ultimately could lead to neuronal dysfunction . WNV , a closely related flavivirus that is also associated with encephalitis and other neurological disorders , has also been described to target neurons in the CNS , leading to their alteration or death ( by caspase-3 driven apoptosis ) [61] . In addition to direct damage , WNV infection may also trigger apoptosis in neurons as a result of bystander effects caused by cytotoxic factors released by ( dying ) neuronal and non-neuronal cells as it can also infect astrocytes [44] . Neuroinvasion does not seem to be a major feature of ZIKV virus infection as only few cases of encephalitis and meningoencephalitis have been reported for ZIKV . Studies indicate that it can poorly infect mature neurons , suggesting that its effects in the adult CNS could be mostly due to infection of glial cells such as astrocytes [51] . Following flavivirus infection activated glial cells release TNF , IL1β , IL6 , and RANTES , all of which promote bystander damage to neurons . However , while the extent to which glial infection contributes to JEV-induced neurological disease has been well studied , its relevance in WNV-induced disease has received less attention [62] . Our data show that USUV can efficiently infect mature neurons that undergo cell death at late stage of infection . Resident cells of the CNS have developed innate immune antiviral strategies to defend against neurotropic viruses . While neurons are a target of neurotropic flaviviruses , other cell types ( e . g . , astrocytes , microglia , oligodendrocytes ) might also be infected and contribute to the resolution of infection by generating immune responses against viral infections . Astrocytes are one of the most abundant cell types in the brain and spinal cord and mediate diverse supportive functions and immune regulation . Activated astrocytes produce a wide variety of cytokines and chemokines , including IFNs [63] . In our study , we found that primary human astrocytes respond to USUV infection by strongly upregulating chemokines and cytokines . The inflammatory profile is partially different following ZIKV infection , which is mainly characterized by less inflammatory response and by the specific induction of several MAPK genes ( MAP2K1 , MAP2K3 , MAP3K7 , MAPK1 , MAPK3 ) . Notably , we observed upregulated mRNA expression levels of IFNα/β , IL6 , TNF and several chemokines as CXCL10 or CXCL11 after USUV infection . Generally , innate immunity is mandatory for clearance of viral infections [64] , and when clearance is inefficient , an exaggerated cytokine release could be detrimental and associated with adverse effects such as cancers or CNS disorders [65] . Therefore , the highest risk during neurotropic viral infection is the spread of the virus to the CNS , causing induction of inflammatory responses and the destruction of neuronal cells . For example IL-6 and TNF production by astrocytes can lead to increased permeability of the BBB [66] , moreover CXCL10 has been reported to induce neuron apoptosis or direct damage in neuronal cells [67 , 68] and has been described to be activated in astrocytes after WNV infection [69] . Notably , we found that USUV induces astrocyte proliferation arrest . This phenotype could be related to the strong immune response observed after USUV infection . Indeed , some cytokines such as IFN-β and CXCL10 are known to cause cell death directly or to inhibit cell proliferation [70 , 71] . As astrocytes are essential for providing trophic support to neurons and maintain synaptic functions , loss of astrocytes can induce significant neuronal dysfunction and damage . In fact , when activated in an uncontrolled manner , astrocytes can release various substances , such as reactive oxygen species and inflammatory cytokines , triggering the cascade of events leading to neuronal degeneration . The most notable inflammatory response following USUV infection is the very strong upregulation of IFNβ ( >7000 times ) . Interestingly , this strong IFN-β induction did not prevent USUV replication . These findings further support the hypothesis that USUV do not possess mechanisms that interfere with IFN induction as previously suggested in other studies on dendritic and epithelial cell [33 , 34] . The type I IFN response can limit viral dissemination by different mechanisms: restricting the spread of progeny viruses to neighboring cells or/and reducing overall viral replication . In this context , USUV infection inhibits IFN antiviral activity through a mechanism that remains to be determined and that could allow USUV to overcome this response to establish a productive infection . The absence of efficient protective effect of type 1 IFN has been demonstrated for other neurotropic flavivirus . For example in the case of DENV , type I IFN response limits only initial viral replication but has no apparent effect on controlling the virus from the CNS and disease development [72] . Similarly , functional type I IFN response was not protective against lethal encephalitis during WNV infections [73] . Moreover , there are several examples demonstrating that flaviviruses produce effective immune modulatory proteins and use multiple immune evasion mechanisms that limit host immune responses and favor viral replication [74 , 75] . Indeed , viruses possess specific mechanisms to subvert IFN antiviral effects through proteins that mimic and interfere with host proteins by delaying the interaction of pathogen-associated molecular patterns ( PAMPs ) with the cellular PRRs , suppressing the IFN-signaling or impairing functions of antiviral ISG . Thus , it could be hypothesized that the association of USUV with human diseases , such as encephalitis or meningitis , could be linked to the inability of the virus to suppress type I IFN production combined with the ability of the virus to overcome response in infected cells . Finally , several receptors have been reported to facilitate flavivirus entry , including DC-SIGN , a type 2 transmembrane C-type lectin and AXL that belongs to the Tyro3 AXL Mer ( TAM ) family , a group of tyrosine kinase receptors [76] . AXL is known to be present in brain cells , including radial glial cells , astrocytes , and microglial cells [77] . We observed that the blocking antibody anti-AXL , and in a lesser extend anti-DC-SIGN , inhibits ZIKV replication in human astrocytes as previously described for AXL in human glia cells [50–52] . However , neither blocking AXL nor DC-SIGN has effect on USUV replication , suggesting that this flavivirus uses other entry receptor ( s ) in astrocytes that remain to be identified . Recent observations showed that some flavivirus as ZIKV infection can also impair neurodevelopment while other neurovirulent Flaviviruses such as JEV and WNV are rarely linked to congenital malformations . Indeed , numerous studies demonstrated that ZIKV can infect human cortical NSCs , attenuates their proliferation and induce apoptosis , both in monolayer culture and in cerebral organoids or neurospheres [45–48 , 53 , 54] . In this study , we show that USUV can also infect neuronal progenitors with high efficiency and induces massive caspase-dependent apoptosis . A previous study showed that deleterious consequences of ZIKV infection in human NSCs are not a general feature of the flavivirus family , as this effect was not observed with DENV [47] . Our results show that USUV has also the ability to infect human NSCs ex vivo . It remains to be determined if USUV can access the fetal brain , but in regards of our results more investigations are necessary to investigate whether USUV infection can cross placental and blood brain barrier and have an impact during different stages of fetal development . In conclusion , we showed that USUV is capable of infecting mature neurons , microglia , human neuronal precursors and primary human astrocytes . Whereas USUV infection kills neurons and NSCs , astrocyte infection causes cell proliferation arrest and induction of cytokines and chemokines . Our findings suggest that USUV infection may lead to encephalitis and/or meningoencephalitis via neuronal destruction and inflammatory response . There results , and the recent observations that USUV circulation in human may be more common than previously thought , highlight the need to include USUV in the differential diagnosis of encephalitis/meningoencephalitis cases of unknown etiology in areas where the virus is known to circulate . A better understanding of the epidemiological and biological characteristics of USUV infection is necessary to provide tools for anticipating the potential threat of USUV emergence .
Antibodies used in this study are: anti-pan-flavivirus ( MAB10216 , clone D1-4G2 ) anti NeuN ( Abcam ) , anti Iba-1 ( Abcam ) , anti Tau ( Abcam ) , anti-GFAP ( Abcam ) , anti-caspase 3 and anti-activated caspase 3 ( Cell Signalling Technology ) and secondary antibodies coupled to Alexa dyes ( 488 , 555 or 647 , Thermofischer Scientific ) . One hour before infection , cells were treated with AXL-blocking antibody or goat IgG control at 10μg/mL and with DC-SIGN at 5μg/mL ( R&Dsystems ) . For apoptotic test cell cultures were incubated with or without zVAD-fmk ( 100 μM ) ( Abcam ) for 90 min before infection and maintained during all the infection process . PF-13 ZIKV was produced and provided by the National Reference Center for arboviruses ( NRC ) and has no more than 5 passages on Vero cells ( ATCC ) . USUV Vienna Austrian strain of USUV ( Vienna2001-blackbird , USUV 939/01 , GenBank acc: AY453411 . 1 ) , was provided from Department of Biotechnology , INIA Madrid and was propagated three times in Vero cells . Viral stocks were prepared by infecting sub confluent Vero cells at the multiplicity of infection ( MOI ) of 0 . 01 in D-MEM medium ( Thermoscientific ) supplemented by 2% heat-inactivated fetal bovine serum ( Sigma ) . Cell supernatant was collected 6 days later and viral stock harvested after centrifugation at 300 g to remove cellular debris . Viral titers were determined by the 50% tissue culture infective dose ( TCID50 ) , which was calculated using the Spearman-Kärber method [78] and were expressed as TCID50 per mL . Cells at 60–70% confluence were rinsed once with phosphate-buffered saline ( PBS ) , and ZIKV and USUV diluted to the required MOI was added to the cells in a low medium volume . Cells were incubated for 2 h at 37°C with permanent gentle agitation and then culture medium was added to each well , and cells were incubated at 37°C and 5% CO2 . As control , cells were incubated with the culture supernatant from Vero cells ( mock condition ) . All pups were anesthetized prior to brain extraction . Briefly , hippocampi from 6- to 7-day-old mice ( Janvier , France ) were dissected under aseptic conditions and transverse sections were obtained using a tissue chopper . Slices were placed on a 30-mm porous membrane ( Millicell-CM ) and kept in 100-mm diameter dish . Petri dishes were filled with 5 ml of culture medium composed of 25% heat inactivated horse serum , 25% Hank’s Balanced Salt Solution ( HBSS ) , 50% minimum essential medium ( MEM ) , 25 U/ml penicillin , 25-μg/ml streptomycin ( Invitrogen ) . Cultures were maintained in a humidified incubator at 36°C and 5% CO2 . Two days later , media were changed and the temperature set to 33°C . NSCs were obtained from the SAFE-IPSc platform at IRMB ( http://www . chu-montpellier . fr/fr/chercheurs/plateformes/les-plateformes-recherche/safe-ips/ ) . Briefly , iPSCs were individualized with Gentle Cell Dissociation Reagent ( Stemcell , 07174 ) . They were rinsed out with Dulbecco’s modified Eagle’s medium/Ham’s F12 ( DMEM/F-12 , Gibco , 31330038 ) and centrifuged at 300 g for 5 min . Dissociated cells were plated on matrigel at a density of 20 , 000–40 , 000 cells/cm2 and cultured in neural induction medium ( Stemcell , 05835 ) supplemented with 10 μM ROCK-inhibitor ( Y-27632 ) . Cells were allowed to reach 80–90% confluence over 6 days . Medium was changed daily with neural induction medium without Y-27632 . IPSc-derived NSCs were passaged by incubation with trypsin at 0 . 005% to allow dissociation , and then seeded on poly-D-ornithine/laminin coated plates at 20 , 000 cells/cm2 in 50% DMEM/F-12 and 50% Neurobasal medium ( Thermoscientific ) supplemented with 1X N2 ( Thermoscientific ) , 1X B27 ( Thermoscientific ) , glutamax ( Thermoscientific ) and β-FGF plus EGF ( Peprotech , 20 ng/mL each ) . The medium was changed every two days . Cells were used between passage 5 and 8 . Astrocytes were purchased from ScienCellTM and cultured according to the manufacturer’s instruction . Cells were cultured on poly-D-lysine coated plates and were used between passage 4 and 8 . Cell proliferation was assessed seeding 5000 cells in 96 well plates . At days 1 , 3 , 6 a bromodoxyuridine ELISA assay was performed ( Calbiochem BrDU cell proliferation assay ) following manufacturer instructions . Absorbance at 450 nm was measured using a spectrophotometer ( TECAN ) . Mouse hippocampal neurons were obtained from OF1 embryonic day 18 ( E18 ) embryos using standard procedures ( Harlan ) . Briefly , hippocampi were isolated , dissociated with 0 . 025% trypsin and plated in Neurobasal medium ( ThermoFischer ) containing B27 ( ThermoFischer ) , L-glutamine ( Sigma ) , Glutamax ( ThermoFischer ) , 10% fetal bovine serum ( FBS , Sigma ) and antibiotics . Hippocampal neurons were then incubated at 37°C and 5% CO2 under a humidified environment . At day in vitro ( DIV ) 4 , 2/3r of the medium was replaced with medium without L-glutamine and FBS . Neurons were used at DIV8-10 . Primary cultures of glial cells were established from the spinal cord of 16-day-old C57/Bl6 mice ( Harlan ) . Animals were sacrificed in aseptic conditions . Spinal cords were dissected , freed from meninges and collected in cold HBSS supplemented with calcium and magnesium ( Gibco ) , glucose ( Sigma , 6 g/L ) and 1% antibiotic solution ( penicillin/streptomycin , Gibco ) . Tissues were chopped and rinsed ( 3 times ) in HBSS ( without calcium and magnesium , 1% antibiotic solution ) , supernatant was removed . Tissues were re-suspended in 1 . 5 ml of DMEM/F12 medium ( Invitrogen ) and 1% penicillin/streptomycin and treated with 2 ml 0 . 25% trypsin-EDTA ( Gibco ) for 20 minutes at 37°C . Trypsin was inactivated by adding 10% FBS . DNAse1 ( 10mg/ml , Rotkreuz . Switzerland ) was then added . Cells were mechanically dissociated and re-suspended in 10ml of culture medium consisting of DMEM/F12 , glucose 6 g/L , glutamax 100X ( Gibco ) , 10% FBS ( Gibco ) and 1% penicillin/streptomycin . Centrifugation ( 5min , 500g , room temperature ) was done and supernatant removed . Cells were re-suspended in the same culture medium and plated at a final concentration of 50 000 cells/well on glass coverslips treated with 25 μg/ml of low-molecular weight poly-D-lysine ( Sigma-Aldrich , St Louis , MO ) in 24-well dishes ( Nunc , Roskilde , Danemark ) . NSCs were plated on poly-D-ornithine/laminin coverslips and astrocytes plated on poly-D-lysin coverslips . For indirect immunofluorescence , cells were fixed with 4% PFA and permeabilized with 0 . 1% Triton X-100/PBS for 5 min at room temperature ( RT ) , followed by a blocking step with 2% bovine serum albumin ( BSA ) and 10% horse serum for 30 min to 1 h at RT . Primary and secondary antibodies were diluted in blocking solution and incubated sequentially for 1h at RT . Samples were then mounted with fluorescent mounting medium ( Prolongold , Thermofischer ) with DAPI ( Sigma ) and imaged by confocal microscopy using the Zeiss SP85 confocal microscope , with 40× or 63× 1 . 4 NA Plan Apochromat oil-immersion objectives . Cells were lysed by boiling in SDS sample buffer , sonicated , and complemented with dithiothreitol ( DTT ) . Protein concentrations were measured by a bicinchoninic acid ( BCA ) protein assay kit ( Pierce , MA , USA ) . Equal amounts of protein from total cell lysates ( 10 μg ) were loaded on SDS-PAGE gels and transferred onto nitrocellulose membranes . The membranes were blocked and incubated overnight at 4°C with primary antibodies and then incubated with horseradish peroxidase ( HRP ) -conjugated secondary antibodies ( Amersham ) for 1 h , bands were visualized by ChemiDoc XRS plus ( Biorad Laboratories Hercules , CA ) . Astrocytes infected with USUV and ZIKV or mock-treated cells were harvested in RLT buffer ( Qiagen ) . Total RNA was extracted using RNeasy mini-kit ( Qiagen ) . Complementary DNA was synthesized using Omniscript reverse transcriptase ( Life Technologies ) . RT2 Profiler PCR arrays for Human Antiviral Response ( 96 well format , Qiagen ) were used for real-time quantitative PCR analysis , with the use of the LC480 real time PCR instrument ( Roche ) and the Light Cycler 480 SYBR Green I master Mix ( Roche ) . Volumes of mix , cDNA , RNAse-free water , and cycling conditions were determined according to manufacturer’s instructions . Data on gene expression were normalized according to data from the HPRT housekeeping gene . Genes with uninterpretable amplifying curves were excluded from the analysis . Mice were bred and maintained according to the French Ministry of Agriculture and European institutional guidelines ( appendix A STE n°123 ) . Animals were killed by cervical dislocation . Experiments were performed according to national regulations of the French Ministry of Agriculture and was specifically approved ( ID approval N° 34118 ) by the regional ethics committee of Languedoc-Roussillon ( Comité Régional d’Ethique sur l’Expérimentation Animale- Languedoc-Roussillon ) , France . For all quantitative analyses , a minimum of three independent experiments were performed . Student’s t-test were performed to analyze unpaired data . | Usutu virus ( USUV ) is an African mosquito-borne virus closely related to West Nile virus and belongs to the Japanese encephalitis virus serogroup in the Flavivirus genus . Recently several neurological disorders such as encephalitis , meningitis and meningoencephalitis were associated with USUV-infection in immunocompromised and immunocompetent patients . The goal of our work was to study the ability of USUV to infect neuronal cells and to characterize the effects of USUV infection in these cells . We have shown that USUV can infect efficiently several neuronal cells ( mature neurons , astrocytes , microglia , IPSc-derived human neuronal stem cells ( NSCs ) ) . Interestingly , USUV replicates in human astrocytes more efficiently than another mosquito-borne flavivirus , Zika virus , reduces cell proliferation and induces strong anti-viral response . Moreover , USUV induces caspase-dependent apoptosis in NSCs . Our results suggest that USUV infection may lead to encephalitis and/or meningoencephalitis via neuronal toxicity and inflammatory response . | [
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] | 2017 | Deleterious effect of Usutu virus on human neural cells |
A mismatch between optical power and ocular axial length results in refractive errors . Uncorrected refractive errors constitute the most common cause of vision loss and second leading cause of blindness worldwide . Although the retina is known to play a critical role in regulating ocular growth and refractive development , the precise factors and mechanisms involved are poorly defined . We have previously identified a role for the secreted serine protease PRSS56 in ocular size determination and PRSS56 variants have been implicated in the etiology of both hyperopia and myopia , highlighting its importance in refractive development . Here , we use a combination of genetic mouse models to demonstrate that Prss56 mutations leading to reduced ocular size and hyperopia act via a loss of function mechanism . Using a conditional gene targeting strategy , we show that PRSS56 derived from Müller glia contributes to ocular growth , implicating a new retinal cell type in ocular size determination . Importantly , we demonstrate that persistent activity of PRSS56 is required during distinct developmental stages spanning the pre- and post-eye opening periods to ensure optimal ocular growth . Thus , our mouse data provide evidence for the existence of a molecule contributing to both the prenatal and postnatal stages of human ocular growth . Finally , we demonstrate that genetic inactivation of Prss56 rescues axial elongation in a mouse model of myopia caused by a null mutation in Egr1 . Overall , our findings identify PRSS56 as a potential therapeutic target for modulating ocular growth aimed at preventing or slowing down myopia , which is reaching epidemic proportions .
The molecular and cellular mechanisms involved in ocular size regulation and refractive development are poorly understood . Here , we have used a combination of genetic mouse models to elucidate the role of PRSS56 in ocular axial growth . We show that loss of PRSS56 function causes ocular axial length reduction and hyperopia . Moreover , utilizing a lineage tracing strategy and a combination of molecular approaches , we demonstrate that Prss56 ocular expression is first detected in a pool of late RPCs and then in a subset of Müller glia following retinal cell differentiation . Importantly , our findings demonstrate that PRSS56 derived from Müller glia contributes to ocular axial length elongation , uncovering a previously unrecognized role for Müller glia in ocular growth . Furthermore , we show that continuous PRSS56 activity is required to sustain ocular growth throughout distinct stages of ocular development spanning the pre- and post-eye opening periods . Thus , findings from our mouse models suggest that at least some of the factors guiding ocular growth are conserved across the prenatal and postnatal stages of ocular development .
All experiments were conducted in accordance with the Association for Research in Vision and Ophthalmology’s statement on the use of animals in ophthalmic research . Mouse studies were performed in compliance with protocols approved by the Institutional Animal Care and Use Committee at University of California San Francisco ( Approval numbers: AN153083 and AN120008 ) . Animals were given access to food and water ad libitum and housed under controlled conditions including a 12-h light/dark cycle in accordance with the National Institutes of Health guidelines . For some of the experiments mice were anesthetized with ketamine/xylazine ( 100 mg/kg and 5mg/kg , respectively ) . Ocular anterior segment examinations were performed on 1–5 months old mutant mice and control littermates using a slit lamp biomicroscope ( Topcon SL-D7; Topcon Medical Systems , Oakland , NJ , USA ) attached to a digital SLR camera ( Nikon D200; Nikon , Melville , NY , USA ) . Observers were masked to mouse genotypes while evaluating clinical phenotypes . Phenotypic evaluation included considerations for iris structure , pupillary abnormalities , cataracts and the overall dimensions of the anterior chamber . Ocular biometry was performed using optical coherence tomography or a digital Vernier caliper . Envisu R4300 spectral-domain optical coherence tomography ( SD-OCT , Leica/Bioptigen Inc . , Research Triangle Park , NC , USA ) was employed to measure the ocular axial length , retinal thickness , vitreous chamber depth ( VCD ) and anterior chamber depth ( ACD ) as previously described with minor modifications [25] . Briefly , mice were anesthetized with ketamine/xylazine ( 100 mg/kg and 5mg/kg , respectively; intraperitoneal ) and their eyes dilated before placing the animal in a cylindrical holder . The eye was hydrated with Genteal ( Alcon , Fort Worth , TX , USA ) and positioned in front of the OCT light source . Correct alignment of the eye was achieved by placing the Purkinje image in the center of the pupil . The images were acquired in rectangular volume and radial volume scans to capture the retinal thickness and axial length measurements , respectively . The axial length was calculated by measuring the distance from the corneal surface to the RPE/choroid interface . The distance between the innermost layer of the retina and the lens was used to calculate the VCD . ACD is the distance between the innermost cornea layer and lens . For biometric analyses performed before eye opening ( before P13 ) , mice were anesthetized and their eyelid carefully slit open using fine scissors . Thereafter , the eye was gently protruded using a Q-tip and aligned to the light source as described above . Digital Vernier caliper ( Fowler Ultra-Cal Mark III ) was used to measure the equatorial diameter as described previously [36] . Eyes were enucleated and magnified under a dissecting scope . The Vernier caliper was positioned along the nasal and temporal plane at a point of maximum diameter . Ocular biometry was performed on both the left and right eyes of a given mouse . S1–S3 Tables summarize the details about sample size , body weight , and biometric measurements of mice in each experimental cohort . To minimize the possible effect of body weight on ocular size , we ensured that body weight of littermates was within a narrow range in each of the comparative groups . Ocular refractions were acquired using an automated infrared photorefractor as described previously with some minor modifications [28] . Refraction was measured following treatment of mouse eyes with cyclopentolate ( Alcon , Fort Worth , Tx , USA ) to temporarily paralyze the ciliary body ( cycloplegic refraction ) . Mice were placed on a pedestal with their eyes facing the photorefractor . The photorefractor was maintained at a distance and maneuvered to obtain a clear focused image of the eye . The photorefractor registers a successful refraction measurement only when the Purkinje image is positioned in the center of the pupil as detected and marked by a green LED flash . Centering of the Purkinje image ensures the infrared rays pass along the optical axis . In a typical recording , around 30–50 refraction measurements are acquired , which are then used to calculate the mean OD . Refraction measurements were performed on both the left and right eyes of a given mouse . A total of at least 6 eyes per experimental group were used for refraction measurements and experimental mice of both sexes were used for refraction measurements . Eyes were enucleated and retinas were immediately dissected . Total RNA was isolated from retina using Qiagen RNeasy Mini Kit with on-column DNase I treatment ( Qiagen , Valencia , CA , USA ) and reverse transcribed using iScript cDNA Synthesis Kit ( Bio-Rad , Hercules , CA , USA ) . qPCR was performed on a Bio-Rad C1000 Thermal Cycler/CF96 Real-Time System using SsoAdvancedTM SYBR Green® Supermix ( Bio-Rad , Hercules , CA , USA ) , and primer sets listed in S5 Table . Briefly , 15ng of cDNA and 0 . 25 μM primers were used per reaction in a final volume of 10 μl of Supermix . Each cycle consisted of denaturation at 95°C for 5s , followed by annealing and extension at 60°C for 25s . Each reaction was run as technical duplicates and a minimum of 4 biological replicates was used per group . The relative expression level of each gene was normalized to housekeeping genes ( Actb , Hprt1 , and/or Mapk1 ) and analyzed using the CFX manager software ( Bio-Rad , Hercules , CA , USA ) . Prss56Cre mice were bred to tdTomato reporter mice ( R26tdTomato ) to generate offsprings with one copy of each of tdTomato and Cre recombinase under the control of the Prss56 promoter . The offsprings ( heterozygous control Prss56Cre/+; R26tdTomato ) were utilized for Prss56 lineage tracing and expression analysis . The eyes were enucleated at both embryonic and postnatal time points . The eyes were processed , sectioned and visualized for tdTomato fluorescence as described below . To assess tdTomato expression ( S3 Fig ) and organization of Müller glial endfeet ( Fig 7G and 7H ) in a Prss56 mutant context ( homozygous mutant ) , we designed our breeding strategy such that Prss56 mutant mice carried a single copy of both tdTomato and Prss56Cre ( similar to control mice ) and a copy of the Prss56glcr4 allele ( Prss56Cre/glcr4; R26tdTomato ) . Our design ensured uniform copy number of tdTomato and Prss56Cre , allowing a direct comparison of tdTomato expressing cells between Prss56 mutant and heterozygous control eyes . Eyes were enucleated and immersion-fixed in 4% paraformaldehyde ( PFA ) in phosphate buffered saline ( PBS ) overnight at 4°C , cryoprotected in 20% sucrose in PBS , and embedded in Optimal Cutting Temperature ( O . C . T . ) compound ( Tissue-Tek; Sakura Finetek , Torrance , CA , USA ) . Twelve micron cryosections were immunolabeled with anti-Sox2 ( 1:500 dilution , goat , cat#AF2018 , R&D systems , MN , USA ) , or anti-PKCα ( 1:250 , rabbit , P4334 , Sigma , St . Louis , MO , USA ) or anti-Vimentin ( 1:100 dilution , mouse IgM , clone 40E-C , IA , DSHB , USA ) , antibodies in PBS containing 10% normal donkey serum , 0 . 1% TritonX-100 ( PBS-T ) . Immunolabeling was visualized using AlexaFluor 594 or 488 conjugated secondary antibodies raised in donkey ( 1:500 , Life Technologies , Carlsbad , CA , USA ) in PBS-T . Slides were mounted in Mowiol containing DAPI ( 2 μg/ml ) . Mice were transcardially perfused with ice-cold RNase-free PBS followed by 4% PFA ( in RNase-free PBS ) . Enucleated eyes were post-fixed in RNAse-free 4% PFA , cryoprotected in 20% sucrose , and embedded in OCT and sectioned within 24 hours for in situ hybridization . QuantiGene View RNA ( Affymetrix , Santa Clara , CA , USA ) in situ hybridization assay was performed according to the manufacturer protocol . Briefly , 12μm cryosections were fixed overnight in 4% PFA , dehydrated through a graded series of ethanol , were subjected to 2X protease digestion for 10 minutes , postfixed with 4% PFA and hybridized with probe sets against the gene of interest for 3 hours at 40°C using a ThermoBrite system ( Abbott Molecular , Des Plaines , IL , USA ) . Cryosections were then washed and subject to signal amplification and detection using fast red substrate , counterstained and mounted for subsequent imaging . For dual fluorescent in situ hybridization , Digoxigenin- and Fluorescein-labeled riboprobes were synthesized from full-length cDNA clones ( MGC Mouse glutamine synthetase cDNA Clone Id:4224865 ) . The hybridized mRNA was detected using the TSA-FITC/ TSA-CY5 Tyramide Signal Amplification System ( PerkinElmer , Waltham , MA , USA ) . Tamoxifen ( T-5648 , Sigma , St . Louis , MO , USA ) was dissolved in ethanol ( 200mg/ml ) and diluted in corn oil ( final concentration of 20mg/ml tamoxifen ) . Each experimental mouse received a single intraperitoneal injection of tamoxifen ( 0 . 6 mg or 30 μl of tamoxifen solution/mouse for P6 and P8 pups and 0 . 8 mg or 40 μl for P13 and P18 mice ) . Mice were euthanized and eyes enucleated and immediately immersed in cold fixative ( 1% PFA , 2% glutaraldehyde , and 0 . 1 M cacodylate buffer ) for 24 hours , after which they were transferred to cold 0 . 1 M cacodylate buffer solution for an additional 24 hours . Samples were embedded in glycol methacrylate , and serial sagittal sections ( 2μm ) passing through the optic nerve were cut and stained with hematoxylin and eosin ( H&E ) . Retina was dissected from Prss56Cre/+; R26tdTomato ( control ) or Prss56Cre/glcr4; R26tdTomato ( mutant ) mice and four radial incisions made and mounted on a slide . tdTomato positive terminal endings of Müller glia projection ( endfeet ) were visualized and images captured using a Confocal mircroscope ( Carl Zeiss LSM700 ) . Eight equivalent areas of the retina were consistently selected for each whole mount . Müller glia endfeet were classified into two groups based on their morphology: 1 ) Müller glia endfeet showing simple cohesive arrangement and occupying smaller area of the retina . 2 ) Müller glia endfeet exhibiting more spread-out morphology and covering a larger area , suggestive of increased branching and elaboration . Two independent observers masked to genotypes manually quantified the relative distribution of the two types of endfeet . Eyes were enucleated and retina was isolated and minced in DMEM ( Dubelcco’s Modified Eagles Medium , Gibco-Invitrogen Corporation , Carlsbad , CA , USA ) . Retina was then dissociated in 15IU papain ( Worthington Biochemicals Freehold , NJ , USA ) and 20μg/ml DNase I ( Roche Applied Science , Mannheim , Germany ) for 30 minutes at 37°C , gently triturated using a glass Pasteur pipet and passed through a 40 μm cell strainer . Tissue trapped by the strainer was digested with 1 mg/ml collagenase type I ( Worthington Biochemicals Freehold , NJ , USA ) and 15 μg/ml DNAse I ( Roche Applied Science , Mannheim , Germany ) for 30 min at 37°C . Flow-through was mixed with DMEM with 10% fetal bovine serum ( FBS , Gibco-Invitrogen Corporation , Carlsbad , CA , USA ) and washed 2X ( 300g for 2 minutes at RT ) . The retinal cell suspension was used for flow cytometry . Retinal cell suspension was fixed in 4% PFA and subjected to indirect immunolabeling using anti-GS ( mouse , 1:500 , MAB302 , EMD Millipore , Billerica , MA , USA ) or anti-Rhodopsin ( mouse , 1:1000 , MAB5336 , EMD Millipore , Billerica , MA , USA ) and fluorochrome labeled secondary antibodies ( AlexaFluor 488 conjugated secondary antibodies raised in donkey , 1:500 , Life Technologies , Carlsbad , CA , USA ) in 10% NDS/PBS containing 0 . 1% Triton X-100 at 4°C . Flow cytometry of immunolabeled cell suspension was performed using a BD™ LSRII Fortessa flow cytometer and FACS Diva Software ( BD Biosciences , San Jose , CA ) . Retinal cell suspension from Prss56Cre/+; R26TdTomato/+ mice incubated with AlexaFluor 488-conjugated secondary antibody were used as negative controls to establish gating parameters . Bright-field images were captured using AxioVision software and an AxioImager M1 microscope equipped with an AxioCam ICc3 digital camera ( Carl ZeissMicroscopy , LLC , Germany ) . Fluorescent images were acquired using AxioImager M1 microscope equipped with an MRm digital camera and AxioVision software , with an LSM700 confocal microscope and a Zen software ( Carl Zeiss Microscopy , LLC , Germany ) . Amira software was used for 3D visualization and analysis . We obtained illustrative biometric data from a nanophthalmic patient affected with a homozygous missense variant ( p . G320R ) in PRSS56 previously identified by one of the authors ( ACO ) [38] , and also a representative normal volunteer for comparison . Approval for this study was obtained from the Research Ethics Board of the Nova Scotia Health Authority , Halifax , Nova Scotia , Canada . In brief , ocular dimensions measured via ultrasound biomicroscopy A- and B-scan techniques revealed a very small globe bilaterally featuring crystalline lenses that were normally positioned , but large in size relative to that of the eye . The choroid was also observed to be diffusely thickened . Statistical comparisons between control and mutant samples were performed by a two-tailed unpaired Student’s t-test using Prism version 6 . 0f software . p values of <0 . 05 were considered significant . Power for a two-tailed two-sample t-tests was calculated using a range of means and standard deviation values of axial length that could be reasonably expected based on published data and our initial assessment [26] . Although the difference between the two group means and within-group standard deviation are statistically independent parameters , in many biological data sets they show various levels of collinearity . Therefore , for small , medium and large μ1- μ 2 values , we based our power calculation on correspondingly increasing expectations of SD . The detection of differences with 80% power is possible in these scenarios with sample sizes ≤ 7 . For example , with respect to axial length , a mean difference of 50 μm , SD = ± 30 , the effect size is 1 . 67 requiring a sample size of 6 . Where logistically possible , and partly as a precaution against the possibility of some failed experiments , we collected data from more samples . Number of eyes , mean and standard deviation of all measurements in each group are presented in S1–S3 Tables . | Refractive errors mainly occur when changes in ocular size ( ocular axial length ) prevent light from focusing directly on the retina . Myopia ( nearsightedness ) is the most common form of refractive errors in which the focused image falls in front of the retina . The recent unprecedented rise in the incidence of myopia has significant implications as individuals with high myopia are at an increased risk of developing irreversible blinding conditions , including retinal detachment , macular degeneration , and glaucoma . Ocular axial growth is a key determinant of normal refractive development . Although the retina has been established as a central player involved in the regulation of ocular growth , the specific retinal cell type ( s ) and molecular pathways involved are poorly defined . Here , we have utilized genetic mouse models to provide significant insight into spatial and temporal requirements of the retinal factor PRSS56 in ocular size determination . Importantly , we have uncovered a previously unrecognized role for retinal Müller glia in ocular growth and demonstrated that Prss56 inactivation has translational potential to rescue axial length elongation in a mouse model of myopia . Collectively , our findings suggest that therapeutic strategies targeting PRSS56 to modulate ocular growth could have important clinical implications to prevent or slowdown the progression of myopia and associated blinding conditions in humans . | [
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] | 2018 | Müller glia-derived PRSS56 is required to sustain ocular axial growth and prevent refractive error |
Variation among individuals is a prerequisite of evolution by natural selection . As such , identifying the origins of variation is a fundamental goal of biology . We investigated the link between gene interactions and variation in gene expression among individuals and species using the mammalian limb as a model system . We first built interaction networks for key genes regulating early ( outgrowth; E9 . 5–11 ) and late ( expansion and elongation; E11-13 ) limb development in mouse . This resulted in an Early ( ESN ) and Late ( LSN ) Stage Network . Computational perturbations of these networks suggest that the ESN is more robust . We then quantified levels of the same key genes among mouse individuals and found that they vary less at earlier limb stages and that variation in gene expression is heritable . Finally , we quantified variation in gene expression levels among four mammals with divergent limbs ( bat , opossum , mouse and pig ) and found that levels vary less among species at earlier limb stages . We also found that variation in gene expression levels among individuals and species are correlated for earlier and later limb development . In conclusion , results are consistent with the robustness of the ESN buffering among-individual variation in gene expression levels early in mammalian limb development , and constraining the evolution of early limb development among mammalian species .
Phenotypic variation within populations is a prerequisite of evolution by natural selection , and in theory has the potential to bias the trajectory and rate of evolutionary change [1–6] . As such , identifying the processes that shape phenotypic variation has long been a fundamental pursuit of evolutionary biologists . Historically , evolutionary biologists have tended to focus on the sorting of population-level variation by selective processes , rather than on the production of that variation by developmental processes [7] . As a result , the effect of developmental processes on the distribution and magnitude of phenotypic variation among individuals and species remains unclear for most systems . In this study we use the mammalian limb as a study system to investigate two questions that address the relationship between developmental processes and phenotypic variation at the level of gene expression dynamics: ( 1 ) Does the structure of the gene network affect the distribution of variation in gene expression among individuals ? , and ( 2 ) Is the distribution of variation in gene expression among individuals correlated with the evolutionary divergence in gene expression among species ? The mammalian limb is an ideal system for examining these questions because its development is well characterized , its morphology diverse , and since its form is central to many mammalian behaviors , its morphology is certainly under selection [8–12] . Many of the critical gene interactions that regulate limb outgrowth and patterning in mouse , the traditional mammal model , have been identified [9 , 10 , 13] . Initial budding of the limb from the body and limb outgrowth ( embryonic day [E] 9 . 5 –E11 ) are regulated by interactions between several genes , including Bmp4 , Gli3 , Grem1 , Shh , AER-Fgf’s ( e . g . , Fgf4 , Fgf8 ) , Fgf10 , and Hox genes ( Fig 1A ) . Knockouts of these genes result in pathological phenotypes ranging from severe ( e . g . , complete limb agenesis; AER-Fgf’s , Fgf10 ) to moderate ( e . g . , limb truncations; Bmp4 ) to mild ( e . g . , malformed digits; Shh , Gli3 , Grem1 ) [13–18] . Most of these genes ( e . g . , Bmp4 , Gli3 , Grem1 , Shh , AER-Fgf’s , and Hox genes ) are also involved in later limb outgrowth and patterning ( E11 –E13 ) , but some of their interactions differ ( e . g . , Hox genes and Gli3 , Shh , AER-Fgf’s; Fig 1B ) . As a result , the structure of the gene regulatory network differs for earlier ( E9 . 5 –E11 ) and later ( E11 –E13 ) limb development . This structural difference provides two opportunities to investigate the relationship between network structure and gene expression variation among individuals . This structural difference also provides an opportunity to contrast earlier and later limb development . Research suggests that the main segments of the limb ( e . g . , stylopod , zeugopod , and autopod ) are specified by or during the time of initial limb outgrowth [19 , 20] . As a result , disruption of early limb development could have potentially catastrophic effects on limb formation that are not likely to be selectively advantageous ( e . g . , limb agenesis ) . In contrast , disruptions of later limb development are less likely to have as severe an impact on the overall limb structure . While later disruptions might impact the relative size of limb segments , they are less likely to result in no limb at all . Following this logic , we might hypothesize that genes regulating early limb development generally exhibit less variation in expression among individuals than those regulating later limb development [21–33] . Additionally , it is possible that select early limb genes might vary at a level equal to or greater than that of individual later genes , but that this variation is dampened at the system level by the interactions among genes that characterize the gene network ( i . e . , developmental buffering ) [34–37] . As population-level variation provides the raw material upon which natural selection acts , we can further hypothesize that the genes regulating early limb development also exhibit less variation in expression among species [38] . Support for these hypotheses would reinforce the importance of network structure ( i . e . , development ) in shaping variation in mammalian limbs among individuals and over evolutionary time , while failure to support these hypotheses would suggest that network structure does not play a critical role in the generation of limb variation . To test these hypotheses , we computationally modeled the gene networks regulating mouse limb development , and determined the sensitivity of network genes to system perturbation and the ability of network genes to perturb the system when altered . We also assessed the sensitivity of the system as a whole to perturbations in gene interactions and expression . We experimentally quantified naturally occurring variation in the expression of several network genes within a population of mouse individuals . To compare variation among species , we used transcriptomic data ( RNASeq ) from four mammals with divergent limb morphologies ( bat , opossum , pig and mouse ) . We then assessed the relationship between gene and network sensitivity and gene expression variation among mouse individuals and among mammalian species . Our results suggest that the gene network that regulates early limb development is more robust than that regulating later limb development , and that this robustness buffers variation in early limb gene expression among individuals , and constrains the evolution of early limb development among species .
We assembled early ( ESN ) and late ( LSN ) stage networks for key genes regulating limb development from previously published experimental studies [9 , 13] ( Fig 1 ) . The ESN regulates initial limb outgrowth and the initiation of the epithelial-mesenchymal interactions that are critical to continued limb development . These events occur from embryonic days ( E ) 9 . 5 to E11 in mouse . The LSN , in contrast , regulates the limb’s differentiation along its anterior-posterior ( i . e . , thumb to pinky ) axis and elongation along its proximal-distal ( shoulder to fingertips ) axis from E11 to E13 in mouse . To describe the temporal behavior of the activity ( i . e . , expression ) levels of genes in these networks we built mathematical models ( see S1 Methods ) . After building the models , we ran a series of simulations in which we computationally interrupted interactions between genes and compared the resulting expression levels with those of the unaltered , default model ( Table 1 ) . Within the ESN , removal of the Hox to Grem1 or Gli3R to Grem1 link affects Grem1 and Bmp4 expression levels ( i . e . , alters expression by 10% or more ) , but does not affect the expression levels of other genes . Removal of the AER-Fgf’s to Shh or Hox to Shh links only affects the expression levels of Gli3R and Shh , removal of the Fgf10 to AER-Fgf’s or Bmp4 to AER-Fgf’s links only affects the expression level of the AER-Fgf’s , and removal of the Hox to Fgf10 or AER-Fgf’s to Fgf10 links only affects the expression level of Fgf10 . Removal of the Shh to Gli3R link affects the expression level of Gli3R , while removal of the Grem1 to Bmp4 affects the expression level of Bmp4 . Removal of the Bmp4 to Grem1 link results in no significant change in expression levels . In total , 14 of 77 possible interactions are affected ( i . e . expression levels change by 10% or more ) by alterations in the ESN ( 18% ) ( Table 1 ) . For the LSN , removal of the Shh to Gli3R link affects Gli3R expression levels , but does not affect the expression levels of other genes . Removal of the AER-Fgf’s to Shh and Hox to Shh links affects only Shh and Gli3R expression levels . Removal of the Repressor X to Grem1 or Grem1 to Bmp4 link disrupts the expression levels of Bmp4 , Grem1 , and the AER-Fgf’s . Removal of the AER-Fgf’s to Repressor X link also disrupts the expression levels of Bmp4 , Grem1 , and the AER-Fgf’s , but also affects Repressor X expression levels . Removal of the Hox to Grem1 link affects the expression levels of Bmp4 , Gli3R , Grem1 , Shh , and the AER-Fgf’s . Removal of the Gli3R to Hox , AER-Fgf’s to Hox , or Gli3R to Grem1 links disrupts the expression levels of all genes save Repressor X . Finally , when the Bmp4 to Grem1 , Bmp4 to AER-Fgf’s link is removed , expression levels of all genes are affected . In total , 52 of 84 possible interactions are affected ( i . e . expression levels change by 10% or more ) by alterations in the LSN ( 62% ) ( Table 1 ) . For each gene in each model , we then determined the number of genes whose removal alters expression of the gene in question ( i . e . expression levels change by 10% or more ) , and the number of genes that exhibit expression changes when the gene in question is removed . We used the resulting values to generate a simulation space that was used to evaluate the ability of genes to affect other genes , and to be affected themselves by network perturbations ( Fig 2C and 2D ) . Simulation spaces for the ESN and LSN were generated using the same scales to allow comparisons . For the ESN ( Fig 2C ) , all genes fall in the lower left quadrant of the space , suggesting that they do not greatly affect expression of other genes , and are not greatly affected by others . In contrast , most LSN genes ( Fig 2D ) fall in the right upper and lower quadrants of simulation space . Genes in both the upper ( e . g . , Bmp4 , Gli3R ) and lower ( e . g . , AER-Fgf’s , Grem1 , Shh ) right quadrants and their boundaries are affected by perturbations in other genes , but genes in the upper right quadrant also affect the expression of other genes while genes in the lower right quadrant do not . Hox A/D falls in near the middle of the simulation space , suggesting that it moderately affects others and is affected by them . Only Repressor X falls in the lower left quadrant for the LSN , suggesting that it does not affect others and is not affected itself . We next varied the parameter values used in the models and compared the resulting gene expression levels to those of the unaltered , default model ( S1 Table ) . Results indicate that the ESN is most sensitive to changes in Hox A/D parameters ( 39% of Hox A/D parameter changes result in a ≥10% change in the expression level of another gene ) , followed by Shh ( 20% ) and Bmp4 ( 18% ) , Gli3R ( 18% ) , and Grem1 ( 17% ) . The ESN is less sensitive to changes in AER-Fgf ( 10% ) and Fgf10 ( 9% ) parameters . Bmp4 is the most sensitive of the ESN genes to changes in parameters of other genes ( 32% of parameter changes result in a ≥10% change in the expression level of Bmp4 ) , followed by Gli3R ( 26% ) , Grem1 ( 19% ) , Fgf10 ( 17% ) , and Shh ( 13% ) . Hox A/D ( 9% ) and Fgf8 ( 7% ) expression levels are less sensitive to ESN parameter changes . The LSN is more sensitive to changes in Repressor X ( 73% ) , Bmp4 ( 60% ) , and the AER-Fgf’s ( 53% ) , and less sensitive to changes in Hox A/D ( 39% ) , Grem1 ( 39% ) , Shh ( 25% ) and Gli3R ( 18% ) . Within the LSN , the AER-Fgf’s ( 61% ) and Gli3R ( 63% ) are the most sensitive to parameter changes in other genes , followed by Bmp4 ( 51% ) , Grem1 ( 47% ) , and Shh ( 40% ) , while Hox A/D ( 30% ) and Repressor X ( 17% ) are less sensitive . The percentages listed above were used to generate a sensitivity space , similar to the simulation space described above ( Fig 2A and 2B ) . ESN and LSN sensitivity spaces were generated using the same scales to facilitate comparisons . Similar to the simulation results , all ESN genes group within or on the boundary of the lower left quadrant of the space , suggesting that alteration of the values of their related-parameters does not greatly affect expression of other genes , and that their expression levels are not greatly affected by alterations in the values of the related-parameters of other genes . LSN genes are more distributed in the sensitivity space . Bmp4 and the AER-Fgf’s lie in the upper right quadrant , similar to their location in the simulation space ( Fig 2C ) . Grem1 , Shh , Gli3R and fall in or on the boundary of the lower right quadrant , indicating that they do not affect others but are affected themselves . Of these , Grem1 and Shh also fall within the lower right quadrant of the simulation space ( Fig 2C ) . Repressor X lies within the upper left quadrant , suggesting that alteration of the values of its related-parameters affects the expression of other genes but that its expression is not greatly affected alterations in the values of the related-parameters of other genes . Repressor X also falls on the left side of the simulation space , but in the lower quadrant . Similar to its location in the simulation space , Hox A/D falls near the center of the plot . We performed a series of real-time quantitative PCR ( qPCR ) assays to quantify the expression levels of genes that appear in both the ESN and LSN models ( Bmp4 , Gli3 , Grem1 , Shh , and the AER-Fgf Fgf8 ) in mouse embryos . For the early developmental stages ( ES ) , the averaged , scaled expression level was 2 . 02 for Bmp4 , 2 . 58 for Fgf8 , 2 . 19 for Grem1 , 1 . 68 for Shh , and 0 . 02 for Gli3 . For the later developmental stages ( LS ) , the average , scaled expression level was 2 . 29 for Bmp4 , 0 . 83 for Fgf8 , 1 . 92 for Grem1 , 2 . 32 for Shh , and 0 . 02 for Gli3 . Statistical tests reveal that the mean-standardized variances of expression levels significantly differ among genes in the earlier ( ES , E10-E11; Bartlett’s Test , F-ratio = 8 . 614 , DF = 4 , P < 0 . 001* ) and later ( LS , E11-E13; Bartlett’s Test , F-ratio = 5 . 823 , DF = 4 , P < 0 . 001* ) stages of development . In the ES , Shh displays the highest average mean-standardized variance ( coefficient of variation , CoV ) ( 0 . 847 ) , followed by Bmp4 ( 0 . 567 ) , Grem1 ( 0 . 531 ) , Fgf8 ( 0 . 474 ) , and Gli3 ( 0 . 380 ) . Fgf8 displays the highest average CoV in the LS ( 1 . 701 ) , followed by Shh ( 1 . 523 ) , Bmp4 ( 1 . 037 ) , Gli3 ( 0 . 910 ) , and Grem1 ( 0 . 703 ) . Litter membership also has the power to significantly explain the variance in expression levels in a given gene ( e . g . , Bmp4 ) that are observed among individuals ( ANOVA; Bmp4 F-ratio = 2 . 379 , DF = 8 , P = 0 . 026*; Gli3 , F-ratio = 5 . 742 , DF = 8 , P = < 0 . 001*; Grem1 , F-ratio = 4 . 412 , DF = 8 , P = < 0 . 001*; Fgf8 , F-ratio = 7 . 097 , DF = 8 , P < 0 . 001*; Shh F-ratio = 2 . 162 , DF = 8 , P = 0 . 043* ) . We next compared the among-individual , standardized variation in the expression level of a gene in vivo ( CoV , from qPCR ) with the: ( 1 ) number of genes whose removal alters expression of the gene in question ( i . e . , alters expression level by 10% or more ) , and ( 2 ) number of genes that exhibit expression changes when the gene in question is removed ( from the simulation analyses ) . For both the ES and the LS , neither the relationships between the number of genes whose removal alters expression of the gene in question ( #1 ) and the CoV ( ES—Least-Squares Regression , R2 = 0 . 202 , P = 0 . 448; LS—R2 = 0 . 214 , P = 0 . 433 ) , nor the relationships between the number of genes that exhibit expression changes when the gene in question is removed ( #2 ) and CoV ( ES—R2 = 0 . 503 , P = 0 . 180; LS—R2 = 0 . 142 , P = 0 . 532 ) are significant ( Fig 3 ) . For both the ES and LS , Shh is among the genes that are the least sensitive to perturbations in other genes , has a relatively low impact on the expression of other genes when altered , and displays a relatively high CoV . The opposite is true for Gli3R during the ES . Fgf8 displays the highest CoV during the LS and is highly sensitive to perturbations in other genes , like all LS genes . Given the large difference in the percentage of possible interactions that are affected by computational alterations in the ESN and LSN ( 18% and 62% , respectively ) , we next compared the level of variation in measured gene expression during early ( ES ) and later ( LS ) development ( from qPCR ) . For every model gene ( 5 of 5 ) , the average CoV is greater later than earlier in development ( P = 0 . 031* ) ( Fig 3 ) . When the variation around the averages is taken into account using a resampling technique , the average CoV remains significantly greater later than earlier in development for four of the five genes ( Bmp4 , P = 0 . 028*; Gli3 , P < 0 . 001*; Grem1 , P = 0 . 024*; Fgf8 , P < 0 . 001* ) . Only for Shh , a gene with among the highest CoV in both the early and later stages , does the average CoV does not remain significantly greater later than earlier in development ( P = 0 . 092 ) . The average CoV of the housekeeping gene β-actin does not significantly differ during earlier ( 0 . 832 ) and later ( 0 . 929 ) development ( P < 0 . 217 ) . To calculate gene expression variation among species , we first generated transcriptomic libraries for bat ( Carollia perspicillata ) , opossum ( Monodelphis domestica ) , pig ( Sus scrofa ) and mouse ( Mus musculus ) forelimbs for early ( ES; early limb bud ) and late ( LS; paddle ) limb stages . We then used a set of 6 , 583 genes orthologous to all four species ( see S1 Methods ) to calculate the among-species conservation of gene expression at each developmental stage , using the mean of all species pairwise Spearman coefficients . All resulting pairwise Spearman coefficients are positive and > 0 . 50 , suggesting that the orthologous genes might perform similar functions between species ( Fig 4A and 4B ) . However , the degree of gene expression conservation decreases from 0 . 5667 at ES to 0 . 5612 at LS of forelimb development . To test the robustness of this difference with respect to the selection of orthologous genes , we randomly sub-sampled 500 sets of orthologous genes at early and late stages at intensities ranging from 50 to 100% of all orthologous genes ( Fig 4C ) . For each intensity , the distributions of gene expression conservation levels between early and late stages were significantly different ( T-test , P-value < 0 . 05* ) . These results suggest that gene expression patterns vary more among species during the LS than ES of limb development , consistent with patterns of variation among individuals . We also calculated the among-species conservation of mean-standardized expression at each developmental stage for the five genes that appear in both the ESN and LSN models ( Bmp4 , Gli3 , Grem1 , Fgf8 , and Shh ) . At the ES , Gli3 ( standard deviation [SD] = 0 . 177 ) falls within the top 25% of conserved genes while Shh ( SD = 0 . 975 ) and Fgf8 ( SD = 0 . 475 ) fall within the top 25% of divergent genes . Bmp4 ( SD = 0 . 257 ) and Grem1 ( SD = 0 . 260 ) fall near the middle of the range of genes . Fgf8 ( SD = 0 . 971 ) and Shh ( SD = 1 . 196 ) are also among within the top 25% of divergent genes at the LS . However , Grem1 ( SD = 0 . 123 ) is among the 25% most conserved genes at this stage , and Bmp4 ( SD = 0 . 253 ) and Gli3 ( SD = 0 . 235 ) fall near the middle of the range of genes . Average divergence level and average variation in expression levels among mouse individuals ( CoV , as measured with qPCR ) are positively correlated for the ES ( R = 0 . 906 ) and LS ( R = 0 . 854 ) ( Fig 5 ) , and the correlation between divergence level and CoV is significant for the ES ( P = 0 . 019* ) and LS ( P = 0 . 016* ) after bootstrapping .
The results reported here suggest that the structure of the early stage network ( ESN ) renders it more robust to perturbation than the later stage network ( LSN ) . Findings also suggest that among individual variation in expression levels is lower for genes regulating early ( ES ) than late ( LS ) limb development , and that gene expression levels are heritable . Results of this study also suggest that the expression levels of genes at early stages generally vary less among species . Additionally , results suggest that among individual and among species variation in the expression levels of several model genes are significantly correlated for the early and later stages of limb development . Taken together , these findings suggest a scenario in which a robust ESN buffers among individual variation in gene expression early in limb development , and , as variation is a prerequisite of evolution by natural selection , limits the evolution of early limb development among species . The findings of this study are therefore consistent with the hypotheses that: ( 1 ) the structure of the early limb gene network influences the distribution of variation in mammalian limbs among individuals and over evolutionary time [21–23 , 38–43] , and , more generally , ( 2 ) the process of early limb development generally precludes the random accumulation of variation in gene expression across the network [21 , 44] . Results of this study are also consistent with a scenario in which species-specific differences accumulate as development progresses . However , it is important to note that the results of this study are based on a limited number of RNASeq samples per species . Analyses of additional samples are needed to determine the degree to which the RNASeq-driven results of this study are robust to experimental variation among samples . Furthermore , while the network models used in this study are based on solid experimental data [9 , 13] , the results of this study are only as accurate as the network models being used . This study did not find a significant correlation between individual gene’s sensitivity to network perturbation or impact on the network when perturbed and variation in expression among individuals . This result could stem from the lack of a relationship between these variables , or from the incompleteness of the limb gene network used in this study . However , this study did find evidence that variation in expression level significantly differs among genes , with some genes being more variable among individuals in a population , and others less so . Shh displays among the greatest variation in expression levels during both the early and later stages of limb development among individuals and species , and has among the least impact on the system when altered . During the early stages of limb development , Gli3 displays the least variation in expression levels and has among the greatest effect on the system when altered . As population-level variation provides a necessary prerequisite for evolution by natural selection , we might expect genes with the greatest expression variation among individuals to also display the most variation in expression among species . In this study this would be late stage Shh , Bmp4 , and Fgf8 . This study did find a significant correlation between the variation in the expression of these and the other key model genes among individuals and species during late limb development . Furthermore , of the few studies that have compared gene expression among mammalian limbs , a disproportionally high number have found differences in later stage Shh , Bmp4 , and Fgf8 expression among species . Evolutionary changes in Bmp4 expression contribute to digit reduction in horses and jerboa , while evolutionary changes in Shh signaling contribute to digit reduction in pigs [45] . A broader initial range and secondary redeployment of Shh signaling helps generate the unique phenotype of the bat wing [46] , and hind limb loss in dolphins is initiated by a disruption in Shh signaling [47] . Shh signaling is also activated exceptionally early during the rapid outgrowth of opossum forelimbs [48 , 49] . Fgf8 expression is higher in the AER of bat wings than mouse limbs and is also secondarily redeployed later in bat wing development [50] . However , genes beyond Shh , Bmp4 , and Fgf8 also display expression differences in mammalian limbs . For example , the expression of 5’ Hox A/D genes differs in bat and kangaroo limbs [51 , 52] , compared to mouse [53] . Clearly more studies of limb development in diverse mammalian species are needed to resolve this issue , but results to date are consistent with alterations in the expression of genes acting during late limb development ( E11 to E13 in mouse ) including Shh , Bmp4 , and Fgf8 frequently contributing to mammalian limb evolution . In line with the proposed predominance of changes in late limb development in limb evolution , this study found no evidence for the existence of significant variation in the expression of early limb genes that is masked by systems-level processes . However , the expression-based findings of this study do not rule out the existence of cryptic genetic variation in genes with roles in early limb development . Cryptic genetic variation can provide a source of evolutionary potential when uncovered by environmental or genetic perturbations [54 , 55] , and thereby can expedite evolutionary change . Thus , if the genes regulating early limb development possess significant cryptic genetic variation that has been uncovered over evolutionary time , we would expect early limb development to vary among species . Early limb development , as defined in this paper , encompasses establishment of the limb field and the initial outgrowth of the limb . The primary limb segments ( e . g . , stylopod , zeugopod and autopod ) are also likely specified during this time [19 , 20] . Initial limb outgrowth appears to be generally conserved in tetrapods across large phylogenetic distances [56 , 57] . Initial limb outgrowth is even conserved in some tetrapods that do not possess limbs in their adult form ( e . g . , boas , dolphins ) [47 , 58] . The primary segments of the limb are also broadly conserved across limbed tetrapods [8 , 59] . These observations together with the findings of this study suggest that the genes regulating early limb development either do not possess significant cryptic genetic variation , or that the robustness of the ESN has inhibited the ability of environmental or genetic perturbations to uncover this variation . Whichever the case , the early development of the limb appears to have been relatively conserved over the evolutionary history of tetrapods .
All animal work was conducted according to relevant national and international guidelines . Animals were euthanized using CO2 inhalation followed by cervical dislocation . The University of Illinois IACUC approved this research ( protocols #13128 , 14159 , 14199 , 14209 ) . The starting point for the mathematical models used in this work was the seminal paper by Bénazet et al . 2009 [13] . These models were designed to pertain to the entire limb . Two interconnected feedback loops were incorporated into their model: a fast loop between Grem1 and Bmp4 and a slower loop between Shh , Grem1 and the AER-Fgf’s . Following the findings reported in Sheth et al . 2013 [9] , this network was divided into an Early ( ESN ) and Late ( LSN ) Stage Network and augmented to also include Hox genes ( specifically , Hox A and D genes ) , Gli3R , Fgf10 , and an as yet unidentified repressor , Repressor X . It is important to note that the specific genes , gene interactions , and equations that we include in our models match those presented in Bénazet et al . 2009 and Sheth et al . 2013 , which are well supported by experimental , biological evidence . Mathematical models to describe gene interactions were constructed in MATLAB and were based on ordinary differential equations , which are outlined in S1 Methods , along with the model parameters . We ran a series of simulations in MATLAB in which we removed interactions between genes . Only one interaction ( i . e . link between two genes ) was removed in each simulation , and removal simulations were performed for all interactions . We ran a series of simulations in MATLAB in which we varied the parameter values used in the models . Only one parameter was modified in each analysis . We performed a series of real-time quantitative PCR ( qPCR ) assays to quantify the expression levels of the genes that appear in both the ESN and LSN , namely Bmp4 , Gli3 , Grem1 , Shh , and the AER-Fgf Fgf8 , in the forelimbs of 71 mouse embryos ( outbred ICR strain , Taconic ) from 9 litters . These litters ranged in age from E10 to E13 . The limbs of the animals in these litters were staged according to Wanek’s staging guide , which divides mouse limb development into 15 stages [60] . Embryonic limbs from limb ridge ( Wanek stage 1 ) through bud formation ( Wanek stage 4; 4 stages total; E10 –E11 ) were grouped into an “early stage” ( ES ) for analyses , while limbs in the paddle stages of development ( Wanek stages 5–8; 4 stages total; >E11 –E13 ) were grouped into a “late stage” ( LS ) . Limb samples were evenly spread over all stages . The Coefficient of Variation ( CoV ) , which is the standard deviation divided by the mean , was used to quantify variation in expression level for a given gene for early and later limb development [61] . Additional details for the qPCR analyses are in S1 Methods , and standard and dissociation curves for each gene are in S2 Methods . Bartlett’s Test was used to compare the variance of expression levels in the ES and LS [61] . ANOVA was used to examine the contribution of litter membership ( i . e . , heredity ) to observed patterns of gene expression [61] . The relationship between among individual variation in gene expression level with the number of genes whose removal alters expression of the gene in question by 10% or more from the default value ( from simulation analyses ) and whose values deviate by 10% or more from the default values when the gene in question is removed ( from simulation analyses ) was statistically assessed using Least-Squares Regression [61] . The average levels of variation in measured gene expression during early and later development ( CoV ) were statistically compared [61] . To determine the significance of the observed differences in CoV , we used a Monte Carlo approach , in which we shuffled the observed CoV from early and later development , to generate a null distribution of CoV differences between them . Specifically , we pooled all replicate CoV irrespective of developmental stage , then randomly drew , with replacement , two samples equal in size to the measured early and later samples , respectively . We used as a measure of significance the proportion of 10 , 000 replicates in which the difference between CoV’s of randomly shuffled samples was greater than or equal to the observed difference . Embryonic mice , opossums , bats and pigs with early ( ES ) or late ( LS ) stage forelimbs were obtained from a variety of sources ( see S1 Methods ) . Forelimbs for the ES were harvested at Stage 14 for bat , E11 for mouse , Stage 28 for opossum and E22 for pig [62–65] . For the LS , forelimbs were harvested at Stage 15 for bat , E12 for mouse , Stage 29 for opossum and E26 for pig . Limbs were removed from embryos and stored in RNALater in -20°C until further processing . RNA was extracted from tissues using E . Z . N . A . Total RNA Kit I ( OMEGA bio-tek #R6834 ) , and converted into RNASeq libraries with the Illumina TruSeq RNA Sample Preparation Kit ( Illumina RS-122-2001 ) . Libraries were sequenced on an Illumina HiSeq 2500 housed in the Roy G . Carver Biotechnology Center at the University of Illinois . Resulting reads were processed , aligned to published genomes or de novo assemblies , and gene expression levels assessed ( see S1 Methods ) . All data from this study have been deposited in the Gene Expression Omnibus ( GEO ) with the accession number GSE71390 . We analyzed the conservation of the gene expression profiles of bat , mouse , opossum , and pig across embryonic limb development , using the mean of all species pairwise Spearman coefficients ( see S1 Methods ) . The relationship between these average species pairwise coefficients and among individual gene expression variation was assessed using Pearson Product-Moment Correlation [61] . To account for the variation among samples we used a bootstrap approach . Specifically , for the comparisons of the average species pairwise coefficients and among individual gene expression variation , we resampled , with replacement , the CoV’s ( among individuals ) and mean-standardized expression levels ( among species ) and recalculated the Pearson product-moment correlation coefficient ( R ) between the resampled CoV’s and mean-standardized expression levels . We used as a measure of significance the proportion of 10 , 000 replicates in which the calculated R was greater than or equal to zero . We also ordered the species pairwise Spearman coefficients for each individual gene from highest ( most conserved ) to lowest ( most divergent ) . | The variation generating mechanisms of development interact with the variation sorting mechanism of natural selection to produce organismal diversity . While the impacts of natural selection on existing variation have received much study , those of development on the generation of this variation remain less understood . This fundamental gap in our knowledge restricts our understanding of the key processes shaping evolution . In this study , we combine mathematical modeling , and population-level and cross-species assays of gene expression to investigate the relationship between the structure of the gene interactions regulating limb development and variation in the expression of limb genes among individuals and species . Results suggest that the way in which genes interact ( i . e . , development ) biases the distribution of variation in gene expression among individuals , and that this in turn biases the distribution of variation among species . | [
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] | [] | 2015 | The Relationship between Gene Network Structure and Expression Variation among Individuals and Species |
MDA5 belongs to the RIG-I-like receptor family and plays a non-redundant role in recognizing cytoplasmic viral RNA to induce the production of type I IFNs . Upon RNA ligand stimulation , we observed the redistribution of MDA5 from the cytosol to mitochondrial membrane fractions . However , the molecular mechanisms of MDA5 activation remain less understood . Here we show that 14-3-3η is an essential accessory protein for MDA5-dependent type I IFN induction . We found that several 14-3-3 isoforms may interact with MDA5 through the CARDs ( N-MDA5 ) , but 14-3-3η was the only isoform that could enhance MDA5-dependent IFNβ promoter activities in a dose-dependent manner . Knock-down of 14-3-3η in Huh7 cells impaired and delayed the kinetics of MDA5 oligomerization , which is a critical step for MDA5 activation . Consequently , the MDA5-dependent IFNβ promoter activities as well as IFNβ mRNA expression level were also decreased in the 14-3-3η knocked-down cells . We also demonstrated that 14-3-3η is essential in boosting the activation of MDA5-dependent antiviral innate immunity during viral infections . In conclusion , our results uncover a novel function of 14-3-3η to promote the MDA5-dependent IFNβ induction pathway by reducing the immunostimulatory potential of viral dsRNA within MDA5 activation signaling pathway .
Among the RIG-I-like Receptor ( RLR ) family , RIG-I and MDA5 share a number of structural similarities , and both of them include three distinct domains . The N-terminus caspase activation and recruitment domains ( CARDs ) function as the activation domain to directly interact with the CARD of downstream adaptor protein MAVS , of which interaction is critical for the activation of the type I interferon signaling pathway [1–4] . The molecular mechanisms of RIG-I activation have been extensively studied in the past decade . Once bound on the RNA with 5’-triphosphate ( 5’-ppp ) or short dsRNA , a conformational change of RIG-I will occur , and the C-terminus repressor domain ( RD ) of RIG-I will release the CARDs for interactions with accessory proteins for redistribution and then interaction with downstream signaling molecules [5 , 6] . We have previously identified that activated RIG-I will then redistribute to the mitochondrion-associated membrane ( MAM ) through interaction with mitochondrial chaperone protein 14-3-3ε via the CARDs of RIG-I [7] . The interaction between RIG-I and MAVS at MAM is critical for triggering IFNβ induction [8] . MDA5 activation , however , is not as well-understood as RIG-I . MDA5-mediated antiviral signaling has been shown important in the clearance of flavivirus , picornavirus , paramyxovirus , and reovirus infections [9] . It has been shown that MDA5 has an essential and non-redundant role in detecting RNA virus infection [10] . When viral long dsRNA appears in the cytoplasm , MDA5 can recognize with these long dsRNA by its helicase and C-terminal domains [11] . The interaction between MDA5 to the long dsRNA will then cooperatively form a tandem MDA5 filament along the dsRNA , and the CARDs of MDA5 will then oligomerize and interact with MAVS to trigger antiviral signaling pathway [12] . Finally , this signaling pathway leads to the phosphorylation and activation of transcription factors , such as interferon regulatory factor 3 ( IRF3 ) , interferon regulatory factor 7 ( IRF7 ) , and nuclear factor κ-light-chain-enhancer of activated B cells ( NF-κB ) to induce interferon β ( IFNβ ) and a set of other antiviral genes to restrict virus replication [13–15] . To establish successful infections , viruses have evolved viral components antagonize the host innate immune system . For instance , dengue virus NS3 protein uses a protease-independent phosphomimetic-based mechanism to retain the essential chaperone protein for RIG-I activation , 14-3-3ε , in the cytosol and lead to the attenuation of IFNβ induction in the infected cells [16] . Successful type I IFN induction requires both effective recognition and activation of the receptors as well as complete downstream signaling transduction . The race between the proper establishment of host antiviral state and the virus antagonism at early infection is critical to determine the infection outcome [17] . The 14-3-3 proteins are conserved in most animals and plants . In mammalian cells , there are 7 isoforms known as 14-3-3β ( α ) , 14-3-3γ , 14-3-3ε , 14-3-3η , 14-3-3σ , 14-3-3θ ( τ ) and 14-3-3ζ ( δ ) [18 , 19] . There are tremendous amount of intracellular proteins , and the intracellular localization of each proteins serves as a regulatory event during cell cycle control , signaling transduction , cell programmed death , protein trafficking , and etcetera [20–23] . Each isoforms of the 14-3-3 family may form homo- or hetero-dimers to serve as chaperone proteins and relocalize their target proteins from one intracellular compartment to another among cytoplasm , cellular membrane , endoplasmic reticulum ( ER ) , mitochondrion , or the nucleus [7] . In most cases , 14-3-3 chaperone proteins bind onto the target proteins at the original localizations , and upon certain stimulations or post-translational modification of the protein , the 14-3-3 proteins will then bring their target proteins to a specific sites where the proteins can properly function [20 , 24 , 25] . Previous studies have shown that the expression level of 14-3-3 isoforms were upregulated during virus infection , and overexpression of 14-3-3 protein could attenuate the production of virus particles [26–28] . Also , we previously identified that the redistribution of RIG-I from the cytosol to a membrane fraction upon ligand recognition was controlled by 14-3-3ε [7] . A followed-up study showed that dengue virus ( DENV ) NS3 protein uses a phosphomimetic-based mechanism to occupy 14-3-3ε to restrict RIG-I activation in the DENV-infected cells [16] , suggesting that 14-3-3 family serve as an important regulator in the type I IFN induction pathway . In this study , we anticipated to further understand the molecular mechanisms of MDA5 activation during virus infection or dsRNA stimulation . We have identified that among the 3 isoforms of 14-3-3 proteins that binds to MDA5 , 14-3-3η specifically promotes MDA5 activation during viral infections and/or under poly ( I:C ) stimulation . We found that in the presence of 14-3-3η , MDA5 activation obtained a higher kinetics in both MDA5 oligomerization and MDA5-mediated type I IFN induction , when compared to those in the 14-3-3 knock-down conditions . Our studies reveal that 14-3-3η is the crucial chaperone protein for MDA5-dependent signaling to innate antiviral immunity .
Human embryonic kidney cell lines , HEK293 and HEK293T ( from ATCC ) , and human hepatoma cell line , Huh7 ( obtained from Dr . Michael Gale Jr . , University of Washington ) , were maintained in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) . Huh7 Non-Targeting Vector ( NTV ) cells , Huh7 RIG-I knock-down ( K/D ) cells , Huh7 14-3-3η K/D cells were first transfected with pSUPER . retro . shRNA plasmids ( Oligoengine ) which contain the shRNA sequences for targeting genes respectively and subsequently selected and maintained in DMEM supplemented with 10% FBS and 1 μg/mL puromycin . Huh7 , Huh7 NTV , Huh7 RIG-I K/D , Huh7 14-3-3η K/D cells were infected with Sendai virus ( SeV ) at a concentration of 200 HA unit/mL in DMEM supplemented with 10% FBS at 37°C . For encephalomyocarditis virus ( EMCV ) infection , cells were first washed with PBS twice , then were incubated with virus with designated MOI in serum-free medium for 1 hour at 37°C . The cells were rinsed by PBS twice and incubated in DMEM supplemented with 2% FBS post virus solution absorption . For poly ( I:C ) stimulation , cells were transfected with high molecular weight ( HMW ) poly ( I:C ) ( Invivogen ) by TransIT-mRNA reagent ( Mirus ) according to manufacturer’s instructions . To generate FLAG-tagged MDA5 , MDA5 cDNA sequence was amplified and cloned into pEFTak vector plasmid with an N-terminus FLAG tag . FLAG-N-MDA5 and FLAG-C-MDA5 which contained MDA5 amino acids 1–205 , 205–1025 respectively were amplified from full-length MDA5 cDNA sequence and sub-cloned into pEFTak plasmids . FLAG-N-MDA5 S88A and S88D were generated by site-directed mutagenesis with QuikChange Lightning Site-Directed Mutagenesis Kit ( Agilent ) . The detailed experimental procedures were according to manufacturer’s instructions . myc-tagged 14-3-3 isoforms expression constructs were described previously [7] . shRNA constructs targeting 14-3-3η was designed by cloning a hairpin sequence which targeting at human 14-3-3η gene 5’-UTR region into pSUPER . retro . shRNA plasmids . Dual luciferase assays to measure Interferon β promoter activity were conducted as described ( Saito et al . , 2008 ) . Cells were lysed in ice-cold RIPA buffer ( 50 mM Tris-Cl pH 7 . 5 , 150 mM NaCl , 5 mM EDTA , 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS ) in the presence of Protease Inhibitor Cocktail ( Roche ) for 10 min . Lysates were clarified by centrifugation and incubated with 2 μg of antibodies for 16 h followed by Protein A/G agarose for 1 h at 4°C . The immunocomplexes were washed 3 times with cold RIPA buffer and resuspended in 15 μl of 2 × SDS sample buffer for SDS-PAGE . Commercial antibodies used in this study were: FLAG-tag mAb ( Sigma-Aldrich , F3165 ) , c-myc tag ( BETHYL , A190-205A ) , GAPDH ( GeneTex , GTX100118 ) , MDA5 ( Enzo , ALX-210-935 ) , RIG-I ( AdipoGen , AG-20B-0009 ) , 14-3-3 Family Antibody Sampler Kit ( Cell Signaling Technology , #9769 ) , VDAC1 ( Abcam , ab15895 ) , ACSL4/FACL4 ( OriGENE , TA324720 ) , MAVS/IPS-1 ( Enzo , ALX-210-929-C100 ) , Tubulin ( Cell Signaling Technology , #2128 ) . Cells were first lysed with Triton X-100 lysis buffer ( 0 . 5% Triton X-100 , 50 mM Tris pH7 . 5 , 150 mM NaCl , 10% glycerol ) supplemented with protease inhibitor ( Roche ) at 4°C for 20 minutes and followed by centrifugation at 13300 rpm for the clearance of lysates . Equal amounts of total proteins for each sample were incubated with 4X SDD-AGE sample buffer ( 2X TBE , 4% glycerol , 8% SDS ) at room temperature for 10 minutes . Samples were subsequently separated in 1 . 5% agarose gel which contained 0 . 1% SDS with 1X running buffer ( 1X TBE , 0 . 1% SDS ) at 80V , 4°C for 90 minutes . Proteins were transferred to NC membrane and blocked in TBST buffer with 5% non-fat milk . Cells were trypsinized and collected in 1 . 5 mL microtubes for further steps to fractionate the cells . In brief , cells were first suspended in 1X cytosol fraction buffer ( BioVison ) and incubated for 10 minutes on ice . Cells suspension was then homogenized by using G21 needles pipetting up and down for 20 times and followed by centrifugation at 700g for 10 minutes to precipitate the remaining unsolved components . Supernatants were subsequently centrifuged at 10000g for 30 minutes to separate cytosol and mitochondria-MAM fractions . The pellet of mitochondria-MAM fraction was re-suspended in 1X mitochondria fraction buffer . All the experiments were repeated at least three times , and the results were presented as mean ± standard deviation ( SD ) . Data were analyzed by two-tailed Student’s t-test , *p<0 . 05 , **p<0 . 01 , ***P<0 . 001 .
To assess the distribution of MDA5 , we conducted mitochondria fractionation of human hepatoma ( Huh7 ) cell extracts to separate mitochondria as well as mitochondria-associated-membrane from the cytosolic cell compartments for analysis of protein localization . Huh7 cells were stimulated with poly ( I:C ) to induce MDA5 activation . The increased protein levels of both MDA5 and IFIT3 were determined by immunoblotting to confirm the effects of poly ( I:C ) stimulations ( Fig 1A ) . These cells were then subjected to the fractionation assay . We confirmed the mitochondria-MAM ( Mito-MAM ) and the cytosolic ( Cyto ) fractions were well-separated by detecting the distribution of MAVS , the mitochondria marker VDAC1 , the MAM marker ACSL4 , and the cytosol maker tubulin by immunoblotting ( Fig 1A ) . We found that the endogenous MDA5 was retained in the cytosolic fraction in the uninfected cells , and would be relocalized to the Mito-MAM fraction upon poly ( I:C ) stimulation ( Fig 1A ) . In order to determine which domain of MDA5 is responsible for its relocalization , we ectopically expressed wildtype ( WT ) MDA5 , N-MDA5 or C-MDA5 in Huh7 cells and evaluated the distribution of these constructs ( S1A Fig ) . We first confirmed that ectopic expression of full-length MDA5 alone could induce IFNβ promoter activity when compared to the vector control ( Fig 1B ) . The expression of FLAG-tagged N-MDA5 , which contains only 2 CARDs of MDA5 ( S1A Fig ) , led to the highest activation of IFNβ promoter activity although the protein level detected by immunoblotting showed that FLAG-N-MDA5 expression was the lowest among the three ( Fig 1B and 1C ) . The expression of FLAG-C-MDA5 did not induce IFNβ promoter activity ( Fig 1B and 1C ) . We next assessed the distribution of FLAG-N-MDA5 and FLAG-C-MDA5 by Mito-MAM fractionation assay . We ectopically expressed FLAG-N-MDA5 and FLAG-C-MDA5 in Huh7 cells and fractionated cell lysates into cytosol or Mito-MAM fractions . The fractionation competence was analyzed by detecting the distribution of MAVS , VDAC1 , ACSL4 , and tubulin by immunoblotting ( Fig 1D and S1B Fig ) . FLAG-N-MDA5 , the constitutively active MDA5 mutant , could distribute in both cytosol and mitochondria-MAM fractions , whereas FLAG-C-MDA5 was only detected in the cytosol ( Fig 1D ) . The expression of IFIT3 in the total cell lysate suggested the induction of type I IFN by ectopic expression of FLAG-N-MDA5 ( Fig 1D ) . These results indicated that similar to RIG-I , activated MDA5 was redistributed from the cytosol to the Mito-MAM fraction , and this redistribution was governed by the N-terminal CARDs of MDA5 . It has been reported that RIG-I bind accessory proteins such as TRIM25 and 14-3-3ε as a virus-inducible complex via its CARDs which facilitate the redistribution to the mitochondria associated membrane ( MAM ) for MAVS interaction . Due to the similarity in structure between RIG-I and MDA5 and the mutual importance of the N-terminal CARDs , we hypothesized that during RNA virus infections or agonists stimulation , MDA5 may also interact with certain 14-3-3 isoforms through the N-terminal CARDs to activate IFNβ signaling pathway to counteract viral infections . To assess whether any of the 14-3-3 isoforms could interact with MDA5 , we co-transfected myc-tagged 14-3-3 isoforms and FLAG-tagged WT MDA5 and stimulated the transfected cells with poly ( I:C ) in preparation for the co-immunoprecipitation assays ( Fig 2A and S2A Fig ) . Six hours after poly ( I:C ) transfection , the cell lysates were applied to immunoprecipitation by anti-FLAG antibodies . The amount of myc-tagged 14-3-3 proteins co-recovered with FLAG-tagged MDA5 was assayed by immunoblotting ( Fig 2A ) . We found that 14-3-3γ , 14-3-3η and 14-3-3θ could be co-immunoprecipitated with full-length MDA5 in unstimulated cells ( Fig 2A ) . However , we found that MDA5 was in complex with increasing amounts of 14-3-3η post poly ( I:C ) stimulation , as revealed by the immunoblot analysis of anti-myc 14-3-3 immunoprecipitation products ( Fig 2A ) . In order to evaluate whether MDA5-interacting 14-3-3 isoforms serve as positive or negative regulators in the MDA5-dependent signaling , we ectopically co-expressed MDA5 and two different doses of 14-3-3 isoforms ( 14-3-3γ , 14-3-3η and 14-3-3θ ) in Huh7 cells , and the IFNβ promoter activities were determined ( Fig 2B–2D ) . In RIG-I knock-down ( K/D ) Huh7 cells , of which the IFNβ promoter activities were primarily driven by MDA5-dependent signaling ( S2B and S2C Fig ) , we found that the co-expression of 14-3-3η with MDA5 without other stimuli could readily enhance the activation of IFNβ promoter activity in a dose-dependent manner ( Fig 2B ) . The protein expression levels of each constructs were examined by immunoblotting ( Fig 2C ) . With the ability to interact with MDA5 , 14-3-3γ and 14-3-3θ , however , did not show an activity to enhance the MDA5-depedent IFNβ promoter activities in either doses tested ( Fig 2B and 2C ) . We further investigated the role of 14-3-3γ , 14-3-3η and 14-3-3θ in promoting the MDA5-dependent IFNβ promoter activities after poly ( I:C ) stimulation . We found that with the ectopic expression of 14-3-3η in RIG-I K/D Huh7 cells , the IFNβ promoter activities were significantly higher than the control in response to poly ( I:C ) stimulation ( Fig 2D ) . Similarly , the enhancement by ectopic 14-3-3η to the poly ( I:C ) -induced MDA5-dependent IFNβ signaling was in a dose-dependent manner of 14-3-3η expression ( Fig 2D ) . In contrast , although ectopic expressions of high amount of 14-3-3γ and 14-3-3θ could slightly induce IFNβ promoter activities than those in the control , these 2 isoforms could not further induce IFNβ promoter activity when high levels of these isoforms were ectopically expressed ( Fig 2D ) . These data suggested that three different 14-3-3 isoforms which could interact with MDA5 might play different roles in MDA5-dependent signaling pathway . Moreover , these results indicated that 14-3-3η could facilitate the activity of MDA5-dependent signaling in upon foreign dsRNA stimulation . Our data suggested that 14-3-3η was a positive regulator of MDA5 activation and the downstream type I IFN induction pathway . We next determined the molecular mechanisms how 14-3-3η could control MDA5 activation . A stable clone of Huh7 cells expressing 14-3-3η targeting shRNA , 14-3-3η K/D Huh7 C#4 , was generated for further experiments ( S2A Fig ) . We first assessed the distribution of endogenous MDA5 in NTV or 14-3-3η K/D cells post poly ( I:C ) stimulation . We found that the redistribution of MDA5 from cytosol to the Mito-MAM fraction in 14-3-3η K/D cells was strongly attenuated when compared to the control NTV Huh7 cells ( Fig 3A ) . The ratio between cytosol MDA5 and Mito-MAM MDA5 is significantly reduced in the 14-3-3η K/D cells when compared to that in the NTV Huh7 cells ( Fig 3A ) . MDA5 has been shown to be an interferon stimulated gene ( ISG ) , of which the expression level increases in response to the presence of interferon . Indeed , the protein level of endogenous MDA5 in NTV Huh7 cells was increased after poly ( I:C ) stimulation ( Fig 3A ) . However , in the 14-3-3η K/D cells , the endogenous protein level of MDA5 was barely increased post poly ( I:C ) stimulation when compare to the control NTV Huh7 cells ( Fig 3A ) . To minimize the effects of endogenous MDA5 expression levels in the NTV and 14-3-3η K/D Huh7 cells , the cells were first treated with IFNβ to increase MDA5 expression levels and then infected with encephalomyocarditis virus ( EMCV ) , of which the viral RNA has been reported to specifically serve as ligands of MDA5 to induce MDA5-dependent signaling pathway . IFNβ-containing DMEM was removed right before EMCV infection . In both NTV and 14-3-3η K/D Huh7 cells , we could detect an increase in MDA5 protein expression levels in the total cell lysates , suggesting that the expression levels of 14-3-3η did not affect the induction of MDA5 expression in response to the stimulation of IFNβ ( Fig 3B ) . Previous reports have shown that EMCV replication was highly sensitive to type I IFN treatment [29] , and therefore , we first determined whether EMCV entry was diminished by pretreatments of IFNβ by measuring the intracellular EMCV RNA levels ( S2 and S3C Figs ) . We found that although the entry was greatly reduced at the IFNβ pretreatment dose we used ( 100 IU/ml ) , EMCV was still able to replicate viral RNA in these cells ( S2B and S3C Figs ) . Also , comparable intracellular EMCV RNA levels , which are the ligands specific for MDA5 , were detected in mock- or IFNβ-pretreated NTV and 14-3-3η K/D Huh7 cells after adsorption ( S3C Fig ) . At 18-hour post-infection , the mock- or IFNβ-pretreated NTV and 14-3-3η K/D Huh7 cells lysed and subjected to Mito-MAM fractionation . We found that IFNβ treatment could increase endogenous MDA5 as well as RIG-I protein expressions in the total cell lysates , and the intracellular distribution of RIG-I was not affected by either IFNβ treatment nor EMCV infection ( Fig 3B ) . MDA5 Mito-MAM redistribution was observed in the EMCV-infected NTV cells , and in the 14-3-3η K/D cells , although the MDA5 expression levels were comparable , the Mito-MAM redistribution of MDA5 was marginal ( Fig 3B ) . As a control , the NTV and 14-3-3η K/D Huh7 cells were infected with SeV to assess whether RIG-I activation and Mito-MAM redistribution during viral infection would be affected by 14-3-3η ( S3 Fig ) . We found that the redistribution of RIG-I during SeV infections in the NTV and 14-3-3η K/D Huh7 cells were similar ( S3D Fig ) . We next assessed prior to the activation-dependent redistribution of MDA5 , how 14-3-3η may control MDA5 redistribution . First , we investigated which domain of MDA5 was required for interactions with these 14-3-3 isoforms by co-expressing myc-tagged 14-3-3 proteins with whether FLAG-N-MDA5 or FLAG-C-MDA5 ( Fig 3C and 3D ) . The co-immunoprecipitation results suggested that the N-terminus CARDs of MDA5 had interactions with 14-3-3γ , 14-3-3η and 14-3-3θ and interactions with 14-3-3σ and 14-3-3ζ to some extent . On the other hand , none of the 14-3-3 isoforms had association with C-terminus helicase region of MDA5 ( Fig 3C and 3D ) . Previous reports have shown that after ligand binding , MDA5 undergoes a dephosphorylation event at S88 for its activation [4] . We first confirmed the activities in inducing IFNβ promoter activities by ectopic expression of MDA5 , N-MDA5 , and N-MDA5 with S88A or S88D substitutions . As expected and consistent with previous reports , MDA5 , N-MDA5 WT , and N-MDA5 S88A could all induce IFNβ promoter activities , and N-MDA5 S88D could not ( S3E Fig ) [4] . We then assessed whether the interaction between 14-3-3η and MDA5 occurred before or after the dephosphorylation of S88 during MDA5 activation ( Fig 3E ) . Here we again observed that both WT MDA5 and N-MDA5 WT could interact with 14-3-3η ( Fig 3E ) . The S88A mutant of N-MDA5 was able to pull-down comparable amount of 14-3-3η , which is in consistence with previous studies , showing that S88A MDA5 mutant maintained a similar capacity to induce the IFN-β promoter as compared with wild type MDA5 . However , only very little or none 14-3-3η could be pulled-down by S88D MDA5 mutants , suggesting that S88 in the N-terminus of MDA5 has the crucial role in the activation of MDA5 as well as 14-3-3 protein interactions ( Fig 3E ) , and the interaction between MDA5 and 14-3-3η may be correlated with MDA5-induced antiviral activities . According to previous study [12] , it has been shown that MDA5 could form oligomers to trigger the activation and interaction between down-stream adaptor protein MAVS and MDA5 itself . We therefore utilized the semi-denaturing detergent agarose gel electrophoresis ( SDD-AGE ) to detect the aggregation formation of MDA5 in NTV and 14-3-3η K/D cells post poly ( I:C ) stimulation ( Fig 3F ) . NTV or 14-3-3η K/D cells were first treated with IFNβ to induce the basal protein expression of endogenous MDA5 for 3 hours , and then these cells were stimulated with poly ( I:C ) for 6 or 24 hours . The kinetics of MDA5 aggregation was delayed in 14-3-3η K/D cells compared to that in the NTV cells post poly ( I:C ) stimulation ( Fig 3F ) . We next determine the aggregation formation of ectopically expressed FLAG-MDA5 in the NTV and/or 14-3-3η K/D Huh7 cells by SDD-AGE ( Fig 3G ) . The ectopically expressed FLAG-MDA5 were at comparable levels in the NTV andr 14-3-3η K/D Huh7 cells . In the NTV cells , the oligomerization levels of MDA5 was enhanced with the increasing protein levels of FLAG-MDA5; however , although the ectopic expressions of FLAG-MDA5 were at the similar levels , the aggregation formation of MDA5 was much impaired in the 14-3-3η K/D cells ( Fig 3G ) . All these data suggest that 14-3-3η may change/decrease the threshold of innate immunostimulatory potential of foreign viral dsRNA thus to promote MDA5 oligomerization and the redistribution of MDA5 from the cytosol to the Mito-MAM fraction . To verify if the 14-3-3η/MDA5 complex is critical for MDA5-dependent type I IFN induction and antiviral activities , we evaluated whether the depletion of 14-3-3η could affect MDA5-dependent signaling pathway . We first evaluated the induction of IFNβ promoter activity during Sendai virus ( SeV ) infection or poly ( I:C ) stimulation in NTV and/or 14-3-3η K/D cells . IFNβ promoter activities were induced at comparable levels by SeV infection between NTV and 14-3-3η K/D cells; however , the induction levels of IFNβ promoter activities were significantly decreased in the 14-3-3η K/D cells post poly ( I:C ) stimulation , compared to those in the NTV cells ( Fig 4A ) . These data together with data from the fractionation assays indicated that loss of 14-3-3η would specifically affect the activation of MDA5-dependent but not the RIG-I-dependent antiviral activity ( Figs 3B and 4A and S3 Fig ) . We also detected the induction of IFNβ mRNA expressions in the NTV and 14-3-3η K/D cells by SeV infection or poly ( I:C ) stimulation ( Fig 4B ) . A similar phenotype was observed that post poly ( I:C ) stimulation , the induction of IFNβ mRNA expression was strongly attenuated in the 14-3-3η K/D cells when compared to the NTV cells ( Fig 4B ) . We next stimulated NTV and 14-3-3η K/D cells with poly ( I:C ) and performed a time-course study of the mRNA expression levels of IFNβ and other ISGs to investigate whether the depletion of 14-3-3η would lead to delayed activation of MDA5-dependent signaling pathway ( Fig 4C–4E ) . The significant inductions of the IFNβ mRNA expression in both NTV and 14-3-3η K/D cells were first observed at 3 hours post poly ( I:C ) stimulation , and the IFNβ mRNA levels in both cell lines peaked by 36 hours post poly ( I:C ) stimulation ( Fig 4C ) . With increasing time post poly ( I:C ) stimulation , the differences between the levels of induced IFNβ mRNA in NTV and 14-3-3η K/D cells were gradually increased up to 36 hours of poly ( I:C ) stimulation ( Fig 4C ) . The difference between mRNA expression levels became insignificant at 48 hours of poly ( I:C ) stimulation ( Fig 4C ) . An independent experiment was performed to observe the mRNA expression levels of IFNβ and other ISGs after poly ( I:C ) stimulation . Similarly , we observed an impaired IFNβ mRNA induction in the 14-3-3η K/D cells when compared to the NTV cells at the same time point ( Fig 4D ) . Besides , the induction of OAS1 mRNA , an interferon stimulated gene , was also strongly attenuated in 14-3-3η K/D cells in response to poly ( I:C ) stimulation ( Fig 4E ) . Since the poly ( I:C ) we used in these experiment primarily induced MDA5-dependent signaling rather than RIG-I-dependent signaling ( S2B Fig ) , these data indicate that 14-3-3η has a critical role in controlling the kinetics of MDA5-dependent type I IFN induction . Lastly , to determine the critical role of 14-3-3η in the MDA5-dependent type I IFN induction and antiviral activities , we infected the cells with EMCV to determine the activation of MDA5 in 14-3-3η K/D cells ( Fig 4F and 4G ) . After EMCV infection , the production of IFNβ mRNA was significantly decreased in 14-3-3η K/D cells compared to NTV Huh7 cells ( Fig 4F ) . In the NTV cells , we found that the higher M . O . I . of EMCV was introduced , the higher the induction of IFNβ mRNA was detected ( Fig 4F ) . Nevertheless , in 14-3-3η K/D cells , the induction of IFNβ mRNA was drastically reduced in both M . O . I . tested ( Fig 4F ) . We then also analyzed the EMCV vRNA levels in the infected NTV and/or 14-3-3η K/D cells . We found that EMCV vRNA levels were significantly higher in the 14-3-3η K/D cells than those in the NTV cells ( Fig 4G ) . These data together suggest that 14-3-3η indeed has an essential role in the MDA5-dependent antiviral activity . These results indicate that 14-3-3η interaction with MDA5 serves to activate robust MDA5-dependent antiviral activities and innate immune signaling actions .
Previous studies have reported that the activation of RIG-I signaling acquires certain accessory proteins , such as the E3 ubiquitin ligase TRIM25 and the mitochondrial chaperone protein 14-3-3ε [7 , 30] . In addition , the C-terminus of RIG-I which contains repressor domain ( RD ) has the ability to inhibit RIG-I auto-activation [6] . As to MDA5 , PP1α/β and RNF125 have been reported to post-translationally modify the CARDs of MDA5 for MDA5 activation [4 , 31] . Also , it has been shown that the CARDs of MDA5 oligomerize in response to the interaction of MDA5 and its RNA ligands [12] . The excessive MDA5 accumulation in the cytoplasm may lead to the spontaneous self-association and trigger the oligomerization of CARDs . According to previous reports , it has been shown that a member within double-stranded RNA-specific adenosine deaminase , ADAR1 , serves as an RNA-editing protein that can change the structures of 3’UTR mRNAs to prevent MDA5-autorecognizing reaction . Through changing the secondary structures of UTRs within many mRNAs , ADAR1 enhances the innate immunostimulatory potential of endogenous transcripts thus to reduce the possibilities that MDA5 recognizes self-RNA to activate type I interferon signaling pathway and causes auto-inflammatory diseases[32] . Here we suggest that 14-3-3η may play an opposite role from ADAR1 in host cells to reduce the threshold of MDA5-dependent antiviral signaling pathway activation . 14-3-3η can boost and accelerate the activation of MDA5 signaling that helps host cells establishing well antiviral response as soon as possible to prevent virus infections . Our data indicated that 14-3-3 proteins could only interact with MDA5 through N-terminus but not the C-terminal domain . Therefore , it is likely that 14-3-3η affects the activation of MDA5 through the interaction between MDA5 N-terminus with no influence the dsRNA binding affinity of MDA5 . We analyzed the amino acid sequences of both the CARDs of RIG-I and MDA5 in search of the consensus binding motif of 14-3-3 family . Two modes of consensus phosphor-serine/phosphor-threonine dependent 14-3-3 protein binding motifs were described: R[SFYW]XpSXP ( mode 1 ) and RX[SYFWTQAD]Xp ( S/T ) X[PLM] ( mode 2 ) ( Muslin , A . J . , et al . 1996 ) . Neither modes were found within RIG-I CARDs; in contrast , within the amino acid 84 to 90 of MDA5 , the R-R-T-G-pS-P-L sequence may serve as the binding motif of 14-3-3 proteins . We initially thought that the S88 phosphorylation of MDA5 may play a role in the binding preference of 14-3-3 isoforms . However , our data showed that the MDA5 S88A mutant served as a better binding partner to 14-3-3η than the S88D mutant , indicating that MDA5 may bind to 14-3-3η in an S88 dephosphorylation-dependent manner . On the other hand , during poly ( I:C ) stimulation or EMCV infection , we did not observe obvious redistribution of 14-3-3η . This phenomenon was also observed in the RIG-I chaperone , 14-3-3ε [7] . Due to the fact that the 14-3-3 family is a highly conserved chaperone protein family to regulate the intracellular localization of their target proteins , these proteins are likely shuttling around and would be hard to determine the distribution ratio of the 14-3-3 proteins . Each of the monomer in 14-3-3 contains a binding groove to recognize phosphoserine or phosphothreonine motif . The interaction with 14-3-3 not only may regulate the intracellular localization of proteins but also may stabilize multi-protein complex [33] . In recent reports , phosphoserine- and/or phosphothreonine-independent interaction with 14-3-3 proteins has also been described [34] . For example , the interaction between 14-3-3 proteins and RIG-I CARD domain , exozyme S cytotoxin or phage Raf inhibitor R18 peptide have been found to be phosphorylation-independent [7 , 35 , 36] . Whether MDA5/14-3-3η interaction is indeed phosphothreonine/phosphoserine independent remains to be further investigated . Several viral proteins have been suggested to interact with 14-3-3 , such as HCV core and Parainfluenza Virus 5 M proteins [28 , 37] . A recently proteomics report showed that influenza NS1 protein could interact with several 14-3-3 isoforms including 14-3-3γ , 14-3-3η , 14-3-3θ , 14-3-3β and 14-3-3ε , though the effects of NS1 on 14-3-3 proteins remain unclear [38] . These viral protein-14-3-3 interactions may change the typical distribution of 14-3-3 proteins and disrupt the original functions of 14-3-3 family , including to serve as intracellular chaperone proteins regulating the localization of their target proteins . For example , DENV NS3 protein utilizes a phosphomimetic-based mechanism to compete 14-3-3ε in cytosol and thus blocks RIG-I redistributing from cytosol to the mitochondria-associated membrane during DENV infection to impair RIG-I-dependent type I IFN induction [16] . It is suggested that a recombinant mutant DENV deficient in 14-3-3ε binding activates RIG-I-dependent type I IFN induction and might serve as an attenuated vaccine candidate [16] . Here we report that 14-3-3η facilitates MDA5 activation , and it is intriguing to understand whether RNA viruses have developed means to interfere the interaction between 14-3-3η and MDA5 to counteract antiviral activities in the infected cells . Nevertheless , the importance of how each 14-3-3 isoforms may regulate antiviral signaling pathway has arisen in the field of antiviral innate immunity . In summary , our work has defined 14-3-3η as a key component to promote MDA5 activation required for innate antiviral immunity ( Fig 5 ) . | In this study , we utilized biochemistry and molecular biology approaches to defines the molecular mechanisms by which melanoma differentiation-associated protein 5 ( MDA5 ) , a cytoplasmic RNA helicase and pattern recognition receptor molecule , is regulated by 14-3-3η to govern its innate immune signaling activity . During viral infection RIG-I-like receptors ( RLRs ) , including MDA5 , play essential roles in initiating type I interferon signaling pathway and preventing virus infection or replication in host cells . Besides , the establishment of well functional adaptive immune response to viruses is depending on the timely activation of innate immune antiviral signaling pathway . Our results suggested that the activation of MDA5 is promoted by the chaperone protein 14-3-3η . The lack of 14-3-3η in host cells leads to the kinetically-delayed oligomerization of MDA5 , which is a key steps of the activation of MDA5-mediated anti-viral signaling pathway . These findings reveal a novel component which participating in the control system of MDA5-dependent signaling pathway . Viral proteins which antagonize 14-3-3η to impair MDA5-dependent antiviral signaling may be suitable targets for antiviral therapy or be modified to generate potential vaccine strains . | [
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] | 2019 | The 14-3-3η chaperone protein promotes antiviral innate immunity via facilitating MDA5 oligomerization and intracellular redistribution |
IgA nephropathy ( IgAN ) , major cause of kidney failure worldwide , is common in Asians , moderately prevalent in Europeans , and rare in Africans . It is not known if these differences represent variation in genes , environment , or ascertainment . In a recent GWAS , we localized five IgAN susceptibility loci on Chr . 6p21 ( HLA-DQB1/DRB1 , PSMB9/TAP1 , and DPA1/DPB2 loci ) , Chr . 1q32 ( CFHR3/R1 locus ) , and Chr . 22q12 ( HORMAD2 locus ) . These IgAN loci are associated with risk of other immune-mediated disorders such as type I diabetes , multiple sclerosis , or inflammatory bowel disease . We tested association of these loci in eight new independent cohorts of Asian , European , and African-American ancestry ( N = 4 , 789 ) , followed by meta-analysis with risk-score modeling in 12 cohorts ( N = 10 , 755 ) and geospatial analysis in 85 world populations . Four susceptibility loci robustly replicated and all five loci were genome-wide significant in the combined cohort ( P = 5×10−32–3×10−10 ) , with heterogeneity detected only at the PSMB9/TAP1 locus ( I2 = 0 . 60 ) . Conditional analyses identified two new independent risk alleles within the HLA-DQB1/DRB1 locus , defining multiple risk and protective haplotypes within this interval . We also detected a significant genetic interaction , whereby the odds ratio for the HORMAD2 protective allele was reversed in homozygotes for a CFHR3/R1 deletion ( P = 2 . 5×10−4 ) . A seven–SNP genetic risk score , which explained 4 . 7% of overall IgAN risk , increased sharply with Eastward and Northward distance from Africa ( r = 0 . 30 , P = 3×10−128 ) . This model paralleled the known East–West gradient in disease risk . Moreover , the prediction of a South–North axis was confirmed by registry data showing that the prevalence of IgAN–attributable kidney failure is increased in Northern Europe , similar to multiple sclerosis and type I diabetes . Variation at IgAN susceptibility loci correlates with differences in disease prevalence among world populations . These findings inform genetic , biological , and epidemiological investigations of IgAN and permit cross-comparison with other complex traits that share genetic risk loci and geographic patterns with IgAN .
IgA nephropathy ( IgAN ) is a common kidney disease with a complex genetic determination . This disorder is diagnosed based on detection of mesangial proliferation and glomerular deposits of IgA1 . Most frequently , IgAN has a progressing course and 20–50% of cases develop end-stage renal disease ( ESRD ) within 20 years of follow-up [1] . The disease has been detected among all ethnicities worldwide , but displays a striking geographic variation . It is the most common cause of kidney failure in East Asian countries , has intermediate prevalence in European and US populations but is rarely reported in populations of African ancestry . The diagnosis of IgAN requires a kidney biopsy , complicating accurate determination of heritability and population prevalence of disease . Autopsy and donor biopsy series suggest a prevalence of up to 1 . 3% in Finland [2] and 3 . 7% in Japan [3] . Familial aggregation of IgAN has also been recognized throughout the world [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] and up to 14% of cases may be familial [8] . Moreover , family members frequently have aberrant glycosylation of the hinge region of circulating IgA1 , a defect with an estimated heritability of 40–50% [12] , [13] . These data suggest a strong genetic contribution to disease . Recently , we have completed a large-scale genome-wide association study ( GWAS ) involving a cohort of 3 , 144 sporadic IgAN cases [14] . The discovery phase samples ( 1 , 194 cases and 902 controls ) were recruited in Beijing , China and were comprised of individuals of Han Chinese ancestry . The most associated SNPs were then followed up in additional cohorts of Han Chinese and Europeans ( 1 , 950 cases and 1 , 920 controls ) . In the combined analysis , we discovered 5 novel susceptibility loci with consistent effects across individual cohorts . These include 3 distinct intervals in the MHC-II region on chromosome 6p21 , with the strongest signal encompassing the HLA DQB1/DQA1/DRB1 locus ( abbreviated as DQB1/DRB1 hereafter ) . Imputation of classical alleles suggested that this signal was partially conveyed by a strong protective effect of the DRB1*1501-DQB1*0602 haplotype . The second signal on Chr . 6p21 encompassed a ∼100 Kb region containing TAP2 , TAP1 , PSMB8 , and PSMB9 genes ( TAP2/PSMB9 locus ) and the third signal on Chr . 6p21 contained the HLA DPA1/DPB1/DPB2 genes ( DPA1/DPB2 locus ) . Independence of these three regions on Chr . 6p21 was demonstrated by their localization within distinct LD blocks as well as genome-wide significant associations after rigorous conditional analyses . We also detected significant association within the Complement factor H ( CFH ) gene cluster on Chr . 1q32 , where alleles tagging a common deletion in the CFHR3 and CFHR1 genes imparted a significant protective effect ( CFHR3/R1 locus ) . Finally , a fifth signal centered on the HORMAD2 gene on Chr . 22q12 and containing multiple genes demonstrated significant association with risk of IgAN ( HORMAD2 locus ) . These five loci individually conferred a moderate risk of disease ( OR 1 . 25–1 . 59 ) , but together explained 4–5% of the variation in risk across the populations examined . To follow-up these studies and better assess the risk imparted by susceptibility alleles in diverse populations , we performed a replication study in eight independent case-control cohorts and performed a meta-analysis of all available genetic data including the original GWAS , totaling in 10 , 755 individuals . The expanded sample size allowed us to formally assess locus heterogeneity , identify new independent risk variants by conditional analyses and search for first-order genetic interactions . Finally , we refined a genetic risk score for IgAN and analyzed differences in the distributions of the IgAN susceptibility alleles among the major world populations .
For replication we examined eight cohorts ( five European , two East Asian , and one African-American cohort , totaling 2 , 228 cases and 2 , 561 controls , described in Table S1 ) . While each individual cohort at best had 40–50% power to replicate original GWAS findings , the combined replication cohort ( 2 , 228 cases and 2 , 561 controls ) provided essentially 100% power for replication across the range of allele frequencies and odds ratios initially observed ( Table S2 ) . We genotyped the two top-scoring SNPs for the CFHR3/R1 , TAP2/PSMB9 , DPA1/DPB2 , and HORMAD2 loci , but four SNPs were included for the DQB1/DRB1 locus to test for independent alleles at this interval by conditional analysis . After a standard assessment of genotype quality control , we performed association testing within each cohort using the standard Cochrane-Armitage trend test ( Table S3 ) . We also tested for heterogeneity of associations and performed a meta-analysis under both fixed and random effects models ( Table 1 ) . Four of the five original GWAS loci displayed significant replication with direction-consistent ORs and no heterogeneity comparable to the original findings ( Table 1 ) . The strongest replication was at the DQB1/DRB1 locus and achieved genome-wide significance in the replication cohort ( fixed effects OR 0 . 75 , P-value 4×10−11 ) . The CFHFR3/R1 locus on Chr . 1q32 , the HORMAD2 locus on Chr . 22q12 , and the DPA1/DPB2 locus on Chr . 6p21 were also robustly replicated ( fixed effects p-values 3×10−3–7×10−7 ) , with minimal between-cohort heterogeneity ( I2<25% ) . Accordingly , when combined with the four cohorts studied in the original GWAS , these four loci provided highly significant evidence of association ( fixed effects p-values 3×10−10–5×10−32 ) . In contrast , the TAP2/PSMB9 locus on Chr . 6p21 displayed direction-consistent replication only in the Italian , German , Czech , and Japanese cohort but the full replication cohort did not support this association ( Table 1 , Table S3 ) . However , when combined with the four cohorts from the original GWAS , this locus remained genome-wide significant ( fixed effects p-values 1×10−8 and 6×10−10 for rs9357155 and rs2071543 , respectively , Table 1 ) . As expected , I2 and Q-tests provided evidence of heterogeneity and random effects meta-analysis , which explicitly models heterogeneity , was 1–3 orders of magnitude more significant than fixed effect meta-analysis at this interval ( e . g . random effects p-value 3×10−11 , I2 = 61% for rs9357155; Table 1 ) . The heterogeneity was not attributable to differences in ethnicity or cohort size as the association results varied within Asian and European cohorts of differing size ( Table S3 ) . The top signals in the original GWAS , represented by rs9275596 and located within the DQB1/DRB1 locus , were mediated by a very strong protective effect of the DRB1*1501-DQB1*602 haplotype [14] . However , the SNPs in this interval are in incomplete LD and conditional analyses in our GWAS [14] and in an independent study of Europeans [15] had indicated that additional independent haplotypes also contributed to the signal . Therefore , taking advantage of our expanded cohort size , we examined additional SNPs that were in partial LD with rs9275596 to detect potentially independent effects ( rs9275224 , rs2856717 and rs9275424 , which had an r2 of 0 . 09 to 0 . 7 with rs9275596 , Table S4 ) . After mutually conditioning each SNP on the remaining SNPs , three of the four SNPs in the DQB1/DRB1 region exhibited a genome-wide significant independent effect ( rs9275596 , rs9275224 and rs2856717 , conditioned p-vales<5×10−8 , Table 2 ) . Interestingly , the conditioned effect of the minor allele of rs2856717 was reversed compared to the crude effect estimate , suggesting that the adjustment for LD structure has uncovered a risk haplotype in this region ( conditioned OR 1 . 61 , p = 2×10−10 ) . The above data indicated that there are multiple risk haplotypes within the DQB1/DRB1 locus . To better define these findings , we next phased four-SNP haplotypes at this locus and tested associations with disease ( Table 3 ) . We confirmed a very strong protective effect of the ATAC haplotype ( freq . 0 . 21 ) which , based on our previous imputation analysis , carries the DRB1*1501/DQB1*602 classical alleles . In addition , we defined a new protective haplotype ( ACAT , freq . 0 . 13 ) and a new risk haplotype ( ATAT , freq . 0 . 05 ) . The ATAC protective haplotype and the ATAT risk haplotype differ only by the rs9275596-C/T allele , explaining the reversal of OR for the rs2856717-T allele after conditioning for rs9275596 ( Table 3 ) . Additionally , the GCGT risk haplotype , tagged by the rs9275424-G allele , exhibited a weaker protective effect . These results were supported by both Asian and European cohorts ( Table S5 ) . Further support is provided by the global haplotype association test , which achieved a p-value of 3×10−43 . Based on these analyses , we concluded that there are at least three independent haplotypes conferring risk of IgAN within this region . Nonetheless , these 3 independent haplotypes in DQB1/DRB1 locus still did not explain associations in other Chr . 6p21 regions ( TAP2/PSMB9 and DPA1/DPB2 loci , respectively represented by rs9357155 and rs1883414 ) , and a fully adjusted model that included all independently associated SNPs continued to support the original GWAS findings of three discrete genome-wide significant intervals on Chr . 6p21 ( Table 4 ) . We tested the possibility of interaction between the 7 risk-contributing SNPs and therefore tested for all possible pairwise interactions ( Table S6 ) . We detected strong evidence for a multiplicative interaction ( defined as departure from additivity on the log-odds scale ) between the CFHR3/R1 ( rs6677604 ) and the HORMAD2 loci ( rs2412971 ) . In this interaction , the rs2412971-A allele has a strong and consistent protective effect among all genotypic subgroups , but its effects are reversed among homozygotes for the rs6677604-A allele , which closely tags a CFHR3/R1 deletion ( Figure 1 , Table S6 ) . The significance of this interaction ( p = 2 . 5×10−4 ) exceeds a Bonferroni-corrected threshold for 21 tests , and is most discernable among the European cohorts ( p = 1 . 4×10−3 ) , where both SNPs have higher minor allele frequencies . The 4-df genotypic interaction test was also significant for these two loci ( p = 6 . 4×10−3 ) , but the 1-df multiplicative interaction model provided a better fit . The original IgAN risk score model was based on the genotypes of the top scoring SNPs at the 5 independent loci discovered in the GWAS [14] . We refined this risk score by incorporating the newly discovered independent effects of rs9275224 and rs2856717 and the interaction between the CFHR3/R1 and the HORMAD2 loci . A stepwise regression algorithm in the entire cohort defined a new risk score that retained the 7 SNPs exhibiting an independent effect as well as the rs6677604* rs2412971 interaction term ( Table 4 ) . When compared with the original GWAS model , the newly refined score was more strongly associated with disease risk and explained a greater proportion of the disease variance in both the replication and the original GWAS dataset ( Table 5 ) . Moreover , the refined risk score was a highly significant predictor of disease in each individual replication cohort ( Table S7 ) . In all datasets combined , the new risk score explained 4 . 7% in disease variance and was 13 orders of magnitude more significant than the original score . In this model , one standard deviation increase in the score was associated with nearly 50% increase in the odds of disease ( OR = 1 . 47 , 95% CI: 1 . 42–1 . 54 , P = 1 . 2×10−72 ) . This translates into nearly a 5-fold increase in risk between individuals from the opposing extremes of the risk score distribution ( with tails defined by ≥2 standard deviations from the mean ) . Similar to the GWAS study , we detected pronounced differences in the distributions of risk alleles among the three different ethnicities studied: for each of these seven risk loci , the frequency of the risk alleles was highest in East Asians and lowest in African-Americans ( Figure S1 ) . These differences were also reflected in highly significant disparities in the risk score distributions by ethnicity ( Figure 2 ) . Motivated by these observations , we examined global geographic variation in the genetic risk for IgAN by applying the newly refined IgAN risk score in 6 , 319 healthy individuals across 85 worldwide populations . We observed marked differences in the genetic risk across the world . Overall , the mean standardized risk score was lowest for Africans , intermediate for Middle Easterners and Europeans , and highest for East Asians and Native Americans ( Figure 3 and Figure S2 ) . Accordingly , the risk increased sharply with eastward distance from the prime meridian ( Pearson's r = 0 . 27 , p = 3 . 5×10−108 ) . The same geospatial pattern were detected if we included only native populations of HGDP and HapMap-III ( Figure S3 ) , demonstrating that the findings are not biased by inclusion of control populations from the genetic association study . These data are consistent with the known East-West gradient in prevalence of IgAN , suggesting that genetic risk predicts prevalence . Unexpectedly , higher resolution analysis of the European continent revealed an additional increase in the risk from South to North ( Pearson's r = 0 . 11 , p = 1 . 3×10−9 ) . For example , northwestern Russians and northern inhabitants of Orkney Islands ( Scotland ) have the highest risk scores when compared with the rest of the European continent ( Tables S8 and S9 ) . To confirm these finding and test whether North-South variation in genetic risk is also reflected in differences in IgAN occurrence , we obtained genetic data from additional European populations ( Belgian , British , Finnish , Swedish and Icelandic ) and compared genetic risk scores with the incidence and point prevalence of IgAN among end-stage renal disease ( IgAN-ESRD ) populations across Europe ( Table S10 ) . As predicted by the genetic risk score , our analysis confirmed a strong North-South cline of both incidence and prevalence across the European continent ( Figure 4 ) . Notably , this analysis includes only patients with end-stage IgAN , on dialysis or after kidney transplantation , thus it underestimates the true incidence and population prevalence of IgAN . Because the point prevalence of IgAN-ESRD ( Figure 4b ) can be confounded by differential survival on renal replacement therapy and differences in kidney biopsy practice by country , we also examined IgAN-ESRD prevalence expressed as a percentage of all ESRD ( Figure 4c ) , and ESRD due to biopsy-diagnosed primary glomerulonephritis ( Figure 4d ) . Regardless of the metric used to quantify differences in IgAN occurrence , regression of the genetic risk score and the prevalence data on the average latitude resulted in positive correlations and parallel trends . The co-variation in genetic risk score and IgAN-ESRD occurrence among world populations may also be in part influenced by differences in environment , or by other factors such as local medical guidelines for screening and treatment . To better distinguish these possibilities , we examined native populations that live under a uniform environment yet show variation in IgAN risk . In the densely sampled North Italian populations , the Alpine villagers of the Valtrompia region have a 3 . 5-fold higher prevalence of ESRD attributable to IgAN and primary glomerulonephritis when compared to the national average [16] . Consistent with this prevalence data , the median standardized risk score in this population was comparable to some of the Northern European countries and ranked as number one among the 17 Italian populations sampled in our study ( Figure 5 , Table S8 ) . Conversely , we compared the genetic risk score and IgAN-ESRD prevalence in populations in the United States , where diverse ethnicities live under different environments and health care systems compared to the ancestral populations . The analysis of the USRDS dataset confirmed the striking ethnic differences in IgAN-ESRD prevalence ( Table S11 ) : the percentage of ESRD attributable to IgAN was 5-fold greater for Caucasian and 15-fold greater for Asian Americans compared to African-Americans . This increased IgAN-ESRD occurrence in Asian- compared to African-Americans far exceeds the 50% increase in risk predicted by genetic risk-score ( one standard deviation difference ) , suggesting the presence of additional unaccounted genetic and environmental factors ( Figure 6 ) .
In this study , we examined the largest IgAN case-control cohorts reported to date . We first verified the five top signals identified in a recent GWAS for IgAN in independent cohorts and demonstrated robust replication of four loci , and heterogeneity at one locus . Using combined dataset of 10 , 755 individuals , we also identified novel risk alleles for IgAN in the DQB1/DRB1 locus and detected a significant interaction between the CFHR3/R1 and the HORMAD2 loci . We also defined a more powerful genetic risk score that explained 4 . 7% in disease variance across all cohorts . Finally , in examination of 85 world populations , the genetic risk score paralleled the prevalence of IgAN , confirming the known East-West cline but also led to the detection of an association of IgAN-ESRD prevalence with latitude in Europe . While ten of twelve tested SNPs ( four susceptibility loci ) were robustly replicated with direction-consistent ORs across all cohorts , the TAP2/PSMB9 locus demonstrated moderately high level of heterogeneity . This locus remained genome-wide significant in the combined analyses under both fixed and random effects model . Family-based studies [17] , [18] , sperm typing experiments [19] and HapMap data have identified a recombination hotspot directly centered over the TAP2 gene ( 22 cM/Mb , 5 . 5-kb centromeric from the 2 SNPs selected for replication ) . We can therefore hypothesize that high heterogeneity at this locus is due to the unusually high rates of recombination in this region , which perturbs LD patterns between tag-SNPs and causal variants; this situation has been shown to cause a “flip-flop” phenomenon in association results [20] . Therefore , higher density of SNP coverage on either side of the recombination hotspot will be needed to guide future replication and fine mapping efforts . In addition to the independent replication of GWAS data , we identified two new signals in the DQB1/DRB1 region that exhibit independent genome-wide significant effect in conditional analyses , providing support for multiple causal variants at this locus . These findings are consistent with previous studies of IgAN [15] , [21] and other autoimmune diseases [22] , [23] , [24] , [25] , highlighting the complexity of associations in the MHC region . In our study , the strongest association signal originates in a protective haplotype tagged by rs9275596-C that carries HLA-DRB1*1501 and DQB1*602 , also associated with protection against type I diabetes [24] . The causal variants underlying the other haplotypes remain obscure and their discovery will likely require comprehensive re-sequencing to define classical alleles . Genetic interactions have been seldom described in association studies [26] . We detected a multiplicative interaction between the CFHR3/R1 and the HORMAD2 loci , which was most evident in the European cohorts , likely because the frequencies of both protective variants are considerably higher in this population . While this interaction was robust to multiple-testing correction for 7 SNPs , it will require confirmation in additional independent cohorts or via functional studies that examine whether these two loci are involved in a common biological pathway . Because the rs6677604-A allele tags a deletion in the CFHR3/CFHR1 genes , this finding suggests that the absence of these proteins abrogates the benefit imparted by HORMAD2 protective alleles . It is thus noteworthy that the HORMAD2 locus encodes several cytokines ( LIF , OSM ) that can interact with complement factors [27] . A seven-SNP genetic risk score explained nearly 5% of IgAN variance and demonstrated co-variation with IgAN prevalence across multiple settings . The major limitations of geospatial modeling include variable sampling density and inadequate coverage of certain geographic regions . Using the most comprehensive resources presently available for geo-genetic analyses , we found that the genetic risk score strongly paralleled the well-known East-West gradient in IgAN prevalence [3] , [28] , [29] , [30] , [31] , [32] . For each of these seven risk loci , the frequency of the risk alleles was highest in East Asians , lowest in African-Americans and intermediate in European populations . Accordingly , we detected co-variation of genetic risk with IgAN-ESRD incidence and prevalence among Asian- , White- and African-Americans , which share genetic background but not environment with their ancestral populations . Representative genetic data for U . S . Native Americans was not available from HGDP nor HapMap projects , precluding a direct comparison of their risk score with prevalence . However , the USRDS data and other reports indicate a high prevalence of IgAN-ESRD in US Native Americans [33] , [34] , [35] , [36] , [37] , consistent with their ancestral origin from an Asian subpopulation that migrated across the Bering land bridge over 15 , 000 years ago [38] . In the more homogeneous population of Northern Italy , the median risk score in the Valtrompia valley was the highest among Northern Italian populations and comparable with the Northern European scores , consistent with Valtrompia's 3 . 5-fold higher prevalence of ESRD , which is largely attributable to IgAN [16] . Taken together , these data strongly suggested that variation in genetic risk partly explains the variation in geo-epidemiology of disease . Because the genetic score captured general trends in IgAN epidemiology , we also tested whether the Northward gradient in genetic risk in Europe is mirrored by higher prevalence of kidney failure from IgAN . The ERA-EDTA data , which are the most unbiased source of information available , demonstrate that Nordic countries have over 2-fold higher incidence and prevalence of IgAN-ESRD compared to the Southern European countries . Although higher risk of IgAN in Northern Europe has not been previously appreciated , similar latitudinal risk gradients in prevalence and incidence have been well established for several other immune-mediated diseases , including type 1 diabetes [39] , [40] , multiple sclerosis [41] , [42] , and inflammatory bowel disease [43] . Interestingly , these disorders share risk alleles with IgAN , suggesting that variation in common genetic risk factors may mediate variation in prevalence of autoimmune disorders . Since our analysis was limited to prevalent IgAN-ESRD in countries with epidemiological data available and only a portion of IgAN cases progresses to ESRD , studies that can better estimate the population prevalence of all IgAN can confirm these findings and better delineate epidemiological connections to other immune mediated disorders . The genetic and environmental factors leading to the observed geospatial pattern of genetic risk and disease prevalence are not clear . The pre-modern history of IgAN is not known because this disease was only first described in 1968 [44] , shortly after the discovery and application of immunofluorescence in the analysis of kidney tissue . It is well known that mucosal infections can exacerbate disease , but specific environmental factors influencing the development of IgAN are not known . Based on a recently proposed pathogenesis model , the IgAN risk loci participate in sequential processes leading to the initiation and exacerbation of IgAN [45] . This may further explain the correlation of the genetic risk score with disease epidemiology . Interestingly , many of the IgAN loci are known to exhibit opposing effects on other autoimmune conditions [14]; for example , the HLA-DQB1 and HORMAD2 risk alleles are respectively protective for systemic lupus erythematosus , and inflammatory bowel disease . Thus balancing selection , in conjunction with local environmental factors , may be responsible for maintenance of risk alleles in different populations . The current IgAN risk score captures a greater proportion of the disease variance compared to other GWAS for kidney functions , such as a recent study of 60 , 000 individual that reported 13 loci explaining only 1 . 4% of the variance for estimated glomerular filtration rate [46] . Nonetheless , the fraction of the IgAN variation explained remains modest . For example , the one standard deviation risk-score difference between Asian- and African-Americans predicts a 50% increase in risk , yet there is over 10-fold difference IgAN-ESRD occurrence between these two groups . These data suggest that additional genetic and environmental factors influence risk . Based on the effect sizes and allelic frequencies of the discovered SNPs , we estimate that doubling the GWAS sample size is likely to find up to 7 additional loci , while tripling the sample size would identify up to 11 additional loci at genome-wide significant p-values<10−8 ( calculation performed as proposed by Park et al . [47] ) . Conditional analyses and higher-level interaction screens of more risk loci are likely to explain additional fraction of the missing heritability and better explain differences in population prevalence of this disease . In summary , we report results of the largest collaborative genetic study of IgAN . We confirm that the IgAN risk loci discovered in GWAS explain a significant proportion of the disease risk worldwide and likely contribute to the geographic variation in disease prevalence . Our geospatial model suggests previously unrecognized northward risk gradient in Europe , which will require further confirmation by alternative sources of prevalence data , such as country specific biopsy-registry data or kidney donor-biopsy series . The approach presented in this study may serve as a blueprint for geo-genetic modeling of other complex traits that exhibit marked geographic differences in prevalence .
This investigation was conducted according to the principles expressed in the Declaration of Helsinki . All subjects provided informed consent to participate in genetic studies and the Institutional Review Board of Columbia University as well as local ethic review committees for each of the individual cohorts approved our study protocol . The case-control cohorts analyzed in this study were contributed by clinical nephrology centers across Europe , Asia , and North America ( Table S1 ) . All cases carried a biopsy diagnosis of IgAN defined by typical light microscopy features and predominant IgA staining on kidney tissue immunofluorescence , in the absence of liver disease or other autoimmune conditions . Each individual cohort of cases was accompanied by a control cohort of similar size , matched based on self-reported ethnicity and recruited from the same clinical center . The French cohort was composed of two sub-cohorts: the St . Etienne cohort recruited in the University North Hospital of Saint Etienne ( 289 cases and 244 controls ) , and the GN-Progress cohort recruited from the nephrology departments of the Paris region ( 207 cases and 159 controls ) . The Italian cohort was also composed of two sub-cohorts: the North Italian cohort recruited in the clinical centers of Genova , Torino , Brescia , Trento , Modena , Bologna , and Trieste ( 410 cases and 524 controls ) , and the South Italian cohort recruited in Foggia ( 81 cases and 80 controls ) . The German cohorts also represent two recruitment sites: the Stop-IgAN cohort recruited among the participants of the Stop-IgAN clinical trial based in Aachen ( 150 cases and 293 controls ) , and the Hamburg-Eppendorf cohort from northern Germany ( 101 cases and 80 controls ) . The Czech and the Hungarian cohorts were recruited through the Department of Nephrology , 1st Faculty of Medicine and General University Hospital , Charles University in Prague ( 245 cases and 223 controls ) and the Nephrology Department of the University of Pécs ( 139 cases and 305 controls ) , respectively . The Japanese participants ( 264 cases and 294 controls ) were recruited by the nephrologists of Niigata University . The Beijing cohort ( 333 cases and 289 controls ) was recruited by the Renal Division of the Peking University First Hospital . Finally , our African-American cohort ( 34 cases and 60 controls ) was recruited at Columbia University ( New York , NY ) and at the University of Alabama ( Birmingham , AL ) . This smaller cohort is unique , as IgAN is exceedingly rare among individuals of African ancestry . In total , 2 , 253 cases and 2 , 621 controls were available for genotyping in the replication study . The composition and recruitment of the GWAS cohorts have been discussed in detail elsewhere [14] . The genotyping was performed by KBiosciences ( Hoddeston , England ) . and genotype calls were determined using an automated clustering algorithm the ( SNP Viewer v . 1 . 99 , KBiosciences , 2008 ) . The genotype clusters were also examined visually across all plates , to assure lack of technical artifacts . The overall genotyping rate across all samples was 98 . 2% . For quality control we calculated minor allele frequencies , as well as per-SNP and per-individual rates of missingness within each case-control cohort separately . Additionally , we tested for Hardy-Weinberg equilibrium among the control groups from each cohort to assure lack of bias due to genotyping artifacts or population stratification . All SNPs included in the final analyses had minor allele frequency greater than 1% , per-SNP missingness rate less than 5% , and all passed the HWE test in controls ( p>1×10−2 ) . Individuals with more than 2 missing genotypes out of the 12 loci were also excluded from the analysis . The participants of the smaller GN-Progress study ( 207 cases and 159 controls ) were genotyped using the Illumina HumanCNV370-duo chip at the Centre National de Génotypage ( CEA , Evry , France ) . The analysis of intensity clusters and genotype calls were performed using the Illumina Genome Studio software . Of 366 genotyped individuals , two cases and 1 . 8% of SNPs were excluded based on low call rates ( <95% ) . The overall genotyping rate was 99 . 6% . In total , 6 of 12 SNPs analyzed for replication were also present on the Illumina HumanCNV370-duo chip . The genotypes at the reminder loci were imputed using the phased HapMap-III CEU reference dataset ( see Web Resources ) . The imputation was performed simultaneously for cases and controls , using MACH 1 . 0 software ( see Web Resources ) . We used a standard single-step imputation approach , with 60 rounds of Markov Chain iterations to estimate the crossover maps , error rate maps , and all missing genotypes across each analyzed locus . The imputed SNPs had an excellent imputation quality , with an average estimated correlation between imputed genotypes and experimental genotypes of 0 . 98 ( range 0 . 94–1 . 0 ) . Consequently , association analyses using either the allelic dosage approach that accounts for imputation uncertainty , or the most likely genotype approach yielded similar results . Therefore , the most probable genotype calls were used in the downstream analyses . In the final quality control step , we compared the allelic frequencies and effect estimates between the two French cohorts ( GN-Progress and St . Etienne ) . For each locus , we observed nearly identical frequencies among cases and controls and the odds ratios were homogenous between the two cohorts . The formal heterogeneity tests were not statistically significant for any of the tested loci ( Cochrane's Q-test P>0 . 05 , average I2 = 0 ) . Therefore , these two cohorts were combined into a single cohort of 493 cases and 402 controls . Similarly to the French cohorts , there was no significant heterogeneity at any of the loci for the two smaller German cohorts ( STOP-IgAN and Hamburg-Eppendorf ) , and these were also combined into a single cohort of 249 cases and 372 controls . Analysis of the Northern and Southern Italian cohorts suggested some heterogeneity at 3 out of 12 SNPs ( I2 = 40–50% ) . Although these observations were not statistically significant ( Q-test P>0 . 05 ) , we used a conservative stratified approach for all downstream analyses for these two cohorts . The final summary of all study cohorts before and after quality control is provided in Table S1 . We performed a power calculation for the final replication cohort size of 4 , 789 individuals ( 2 , 228 cases/2 , 561 controls ) as a function of disease allele frequency and genotype relative risk ( Table S2 ) . The power was calculated in reference to a protective allele , with the range of allelic frequencies and effects comparable to the ones observed in the original GWAS . Assumptions included disease prevalence of 1% , log-additive model , no heterogeneity , and alpha = 0 . 01 ( Bonferroni-adjusted considering five independent loci tested ) . This analysis confirmed that our study had ample power ( nearly 100% for most loci ) to replicate the associations observed in the initial GWAS . The power calculations were performed using QUANTO v . 1 . 2 software [48] . The primary association analyses were performed using PLINK version 1 . 07 [49] . Similar to GWAS , we selected a standard 1-df Cochran-Armitage trend test as the primary association test . We also estimated the per-allele odds ratios and 95% confidence intervals for all tested SNPs within each individual cohort . The results across multiple cohorts were combined using an inverse variance-weighted method under a fixed-effects model ( PLINK ) , as well as using a random effects model as proposed by Han and Eskin ( METASOFT ) [50] . We also tested for heterogeneity across cohorts by performing a formal Cochrane's Q heterogeneity test as well as by estimating the heterogeneity index ( I2 ) [51] . The conditional association tests of the HLA loci were performed after controlling for the genotypes of the conditioning SNPs within each cohort using logistic regression ( PLINK ) . The adjusted ( conditioned ) effect estimates were then combined across cohorts using a fixed effect meta-analysis considering no significant heterogeneity across these loci . For the purpose of validation of this approach , we also combined the results by adding cohort information as an additional covariate in the stratified analysis within the logistic regression framework . As expected , the results of both approaches were similar . These analyses were carried out in PLINK v1 . 07 [49] . Haplotypes were first phased using EM algorithm across the HLA-DQB1 , HLA-DQA1 , HLA-DRB1 region . The haplotype frequencies were estimated in the cases and controls separately , as well as jointly in the entire cohort . Only common haplotypes with overall frequency >1% were included in the association tests . Global haplotype association test was performed using a χ2 test with n-1 degrees of freedom for n common haplotype groups . The ORs and the corresponding 95% confidence intervals were estimated in reference to the most common haplotype ( GCAT , frequency ∼35% ) . To explore the possibility of interactions between the 7 independent risk variants , we screened all possible pairwise interaction terms for association with disease within the framework of logistic regression models ( R version 2 . 10 ) . As a screening test , we used 1-df LRT to compare two nested models: one with main effects only and one with main effects and a multiplicative ( logit-additive ) interaction term . We included cohort membership as a fixed covariate in both of these models . For this analysis we selected a Bonferroni-adjusted significance of 2 . 4×10−3 , a conservative threshold that accounts for all 21 pairwise interaction terms tested . Significant interactions from this analysis were also tested using a 4-df genotypic interaction test . In this test , we compared a model with allelic effects , dominant effects , and their interaction terms with a reduced model with no interaction terms . We followed the coding proposed by Cordell and Clayton: for each SNP i we modeled its allelic effect xia by coding the genotypes AA , AB , and BB as xia = −1 , 0 , 1; we modeled dominance effects as xid = −0 . 5 , 0 . 5 , −0 . 5 for the genotypes AA , AB , and BB , respectively [52] . Each study participant was scored for the number of risk alleles and the distributions of protective alleles were compared between cohorts of different ethnicity . Only individuals with complete genotype information at the 7 scored loci ( 14 alleles ) were included in this analysis . The distributions were analyzed separately for cases and controls . A χ2 goodness-of-fit test was used to derive p-values for comparison of distributions . Because of a relatively small number of individuals at the tails of the distributions , for the purpose of statistical testing the tails of the distributions were binned into single-bin categories to achieve expected cell counts >5 . To confirm the results of conditional analyses and refine the genetic risk score proposed in the original GWAS , we subjected the genotype data from the entire cohort to a stepwise regression algorithm that selects significant covariates for the best predictive regression model based on Bayesian Information Criterion ( the step function , R version 2 . 10 ) . At model entry , we included all 12 genotyped SNPs , all 21 tested interactions , as well as cohort membership as a fixed covariate . Consistent with the results of our conditional analysis , the stepwise algorithm retained only the 7 SNPs exhibiting an independent effect along with the rs6677604*rs2412971 interaction term . All other terms were automatically dropped from the regression model . The risk score was calculated as a weighted sum of the number of protective alleles at each locus multiplied by the log of the OR for each of the individual loci from the final fully adjusted model . Only individuals with non-missing genotypes for all 14 alleles were included in this analysis . The risk score was standardized across all populations using a z-score transformation , thus the standardized score represented the distance between the raw score and the population mean in units of standard deviation . The percentage of the total variance in disease state explained by the risk score was estimated by Nagelkerke's pseudo R2 from the logistic regression model with the risk score as a quantitative predictor and disease state as an outcome . The C-statistic was estimated as an area under the receiver operating characteristic curve provided by the above logistic model . These analyses were carried out with SPSS Statistics version 19 . 0 . For this purpose , we used publicly available genotype data of the Human Genome Diversity Panel ( HGDP; 1 , 050 individuals representative of 52 worldwide populations ) , HapMap III ( 1 , 184 individuals representative of 11 populations ) , along with healthy controls genotyped as part of this study ( 4 , 547 individuals representative of 25 recruitment sites ) . The HGDP individuals have been previously genotyped for 660 , 918 markers using Illumina 650Y arrays ( Stanford University ) . First , SNPs with genotyping rate<95% and samples with an overall call rate<98 . 5% were removed from the genome-wide data . Only 1 , 042 individuals with all 14 non-missing alleles at the 7 analyzed risk score loci were included in the final analysis . The geographic coordinates for the HGDP populations were downloaded from the CEPH website ( see Web Resources ) . The HapMap III genotype data have been generated using two platforms: the Illumina Human1M ( Wellcome Trust Sanger Institute ) and the Affymetrix SNP 6 . 0 ( Broad Institute ) . These files were merged into a single dataset of 1 , 440 , 616 markers , from which we removed ( 1 ) SNPs with genotyping rate<95% , ( 2 ) samples with an overall call rate<98 . 5% , ( 3 ) all non-founders from mother-father-child trios , and ( 4 ) individuals with missing genotypes at any of the 7 SNP loci used for risk scoring . In the global geospatial analyses , we excluded US-recruited individuals of African American ( ASW ) , European ( CEU ) , and Asian ( CHD ) ancestry considering non-specific geographic origin of these populations . However , the population of Guajarti Indians recruited in Houston ( GIH ) was mapped to the northwestern part of the Indian subcontinent , as these individuals reported having at least three out of four Gujarati grandparents , speak the Gujarati language , and trace their ancestry to the region of Gujarat . In total , 730 HapMap III individuals representative of 8 populations met our selection criteria and were included in the final analysis . Because many European populations are underrepresented in HGDP and HapMap III datasets , we also included a total of 4 , 462 healthy controls from the GWAS and replication studies that were collected across 25 recruitment centers participating in our studies . Similar to the above criteria , only individuals with non-missing genotypes at all 7 scored SNPs were included in this analysis . The geographic coordinates for our populations were based on the location of recruitment centers and determined with Google Earth ( see Web Resources ) . This resulted in a final dataset of 6 , 319 individuals sampled across 85 worldwide populations for geospatial analysis . We fitted a 3rd degree polynomial trend surface based on the latitude , longitude , and median standardized risk score for each of the 85 populations using least squares approach ( Spatial package version 7 . 3-2 , R version 2 . 10 ) . For higher resolution maps , we used kriging technique and accounted for the possibility of spatial correlation of errors among more densely sampled populations by modeling the covariance function in an exponential form . The estimated risk surfaces were projected over the major continents using Maps package version 2 . 1–6 ( R version 2 . 10 ) . We obtained case counts of prevalent and incident ESRD stratified by primary renal diagnosis and by ethnicity from the United States Renal Data Systems ( 2011 USRDS Data Atlas , see Web Resources ) . For Europe , we obtained prevalent and incident ESRD case counts from the European Renal Association and European Dialysis and Transplant Association ( ERA-EDTA Renal Registry , see Web Resources ) . Comprehensive data were available for a total of 13 European countries participating in this registry . We calculated the prevalence of ESRD due to IgAN using three definitions: ( 1 ) proportion of all ESRD cases attributable to IgAN , ( 2 ) proportion of all ESRD cases from primary glomerulonephritis attributable to IgAN , and ( 3 ) total number of ESRD cases due to IgAN per million population ( PMP ) . The prevalence data for both USRDS and ERA-EDTA datasets were calculated for the same timepoint of December 31st , 2009 . The incidence of ESRD due to IgAN was estimated using all the available data over a 3-year period for the ERA-EDTA registry ( 2007–2009 ) , and a 5-year period for the USRDS registry ( 2005–2009 ) . For correlation of genetic risk score with disease prevalence in the US , we scored representative samples of the three major US ethnic groups: 303 US Caucasians ( CEU founders from HapMap-3 and healthy US controls from our original GWAS ) , 103 African-Americans ( ASW founders from HapMap-3 and healthy controls from this study ) , and 74 Asian-Americans ( CHD founders from HapMap-3 ) . For correlation of genetic risk with disease prevalence in Europe , we calculated median standardized risk scores at a country level for 13 European countries for which we obtained genotype data . We confirmed the South-North disease gradient by regressing the prevalence and risk score data against each country's average latitude . The correlation and regression analyses were conducted in SPSS Statistics version 19 . 0 . HAPMAP PHASE III Data: http://hapmap . ncbi . nlm . nih . gov/downloads/phasing/2009-02_phaseIII HGDP Genotype Data: http://hagsc . org/hgdp HGDP Population Data: http://www . cephb . fr/en/hgdp MACH: http://www . sph . umich . edu/csg/abecasis/MaCH PLINK: http://pngu . mgh . harvard . edu/~purcell/plink METASOFT: http://genetics . cs . ucla . edu/meta CRAN: http://cran . r-project . org GOOGLE EARTH: http://www . google . com/earth SPATIAL: http://cran . r-project . org/web/packages/spatial MAPS: http://cran . r-project . org/web/packages/maps USRDS Data Atlas 2011: http://www . usrds . org/atlas . aspx ERA-EDTA Registry Annual Report 2009: http://www . era-edta-reg . org | IgA nephropathy ( IgAN ) is the most common cause of kidney failure in Asia , has lower prevalence in Europe , and is very infrequent among populations of African ancestry . A long-standing question in the field is whether these differences represent variation in genes , environment , or ascertainment . In a recent genome-wide association study of 5 , 966 individuals , we identified five susceptibility loci for this trait . In this paper , we study the largest IgAN case-control cohort reported to date , composed of 10 , 775 individuals of European , Asian , and African-American ancestry . We confirm that all five loci are significant contributors to disease risk across this multi-ethnic cohort . In addition , we identify two novel independent susceptibility alleles within the HLA-DQB1/DRB1 locus and a new genetic interaction between loci on Chr . 1p36 and Chr . 22q22 . We develop a seven–SNP genetic risk score that explains nearly 5% of variation in disease risk . In geospatial analysis of 85 world populations , the genetic risk score closely parallels worldwide patterns of disease prevalence . The genetic risk score also predicts an unsuspected Northward risk gradient in Europe . This genetic prediction is verified by examination of registry data demonstrating , similarly to other immune-mediated diseases such as multiple sclerosis and type I diabetes , a previously unrecognized increase in IgAN–attributable kidney failure in Northern European countries . | [
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] | 2012 | Geographic Differences in Genetic Susceptibility to IgA Nephropathy: GWAS Replication Study and Geospatial Risk Analysis |
B cells develop high affinity receptors during the course of affinity maturation , a cyclic process of mutation and selection . At the end of affinity maturation , a number of cells sharing the same ancestor ( i . e . in the same “clonal family” ) are released from the germinal center; their amino acid frequency profile reflects the allowed and disallowed substitutions at each position . These clonal-family-specific frequency profiles , called “substitution profiles” , are useful for studying the course of affinity maturation as well as for antibody engineering purposes . However , most often only a single sequence is recovered from each clonal family in a sequencing experiment , making it impossible to construct a clonal-family-specific substitution profile . Given the public release of many high-quality large B cell receptor datasets , one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles . In this paper , we present the method “Substitution Profiles Using Related Families” ( SPURF ) , a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence . Using this framework , we show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction . We fit this model on a large public dataset and validate the robustness of our approach on two external datasets . Furthermore , we provide a command-line tool in an open-source software package ( https://github . com/krdav/SPURF ) implementing these ideas and providing easy prediction using our pre-fit models .
In the therapeutic antibody discovery and engineering field , researchers commonly isolate antibodies from animal or human immunizations and screen for functional properties such as binding to a target protein . Following the initial screening process , a small number of well-behaving antibodies ( hits ) are isolated for more rigorous examination of their biophysical properties in order to determine their potential as a therapeutic . After this stage , only a few final antibodies remain as lead candidates . However , even these carefully selected antibodies often have immunogenic peptides or other undesirable properties such as poor thermo/chemical stability and aggregation tendencies . To address these problems , the art of antibody engineering has emerged [1] , with numerous rational design strategies developed to mitigate aggregation . Researchers have removed hydrophobic surface patches to avoid aggregation [2–5] , “deimmunized” complementarity-determining regions by screening immunogenic peptides and mutating positions detrimental for peptide MHCII binding [6] , and improved thermostability through stable framework grafting [7] and targeted mutagenesis using predictions from proprietary structure/sequence analysis software [8] . Although referred to as “rational” , the choice of which amino acid to use for a site-directed mutation is often made using 1 ) the germline as a reference , 2 ) biochemical similarity between amino acids , or 3 ) the highest probability amino acid from a generic substitution matrix ( e . g . BLOSUM ) [9] . However , neither of these three methods are explicitly designed to conserve antibody functionality ( i . e . binding to the same epitope with the same kinetics ) , so mutations are likely to have negative side effects on affinity . These considerations motivate a prediction problem: given a B cell receptor ( BCR ) sequence , which positions can be modified , and to which amino acids , without drastically changing the binding properties of the resulting BCR ? An immunization-derived antibody has already implicitly explored the mutational space through the population of B cells sharing the same naive ancestor , referred to as its clonal family ( CF ) . The members of a CF arise during affinity maturation in a germinal center and carry fitness information about the effect of amino acid substitutions . A profile of the observed substitutions aggregated over all the B cells in a CF reveals which sites are more conserved , which sites can be more freely edited , and which amino acids can be used for replacements . However , we generally do not sequence all the B cells that are released from a germinal center so the information to make such a substitution profile is lost . Thus , we can formulate a more specific version of our prediction problem: given bulk BCR data and a single input sequence , can we infer the most likely per-site substitutions that are allowed in its true germinal center clonal family ? We begin by reviewing the natural mutation and selection process of germinal center affinity maturation . The Darwinian selection undertaken inside a germinal center is driven by B cells’ ability to bind the antigen through the membrane-embedded BCR . The highly-mutated population of B cells in a germinal center is under stringent selection , driving the cell population towards higher and higher affinity until the germinal center is dissolved . Each germinal center is seeded by around one hundred naive B cells , but eventually internal competition makes one or a few of these lineages take over the whole germinal center [10] . Although B cells in the germinal center reaction experience an extraordinarily high mutation rate ( 106 fold higher than the regular somatic mutation rate [11] ) , they rarely harbor more than 15% mutations at the DNA level [12] . However , since they must maintain some degree of antigen specificity to survive during the course of the germinal center reaction , lineages evolve in small incremental steps [13 , 14] and therefore , even lineages that drift far away from their naive B cell ancestor most likely maintain the same epitope specificity throughout the germinal center reaction [15] . We can describe the combination of germinal center mutation and selection dynamics by computing per-site amino acid frequency vectors from observed BCR sequence data . We follow previous authors in calling site-specific amino acid probability vectors “substitution profiles” , where each vector in a profile stores the probabilities of observing the 20 different amino acids at a given site [16] . We use the concept of a clonal family , defined by a shared heavy chain inferred naive DNA sequence , to segment BCR sequences into evolutionarily-related groups [17]; some practitioners refer to these groups as lineages . CF inference is highly informed by nucleotide sequences and therefore performed using DNA sequences . This makes DNA-level information necessary even though germinal center selection operates at the protein level and synonymous codons do not possess any fitness advantages ( modulo transcription rate differences and codon bias , which we follow many others in ignoring here ) . The per-site amino acid frequency vectors described above form the substitution profile estimates; the substitution profile estimates converge to the true substitution profiles as the number of sequences sampled from the same CF tends to infinity . Most CFs do not contain enough sequences in order to get a detailed substitution profile estimate . Indeed , most CFs in repertoire sequencing ( Rep-Seq ) samples have few members and a large fraction are singletons due to the exponential nature of the CF size distribution [17] . Additionally , many antibody screening methods are not geared towards whole repertoire sequencing . One may wish , then , to enhance the substitution profile estimates for data-sparse CFs with substitution profile information from similar CFs . In this paper , we present “Substitution Profiles Using Related Families” ( SPURF ) , a penalized tensor regression framework that integrates multiple sources of information to predict the CF-specific amino acid frequency profile for a single input BCR sequence ( Fig 1 ) . Some of these information sources include substitution profiles for CFs in large , publicly available BCR sequence datasets and germline gene sequence information . We combine the local context-specific profile information with global profile information derived from other related germinal centers by regularizing the noisy local substitution profile estimate and pooling it closer towards more robust global profile estimates . Even though each germinal center focuses on binding to a unique epitope context , there are structural and possibly functional properties associated with BCR sequences that are common across germinal centers that we can leverage . In addition , our inference machinery uses both standard and spatial lasso penalties as model regularizers and , as a result , furnishes sparse , interpretable parameter estimates . While our output type shares some similarities to that described by [16] , the proposed objective , approach , and details differ ( e . g . they predict substitution profiles for gene families , we predict substitution profiles for CFs ) . We enable substitution profile prediction for single input BCR sequences based on profiles derived from a high-quality repertoire dataset that contains B cell samples from many human donors . To demonstrate the usefulness of our technique , we validate SPURF on two external datasets—one containing CFs extracted from a single human donor and the other focusing on a single CF of a HIV broadly neutralizing antibody . Lastly , we implement SPURF in an open-source software package ( https://github . com/krdav/SPURF ) , which outputs a predicted CF-specific substitution profile and an associated logo plot based on a single input BCR sequence .
The aim of our model is to take a single sequence and predict the site-wise amino acid frequencies as would be found in the full CF from which this single sequence derived . We will refer to this as the sequence’s CF-specific substitution profile . For this prediction problem , we have no direct information about this desired substitution profile other than the information contained in the input sequence itself , but we may use other information ( e . g . from the inferred germline gene , simulated substitutions , or information derived from published BCR sequence datasets ) . For large CFs , a CF-specific substitution profile can be constructed simply by counting and making a per-site frequency matrix , with the rows of the matrix representing each of the 20 amino acids , and the columns being the sequence positions . For training , we extract a collection of such large CFs and use them to build “ground truth” CF-specific substitution profiles as a training set for fitting the model . A randomly sampled single sequence is then taken out from each of these large CFs to predict the substitution profile , which is compared to the ground truth . We refer to these single sequences , sampled from large CFs , as subsamples . To make the best possible prediction , we need a flexible model framework that can accommodate different sources of information seamlessly ( Fig 2 ) . For example , previous work by [18] and [19] suggests that the various V genes have different characteristic paths of diversification . We can obtain a data-driven summary of that intuition by building profiles from large Rep-Seq data sets stratified by V gene . We may also think that the neutral substitution process is an important factor in determining substitution profiles [18] . We can quantify that sort of information by repeatedly simulating the neutral substitution process using a context-sensitive model [20] . We call each external data set ( e . g . , V gene alignments and sequences simulated from the neutral substitution process ) that can be used to predict the CF substitution profile of interest a source of profile information . To make predictions using these types of information , we need a way of describing the various sites , and a way of integrating the information across the sites . We use the AHo numbering scheme [21] to provide a single coordinate system to all sequences via its fixed-length numbering vector going from 1 to 149 . Given this coordinate system , we use a site-wise weighted average of the input predictive profiles using a α weight vector for each source of profile information . To train this model , we fit the α vectors by minimizing some objective function that quantifies the difference between the predicted profiles ( where the prediction uses the subsampled sequence and the external profile information ) and the “ground truth” substitution profiles from the large CFs . Any objective function could be used , but here we provide implementations of two such functions , a “fine-grained” L2-error-based objective and a “coarse-grained” Jaccard-similarity-based objective [22] . We use two forms of regularization to avoid overfitting the many parameters of this model . This includes a standard lasso penalty to shrink weights to zero that do not contribute significantly to prediction performance [23] . We also use a fused lasso penalty [24 , 25] to smooth differences between parameters at nearby sites in the sequence . These regularization terms have tuning parameters that regulate the strength of the penalties and are estimated using cross-validation . Given this setup , a forward stepwise selection procedure is run with cross-validation to pick the set of external profiles to use in the final model . As a last check , this model is tested on two external datasets to give a fair estimate of the prediction performance . We divide input data into two parts , with each part for a respective purpose: 1 ) model fitting and model testing and 2 ) providing “public” substitution profiles over clustered data to be used by our model . Throughout this work , we are careful to not use the same data for both purposes as this would bias our estimates; as a final validation , we test SPURF on two external datasets which are only used in this validation . Because we do not model sequence error , we only include high-quality data that we have high confidence in . We collect post-processed data files from 6 published works on Rep-Seq , which we refer to as repertoire data 1 to 6 ( RD1-6 ) : All datasets are acquired in their post-processed form with read processing performed as described in their respective publications . The first five datasets ( RD1-5 ) are prepared from unique molecular identifier ( UMI ) barcoded cDNA spanning the whole VDJ region and sequenced on the Illumina MiSeq platform using overlapping paired-end reads . Using the UMI , these reads are processed to address both PCR and sequencing errors giving high confidence reads [33] . Briefly , UMIs are used for error correction in conjunction with either of the pRESTO [34] or MIGEC [33] processing pipelines and an appropriate Phred quality score cutoff . Paired-end reads are assembled using pRESTO and only the set of high confidence assembled reads constitute the final dataset used in this work . RD6 is the only dataset not prepared with UMIs; however , it is sequenced directly from genomic DNA ( gDNA ) instead of the more common practice of sequencing mRNA . Sequencing gDNA has the benefit of avoiding mutations introduced by the transcription machinery as well as mutations introduced in the RT-PCR step . On the other hand , DNA sequencing is not able to discriminate between expressed versus unexpressed BCRs ( e . g . in the case of faulty VDJ recombination ) and therefore we apply aggressive filtering of non-functional BCR sequences . We prefer quality over quantity and therefore avoid datasets from the 454 technology because of their higher indel frequencies compared to those from Illumina technologies [35] . Individual sequence files are merged based on donor identity so that the number of sample files matches the number of donors; this process yields 33 donor files . The donor files are then annotated and partitioned into CFs using the partis software [17 , 36] . Each donor file is run separately from the other files so CFs are defined by their unique partis-inferred naive sequence and donor identity . To ensure we obtain the highest quality and most biologically relevant sequences , partis is run in its most restrictive mode , discarding all reads with VDJ recombinations that are deemed as unproductive because of out-of-frame N/P junction nucleotides , missing invariant codons , or stop codons inside the VDJ region; furthermore , the most accurate partis partitioning mode ( “full” ) is used to get the best CF estimates . Lastly , productive VDJ-recombined sequences are removed if they contain indels to assure concordance between the length of the naive sequence and the length of the read sequences in its CF . At this stage , some sequences contain ambiguous bases ( e . g . , because of primer masking ) ; these are allowed to pass only if the ambiguous bases are inside the first or last 30 nucleotides of the VDJ region ( equivalent to the length of the potentially masked PCR primers ) , otherwise they are discarded . This is a way of substituting the error-prone ends with neutral bases that minimize variance and maintain a conservative estimate of the substitutions; we also note that this has no apparent effect on the subsequently-described estimates ( Figs 3 and 4 ) . For all sequences that pass this requirement , ambiguous bases are substituted with bases from the naive sequence in batches of 3 nucleotides ( i . e . one codon ) at a time until all ambiguous bases are resolved . Sequences are then translated into their respective amino acid sequences and de-duplication of repeated amino acid sequences is done within each CF . Because our statistical methodology operates on these amino acid sequences , we use the word “sequence” in subsequent sections to refer to these amino acid sequences . All CFs with fewer than 5 unique sequences are discarded . From these remaining CFs , their inferred naive sequences are used for antibody sequence numbering with the ANARCI software [37] under the AHo numbering scheme [21] . As a result of our restriction to non-indel sequences , all sequences within a given CF have equal length; thus , the AHo numbering from the naive sequence can be positionally transferred to all its CF-related read sequences . Finally , for each CF , the amino acid usage is extracted as a vector of counts at each AHo position . This overall dataset , which we call the “aggregated” dataset , contains 518 , 174 sequences distributed over 31 , 893 CFs and is built as a matrix of counts with rows denoting CFs and columns representing AHo positions and amino acid identities . All data used to build this aggregated dataset is public and freely available . We provide the data partitioned into CFs and numbered into AHo numbering for download on Zenodo ( https://doi . org/10 . 5281/zenodo . 1289984 ) . Before we present our penalized tensor regression model , we first describe how the input data for the model is constructed , building off the data descriptions in the last subsection . Throughout the rest of this section , we assume the count matrices are normalized to frequencies and reorganized into three-dimensional tensors ( i . e . arrays ) as follows . For any substitution profile tensor T = {Ti , j , k} , let Ti , j , k denote the frequency of the kth amino acid at the jth AHo position for the ith CF; we represent the subsampled , full , and public substitution profile tensors as X , Y , and Z , respectively . Our goal is to use the subsampled profiles X to predict the corresponding full substitution profiles Y ( i . e . we want to construct a function F ( X ) such that F ( X ) ≈ Y ) . We incorporate information from the public dataset Z to enhance these predictions . In addition to the subsampled profiles , we use other types of substitution profiles within F ( X ) : To compute the external profiles in X ^ vsubgrp ( resp . X ^ vgene ) , we cluster the public dataset Z by averaging its CF-specific substitution profiles according to the partis-inferred [36] IMGT defined [39] V-subgroup ( resp . V-gene ) labels and then assign each row in X to a V-subgroup ( resp . V-gene ) cluster profile according to its V-subgroup ( resp . V-gene ) identity . We obtain the second set of profiles X ^ naiveAA by using the partis-inferred naive sequences as substitution profiles ( these profiles contain zeros and ones because they are based on one sequence only ) ; we re-emphasize that these naive sequences are inferred based only on the corresponding subsampled sequences in X . We cluster the public dataset Z once more by running K-means clustering based on the inferred naive sequences in Z and obtain our third set of substitution profiles X ^ naiveAA-clust by assigning each CF in X to its closest cluster centroid . The additional cluster profiles X ^ clust are obtained similarly as above , except in this case , we run K-means clustering based on the original frequency profiles in Z . The K-means clustering procedure is run over a grid of cluster sizes ranging from 2 to 120 using the algorithm described by [40] with the standard euclidean distance metric . Lastly , the tensor X ^ neut contains the simulated S5F neutral substitution profiles , which are described in the previous subsection . The frequency tensors X ^ vsubgrp and X ^ vgene are important to include in our analysis because these profiles capture substitution information at the level of the V subgroup ( V1 , V2 , … ) and V gene ( V1-5 , V2-2 , … ) , respectively; this is similar to the types of profiles obtained in [16] . Even though we expect the X ^ vsubgrp and X ^ vgene tensors to be correlated , we are interested in seeing whether either of these profiles will dominate the other in our regression model . As described in the introduction , most germinal center lineages do not accumulate many mutations relative to the naive sequence so substitution profiles based solely on the naive sequence ( like X ^ naiveAA ) may be informative for predicting the mutational patterns at conserved residue positions . In addition , we believe that the X ^ naiveAA-clust cluster profiles are useful as the naive sequence can greatly influence the pattern of substitutions in a CF due to local sequence context . Unlike the X ^ vsubgrp and X ^ vgene substitution profiles , which are based on IMGT labeling schemes , the profiles in X ^ naiveAA-clust ( and X ^ clust ) are determined by a data-driven clustering procedure , which allows us to group CFs in Z in a more intricate fashion . The simulated neutral substitution profiles X ^ neut are able to provide some insight into the CF-specific SHM processes without the corresponding clonal selection effects . To condense our model presentation , we introduce a four-dimensional tensor X* that combines as many of the input profiles mentioned previously as we would like , where p , the size of the fourth tensor dimension , represents the number of external profiles used . We define X * ≡ { X i , j , k , l * } to be the input data tensor that incorporates all the external information we want to use in our substitution profile predictions; note that i ∈ {1 , … , NCF} ( NCF CFs in the tensors ) , j ∈ {1 , … , 149} ( 149 AHo positions ) , k ∈ {1 , … , 20} ( 20 amino acids ) , and l ∈ {1 , … , p} ( p external profiles ) . Each element X i , j , k , l * represents an amino acid frequency as described above for Ti , j , k; for instance , X 5 , 130 , 1 , 4 * represents the amino acid frequency of the first amino acid ( i . e . alanine ) at the 130th AHo position for the 5th CF in the 4th profile in the tensor . In addition , we use the indexing symbol • to extract all elements of a particular array dimension of a tensor ( i . e . X 10 , 50 , • , 2 * specifies the full substitution profile of the 20 amino acids at the 50th AHo position for the 10th CF in the 2nd profile in the tensor ) . This setup allows us to easily include as many external profiles as we would like . Given the subsampled profiles X and all the external profiles X* , we compute a weighted average to form an estimator of Y . Our independent-across-sites model F ( X ) = [f ( X• , 1 , • ) , … , f ( X• , 149 , • ) ] is specified as follows: f ( X • , j , • ) ≡ f ( X • , j , • ; α j , • ) = ∑ l = 1 p α j , l · X • , j , • , l * + ( 1 - ∑ l = 1 p α j , l ) · X • , j , • , ( 1 ) where α = {αj , l}; 0 ≤ αj , l ≤ 1; 0 ≤ ∑ l = 1 p α j , l ≤ 1 represents the site-specific weights of the different external profiles for j = 1 , … , 149 and l = 1 , … , p . Although we consider f to be a function of the per-site data X• , j , • , the frequencies X • , j , • , l * are computed using sequence-level , site-dependent information . With 149 × p parameter values of α , this is a highly parameterized model so we include regularization terms to prevent overfitting and obtain sparse , interpretable parameter estimates . Specifically , we use standard and spatial ( fused ) lasso penalties to achieve these goals . Standard lasso penalties shrink individual parameters to zero and are commonly used to obtain sparse solutions in regression problems [23] . It has been shown that regression models using standard lasso penalties provide more accurate predictions than models using best subset selection penalties when there is a low signal-to-noise ratio [41] , which probably holds true in our problem as well . In addition , standard lasso penalties are convex functions , which is important in a regression problem as it guarantees that a local minimum is indeed a unique global solution [42] . On the other hand , fused lasso penalties shrink the differences between parameters to zero and are useful in regression problems with spatially-related covariates [24] . We believe that the α parameters have a spatial relationship ( i . e . adjacent residues are under similar constraints ) ; for instance , given that the mutations in the framework regions are largely related to antibody stability , it makes sense that we would weight external profile information similarly in those regions . The fusion penalty in this setting enforces smoothness of the α trend across the AHo positions . For example , if we penalize first-order differences of the α trend , the fitting procedure will necessarily favor trends that have no slope ( i . e . that are piecewise constant ) . We can obtain more flexible piecewise polynomial α trends by penalizing higher-order successive differences of α [25] . In our modeling framework , the standard lasso penalty is represented as ∑ j = 1 149 ∑ l = 1 p | α j , l | = ∥α∥1 and the fused lasso penalty is specified by ∑ l = 1 p ∥∇ d ( α • , l ) ∥1 , where ∥⋅∥q denotes the Lq norm and ∇d ( ⋅ ) represents the dth difference operator . This ∇d ( ⋅ ) operator accepts a vector v as input ( call its length nv ) and outputs a length- ( nv − d ) vector that results from successively differencing adjacent elements d times . In the special case when d = 1 , the fusion penalty becomes ∑ l = 1 p ∥∇ 1 ( α • , l ) ∥1 = ∑ j = 2 149 ∑ l = 1 p | α j , l - α j - 1 , l |; the |αj , l − αj−1 , l| terms can be interpreted as first-order discrete derivatives . Our unpenalized objective function can be written as: L 2 α ≡ L 2 α ( Y , F ( X ) ) = 1 149 · N C F ∑ j = 1 149 | | Y • , j , • - f ( X • , j , • ; α j , • ) | |2 2 , ( 2 ) where , as in the last subsection , NCF denotes the number of CFs in X and Y; we refer to this objective as “L2 Error” . Our penalized estimation problem is defined in the following manner: α ^ = argmin α L 2 α ( Y , F ( X ) ) + λ 1 ∥α∥1 + λ 2 ∑ l = 1 p ∥∇ d ( α • , l ) ∥ 1 , s . t . 0 ≤ α j , l ≤ 1 , 0 ≤ ∑ l = 1 p α j , l ≤ 1 , ∀ j , l , ( 3 ) where λ1 , λ2 ≥ 0 and d ∈ N signify tuning parameters . The differencing order d is used to specify a given level of smoothness in the spatial α trend estimates because the ∑ l = 1 p ∥∇ d ( α • , l ) ∥ 1 term in the above minimization problem encourages α trends that have dth order discrete derivatives close to 0 ( i . e . that are piecewise polynomials of order d − 1 ) . In addition , careful selection of λ1 and λ2 is required to obtain an adequate model fit . Unfortunately , this is a constrained optimization problem with a multivariate output and there are not any obvious ways to minimize such an objective without resorting to general-purpose optimizers . Therefore , in all our experiments , we use the L-BFGS-B algorithm [43] to fit the above model . We note that the above penalized optimization problem is ( non-strictly ) convex so any local minimum is , in fact , a global solution too . While the model described above has computational and statistical appeal , in engineering applications it is mostly interesting to know the high-frequency amino acid predictions; however , our penalized objective function focuses attention on the complete substitution profiles and not exclusively the high-frequency amino acids . To provide a metric with exclusive focus on high-frequency amino acids , we utilize the Jaccard similarity metric , which can be used to measure differences between predicted and observed sets . Sets of high-frequency amino acids are defined at each position by a minimum frequency cutoff t; Jaccard similarities are then computed between the observed and predicted sets and averaged across each CF and AHo position in the dataset . The Jaccard similarity metric [22] measures the similarity between two finite sets . Specifically , for any sets A and B , the similarity metric J ( A , B ) is defined as the ratio of the intersection size |A ∩ B| to the union size |A ∪ B| . It has these properties: 0 ≤ J ( A , B ) ≤ 1; J ( A , B ) = 1 when A = B and J ( A , B ) = 0 when A ∩ B = ∅ ( empty set ) . To formally establish our use of Jaccard similarity , we define the following notation . Let Y i , j = { y ∈ Y i , j , • ∣ y ≥ t } represent the set of amino acid frequencies at AHo position j for CF i that has observed frequencies greater than or equal to the cutoff t and denote Y ≡ { Y i , j } for i = 1 , … , NCF and j = 1 , … , 149 . We define F^ i , j X and F^ X ≡ { F ^ i , j X } to be the analogous quantities for the predicted amino acid frequencies . If we let A ( Y ′ ) denote a function that accepts as input an amino acid frequency set Y ′ ( i . e . Y i , j or F ^ i , j X ) and outputs the corresponding set of amino acid identities , then our Jaccard similarity objective can be written as: J t α ≡ J t α ( Y , F ( X ) ) = 1 149 · N C F ∑ i = 1 N C F ∑ j = 1 149 J ( A ( Y i , j ) , A ( F ^ i , j X ) ) , ( 4 ) which is referred to as the “Jaccard Similarity” objective . We can define a penalized Jaccard estimation problem by substituting - J t α ( Y , F ( X ) ) for L 2 α ( Y , F ( X ) ) in Eq ( 3 ) . Jaccard similarity optimization is difficult using derivative-based optimization because of its discrete nature , so we use a smooth approximation of the aforementioned metric for model fitting in our experiments ( see S2 Text for detailed explanation ) . We devise a forward stepwise selection procedure to help us determine the combination of external profiles that best predict the outcome of interest , which can be penalized L2 Error or Jaccard Similarity . In this procedure , we initially try all possible external profiles in the model separately and determine the best fit using 5-fold cross-validation . We cache the best model from the initial step and continue fitting models with two external profiles; the first external profile is fixed to be the best profile from the previous round and the second profile can be any possible remaining external profile . We continue this iterative scheme until we reach a prespecified limit on the number of external profiles allowed in X* . It is important to note that to ease computation , we perform forward selection using the unpenalized variants of our models . Even though this procedure is greedy and not as thorough as all-subsets selection , we believe this technique provides the best trade-off between accuracy and efficiency . We provide the implementation of our stepwise procedures at https://github . com/krdav/SPURF . We apply a 80%/20% training/test split to the model fitting dataset described above . We first run the forward stepwise selection procedure with a maximum profile limit of five to approximately determine the best profile groupings starting with a single profile and ending with a group of five profiles . Using the profile groupings from the previous step , we fit the penalized version of the model and use 5-fold cross-validation to obtain estimates of the relevant tuning parameters , which consist of the lasso penalty weights λ1 , λ2 and the differencing order d; note that we report unpenalized performance estimates when we run cross-validation . After we determine the optimal tuning parameters via cross-validation , we fit the penalized model using the entire training portion of the model fitting dataset and the best tuning parameters and cache the resulting parameter estimates of α . Once we obtain the estimates of α from the penalized model , we can use them to compute the chosen performance metric on the testing portion of the model fitting dataset and any other validation dataset of interest .
As described in the methods ( the Inference Pipeline subsection ) , we first need to infer the best profile groupings to use in penalized model fitting . To determine these groupings , we run the forward stepwise selection procedure for both the L2 error function and the smoothed Jaccard objective function with a frequency cutoff t = 0 . 2 ( Table 2 ) . For both objective functions , the forward selection path is the same until X * = { X ^ naiveAA , X ^ vgene , X ^ neut , X ^ vsubgrp } . For the L2 loss function , model performance is the best when X * = { X ^ naiveAA , X ^ vgene , X ^ neut , X ^ vsubgrp } even though there are diminishing returns for using profiles beyond X * = { X ^ naiveAA , X ^ vgene } . In a similar fashion , the Jaccard similarity estimates tend to be highest when X * = { X ^ naiveAA , X ^ vgene } , despite the almost identical model performance from just using X * = { X ^ naiveAA } . For the subsequent penalized model fitting step , we choose to evaluate the { X ^ naiveAA , X ^ vgene , X ^ neut } and { X ^ naiveAA , X ^ vgene , X ^ neut , X ^ vsubgrp } profile groupings with the L2 objective and { X ^ naiveAA } and { X ^ naiveAA , X ^ vgene } with the smoothed Jaccard similarity objective . The inclusion of the X ^ vgene tensor puts a notable restriction on the model; no prediction can be made for a sequence annotated to a V gene which has not been observed in our Public dataset . We now use the approximate profile groupings obtained from the forward stepwise selection procedure to fit our regularized models . The penalized estimation problem has additional tuning parameters that must be determined . In our experiments , we cross-validate over penalty parameters; λ1 , λ2 = 10−7 , 5 . 05 × 10−6 , 10−5; the differencing order , d = 1 , 2 , 3; and the two profile groupings specified above for both the L2 error and Jaccard similarity objectives . The best regularized L2 model uses X * = { X ^ naiveAA , X ^ vgene , X ^ neut , X ^ vsubgrp } , while the best regularized Jaccard model utilizes X * = { X ^ naiveAA } ( S1 Table and S7 Fig ) . In summary , using many external profiles is important for predicting the complete substitution profiles , while the inferred naive sequence is the only external profile deemed useful for our model to accurately predict the observed high-frequency amino acids ( where high-frequency is defined by being at least 20% of the observed amino acids ) . Our optimization times for both L2 loss and Jaccard Similarity on the 500 CFs ranged from 12 to 15 minutes . Our optimization is based on evaluating the objective function at different points and each objective function call has linear complexity in the number of CFs so increasing the number of CFs should result , on average , in a linear increase in time complexity . Computational time invested in pre-processing is one-time and negligible . In addition to predictive performance , we are also interested in understanding how the estimated parameter weights from our best regularized L2 model vary across the different external profiles in X* and antibody regions . For convenience , we aggregate the estimates of α associated with the V gene ( X ^ vgene and X ^ vsubgrp ) and with the full naive sequence ( X ^ naiveAA and X ^ neut ) as these sets of profiles are intuitively similar ( Fig 3 ) ; the V-gene and V-subgroup profiles are both derived by averaging over different IMGT V germline gene labeling schemes and the simulated S5F neutral substitution profiles originate from the CF-specific inferred naive sequence . Antibody heavy chain ( and light chain ) sequences can be partitioned into framework regions ( FWKs ) and complementarity-determining regions ( CDRs ) by the AHo definitions [21]; the BCR binding affinity is largely determined by the CDRs ( especially by the heavy chain CDR3 ) , while the FWKs encode the structural constraints of the BCR and thus can be strongly conserved [44] . The X ^ vgene and X ^ vsubgrp profiles are extremely important for prediction at FWK1-FWK3 , which is not surprising as V germline genes extend from the FWK1 to the beginning of the CDR3 . In contrast , the X ^ naiveAA and X ^ neut external profiles are heavily weighted in the CDR3 and FWK4; this result is also intuitive because the CDR3 is highly variable across CFs as it is a strong determinant of antigen-binding specificity , the X ^ naiveAA and X ^ neut profiles are our only CF-specific sources of external information , and the V gene specific profiles cannot provide any information beyond the end of the V gene . Furthermore , the FWKs have , on average , more support from the external profiles compared to the CDRs , which is consistent with our understanding of antibody biochemistry as the FWKs are structurally constrained and thus need to be more conserved compared to the more flexible CDRs . We note that the middle of the CDR3 has artificially low estimates of α because most of the AHo positions in the CDR3 have only a few or no defined sequence positions in the dataset ( S1 Fig ) . While our penalized modeling framework allows for easy interpretation of the parameter estimates , ultimately the quality of the α estimates is determined by their performance on independent test datasets . Specifically , we compute the L2 error ( L 2 α ) and Jaccard similarity ( J 0 . 2 α ) between the predicted and observed profiles associated with both the testing portion of the model fitting dataset and the Briggs validation dataset ( Table 3 ) ; we remind readers that these predictions are made based on the subsampled ( i . e . single-sequence ) profiles in the aforementioned datasets and compared to the corresponding actual amino acid frequencies through the L 2 α and J 0 . 2 α performance metrics ( Fig 2 ) . Additionally , we compute the L2 error and Jaccard similarity on all sequences in the Liao dataset , comparing the baseline and SPURF predictions to the full amino acid frequencies ( Table 3 , S2 Table , and S6 Fig ) . Our model improves upon the “baseline” prediction performance , where “baseline” refers to predictions made using only the input sequence ( i . e . model predictions with all parameter values of α set to 0 ) . In addition , we also want to know how well our model performs in the different antibody regions ( i . e . FWKs/CDRs ) . To answer this question , we compute the same metrics as shown in Table 3 for the different FWKs and CDRs ( Fig 4 ) . To provide some insight into the variability of the model performance estimates in the different regions , we calculate bootstrap standard errors , which are expressed as error bars in Fig 4 . We see that our substitution profile prediction model performs well in the CDRs relative to the baseline model . This is an important finding because antigen binding is largely determined by the sequence segments in the CDRs , and especially CDR3 . In fact , our models seem to provide the greatest improvement in performance in the CDR3 , which is also the hardest region to predict because it has the highest amount of sequence variability . Another important takeaway is that the prediction performance is better in FWKs than CDRs , which is presumably because FWKs have lower variance and are more conserved compared to CDRs . In summary , our prediction models are able to systematically integrate different data sources to make better predictions of the per-site amino acid compositions in CFs . Our model also improves the prediction of the highest-frequency amino acid at a given position , referred to here as the mode ( Table 4 ) . Indeed , the counts in the bottom-left cells ( cases where the model is correctly predicting the actual mode given an incorrect input sequence amino acid ) are larger than the counts in the top-right cells ( vice-versa ) . In addition , the input sequence amino acids that are not the true modes but correctly predicted by the model to be the actual modes are all germline reversions , which is consistent with the X ^ naiveAA profile being heavily weighted in our prediction model ( Fig 3 ) . In the opposite case , where the input sequence amino acid is correct but the model prediction is wrong , all the counts consist of germline predictions as well . In summary , many of the mode predictions are just germline reversions and , in fact , most of these predictions are to the true modes ( i . e . the actual highest-frequency amino acids ) ; however , most of the input sequence amino acids are the true modes already ( ≈ 99% ) . The in-sample and out-of-sample prediction performances demonstrate that our SPURF inference pipeline is able to obtain accurate and robust estimates of α . Specifically , prediction performance is consistently similar but slightly worse when comparing the Briggs dataset to the model fitting test set , which likely reflects two things: 1 ) the median number of sequences per CF in the Briggs set is lower than in the test set ( Tables 1 and 2 ) the model fitting dataset is sampled from the same donors as the dataset for cross-validation . Regardless , the differences between the test and Briggs datasets are small , which provides evidence in support of our model performance estimates . We also note that the test on the Liao data yielded results strongly favoring SPURF over baseline . Since the Liao dataset carries a high mutation frequency compared to the average CF of the other dataset it is ( as expected ) harder to predict the amino acid frequency , which is reflected in the magnitude of both the L2 error and Jaccard similarity for all predictions . Subjective assessments of the inferred substitution profiles coincide with our description of the L2 error metric , namely that fine-grained amino acid substitution information is captured by SPURF ( S2 Fig ) . The SPURF model setup produces interpretable and meaningful profile weights ( Fig 5; per-profile decomposition in S3 Fig ) . The input sequence is strongly weighted in the CDRs , indicating that substitutions in these regions are both specific and conserved within the CF and , therefore , cannot easily utilize the information from other CFs . The weight on the V gene specific profiles spikes at CDR1 and at the end of FWK3 , which is at the heavy and light chain interface . We note that , as expected , the weight on the V gene specific profiles is minimal downstream of FWK3 as this is the end of the V gene and the beginning of the V-D junction region . As such , nothing prevents the V gene profiles from having a high weight downstream of FWK3 , but the model framework has chosen these meaningful weights without any manual interference . We ascribe this shrinkage feature of the weights to the standard lasso penalty built into SPURF . The profiles that are derived from the inferred naive sequence ( X ^ naiveAA , X ^ neut ) take up the missing weight of the V gene profiles as these are highly weighted in the CDR3 and FWK4 .
In this paper , we present SPURF , a statistical framework for predicting CF-specific amino acid frequency profiles from single input BCR sequences by leveraging multiple sources of external information . We use standard and spatial lasso penalties to prevent our model from overfitting and obtain sparse , interpretable estimates of the profile weights , expressed by an α matrix . The spatial lasso penalizes extreme differences between spatially-adjacent profile weights , while the standard lasso penalties promote simpler models by shrinking parameter values in α to 0 if the associated external profiles are not useful predictors . We show that our method not only performs well on the held-out ( test ) portion of our model fitting dataset but also provides accurate predictions on the Briggs and Liao external validation datasets . Indeed , we did not obtain the Briggs or Liao validation datasets until after we ran our model inference pipeline on the model fitting dataset . Using two different objective functions we fitted SPURF to predict the frequencies of all amino acids ( L2 objective ) and only the >20% frequency amino acids ( Jaccard similarity objective ) . With the L2 objective we obtained a large difference ( 0 . 114 to 0 . 0492 ) between the baseline model and SPURF , which was confirmed using repertoire wide data from [12] and single clone data from [38] ( Fig 4 and S6 Fig ) . With the Jaccard similarity objective improvements over baseline were more modest ( 0 . 9156 to 0 . 9289 ) showing that SPURF is strongest at predicting the full spectrum of amino acid frequencies ( Table 3 ) . Still , fitted using the L2 objective , SPURF can recover the highest frequency amino acid of a clonal family ( mode prediction ) much better than a random sequence from the corresponding clonal family ( Table 4 ) , showing the versatility of the L2 objective . Our work can be seen as a prediction-based extension of the work of [16] and [19] . This previous work illustrates that amino acid substitution profiles differ between germline genes , a finding supported by the context specificity of somatic hypermutation [20] . In our work , we provide a prediction algorithm that takes a single BCR sequence from a clonal family as input and outputs a CF-specific substitution profile estimate for the whole VDJ region . As SPURF relies on large CFs to establish a ground truth substitution profile it is possible that certain types of rare clones or V/J gene combinations are not included in our training/test data . For such rare events the error estimates reported cannot be reliably used , however , we note that our training/test data cover a broad set of V/J combinations ( S4 Fig ) and that the substitution profile of a rare , but expanded , broadly HIV neutralizing clone is well predicted ( S6 Fig ) . We believe that this work will be a useful tool for antibody engineering in situations when it is important to maintain antibody binding affinity to the same epitope . The predicted profiles from SPURF can be used to choose the sites that are most tolerable for mutagenesis and the substitutions that are most likely to maintain binding specificity; as such , this information can be used to engineer antibodies with better biophysical properties . The seven datasets utilized in the present study were all derived from different laboratories employing varying strategies to obtain their processed data which served as input for SPURF . We carefully examined available resources and selected the datasets to be used in our model . However , our approach would greatly benefit from a large and uniformly accessible repository of Rep-seq datasets . For this to happen , data has to be discoverable and usable , including having all information about the study and data processing available together with the raw and processed data in publicly accessible data repositories . Recently , the Adaptive Immune Receptor Repertoire ( AIRR ) community [45] proposed MiAIRR [46] , a set of minimal standard elements to be published alongside the raw and processed data . Future Rep-seq studies following this initiative and making their data available under the MiAIRR-standard will facilitate the development of SPURF and future approaches with similar goals . To our knowledge , SPURF is the first prediction algorithm for B cell CF substitution profiles . There are many possible extensions; in our SPURF inference pipeline , we subsample single BCR sequences from CFs to use as model input; unfortunately , this means that our modeling analysis is conditional on a dataset that does not account for the variability associated with the subsampling process . One obvious means of fixing the above problem is to draw multiple subsamples from each CF and treat these multiple “observations” per CF within a dataset as a clustered data or weighted least squares problem . In addition , our model fitting dataset consists of only the largest CFs because we need accurate CF-specific substitution profile estimates to serve as the ground truth . This non-random sampling technique could potentially bias our analysis results; however , this appears unlikely given our model’s performance on the external Briggs and Liao validation datasets . Furthermore , our approach models per-site amino acid composition in a CF and accounts for interactions between sites only through the fusion lasso penalties . It is well known from other protein studies that spatially-adjacent amino acid residues evolve jointly [47 , 48] , presumably to maintain structural stability , or in the case of antibodies to stabilize the interface between heavy and light chains [49] . In the context of antibodies , residues in the FWKs have the potential to co-evolve ( e . g . FWK residues flanking the CDRs could co-evolve to stabilize the stem leading to the more flexible CDRs ) . Thus , figuring out how to incorporate more detailed interaction effects in our model is an important avenue for future research . | Antibody engineering can be greatly informed by knowledge about the underlying affinity maturation process . As such this can be probed by sequencing , but unfortunately , in practice often only one member of the clonal family is sequenced , making it difficult to determine a set of possible amino acid mutations that would retain the original antibody antigen binding affinity . We overcome this data sparsity by developing a statistical learning approach that leverages vast information about amino acid preferences available in public immune system repertoire data . We use a penalized regression approach to devise a flexible statistical model that integrates multiple sources of information into a coherent prediction framework and validate our prediction algorithm using subsampling and held out data . | [
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] | 2018 | Predicting B cell receptor substitution profiles using public repertoire data |
Immunological checkpoints , such as the inhibitory CD200 receptor ( CD200R ) , play a dual role in balancing the immune system during microbial infection . On the one hand these inhibitory signals prevent excessive immune mediated pathology but on the other hand they may impair clearance of the pathogen . We studied the influence of the inhibitory CD200-CD200R axis on clearance and pathology in two different virus infection models . We find that lack of CD200R signaling strongly enhances type I interferon ( IFN ) production and viral clearance and improves the outcome of mouse hepatitis corona virus ( MHV ) infection , particularly in female mice . MHV clearance is known to be dependent on Toll like receptor 7 ( TLR7 ) -mediated type I IFN production and sex differences in TLR7 responses previously have been reported for humans . We therefore hypothesize that CD200R ligation suppresses TLR7 responses and that release of this inhibition enlarges sex differences in TLR7 signaling . This hypothesis is supported by our findings that in vivo administration of synthetic TLR7 ligand leads to enhanced type I IFN production , particularly in female Cd200−/− mice and that CD200R ligation inhibits TLR7 signaling in vitro . In influenza A virus infection we show that viral clearance is determined by sex but not by CD200R signaling . However , absence of CD200R in influenza A virus infection results in enhanced lung neutrophil influx and pathology in females . Thus , CD200-CD200R and sex are host factors that together determine the outcome of viral infection . Our data predict a sex bias in both beneficial and pathological immune responses to virus infection upon therapeutic targeting of CD200-CD200R .
To generate an appropriately controlled response during infections , the immune system is balanced by the action of activating and inhibitory receptors . Lack of inhibition leads to excessive inflammation and autoimmunity and other severe disease symptoms . One of the receptors regulating this balance is CD200 Receptor ( CD200R ) [1] . CD200R was originally described as a myeloid receptor [2] , being expressed on macrophages , granulocytes and DCs , but later we and others recognized that it is also expressed on T cells , B cells and NK cells [3] , [4] . The CD200R intracellular domain is devoid of the classical immunoreceptor tyrosine-based inhibition motif ( ITIM ) present in most immune inhibitory receptors but it does have three tyrosine residues that can be phosphorylated , one of which is embedded in an NPXY motif . CD200R-downstream signaling is dependent on the recruitment of Dok2 and RasGAP [5] . The signal that triggers CD200R and results in delivery of an inhibitory intracellular signal to the cell is given by its ligand CD200 , which has a short intracellular tail devoid of any known signaling motifs . CD200 is expressed on thymocytes , activated T cells , B cells , dendritic cells ( DCs ) , vascular endothelial cells , hair follicular cells , in the central nervous system and in the retina ( reviewed in [6] ) . Both in mice and humans , CD200 exclusively binds to the inhibitory CD200R . In contrast to humans , the mouse CD200R family contains several activating receptors , but these do not bind CD200 [7] . Cd200−/− mice were first described to be more susceptible to autoimmune disorders [8] . Later its role in microbial infections was recognized . Infection of Cd200−/− mice with the gram negative N . meningitides causes increased lethality , proinflammatory cytokine production and lymphocyte activation [9] . We and others showed that in mouse influenza A virus infection CD200-deficiency aggravates immune pathology [10] , [11] . These studies were exclusively performed in female mice . They indicate that CD200-CD200R signaling controls the strength of the initial anti-microbial response and the return to homeostasis . We here studied the influence of CD200-CD200R blockade on clearance and pathology in two different virus infection models , coronavirus and influenza virus , in both male and female mice . Mouse hepatitis coronavirus ( MHV ) is an accepted model for the most illustrious coronavirus ( CoV ) : severe acute respiratory syndrome ( SARS ) -CoV . Host control of MHV infection is completely dependent on an immediate type I IFN response , initiated upon TLR7 triggering by viral RNA . Mice lacking this pathway show massive MHV replication and fatal infection within a few days [12] , [13] . As a model where a strong anti-viral response causes immune mediated pathology we studied influenza A virus infection in which immune pathology is known to be important for clinical outcome . We here report that lack of CD200R signaling has a more profound effect on the beneficial but also on the pathological immune responses to viruses in female mice as compared to male mice , which can be attributed to the capacity of CD200R to inhibit TLR7 responses .
To determine the role of CD200-CD200R signaling in CoV infection , we intraperitoneally inoculated male and female wild type ( WT ) and Cd200−/− mice with a recombinant MHV encoding luciferase ( MHV-EFLM ) . We monitored viral spread using bioluminescence imaging ( BLI ) at day 2 and 4 after infection [14] , [15] . Interestingly , at both time points we observed a decreased viral spread in female WT mice when compared to males . Moreover , lack of CD200 resulted in a significantly lower level of viral replication in females ( Figure 1A , B and Figure S1A , B , C ) . The viral RNA load in the livers at day 4 was assessed by quantitative PCR and confirmed the imaging results: WT female mice had significantly lower viral RNA levels than WT male mice ( Figure 1C ) . Again , CD200-deficiency greatly decreased virus load in female mice . This was confirmed in histological liver sections stained with a monoclonal antibody against MHV ( data not shown ) . The number of focal lesions in the liver , typical for MHV was also lower in female mice and again CD200-deficiency had a significant effect on these lesions only in female mice ( Figure 1D , E ) . Clearance of MHV critically depends on TLR7-mediated type I IFN production by hematopoietic cells [12] , [13] . In WT mice , MHV infection resulted in detectable IFN-α production only in female mice ( Figure 2A ) . In Cd200−/− animals , all females and 2 out of 8 males produced detectable amounts of IFN-α and Cd200−/− female mice produced the highest amounts of IFN-α ( Figure 2A ) . IFN-α concentrations in serum inversely correlated with viral load at day 4 ( p = 0 . 04 ) ( data not shown ) . Thus , the combination of female sex and CD200 deficiency results in increased type I IFN production and decreased viral load and pathology upon MHV infection . Sex differences in TLR7 responses have previously been reported for humans [16] , [17] . Our observed sex difference in IFN-α production and viral clearance upon MHV infection in mice is likely due to a similar sex bias in TLR7 responses . We hypothesized that CD200R signaling suppresses TLR7 responses in WT mice . Release of this inhibition would further reveal the intrinsically higher TLR7 responses in females and result in more rapid viral clearance . To test this hypothesis , we first administered a TLR7 ligand in vivo . As described previously [18] , intraperitoneal injection of a synthetic TLR7 ligand ( imiquimod ) leads to rapid release of type I IFN into the circulation . One-hour after injection we only detected significant amounts of IFN-α in the sera of female Cd200−/− mice , confirming the notion that CD200R inhibits the intrinsic potential for a higher TLR7 response in females ( Figure 2B ) . We sacrificed the mice at 24 hrs after ligand injection , at which time point no serum IFN-α was detectable ( data not shown ) but type I IFN mRNA could be detected in the livers of these mice . Again , females expressed more IFN-β mRNA than males , with CD200-deficient female mice displaying even more elevated IFN-β mRNA levels ( Figure 2C ) . Thus , release of CD200-mediated inhibition leads to increased production of type I IFN in response to TLR7 ligands , particularly in female mice . We next tested whether CD200R-mediated signaling directly inhibits signals transduced via TLR7 . We generated a chimeric construct containing a LAIR-1 receptor in which the intracellular tail was replaced by that of CD200R . This allows for efficient cross-linking using anti-LAIR-1 antibodies to induce signaling via the CD200R cytoplasmic tail . We transfected HEK 293 cells with plasmids encoding a LAIR-1-CD200R chimeric receptor , human TLR7 and a luciferase reporter under control of a NF-κB driven promoter . Cross-linking of the chimeric receptor by anti-LAIR-1 antibody , but not by isotype-matched control antibody , resulted in robust inhibition of imiquimod-induced NF-κB activity ( Figure 3A ) . CD200R contains three intracellular tyrosine residues . A chimeric LAIR-1-CD200R protein in which all three tyrosines are mutated to phenylalanine ( FFF ) did not suppress TLR7 responses upon cross-linking , indicating that the observed inhibitory effect is indeed dependent on CD200R-signaling ( Figure 3A ) . In a cell line with stable ectopic expression of TLR7 and transient expression of the NF-κB-reporter and the LAIR-1-CD200R constructs we also observed that TLR7 signaling was inhibited through CD200R-mediated signaling . A similar effect was observed when luciferase expression was driven by an IFN-β promoter ( Figure 3B ) . Thus , the enhanced type I IFN production and viral clearance of MHV in female Cd200−/− mice can be explained by the release of CD200R-mediated inhibition of the intrinsically higher TLR7 responses in females . A strong anti-viral response can also cause immune mediated pathology that can be detrimental to the host . We therefore moved to a virus infection model in which immune pathology is known to be important for clinical outcome . Upon intranasal infection with influenza A virus we again observed a sex bias in the viral load , measured in the lungs at day 8 post infection ( Figure 4A ) . Female mice had lower viral loads compared to male mice , which was accompanied by enhanced IFN-α concentrations in the bronchoalveolar lavage ( BAL ) fluid ( Figure 4B ) . However , as opposed to MHV infection , CD200-deficiency did not enhance type I IFN production in influenza virus infection ( Figure 4B ) . Confirming previous reports , we found a significantly enhanced body weight loss in female Cd200−/− mice compared to WT females ( Figure 4C ) [11] , [10] . Although male Cd200−/− mice lost more weight than WT males , the weight loss started later and the difference was not as prominent ( Figure 4D ) . This may indicate that lack of CD200 results in a more severe pathology in females . We observed an increased level of cellular infiltration in lung tissue of females ( Figure S2A ) . Therefore we determined lung cellular influx by differential cell count in BAL fluid ( Figure S2B–D ) . The total number of cells in BAL fluid was higher in all female groups but differences were not significant ( Figure S2B ) . The number of lymphocytes was increased in females of both genotypes ( Figure S2D ) . At day 4 after infection , the number of lymphocytes was increased in females of both WT and Cd200−/− groups . At day 8 , the number of neutrophils in the BAL fluid was decreased in all groups , still the WT females displayed elevated numbers over males . Importantly , lack of CD200 resulted in significantly higher neutrophil counts in females but not in males ( Figure 4E ) . As an additional parameter of lung damage we measured the total protein content in the BAL fluid . Lack of CD200 resulted in increased protein levels in the BAL fluid of both males and females , especially at day 8 after infection ( Figure 4F ) . Overall these data indicate that female mice experience increased lung pathology upon influenza A virus infection , which is aggravated by the lack of the CD200R-regulatory pathway . In agreement with the increased neutrophil counts we measured elevated levels of KC ( IL-8 ) in the BAL fluid ( Figure 4G ) . IL-6 concentrations were increased in all females at 4 and 8 days after infection , which was further enhanced by the lack of CD200 only at day 4 ( Figure 4H ) . TNF-α was hardly detectable , but significantly increased levels were measured in Cd200−/− female mice at day 4 after infection ( Figure 4I ) . Thus , from two different viral infection models we can conclude that sex has a profound effect on type I IFN production and viral clearance . This study is the first to report a significantly enhanced viral clearance in female mice due to a sex bias in TLR7 responses . Sex differences in TLR7 induced type I IFN production have previously been reported for humans [16] , [17] and our data show that in mice this has a strong impact on the course of a viral infection . The mechanism for this is not understood . On the one hand , incomplete inactivation of the Tlr7 gene , on the X chromosome , resulting in higher TLR7 expression in females has been proposed [19] , [20] . In our experiments , TLR7 mRNA expression was equal in male and female mice ( Figure S3 ) . This is consistent with a prior report in which no evidence for escape from X-inactivation of the Tlr7 gene in humans was found [16] . There are conflicting reports concerning the influence of sex hormones on TLR7 responses [16] , [17] . Alternatively , sex-dependent epigenetic mechanisms may contribute [16] . We demonstrate direct inhibition of TLR7 signaling through CD200R . Previously , CD200R-mediated inhibition of LPS-induced cytokine production was reported [5] , [21] , [22] . This suggests that CD200R affects proximal events in the TLR signaling pathway . CD200R is a unique inhibitory receptor , since its intracellular tail does not contain ITIMs . CD200R does contain three intracellular tyrosine residues . Mutation of all three tyrosine completely abrogates its inhibitory function [23] . The most distal tyrosine is located in an NPXY motif , to which the adaptor molecule Dok2 is recruited [24] . Dok 2 activates RasGAP and knockdown of these proteins diminishes the inhibitory action of CD200R [5] . However , the down-stream targets for CD200R mediated inhibition are not yet identified . Upon influenza A virus infection , CD200-deficiency strongly enhances neutrophil influx into the lungs of female mice possibly leading to pathology , but it does not affect viral clearance and type I IFN production . This implies that , for influenza virus , the sex-biased type I IFN production and viral clearance are not regulated by CD200R , while the events leading to increased neutrophil recruitment and lung pathology are . Neutrophil responses to influenza virus infection were shown to be dependent on TLR7 [25] . Since neutrophils express CD200R , the strongly increased neutrophil influx in female Cd200−/− mice is in line with our finding that CD200R inhibits sex-biased TLR7 responses . In contrast to MHV infection , clearance of influenza A virus is not dependent on plasmacytoid dendritic cells [26] . Although influenza RNA triggers TLR7 [27] , the main source of type I IFN is the infected respiratory epithelium [28] . These cells do not express CD200R and hence are not influenced by CD200-deficiency , explaining the lack of effect of CD200-deficiency on type I IFN production . There is emerging evidence that tumor cells employ immunological checkpoints for their benefit . As a result of this , inhibitory immune pathways have become therapeutic targets to strengthen anti-tumor responses and develop ( adjuvant ) therapeutic strategies in cancer treatment . The successful application of anti-CTLA4 ( Cytotoxic T-Lymphocyte Antigen 4 ) in melanoma is followed up with blocking agents for other checkpoints , among which the CD200-CD200R immune inhibitory pathway . Strong evidence for a role for CD200 in tumor progression comes from studies in patients . Expression of CD200 is an independent prognostic factor for multiple myeloma and acute myeloid leukemia predicting worse overall survival of these patients [29] , [30] . A clinical trial with a blocking anti-CD200 antibody aims to enhance anti-tumor responses towards CD200-expressing malignancies ( ClinicalTrials . gov Identifier: NCT00648739 ) . On the basis of our data , one of the predicted side effects would be severe immune pathology to infections . Our finding that the combination of lack of CD200R signaling and female sex has such a profound impact on the control of virus infection as well as on immune pathology raises some important issues . We are the first to demonstrate a strong sex bias in type I IFN production and viral clearance in mice utilizing two different models of virus infection . This is of importance for scientists studying these widely used models and may result in a completely different interpretation of data obtained , depending on the sex of the mice used . Moreover , sex biased clinical responses to virus infections have been reported in humans [31] . For influenza A virus ( H5N1 ) a significantly enhanced case-fatality rate was found in women [32] . In agreement with these findings we now show increased lung damage , enhanced neutrophil influx and elevated IL-8 , IL-6 levels in BAL fluid of female mice upon influenza A virus infection . A few reports discuss the possibility of a sex bias in severity of SARS-CoV infection [33] , [34] . Also for HIV-1 infection , sex-related differences have been well-established [35] , [36] . Our results underscore the importance of the issue of sex bias in scientific research , clinical trails , and vaccine studies , previously raised by others [19] , [37] . Particularly , CD200 blocking antibodies are currently entering clinical trials for cancer treatment . Our data point to a possible pathological outcome of e . g . influenza virus infection in women as a result of CD200 blocking therapies .
Wild-type C57BL/6J mice and Cd200−/− mice , which were made and maintained on a full C57BL/6J background [8] , were bred at the Specified Pathogen Free ( SPF ) unit at the Utrecht University Central Animal Laboratory and used between 8 and 10 weeks of age . Mice were injected intraperitoneally with 106 TCID50 of MHV strain A59 expressing the firefly luciferase ( FL ) reporter gene ( MHV-EFLM ) [14] in 200 µl PBS . Intranasal infection with 3 . 0×104 TCID50 of influenza strain A/HK/2/68 was performed as described [11] . Mice were monitored once every 24 hours for symptoms of illness . In additional experiments we injected the mice intraperitoneally with the TLR7 agonist imiquimod ( Invivogen; 50 µg in 200 µl PBS ) . The Utrecht University Ethical Committee for Animal Experimentation approved the animal study protocols , in accordance with the advice of the Central Committee on Animal Experimentation ( 20 januari 1997 ) and the Dutch Law on Animal Experimentation ( art . 18a ) . After MHV-EFLM injection ( day 0 ) , mice were imaged at day 2 and day 4 as described previously [14] with minor modifications . Briefly , mice were anaesthetized with isoflurane and subsequently injected with 100 µl of the FL substrate D-luciferin ( Synchem Laborgemeinschaft OHG ) dissolved in PBS ( 25 mg/kg ) . Mice were positioned to the ventral side in a specially designed box and placed onto the stage inside the light-tight photon imager ( Biospace Laboratory ) . Five mice were imaged simultaneously exactly 5 min after the injection of D-luciferin . The bioluminescence signals were acquired with PhotoVision software ( Biospace Laboratory ) over a 10-min interval and are expressed as integrated light intensity ( photons/min ) . A low-intensity visible light image was generated and used to create overlay ( heatmap ) images for each individual animal . Whole livers or left lungs were dissected from the mice . The tissues were processed in Lysing Matrix D tubes ( MP Biomedical ) , containing 1 ml of PBS , using a FastPrep instrument ( MP Biomedical ) . The tissues were homogenized at 3300× g for 40 sec and immediately placed on ice . Subsequently , the homogenates were centrifuged at 18600× g for 10 minutes at 4°C and supernatants were harvested and stored at −80°C . Total RNA was isolated from the homogenates using the TRIzol reagent ( Invitrogen ) according to manufacturer's instructions . Gene expression levels of IFN-α , IFN-β1 , and TLR7 were measured by quantitative PCR using LightCycler 480 RNA Master Hydrolysis Probes in combination with a LightCycler 480 system ( both from Roche Applied Science ) , according to the manufacturer's instructions . The housekeeping gene GAPDH was used as a reference in all experiments , and expression of this gene was found relatively constant among samples . The amounts of MHV RNA were determined by quantitative RT-PCR using primers and probe directed against the N gene of MHV-A59 [38] . For the influenza RNA quantification the primers mapping to the influenza A nucleoprotein ( N ) gene were used . Amplification and detection were performed with an ABI Prism 7700 system . Samples were controlled for the presence of possible inhibitors of the amplification reaction by internal control ( murine encephalomyocarditis virus DNA ) . Bronchoalveolar lavage ( BAL ) fluid was obtained by flushing the lungs two times with 1 ml PBS using a canula inserted into the trachea , yielding around 1 . 7 ml BAL fluid . Pelleted cells from BAL fluid were counted and cytospins were prepared and stained with May-Grunwald/Giemsa and neutrophils were scored on the basis of morphology ( Dade Behring , Switzerland ) . BAL fluids were kept on ice or stored at −80°C until further processing . BAL fluid was centrifuged , and 20 µl of aliquot was used to determine the protein concentration with a BCA kit ( Pierce ) according to the manufacturer's instructions . To measure the interferon concentration in the sera , blood was sampled from naïve mice or four days after MHV infection or one hour after imiquimod treatment . Sera were separated by spinning the blood at 2300× g for 15 minutes at 4°C . For measurement of cytokines in BAL , samples were prepared by spinning 5 minutes at 530× g . IFN-α was measured with a mouse interferon alpha ELISA kit ( PBL Interferon Source ) . For the IL-6 , IL-8 and TNF-alpha measured by Mouse IL-6 Mni ELISA Development Kit , Murine KC ( IL-8 ) ELISA Development Kit and Murine TNF-alpha Mini ELISA Development Kit ( PeproTech ) respectively . Experiments were done according to manufacturer's instructions . Livers of MHV-infected mice were sampled , fixed in 4% neutral buffered formalin , and embedded in paraffin . Seven µm liver sections were stained with hematoxylin and eosin . Total liver sections were examined by light microscopy and foci of hepatocellular necrosis and inflammation were scored in a semi-quantitative manner . HEK 293 T cells were transiently co-transfected with: human TLR7 ( kindly provided by Rogier Sanders , AMC , Amsterdam , the Netherlands ) , and NF-κB-reporter or IFNα-reporter constructs , kindly provided by Dr Paul Moynagh ( National University of Ireland ) . A chimeric construct containing the extra cellular region of human LAIR-1 ( amino acids 1–160 ) fused with the transmembrane and intracellular rat CD200R ( rCD200R ) ( amino acids 236–327 ) was cloned into pcDNA3 . 1/zeo ( Invitrogen , Breda , the Netherlands ) . A tyrosine ( Y ) to phenylalanine ( F ) mutant of tyrosines 287 , 290 , and 298 in the intracellular rCD200R tail were generated with PCR-based mutagenesis . The mutant was cloned into the same vector and all sequences were confirmed by automated DNA sequencing . The LAIR-CD200R plasmid was co-transfected with the TLR7 and reporter constructs . Twenty-four hours after transfection cells were trypsinized and seeded in 48-well plates coated with 3 ug/ml anti-LAIR-1 monoclonal antibody ( clone 8A8 ) . Forty-eight hrs after transfection cells were stimulated with imiquimod 3 µg/ml ( Invivogen ) in PBS . On the next day , cells were lysed with Passive Lysis Buffer ( Promega ) , luciferase activity was measured on a luminometer ( Berthold technologies Centro LB 960 ) , and data were analyzed with Microwin software . Total protein content was determined with a Pierce BCA Protein Assay ( Thermo Scientific ) . All luciferase values were normalized to protein concentration . Alternatively , we used HEK 293 cells stably expressing human TLR7 ( Invivogen ) . Significance was calculated with Mann-Whitney test using GraphPad Prism software . Figure S1 shows BLI measurement in naïve mice and viral spread of MHV at day 2 after infection . Figure S2 depicts the quantification of lung pathology and differential cell count in the BAL fluid in influenza A virus infected mice . Figure S3 is a quantitative analysis of TLR7 mRNA in male and female mice . | Immune responses need to be carefully orchestrated to prevent disease due to an overactive immune system . Immunological checkpoints are provided by immune inhibitory receptors , which set a threshold for activation and dampen the immune system . In the case of a viral infection , this prevents pathology induced by the immune system , but on the other hand may prevent adequate removal of the virus . In this paper , we show that removal of such an immunological checkpoint in mice leads to rapid removal of corona virus , but also to more immune-induced disease symptoms in case of influenza virus infection . We observe this predominantly in female mice . We demonstrate that this particular checkpoint inhibits anti-viral responses that are naturally stronger in females . Release of this checkpoint enlarges these sex differences . Our findings have major implications for therapeutic use of blockers of this pathway , which are currently in clinical trials for the treatment of cancer , as we predict that female patients will have a stronger response to such therapeutics . | [
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] | 2012 | CD200 Receptor Controls Sex-Specific TLR7 Responses to Viral Infection |
The dentate gyrus has an important role in learning and memory , and adult neurogenesis in the subgranular zone of the dentate gyrus may play a role in the acquisition of new memories . The homeobox gene Prox1 is expressed in the dentate gyrus during embryonic development and adult neurogenesis . Here we show that Prox1 is necessary for the maturation of granule cells in the dentate gyrus during development and for the maintenance of intermediate progenitors during adult neurogenesis . We also demonstrate that Prox1-expressing intermediate progenitors are required for adult neural stem cell self-maintenance in the subgranular zone; thus , we have identified a previously unknown non-cell autonomous regulatory feedback mechanism that controls adult neurogenesis in this region of the mammalian brain . Finally , we show that the ectopic expression of Prox1 induces premature differentiation of neural stem cells .
In the brain , the dentate gyrus ( DG ) is the primary afferent pathway into the hippocampus . The DG has a crucial role in learning and memory [1] , [2] , [3] . In mammals , neurogenesis occurs in the subgranular zone ( SGZ ) of the DG throughout adulthood [4] , [5] , [6] , [7]; this activity is thought to be the basis for the acquisition of new memories [3] , [8] , [9] . The formation of the DG is a complex process that involves cell migration and neuronal differentiation [10] , [11] . Factors that regulate DG development are thought to have a similar function during adult neurogenesis . In the SGZ , astrocyte-like adult neural stem cells ( NSCs ) give rise to a series of intermediate progenitors that eventually differentiate into neurons [12] . Several signaling molecules , including Wnt , Noggin/BMP , Shh , and Notch , regulate adult NSC self-maintenance , proliferation , and progenitor differentiation [13] , [14] . However , little is known about how the generation of the proper number of descendants is controlled . It has been proposed that once generated , NSC descendants can trigger some type of feedback mechanism to stop stem cell differentiation [15] . In this context , Notch signaling has been considered a candidate to regulate such a feedback mechanism during adult neurogenesis [13] . The homeobox gene Prox1 is expressed in several brain regions ( i . e . , cortex , DG , thalamus , hypothalamus , cerebellum ) during prenatal and postnatal stages of development [16] , [17] , [18] . Interestingly , Prox1 is expressed throughout all stages of DG development and in adult granule cells; therefore , Prox1 is commonly used as a specific marker for these cells [15] , [19] . However , no data are yet available on the functional role ( s ) of Prox1 during brain development . We have now determined that functional inactivation of Prox1 during DG development results in defective granule cell maturation and the loss of this cell population . We also report that conditional inactivation of Prox1 in the SGZ during adult neurogenesis leads to the lack of intermediate progenitors , and as a consequence , the disruption of the mechanism involved in NSC self-maintenance . Therefore , we have identified a previously unknown non-cell autonomous regulatory feedback mechanism that links adult NSC self-maintenance with the generation of the proper number of descendants in the SGZ . Finally , we show that ectopic expression of Prox1 in NSCs promotes premature differentiation during DG development and adult neurogenesis in the SGZ .
Standard Prox1-null embryos die during midgestation [20]; therefore , to evaluate the possible functional roles of Prox1 in the mammalian brain , we used a conditional-inactivation approach . An available Prox1-floxed strain [21] was initially bred with Nestin-Cre mice in which constitutively active Cre recombinase is expressed in neural progenitors from embryonic day ( E ) 10 . 5 [22] . Adult Nestin-Cre;Prox1F/F mice were viable but had only a few scattered Prox1+/NeuN+ wild-type granule cells in their hippocampi ( Figure 1B , D ) . During embryonic development , Prox1 expression is detected in both the dentate neuroepithelium ( DNE ) and the DG [17] , [23] . Therefore , we performed a detailed characterization of the development of the DG in Nestin-Cre;Prox1F/F conditional-mutant embryos . At E14 . 5 , the DG of Nestin-Cre;Prox1F/F embryos showed normal Ammon's horn formation ( Figure 2A–L ) . At E16 . 5 , and as shown by an anti-C-Prox1 antibody that recognizes only the wild-type form of Prox1 ( see Figure S1A , B for more details ) , only a few cells escaped Cre-mediated deletion in the DG of Nestin-Cre;Prox1F/F embryos ( Figure 1F ) . However , as indicated by an anti-N-Prox1 antibody that recognizes both wild-type and conditional-mutant forms of Prox1 ( Figure S1A , B ) , the number of N-Prox1+ cells was reduced in the DG of Nestin-Cre;Prox1F/F embryos at this stage ( Figure 1H; Figure S1C , D ) . Reduced numbers of Notch1+ , Ngn2+ , and NeuroD1+ cells were also observed by ISH in the mutant DG ( Figure 1J , L , N ) ; however , we found no obvious alterations in the expression of Wnt3a in the FNE ( Figure S2A , B ) [24] , or lef1 in the migratory stream ( MS ) ( Figure S2C , D ) [25] . We also did not find obvious anomalies in radial glia scaffolding [26] in the mutant DG at E16 . 5 ( Figure S2E–H ) . These results indicate that in the Nestin-Cre;Prox1F/F embryos , the N-Prox1+ cells are capable of migrating out of the DNE and reach the DG . Prox1 absence leads to cell cycle alterations in the developing neuroretina [27] . To evaluate possible alterations in cell proliferation during DG development , we compared the number of cycling cells in the DNE of wild-type and Nestin-Cre;Prox1F/F embryos by using a 1-h BrdU pulse starting at E14 . 5 . Significantly fewer BrdU+ cells were observed in the Nestin-Cre;Prox1F/F DNE at E16 . 5 ( Figure 3D , G ) and E18 . 5 ( Figure 3F , G ) . Similar results were obtained with Ki67 ( Figure 3E , H ) . Results using BrdU/Ki67 double immunostaining ( Figure 3I ) determined that cells in the DNE were cycling more slowly in Nestin-Cre;Prox1F/F embryos at E16 . 5 and E18 . 5 . We also found that CyclinE expression was reduced in the Nestin-Cre;Prox1F/F DNE at E16 . 5 ( Figure 3K ) . To confirm that the reduced proliferation of cells in the DNE was caused by the lack of Prox1 in that region at around E16 . 5 , we used a tamoxifen ( TM ) -inducible Nestin-CreERT2 strain [28] to induce Prox1 deletion later during development . Following TM administration at E16 . 5 ( see Figure S3A for details ) , most C-Prox1+ cells were missing in the DNE and MS of E18 . 5 Nestin-CreERT2;Prox1F/F embryos ( Figure 3M ) . Fewer Ki67+ cells were also observed in the DNE of these mutant embryos ( Figure 3N ) . Thus , Prox1 is required to regulate cell proliferation in the DNE during early stages of DG development . During DG formation , Prox1 expression is upregulated in intermediate progenitors and immature granule cells , and throughout adulthood , its expression is maintained in mature granule cells [17] , [23] . Therefore , we analyzed whether Prox1 is necessary for the acquisition of granule cell identity by monitoring the Δ-Prox1 cells . Using this approach , we observed an increase in the number of Δ-Prox1 cells ( cells recognized by the anti-N-Prox1 antibody but not by the anti-C-Prox1 antibody; see Figure S1A , B for details ) in the DG region of Nestin-Cre;Prox1F/F embryos until E18 . 5 ( Figure S1D , F and Figure 4E ) ; however , at later stages , the number of Δ-Prox1 cells in the DG decreased . This reduction was particularly apparent at postnatal stages; by postnatal day ( P ) 15 , most Prox1+ cells in the mutant DG corresponded to those that escaped deletion ( Figure 4B , D , E ) . As revealed by TUNEL assay , starting at E16 . 5 the number of TUNEL+ cells increased in the DG of Nestin-Cre;Prox1F/F embryos ( Figure 4F ) , a finding suggesting that Prox1 is necessary for the survival of intermediate progenitors and immature granule cells . In the DG , terminal differentiation of granule cells occurs during early postnatal stages . Therefore , we analyzed whether the observed increase in the number of TUNEL+ cells was due to defective differentiation of Δ-Prox1 granule cells . Previous studies have shown that the bHLH protein NeuroD1 is required for the maturation of granule cells [15] , [29] , [30] . We found that at P10 , N-Prox1+ cells in the mutant DG were also NeuroD1+ ( Figure 4H ) ; however , they did not co-express Dcx+ [31] or Calretinin+ [32] ( Figure 4J , L ) , and only the few C-Prox1+ granule cells that escaped Cre deletion co-expressed NeuN ( Figure 4N ) [33] . These results suggest that lack of Prox1 activity arrested granule cell differentiation . Next , we used an in vitro assay to determine whether the functional inactivation of Prox1 affects neuronal differentiation . Nestin-Cre;Prox1F/+ and Nestin-Cre;Prox1F/F neurospheres were isolated from E16 . 5 hippocampal regions . Nestin-Cre;Prox1F/F neurospheres produced cells that were positive for the early neuronal marker β-tubulin-III at a relatively similar rate ( 119 of 135 ) compared to that of wild-type neurospheres ( 159 of 175 ) ( Figure S4A–C ) . However , fewer Nestin-Cre;Prox1F/F neurospheres produced Dcx+ cells ( 8 of 190 ) , as compared with wild-type controls ( 199 of 216 ) ( Figure S4D–F ) . To determine whether Prox1 is necessary for Dcx expression , we cotransfected Nestin-Cre;Prox1F/F neurospheres with GFP-expressing and full-length Prox1 cDNA-expressing plasmids . We found that Nestin-Cre;Prox1F/F neurospheres transfected with Prox1 cDNA produced Dcx+ cells ( 20 of 76 GFP+ neurospheres ) , but those transfected only with the GFP-expressing plasmid did not ( 0 of 73 GFP+ neurospheres ) ( Figure S4G , H ) . Thus , as seen in vivo , Prox1 is not necessary to induce neuronal differentiation in vitro but is required for the expression of later neuronal markers such as Dcx . To confirm that the conditional inactivation of Prox1 at postnatal stages is directly responsible for the defective granule cell maturation phenotype observed in Nestin-Cre;Prox1F/F mice , we next deleted Prox1 during postnatal stages by using the Nestin-CreERT2 transgenic mouse strain [28] . TM was administered to Nestin-CreERT2;Prox1F/F pups at early postnatal stages ( see Figure S3B for details ) ; at these stages , there are no Prox1+ NSCs in the hilus ( Figure S5A–E ) . At 2 mo of age , TM-treated Nestin-CreERT2;Prox1F/F mice had smaller DGs than their control littermates ( Figure 5B ) . The number of Tbr2+ intermediate progenitors [34] was reduced in both P5 ( Figure S5R , S ) and P10 ( Figure 5P , Q ) pups . They also exhibited fewer Dcx+ cells at P5 ( Figure S5T , U ) and P10 ( Figure 5R , S ) . Moreover , the ratio of Tbr2+:Calretinin+ cells was increased in the TM-treated Nestin-CreERT2;Prox1F/F ( 1 . 55±0 . 25:1; N = 3 ) pups at P10 ( control: 1 . 09±0 . 28:1; N = 3; p<0 . 1 ) . These results suggest that the reduced size of the DG was caused by a reduction in the number of intermediate progenitors and by an arrest in granule cell differentiation . Moreover , as indicated by TUNEL ( Figure S5V–Y and Figure 4T–V ) and active Caspase-3 assays ( Figure S6A , B ) , an increase in cell death was observed in the mutant DG at these stages . On the other hand , cell proliferation was reduced at P5 but not at P10 in the DG of Nestin-CreERT2;Prox1F/F pups ( Figure S5P; Figure 5N ) . The number of Nestin+ , Sox2+ , or Id1+ NSCs was normal at P5 and P10 ( Figure S5J , M; Figure 5C–K ) . These data suggest that the lack of Prox1 during postnatal stages is directly responsible for the reduction in the number of intermediate progenitors and the defect in granule cell differentiation that secondarily promotes an increase in cell death and ultimately a reduction in the size of the DG . In the SGZ , Prox1 expression is initially detected in Tbr2+ ( Figure 6E ) [35] and Dcx+ ( Figure 6F ) Type-IIb intermediate progenitors [36] and is absent from adult NSCs ( Figure 6A , B , C ) [37] , [38] and Ascl1+ intermediate progenitors ( Figure 6D ) [36] . As neurogenesis progresses , Prox1 is detected in Calretinin+ cells ( Figure 6H ) . Analysis of the SGZ of Nestin-CreERT2;Prox1F/F pups treated with TM daily from P0 to P15 ( see Figure S3B for the details of the TM treatment ) identified a reduced number of Tbr2+ ( Figure 6K ) and Dcx+ ( Figure 6N ) cells at P20; these cells were nearly absent at 4 and 8 mo of age ( Figure 6J , K , M , N ) . As indicated by C-Prox1 immunostaining , the few remaining Dcx+ cells were those that escaped deletion ( Figure S7 ) . Fewer Calretinin+ immature neuron cells were also observed in the mutant brains ( Figure 6P , Q ) . To determine whether the lack of intermediate progenitors in adult stages is a direct or indirect result of previously identified alterations in postnatal DG development , we treated 8-wk-old ( adult ) Nestin-CreERT2;Prox1F/F mice with TM 3 d/week ( see Figure S3C for the details of the TM treatment ) . Four weeks after the initial TM induction , the number of Tbr2+ ( Figure 7A–C ) and Dcx+ ( Figure 7D–F ) cells was reduced in the SGZ of 12-wk-old Nestin-CreERT2;Prox1F/F mice . Accordingly , the number of Calretinin+ cells was also reduced ( Figure 7G–I ) . Similar results were observed 8 wk after induction ( in 16-wk-old animals ) ( Figure 7A , D , G ) . To evaluate whether the lack of Prox1 activity leads to an increase in cell death , as it does during developmental stages , we performed double TUNEL immunohistochemistry in 12- and 16-wk-old mice after TM induction . At both time points , we observed an increased number of TUNEL+Tbr2+ ( Figure 7J , K , M ) and TUNEL+Dcx+ ( Figure 7J–L ) cells in the Nestin-CreERT2;Prox1F/F SGZ , a result indicating that Prox1 is required for the survival of Tbr2+ and Dcx+ cells . To evaluate whether the lack of Tbr2+ intermediate progenitors and Dcx+ cells affects adult neurogenesis in the SGZ , we performed BrdU labeling 15 d prior to collection of the brains for analysis . Fewer BrdU+ newborn cells were observed in the SGZ of 12-wk-old ( Figure 7N–P ) and 16-wk-old ( Figure 7Q–S ) Nestin-CreERT2;Prox1F/F mice . This result suggests that the lack of Prox1 in the SGZ affects adult neurogenesis . Next , to determine whether the defective neurogenesis in the SGZ is solely because of a reduction in the number of intermediate progenitors or it is also because of an alteration in immature neurons , we analyzed the ratio of Tbr2+:Calretinin+ cells in the SGZ of 12-wk-old and 16-wk-old Nestin-CreERT2;Prox1F/F mice . A consistent increase in the ratio of Tbr2+:Calretinin+ cells was observed in 12-wk-old ( N = 3 ) ( 2 . 39±0 . 58:1 ) and 16-wk-old ( N = 3 ) ( 4 . 39±1 . 92:1 ) TM-treated Nestin-CreERT2;Prox1F/F mice ( 12-wk-old control: 1 . 77±0 . 34:1; 16-wk-old control: 1 . 51±0 . 17:1; p value for both cases was p<0 . 1 ) . This result suggests that granule cell maturation is also affected by the lack of Prox1 in the SGZ during adult neurogenesis . We next examined whether the induction of neurogenesis rescues the reduction in the number of progenitor cells observed in the SGZ of adult Nestin-CreERT2;Prox1F/F mice . At 12 wk of age , TM-induced and control mice were treated with kainic acid ( KA ) , a compound that induces adult hippocampal neurogenesis ( see Figure S3D for the details of the TM/KA treatment ) [39] , [40] . Eight days after KA administration , the brains of control mice treated with TM and KA or only with KA showed an increased number of Tbr2+ ( Figure S8A , B ) , Dcx+ ( Figure S8D , E ) , or Calretinin+ ( Figure S8G , H ) cells in the SGZ . However , similar to that in the TM-treated Nestin-CreERT2;Prox1F/F mice , the SGZ of Nestin-CreERT2;Prox1F/F mice treated with TM and KA had fewer Tbr2+ intermediate progenitors ( Figure S8A , C ) and fewer Dcx+ ( Figure S8D , F ) and Calretinin+ ( Figure S8G , I ) cells . These results show that KA-mediated induction of neurogenesis cannot rescue the reduction in the number of Tbr2+ , Dcx+ , or Calretinin+ cells in the SGZ of TM-treated adult Nestin-CreERT2;Prox1F/F mice . It has been proposed that upon their differentiation , intermediate progenitors trigger a feedback mechanism necessary to stop NSC differentiation and support NSC maintenance [13] , [15] . Therefore , the lack of intermediate progenitors observed in the SGZ of Nestin-CreERT2;Prox1F/F mice may impinge on this feedback mechanism and ultimately affect the number of adult NSCs produced in this region . To assess whether the reduced number of intermediate progenitors in the SGZ of Nestin-CreERT2;Prox1F/F mice treated postnatally with TM affected that of NSCs , we compared the number of Type-I NSCs in wild-type and Prox1 conditional-mutant brains . We detected no difference in the number of Nestin+ , Sox2+ , or Id1+ adult NSCs at P20 ( Figure 8A , D , G ) . However , we unexpectedly observed fewer Nestin+ , Sox2+ , or Id1+ NSCs in the SGZ of Nestin-CreERT2;Prox1F/F mice older than 2 mo ( Figure 8A–I ) . We also observed a similar reduction in the number of Nestin+ Gfap+ Blbp+ NSCs in the SGZ of 4-mo-old Nestin-CreERT2;Prox1F/F mice ( Figure S9 ) . This result argued that the absence of intermediate progenitors observed in the SGZ of Nestin-CreERT2;Prox1F/F brains leads to a non-cell autonomous reduction in the number of adult NSCs . To determine whether defective maintenance of adult NSCs is a direct result of the lack of intermediate progenitors or an indirect result because of alterations in DG development , we treated 8-wk-old control and Nestin-CreERT2;Prox1F/F mice with TM ( see Figure S3C for the details of the TM treatment ) and counted the number of adult NSCs . Four weeks after the beginning of the treatment , the number of Nestin+ , Sox2+ , or Id1+ adult NSCs was higher in the SGZ of Nestin-CreERT2;Prox1F/F mice than in wild-type littermates ( Figure 8J , M , P ) ; however , 8 wk after TM treatment , we detected fewer Nestin+ , Sox2+ , or Id1+ adult NSCs in the SGZ of Nestin-CreERT2;Prox1F/F mice ( Figure 8J–R ) . To examine whether Prox1 has a direct role in this defective maintenance of adult NSCs , we generated Nestin-CreERT2;Prox1F/F;R26R mice and treated them with TM ( 3 d/wk ) starting at 8 wk of age ( see Figure S3C for the details of the TM treatment ) . After 4 wk of treatment , the ratios of Nestin+β-gal+ cells and Sox2+β-gal+ cells were the same in wild-type and Prox1 conditional-mutant mice ( Figure 8S ) . This result indicates that the absence of Prox1 did not directly affect the adult NSC population . Similar results were seen in mice analyzed 8 wk after the beginning of TM administration ( Figure 8T ) . Only the Tbr2+β-gal+ and the Dcx+β-gal+ cell populations were significantly reduced in the SGZ of Nestin-CreERT2 , Prox1F/F;R26R mice at 4 and 8 wk after the beginning of TM treatment ( Figure 8S , T ) . These results , together with the absence of increased apoptosis in adult NSCs ( Figure 7J , K ) , suggest that the defective maintenance of adult NSCs in the mutant SGZ is indirectly caused by the absence of intermediate progenitors . We next investigated the possible mechanisms of this intermediate progenitor-dependent control of adult neurogenesis in the SGZ . In the developing telencephalon [41] and in the SGZ [42] , Notch signaling is necessary for NSC maintenance , and the expression of Jagged1 , a Notch receptor ligand , is restricted to intermediate progenitors in the SGZ [42] . We found a reduced number of Jagged1+ cells in the SGZ of Nestin-CreERT2;Prox1F/F mice treated with TM starting at 8 wk of age and analyzed 4 wk later ( Figure 9C , D ) . To examine the relationship between the number of Nestin+ Type-I cells in the SGZ and active Notch signaling , we performed Hes1 immunohistochemistry . In the SGZ of control mice , only 17% of the Type-I cells were Nestin+Hes1– ( Figure 9F ) ; however , in the Nestin-CreERT2;Prox1F/F mice , this number increased to 55% ( Figure 9F ) . The number was also higher in the SGZ of Nestin-CreERT2;Prox1F/F mice ( 59% ) when compared with wild-type controls ( 25% ) at 16 wk of age ( Figure 9G ) . A reduced number of Hes5-expressing cells was also observed in the SGZ of Nestin-CreERT2;Prox1F/F mice at 16 wk of age ( Figure S10 ) . These results show that in the absence of intermediate progenitors , the proportion of adult NSCs with active Notch signaling at 12 and 16 wk is reduced . This mechanism may explain why the self-maintenance of adult NSCs is eventually overruled at 16 wk of age . Our results showed that Prox1 is necessary for granule cell differentiation and intermediate progenitor maintenance . Therefore , we next determined whether Prox1 activity is sufficient to induce granule cell differentiation . To do this , we generated a new mouse transgenic line in which Prox1 expression was under the control of the ubiquitous CMV promoter ( CMV-CAG-loxP-eGFP-Stop-loxP-Prox1-Ires-β Gal; JoJo-Prox1 for brevity ) [43] . Crossing this line with any available Cre strain will lead to the release of the Stop-signal cassette and the transcription of Prox1 in a tissue-specific manner . We used this novel strain to generate adult Nestin-Cre;JoJo-Prox1 mice that ectopically expressed Prox1 in several brain regions ( Figure S11 ) . Importantly , we found that the number of granule cells was reduced in the DG of these mice ( Figure 10A , B , V ) . Prox1 was ectopically expressed in the ventricular , subventricular , and mantle zones of several brain regions of Nestin-Cre;JoJo-Prox1 embryos ( Figure S11 ) , including the hippocampal neuroepithelium and the hippocampal field ( Figure 10D ) . However , we observed no change in the number of Prox1+ ( Figure 10E ) , Sox2+ ( Figure 10F ) , or Ki67+ cells ( Figure 10G ) in the DNE or DG . These results suggest that ectopic expression of Prox1 in embryonic Nestin-expressing neuroepithelium is not sufficient to affect embryonic NSC differentiation . To perform a similar analysis during postnatal stages , we administered TM to Nestin-CreERT2;JoJo-Prox1 pups daily starting at P0 ( see Figure S3B for the details of the TM treatment ) . At P8 , the DG of Nestin-CreERT2;JoJo-Prox1 pups exhibited an increased number of Dcx+ ( Figure 10I ) and NeuN+ ( Figure 10L ) cells and a reduced number of Nestin+ cells ( Figure 10N ) . At this stage , proliferation was already reduced in the DG of Nestin-CreERT2;JoJo-Prox1 mice , as shown by Ki67 staining ( Figure S12G ) . To determine the fate of cells that ectopically expressed Prox1 , we performed double immunohistochemistry using antibodies against β-gal and Nestin , Dcx , NeuN , Gfap , or NG2 ( Figure S12I–M ) . We determined that 2 . 11%±0 . 38% of the β-gal+ cells in the DG of Nestin-CreERT2;JoJo-Prox1 mice were Nestin+; 64 . 57%±12 . 99% were Dcx+; 34 . 52%±6 . 32% were NeuN+; and less than 0 . 1% were Gfap+ or NG2+ ( Figure 10O ) . No increase in the number of TUNEL+ cells was observed in the DG of these mice ( Figure S12H ) . These results suggest that there is premature neuronal differentiation in the DG of Nestin-CreERT2;JoJo-Prox1 mice during postnatal stages of brain development . Finally , we addressed the consequences of ectopic expression of Prox1 in the SGZ during adult neurogenesis . Analysis of the brains of P30 Nestin-CreERT2;JoJo-Prox1 mice revealed the presence of Nestin+;Prox1+ adult NSCs ( Figure S12N ) . As a consequence of Prox1 ectopic expression , the numbers of Nestin+ ( Figure 10Q , T ) and Sox2+ ( Figure S12P ) cells were reduced at this stage . Accordingly , the numbers of Dcx+ ( Figure 10S , U ) and Calretinin+ ( Figure S12R ) intermediate progenitors were also reduced . These results indicate that the ectopic expression of Prox1 depleted the adult NSC population .
In this article , we report for the first time the functional role for Prox1 in mammalian brain development . We determined that in the mouse , Prox1 is required for the maturation of granule cells during DG development . Prox1 is expressed through all the stages of DG formation; therefore , it is possible that the defective granule cell maturation observed in Nestin-Cre;Prox1F/F mice is an indirect consequence of the earlier absence of Prox1 at the intermediate progenitor level . However , it is also possible that Prox1 plays additional functional roles during granule cell formation . Moreover , the granule cells in the DG are one of the few types of brain cells that express Prox1 throughout adulthood . This suggests that Prox1 might be necessary not only for the maturation of granule cells but also for the regulation of other aspects of granule cell function . NeuroD1 is also required for granule cell maturation [15] , [29] , [30] . During embryogenesis and in the absence of Prox1 or NeuroD1 , mutant granule cells express some neuronal markers but fail to fully differentiate and undergo apoptosis [15] . As a consequence , Nestin-Cre;Prox1F/F and NeuroD1–/– mice have very few granule cells ( Figure 1 ) [15] , [30] . Few Prox1+ granule cells are present in the DG of NeuroD1–/– mice [15] , and NeuroD1 is expressed in the DG of Prox1-conditional mutants . These results suggest that although NeuroD1 and Prox1 might control similar or parallel pathways of granule differentiation , they might not necessarily induce each other's expression . We also showed that during adult neurogenesis , Prox1 is necessary for the survival of Tbr2+ intermediate progenitors in the SGZ . In this case and similarly to DG development , our data suggest that the lack of Prox1 results in defects in granule cell maturation that could also be an indirect consequence of the earlier lack of Prox1 at the progenitor level . Nevertheless , the lack of Prox1 leads to an increase in apoptosis in Tbr+ and Dcx+ cells and the absence of adult neurogenesis . Also in this case , similar results have been reported for NeuroD1-mutant mice during adult neurogenesis [44] , [45] . Our results provide the strongest evidence so far about a feedback mechanism involved in the regulation of adult neurogenesis and progenitor cell numbers in the adult SGZ . We also provide evidence supporting the proposal that Tbr2+ intermediate progenitors and Dcx+ cells are required to maintain the adult NSC population in the SGZ niche . We showed that in the absence of Tbr2+ and Dcx+ cells and as this feedback regulation becomes defective , adult NSCs continue generating new progeny such that the adult NSC population in the SGZ of Prox1-conditional mutants transiently expands but is ultimately depleted . We do not yet know whether this depletion of NSCs is due to their premature differentiation or their exhaustion resulting from increased proliferation [46] . How is this feedback mechanism regulated ? We showed that in the SGZ of Prox1 conditional-mutant mice the lack of intermediate progenitors leads to a reduction in Jagged1 expression . Moreover , we determined that in these conditional-mutant mice the lack of intermediate progenitors leads to the absence of active Notch signaling in adult NSCs; active Notch signaling is necessary for the maintenance of adult stem cells in the brain , bone marrow , and gut [42] , [47]–[51] . Previous work has shown that during embryonic development , the activity of mind bomb homolog 1 ( Mib1 ) is required for Jagged and Delta-like-mediated Notch signaling . In the absence of Mib1 , Notch signaling is not activated in radial glia cells , which results in their premature differentiation [41] , [52] . In the SGZ , the absence of Notch1 is sufficient to induce neuronal differentiation and reduce the proportion of adult NSCs and intermediate progenitors , which suggests that this receptor is involved in NSC self-renewal in the SGZ [42] . Therefore , the role of other Notch receptors during adult neurogenesis could be either redundant or restricted to a subpopulation of cells in the SGZ [42] , [53] . Together , these and others results [42] suggest that in the SGZ , the Notch1/Jagged1 pathway may regulate NSC self-maintenance . However , we cannot exclude the possibility that Notch activation in adult NSCs is also mediated by other Notch ligands expressed by other cell types ( e . g . , vascular endothelial cells ) [54] . Also , we cannot rule out the possibility that other signaling molecules or trophic factors produced by Prox1-expressing intermediate progenitors may be involved in the feedback mechanism that controls adult neurogenesis in the SGZ . In summary , we have provided evidence demonstrating that Prox1 plays a key role as a neurogenic factor during granule cell formation . In the absence of Prox1 , granule cell maturation is affected at later stages of differentiation; ectopic expression of Prox1 in the brain is sufficient to overrule the mechanisms that control NSC self-maintenance and induce premature differentiation in the SGZ during postnatal stages of brain development and adult neurogenesis .
Prox1F/F [21] mice , Nestin-Cre [22] mice , and Nestin-CreERT2 [28] mice were previously described . The JoJo-Prox1 construct was generated by the introduction of a 2 . 2 kb Prox1 cDNA into a CMV-CAG-loxP-eGFP-Stop-loxP-IRES-βGal expression vector [43] . Mice were kept in the NMRI background . TM ( Sigma ) was dissolved in safflower oil at 20 mg/ml . For prenatal induction , time-mated females were treated with TM by gavage at E16 . 5 and embryos were harvested at E18 . 5 . To induce Cre recombination postnatally , pups were fed TM ( 4 mg/20 g body weight ) daily from P0 until the collection day . As a consequence of postnatal TM administration , body weight was reduced in TM-treated versus non-TM-treated pups . This reduction was temporary; 2-mo-old postnatally TM-treated Nestin-CreERT2;Prox1F/+ and Nestin-CreERT2;Prox1F/F mice had a similar weight than non-TM-treated animals . In adults ( 8-wk-old ) , TM ( 4 mg/20 g body weight ) was administered by gavage 3 times/wk for 4 wk . No obvious weight differences were observed in the TM-treated animals at the time of sample collection . Genotypes were determined by PCR analysis . Nestin-Cre;Prox1F/+ , Nestin-CreERT2;Prox1F/+ , and JoJo-Prox1 mice were used as controls . For Hes1 staining during adult stages , brains were perfused with 4% PFA and embedded in paraffin . Antigen retrieval was performed for 7 min at 105°C in a decloaking chamber with Antigen Retrieval Solution ( Dako ) . The ABC Kit ( Vector Laboratories ) and TSA Fluorescein System ( Perkin Elmer ) were used for signal amplification . Jagged1 staining at adult stages was performed on brains perfused with 4% PFA and cryoprotected in 30% sucrose . Signal amplification was obtained with the ABC Kit and the TSA Fluorescein System . Immunohistochemistry for the rest of the antibodies was performed as described [17] . The following antibodies and dilutions were used: rabbit anti-C-Prox1 ( 1∶1000; Millipore ) , guinea pig anti-C-Prox1 ( 1∶100; our own ) , rabbit anti-N-Prox1 ( 1∶1000; a gift from B . Sosa-Pineda ) , goat anti-N-Prox1 ( 1∶100; R&D systems ) , goat anti-Nestin ( 1∶100; R&D systems ) , rabbit anti Id-1 ( 1∶200; Biocheck ) , rat anti-PECAM ( 1∶500; Pharmingen ) , mouse anti-Gfap ( 1∶500; Sigma ) , rabbit anti Blbp ( 1∶200; Chemicon ) , rabbit anti-Sox2 ( 1∶1000; Milipore ) , rabbit anti-Sox2 ( 1∶500; Invitrogen ) , mouse anti-Sox2 ( 1∶50 , Milipore ) , mouse anti-Ascl1 ( 1∶100; Milipore ) , mouse anti-βTubIII ( 1∶500; BabCO ) , rabbit anti-NeuroD1 ( 1∶500; Chemicon ) , rabbit anti-Dcx ( 1∶100; Abcam ) , rabbit anti-Dcx ( 1∶1000; Chemicon ) , rabbit anti-Dcx ( 1∶500; Cell Signaling ) , rabbit anti-Calretinin ( 1∶5000; Millipore ) , mouse anti-NeuN ( 1∶100; Millipore ) , rabbit anti-active caspase-3 ( 1∶100; BD-Pharmingen ) , rabbit anti-Hes1 ( 1∶50 , Santa Cruz ) , goat anti-Jagged1 ( 1∶50 , Santa Cruz ) , rabbit anti-GFP ( 1∶1000; Molecular Probes ) , rabbit anti-bGal ( 1∶1000 , ICN ) , and chicken anti-bGal ( 1∶1000 , Abcam ) . The following secondary antibodies were used: anti-rabbit , anti-mouse , anti-guinea pig , anti-chicken , or anti-goat Alexa 488 , Alexa 594 ( Molecular Probes ) , Cy3 , or Cy5 ( Jackson Immunoresearch ) . Low-magnification images were obtained with a Leica MZFLIII stereomicroscope equipped with a Hanamatsu C5810 camera and a Zeiss Axiovert 1 . 0 microscope equipped with a ProgRes C14 camera . The remaining images were obtained with a Leica SP1 confocal microscope or a Zeiss LSM 510 NLO Meta confocal microscope . In situ hybridization of sections was performed as previously described [55] . The following probes were obtained from: Ngn2 ( Q . Ma ) , Wnt3a ( A . McMahon ) , Lhx2 ( H . Westphal ) , Notch1 ( G . Weinmaster ) , Lef1 ( G . Kardon ) . Double–in situ hybridization/immunohistochemistry was performed as described [17] . TUNEL assay was performed on tissue sections as previously described [56] . For proliferation assays at embryonic stages , time-mated female mice were injected with BrdU ( 100 µg/g body weight , intraperitoneally ) , and embryos were harvested 1 h later . Embryos were fixed o/n in 4% PFA and cryoprotected in 30% sucrose . For proliferation assays at early postnatal stages , P5 and P10 pups were injected with BrdU ( 100 µg/g body weight , intraperitoneally ) 1 h before harvest . Brains were perfused with 4%PFA and cryoprotected in 30% sucrose . For proliferation assays at adult stages , animals were injected with BrdU ( 100 µg/g body weight , intraperitoneally ) 15 d before harvest . Brains were perfused in 4% PFA and cryoprotected in 30% sucrose . BrdU incorporation was exposed after 20-min treatment in 2N HCl . Mouse anti-BrdU ( 1∶10; BD Biosciences ) antibody was used . Sections were counterstained with DAPI . Neurosphere cultures were established as described , with modifications [57] . Briefly , E16 . 5 hippocampi were dissected , disaggregated in trypsin , and maintained in culture in neurosphere culture medium ( Neurobasal medium with GlutaMAX , Pen/Strep , B27 , and N2 ) ( Gibco ) supplemented with 20 ng/ml EGF ( Upstate ) and 20 ng/ml FGF ( Millipore ) . After the fourth passage , neurospheres were differentiated for 4 d in neurosphere culture medium with 10% fetal calf serum ( FCS ) without supplements , in Lab-Tek II CC2 chamber slides ( Nunc ) . Cells were fixed in 2% PFA for 15 min at room temperature ( RT ) . Cells were blocked in 10% FCS for 30 min at RT and incubated with the appropriate primary and secondary antibodies ( see above ) in 2% FCS for 2 h . For the transfection of the neurospheres , a Mouse NSC Nucleofector® Kit ( Amaxa ) was used according to the manufacturer's specifications . For Prox1 expression , a Prox1 cDNA was cloned in an expression plasmid under the PGK promoter . Adult neurogenesis was induced by KA as described [39] with minor modifications . Briefly , TM ( 4 mg/20 g body weight ) was administered by gavage 3 times/wk for 4 wk to 8-wk-old Nestin-CreER;Prox1F/F and Nestin-CreER;Prox1F/+ mice . At the end of TM treatment , animals were injected intraperitoneally with KA ( 30 mg/kg body weight; Sigma ) dissolved in PBS . Approximately 40 min after KA injection , all mice displayed status epilepticus for 2 to 3 h . Approximately 15% of control and conditional mutant mice died 12 h after KA administration . Eight days after KA injection , the surviving animals were euthanized and their brains perfused with 4% PFA and cryoprotected in 30% sucrose . | In the brain , the hippocampus has a crucial role in learning and memory . In mammals , neurogenesis ( the birth of new neurons ) occurs in the dentate gyrus region of the hippocampus throughout adulthood , and this activity is thought to be the basis for the acquisition of new memories . In this study we describe for the first time the functional roles of the transcription factor Prox1 during brain development and adult neurogenesis . We demonstrate that in mammals , Prox1 is required for the differentiation of granule cells during dentate gyrus development . We also show that conditional inactivation of Prox1 results in the absence of specific intermediate progenitors in the subgranular zone of the dentate gyrus , which prevents adult neurogenesis from occurring . This is the first report showing blockade of adult neurogenesis at the level of progenitor cells . Next , we demonstrate that in the absence of Prox1-expressing intermediate progenitors , the stem cell population of the subgranular zone becomes depleted . Further , we show that Prox1-expressing intermediate progenitors are required for adult neural stem cell self-maintenance in the subgranular zone . Finally , we demonstrate that Prox1 ectopic expression induces premature granule cell differentiation in the subgranular zone . Therefore , our results identify a previously unknown non-cell autonomous feedback mechanism that links adult stem cell self-maintenance with neuronal differentiation in the dentate gyrus and could have important implications for neurogenesis in other brain regions . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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] | [
"developmental",
"biology/stem",
"cells",
"developmental",
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"evolution",
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] | 2010 | Prox1 Is Required for Granule Cell Maturation and Intermediate Progenitor Maintenance During Brain Neurogenesis |
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