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journal.pgen.1004951

Modeling of the Human Alveolar Rhabdomyosarcoma Pax3-Foxo1 Chromosome Translocation in Mouse Myoblasts Using CRISPR-Cas9 Nuclease

Rhabdomyosarcoma ( RMS ) is the third most common soft-tissue sarcoma in children with an annual incidence of five new cases per million ., It accounts for 5–8% of all pediatric cancer ., RMS belongs to the family of small round blue cell tumors of childhood and exhibits histological features of skeletal muscle ., Two major histological subtypes of RMS can be distinguished , embryonal ( E-RMS ) and alveolar ( A-RMS ) ., E-RMS has its highest incidence in infants and young children whereas A-RMS is more frequent in older children and adolescents ., A-RMS has a more aggressive clinical behavior with early dissemination , a poor response to chemotherapy , frequent relapses , and a 5-year failure-free survival of 65% after treatment 1 ., A-RMS is found predominantly in the extremities ( 42% ) , parameningeal ( 17% ) , head and neck ( 11% ) and other locations ( 21% ) 1 including the trunk , perirectal and perianal areas 2 , 3 ., Cytogenetically A-RMS is distinguished from E-RMS by one of two recurrent chromosome translocations: t ( 2;13 ) or t ( 1;13 ) , which result in fusion of PAX3 or PAX7 to FOXO1 , respectively 4 ., In spite of multiple attempts to identify the cell of origin in which the t ( 2;13 ) occurs the question remains unanswered ., It was shown previously that transcription occurs at a few hundred discrete nuclear sites called transcription factories 5 ., Some genes frequently involved in a recurrent chromosome translocation ( MYC and IGH in B lymphoid progenitors , TMPRSS2 and ERG or ETV1 in prostate cancer , RET and H4 in in radiation-associated papillary thyroid cancer ) co-localize to the same transcription factory 6–9 ., Initial chromosome conformation capture experiments in activated mouse B cells suggested that physical proximity of the IGH and MYC loci is a minor contributor to the frequency of chromosome translocation 10 ., However , combined high resolution Hi-C mapping and genome-wide translocation sequencing in transformed mouse pre-B cells found good coincidence between chromosomal translocation and spatial proximity 11 ., A possible driver of double strand DNA breaks might be the co-localization of replication stress-induced early replication fragile sites ( ERFSs ) with highly expressed gene clusters 12 ., Though it was demonstrated that ectopic expression of PAX3-FOXO1/Pax3-Foxo1 can transform mouse mesenchymal stem cells in vitro 13 as well as Myf6+ myofibers in vivo 14 in view of the above these cell types seem unlikely hosts for the chromosome translocation given that they do not express Pax3 ., In fact , the suggestion that Myf6+ myofibers might be the host of the PAX3-FOXO translocation was recently rectified 15 ., In contrast , Pax3 is expressed in activated myoblasts upon muscle injury or in growing muscles during normal development 16 ., Moreover , PAX3-FKHR , in cooperation with loss of p16INK4A expression , transforms both fetal and postnatal primary human skeletal muscle cell precursors 17 ., Together these observations suggest that translocation might occur in a population of activated myoblasts that express PAX3 ( PAX3+ ) ., It has been shown that Pax3 expression differs among different muscles in the mouse 18 , 19 ., There are many more Pax3+ cells in fore limb than in hind limb muscles 19 ., Muscle satellite cells from the masseter and soleus did not express Pax3 while only 7% of those from the extensor digitorum longus ( EDL ) did ., In contrast 49% of satellite cells from the biceps were Pax3+ ., In addition , most ventral trunk muscles were Pax3-positive and 64% of satellite cells from the diaphragm expressed Pax3 ., Importantly , primary myoblast cultures of Pax3+ satellite cells remain Pax3+ , while Pax3- satellite cells from hind limb remain negative 19 ., Studies addressing the relation between spatial chromosome proximity and translocation have been performed in cells of the B-lymphoid lineage or of hormone-responsive lineages mostly using transformed cell lines 6 , 7 , 9 ., Recently CRISPR-Cas9 nuclease ( Clustered Regularly Interspaced Short Palindromic Repeats ( CRISPR ) /CRISPR-associated systems ) 10 was used to engineer human tumor-associated translocations 20 ., To answer the question if locus proximity of Pax3 and Foxo1 in low-passage primary mouse myoblasts contributes to the frequency of Pax3-Foxo1 fusion gene formation we used the CRISPR-Cas9 system to induce double strand DNA breaks ( DSBs ) , which spurred by non-homologous end joining repair ( NHEJ ) produce chromosome translocations between these two loci ., We used synthetic single-guide RNAs ( sgRNA ) to program Cas9 to induce DNA double-strand breaks ( DSBs ) in Pax3 and Foxo1 21–23 ., Unlike the human PAX3/7 and FOXO1 genes , mouse Pax3/7 and Foxo1 are in opposite orientation on their respective chromosomes ( 1 , 4 , and 3 ) ., Compared with human chromosome 13 , Foxo1 is part of an inverted 4 . 9Mb syntenic region on mouse chromosome 3 ., Although a recurrent complex inversion/translocation event involving the oppositely oriented ETV6 and c-ABL genes in humans gives rise to the ETV6-ABL fusion gene in some myeloid and lymphoid malignancies , the frequency of this event is extremely low 24 ., Therefore , to successfully generate a CRISPR-Cas9-mediated Pax3-Foxo1 fusion gene we used chromosomal engineering via Cre recombinase-mediated genetic alterations to create a mouse in which the Foxo1 containing 4 . 9 Mb syntenic region is inverted ( Foxo1-inv+/+ mice ) ., Previously , Cre recombinase-mediated inversions of large fragments of chromosomes have been used to create balanced chromosomes 25–29 ., We show that myoblasts isolated from fore and hind limb keep their Pax3-expressing identity and co-localization of Pax3 and Foxo1 loci strongly correlates with the level of Pax3 expression and generation of a CRISPR-Cas9 induced t ( 1;3 ) , which is more frequent in fore limb myoblasts ., Our Foxo1inv+/+ mice will be a valuable tool for studying mechanisms underlying the initial stages of the A-RMS implicated chromosome translocations resulting in development of better animal models for this pediatric cancer and other human diseases caused by chromosome translocations ., Since close physical proximity of translocation partners might facilitate chromosome translocation , we determined if Pax3 and Foxo1—the translocation partners in A-RMS—are co-localized in actively proliferating low-passage primary mouse myoblasts ., DNA-FISH analyses of the Pax3 and Foxo1 loci in interphase nuclei of primary limb myoblasts of newborn pups , after one week in culture showed 13% co-localization , which was significantly higher than in similarly cultured MEFs ( 2% , the background of this method; Fig . 1A ) ., We hypothesized that co-localization of Pax3 and Foxo1 loci in myoblasts reflects the percentage of Pax3+ cells in the original newborn muscles ., To test this hypothesis we isolated myoblasts from hind and fore limbs of newborn pups and compared the frequency of co-localization of Pax3 and Foxo1 loci in proliferating myoblasts from these two sources ., It was shown previously with a Pax3 knock-in reporter gene that many more satellite cells in fore limb muscle express Pax3 than in hind limb muscle 19 ., In accordance , the percentage of co-localized Pax3 and Foxo1 loci was notably higher in fore limb than in hind limb myoblasts in 8 independent experiments ( Fig . 1A–C ) ., In addition , Q-RT-PCR of RNA from these myoblasts confirmed that expression of Pax3 was six-fold higher in fore limb myoblasts ( Fig . 1D ) ., These results are in agreement with the published observation that satellite cells maintain their Pax3+ identity upon activation in vitro ., Expression of other genes such as Foxo1 and Pax7 was similar in the two types of myoblasts ( Fig . 1D ) ., The results for Pax3 expression were reproducible given that a number of independent experiments produced similar data ( S1 Fig . ) ., Because diaphragm was shown to contain the highest number of Pax3+ myoblasts 19 , we compared by FISH the co-localization of the Pax3 and Foxo1 loci in myoblasts isolated from fore limb , hind limb , and diaphragm of the same adult mouse ., Indeed , diaphragm myoblasts showed a higher co-localization of the two loci ( 20% ) than fore limb ( 11% ) or hind limb ( 9% ) myoblasts ., The mouse Foxo1 gene is located on chromosome 3 in a 4 . 9 Mb DNA fragment ( ch3:52 , 059 , 615–56 , 995 , 963 ) that is syntenic with human chromosome 13 ( ch13:41 , 254 , 213–34 , 463 , 185 ) but positioned in the opposite orientation ( Fig . 2A ) ., This places Foxo1 in the mouse in a reverse transcriptional direction with respect to that of the Pax3 or Pax7 genes ., To engineer a mouse capable of acquiring productive Pax3/7-Foxo1 fusion genes via a simple balanced t ( 1;3 ) or t ( 4;3 ) , we performed two consecutive rounds of ES cell targeting in which we introduced two pairs of non-compatible LoxP sites at either border of this syntenic region with the goal to create a Cre-recombinase mediated permanent inversion of the 4 . 9Mb DNA fragment ( S2 Fig . ) ., Without inversion there would only be two ways to produce a productive fusion:, 1 ) Via a translocation in which the resulting chromosomes would carry a double centromere and no centromere , respectively , an option likely to be non-viable in primary myoblasts and, 2 ) Via a complex inversion/translocation event as described for the human ETV6-ABL fusion gene 24 , a rare event , which likely would reduce the frequency of fusion gene formation below detectable levels ., The centromeric border of the mouse/human syntenic region is located 15 kb upstream of the Foxo1 start codon ( Fig . 2B ) ., To precisely target this border in ES cells we used recombineering in E . coli 30 to modify the RP24–391O12 BAC ( bacterial artificial chromosome ) clone , so that it carries non-compatible mutant 511-ILoxP and wtLoxP sites 31 flanking the hph ( hygromycin B resistance ) and tk ( HSV1-thymidine kinase ) selectable marker genes ( Figs . 2B , S2 ) ., The precise targeting of the border of the syntenic region minimizes the chance of disturbing any potentially important regulatory sequences that might affect Foxo1 expression ( Fig . 2B , top ) ., We targeted ES cells with linearized RP24–391O12-LoxP-hygro-TK BAC DNA and counter selected hygromycin B resistant clones carrying random integrations by screening for the presence of vector sequences remaining on either side of the insert ., Colonies containing such vector segments were discarded 32 ., The remaining clones were subjected to the ‘loss-of-native-allele’ assay using real-time quantitative PCR 33 ., For copy number control of stably integrated target DNA we used the telomeric border of the syntenic region as a reference ., In total 273 clones were analyzed , two of which contained a single copy of the wild type locus ( Fig . 2C ) ., These clones were submitted to FISH analysis and karyotyping which confirmed the presence of only two native signals on chromosome 3 when hybridized with a wild type BAC RP24–391O12 probe ( Fig . 2D ) ., For consecutive targeting of the telomeric border of the syntenic region we selected clone XIIB3 , which had a 100% normal diploid karyotype ., For targeting of the telomeric border of the syntenic region we followed the same strategy and engineered a BAC clone carrying the 511-ILoxp-Neo-TK-wtLoxP cassette inserted at the precise syntenic border ( Fig . 2B , bottom ) ., The recombinant RP23–422I13-LoxP-Neo-TK BAC was linearized in such a way that only very short vector fragments remained at either side of the insert ., After targeting in ES cells , analysis with the ‘loss-of-native-allele’ assay of 48 clones proved sufficient to obtain the desired recombinant ., Two clones carrying a single copy of the wild type telomeric locus ( Fig . 2E ) were analyzed by FISH using the RP24–391O12 and RP23–422I13 BAC probes ., One of them ( 13D3 ) showed two native signals on chromosome 3 with either BAC probe ( Fig . 2F ) ., This clone had a 90% normal diploid karyotype and we next determined if it carried cis- or trans-targeted borders of the 4 . 9 Mb syntenic region ., To discriminate between these two possibilities we transiently transfected a Cre recombinase plasmid into the double-targeted 13D3 ES cells and DNA isolated from the pool of electroporated cells was analyzed by PCR using only forward or reverse primers from both targeted borders ., Both PCRs produced bands indicating that the pool contained cells carrying the 4 . 9Mb inversion ., The same pool of cells was counter selected with FIAU and 23 resistant clones were analyzed by PCR ( Fig . 3A ) ., Sixteen clones harbored the 4 . 9Mb inversion and two of these were selected , A6 and C5 , which had a 100% and 93% normal karyotype , respectively ., Inversion of the 4 . 9Mb region in these clones was subsequently confirmed by FISH analysis ( Fig . 3B ) using the RP24–391O12 and RP23–422I13 BAC probes ., The chromosome containing the inversion showed split hybridization signals while the wild type chromosome produced contiguous signals with these probes ., These ES cell clones were used to generate chimeric mice that transmitted the inversion of the Foxo1 syntenic region to heterozygous Foxo1-inv+/- offspring ., Foxo1-inv+/- mice were fertile and produced Foxo1-inv+/+ offspring at the expected Mendelian frequency ., Foxo1-inv+/+ animals did not exhibit any obvious phenotypic abnormalities and showed normal fecundity and life span ., Moreover , western blot analysis confirmed that Foxo1-inv+/+ primary myoblasts and wild type myoblasts expressed equal amounts of Foxo1 protein ( Fig . 3C ) and co-localization of the Pax3 and Foxo1 loci was equal in Foxo1-inv+/+ and wild type myoblasts ( 8% , S5 Fig . ) ., Finally , DNA-FISH analysis of Foxo1-inv+/+ fibroblasts with RP24–391O12 and RP23–422I13 BAC probes confirmed that both chromosomes 3 carried the 4 . 9Mb inversion ( Fig . 3D ) ., Nuclear receptor-induced chromosomal proximity of TMPRSS2 and ERG or TMPRSS2 and ETV1 promotes the occurrence of nonrandom ligation sites upon translocation between these partner genes , thereby generating unique breakpoint “hot spots” 6 ., It is possible that translocations in A-RMS are non-random and occur predominantly at sites , coming in close proximity during co-regulated expression ., We hypothesized that directing DSBs to sites in mouse Pax3 and Foxo1 homologous to those in PAX3 and FOXO1 in an ARMS cell line carrying a t ( 2;13 ) might increase the chance of generating a t ( 1;3 ) in proliferating Foxo1-inv+/+ myoblasts after Cas9 induced DSBs ., We chose to mimic the breakpoints of the widely used ARMS cell line RH30 ( S3 and S4 Figs . ) ., Alignment of human and mouse Pax3 and Foxo1 sequences mapped the RH30-like breakpoints at positions 78105273 on mouse chromosome 1 and 52300558 on mouse chromosome 3 ( Fig . 4A ) ., We chose unique protospacer sequences followed by a 5’-GGT PAM as close as possible to the RH30-like breakpoints in both Pax3 and Foxo1 ( Fig . 4B ) ., Cas9 introduces DSB three nucleotides downstream of the two PAM sequences , which would result in DSBs between nucleotides 78105248 and 78105247 on chromosome 1 in intron 7 of Pax3 and between nucleotides 52300541 and 52300542 ( coordinates in the non-inverted sequence ) on chromosome 3 in intron 1 of Foxo1 ( Fig . 4B ) ., For gene delivery to the myoblasts we cloned the human codon optimized Cas9 ( hCas9 ) into the pCL20C 34 lentiviral vector downstream of the MSCV promoter and upstream of an IRES-YFP fluorescent marker ( Fig . 4C ) ., In order to express two different sgRNAs form a single vector we constructed a second pCL20C dual sgRNA vector in which the Pax3-specific sgRNA was driven by the human U6 promoter and the Foxo1-specific sgRNA by the mouse U6 promoter ( Fig . 4C , D ) ., We first determined that with our current batch of serum maximum co-localization of Pax3 and Foxo1 occurred at 7–8 days of culture after myoblast isolation ., This time point synchronized with Cas9 and sgRNA expression should therefore maximize the probability of introducing DSB in closely positioned Pax3 and Foxo1 loci ., Hence 24 hours after isolation we transduced primary fore and hind limb myoblasts of Foxo1-inv+/+ pups , Foxo1-inv+/+ MEFs and fore limb myoblasts from wild type mice with Cas9 lentivirus ( Fig . 4C ) ., After FACS sorting for YFP , cells were expanded and transduced with lentivirus expressing the RH30-like sgRNAs at day 7 after isolation and with an SV40 large T antigen expressing lentivirus at day 8 ., The latter was done to prevent senescence of the myoblasts during puromycin selection and allows subsequent expansion of the culture ., To detect the Pax3-Foxo1 fusion DNA fragments from Cas9/sgRNAs expressing myoblasts and MEFs we used the Pax3-RH30F ( forward ) and Foxo1-RH30R ( reverse ) primers for PCR analysis , which are positioned downstream and upstream of the putative Cas9-induced Pax3 and Foxo1 DSBs ( Fig . 5A ) ., PCR amplification of DNA from 104 cells produced bands of 250 bp or shorter in Cas9/sgRNAs expressing hind limb and fore limb Foxo1-inv+/+ myoblast ( Fig 5B , lanes 2 and 4 ) ., However , no product was detected upon PCR amplification of DNA from 104 hind and fore limb Foxo1-inv+/+ myoblasts not treated with Cas9/sgRNAs ( Fig ., 5B , lanes 1 and, 3 ) or from 104 Cas9/sgRNAs expressing Foxo1-inv+/+ MEFs or wild type fore limb myoblasts ( Fig . 5B , lanes 5 and 6 ) ., As a control we verified that the difference in translocation frequency between myoblasts and MEFs was not caused by differences in CRISPR-Cas9’s accessibility to chromatin , given that Pax3 is not expressed in MEFs ., The CRISPR-Cas9 breakpoint in Pax3 falls within a MaeIII restriction endonuclease site and that in Foxo1 within a DdeI site ., Therefore we PCR amplified the Pax3 and Foxo1 fragments spanning the breakpoints and digested them with MaeIII or DdeI ., This showed that 96% ( Pax3 ) and 97% ( Foxo1 ) of the PCR products of CRISPR-Cas9 treated myoblasts were resistant to MaeIII or DdeI digestion , whereas in CRISPR-Cas9 treated MEFs these numbers were 72% for both enzymes ( Fig . 5C , D ) ., Thus , there was no great difference in chromatin accessibility ., Moreover , the Pax3 and Foxo1 chromatin in MEFs was equally accessible to CRISPR-Cas9 , despite the fact that Foxo1 is and Pax3 is not expressed in these cells ., Cloning of the CRISPR-Cas9 induced fusion DNAs , followed by sequencing of 45 individual clones of each of the PCR products , produced 39 and 34 translocation breakpoint sequences from fore and hind limb myoblasts , respectively ., This identified 6 different breakpoint sequences from fore limb and 3 different breakpoint sequences from the hind limb myoblasts ., This represents the minimal number of translocation events per 104 cells ( Fig . 5E , top and bottom ) ., Taking into account the percentage of locus co-localization ( Fig . 5F ) these numbers translate to a minimal translocation frequency of 1 in 150 in fore limb and 1 in 200 in hind limb myoblasts , respectively ., The only sequence in common between the fusion fragments from these two types of myoblasts was the cleanly re-ligated fusion , without missing or added base pairs ., The other 7 ( 5 from fore limb myoblast and 2 from hind limb myoblast ) were all unique and carried NHEJ-mediated deletions varying from 6 to 71 bp ., Superimposed on the deletion , two of the clones also contained randomly added base pairs ., Notably , three additional breakpoint sequences obtained from an independent experiment ( S5 Fig . ) were different from the 7 shown in Fig . 5E and underline the mutation-prone repair of the NHEJ DNA-repair machinery during the translocation event ., Together these results show excellent correlation between the frequency of translocation , co-localization , and expression of the Pax3 and Foxo1 loci in primary myoblasts ., It was highest in fore limb myoblasts , lower in hind limb myoblasts and undetectable in MEFs ., Although wild type myoblasts show the same frequency of locus co-localization as Foxo-inv+/+ myoblasts ( S6 Fig . ) , the opposite orientation of Foxo1 prevented the formation of a productive Pax3-Foxo1 fusion gene ., Next we performed RT-PCR on equal amounts of total RNA from fore limb and hind limb myoblasts to detect the Pax3-Foxo1 fusion mRNA ., In support of the higher frequency of chromosome translocation in fore limb myoblasts , we were able to RT-PCR amplify the Pax3-Foxo1 cDNA from these myoblasts ( Fig . 5G ) but not from the hind limb myoblasts using an equal amount of input RNA ( not shown ) ., Sequence analysis of the cDNA confirmed the correctly spliced Pax3 exon 7-Foxo1 exon 2 fusion ( Fig . 5G ) ., To further characterize the t ( 1;3 ) we repeated the experiment in Foxo1-inv+/+/Ink4a-ARF-/- myoblasts ., Due to loss of a functional p53 pathway Ink4a-ARF-/- myoblasts do not senesce during further experimental manipulation ., Based on the Pax3 and Foxo1 co-localization data at the time of induction of the t ( 1;3 ) ( 11% in fore limb myoblasts and 7% in hind limb myoblasts ) we assumed that the frequency of translocation events in these myoblasts should not be lower than in the myoblasts used in Fig . 5 , i . e . at least 6 independent translocation events per 104 fore limb myoblasts ., This frequency is too low for further molecular and functional analyses ., To enrich the cell pool for the t ( 1;3 ) carrying cells , we evenly distributed 104 cells between the wells of three 96-well plates ( on average 30 cells per well ) ., PCR analyses of the DNA of 95 wells from the first plate identified 3 potentially t ( 1;3 ) -enriched cell pools ( S7 Fig . ) ., Pool 1E10 was lost during the freeze-thawing cycle but FISH analyses detected the reciprocal t ( 1;3 ) in 64% of pool 1G3 metaphase cells ( Fig . 6A–C ) and in 4% of pool 1D10 metaphase cells ., Both the derivative chromosomes 1 and 3 were detected in all t ( 1;3 ) positive cells , confirming that the translocation was reciprocal ., To determine if the t ( 1;3 ) resulted in expression of Pax3-Foxo1 protein we immunoprecipitated three cell lysates each of the t ( 1;3 ) -negative ( 1H3 ) and t ( 1;3 ) -positive ( 1G3 ) pools with either an anti-Pax3 or an anti-Foxo1 antibody ., The Pax3 IPs were then immunoblotted with the anti-Pax3 antibody , showing the Pax3 and Pax3-Foxo1 bands ( Fig . 6D , Pax3/Pax3 panel ) , or with anti-Foxo1 antibody showing only the Pax3-Foxo1 band ( Fig . 6D , Pax3/Foxo1 panel ) ., Similarly , immunoblotting the Foxo1 IPs with anti-Pax3 antibody again showed the Pax3-Foxo1 fusion protein ( Fig . 6D , Foxo1/Pax3 panel ) while immunoblotting with the Foxo1 antibody showed both Foxo1 and the fusion protein ( Fig . 6D , Foxo1/Foxo1 panel ) ., This confirmed that the engineered t ( 1;3 ) expressed the fusion protein , which allowed us to assess if it affected the expression of Pax3-Foxo1’s transcriptional targets ., We performed RNA-seq analysis comparing the mapped sequence reads of presumed PAX3-FOXO1 target genes 2 in the 1G3 pool ( 64% Pax3-Foxo1 positive ) with those in the 1H3 pool ( Pax3-Foxo1 negative ) ( S1 Table ) ., This showed that roughly half the targets of PAX3-FOXO1 were correctly up or down regulated in the 1G3 pool ., The same comparison with a PAX3-FOXO1 expression signature obtained with the ectopic PAX3-FOXO1 expressing ERMS cell line RD 35 , also showed coincident regulation of half the targets ( S2 Table ) , suggesting that the t ( 1;3 ) generated fusion protein is active ., For the precise modeling of human recurrent chromosome translocations and their impact on disease development in mice , reenactment of the actual translocation would be the closest possible recapitulation of the sequence of events in humans ., Until now such reenactment was a daunting task as the translocation would require introduction of LoxP 36 , 37 or Frt recombination sites into both translocation partners via homologous recombination in ES cells , followed by expression of Cre or Flp recombinase to create DSBs that would mediate the translocation ., As shown by others 20 and here , the availability of the CRISPR-Cas9 system has paved the way to implementing this approach without such major technical or time investment ., Given the high homology between mouse and human genes and their regulatory sequences , this approach is likely to include all sequences that are important for the precise regulation of the mouse fusion gene as it occurs in humans ., The first and only published model for ARMS 38 in which expression of a conditional Pax3-Foxo1 knock-in fusion oncogene is induced by a Myf6 driven Cre had a low incidence and long latency of tumor development , requiring the presence of two Pax3-Foxo1 alleles on a Trp53-null or Ink4a/Arf-null background ., One reason for this might be that the level of expression of the fusion oncogene in this KI model is inadequate for shorter latency tumor development ., An argument against this possibility is that a high level of PAX3-FOXO1 expression induces cell death 39 , most likely due to transcriptional activation of the Pax3-Foxo1 pro-apoptotic target gene Noxa1 40 ., Unlike other studies 41 , 42 , the KI Pax3-Foxo1 gene contained some Foxo1 genomic sequences that allowed expression of the fusion gene in adult mice , but despite their presence the construct might lack sequences that mediate human-like regulation of fusion gene expression , which in turn might be crucial for efficient tumor development ., In agreement with published data 19 we established that co-localization of Pax3 and Foxo1 in our culture system was higher in forelimb than in hind limb myoblasts , which coincided with higher Pax3 expression in forelimbs ., Due to experimental variability the percentage of co-localization of the two loci varied in 8 independent experiments , but co-localization in the fore limbs was always higher than in the hind limbs ., Therefore our myoblast model represents a graded system to determine if these features contributed to the frequency of chromosome translocation in low passage primary myoblasts upon introduction of targeted DSBs ., To perform these experiments and to eventually develop a precise mouse model of ARMS , the transcriptional orientation of Foxo1 on chromosome 3 needed to be inverted ., We followed the Cre-dependent one-way inversion of a DNA fragment in mice as was previously demonstrated by Schnütgen and colleagues 43 ., To avoid disturbing the transcriptional regulation of the inverted Foxo1 , we decided to invert the mouse/human 4 . 9 Mb syntenic region encompassing Foxo1 , rather than the gene itself ., Although the centromeric border of this region is only 15 kb upstream of Foxo1 , we reasoned that all important Foxo1 regulatory sequences should be contained within this region otherwise it would not be syntenic with human FOXO1 on chromosome 13q14 . 1 ., Although we did not analyze the detailed expression of Foxo1 in Foxo1-inv+/+ mice during pre- and postnatal life , the animals did not show any obvious phenotypic abnormalities ., In addition , they had a normal lifespan , normal fecundity , and the level of Foxo1 protein expression and co-localization of the Pax3 and Foxo1 loci in myoblasts were identical to those of wild type mice ., Together these observations made the Foxo1-inv+/+ myoblasts suitable for our translocation experiments ., To determine if the level of co-localization of Pax3 and Foxo1 in primary myoblasts affected the frequency of chromosome translocation between these loci upon induction of targeted DSB , we transduced the cells with Cas9 and dual sgRNA expressing lentiviruses ., Combining the three genes into a single lentiviral vector failed to produce viral particles ., We targeted the CRISPR-Cas9 DSBs to sequences in Pax3 and Foxo1 that mediated the t ( 2;13 ) in the A-RMS cell line Rh30 ., Both breakpoints are present in sequences conserved between the mouse and human genes , suggesting that they occurred in non-redundant sequences that might bind factors with a role in expression regulation of both genes ., Currently we do not know if this affects the frequency of translocation , which is a possibility that can be tested in future by choosing sgRNAs targeting non-conserved sequences within the target Pax3 and Foxo1 introns ., We found excellent positive correlation between the frequency of the t ( 1;3 ) and the percentage of locus co-localization using FISH analysis ., This also correlated with the level of Pax3 expression , which is much higher in fore limb than hind limb myoblasts and absent in MEFs , while Foxo1 expression is ubiquitous ., Given that the frequency of CRISPR-Cas9 induced DSBs in Pax3 and Foxo1 is comparable in myoblasts and MEFs , it is the proximity of the loci in these primary cells that facilitates trans-chromosomal ligation producing the two expected derivative chromosomes during NHEJ DNA repair ., The derivative chromosome 3 produced correctly spliced Pax3-Foxo1 mRNA , encoding active Pax3-Foxo1 protein that up/down-regulated expression of approximately half the presumed PAX3-FOXO1 targets in the 64% Pax3-Foxo1-positive cell pool ( S1 Table ) ., The genes compiled in this table are differentially expressed in ARMS versus ERMS tumors or have been identified by forced expression of PAX3-FOXO1 in different cell lines , including NIH3T3 cells , MEFs , SAOS2 cells and C2C12 cells ( 2 and references therein ) ., Because the cell background affects the range of PAX3-FOXO1 target gene expression 44 , none of the published scenarios reflect expression of Pax3-Foxo1 in primary p16/Arf-/- mouse myoblasts ., Possibly this is the reason for the 45% match of reported PAX3-FOXO1 up or down regulated genes ., Comparison with genes up or down regulated in the ERMS cell line RD transduced with PAX3-FOXO1 retrovirus 35 showed 52% coincident regulation ( S2 Table ) ., Clearly , the t ( 1;3 ) generated Pax3-Foxo1 protein in mouse myoblasts is active and changes the expression of target genes in an ARMS-like manner ., One would expect that the frequency of translocation in myoblast that show co-localization of the two translocation partners would be the same irrespective of the source of myoblasts ., We found a frequency of 1/150 and 1/200 in fore and hind limb myoblasts , respectively , which we believe does not represent a difference given the uncertainty of how many translocation events actually took place ( we can only count those that give distinguishable fusion products ) ., Our results in mouse myoblasts suggest that human myoblasts can be a cell of origin for the PAX3-FOXO1 translocation as they would provide a favorable environment for the translocation to occur , i . e . expression of both genes and spatial co-localization ., It is curious that A-RMS is more frequent in the lower than in the higher extremities in humans , as reported by Neville and co-workers 45 ., This apparent inconsistency with our mouse data might be explained by the possibility that humans may not have a difference in the distribution of PAX3 expression in the upper and lower extremities ., In addition , the muscle mass and presumably the number of satellite cells in the lower extremities in humans is much higher than in the upper extremities , hence increasing the number of translocation-competent cells and frequency of translocation ., By using CRISPR-Cas9 nuclease we showed that targeted chromosome translocations could be induced with high efficiency ., Unlike other approaches that have relied on induction of chromosome translocation using γ−irradiation , DSB-inducing chemicals , or the lymphoid cell-specific gene rearrangement machinery , CRISPR-Cas9 can be employed in any cell type ., Due to its specificity the system is suitable for use in vivo in cell culture or in mice ., Application of this system will greatly facilitate the development of mouse models that precisely recapitulate chromosome translocation-induced human diseases ., A complete list of E . coli strains used for this work can be found in S1 Protocol ., BAC clones RP24–391O12 ( centromeric border of the 4 . 9 Mb syntenic region ) and RP23–422I13 ( telomeric border of the 4 . 9 Mb syntenic region ) were

Introduction, Results, Discussion, Materials and Methods

Many recurrent chromosome translocations in cancer result in the generation of fusion genes that are directly implicated in the tumorigenic process ., Precise modeling of the effects of cancer fusion genes in mice has been inaccurate , as constructs of fusion genes often completely or partially lack the correct regulatory sequences ., The reciprocal t ( 2;13 ) ( q36 . 1;q14 . 1 ) in human alveolar rhabdomyosarcoma ( A-RMS ) creates a pathognomonic PAX3-FOXO1 fusion gene ., In vivo mimicking of this translocation in mice is complicated by the fact that Pax3 and Foxo1 are in opposite orientation on their respective chromosomes , precluding formation of a functional Pax3-Foxo1 fusion via a simple translocation ., To circumvent this problem , we irreversibly inverted the orientation of a 4 . 9 Mb syntenic fragment on chromosome 3 , encompassing Foxo1 , by using Cre-mediated recombination of two pairs of unrelated oppositely oriented LoxP sites situated at the borders of the syntenic region ., We tested if spatial proximity of the Pax3 and Foxo1 loci in myoblasts of mice homozygous for the inversion facilitated Pax3-Foxo1 fusion gene formation upon induction of targeted CRISPR-Cas9 nuclease-induced DNA double strand breaks in Pax3 and Foxo1 ., Fluorescent in situ hybridization indicated that fore limb myoblasts show a higher frequency of Pax3/Foxo1 co-localization than hind limb myoblasts ., Indeed , more fusion genes were generated in fore limb myoblasts via a reciprocal t ( 1;3 ) , which expressed correctly spliced Pax3-Foxo1 mRNA encoding Pax3-Foxo1 fusion protein ., We conclude that locus proximity facilitates chromosome translocation upon induction of DNA double strand breaks ., Given that the Pax3-Foxo1 fusion gene will contain all the regulatory sequences necessary for precise regulation of its expression , we propose that CRISPR-Cas9 provides a novel means to faithfully model human diseases caused by chromosome translocation in mice .

Many cancers carry recurrent chromosome translocations , which often result in the formation of fusion genes that are directly involved in the tumorigenic process ., Alveolar rhabdomyosarcoma , a muscle tumor in children , is typified by a translocation that fuses the PAX3 gene on chromosome 2 to the FOXO1 gene on chromosome 13 ., For translocation to occur both genes need to break and the disparate ends need to fuse via a process called non-homologous end joining ., We determined that physical proximity of Pax3 and Foxo1 in mouse muscle progenitor cells ( myoblasts ) facilitates fusion gene formation ., Because Pax3 and Foxo1 in the mouse are in an opposite orientation , we used a chromosome engineering strategy to invert the orientation of Foxo1 so that upon translocation a productive Pax3-Foxo1 fusion gene is created ., Co-localization of the Pax3 and Foxo1 loci is higher in fore limb than in hind limb myoblasts ., Simultaneous induction of a targeted double strand DNA break in each gene by CRISPR-Cas9 nuclease generated more fusion genes in fore limb than in hind limb myoblasts ., Thus , gene proximity facilitates fusion gene formation ., We propose that CRISPR-Cas9 nuclease can be used for the precise modeling of chromosome translocations of human cancer in mice .

journal.pntd.0001266

Cryptic Diversity within the Major Trypanosomiasis Vector Glossina fuscipes Revealed by Molecular Markers

Control of Human African Trypansomiasis ( HAT ) has largely been based upon the detection and treatment of human cases 1 ., Anti-vector interventions , whilst hugely successful in reducing transmission of Animal African Trypanosomiasis ( AAT ) , have rarely been implemented on a programmatic scale 2 , 3 ., Part of the explanation for the relative neglect of anti- vector interventions is that the majority of cases of HAT are transmitted by flies within the Glossina palpalis group which are less amenable to control using natural ( insecticide-treated cattle ) or artificial ( traps and insecticide-treated targets ) baits ., The recently launched Pan African Tsetse and Trypanosomiasis Eradication Campaign ( PATTEC ) has placed anti-vector interventions back on the agenda for HAT control ., This initiative aims to identify , then eradicate discrete populations of tsetse flies ., The programme is not reliant upon a single intervention but will take an integrated vector management ( IVM ) approach which tailors the interventions to the ecology and bionomics of the target species ., Most interventions , such as aerial spraying , bait and trap methods and release of sterile irradiated-males ( SIT ) , require a detailed understanding of the biology and population genetics of the target species ., As discussed in two recent papers by Solano et al we are beginning to see population genetic data being used to target and tailor control strategies for some species within the palpalis group 4 , 5 ., However , for Glossina fuscipes s . l . , which is thought to vector approximately 90% of cases of HAT 6 , very few molecular genetic studies have been conducted 7 , 8 , 9 , ., Consequently , at present , our understanding of the taxonomy and population structure of this “species” is too incomplete to fully inform intervention strategies ., A recent initiative to develop improved bait technologies for G . fuscipes spp ., flies has revealed marked geographical differences in the response of flies to both odour and trap design ., In Kenya G . f ., fuscipes were unresponsive to any mammalian odour whilst in the Democratic Republic of Congo ( DRC ) G . f ., quanzensis was responsive to pig odour 12 ., Similarly , studies investigating the optimal orientation for the insecticide-treated , oblong cloth traps which are commonly used to control tsetse suggest that the visual responses of the putative sub-species may differ ., Glossina f ., fuscipes was equally attracted to traps in which the longest axis of the oblong was either parallel ( horizontal ) or orthogonal ( vertical ) to the ground 13 whereas G . f . quanzensis was apparently more attracted to horizontal oblongs ( S . Torr , unpublished ) ., If these , and other , differences in vision and odourant-mediated behaviour between the putative fuscipes subspecies reflect genetic differences population genetic approaches may be used to target interventions to populations with specific behaviours ., Glossina fuscipes s . l . has an extensive distribution centralised on the Congo basin but also extending as far north as Ethiopia/Sudan and as far south as Angola ( Figure 1 ) ., The sister group to Glossina fuscipes is the predominantly parapatric Glossina palpalis complex 9 whose species range lies largely to the west ., Machado revised the systematics of the palpalis group , 14 , and described three G . fuscipes subspecies on the basis of morphology ., The first , G . fuscipes fuscipes inhabits the most humid , equatorial forest habitats across the northern part of the species range ., The second subspecies , G . f ., martinii , inhabits the south Eastern part of the range , around Lake Tanganyika , and in the drainage of river Lualaba from the south up to where it is joined by the Luama , and was described as the most tolerant of low humidity levels of the three subspecies ., The third subspecies , G . f ., quanzensis , is distributed in the south western part of the species range , in the drainages of the tributaries joining the Congo River south of Mushie ., Machado asserted that the habitat of G . f ., quanzensis is intermediate in character between fuscipes and martinii ., Whether the present distributions are limited by the tolerance of the flies to different humidity levels is unknown , since only G . f ., fuscipes has been empirically tested for desiccation tolerance 15 ., The three subspecies are thought to have contiguous , non overlapping distributions ., Machado concluded that the three fuscipes subspecies are probably the result of vicariant ( allopatric ) speciation events ., From the work of Vanderplank there is evidence for barriers to mating between some of the subspecies 16 ., Glossina fuscipes fuscipes ( then called palpalis fuscipes ) from Uganda were reciprocally crossed with G . fuscipes martinii from Zambia 16 ., In the female G . f . fuscipes×male G . f ., martinii the superior claspers of the male genitalia punctured the female abdomen leading to death of the female ., The reciprocal cross showed partially sterility , with approximately10 times fewer pupae produced than in intraspecific crosses ., The area the subspecies inhabit has long been problematic to sample due to a combination of physical and socio-political difficulties and hence classical approaches of crossing different putative species are scant ., In this paper by collecting samples over a wide geographical range and using molecular genetic approaches we attempt to determine whether the subspecies of G . fuscipes sensu Machado 14 are supported or if there is evidence for alternative genetic stratification within G . fuscipes ., Given that methods of tsetse control often exploit species-specific behaviours there is a pressing need to establish the taxonomic status and ranges of the taxa within G . fuscipes s . l ., G . fuscipes were collected using biconical traps or pyramidal traps 17 , 18 at the locations and dates shown in table 1 and figure, 1 . After preliminary morphological identification in the field , flies were stored in either acetone or 90% ethanol ., In the laboratory samples were assigned to the three morphological subspecies proposed by Machado 14 using the identification key of Jordan 19 ., For mitochondrial and nuclear sequence data , DNA was extracted from three legs per tsetse using a modified version of the Ballinger-Crabtree protocol 20 , 21 ., The same method was used to extract DNA from tsetse abdomens for the amplification of DNA from the tsetse symbiont Wigglesworthia glossinidia ., The abdomen was used because Wigglesworthia is concentrated in a specialized organ , the bacteriome , on the tsetse midgut ., Table S1 details which loci were examined in each specimen ( Accession numbers HQ387026–HQ387133 ) ., The sequencing-based analyses were conducted at the Liverpool School of Tropical Medicine whilst microsatellite analyses were conducted at the Institut de Recherche pour le Développement ., A Chelex method 22 was used to extract DNA from 3 legs of individuals used solely for microsatellite analysis ., An 850 bp fragment of the 3′ end of the Glossina mitochondrial Cytochrome Oxidase 1 gene , a 764 bp fragment of the Glossina mitochondrial NADPH dehydrogenase 2 gene , and a 618 bp fragment of Glossina ribosomal internal transcribed spacer 1 ( ITS1 ) were amplified as described previously 9 using primer pairs COI-CULR , TW-N1284- N2-J586 and Glossina ITS1for- GlossinaITS1rev respectively ., Putative Glossina period gene sequences were identified from genome reads produced by the Wellcome Trust Sanger Institute available from http://www . sanger . ac . uk/resources/downloads/vectors/glossina-morsitans-morsitans . html using tBLASTn with the Drosophila melanogaster period protein sequence ( NP_525056 ) as a query seqeuence ., period was selected as it is a single copy nuclear gene in Drosophila and other insects and has been previously used to study closely related taxa 23 , 24 ., tBLASTn hits to the G . m ., morsitans genome ( downloaded from Sanger website in February 2008 , cut off probability 1×e−20 ) were assembled using CodonCode aligner ( CodonCode corporation ) together with the cDNA GMsg-3911 found using a description search of “period” in GeneDB ( http://old . genedb . org/genedb/glossina ) 25 ., A possible intron-exon structure was inferred by comparison with Drosophila cDNA and genomic DNA , and the protein sequences of other insect period genes ., Primers Perfor1 ( GATTTCGTTCATCCCAAGGA ) and Perrev1 ( GAGGCTAAAGCCTGACAACG ) were designed to amplify a fragment at the 5′ end of the putative Glossina period gene up to the highly conserved PAS domain ( due to a gap in the blast hits , the precise length of the fragment was determined by PCR and sequencing to be 1026 bp ) ., This fragment was initially amplified and sequenced from G . m ., morsitans genomic DNA , and the same primers were subsequently used to amplify the same region from G . fuscipes genomic DNA ., 25 µl reactions contained 1 µl template , 0 . 8 mM dNTP , 3 mM MgCl2 , each primer at 0 . 5 µM and 0 . 08 µl ( 0 . 4 units ) Kapa Taq polymerase ., 30 amplification cycles of 94°C for 30 seconds , 60°C for 30 seconds and 72°C for 2 minutes were used ., Primers and reaction conditions for the less variable 3′ region are given in the table S2 and Text S1 ., Wigglesworthia ., Genes for use as G . fuscipes genotyping markers were identified by comparative genomics between W . glossinidia - G . brevipalpis 26 and W . glossinidia - G . m ., morsitans genomes ( Serap Aksoy , pers . comm . ) ., No W . glossinidia - G . fuscipes genome was available and differential gene loss in symbiont lineages was anticipated ., To allow for the different gene content orthologous single copy genes were identified between E . coli ( K1 ) , W . glossinidia from G . brevipalpis and G . m . morsitans , using ORTHOMCL 27 ., A total of 355 genes were found to be single copy and present in all three genomes ., The gene orthologous groups where aligned using MUSCLE 28 , only five genes showed high levels of divergence at the nucleotide level in genome regions with conserved synteny ., Degenerate primers were designed to all regions , but the hypothetical protein YcfW was the only one which yielded amplicons of the selected size ., The gene encoding the hypothetical protein , YcfW , was initially amplified using degenerate primers DG11F ( 5′-ACWTGGATKTYAAAATACGG-3′ ) and DG11R ( 5′-ACWCCTGAWAARTAYATTGG-3′ ) based upon sequences of W . glossinidia from G . brevipalpis ( genome accession number: NC_004344 ) and G . m . morsitans ( Serap Aksoy , pers . comm . ) ., The degenerate primers amplified a 600 bp fragment from G . fuscipes derived material which was then sequenced and used to design specific primers for G . fuscipes Wigglesworthia ( Gp11fusc_for 5′-GCGCTATTTTAATATCTTTTATTTTTG-3′; Gp11fusc_rev 5′-TGGATTWTCAGAACAAATDGTTAATC-3′ ) ., YcfW was amplified for 35 cycles of 94°C for 30 seconds , 58°C for 30 seconds and 72°C for 30 seconds from roughly 40 ng of template DNA extracted from either abdomen or the whole fly , with primers 0 . 5 µM each , MgCl2 3 mM , dNTP , 0 . 8 mM ., These primers amplified a 499 bp fragment and were also used for direct sequencing ., Sanger sequencing of PCR products was performed by Macrogen Korea using an ABI3730XL sequencer ., PCR products were purified prior to sequencing using Sureclean ( Bioline ) according to the manufacturers instructions ., All the individuals studied were genotyped using 5 microsatellite loci with dinucleotide repeats , using a LI-COR sequencer ., All these microsatellite loci were originally isolated by Alan Robinson ( Entomology Unit , Food and Agricultural Organization of the United Nations/International Atomic Energy Agency , Austria ) ., GfA3 , GfB8 , and GfB101 were redesigned to produce smaller amplicons 29 ., The PCR reactions were carried out in a thermocycler ( MJ Research , Cambridge , UK ) in 20 µl final volume using 10 µl of the diluted supernatant from the extraction step ., After PCR amplification , allele bands were routinely resolved on a 4300 DNA Analysis System from LI-COR ( Lincoln , NE ) after migration in 96-lane reloadable ( 3× ) 6 . 5% denaturing polyacrylamide gels ., This method allows multiplexing by the use of two infrared dyes ( IRDye™ ) , separated by 100 nm ( 700 and 800 nm ) , and read by a two channel detection system that uses two separate lasers and detectors to eliminate errors due to fluorescence overlap ., To determine the different allele sizes , a large panel of about 70 size markers was used ., These size markers had been previously generated by cloning alleles from individual tsetse flies into pGEM-T Easy Vector ( Promega Corporation , Madison , WI , USA ) , by sequencing the cloned alleles to determine their exact size ., PCR products from these cloned alleles were run in the same acrylamide gel as the samples , allowing the allele size of the samples to be determined accurately 30 ., Allele sizes were scored twice by two independent readers using the LI-COR SagaGT genotyping software ., Primers , repeat motifs , allele size ranges and the dye used are given in table S2 ., Sequence data: An incongruence length difference ( ILD ) /partition homogeneity test 31 was performed in PAUP 32 to determine whether Cytochrome oxidase 1 and NADH dehydrogenase 2 sequences could be used together for estimating phylogenetic trees ., No significant difference was detected between tree lengths of the COI∶ND2 partition compared to random partitions of the same size , so subsequent tree inference was performed on the combined data set ., JModeltest 33 , 34 was used to perform a hierarchical likelihood ratio tests on all markers to find which substitution model best describes their evolution ., Using the Akaike information criterion ( AIC ) , the Tamura Nei 1993 model 35 was specified for the COI+ND2 dataset ., This model was used to make maximum likelihood ( ML ) trees using PhyML online 36 ., Neighbour joining trees were inferred using PAUP version 4 . 0 32 ., Other than specifying the substitution model and a gamma distribution of rates among sites , PAUP settings for distance trees were default except that base frequencies were determined empirically from the data , tree searching was heuristic with a random order of sequence addition repeated 10 times , and 2000 bootstrap replicates were performed ., Using JModeltest , the “Transversion” model was specified for YcfW ( AIC ) ., This model is equivalent to the Generalised time-reversible ( GTR ) model but with only one transition rate ., We therefore used the similar GTR model for neighbour joining trees in PAUP and for ML tree inference in PhyML ., Bayesian phylogenies of COI+ND2 , YcfW and Period and ITS1 were made using MrBayes 37 , in each case the substitution model was selected using the Bayesian information criterion in JModeltest 33 , 34 ., For COI+ND2 each gene was designated as a partition of the dataset ., Both YcfW and COI+ND2 were allowed 6 substitution rates with a gamma distribution of rates across sites and a proportion of invariable sites allowed ., Period was permitted 2 substitution rates and no variation of rates among sites or invariable sites ., ITS1 was permitted one substitution rate and no variation of rates among sites or invariable sites ., The rate prior was set to variable ( dirichlet ) , with other priors left on default ., Two runs of four chains were run for 2000000 generations , sampling every 100 generations ., The first 100000 generations ( 1000 samples ) were discarded as burn-in ., Runs and burn-in of this length gave good convergence as assessed by examining plots of log probability against generation and observing that potential scale reduction factor for all parameters was close to, 1 . The analysis was repeated three times with different seeds for the random number generator ., For period full length sequences Jmodeltest specified the TPM3uf+G 38 substitution model under the Bayesian information criterion , and TIM3+G under the AIC ., The TPM3uf+G model was used to infer a NJ tree using PAUP ., The GTR model was used to infer a maximum likelihood phylogeny using PhyML online as this does not implement the TPM3uf or TIM3 models ., Molecular clock calculations on COI data were performed using divergence rate of 1 . 5% per million years appropriate for insect COI 39 ., The assumption of uniform rates across the tree was not rejected by the two cluster test implemented in Lintre 40 ., Hypotheses about monophyly were tested in a Bayesian framework by observing the frequency of particular groups being monophyletic in the posterior distribution of trees , which is the posterior probability of monophyly 41 , 42 ., The probability of monophyly of the three morphological subspecies ( fuscipes , quanzensis and martinii ) were tested , and we also tested the monophyly of Ethiopian G . f ., fuscipes since these flies are geographically separated from other G . fuscipes by a discontinuity in their distribution , and the monophyly of Lake Victoria Basin ( LVB ) and Tanzanian specimens , since this seemed like a possible taxonomic unit in the COI+ND2 tree ., This was done by using PAUP 32 to filter the posterior distribution of trees excluding the burn-in ( i . e . 19001 trees from each run ) to find the trees which agree with the hypothesis of monophyly ., The Shimodaira and Hasegawa ( SH ) test 43 was also used to test the Maximum Likelihood tree topology under the constraints of the three morphological subspecies and the monophyly of Ethiopian G . fuscipes , estimating the bootstrap probabilities by bootstrap resampling the estimated log likelihoods of sites 1000 times ( the RELL method ) 44 ., The monophyly of LVB+Tanzanian flies , a taxonomic unit noticed only after tree construction , was not tested using the SH test because hypotheses for this test should be a priori hypotheses , independent of the observed data 45 ., Linkage disequilibrium ( LD ) between microsatellite loci was tested in each population using Genepop V 46 ., A log likelihood ratio statistic ( G test ) was calculated for contingency tables of genotypes of each pair of loci in each sample ., A global test for each pair of loci across all sample sites was also performed using Fishers method ., The Ethiopian sample sites were all considered as one due to their geographic proximity ( <10 Km ) ., Although the straight line distance was shorter between Manga and Rusinga islands ( <5 km ) , they were considered separately because this distance is over open water ., FST 47 was estimated with correction for null alleles , 48 ., Null allele frequency was estimated using the expectation maximization algorithm of 49 using FreeNA 48 , and was also estimated simultaneously with the inbreeding coefficient as described by Chybicki and Burczyk 50 ., After re-coding positions in the matrix containing no data with a unique code , the ‘excluding null alleles’ ( ENA ) corrected and uncorrected genotype data was converted into PHYLIP format for further analysis with programmes within the PHYLIP package 51 ., Recoding of missing data genotypes with a code unique for each locus was necessary to make the sum of allele frequencies, 1 . This makes the assumption that all missing data at a particular locus are the result of a single mutation that results in a null allele , which is an oversimplification ., However , trees made using the original ( non-recoded ) dataset using populations 52 results in similar topology of the well supported clades , with only the poorly supported nodes changing ., Allele frequencies were bootstrapped over loci using seqboot ., The Cavalli-Svorza chord distance 53 was calculated using gendist and neighbour-joining trees made for each of the bootstrapped datasets using neighbour ., An extended majority rule consensus tree of the bootstrap replicates was calculated using consense , the tree converted to an unrooted tree using retree , and branch lengths based on the non bootstrapped Cavalli-Svorza distance matrix were imposed on that tree topology using fitch , where negative branch lengths were not allowed ., Hierfstat 54 was used to test the contribution of hierarchical levels of population structure on departures from Hardy Weinberg equilibrium ., Specifically , we aimed to test whether the morphological subspecific classification ( Fsubspecies/total ) accounts for a significant level of genetic differentiation once the geographical sampling is taken into account ( Fcluster/subspecies , Fsample site/cluster and Findividual/sample site ) ., Hierfstat tests the significance of higher levels of the hierarchy by permuting predefined units at a lower level between the bigger units defined by the higher level ., Since G . f ., martinii was only sampled from one site ( Kigoma ) , this sample was removed from the dataset for Hierfstat analysis ., Three levels of structure were considered above “individual” , which were sample site , geographic cluster ( Kinshasa , Madimba and Kisantu were grouped into one cluster , Ungoye , Manga and Rusinga into another , and Busime and Buvuma into another , with the remaining sample sites classified individually ., 1000 permutations were used to test the significance of F statistics at each level of the hierarchy , for all 5 autosomal loci and across all loci ., STRUCTURE 2 . 3 . 1 55 , 56 was used to infer population structure without prior information about sample locations ., STRUCTURE assigns individuals to each of K clusters with different probabilities ., STRUCTURE was run with K\u200a=\u200a1 to K\u200a=\u200a12 , using 10 replicate runs for each value of K with sequential random seeds ., A burn-in period of 12000 iterations and a subsequent 60000 iterations were used to estimate parameters ., The admixture model was used , which assumes that a fraction of the genome of each individual can come from each of the K populations ., Allele frequencies were allowed to be correlated between clusters , as each cluster is thought to have undergone genetic drift away from a common ancestral population ., The optimal value of K was assessed using the DeltaK method of Evanno et al 57 ., When the whole dataset was entered , K\u200a=\u200a2 was the optimal number of clusters using this criterion , which is the uppermost level of hierarchical structure ., We then aligned the results of the 10 runs with K\u200a=\u200a2 using the full search algorithm implemented in CLUMPP 58 ., The proportionate assignment of each individual output by CLUMPP was then used to assign each individual to one of three groups:, 1 . Assigned to cluster 1 with >90% probability ,, 2 . Assigned to cluster 2 with >90% probability and, 3 . Assigned to neither cluster with >90% probability ., Data from the third group was discarded for further analysis ., Groups 1 and 2 were analysed separately in STRUCTURE as above , except that only K\u200a=\u200a1−K\u200a=\u200a10 was considered ., For group 2 , the greedy method , which selects the locally optimal solution at each stage in the hope of finding the global optimum , was used on CLUMPP since the full search algorithm took >5 minutes to run ., STRUCTURE analysis was run with the original genotypes , and also with missing data genotypes replaced with a code unique for each locus ., Bayesian tests of monophyly were performed for all sequence data sets ( Table 2 , figures S3 , S4 , S5 ) ., No marker supported the monophyly of G . f ., fuscipes or G . f ., quanzensis , although ITS1 did provide weak support for the monophyly of G . f ., fuscipes ( P\u200a=\u200a0 . 917 ) ., All the markers give support to the monophyly of G . f ., fuscipes from Ethiopia ., The monophyly of G . f ., martinii was supported by the nuclear marker ( Period ) , but neither of the maternally inherited markers ., The hypothesis of the monophyly of flies inhabiting Lake Victoria basin down to Tanzania ( LVB+martinii ) is supported by mitochondrial DNA but rejected by the nuclear marker period , with Wigglesworthia YcfW being inconclusive ., This contrast between nuclear and maternally inherited markers may reflect the repeated adaptive sweeps to which maternally inherited markers are prone which can result in dissociation between nucleotide diversity and population demography 59 ., The more conservative Shimodaira Hasegawa ( SH ) test of monophyly was performed on the same data sets ., SH tests rejected monophyly for G . f ., quanzensis only for the full COI+ND2 dataset ( P\u200a=\u200a0 . 003; n\u200a=\u200a29 ) , but when only the individuals genotyped at other loci were considered , monophyly could not be rejected ( P\u200a=\u200a0 . 729; n\u200a=\u200a16 ) ., Monophyly was also rejected for G . f ., fuscipes ( P\u200a=\u200a0 . 029 ) for the YcfW dataset ( table S3 ) ., No hypothesis could be rejected with the period or ITS1 data sets ., No pair of loci showed significant LD after Bonferroni correction for multiple testing ., Exact tests of heterozygote deficit 60 and highly variable FIS values suggested the presence of null alleles ( table S4 ) ., Estimated null allele frequencies and the population inbreeding coefficient ( F ) for each population are shown in table S5 ., For each locus estimated null allele frequency was >0 . 1 in at least one population ., Once the data set was adjusted to account for the presence of null alleles , the population inbreeding coefficient was low ( <0 . 1 ) for all populations except Ungoye and Bena Tshibangu ., If the three morphological subspecies , sensu Machado are valid phylogenetic entities , subspecific classification should account for a proportion of the genetic differentiation between populations ., However , using a hierarchical analysis of F-statistics morphological subspecific classification was not found to be a major determinant of genetic differentiation among G . fuscipes ., Subspecific classification was defined as one level of the hierarchy , and sampling site/clusters of sampling sites as other levels ., It was not possible to test G . f ., martinii using this method since this subspecies was only sampled at one site ., With uncorrected genotypes , significant levels of genetic differentiation were accounted for by sample site ( Fsample site/cluster\u200a=\u200a0 . 020–0 . 113 , P\u200a=\u200a0 . 001–0 . 003 ) and sample site cluster ( Fcluster/subspecies\u200a=\u200a0 . 050–0 . 210 , P\u200a=\u200a0 . 001–0 . 003 ) but not by subspecific classification ( Fsubspecies/total\u200a=\u200a−0 . 045–0 . 056 , P>0 . 05 ) at all 5 autosomal loci ., P values and F statistics were similar for both uncorrected and ENA corrected 48 genotypes ., To test further the morphological subspecies hypothesis we used STRUCTURE software in the expectation that the genotypes would separate into three main clusters corresponding to the martinii , quanzensis and fuscipes subspecies within which there might be additional geographical sub-structuring ., The optimal number of clusters based upon the DeltaK statistic was K\u200a=\u200a2 57 , with a local peak at K\u200a=\u200a7 ., Genotypes from Kinshasa , Madimba , Kisantu , Ethiopia , Kigoma fell into cluster 1 , whereas those from Ungoye , Manga , Rusinga , Busime and Buvuma fell into cluster 2 ( figure 4A ) ., Bunghazi and Bena Tshibangu populations showed admixture between the two clusters , and Moyo was sometimes assigned to cluster 1 and sometimes to cluster 2 ., When K\u200a=\u200a7 , 5 of the clusters correspond to clades seen in the mitochondrial DNA trees , with the fifth mtDNA clade ( Kenya and south Eastern Uganda ) corresponding to the fifth ( Kenya ) and seventh clusters ( SE Uganda ) ( figure 4B ) ., To further test the ability of the microsatellites to distinguish the morphological subspecies , genotypes assigned to cluster 1 or cluster 2 when K\u200a=\u200a2 , with probabilities greater than 0 . 9 were pooled into two separate data sets and the analysis re-run ., For cluster 1 , an optimal number of 2 sub clusters was found , which corresponded to i ., West DRC ( Kinshasa , Madimba and Kisantu ) , with ii ., Ethiopia and western Tanzania ( Kigoma ) ( figure 4C ) ., Cluster 2 was separated into 3 subclusters , corresponding to i ., Non admixed individuals from Bena Tshibangu , ii ., eastern Lake Victoria Basin ( Ungoye , Manga and Rusinge ) and iii ., northern Lake Victoria Basin ( Busime and Buvuma ) in the other , although Ungoye and Busime did show a moderate level of admixture ( figure 4D ) ., Thus the first level of clustering split G . f ., fuscipes into two groups , one of which clustered together with martinii ( Kigoma population ) , and the other of which corresponds to fuscipes living in the lake Victoria basin ., G . f . quanzensis individuals ( Western DRC and Bena Tshibangu ) also failed to cluster together ., At the next level of clustering , martinii was resolved as separate from quanzensis , but still grouped together with a fuscipes population ( although at K\u200a=\u200a3 or higher , martinii did cluster alone ) ., When the analysis was run with null homozygotes recoded as homozygous for recessive alleles , the results were largely similar , except that Bena Tshibangu showed a higher level of admixture and therefore contributed very few individuals to the second runs on clusters 1 and 2 ., Cluster 1 split into an optimal ( Max DeltaK ) 3 clusters , which were i ., West DRC , ii ., Ethiopia and iii ., Kigoma ., STRUCTURE analysis does not support the hypothesis that the subspecies account for the deepest level of structuring amongst fuscipes populations ., Trees made using ENA corrected or uncorrected datasets were very similar in topology , only differing at nodes with <70% bootstrap support ., As demonstrated by the low bootstrap values at internal nodes in this tree ( figure S6 ) , the phylogenetic relationships of widely geographically distributed G . fuscipes populations are not well resolved by this method ., The only well supported clades are the Lake Victoria Basin ( blue ) and south west DRC ( green ) ., The distance of the morphologically similar Bena Tshibangu population from the other quanzensis flies is great , and they are not resolved as sister taxa in this tree ., There was not strong support for the three morphological subspecies proposed by Machado 14 ., With the exception of ITS1 , sequence data from both nuclear , mitochondrial and endosymbiont genomes rejected one or more of the morphological subspecies in tests of monophyly ., Microsatellite data lends little support to the monophyly of G . f ., fuscipes: in the STRUCTURE analysis the major subdivision between two clusters split G . f ., fuscipes between these two clusters ., The Hierfstat analysis showed that once the population differentiation due to sampling sites has been taken into account , subspecific identity does not contribute significantly to differentiation ., Also , in the neighbour joining tree there was no clear separation into three clades according to Machados subspecies ., Both sequence and microsatellite data does however support Machados statement that the subspecies are allopatrically distributed; no mixed taxonomic units or admixture between morphological subspecies is observed in any population ., However , microsatellite and mitochondrial DNA , and to a lesser extent Wigglesworthia DNA and single copy nuclear DNA did reveal strong support for marked genetic discontinuities within G . fuscipes s . l . Taking the results from the various markers together , five clear sub divisions were observed: The status of the central DRC population from Bena Tshibangu was harder to resolve , despite being well supported by mtDNA , and forming a sister taxa to all other fuscipes in both the ML and NJ distance trees ., The Wigglesworthia YcfW gene did not amplify from samples in this population using the same primers used to amplify the rest of the G . fuscipes specimens , which may suggest they harbour a very divergent sequence with mutations in the primer binding site ., Judging by the number of locus specific non amplifications and heterozy

Introduction, Materials and Methods, Results, Discussion

The tsetse fly Glossina fuscipes s . l . is responsible for the transmission of approximately 90% of cases of human African trypanosomiasis ( HAT ) or sleeping sickness ., Three G . fuscipes subspecies have been described , primarily based upon subtle differences in the morphology of their genitalia ., Here we describe a study conducted across the range of this important vector to determine whether molecular evidence generated from nuclear DNA ( microsatellites and gene sequence information ) , mitochondrial DNA and symbiont DNA support the existence of these taxa as discrete taxonomic units ., The nuclear ribosomal Internal transcribed spacer 1 ( ITS1 ) provided support for the three subspecies ., However nuclear and mitochondrial sequence data did not support the monophyly of the morphological subspecies G . f ., fuscipes or G . f ., quanzensis ., Instead , the most strongly supported monophyletic group was comprised of flies sampled from Ethiopia ., Maternally inherited loci ( mtDNA and symbiont ) also suggested monophyly of a group from Lake Victoria basin and Tanzania , but this group was not supported by nuclear loci , suggesting different histories of these markers ., Microsatellite data confirmed strong structuring across the range of G . fuscipes s . l . , and was useful for deriving the interrelationship of closely related populations ., We propose that the morphological classification alone is not used to classify populations of G . fuscipes for control purposes ., The Ethiopian population , which is scheduled to be the target of a sterile insect release ( SIT ) programme , was notably discrete ., From a programmatic perspective this may be both positive , given that it may reflect limited migration into the area or negative if the high levels of differentiation are also reflected in reproductive isolation between this population and the flies to be used in the release programme .

Glossina fuscipes s . l . tsetse flies are responsible for transmission of approximately 90% of the cases of Human African Typanosomiasis in Sub Saharan Africa ., It was previously proposed on the basis of morphology that G . fuscipes is composed of three sub-species ., Using genetic evidence from G . fuscipes nuclear , mitochondrial and symbiont DNA , we show that the morphological subspecies do not correspond well to genetic differences between the flies and morphologically similar flies may have arisen more than once in the evolution of this species ., Instead , we found at least 5 main allopatrically distributed groups of G . fuscipes flies ., The most genetically distinct group of flies originated from Ethiopia , where a sterile insect release programme is planned ., Given that tsetse control often exploits species-specific behaviours there is a pressing need to establish the taxonomic status and ranges of these five groups ., Moreover given that we were only able to perform limited sampling in many parts of the species distribution further groups within G . fuscipes are likely to be awaiting discovery .

taxonomy, medicine, infectious diseases, african trypanosomiasis, molecular systematics, neglected tropical diseases, biology, evolutionary biology, infectious disease control, evolutionary systematics

journal.pntd.0003810

A Multi-country Study of the Household Willingness-to-Pay for Dengue Vaccines: Household Surveys in Vietnam, Thailand, and Colombia

Dengue fever is a major public health concern in South-East Asia and South America ., Dengue virus is transmitted to humans by Aedes mosquitoes ., Clinical presentation ranges from self-limited , mild febrile illness to classic dengue fever ( DF ) to the more severe form of illness , dengue hemorrhagic fever ( DHF ) ., The global burden of dengue has increased dramatically in the past five years , and presently , DF and DHF are recognized as a major cause of mortality and morbidity in tropical and sub-tropical countries1 , 2 ., The recent study shows that there are 96 million apparent and 294 million inapparent dengue infections occurring yearly , and the total 390 million infections are more than three times the previous estimate of the World Health Organization ( WHO ) 3–6 ., At present , there is no specific treatment for dengue infection ., Mosquito control prevention efforts have not been sufficient to control the disease ., Vaccine development is still in progress ., The Dengue Vaccine Initiative ( DVI ) has conducted extensive multidisciplinary dengue fever studies for decision makers in three countries: Vietnam , Thailand , and Colombia ., In Vietnam , annual disease incidence is reported to be 145/100 , 000 population according to the national surveillance system in 20107 ., Because extensive studies in Vietnam are lacking , a better understanding of dengue and its impact is necessary for disease control , especially in making decisions in regard to future implementation of dengue vaccines ., In Thailand , DF/DHF has steadily increased in both incidence and range of distribution despite mosquito control efforts ., The dengue incidence rate in Thailand was estimated to be 177/100 , 000 population in 20108 ., Dengue epidemiology in Thailand can be characterized by the circulation of all four dengue serotypes and the presence of a well-established national dengue surveillance system ., Colombia has seen a significant increase in cases of DF/DHF during the last 10 years , with epidemic waves occurring every 3–4 years ., In 2010 , Colombia experienced the largest recorded epidemic with the dengue incidence rate of 685/100 , 000 population9 ., All four dengue serotypes are found in Colombia , and in significant contrast to Asian countries , the disease occurs in people of all ages ., The rising tide of the Dengue Fever and its associated morbidities underscore the need for vaccines against dengue10–12 , but there continues to be a lack of economic assessment on dengue fever vaccines ., Vaccines currently under development may significantly reduce the burden of disease ., The three countries mentioned above are each candidates to become early adopters of future dengue vaccines ., However , like many other low- and middle-income countries , these countries will face decisions on whether and how to incorporate new and potentially expensive vaccines within their budget-constrained national vaccination programs ., Therefore , understanding the private economic benefits of potential dengue vaccines is necessary for accelerated introduction of vaccines into the public sector program and private markets of high-risk countries ., To estimate household demand and WTP for hypothetical vaccines against dengue infection , we administered a study questionnaire to 400 households in each of three countries ( Fig 1 ) ., All respondents ( N = 400 ) at each site received a description of the hypothetical dengue vaccine scenarios of 70% or 95% efficacy for 10 or 30 years ., Five pre-assigned prices were determined after performing pilot tests ( 40 pretest interviews ) and open-ended focus group discussions ., In this analysis , the dichotomous choice method was adopted ., Unlike other CV studies such as open-ended , bidding game , and payment card which have shown incentive compatibility problems , dichotomous choice eases the burden on the respondents , decreasing the number of protest answers13 ., Respondents were asked if they would be willing to buy a vaccine for their youngest child and other household members at randomly pre-assigned prices ., Interviewers reminded respondents of their budget constraints and mentioned that there were no right or wrong answers ., We adopted a time-to-think approach in Colombia and Vietnam , which gives respondents time to deliberate their decision on whether they would like to purchase a new vaccine ., In previous split sample studies14–17 , respondents tended to demand significantly fewer vaccines when provided with more time to think about their purchasing decision compared to respondents that completed interviews in one sitting ., In Colombia and Vietnam , respondents received general information on dengue illness , risk factors and a hypothetical vaccine ., They were instructed to consider their vaccine purchasing decision overnight ., The time-to-think design was not implemented in Thailand because of cost and logistical issues ., Instead , respondents completed the entire survey in one interview ., The three sites were selected in support of multidisciplinary research goals of the Dengue Vaccine Initiative ., In addition to economic studies , these sites were chosen to provide an in-depth picture of dengue epidemiology and transmission within high risk populations of the chosen countries ., All three sites share several common characteristics—high levels of dengue virus transmission; stable population with low rates of migration and high rates of ethnic homogeneity; sites are easily accessible with good health services; and local dengue control officers and provincial public health officials are exceptionally motivated and committed to dengue research ., Interviews were administered to the head /senior of selected households ., The questionnaire consists of six sections ., The first section collected demographic information about the respondent and members of the household ., The second section asked about respondent’s perception and experience regarding dengue fever , including activities undertaken by the household to reduce their risk of dengue infection ., The third section included information for the respondents on general conditions of dengue fever including how the disease is transmitted and dengue fever risk may be mitigated through community-wide efforts ., This section also recorded previous vaccination history , and provided a description of the hypothetical dengue vaccine , including efficacy and duration of protection which can be found in S2 Text and S2 Fig . A series of questions were asked to ensure that respondents had understood how the vaccine works ., In the fourth section , household demand was collected ., For example , the first WTP question was framed as: “Suppose that the total cost for the dengue fever vaccine would be VND 450 , 000 for three dose needed for one person . Would you buy this vaccine for your youngest child ? ”, To access household WTP , the additional question was followed: “Suppose that this dengue fever vaccine costs VND 450 , 000 for the 3-dose series required for each person ( same price for adults and children ) , how many people in your household ( not including your youngest child ) would you be willing to purchase vaccines for ? …Who would you buy this vaccine for ? ”, The responses were recorded in a table which is linked to the demographic information in the first section ., Respondents who said that they would not buy the vaccine at the specified price were asked if they would take the vaccine if it were provided free ., For those who did not want to take a free vaccine or pay any positive price , an additional question was posed to see why they would not take the vaccine ., The respondents refused to take the vaccine because they did not think that vaccines are safe or prevent the disease were determined as out-of-market respondents and did not proceed to the next step ., In the fifth section , socioeconomic information was collected , such as education , occupation , income , and economic status ., The sixth section included questions regarding the time-to-think approach ., Pilot studies , focus group discussion and pre-final questionnaires , were conducted in each of the three studies to refine the survey instrument and to help determine an appropriate set of prices for each setting ., Results from the pilot studies were not included in this analysis ., Our underlying economic model assumes that respondents maximize their household utility , subject to their budget constraints ., Vaccines are one of many purchases that can be used to build health capital and household health is one of many competing spending choices ., Household vaccine demand is a non-negative integer value and the number of vaccines demanded ( dependent variable ) can be estimated as a function of vaccine price , efficacy , household perceptions of dengue severity and likelihood , as well as household socio-economic characteristics ., Count models are suitable for our household demand analysis because the count model estimates non-negative integer values and specifies the quantity demanded with a mean that is dependent on exogenous variables13 , 21 ., The Poisson or its variants ( e . g . , negative binomial ) typically takes the exponential form for expected demand , and the Poisson probability density function can be written as, Pr ( xi=n ) =e−λiλinn !, , n=0 , 1 , 2…, where n is observed demand , and λi is the mean , λi = exp ( ziβ ) ., For the Poisson model , the mean is equal to the variance of the distribution ., If the variance is greater than the mean , the model is mis-specified due to overdispersion22 , 23 ( S1 Text ) ., Overdispersion may not affect the coefficient estimates significantly , but causes standard errors to be underestimated ., For this reason , the Z-score test and the boundary likelihood ratio test were performed to test for overdispersion for each country22 ., The negative binomial technique relaxes the assumption of equality of the mean and variance by adding a gamma distributed error term24 ., A common version of the negative binomial model is as follows:, E ( xi|ziβ ) =λi=exp ( ziβ ) log ( E ( xi ) ) =ziβ+θi, where θi represents unobserved individual differences ( or unobserved heterogeneity ) ., Pr ( xi ) =Γ ( xi+1α ) Γ ( xi+1α ) Γ ( 1α ) ( 1α1α+λi ) 1α ( λi1α+λi ) xi, where λi = exp ( ziβ ) ., The mean of the negative binomial distribution is E ( xi ) = λi = exp ( ziβ ) ., However , now the variance of the dependent variable is V ( xi ) = λi ( 1 + αλi ) ., The parameter α can be interpreted as the overdispersion parameter ., When α is equal to zero , the variation becomes equal to the mean , and the distribution can be modelled by Poisson regression ., However , if α is greater than zero , overdispersion exists , and the Poisson model is rejected in favor of the negative binomial model13 ( S1 Fig ) ., Standard errors were corrected for the cluster sampling procedure to improve the accuracy of the estimates ., Model validation is critical to check whether a model is appropriate and useful ., There are several statistics which estimate how well the model fit the data , how much error was in the model25 ., Mean Absolute Deviation ( MAD ) and Mean Squared Prediction Error ( MSPE ) were used to estimate how well the model fit the data 24–26 ., MAD provides a measure of the average mis-prediction of the model , and MSPE is typically used to assess the error associated with a validation or external dataset26 ., MAD=∑i=1n|Y^i−Yi|nMSPE=∑i=1n ( Y^i−Yi ) 2n, where n is validation data sample size , Y^iis the predicted value , and Yi is the observed value ., 50% of the full dataset for each country were randomly selected as a validation dataset ., The two statistics were used to measure how well the original models estimated on estimation data predict the validation data ., The smaller the value of MAD and MSPE represents the more desirable model which fits the data as closely as possible24 ., The mean household WTP can be calculated by aggregating the area beneath the demand curve ., WTP ( vaccine ) =∫0∞eβixi+βpPidP=−eβixiβp, where βp is the estimated coefficient for price and βi is an array of the estimated coefficients for the other independent variables ., The median WTP was also calculated by estimating the price at which an estimated 50% of the population would purchase vaccines ., Parametric estimates of WTP are sensitive to the choice of distribution and functional forms of household demand ., Non-parametric models , Turnbull lower bound and Kristrom’s midpoint models13 , 27–29 , were also estimated ., The advantages of non-parametric models are in their simplicity and transparency ., The Turnbull estimator does not impose any statistical assumptions about how WTP is distributed13 , and is considered to be a conservative measure ., The Kristrom’s midpoint estimator assumes that the distribution between bid points is symmetrical15 ., Both models provide a useful comparison of mean and median WTP with the parametric WTP measures ., The contingent valuation studies and survey questionnaires were approved by the ethical review committees in three countries ( National Institute of Hygiene and Epidemiology in Vietnam , Faculty of Tropical Medicine , Mahidol University in Thailand , Universidad de Antioquia in Colombia ) , as well as Ministry of Health in three countries and Institutional Review Board of the International Vaccine Institute ., Written informed consent was obtained prior to conducting interviews and respondents were informed that they could terminate interviews at any time ., Respondents received compensation for their time ., Table 1 shows household characteristics for each study site ., Average respondent age is from 37 to 47 years , and average household size is around 5 members ., Most of the respondents are females ., In order to make sure that their responses reflect the decisions made at the household , the respondents were asked who would be primarily involved in making decision for their household members ., Over 80% of the respondents confirmed that they would be primarily involved in making vaccine purchasing decisions ., The respondents had 6~9 years of median school education in Vietnam and Colombia , and 1~5 years of median school education in Thailand ., The self-reported mean household income per month is $351 , $788 , and $367 in Vietnam , Thailand , and Colombia , respectively ., For all three countries , more respondents thought that dengue fever is serious for children than respondents who thought so for adults , although the differences are not significant ., Approximately 35% , 60% , and 87% of the respondents in Vietnam , Thailand , and Colombia said that their children would likely contract dengue in the next five years ., In Vietnam and Thailand , about 28% of the respondents reported that at least one member of their household had contracted dengue fever in the past , compared to 10% in Colombia ., Around 52% , 47% , and 27% of the respondents in Vietnam , Thailand , and Colombia mentioned that they know someone who had dengue fever in their neighborhoods ., In response to past vaccine purchase history , 68% and 13% of the respondents in Vietnam and Colombia answered that they had previously purchased other vaccines ., This question was not asked in Thailand ., Over 99% of the total respondents from all three countries correctly answered questions designed to test their understanding of vaccine duration and efficacy ., Table 2 summarizes the raw data for average household vaccine demand as a function of price and efficacy ., Vaccine demand decreases with price in all three countries ., We did not find any significant difference in demand between the 70% and 95% efficacy scenarios ., The regression results are shown in Table 3 ., Type 2 includes all possible covariates , while type 1 is a parsimonious model which includes only non-attitudinal variables ., As expected , the price variable is statistically significant at the 1% level and has a negative sign ., Income per capita ( in log form ) is also highly significant across countries with positive signs indicating that vaccine demand increases with income ., The respondent age variable is inversely related to the likelihood that a respondent would purchase the vaccine in Vietnam , but this variable is not significant in Thailand and Colombia ., The relationship between education and demand was not consistent across countries ., Compared to no education , respondents with some education had significantly greater demand in Vietnam , but lower demand in Colombia ., The coefficient was positive , but not significant in Thailand ., The interaction between earning and price is positive and significant , meaning that respondents with a higher income can afford a more expensive vaccine ., Neither the perceived seriousness of dengue nor the likelihood of contracting the disease in the next 5 years was a statistically significant determinant except the perceived seriousness in Colombia ., Respondents in Vietnam who knew a person who had contracted dengue were more likely to be willing to purchase a vaccine ., There is some evidence that respondents who had purchased other vaccines tend to demand more for our hypothetical dengue vaccine than those without previous vaccine purchase experience ., The overall robustness of the model was examined by the MAD and MSPE statistics ., In the field of transportation and accident analyses where the negative binomial models are more commonly used , 1 . 8 of MAD and 7 . 2 of MSPE were considered to be relatively small values for a mean dependent variable of 2 . 8524 , 30 ., Given that the mean value of the dependent variable in this study ranges from 1 . 54 to 2 . 66 , the models for all three countries produced fairly satisfactory predictive performance ., In particular , MSPE values are closer to 1 for both short and long models in Vietnam , meaning that the model fits the data better than the other countries ., While the long model is preferred over the short model in Vietnam , the short models fit the data better for Thailand and Colombia ., Fig 2 depicts the observed and predicted fractions of household members vaccinated by price ., Table 4 shows parametric and non-parametric mean WTP estimates for the three-dose series described in the hypothetical scenario ., The mean WTP per dose is included in parentheses ., We did not generate separate estimates by vaccine efficacy/duration since the difference in vaccine demand was not statistically significant ., The conservative Turnbull-lower bound mean WTP is $42 . 3 ( $14 . 1 per dose ) for a 3 dose-series vaccine in Vietnam , $68 . 8 ( $22 . 9 per dose ) in Thailand , and $48 ( $16 per dose ) in Colombia ., The parametric mean WTP estimates lie in between Turnbull lower bound and Kristrom midpoint values except Colombia ., The mean WTP in Thailand is higher than for the other two countries ., Table 5 summarizes the median WTP estimates for both parametric and non-parametric models ., The median WTP is calculated based on the price in which an estimated 50% of the population would purchase vaccines ., Median estimates tend to be less sensitive to the unexpected responses and functional form assumptions than mean estimates , as long as the empirical 50th percentile value lies in between the lowest and the highest price points13 ., For all three countries , the observed median WTP estimates fall between the lowest price and the highest price offered in the surveys ., In the case of non-parametric models , the point estimates were linearly interpolated ., The parametric median WTP is $26 . 4 ( $8 . 8 per dose ) for a 3 dose-series vaccine in Vietnam , $70 ( $23 . 4 per dose ) in Thailand , and $23 ( $7 . 7 per dose ) in Colombia ., The median WTP in Thailand is again higher than the other countries ., It is also possible to create separate sub-models and estimate vaccine demand for different age groups ., We divided households into three groups: young children ( age under 5 years ) , school age children ( age 5–18 years ) , and adults ( age over 19 years ) ., Fig 3 shows the predicted coverage as a function of price for the three age groups ., For all three countries , the predicted fractions of young children vaccinated are higher than those for the other age groups at any price , suggesting that respondents place more value on vaccinating young children than school age children and adults ., This study provides insight into the private economic benefits of potential dengue vaccine in three countries ., The median WTP per household member was $26 . 1 ( $8 . 7 per dose ) in Nha Trang , Vietnam , $69 . 8 ( $23 . 3 per dose ) in Ratchaburi , Thailand , and $22 . 6 ( $7 . 5 per dose ) in Medellin , Colombia ., Our models showed that household demand for the dengue vaccine is sensitive to price and income , suggesting that respondents took the hypothetical purchasing scenario seriously ., These results suggest the possibility that a private market for dengue vaccines exists in these three countries and that sales may be robust if vaccine prices are lower than the median estimates from our study ., Since respondents were not bound by their stated purchasing decisions , it is possible they may not act as they reported ., There was a relatively large fraction of respondents who were willing to purchase vaccines at high price points in Thailand ( 11% ) compared to those in Vietnam ( 9% ) and Colombia ( 3% ) ., One explanation for higher WTP in Thailand is that the mean reported household income in Thailand is almost two times greater than that in Vietnam and Colombia; therefore , Thai respondents had more purchasing power ., In addition , we were not able to employ the time-to-think research design in Thailand due to budget and logistical constraints ., Other researchers have found that people tend to report lower WTP when they have more time to think about a new vaccine product and their budget constraints14–17 ., It is worth noting that this study did not attempt to test the validity of the time-to-think approach ., Rather , the study was designed based upon evidence from the time-to-think option used in the previous studies 17 ., The time-to-think approach allowed respondents to think carefully about their budget constraints and may more accurately reflect their willingness-to-pay for the vaccine ., While the absence of the time-to-think option in Thailand may contribute to higher WTP compared to Vietnam and Colombia , the exact magnitude could not be measured in this study ., The detailed methodology and comparison for the time-to-think approach were extensively discussed by Cook J et al . 17 ., It should be noted that two parameter estimates among the significant determinants differ in sign by country: education 1 and age group 2 ., While it is common for regression coefficients to have the same direction towards the underlying concept in the similar circumstance , some of the socio-economic variables may behave differently across countries to explain variance of the dependent variable ( vaccine demand ) due to the diverse contexts of local specific situations ., Ideally , our study samples would be more heterogeneous and more representative of the entire countries; however , we were limited to performing the studies in locations where epidemiologic studies were being conducted at the same time ., As a result , these results may not be generalizable beyond the communities in which the studies were conducted ., In comparison with previous studies , a study from Philippines suggests a median WTP of $60 for a 10 year efficacy scenario of dengue vaccine31 , and a study from Indonesia shows a median WTP of $1 . 94 for a fully efficacious dengue vaccine32 ., The estimates may differ depending upon income levels of study populations and previous experience in receiving other vaccines in the study communities ( i . e . availability of free vaccines from local health centers , etc . ) ., The rise in dengue fever cases and the absence of dengue vaccines will likely cause governments to consider various types of effective means for controlling the disease ., The contingent valuation study proposed here provides important information—how much people are willing to pay for a dengue fever vaccine to avoid the risk of getting infected ., The WTP estimates provide quantification of the private benefit of disease reduction ., Results can be incorporated into cost benefit analyses , which can inform prioritization of different health interventions at the national level ., The study can also assist decision makers to understand how much of population can be covered by subsidizing dengue vaccines when implementing the nationwide campaigns and can help inform how households would allocate vaccines across age groups given household budget constraints ., Further , the WTP study provides vaccine manufacturers a better picture of people’s perceptions of dengue fever and dengue vaccines .

Introduction, Methods, Results, Discussion

The rise in dengue fever cases and the absence of dengue vaccines will likely cause governments to consider various types of effective means for controlling the disease ., Given strong public interests in potential dengue vaccines , it is essential to understand the private economic benefits of dengue vaccines for accelerated introduction of vaccines into the public sector program and private markets of high-risk countries ., A contingent valuation study for a hypothetical dengue vaccine was administered to 400 households in a multi-country setting: Vietnam , Thailand , and Colombia ., All respondents received a description of the hypothetical dengue vaccine scenarios of 70% or 95% effectiveness for 10 or 30 years with a three dose series ., Five price points were determined after pilot tests in order to reflect different local situations such as household income levels and general perceptions towards dengue fever ., We adopted either Poisson or negative binomial regression models to calculate average willingness-to-pay ( WTP ) , as well as median WTP ., We found that there is a significant demand for dengue vaccines ., The parametric median WTP is $26 . 4 ( $8 . 8 per dose ) in Vietnam , $70 . 3 ( $23 . 4 per dose ) in Thailand , and $23 ( $7 . 7 per dose ) in Colombia ., Our study also suggests that respondents place more value on vaccinating young children than school age children and adults ., Knowing that dengue vaccines are not yet available , our study provides critical information to both public and private sectors ., The study results can be used to ensure broad coverage with an affordable price and incorporated into cost benefit analyses , which can inform prioritization of alternative health interventions at the national level .

Dengue is complicated ., There are four serotypes of the dengue virus , and dengue infection occurs in almost all age groups ., Infection with one serotype provides life-long immunity to that specific serotype but does not protect against the other three serotypes ., Unlike other diseases which already have preventable vaccines developed , currently there are no commercially available vaccines for dengue fever ., Even if the first vaccine becomes available , it is expected that there will be a limited number of vaccine doses available in the first few years ., Due to the increase in dengue fever cases , there is already huge public and private interest in potential dengue vaccines ., This study reports the household willingness-to-pay for a hypothetical dengue vaccine in three dengue endemic countries ., We found that household demand is strongly related to price and income ., It was also observed that more than half of the study populations are willing to pay for vaccines when price is lower than the median estimates reported here ., This study may contribute to a more effective decision on dengue vaccine introduction .

journal.pgen.1007680

Recombination hotspots in an extended human pseudoautosomal domain predicted from double-strand break maps and characterized by sperm-based crossover analysis

The major pseudoautosomal region ( PAR1 ) , located at the tips of the short arms of the human sex chromosomes , is a region of interchromosomal homology 1 , 2 ., In contrast to its smaller counterpart ( PAR2 ) on the long arms of the sex chromosomes 3 , PAR1 plays an essential role in male meiosis by supporting pairing and obligatory exchange between the X and Y 4 , failure of which can lead to sex-chromosomal aneuploidy such as Klinefelter syndrome ( 47 , XXY ) , and is associated with increased infertility 5–7 ., The human PAR1 is ~2 . 7 Mb in length , and until recently it was thought to have been stable during most of primate evolution 8 ., Indeed , since its initial molecular characterization , it was widely accepted that the boundary was fixed approximately at its present location before the divergence of the old world monkeys and great apes 27–32 million years ago 9 and is delineated by an Alu element insertion on the human Y chromosome ., However , despite this , there is evidence that its boundary , PAB1 , has shifted distally in the past , as the proximal 240 bp of sex-specific DNA shows 77% sequence similarity between the human X and Y 8 , 10 ., More recently , direct evidence of pseudoautosomal region plasticity came from a chance discovery during an aCGH ( array comparative genomic hybridization ) screen for copy number variation ( CNV ) in ~4 , 300 patients with developmental disorders , which showed that a small subset of men carry an extended PAR1 ( ePAR ) : this demonstrates that the PAR1 boundary is not static , but polymorphic in modern humans 11 ., The Mensah et al . study 11 established that creation of the ePAR involved transfer of ~110 kb of X-chromosomal PAR1-proximal sequence and concomitant duplication of a ~5-kb portion of PAR1 to the Y chromosome ., Furthermore , this insertional translocation was deemed most likely to be the result of non-allelic homologous recombination ( NAHR ) mediated by flanking ~550-bp LTR6B elements , and consistent with this , a family segregating the predicted reciprocal ~115-kb deleted form of the X was also identified 11 ( Fig 1A ) ., In contrast to most males but akin to females , men carrying the ePAR have two full-length copies of the apparently clinically irrelevant XG blood group gene 12 , as well as two copies of the GYG2 gene , encoding a precursor for glycogen synthase particularly important in the liver 13 , 14 ( Fig 1B ) ., Interestingly , both these genes escape inactivation in females 15 ., In the Mensah et al . study , the ePAR was observed in 15 independent Belgian and French families: all Belgian ePAR Y chromosomes belonged to one sub-haplogroup , I2a ( I-P37 . 2 ) , while those of the two first-degree relative French carriers belonged to a sub-haplogroup within the distantly-related lineage R1b , namely R-P312 11 ., This indicated that the creation of ePAR is recurrent and has occurred at least twice based on the global Y-chromosomal phylogeny 16 ., Mensah et al . presented indirect evidence that the translocated region within the ePAR actually functions pseudoautosomally ., PacBio sequencing of <5% of the total ePAR indicated that at least two haplotypes exist amongst the haplogroup-I2a men; these were interpreted as a consequence of recombination between X and ePAR rather than mutation accumulation , because they differed by twelve single nucleotide polymorphism ( SNP ) variants all of which are also observed on X chromosomes 11 ., More recently a gradual decline in X-chromosome genetic diversity spanning the canonical boundary was noted 17: this contrasts with the expected abrupt drop at the boundary given the lower effective population size of strictly X-linked sequences ( i . e . two copies in females but only one in males ) compared with a truly pseudoautosomal sequence ( two copies in both sexes ) and provides further evidence consistent with the ePAR supporting exchange between the X and Y . Here we build on these initial studies to seek direct evidence that the ePAR supports meiotic exchange by identifying de novo sperm recombinants ( crossovers COs , and noncrossovers NCOs ) that map to this region from two men carrying ePAR-bearing Y chromosomes belonging to the I2a haplogroup ., Since double-strand breaks ( DSBs ) induced by the protein SPO11 are known to initiate meiotic recombination 18 , we target two subregions of the X chromosome involved in the translocation that are known , via single-stranded DNA sequencing ( SSDS ) data , to support DSBs in the male germline of presumed non-ePAR-carrying individuals 19 ., Furthermore , we sequence >90% of the entire translocated region to extend our understanding of the recombinational history of the region as a whole ., The ePAR has been found in two Y-chromosome haplogroups , I2a and R1b , that are frequent in Europe 20 , 21 so we focused on North European semen donors in our collection ., Man 20 had previously been found to have a duplication of at least 17 kb of X-chromosome sequence that spanned PAB1 and was therefore a candidate ePAR-carrier ( S1 Fig ) ., A second candidate , man 53 , was identified on the basis of a similar Y microsatellite or Short Tandem Repeat ( Y-STR ) haplotype and therefore predicted to share Y-chromosome haplogroup I2a-L233 with man 20 ., ePAR status was confirmed by sequencing of the proximal insertion junction; both sperm donors carry the “Junc1” sequence shared by eight of the nine independently sampled haplogroup I2a ePARs studied by Mensah et al . 11 ., Since human recombination events cluster into narrow 1–2 kb-wide hotspots 22–27 we sought to identify potential hotspot sites within the ePAR ., Hotspot location is largely determined by PRDM9 28–30 , a presumed chromatin-remodelling protein , which binds DNA via its highly polymorphic zinc finger domain , and thus targets the induction of DSBs to specific locations ( for a review see 31 ) ., As both ePAR-carrying sperm donors were known to be homozygous for the common A-type zinc finger allele at the PRDM9 locus , we considered the distribution and strength of meiotic DSB clusters induced by the PRDM9 A allele on the X chromosome region that makes up the ePAR , as ascertained by read depth in a previous SSDS study using testis biopsy material from presumed normal-PAR1-carrying men 19 ( Fig 2A ) ., We also considered the distribution of so-called hotspot motifs believed to be the cognate binding sites for the most common form of PRDM9 30 , as well as the SNP density across the entire region as reported in dbSNP 32 , because recombination and DNA diversity often show a positive correlation 33; neither showed a clear correspondence to the SSDS signals ( S2 Fig ) ., Finally , since our ability to detect recombinants is wholly reliant on informative SNPs in the sperm donors under study , we determined the distribution of heterozygous SNPs for >90% of the entire ePAR in both man 20 and man 53 using Ion Torrent sequencing ( Fig 2B ) ., These data suggested that recombination assays could be developed for both donors in each of two DSB clusters , as indicated in the figure ., The distal assay region was located ~ 2 . 6 kb proximal to the canonical X-specific PAB1 and coincided with a moderately strong PRDM9-A-induced DSB cluster ., The proximal assay region was some 86 kb upstream of this and coincided with the strongest DSB cluster as determined by SSDS read depth ., We also compared the male meiotic DSB data with the pattern of historical female-dominated X-chromosomal recombination activity as determined by linkage disequilibrium ( LD ) , using SNP data from the 1000 Genomes Project 34 ( Fig 2C ) ., Both the distal and proximal assay intervals coincide with regions of LD breakdown , suggesting that these intervals have been active in the female germline too ., In fact , five of the six regions of LD breakdown were found to correspond to either PRDM9-A- or PRDM9-C-induced DSB clusters ( PRDM9 C and related alleles are known to activate different subsets of hotspots compared with the A allele , and collectively encode the next most common class of PRDM9 protein 35 ) ., Conversely , only six of the ten male DSB clusters coincide with historical recombination activity in the female germline ., We found no clear relationship between DSB strength and LD breakdown; of the six PRDM9-A-induced DSB clusters , the two weakest map to regions of historical recombination in the female germline , but the next two weakest do not ., Each sperm donor was found to be heterozygous for at least two SNPs both upstream and downstream of the DSB cluster in the distal region ., This allowed development of a full CO assay for each , whereby forward allele-specific primers ( ASPs ) from one parental haplotype are used in conjunction with reverse ASPs from the opposite haplotype to selectively amplify de novo recombinants from multiple PCR reactions each containing several hundred molecules 36 ., This is an efficient means by which to both estimate CO frequencies and to recover CO molecules for breakpoint mapping by subsequent typing of intervening SNPs ., Reciprocal assays were carried out for each of the two men ., Collectively , 200 de novo COs were isolated and mapped from a total of 168 , 800 sperm molecules screened ., Ninety-five percent of events clustered into a 1 . 3-kb-wide interval , entirely consistent with both autosomal and pseudoautosomal sperm CO hotspots 22 , 23 , 37 , and with the peak of CO activity almost exactly mapping to the centre of the DSB cluster ( Fig 3A ) ., Despite a shared distribution of events ( the inferred centre points of each donor’s distribution are estimated to be offset by <10 bp ) , the two men exhibited a ~4-fold difference in rate ( man 53 RF = 0 . 21% ( 95% CI 0 . 18–0 . 24% ) , man 20 RF = 0 . 05% ( 95% CI 0 . 03–0 . 06% ) , P << 0 . 0001 , 2-tailed goodness of fit test ) ., This is within the observed range noted at other characterized sperm CO hotspots , controlling for both PRDM9 status and cis-effects influencing initiation ( see below ) 38 , and is comfortably within the 30-fold range of DSB strength as measured by SSDS read depth across the five men tested over this interval 19 ., Ordinarily , reciprocal events should show a 50:50 ratio of alleles at heterozygous SNP sites; however , several CO hotspots have been shown to exhibit significant transmission distortion ( TD ) between alleles for markers close to the hotspot centre 37 , 39–42 ., This phenomenon is most readily explained by differences in the frequency of recombination-initiating DSBs between the two parental haplotypes , since the repair of such lesions uses the intact homologue which in turn leads to over-transmission of the recombination-suppressing haplotype ., TD is also referred to as CO asymmetry because the centre point of events is shifted between the reciprocal orientations even though the rates remain the same ., Man 53 showed evidence of TD at the rs1970797 C/T polymorphism , with significant over-transmission of the T-allele ( 0 . 62 cf . 0 . 50 , P = 0 . 008 , two-tailed exact binomial test ) and a displacement of the centre points of the reciprocal distributions of 126 bp ., This SNP is the closest informative marker to the overall hotspot centre ( Fig 3B ) ., Given the CO rate estimate for man 53 and this level of TD observed amongst his COs , this equates to a gametic ratio of 50 . 024:49 . 976 and demonstrates that this hotspot , like some autosomal hotspots 39 , 40 , is subject to a form of meiotic drive that will ultimately lead to its demise 43 ., The distribution of informative markers for both men in the proximal interval was such that similar CO assays could not be developed without requiring >20 kb amplicons that would at best result in very low PCR efficiencies ., Instead , we designed assays in which ASPs are used in conjunction with universal primers to amplify one haplotype , and recombinants are detected by the presence of alleles from the non-amplified haplotype 36 ., Since the latter is dependent on hybridization , this approach is less efficient as pool sizes are of the order of tens , not hundreds , of sperm per PCR , but it offers the advantage that both CO and NCO events can be detected ., Across the two men , a total ( i . e . CO+NCO ) of 120 recombinants were detected from 21 , 690 sperm , and comparable recombination fractions were noted for each ( man 20 , 0 . 60% ( 95% CI 0 . 47–0 . 77% ) and man 53 , 0 . 51% ( 95% CI 0 . 39–0 . 66% ) , P >0 . 05 , 2-tailed goodness of fit test ) ., Despite the need to design different assays ( Fig 4 ) , in both cases , the most common type of event involved a switch of haplotype only at the terminal marker adjacent to the universal primer ., In such cases it is impossible to distinguish COs from NCOs; furthermore , from analysis of other recombination hotspots , both are expected to co-localise , albeit with varying proportions 24 , 37 , 44 , 45 ., In order to gain insight into the hotspot morphology , we therefore arbitrarily assigned half of such events as COs ., Under this scenario , the proximal DSB cluster encompasses a 1 . 1-kb-wide hotspot with a peak activity of ~385 cM/Mb ( see Fig 4A ) ., Sperm recombination data from the two assay intervals show comparable trends to those observed by Pratto et al . for the two DSB clusters ., However measured , the proximal region shows more modest variability in recombination ( at most a 1 . 2-fold difference between the sperm donors ) , compared with the distal region where a 4-fold difference in CO was noted , whilst DSB strength differed ~7- and ~30-fold respectively amongst the four men analysed by Pratto et al . 19 ., Similarly , overall higher rates of recombination are observed in the proximal than distal region , though at best there is only ~12-fold difference compared with ~50-fold noted in mean DSB strength ., Of course , only DSBs repaired using the homologue can be identified in our assays , and NCO events that do not encompass informative SNPs will go undetected but still contribute to the single-stranded DNA signal used to generate the DSB maps ., Unsurprisingly , given the distribution of markers , all twenty-one events that could be scored unambiguously as NCOs encompassed just a single polymorphic site with maximal conversion tract lengths ranging from 1853–2812 bp ., Nineteen of these were observed for man 53 with peak numbers seen at SNP 97 . 4 , the marker that lies nearest to the predicted hotspot centre ( Fig 4B ) , entirely consistent with previous characterization of human meiotic NCOs 37 , 44 , 45 ., Indeed , the closest adjacent marker to SNP 97 . 4 lies just 413 bp away , yet no co-conversions were observed suggesting , as seen in other studies , that most of the NCO tracts not only occur at the centre of the CO hotspot but are in fact short 44 ., Assays were carried out in both orientations so it was possible to also test for TD in the proximal region ., In contrast to the distal assay , none was observed amongst the COs for either man; however , significant bias was observed amongst the NCOs for the central-most SNP , 97 . 4 , for man 53 ., Nine of the ten NCOs spanning SNP 97 . 4 contained the G- rather than A-allele indicating a preferential repair of ‘weak’ to ‘strong’ base pairs ( Fig 4B ) as noted in other studies 46 ., TD confined to NCOs has previously been noted at two autosomal hotspots indicating differences in CO and NCO heteroduplex formation and/or mismatch repair; it is noteworthy that in both of these cases there was also a significant GC bias 45 ., To gain a comprehensive understanding of the recombination history of ePAR we set out to sequence the entire region for the two sperm donors , six of the originally reported families of the Mensah et al . study 11 , plus a further three carriers including one who is part of a CEPH pedigree ( see Methods ) ., Including family members to aid with subsequent phasing of alleles , this equated to twenty individuals , and ten independent I2a ePARs plus one R1b ePAR ( S1 Table ) ., We designed overlapping amplicons spanning the 110-kb transferred region of the X and sequenced them on an Ion Torrent platform to a mean read depth of 300x ., We observed some unintended amplification from the long arm of the male-specific region of the Y ( see Methods ) ; this technical issue reflects the fact that the region of the X chromosome that transferred to form the ePAR shares a common evolutionary origin with this proximal portion of Yq , dating back ~30 Mya 47 ., We therefore excluded approximately 9 kb from further analysis and determined SNP haplotypes for the remaining ~92% of the ePAR using the program PHASE 48 , 49 ., We made use of family relationships where appropriate to determine which haplotype most likely corresponded to the ePAR ., Two of the ePAR men for whom there were no first-degree relatives to analyse shared the same uncommon British surname indicative of shared ancestry ( ~1000 carriers in Great Britain in the year 1998 ) 50 ., Whilst genealogical records suggest a putative common ancestor more than five generations ago , Y-STR profiling provides evidence of close paternal line relatedness of these two men ( S4 Table ) ; we took this into account when assigning their ePAR haplotypes ., We focused on SNPs that overlap with those in the 1000 Genomes Project dataset for CEU ( Utah Residents CEPH with Northern and Western European Ancestry ) and GBR ( British in England and Scotland ) , reasoning that Western European X chromosomes were most relevant for understanding the history of ePARs identified in the same geographical region ( Fig 5 ) ., To aid interpretation , we focussed on SNPs that fall outside of the DSB clusters , as the signature of CO is most easily detected by new combinations of pre-existing and well-defined flanking LD haplotype blocks ., This left a core set of 213 markers ( S2 Table ) split across nine regions or “blocks” , ranging in size from 558 to 16 , 143 bp ., Of the ten independently sampled I2a ePARs , only two were found to have the same compound haplotype which was designated as the consensus ( Fig 6A ) ., The eight remaining I2a ePARs differed by up to four of the nine blocks ( mode and median = 2 ) with changes from the consensus ranging from 1 to 29 SNP sites per block ( mode = 1 , median = 2 ) ., No complete matches were observed with phase-known X chromosomes from either the CEU or GBR males , though matches at the level of individual blocks were observed ( 9/19 that differ from the consensus , Fig 6B , S3 Table ) ., The simplest explanation of the diversity of the I2a ePARs would be that each unique haplotype is the outcome of a single , different CO event with an X chromosome ( Fig 6 ) ., We therefore looked for matches for the predicted incoming ( i . e . strictly X-linked ) haplotype among the 95 phase-known CEU/GBR male X-haplotypes , but failed to identify any ., Only five different compound haplotypes were seen more than once amongst this data set , consistent with high diversity in this region 17 and so it is possible that single exchanges involving unsampled X chromosomes could account for our observed ePAR haplotypes ., Using published mutation rates for 23 Y-STRs we estimated the time to most recent common ancestor ( TMRCA ) for the I2a ePARs of our ten sequenced lineages at 3 , 877 ± 779 yrs , ( S4 Table ) 51–53 , equating to 125 generations averaging 31 years ., Assuming a minimum of eight recombination events to account for the nine extant I2a ePAR variants amongst the ten lineages examined , we thus obtain a minimum recombination rate of 0 . 64% ( i . e . 8/ ( 125 x10 ) ) ., This recombination rate is likely an underestimate of the true rate for two reasons; not all ten sequenced lineages radiated in one generation directly from the common ancestor ( S3 Fig ) , and we have no way of definitively identifying multiple recombination events in these data ., Interestingly , the two most diverged I2a sub-haplogroups also carry the most differentiated ePARs and importantly the variation from the consensus extends close to the proximal boundary , so it is entirely possible that these ePARs have experienced additional distal recombination events ., Conversely , although the 23-Y-STR haplotypes of two of the lineages differ by a single repeat at just one STR , suggesting very recent shared ancestry , their respective ePAR haplotypes differ greatly , implying that a recent recombination has occurred close to the new boundary ( P2/F2 and P3/F3 in Fig 6 and S3 Fig ) ., This recent shared ancestry is also confirmed by the fact that both families have an identical surname that has a low frequency in Belgium ( ca . 550 carriers in 2008 ) suggesting a close genealogical relatedness in the patrilineal line 54 ., Nonetheless , our minimum recombination estimate is compatible with the sperm CO data for the two intervals surveyed , and suggests that the entire ePAR has a recombination rate of at least six times genome-average ( ~5 . 8 cM/Mb , compared with a genome-average male recombination rate of at most 0 . 9 cM/Mb 55 ) ., The canonical PAR1 supports a male crossover rate seventeen times higher than genome-average and four times greater than the next most recombinogenic region of comparable physical length 56; our data therefore demonstrate that the ePAR is an active , recombinationally-hot domain in the male germline ., Despite comprising less than 5% of the human Y chromosome , PAR1 plays a fundamental role during male meiosis ., Indeed , failure of the human X and Y chromosomes to pair and exchange genetic information within this region is not only associated with paternal inheritance of sex-chromosomal aneuploidy but also intimately linked with male fertility per se 5–7 , 57 , 58 ., Our appreciation of the latter has been furthered by mouse studies demonstrating that high levels of achiasmate X and Y trigger a spindle assembly checkpoint resulting in an apoptotic response 59 ., Increased infertility in male mice has also been linked with disruption of sequence homology across the mouse PAR 60 , demonstrating the importance of the length of sequence identity for successful X-Y pairing ., Given the recent discovery that the human PAR1 varies in length among humans 11 , we therefore sought to examine the recombination behaviour of this proximally extended 110-kb X-derived ePAR ., We measured recombination activity in the ePAR by directly examining gametic DNA from appropriate sperm donors ., Since thousands of sperm can be screened per donor , this approach not only allows efficient estimation of rates ( down to 0 . 0004% , 36 ) but can give detailed insight into the dynamics of recombination , even when only one or two men are available for study ., Such analyses have been instrumental in establishing that human meiotic recombination , including that in PAR1 , is not randomly distributed , but clusters into narrow 1-2-kb-wide intervals , or hotspots 61 , 62 ., However , this approach , which is based on long PCR , is not easily scalable to even modestly-sized genomic regions such as the ePAR , so here we exploited published human male meiotic DSB maps 19 in order to target tractable sub-regions for bulk sperm analysis ., De novo recombinants were detected in both sub-regions analysed , and their frequencies , distributions and characteristics were entirely consistent with classic hotspots shaping the recombination landscape of the ePAR ., We complemented these sperm data by examining ePAR diversity amongst men of the I2a Y sublineage , estimating that the entire region has a historical recombination frequency of at least six times the male genome average , and thus we conclude that the ePAR very likely contributes to the critical crossover function attributed to the canonical PAR1 ., Whether this expansion leads to a selective advantage , as proposed for rearrangements altering the mouse PAR ( see 60 ) , remains to be seen ., Sperm DNA approaches have given unprecedented insight into the dynamics of recombination at the sub-kilobase scale , ranging from inter-individual differences in activity 35 , 38 through to haplotype-specific differences for a given man 37 , 39 but have traditionally relied on pedigree or LD analysis to identify suitable target regions 22 , 42 ., Here , for the first time , we primarily made use of recombination initiation maps to guide our efforts ., As noted on a genome-wide scale , the male-specific DSB clusters on the X chromosome relating to the ePAR show reasonable correspondence with LD-based hotspot prediction ( 6/10 60% DSB clusters map to LD hotspots , cf . 73% genome-wide , whereas 5/6 83% LD hotspots in the region map to DSB clusters , cf . 68% genome-wide 19 ) ., Since the LD landscape in this region is dominated by female recombination , this indicates that the chromatin structure of this portion of the X chromosome during prophase I in most males must be very similar to that in females , though of course repair of such DSBs in these non-ePAR carriers must be via the sister chromatid ., Since we observe NCOs in both orientations , it seems this 110-kb region probably experiences the same clustering of initiating lesions when embedded on the Y chromosome , and that subsequent spreading of the synaptonemal complex from the canonical PAR1 ensures engagement and repair with whichever homologue is intact ., Although we observed reasonable correspondence with LD hotspot locations , there were some exceptions and it is tempting to speculate that these may be indicative of sex-specific differences in DSB induction ., However , as acknowledged by Pratto et al . , LD-only hotspots could be the consequence of lower-frequency PRDM9 alleles not assessed in their study , and it is possible that DSB clusters could reflect activity that has yet to make an impact at the population level 19 ., Alternatively , repair of DSBs to give rise to NCOs exclusively would have extremely localised effects on haplotype diversity and may even go undetected in the absence of suitably located polymorphisms ., Recent refined sex-specific genetic maps derived from >100 , 000 meioses in pedigrees indicate that there are in fact only a few hundred female- or male-specific recombination hotspots throughout the autosomes in comparison to the tens of thousands of total hotspots predicted by LD 63 ., On the other hand , sexually dimorphic regions , i . e . 10-kb intervals with significant sex differences in rate , are observed to be more common by an order of magnitude ., Overall , population-based methods are generally good at predicting hotspot location , as noted here and elsewhere 42 , but they do not perform so well in predicting hotspot activity ., Certainly there is no consistent relationship between LD breakdown and DSB strength ( i . e . DMC1-SSDS signal ) in our data , though the latter were ascertained in men unlikely to be ePAR carriers and may therefore be particularly influenced by the lifetime of ssDNA intermediates 64 and/or differences in DMC1 loading 65 since SSDS signal on the strictly sex-specific portions of the X and Y is 3-7x higher than on the autosomes 19 ., Our sperm data show comparable rates to those observed at autosomal and PAR1 hotspots and although limited to just two intervals , nonetheless show the expected relative relationship with DSB strength ., Future sperm CO+NCO analyses might therefore specifically target the strongest DSB clusters reported by Pratto et al . 19 to see if they manifest as hotter than characterized sperm hotspots within the autosomes ., Such hotspots would offer the opportunity to recover efficiently even atypical events that might provide further mechanistic insight into human meiotic recombination ., Our study suggests that the haplogroup I2a-associated ePAR is likely to have a more geographically restricted distribution than originally proposed 11 ., In the course of identifying carriers we established by junction PCR that the ePAR was present within the two sister I2a sub-lineages I-L1286 and I-L1294 , both of which occur predominantly within Northwestern Europe , but was absent from two Hungarian males within the I-M423 sub-haplogroup as determined by resequencing of 3 . 7 Mb of Y-specific DNA 66 ( see S3B Fig ) ., The majority of I-P37 . 2 men belong to the sub-lineage I-M423 , which is predominantly found within Southeastern Europe , and rarely encountered in Northwestern Europe 67 , hence its probable absence from the dataset tested by Mensah et al . So , whilst we would expect to find haplogroup-I2a ePAR carriers at a frequency of approximately 1% among Northwest European men as originally reported 11 , we would expect only a minority of I2a men in Southwest Europe to be carriers of the ePAR ., Breakpoint sequence analysis 11 has shown that the ePAR owes its origin to NAHR between repeated sequences ( LTR6B elements ) , so it is inherently likely to be recurrent ., Indeed , its presence in two distinct Y haplogroups shows that it has occurred at least twice ., The increasing size of population-based genome-wide SNP datasets , ( e . g . 68 ) , may allow further examples of the ePAR , or , indeed , other PAR1 extensions , to be identified and characterized ., With sufficient numbers of independent occurrences in hand , the influence of sequence diversity of the mediating LTR6B sequences will be able to be understood in detail ., North European semen samples were collected with written informed consent , and ethical approval for their use in recombination studies has been granted to CAM by NRES-East Midlands ( REC ref . 6659 ) ., Sperm DNA was prepared as described in 36 ., Additional DNA samples were also collected with written informed consent following University of Leicester ethical review ( refs . : maj4-46d9 and maj4-cb66 ) ., Blood DNA samples originally analysed in 11 were part of an institutional genome-wide CNV study that was approved by KU Leuven review board ( protocol number S55513 ) ., Lymphoblastoid cell-line DNA from CEPH family 1334 is available from the Coriell Institute ( https://www . coriell . org/ ) ., One sperm donor ( man 20 ) was previously identified as carrying a duplication of the X chromosome that encompassed the canonical PAR1 boundary and extended at least 12 kb proximal to this ( S1 Fig ) ., Twenty-three Y-STRs were typed in 81 donors , including man 20 , using the PowerPlex Y23 kit ( Promega ) ., Y-chromosome haplogroups were predicted from the resulting STR haplotypes using a Bayesian Allele Frequency approach ( http://www . nevgen . org/ ) ., Man 20 and man 53 were predicted to carry the haplogroup I2a-L233 sublineage ., Two further unrelated ePAR carriers were found by surveying PowerPlex Y23 data to predict haplogroup I2a Y chromosomes among laboratory collections of DNA samples ., A first-generation male from CEPH family 1334 ( NA12146 ) was identified as another candidate carrier; he was reported to have an apparent duplication of X-linked SNPs in the vicinity of the ePAR1 ( hg19 chrX:2694151–2808548; hg38 chrX:2776110–2890507 ) in DGV ( http://dgv . tcag . ca/dgv/app/home ) , and predicted to belong to the same I2a sub-haplogroup based on his Y-STR profile ( data kindly provided by C . Tyler-Smith , Wellcome Trust Sanger Institute ) ., We also typed two Hungarian males known from sequencing of 3 . 4Mb of their male specific Y to have the most distantly related I2a sublineage ( I2a-M423 ) 66 to determine whether all males within I2a possessed an ePAR ., A duplex PCR consisting of a 848-bp fragment spanning the ePAR junction ( i . e . distal X-specific LTR6B and proximal PAR1-specific LTR6B ) together with a 1551-bp control fragment from the SRY gene was used to verify the ePAR rearrangement ., PCRs were carried out in the buffer described in 69 using primers ePARjunc-F ( 5´-TGGCAATGTTACTGGAGACG ) , ePARjunc-R ( 5´-CAAGGAGTCTGCTGGAAGTC ) , SRY-F ( 5´-GGGGTCCCGAGATTTATGTT ) and SRY-R ( 5´-GCTAGAACAAGTTACCCCTC ) , with an annealing temperature of 60°C and extension temperature of 65°C ., A multiplex PCR encompassing nine haplogroup-identifying SNPs within I2a was developed with an annealing temperature of 59°C and extension temperature of 65°C ( S5A Ta

Introduction, Results, Discussion, Methods

The human X and Y chromosomes are heteromorphic but share a region of homology at the tips of their short arms , pseudoautosomal region 1 ( PAR1 ) , that supports obligate crossover in male meiosis ., Although the boundary between pseudoautosomal and sex-specific DNA has traditionally been regarded as conserved among primates , it was recently discovered that the boundary position varies among human males , due to a translocation of ~110 kb from the X to the Y chromosome that creates an extended PAR1 ( ePAR ) ., This event has occurred at least twice in human evolution ., So far , only limited evidence has been presented to suggest this extension is recombinationally active ., Here , we sought direct proof by examining thousands of gametes from each of two ePAR-carrying men , for two subregions chosen on the basis of previously published male X-chromosomal meiotic double-strand break ( DSB ) maps ., Crossover activity comparable to that seen at autosomal hotspots was observed between the X and the ePAR borne on the Y chromosome both at a distal and a proximal site within the 110-kb extension ., Other hallmarks of classic recombination hotspots included evidence of transmission distortion and GC-biased gene conversion ., We observed good correspondence between the male DSB clusters and historical recombination activity of this region in the X chromosomes of females , as ascertained from linkage disequilibrium analysis; this suggests that this region is similarly primed for crossover in both male and female germlines , although sex-specific differences may also exist ., Extensive resequencing and inference of ePAR haplotypes , placed in the framework of the Y phylogeny as ascertained by both Y microsatellites and single nucleotide polymorphisms , allowed us to estimate a minimum rate of crossover over the entire ePAR region of 6-fold greater than genome average , comparable with pedigree estimates of PAR1 activity generally ., We conclude ePAR very likely contributes to the critical crossover function of PAR1 .

95% of our genome is contained in 22 pairs of chromosomes shared by all humans ., However , women and men differ in their sex chromosomes: while women have two X chromosomes , men have an X and a smaller , sex-determining Y chromosome ., To ensure correct partition of X and Y into sperm , genetic exchange ( crossover ) must occur between these very different chromosomes in a short , shared region ., The location of the boundary of this region was thought to have been conserved since before the divergence from old world monkeys at least 27 million years ago , but recently it has been shown that some human males carry an extended version on their Y chromosomes , thanks to the transposition of a piece of DNA from the X chromosome ., Here , we asked if genetic exchange occurs in this newly extended region ., To do this , we used previously published information that signposted the positions within the X chromosome segment which exhibit the hallmarks of crossover initiation ., We then sought direct evidence of crossover in the sperm of men carrying the extension ., This work showed that the signposts were accurate , pointing to frequent crossover in this novel shared sex-chromosomal domain .

recombination-based assay, population genetics, genetic mapping, germ cells, molecular biology techniques, population biology, dna, haplogroups, sperm, homologous recombination, research and analysis methods, sex chromosomes, animal cells, chromosome biology, y chromosomes, x chromosomes, molecular biology, molecular biology assays and analysis techniques, biochemistry, haplotypes, cell biology, nucleic acids, heredity, library screening, genetics, biology and life sciences, cellular types, dna recombination, evolutionary biology, chromosomes

journal.ppat.1006541

HIV-1 epitopes presented by MHC class I types associated with superior immune containment of viremia have highly constrained fitness landscapes

HIV-1-specific CD8+ cytotoxic T-lymphocytes ( CTLs ) play a significant protective role in the pathogenesis of HIV-1 infection 1–3 , but ultimately fail to prevent disease progression in most persons ., Myriad failure mechanisms have been proposed , but given the remarkable mutation rate and sequence plasticity of HIV-1 4 , 5 , the major factor is viral epitope escape mutation resulting in a cascade of viral persistence , CTL exhaustion , dysfunction , and senescence in chronic infection 6 ., Indeed , evasion of CTLs is the major determinant of viral evolution in vivo 7–10 ., Moreover , the major histocompatibility complex class I ( MHC-I ) locus is the best defined genetic determinant of disease progression rate in genome-wide association 11–13 and epidemiologic studies 14 , 15 , indicating that MHC-I-associated properties of CTLs are important determinants of their efficacy ., Several studies of persons with “protective” MHC-I types who contain viremia without treatment have shown limited variation in targeted epitopes ., Some have suggested that these are limited escape mutations with high fitness costs , based on examination of a few epitope variants observed in vivo 16–24 ., However , the generality and mechanisms behind this observation are unclear , and the contributions of viral versus immune constraints for HIV-1 escape from CTLs are incompletely understood ., Properties of the targeted epitope could be important; HIV-1 sequence plasticity is not uniform and epitopes likely vary in their constraints for mutation 25 ., Alternatively , properties of the CTLs could differ; it has been proposed that the T cell receptors ( TCRs ) associated with protective MHC-I types either have greater cross-reactivity for epitope mutants and thus better limit possibilities for escape 26–28 , or rather are better matched to common epitope variants 29 ., Thus it is unresolved whether the limited escape is due to properties of the epitopes versus CTLs ., Finally , CTL responses against a given epitope are generally comprised of multiple clones with differing TCRs 30 , 31 ., Because individual clones recognizing the same epitope can vary in the recognition of different variants 32–34 , it has been proposed that clonal breadth may be important for preventing escape 30 , but protective MHC-I types do not appear to yield greater TCR breadth overall 31 ., This suggests qualitative differences in the composition or function of TCRs , and it is unclear to what degree the constraints for HIV-1 to escape CTLs are shared ( “public escape” ) versus specific for each clone ( “private escape” ) ., Such issues are difficult to address in vivo , where the CTL response is polyclonal , the starting sequences of HIV-1 are typically undefined , and it is impossible to normalize selective pressure between epitopes ., Here we assess the effect of HIV-1-specific CTLs on the fitness landscape of viral epitope mutation at clonal resolution ., Libraries of HIV-1 epitope mutants are propagated under selective pressure to define the options for immune escape for multiple CTL clones associated with protective and non-protective MHC-I types , addressing these issues with an experimentally controlled approach to reveal CTL escape pathways for HIV-1 ., Saturation mutagenesis was applied to three immunodominant HIV-1 epitopes in Gag ( Table 1 ) : SLYNTVATL ( SL9 , Gag 77–85 , A*02-restricted ) , KAFSPEVIPMF ( KF11 , Gag 162–172 , B*57-restricted ) , and KRWIILGLNK ( KK10 , Gag 263–272 , B*27-restricted ) ., Degenerate nucleotide DNA synthesis was utilized for each codon encoding the epitope and its flanking amino acids , as well as every combination of two codons , followed by substitution into the whole proviral genome of HIV-1 strain NL4-3 ( Fig 1 ) ., The resulting plasmid libraries were found by deep sequencing to contain a full representation ( 100% for each epitope ) of single amino acid variants and partial representation of double amino acid variants ( 38 to 43% ) achieving the threshold frequency of 2 . 5x10-5 that was considered adequate for detectable virus production after transfection ( Table 2 ) ., As expected , the consensus epitope sequence was overrepresented in each library because consensus amino acids were included in every degenerate codon ( Fig 2A ) ., These proviral DNA libraries were transfected into producer cells to yield starting virus libraries after a week of expansion ., Deep sequencing of viral RNA in these libraries again demonstrated that the consensus epitope variant was predominant , but also demonstrated that a minority of the adequately represented variants in the plasmid library persisted as replication-competent variants ( Table 2 and Fig 2A ) , suggesting that most epitope mutations were deleterious ( 36 . 4 to 86 . 6% of single codon mutants , 99 . 12 to 99 . 97% of double codon mutants ) ., Epitope variants with a threshold frequency <10−4 in two experimental replicates of virus libraries were considered to be nonviable , because they tended to decay if present in only one library , indicating insufficient replicative capacity ., To investigate the properties of the epitope variants , each library was passaged in the absence and presence of CTLs ( Table 1 ) that had been confirmed to have antiviral activity in virus suppression assays ( S1 Fig ) ., Epitope sequences were obtained by deep sequencing at baseline and after each of two serial passages of one week each ( S2 Fig ) ., Significant shifts in the frequencies of epitope variants within a library occurred in the absence of CTLs , reflecting replicative capacity differences between variants ( Fig 2A ) ., Library propagation with the addition of epitope-targeted CTLs yielded distinctly different profiles of epitope variants , indicating superimposed selective pressure by the CTLs ( Fig 2B ) ., Control CTLs targeting an irrelevant epitope did not induce a profile distinct from passaging without CTLs , and the magnitude of the epitope-specific CTL-induced change was dose-dependent ( Fig 2C ) ., The small minority of variants containing stop codons that achieved the detectable threshold in the initial virus libraries generally showed sharply decaying frequencies ( S3 Fig ) , confirming the reflection of replicative capacity ., The outcome for each epitope variant was quantified as a relative enrichment value ( RE ) compared to the subtype B consensus epitope sequence , calculated as the log10 transformed ratio of frequencies normalized to subtype B consensus variant ( Fig 2D ) in the absence or presence of CTLs ( RE-CTL and RE+CTL respectively ) ., Thus RE-CTL values reflected intrinsic replicative capacity relative to the consensus variant , with values <0 and >0 indicating variants replicating less and more efficiently ( relative to the consensus variant ) respectively ., RE+CTL values reflected the impact of CTL selection relative to the consensus variant , independently of replicative capacity ( e . g . a variant with RE-CTL<0 and RE+CTL>0 indicates that it replicates less efficiently and is less suppressed by CTL than consensus ) ., The REs between experimental replicates were highly correlated ( Fig 2E and 2F ) , demonstrating the robustness of this measurement ., Two separately produced virus libraries were utilized for all further determinations of RE-CTL and RE+CTL values , which were calculated as averages of quadruplicates ( duplicate virus libraries each passaged in duplicate without CTLs ) and duplicates ( duplicate virus libraries each passaged singly with CTLs ) respectively ., The impacts of mutations at each epitope amino acid position were evaluated by examining the subsets of single codon mutants in each library ( Figs 3–5 ) ., Passaging in the absence of CTLs revealed the effects of point mutations on intrinsic viral replication ., For each epitope , most mutations had negative effects on replication ( RE-CTL<0 ) ., However , each epitope also demonstrated mutations that were tolerated or advantageous ( RE-CTL≥0 ) ., For SL9 , substitutions at multiple positions yielded enrichment , particularly at residues -1 , 5 , and 8 of the epitope ( Fig 3 “No CTL” panel ) ., KF11 appeared to have fewer tolerated mutations ( Fig 4 “No CTL” panel ) , mostly at residues 2 and 4 , while KK10 ( Fig 5 “No CTL” panel ) had several tolerated mutations mostly at residues 2 , 5 , and 6 ., Evaluation of these epitopes under additional CTL selection also demonstrated patterns of epitope enrichment relative to the consensus epitope sequences ( RE+CTL >0 ) ., The addition of CTL generally appeared to augment enrichment of epitope variants with intrinsic growth advantages in the absence of CTLs ( Figs 3–5 ) , although there were also some intrinsically disadvantageous variants that gained enrichment with the addition of CTLs ., Conversely , some intrinsically advantageous variants were selected against with the addition of CTLs , particularly those with substitutions at the -1 position of the SL9 epitope ., The net effect of CTL selection ( ΔRE = RE+CTL- RE-CTL ) was examined for each epitope variant ( Fig 6 ) ., The relevance of this value to identify potential CTL escape variants was confirmed by generating HIV-1 clones corresponding to library variants with defined ΔRE values , and testing their susceptibility to inhibition of replication by CTLs ( Fig 7 ) ., Thus this parameter showed that many single substitutions conferred benefits against CTL selection ( Fig 6 ) ., A major exception was the N-terminal flanking amino acid of the SL9 epitope ( position -1 ) , where most substitutions increased susceptibility to CTLs ., Overall , these data demonstrate epitope-specific constraints for mutation and evasion of CTLs ., Quantitative analyses were extended to all epitope variants in the libraries , including double amino acid mutants ( Table 1 ) , to compare epitopes ., First examining RE-CTL ( Fig 8 , S4–S6 Figs first columns ) , the SL9 library yielded more variants with neutral to moderately decreased replication capacity ( RE-CTL≥0 or RE-CTL≥-0 . 5 ) compared to KF11 and KK10 , whereas KF11 and KK10 were similar ( Fig 9 ) ., There were 30 and 59 ( 0 . 34% and 0 . 68% of all single and double SL9 mutants adequately represented in the plasmid library ) SL9 variants with RE-CTL ≥0 and -0 . 5 respectively , compared to 2 and 17 ( 0 . 018% and 0 . 031% ) and 3 and 16 ( 0 . 16% and 0 . 16% ) of KF11 and KK10 epitopes reaching those thresholds ( Fig 9 top ) ., The distributions of measurements showed increasing numbers of lower RE-CTL variants , consistent with insufficient replicative capacity for the variants in the plasmid library that were not detected in the virus library ( Fig 9 bottom ) ., Comparing susceptibilities of epitope variants to CTLs ( ΔRE ) , many variants had neutral to enriched effects under CTL selection ( Fig 8 ) ., Across all variants in the virus libraries ( excluding variants with mutations in epitope flanking residues , to isolate effects of changes in CTL epitope recognition from epitope processing ) , this parameter displayed a range of values that was normally distributed ( Fig 10 ) ., The mean ΔRE value across all SL9-specific CTLs was similar to KF11- and KK10- specific CTLs ( 1 . 31 versus 1 . 54 and 1 . 32 respectively ) , although the percentages of variants with at least 5-fold advantage under CTL selection ( ΔRE≥0 . 7 ) was significantly higher ( 92 . 9% versus 78 . 1% and 74 . 8% respectively , Fig 10 top ) ., Over the range of 2-to 10-fold relative enrichment with CTLs , a stable profile of selected variants was observed ( S4-S6 third columns ) , and thus 5-fold selection ( ΔRE≥0 . 7 ) was chosen as a definition of potential escape ., Finally , considering the numbers of potential escape variants under this definition with at least moderate replicative capacity ( RE-CTL≥-0 . 5 ) as viable options for escape , SL9 had significantly more options than KF11 or KK10 ., Using mean values across CTLs , SL9 had 19 variants ( 0 . 22% of variants in the virus library ) compared to 4 ( 0 . 036% ) and 3 ( 0 . 031% ) variants for KF11 and KK10 respectively meeting these criteria ( Fig 11 ) ., In summary , the SL9 epitope offers more options for viable mutations than KF11 or KK10 , and average CTL coverage of those mutations is similar or perhaps modestly decreased for SL9 compared to KF11 and KK10 ( Fig 12 ) ., This study addresses the fitness landscape for mutational variation of three HIV-1 epitopes and the restrictions imposed by CTLs ., While in vivo observations have revealed the effects of CTL on viral evolution to escape , our data dissect this process in greater detail , resolving the interaction at the level of individual CTL clones and defined starting virus quasispecies populations ., For each epitope , the effect of every single amino acid polymorphism ( as well as about a third of all double amino acid polymorphisms ) versus the subtype B consensus sequence is assessed by frequency change as a reflection of fitness during serial passaging , as well as the impact of clonal CTL selection on these variants ., Two epitopes presented by protective MHC-I types B*57 ( KF11 ) and B*27 ( KK10 ) and an epitope presented by the non-protective type A*02 ( SL9 ) are examined in detail ., The quantities of mutation options in the absence of CTL selection markedly differ between these epitopes ., The SL9 epitope exhibits many variants with similar or higher fitness compared to consensus , whereas KF11 and KK10 epitopes appear to have very few ., This finding indicates that the SL9 epitope is much less constrained for mutation than KF11 and KK10 , suggesting that HIV-1 generally has fewer options for mutational escape in KF11 and KK10 ( Gag p24 ) than SL9 ( Gag p17 ) epitopes ., This result agrees with prior observations that: efficient immune containment of HIV-1 corresponds to CTL targeting of p24 35 , that immunodominance of p24 targeting is commonly associated with protective MHC-I types ( including B*57 and B*27 ) 36 , 37 , and that p24 is highly conserved overall 25 ., However , studies delineating associations of particular CTL responses with immune containment of HIV-1 demonstrate that protective epitope targeting is not limited to p24 37 , 38 , suggesting that sequence constraint at the level of the individual epitope overrides the particular source protein in importance for escape and thus CTL efficacy ., The epitope variants that were enriched under CTL selection further illuminate the constraints for escape mutation ., For SL9 , there are several highly CTL-enriched variants with intrinsic fitness near the consensus epitope ., In contrast , KF11 and KK10 both exhibit few CTL-enriched variants with preserved fitness , in agreement with prior studies showing that CTL escape mutations for these epitopes require high fitness costs 16–19 , 21–24 ., Moreover , the variants enriched by CTL selection recapitulate several previously reported escape variants in vivo , such as Y79F in SL9 16 and A163G in KF11 18 , although some other reported escape variants such as KF11 A163G/S165N 18 were present in the initial plasmid library but appeared replication incompetent ., As a whole , these data support the concept that protective MHC-I types such as B*27 and B*57 are beneficial through generating CTL responses against epitopes for which escape occurs only at a high fitness cost to HIV-1 ., Regarding the alternative hypothesis that protective MHC-I types yield TCRs with greater promiscuity for epitope variation 26–28 , our findings do not provide definitive evidence ., While KF11- and KK10- specific CTLs do appear to recognize more variants on average than SL9-specific CTLs , the average impacts of CTLs on epitope variants do not vary significantly between epitopes ., However , these measurements are limited to CTL interactions only with viable variants , and are thus not a comprehensive evaluation of promiscuity across all epitope variation ., Within the subset of viable mutants , there is no clear difference in coverage by CTLs across the three epitopes , and the findings are consistent with a study suggesting that better immune containment of HIV-1 is mediated by CTL responses that are more focused on viable epitope variants despite recognizing fewer epitope variants overall 29 ., An unexpected finding is that CTL recognition of SL9 is enhanced by various substitutions at the N-terminus flanking amino acid ., This suggests that these substitutions increase epitope presentation compared to the consensus sequence ., Although the influence of various mutations within the SL9 epitope reducing its proteasomal processing and presentation have been demonstrated 39 , the impairment of processing associated with the N-terminus flanking residue in the consensus sequence has not been reported ., Given the high prevalence of A*02 and the capacity of other MHC-I types such as B*40 to present the SL9 epitope , it is plausible that the consensus sequence represents escape adaptation across the human population ., Also unexpected is the observation that several SL9 epitope variants had apparently higher fitness than the consensus sequence ., Both these findings support the proposal that HIV-1 can accumulate escape mutations in the consensus sequence for circulating strains , as has been suggested specifically for SL9 40 and more generally across the HIV-1 genome 7 , 8 ., We previously reported the differential ability of CTL clones targeting the same epitope to cross-recognize escape variants 32–34 ., Here we confirm such differences between clones , but find that the overall options for escape are strikingly similar even between TCRs with entirely different variable chains ., For each epitope , the amino acid substitutions resulting in CTL evasion follow stereotypic patterns mostly sparing the main MHC-I anchor-binding residues ., Although such substitutions could affect proteasomal processing , epitope stability , or MHC-I binding , this suggests shared mutational pathways for ablating binding of sequence-distinct TCRs , and that these “public escape” pathways may predominate for these epitopes , consistent with prior population-based studies of HIV-1 escape “footprints” in vivo 8 , 41 ., Several caveats must be considered for the interpretation of our data ., Our libraries provide complete coverage for single amino acid polymorphisms in the epitopes , but incomplete coverage for double amino acid polymorphisms , and no coverage for three or more changes ., However , most reported escape mutations are single or double polymorphisms compared to consensus , and our data show sharply decreased viability for double mutants compared to single mutants , suggesting that very few triple mutants would be viable ., The RE values for epitope variants are semiquantitative reflections of HIV-1 fitness , given the saturating conditions for viral growth that can exaggerate the competitive advantage of the most fit variants ., Moreover , the selective pressure exerted by CTLs is dependent on the experimental conditions , i . e . the number of added cells and functional activity of the cells ., While these parameters are kept as constant as possible between experiments , there is biologic variability that is difficult to control entirely; thus setting RE values based on consensus sequence epitopes provides a frame of reference for comparisons between different experiments and SL9 , KF11 , and KK10 epitopes , because HIV-1 with consensus sequences in all three epitopes is shared between all libraries ., Finally , fitness costs for sequence polymorphisms can vary considerably in different genomic contexts , and our results in HIV-1 strain NL4-3 using single epitope targeting may not reflect the outcome for different virus with CTL pressure on multiple epitopes simultaneously ., Related to this point is the inability to assess for compensatory mutations ., However , the general patterns we observe are striking , and provide insight into the overall levels of constraints for these epitopes ., In summary , our findings indicate that two immunodominant epitopes associated with protective MHC-I types have highly restricted fitness landscapes for mutation compared to one that is not associated with protection , and that this allows very limited options for escape from CTLs ., Additionally , most escape pathways appear to be public and shared between different clones recognizing these epitopes ., These results have implications for harnessing CTL responses as vaccines and/or immunotherapies ., An early attempt at therapeutic adoptive transfer of CTLs resulted in rapid viral escape 42 , and analysis of the failed Step trial demonstrated a “sieve” effect in infected individuals , reflecting viral escape from vaccine-induced CTLs 43 ., Thus , a successful CTL-based approach will require understanding of the constraints for escape and strategies to block HIV-1 escape routes through reducing HIV-1 options for mutational escape and/or increasing CTL coverage of mutation options ., Double-stranded DNA spanning the Gag epitope regions of interest were commercially synthesized ( gBlock , Integrated DNA Technologies , Coralville , IA ) using NNK degenerate codons ( where “N” is any nucleotide , and “K” is guanine or thymidine ) at each single or double codon position for the epitope and its flanking codons ., These gBlock DNA fragments were then PCR amplified using primers 5’-ATCTCTAGCAGTGGCGCCC-3’ with 5’-TTTGGCTGACCTGGCTGTTG-3’ for the fragment containing the SLYNTVATL ( Gag 77–85 , SL9 ) epitope , and 5’-AGACACCAAGGAAGCCTTAGATAAGA-3’ with 5’-TACCTCTTGTGAAGCTTGCTCG-3’ for the fragments containing the KAFSPEVIPMF ( Gag 162–172 , KF11 ) and KRWIILGLNK ( Gag 263–272 , KK10 ) epitopes ., These primer sequences corresponded to the start and end sequences of the synthesized DNA fragments ., A modified HIV-1 NL4-3 provirus plasmid was created to reduce LTR-driven recombination during cloning , with 5’ U3 and 3’ U5 regions of the HIV LTR removed ( to reduce LTR homology ) , flanked by the CMV immediate-early promoter and the BGH polyA sequence ( Fig 1 ) ., Additionally , this vector was modified to delete the synthesized epitope regions except the first and last 15 nucleotides; the junction of the deleted regions were modified to have blunt cutting restriction enzyme sites: SfoI for the region containing SL9 , AfeI for the region containing KF11 and KK10 ., After linearizing each plasmid vector with the appropriate enzyme , the PCR-amplified gBlock DNA fragments were inserted via the 15 nucleotide homology by “Infusion” ( Clontech , Mountain View , CA ) to created whole genome plasmid libraries ., The resulting plasmids were then transformed into Stellar chemocompetent E . coli ( Clontech , Mountain View , CA ) , plated onto 100mm LB/ampicillin plates at ~2x104 colonies/plate and grown for 24 hours at 30°C ., Colonies were collected by washing the bacteria from the plates with Luria broth with ampicillin ., The plasmid DNA isolated from these bacteria served as the initial “plasmid libraries” for each epitope ., The plasmid libraries of each epitope were lipofected into two T75 flasks of 70% confluent HEK 293T cells ( obtained from Dr . Irvin S . Y . Chen , University of California , Los Angeles ) using 20μg DNA with BioT lipofection reagent ( Bioland Scientific , Paramount , CA ) ., After 24 hours the media was removed , and 107 T1 cells 44 ( obtained from Dr . Bruce D . Walker , Harvard University ) in 20mL RPMI 1640 medium supplemented with 10% FCS , L-glutamine , HEPES , and penicillin-streptomycin ( R10 ) were added to each flask to promote cell-cell infection of the T1 cells ., After 24 hours , the nonadherent cells were removed and transferred to a new flask ., These cells were then cultured for 6 to 8 days in R10 media until at least 50% of the cells were infected with HIV-1 ( determined by expression of p24 antigen in the cells by intracellular staining and flow cytometry ) ., The supernatant was then filtered through a 0 . 45 micron filter and cryopreserved to be utilized as the “starting virus library . ”, All virus libraries were produced in duplicate , and all experiments utilized both libraries in parallel , with duplicates for cultures without CTLs ( two replicates for each library , four total ) and singles for cultures with CTLs ( one replicate for each library , two total ) ., Cell lines utilized for passaging of HIV-1 included T144 ( expressing A*02 for the SL9 library and A*02-restricted CTLs ) , 1CC4 . 14 cells ( expressing B*57 for the KF11 library and B*57-restricted CTLs , previously produced in our laboratory 45 ) , and Subject 00076 EBV-transformed B-cells ( previously produced in our laboratory from PBMC ) that were transduced with human CD4 ( expressing B*27 for the KK10 library and B*27-restricted CTLs ) ., CTL clones ( Table 1 ) were previously isolated from chronically HIV-1-infected persons and maintained as previously described 46–48 from blood obtained with written informed consent under a University of California , Los Angeles Institutional Review Board-approved protocol , with the exception of 68A62 provided by Dr . Bruce D . Walker ( Harvard University ) ., In brief , peripheral blood mononuclear cells ( PBMCs ) were enriched for the CTLs of interest by culture with the appropriate epitope , followed by cloning at limiting dilution ., Some experiments utilized KK10-specific CTLs previously produced by stable lentiviral transduction of allogeneic CD8+ T-cells with a KK10-specific T cell receptor ( TCR ) sequence identified by quantitative spectratyping 31 ( TCR5 ) that had been cloned into a lentiviral vector as previously described 34 ., CTLs were maintained by periodic stimulation with 200ng/mL of the monoclonal anti-CD3 12F6 antibody 49 with irradiated allogeneic PBMCs ( obtained anonymously through the UCLA AIDS Institute Virology Core Facility ) in R10 media supplemented with recombinant human interleukin-2 ( NIH AIDS Reference and Reagent Repository ) at 50IU/mL ( R10-50 ) ., For the CTL clones , TCR beta variable ( BV ) chain sequences were determined after RNA isolation using Trizol reagent ( ThermoFisher Scientific , Waltham , MA ) , amplification and cloning of the BV gene using the SMARTER 5’ RACE kit ( Clontech , Mountain View , CA ) with a constant region primer ( 5’-CTTCTGATGGCTCAAACAC-3’ ) , and sequencing using the same primer ., 5x106 permissive cells ( 106 cells for the SL9 library passaged with the 1 . 9 CTL ) were infected with the starting virus library , yielding about 10–20% infected cells after 72–96 hours ( determined by intracellular staining for p24 ) ., The cells were then washed twice and resuspended at 5x105 cells/mL in R10-50 ., CTLs were added at effector:target ratios of 1:8 ( except 1:2 for the SL9 library with CTL 1 . 9 ) , with parallel no-CTL controls ., These cultures were fed every 3 days by removing and replacing half of the media ., After 7 days the supernatant was filtered through a 0 . 45 micron filter and cryopreserved; virus in the supernatant was quantified via p24 ELISA ( Xpress Bio , Frederick , MD ) ., This virus was utilized to infect cells for a second passage in the same manner using 5x103 pg p24 per 106 target cells ( 103 pg p24 per 106 target cells for the KK10 library ) , followed by collection and cryopreservation as before ., All passaging with CTLs was performed with duplicate virus libraries , and passaging without CTLs was done in quadruplicate ( 2 replicates for each virus library ) ., The passaged virus supernatant was treated with DNAse I ( New England Biolabs , Ipswich , MA ) to remove residual plasmid DNA ., HIV-1 RNA was isolated with the QIAmp viral RNA mini kit ( Qiagen , Hilden , Germany ) , and reverse-transcribed with the high capacity cDNA reverse transcription kit ( ThermoFisher Scientific , Waltham , MA ) and quantified by real-time PCR with ssoFast EvaGreen supermix on a CFX96 ( Bio-Rad , Hercules , CA ) with gag-specific primers ( 5’-ATCTCTAGCAGTGGCGCCC-3’ and 5’-TTTGGCTGACCTGGCTGTTG-3’ ) compared to NL4-3 plasmid standard to ensure ≥5x105 copies/μL of cDNA per specimen ., This cDNA and the starting plasmid libraries were prepared for deep sequencing by PCR amplification using primers tagged with 6 base-pair customized barcodes ., The gene specific portions of the primers were: Deep sequencing was performed with Hiseq PE150 sequencing ( Illumina , San Diego , CA ) ., The sequence data were parsed using the SeqIO function of open source BioPython software ( http://biopython . org/ ) ., Sequences from different samples were de-multiplexed by the barcodes and mapped to the corresponding region in the HIV-1 genome ., Since both forward and reverse reads covered the mutated region , paired reads were used to compensate for sequencing errors ., A polymorphism was accepted as valid only if observed in both reads and with a quality score ≥30 ., Further filtering for errors was done by comparison to control deep sequencing of the index NL4-3 plasmid; variants present at a frequency <10−4 were only accepted if their frequencies in duplicate virus libraries exceeded 10-fold the observed frequency of the variant in the control plasmid sequences ( due to background error ) ., The sequencing depth was >6x105 and >4x106 for the virus and plasmid libraries respectively ., All the data processing and analysis was performed with customized python scripts , which are available upon request ., Variants above threshold in initial virus libraries whose frequencies decayed to 0 after passaging were assigned a frequency of 10−6 for calculation of RE values ., All sequences have been uploaded to GenBank ( PRJNA394927 ) ., Site-directed mutagenesis was performed with the Q5 mutagenesis kit ( New England Biolabs , Ipswich , MA ) on the modified pNL4-3 vector described above , which had been further modified to contain the M20A mutation that ablates Nef-mediated MHC-I downregulation 50 , 51 ., The SL9 epitope was modified to create variants SLYNAVAVL ( codon 4 = GCT , codon 7 = GTG ) , SLYNTVACL ( codon 8 = TGT ) , SLYITVATL ( codon 4 = ATA ) , SLYNCVACL ( codon 5 = TGT , codon 8 = TGT ) , SLYCTVATL ( codon 4 = TGT ) , and the resulting plasmids were lipofected into HEK 293T cells as above to produce virus ., Evaluation of HIV-1 susceptibility to CTL suppression was performed as previously described 32 , 48 ., Briefly , T1 cells 44 were infected with 500pg p24/106 cells of the indicated viruses , and 5x104 infected cells with 5x104 CTL ( S2 Fig ) or 1 . 25x104 CTL ( Fig 9 ) were cultured in 200μL R10-50 U/ml IL-2 in a 96 well flat-bottom plate , with monitoring of supernatant p24 antigen by ELISA ( Xpress Bio , Frederick , MD ) ., Comparisons for correlations of replicate experiments and selection of epitope variants by different CTL clones were performed using Spearman rank correlation ., Comparisons of means of two groups were performed using Student’s t-test ., Comparisons of frequencies between two groups were performed using Fisher’s exact test .

Introduction, Results, Discussion, Materials and methods

Certain Major Histocompatibility-I ( MHC-I ) types are associated with superior immune containment of HIV-1 infection by CD8+ cytotoxic T lymphocytes ( CTLs ) , but the mechanisms mediating this containment are difficult to elucidate in vivo ., Here we provide controlled assessments of fitness landscapes and CTL-imposed constraints for immunodominant epitopes presented by two protective ( B*57 and B*27 ) and one non-protective ( A*02 ) MHC-I types ., Libraries of HIV-1 with saturation mutagenesis of CTL epitopes are propagated with and without CTL selective pressure to define the fitness landscapes for epitope mutation and escape from CTLs via deep sequencing ., Immunodominant B*57- and B*27- present epitopes are highly limited in options for fit mutations , with most viable variants recognizable by CTLs , whereas an immunodominant A*02 epitope-presented is highly permissive for mutation , with many options for CTL evasion without loss of viability ., Generally , options for evasion overlap considerably between CTL clones despite highly distinct T cell receptors ., Finally , patterns of variant recognition suggest population-wide CTL selection for the A*02-presented epitope ., Overall , these findings indicate that these protective MHC-I types yield CTL targeting of highly constrained epitopes , and underscore the importance of blocking public escape pathways for CTL-based interventions against HIV-1 .

Certain MHC class I types are associated with superior immune containment of HIV-1 , underscoring the importance of CD8+ cytotoxic T lymphocytes ( CTLs ) ., Epitope escape mutations for these types is limited , indicating reduced immune evasion ., Two proposed mechanisms are:, 1 ) CTL targeting of highly sequence-constrained epitopes , or, 2 ) more promiscuous CTLs for epitope variation ., However , the in vivo complexity of undefined starting virus , multiple targeted epitopes , polyclonal CTL responses against each epitope , and post-hoc evaluation of the interaction renders examination of mechanisms difficult ., Here we approach this question with controlled prospective in vitro experiments using saturation mutagenesis of epitopes in clonal HIV-1 , propagated in the absence or presence of CTL clones to define the options for epitope mutation and immune evasion by deep sequencing ., We find that two immunodominant epitopes presented by protective MHC types are highly mutation-constrained compared to one presented by a non-protective MHC type , whereas CTL promiscuity for epitope variation is not appreciably different ., These results suggest that these protective MHC types are associated with limited HIV-1 escape predominately due to intrinsic constraints on epitope mutation , and underscore the importance of focusing the CTL response on highly conserved epitopes for immunotherapies and vaccines .

medicine and health sciences, pathology and laboratory medicine, chemical compounds, pathogens, immunology, microbiology, cloning, organic compounds, retroviruses, viruses, immunodeficiency viruses, mutation, substitution mutation, rna viruses, amino acid substitution, genome analysis, amino acids, dna libraries, molecular biology techniques, dna, research and analysis methods, immune system proteins, genomic libraries, genomics, proteins, medical microbiology, hiv, microbial pathogens, chemistry, hiv-1, viral replication, molecular biology, biochemistry, signal transduction, t cell receptors, organic chemistry, nucleic acids, cell biology, virology, viral pathogens, genetics, biology and life sciences, immune receptors, physical sciences, computational biology, lentivirus, organisms

journal.pcbi.1005136

Dynamic Nucleosome Movement Provides Structural Information of Topological Chromatin Domains in Living Human Cells

Genomic DNA is packed and folded three-dimensionally in the cell nuclei ., In the nuclei of eukaryotic cells , the nucleosome is a basic unit consisting of an approximately 147-bp DNA wrapped around core histones 1 ., Recent experimental evidences suggest that the nucleosome is irregularly folded without the 30-nm chromatin fiber 2–7 ., On the other hand , at the scale of the whole nucleus , interphase chromosomes occupy distinct chromosome territories 8 ., This highly organized chromosome structure allows for effective regulation of various genome functions ., By virtue of recent developments of chromosome conformation capture ( 3C ) techniques , the genome-wide chromosome organization has been revealed by detecting the physical contact frequencies between pairs of genomic loci 9 ., More recently , 3C derivatives , Hi-C and 5C profiles demonstrated that metazoan genomes are partitioned into submegabase-sized chromatin domains ( CDs ) including topologically associating domains ( TADs ) 10–12 ., TADs are considered to be a regulatory and structural unit of the genome 13; genome loci located in the same TAD are associated with each other , whereas genomic interactions are sharply depleted between adjacent domains ., For even single-cell Hi-C , individual chromosomes maintain domain organization 14 ., Furthermore , kilobase-resolution in situ Hi-C maps identified not only small contact domains but also CTCF-mediated loop domains 15 , 16 ., In contrast , dynamic aspects of chromatin have been shown by live-cell imaging experiments 17–24 ., In particular , single-nucleosome imaging in living mammalian cells has revealed local nucleosome fluctuations caused by the thermal random force 25–27 ., The mean-squared displacement ( MSD ) of dynamic nucleosome movement clearly shows subdiffusive motion ,, MSD ( t ) = D app · t β ( 0 < β < 1 ) , ( 1 ), where Dapp is the apparent diffusion coefficient with dimension m2/sβ ., This means that nucleosome movement must be affected by restrictions from some factors but thermal noise ., Therefore , there must be a way that the dynamic aspect is consistent with aspects of the genome organization ., A theory is required to relate the dynamic aspects described by Dapp and β to the structural features of CDs ., To date , the subdiffusive exponent β has been considered to depend on the folding structure of nucleosome fibers 28 and the viscoelasticity of the thermal environment 29 , 30 ., The fractal nature of chromatin architecture as well as nucleus environment has been revealed recently 9 , 31 , 32 ., The topological structure of CDs can be described by use of the fractal manner ., Here , we propose a polymer model for a CD , whose conformational state is assumed to be expressed by the fractal dimension df in a viscoelastic medium with the exponent 0 < α < 1 ., Although not only the strings and binders switch model 33 but also the block copolymer model 34 can explain aspects of chromatin folding and chromosome architecture in Hi-C experiment datasets , in our model we abstract information on the conformational states of CDs and interpret their dynamic features by using size scaling according to the fractal dimensions ., Accordingly , the analytical form of the MSD of nucleosomes in CDs can be derived in terms of polymer physics ., As a result , the structural information of CDs , such as the size and conformational state expressed by the fractal dimension , can be derived from the MSD data of dynamic nucleosomes ., A standard approach for treating Eq 3 is to use the normal coordinates X p ( t ) ≡ 1 N ∫ 0 N cos ( p π n N ) R ( n , t ) d n for p = 0 , 1 , 2 , ⋯; however , the nonlinearity of the long-range interaction makes it difficult to deal with the equation in this manner ., Therefore , to simplify the analysis , firstly , we assume that nucleosome fluctuations within the CD reach thermal equilibrium after the relaxation time τdf , α , which is explicitly described below ( Eqs 11 and 12 ) ., Second , we use an approximation to transform the nonlinear Langevin equation ( Eq 3 ) into a linear equation by averaging under thermal equilibrium with respect to the normal coordinates, ∫ 0 t γ ( t - t ′ ) d X p ( t ′ ) d t ′ d t ′ = - k p X p ( t ) + g p ( t ) ., ( 5 ), The term in the left hand side and the second term in the right hand side ( RHS ) are straightforwardly derived according to the normal coordinates , in which g p ( t ) ≡ 1 N ∫ 0 N cos ( p π n N ) g ( n , t ) d n satisfies 〈gp ( t ) 〉 = 0 and the FDR 〈 g p κ ( t ) g q λ ( t ′ ) 〉 = k B T N γ ( t - t ′ ) δ κ λ δ p q ( 1 + δ p 0 ) / 2 ( see S1 Text , Section IA ) ., Instead of the linearity of Eq 5 , the parameter kp implicitly includes the nonlinear effect such as the long-range interactions , and is determined by the variance of Xp over the thermal relaxation time 30 ( see S1 Text , Section IB ) :, k p = 3 k B T 2 N X p 2 CD for p ≥ 1 and k 0 = 0 ., ( 6 ), Finally , to calculate the thermal average 〈 X p 2 〉 CD , the effective size scaling ( Eq 4 ) generated by the long-range interactions is used ., The asymptotic form for large p is calculated as follows ( see S1 Text , Section IC ) :, X p 2 CD ≃ 〈 R 2 〉 CD 2 A d f p - 1 - 2 / d f ., ( 7 ) Adf is a dimensionless constant depending on the fractal dimension: A d f = π 1 + 2 / d f Γ ( 1 + 2 / d f ) sin ( π / d f ) ., We shall refer to the above approximation as the linearization approximation , which is on the same level of the approximation as the preaveraging approximation in terms of polymer physics 35 , 50 ., From this point forward , to avoid complicated expressions caused by this asymptotic form , we regard the asymptotic sign ‘≃’ as equality ., Next , let us consider the MSD of nucleosomes in CDs ., Since the inverse transform of normal coordinates is R ( n , t ) = X 0 ( t ) + 2 ∑ p = 1 ∞ cos ( p π n N ) X p ( t ) and the correlation between different modes vanishes , the MSD of the n-th nucleosome , ϕ ( n , t ) ≡〈R ( n , t ) − R ( n , 0 ) 2〉 , is expressed as, ϕ ( n , t ) = X 0 ( t ) - X 0 ( 0 ) 2 + 8 ∑ p = 1 ∞ cos 2 p π n N X p 2 CD - C p ( t ) , ( 8 ), where the correlation function is defined as Cp ( t ) ≡〈Xp ( t ) ⋅ Xp ( 0 ) 〉 ., Multiplying Eq 5 by Xp ( 0 ) and averaging with 〈gp ( t ) ⋅ Xp ( 0 ) 〉 = 〈gp ( t ) 〉⋅〈Xp ( 0 ) 〉 = 0 , we can derive that the correlation function for p ≥ 1 satisfies, ∫ 0 t γ ( t - t ′ ) d C p ( t ′ ) d t ′ d t ′ = - k p C p ( t ) ., ( 9 ), The first term for p = 0 in the RHS of Eq 8 corresponds to the MSD of the center of the CD , and the motion obeys ∫ 0 t γ ( t - t ′ ) d X 0 ( t ′ ) d t ′ d t ′ = g 0 ( t ) and the FDR 〈 g 0 κ ( t ) g 0 λ ( t ′ ) 〉 = k B T N γ ( t - t ′ ) δ κ λ ., According to the fluctuation-dissipation theorem 49 , the motion of the center of mass is subdiffusive with exponent α ( see S1 Text , Section IE ) :, X 0 ( t ) - X 0 ( 0 ) 2 = 2 〈 R 2 〉 CD A d f Γ ( 1 + α ) t τ d f , α α , ( 10 ), where, τ d f , α ≡ N γ α 〈 R 2 〉 CD A d f · 3 k B T 1 / α ( 11 ), represents the relaxation time of nucleosome fluctuations in the CD ., On the other hand , the second term in the RHS of Eq 8 describes the fluctuations of many modes inside the CD ., Using the Laplace transformation and the thermal equilibrium initial state , the solution of Eq 9 can be derived as follows ( see S1 Text , Section ID ) :, C p ( t ) = X p 2 CD E α - p 1 + 2 / d f t / τ d f , α α , ( 12 ), where Eα ( x ) is the Mittag-Leffler function ., According to the polymer physics 35 for t ≪ τdf , α , ϕ ( n , t ) is dominated by terms with large p ., Moreover , since the MSD in our experiment ( Fig 2E ) is calculated by averaging the nucleosome trajectories at various positions in CDs , the term cos 2 ( p π n N ) can be replaced by the average 1/2 ., Therefore , according to the asymptotic form of the Mittag-Leffler function , Eα ( −x ) ≃ exp−x/Γ ( 1 + α ) for x ≪ 1 , and the conversion of the sum into the integral , we obtain for t ≪ τdf , α MSD ( t ) ≃ 2 B d f , α 〈 R 2 〉 CD A d f Γ ( 1 + α ) t τ d f , α α · 2 / ( 2 + d f ) , ( 13 ), where B d f , α = d f 2 Γ ( 1 + α ) d f / ( 2 + d f ) Γ d f / ( 2 + d f ) is a dimensionless constant ( see S1 Text , Section IF ) ., Thus , in our model , subdiffusive motion of single nucleosomes is a typical feature , assuming both fractal CDs and viscoelastic medium ., In order to apply our model to living human cells , single-particle imaging of nucleosomes was performed by observation of PA-mCherry labels 51 attached to histone H2B in human HeLa cells ( Fig 2A ) ., The clear single-step photobleaching profile of the H2B-PA-mCherry dots shows a single H2B-PA-mCherry molecule in a single nucleosome ( Fig 2B ) ., We tracked approximately 40 , 000 dots representing single nucleosomes ( S1 Table ) ., Fig 2D shows representative trajectories of the dynamic nucleosome movement in single cells ., Here , to evaluate the state of CDs according to their position in the nucleus , we focused on the nuclear interior and periphery ( or surface ) ( Fig 2C and S1 Fig ) , and calculated the MSD ., The nuclear periphery is a heterochromatin-rich region , which presumably shows much less active transcription than the interior ., The plots of the MSD at each region , in time interval t up to 0 . 5 s , are shown in Fig 2E ( normal scale ) and S2 Fig ( log-log scale ) ( also see S1 Table ) ., The MSD at the interior is higher than that at the periphery ., This result implies that nucleosome movement within CDs in the euchromatin-rich interior region is higher than that in the heterochromatin-rich periphery region ., As we analytically derived the subdiffusive MSD ( Eq 13 ) , the experimental result clearly shows subdiffusion of single-nucleosomes: using Eq 1 , the plots fit well with the MSD curves 0 . 018 t0 . 44 μm2 and 0 . 013 t0 . 39 μm2 for the interior and the periphery , respectively ., Comparing Eqs 1 and 13 , β and Dapp are calculated as, β = α · 2 2 + d f , ( 14 ) D app = C d f , α · 3 k B T N γ α 2 / ( 2 + d f ) · 〈 R 2 〉 CD d f / ( 2 + d f ) , ( 15 ), where C d f , α = 2 B d f , α ( A d f ) d f / ( 2 + d f ) Γ ( 1 + α ) ., It turns out that these values contain statistical information of the CD structures , 〈R〉CD and df ., Since β and Dapp can be determined by the fitting in our experiments , we can therefore estimate 〈R〉CD and df , inversely ., The lower MSD at the periphery than at the interior , Dapp , periphery < Dapp , interior and βperiphery < βinterior , reflects the fact that the CDs near the periphery are in a more compact conformational state and are smaller in size than those at the interior: df , periphery > df , interior and 〈R〉CD , periphery < 〈R〉CD , interior ., This property is consistent with the conventional distribution of heterochromatin: the CDs in the heterochromatin-rich nuclear periphery are more compact than those in the euchromatin-rich interior 52 ., Our results indicate that our proposed model serves as a strong method for extracting the structural information of CDs from observations of dynamic nucleosome movement ., Super-resolution microscopy techniques can be used to elucidate the spatial size of CDs according to different epigenetic states 53 ., On the other hand , development of an effective imaging technique to reveal the fractal dimensions remains a challenge for the future ., The conformational state of CDs characterized by the fractal dimension must be associated with the accessibility of transcription factors , depending on the physical size of those factors 59 ., Beyond the pioneer computational work of analyzing interphase chromosomes based on the chromatin fibers 60 , further development of not only a large-scale chromosome model based on the results of a genome-wide association study 61 but also restraint-based three-dimensional modeling of genomes 62 is expected to provide novel insight and open the door toward further discovery on the relationship between dynamic genome organization and stochastic gene expression ., To observe single nucleosomes and analyze their local dynamics in living human cells , histone H2B was fused with photoactivatable ( PA ) -red fluorescent protein ( mCherry ) 51 and expressed in HeLa cells as described previously 25 ., The cell lines expressing H2B-PA-mCherry at a very low level were isolated ., The cells were cultured in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) ( vol/vol ) at 37°C in 5% CO2 ( vol/vol ) ., The cells were plated 24–48 h before the experiment onto Iwaki glass bottom dishes treated with poly-lysine ., Before the experiment , the medium was replaced by DMEM F-12 ( non phenol red ) with 15% FBS ., The cells were then set on the microscope stage kept in a custom-built 37°C microscope incubator enclosure with 5% CO2 ( vol/vol ) delivery throughout the experiment ., For single-nucleosome imaging , an oblique illumination microscope was used to illuminate a limited thin area within the cell ( Nikon laser TIRF microscope system Ti with sapphire 564-nm laser ) ., In general , PA-mCherry exhibits red fluorescence only after activation by a 405-nm laser 51 ., However , we unexpectedly found that a relatively small number ( ∼100/time frame/nucleus ) of H2B-PA-mCherry molecules were continuously and stochastically activated even without UV laser stimulation ., Fig 2A shows a typical single-nucleosome image of a living HeLa cell ., Due to the clear single-step photobleaching profile of the H2B-PA-mCherry dots , each dot in the nucleus represents a single H2B-PA-mCherry in a single nucleosome ( Fig 2B ) ., Nucleosome signals were recorded in the interphase chromatin of the nuclear interior and periphery in living HeLa cells at a frame rate of ca ., 50 ms/frame ., Note that the two different focal planes for the nuclear interior and periphery ( Fig 2C ) were precisely ensured by nuclear surface labeling with Nup107 ( a nuclear pore component ) -Venus ( a bright yellow fluorescent protein ) 63 ( see S1 Fig ) ., Local nucleosome fluctuation was observed ( ca . 60 nm movement/50 ms ) , presumably caused by Brownian motion ., The free MATLAB software u-track 64 was used for single-nucleosome tracking ., The dots were fitted to an assumed Gaussian point spread function to determine the precise center of the signals with higher resolution ., Finally , we obtained data set of two-dimensional Mi trajectories { ( x 0 j , y 0 j ) , ( x 1 j , y 1 j ) , … , ( x i j , y i j ) } , where the suffix j ∈ {1 , ⋯ , Mi} represents the sample number for the tracked time-interval 0 , ti; ti ≡ i × 50 ms . Several representative trajectories of fluorescently tagged single nucleosomes are shown in Fig 2D ( bar = 100 nm ) ., According to observed regions , we calculated the ensemble-averaged MSD of single nucleosomes: MSD ( t i ) = 3 2 1 M i ∑ j = 1 M i ( x i j - x 0 j ) 2 + ( y i j - y 0 j ) 2 ., Here , in order to obtain the three-dimensional value , we multiplied the two-dimensional value by 3/2 on the assumption of isotropy ., Plots of the MSDs of single nucleosomes in interphase chromatin at the nuclear interior ( 10 cells ) and the nuclear periphery ( 10 cells ) from 0 to 0 . 5 s are shown in Fig 2E ., The plots for single nucleosomes were fitted with the subdiffusion model ( Eq 1 ) using R-software ., The standard error of the mean ( SEM ) , which is the standard deviation of the sampling distribution of the mean , for MSD ( ti ) was sufficiently small ., The number of trajectories Mi and the SEM of MSD ( ti ) are summarized in S1 Table .

Introduction, Results, Discussion, Materials and Methods

The mammalian genome is organized into submegabase-sized chromatin domains ( CDs ) including topologically associating domains , which have been identified using chromosome conformation capture-based methods ., Single-nucleosome imaging in living mammalian cells has revealed subdiffusively dynamic nucleosome movement ., It is unclear how single nucleosomes within CDs fluctuate and how the CD structure reflects the nucleosome movement ., Here , we present a polymer model wherein CDs are characterized by fractal dimensions and the nucleosome fibers fluctuate in a viscoelastic medium with memory ., We analytically show that the mean-squared displacement ( MSD ) of nucleosome fluctuations within CDs is subdiffusive ., The diffusion coefficient and the subdiffusive exponent depend on the structural information of CDs ., This analytical result enabled us to extract information from the single-nucleosome imaging data for HeLa cells ., Our observation that the MSD is lower at the nuclear periphery region than the interior region indicates that CDs in the heterochromatin-rich nuclear periphery region are more compact than those in the euchromatin-rich interior region with respect to the fractal dimensions as well as the size ., Finally , we evaluated that the average size of CDs is in the range of 100–500 nm and that the relaxation time of nucleosome movement within CDs is a few seconds ., Our results provide physical and dynamic insights into the genome architecture in living cells .

The mammalian genome is partitioned into topological chromatin domains ( CDs ) in the living cell nuclei ., Gene expression is highly regulated within CDs according to their structure , whereas chromatin itself is highly dynamic ., This raises the following question: how is the CD structure in such dynamic chromatin ?, We developed a conceptual framework that unifies chromatin dynamics and structure ., Using a polymer model with a fractal domain structure in a viscoelastic medium , we analytically show that nucleosome movement is subdiffusive and depends on CD structure ., Hence , structural information can be extracted based on nucleosome movement in living cells with single-particle tracking experiments ., This framework provides physical insights into the relationship between dynamic genome organization and gene expression .

relaxation time, hela cells, biological cultures, geometry, mathematics, materials science, cell cultures, epigenetics, macromolecules, chromatin, materials by structure, research and analysis methods, polymers, polymer chemistry, relaxation (physics), fractals, chromosome biology, gene expression, chemistry, cell lines, nucleosomes, physics, mass diffusivity, cell biology, genetics, biology and life sciences, physical sciences, cultured tumor cells, chemical physics, chromosomes

journal.pcbi.1005735

Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

As with many animals , vision plays a key role in a number of behaviours performed by the fruit fly Drosophila melanogaster , including mate-recognition 1 , place homing 2 , visual course control 3 , collision-avoidance 4 , landing 4 and escaping a looming object ( like a rolled newspaper , for example ) 5 ., The benefit of studying these visually guided behaviours in Drosophila is the range of neurogenetic techniques which give a realistic chance of understanding the neural circuits that underpin them ., With that goal in mind , we focus on work by Seelig and Jayaraman 6 which mapped the receptive fields ( RFs ) of a set of visually responsive neurons: the ring neurons of the ellipsoid body ., These neurons are necessary and sufficient for a range of complex behaviours , including short term spatial memory , pattern discrimination and place memory 2 , 7–9 , and yet are surprisingly small in number ., To understand their role in these behaviours , we used modelling to bridge the gap between neurogenetic data and behaviour by evaluating ring neuron responses during simulations of fly experiments ., In this way we investigate how small populations of visual neurons in Drosophila , which might represent a sensory bottleneck , can still provide behaviourally relevant information ., In laboratory assays , flies show interesting spontaneous visual behaviours ., For instance , flies orient towards bar stimuli 10 , 11 and in a circular arena with two diametrically opposed bars will walk between them until exhaustion 12 ., The attraction to vertical bars decreases as the bar is shortened and flies are strongly repulsed by small spots 13 ., In addition , a number of studies have investigated the process of pattern recognition and its neural underpinnings 7 , 14 , 15 ., Flies seem to possess a form of pattern memory analogous to the better-studied pattern memory of bees 16–18 ., Interestingly , both bees 19 and flies 14 systematically fail to discriminate certain pattern pairs ., These visual behaviours require the central complex , a major neuropil which comprises the ellipsoid body , the fan-shaped body , the paired noduli and the protocerebral bridge 20 ., The central complex is thought to be involved primarily in spatial representation , action selection and mediation between visual input and motor output 21 ., One class of neurons with projections in the ellipsoid body is the ‘ring neurons’ , which are known to be involved in certain visual behaviours ( R1: place homing 2 , 22 , 23; R2/R4m: pattern recognition 7 , 14 , 15; R3/R4: bar direction memory 8 ) ., Here we investigate how the ring neurons might contribute to behaviour , by simulating the visual input as it would be processed by this small population of visually responsive cells ., In particular , we can address why flies are unable to discriminate certain pattern pairs , whether these subpopulations of neurons are optimised for pattern recognition and , if not , what visually guided behaviours these cells are suited to ., In order to do this , we leverage research which has described the RF properties of two classes of ring neuron in the Drosophila ellipsoid body 6 ., The two subtypes of neuron investigated were the R2 and the R4d ring neurons , of which only 28 and 14 , respectively , were responsive to visual stimuli ., The cells were found to possess RFs that were large , centred in the ipsilateral portion of the visual field and with forms similar to those of mammalian simple cells 24 ( for details of how the RFs were estimated , see Materials and methods ) ., Like simple cells , many of these neurons showed strong orientation tuning and some were sensitive to the direction of motion of stimuli ., The ring neuron RFs , however , are much coarser than those of simple cells , far larger and less evenly distributed across the visual field and respond mainly to orientations near the vertical ., This suggests that ring neurons might have a less general function than simple cells 25 ., In mammals , the very large population of simple cells means that small , high-contrast boundaries of any orientation are detected at all points in the visual field ., Thus the encoding provided by simple cells preserves visual information and acts as a ‘general purpose’ perceptual network that can feed into a large number of behaviours ., In contrast , the coarseness of the ring neuron RFs , allied to the tight relationship between specific behaviours and specific subpopulations of ring neurons , suggests instead that these cells are providing economical visual information that is likely tuned for specific behaviours 25 ., To investigate such issues , we use a synthetic approach whereby investigations , in simulation , of the information provided by these populations of neurons can be related to behavioural requirements , thus ‘closing the loop’ between brain and behaviour ., We show how the population code is well-suited to the spontaneous bar orientation behaviours shown by flies ., Similarly , we verify that our population of simulated ring neurons is able to explain the success and failure of the fly to discriminate pairs of patterns ., Upon deeper analysis , we demonstrate that certain shape parameters—orientation , size and position—are implicit in the ring neurons’ outputs to a high accuracy , thus providing the information required for a suite of basic fly behaviours ., This contrasts with the rather limited ability of ring neuron populations ( and flies ) to discriminate between abstract shapes , casting doubt on cognitive explanations of fly behaviour in pattern discrimination assays ., We first consider experiments in which flies are presented with bar stimuli , as flies are known to spontaneously orient towards black bars 11 , aiming for the centres of narrow bars and the edges of wide bars 27 ., We therefore decided to examine the responses of simulated ring neurons to bars of different widths ( Fig 1A and 1B ) ., The summed outputs of the ensembles of ring neurons show peaks to the bars of different widths , which broadly matches experimental results ( Fig 1B ) ., For instance , R2 neurons respond maximally to the inside edges of large bars , while peak activity in R4d neurons occurs at bar centres and also at roughly ±90° ., While we do not know the details of mechanisms downstream of the ring neurons and hence how their activity is transformed into action , the simulation is an existence proof that the information needed to control the observed behaviour is present in the sparse ring neuron code ., We further demonstrate this point by closing the loop between sensory systems and behaviour using a simple model of a fly viewing a bar in which the fly’s heading is controlled by the difference between the summed activation of left and right ring neurons ( Fig 1C; see Materials and methods for details ) ., The simulated fly approaches the bar from different distances , demonstrating centre-aiming when far from the bar and fixation of the edges when it is nearer and the bar’s apparent size is thus greater ( Fig 1D ) ., Through this example , we can see how the information present in this small population of visually responsive ring neurons can control a specific behaviour ., We now turn to a more complex behaviour: pattern discrimination ., The standard paradigm for testing pattern discrimination involves putting a fly into a closed-loop system where it is tethered inside a drum , on the inside of which are two different visual patterns , alternating every 90° , giving four visual stimuli in total 7 , 14 , 15 , 28 ( see Fig 1E ) ., As the fly attempts to rotate in one direction , the drum rotates in the other , giving the fly the illusion that it is moving in a stable world ., To elicit conditioned behaviour , if the fly faces one of the four pattern stimuli it is punished by a heat beam ., Over time , if the fly is able to differentiate the patterns , it should preferentially face the unpunished pattern ., This procedure has been used to demonstrate that flies can differentiate stimulus pairs such as upright and inverted ‘T’ shapes , a small and a large square , and many others 14 ., The ability to discriminate patterns in such an assay requires R2 neurons 7 , 14 , 29 ., More specifically , synaptic plasticity afforded by rutabaga in these neurons is sufficient and necessary for observed pattern learning 15 ., We therefore investigate the responses of ring neurons in simulations of the classic pattern discrimination paradigm ., To recreate the visual information perceived by flies in such experiments , we simulated the typical experimental flight arena with a fly tethered in the centre ., We then examined the output of the ensembles of ring neurons for a fly rotating in the drum and looked at the difference in the activation code when the agent was facing the different patterns of a pair ., Our logic is that if the ensemble codes were identical , it would be impossible for the patterns to be discriminated by interrogating the outputs of ring neurons alone ., Similarly , the greater the difference in the ring neuron ensemble activation codes when looking at the pattern pairs , the easier they would be to discriminate ( Fig 1F and 1G; see Materials and methods for details ) ., Our discriminability measure is the root mean square ( r . m . s . ) difference between ensemble outputs when the ( virtual ) fly faces different azimuths in the drum ., In this way , we can compare the ensemble output when the ‘fly’ is oriented at 0° ( i . e . with the view centred on one pattern ) and the ensemble output at other azimuths ( Fig 1 ) ., We henceforth treat this as a measure of ‘discriminability’ of patterns , following the experimental work that we are modelling , though of course in reality an animal’s ability to discriminate stimuli is not an absolute value and varies depending on many factors , including task and training procedure 30 ., The r . m . s . difference , as compared to the view at 0° , rises as the fly rotates in the drum , peaking as it faces the space in between the patterns and dropping to a minimum when facing the centre of the next pattern ( Fig 1F and 1G ) ., For some pairs of patterns , there is still an appreciable r . m . s . difference between the codes when facing the centres of each pattern , thus enabling their discrimination ., However , in the example of Fig 1F and 1G , if we displace the patterns vertically , we see a drop in the r . m . s . difference between activation codes when the fly fixates the patterns ., This is despite the fact that , to the human eye , the patterns still appear very different ., Interestingly , the pattern pair in Fig 1G is also harder to discriminate for flies ., In this way , we can use the difference between ensemble codes when flies face the patterns to re-examine the discriminability of pattern pairs tested with flies ., One illustrative example is shown in Fig 2 ( see pattern set ( 9 ) in Fig 3 ) , which contains pairs of ‘triangles’ , one facing up and the other down ., Drosophila are able to discriminate these pattern pairs when they are aligned along the top and bottom , but not when aligned about the vertical centres of mass 14 ., Looking at the placement and form of the R2 RFs allows us to determine where this difference comes from ( Fig 2 ) ., The excitatory regions of the RFs fall roughly across the middle of triangles that are not aligned about their vertical centres of mass and therefore the difference in width at this point will lead to differences in activation ., If the triangles are offset ( Fig 2B ) so as to be aligned about their vertical centres of mass , their width will be similar for the regions of peak R2 coverage and the difference in activation will be lower ., Thus the failure to discriminate features with an equivalent vertical centre of mass can be explained by the shape of the RFs interacting with the patterns directly ., It is not necessary to invoke an additional system that extracts and compares the vertical centres of mass of the patterns ., Performance on poorly discriminated patterns can be improved , however , by simply adding more RFs of the same form ., Fig 2D and 2E show the increase in performance with number of RFs for two such pattern pairs: triangles and triangularly shaped horizontal bars aligned about the centre of mass ( from pattern set ( 9 ) in Fig 3; see Materials and methods for details ) ., This demonstrates that the patterns could be discriminated by flies simply with the addition of more RFs centred on other portions of the visual field ., Similarly , pattern set ( 2 ) in Fig 3 gives examples of pattern pairs that are not discriminable by flies and also give only small differences in the outputs of R2 filters ., This may seem surprising , given that these patterns appear quite different to human observers and are also very dissimilar if compared retinotopically ., Thus we can see that the R2 ring neuron encoding is informationally sparse ., Whilst the V1 region of human visual cortex contains neurons representing the full range of orientations across the visual field , R2 neurons have large RFs and poor orientation resolution ., Hence , a pattern pair consisting of a diagonal line facing left and a diagonal line facing right , for example , have only a small difference in R2 outputs in our simulation and are also not discriminable by flies ., This could , in the light of behavioural experiments alone , be interpreted as evidence that flies do not discriminate patterns on the basis of orientation ., A more parsimonious explanation , however , is that the flies are failing because the form of the RFs means that the output code is similar for these particular orientations ., To emphasise the independence of apparent similarity of patterns and the visual encoding from R2 cells , we designed shape pairs that appear similar to humans , but are easily discriminable by the R2 population ( white bars in Fig 2F ) , as well as shape pairs that are considered similar by the R2 population but not by human observers or in terms of retinal overlap ( black bars in Fig 2F; see Materials and methods for details ) ., Despite the similarity between the pairs of patterns , the first is readily discriminable , especially from the outputs of glomeruli 1 , 3 , 5 and 11 , while the second pair—which we easily see as having a different orientation—has very low overall differences across the glomeruli ., This shows that the irregular RF shapes can lead to counterintuitive results ., The small population of visually responsive R2 neurons can be thought of as a sensory bottleneck ., If the information that passes through this bottleneck is all that a fly has available for pattern discrimination , then we should see a close relationship between the r . m . s . difference in simulated R2 output for a pattern pair and the flies’ ability to learn to discriminate that pair ., We thus examined the difference in the outputs of the R2 filters between patterns from pairs drawn from work by Ernst and Heisenberg 14 ( Fig 3 ) ., In general , the pattern pairs for which flies show a significant learned discrimination have a greater r . m . s . difference in R2 population activity 14 ., All of the pattern pairs where flies show significant learning ( n = 8 ) have R2 r . m . s . differences above the overall mean ( Fig 3A and 3B ) , whereas 13 out of 18 patterns that flies found more difficult to learn had below-average r . m . s . differences ( there were nine pattern pairs for which a significance level was not given that were excluded . ) Across all pattern pairs , we find a significant correlation between the strength of the learning index reported for flies in 14 and the r . m . s . difference in R2 activation ( Spearman’s rank , n = 30 , ρ = . 420 , p < . 05 ) ., Of course , these differences could simply result from the apparent similarity of the patterns ., Therefore , as a control comparison , we quantified the similarity of pattern pairs based on the degree to which the patterns overlap in a pixel-by-pixel manner ( see Materials and methods ) ., There was no significant correlation with the flies’ learning index over the pattern pairs ( Spearman’s rank , n = 32 , ρ = − . 068 , p = n . s . ) ., We additionally looked at the relationship between the two visual similarity metrics ( R2 population code and pixelwise retinal overlap ) and the degree to which flies show a spontaneous preference ( i . e . without any conditioning ) for one of the patterns within a pair ( Fig 3D and 3E ) ., There was no correlation for R2 population codes ( Spearman’s rank , n = 29 , ρ = . 289 , p = n . s . ) , but for retinal overlap there was a weakly significant correlation ( Spearman’s rank , n = 29 , ρ = − . 371 , p < . 05 ) ., This is consistent with research showing that R2 neurons alone are critical for learned pattern differences 14 , but not spontaneous preferences which , by contrast , seem to result from activity across all subsets of ring neurons 31 ., There are , however , some discrepancies where the learning performance of flies for a particular pattern pair does not match the r . m . s . difference of our R2 population code ., In some cases flies are better at discriminating pairs of patterns that differ along the vertical rather than horizontal axis ( set ( 3 ) vs set ( 4 ) , and the pairs in set ( 12 ) , marked with red Xs in Fig 3 ) ., In contrast , the r . m . s . difference in the R2 population code discriminates horizontal and vertical patterns equally ., This is because while our R2 filters are presented with static stimulus pairs to simulate a fly facing the centre of a pattern , for real flies the patterns were moving horizontally but fixed in the vertical axis making it harder for flies to resolve horizontal information 14 ., Overall , we have shown that the behavioural performance of flies on a pattern discrimination task is approximated by a simple discriminability metric applied to the population activity of a small number of simulated R2 cells ., There were , however , a number of seemingly ‘easy’ pattern pairs which neither flies nor the simulated population of R2 cells , perhaps surprisingly , could discriminate ., On further investigation , we found that performance for poorly discriminated pattern pairs could be improved with the addition of extra R2-type RFs ., Thus , it seems likely that the pattern discrimination capability of a set of R2-like neurons could easily have been improved over evolutionary time with the simple addition of more cells and we therefore suggest that there must have been little selection pressure specifically for a specialised pattern recognition module in fruit flies ., Information from 3000 ommatidia is funnelled to just 28 R2 and 14 R4d ring neurons , yet these cells are able to support a number of complex behaviours ., We have shown how the R2 population code provides sufficient information to discriminate some pattern pairs , and also that , as performance could be improved with the addition of more ring neurons , general-purpose pattern recognition seems unlikely to be the purpose of the ring neuron system ., So what information is this system tuned to extract ?, Examining the pattern pairs which flies and the R2 population were able to discriminate , we see that certain pattern parameters are implicitly coded for in the R2 population ., Pattern sets ( 6 ) and ( 9 ) ( Fig 3 ) suggest that , for instance , stimulus size and vertical centre of mass are parameters that can be recovered from the R2 population code after this sensory bottleneck ., We now address in more general terms the question of what shape information is implicitly conveyed in the ring neuron population code ., To do this , we generated large sets of ellipse-like ‘blob’ stimuli varying in size ( specified by major-axis length ) , position ( azimuth and elevation ) and orientation ., The blob generation procedure was stochastic and so the precise shape of each blob was random and unique ( see Materials and methods ) ., We then trained an artificial neural network ( ANN ) to recover this shape information from either a raw image of the shape ( the control condition ) or from the output of the R2/R4d populations on presentation of the blobs ., We are using ANNs here as statistical engines interrogating the output of the ring neuron population code to determine if shape information is implicit to the code and has therefore passed through the sensory bottleneck ., We first examined whether ANNs could be trained to extract positional information ( the elevation and azimuth ) of randomly generated blobs ., Note that as the blobs are initially aligned about their centres of mass , elevation is equivalent to vertical centre of mass , except where the blobs are partially outside the visual field ., The blobs varied along four parameters: elevation ( ≥ −60° and ≤ 60° ) , azimuth ( ≥ −135° and ≤ 0° ) , orientation ( ≥ 0° and ≤ 90° ) and major-axis length ( ≥ 12 . 79° and ≤ 60° ) ., Each parameter had 22 possible values , giving a total of 234 , 256 ( = 224 ) stimuli ., Of these , approximately 40% ( n = 93 , 702 ) were used for training and the remainder ( n = 140 , 554 ) for testing ., Results for the test set ( Fig 4A–4D ) show that ANNs are indeed able to extract information about elevation and azimuth from any of the input types ( ‘raw view’ , ‘R2’ , ‘R4d’ or ‘R2 + R4d’ ) ., Performance was better with parameter values near the middle: at the extremes , portions of the stimuli lay outside the visual field of the simulated fly , meaning stimuli begin to disappear ‘off the edge’ of the visual field ( Fig 4A and 4B ) , making the task harder ( i . e . , is this a large object projecting outside the visual field , or a smaller object at the edge ? ) ., While performance was best with raw views as inputs ( Fig 4C and 4D ) , positional information could still be reliably extracted from ring neuron outputs ., The R2 code performs better than the R4d and the addition of R4d RFs to the R2 code ( ‘R2 + R4d’ ) , while adding dimensionality , does not improve performance , suggesting that either an R2-like encoding is sufficient to extract positional information , or that the information in the two codes is redundant ., Thus small populations of ring neurons retain positional information ., We next trained ANNs to decode information about stimulus orientation and size ., The stimuli were random blobs , as before , with the same possible values for elevation , orientation and size ., This time , however , azimuthal position was fixed at −90° ., The reason for this was that the neural network struggled to encode information about orientation when azimuth also varied , presumably because the centres of the receptive fields—and thus the position on the visual field where they can best extract information—are clustered at around −90° ., For this experiment there were therefore 10 , 648 ( = 223 ) stimuli , of which approximately 40% ( n = 4259 ) were used for training and the remainder ( n = 6389 ) for testing ., The ANNs were able to extract this shape information from both raw images and the ring neuron outputs ( Fig 4E–4H ) ., Orientation was the parameter with the highest error score , possibly because its calculation requires a second-order statistic ( the covariance of the shape ) ., Nonetheless , both parameters could be simultaneously estimated by an ANN neural network fed with ring neuron outputs ., In summary , we have shown that information about a number of shape properties passes through the bottleneck created by the small number of ring neurons ., This indicates that such information is available downstream of the ring neurons for the guidance of behaviour ., One striking feature of the ring neuron receptive fields is that they are in general tuned to vertically oriented objects ., We know that fruit flies are strongly attracted to vertical bars , a finding that has been leveraged across a range of behavioural paradigms ( e . g . bar fixation: 8 ) ., In one , individual flies are placed into a virtual-reality arena with two vertical stripes 180° apart: flies will typically head back and forth between the two bars repeatedly ., Occasionally , when a fly crosses the arena’s midline , the bars disappear and a new bar is presented at 90° to the originals , to which the flies reorient ., The new target then also disappears , and the flies resume their initial heading , even though the original bar is no longer visible ., This indicates that directional information is stored in short-term memory and updated ., Work by Neuser and colleagues 8 has shown that R4 ( and R3 ) ring neurons are involved in this spatial orientation memory ., We found that both R2 and R4d neurons were responsive to vertical bars of varying widths , mimicking flies’ preference for the edges of larger bars and the centres of narrower ones 27 ., We also showed that the cells provide sufficient information to guide homing towards a large vertical object and , separately , that the azimuth of bar stimuli makes it through the sensory bottleneck ., Taken together , these findings demonstrate a viable role for the small R4d population in the behaviours described above ., The more general role of R4d cells within the central complex is still unknown ., There is evidence that R4d neurons are able to act as a ring attractor , maintaining a stable encoding of the fly’s orientation with respect to a landmark 9 , 32 ., Therefore , R4d neurons could be conceived variously as functioning like mammalian head-direction cells 33 , playing a part in a path integration system 8 or in conditioning of visual orientation 34 ., These possibilities are not mutually exclusive , of course , and their true function ( or functions ) will become apparent only with a better understanding of the behaviours in which they are involved ., Drosophila can discriminate patterns differing in size , orientation and elevation and other complex shape parameters , an ability for which R2 cells are critical 7 , 14 , 15 ., We have shown that the discriminability of a given pattern pair is predicted by the outputs of the small population of R2 cells , which have coarse receptive fields and therefore do not encode higher-order visual properties explicitly ., Does this limited ability of the R2 population ( and , of course , the fly ) to discriminate patterns suggest that flies might be a good model for the study of a universal perceptual process of pattern recognition , or might limited pattern recognition be an artefact of a perceptual system tuned to other tasks ?, Any selection pressure on flies’ ability to discriminate patterns ( as bees need to do , for instance ) would surely have led to a larger R2 population or , possibly , visual input to the mushroom body 35 , 36 , and we can therefore be confident that ring neurons have not been tuned for arbitrary , general-purpose pattern recognition ., Accordingly , we must suggest caution if research on flies is used with the aim of understanding the neural basis of pattern recognition or even visual cognition more generally 37 ., So what behaviours are served by the information that makes it through this sensory bottleneck ?, It is interesting to consider to what extent Drosophila’s ecological needs are served by general learning mechanisms—such as a capacity to learn arbitrary visual stimuli—and to what extent by domain-specific abilities ., For example , bees have a well-attested ability to learn many varied patterns , which presumably derives from a need to learn about flowers 38; it is not apparent , however , that there has been a comparable selection pressure on Drosophila for such general-purpose learning ., Across the animal kingdom there are many cases where a task-specific heuristic can provide an elegant solution ., For example , male fiddler crabs ( Uca pugilator ) treat salient objects above the horizon as predators and everything below as conspecifics 39 ., Similarly , Drosophila have a mechanism to approach bars and to avoid small objects 13; presumably to approach vegetation ( for oviposition , etc . ) and avoid predators , respectively ., In order to fully understand these circuits we need to examine further how flies depend on a balance of innate visual responses versus learned visual information ., So , if the R2s are not truly ‘pattern-recognition cells’ , the question remains: what are they for ?, Though we have not attempted to answer this question here , we have shown that there is implicit information about higher-order properties , such as stimulus position , orientation and size , in the RFs’ code , which could drive any number of natural behaviours ., For example , elsewhere we have shown that the information content of ring neuron RFs is suitable for place learning and homing 26 , and although this behaviour in flies involves a subset of ring neurons other than those examined here ( R1 ) , it gives an indication of how small populations of coarse , wide-field cells can be used to drive behaviour ., The goal of this work was to investigate the information encoded in a population of visually responsive ring neurons , in simulations of classic pattern discrimination assays ., Our aim was to examine the behavioural uses to which the information encoded in this population of cells could be put by a fruit fly ., Of course , a full understanding of these neurons requires detailed knowledge of how they interact with other neural circuits for behaving flies in natural environments ., Hence future work needs to address the interaction between brain , behaviour and environment 40 ., For the brain , a sensible starting point is to ask how ring neurons and the information they carry are integrated in the central complex circuitry ., Recent work has shown the presence of a ring attractor network 9 , 32 , 41 , 42 in the ellipsoid body of the central complex which integrates both visual and proprioceptive information ., This circuit is able to retain a heading in short-term memory 8 and thus the cells we have modelled could be useful in contributing information about the position of behaviourally relevant objects ., Of course , there are many details to be determined , such as the dynamics of neural coding in this circuit and the sensory pathways that lead to the observed receptive fields 43 ., In the current study we have not considered neural dynamics and have assumed that the information would be extracted as rate codes ., While this is a common assumption for models of visual perception ( e . g . 24 ) we note that information could be extracted via a timing code , perhaps even more efficiently—especially if the fly is actively perceiving its environment ., Though it is possible to convert from an analogue neural network to a spiking neural network 44 , more work would be needed to establish this ., Finally , the story is complicated further by the sheer variety of behaviours in which these cells have been implicated: for example , different subsets of R2 neurons have also been implicated in an olfactory decision task 45 and in sleep drive 46 ., The sensory ecology of fruit flies is still largely a mystery ( see 47 ) , despite the immense promise and productivity of Drosophila neuroscience research ., We thus know relatively little about ‘natural’ Drosophila visual behaviours—in contrast to bees , for which we know much about behaviour but comparatively little about the nervous system ., One pertinent example of this is visual pattern recognition , where for bees we have a good understanding of the real-world challenge facing a forager ., This has enabled models of pattern recognition to be developed for bees 48 ., Without a detailed understanding of sensory ecology and the natural behaviour of flies , it is hard to understand what type of pattern vision flies might need ., However , some fly behaviours are easier to relate to the natural environment ., Flies show an innate attraction to long bar-like objects , on which they might perch 13 and in Fig 5 we show one example of how behaviourally relevant information is maintained in the output of R4d cells even for

Introduction, Results, Discussion, Materials and methods

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex , changing world ., Yet the computational power available is constrained by biology and evolution , favouring mechanisms that are parsimonious yet robust ., Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster ., These so-called ‘ring neurons’ , projecting to the ellipsoid body of the central complex , are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation ., Recently the receptive fields of these neurons have been mapped , allowing us to investigate how well they can support such behaviours ., For instance , in a simulation of classic pattern discrimination experiments , we show that the pattern of output from the ring neurons matches observed fly behaviour ., However , performance of the neurons ( as with flies ) is not perfect and can be easily improved with the addition of extra neurons , suggesting the neurons’ receptive fields are not optimised for recognising abstract shapes , a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays ., Using artificial neural networks , we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes ., We show that these neurons are well suited for encoding information about size , position and orientation , which are more relevant behavioural parameters for a fly than abstract pattern properties ., This leads us to suggest that in order to understand the properties of neural systems , one must consider how perceptual circuits put information at the service of behaviour .

A general problem in neuroscience is understanding how sensory systems organise information to be at the service of behaviour ., Computational approaches can be useful for such studies as they allow one to simulate the sensory experience of a behaving animal whilst considering how sensory information should be encoded ., In flies , small subpopulations of identifiable neurons are known to be necessary for particular visual tasks , and the response properties of these populations have now been described in detail ., Surprisingly , these populations are small , with only 14 or 28 neurons each , which suggests something of a sensory bottleneck ., In this paper , we consider how the population code from these neurons relates to the information required to control specific behaviours ., We conclude that , despite previous claims , flies are unlikely to possess a general-purpose pattern-learning ability ., However , implicit information about the shape and size of objects , which is necessary for many ecologically important visually guided behaviours , does pass through the sensory bottleneck ., These findings show that nervous systems can be particularly economical when specific populations of cells are paired with specific visual behaviours ., This is a general-interest finding for computer vision and biomimetics , as well as sensory neuroscience .

pattern recognition receptors, invertebrates, medicine and health sciences, immunology, social sciences, neuroscience, animals, artificial neural networks, animal models, drosophila melanogaster, model organisms, cognition, artificial intelligence, experimental organism systems, computational neuroscience, vision, drosophila, neuronal tuning, research and analysis methods, immune system proteins, computer and information sciences, animal cells, proteins, behavior, insects, arthropoda, biochemistry, signal transduction, cellular neuroscience, psychology, eukaryota, cell biology, neurons, biology and life sciences, cellular types, sensory perception, immune receptors, cognitive science, computational biology, organisms

journal.pcbi.1006689

Kinetic and thermodynamic insights into sodium ion translocation through the μ-opioid receptor from molecular dynamics and machine learning analysis

Evidence of allosteric modulation of receptor signaling by cations was first presented in the literature for opioid receptors ( ORs ) ., Specifically , sodium ( Na+ ) and lithium , but not other monovalent or divalent cations , were shown to enhance receptor binding of opiate antagonists and to reduce the binding of opiate agonists , thus altering ligand properties in vivo 1 ., Over the course of years , the original hypothesis that Na+ stabilizes an inactive conformation of the receptor was extended to several other G protein-coupled receptors ( GPCRs ) 2 , but it was only recently supported by various ultra-high resolution crystal structures of inactive GPCRs , including that of δ-OR 3 ., In this structure , Na+ was found to be bound at an allosteric site through coordination with two water molecules as well as receptor residues N1313 . 35 , S1353 . 39 , and D952 . 50 ( superscripts refer to the Ballesteros-Weinstein generic numbering scheme 4 ) ., Notably , we observed a similar ion coordination in molecular dynamics ( MD ) simulation studies 5 of Na+ binding from the bulk solvent to the inactive μ- and κ-OR ( MOR and KOR , respectively ) crystal structures embedded in a hydrated 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) /10% cholesterol lipid bilayer and at physiological concentrations of Na+ ., Unlike their inactive crystal structures , experimental structures of active GPCRs ( e . g . , those of MOR 6 , 7 ) , show a collapsed ion binding site which likely results in weaker Na+ binding affinity and consequent ion departure from the receptor ., How Na+ migrates into the cytosol has recently been described in the literature for the active state of the M2 muscarinic receptor 8 ., One conclusion of that study is that Na+ egress into the cytosol occurs without significant energy barriers when the D2 . 50 is protonated and another fairly conserved residue , Y7 . 53 , is in an upward configuration ., Notably , the intracellular egress of Na+ is further facilitated by the formation of a hydrated pathway connecting the orthosteric ligand binding pocket to the G protein binding site , a feature that has also been seen in experimental structures ( e . g . , those of active MOR 6 , 7 ) as well as in recent simulation studies of GPCRs such as the adenosine A2A receptor 9 and the serotonin 5-HT1A receptor 10 ., Herein , using a combination of molecular dynamics ( MD ) , Markov State Models ( MSMs ) , and machine learning tools , we provide , for the first time , estimates of the timescales associated with Na+ translocation through the TM helix bundle of either active or inactive MOR conformations embedded in an explicit POPC/10% cholesterol lipid bilayer at a physiological concentration of Na+ ., Moreover , we present complete free-energy profiles of Na+ movement through these receptor states , estimates of Na+-induced changes to agonist binding validated by radioligand measurements , and testable hypotheses of the most important underlying motions and molecular determinants involved in Na+ translocation ., The three-dimensional spatial density distributions of Na+ across inactive or active MOR , the latter with either a charged or a protonated D1142 . 50 , were obtained from the corresponding combined trajectories of unbiased MD and US simulations ( see the Methods section for details ) ., As shown in Fig 1 , clear differences exist in the spatial distribution of Na+ across the active and inactive MOR , with the inactive receptor structure ( Fig 1a ) exhibiting several more localized high-density regions for the ion ( red hotspots ) compared to active MOR ( Fig 1b and 1c ) ., Perhaps the most striking difference between the active and inactive MOR is the continuous Na+ density distribution observed through the entire TM bundle in the active , but not the inactive , MOR ., To generate testable hypotheses of the most important molecular determinants involved in Na+ translocation among residues close to high ion densities , we extracted lists of residue pairs ( S3 Table ) whose minimum heavy atom distance fluctuations had a correlation larger than 0 . 6 to the most dominant tIC0 and tIC1 components ., Graphs illustrating these selected residue pairs on the inactive or active MOR crystal structures are shown in S2 Fig . These highly correlated inter-residue distance fluctuations to tIC0 and tIC1 ( e . g . , those involving the conserved , functionally important , NPxxY ( x ) 5 , 6F motif in rhodopsin-like GPCRs ) can be interpreted as the main contributors to the slowest ( and most important ) motion modes in the simulated inactive and active MOR systems ., For instance , out of a total of 176 residue pairs that are highly correlated to tIC0 in the simulated inactive MOR ( S3 Table ) , Y3367 . 53 , F343H8 and N3327 . 49 are involved in 89 , 58 , and 30 pairs , respectively , while other residues are only involved in 3 or fewer pairs ., Notably , disruption of the interaction between rhodopsin residues corresponding to positions Y3367 . 53 and F343H8 in MOR has been shown to lead to rhodopsin activation 14 ., Overall , the simulated active MOR systems exhibited a reduced number of highly correlated inter-residue distance fluctuations to the most dominant tIC0 and tIC1 components compared to the inactive simulated MOR ( S3 Table ) , although most of them still involved residues of the NPxxY ( x ) 5 , 6F motif ., While N3327 . 49 also stood out as a main contributor to the most important motions of the two simulated active MOR systems , Y3367 . 53 and F343H8 were found not to be involved in the most important motions in the active MOR system with a protonated D1142 . 50 ., Notably , recently published MD simulations of three different rhodopsin-like GPCRs 9 revealed that three distinct rotamer conformations of the conserved Y7 . 53 residue were correlated with the occlusion or opening of a continuous intrinsic water channel characteristic of an inactive or active conformational state of the receptor , respectively ., The observed larger number of relevant tIC0 and tIC1 components involving Y3367 . 53 in the inactive MOR compared to the active receptor ( S3 Table ) suggests that Na+ translocation to the cytosol in inactive MOR is hindered by several residues that have to move in a concerted manner to enable Y3367 . 53 to change its rotameric state and allow opening of the continuous intrinsic water channel ., The highly correlated inter-residue distance fluctuations to tIC1 are very different in the simulated inactive and active MOR systems ( S3 Table ) ., While in the inactive MOR all highly-correlated pairs to tIC1 involve the conserved F2896 . 44 residue in concerted motion with TM1 , TM2 , and TM3 residues , in the active MOR simulated with a charged D1142 . 50 , roughly half of the pairs are between residues located in the orthosteric ligand binding pocket ( e . g . , Y1483 . 33 , Y1493 . 34 , and N1503 . 35 ) and residues at the allosteric Na+ binding site ( e . g . , A1132 . 49 and D1142 . 50 ) ., In contrast , in the active MOR simulated with a protonated D1142 . 50 , all but one highly-correlated pairs to tIC1 involve the so-called “rotamer toggle switch” W2936 . 48 in concerted motion with TM3 , TM4 , TM5 , and TM7 residues ., Notably , many of these residues have a known functional role 15 , which , based on the above , might be due to their contribution to Na+ translocation ., Using the MEMM framework ( see Methods ) , we calculated the thermodynamics of Na+ translocation through both the inactive and active MOR ., The derived free-energy profiles of inactive and active MOR are reported in Fig 2 ., As seen in this figure , Na+ binding at its allosteric site ( Na+ z-coordinate = 0 ) is ~6 kcal/mol more energetically favorable in the inactive MOR compared to the receptor active state at pH = 7 ( up to ~11 kcal/mol compared to active MOR with a neutral D2 . 50 ) ., This means that in the inactive MOR , the Na+ ion needs to overcome a significantly higher free-energy barrier to egress to the cytosol compared to the active MOR ( especially the state with a neutral D2 . 50 , where the free energy barrier for the translocation is the lowest ) , making ion egress less likely to occur in an inactive MOR structure ., We also built MSMs to elucidate the kinetics of Na+ translocation in the inactive or active MOR ., For this , we not only present the results of MOR simulated with either a charged or protonated D1142 . 50 , but also those of a mixed model system at pH 7 that would capture , in principle , protonation changes of the residue ( see Methods for details ) ., Details of the MEMM construction and its validation are provided in the Methods section and S3 Fig , respectively ., The microstates of the Markov model for each system were divided into a small set of metastable states , which were labeled according to the Na+ position relative to the receptor as “extracellular” , “bound” , or “cytoplasmic” states ., The transition networks between these states are shown in Fig 3a for the inactive MOR , and in Fig 3b and 3c for active MOR with charged and protonated D1142 . 50 , respectively ., S4 , S5 and S6 Tables report the transition times between these metastable states for inactive MOR , active MOR with charged D1142 . 50 , and active MOR with protonated D1142 . 50 , respectively ., The observed larger number of Na+ bound states in the inactive MOR ( Fig 3a ) indicates a more rugged energy landscape than in the active receptor with multiple local minima that can trap the Na+ ion ., In order to obtain kinetic estimates of Na+ translocation that can be compared to experiments , we coupled the Markov model obtained from TRAM to a bulk state with a fixed ion concentration , and calculated Na+ transition times from extracellular bulk to bound states ( Na+ binding; Fig 4a ) , from bound to extracellular states ( Na+ dissociation; Fig 4b ) , and from bound to intracellular states ( Na+ egress; Fig 4c ) at different concentrations ( see the Methods section ) ., As expected , the Na+ binding kinetics is highly concentration-dependent while the Na+ dissociation and egress kinetics are virtually independent on concentration ., While the timescale of Na+ binding to the receptor is similar for the different receptor conformations , the timescales of Na+ dissociation and egress differ between inactive and active MOR ., Specifically , based on the “mixed model” at pH 7 , Na+ dissociation from the active MOR is estimated to take ~0 . 3 ( 0 . 2 , 2 . 0 ) μs , which is significantly faster than in the inactive MOR ( ~5 . 5 ( 3 . 0 , 8 . 0 ) μs ) ., This is consistent with the prediction that a Na+ bound state in the active MOR has significantly higher free energy compared to the inactive MOR ., The Na+ egress timescales are estimated to be of the order of hundreds of milliseconds to ~60 s depending on whether the active MOR has a protonated or charged D1142 . 50 , whereas Na+ leaves the inactive MOR state in ~100 seconds , suggesting that Na+ can more easily migrate to the cytosol in an active MOR with a protonated D1142 . 50 than in the inactive MOR ., Notably , these predicted timescales are similar to the experimentally derived lifetimes of GPCR/G protein complexes 16 , 17 ., From the concentration-dependent MSM model , we estimated the Na+ binding affinity to be 23 mM ( from 14 mM to 50 mM with errors ) at a ligand-free , inactive MOR or significantly lower ( 850 mM; from 650 mM to 1 . 3 M with errors ) at active MOR models with charged D2 . 50 ( S4 Fig ) ., The corresponding values for the active models with a neutral D2 . 50 and the model at a constant pH = 7 . 0 are in excess of 1 M . To explain Na+ modulation of ligand binding to MOR , we applied the two-state receptor theory ( see Methods ) , and used the free-energies obtained from the aforementioned models to calculate sodium-induced stabilization of the receptor inactive state relative to the active one ., Given that antagonists bind with equal affinity to both active and inactive conformations of the receptor , this model is consistent with the observation that antagonist bound fractions are not affected by ion concentrations ., In the case of a full agonist , we can quantitatively estimate the extent to which its bound fraction is modulated by the ion concentration ., Specifically , assuming a binding affinity of ~4 nM 18 for MOR agonist DAMGO at the active conformation of the receptor , we estimated the relative bound fraction of DAMGO to active MOR at constant pH = 7 as a function of the ion concentration as described in the Methods section , and report these results in Fig 5 , together with experimental values obtained by radioligand binding experiments ., The calculated percent reduction in agonist binding at increasing ion concentrations ( solid line in Fig 5 ) by this simple model is in good agreement with the experimental data , and indicates that ligand binding modulation is triggered by the stabilization of the inactive conformation of the receptor in the presence of sodium , which affects orthosteric ligand binding affinity ., The model estimates that 60 mM of Na+ ( with a confidence interval between 50 and 150 mM ) are required to achieve a 50% reduction of DAMGO binding , in excellent agreement with the experimental value of 60 mM ., For sodium concentrations above 200 mM , the simple model employed here slightly underestimates the effect of sodium , suggesting that direct interactions with the ligand or double occupation of the receptor by multiple ions might play a role at high ionic strength ., Saturation experiments ( see S5 Fig ) are also in reasonable agreement with this simple allosteric model ., In the presence of sodium ( 25 mM ) , the affinity of 3H-DAMGO does not appreciably change ( KD 1 . 8 and 0 . 89 nM for control and sodium conditions ) while the Bmax is lowered by 50% ( 333 fmol/mg protein to 156 fmol/mg protein ) ., This is consistent with a shift of the receptor to an inactive state from an active one ., In summary , the combination of MD , MSMs , and machine learning tools is powerful in that it provides , for the first time , both kinetic and thermodynamic estimates of Na+ translocation through active or inactive MOR states in a membrane mimetic environment and at a physiological concentration of Na+ ., The results provide quantitative support to the notion that Na+ can more easily egress from the cytosol in an active MOR with a protonated D1142 . 50 than in an inactive receptor , as well as testable hypotheses of the most important underlying motions and molecular determinants involved in Na+ translocation ., The active and inactive MOR systems were modeled based on the respective crystal structures ( PDB entries 5C1M and 4DKL , respectively ) ., The missing loop between TM5 and TM6 in 4DKL was added as described previously 14 , 19 ., For the active MOR , the N-terminal region preceding residue M65 was removed and the missing residues on the helix 8 at the C-terminus was rebuilt using the Prime package included in the Schrödinger’s suite 20 to ensure both simulated systems had identical primary sequences ., Both inactive and active MOR models were embedded in a POPC and cholesterol bilayer with a mixing POPC:cholesterol≈9:1 ratio and an area of 79×79 Å2 ., The membrane and protein were then neutralized and solvated with explicit TIP3P water and a NaCl concentration of 150 mM ., The entire simulation systems contained approximately 60 , 000 atoms with a volume of 79×79×106 Å3 and were assembled using the CHARMM-GUI webserver 21 ., The CHARMM36 force field 22 , 23 was used to model protein , lipids and ions and all molecular dynamics ( MD ) simulations were carried out using the NAMD software package 24 ., Both inactive and active MOR systems were simulated in the NPT ensemble using the Nosé-Hoover Langevin piston method 25 , 26 to maintain the pressure at 1 atm , and a Langevin thermostat to maintain the temperature around 310 K . Long-range electrostatic interactions were calculated using the Particle-Mesh Ewald ( PME ) algorithm 27 ., The van der Waals interactions were switched off gradually between 10 and 12 Å ., Periodic boundary conditions were applied to the simulation boxes , and an integration time step of 2 fs was used for all simulations ., After a multi-step equilibration with gradually decreasing harmonic constraints on lipid and protein heavy atoms , following the CHARMM-GUI membrane builder equilibration protocol , an additional 60 ns unconstrained equilibration run was carried out ., The last snapshot of this equilibration run was used as a starting point for umbrella sampling simulations ., US simulations were carried out to enhance sampling of the Na+ ion translocation across the membrane through the interior of the TM helix bundle of MOR ., A bias was applied to the Z-coordinate of a reference sodium ion , parallel to the normal vector of the membrane surface and measured from a reference position ( Z = 0 ) corresponding to the location of the Na+ allosteric binding site defined as the center of mass of the Cα atoms of residues D1142 . 50 , N1503 . 35 , W2936 . 48 , and Y3267 . 43 ( the superscripts refer to the Ballesteros-Weinstein numbering scheme 4 ) ., For each active and inactive MOR system , 157 starting configurations for US windows , uniformly spaced by 0 . 5 Å , were selected to cover the entire TM region of the protein and part of the bulk solvent ( from Z = +40 Å in the extracellular region to Z = -35 Å in the intracellular region ) ., The reference sodium ion was slowly pulled from one window to another ., A harmonic biasing potential with a force constant of 10 kcal/ ( mol·Å2 ) was applied along the Z variable to constrain the Na+ ion in the center of each window ., A flat-bottom cylindrical constraint with radius of 15 Å was applied to avoid insufficient sampling of the reference ion in the bulk solvent and to prevent the disturbance by other ions ., To prevent the drift of MOR in the membrane , a harmonic potential was applied to the head groups of POPC lipids with a force constant of 10 kcal/ ( mol·Å2 ) ., Each US window was run for at least 7 ns ( in addition to 1 ns of equilibration run ) or until the relative entropy 28 reached values below 0 . 2 for an average simulation length of 11 . 8 ns and a maximum simulation length of 100 ns ., To assess the kinetic behavior of the ion across the protein , we also carried out a set of unbiased simulations , starting from the last frame of each umbrella sampling window and running additional 12 ns of simulation ., The same simulation settings as the biased simulations were used , while all restraint potentials on the sodium and the lipids were removed ., 3D density distribution maps were built using a grid-based approach ., First , global translational and rotational motions of the protein in all simulation trajectories were removed by fitting to a reference structure using the protein Cα atoms root mean square deviation ( RMSD ) ., Then , a 3D rectangular grid covering the entire TM domain of the protein and a small part of the bulk solvent was built using a uniform grid spacing of 1 . 25 Å in all directions and amounting to a volume of 30×30×70 Å3 , and a total of 32 , 256 grid points ., The 3D bins defined by the grid were used to obtain the reweighted probability for the reference ion’s 3D position using the data from both umbrella sampling and unbiased simulations and the weighted histogram analysis method ( WHAM ) estimator implemented in the python library PyEmma 29 ., The reweighted density values were normalized to the averaged density values of the grid points in the bulk solvent ( 0 . 01 particles/nm3 ) ., The results were saved as a dx grid file , which was subsequently rendered as layered 3D color maps using the visualization software Pymol 30 ., Sodium-interacting protein residues across the TM bundle were selected using the active MOR with charged D1142 . 50 as a reference structure , which has sodium density registered over a larger area compared to the three simulated MOR systems ., A total of 105 residues within 4 . 4 Å from density grid points with values at least 7 times larger than the bulk region density were identified using an in-house python code ., The pair-wise distances between residue heavy atoms as a function of time were extracted using PyEmma and used as input features for tICA 31 ., tICA uses a linear transformation to map the original input data r ( t ) onto a new set of time-lagged independent components ( see reference 31 for details ) ., These components are correlated and their autocorrelation is maximal at a fixed lag-time 31 ., Notably , the most dominant components span a linear subspace that contains the slowest , and therefore most relevant , degrees of freedom ., These components can therefore provide the dimensional reduction that is necessary for the construction of a MSM 31 , 32 using PyEmma ., A lag time of 0 . 1 ns was used for our tICA calculations and the first two most dominant independent components , tIC0 and tIC1 , were used to describe the protein dynamics portion of our final MSM , which includes the Na+ motions as well ., The residue pairs whose minimum heavy atom distance fluctuations exhibited a larger than 0 . 6 correlation ( the absolute value of the Pearson correlation coefficient ) relative to tIC0 and tIC1 were considered the slowest ( and most important ) motion modes ., We projected trajectories of the inter-residue minimum distance fluctuations between heavy atoms of the 105 selected residues near Na+ high-density regions onto the two most dominant independent components tICA0 and tICA1 with PyEmma and calculated the free energy landscapes of all three simulated MOR systems i . e . , active MOR with either charged or protonated D1142 . 50 , and the inactive MOR ., We included tICs extracted from both unbiased MD simulations and umbrella sampling to construct the combined free energy landscapes sampled in both sets of simulations ., The combined landscapes were then subjected to k-means clustering using PyEmma ., Different k values were selected depending on the complexity of the individual free-energy landscape ., Specifically , we chose k = 5 , 7 and 4 for the inactive MOR , active MOR with charged D1142 . 50 , and active MOR with protonated D1142 . 50 MOR , respectively ., The resulting cluster centers were then used to assign the frames in trajectories from the unbiased and umbrella samplings simulations individually ., In order to optimally utilize both sets of unbiased MD and umbrella sampling simulations to derive equilibrium and kinetic properties of the system , we used the recently published transition-based reweighing method ( TRAM ) 11 to estimate a MEMM ., This approach aims at overcoming the limitations of standard MD simulations ( e . g . , insufficient sampling of transition states ) by integrating the results of enhanced sampling techniques such as umbrella samplings 11 , 33 ., Since the Na+ binding , dissociation and egress from the TM bundle does not only depend on the Na+ movement alone , but also on the protein conformational dynamics , we designed a MEMM that takes both aspects into account ., The trajectories from both the biased and unbiased simulations were discretized into microstates encoding the Na+ position , as well as the slowest protein degrees of freedom captured by tICA , which are represented by the transitions between the conformational states on the free energy landscape in the space of tIC0 and tIC1 approximated by the k-mean clusters of the landscape ., Specifically , microstates were defined based on, ( i ) the z coordinate of the Na+ ion , which was divided into 100 bins covering the entire range of the umbrella sampling , and, ( ii ) N k-means clustering of the two slowest tICA0 and tICA1 components , leading to a total of N×100 microstates , with N = 6 , 5 , and 4 for the inactive MOR , active MOR with charged D1142 . 50 , and active MOR with protonated D1142 . 50 , respectively ., We label the microstates as ( z ,, i ) , with 1 ≤ z ≤ 100 and 1 ≤ i ≤ N . The discretized trajectories were used together to obtain a maximum-likelihood TRAM estimation of the transition matrix in the unbiased thermodynamic state via the python package PyEmma 29 ., A lag time of 200 frames ( or 0 . 4 ns ) , selected based on the convergence of the implied time scales , was used for TRAM ., The free-energy of the microstates G - ( z , i ) was obtained from the steady-state probabilities from the Markov model estimated from TRAM ., In order to obtain the one-dimensional free energy profiles of the active and inactive MOR systems as a function of z , we integrated out the tICA dimensions via the relation:, G ( z ) = - k B T l n ∑ i = 1 N e x p ( - G - ( z , i ) k B T ) , ( 1 ), Next , we calculated the timescales employed by the reference sodium ion to bind to and dissociate from the extracellular side of the receptor , as well as to egress from the cytoplasmic side ., Considering the much higher Na+ concentrations in the extracellular region compared to the cytoplasmic side of the cell membrane under physiological conditions and the resulting unfavorable membrane potential , no ion binding from the intracellular side was taken into account ., For the kinetics estimates , sodium trajectories that crossed the periodic boundary between two unit cells along the z-direction were split in order to remove artificial transitions between microstates close to the intracellular side and the extracellular side ( i . e . z~0 and z~100 , respectively ) without actually going through the receptor ., A kinetic model was constructed by first assigning all microstates from the MSM estimated from TRAM into a small number of metastable states ( Npcca = 9 , 5 and 7 for inactive , active with charged D2 . 50 and active with neutral D2 . 50 MORs , respectively ) by using the Perron-cluster cluster analysis 34 ( PCCA+ ) ., A Npcca × Npcca transition matrix between the Npcca metastable states was then estimated using the Hummer-Szabo method 35 , and a Markov model estimated from this transition matrix using the PyEMMA package ., Furthermore , metastable states were clustered into three groups depending on whether Na+ , occupied the intracellular , bound , or cytoplasmic regions , respectively ., Specifically , microstate ( i , z ) was assigned to the cytoplasmic state if 1 ≤ z ≤ 10 ., A microstate belonging to one of the bins with 30 ≤ z ≤ 60 was considered to belong to the ion bound state , whereas microstates with 90 ≤ z ≤ 100 were considered to belong to the extracellular state ., Each of the Npcca metastable states was then assigned to one of the three groups ( intracellular , bound , or cytoplasmic ) if 90% of its microstates belonged to such a group ., In order to assess the effect of D2 . 50 protonation on sodium binding , we constructed a kinetic model that combines the properties of the sodium binding to the receptor with charged and neutral D2 . 50 , which we label with indices α and β , respectively ., Specifically , to establish a common reference state for the active model of MOR , we assumed that the pKa of D2 . 50 in the absence of Na+ nearby is pKa ≈ 9 13 , which corresponds to a free-energy difference of ΔG0 ≈ 2 . 6 kcal/mol at physiological pH ≈ 7 . 0 ., Using this shift , we expressed the free energy of the microstates z of the MOR system with charged D2 . 50 as, ε α ( Act . ) ( z ) = e α ( Act . ) ( z ) + Δ G 0, ( 2 ), where e α ( Act . ) ( z ) are the free-energies obtained from the simulation trajectories of the MOR system with charged D2 . 50 ., We then obtained the thermodynamic properties for the combined system as a function of the ion position as, exp ( - ε ( Act . ) ( z ) k B T ) = exp ( - e α ( Act . ) ( z ) + Δ G 0 k B T ) + exp ( - e β ( Act . ) ( z ) k B T ), ( 3 ), where e β ( Act . ) ( z ) are the free-energies obtained from simulation trajectories of the MOR system with neutral D2 . 50 , while the probability of observing a charged sidechain as a function of the ion position is:, p α ( z ) = 1 1 + exp ( − e β ( Act . ) ( z ) − e α ( Act . ) ( z ) − Δ G 0 k B T ) = 1 1 + π β ( Act . ) ( z ) π α ( Act . ) ( z ) exp ( Δ G 0 k B T ) ≡ exp ( − Δ G ( z ) k B T ), ( 4 ), We then modeled the rates for a fixed protonation state of D2 . 50 using the kinetic models obtained from analysis of the simulation run on MOR with either a charged or neutral D2 . 50:, K ( z , i , x ; z ′ , i ′ , x ) = K x ( z , i ; z ′ , i ′ ), ( 5 ), where x = α , β ( i . e . , charged and neutral D2 . 50 states , respectively ) , while z and i indicate , as before , the position of the sodium ion and the conformational microstate of the protein ., Following the evidence from NMR 36 we modeled the protonation process for given z and i with a constant deprotonation rate ( off-rate ) koff, K ( z , i , β ; z ′ , i ′ , α ) = δ ( z , z ′ ) δ ( i , i ′ ) k off, ( 6 ), Based on published work 36 , we used koff = 106 s−1 ., The protonation state that ensures that the free energy difference between the two protonation states is preserved is therefore, K ( z , i , α ; z ′ , i ′ , β ) = δ ( z , z ′ ) δ ( i , i ′ ) k off e x p ( Δ G ( z ) k B T ), ( 7 ), while δ ( z , z′ ) δ ( i , i′ ) guarantees that only protonation events for a fixed ion position and side-chain conformations are possible ., Finally , the rates between the PCCA macrostates a and b defined above were approximated as:, K ( a , x ; b , x ′ ) = ∑ j , z ′ ∈ b ∑ i , z ∈ a π x ( i , z ) K ( z , i , x ; z ′ , i ′ , x ′ ), ( 8 ), The matrix K was used to calculate kinetic rates for the constant pH = 7 . 0 model of the active MOR system ., In order to address the effects of sodium at physiological concentrations , we supplemented the Markov model by coupling it to reference states corresponding to the intracellular and extracellular bulk with constant sodium concentrations Na+IC and Na+EC , respectively ., We modeled the kinetics of ions across the receptor stepwise 37 , as follows:, Na EC + R k EC + ⇌ k EC - ( Na ∙ R ) EC k i j ⇌ k j i Na R k j l ⇌ k l j ( Na ∙ R ) IC k IC - ⇌ k IC + Na IC + R, ( 9 ), where NaEC and NaIC indicate a cation in the extracellular or intracellular space , respectively and parenthesis indicate the formation of an encounter complex , defined as the presence of an ion within a cylinder of radius r0 = 1 . 5 nm in the extracellular or intracellular region of the bulk ., Rates kab were obtained from the estimated Markov model , while the rates for the formation of the encounter complexes , k EC + and k IC + , were obtained from the 3D Smoluchowski expressions for a given ion diffusion constant DNa ≅ 20 nm2/μs and bulk concentration ,, k EC + = 4 π D Na r EC Na + EC, ( 10 ), where rEC is encounter complex radius ., The rates of ion dissociation from the encounter complex , k EC - and k IC - determine the capture probabilities , γEC and γIC defined as the probability of an ion to take part in the binding reaction , conditional on having formed the encounter complex:, γ EC = k EC - k EC - + ∑ j k i j, ( 11 ), A similar equation for γIC was defined for the intracellular encounter complex ., The values of γ were estimated 38 from the unbiased simulations described in the text ., The Na+ binding , dissociation , and egress rates were calculated by coarse-graining the transition matrix corresponding to the stepwise kinetic model and defined , respectively , as the rate of transition between the extracellular unbound and the bound state , between the bound and the extracellular unbound state , and between the bound and the intracellular unbound state ., We employed a minimal two-state model for receptor activation , which resulted in the same functional form as the operational Black-Leff model ., Let τu and τb be the equilibrium constants between the active and inactive states of the ligand-free and ligand-bound receptors , respectively , and let K and K⋆ be the binding affinities of the ligand to the inactive and active receptor states , respectively ., Then the fraction of receptors bound to a ligand is:, f b = L L 50 + L , ( 12 ), where, L 50 = K ⋆ 1 + τ u 1 + τ b, ( 13 ), Notably , for antagonists ,

Introduction, Results and discussion, Methods

The differential modulation of agonist and antagonist binding to opioid receptors ( ORs ) by sodium ( Na+ ) has been known for decades ., To shed light on the molecular determinants , thermodynamics , and kinetics of Na+ translocation through the μ-OR ( MOR ) , we used a multi-ensemble Markov model framework combining equilibrium and non-equilibrium atomistic molecular dynamics simulations of Na+ binding to MOR active or inactive crystal structures embedded in an explicit lipid bilayer ., We identify an energetically favorable , continuous ion pathway through the MOR active conformation only , and provide , for the first time:, i ) estimates of the energy differences and required timescales of Na+ translocation in inactive and active MORs ,, ii ) estimates of Na+-induced changes to agonist binding validated by radioligand measurements , and, iii ) testable hypotheses of molecular determinants and correlated motions involved in this translocation , which are likely to play a key role in MOR signaling .

Notwithstanding years of research supporting the notion that μ-opioid receptor ( MOR ) function can be modulated by sodium ions ( Na+ ) , a complete understanding of Na+ translocation through the receptor and its effect on ligand binding at MOR requires additional information ., Here , we use computer simulations to elucidate the energetics involved in sodium binding at inactive and active MOR , the timescales of sodium translocation through these receptor conformations , and the molecular determinants involved in this process .

crystal structure, molecular dynamics, markov models, condensed matter physics, sodium, simulation and modeling, mathematics, crystallography, thermodynamics, g protein coupled receptors, research and analysis methods, solid state physics, proteins, chemistry, transmembrane receptors, probability theory, free energy, physics, biochemistry, biochemical simulations, signal transduction, cell biology, biology and life sciences, physical sciences, computational chemistry, computational biology, chemical elements

journal.pcbi.1000856

Do Humans Optimally Exploit Redundancy to Control Step Variability in Walking?

Walking is an essential task most people take for granted every day ., However , the neural systems that regulate walking perform many complex functions , especially when we walk in unpredictable environments ., These systems continuously integrate multiple sensory inputs 1–4 and generate motor outputs to coordinate many muscles to achieve efficient , stable , and adaptable locomotion ., Establishing the fundamental principles that guide this control is central to understanding how the central nervous system regulates walking ., The principal idea used to explain how humans and animals regulate walking has been energy cost 5–12 ., At a given speed , humans choose an average step length and frequency that minimizes energy cost 7 , 9 , 10 , 12 ., Small changes in either average stride length or average stride time increase energy cost in humans similarly ( Fig . 1 , and Supplementary Text S1 ) 7 ., These experimental findings have been supported by multiple computational models 9–11 , 13 , 14 ., Such optimality principles have been a major focus for understanding the control of complex movements 15–20 ., However , these optimization criteria have been used primarily to predict average behavior , not to explain the variability ubiquitously observed in movements like walking 21–24 ., Understanding the nature of this variability may be critical to understanding how humans perform skilled movements 25–34 ., Most optimization approaches do not address whether the nervous system must overcome all variability as a limiting constraint 16 , 26 , 29 , 32 , or instead exploits redundancy to regulate variability in ways that help maximize task performance 25 , 27 , 28 , 34 ., Others have sought to determine how muscles are organized into functional synergies to resolve the inherent redundancy of complex movements 35–37 ., These efforts likewise characterize average behavior and so also provide few insights into movement variability ., Conversely , redundancy gives rise to equifinality: i . e . , there are typically an infinite number of ways to perform the same action 25 , 38 ., Equifinality permits individuals to perform complex tasks reliably and repeatedly while allowing variability in a movements particulars ., This is thought to facilitate adaptability in motor performance 25 ., Recent researchers have addressed this issue experimentally using the geometry-based uncontrolled manifold ( UCM ) approach 39 , 40 ., A related concept , the minimum intervention principle ( MIP ) 27 , 28 , 41 ties these ideas to stochastic optimal control theory and provides a concrete computational framework for predicting precisely how trial-to-trial movement variability arises in redundant motor systems performing tasks with well prescribed goals 18 , 27 , 28 , 41 , 42 ., During walking , humans need to adapt at every step ( not just on average ) to be able to respond to externally and/or internally generated perturbations 23 , 43 , 44 ., While the neurophysiological mechanisms that enact these responses are well known 1–4 , the fundamental principles governing adaptation from stride to stride remain unknown ., Small stride-to-stride fluctuations in gait dynamics are typically assumed to reflect random noise ., Indeed , there is ample evidence supporting multiple sensory and motor sources of physiological noise 31 , 45–48 ., However , stride-to-stride variations in gait cycle timing exhibit statistical persistence 22 , 49 , 50 , which has been argued to be “indispensible” to healthy physiological function 51 , 52 ., Stride intervals become more uncorrelated ( i . e . , less persistent ) in elderly subjects and patients with Huntingtons disease 53 , but not in patients with peripheral sensory loss 54 ., Understanding how stride-to-stride control is enacted therefore requires quantifying not only average magnitudes of variations across strides , but also the specific temporal sequencing of those variations ., Here , we formulate goal functions 25 that give concrete mathematical form to hypotheses on the strategies used to achieve a given task ., This provides a unifying framework for reconciling issues of optimality , redundancy , and stochasticity in human walking ., Walking on a motor driven treadmill only requires that subjects do not “walk off” either the front or back end of the treadmill ., While subjects must , over time , walk at the same average speed as the treadmill , variations in speed due to changes in stride length and/or stride time do occur and can be sustained over several consecutive strides 23 , 24 , 55 , 56 ., The main question addressed here is how do people regulate these variations ?, We present a mathematical definition of a specific hypothesized task strategy 25 , 57 with the goal to maintain constant walking speed at each stride ., This yields a decomposition of stride-to-stride variations into new gait variables explicitly related to achieving this strategy ., Time series analyses confirm that humans do indeed adopt this hypothesized strategy ., We similarly analyze three alternative strategies that equally achieve the task requirements , but make no reference to the hypothesized goal function ., Humans do not adopt any of these alternatives ., Finally , we develop a sequence of stochastic optimal control models of stride-to-stride dynamics to determine if they replicate our observations ., These models confirm that healthy humans do carefully regulate their movements explicitly to maintain constant speed at each stride ., However , humans do not use strategies that are precisely “optimal” with respect to the employed cost functions , but instead slightly but consistently over-correct small deviations in walking speed from each stride to the next ., To test GEMs of different location/orientation , subjects walked on a motorized treadmill at each of 5 constant speeds , from 80% to 120% of their preferred walking speed ( PWS ) ., Time series of stride times ( Tn ) , stride lengths ( Ln ) , and stride speeds ( Sn\u200a=\u200aLn/Tn ) for all strides within each trial were obtained and analyzed ., As expected , when subjects walked faster , they increased stride lengths ( Fig . 3A ) , decreased stride times ( Fig . 3B ) , and increased stride speeds ( Fig . 3C ) ., Stride length variability ( Fig . 3D ) increased slightly at speeds faster and slower than PWS , while stride time variability ( Fig . 3E ) increased at slower walking speeds , and stride speed variability ( Fig . 3F ) increased at faster walking speeds ., However , standard deviations only quantify the average magnitude of differences across all strides , regardless of temporal order ., They yield no information about how each stride affects subsequent strides ., Therefore , to quantify temporal correlations across consecutive strides , we computed scaling exponents , α , using Detrended Fluctuation Analysis ( DFA ) 22 , 49 , 51 , 52 ( see Methods ) ., α>½ indicates statistical persistence: deviations in one direction are more likely to be followed by deviations in the same direction ., α<½ implies anti-persistence: deviations in one direction are more likely to be followed by deviations in the opposite direction ., α\u200a=\u200a½ indicates uncorrelated noise: all deviations are equally likely to be followed by deviations in either direction ., In the context of control , statistical persistence ( α>½ ) is interpreted as indicating variables that are not tightly regulated ., Conversely , variables that are tightly regulated are expected to exhibit either uncorrelated or anti-persistent fluctuations ( α≤∼½ ) ., Consistent with previous results 22 , 50 , 54 , Tn and Ln time series ( Figs . 3G , 3H ) both exhibited significant statistical persistence ( α>½ ) ., Conversely , Sn time series ( Fig . 3I ) exhibited consistent and statistically significant anti-persistence ( ∼0 . 4<α<0 . 5 ) ., Thus , at all walking speeds , deviations in both Tn and Ln were allowed to persist , while deviations in Sn were rapidly reversed on subsequent strides ., This provides indirect evidence that subjects did not regulate Tn or Ln independently , but instead adjusted both Tn and Ln in a coordinated manner to maintain walking speed ., As expected 23 , 24 , 55 , 56 , subjects did “drift” forward and backward ( Eq . 1 ) over time along the treadmill belt ( Fig . 4A ) ., Most of these drifting movements remained contained within approximately the middle one third of the treadmill belt ( Fig . 4B ) ., This suggested that subjects adopted a more “conservative” walking strategy than actually required by the inequality constraint of Eq ., ( 1 ) ., However , these movements also exhibited a high degree of statistical persistence ( ∼1 . 25<α<∼1 . 55 ) at all walking speeds ( Fig . 4C ) ., Thus , deviations in absolute position along the treadmill belt were allowed to persist even more so than deviations in either Tn or Ln ., Thus , absolute position itself was not a tightly controlled variable for this task ., Plots of Ln versus Tn ( e . g . , Fig . 5A ) exhibited distributions elongated along the GEM ., As hypothesized , subjects exhibited far greater variability along the GEM than perpendicular to it ( F ( 1 , 16 ) =\u200a139 . 93; p\u200a=\u200a2 . 53×10−9; Fig . 5C ) ., This contrasts with what would be expected if the distributions of Tn , Ln points were solely a reflection of average metabolic costs , given the nearly circular energy contours seen in Fig ., 1 . Additionally , the δT time series all exhibited standard deviations >>1 , while the δP time series all exhibited standard deviations <<1 ( Fig . 5C ) ., Thus , subjects consistently exhibited much greater δT variability and much less δP variability than they did for either normalized ( i . e . , standard deviation\u200a=\u200a1 ) Tn or Ln time series ., The δT and δP time series exhibited temporal correlation structures qualitatively very different from each other ( Fig . 5B ) ., As hypothesized , subjects exhibited far greater statistical persistence for δT than for δP ( F ( 1 , 16 ) =\u200a368 . 21; p\u200a=\u200a1 . 81×10−12; Fig . 5D ) ., Additionally , all subjects exhibited significant statistical anti-persistence ( i . e . , 95% CI upper bounds for α<½ ) for the goal-relevant δP deviations at all five walking speeds ., Thus , subjects rapidly corrected δP deviations from each stride to the next , while allowing δT deviations to persist across multiple strides , independent of the magnitudes of these fluctuations ., One obvious question is whether these observed dynamics represented the only viable strategy subjects could have used ., Rejecting this possibility requires only that we identify at least one alternative strategy that still satisfied the fundamental task requirements ( Eq . 1 ) , but was completely “ignorant” of the proposed GEM defined by Eq ., 2 . Here , we present three such alternatives using “surrogate” data 60 , 61 that each represent the output of a particular type of data-based model of the observed stride-to-stride dynamics ., Each surrogate model directly tested a specific null hypothesis that subjects could have successfully completed the treadmill walking task ( i . e . , satisfied Eq . 1 ) using a strategy that made absolutely no reference to the GEM ., The first alternative strategy was to choose a reference point , T* , L* ( e . g . , Fig . 1 ) , on the GEM and maintain sufficiently small variance about this point to satisfy Eq ., ( 1 ) ., Here , “control” would consist entirely of suppressing variability in both Ln and Tn caused by neuro-motor noise ., This controller would therefore be completely ignorant of the GEM ., We implemented this hypothetical controller by generating 20 randomly shuffled surrogates 22 , 60 , 61 for each experimental trial ., This procedure maintained the exact same means and variances of the original Ln and Tn time series ( Fig . 6A ) ., However , all effects of temporal order were eliminated , yielding statistically uncorrelated time series ( α≈½; Fig . 6B ) ., By construction , all surrogates were constrained to not “walk off” the front or back end of the treadmill ( Fig . 6C ) , thus satisfying Eq ., 1 . These surrogates exhibited approximately isotropic distributions ( i . e . , no obvious directionality ) about T* , L* within the Tn , Ln plane ( Fig . 6D ) ., Likewise , δP and δT time series were qualitatively very similar to each other ( Fig . 6E ) ., Standard deviations for δP and δT were both≈1 and not significantly different ( F ( 1 , 16 ) =\u200a2 . 614; p\u200a=\u200a0 . 125; Fig . 6F ) ., DFA α exponents for δP and δT were both≈½ and also not significantly different ( F ( 1 , 16 ) =\u200a0 . 413; p\u200a=\u200a0 . 529; Fig . 6G ) ., Most importantly , these surrogates exhibited statistical and dynamical properties drastically different from the experimental data ( Fig . 5 ) ., Thus , the null hypothesis that subjects used this alternative “GEM ignorant” strategy to accomplish the treadmill walking task was rejected ., Fig . 6 demonstrates unequivocally that the strategy subjects used ( Fig . 5 ) was not the only successful strategy they could have adopted ., They could have adopted a control policy that equally achieved the task requirement defined by Eq ., 1 without using the GEM-based control strategy defined by Eq ., 2 . We also used surrogate data techniques to test two additional model hypotheses of how subjects might have controlled their stride-to-stride dynamics ., We tested a second alternative strategy that also regulated Tn and Ln independently of the GEM , but in a way that retained the statistical persistence observed in humans ( Fig . 3G , H ) 22 , 53 , 54 ., We then tested a third possibility that the covariation observed in Tn , Ln ( Figs . 5A , C ) was not due to stride-to-stride “control , ” but to simple biomechanics 42: i . e . , taking longer ( or shorter ) Ln naturally required longer ( or shorter ) Tn ., Subjects did not adopt either of these two viable alternative control strategies ., Full details and results of these analyses are presented in Supplementary Text S2 ., To obtain more definitive conclusions about the underlying control policies used , we first hypothesized that subjects controlled their movements based on the minimum intervention principle ( MIP ) 27 , 28 , 41 , 42 ., We created a model “walker” ( see Methods ) , where a two-dimensional state variable , xn\u200a=\u200aTn , LnT , defined each stride ., We implemented a stochastic optimal control policy that directly corrected δP deviations at each stride , but ignored δT deviations ., By construction , this MIP model walked with nearly the same average stride parameters ( Fig . 7A ) and stride speed ( Sn ) standard deviations ( Fig . 7B ) as humans ., However , the MIP model exhibited substantially greater variability in both Ln and Tn ( Fig . 7B ) ., The MIP model also exhibited much greater statistical persistence for Ln and Tn than humans , while Sn was statistically uncorrelated ( Fig . 7C ) ., Data points were aligned very closely to the GEM ( Fig . 7D ) ., The δT time series exhibited both much greater variability ( F ( 1 , 39 ) =\u200a6 , 076 . 51; p\u200a=\u200a1 . 53×10−43; Fig . 7E , F ) and more persistent fluctuations ( F ( 1 , 39 ) =\u200a1 , 969 . 18; p\u200a=\u200a2 . 40×10−34; Fig . 7E , G ) than did δP ., Because no control effort was applied along the GEM , consecutive strides exhibited approximately random walk behavior , or Brownian motion , ( i . e . , α ≈1 . 5 ) in δT ., Thus , our hypothesis that subjects adopted this stochastically optimal MIP control 27 , 28 was rejected ., However , the MIP model did not incorporate any additional physiological and/or biomechanical constraints ., Because human legs have finite length , they cannot take extremely long steps easily ., Because they have inertia , they cannot easily move extremely fast ., Likewise , the MIP model incorporated no capacity to minimize energy cost 5–12 ., Each of these factors would act to constrain the choices of Ln and Tn to a smaller range along the GEM ., We therefore hypothesized that subjects adopted a different MIP-based control policy that also used a “preferred operating point” ( POP ) on the GEM , where this POP , T* , L* , was assumed to be equal to the mean stride time and stride length ( Fig . 8 ) ., By construction , this POP model also walked with nearly the same average stride parameters ( Fig . 8A ) and variability ( Fig . 8B ) as humans ., Likewise , this model exhibited statistical persistence ( α>½ ) for both Ln and Tn that , while still greater , were much closer to those of humans ( Fig . 8C ) ., This model did not , however , capture the anti-persistence ( α<½ ) exhibited by humans for Sn ( Fig . 8C ) ., The POP model exhibited greater relative δP variability than did the MIP model ( Fig . 8D , E ) , very similar to humans ( Fig . 8F ) ., The magnitudes of the δT fluctuations were much greater than those of the δP fluctuations ( F ( 1 , 39 ) =\u200a2 , 916 . 30; p\u200a=\u200a1 . 55×10−37; Fig . 8F ) ., This model also exhibited larger DFA α exponents for δT fluctuations than for δP fluctuations ( F ( 1 , 39 ) =\u200a597 . 27; p\u200a=\u200a7 . 61×10−25; Fig . 8G ) ., As expected , α exponents for δT were greatly reduced compared to the MIP model ., However , this model still failed to replicate the anti-persistent ( α<½ ) δP fluctuations exhibited by humans ( Fig . 8G ) ., Thus , our hypothesis that subjects adopted this modified control policy was partly supported , but ultimately rejected ., The MIP and POP models both optimally corrected deviations away from the GEM at the next stride ., Thus , the δP fluctuations in each case ( Figs . 7G , 8G ) reflected nearly uncorrelated white noise ( α≈½ ) ., Conversely , humans consistently exhibited statistical anti-persistence ( α<½ ) in their δP fluctuations ( Fig . 5D ) ., This suggests that humans corrected these δP deviations more than would be expected from a single stride optimal control policy ., To test this hypothesis , we implemented an “OVC” controller that slightly over-corrected δP deviations at each successive stride ( Fig . 9 ) ., By construction , this OVC model walked with nearly the same average stride parameters ( Fig . 9A ) , stride variability ( Fig . 9B ) , and statistical persistence for both Tn and Ln ( Fig . 9C ) as humans ., Unlike the MIP and POP models , this OVC model did capture the anti-persistence ( α<½ ) exhibited by humans for Sn ( Fig . 9C ) ., The OVC model yielded GEM decomposition results qualitatively ( Figs . 9D , E ) and quantitatively ( Figs . 9F , G ) consistent with humans ., Most importantly , this model now exhibited the anti-persistent δP fluctuations ( Fig . 9G ) observed in humans ( Fig . 5D ) ., Thus , our hypothesis that subjects adopted a control policy that slightly over-corrected deviations away from the GEM was supported ., This study set out to determine how humans regulate stride-to-stride variations in treadmill walking ., We specifically sought to determine if the nervous system always overcomes all variability as a fundamental performance limitation 16 , 26 , 29 , 32 , or if it instead exploits redundancy to selectively regulate the effects of variability and enhance task performance 25 , 27 , 28 ., We demonstrate that formulating mathematical hypotheses on specific strategies ( e . g . , Eq ., 2 ) used to achieve task requirements ( e . g . , Eq ., 1 ) can reconcile issues of optimality , redundancy , and stochasticity in human walking ., Our results reveal a new governing principle for regulating stride-to-stride fluctuations in human walking that acts independently of , but in parallel with , the principle of minimizing energy cost 5–12 ., We hypothesized that humans walking on a treadmill would adopt a specific strategy 25 , 57 to maintain constant speed at each consecutive stride ( Eq . 2 ) , something not absolutely required to complete this task ., This yielded a decomposition of stride-to-stride variations into new gait variables ( δP and δT ) ( Fig . 2 ) ., Human subjects adjusted their steps specifically to achieve this hypothesized strategy ( Fig . 5 ) ., Moreover , they did so across a range of walking speeds , demonstrating that this strategy is robust to alterations in task requirements ., Subjects did not use perfectly viable alternative strategies , including three that completely ignored the GEM ( Figs . 6 and Supplementary Text S2 ) , and two based on optimal control models ( Figs . 7–8 ) ., Instead , stride-to-stride dynamics were directly consistent with a control strategy that first seeks to minimize goal-relevant δP errors ( Fig ., 7 ) 25 , 27 , but then also weakly limits δT variations ( Fig ., 8 ) and slightly over-corrects δP deviations ( Fig . 9 ) ., These results confirm that the neuromotor control of treadmill walking is organized around the hypothesized goal function ( Eq . 2 ) ., Beyond the five alternative control strategies clearly rejected by our results ( Figs . 6–8 and Supplementary Text S2 ) , other plausible alternatives were considered ., One seemingly reasonable strategy might be to try to stay at a fixed location on the treadmill ., Such absolute position control would necessitate regulating dnet ( n ) ( see Methods , Eq . 4 ) , in contrast with the controllers derived here that regulate stride speed , ( Eq . 2 ) ., However , the statistical persistence in the experimental dnet ( n ) data ( Fig . 4A , C ) strongly suggests that people do not regulate their walking this way ., Our stochastic optimal control models demonstrate that the level of control strongly determines the statistical persistence of a time series ., For both the MIP and POP models ( Figs . 7–8 ) , stochastic optimal control with respect to the hypothesized GEM ( Eq ., 2 ) yielded δP fluctuations with α ( δP ) ≈½ ( Figs . 7G & 8G ) ., Increasing the control gains above unity for the OVC model ( so the model over-corrected errors in δP ) yielded α ( δP ) <½ ( Fig . 9G ) ., Likewise , decreasing these control gains ( so the model under-corrected errors in δP ) would yield α ( δP ) >½ ., This phenomenon was also observed along the GEM ., The POP and OVC models that applied weak control along the GEM yielded ½<α ( δT ) <1 ( Figs . 8G & 9G ) ., The MIP model that applied no control along the GEM yielded α ( δT ) ≈1½ ( Fig . 7G ) , as predicted ., A value of α\u200a=\u200a1½ corresponds to Brownian motion , where each deviation is simply a random change from the previous value ., Thus , a position controller that minimized dnet ( n ) in a stochastically optimal way would similarly yield α ( dnet ) ≈½ ., This was clearly not observed in our experiments , where we instead found α ( dnet ) ≈1½ ( Fig . 4C ) ., Thus , the possibility of absolute position control was also rejected in favor of speed control ., Minimizing energy cost has been the primary explanation for how humans and animals regulate walking 5–12 ., This criterion predicts the presence of a single optimal operating point , TOpt , LOpt , in the Tn , Ln plane 7 , 9 , 10 ., Deviations away from TOpt , LOpt , induced for example by neuromuscular noise 31 , 45–47 , would increase energy cost approximately equally for equivalent relative changes in all directions ( Fig . 1 ) ., If variability were merely a limiting constraint the central nervous system must overcome 16 , 26 , 29 , 32 , the distributions of the variations around TOpt , LOpt should , on average , approximate the shape of the contours shown in Fig . 1 to minimize deviations from TOpt , LOpt ., We did not observe that here ., Instead , all Tn , Ln data were strongly oriented along the GEM ( Fig . 3A , C ) ., Indeed , the failure of the surrogates ( Fig ., 6 ) to capture the experimentally observed gait dynamics clearly refutes the idea that humans only try to minimize variations in Tn , Ln about a single operating point ., Instead , while subjects rapidly corrected δP deviations , they allowed δT deviations to persist ( Fig . 5B , D ) , even though these deviations would increase energy cost ., Our findings , however , remain compatible with the idea that humans also try to minimize energy cost while walking ., The failure of the MIP model ( Fig ., 7 ) to capture the experimentally observed gait dynamics demonstrates that humans do not only minimize deviations away from the GEM ., The POP model ( Fig . 8 ) , is precisely compatible with adding the secondary goal of minimizing energy cost ., For the average walking speed modeled ( v\u200a=\u200a1 . 21 . m/s ) , we computed a POP of T* , L*\u200a=\u200a1 . 105 s , 1 . 337 m ., Mechanical walking models of Minetti 9 and Kuo 10 predict similar energetically optimal POPs of TOpt , lOpt\u200a=\u200a1 . 029 s , 1 . 247 m and TOpt , LOpt\u200a=\u200a1 . 013 s , 1 . 228 m , respectively , for this speed ., Simplifications in both models account for their slightly under-estimating the preferred T* , L* of actual humans 9 ., Humans also consistently over-corrected δP deviations ( Fig . 5D ) ., Our OVC model ( Fig . 9 ) provides one possible explanation: that humans use sub-optimal control to correct stride-to-stride deviations ., In the model , anti-persistence in δP implies sub-optimal and vice-versa ., More importantly , data analysis methods currently used to substantiate UCM 39 , 40 and MIP 18 , 27 , 28 , 42 predictions would not have captured this because they only consider variability in the data ., However , taken alone , our variability results are entirely compatible with either the optimal POP ( Fig . 8F ) or sub-optimal OVC ( Fig . 9F ) controllers ., Only the DFA analyses ( Figs . 8G , 9G ) allow us to distinguish these two models , by offering an additional measure of stride-to-stride dynamics 57 , 62 that is independent of variability 22 , 49 , 51 ., Perhaps most explicitly , the paired surrogates ( see Supplementary Text S2 ) exhibited very strong alignment of variance along the GEM , even though these surrogates , by definition , represented an explicitly GEM-ignorant control strategy ., Thus , quantifying variance ratios alone ( as done in experimental applications of UCM and MIP ) can very easily lead to incorrect conclusions about control ( see also 42 ) ., Our results demonstrate that it is critical to quantify both variability and temporal dynamics 57 , 62 to fully determine how repetitive movements are controlled ., The principal contribution of our work is thus to demonstrate that considerations other than minimizing energy cost help determine Tn , Ln at each stride ., Subjects instead choose Tn , Ln based on a hierarchy of defined goals 25 , with at least one short-term goal to maintain walking speed , and one long-term goal to reduce energy cost ., Humans adopt GEM-aware control over short ( stride-to-stride ) time scales , while still minimizing energetic cost over longer ( on average ) time scales ., They readily exploit this Tn , Ln redundancy during level treadmill walking , even though they do not have to ( Fig . 6 and Supplementary Text S2 ) ., This ability to fully exploit the redundancy available could become critical when tasks become more demanding ., In walking for example , rapidly and effectively adjusting successive steps could become critical when negotiating uneven terrain 63 ., However , these adjustments need to be made at each step and not just on average ., Thus , GEM-aware control exploits inherent task redundancy 25 , 27 , 28 to simultaneously achieve high task performance ( low error ) while allowing possibly beneficial motor variability ., The nervous system appears to estimate both motor errors and the sources of those errors to guide continued adaptation 30 , 31 , 33 ., The neural structures involved in decision making may even deliberately insert noise into the process to enhance adaptation 64 , 65 ., Exposing humans to tasks that share similar structural characteristics but vary randomly may even help facilitate the ability to generalize to novel tasks 33 ., Similar capacities were recently demonstrated even in highly-learned ( i . e . , “crystallized” ) adult bird song 66 , where residual variability in this skill represented “meaningful motor exploration” to enhance continued learning and performance optimization 31 , 66 , 67 ., Our findings suggest that similar purposeful motor exploration occurs in the highly-learned task of human walking ., It has been widely argued that statistically persistent fluctuations are a critical marker of “healthy” physiological function 51 , 52 and that uncorrelated or anti-persistent fluctuations are a sign of disease or pathology 51–53 ., The present results strongly refute this interpretation ., The subjects tested here clearly cannot be simultaneously both “healthy” ( according to α ( δT ) ) and “unhealthy” ( according to α ( δP ) ) ( Fig . 5D ) ., Instead , our findings argue for interpreting these DFA exponents specifically within the context of the control processes involved ( Figs . 7–9 ) ., This interpretation is fully consistent with the fact that many random processes can yield time series with a wide range of α values 68 ., In previous work , this was directly supported by a simple mechanical model of walking with minimal feedback control that still exhibited a wide range of statistically persistent and anti-persistent walking behaviors 62 ., One question is whether the theoretical framework developed here will generalize to other contexts ., During unconstrained overground walking 50 , humans exhibited strong statistical persistence for Tn and Ln similar to Fig . 5G–H ., However , unlike Fig . 5I , they also exhibited strong persistence for Sn 50 ., When those subjects walked in time with a metronome , Ln and Sn remained strongly persistent 50 , but Tn became anti-persistent 50 , 69 , 70 ., All three results ( treadmill , overground , and metronome ) are precisely compatible with the idea that humans adopt generalized “Minimum Intervention” 27 strategies to tightly regulate only those variables that are directly relevant to achieving some specified task goal 25 ., On the treadmill , humans tightly regulate walking speed ( Fig . 5 ) ., Remove the treadmill , and subjects no longer tightly regulate any one individual stride parameter 50 ., Introduce a metronome , and subjects tightly regulate gait cycle timing ( Tn ) , but not Ln or Sn 50 ., In all three contexts , factors beyond minimizing energy cost help determine how stride-to-stride movements are regulated ., The critical first step is to identify the appropriate goal function for each task 25 ., All participants provided written informed consent , as approved by the University of Texas Institutional Review Board ., Seventeen young healthy adults ( 12M/5F , age 18–28 , height 1 . 73±0 . 09 m , body mass 71 . 11±9 . 86 kg ) , participated ., Subjects were screened to exclude anyone who reported any history of orthopedic problems , recent lower extremity injuries , any visible gait anomalies , or were taking medications that may have influenced their gait ., Subjects walked on a level motor-driven treadmill ( Desmo S model , Woodway USA , Waukesha WI ) while wearing comfortable walking shoes and a safety harness ( Protecta International , Houston TX ) that allowed natural arm swing 44 ., First , preferred self-selected walking speed ( PWS ) was determined 23 ., Subjects reported the limits of their PWS while the treadmill was slowly accelerated and then decelerated three times ., These upper and lower limits were averaged to determine PWS 23 ., Following a 2-minute rest , subjects completed two 5-minute walking trials at each of five speeds ( 80 , 90 , 100 , 110 and 120% of PWS ) , presented in pseudo-random order 44 ., Subjects rested at least 2 minutes between each trial to prevent fatigue ., Subjects were instructed to look ahead

Introduction, Results, Discussion, Methods

It is widely accepted that humans and animals minimize energetic cost while walking ., While such principles predict average behavior , they do not explain the variability observed in walking ., For robust performance , walking movements must adapt at each step , not just on average ., Here , we propose an analytical framework that reconciles issues of optimality , redundancy , and stochasticity ., For human treadmill walking , we defined a goal function to formulate a precise mathematical definition of one possible control strategy: maintain constant speed at each stride ., We recorded stride times and stride lengths from healthy subjects walking at five speeds ., The specified goal function yielded a decomposition of stride-to-stride variations into new gait variables explicitly related to achieving the hypothesized strategy ., Subjects exhibited greatly decreased variability for goal-relevant gait fluctuations directly related to achieving this strategy , but far greater variability for goal-irrelevant fluctuations ., More importantly , humans immediately corrected goal-relevant deviations at each successive stride , while allowing goal-irrelevant deviations to persist across multiple strides ., To demonstrate that this was not the only strategy people could have used to successfully accomplish the task , we created three surrogate data sets ., Each tested a specific alternative hypothesis that subjects used a different strategy that made no reference to the hypothesized goal function ., Humans did not adopt any of these viable alternative strategies ., Finally , we developed a sequence of stochastic control models of stride-to-stride variability for walking , based on the Minimum Intervention Principle ., We demonstrate that healthy humans are not precisely “optimal , ” but instead consistently slightly over-correct small deviations in walking speed at each stride ., Our results reveal a new governing principle for regulating stride-to-stride fluctuations in human walking that acts independently of , but in parallel with , minimizing energetic cost ., Thus , humans exploit task redundancies to achieve robust control while minimizing effort and allowing potentially beneficial motor variability .

Existing principles used to explain how locomotion is controlled predict average , long-term behavior ., However , neuromuscular noise continuously disrupts these movements , presenting a significant challenge for the nervous system ., One possibility is that the nervous system must overcome all neuromuscular variability as a constraint limiting performance ., Conversely , we show that humans walking on a treadmill exploit redundancy to adjust stepping movements at each stride and maintain performance ., This strategy is not required by the task itself , but is predicted by appropriate stochastic control models ., Thus , the nervous system simplifies control by strongly regulating goal-relevant fluctuations , while largely ignoring non-essential variations ., Properly determining how stochasticity affects control is critical to developing biological models , since neuro-motor fluctuations are intrinsic to these systems ., Our work unifies the perspectives of time series analysis researchers , motor coordination researchers , and motor control theorists by providing a single dynamical framework for studying variability in the context of goal-directedness .

computational biology/systems biology, neuroscience/motor systems, computer science/systems and control theory, computational biology/computational neuroscience, neuroscience/theoretical neuroscience

journal.pgen.1004281

Genome Sequencing and Comparative Genomics of the Broad Host-Range Pathogen Rhizoctonia solani AG8

Rhizoctonia solani ( formerly , teleomorph: Thanetophorus cucumeris ) is a globally-distributed , soil-borne fungal phytopathogen employing a necrotrophic lifestyle ., Collectively , the host-range of the R . solani species spans numerous plant species vital to the agriculture , forestry and bioenergy industries , including but not limited to: wheat , rice , barley , canola , soybean , corn , potato and sugar beet 1 ., Chemical control methods may not be feasible nor economical for the control of many soil-borne pathogens 2 ., Hence , agronomic controls such as crop-rotation are heavily relied upon to fight this disease , though the polyphagous habit of some isolates can include commonly rotated crop species ., For example , cereal and legume rotations are susceptible to AG8 1 , 3; and corn , canola and soybean rotations are susceptible to AG1 and AG2 4–5 ., Susceptible crop species possess at best , low to moderate levels of genetic resistance which are of limited use to conventional breeding strategies 6–8 ., The impact of R . solani has been observed to increase in incidence and severity with increased adoption of conservation ( no-till ) farming techniques 2 ., The combinations of these factors places R . solani as a significant threat to global food security and other agro-forestry industries ., The R . solani species complex is comprised of fourteen anastomosis groups ( AGs ) , most of which are reproductively incompatible with each other and are numbered AG-1 through AG-13 ., The ‘bridging isolate’ AG-BI is the exception , being compatible with multiple AGs 1 , 9 ., Despite an apparently low level of phylogenetic divergence between AGs 10 they exhibit diverse phenotypic variation , particularly with respect to the host-ranges of phytopathogenic AGs ( Supporting Table S1A ) ., Less frequently , certain AGs have been observed to have a predominantly saprophytic or mycorrhizal life-cycle ., Our study presents a comprehensive genome assembly and functional analysis of R . solani AG8 , causative agent of bare patch of wheat , barley and legume species 3 , 11–12 ., Of the AGs that infect wheat , AG8 is the most damaging ., In Australia , the impact of R . solani on wheat and barley production is estimated upwards of $77 million per annum and bare patch also remains a major problem for the production of wheat and other crops in the US 13 ., The host-range of the sequenced isolate WAC10335 ( zymogram group ZG1-1 14 ) also extends to legume species of agricultural and scientific importance: Lupinus spp ., ( lupin ) 15 and Medicago truncatula ( barrel medic ) 16 , but not to the non-legume Arabidopsis 17 ., As a basidiomycete , the plant pathogens most closely related to R . solani with genome sequences available are the biotrophic smuts 18–20 , rusts 21–22 and the tree-pathogenic Moniliophthora spp ., 23 , which possess vastly different lifestyles ., Thus , the information gained from R . solani is expected to be of importance in filling gaps in our knowledge of plant pathogen biology , which apart from rusts and smuts , is skewed towards the ascomycetes ., Significant genomic resources for other AGs of R . solani have also recently become publicly available , formerly being limited to EST libraries of AG1-IA 24 and AG4 25 ., The recent generation of whole genome sequences of R . solani AGs presents new opportunities for comparative genomics between R . solani anastomosis groups ., The most comprehensive whole-genome study to date has been that of the rice pathogen AG1-IA 26 GenBank: AFRT00000000 ., The genome assembly of the closely related AG1-IB was published recently 27 GenBank: CAOJ00000000 , however full scaffold sequences were not in the public domain at the time of writing and thus AG1-IB data has not been used for synteny comparisons in this study ., The mitochondrial genome sequence of the potato pathogen AG3 strain Rhs1AP and its comparison to that of AG1-IB has been published recently 28 ., A draft nuclear genome for AG3 is also available ( http://www . rsolani . org with kind permission from Cubeta et al . ) , however a nuclear gene dataset and genome survey have not yet been published 29 ., R . solani AG8 exists as a multi-nuclear heterokaryon in which individual R . solani cells may carry multiple nuclei and copy number can vary between cells ., An average of 8 nuclei per cell has previously been observed in AG8 , but numbers commonly ranged from 6 to 15 1 ., While reduction of nuclear complexity via protoplast isolation has been reported for R . solani 30–32 , we chose to assemble a representative haploid assembly of all AG8 nuclei in an agriculturally-relevant isolate and investigate mechanisms and type of sequence variations between nuclei in this largely asexual isolate ., We report evidence of SNP-level diversity between heterokaryotic nuclei of a complex fungal genome , which has not previously featured extensively in genome studies of fungal phytopathogens ., The heterozygosity between nuclei of AG8 compounded the complexity of its de novo genome assembly available from GenBank: AVOZ00000000 and we also describe novel bioinformatic approaches used to overcome these challenges ., This study also compares whole-genome synteny between R . solani anastomosis groups ( AG8 , AG1-1A and AG3 ) and uses comparative genomics techniques to highlight genes and functions unique to AG8 and AG1-1A ., Predicted properties of AG8 proteins have been leveraged to generate a list of 308 ‘effector-like’ genes that may be related to plant-pathogenicity ., These collective resources will be important for further experimentation in this pathosystem ., The heterokaryotic nature of the R . solani genome posed considerable challenges for genome assembly ., To overcome these challenges we developed a novel genome assembly pipeline ( Figure 1 ) ., The assembly process , including software and parameters , is described in the Materials and Methods section with additional information in Supporting Text S1 ., Preliminary de novo assemblies exhibited high levels of sequence redundancy and heterozygosity across gene-encoding regions ., We confirmed that multiple nuclei were present in variable numbers within cells of the sequenced isolate ( Figure 2A ) ., In order to reduce sequence redundancies caused by the assembly of heterozygous homeologs , the process used to assemble the AG8 genome included a step to merge haplotype contigs prior to scaffolding ., This step was followed by generation of a haploid ‘majority consensus’ sequence from alignments of genomic sequence reads to merged scaffolds ., However prior to this study , the extent of sequence variation between homeologous chromosomes originating from different nuclei was unknown ., Alignment of genomic deep-sequencing reads to the genome assembly indicated an abundance of heterozygous SNP mutations throughout the AG8 assembly ( Figure 2B ) ., As many as 74% of heterozygous SNP alleles were transition mutations between cytosine and thymine ( or their complementary bases guanine and adenine ) ( Figure 2C , Supporting Table S2A ) ., Nucleotides flanking these C→T ‘hypermutations’ exhibited a moderate bias of approximately 40% for a G at the 3′ base ( i . e . CpG→TpG ) ( Supporting Table S2B ) ., These cytosine and CpG hypermutations were widespread across the AG8 genome and occurred within protein-coding genes and repetitive DNA regions at similar levels ( Figure 2D ) , with only a slight reduction in CpG frequency in genes relative to repeats ., One of the consequences of C→T mutation is the introduction of stop codons into protein-coding open-reading frames ( ORFs ) 33 ., We reason that it is possible for ORFs to be inactivated by nonsense mutations in the majority of nuclei , yet still produce functionally active , full length proteins from a low number of non-mutated nuclei in R . solani AG8 ., Thus the assembly process also included a step which reverted heterozygous mutations between C and T to cytosine , regardless of allele frequencies ., The final R . solani AG8 draft assembly comprises 861 scaffolds , has a total length of 39 . 8 Mbp which is consistent with previous haploid cytogenetic estimates of 37 to 46 Mbp 34 , an N50 of 65 and an N50 length of 160 . 5 kbp ( Table 1 ) ., The AG8 genome assembly statistics compared favorably with those of other R . solani isolates AG1-1A , AG1-1B and AG3 as shown in Table 1 ., Sequence comparisons between the whole genome assemblies of R . solani AG8 , AG1-1A and AG3 exhibited widespread co-linearity or macrosynteny 35 ( Figure 3 , Supporting Table S3 ) ., No conclusive evidence for dispensable chromosomes , as reported for F . oxysporum 36 , was observed ., A single scaffold ( Scaffold_77 ) of ∼140 kbp in length was predicted to represent the mitochondrial genome ., The ends of the mitochondrial scaffold sequence were confirmed to be physically joined in a circular configuration by PCR ( Supporting Text S2 ) ., The mitochondrial scaffold contained the expected set of fungal mitochondrial genes ( atp6 , cytb , cox1-3 , nad1-5 & nad4L , rps5 , rns & rnl ) and was abundant with LAGLIDADG and GIY-YIG intronic endonucleases ., This is consistent with recent reports for the mitochondrial genomes of AG3 and AG1-IB , which are of similarly large sizes ( 235 . 8 kbp and 162 . 8 kbp respectively ) and possess high abundances of endonucleases 28 ., Within the nuclear genome , repetitive DNA sequences ( Supporting Table S4A ) represented just over 10% of its total length ., Gypsy retrotransposons were the most abundant repeat type and represented 4% of the nuclear genome ., Protein-coding gene-based tri-nucleotide simple sequence repeats , WD40-like and tetratrichopeptide repeats , represented approximately 1% ., Comparing the repetitive content of AG8 with available repeat data for AG1-1A , we observed more repetitive DNA in the assembly of AG8 ( 10 . 03% of the assembly ) compared to that of AG1-1A ( 5 . 27% ) 26 ., It should be noted that critical differences in assembly , de novo repeat prediction and repeat classification methods may limit the comparability of these two datasets , however the proportions of the most dominant repetitive elements was strikingly similar ., The most dominant transposable elements in both AG8 and AG1-1A were LTR retrotransposons: the most common being the Gypsy/Dirs1 family at 3 . 98% and 3 . 43% respectively; followed by the Ty1/Copia family at 0 . 14% and 0 . 60% respectively ., This pattern of Gypsy being more numerous than Copia retroelements , appears to be typical of most fungal genomes 37 ., Non-coding RNA ( ncRNA ) genes were predicted in silico ( Supporting Table S5A ) , which overall made up less than 0 . 007% ( 26 . 5 kbp ) of the total genome length ., To enable discovery and accurate annotation of protein-coding genes present in the AG8 assembly , particularly those expressed in the presence of plant tissues , three high-coverage Illumina RNA-seq libraries were aligned to the genome to delineate gene exon boundaries ., To obtain transcript data for as many genes as possible , the libraries included one library of AG8 undergoing vegetative growth in culture and two “infection-mimicking” libraries ., These libraries were derived from AG8 grown on water agar containing wheat ( Triticum aestivum ) or Medicago truncatula seedlings separated by a permeable nitrocellulose membrane ., This enabled collection of fungal tissue whilst reducing plant tissue contamination to negligible amounts ., Alignment of RNA-seq data and proteins from related fungal species and pathogenicity gene databases were combined with in silico gene predictions to automatically predict gene structure annotations , which were then manually curated ., The density of gene-coding regions was relatively even throughout the assembled genomic scaffolds ( Figure 2Eii ) , with reduced density at some scaffold termini with high levels of repeats ( Figure 2Eiii ) ., A total of 13 , 964 protein-coding AG8 genes that can serve as a reference for R . solani comparative genomics were predicted after RNA-seq-assisted manual gene annotation ., Of these , 8 , 449 proteins had a BLASTP match to the NCBI NR protein database ( Supporting Figure S1 , Supporting Table S6 ) ., The taxonomic distribution of lowest-common ancestor taxa for these BLASTP matches indicated wide conservation of 83% ( 7016/8449 ) of R . solani AG8 with fungal proteins , 52 . 5% ( 4436/8449 ) specifically conserved within the Basidiomycota ( Supporting Figure S1 ) and 17 . 9% conserved within the class Agaricomycetes ., The extracellular secreted component of these proteins was predicted using a combination of SignalP 38 , WolfPsort 39 and Phobius 40 ( Figure 4 ) ., A total of 1 , 959 proteins ( 14 . 0% of all proteins ) were predicted to be secreted by one or more methods and 608 ( 4 . 4% ) were predicted to be secreted by all three methods ., For comparative purposes , SignalP predictions were applied to R . solani AG8 and across 86 fungal species ( Supporting Table S7 ) ., There were 911 secreted proteins predicted by SignalP for AG8 , which was similar to the numbers predicted for closely-related plant-pathogenic species of the class Agaricomycetes ., The secretome counts across biotrophic Basidiomycetes of other classes were relatively variable ,, e . g ., Puccinia striformis ( 1 , 264 ) , P . graminis, f . sp ., tritici ( 2 , 012 ) and Ustilago maydis ( 595 ) ., However AG8 was within a similar range to the average predicted secretome count across all fungi ( 1 , 052 ) , which was predominantly comprised of necrotrophs ., To surmise the biological processes important to R . solani AG8 in the infection process , we predicted the functions of its 13 , 964 genes by comparison to the CAZy ( Carbohydrate-Active enZyme ) and Pfam ( Protein family ) databases ., In total , we assigned CAZy annotations to 1 , 137 genes ( Supporting Table S8B , C ) and Pfam annotations to 6 , 099 genes ( 44 . 5% ) ( Supporting Table S9A ) ., Analysis of CAZymes present in the R . solani AG8 genome ( Figure 5 ) revealed a dual bias for the degradation of the structures of plant cells and modification of the fungal cell wall for growth or protection from host-defences ( Supporting Table S8C ) ., The most abundant CAZy families are described here ., The most prevalent glycoside hydrolase ( GH ) CAZyme class ( GH18 ) represented chitinases , followed in frequency by classes representing cellulases ( GH5 ) , polygalacturonases ( GH28 ) and beta-glucanases ( GH16 ) , which degrade major components of plant cell walls ., The most abundant glycosyltransferase ( GT ) classes were strongly geared towards cellulose ( GT2 , GT41 ) , hemicellulose ( GT77 , GT4 , GT34 ) and chitin ( GT2 ) degradation ., The most common carbohydrate esterase ( CE ) class contained choline esterases ( CE10 ) ., Polysaccharide lyase ( PL ) CAZymes were strongly biased towards pectin-degradation , with the two most dominant classes ( PL1 and PL3 ) both representing pectate lyases ., The three most abundant carbohydrate-binding ( CBM ) class CAZymes were lectin-like proteins ., Two of these ( CBM13 and CBM57 ) are predicted to bind cellulose and hemicelluloses and include ricinB-like lectins ., The third ( CBM18 ) contains sialic-acid-binding lectins , which may play a role in protection from plant host-defenses by ‘shielding’ sugars protruding from the fungal cell wall 41 ., The fourth most frequent CBM class ( CBM1 ) binds chitin and cellulose and appears to be conserved exclusively within fungal species ., Pfam domains in R . solani AG8 were compared to Pfam annotations assigned to a panel of 50 pathogenic and non-pathogenic fungal species ( obtained from the JGI Integrated Microbial Genomes database ) ( Supporting Figure S2 , Supporting Table S9A ) 42 ., R . solani AG8 exhibited high abundance of tyrosine protein kinase signalling , membrane transport , protein-protein binding , reduction-oxidation , DNA methylation and a bias among cell-wall degrading enzymes towards pectin and peptidase degradation ., Pfam domains with protein-protein binding functions were dominated by various classes of tetratrichopeptide repeats , but also included other domains involved in protein binding interactions: ( WD40-like ) PD40 beta-propeller Pfam: PF07676; Ankyrin Pfam: PF13606 and leucine-rich repeats Pfam: PF00560 ., The most abundant peptidase domain was the CHAT ( Caspsase HetF-Associated with TPRs ) domain Pfam: PF12770 which may be involved in programmed cell death ., In summary , R . solani AG8 possesses a number of gene families whose members have a broad range of potential biological roles , for example those encoding caspases or protein-binding functions ., Further study would be required to determine their relevance to plant pathogenicity or other lifestyle characteristics ., These findings do however indicate that R . solani AG8 possesses a large number of carbohydrate-binding lectins of unknown function as well as a battery of CAZymes suitable for consumption of carbohydrates commonly found in cereal hosts , but also is geared towards the degradation of pectin ., Publicly-available protein data for AG1-IA 26 was also used to generate functional annotations for AG1-IA ., Statistical comparisons between functions predicted in AG8 and AG1-IA were performed using Fishers exact test ( p≤0 . 05 ) ( Supporting Table S10A ) ., R . solani AG8 and AG1-IA primarily infect two different hosts - wheat and rice respectively ., Differences between them in their relative abundances of functionally-annotated genes may reveal important differences in their infection strategies ., Overall , fewer Pfam domains were found to be significantly higher in AG1-IA than in AG8 ., In AG1-IA ( Supporting Table S10B ) , the Pfams that were significantly more abundant and may be related to pathogenicity included several types of transmembrane transporter domain and formin-like proteins that may be involved in cytokinesis ., Many more functions were found to be increased in AG8 relative to AG1-IA ( Supporting Table S10C ) , however most of these were of too broad or unknown function to infer their biological roles ., Nevertheless , several functions stood out as potentially important for plant pathogenicity in AG8 , including CAZymes , peptidases , membrane transporters , transcription factors and toxin-like proteins ., Peptidases abundant in AG8 included the CHAT and C14 domain caspases as well as fungalysin-like peptidases ., The CAZyme functions that were significantly more numerous in AG8 were predominantly glycosyl-hydrolases ( polygalacturonases , β-galactosidases ) , pectate lyases and carbohydrate binding proteins ( ricin-like and jacalin lectins and fungal-specific CBM1 proteins ) ., Fungal pathogens of dicots generally possess higher numbers of pectin-degrading enzymes than monocot pathogens 43 ., Though an important pathogen of monocot cereals , most notably wheat , the sequenced isolate of R . solani AG8 was isolated from the dicot lupin and is also an important pathogen of other leguminous dicots ., The abundance of pectate lyases in AG8 relative to AG1-IA is likely to reflect the broad host range of the sequenced AG8 isolate ., Interestingly , AG8 had more members of two Pfams similar to ricinB lectins 44 and delta endotoxins 45 , highly toxic proteins commonly associated with defence against insect predators which have been prioritised for further study ., In contrast to AG1-IA which had none , AG8 possessed 3 delta-endotoxin-like proteins ( RSAG8_06697 , RSAG8_07821 and RSAG8_07820 ) with the Pfam domain Bac_thur_toxin Pfam: PF01338 ., This domain was originally defined based on the insecticidal delta endotoxins of Bacillus thuringiensis ., Pfam matches and orthology analysis suggested the presence of orthologous delta endotoxin-like proteins in other phytopathogenic species including Fusarium graminearum ( Fusarium head blight of wheat and barley ) and the bacteria Dickeya dadantii ( syn . Erwinia chrysanthemi , soft-rot , wilt and blight on a range of plant hosts and septicaemia of pea aphid ) 46 ( Supporting Table S9A , Supporting Table S11 ) ., Whether these ricinB and delta-endotoxin homologs confer an advantage against competitors or predators or may instead be toxic to the plant host remains to be determined ., Effector proteins have been observed to be secreted by several microbial pathogens 47 and cause disease on their respective hosts ., A set of characteristics common to plant pathogenicity effectors from fungi that would allow reliable bioinformatic predictions has not yet been accurately defined ., However experimentally validated effectors tend to be low molecular weight , secreted , cysteine-rich proteins which may contain certain conserved amino-acid motifs near the N-terminus 47–48 ( Supporting Table S12 ) ., Effector-like proteins were predicted in AG8 , requiring: complete annotation from translation start to stop with <3 consecutive unknown ( ‘X’ ) amino acids; predicted molecular weight ≤30 kDa; predicted as secreted with 0–1 predicted transmembrane domains; and with ≥4 cysteine residues ., A total of 308 AG8 proteins matched all of these criteria ., These candidates were searched for known motifs previously associated with plant pathogenicity , however the occurrence of these motif matches was not significant relative to the complete protein dataset ., As an example the RxLR-like motif ( Kale et al . , 2011 ) , though found in 73% of the predicted effector candidates , was also found in 77% of the whole R . solani AG8 proteome ( Supporting Table S13 ) indicating this permissive motif may not be useful for effector candidate prediction in R . solani AG8 ., We were also unable to identify any novel N-terminal-associated motifs that were highly conserved among these 308 proteins ( Supporting Text S3 ) ., However , we observed the ratio of non-synonymous to synonymous mutations ( dN/dS ) within these 308 candidate genes to be 0 . 97 compared to 0 . 77 across all genes ., Our understanding of the roles of these 308 effector candidates will benefit from the addition of further experimental data , resulting in a more succinct list of candidates with a potential direct role in disease on one or more of the many plant hosts of R . solani AG8 ., Unfortunately , no method for the stable transformation of R . solani AG8 is presently available and thus functional testing of candidate pathogenicity genes will be challenging ., To gain further support for an association with pathogenicity , approximately 10% ( 29 ) of the 308 predicted ‘effector-like’ genes were randomly selected and their mRNA expression relative to a set of 7 constitutively expressed genes was compared between R . solani AG8 sampled at 7 days post-infection of wheat and 7 day-old AG8 mycelia grown on media ., Of these 29 genes , 25 ( 85% ) had a positive fold-change and 17 had a significantly higher relative expression in-planta ( Students t-test; p≤0 . 05 , log2 fold change ≥1 ) ( Supporting Table S14B ) ., This dataset highlights several plant-pathogenicity candidates , but other genes also important for pathogenicity may not be changing in abundance during infection relative to in-vitro growth ., Repeat-induced point mutations ( RIP ) are fungal-specific SNP mutations previously reported to be restricted to the filamentous Ascomycota ( Pezizomycotina ) 49 ., RIP in the Pezizomycotina involves transition mutations converting cytosine to thymine , with a moderate bias for CpA dinucleotides 49 ., Other features of RIP include targeted mutation of repetitive DNA , with single-copy DNA regions being largely unaffected ., An important exception to this is where RIP mutations ‘leak’ into single-copy DNA regions from neighbouring repetitive DNA which occurs more frequently closer to repeats 50 ., The small number of studies looking for RIP-like mutations in the Basidiomycota do not exhibit the characteristic CpA mutation bias observed in the Pezizomycotina 49 , however two studies have reported a CpG dinucleotide bias between repetitive DNA sequences within the Basidiomycota and a TpCpG trinucleotide bias specific to the subphylum Pucciniomycotina 51–52 ., As an Agaricomycete , we expect R . solani to exhibit a bias towards CpG but not TpCpG ., However , it should also be noted that hypermutations of CpG may also be caused by widely conserved processes involving the methylation of cytosine to 5-methylcytosine ( 5mC ) and subsequent deamination which converts 5mC to thymine 53 ., Importantly , conversion of cytosine to thyimine via methylation and deamination does not actively target repetitive DNA or ‘leak’ in the same manner as RIP ., Analysis of nucleotides immediately flanking heterozygous C↔T SNP sites in AG8 exhibited a CpG dinucleotide bias consistent with previous observations of ‘RIP-like’ cytosine hypermutations in the Basidiomycota 52 ( Figure 2D ) ., The distribution of these RIP-like mutations in AG8 was observed to occur across repetitive and gene-encoding regions alike at a relatively constant ratio versus non-RIP-like mutations , where heterozygous C↔T alleles comprised ∼70–80% of all SNP mutations ( Figure 2Eiv-v ) and in turn CpG dinucleotides comprised ∼40–50% of heterozygous C↔T alleles ., In mononuclear fungal genomes , RIP has previously only been observed to act upon repetitive DNA or to ‘leak’ into adjacent non-repetitive sequences 50 ., Due to the novel genome assembly process for AG8 which involved merging of redundant haplotypes , a survey of SNP mutations in its annotated repetitive DNA would likely lead to incorrectly inflated counts of RIP-like mutations ., Therefore we instead looked at the frequency of CpG↔TpG mutations versus their distance from the nearest repeat , which indicated that CpG mutations were more frequent closer to repeats ( Figure 6 ) ., Furthermore , although the ratios of ( C↔T/all SNPs ) and ( CpG↔TpG/C↔T ) were relatively similar between genes and other regions of the genome , the frequency of mutations in gene regions were lower than in the genome as a whole , suggesting strong selection pressures to retain protein function ., The ratio of CpG/CpH ( where H\u200a=\u200anot G ) was slightly lower in repeats ( 0 . 3 ) than in genes ( 0 . 4 ) ( Table, 2 ) and we speculate that this likely to be due to complete ( i . e . homozygous ) conversion of C→T occasionally occuring across all copies of a repeat , as they are under no selection pressure to retain their pre-RIP sequences ., Thus there would be fewer sites that can be detected as heterozygous SNPs by aligning genomic reads to the genome assembly ., Regardless of whether the underlying process is similar to RIP or not , CpG-biased hypermutation is likely to play an important role in the evolution of the AG8 genome ., RIP has been recently proposed to have the potential to randomly introduce nonsense mutations , converting longer secreted proteins into small , secreted proteins thus making them gradually more effector-like 54 ., Stop-codon frequency across the 12 , 771 annotated AG8 genes possessing stop codons is highest for TGA ( 40% ) compared to TAA ( 31% ) and TAG ( 29% ) ., As TGA stop codons would be the primary nonsense product of CpG-biased hypermutation , similar evolutionary processes may also occur in AG8 ., Furthermore , the presence of multiple nuclei in AG8 could potentially compensate for loss of gene function due to hypermutation in one or more nuclei , allowing for a higher tolerance for the accumulation of mutations in gene-coding regions ., Analysis of total SNP , and CpN dinucleotide frequencies ( expressed in Table 2 as average distance in bp between mutations ) , showed that a SNP mutation occurred on average every 70 bp , cytosine hypermutations occurred every 89 bp and that there was a 40% bias towards CpG mutations occurring every 307 bp ., Within the 308 predicted ‘effector-like’ genes , SNP mutations occurred on average every 55 bp , cytosine hypermutations every 81 bp and CpG mutations occurred every 265 bp ., However , the ratios of ( C↔T/all SNPs ) and ( CpG↔TpG/C↔T ) were not significantly different between the complete set of 13 , 964 AG8 genes and the 308 effector-like genes ., Interestingly , despite apparently similar mutation ratios , the ratio of non-synonymous to synonymous SNP mutations ( dN/dS ) was 0 . 97 in ‘effector-like’ candidates compared to 0 . 77 across all genes ., This may suggest that the increased mutation rate conferred by CpG-biased hypermutation is advantageous for accelerating the adaptation of pathogenicity genes which , if being actively counter-acted by plant defences , are likely to be under diversifying selection ., The density of heterozygous SNP mutations within AG8 was compared to SNP densities between the genome assemblies of AG8 and AG1-IA , AG1-IB and AG3 ( Table 3 ) ., SNP density in AG8 was highest within intronic regions ( 19 . 6 SNPs/kbp ) , moderate in coding exons and genes ( 14 . 5–15 . 9 SNPs/kbp ) and lowest in intergenic regions ( 11 . 5 SNPs/kbp ) ., Comparisons of SNP mutations between AG8 and alternate AGs exhibited an approximately ten-fold increase in SNP density compared to the rate of heterozygous SNPs within AG8 ., The corresponding values within for comparisons between AG8 and AG1-IA ranged from 162 . 8–228 . 2 SNPs/kbp , AG1-IA from 141 . 6–200 . 3 SNPs/kbp and AG3 from 98 . 5–145 . 3 SNPs/kbp ., We note however that in these comparisons between the genome assemblies of AG8 and other AGs , it was not possible to ascertain whether these SNPs ( or homologous bases ) were homozygous or heterozygous in the alternate AG ., Nevertheless a higher SNP density between the AG8 genome and those of the other three AGs , relative to heterozygous AG8 SNPs , was consistent in all three comparisons ., Comparisons between individual genomes and fungal population genetics studies were also used to place the SNP diversity within R . solani AG8 into a wider context ., Similar to AG8 , the Basidiomycete stripe rust fungus Puccinia striformis is heterokaryotic but exhibits a lower SNP density within its genome assembly of 5 . 98 SNPs/kbp 21 ., It may be significant that P . striformis is binucleate and therefore only possesses 2 nuclei per cell as opposed to the 6–15 nuclei that have been observed within cells of R . solani AG8 1 ., Similarly , SNP variation across a population of shiitake mushroom ( Lentinula edodes ) was reported to be 4 . 6 SNPs/kbp ( 186 , 0789 SNPs in 40 . 2 Mbp ) 55 ., In barley powdery mildew ( Blumeria graminis ) , the SNP rate observed between pairs of isolates was lower at 1 SNP/kbp 56 ., Across isolates of the multinucleate endomycorrhizal Glomeromycete Rhizophagus irregularis 57 and the beetle-symbiont Leptographium longiclavatum 58 , even lower SNP densities of 0 . 2 SNPs/kbp ( 28 , 872 SNPs in 140 . 9 Mb ) and 0 . 6 SNPs/kbp ( 17 , 266 in 28 . 9 Mbp ) respectively , were observed ., In contrast , a population study of the multinucleate human pathogens Coccidioides immitis and C . posadasii reported a rate of 23 . 7 SNPs/kbp relative to the C . immitus RS reference genome assembly ( 687 , 250 SNPs in 28 . 95 Mb ) 59 , which though slightly higher is within a similar range to R . solani AG8 ( Table 3 ) ., In conclusion , the SNP diversity in R . solani AG8 appears to be higher than that observed thus far within individual isolates of binucleate rusts , between isolates of the same pathogenic species and across non-pathogen populations ., Furthermore , diversity within R . solani AG8 is comparable to a population of another multinucleate pathogen ( C . immitus ) and much higher than that observed within a population of a multinucleate non-pathogen ( R . irregularis ) ., We speculate that the combination of multinuclearity and selection pressures relating to pathogenicity may be driving the accumulation of widespread heterozygous SNP diversity in R . solani AG8 ., In this study , we present a novel bioinformatics pipeline for the accurate and comprehensive assembly of a complex fungal genome , the heterozygous and multinucleate pathogen Rhizoctonia solani AG8 ( Figure 1 ) ., The combin

Introduction, Results & Discussion, Materials and Methods

Rhizoctonia solani is a soil-borne basidiomycete fungus with a necrotrophic lifestyle which is classified into fourteen reproductively incompatible anastomosis groups ( AGs ) ., One of these , AG8 , is a devastating pathogen causing bare patch of cereals , brassicas and legumes ., R . solani is a multinucleate heterokaryon containing significant heterozygosity within a single cell ., This complexity posed significant challenges for the assembly of its genome ., We present a high quality genome assembly of R . solani AG8 and a manually curated set of 13 , 964 genes supported by RNA-seq ., The AG8 genome assembly used novel methods to produce a haploid representation of its heterokaryotic state ., The whole-genomes of AG8 , the rice pathogen AG1-IA and the potato pathogen AG3 were observed to be syntenic and co-linear ., Genes and functions putatively relevant to pathogenicity were highlighted by comparing AG8 to known pathogenicity genes , orthology databases spanning 197 phytopathogenic taxa and AG1-IA ., We also observed SNP-level “hypermutation” of CpG dinucleotides to TpG between AG8 nuclei , with similarities to repeat-induced point mutation ( RIP ) ., Interestingly , gene-coding regions were widely affected along with repetitive DNA , which has not been previously observed for RIP in mononuclear fungi of the Pezizomycotina ., The rate of heterozygous SNP mutations within this single isolate of AG8 was observed to be higher than SNP mutation rates observed across populations of most fungal species compared ., Comparative analyses were combined to predict biological processes relevant to AG8 and 308 proteins with effector-like characteristics , forming a valuable resource for further study of this pathosystem ., Predicted effector-like proteins had elevated levels of non-synonymous point mutations relative to synonymous mutations ( dN/dS ) , suggesting that they may be under diversifying selection pressures ., In addition , the distant relationship to sequenced necrotrophs of the Ascomycota suggests the R . solani genome sequence may prove to be a useful resource in future comparative analysis of plant pathogens .

The fungus Rhizoctonia solani is divided into several sub-species which cause disease in a range of plant species that includes most major agriculture , forestry and bioenergy species ., This study focuses on sub-species AG8 which causes disease of cereals , canola and legumes , and compares its genome to other R . solani sub-species and a wide range of fungal and non-fungal species ., R . solani is unusual in that it can possess more than one nucleus per cell ., The multiple nuclei and sequence mutations between them made assembly of its genome challenging , and required novel techniques ., We observed signs that DNA sequences originating from multiple nuclei in AG8 exhibit a high frequency of single nucleotide polymorphisms ( SNPs ) and more SNP diversity than most fungal populations ., These SNP mutations also have similarities to repeat-induced point mutations ( RIP ) ., Moreover in AG8 , RIP-like SNPs are not restricted to intergenic regions but are also widely observed in gene-coding regions ., This is novel as RIP has previously only been reported in repetitive DNA of distantly-related fungi that have only a single nucleus per cell ., We generated a list of 308 genes with similar properties to known plant-disease proteins , in which we found higher rates of non-synonymous mutations than normal .

sequencing techniques, functional genomics, genome evolution, sequence assembly tools, genome sequencing, fungi, plant science, genome analysis, crops, plant pathology, molecular genetics, molecular biology techniques, sequence analysis, genome complexity, mycology, gene expression, comparative genomics, crop diseases, molecular biology, agriculture, plant pathogens, genetics, biology and life sciences, genomics, computational biology, organisms

journal.ppat.1006264

The E3 ubiquitin ligase RNF185 facilitates the cGAS-mediated innate immune response

The innate immune system serves as the first line of host defense against invading microbes ., Upon recognition by an array of host germline-encoded pattern recognition receptors ( PRRs ) , including Toll-like receptors ( TLRs ) , RIG-I-like receptors ( RLRs ) and DNA sensors , microbial nucleic acids trigger the initiation of intracellular signaling cascades that lead to the induction of type I interferons as well as pro-inflammatory cytokines , which are a prerequisite for eliciting immediate antiviral responses and adaptive immunity to ultimately eradicate the infection 1 , 2 , 3 , 4 ., Microbial RNA-sensing machinery and the corresponding downstream signaling cascade have been well characterized during the past decade , whereas the microbial DNA sensing represents a fast evolving field for understanding the corresponding innate immune signaling pathways 5 , 6 , 7 , 8 , 9 ., Various studies have identified several proteins , including Mre11 , DAI , RNA polymerase III , IFI16 , DDX41 as the potential DNA sensors 10 , 11 , 12 , 13 , 14 ., However , these proteins are not universally essential for detecting microbial DNAs in distinct cell types or in vivo 6 ., Recently , cyclic GMP-AMP synthase ( cGAS ) is characterized as a sequence-independent DNA sensor by classical biochemical fractionation strategies coupled with quantitative mass spectrometry 15 ., Analyses of cGAS knockout mice reveal its essential roles in fibroblasts , macrophages , and dendritic cells in response to various DNA stimuli transfections and DNA pathogens ( DNA viruses , retroviruses , Listeria monocytogenes and Mycobacterium tuberculosis ) infection 16 , 17 , 18 , 19 , 20 ., In addition , cGas-/- mice are more vulnerable to lethal infection after exposure to herpes simplex virus 1 ( HSV-1 ) than wild-type mice 16 ., Notably , cGAS possesses nucleotidyl transferase activity , converting ATP and GTP into noncanonical cyclic dinucleotide 2′3′-cGAMP in the presence of DNA 21 , 22 ., As a second messenger , cGAMP directly binds to and activates ER-resident stimulator of interferon genes ( STING ) 23 , 24 , 25 ., STING traffics from ER , through the Golgi apparatus , and to the perinuclear microsomes or punctuate structures 25 ., During the trafficking processes , the K27-linked poly-ubiquitin chain anchored on STING by AMFR-INSIG1 complex recruits the TANK-binding kinase 1 ( TBK1 ) , which causes STING and TBK1 to congregate simultaneously in the same compartment 26 ., Importantly , the DNA-triggered assembly of STING-TBK1 complex is critical for TBK1 activation , followed by activating the transcriptional factor IRF3 , thus inducing expression of type I interferons and pro-inflammatory cytokines ., Protein post-translational modifications , such as phosphorylation , ubiquitination , and SUMOylation , are central to the host innate immune regulations 27 , 28 ., cGAS is potentially subjected to a couple of modifications 29 , 30 , 31 ., For example , the glutamylases TTLL6 catalyzes poly-glutamylation of cGAS and impedes its DNA-binding activity , whereas TTLL4-mediated mono-glutamylation of cGAS blocks its synthase activity ., The carboxypeptidases CCP6 and CCP5 reverse the above processes respectively , thus promoting the cGAS activation 29 ., The protein kinase Akt phosphorylates cGAS and suppresses its enzymatic activity 30 ., However , it remains unknown whether cGAS is modulated by ubiquitination ., A thorough study on the regulation of cGAS activity is deserved because the aberrant activation of cGAS causes severe autoimmune or autoinflammatory disorders , such as systemic lupus erythematosus ( SLE ) and Aicardi Goutières syndrome ( AGS ) 32 , 33 , 34 ., The E3 ubiquitin ligase RNF185 potentially modulated the osteogenesis or protein quality control on the ER 35 , 36 , 37 ., In this study , we characterized the ER-resident RNF185 as a positive regulator of the cGAS-STING signaling ., RNF185 interacted with cGAS and catalyzed the K27-linked poly-ubiquitination of cGAS upon HSV-1 challenges , which markedly potentiated the enzymatic activity of cGAS ., Additionally , SLE patients exhibited elevated RNF185 mRNA expression ., Because polyubiquitination has emerged as an important regulatory mechanism for cGAS-STING signaling 27 , 28 , we speculated whether additional E3 ubiquitin ligases catalyze the ubiquitination of key signaling molecules and thereby regulate innate antiviral response ., We noticed that RNF185 contains a RING domain , a signature of ubiquitin E3 ligases , and shares a high degree of sequence identity ( approximate to 70% ) with RNF5 , which catalyzed the ubiquitin-mediated degradation of STING ( S1A Fig ) ., To explore the potential role of RNF185 , we screened out the specific and effective siRNAs ( mouse Rnf185 siRNA 1# and mouse Rnf185 siRNA 2# ) ( Fig 1A ) ., As expected , silencing of Rnf185 markedly attenuated the expression of the IRF3-responsive genes ( Ifnb , Ifna4 and Cxcl10 ) in L929 cells , stimulated by the herring testis DNA ( HT-DNA ) transfection ( S2A Fig ) or the DNA virus HSV-1 infection ( Fig 1B , left panel ) ., In contrast , the abundance of Ifnb , Ifna4 or Cxcl10 mRNAs induced by RNA mimic poly ( I:C ) transfection ( S2A Fig ) or RNA virus Sendai virus ( SeV ) infection ( Fig 1B , right panel ) was comparable between Rnf185 knockdown and wild-type L929 cells ., Similarly , knockdown of Rnf185 in Raw264 . 7 cells also significantly attenuated the expression of the IRF3-responsive genes ( Ifnb , Ifna4 and Cxcl10 ) , when challenging cells with HSV-1 ( S3A Fig ) ., In contrast , the induction of the IRF3-responsive genes was marginally affected in Rnf185 knockdown Raw264 . 7 cells when challenging cells with SeV ( S3B Fig ) ., To make it more physiologically relevant , we next probed the role of RNF185 in primary cells ., We confirmed RNF185 expression was also efficiently reduced in the BMDMs ( bone marrow derived macrophages ) transfected with the indicated siRNAs ( S3C Fig ) ., Consistently , silencing of Rnf185 markedly attenuated the expression of the IRF3-responsive genes ( Ifnb , Ifna4 and Cxcl10 ) in BMDMs stimulated by HSV-1 ( S3D Fig ) ., In contrast , the abundance of Ifnb , Ifna4 or Cxcl10 mRNAs induced by SeV infection was comparable between Rnf185 knockdown and wild-type BMDMs ( S3E Fig ) ., Furthermore , RNF185 knockdown in BMDMs resulted in obvious increase in HSV-1 titer as compared with controls by standard plaque assay ( S3F Fig , left panel ) ., However , RNF185 knockdown did not influence Sendai virus replication as checked by qPCR analysis ( S3F Fig , right panel ) ., These data suggest that RNF185 specifically regulates cytosolic DNA sensing pathway ., To rule out potential off-target effects of the RNF185 siRNA , we generated two RNA interference ( RNAi ) -resistant RNF185 constructs , named rRNF185 WT and rRNF185 C39A , in which silent mutations were introduced into the sequence targeted by the siRNA without changing the amino acid sequence of the corresponding proteins ., L929 cells were first transfected with control or RNF185 siRNA followed by introduction of control or indicated rRNF185 plasmids , respectively ., Then the induction of IRF3-responsive genes ( Ifnb , Ifna4 and Cxcl10 ) was measured after HT-DNA stimulation ., As shown in Fig 1C , the induction of Ifnb , Ifna4 and Cxcl10 was restored by rRNF185 WT , but not rescued by rRNF185 C39A ., These data suggest that RNF185 potentially modulates the cytosolic DNA sensing pathway depending on its enzymatic activity ., The immune sensing of microbial DNA is critical for triggering immediate immune responses and the subsequent adaptive immunity 3 ., However , inappropriate provocation of the immune system by aberrant self-DNA , which should be cleared under normal conditions , contributes to the pathogenesis of certain autoimmune diseases , such as systemic lupus erythematosus ( SLE ) 38 , 39 ., Since RNF185 might be involved in regulating cGAS-mediated DNA sensing pathway , we further examined the mRNA expression levels of RNF185 as well as ISG15 and OASL-1 ( type I IFNs inducible genes ) in peripheral blood mononuclear cells ( PBMCs ) isolated from SLE patients and healthy controls by QPCR analysis ., The RNF185 mRNA expression was significantly up-regulated in SLE patients as compared with healthy controls ( Fig 1D ) ., The ISG15 and OASL-1 mRNA expression were also increased in SLE patients as compared with healthy controls ( Fig 1D ) ., Interestingly , ectopic-expression of wild-type RNF185 in PBMCs potentiated the expression of ISG15 and OASL-1 mRNA as well as IFNA2 , IFNA5 and IFNB mRNA , whereas the mutant RNF185 C39A could not ( Fig 1E ) ., In addition , we treated PBMCs with purified IFNα2a in different doses , and observed that IFNα2a could efficiently induce the expression of ISG15 and OASL-1 mRNA in early and late time points ( S2B and S2C Fig ) ., In contrast , the RNF185 mRNA expression was barely affected at the early and late phase of IFNα2a treatment ( S2B and S2C Fig ) ., To make the experiment more physiologically relevant , we stimulated the PBMCs with different titrations of serum isolated from SLE patients and healthy controls ., As expected , the cells treated with the serum from SLE patients produced much more ISG15 and OASL-1 mRNA than did those from healthy controls ( S2D and S2E Fig ) ., Notably , serum from SLE patients displayed no substantial effect on the expression of RNF185 mRNA as compared with those from healthy controls in early and late time points ( S2D and S2E Fig ) ., These data suggest that no positive feedback loop exists between RNF185 mRNA expression and Interferons production ., The dimerization and phosphorylation of IRF3 as well as the phosphorylation of TBK1 are hallmarks of the cytosolic DNA-triggered signaling ., These processes were apparently inhibited in Rnf185 knockdown L929 cells , when stimulating cells with HT-DNA ( Figs 2A and S4A ) ., However , poly ( I:C ) -induced dimerization or phosphorylation of IRF3 as well as the phosphorylation of TBK1 were barely affected when silencing Rnf185 ( Figs 2B and S4B ) ., In addition , the nuclear translocation of IRF3 triggered by HT-DNA was markedly crippled when knocking down Rnf185 in L929 cells ( Fig 2C and 2D ) , whereas the nuclear translocation of IRF3 triggered by poly ( I:C ) remained intact in Rnf185 knockdown L929 cells ( Fig 2C and 2D ) ., Collectively , these data indicate that RNF185 is essential for the cytosolic DNA-induced IRF3 activation ., Interestingly , silencing of RNF185 apparently did not affect the expression of IRF3-responsive genes ( Ifnb , Ifna4 and Cxcl10 ) induced by cGAMP ( Fig 2E ) ., Consistently , cGAMP-triggered dimerization and phosphorylation of IRF3 were barely affected when silencing Rnf185 ( Fig 2F ) ., Therefore , we reasoned that RNF185 played a role on the upstream of STING ( Fig 2G ) ., To substantiate , silencing of Rnf185 markedly attenuated the induction of Cxcl10 and Ifnb as well as the phosphorylation of TBK1 and IRF3 by cGAS in L929 cells , stimulated with or without HT-DNA ., ( Figs 2H , S4C and S4D ) , which suggest that RNF185 may modulate the cGAS signalsome ., To address the association between RNF185 and cGAS , HA-tagged RNF185 and Flag-tagged cGAS were transfected individually or together into HEK293T cells , followed by coimmunoprecipitation ( coIP ) assays ., As expected , HA-tagged RNF185 associated with Flag-tagged cGAS ( Fig 3A and 3B ) ., It was predicted that the cysteines in the RING domain of RNF185 are critical for its catalytic activity ., Several RNF185 mutants were therefore generated , including RNF185 C39A ( Cys to Ala mutation at 39 residues ) , RNF185 C39/42A ( Cys to Ala mutation at both 39 and 42 residues ) and RNF185 C39/79A ( Cys to Ala mutation at both 39 and 79 residues ) , all of which were deprived of the potential E3 ubiquitin ligase activity ( see below ) ., It was observed that cGAS associated with these RNF185 mutants as well as with wild-type RNF185 ( Fig 3A and 3B ) , indicating that the E3 ubiquitin ligase activity of RNF185 was dispensable for its association with cGAS ., We further confirmed the weak endogenous association between cGAS and RNF185 ( Fig 3C ) ., Notably , the endogenous association between cGAS and RNF185 was substantially enhanced upon HSV-1 infection ( Fig 3C ) ., A series of deletion mutants of cGAS and RNF185 were employed to map the domains responsible for RNF185-cGAS interaction ( Fig 3D and 3E , left panel ) ., The C-terminal domain of cGAS ( amino acids 201–522 ) and the RING domain of RNF185 ( amino acids 39–80 ) were required for the interaction ( Fig 3D and 3E , right panel ) ., Confocal microscope imaging revealed that endogenous RNF185 partially co-localized with endogenous cGAS in resting cells ( Figs 3F and S5A ) , and this co-localization was enhanced after HSV-1 infection ( Figs 3F and S5A ) ., Confocal microscopy and subcellular fractionation analysis confirmed that RNF185 were predominantly expressed on ER membrane , but not on mitochondria membrane ( Fig 3G and 3H ) ., Taken together , these data indicate that RNF185 is a novel member of the cGAS signalsome in vivo ., As an E3 ubiquitin ligase , RNF185 could catalyze the ubiquitin-mediated degradation BNIP1 , CFTR and Dvl2 35 , 36 , 37 ., Our in vitro ubiquitination assays confirmed that RNF185 could catalyze the formation of poly-ubiquitin chains , whereas RNF185 C39A , RNF185 C39/42A or RNF185 C39/79A could not ( Fig 4E ) ., Our above data uncovered the importance of the E3 ubiquitin ligase activity of RNF185 for the cytosolic DNA sensing pathway ( Fig 1C ) ., Therefore , we wondered whether the cGAS was the authentic substrate of RNF185 ., To explore this possibility , Flag-tagged cGAS was co-transfected respectively with RNF185 or other E3 ligases known in the STING pathway ., The cell lysates were subjected to immunoprecipitation with anti-Flag , and then the immunoprecipitates were denatured , followed by re-immunoprecipitation again with anti-Flag; the precipitates were finally analyzed by immunoblotting with anti-ubiquitin ., Notably , cGAS was markedly poly-ubiquitinated in the presence of RNF185 ( Fig 4A ) ., In contrast , other E3 ligases could not catalyze the ubiquitination of cGAS ( Fig 4A ) ., Apparently , RNF185 could not catalyze the polyubiquitination of other potential DNA sensors 11 , 13 , 14 ( Fig 4B ) , neither could it catalyze the polyubiquitination of STING , TBK1 or IRF3 ( Fig 4C ) ., In addition , the catalytically inactive mutants RNF185 C39A , RNF185 C39/42A or RNF185 C39/79A failed to catalyze the polyubiquitination of cGAS inside cells ( Fig 4D ) ., In vitro ubiquitination assay further confirmed that the wild-type RNF185 catalyzed the formation of poly-ubiquitin chains on cGAS , whereas the RNF185 C39A , RNF185 C39/42A or RNF185 C39/79A could not ( Fig 4E ) ., Thus , cGAS is a new substrate of the RNF185 ., A panel of ubiquitin mutants , including those containing a point mutation at a corresponding lysine and those with all lysines mutated to arginines except for the indicated one , were employed to dissect the polyubiquitin chain linkage on cGAS ., As expected , RNF185 catalyzed the poly-ubiquitination of cGAS in the presence of wild-type ubiquitin , whereas the poly-ubiquitination of cGAS was completely abolished when using the ubiquitin K0 mutant ( Ubiquitin with all lysine residues mutated to arginine ) ., Notably , the modification reappeared when K27 , rather than other lysines , was reintroduced into the ubiquitin K0 mutant ( Fig 4F ) ., Moreover , cGAS was not poly-ubiquitinated when using ubiquitin K27R ( S5B Fig ) , whereas cGAS was polyubiquitinated as well by K6R , K11R , K29R , K33R , K48R and K63R ( S5B Fig ) ., Collectively , the data indicate that RNF185 catalyzes the formation of the K27-linked polyubiquitin chains on cGAS ., To identify the potential poly-ubiquitination sites on cGAS , we carried out a systematic lysine ( K ) to arginine ( R ) mutation scanning ., When the two lysines ( K173 and 384 ) on cGAS were all mutated to arginines , the poly-ubiquitination of cGAS was almost completely abolished ( Fig 4G ) ., To substantiate , cGAS mutants could induce the IFN-β-luciferase reporter gene to a much lower level than their wild-type one ( Fig 4H ) ., In addition , the expression of Cxcl10 and Ifnb mRNAs as well as the phosphorylation of TBK1 and IRF3 triggered by cGAS K173R/384R mutant were much lower than by wild-type cGAS in L929 cells stimulated with or without HT-DNA ( S5C and S5D Fig ) ., There was some background poly-ubiquitination of the endogenous cGAS in resting cells ( Fig 4I ) ., The endogenous cGAS was robustly poly-ubiquitinated upon HSV-1 infection ., Importantly , the poly-ubiquitination of cGAS was markedly reduced in Rnf185 knockdown L929 cells ( Fig 4I ) ., Taken together , these data establish that cGAS is an authentic substrate of RNF185 , which catalyzes the K27-linked poly-ubiquitination of cGAS ., To probe the functional role of the ubiquitination of cGAS , L929 cells were transfected with RNF185 siRNA and stimulated with HT-DNA ., cGAMP levels in the infected cell lysates were indirectly measured through incubation of PFO-permeabilized fresh L929 cells with those lysates , and the IRF3 dimerization was checked ., As expected , knocking down RNF185 in L929 cells resulted in a significant reduction of cGAMP production upon HT-DNA transfection ( Fig 5A ) , which indicate that RNF185 is required for activating cGAS in response to cytosolic DNA challenge ., The in vitro enzymatic activity assay was further employed to assess the effect of cGAS ubiquitination on its cGAMP synthetic activity ., Briefly , the purified recombinant proteins of cGAS or cGAS mutants from bacteria were subjected to the in vitro ubiquitination reaction , and then they were incubated with the salmon sperm DNA in the presence of ATP and GTP ., The production of cGAMP was analyzed by ion exchange chromatography ., As expected , recombinant RNF185 ( rRNF185 ) promoted recombinant cGAS to produce much more 2′3′-cGAMP than the rRNF185 C39A mutant ( Fig 5B and 5C ) ., In addition , the polyubiquitin-chain-deficient cGAS mutant could marginally synthesize 2’3’-cGAMP in the presence of rRNF185 ( Fig 5B and 5C ) ., Taken together , these data reveal that the poly-ubiquitination of cGAS promotes its catalytic activity ., The robust induction of IFN-β and interferon-stimulated genes ( ISGs ) represents one of the immediate responses to cytosolic DNA virus infections ., ELISA assays indicated that the production of IFN-β was markedly reduced in Rnf185 knockdown L929 cells stimulated with HT-DNA ( Fig 6A ) ., Standard plaque assay revealed that RNF185 knockdown resulted in nearly 3-fold increase in HSV-1 virus titer as compared with controls ( Fig 6B and 6C ) ., Since IFN-β protects host cells against viruses , we assessed if RNF185 played a role in restricting HSV-1 infection ., MEF cells were pretreated respectively with culture supernatants from HT-DNA-stimulated Rnf185 knockdown L929 cells or wild-type L929 cells , followed by HSV-1 infection ., Fresh cells pretreated with culture supernatants from Rnf185 knockdown L929 cells were more permissive to HSV-1 infection ( Fig 6D ) ., We next investigated whether RNF185 modulated virus replication by challenging cells with HSV-1-GFP ., It was observed that the cells with Rnf185 knockdown showed considerably increased numbers of HSV-1-GFP positive cells ( Fig 6E and 6F ) ., Taken together , these data indicate that RNF185 is important for innate antiviral responses ., Much is known about the signal transduction triggered by the cytosolic DNAs 24 , 40 ., Unlike other sensors ( DAI , IFI16 , DDX41 , Mre11 ) 10 , 11 , 13 , 14 , cGAS was characterized as a universal sensor that initiates the STING signaling in multiple cell types triggered by many stimuli 15 ., Although several recent studies have uncovered key structural features associated with DNA recognition by cGAS as well as the catalytic mechanisms of cGAS generating cGAMP 22 , 41 , 42 , 43 , 44 , it is not well understood how the cGAS activity is modulated dynamically in response to pathogenic or self DNA ., In this study , we performed unbiased RNAi-based screening ( S6 Fig ) , and identified a novel E3 ubiquitin ligase RNF185 to directly modulate cGAS action ., Several lines of evidence substantiate the important function of RNF185 in the cytosolic DNA sensing pathway ., ( a ) Knocking down RNF185 specifically attenuated the expression of IRF3-responsive genes induced by DNA mimics transfection or DNA virus HSV-1 infection , but not by RNA mimic transfection or RNA virus SeV infection ., ( b ) The effect produced by RNF185 knockdown was reversed by exogenously expressing a siRNA-resistant rRN185 , not by expressing the enzymatic inactive RNF185 mutant , indicating that the regulatory function of RNF185 was dependent on its enzymatical activity ., ( c ) The phosphorylation , dimerization and nuclear translocation of IRF3 triggered by cytoslic DNAs were markedly crippled in RNF185 knockdown cells ., ( d ) Silencing of RNF185 was more permissive to the HSV-1infection , establishing that RNF185 was important for the innate antiviral responses ., In most cases , HSV-1 infection brings about herpetic encephalitis or genital disease in a living host 45 , 46 ., In our data , we noticed that RNF185 knockdown resulted in nearly 3-fold increase in HSV-1 virus titer as compared with controls ., However , it is worthwhile to explore in the future clinical study whether a 3-fold statistically significant difference in a HSV titer could potentially affect herpetic encephalitis or genital disease in a living host ., RNF185 was previously shown to catalyze the ubiquitin-mediated degradation of several proteins ( BNIP1 , CFTR and Dvl2 ) and modulate the protein quality control on ER 35 , 36 , 37 ., In this study , we characterized cGAS as a new substrate of RNF185 ., ( a ) RNF185 specifically associated with cGAS , and this association was markedly increased upon HSV-1 challenge , indicating that this association was transient and dynamic ., ( b ) Wild-type RNF185 , but not its enzymatic inactive mutants , could catalyze the poly-ubiquitination of cGAS , as evidenced by the two-step immunoprecipitation or in vitro ubiquitination assay ., ( c ) Site-directed mutagenesis revealed that lysines 173 and 384 on cGAS were major acceptor sites of the polyubiquitin chain ., ( d ) RNF185 specifically catalyzed the K27-linked polyubiquitin chain on cGAS ., ( e ) DNA virus infection induces the ubiquitination of endogenous cGAS by RNF185 , as evidenced by the observation that RNAi-mediated silencing of RNF185 diminished these effects ., ( f ) The ubiquitination of cGAS potentiates its enzymatic activity and boosts the production of cGAMP ., Taken together , RNF185 is an authentic E3 ubiquitin ligase for cGAS and promotes its activation ., It is recently well established that the aberrant activation of the cGAS-STING signaling by self-DNA causes severe autoimmune or auto-inflammatory disorders , such as SLE 38 , 39 , 47 ., We found that RNF185 mRNA expression is substantially elevated in PBMCs from SLE patients ., Generation of RNF185-deficient mice in the future will further elucidate the functional relevance of RNF185 in SLE ., cGAS-STING signaling is essential for monitoring mitochondrial DNA ( mtDNA ) released into cytoplasm during mitochondrial membrane permeabilization or stress 48 , 49 , 50 ., It is also indispensable for sensing damaged DNA leaked into cytoplasm , resulting from ATM ( Ataxia-telangiectasia mutated ) deficiency or exogenous genotoxic stress 51 ., In particular , cGAS-STING signaling is important in sensing and responding to tumor cell-derived DNA 52 , 53 ., Future investigation is expected to uncover the potential roles of RNF185 in mitochondrial stress , DNA damage , and tumor immunity ., Insights from these studies might substantiate RNF185 as a potential therapeutic target for further clinical trials ., Ubiquitination is a versatile post-translational modification critical in innate immunity 27 , 54 ., Different linkages of polyubiquitin chains anchored on target proteins produce specific physiological or pathological consequences ., K48- and K63-linked polyubiquitin chains have been extensively used in regulating the TLR and RLR signaling pathways 27 , 54 ., Apparently , ubiquitin-mediated modulation of the cGAS-STING signaling is no exception ., For example , RNF5 promotes K48-linked poly-ubiquitination of STING , thus dampening the cytosolic virus-triggered immune responses 55 ., Additionally , E3 ubiquitin ligases TRIM56 and TRIM32 respectively facilitate the K63-linked poly-ubiquitination of STING and positively regulate the host anti-microbial responses 56 , 57 ., Given the diversity of the STING poly-ubiquitination , it is worthwhile to explore whether cGAS is also modulated by other forms of poly-ubiquitination ., It remains to address whether the stability of cGAS is dynamically modulated by the ubiquitin-proteasome system ., Ethical approval for this study was granted by the Clinical Research Ethics Committee of Zhongshan Hospital , Fudan University School of Medicine ., All the participants gave written informed consent before enrollment ., Age matched 32 healthy volunteers were recruited as controls ., All healthy volunteers used as controls also provided written informed consent ., We collected 34 patients all fulfilling the American College of Rheumatology classification criteria for SLE 58 ., These patients included two male patients and thirty-two female patients , 18 to 48 years old , averaging 37 years old ., All patients were new-onset and not being treated before ., Patients who coincided with other autoimmune diseases were excluded ., All subjects were screened for infectious conditions ., Peripheral blood ( 8 ml ) was sampled in 10ml EDTA containing Vacutainer K2E ( BD biosciences ) ., Peripheral blood mononuclear cells ( PBMCs ) were separated by density gradient centrifugation using Ficoll-Paque PLUS ( GE Healthcare ) and the RNA was extracted from PBMCs using TRIzol reagent ( Invitrogen ) according to the manufacturer’s instructions ., L929 cells ( ATCC ) were cultured in RPMI 1640 medium ( Invitrogen ) supplemented with 10% FBS and 1% penicillin-streptomycin ., HEK293T ( ATCC ) , HEK293FT ( kindly provided by Ke Lan’ lab in Shanghai Pasteur Institute ) , MEFs ( ATCC ) and RAW264 . 7 cells ( ATCC ) were maintained in DMEM plus 10% FBS ( Gibco ) , supplemented with 1% penicillin-streptomycin ( Invitrogen ) ., Vero cells ( kindly provided by Ke Lan’ lab in Shanghai Pasteur Institute ) were cultured in MEM ( SAFC Biosciences ) supplemented with 10% FBS and 1% penicillin-streptomycin ., BMDMs ( bone marrow derived macrophages ) were prepared as described previously 59 ., HEK293T cells and HEK293FT cells were transfected by standard calcium phosphate precipitation method ., Other cells were transfected by Lipofectamine 2000 ( Invitrogen ) according to the manufacturer’s instructions ., The individual SMART pool siRNA probes ( Dharmacon ) against a mouse ubiquitin-E3-ligase sub-library of 43 genes encoding RING finger proteins were transfected into L929 cells ., 48h after transfection , cells were left uninfected or infected with HSV-1 for 6h , and then cells were directly collected into lysis buffer and cDNA was obtained according to the manufacturers instruction ( Cells-to-cDNA II Kit , Invitrogen ) ., The quantifications of gene transcripts were performed by real-time PCR ., HEK293T/STING cells were originated from HEK293T cells selected by Zeocin ™ ( Invitrogen , 500ug/ml ) following transfection with pCMV-Zeo-STING 60 ., L929/cGAS cells and L929/cGAS K173/384R cells were established by transducing the phageflag-cGAS or phageflag-cGAS K173/384R lentiviruses into L929 cells followed by sorting with flow cytometry ., Lentiviruses production was performed according to the manufacturer’s instructions ., Briefly , HEK293FT cells plated on 100-mm dishes were transfected with the indicated lentiviral expression plasmid ( 15ug ) together with the PSPA ( 10ug ) and the PMD2G ( 5ug ) ., The viral particles were collected at 48h , filtered by 0 . 45um membrane filter and used to infect the indicated cells in the presence of polybrene ( 4ug/ml ) ., After transfection for 48h , the positive cells were sorted by flow cytometry ( AriaII , BD Biosciences ) , then cultured in complete RPMI 1640 medium ., RNF185 , cGAS , STING , TBK1 , IRF3 , AMFR , Trim32 , Trim56 , DDX41 , DAI , IFI16 cDNAs were obtained by standard PCR techniques from thymus cDNA library and subsequently inserted into mammalian expression vectors as indicated ., pCMV-Zeo-STING was kindly provided by Fanxiu Zhu ( Florida State University , Tallahassee , USA ) ., pET21b-PFO 85–1500 a . a . was a gift from Zhengfan Jiang ( Peking University , Beijing , China ) ., The reporter plasmids ( IFNβ-luciferase and pTK-Renilla ) have been described previously 61 ., All point mutations were introduced by using a QuickChange XL site-directed mutagenesis method ( Stratagene ) ., All constructs were confirmed by sequencing ., The rabbit polyclonal antibody against cGAS was from Cell Signaling Technology and Sigma-Aldrich ., The polyclonal antibody against RNF185 was from Abcam ., The antibodies against hemagglutinin ( HA ) , Myc , GFP , and ubiquitin were purchased from Santa Cruz Biotechnology ., Mouse monoclonal Flag antibody and β-actin antibody were obtained from Sigma-Aldrich ., The TBK1 antibody was from Abcam ., The IRF3 antibody was from Santa Cruz Biotechnology ., Phospho-TBK1 and Phospho-IRF3 antibody was from Cell Signaling Technology ., The antibodies against CoxIV , Calreticulin and Calnexin were from Abcam ., Anti-Flag ( M2 ) -agarose was from Sigma-Aldrich ., Herring testis ( HT ) DNA was from Sigma ., Salmon sperm DNA was from TREVIGEN ., Poly ( I:C ) was purchased from Invivogen ., IFNα2a was from PBL Assay Science ., cGAMP was obtained from InvivoGen ., In some experiments , cGAMP was delivered into cultured cells by digitonin permeabilization method as previously described 62 ., Chemically synthesized 21-nucleotide siRNA duplexes were obtained from Invitrogen and Gene-Pharma , and transfected using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer’s instructions ., RNA oligonucleotides used in this study are as follows: N . C . : 5-UUC UCC GAA CGU GUC ACG UTT-3; RNF185 #1: 5′- AAU CUU CCC UGG AAG CUU UTT-3′; RNF185 #2: 5′- GCC ACA GCA UUU AAC AUA ATT -3′ ., Luciferase reporter assays were performed as described previously 63 ., Total RNA was isolated from cultured cells using TRIzol reagent ( Invitrogen ) according to the manufacturer’s instructions , and then subjected to reverse transcription with PrimeScript RT Master Mix ( Takara ) ., The quantifications of gene transcripts were performed by real-time PCR using Power SYBR GREEN PCR MASTER MIX ( ABI ) ., GAPDH served as an internal control ., PCR primers used to amplify the target genes were shown as follows: Gapdh: sense ( 5′-GAA GGG CTC ATG ACC ACA GT-3′ ) , antisense ( 5′-GGA TGC AGG GAT GAT GTT CT-3′ ) ; Rnf185: sense ( 5′-AGC AGA CTG GGA TTG TCT TG-3′ ) ; antisense ( 5′-CCA TTG CTG CTG CCA CTG GG -3′ ) ; Ifnb: sense ( 5′-AGA TCA ACC TCA CCT ACA GG-3′ ) , antisense ( 5′-TCA GAA ACA CTG TCT GCT GG-3′ ) ; Ifna4: sense ( 5′-ACC CAC AGC CCA GAG AGT GAC C-3′ ) , antisense ( 5′-AGG CCCT CTT GTT CCC GAG GT-3′ ) ; Cxcl10: sense ( 5′-CCT GCC CAC GTG TTG AGA T-3′ ) , antisense ( 5′-TGA TGG TCT TAG ATT CCG GAT TC-3′ ) ; GAPDH: sense ( 5′-CGG AGT CAA CGG ATT TGG TC-3′ ) , antisense ( 5′-GAC AAG CTT CCC GTT CTC AG-3′ ) ; RNF185: sense ( 5′-AGG ACC CCA GAG AGA AGA CC -3′ ) , antisense ( 5′-CAA TTC CAA AAG ACA TCT GG-3′ ) ; IFNB: sense ( 5′-ATT GCC TCA AGG ACA GGA TG-3′ ) , antisense ( 5′-GGC CTT CAG GTA ATG CAG AA-3′ ) ; IFNA2: sense ( 5′-CCT GAT GAA GGA GGA CTC CAT T-3′ ) , antisense ( 5′-AAA AAG GTG AGC TGG CAT ACG-3′ ) ; IFNA5: sense ( 5′-TCC TCT GAT GAA TGT GGA CTC T-3′ ) , antisense ( 5′-GTA CTA

Introduction, Results, Discussion, Materials and methods

The cyclic GMP-AMP synthase ( cGAS ) , upon cytosolic DNA stimulation , catalyzes the formation of the second messenger 2′3′-cGAMP , which then binds to stimulator of interferon genes ( STING ) and activates downstream signaling ., It remains to be elucidated how the cGAS enzymatic activity is modulated dynamically ., Here , we reported that the ER ubiquitin ligase RNF185 interacted with cGAS during HSV-1 infection ., Ectopic-expression or knockdown of RNF185 respectively enhanced or impaired the IRF3-responsive gene expression ., Mechanistically , RNF185 specifically catalyzed the K27-linked poly-ubiquitination of cGAS , which promoted its enzymatic activity ., Additionally , Systemic Lupus Erythematosus ( SLE ) patients displayed elevated expression of RNF185 mRNA ., Collectively , this study uncovers RNF185 as the first E3 ubiquitin ligase of cGAS , shedding light on the regulation of cGAS activity in innate immune responses .

Ubiquitination has been demonstrated to serve as an effective means to catalyze the rapid , dynamic and versatile regulatory processes that are activated when hosts face microbes ., Given the critical functions of cytosolic DNA sensing pathway in anti-viral innate immune responses and the pathogenesis of autoimmune diseases , they are subjected to manifold spatial and temporal modulations shaping the strength and duration of the signaling pathways ., Recent progress has characterized the cyclic GMP-AMP synthase ( cGAS ) as the primary DNA sensor that initiates stimulator of interferon genes ( STING ) -dependent signaling pathway ., However , it remains poorly understood how cGAS activity is modulated dynamically ., In this study , we identify E3 ubiquitin ligase RNF185 as a positive regulator of cGAS-STING signaling ., Knockdown of RNF185 significantly attenuates IRF3-responsive gene expression ., ER-resident RNF185 interacts with cGAS and catalyzes the K27-linked poly-ubiquitination of cGAS upon HSV-1 challenges , which thus potentiates cGAS enzymatic activity ., Notably , systemic lupus erythematosus ( SLE ) patients have elevated expression of RNF185 mRNA ., Our study uncovers RNF185 as the first E3 ubiquitin ligase of cGAS and suggests RNF185 as an important target for modulating antiviral response .

transfection, phosphorylation, rheumatology, medicine and health sciences, gene regulation, enzymes, biological cultures, immunology, enzymology, ubiquitin ligases, clinical medicine, immunoprecipitation, molecular biology techniques, ligases, research and analysis methods, systemic lupus erythematosus, l929 cells, small interfering rnas, proteins, lupus erythematosus, gene expression, ubiquitination, cell lines, molecular biology, precipitation techniques, biochemistry, rna, nucleic acids, post-translational modification, clinical immunology, genetics, autoimmune diseases, biology and life sciences, non-coding rna

journal.pntd.0004980

West Nile Virus Spreads Transsynaptically within the Pathways of Motor Control: Anatomical and Ultrastructural Mapping of Neuronal Virus Infection in the Primate Central Nervous System

West Nile virus ( WNV ) is a mosquito-borne neurotropic flavivirus that has emerged as a human pathogen of global scale 1 ., During the latest and largest outbreak of human WNV disease in US history during 2012–2013 2 , 3 , over half of the reported cases ( 51% ) were classified as WNV neuroinvasive disease ., Although much research has been done , there are still gaps in our understanding of WNV neuropathogenesis 4 ., There is no consensus on how WNV infects the central nervous system ( CNS ) ., Two hypotheses of neuroinvasion are being considered:, ( i ) hematogenous route and, ( ii ) transneural entry through peripheral nerves ., However , regardless of the mode of virus entry from the periphery , it is not clear how virus spreads once within the CNS ., Cell-to-cell spread of viruses contributes significantly to the pathogenesis of viral infections by facilitating virus dissemination and immune evasion 5 ., The ability of several neurotropic viruses to spread between neurons via neuronal synapses and use axonal transport ( anterograde , retrograde , or both ) is well recognized and has been successfully exploited to trace neuronal connectivity ., Of these viruses , the most studied are the alpha herpes viruses and rabies virus ( reviewed in 6–9 ) ., A study using an in vitro system of compartmentalized neuronal cultures showed that WNV can spread between neurons in both anterograde and retrograde directions via axonal transport 10 ., However , how WNV spreads in vivo , especially within the CNS , is less clear ., Transneuronal WNV spread was reported as a putative route of neuroinvasion after sciatic nerve inoculation in hamsters 10 , 11 , but the brain was not studied in this model , and it was speculated that in the CNS , both anterograde and retrograde neuronal transport contributes to “centrifugal” spread of WNV among neurons in the brain 10 ., It is also not clear how well rodent models reproduce all aspects of WNV neuropathogenesis in humans 4 ., Nonhuman primates ( NHP ) represent a more suitable model due to their natural susceptibility to a wide range of human pathogens and the high degree of genetic similarity to humans 12 ., However , primates do not develop neurological WNV disease after peripheral ( either natural or experimental ) infection 13–17 ., On the other hand , the NHP model of neuroinfection , in which animals are inoculated intracerebrally , remarkably recapitulates the features of WNV encephalomyelitis seen in humans 15–19 ., In humans with WNV neuroinvasive disease , the CNS structures that are often involved include ( listed by an increasing gradient of the severity of infection ) : cerebral cortex ( least severe ) , basal ganglia , thalamus , brainstem , cerebellum , and spinal cord ( most severe ) 20–24 ., The possibility of WNV propagation via neuronal processes within the CNS was suggested from autopsy findings in an immunosuppressed patient with a fatal WNV encephalitis 25 ., However , the connectivity between affected structures and its possible role in virus spread within the CNS have not been studied ., Here , we used the CNS tissues from our previous NHP study 19 , in which animals were inoculated intrathalamically with WNV , and investigated the role of the neuroanatomical connectivity in the spread of WNV within the brain and spinal cord ., The rhesus macaques ( Macaca mulatta ) used for this study were housed in a BSL-3 facility in compliance with the National Institute of Allergy and Infectious Diseases ( NIAID ) , Division of Intramural Research ( DIR ) Animal Program Policy on Social Housing of Non-Human Primates , and Comparative Medicine Branch NHP enrichment programs ., Animals were provided with commercial food pellets supplemented with appropriate treats ., Drinking water was provided ad libitum ., All steps were taken to minimize suffering ., The experimental procedures requiring anesthesia were performed using ketamine hydrochloride or other anesthetics at the discretion of the attending veterinarian ., For euthanasia , ketamine hydrochloride pre-anesthesia and sodium pentobarbital were used ., The NIAID DIR Animal Care and Use Committee approved the animal study proposal ( #LID 7E ) ., The NIAID DIR Animal Care and Use Program , as part of the NIH Intramural Research Program ( IRP ) , complies with all applicable provisions of the Animal Welfare Act ( http://www . aphis . usda . gov/animal_welfare/downloads/awa/awa . pdf ) and other Federal statutes and regulations relating to animals ., The NIAID DIR Animal Care and Use Program is guided by the U . S . Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research , and Training ( http://oacu . od . nih . gov/regs/USGovtPrncpl . htm ) ., The NIAID DIR Animal Care and Use Program acknowledges and accepts responsibility for the care and use of animals involved in activities covered by the NIH IRP’s PHS Assurance #A4149-01 , last issued 11/24/2014 ., As partial fulfillment of this responsibility , the NIAID DIR Animal Care and Use Program ensures that all individuals involved in the care and use of laboratory animals understand their individual and collective responsibilities for compliance with that Assurance , as well as all other applicable laws and regulations pertaining to animal care and use ., The NIAID DIR Animal Care and Use Program has established and will maintain a program for activities involving animals in accordance with the most recent ( 2011 , 8th edition ) of “The Guide for the Care and Use of Laboratory Animals” ( ILAR , NRC ) ( http://oacu . od . nih . gov/regs/guide/guide_2011 . pdf ) ., The policies , procedures and guidelines for the NIH IRP are explicitly detailed in NIH Policy Manual 3040–2 , “Animal Care and Use in the Intramural Program” ( PM 3040–2 ) and the NIH Animal Research Advisory Committee Guidelines ( ARAC Guidelines ) ., Those documents are posted on the NIH Office of Animal Care and Use public website at: http://oacu . od . nih . gov ., Our animal model of WNV neuropathogenesis in NHPs ( Macaca mulatta; WNV-seronegative; 2–3 year old ) that were inoculated intrathalamically ( bilaterally ) with a dose of 5 . 0 log10 PFU of wild-type WNV strain NY99-35262 ( hereafter WNV ) has recently been described 19 ., We performed a systematic collection of all major CNS regions from these animals for downstream analyses ., In this study , CNS tissues were examined by immunohistochemistry and electron microscopy ., CNS tissues were from twelve WNV-infected monkeys ( 3 days post infection ( dpi ) n = 3; 7 dpi n = 3; and 9/10 dpi ( 9 dpi n = 5; 10 dpi n = 1 ) and two mock-inoculated monkeys ( 7 dpi n = 1 and 10 dpi n = 1 ) ., WNV-infected animals developed a fulminant encephalomyelitis by 9/10 dpi ( details of the clinical course , CNS virus burden , and histopathological scores can be found in our prior publication 19 ) ., Brains and spinal cords were collected immediately after euthanasia and cardiac perfusion with sterile saline ., After a parasagittal cut , the right brain hemisphere was fixed in 10% buffered formalin ., Rhesus Mon-key Brain Matrix ( Ted Pella , Redding , CA ) was used to make 4 mm coronal brain slices that were further cut to facilitate mounting of subsequent sections onto standard 1 x 3 inches slides ., Slices were routinely processed and embedded in paraffin ., Two 5 μm sections ( 1st and 4th ) from each paraffin block were mounted onto single slides and processed for immunohistochemistry ., Spinal cord was dissected transversely and sections from cervical , thoracic , and lumbar regions were mounted onto single slides and also processed for immunohistochemistry ., Immunohistochemical detection of WNV antigens in the CNS of rhesus monkeys was performed using WNV-specific primary antibodies in hyperimmune mouse ascitic fluid ( ATCC VR-1267 AF; 1:1000 ) and subsequent steps were according to previously described procedures 26 ., Diaminobenzidine was used for colorimetric detection of WNV antigens ., Sections were counterstained with hematoxylin ., Whole tissue section imaging was performed at 20x magnification using ScanScope XT ( Aperio , Vista , CA ) ., Aperio Spectrum Plus and ImageScope software was used for digital slide organization , viewing , and analysis ., We analyzed all major CNS regions including: cerebral cortex , basal ganglia , thalamus , midbrain , pons , medulla oblongata , cerebellum ( cerebellar cortex and deep cerebellar nuclei ) , and spinal cord ( cervical , thoracic , and lumbar regions ) ., The “Primate Brain Maps: Structure of the Macaque Brain” 27 were used for neuroanatomical orientation and mapping ., To examine the WNV-immunoreactivity and to add to the visualization of WNV-antigen positive cells in the cerebellar cortex , a custom “WNV-labeled cell segmentation” image analysis algorithm was developed based on the ImageScope nuclear algorithm ., For ultrastructural analysis , core tissue samples ( 2 mm in diameter; 4 mm thick ) were extracted using sterile Harris Uni-Cores ( Ted Pella , Redding , CA ) ., Samples that included the gray matter ( wherever possible ) were extracted from the following CNS regions: cerebral cortex , basal ganglia , thalamus , pons , medulla oblongata , cerebellar cortex , and spinal cord ( cervical and lumbar regions ) ., For the cerebellar cortex , core samples were extracted from the folia in a manner that included the molecular layer , Purkinje cell layer , and granule cell layer ., For the spinal cord , core samples were extracted from the ventral horns ., Collected core tissue samples were fixed in 2 . 5% glutaraldehyde and 2% paraformaldehyde ( Electron Microscopy Sciences , Hatfield , PA ) , then washed in Millonig’s sodium phosphate buffer ( Tousimis Research , Rockville , MD ) , post-fixed in 1% osmium tetroxide ( Electron Microscopy Sciences ) , stained en bloc with 2% uranyl acetate ( Fisher Scientific , Waltham , MA ) , dehydrated in increasing concentrations of ethanol , and then infiltrated and embedded in Spurr plastic resin ( Electron Microscopy Sciences ) ., Embedded tissue samples were sectioned using a Leica UC7 Ultramicrotome ( Leica Microsystems , Buffalo Grove , IL ) ., Ultra-thin sections ( 60–80 nm in thickness ) were collected , mounted onto 200 mesh copper grids , and contrasted with lead citrate ( Fisher Scientific ) ., The grids were then examined and imaged using a transmission electron microscope ( FEI G2 Tecnai ) ., The method of circular representation , named a “connectogram” , is an intuitive and suitable approach for the visualization and interpretation of neuroanatomical connectivity using magnetic resonance imaging 28 , 29 ., This type of representation is also highly suitable for visualization of complex neuroanatomical connections with an attempt to reconstruct virus spread between the infected CNS structures in this study ., For this purposes , we adopted the connectogram idea and manually created our connectograms using Adobe Illustrator ., The information used to create the connectograms is based on the literature review of established connectivity only between neuroanatomical structures relevant to this study ., Our first goal was to identify WNV-labeled cells using immunohistochemistry and then map their distribution to specific anatomical structures within the CNS ., We did not detected WNV antigens at 3 dpi in any CNS region ., WNV-labeled neurons became readily detectable in the CNS at 7 dpi and 9/10 dpi ., WNV-infected CNS regions , anatomical structures/types of neurons , reference virus titers 19 , extent/intensity and timing of WNV-labeling , as well as references to representative images in this report are summarized in Table 1 ., A general pattern of anatomical localization and extent of neuronal WNV-labeling closely followed the distribution and amounts of infectious virus at the same time points ., To add to these comparisons , the changes in WNV loads within each major CNS region during the time course of neuroinfection are provided by radar charts in S1 Fig . One caveat of this study is the fact that we were unable to detect WNV antigens by immunohistochemistry at 3 dpi ., This could be explained by the immunohistochemical limit of flavivirus detection of approximately 3 log10 PFU/g ( personal observation and compare mean virus titers and average WNV-labeling in Table 1 ) ., Whether other neuronal cells could be infected at the levels below our limit of detection remains an open question ., WNV-labeling was detected in the neuronal cytoplasm and processes of the following anatomical structures and/or neuronal types: motor cortex ( corticospinal motor neurons Betz cells ) ; subcortical regions ( neurons in the motor ventrolateral thalamus and basal ganglia ) ; midbrain ( substantia nigra pars compacta and red nucleus magnocellular ) ; and pons/medulla oblongata ( pontine nuclei , vestibular nuclei , medullary reticular formation , inferior olivary nuclei , and accessory cuneate nucleus ) ( Figs 1 and 2 ) ., In the cerebellum , WNV-labeling was unambiguously detected in neurons of the deep cerebellar nuclei ( Fig 3 ) and in the Purkinje cells ( Fig 4 and S2 Fig ) ., WNV-labeling of the granule neurons was much less frequent ., To better appreciate the differences in WNV immunoreactivity between the infected cells of the cerebellar cortex , we developed a custom “WNV-labeled cell segmentation” image analysis algorithm , which produced markup images highlighting the intensity of WNV immunoreactivity in the Purkinje cells ( Fig 4B and 4D ) and granule neurons ( Fig 4D ) ., Of note , despite the fact that only a few small groups of granule neurons were infected , we observed a substantial focal rarefaction of the granule cell layer at 9 dpi ., This phenomenon cannot be explained by virus-induced cell death since only a small number of these cells were WNV-positive , nor can it be ascribed to a known artefact of granule cell dissolution due to postmortem autolysis ., The latter is because necropsy and tissue fixation were performed immediately after euthanasia and also because our experiments were well controlled by inclusion of mock-inoculated animals that were euthanized at the same time points as WNV-infected animals ( compare anti-NeuN immunostaining highlighting the rarefaction of the granule cell layer in WNV-infected animals and normal granule cell layer in mock-inoculated animals , S4 Fig ) ., This phenomenon therefore remains unexplained and deserves further investigation ., In the spinal cord , WNV-labeling was detected in the lower motor neurons residing in the Rexed’s laminae IX of the ventral horns , spanning cervical , thoracic , and lumbar regions ( Fig 5 ) ., However , in addition to this , an intriguing finding was that many spinocerebellar relay neurons that occupy a discrete nucleus known as Clarke’s column also contained large amounts of WNV antigens in their cytoplasm and transverse axonal profiles ( Fig 5E ) ., These mapping results , when taken together , provided a detailed picture of WNV infection of the CNS ., Interestingly , all structures identified as harboring WNV-labeled neurons are thought to participate in the control of movement ( Table 2 ) Of note , during the terminal stage of neuroinfection , WNV also infected the structures that relay proprioceptive signals from the upper parts of the body ( accessory cuneate nucleus ) and from the lower parts of the body ( Clarke’s column ) to the cerebellum ., Clarke’s column ( medial portion ) also integrates the corticospinal inputs with relevance to motor planning and evaluation 46 ., Remarkably , all WNV-infected structures and/or neuronal groups identified in this study were also reported to be affected in humans with WNV encephalomyelitis ( i . e . , cerebral cortex , basal ganglia , thalamus , substantia nigra , red nucleus , pons , vestibular nuclei , medulla , inferior olive , cuneate nucleus , Purkinje cells , dentate nucleus , Clarke’s column , and ventral horns of spinal cord 20–25 , 47–51 ., Our findings are also in line with the pioneering studies of WNV encephalitis in intracerebrally inoculated nonhuman primates 15–18 ., These early studies , although not well equipped to precisely detect specific groups of infected neurons , clearly showed infection of brainstem , cerebellum and spinal cord ., Our findings provide the evidence that , within the primate CNS , WNV preferentially infects specific neuroanatomical structures responsible for the control of movement ., We next used electron microscopy ( EM ) to determine intraneuronal localization of WNV particles ., It is generally accepted that in order to detect virus particles in tissue culture by electron microscopy ( EM ) , the virus titers have to be high ( i . e . , 105 to 106 particles per milliliter ) 52 ., The same is true for detection of viruses in tissues ., A major limitation of virus detection in tissues by EM is that the sampling might miss the areas containing viruses ., With this in mind , we focused on the most heavily infected CNS regions with highest virus loads ( i . e . , cerebellum and spinal cord; see Table 1 ) from animals which developed fulminant encephalitis ( at 9/10 dpi ) ., In order to maximize the probability of virion detection , we used a targeted small-volume sampling of the gray matter for the ultrastructural analysis ( see Materials and Methods ) ., Tissue-core samples of the cerebellar cortex included the molecular layer , Purkinje cell layer , and granule cell layer ., Tissue-core samples of the spinal cord contained the gray matter from the ventral horns ., Ultrastructural analysis confirmed WNV infection of the Purkinje cells in the cerebellar cortex and motor neurons in the ventral horns of the spinal cord ., We also examined many other CNS regions with lower virus burden ( i . e . , cerebral cortex , basal ganglia , thalamus , pons , and medulla oblongata ) but , as expected , were unable to detect virions ., Similar to well-defined structures that can be seen in non-polarized cells and have been linked to sites of virus replication 53–55 , many infected neurons in this study showed smooth-membrane structures , convoluted membranes , and tubular structures that are characteristic of flavivirus infection ., We also observed the formation of prominent paracrystalline arrays ( S3 Fig ) ., However , there were several unique findings related exclusively to WNV infection of neurons in this study: It is also important to note that during EM examination of neurons , the cell organelles such as neuropeptide-containing dense-core vesicles 56 should not be mistaken for virions ., This has been also emphasized by other investigators 57 ., In this study , we often observed large dense-core vesicles in axons and axon terminals ., Two features helped to distinguish between the dense-core vesicles and WNV particles:, ( i ) each dense-core vesicle had a single membrane and, ( ii ) dense-core vesicles were larger in diameter ( 75–120 nm ) compared to WNV virions ( 40 nm ) ( compare Fig 7C and 7D , insets ) ., In human cases of WNV encephalitis , the visualization of WNV particles in neurons by electron microscopy is very rare , likely due to the difficulties in performing a sufficient sampling of particular tissue areas with high virus loads ., Interestingly , when found , WNV particles were present in the cerebellar neurons ( type of neurons was not specified ) 22 ., We found virions grouped in the vesicular structures within the dendrites ( shafts and spines ) as well as within axon terminals in very close vicinity to the synaptic clefts ., To our knowledge , this is a first in vivo electron microscopy evidence suggesting transsynaptic spread of WNV between synaptically connected neurons in the primate CNS ., Another intriguing ultrastructural finding in this study was that WNV virions in the axon terminals were enclosed in the double-membrane vacuoles indicative of autophagosomes ., The role of autophagy in WNV-infected cells in vitro is not clear reviewed in 58; however , a recent study in a neonatal mouse model of WNV infection of the CNS showed that pharmacological activation of autophagy by a pro-autophagic peptide can protect against WNV-induced neuronal cell death and improve the clinical outcome 59 ., To this end , our finding of WNV virions within the autophagosomes that were positioned pre-synaptically might indicate sequestration of virions for degradation 60 to prevent their transsynaptic release and infection of post-synaptic neuron ., Alternatively , since the autophagosomes in neurons are initiated distally at axon terminals and fuse with late endosomes to form the amphisomes that are then transported retrogradely to reach acidic lysosomes in the cell soma 61 , 62 , WNV particles encapsulated within the autophagosomes/amphisomes might take advantage by using retrograde axonal transport to the neuronal perikarya as a way of transneuronal spread ., Whether this transport would result in the virion degradation upon fusion with lysosomes or would deliver the virions to the neuronal perikarya for successful subsequent replication remains to be investigated ., Intraneuronal movement of WNV most likely involves microtubules 63–65 and their associated motors for anterograde , retrograde , and/or bidirectional transport 66–69 ., In agreement with this scenario , the analysis by electron microscopy in this study revealed virions inside the vesicles that were adjacent to microtubule structures ( Fig 6B ) ., Assuming that WNV is transported in neurons within the vesicular structures , it is possible that already established mechanisms for vesicular intraneuronal transport are in use 8 ., For example , neuropeptide-containing dense core vesicles can move bidirectionally , switching between anterograde and retrograde axonal transport motors in a conveyor belt-like manner for continuous circulation 70 , 71 ., As mentioned above , it is possible that WNV particles are captured by autophagosome formations at distal axons ( whether delivered there by anterograde transport from neuronal soma or transmitted transsynaptically from a post-synaptic neuron ) and then delivered by retrograde transport to the neuronal soma for degradation or release and replication ., Since the directionality of virus spread cannot be determined based on the “snapshots” revealed by immunohistochemistry and EM , we next attempted to reconstruct the directionality of transsynaptic spread of WNV based on the neuroanatomical connectivity between identified infected structures ., For this purpose , we compiled known connectivity information and designed connectograms to capture and visualize the complex neuroanatomical connections between WNV-infected structures in an intuitive and concise way ., We created two connectograms ( Fig 8 ) showing a proposed directionality of WNV spread in our model based on the neuroanatomical connectivity and time of immunohistochemical virus detection ( i . e . , 7 and 9/10 dpi ) ., Reference connectivity information is provided in the S1 Table ., Each connectogram contains two concentric rings and a black core ., The names for each major CNS region are given on the outside periphery of outermost ring ( i . e . , subcortical regions , motor cortex , midbrain , spinal cord , cerebellum , medulla oblongata , and pons ) in no particular order ., For the outermost ring , each CNS region was assigned a spectrum domain color counterclockwise as follows: subcortical regions ( red ) , motor cortex ( orange ) , midbrain ( yellow ) , spinal cord ( green ) , cerebellum ( light blue ) , medulla oblongata ( blue ) , and pons ( purple ) ., The next ring toward the center of the connectogram is divided in sixteen segments ., Each segment representing a specific structure/type of neurons includes an abbreviation and is assigned a unique color within the spectrum domain color of the corresponding larger anatomical CNS region ., Within the black core , the directions of proposed spread of WNV between the structures/types of neurons are depicted by arrows ( anterograde virus spread—solid arrows; retrograde virus spread—dashed arrows; anterograde/retrograde virus spread due to existence of reciprocal connections—white lines ) ., Each arrow has the same color as the segment representing a structure/type of neurons from which it originates ., The direction of the arrow indicates a proposed direction of virus spread between one neuronal order to the next ., The proposed orders of neurons are indicated by circled numbers ., Assuming that the neurons of motor thalamus ( Mthal ) represent the neuronal order “0” ( starter cells ) , the next neuronal order “1” will be neurons of the deep cerebellar nuclei ( DCN ) , basal ganglia ( BG ) , and corticospinal motor neurons ( CSMN ) ( Fig 8A ) ., The arrows connecting these neuronal groups show that there are three possibilities of the retrograde axonal virus spread ( Mthal → DCN; Mthal → BG; and Mthal → CSMN ) and one possibility of the anterograde axonal virus spread ( Mthal → CSMN ) ., The possibility of both retrograde and anterograde virus spread between Mthal and CSMN exists because of reciprocal connections between these structures ., From the neurons of the order “1” , the virus spread to the next neuronal order “2” could also occur by the anterograde axonal transport ( BG → SNC; CSMN → SMN; CSMN → Pn; DCN → MeRF; DCN → IO ) and by the retrograde axonal transport ( BG → SNC; DCN → Pn; DCN → IO; DCN → Purkinje cells ) ., By the terminal stage of neuroinfection ( 9/10 dpi; Fig 8B ) , the directionality of virus spread to the next neuronal orders could occur by anterograde axonal transport ( CSMN → RnM; CSMN → CC; DCN → RnM; DCN → Ve; Pn → Granule cells ) and by retrograde axonal transport ( SMN/Cervical → RnM; SMN → Ve; Purkinje cells → Granule cells; Granule cells → ACu; Granule cells → CC ) ., The maximal neuronal order reached by virus in this model is “4” ( ACu and CC ) ., WNV infection of the neurons of accessory cuneate nucleus ( ACu ) in the medulla oblongata and Clarke’s column ( CC ) in the spinal cord is intriguing ., These structures relay proprioceptive information from the upper ( ACu ) and lower ( CC ) parts of the body to the cerebellum ., The axons of these relay neurons terminate as mossy fibers on the granule neurons ., It is conceivable that these structures may have been infected by retrograde spread of the virus along the dorsal spinocerebellar tract from granule neurons ., However , we found very few WNV-labeled granule neurons in the cerebellum ( Fig 4C and 4D ) ., Similarly , the granule cells do not appear to be infected in humans with WNV encephalomyelitis 20 ., This is consistent with the recently reported enhanced antiviral response in the granule neurons and might explain their relatively low permissiveness to WNV infection 72 ., These considerations suggest a limited contribution of granule neurons to the retrograde spread of WNV along the dorsal spinocerebellar tract to the proprioceptive relay neurons ., Recently identified inputs from the descending corticospinal axons 46 , better explain the infection of Clarke’s column ., By analogy , the infection of the accessory cuneate nucleus could probably be also explained by the cortical inputs ( yet to be identified ) since this nucleus is the anatomical and functional correlate of Clarke’s column in the medulla ., In summary , our reconstruction of the directionality of WNV spread within the CNS of intrathalamically inoculated NHPs suggests both anterograde and retrograde axonal transport ., The connectograms ( Fig 8 ) show eleven possible routes of anterograde and twelve possible routes of retrograde axonal spread ( with three unidentifiable directionalities between the same neuronal orders ) ., Collectively , our results of the anatomical and ultrastructural mapping of WNV neuronal infection in the primate CNS , together with the connectivity-based reconstruction of the directionality of virus spread strongly suggest the following: Progression of WNV neuroinfection transsynaptically along specific pathways governing motor control in both , anterograde and retrograde directions suggested by this study may open a way for future therapeutic approaches ., Although it seems unlikely that antiviral interventions would be justified for asymptomatic or self-limiting WNV cases in humans , we cannot neglect the necessity for development of rational treatments of flavivirus neurological disease ., Our findings imply that the focus of such treatments should not only be on limiting virus replication but also on blocking neuron-to-neuron virus transmission , thus preventing further damage to the CNS .

Introduction, Materials and Methods, Results and Discussion

During recent West Nile virus ( WNV ) outbreaks in the US , half of the reported cases were classified as neuroinvasive disease ., WNV neuroinvasion is proposed to follow two major routes: hematogenous and/or axonal transport along the peripheral nerves ., How virus spreads once within the central nervous system ( CNS ) remains unknown ., Using immunohistochemistry , we examined the expression of viral antigens in the CNS of rhesus monkeys that were intrathalamically inoculated with a wild-type WNV ., The localization of WNV within the CNS was mapped to specific neuronal groups and anatomical structures ., The neurological functions related to structures containing WNV-labeled neurons were reviewed and summarized ., Intraneuronal localization of WNV was investigated by electron microscopy ., The known anatomical connectivity of WNV-labeled neurons was used to reconstruct the directionality of WNV spread within the CNS using a connectogram design ., Anatomical mapping revealed that all structures identified as containing WNV-labeled neurons belonged to the pathways of motor control ., Ultrastructurally , virions were found predominantly within vesicular structures ( including autophagosomes ) in close vicinity to the axodendritic synapses , either at pre- or post-synaptic positions ( axonal terminals and dendritic spines , respectively ) , strongly indicating transsynaptic spread of the virus between connected neurons ., Neuronal connectivity-based reconstruction of the directionality of transsynaptic virus spread suggests that , within the CNS , WNV can utilize both anterograde and retrograde axonal transport to infect connected neurons ., This study offers a new insight into the neuropathogenesis of WNV infection in a primate model that closely mimics WNV encephalomyelitis in humans ., We show that within the primate CNS , WNV primarily infects the anatomical structures and pathways responsible for the control of movement ., Our findings also suggest that WNV most likely propagates within the CNS transsynaptically , by both , anterograde and retrograde axonal transport .

West Nile virus ( WNV ) is a mosquito-borne neurotropic flavivirus that has emerged as a human pathogen of global scale ., During recent WNV outbreaks in the US , half of the reported human cases were classified as neuroinvasive disease ., Although much research has been done , there are still gaps in our understanding of WNV neuropathogenesis ., While WNV neuroinvasion is proposed to occur by the hematogenous route and/or by axonal transport along the peripheral nerves , how virus spreads once within the central nervous system ( CNS ) remains unknown ., In this study , we examined the expression of viral antigens in the CNS of monkeys that were intrathalamically inoculated with WNV ., Next , we mapped the localization of WNV-infected neurons to specific anatomical structures , identified the intraneuronal localizations of WNV particles and investigated the role of neuronal connectivity in the spread of WNV within the CNS ., Our results revealed that all structures containing WNV-labeled neurons belonged to the pathways of motor control ., Virions were found in close vicinity to the axodendritic synapses , strongly indicating transsynaptic spread of the virus ., Neuronal connectivity-based reconstruction of the directionality of transsynaptic virus spread suggests that , within the CNS , WNV can utilize both anterograde and retrograde axonal transport to infect connected neurons .

medicine and health sciences, pathology and laboratory medicine, nervous system, pathogens, microbiology, brain, viral structure, neuroscience, motor neurons, viruses, rna viruses, spinal cord, animal cells, medical microbiology, microbial pathogens, virions, cellular neuroscience, cerebellar nuclei, west nile virus, anatomy, flaviviruses, central nervous system, cell biology, virology, viral pathogens, neurons, neuroanatomy, biology and life sciences, cellular types, cerebral cortex, organisms

journal.pntd.0001517

Clinical Forms of Chikungunya in Gabon, 2010

Chikungunya fever ( CHIK ) is a neglected tropical disease caused by the Chikungunya virus ( CHIKV ) , an arthropod-borne virus belonging to the genus Alphavirus of the Togaviridae family ., This virus is transmitted to humans via the bite of infected Aedes mosquitoes ( Aedes aegypti , Aedes albopictus ) ., The genome consists of a single positive strand of RNA that encodes four nonstructural proteins involved in virus replication and pathogenesis , and five structural proteins that compose the virion 1 ., CHIKV is subdivided into three genotypes based on phylogenetic analyses ., These genotypes , based on the gene sequences of an envelope protein ( E1 ) , are Asian , East/Central/South African ( ECSA ) , and West African 2–6 ., Over the past two decades , this virus has caused multiple outbreaks worldwide , particularly in tropical and subtropical areas ., Since its initial isolation in Tanzania in 1953 , sporadic cases and numerous outbreaks have been reported in Africa , India Ocean Islands , India , and even in Italy , a temperate region ., CHIKV circulated in West and East Africa at low levels until 1999–2000 , when an outbreak occurred in the Democratic Republic of the Congo ( DRC ) with around 50 , 000 cases 7 ., From 2004 , successive epidemics occurred , starting in Kenya with 13 , 500 cases and then spreading through the Indian Ocean region , including the Comoros Islands , Mauritius , Mayotte , La Réunion Island , Madagascar and the Seychelles 8–13 ., On La Réunion Island alone , which has a population of 760 , 000 inhabitants , at least 266 , 000 cases were reported 14 ., Thereafter , the epidemic arrived on the Indian subcontinent in 2006–2007 and caused more than 1 . 3 million cases 15 ., Genetic analyses showed that the ECSA genotype was responsible for these outbreaks 8 , 16 ., Numerous cases were subsequently reported all over the world 17 , directly associated with the return of tourists from India and affected India Ocean islands 2 ., The first reported European outbreak occurred in two contiguous villages of northeastern Italy 18 ., A unnoticed and retrospectively diagnosed large outbreak hit Cameroon in 2006 19 , then Gabon one year later , where a concomitant CHIKV/dengue virus- serotype 2 ( DENV-2 ) epidemic raged in the northwest and north; 20 , 000 cases were recorded and Aedes albopictus was identified as the main vector 20 ., Isolates from the 2007 Gabon outbreak belonged to the ECSA phylogroup and harbor the A226V mutation 6 ., Thus , the virus has proven able to expand to novel ecological niches , together with the vector Aedes albopictus 21 , 22 ., Recently , autochthonous cases of dengue fever ( DF ) and CHIK were reported in southern France , where Aedes albopictus has also been detected , raising serious concerns 23 ., CHIK , a word from the Bantu language , means “that which contorts or bends up” referring to the stooped posture that develops in infected patients due to severe joint pain and impaired walking ability ., The clinical manifestations of CHIK are now well described ., The infection is characterized by three distinct forms: asymptomatic , classical and severe ., The asymptomatic form is revealed by serology , in naïve populations ., In its classical form , the illness appears as a “dengue-like” disease , sometimes being confused with DF , particularly in areas where the two viruses cocirculate 3 , 22 , 24 ., After an incubation period of 3–7 days , symptoms start abruptly with acute fever , followed by severe and often debilitating polyarthralgias sometimes lasting months or years ., Additional symptoms include a maculopapular rash , myalgias and headaches 3 , 22 , 24 ., In La Reunion Island , severe forms were reported , mainly in patients with underlying medical conditions ., These forms included neurological and cardiovascular disorders , acute hepatitis , skin diseases , and respiratory and renal failure ., Miscarriages and neonatal infections were also reported , and some deaths were directly attributed to CHIKV 3 , 14 , 25 , 26 ., In previous studies , clinical and biological descriptions were mostly retrospective , and included a limited number of patients ., Furthermore , except for the first known CHIKV outbreak in Tanganyika 27 , most studies were conducted in Asia , the Indian Ocean Islands and Europe 18 , 25 , 28 , 29 , while none have concerned Africa , where multiple pathogens co-circulate 7 , 19 , 20 , 30–32 ., Here , we report the findings of a prospective , exhaustive clinical and biological study of 270 laboratory-confirmed cases during the simultaneous CHIKV/DENV outbreak which occurred in Gabon , a Central African country , in 2010 33 , focusing on the high clinical variability ., A simultaneous CHIKV/DENV outbreak was reported in two provinces ( Ogooue Lolo and Haut Ogooue ) of southeast Gabon , from April to July 2010 ., This study took place in Franceville , the main town of Haut Ogooue province , located 512 km south-east from Libreville ( capital of Gabon ) ( Figure 1 ) ., In Franceville , there are 4 healthcare capacities , including 2 public hospitals with a total of 170 beds ., The Centre International de Recherches Medicales de Franceville ( CIRMF ) , which includes a medical unit , is located in the heart of the city ., The CIRMF team partnered the Ministry of Health ( MoH ) response team during this outbreak ., The investigations ( epidemiological and clinical inquiries , blood sampling for laboratory confirmation ) were thus considered as part of the public health response ., According to the MoHs directives , written consent was not required due to emergency diagnosis ., An oral consent was obtained for each patient during interviews ., The study was approved by the Regional Health Director , including individual oral consent for blood sampling ( Authorization n°189 , Figure S1 ) ., The results were transmitted to patients and MoH ., The study was conducted in the field by two doctors who investigated cases in all healthcare facilities of Franceville , while free medical consultations were provided by another in the CIRMF medical unit ., At the time of this outbreak , all the hospitals were requested to sample suspected cases ., The case definition adopted by the MoH included suspected and confirmed cases ., Patients were suspected of having CHIK if they presented with at least one of the following symptoms or signs: fever ( defined as a temperature ≥38°5 , measured by a HCW ) , arthralgias , myalgias , headaches , rash , fatigue , nausea , vomiting , diarrhea , bleeding , or jaundice ., A ‘confirmed’ case met the clinical case definition and was PCR-positive ., Patients with suspected CHIK who were identified by the CIRMF team were examined physically , then data were collected on a standardized questionnaire , including age , sex , residence , time of onset and intensity of symptoms , and location of arthralgias ( Figure S2 ) ., Analgesics and non steroidal antiinflammatory drugs ( paracetamol , ibuprofen ) were provided to patients according to MoH recommendations ., If necessary , patients were hospitalized , and the length of stay was transmitted to our team ., Finally , clinical data on the disease course and outcome were collected during a 3-month period ., Blood samples were collected in two 7-ml EDTA Vacutainer tubes and one 7-ml dry tube ( VWR International , France ) ., The tubes were stored in the dark at +4°C until arrival at the laboratory ., Thick and thin blood films were stained with 20% Giemsa and examined for malaria parasites ., Patients with positive test were excluded from the study ., Hematological ( Hematology Analyser ACT 10 , Beckman Coulter ) and biochemical ( creatinine , AST , ALT ) tests were performed ( Automatic Analyser Hitachi model 902 , Roche Diagnostics ) ., For molecular studies , RNA was extracted from 140 µL of plasma by using the QIAamp Viral RNA Mini Kit according to the manufacturers recommended procedures ( Qiagen , Courtaboeuf , France ) ., cDNA was synthesized in a 9700 thermocycler ( Applied Biosystems , Foster City , CA , USA ) , where 25 µL of extracted RNA was mixed with 25 µL of High Capacity cDNA kit ( Applied Biosystems , Foster City , CA , USA ) ., Finally , 5 µL of newly synthesized cDNA was used as template in 25 µL of TaqMan Universal PCR Master Mix and then , thermocycled in a 7500 Real-Time PCR system ( Applied Biosystems ) ., RNA positive and negative controls were added in each run ., The TaqMan PCR products were identified by curves using a 7500 system SDS software ., The quantitative PCR mix were run with 400 nM of each primer and 200 nM of probe ., The E1 gene ( 208 bp ) was targeted for the CHIKV detection ( genome position , 10387–10595 ) : CHIK-S ( S\u200a=\u200aSense ) : AAGCTYCGCGTCCTTTACCAAG; CHIK-R ( R\u200a=\u200aReverse ) : CCAAATTGTCCYGGTCTTCCT; CHIK-P ( P\u200a=\u200aProbe ) : CCAATGTCYTCMGCCTGGACACCTTT 34 ., For DENV detection , the 3′ UTR ( 107 bp ) was targeted ( genome position , 10590–10697 ) : DENt-S ( t\u200a=\u200atotal , for the 4 serotypes ) : AGGACYAGAGGTTAGAGGAGA; DENt-R: CGYTCTGTGCCTGGAWTGAT; DENt-P: ACAGCATATTGACGCTGGGARAGACC 35 ., The probes used for CHIKV , then for DENV assays were labeled with FAM-reporter and TAMRA-quencher ( Applied Biosystems ) ., Quantified RNA transcripts and cell-culture supernatants of CHIKV and DENV were used in 10-fold dilutions as standards for viral load ( VL ) determination ., The VL was determined by comparison to a standard curve ., This standard curve was obtained from standard RNA which was diluted at 10 in 10 times ., Exponential regression was used to determine the CHIKV viral loads from the threshold cycle ., The standard linearity minimum was <101 cDNA genome equivalents/mL ., DENVs were typed as previously described 35 ., Co-infected patients were excluded from this study ., Statistical analyses were performed using Epi Info software ( 6 . 04 , Epiconcept ) ., Results were expressed as averages ( with their SD ) and percentages ( with their 95% confidence interval , CI ) ., Students t test was used to compare laboratory parameters ( samples from 50 healthy volunteers recruited at the CIRMF medical unit , with sociodemographic characteristics similar to those of the study subjects , were selected as controls ) and continuous clinical variables ., For qualitative variables , the Chi square test or Fishers exact test was used as appropriate ., A p value <0 . 05 was considered to denote statistical significance ., From May to July 2010 , 2731 suspected cases were recorded in the two provinces , and 1208 cases ( 44 . 2% ) were laboratory confirmed ( Table 1 ) ., There were 1139 CHIKV+ cases ( 94 . 2% ) in Haut Ogooue province ., In all , 933/2063 ( 45 . 2% ) cases were confirmed in Franceville , representing 81 . 9% of the confirmed cases in the province ., The first cases were confirmed on April 25 ( week 17 ) , the peak incidence was reached at weeks 21 and 22 ( 400 confirmed cases ) , and the outbreak ended at week 27 ( Figure 2 ) ., Of the 2063 suspected cases detected in Franceville , 408 ( 19 . 8% ) were examined by the CIRMF medical team , of whom 289 were CHIKV+ , 19 were co-infected ( with 18 CHIKV+/DENV+ patients and 1 CHIKV+/DENV+/malaria+ ) ., So , 270 CHIKV+ ( 66 . 2% ) were selected for the study ( Figure 3 ) ., The 119 CHIKV- patients did not receive a diagnosis ., They represented 28 . 9% of all CHIKV+ patients in Franceville ., They were distributed throughout the outbreak , with 99 ( 36 . 6% ) patients included at the epidemic peak , in weeks 21 and 22 ( Figure 2 ) ., The M/F sex ratio of the study population was 0 . 85 and mean age was 30±16 years ( range , 1–77 ) ., Fifty-six patients ( 20 . 7% ) were under 16 years old , 70 ( 26 . 3% ) 16–30 years old , 92 ( 34 . 1% ) 31–45 years old , and 53 ( 18 . 9% ) over 45 years old ., CHIK patients consulted an average of 2 days ( range , 0 to 18 days ) after the onset of symptoms , 232 ( 85 . 9% ) from day 0 to 3 , 30 ( 11 . 1% ) from day 4 to 7 , and 8 ( 3% ) after day 7 ., The mean duration of symptoms in the acute phase was 7 days ( range , 1–24 days ) ., Hospitalization was necessary for 42 patients ( 15 . 5% ) with more pronounced manifestations , and the mean length of stay was 2 . 6 days ( range , 1 to 6 days ) ., At the time of the initial consultation , 230 ( 85% ) patients complained of fever , 246 ( 90 . 4% ) had arthralgias , 197 ( 72 . 9% ) myalgias and 194 ( 71 . 8% ) headaches ( Figure 4 ) ., They all described an abrupt onset of the illness ., Joint pain was mostly polyarticular , bilateral and symmetrical ., On average , 7 joints per patient were affected ., Among the 246 patients with arthralgias , 100 ( 40 . 7% ) had fewer than 5 affected joints , and 146 ( 59 . 3% ) had 5 or more affected joints ., Arthralgias occurred in the large joints ( shoulders , elbows , wrists , knees , ankles ) in 242 ( 98 . 4% ) patients , and in the lower limbs in 220 ( 89 . 4% ) patients; the spine was affected in 146 ( 59 . 3% ) patients ., Incapacitation was noted in 158 ( 64 . 2% ) patients ( Table 2 ) , and swelling of the elbows , wrists , knees or ankles was noted in one-quarter of these patients ( Figure 5A ) ., Myalgias mainly affected the forearms , arms , thighs and calves , and sometimes became increasingly incapacitating ., No cases of myositis were seen ., Headaches were beating or weighty , and were located in the frontal , parietal , retro-orbital or , rarely , occipital regions ., Skin lesions were noted in 113 ( 41 . 8% ) patients , in the form of macular or maculopapular exanthema ( Figure 5B ) , morbiliform or bullous rash in a few children ( Figure 5C ) , and was accompanied by pruritus in one-quarter of cases ., A more aggressive form , with facial edema , was seen ( Figure 5D ) ., Peeling of the affected skin occurred a few days after ., Digestive symptoms , consisting of abdominal pain , nausea , vomiting and diarrhea ( 87 , 32% ) , were described , and mild bleeding of the nose and gums reported ( 6 , 2 . 2% ) in patients with normal platelet counts ., Seizures occurred in 2 children ( CSF was not sampled ) , who recovered without sequelae ., No complications or deaths were reported ., The disease was classified in four forms according to the existence of fever and/or arthralgias ( plus any additional symptoms ) , namely mixed ( fever and arthralgias both present ) , pure febrile ( fever without arthralgias ) , pure arthralgic ( arthralgias without fever ) , and unusual ( neither fever nor arthralgia ) ., The mixed form was found in 212 ( 78 . 5% ) patients , the arthralgic form in 34 ( 12 . 6% ) , the febrile form in 18 ( 6 . 7% ) , and the unusual form in 6 ( 2 . 2% ) ( Figure 6A ) ., Patients with the unusual form mainly had digestive symptoms ( Table 3 ) ., The performance indicators of the “fever and arthralgia” combination were estimated in suspected patients ( including PCR negative patients ) , taking into account the true positive ( TP ) , true negative ( TN ) , false positive ( FP ) and false negative ( FN ) results ., Sensitivity was calculated as TP/ ( TP+FN ) , specificity as TN/ ( TN+FP ) , positive predictive value ( PPV ) as TP/ ( TP+FP ) and negative predictive value ( NPV ) as TN/ ( FN+TN ) ., The “fever and arthralgia” combination showed a sensitivity and specificity of respectively 73 . 1% and 41% for PCR positivity , and PPV and NPV of 78 . 5% and 34 . 4% ( Table 4 ) ., All four forms occurred throughout the outbreak , with a majority of patients in the mixed form between week 18 to 23 ( Figure 6B ) ., There was no significant difference in the mean duration of symptoms ( p\u200a=\u200a0 . 33 ) across the 4 clinical forms ., Hospitalization was necessary for 35 ( 16 . 5% ) patients in the mixed form , 3 ( 8 . 8% ) patients in the arthralgic form , 4 ( 2 . 2% ) patients in the febrile form and none of the unusual form ( p\u200a=\u200a0 . 33 ) , and there was no significant difference in the mean length of hospital stay ( p\u200a=\u200a0 . 16 ) ., Hematological and biochemical parameters were available for 224 patients ., Mean counts of leukocytes ( 5243±1676/mm3 , range 1900–11000/mm3 ) and platelets ( 233 089±81 750/mm3 , range 52000–455000/mm3 ) did not differ from the controls ., Nevertheless , anemia ( mean hemoglobin 12 . 3±1 . 7 g/dl , range 8–17 g/dl ) , and lymphocytopenia ( mean lymphocyte count 2228±216/mm3 , range 184–7150/mm3 ) were significantly frequent in CHIKV+ patients ( respective p values 0 . 0009 and <0 . 0001 ) than in the controls ., Liver enzymes ( AST and ALT ) and creatinine levels were significantly higher ( respective p values 0 . 03 , 0 . 003 and <0 . 0001 ) in CHIKV+ patients than in controls ( Table 5 ) ., There was no significant difference in hemoglobin rate , leukocyte , lymphocyte , or platelet count , or biochemical parameters across the four clinical forms ., A total of 123 patients were selected for viral load ( VL ) assay ., The selection criteria included age , sex , site of consultation , area of residence , week of disease onset , and clinical form ., Their characteristics were comparable to those of the initial sample ., The mean VL was 1 . 2×107 ( range , 1–4 . 4×108 ) ., Fifty eight patients ( 47 . 2% ) had low VL ( <100 000 DNA copies per mL , Group 1 ) , and 65 ( 52 . 8% ) had high VL ( ≥100 000 DNA copies , Group 2 ) ., There was no difference in hemoglobin rate , leukocyte or platelet count or biochemical data between the two groups ., However , lymphocytopenia was significantly more frequent ( p\u200a=\u200a0 . 01 ) in group 2 than in group 1 ., Moreover , there was no difference in symptoms , affected joints ( large vs small ) , their location or intensity ( Table 2 ) ., There was no difference in VL according to the day of sampling or symptom onset ( day 0 to 3 , day 4 to 7 , and after day 7 ) ., VL did not differ across the four clinical forms ., Of the 270 CHIKV+ patients included in the study , 225 ( 83 . 3% ) had completely recovered by day 30 ., The other 45 patients complained of persistence or relapse of fever ( n\u200a=\u200a6 ) , arthralgias ( n\u200a=\u200a36; incapacitating arthragias were still present in 5 patients ) , myalgias ( n\u200a=\u200a11 ) , headaches ( n\u200a=\u200a20 ) , pruritus ( n\u200a=\u200a5 ) or fatigue ( n\u200a=\u200a10 ) ., At day 90 , 11 patients had persistent arthralgias ., Among the 5 patients with incapacitating arthralgias , 4 recovered by day 90 and one was lost to follow-up ., Three patients had headaches ., During a recent concomitant CHIKV/DENV outbreak which occurred in south-east Gabon , we conducted a prospective study in the most affected town , obtaining clinical and biological descriptions of 270 laboratory-confirmed cases of CHIK ., We found variable clinical manifestations , including unusual forms ., This is the second large epidemic to be reported in Gabon , after the concurrent CHIKV/DENV-2 intrusion in 2007 , in which 2 provinces and 7 towns were affected ., International shipping was suspected of providing the portal of entry for both viruses 20 ., Inside the country , the infection spread insidiously , along a north-west/south-east axis via the railway and roads , leading to a new outbreak in 2 provinces and 10 towns , 600 km distant and 3 years later ., Identified in Gabon just before 2007 , in an area where Aedes aegypti was previously predominant 36 , Aedes albopictus was the main vector of the first outbreak 20 , and the second ., Given the rapid spread of this mosquito , even in temperate areas 18 , gradual invasion of the entire country by these viruses is foreseeable ., This descriptive and prospective African study provides exhaustive clinical and biological data than previous outbreaks in the DRC , Republic of Congo , Cameroon and Gabon where descriptions were succinct 7 , 19 , 20 , 37 ., Available descriptions have also been made in patients from la Reunion Island 25 , 26 , notably , in a prospective study 38 ., A large number of patients were enrolled in this study ., They represented about one-third of all laboratory-confirmed cases , and were distributed throughout the outbreak period , following the global epidemic curve ., This series is therefore largely representative of the epidemic , and is as large as two other studies which included respectively 157 and 274 patients 25 , 38 ., Other studies included less than 100 patients 28 , 39 , 40 ., However , some patients with mild symptoms did not consult at the hospitals , and constituted the unique bias of recruitment ., So , the number of enrolled patients is underestimated ., This study did not provided any information on asymptomatic because patients recruitment occurred in the hospitals ( individual with symptoms only ) ., In a recent study , asymptomatic CHIKV infection was estimated at 28% 41 ., With regard of the number of CHIKV negative cases , the circulation of other arboviruses during this epidemic cannot be ruled out ., During the 2007 outbreak , DENV-2 was detected 20 and a fatal case of West Nile virus infection was diagnosed in Libreville 42 ., Moreover , recent sero-epidemiological studies have suggested the circulation of DENV , West Nile and Rift Valley Fever viruses in rural populations 43–45 ., We observed marked clinical variability ., As previously described , fever and polyarthralgias were the most frequent manifestations , affecting nearly all the patients 3 , 14 , 22 , 25 , 38 , 41 ., They were found in respectively 85% and 90% of our patients , and were both present in three-quarters of cases ., We found a weak association between the “fever and arthralgia” combination and PCR positivity , with a sensitivity and specificity of 73 . 1% and 41% , while in another recent study this combination had a diagnostic sensitivity of 84% and a specificity of 89% in an epidemic setting 41 ., Differences in the case definition and the assay could explain this discrepancy ., In its classical form , CHIK is a painful febrile illness characterized by incapacitating arthralgias ., Patients presenting with a bent gait due joint pain , mainly affecting the wrists and ankles , are easily recognizable ., Incapacitating arthralgias are considered pathognomonic for the disease ., When the symptoms are less severe , the clinical diagnosis becomes less clear-cut , and they may also be seen in other diseases such as malaria , DF and typhoid fever ., To date , only the classical DF has been reported in Africa ., To our knowledge , there is no comparative study of the two diseases ., Complications such as dengue hemorrhagic fever and dengue with shock syndrome and persistent arthralgia following CHIK constitute the noteworthy differences ., In our field experience , these diseases are clinically indistinguishable , and the problem of differential diagnosis may be compounded during concomitant CHIKV/DENV outbreaks in a malaria endemic area ., Pure febrile and arthalgic forms were also seen , adding to the clinical variability ., The other symptoms were as frequent as in previous studies 14 , 25 , 38 , 41 ., Myalgias sometimes resulted in incapacity ., Rashes were easily diagnosed in our darker-skinned population , and uncomplicated bullous lesions were also seen in children ., The mechanism by which bleeding occurs despite normal platelet counts is unclear , but this classifies CHIK in the viral hemorrhagic fever group , in this area where ebolavirus and yellow fever virus also circulate ., We identified 2 . 2% of patients who had neither fever nor arthralgias ., These unusual forms were evoked in the Reunion Island ., This is their first description due to a strong clinical presumption of physicians and a less sensitive and specific case definition ., Furthermore , no severe forms ( requiring maintenance of vital function ) and deaths as described in the Reunion Island 14 , 26 were noted in Gabon ., In a context of frequent self-medication , as in our study , many patients with unusual and non severe forms did not consult a health service , and some of those who did may have been misdiagnosed ., So , the total number of cases , in this epidemic , and the proportion of unusual forms , are probably underestimated ., Our findings imply that , during epidemic periods , clinicians should not focus solely on fever and arthralgias ., The four clinical forms were present throughout the outbreak period , suggesting that pathogenicity did not vary markedly ., We also observed rare relapses or persistence of arthralgias , as previously described 3 , 14 , 22 ., The low rate of persisting arthralgia in our sampling contrasts with previous studies 46–48 ., The little number of individuals followed up and the low mean age ( at 30 years ) of our sampling could explain it ., In previous studies , the mean age of the population study was higher ( at 50 years ) and the incidence of persistent arthralgia was higher in older patients 46–48 ., Immunonologic and genetic factors ( anti CCP antibodies , antinuclear antibodies and HLA DR alleles ) are associated with rheumatoid arthritis following Chikungunya fever 49 ., Their absence in our patients could also explain low rate of persistent arthralgia ., In CHIKV infections , biological abnormalities are varied , transient and nonspecific , as in many other viral diseases 25 , 38 ., In our study , biological data from CHIKV+ patients were compared with those of healthy volunteers than CHIKV- , in order to avoid a bias in results interpretation ., CHIKV- patients were considered as having another disease ., Varied biological abnormalities were found ., Anemia may have been due to parasitic coinfection ( ankylostomiasis , ascariasis ) ., Lymphocytopenia correlated with viremia , as recently described 38 , and is probably due to excessive apoptosis with peripheral lymphocyte destruction ., The increase in liver enzyme and creatinine levels may have been due to drug toxicity ( particularly when ibuprofen and paracetamol are associated ) , muscle damage or even rhabdomyolysis ., Finally , the disease severity did not correlate with VL in our study , contrary to a recent study in which the severity criteria were different 50 ., Together , these findings imply that symptoms are not directly related to the virus but rather to the immune reaction ., Theoretically , PCR is the preferred method for detecting and quantifying CHIKV viral RNA , mainly during the first week after infection 4 , 5 ., The detection of the virus later than 7 days after symptom onset in 3% of our patients is surprising ., It is conceivable that their initial symptoms were due to another disease such as malaria or dengue , before they contracted CHIKV ., The genotype of CHIV implicated in Gabonese and Cameroonian outbreaks belongs to the ESCA phylogroup and harbor the A226V mutation 20 ., This mutation has also been found in samples from La Reunion Island which the genotype belongs to the Asian phylogroup ., This mutation improves replication and transmission efficiency in Aedes albopictus mosquitoes 6 ., To date , no genotype and no mutation have been found associated with specific clinical forms ., Finally , our study showed that this outbreak , as the first one , occurred during the rainy season and Aedes albopictus was the main vector , with an extension in other parts of the country ., Globally , the clinical picture did not differ from Asian , European and Indian Ocean Islands studies ., The acute phase and outcome seemed similar , as biological abnormalities and treatment efficacy ., In the other hand , there were some noteworthy distinctive features: the mean duration of symptoms in the acute phase is long ( 7 days reaching to 24 days ) , relapses and persistent arthralgia were rare , there were no severe forms or deaths , and unusual forms were well described ., Epidemiological surveillance must continue in order to detect any change in the pathogenicity of CHIKV .

Introduction, Materials and Methods, Results, Discussion

Chikungunya virus ( CHIKV ) has caused multiple outbreaks in tropical and temperate areas worldwide , but the clinical and biological features of this disease are poorly described , particularly in Africa ., We report a prospective study of clinical and biological features during an outbreak that occurred in Franceville , Gabon in 2010 ., We collected , in suspect cases ( individuals presenting with at least one of the following symptoms or signs: fever , arthralgias , myalgias , headaches , rash , fatigue , nausea , vomiting , diarrhea , bleeding , or jaundice ) , blood samples , demographic and clinical characteristics and outcome ., Hematological and biochemical tests , blood smears for malaria parasites and quantitative PCR for CHIKV then dengue virus were performed ., CHIKV+ patients with concomitant malaria and/or dengue were excluded from the study ., From May to July 2010 , data on 270 laboratory-confirmed CHIK patients were recorded ., Fever and arthralgias were reported by respectively 85% and 90% of patients , while myalgias , rash and hemorrhage were noted in 73% , 42% and 2% of patients ., The patients were grouped into 4 clinical categories depending on the existence of fever and/or joint pain ., On this basis , mixed forms accounted for 78 . 5% of cases , arthralgic forms 12 . 6% , febrile forms 6 . 7% and unusual forms ( without fever and arthralgias ) 2 . 2% ., No cases of organ failure or death were reported ., Elevated liver enzyme and creatinine levels , anemia and lymphocytopenia were the predominant biological abnormalities , and lymphocytopenia was more severe in patients with high viral loads ( p\u200a=\u200a0 . 01 ) ., During CHIK epidemics , some patients may not have classical symptoms ., The existence of unusual forms and the absence of severe forms of CHIK call for surveillance to detect any change in pathogenicity .

Chikungunya fever ( CHIK ) is a disease caused by a virus transmitted to humans by infected mosquitos ., The virus is responsible for multiple outbreaks in tropical and temperate areas worldwide , and is now a global concern ., Clinical and biological features of the disease are poorly described , especially in Africa , where the disease is neglected because it is considered benign ., During a recent CHIK outbreak that occurred in southeast Gabon , we prospectively studied clinical and biological features of 270 virologically confirmed cases ., Fever and arthralgias were the predominant symptoms ., Furthermore , variable and distinct clinical pictures including pure febrile , pure arthralgic and unusual forms ( neither fever nor arthralgias ) were detected ., No severe forms or deaths were reported ., These findings suggest that , during CHIK epidemics , some patients may not have classical symptoms ( fever and arthralgias ) ., Local surveillance is needed to detect any changes in the pathogenicity of this virus .

medicine

journal.pcbi.1006635

Modelling the mechanics of exploration in larval Drosophila

Exploratory search is a fundamental biological behaviour , observed in most phyla ., It has consequently become a focus of investigation in a number of model species , such as larval Drosophila , in which neurogenetic methods can provide novel insights into the underlying mechanisms ., However , appropriate consideration of biomechanics is needed to understand the control problem that the animal’s nervous system needs to solve ., When placed on a planar substrate ( typically , an agar-coated petri dish ) , the Drosophila larva executes a stereotypical exploratory routine 1 which appears to consist of a series of straight runs punctuated by reorientation events 2 ., Straight runs are produced by laterally symmetric peristaltic compression waves , which propagate along the larval body in the same direction as overall motion ( i . e . posterior-anterior waves carry the larva forwards relative to the substrate , anterior-posterior waves carry the larva backwards ) 3 ., Reorientation is brought about by laterally asymmetric compression and expansion of the most anterior body segments of the larva , which causes the body axis of the larva to bend 2 ., Peristaltic crawling and reorientation are commonly thought to constitute discrete behavioural states , driven by distinct motor programs 2 ., In exploration , it is assumed , alternation between these states occurs stochastically , allowing the larva to search its environment through an unbiased random walk 1 , 4–6 ., The state transitions or direction and magnitude of turns can be biased by sensory input to produce taxis behaviours 4 , 5 , 7–13 ., The neural circuits involved in producing the larval exploratory routine potentially lie within the ventral nerve cord ( VNC ) , since silencing the synaptic communication within the brain and subesophageal ganglia ( SOG ) does not prevent substrate exploration 1 ., Electrophysiological and optogenetic observations of fictive locomotion patterns within the isolated VNC 14 , 15 support the prevailing hypothesis that the exploratory routine is primarily a result of a centrally generated motor pattern ., As such , much recent work has focused on identifying and characterising the cells and circuits within the larval VNC 16–32 ., However , behaviour rarely arises entirely from central mechanisms; sensory feedback and biomechanics often play a key role 33–35 including the potential introduction of stochasticity ., Indeed , thermogenetic silencing of somatosensory feedback in the larva leads to severely retarded peristalsis 36 or complete paralysis 37 , 38 ., In line with the ethological distinctions drawn between runs and turns , computational modelling of the mechanisms underlying larval behaviour has so far focused on either peristaltic crawling or turning ., An initial model based on neural populations described a possible circuit architecture and dynamics underlying the fictive peristaltic waves observed in the isolated ventral nerve cord 39 ., A subsequent model described the production of peristaltic waves through interaction of sensory feedback with biomechanics , in the absence of any centrally generated motor output 40 , in a manner similar to earlier models of wave propagation via purely sensory mechanisms in C . elegans 41 , 42 ., This model produced only forward locomotion as it incorporated strongly asymmetric substrate interaction ., Recently , a model combining biomechanics , sensory feedback , and central pattern generation reproduced many features of real larval peristalsis 43 ., However , this model only aimed to explain forward locomotion , and accordingly contained explicit symmetry-breaking elements in the form of posterior-anterior excitatory couplings between adjacent segments of the VNC , and posterior-anterior projections from proprioceptive sensory neurons in one segment into the next segment of the VNC ., No biomechanical models of turning in the larva have yet been published , but the sensory control of reorientation behaviour has been explored in more abstract models 4 , 5 , 8 , 11–13 , 44 ., No current model accounts for both peristalsis and reorientation behaviours , and no current model of peristalsis can account for both forward and backward locomotion without appealing to additional neural mechanisms ., Here we present a model of unbiased substrate exploration in the Drosophila larva that captures forward and backward peristalsis as well as reorientation behaviours ., We provide a deterministic mathematical description of body mechanics coupled to a simple , reflexive nervous system ., In contrast to previous models , our nervous system has no intrinsic pattern-generating ability 39 , 43 , 44 , and does not explicitly encode discrete behavioural states or include any stochasticity 4 , 5 , 8 , 11–13 ., Nevertheless , the model is capable of producing apparently random “sequences” of crawling and reorientation behaviours , and is able to effectively explore in a two-dimensional space ., We argue that the core of this behaviour lies in the chaotic mechanical dynamics of the body , which result from an energetic coupling of axial ( “peristaltic” ) and transverse ( “turning” ) motions ., Our choice not to explicitly model navigational decision-making and central pattern generation circuits is motivated by our desire to illustrate the powerful insights offered by focusing upon the mechanics of the body with which the nervous system interacts ., The model neuromuscular system we have constructed is based upon simple physical arguments , yet ultimately bears a striking resemblance to known features of the larval nervous system ., By starting from the mechanics of the body , and not assuming the existence of particular neural circuits , we are able to provide a new explanatory framework within which to re-interpret existing neurophysiological observations , including observations of central pattern generation within the larval VNC , and also provide unique predictions for future neurophysiological experiments ., In what follows , we first outline the key components and assumptions of our model of body mechanics ., We then follow simple arguments to guide the construction of a neuromuscular model capable of producing power flow into the body , and motion of the body’s centre of mass relative to the substrate ., Crucially , the neuromuscular model neither senses nor drives transverse motions ., In analysing the behaviour of our model , we begin by focusing on the small-amplitude , energy-conservative behaviour of the body in the absence of frictive and driving forces ., In this case , the motion of the body is quasiperiodic and decomposes into a set of energetically isolated axial travelling waves and transverse standing waves ., Reintroducing friction and driving forces , we demonstrate the emergence of a pair of limit cycles corresponding to forward and backward peristaltic locomotion , with no differentiation of the neural activity for the two states ., We then shift focus to the behaviour of the model at large amplitudes ., In this case the axial and transverse motions of the body are energetically coupled , and the conservative motion becomes chaotic ., The energetic coupling allows our neuromuscular model to indirectly drive transverse motion , producing chaotic body deformations capable of driving substrate exploration ., Analysis of our model supports a view of larval exploration as an ( anomalous ) diffusion process grounded in the deterministic chaotic mechanics of the body ., To explore larval crawling and turning behaviours , we choose to describe the motion of the larval body axis ( midline ) in a plane parallel to the substrate ( Fig 1 , S1 Fig ) ., The larval body is capable of more diverse motions including lifting/rearing 21 , rolling 45 , digging 46 , self-righting / balancing , and denticle folding which we have recently observed to occur during peristalsis ( S1 Video ) ., However , while exploring flat surfaces , the larva displays fairly little out-of-plane motion ( neither translation perpendicular to the substrate nor torsion around the body axis ) and only small radial deformations 47 ., Furthermore , the majority of ethological characterisations of larval exploration treat the animal as if it were executing purely planar motion 4 , 6 , 8–13 , 48 ., A planar model is thus a reasonable abstraction for the issues addressed in this paper , i . e . , the generation of peristalsis , bending , and substrate exploration ., The segmented anatomy of the Drosophila larva allows us to focus our description of the midline to a set of N = 12 points in the cuticle , located at the boundaries between body segments and at the head and tail extremities ., We assign each point an identical mass , and measure its position and velocity relative to a two dimensional cartesian coordinate frame fixed in the substrate ( the laboratory or lab frame ) ., We therefore have NDOF = 2N = 24 mechanical degrees of freedom ., We note that our assumption of a uniform mass distribution along the midline is somewhat inaccurate , since thoracic segments are smaller than abdominal segments ., However , simulations with non-uniform mass distribution give results which are qualitatively close to those presented here ., We assume that the larval body stores elastic energy in both axial compression/expansion and transverse bending , due to the presence of elastic proteins in the soft cuticle ., We assume that energy is lost during motion due to viscous friction within the larva’s tissues and sliding friction between the body and the substrate ., Sliding friction also allows shape changes ( deformations ) of the body to cause motion of the larva as a whole relative to the substrate ( centre of mass motion ) ., Since the mechanical response of the larva’s tissues is yet to be experimentally determined , we assume a linear viscoelastic model ., This is equivalent to placing linear ( Hookean ) translational and torsional springs in parallel with linear ( Newtonian ) dampers between the masses in the model , as shown in Fig 1 , or to taking quadratic approximations to the elastic potential energy and viscous power loss ( as in S1 Appendix ) ., We note that the accuracy of the approximation may decrease for large deformations , in which nonlinear viscoelastic effects may become important ., As with larval tissue mechanics , there has been little experimental investigation of the forces acting between the larva and its environment ., We therefore assume a simple anisotropic Coulomb sliding friction model , in which the magnitude of friction is independent of the speed of motion , but may in principle depend upon the direction of travel ., This anisotropy could be thought of as representing the biased alignment of the larva’s denticle bands , or directional differences in vertical lifting or denticle folding motions which are not captured by our planar model ., A mathematical formulation of our sliding friction model is given in S1 Appendix ., In addition to power losses due to friction , we also allow power flow due to muscle activation ., For the sake of simplicity , we choose to allow only laterally symmetric muscle tensions ., In this case , the musculature cannot directly cause bending of the midline , and can only explicitly drive axial motions ., We will see later that even indirect driving of bending motion can lead to surprisingly complex behaviour , due to energetic coupling of axial and transverse degrees of freedom ., The choice to neglect asymmetric muscle tensions is made in order to simplify our model and provide a clearer illustration of the potential role of body mechanics in generating complex larval behaviour ., We note that there is only one way for muscle activations to be symmetric—if we were to allow asymmetry we would have to specify exactly what form that asymmetry should take , and we have little empirical or theoretical grounds on which to do so ., Nevertheless , there are some interesting cases which may be considered in passing—the presence of a constant torque about the model’s segment boundaries should cause a shift in the equilibrium posture towards a resting curved shape ., The presence of torques which are a linear function of the local body bending angle or local angular velocity should shift the effective transverse stiffness or viscosity of the body S5 Appendix ., In this sense the model presented here could be considered to already include the effect of asymmetric muscle tensions , they have simply been incorporated into the passive stiffness and viscosity of the body ., We have recently developed an extension of the model presented here which uses a similar local reflex to modulate the body’s effective transverse viscosity in proportion to a stimulus input , allowing the model to exhibit taxis behaviour 49 ., Finally , we model the internal coelomic fluid of the larva ., Given the extremely small speed of the fluid motion compared to any reasonable approximation to the speed of sound in larval coelomic fluid , we can safely approximate the fluid flow as incompressible 50 ., This would ordinarily require that the volume contained within the larval cuticle remain constant ., However , since we are modelling only the motion of the midline and neglecting radial deformations , we constrain the total length of the larva to remain constant ., We note that this constraint is not entirely accurate to the larva , as the total length of the animal has been observed to vary during locomotion 47 ., Nevertheless , for the sake of simplicity we will continue with this constraint in place , noting that this approximation has been used with success in previous work focused on peristalsis 40 , 43 , and that there is experimental support for kinematic coupling via the internal fluid of the larva 3 ., We note that we satisfy the incompressibility condition only approximately in some sections ( Model behaviour—Conservative chaos , Dissipative chaotic deformations , and Deterministic exploration ) , by introducing an additional potential energy associated with the constraint , which produces an energetic barrier preventing large changes in the total length of the body ( see S1 Appendix for details of this approximation along with specifics of the mathematical formulation of our mechanical model ) ., Note that in the absence of transverse bending , the total length constraint causes the head and tail extremities of the larva to become mechanically coupled and move in unison 40 , 43 ., The axial mechanics thus has periodic boundary conditions , and the most anterior ( T1 ) and posterior ( A8 ) segments of the larva may be considered adjacent ., This means , for instance , that a compression wave travelling from tail to head will cause motion of the tail on termination at the head , thus initiating a new compression wave ., This view also allows us to reason about what should happen if we relax the total length constraint ., In particular , if we were to replace the direct coupling of head and tail by a viscoelastic coupling , representing the capacity for storage and dissipation of energy within the internal fluid or in radial expansion of the cuticle , the axial mechanics would still have periodic boundary conditions but would now have a step change in mechanical impedance ., Waves hitting such “sudden” impedance boundaries in their transmission media will generally be partially transmitted ( i . e . passing directly from head to tail in the larva ) and partially reflected ( i . e . changing direction and moving backwards from the head extremity ) , providing one possible cause of transitions between forward and backward locomotion in the animal ., As will be seen , however , these transitions may occur even in the absence of an impedance discontinuity , and we will continue with the total length constraint in place in order to simplify our model ., Let us now consider how we should use muscle activity to produce locomotion ., There are two basic requirements ., First , we must have power flow into the body from the musculature , so that the effects of friction may be overcome and the larva will not tend towards its equilibrium configuration ., Second , we must be able to produce a net force on the centre of mass of the larva , so that it can accelerate as a whole relative to the lab frame ., Note that in this section , we motivate the neural circuits in the model from this purely functional point of view , but will present relevant biological evidence in the discussion ., To satisfy the first criterion , let us examine the flow of power into the body due to the action of the musculature, P = - ∑ i = 1 N - 1 b i MF i q ˙ i ( 1 ) Here , qi describes the change in length of the i’th body segment away from its equilibrium length , q ˙ i is the rate of expansion of the i’th body segment , bi is a ( positive ) gain parameter , MFi is a ( positive ) dimensionless control variable representing muscle activation , and the product biMFi is the total axial tension across the i’th body segment ., From this expression , it is clear that if we produce muscle tensions ( MFi > 0 ) only while segments are shortening ( q ˙ i < 0 ) , we will always have positive power flow into the body ( P > 0 ) ., This is a mathematical statement of the requirement for the larva’s muscles to function as motors during locomotion , rather than as springs , brakes , or struts 33 ., A simple way to fulfil this condition is to introduce a segmentally localised reflex circuit ( Fig 2 , 40 ) ., We place a single sensory neuron in each segment which activates when that segment is compressing ( q ˙ i < 0 ) ., Each sensory neuron then projects an excitatory connection onto a local motor neuron , which in turn projects to a muscle fibre within the same segment ., Assuming for now that there are no other influences on the motor neurons , so that sensory activation implies local motor neuron activation , segmental shortening will produce an immediate muscle tension serving to amplify compression of the segment and thus counteract frictive energy losses ., Let us now consider the second criterion for peristaltic locomotion ., Assuming all segment boundaries are of equal mass , the force on the centre of mass of the larva is proportional to the sum of the forces acting on each segment boundary , i . e ., F C O M ∝ ∑ i = 1 N - 1 F s e g m e n t ( 2 ) Newton’s third law tells us that any forces of interaction between segment boundaries ( i . e . viscoelastic and muscle forces ) must be of equal magnitude and opposite direction , so that they cancel in this summation and we are left only with contributions arising from substrate interaction ., If the motion of the body is such that some number nf of segments move forward at a given time , against a frictional force −μf , while nb segments remain anchored or move backward , experiencing a frictional force μb , then the summation becomes, F C O M ∝ n b μ b - n f μ f ( 3 ) In the limiting case of isotropic ( direction-independent ) substrate interaction we have μb = μf , and this expression tells us that the centre of mass will accelerate in the forward direction only when there are less segments moving forward than are moving backward or anchored to the substrate ., Similarly , moving a small number of segments backward while the others remain anchored will result in backward acceleration of the centre of mass ., Therefore , if the animal is to move relative to its substrate , it must ensure that only a limited number of its segments move in the overall direction of travel at a given time ( indeed , this matches observations of the real larva 3 , 22 ) ., A more lengthy exposition of this requirement on limbless crawling behaviours can be found in 51 ., We fulfil the requirement for a small number of moving segments by introducing mutually inhibitory interactions between the segmentally localised reflex circuits ( Fig 2 ) ., We add a single inhibitory interneuron within each segment ., When the sensory neuron within the local reflex activates , it excites this interneuron , which then strongly inhibits the motor neurons and inhibitory interneurons in non-adjacent segments , effectively turning off the local reflexes in distant neighbours ., Adjacent segments do not inhibit each other in our model , allowing reflex activity to track mechanical disturbances as they propagate from one segment to the next ., We comment on the plausibility of this feature of our model , given the experimental observation of nearest-neighbour inhibitory connections in the larval ventral nerve cord 28 , in the discussion ., Similarly , the head and tail segments do not inhibit each other , which permits peristaltic waves to be ( mechanically ) reinitiated at one extremity as they terminate at the other ( see Discussion at the end of the previous subsection ) ., This effectively introduces a ring-like topology into the neural model , matching our model of axial mechanics which couples head and tail motion through the total length constraint 40 ., We now have a neuromuscular model consisting of four cell types repeated in each segment—sensory neurons , inhibitory interneurons , motor neurons , and muscle fibres ., For the sake of simplicity we model all neurons as having a binary activation state governed by the algebraic relation, V i = { 1 ∑ j w j V j > θ i 0 otherwise ( 4 ), where Vi is the activation of the i’th cell , θi is its activation threshold , Vj is the activation of the j’th presynaptic cell , and wj is the associated synaptic weight ., Numerical values for the weights and thresholds used in our model are given in S1 Table , supplemental ., Note that the muscle tension over a segment either vanishes ( when the muscle fibre is in the inactive state ) or has fixed magnitude bi ( when the muscle fibre is activated by local sensory feedback ) ., For this reason we refer to bi as the reflex gain ., Our choice to neglect neural dynamics is based on the large difference in timescales between the neural and mechanical dynamics ., Typical motor neuron spiking occurs with a timescale on the order of 10−3 seconds ., Spiking is observed to be significantly “averaged out” by the graded ( non-spiking ) muscle fibre responses , and respond on the order of ∼10−1 seconds to prolonged motor neuron spiking 52 , 53 ., During locomotion , segmental compressions are driven by several longitudinal muscle fibres activating simultaneously 3 , 14 , 29 in response to largely independent motor neuron populations 54 , 55 which are unlikely to spike with identical timing ., This spatial integration should further “mask” the effects of neural dynamics ., Note that the lack of neural dynamics in our model immediately rules out central pattern generation ., However , this does not prevent our model from producing complex , larva-like behaviour , and we consider how our model could account for observations of central pattern generation in the discussion ., To summarise , the neural model we have constructed can be seen as consisting of two parts , a segmentally repeating local reflex and a mutual inhibition circuit acting between non-adjacent reflexes ., The local reflex is constructed so that muscles will act as motors , amplifying segmental compressions and counteracting friction ., The mutual inhibition circuit couples distant reflexes to allow only localised amplification ., By limiting the number of moving segments , this should ensure that the model larva can produce a net force on its centre of mass ., One of the advantages of grounding our model of larval exploration in the body’s physics is that we are now able to apply powerful analytical results from classical mechanics in order to understand the model’s behaviour ., In this section we attempt to elucidate the naturally preferred motions of the larva by focusing our attention on the conservative mechanics of the body while neglecting friction forces , which would cause all motion to stop , and driving forces , which might impose a particular pattern of motion ., In this case , the general character of motion is specified by the Liouville-Arnold integrability theorem ., This theorem asks us to look for a set of conserved quantities associated with a mechanical system , which remain unchanged as the system moves ( energy , momentum , and angular momentum are examples of some commonly conserved quantities ) ., If we can find a number of these quantities equal to the number of mechanical degrees of freedom in our model , then the theorem tells us that the motion of the body is integrable—it can be expressed analytically , and must be either periodic or quasiperiodic ., If there are not enough conserved quantities , then the system is said to be nonintegrable ., In this case the motion is much more complicated and will be chaotic for some initial conditions ., These chaotic motions do not permit analytical expression and must be approximated through simulation ., In this section , we explicitly seek a case for which there is a “full set” of conserved quantities ( we provide only major results here , for detailed derivations see S2 Appendix ) ., We begin by restricting ourselves to considering only small deformations of the larval midline , in the case where all segments are of identical axial stiffness ka , transverse stiffness kt , mass m , and length l ., Under these assumptions , the total mechanical energy of the body may be written, H ( x , y , p x , p y ) = 1 2 p x T p x + ω a 2 x T D 2 x + 1 2 p y T p y + ω t 2 y T D 4 y ( 5 ), where x and y are vectors giving the displacement of each segment boundary along the body axis and perpendicular to the body axis , respectively , px and py give the translational momentum associated with each direction , D2 and D4 are difference matrices arising from a Taylor series expansion of our model’s potential energy ( see S2 Appendix ) , and ω a = k a / m and ω t = k t / m l 2 are characteristic axial and transverse frequency scales ., By making a linear change of coordinates {x , y , px , py} → {X , Y , pX , pY} to the eigenbasis of D2 and D4 ( see S2 Appendix ) this simplifies to, H ( X , Y , p X , p Y ) = ∑ i = 1 N - 1 1 2 p X , i 2 + ω a 2 λ a , i X i 2 + ∑ i = 1 N 1 2 p Y , i 2 + ω t 2 λ t , i Y i 2 ( 6 ), where λa , i and λt , i are eigenvalues associated with the coordinate transformation ., This expression is a sum of component energies , each of which is independently conserved ., The Liouville-Arnold theorem immediately tells us that the motion of the body must be ( quasi ) periodic in the case of conservative small deformations ., Indeed , the energy associated with each of the new coordinates Xi , Yi is in the form of a harmonic oscillator , and each coordinate executes pure sinusoidal oscillations ., By transforming back to the original coordinates x , y we obtain a set of collective motions ( modes ) of the body which are energetically isolated and have a sinusoidal time dependence , corresponding to axial and transverse standing waves ., We will refer to the Xi , Yi as modal coordinates since they describe the time dependence of each of the collective motions ., Each transverse standing wave corresponds to a periodic lateral oscillation of the body , with a unique frequency given by ω t , i = ω t λ t , i ., We determined these frequencies numerically , along with the spatial components of the lowest frequency standing waves ( Fig 3A ) ., These can be seen to match the eigenmaggot shapes extracted from observations of unbiased larval behaviour 56 ., The axial standing waves correspond to oscillating patterns of segmental compression and expansion ., While each transverse standing wave had its own unique frequency of oscillation , the axial standing waves come in pairs with identical frequency but different spatial components—each member of the pair corresponds to a different spatial pattern of segmental compression/expansion spread across the body , but these patterns oscillate in time with the same frequency ., We were able to analytically determine the frequency of oscillation of the i’th pair of axial standing waves to be, ω a , i = ω a λ a , i = 2 ω a | sin ( π i N - 1 ) | , i ∈ 0 , N / 2 - 1 ( 7 ) The spatial components of the axial standing waves could also be determined analytically, x k , i = 1 N - 1 cos ( 2 π i k N - 1 ) , or x k , i = 1 N - 1 sin ( 2 π i k N - 1 ) , i ∈ 0 , N / 2 - 1 ( 8 ) Where xk , i is the displacement of the k’th segment boundary for the i’th pair of standing waves ., We plot the axial frequencies ωa , i and spatial components xk , i in Fig 3B ., The fact that the axial oscillation frequencies come in identical pairs allows us to combine the axial standing waves with a ±90° relative phase shift to form pairs of forward and backward travelling wave solutions ( see S2 Appendix for the full derivation ), x k , i ( t ) = cos ( ω a , i t ± 2 π i k N - 1 ) , i ∈ 0 , N / 2 - 1 ( 9 ), where xk , i ( t ) gives the displacement of the k’th segment boundary as a function of time for the i’th pair of travelling waves ., The choice of a plus or minus sign corresponds to the choice between forward or backward wave propagation ., These solutions correspond to propagating waves of segmental compression and expansion similar to those seen during larval peristalsis ., We plot the lowest frequency pair of axial travelling wave solutions in Fig 3C , and directly visualise the synthesis of travelling wave solutions from standing wave solutions in S2 Video ., To summarise , in this section we have shown that for the case of conservative , small oscillations , the motion of the body may be decomposed into a combination of transverse standing waves and axial travelling waves ., This is of clear relevance to understanding the behaviour of the larva , which moves across its substrate by means of axial peristaltic waves while reorienting using lateral oscillations ., Indeed , the transverse modes of oscillation that we have derived here match principal components of bending computed from actual larval behaviour 56 ., Our results can be interpreted as providing a physical basis for these observations—the principal components extracted from real larval data correspond to a “natural” coordinate basis that is grounded in the animal’s mechanics ., Furthermore , the proportion of postural variance explained by each principal component of the experimental data decreases with increasing modal frequency in our model ( and thus increasing energy ) ., We can therefore help to explain the observed ordering of principal components , as this corresponds to the larva “preferring” to occupy low-frequency , low-energy modes during most of its behaviour ., We comment further on this observation in S3 Appendix in the context of axial modes ., We will now focus on the small-amplitude motion of the body in the presence of energy dissipation due to friction and driving forces ., Reintroducing friction will clearly lead the motions described above to eventually terminate due to energy dissipation , unless opposed by transfer of power ., In a previous section ( Models—Neuromuscular system , see also S1 Appendix ) , we introduced a neuromuscular system to produce power flow into the body , but as described , it can only directly transfer power into the axial degrees of freedom ., In the small deformation model we have just analysed , the axial and transverse degrees of freedom are energetically decoupled ., It follows that transverse friction is unopposed and any transverse motion must eventually terminate in the case of small deformations ., In this section we will therefore focus only on the axial degrees of freedom , which correspond to the peristaltic locomotion of the larva ., In Fig 4 , we show the effect of coupling our neuromuscular model to the axial mechanics ., For small reflex gains , the power flow into the body from the musculature is too low to effectively counteract frictive losses and the larva tends towards its passive equilibrium state , in which there is complete absence of motion ., However , increasing reflex gain past a certain critical value leads to the emergence of long-term behaviours in which the

Introduction, Models, Results, Discussion

The Drosophila larva executes a stereotypical exploratory routine that appears to consist of stochastic alternation between straight peristaltic crawling and reorientation events through lateral bending ., We present a model of larval mechanics for axial and transverse motion over a planar substrate , and use it to develop a simple , reflexive neuromuscular model from physical principles ., The mechanical model represents the midline of the larva as a set of point masses which interact with each other via damped translational and torsional springs , and with the environment via sliding friction forces ., The neuromuscular model consists of:, 1 . segmentally localised reflexes that amplify axial compression in order to counteract frictive energy losses , and, 2 . long-range mutual inhibition between reflexes in distant segments , enabling overall motion of the model larva relative to its substrate ., In the absence of damping and driving , the mechanical model produces axial travelling waves , lateral oscillations , and unpredictable , chaotic deformations ., The neuromuscular model counteracts friction to recover these motion patterns , giving rise to forward and backward peristalsis in addition to turning ., Our model produces spontaneous exploration , even though the nervous system has no intrinsic pattern generating or decision making ability , and neither senses nor drives bending motions ., Ultimately , our model suggests a novel view of larval exploration as a deterministic superdiffusion process which is mechanistically grounded in the chaotic mechanics of the body ., We discuss how this may provide new interpretations for existing observations at the level of tissue-scale activity patterns and neural circuitry , and provide some experimental predictions that would test the extent to which the mechanisms we present translate to the real larva .

We investigate the relationship between brain , body and environment in the exploratory behaviour of fruitfly larva ., A larva crawls forward by propagating a wave of compression through its segmented body , and changes its crawling direction by bending to one side or the other ., We show first that a purely mechanical model of the larva’s body can produce travelling compression waves , sideways bending , and unpredictable , chaotic motions ., For this body to locomote through its environment , it is necessary to add a neuromuscular system to counteract the loss of energy due to friction , and to limit the simultaneous compression of segments ., These simple additions allow our model larva to generate life-like forward and backward crawling as well as spontaneous turns , which occur without any direct sensing or control of reorientation ., The unpredictability inherent in the larva’s physics causes the model to explore its environment , despite the lack of any neural mechanism for rhythm generation or for deciding when to switch from crawling to turning ., Our model thus demonstrates how understanding body mechanics can generate and simplify neurobiological hypotheses as to how behaviour arises .

medicine and health sciences, classical mechanics, neuroscience, biological locomotion, motor neurons, developmental biology, reflexes, damage mechanics, waves, traveling waves, bending, animal cells, deformation, life cycles, physics, cellular neuroscience, cell biology, physiology, neurons, biology and life sciences, cellular types, physical sciences, larvae, motion

journal.pbio.1001369

Systematic Dissection of Roles for Chromatin Regulators in a Yeast Stress Response

Packaging of eukaryotic genomes into chromatin has wide-ranging effects on gene transcription in eukaryotes 1 ., There are two major ways in which cells modulate nucleosomal influences on gene expression ., ATP-dependent chromatin remodeling machines utilize the energy of ATP hydrolysis to disrupt histone-DNA contacts , often resulting in nucleosome eviction and changed nucleosomal location or subunit composition 2 ., In addition , the highly conserved histone proteins are subject to multiple types of covalent modification , including acetylation , methylation , phosphorylation , ubiquitination , SUMOylation , and ADP-ribosylation ., These covalent histone modifications often occur during the process of transcription , and in turn have many effects on transcription ., Moderately well-understood effects of histone modifications include epigenetic gene silencing , control of transcript structure via repression of “cryptic” internal promoters , control of splicing , and transcriptional activation 3–7 ., Altogether , there are myriad interactions and feedback loops between chromatin state and transcription ., At present , the effect of most modifications on transcription is unclear , even for reasonably well-characterized ones ., A large number of systematic genome-wide analyses have been carried out to characterize the complex interplay between chromatin regulation and gene transcription ., Genome-wide mapping studies 8 , 9 show that modification patterns are correlated with gene structure and gene activity levels ., Genome-wide mRNA profiling has been used for over a decade to identify transcriptional defects in chromatin mutants 10 ., A recent tour de force from the Holstege lab examined the effects on gene expression of deleting each of 174 different chromatin regulators 11 ., Proteomic studies characterize many of the protein complexes that play a role in chromatin regulation 12 , 13 ., Systematic genetic interaction profiling ( using growth rate as a phenotype ) has been used to identify chromatin complexes , and to delineate interactions between chromatin pathways 14–16 ., Importantly , most of these genomic screens have been carried out in steady-state conditions , typically in yeast actively growing in rich media ., In contrast , single gene studies suggest that chromatin regulators have important roles in dynamic processes that are masked at steady-state ., For instance , deletions of the histone acetylase Gcn5 or the histone chaperone Asf1 have little effect on the eventual induction of PHO5 by phosphate starvation , but both of these deletions cause significant delays in PHO5 induction kinetics 17 , 18 ., Similarly , mutation of H3K56 , whose acetylation plays a role in histone replacement , delays PHO5 induction by slowing nucleosome eviction upon gene activation 19 ., Similar results hold for other classic model genes , such as the galactose-inducible GAL genes 20 ., Because steady-state gene expression in mutants is subject to widespread compensatory or homeostatic mechanisms , we reasoned that analysis of mutant responses to a stressful stimulus would help reveal direct functions of transcriptional regulators ., Thus , the dynamics of response to stimuli should uncover the transcriptional roles of histone-modifying enzymes and other chromatin regulators ., We chose diamide stress in yeast as a model system , as it has been shown to involve a rapid , dramatic reorganization of the yeast transcriptome with 602 genes induced more than 2-fold and 593 genes repressed 21 ., Here , we carried out a time course of diamide stress in 202 yeast mutants and characterized gene expression changes at 170 selected transcripts ( Figure S1A–C ) ., Importantly , analysis of thousands of genome-wide mRNA profiling studies shows that genes typically are co-regulated in coherent clusters 22–24 , meaning that the behavior of the majority of co-regulated clusters can be captured by analyzing ∼100–200 transcripts ., For example , analyzing mutant effects on six ribosomal protein genes suffices to capture the majority of mutant effects on all ∼250 of these genes ., We find that the majority of chromatin regulators have greater effects on gene induction/repression kinetics than they do on steady-state mRNA levels , confirming that dynamic studies can identify unanticipated functions for chromatin regulators ., We show that grouping deletion mutants with similar gene expression defects identifies known complexes , and that joint analysis of histone mutants and deletion mutants associates many histone-modifying enzymes with their target sites ., In addition to known relationships between chromatin regulators , we identify a number of novel connections , including a previously unknown connection between H3K4 and H3S10 modifications ., We further carried out genome-wide mapping of five relevant histone modifications during the same stress time course ( Figure S1D–E ) ., By combining functional data with genome-wide mapping data , we identify a key role for Set1-dependent H3K4 methylation in repression of ribosomal biogenesis genes ., H3K4 methylation and H3S10 phosphorylation are both required for full repression of ribosomal protein genes ( RPG ) and of genes involved in rRNA maturation ( RiBi ) , but repression of RPGs and RiBi genes operate via two distinct pathways downstream of these histone marks ., Thus , the classic “activating” mark H3K4me3 in fact serves primarily to facilitate repression in budding yeast under multiple stress conditions ., Together , these data provide a rich multi-modal view on the role of chromatin regulators in gene induction and repression dynamics , and suggest that understanding the myriad roles of chromatin structure in gene regulation on a genome-wide scale will require extending mutant analyses to kinetic studies ., We used nCounter technology 25 to carry out genome-scale gene expression profiling ., Briefly , this technology utilizes hybridization of labeled oligonucleotides in a flow cell to directly count individual RNA molecules , without any enzymatic steps , for several hundred RNAs in yeast extracts ., For this experiment , we focused on gene expression during a stress response time course ( using the sulfhydryl oxidizing agent diamide ) ., We used whole genome mRNA abundance and Pol2 localization data from prior diamide exposure time courses 21 , 26 , along with a compendium of prior whole genome mRNA analyses and transcript structure analyses in various mutants 23 , 24 , to select 200 probes reporting on 170 transcripts ( 142 genes , of which 30 had two sense probes , as well as another 28 antisense transcription units ) that capture the majority of the different patterns of gene expression behavior in this stress ., Using this probeset , we measured transcript abundances over a 90-min time course of diamide exposure ( Figure 1 ) ., Experimental replicates are highly reproducible ( Table S1 ) , and these data provide a detailed kinetic perspective on gene expression dynamics during the diamide stress response ( Figure 1A–D ) ., We carried out identical time course experiments for 119 deletion strains for chromatin regulatory genes and for 83 mutants in histones H3 and H4 27 , covering the majority of individual K→R , K→Q , K→A , R→K , and S→A mutants , and several H3 and H4 N-terminal tail deletions ., For most mutants , we analyzed mRNA abundance at four time points ( t\u200a=\u200a0 , 15 , 45 , and 90 min ) as these time points capture the major phases of the diamide stress response ., Figure 1A–D show example data for wild-type yeast and three mutants in the HDA1/2/3 complex ., The entire dataset , comprising ∼1 , 000 experiments carried out for 202 mutant strains , is shown in Figure 1F–G , with mutant time courses clustered according to the similarity between their effects on gene expression across all four time points ( see also Table S1 ) ., Close inspection of the cluster in Figure 1G ( Table S1 ) revealed that many of the gene expression defects observed in these mutants were only observed during the stress response , but not before stress ., This is apparent in Figure 1A and 1D , where many more genes exhibit different levels between wild-type and hda mutants at 15 and 45 min of the stress response than at t\u200a=\u200a0 ( midlog growth ) ., These differences include both kinetic delays in gene induction/repression and defects in the extent of gene regulation ( see below ) ., To determine the generality of this phenomenon , we determined the distribution of mutant effects on RNA abundance at each of the four time points in the stress response ., Many more significant gene expression changes relative to wild-type occur at 15 and 45 min ( ∼10% of probe/mutant pairwise interactions ) after diamide addition than at t\u200a=\u200a0 ( ∼3 . 5% of pairwise interactions , Figure 1E ) ., As the yeast acclimate to the stress environment ( e . g . , at t\u200a=\u200a90 ) , the transcriptome reaches a new steady-state where we see fewer large mutant effects , although there are still more changes than at t\u200a=\u200a0 ., Thus , consistent with observations from classical model genes such as PHO5 , we find that chromatin mutants have much more extensive effects during changes in transcription than during steady-state conditions ., We sought to identify major classes of gene expression defect in various chromatin mutants , as a first step in eventually linking chromatin transitions to the genetic requirements for different chromatin regulators ., Immediately apparent in Figure 1G ( red boxes ) are two large groups of mutants with opposing behaviors with respect to the stress response—mutants that appear to be transcriptionally “hyper-responsive” to diamide stress and “hypo-responsive” mutants that exhibit blunted stress responses ., These two major classes of mutants are also captured by principal component analysis ( PCA ) of our dataset ., Here , the first principal component , which explains 30% of the variance in the dataset , corresponds to hyper- and hypo-responsive mutants ( Figure S2A–B ) ., Interestingly , not all genes induced or repressed during diamide stress were affected by hyper- or hypo-responsive mutants ., Genes whose induction was most affected by hyper-responsive mutants , for example , tended to be those with highly nucleosome-occupied promoters in YPD ( Figure S2C ) 28–30 ., Hypo-responsive mutants to diamide stress included a number of expected mutants , including deletion mutants lacking the general stress transcription factors Msn2 and Msn4 , or with compromised coactivator complexes such as Swi/Snf or SAGA ., Hyper-responsive mutants , conversely , included a number of histone deacetylases such as Hda1/2/3 ., Beyond acetylation/deacetylation , hyper-responsive and hypo-responsive mutants included a variety of deletions known to affect histone turnover and/or occupancy ., Several of these factors have previously been shown to affect bulk H3 turnover ( Rtt109 , Cac2/Rtt106 , Htz1 , Hat1 , Rsc1 , and Nhp10; 31–37 ) or histone levels/occupancy ( Rtt109 , Yta7 , Rtt106 , Cac2 , Spt21 , H3K42Q; 38–40 ) ., Interestingly , we noticed that among those histone mutants that decreased the stress response program , the subset of those mutations that are located in the globular domains of H3/H4 ( as opposed to the N-terminal tails ) are all situated at histone-DNA interfaces ( Figure S2D ) , which we speculate could affect nucleosomal stability and/or replacement dynamics ., Taken together , these results support a model in which many chromatin regulators have roles on global transcriptional responsiveness resulting from their overall effects on nucleosome stability ., Our RNA abundance measurements provide a population-averaged view of chromatin effects on gene expression , but hide a great deal of stochastic behavior that can be revealed by single-cell approaches ., For example , RNA data on hyper-responsive mutants come from many thousands of cells , meaning the mechanistic basis for stress hyper-responsiveness is unknown ., Do hyper-responsive mutants have a greater fraction of cells exhibiting diamide-driven gene induction ( as might be observed if gene induction depends on cell cycle stage and mutants exhibit cell cycle delays ) , or do all individual cells exhibit greater amplitude responses ?, We therefore extended our studies to include single cell analysis of protein expression using high throughput microscopy of GFP-tagged proteins in several key mutants ., As protein stability significantly confounds measures of gene repression , we focused on four diamide-induced genes , and examined each reporter in wild type and in nine deletion mutants ., We conducted time-lapse microscopy of yeast cells during the diamide response ( Figure 2A , Methods ) ., After detecting cells ( average n\u200a=\u200a120 for each of 40 strains , two biological replicates ) , we quantified the temporal profile of GFP intensity for each cell ., Figure 2B shows the median intensity as a function of time for one reporter in wild-type and several mutants ., Importantly , we found excellent agreement between defects in protein induction in various hypo- and hyper-responsive mutants and the corresponding nCounter RNA measurements ( Figure 2C ) ., In general , we noted that GFP induction in individual cells followed a sigmoid-like curve consistent with a window of stress-increased protein production followed by a gradual return to baseline production levels ., This behavior is consistent with a simple model in which there is a time window of diamide-induced gene transcription , followed by gradual mRNA decay ., We implemented a simple mathematical model with cells transitioning from low expression to high expression and back , with a constant rate of mRNA production during the open window ( Materials and Methods ) ., This model is clearly oversimplified—each parameter covers multiple processes—but provides very good fit to the measured intensity profiles ( Figure 2D ) ., Fitting the model for each cell , we can estimate the transcriptional time windows for individual cells as well as the rate of protein production during this time and examine the variability in the timing and speed of transcriptional response in a genetically homogenous population of cells ( Figure 2E ) ., We then used the extracted parameters for individual cells to determine whether hyper- or hypo-responsiveness corresponded to a change in the responsive fraction of cells , a population-wide change in promoter open time , and so forth ., In general , we found that most mutants did not affect the fraction of cells responding to diamide ., The fraction of cells exhibiting diamide induction of GFP was 87%±3% across all 40 strains , and no strain differed from wild-type by even 10% of cells responding ., Notably , we found that different hyper-responsive mutants could act at different stages in gene expression ., For example , deletion of YTA7 , which is involved in histone gene transcription and affects nucleosome occupancy 40 , 41 , leads to accelerated promoter opening during diamide stress , whereas deletion of HDA2 predominantly affects GFP production rate rather than promoter opening ( Figure 2F , G ) ., Together , these results independently validate our RNA measurements , confirm that RNA changes are reflected in protein abundance , and show that , for the nine mutants analyzed , mutant effects on transcriptional response occur in the majority of cells rather than reflecting changes in the fraction of diamide-responsive cells ., Beyond the major groups of mutants that affect overall stress responsiveness and likely report on global histone occupancy/dynamics , we observed a wide variety of gene expression effects that were specific to smaller sets of mutants ., For example , the white box in Figure 1G highlights the well-understood gene expression changes that occur in mutants related to the Sir heterochromatin complex—repression of mating-related genes secondary to the pseudodiploid state of these mutants 7 ., To systematically group mutants according to their gene expression phenotypes , we calculated the correlations between the changes ( relative to wild-type ) in stress response in each mutant and clustered mutants according to these correlations ( Figure 3A , Table S2 , Materials and Methods ) ., We kept histone mutants and deletion mutants separate to allow more intuitive interpretation of clusters ., Grouping deletion mutants by this method recovers a great deal of known chromatin biology , validating our approach ., In general , mutants in different subunits of known chromatin complexes exhibit similar defects in gene expression , indicating shared function ., Most white boxes in Figure 3A highlight a subset of clear examples , including the grouping of subunits of the Sir complex , the HDA1/2/3 complex , COMPASS , Cac2/Rtt106 , Set3C , and the Ino80 complex ., Furthermore , several pathways were recovered ., The histone variant H2A . Z ( encoded by HTZ1 ) was linked to components of the Swr1 complex responsible for H2A . Z incorporation 42–44 , the H3K4 methylase Set1 was linked to the H2B ubiquitin ligase Bre1 whose activity is required for K4 methylation 45 , and the H3K36 methylase Set2 was linked to Eaf3 , the binding partner for H3K36me3 16 , 46 , 47 ., In addition to known chromatin regulatory complexes and pathways , our results also suggest a number of hypotheses for novel chromatin pathways ., For example , we find strong correlations between gene expression defects in mutants lacking the H4K16 acetylase Sas2 and those lacking the proline cis/trans isomerase Cpr1 ., Similarly , our results link the H3K36 demethylase Rph1 with ATP-dependent remodeler Chd1 , suggesting the possibility that H3K36 methylation regulates Chd1 in budding yeast , an idea that finds support in prior studies showing that H3K36 mutants and chd1 mutants have similar genetic interactions in vivo 48 ., Analysis of histone mutations revealed similar structure ., We observe two larger clusters that correspond to hyper- and hypo-responsive mutations ( Figure 3A , yellow boxes ) , as well as many smaller groups ., Many of these groups are comprised of several mutations in the same residue ( e . g . , all three mutations in H3K36 are tightly clustered together ) or in the same tail ( e . g . , H3 tail delete and simultaneous K->Q/R mutations in H3 tail lysines 4 , 9 , 14 , 18 , and 23 ) ., Many other groups of histone mutants were unanticipated and may identify functionally relevant nucleosomal surfaces 27 or novel examples of histone crosstalk 49 ., Below , we explore the relevance of one such novel connection , between H3K4A and H3S10A mutants ., Many of the connections between chromatin regulatory genes observed here also can be observed in systematic genetic interaction profiles , or in gene expression studies carried out in midlog growth conditions 11 , 14 ., A unique aspect of our study is the joint analysis of gene deletion mutants with histone point mutants ., Many of the strongest correlations between deletion and histone mutants correspond to known enzyme-substrate and modification-binding partner relationships ., For example , gene expression defects resulting from deletion of the H3K36 methylase Set2 were most strongly correlated with the defects in H3K36R and H3K36Q mutants , and with the H3K36me3-binding protein Eaf3 ( Figure 3A ) ., Analysis of multiple different mutations of the same lysine residue can provide insight into the biochemical function of modifications at this residue ., While both K→R and K→Q mutants disrupt modification-specific binding by proteins ( e . g . , bromo- and chromo domain proteins ) , they differ in their charge ., Indeed , lysine mutants for which K→R and K→Q mutants exhibited similar gene expression defects tend to occur at lysines with well-characterized modification-specific binding partners ( e . g . , Eaf3 , Sir3 ) ., In contrast , lysines for which K→R and K→Q mutants had opposing effects on gene expression often were known acetylation substrates , although we counterintuitively observe that for these lysines the K→R mutations were generally correlated with deletions in histone deacetylases ( Figure S3 ) ., To systematically identify relationships between chromatin factors , we identified significant correlations between mutants ( Materials and Methods ) , recovering for example the Set2→H3K36→Eaf3 pathway ( Figure S4A–B , Table S3 ) ., Data for all correlations above a threshold significance are visualized in a network view in Figure 3B to show not only connections within strongly connected pathways but also connections between pathways ., Other known relationships recovered this way included the association between Set1 and H3K4 , and the association between the Sir complex and H4K16 ( Figure S4 ) ., Furthermore , we found that Cac2 , a CAF-1 subunit , and Rtt106 , histone chaperones that were strongly correlated with one another , exhibited transcriptional effects most related to the H4K91R mutant ( Figure 3A ) ., H4K91 acetylation is a little-studied modification reported to occur on newly synthesized histones 50 , and in systematic genetic interaction studies , H4K91R and mutations in the assembly-related lysine H3K56 exhibited similar genetic interactions 27 ., We therefore hypothesize that H4K91 acetylation might affect chromatin assembly by CAF-1 or Rtt106 ., Other connections have no obvious literature precedent—the HMG protein Nhp6a , which plays a role in nucleosome positioning and dynamics at promoters 51 , 52 , was correlated with the H3R8K mutation ( Table S2 ) —and thus represent potentially novel connections between histone residues and either modifying enzymes or binding partners ., Below , we follow up specifically on one such observation , the surprising linkage between H3K4 methylation mutants and the H3S10A histone mutant ., Our data show that joint analysis of histone mutants with related gene deletion mutants can systematically link histone-modifying enzymes with their substrates , as well as modification-specific binding proteins to the relevant modified histone residue ( Tables S2 and S3 ) ., We next sought to understand why only particular genes were affected by mutants in various chromatin regulators ., One of the central questions in chromatin regulation is why broadly localized histone marks appear to have extremely localized effects on gene expression ?, In other words , given that H3K4me3 occurs at nearly all +1 nucleosomes , why do set1Δ mutants exhibit relatively minor 11 , 53 gene expression changes ?, Our functional results suggest that many transcriptional effects of chromatin mutants are masked at steady-state by feedback mechanisms , but can be uncovered during dynamic changes in gene expression ., To address the relationship between histone mark occurrence and function in a dynamic context , we therefore extended our studies by carrying out genome-wide mapping of several histone modifications ( Tables S4 and S5 ) during a six time point diamide stress time course ( t\u200a=\u200a0 , 4 , 8 , 15 , 30 , and 60 min ) ., We focused these experiments on two relatively well-characterized modifications: H3K36me3 and H3K4me3 , and related marks H3K14ac , H3S10P , and H3R2me2a ., Our mapping data for unstressed yeast are concordant with known aspects of modification localization patterns from either prior genome-wide mapping efforts 8 , 9 or related studies ( Figure 4A , Figure S5 ) ., Given the surprising correlation between H3K4A and H3S10A mutants ( Figure 3A , Figure S4 ) , we focused on how the histone modifications H3K4me3 and H3S10P change genome-wide during diamide stress ., As noted above , H3K4me3 occurs at the 5′ ends of transcribed genes , and genes induced during the stress response gained H3K4me3 over time , as expected ( Figure 4C , Figure S5B ) ., H3S10P , which had not been mapped genome-wide in yeast , is most strikingly localized to ∼20 kb surrounding yeast centromeres ( Figure S5G ) , consistent with its pericentric localization by immunofluorescence in mammalian cells 54 ., However , we also noted that H3S10P on chromosome arms was heterogeneous , and localized to coding regions with a pattern opposite to that of H3/H4 turnover 31 , 35 ., H3S10P is depleted from the 5′ ends of genes , and over coding regions anticorrelates with transcription rate ( Figure 4B ) ., Furthermore , during the stress response H3S10P levels increase over repressed coding regions , and decrease over induced genes , indicating that the anticorrelation between H3S10P and transcription is dynamic ( Figure 4C ) ., Overall , many of the chromatin changes over stress-activated or repressed genes fit expectations ., At stress-activated genes , promoter H3K4me3 levels increased while H3K36me3 increased over gene bodies ., However , we also observed several unexpected dynamic behaviors ( e . g . , increasing H3K36me3 over the promoters of many stress-responsive genes ) ., Furthermore , H3K14 , whose acetylation scales with transcription rate during midlog growth 8 , 9 , was only deacetylated at a small subset of repressed genes during diamide stress , with most repressed genes exhibiting surprisingly minimal changes in H3K14ac ( see below ) ., Most curiously , we found that H3K4me3 levels increase at the 5′ ends of a substantial number of diamide-repressed genes during their repression ( Figure 4C , yellow box ) ., Not only do these genes gain H3K4me3 , they also gain H3S10P , and as noted above H3K4 mutants and H3S10 mutants exhibit similar gene expression defects ( Figure 3A , Figure S4 ) ., Thus these marks are linked both functionally and in terms of dynamic localization changes ., Curiously , the H3K4methylase Set1 and one of the H3S10 kinases , Ipl1 , also share the nonhistone substrate Dam1 55 , indicating a more general connection between H3K4 and H3S10 based on shared nonhistone substrates for their modifying enzymes ., It is unlikely that the gene expression defects observed here stem from nonhistone substrates of these enzymes as the gene expression changes are observed in histone point mutants as well as modifying enzyme deletions , but the connection is curious nonetheless ., Below , we attempt to connect the changes in H3K4me3 and H3S10P localization with the functional effects of relevant mutants ., Are the genes that are misregulated in K4 and S10 mutants the same genes that exhibit dynamic changes in these marks during stress ?, Set1 methylates H3K4 to create a gradient over coding regions from K4me3 at the 5′ end to K4me1 at the 3′ end , and this methylation pattern correlates with transcription rate during midlog growth ( 8 , 9 , Figure S5B ) ., The correlation between H3K4me3 and transcription rate leads to this mark being referred to as an “activating mark , ” yet set1Δ mutants exhibit few gene expression defects in midlog growth , and in fact increasing evidence points to a primarily repressive role for K4 methylation in yeast ., set1Δ mutants exhibit increased basal expression of repressed genes such as PHO5 56 , 57 , and moreover exhibit widespread defects in repression of sense transcription by antisense transcripts 58–61 ., We noted in our initial gene expression dataset that set1Δ and related mutants showed defects in repression of ribosomal protein ( “RPG” ) and ribosomal biogenesis ( “Ribi” ) genes ( Table S1 ) ., We therefore extended these results to whole genome mRNA profiling , finding that the major gene expression defect in set1Δ mutants during diamide stress is a failure to adequately repress RPG and Ribi genes ( Figure 5A ) ., This result is interesting in light of prior observations that Set1 is required for full repression of the rRNA repeats 62 , 63 during steady-state growth ( when a subset of rDNA repeats are silenced ) , and shows that Set1 plays a general role in repression of all aspects of ribosomal biogenesis ., Notably , although some snoRNA genes are found in RPG introns , we observed Set1 effects on the majority of ribosomal protein genes , most of which do not carry snoRNAs in their introns , indicating that the observed effect is not a consequence of Set1s known effects on termination at snoRNA genes 64 ., Overall , deletion of SET1 resulted predominantly in diminished repression of ribosome-related genes , with very few large effects on diamide-activated genes ( Figure 5B , Figure S6A–C ) ., Importantly , loss of Set1 had a distinct effect on ribosomal gene repression from that observed in “hypo-responsive” mutants ., Comparison of a given mutants effects on overall gene repression to its effects on ribosomal gene repression identifies Set1-related and Sir2-related mutants as having specific defects in ribosomal gene repression ( Figure S6D , see also below ) ., We next asked whether Set1s role in ribosomal repression was specific to diamide stress ., We therefore assayed gene expression of our 200 probes in wild type and set1Δ yeast responding to another stress response , heat shock , or responding to nutrient deprivation signals induced by the small molecule rapamycin 65 , 66 ., Each of these stress responses exhibited different repression kinetics of the RPG genes , yet in all three stresses set1Δ strains suffered defects in RPG repression ( Figure 5C ) ., Thus , Set1 appears to act fairly generally as a repressor of ribosomal biogenesis under suboptimal growth conditions ., Comparing set1Δ effects on mRNA abundance with modification mapping data , we noted that many genes repressed in a Set1-dependent manner were often associated with stress-induced gains in H3K4me3 and H3S10P at their 5′ ends ( Figure 6A , Tables S4 and S5 ) ., Focusing on the most highly Set1-dependent diamide-repressed genes revealed two clearly distinct clusters based on chromatin changes at the genes 5′ ends ( Figure 6B ) ., Remarkably , we found that ribosomal protein genes ( RPGs ) were “paradoxically” associated with dramatic gains in H3K4me3 at their 5′ ends , as well as gains in H3S10P ., The changes in H3K4me3 and H3S10P were strongest at the +1 nucleosome but occurred throughout the promoters ( Figure S7A and analysis not shown ) ., Conversely , non-RPG ribosomal biogenesis ( Ribi ) genes exhibited similar increases in H3S10P , but modest increases in 5′ H3K4me3 ., Instead , these genes were among the relatively few diamide-repressed genes associated with decreases in H3K14 acetylation ., Importantly , these specific modification changes are quite specific for the gene classes in question ., RPGs encompass the majority of genes gaining H3K4me3 during diamide repression , whereas Ribi genes provide the majority of cases with H3K14 deacetylation during repression ( Figure S7B–C ) ., The distinct chromatin changes observed over RPG and Ribi genes during repression suggested that Set1-dependent repression of these genesets might operate via distinct pathways downstream of H3K4 methylation ., We therefore sought to identify additional players in the pathways involved in repression of RPG and Ribi genesets ., For each mutant assayed in our nCounter dataset , we compared the effects on diamide repression of RPGs to the effects on Ribi repression ( Figure 7A ) ., In general , mutants had similar effects on both gene classes , with globally hypo-responsive mutants such as H3K42Q failing to repress both RPGs and Ribi genes to similar extents ., Intriguingly , we found a handful of mutants ( several are shown in Figure 7B ) with substantially different effects on RPG and Ribi repression: most notably , mutants in the RPD3L complex ( e . g . , sap30Δ , pho23Δ ) exhibit defective repression of Ribi genes , yet have no effect on RPG gene expression during diamide stress ., These results are consistent with prior genome-wide studies in yeast which found that repression of Ribi genes in response to heat shock , H2O2 , or rapamycin was defective in the absence of RPD3L 66 , 67 ., Together , our results suggest that H3K4me3-dependent recruitment or activation of RPD3L ( presumably via the PHD finger in Pho23; 57 ) is required for Set1-driven repression of Ribi genes , whereas an alternative Set1-d

Introduction, Results, Discussion, Materials and Methods

Packaging of eukaryotic genomes into chromatin has wide-ranging effects on gene transcription ., Curiously , it is commonly observed that deletion of a global chromatin regulator affects expression of only a limited subset of genes bound to or modified by the regulator in question ., However , in many single-gene studies it has become clear that chromatin regulators often do not affect steady-state transcription , but instead are required for normal transcriptional reprogramming by environmental cues ., We therefore have systematically investigated the effects of 83 histone mutants , and 119 gene deletion mutants , on induction/repression dynamics of 170 transcripts in response to diamide stress in yeast ., Importantly , we find that chromatin regulators play far more pronounced roles during gene induction/repression than they do in steady-state expression ., Furthermore , by jointly analyzing the substrates ( histone mutants ) and enzymes ( chromatin modifier deletions ) we identify specific interactions between histone modifications and their regulators ., Combining these functional results with genome-wide mapping of several histone marks in the same time course , we systematically investigated the correspondence between histone modification occurrence and function ., We followed up on one pathway , finding that Set1-dependent H3K4 methylation primarily acts as a gene repressor during multiple stresses , specifically at genes involved in ribosome biosynthesis ., Set1-dependent repression of ribosomal genes occurs via distinct pathways for ribosomal protein genes and ribosomal biogenesis genes , which can be separated based on genetic requirements for repression and based on chromatin changes during gene repression ., Together , our dynamic studies provide a rich resource for investigating chromatin regulation , and identify a significant role for the “activating” mark H3K4me3 in gene repression .

Chromatin packaging of eukaryotic genomes has wideranging , yet poorly understood , effects on gene regulation ., Curiously , many histone modifications occur on the majority of genes , yet their loss typically affects a small subset of those genes ., Here , we examine gene expression defects in 200 chromatin-related mutants during a stress response , finding that chromatin regulators have far greater effects on the dynamics of gene expression than on the steady-state transcription ., By grouping mutants according to their shared defects in the stress response , we systematically recover known chromatin-related complexes and pathways , and predict several novel pathways ., Finally , by integrating genome-wide changes in the locations of five prominent histone modifications during the stress response with our functional data , we uncover a novel role for the “activating” histone modification H3K4me3 in gene repression ., Surprisingly , H3K4 methylation appears to act in conjunction with H3S10 phosphorylation in the repression of ribosomal biosynthesis genes ., Repression of ribosomal protein genes and ribosomal RNA maturation genes occur via distinct pathways ., Our results show that steady-state studies miss a great deal of important chromatin biology , and identify a surprising role for H3K4 methylation in ribosomal gene repression in yeast .

systems biology, genome expression analysis, functional genomics, chromosome biology, gene expression, genetics, epigenetics, biology, genomics, genetics and genomics

Systematic functional and mapping studies of histone modifications in yeast show that most chromatin regulators are more important for dynamic transcriptional reprogramming than for steady-state gene expression.

journal.pgen.1004925

Exome Sequencing in an Admixed Isolated Population Indicates NFXL1 Variants Confer a Risk for Specific Language Impairment

Language deficits form a central feature of many developmental disorders and account for a high number of pediatric referrals and statements of special educational need 1 ., These language impairments often represent a secondary clinical feature of a more pertinent developmental disability such as Down syndrome , Autistic Spectrum Disorder or intellectual disability ., However , in a proportion of cases , the primary clinical concern is the language difficulties , which occur in the absence of any other developmental deficit or neurological impairment and in the presence of normal non-verbal IQ ., In such cases , the diagnosis is Specific Language Impairment ( SLI ) 2 ., SLI affects between 5 and 7% of children in the UK 3 and significantly more boys than girls 4 ., The disorder is highly heritable 5 but genetic contributions are expected to be complex in nature with significant heterogeneity between individuals 6 ., Common risk variants within ATP2C2 ( OMIM#613082 ) , CMIP ( OMIM#610112 ) 7 , ABCC13 ( OMIM#608835 ) 8 , FLNC ( OMIM#102565 ) , RBFOX2 ( OMIM#612149 ) 9 and ROBO2 ( OMIM#602431 ) 10 have been associated with quantitative measures of language skills ., Genome-wide association studies of language-impaired probands have also highlighted potential risk variants in NDST4 ( OMIM#615039 ) , ZNF385D , COL4A2 ( OMIM#120090 ) 11 and NOP9 12 ., Other studies implicate rare genetic events which may have higher penetrance 13 , 14 ., However , it is clear that the contributions of these various genetic effects are complex ., Some may be specific to individuals with certain forms of language deficits , others may contribute across the range of ability 7 , 8 , 11 , 15 , 16 ., The functional impact of these candidate genes has yet to be elucidated and further candidates need to be identified before we can properly understand the molecular pathways underlying SLI ., Clearer links have been made between the presence of language deficits and disruption of the FOXP2 gene ( OMIM#605317 ) , a forkhead/winged-helix transcription factor 17 , 18 ., Reduced functional dosage of FOXP2 , caused by mutation or chromosomal rearrangements , leads to characteristic deficits in coordinating sequences of orofacial movements , impairing speech , producing a disorder known as developmental verbal dyspraxia ( DVD ) or childhood apraxia of speech ( CAS ) 18–22 ., Typically the DVD/CAS features of FOXP2 mutation cases are accompanied by wide-ranging problems with spoken and written language 23 ., Whilst FOXP2 disruptions are rare and account for only a small proportion of DVD/CAS cases , the investigation of this gene , its expression patterns and interactions , have led to the elucidation of genetic networks that are important to language development and contribute to more common forms of language impairment 23–25 ., One of the transcriptional targets of FOXP2 is CNTNAP2 ( OMIM#604569 ) , a member of the neurexin family which mediates interactions between neurons and glia during nervous system development 26 ., Genetic variation across CNTNAP2 has been associated both with language deficits 15 , 27–29 and language ability in the general population 30–32 ., Variations in , and disruptions of , this gene have also been implicated across a range of neurodevelopmental disorders such as autism , epilepsy and schizophrenia 26 , indicating that it is likely to be crucial for brain development ., These investigations demonstrate how the identification of genetic mutations underlying a distinct severe form of disorder provide entry points into mechanisms that are relevant to the wider processes underlying the initial deficit ., In 2008 , Villanueva et al described a population who are affected by an unusually high prevalence of language impairment 33 ., This admixed population inhabits the Robinson Crusoe Island which forms part of the Juan Fernandez Archipelago in the South Pacific Ocean , approximately 400 miles off the coast of Chile ., The Island was last colonized in 1876 by 64 individuals of European and South American descent ., In the 2002 census , the Island population was 633 , the majority of whom were descendants of the founder families ., More than 70% of the current population has a surname from the colonizing families and 14% of marriages involve consanguineous unions 34 ., In their 2008 study , Villanueva et al completed psychometric profiling of 66 island children aged between 3 and 9 years of age , of whom 40 were descendants of the founder party ., They found that 35% of the founder-related children ( 14 of 40 ) were affected by specific language impairment ., No evidence for a male bias was observed in this group ., A further 27 . 5% of the founder-related child population ( 11 of 40 ) had language abilities below that expected for their age but presented with additional developmental concerns or low non-verbal IQ , precluding a diagnosis of SLI ., The remaining 37 . 5% of founder-related children ( 15 of 40 ) had typical language development ., In contrast , only one of 26 children whose parents are not related to the founder families ( 3 . 8% ) had evidence of language impairment , a frequency of language impairment that coincided with that seen in mainland Chile ( 3% ) 33 ., Furthermore , when the genealogical records of the islanders were recompiled , 90% of the individuals affected by SLI were direct descendants of a single pair of founder brothers who formed part of the founder party 33 , 35 ., Given the clear phenotypic differences between founder-related and non-founder-related children on the Island , we postulated that the founder brothers may have carried a rare causative genetic mutation or , alternatively , combinations of common genetic variations that together confer a high risk of language impairment ., A previous genome-wide linkage study of 34 families from the Robinson Crusoe Island identified significant linkage to several chromosome regions , the most consistent of which included a large section ( 48Mb ) of chromosome 7q ( SLI4 – OMIM#612514 ) that included many genes which represent good candidates for language impairment , including FOXP2 and CNTNAP2 35 ., However , in depth genomic profiling has yet to be performed within this population ., In this study , we make use of this admixed isolated population and assess the possibility of a founder mutation , by completing exome sequencing of five individuals from the Robinson Crusoe population affected by SLI ., We substantiate the findings of the exome screen by performing association analyses of selected putative functional variants in the wider Robinson Crusoe population ., The contribution of identified risk variants is subsequently validated by performing targeted sequencing of candidate genes in a UK-based cohort of individuals affected by SLI ., On average , 47 , 276 ( median = 49 , 543 , range = 43 , 075–50 , 112 ) genic variants were identified in each of the five exomes ., This included an average of 17 , 405 ( median = 17 , 326 , range = 15 , 200–19 , 837 ) exonic variants , 8 , 379 ( median = 8 , 089 , range = 7 , 258–9 , 629 ) missense variants and 106 ( median = 90 , range = 72–157 ) nonsense ( including indels ) variants per individual ., Across all five samples , 90 . 0% of targeted exome sequencing had coverage of at least 10-fold ., The average coverage of targeted sequence was 56 . 5-fold and 21% of the reads reached this level ., Sequencing metrics can be found in S1 Table ., To test the hypothesis that the founder brothers carried a rare causative genetic mutation , we focused upon novel variants that caused nonsynonymous protein substitutions or altered canonical splice sites for our downstream analyses ., Comparisons between individuals found that no such variants were shared by all five individuals ., However , allowing for potential genetic heterogeneity between affected individuals , we identified nine novel nonsynonymous or splice-site variants that were shared by at least 3 of the 5 children sequenced ( Table 1 ) ., Eight novel nonsynonymous or splice-site variants were validated in the five exome samples by Sanger sequencing ., None of these variants overlapped with the regions of suggestive linkage ( P<7 . 3×10−4 , chromosomes 2 , 6 , 7 , 8 , 9 , 12 , 13 and 17 , as listed in S2 Table ) previously identified in this population 35 ., S3 Table provides a full list of all shared , high-quality variants that fell within the previously identified regions of linkage ., All of these had previously been reported in dbSNP ( 138 ) and many were non-genic , intronic or synonymous ( see notes column in S3 Table ) ., All shared novel nonsynonymous or splice-site variants identified in the exome screen were subsequently genotyped in 111 members of the Robinson Crusoe population ( 49 individuals with language-impairment and 62 individuals with typical language ability ) ., This validation cohort was ascertained via 35 children living on the Robinson Crusoe Island who had been diagnosed with SLI or who showed typical language development ( as described in methods ) and included the five children used in the exome sequencing ., All children were descendants of the founder families of the Robinson Crusoe Island and , as such , the cases and controls used in these association analyses were inter-related ( Fig . 1 ) ., We therefore employed an association algorithm that allowed for relatedness between cases ( MQLS , 36 ) , and that took into account the shared ancestry of the Robinson Crusoe validation cohort ( 288 individuals over 5 generations ) ., These analyses highlighted one particular coding variant ( chr4:g . 47 , 907 , 320A>T , hg19 ) that was present at a significantly higher frequency in Islanders with language impairment than in Islanders with typical language ability ( Table 1 ) ., Thirty nine percent of Islanders with language impairment were found to carry this variant compared to ten percent of Islanders with typical language skills ( p = 2 . 04 × 10−4 ) ( Table 1 ) ., Across the Robinson Crusoe validation cohort , the minor allele frequency was 11 . 3% ( 25 of 222 chromosomes sampled ) ( Table 1 ) ., Chr4:g . 47 , 907 , 320A>T ( hg19 ) falls in exon 4 of the Homo sapiens nuclear transcription factor , X-box binding-like 1 ( NFXL1 ) gene ( Fig . 2 ) ., The variant causes a nonsynonymous change yielding an asparagine to lysine substitution in the encoded protein ( p . N150K , uncharged amino acid to positively charged amino acid ) ., This change is predicted to be “disease-causing” by MutationTaster with a confidence probability of 0 . 98 ( SIFT = 0 . 67 , PolyPhen-2 = 0 . 178 ) ., The position is conserved at both the amino acid and nucleotide level ( PhyloP = 0 . 66 , PhastCons = 1 ) ; the amino acid N150 is invariant across 36 of the 38 vertebrate species in which an alignment could be made and the thymine nucleotide at this position is conserved across all six ENSEMBL primate species investigated ( Human , chimp , gorilla , orangutan , macque and marmoset ) ( Fig . 2 ) ., The variant at chr4:47 , 907 , 320 was not observed in 127 independent European population controls that were genotyped ( Table 2 ) ., We therefore went on to genotype an additional 320 independent individuals from a Colombian population cohort and 121 independent individuals from a Chilean control population cohort ., In these cohorts , the variant was present with a minor allele frequency of 4 . 2% ( 27 of 640 chromosome sampled ) and 7 . 4% ( 18 of 242 chromosome sampled ) respectively ( Table 2 ) ., Subsequent data released by the 1000 genomes project confirmed that this variant is specific to admixed American populations ( AMR ) with an average minor allele frequency of 4 . 1% ., In the sub-populations of the AMR grouping , the minor allele frequency is reported as 0 . 9% in Puerto Ricans ( PUR – 1 in 110 chromosomes sampled ) , 3 . 3% in Colombians in Medellin ( CLM – 4 in 120 chromosomes sampled ) and 7 . 6% in individuals of Mexican ancestry in Los Angeles ( MXL – 10 of 132 chromosomes sampled ) ( Table 2 ) ., The variant has recently been designated as rs144169475 accordingly ., Parametric and nonparametric linkage analyses were performed for 55 SNPs across the NFXL1 region of chromosome 4 ( 46–49Mb , hg19 ) within seven extended pedigrees from the Robinson Crusoe validation cohort ( S2 Fig . ) ., In these analyses , we did not observe evidence of linkage ( maximum LOD score = 0 . 62 , S3 Fig . ) ., We sequenced the entire coding region of the NFXL1 gene in 117 unrelated probands affected by SLI ( from the UK SLI Consortium ( SLIC ) cohort 7 , 37–39 ) , to assess whether we could replicate a role for NFXL1 in SLI etiology ., In total , we identified 166 high-quality sequence variants across the NFXL1 gene ., 155 of the variants detected were intronic , 4 were in the 3’UTR and 7 affected the coding region ., Of the coding variants , three were nonsynonymous and four were synonymous substitutions ( Table 3 ) ., Nonsynonymous variants and those with estimated allele frequencies of <5% were verified across all the pools of DNA in which they were observed using Sanger sequencing ., This allowed the derivation of accurate allele frequencies within the SLIC cohort ., One of the synonymous variants ( chr4:g . 47 , 916 , 008G>A , hg19 ) was found in a heterozygous state in one SLIC proband ( allele frequency of 0 . 43% ) but had not been documented in any European individuals in the 1000 genomes project 40 or the NHLBI GO ESP Exome Variant Server ( EVS ) , which together consist of data from 4679 control individuals and therefore have the ability to detect rare variants with a population frequency of 0 . 0001 ., A comparison of allele frequencies between SLIC probands ( 1 of 234 chromosomes tested ) and controls ( 0 of 9358 chromosomes tested ) yielded a significant P-value of 0 . 0244 ., Intriguingly , although it is synonymous , this variant was predicted to be “disease-causing” by MutationTaster with a confidence probability of 0 . 98 ( SIFT = 1 . 0 ) ., This variant falls in the most 5’ coding exon of NFXL1 and is part of a CpG island , indicating that it may be important for the regulation of gene expression ., Furthermore , ENCODE data shows that it is part of a H3K4Me3 mark ( which is often associated with promoters ) and binds multiple transcription factors , particularly POLR2A c-MYC and PHF8 ( www . genome . ucsc . edu , accessed April 2014 ) ., The remaining three synonymous variants ( rs2053404 , rs6818556 and rs35139099 ) found in SLIC probands were also found at similar frequencies in control databases ., All had allele frequencies of >5% and are therefore thought to represent common polymorphisms ( Table 3 ) ., One nonsynonymous substitution ( chr4:g . 47 , 887 , 652T>C , hg19 – rs151113647 ) was found in a heterozygous state in a single SLIC proband ( allele frequency of 0 . 43% ) and again , was not observed in 4679 independent European individuals in the control public databases ( Table 3 ) , yielding a significant P-value of 0 . 024 ( 1 of 234 SLIC chromosomes tested vs 0 of 9358 control chromosomes tested ) ., Further investigations found that this variant had been observed in a heterozygous state in a single African American individual from the EVS ., Principal components analysis of genome-wide SNP data in the SLIC proband against the hapmap-3 populations did not detect any African ancestry ., The rarity of the rs151113647 variant and its position within a zinc-finger motif ( Fig ., 2 ) indicates that it may confer negative effects upon protein function ., Nonetheless , because the nucleotide is not highly conserved across species ( phyloP = −0 . 418 , phastCons = 0 . 925 ) , the change was predicted to be a polymorphism by MutationTaster with a confidence probability of 0 . 99 ( SIFT = 0 . 68 , polyphen-2 = 0 . 00 ) ( Fig . 2 ) ., A second nonsynonymous substitution ( chr4:47 , 898 , 575G>A , hg19 - rs35139099 ) was observed in a heterozygous state in two independent SLIC probands ( allele frequency of 0 . 85% ) ., This variant was also found in 44 of 4679 independent European control individuals from public databases ( allele frequency of 0 . 47% , Table 3 ) yielding a P value of 0 . 3097 ., Although , it was not observed to occur at a significantly increased frequency in SLIC probands , the rs35139099 variant occurs at a conserved residue ( phyloP = 1 . 466 , phastCons = 1 ) within a zinc-finger motif ( Fig ., 2 ) and is therefore predicted to be damaging by MutationTaster with a confidence probability of 0 . 99 ( SIFT = 0 . 00 , Polyphen-2 = 1 . 00 ) ( Fig . 2 ) ., The remaining nonsynonymous variant ( chr4:g . 47 , 901 , 476G>A , hg19 - rs12651301 ) was observed to occur across all the sequence pools with an estimated allele frequency of 32% ( Table 3 ) ., This common variant was also observed in independent European controls from public databases with a frequency of 31% ( Table 3 ) and falls outside of any protein motifs and is thus likely to represent a polymorphism ., The three rare variants identified ( rs151113647 , rs35139099 and chr4:g . 47 , 916 , 008G>A , hg19 ) were sequenced in all available family members of the SLIC proband in whom they were observed ( Fig . 3 ) ., The chr4:g . 47 , 916 , 008 variant was inherited from an affected father by two affected children and one child with typical language development ( Fig . 3 ) ., The rs151113647 variant was inherited from a father , who reports a history of language and literacy problems , by the proband , who attends a special language unit , and his sibling , who also has SLI ., The middle child in this family , who also showed evidence of expressive and receptive language deficits , did not inherit the variant ( Fig . 3 ) ., Two SLIC families carried the rs35139099 variant; in the first , the variant is present in the father , who self-reports a history of dyspraxia , and passed onto both the proband and her elder sib , each of whom has expressive and receptive language problems ., The youngest daughter in this family , who was observed to have a similar pattern of language deficits , did not inherit the variant ( Fig . 3 ) ., In the second family carrying the rs35139099 variant , the change was present in both the proband and his younger sib , who had expressive and receptive language scores ∼1SD below that expected for his age , indicating that it is inherited ( Fig . 3 ) ., The variant was not present in the mother and we did not have a DNA sample , or phenotypic data , from the father ., Nonetheless , haplotype analyses of genome-wide SNP data indicated that the two children shared the same paternal chromosome in this region indicating that the rs35139099 variant was likely inherited from the father ., A natural limitation of all studies of founder or isolated populations is the restricted size of the cohort ., Although our study represents a comprehensive profiling of the Robinson Crusoe child population , the total sample consisted of only 111 individuals , 100 of whom were founder-related and 49 of whom had language impairment ., Although it should be noted that the power of this particular sample lies in the close relationships between individuals rather than the absolute number of samples , the issue of sample sizes is especially pertinent when one is considering rare variations ., Thus it is of particular importance that we observed independent evidence implicating NFXL1 rare variants in another cohort ., However , in the absence of a large South American cohort of language-impaired individuals , we were unable to include the rs144169475 variant in our replication investigations ( since this SNP is particular to South American populations ) ., Thus , further studies of larger sample sizes that include language-selected controls and South American individuals will be required to fully evaluate the role of rs144169475 and rare NFXL1 coding variants in SLI susceptibility ., Of note , none of the shared variants identified through exome sequencing co-incided with regions of suggestive linkage reported in a previous genomewide linkage study of the Robinson Crusoe population ( S2 and S3 Tables ) 54 ., Nor did we find evidence for linkage to the NFXL1 region of chromosome 4 ( S4 Fig . ) ., We must therefore acknowledge that the increased frequency of rs144169475 in language-impaired individuals of the Robinson Crusoe validation cohort does not directly indicate pathogenicity ., The result may represent a chance finding or , alternatively , rs144169475 may be a proxy for the causal variant ., Since the exome sequencing performed did not capture 100% of the exome , it is possible that the causal variant was not detected here ., Full genome sequencing would be required to fully investigate this possibility ., However , it is also important to note that a lack of linkage does not preclude the presence of a causal variant and may instead reflect the complexities of analyzing a pedigree of this size and complexity 55 ., The pedigree , which explained the known relationships between the founder brothers and the Robinson Crusoe validation cohort , included 288 individuals ( 321 bits , where a bit is defined as twice the number of non-founders—the number of founders ) and so had to be broken into smaller sets for linkage analyses ., This segmentation process discards information and can reduce the power to detect linkage if individuals sharing the linked chromosome segment are split between sub-pedigrees 56 ., Lastly , since we hypothesize that SLI in this population has a complex genetic basis and involves incomplete and a high phenocopy frequency , it is possible that the power to detect linkage is insufficient ., We observed reduced penetrance at the NFXL1 locus ( of 25 variant carriers , 19 were diagnosed with SLI , penetrance of 76% ) in combination with evidence of a high phenocopy rate in our cohort ( of 49 individuals with language impairment , 19 carried the variant , phenocopy rate of 61% ) ., In combination , these factors break down the correspondence between genotype and phenotype , compromising the ability to detect linkage 57 ., In summary , the Robinson Crusoe admixed founder population represents a rare resource which may assist in the identification of genetic variants that contribute to SLI susceptibility ., Exome sequencing of five individuals from this population identified eight shared coding variants ., One of these variants ( rs144169475 ) was found to be significantly associated ( P = 0 . 0002 ) with language impairment in the wider Robinson Crusoe population ., rs144169475 confers a nonsynonymous change ( N150K ) in the NFXL1 gene at a highly conserved residue ., Subsequent sequencing of the NFXL1 coding regions in 117 independent UK SLI cases identified four individuals with rare heterozygous variants predicted to be of functional consequence ., We conclude that coding variants within NFXL1 confer an increased risk of SLI within a complex genetic model ., The work on the Robinson Crusoe Island was approved by the ethics department of the University of Chile ., Ethical permission for each SLIC collection was granted by local ethics committees ., Guys Hospital Research Ethics Committee approved the collection of families from the Newcomen Centre to identify families from the South East of England with specific language disorder ., Ref No . 96/7/11 ., Cambridge Local Research Ethics Committee approved the CLASP project “Genome Search for susceptibility loci to language disorders” Ref No ., LREC96/212 ., Ethical approval for the Manchester Language Study was given by the University of Manchester Committee on the Ethics of Research on Human Beings ., Ref No . 03061 The Lothian Research Ethics Committee approved the project “Genetics of specific language impairment in children in Scotland” for the use of the Edinburgh samples ., Ref ., No ., LREC/1999/6/20 ., The ethics department of the University of Chile approved the project “Genetic analysis of language-impaired individuals from the Robinson Crusoe Island” ., Project Number 001-2010 ., Informed consent was given by all participants and/or , where applicable , their parents ., The Robinson Crusoe cohort was ascertained on the basis of phenotypic data from 61 children , between the ages of 3 years and 8 years , 11 months ( i . e . the child cohort , described below ) all of whom were descendants of the founder families and represents an extended cohort ( including children who have turned 3 years of age since 2008 ) of that described in 33 ., First-degree relatives of founder-related children found to meet criteria for SLI or typical language development were then also assessed for language performance ( i . e . the family cohort , described below ) ., Age constraints of available standardized tests meant that different language batteries were employed within the child and family cohorts ., The language ability of 61 children , all of whom were related to a founder individual , was assessed by tests of expressive and receptive language ( Toronto Spanish Grammar Exploratory test , TEGE 58 ) and phonology ( Phonological simplification test ( Test para Evaluar Procesos de Simplificación Fonológica—TEPROSIF 59 ) ., Nonverbal IQ was tested using the Colombia Mental Maturity Scale 60 ., In addition , all children were subjected to an auditory screen and oral motor exam 61 ., All tests were validated and normalized in Chilean populations ., On the basis of these tests , all children were classified into one of the three following categories:, “Specific Language Impairment ) ” ( N = 16 , 7 male , 9 female , 26 . 2% ) defined as, ( i ) performance >2SD below expected on TEPROSIF ( for children aged 6 years or less ) or performance >2 years below expected for chronological age on TEPROSIF ( for children aged over 6 years ) and/or performance below the 10th percentile on either the receptive or expressive scales of the TEGE ,, ( ii ) nonverbal IQ not below the 10th percentile ,, ( iii ) normal hearing , oral motor skills and neurological development ., “Typical language development” ( N = 23 , 8 male , 15 female , 37 . 7% ) defined as, ( i ) performance not >2SD below expected on TEPROSIF or performance >2 years below expected for chronological age on TEPROSIF ( for children aged over 6 years ) and performance above the 10th percentile on both the receptive and expressive scales of the TEGE ., “Nonspecific language impairment” ( N = 22 , 13 male , 9 female , 36 . 1% ) defined as, ( i ) performance >2SD below expected on TEPROSIF or performance >2 years below expected for chronological age on TEPROSIF ( for children aged over 6 years ) and/or performance below the 10th percentile on either the receptive or expressive scales of the TEGE , and, ( ii ) nonverbal IQ >1SD below age-expected , and/or, ( iii ) evidence of hearing loss or oral motor disability ( e . g cleft lip ) or abnormal neurological development ., The observed language deficits in the individuals diagnosed with SLI were typical of those described in other SLI cohorts and involved varied deficits across grammatical , morphosyntactical and receptive aspects of language , but not dialectic variations in intonation , vocabulary or phonology ., Since we were particularly interested in genetic contributions to SLI , our family cohort consisted of the first-degree relatives of the 39 founder-related children presenting with SLI or typical language development ., All available first-degree family members ( 92 parents and siblings , 47 male , 45 female ) were assessed for language difficulties using tests of verbal fluency ( Barcelona test 62 ) and verbal comprehension ( Token test 63 ) ., These family members included 11 parents who were not related to a founder member of the Island ( referred to as non-founder-related parents ) ., In addition to these formal language assessments , all individuals ( or their parents or spouses ) completed a family history interview ( provided by P Tallal ) 64 , which specifically asks questions regarding language difficulties ., On the basis of these data individuals were classified as either:, “Language-impaired” ( N = 34 , 15 male , 19 female , 37 . 0% , including 4 non-founder-related parents ) if they scored below the 10th percentile on either the Barcelona test or the token test or they self-reported a need for writing or reading support at school or a history of language support in the family history questionnaire ., “Typical language ability” ( N = 58 , 32 male , 26 female , 63 . 0% , including 7 non-founder-related parents ) if they scored above the 10th percentile on both the Barcelona test and the token test and they indicated no requirement for writing , reading or language support in the family history questionnaire ., Five Islanders ( 3 male , 2 female ) from the child cohort who had been diagnosed with SLI were selected for exome sequencing ., The selection of individuals for sequencing was based upon the amount and quality of DNA available , the severity of observed language impairment and their known relationships with other affected individuals ., The five children were selected to cover the different branches of the founder pedigree and were descendants of the founder families ( Fig . 1 ) ., Exome capture was performed using 10μg of genomic DNA with a first generation ( v1 ) Agilent SureSelect human exome kit ( Agilent , Santa Clara , CA , USA ) , which provide an average target coverage of 80% of the exome at 56-fold across all samples ., Sequencing of the generated fragments was performed on the SOLiD 4 sequencer ( Life Technologies , Carlsbad , CA , USA ) ., Color space reads were mapped to the human reference genome ( hg18 ) in the SOLiD bioscope software ( v1 . 2 ) , which applies an iterative mapping approach ., Variants were called using a diBayes algorithm 65 using high stringency settings , requiring calls on each strand ., Small insertions and deletions were detected using the SOLiD Small Indel Tool ., We assumed a binomial distribution with a probability of 0 . 5 of sequencing the variant allele at a heterozygous position ., Given such a distribution , a minimum of ten reads would be required to provide a 99% probability that two or more reads contain an allele variant call ., We filtered variant calls to have at least four unique ( i . e . different start sites ) variant reads with the variant being present in at least 15% of all reads ., To test the hypothesis that the founder brothers carried a rare causative genetic mutation , for our downstream analyses , we focused upon novel variants that were potentially deleterious ., Each exome file was individually filtered to exclude nongenic , intronic ( other than canonical splice sites ) and synonymous variants ., The remaining nonsynonymous and splice-site mutations were further filtered to exclude known sites of variation ( as described in dbSNP , ( build 130 ) , publically available genome sequences and an in-house sequencing database ) ., The remaining variants were then compared across exome samples to allow the selection of variants that occurred in 3 or more of the 5 children sequenced ., A flow diagram of the methodology can be found in S1 Fig ., ., Shared novel , potentially deleterious variants discovered in the exome data were verified by Sanger sequencing ., Primers for Sanger sequencing were designed in primer3 66 ., Primer sequences are available on request ., All novel nonsynonymous or canonical splice-site variants found to occur in 3 or more of the 5 exome samples were also genotyped in the wider child and family cohorts from the Robinson Crusoe population ., We were able to obtain DNA samples for 35 founder-related children ( from the SLI and typical language development child groups described above ) and their family members ( from the family cohort described above ) ., Forty nine of these individuals ( 16 children , 22 parents ( 4 of whom were non-founder-related ) , 7 siblings and 4 half-siblings ) were language impaired and 62 ( 19 children , 32 parents ( 7 of whom were non-founder-related ) , 9 siblings and 2 half-siblings ) had language ability in the normal range ., These famili

Introduction, Results, Discussion, Materials and Methods

Children affected by Specific Language Impairment ( SLI ) fail to acquire age appropriate language skills despite adequate intelligence and opportunity ., SLI is highly heritable , but the understanding of underlying genetic mechanisms has proved challenging ., In this study , we use molecular genetic techniques to investigate an admixed isolated founder population from the Robinson Crusoe Island ( Chile ) , who are affected by a high incidence of SLI , increasing the power to discover contributory genetic factors ., We utilize exome sequencing in selected individuals from this population to identify eight coding variants that are of putative significance ., We then apply association analyses across the wider population to highlight a single rare coding variant ( rs144169475 , Minor Allele Frequency of 4 . 1% in admixed South American populations ) in the NFXL1 gene that confers a nonsynonymous change ( N150K ) and is significantly associated with language impairment in the Robinson Crusoe population ( p = 2 . 04 × 10–4 , 8 variants tested ) ., Subsequent sequencing of NFXL1 in 117 UK SLI cases identified four individuals with heterozygous variants predicted to be of functional consequence ., We conclude that coding variants within NFXL1 confer an increased risk of SLI within a complex genetic model .

Children affected by Specific Language Impairment ( SLI ) have unexpected problems learning to talk and understand language , despite developing normally in all other areas ., This disorder runs in families but we do not understand how the genetic contributions work , or which genetic mechanisms might be important ., In this paper , we study a Chilean population who are affected by a high incidence of SLI ., Such populations may provide increased power to discover contributory genetic factors , under appropriate conditions ., We identify a genetic change in the population that causes a change to a protein called NFXL1 ., This change is usually very rare but is found at a higher frequency than expected in our population , particularly in those people affected by SLI ., We then looked at this gene in over 100 individuals from the UK affected by SLI and found four more changes that probably affect the protein ., This is a higher number than we would expect by chance ., We therefore propose that the NFXL1 gene and the protein it encodes might be important in risk of SLI .

journal.pbio.1000043

The Structural Basis of Gas-Responsive Transcription by the Human Nuclear Hormone Receptor REV-ERBβ

The closely related REV-ERBα and REV-ERBβ proteins generally act as transcriptional repressors , either on their own , by recruiting co-repressor proteins 1–3 , or by competing with the Retinoid-related Orphan Receptors ( RORs ) α , β , or γ for the same DNA binding sites 4–6 ., Physiologically , the REV-ERB proteins play a number of diverse and important roles ranging from the control of circadian biology to the homeostasis of lipids ., REV-ERBα and β directly regulate circadian rhythm , both in the brain , and in peripheral tissues , by targeting the circadian clock genes Bmal1 and clock 6–11 ., Regulation of lipid metabolism and stimulation of adipogenesis by the REV-ERBs is mediated in part through repression of the apolipoprotein A1 ( ApoA1 ) and apolipoprotein C3 ( ApoCIII ) gene promoters , which play major roles in cholesterol metabolism 12–14 ., REV-ERBs also control inflammatory responses by inducing nuclear factor kappa-light-chain-enhancer of activated B cells ( NFκB ) , interleukin-6 ( IL6 ) , and cyclooxygenase2 ( COX2 ) expression , and by repressing IκBα expression 15 , 16 ., In the liver , REV-ERBs also help regulate gluconeogenesis , consistent with an overall role in energy storage and conservation 17 ., In Drosophila , the REV-ERB homologue , Ecdysone-induced protein 75 ( E75 ) is best known for its role in developmental timing , acting together with the ROR orthologue Drosophila Hormone Receptor 3 ( DHR3 ) to control ecdysone-induced molting , pupariation , and eclosion 18 ., To perform these functions , E75 appears to require heme as a requisite ligand , bound presumably within its ligand-binding domain ( LBD ) ligand pocket ., E75 is not stable in the absence of heme , nor does the bound heme dissociate readily , suggesting that heme is an obligate component of E75 ., Interestingly , the presence of heme allows E75 to also bind the diatomic gases nitric oxide ( NO ) and carbon monoxide ( CO ) , which function to reverse E75-mediated transcription repression 19 ., The REV-ERBs also bind heme but , unlike E75 , the heme can readily dissociate from the REV-ERBs , and this reversible binding regulates REV-ERB transcription activity 17 , 20 ., The REV-ERB LBDs do not appear to share the ability to bind gases or to respond to redox 20 , raising the possibility that E75 and REV-ERBs have evolved two different ways to exploit heme-binding ., The structural basis for heme binding in REV-ERB proteins , and heme and gas binding in E75 , is unknown , although mutagenesis and transcription studies have implicated conserved histidine and cysteine residues in heme binding ., A crystal structure of the REV-ERBβ LBD in the absence of heme has revealed a classic nuclear receptor ( NR ) fold , but the mechanisms of heme binding could not be deduced from the structure , because the conserved histidine residue points away from the putative ligand-binding pocket and the conserved cysteine residue was not present in the construct that generated the structure ., Moreover , the putative pocket is fully occupied by hydrophobic side chains 21 ., The extensive structural similarities between the REV-ERB and E75 LBDs coupled with the apparent mechanistic differences prompted us to explore more deeply the basis for both heme binding and the potential for gas regulation ., The potential involvement of gases in circadian rhythm has been noted in physiological studies for some time , but the molecular mechanisms only began to emerge with the finding that Neuronal PAS domain protein ( NPAS2 ) , a CLOCK protein analog , is a hemoprotein 22–24 ., NPAS2 and CLOCK heterodimerize with Brain and Muscle Arnt-like protein-1 ( BMAL1 ) to activate transcription of various genes , including the molecular clock components Period , Cryptochrome , and Rev-erb 25–29 ., The binding of CO to the NPAS2-heme complex , in vitro , inhibits its binding to BMAL1 and DNA binding 23 ., Using transcription assays from native and model promoters in cells , we report that the REV-ERBs are also gas-binding components of the molecular clock and that gas and redox state modulate the structure and function of the REV-ERB LBDs ., Using crystallography and spectroscopy , we determined the structure of the heme-bound REV-ERBβ LBD from which we are able to propose a model for heme and gas binding ., Overexpression of either of the REV-ERB LBDs in Escherichia coli produces apo forms of the repressors ., The heme-bound form is produced only if the culture medium is supplemented with hemin ( Figure S1 ) ., While REV-ERBs expressed with and without hemin supplementation appear to be equally abundant and soluble ( Figure S1 ) , they differ strikingly in color , with the heme-bound form intensely red , and the apo form colorless ., Incubation of either purified REV-ERBα or β apo LBD with hemin in solution , results in full heme occupancy within seconds or less ( unpublished data ) , while washing the heme-bound LBDs with buffer lacking heme leads to a much slower release of the bound heme ( T1/2 ∼13–16 h; Figure S2 ) ., Overall a Kd of approximately 6 μM was observed ( unpublished data ) ., Thus , unlike E75 , where heme appears to act as a requisite structural component , in the REV-ERBs it can function potentially as a reversible ligand ., Spectroscopic evidence has suggested that the coordination of heme in E75 involves a cysteine residue , and mutagenesis has further suggested that this thiolate bond is contributed by Cys396 ( JR , unpublished data ) 30 , which corresponds to Cys418 in REV-ERBα and Cys384 in REV-ERBβ ., Mutagenesis of E75 has also suggested that the second protein-heme coordinate bond is provided by the side chain of His574 ( His602 in REV-ERBα; His568 in REV-ERBβ ) 19 , 30 ., Consistent with this finding , the mutation of His602 of REV-ERBα ( His568 in REV-ERBβ ) to phenylalanine essentially eliminates heme binding 17 , 20 ., Our mutagenesis of REV-ERBβ confirms that Cys384 and His568 are both key mediators of heme binding ( Figure 1 ) , although the His568 mutant of REV-ERBβ did maintain some heme-binding activity compared with the His602 mutant of REV-ERBα 17 , 20 ., In our REV-ERBβ analysis , the levels of bound heme decrease in each of the purified Cys384 and His568 mutant proteins , but neither of the single mutations to alanine , nor the Cys384A/His568A double mutation , completely eliminate heme binding ( Figure 1 ) ., These differences may be attributable to the different choices for amino acid substitution ( Ala versus Phe ) , or other differences between the two proteins ., Thus , residues in addition to these particular Cys and His residues must also contribute to heme binding ., The structures of other heme-containing transcription factors are modulated by redox state 23 , 31–33 ., To investigate the effects of redox state on the interaction of REV-ERBs with heme , we subjected the heme-bound proteins to electronic absorption spectral analysis ., The heme in coordinately bound proteins absorbs at characteristic wavelengths , producing what are referred to as α , β , and γ ( or Soret ) heme absorption peaks ., The existence , positions , and sizes of these peaks provide insight into the oxidation and spin states of the iron center and the number and types of coordinate bonds formed ., The oxidized Fe ( III ) REV-ERBβ LBD yielded an absorption spectrum ( Figure 2A ) that was almost identical to those produced by aerobically purified Drosophila E75 19 and the bacterial thiolate-heme Fe ( III ) -containing transcription factor CooA 31 ., All three proteins exhibit characteristic α ( ∼575 nm , shoulder ) , β ( 542 nm ) , and γ ( 419 nm ) absorption peaks as well as a prominent δ-band ( 359 nm ) ; altogether , these are indicative of a hexa-coordinated heme bound to at least one thiolate ( e . g . , Cys ) group 30 ., Upon subjecting the REV-ERBβ LBD to the reducing agent sodium hydrosulphite ( dithionite ) , the resultant shifts in absorption peaks ( Figure 2B ) indicate reduction of the iron center from Fe ( III ) to Fe ( II ) , and loss of the thiolate coordinate bond ( diagrammed in Figure 3 ) ; this reduction is seen most readily by the loss of the δ-band ( 359 nm ) ., We have also reported elsewhere 34 , using magnetic circular dichroism and resonance Raman spectroscopy , that reduction of the REV-ERBβ iron center appears to yield both a 5-coordinated system , with a single neutral residue coordinate bond ( e . g . , His or Pro ) , as well as a 6-coordinated system with two neutral residue bonds ( Figure 3 ) ., Thus , the reduced REV-ERB-heme complex comprises at least two structural states in which the heme-coordinating amino acid side chains change ., This form of redox-regulated coordinate bond switching is not unique to REV-ERBs ., For example , similar side chain-switching states have been observed in CooA ( Cys75 to His77 ) , a bacterial CO-responsive heme thiolate protein transcription factor 35–37 , and in NPAS2 ( Cys170 to His171 ) , whose axial coordinate bonds are also different in the Fe ( III ) and Fe ( II ) states 23 , 33 , 38 ., In these transcription factors , these redox-dependent structural changes also result in functional changes for their host proteins ., Redox was also shown to modulate E75 coordinate bonding and function 19 , but it remains to be seen if the redox-dependent structural changes in the REV-ERBs also have analogous functional consequences ., As pointed out earlier , many coordinately bound heme proteins , including proteins such as hemoglobin , cytochromes , and the transcription factors CooA and NPAS2 , have the added ability to bind gases 23 , 31 , 37 ., This ability is also the case for the REV-ERB insect orthologue E75 19 ., As a first step in assessing the gas-binding potential of REV-ERB proteins , the reduced REV-ERBβ LBD was incubated with CO or NO gas and analyzed for characteristic changes in the electronic absorption spectra ., The altered electronic absorption profiles confirm direct binding of both diatomic gases to the heme-bound forms of both REV-ERB LBDs ., When incubated with either NO or CO , the reduced ( but not oxidized ) REV-ERBβ LBD exhibits classic shifts in the absorption peaks ( Figure 2C , 2D ) similar to those seen in other gas-bound hemoproteins 31 , 39 , 40 ., More detailed studies of the heme in REV-ERB LBDs 34 have shown that the NO and CO gases bind opposite to a neutral side chain in a 6-coordinated state ( Figure 3 ) ., Thus , the heme-binding data reported previously 17 , 20 and the spectroscopic data reported here and in Marvin et al . 34 reveal that REV-ERBs comprise modular ligand systems that can adopt a minimum of six different LBD structural states ( Figure 3 ) , each of which has the potential for distinct functional interactions , transcriptional outputs , and biological roles ., Among the many genes targeted for repression by the REV-ERB proteins are their own genes and the clock gene Bmal1 ., To test the effects of NO or CO on the activities of REV-ERBα and β in vivo we monitored transcription levels of the endogenous Bmal1 and Rev-erb genes in human embryonic kidney ( HEK ) 293T and hepatocellular carcinoma ( HepG2 ) cells in response to gas ., We first confirmed that transcription from these genes is regulated by the REV-ERBs by measuring their transcription in the presence and absence of Rev-erb small interfering RNA ( siRNA ) ., Figure 4A shows that each of the Rev-erb siRNAs specifically targets its corresponding Rev-erb gene ., As expected , the knock-down of either Rev-erb gene results in an increase in Bmal1 expression ( 1 . 5–2-fold; Figure 4A ) , due presumably to derepression of ROR-mediated transcriptional activation ., Effects were readily observed within 12 h of siRNA treatment , and peaked at 72 h post-treatment ., We then asked whether NO , supplied by the chemical donor diethylenetriamine/NO ( Deta/NO ) , relieves REV-ERB-mediated repression of the endogenous Bmal1 , Rev-erbα , and Rev-erbβ genes ., Addition of Deta/NO increased levels of Bmal1 , Rev-erbα , and Rev-erbβ mRNAs by 2–3-fold ( Figure 4B ) ., The addition of Li2+ , which has been shown to cause REV-ERB degradation via inhibition of Glycogen Synthase Kinase-3 β ( GSK3β ) kinase-mediated phosphorylation 41 , led to a similar derepression of the endogenous Rev-erb and Bmal1 target genes ., Importantly , combining NO and siRNA treatments did not have additive effects , suggesting that NO acts via derepression of the REV-ERBs and not via a parallel pathway ( Fig 4C ) ., NO-dependent transcription was also observed in HepG2 cells , with the exception that NO-mediated upregulation of ROR/Rev-erb target gene expression was only ∼2-fold ( Figure S3 ) ., This may reflect lower levels of available heme in this cell type , as heme levels vary significantly in different cell types and in other cell states 42 , 43 ., Analogous studies conducted in the presence of 500 ppm CO , yielded only modest changes in REV-ERB target gene expression ( unpublished data ) ., This minimal response may be due to differences in affinity or effectiveness between NO and CO in cells , or to differences in the effectiveness of the different gas delivery protocols used ( see Methods ) ., To provide evidence that NO acts as a direct regulator of heme-bound REV-ERB proteins , the LBDs of either REV-ERBα or β were fused to the DNA-binding domain of yeast GAL4 , and their activities tested using a luciferase reporter regulated by a thymidine kinase promoter containing upstream activating sequences ( UAS ) GAL4 binding sites ., As expected , co-transfection of the GAL4-REV-ERBα or GAL4-REV-ERBβ fusion proteins repressed transcription driven by the UAS-containing thymidine kinase promoter ( Figure 4D ) ., This repression was reversed by greater than 3 . 5-fold by the addition of either of two NO donors , Deta/NO ( Figure 4D ) or S-nitroso-N-acetyl-l , l-penicillamine ( SNAP ) ( unpublished data ) , suggesting that the REV-ERBs are direct targets of NO ., As earlier , similar studies with 750–2 , 000 ppm CO had a more modest effect ( ∼15% of NO effect; unpublished data ) ., In summary , both REV-ERB proteins are transcriptional repressors whose activities can be reversed by NO binding ., To determine if the heme and gas effects on REV-ERB activity might be attributable to the recruitment of co-repressor proteins , GAL4-REV-ERBα and GAL4-REV-ERBβ fusion proteins were co-transfected with full-length co-repressor expression constructs into 293T cells ., As expected , addition of the known REV-ERB co-repressor Nuclear Receptor Co-repressor ( NCOR ) to GAL4-REV-ERB transfection assays increases repression by 2–3-fold ., Similar results were obtained by coexpression with another co-repressor , Receptor Interaction Protein 140 ( RIP140 ) , which has not been previously tested for REV-ERB binding ( Figure 5A , 5B ) ., This augmented NCOR or RIP140-mediated repression is reversed by the addition of Deta/NO ( Figure 5A , 5B ) ., Similar reductions in Gal4-REV-ERB/co-repressor mediated repression were obtained by treating the transfected cells with valproic acid , which is an inhibitor of the histone deacetylases that are recruited by NCOR and RIP140 44–46 ., We conclude that NO signaling reduces REV-ERB repression activity , at least in part , by overcoming the recruitment or activities of these co-repressors ., The effects of heme-binding on REV-ERB function are unclear ., Previous studies have shown that the availability of heme negatively affects the ability of REV-ERB proteins to bind co-repressor peptides in vitro but is required for the REV-ERB proteins to interact functionally with co-repressors in vivo 17 , 20 ., To explain this finding , it has been suggested that co-repressor interactions in vivo must be modulated by interactions or conditions that are not reflected in experiments with purified components ., To test if the in vitro interactions could be influenced by gas , we used fluorescence polarization to follow the recruitment of peptides corresponding to the LXXI/HIXXXI/L interaction domain I ( IDI ) of NCOR ( Figure 6 ) and Silencing Mediator for Retinoid and Thyroid hormone receptor ( SMRT ) ( unpublished data ) in the absence and presence of heme and gas ., As expected , based on the previous study , both peptides interact specifically with the REV-ERBα and β LBDs ( Figure 6 ) , and the addition of heme acts negatively on co-repressor peptide binding ., As might also be expected , only the heme-bound form of the REV-ERB LBDs are responsive to the addition of NO gas ( Figure 6A–6C and unpublished data ) ., As with heme binding though , this effect is the opposite of that which occurs in vivo , with NO acting to increase co-repressor peptide recruitment , rather than blocking it ., We can only conclude , as did Yin et al . 17 , that interactions or conditions that exist in the cell , are not reflected in the in vitro system ., To shed light on the structural basis of heme , gas , and redox regulation , we crystallized the REV-ERBβ LBD in the heme-bound state ., The formation of well-ordered crystals required the addition of trypsin to the crystallization solution 47 ., Two identical structures were obtained using constructs comprising either the complete LBD ( residues 212–579 ) or the LBD with an internal deletion ( residues 241–579 Δ 275–357 ) ., Both 1 . 9 Å resolution structures include α-helices 3–11 , ( residues 381–576; REV-ERBβ381–576 ) , which is slightly larger than the fragment used to derive the unliganded LBD structure 21 ., Both of the crystals were obtained under nonreducing conditions ., The two REV-ERBβ381–576 Fe ( III ) heme structures verify that the heme-binding pocket is in fact present at the same position as ligand-binding pockets observed in other NR family members ( Figure 7B ) ., As predicted by the mutagenesis and spectroscopic analyses for the oxidized state of REV-ERB , a single heme molecule is hexa-coordinated within the pocket by Cys384 and His568 side chains ., As mentioned above , Cys384 was not included in the previously published unliganded receptor structure constructs 21 ., The structural changes that facilitate heme binding are confined primarily to helices 3 , 7 , and 11 ., In the absence of heme , helix 3 breaks at Pro411 allowing its N-terminal portion to move into the unliganded pocket ( Figure 7A ) 21 ., A number of aromatic residues from this helix face into the pocket , contributing substantially to the hydrophobic core that stabilizes the unliganded structure ., In the presence of heme , helix 3 straightens , swinging the end of its N-terminal half ( Cα atom Gly398 ) 16 . 4 Å away from its position in the unliganded structure ( Figure 7B ) ., Although helix 7 shifts in a less dramatic manner , the 3 . 0-Å movement ( Cα atom Leu482 ) further increases the pocket volume ., The movement of residues 480–483 , in particular , allows heme and its propionate side chains to assume their observed planar orientation ( Figure 7C ) ., In the absence of heme , helix 11 shields the hydrophobic core by bridging helices 10 and 3 ., To facilitate heme binding , it also undergoes a major conformational change , swinging its C terminus ( Cα atom Leu576 ) 15 Å away from the ligand-binding pocket , forming a gently curving , uninterrupted α-helix that covers the ligand pocket ( Figure 7A , 7B ) ., The unprecedented formation of LBD-ligand coordinate bonds involves some equally novel and elegant structural changes ., First , the imidazole side chain of His568 makes a ∼120° rotation around the axis of the helix to allow bonding with the Fe ( III ) heme center ( Figure 7D , 7E ) ., Cys384 , the other coordinate bond-forming residue in this Fe ( III ) structure , derives from a flexible loop N-terminal to helix 3 , which does not appear in the apo structure ( Figure 7D , 7E ) ., In addition to opening the pocket and correctly positioning the two heme-coordinating residues , the newly positioned helices and loop also help to shield the hydrophobic heme moiety from the solvent , with only 8% ( 66 Å2 ) of the ligand exposed ., This value falls well within the expected normal range of 1%–28% for hemoproteins 48 ., The majority of residues surrounding heme in the pocket stabilize heme binding via van der Waal interactions ., Within 4 Å of the heme moiety are 25 residues ( Table S1 ) derived from five different regions of the REV-ERBβ secondary structure ( H3 , H5 , H7 , and H11 , and loop N-terminal to H3 ) ., The majority of these residues form the core of the apo-structure 21 , and must swing out and away to facilitate heme binding ( Figure 8A ) ., In other hemoproteins the residues forming hydrophobic heme contacts include Ile , Leu , Val , Phe , Trp , and Tyr 48 ., The REV-ERBβ pocket is also enriched with these residues , along with six phenlyalanines ( Table S1 ) ., With two exceptions , all of these residues are conserved in REV-ERBα and among all the vertebrate REV-ERBβ orthologues ., Although these contacts occur all around the heme ligand , Trp402 , Phe405 , and Phe454 are striking examples of how van der Waal radii of the protein side chains and heme can interlock ( Figure 8A ) ., Taken together , these precisely fitted hydrophobic contacts must contribute significantly to the strength and specificity of heme binding ., Aside from Cys384 and His568 , the only other polar residues within 4 Å of heme are His381 and Glu571 , and while at this point their role is undetermined , their presence in an otherwise nonpolar environment , and their conservation in other REV-ERBβ homologues , suggests a functional role ( Figure 8A , Table S1 ) ., Ligand -binding specificity in many NR ligand-binding pockets often involves hydrogen bonding between polar group ( s ) on the ligand and charged residue ( s ) of the LBD ., The most common polar interaction in the NR pocket is with an arginine side chain that precisely orients ligands to ensure specificity 49 ., While this Arg is conserved in the REV-ERB LBDs , neither it nor any other Arg residue faces the ligand-binding pocket in the apo or liganded forms 21 ., Glu571 , however , is positioned 3 . 8 Å from the negatively charged propionate groups of heme ( Figure 8A ) ., This is unusual because the carboxy termini of heme propionate groups usually interact with positively charged residues such as Arg or Lys 48 ., It may be possible that the negatively charged propionate side chains are repelled by the acid group of Glu571 in a way that helps to properly center the heme group , or perhaps helps to facilitate exchange ., Interestingly , in REV-ERBα and E75 the analogous residue is lysine , which would be predicted to attract the carboxy termini of the heme molecule , as observed in other heme-binding proteins ., The other polar residue within close proximity to the heme group is His381 , which is close to the heme coordinating Cys384 residue , and is highly conserved throughout vertebrate REV-ERBβ homologues ( Figure 8A ) ., Given the spectroscopic data , which suggest switching of coordinate bonds from a Cys to a neutral residue such as histidine upon heme reduction , His381 is a good candidate for this substituting residue ., Indeed coordinate bond switching in other heme proteins tends to involve nearby residues 33 , 35 ., Interestingly , within this loop there are three other His residues that may also be capable of coordinate bond formation ., All three are also within HXXC motifs ( Figure S4 ) , which serve as metal binding sites in the unstructured loops of olfactory receptors 50 and other hemoproteins ., Alternative switching between these Cys/His residues has the potential to ratchet the loop peptide along the plane of the heme molecule , and to reshape the external LBD surface into novel protein interaction sites ., Also worth noting is that the residue next to the coordinately bound Cys384 is a proline ( Pro385 ) ., This highly conserved Cys-Pro duo fits a consensus for “heme regulatory motifs , ” which also include flanking residues such as His , Leu , Val , Met , Lys , Arg , and Asp 51–53 ., This heme regulatory motif in REV-ERBβ includes six of those seven residues ( Figure S4 ) ., Such motifs have been shown to be capable of binding heme reversibly with low micromolar affinity ., Mutational analyses of the corresponding prolines in other heme thiolate proteins suggest that these residues help to direct the Cys residue toward the heme moiety , as well as to contribute to the reversibility of Cys-heme binding 54–56 ., The Drosophila E75 LBD is a notable exception to this reversibility , although this may be explained by the presence of a second heme binding cysteine ( Cys468 ) that is not flanked by a proline and has no counterpart in the REV-ERBs 19 , 30 ., A final consideration based on this structure is how the NCOR and SMRT co-repressor peptide-binding site on the LBD surface changes upon the addition of heme ., Heme binding appears to affect the previously characterized co-repressor binding site in two ways ., First , the hydrophobic groove becomes broader ., Second , helix 11 , at the base of the groove , swings away from the binding site ., This ligand-dependent movement of H11 from the co-repressor binding site supports the notion that H11 serves as a proxy for the missing H12 , which in other NRs would serve as a platform for co-repressor binding 21 ., Both of these heme-induced changes are predicted to impact negatively on co-repressor binding ., It is interesting to note that the helices that show the greatest movement upon heme binding are those that border the co-repressor binding groove H3–5 , H10 , and H11 ( Figures 7 and 8 ) 57–60 ., A number of specific REV-ERBβ residues are critical for co-repressor binding , and have been identified previously 60 ., Examples include residues from H11 , which are in position to form a number of critical co-repressor contacts in the apo form ( L572 , F575 , K576 ) but that are shifted dramatically in position by movement of the helix , making them unlikely to maintain these interactions ( Figure 8B , 8C ) ., Likewise in H3 , F409 , which has also been identified as essential 60 , shifts from presumably holding H11 in position for co-repressor interaction to becoming a hydrophobic contact for heme ., K414 of H3 also appears to make a critical shift that leads to widening of the hydrophobic peptide-binding groove ., At either end of the hydrophobic groove , there are also charged residues ( K421 , R427 , and E570 ) that have been predicted by modeling to play important roles in anchoring the NCOR peptide 21 ., Two of these three residues , R427 and E570 , shift dramatically away from the co-repressor binding groove in the heme-bound form ( Figure 8B , 8C ) 21 ., Notably , hydrophobic vinyl and methyl groups from the heme moiety also extend to the surface of the groove close to the region where H11 was positioned ., While this does not appear to provide interference , it does indicate the possibility for heme to either interact or interfere with co-repressor binding under different conditions ., These alterations in the co-repressor binding site are consistent with the effects of heme on peptide binding in vitro ., Presumably , disruption of one of the coordinating heme ligands by NO would restore peptide binding by relieving the strain imposed on the LBD by the hexa-coordination of heme ., Changes to the structure of the binding site cannot , however , explain why heme and the presence of NO have the opposite effects in vivo ., The answer to this apparent paradox will most likely require structural analyses under different conditions , in the presence of other REV-ERB or co-repressor protein domains , or with other known or unknown cofactors ., Over 20 different protein folds can specifically bind b-type heme , which is the most abundant of the hemes and serves as the functional group for essential proteins such as hemoglobin , myoglobin , and cytochrome b5 ., Under different evolutionary constraints and pressures , these various heme-binding folds have adopted additional functional properties , which include electron transfer , redox sensing , and the sensing or transport of various gases 48 ., The REV-ERBβ381–576/heme structure adds a new and highly dynamic representative to the heme binding-fold family ., The molecular volume of heme ( ∼520 Å3 ) is relatively large in comparison to most other NR ligands ., Hence , the conformational changes that allow entry and occupancy of the apo LBD pocket are considerable ., Such structural plasticity has been observed for an increasing number of NRs ( e . g . , Ecdysone Receptor ECR 61 , Liver X Receptor LXR 62 , and Estrogen Receptor ER 63 ) ., This plasticity is an important point , as it indicates the potential for other “orphan” receptors , with seemingly inadequate ligand-binding pockets , and “constitutive” activities , to also be regulated by novel small molecule ligands within their various natural in vivo environments ., As with many other heme-containing proteins , which include E75 19 , both REV-ERB proteins are also able to monitor redox state and to bind gases ., E75 and the REV-ERBs are unusual however , in that while discriminating against O2 , they are able to bind both NO and CO gases in vitro ., Although the CO gas responses observed in vivo were much weaker than those observed for NO , this may be a consequence of the different methods of gas delivery used , or differences in the cellular functions and biochemistry of the two gases ., The different kinetics of gas and heme binding to the REV-ERB LBDs , and the different rates at which these molecules are produced and metabolized within the body , suggest that these ligands may have different physiological roles in different tissues ., Gas and redox exchange observed in vitro occurs within seconds , whereas heme exchange requires many hours ., In the body , changes in redox and gas levels can be rapid 32 , 64 , whereas heme levels oscillate over hours or days 42 , 43 ., It may also be of relevance that heme exchange does not appear to be possible for the fly orthologue E75 , such that the levels of E75 accumulation in the cell are dependent on the abundance of available heme 19 ., Thus , while both E75 and REV-ERB proteins may function as heme sensors , REV-ERBs appear to have the added ability to function in the absence of heme ., Although we also attempted to capture the structure of REV-ERBβ in reduced Fe ( II ) and gas-bound states , and were able to derive crystals , the latter diffracted poorly due possibly to the predicted multiplicity of Fe ( II ) coordinate bond isoforms ( Figure 3 ) ., This heterogeneity would be consistent with our spectroscopic analyses , and those of Marvin et al . 34 , which suggest that the Cys384-heme coordinate bond is replaced in the Fe ( II ) population by one of several alternative neutral donors ., It is tempting to speculate that His381 , which is conveniently positioned just N-terminal to Cys384 , may serve as one of these residues ., In fact , the ∼133 residue loop between helices 1 and 3 ( Figure S4 ) contains at least 23 residues that could coordinate heme ( nine His residues , seven Met residues , and seven Cys residues ) ., This abundance of His , Met , and Cys residues is around three times their general frequency in the human proteome ., There are also three more histidine residues ( His395 , His399 , and His475 ) surrounding the ligand-binding pocket that could serve as alternate binding partners ., If any of these residues do in fact form alternative coordinate bonds , this would lead to an additional and unprecedented number of LBD conformational and functional variants ., In terms of how heme and gases affect REV-ERB LBD functions , our results suggest a major role for both ligands in co-repressor recruitment ., The presence of heme leads to significant broadening of the co-repressor-binding groove and a highly unfavorable redistribution of interacting residues , consistent with the dramatic drop in co-repressor peptide binding observed in vitro ., Addition of NO to the heme-bound LBD reverses the negative effect of heme on peptide binding , suggesting t

Introduction, Results, Discussion, Methods

Heme is a ligand for the human nuclear receptors ( NR ) REV-ERBα and REV-ERBβ , which are transcriptional repressors that play important roles in circadian rhythm , lipid and glucose metabolism , and diseases such as diabetes , atherosclerosis , inflammation , and cancer ., Here we show that transcription repression mediated by heme-bound REV-ERBs is reversed by the addition of nitric oxide ( NO ) , and that the heme and NO effects are mediated by the C-terminal ligand-binding domain ( LBD ) ., A 1 . 9 Å crystal structure of the REV-ERBβ LBD , in complex with the oxidized Fe ( III ) form of heme , shows that heme binds in a prototypical NR ligand-binding pocket , where the heme iron is coordinately bound by histidine 568 and cysteine 384 ., Under reducing conditions , spectroscopic studies of the heme-REV-ERBβ complex reveal that the Fe ( II ) form of the LBD transitions between penta-coordinated and hexa-coordinated structural states , neither of which possess the Cys384 bond observed in the oxidized state ., In addition , the Fe ( II ) LBD is also able to bind either NO or CO , revealing a total of at least six structural states of the protein ., The binding of known co-repressors is shown to be highly dependent upon these various liganded states ., REV-ERBs are thus highly dynamic receptors that are responsive not only to heme , but also to redox and gas ., Taken together , these findings suggest new mechanisms for the systemic coordination of molecular clocks and metabolism ., They also raise the possibility for gas-based therapies for the many disorders associated with REV-ERB biological functions .

Much of human biology , such as sleeping , eating , and even the prevalence of heart attacks , occurs in daily cycles ., These cycles are orchestrated by a master “clock” located in the brain ., The basic components of this clock are proteins that control the expression of important genes ., In this study , we analyze one of these regulatory proteins , named REV-ERB , and show that it is regulated by the combination of heme and nitric oxide gas , both of which are important regulators of human physiology ., By determining the 3-D structure of the REV-ERB protein , we were able to uncover clues as to how this regulation occurs ., REV-ERB belongs to a protein family called nuclear hormone receptors , which are known to be excellent drug targets ., Thus , this paper opens the door to possible gas-based therapies for diseases known to involve REV-ERB , such as diabetes , atherosclerosis , inflammation , and cancer .

biochemistry, cell biology, diabetes and endocrinology, biophysics, neuroscience

The heme-regulated nuclear hormone receptor REV-ERB is one of the core transcription factors regulating circadian rhythms. We found that transcription by heme-bound REV-ERB is regulated by NO gas, and a crystal structure of the heme-bound protein uncovers the basis for heme and gas binding.

journal.ppat.1003634

NADPH Oxidase-Driven Phagocyte Recruitment Controls Candida albicans Filamentous Growth and Prevents Mortality

Candida albicans is a ubiquitous commensal fungus and a clinically important opportunistic pathogen of humans ., C . albicans is pleomorphic and grows in both yeast and filamentous forms , permitting growth in different environments , tissue invasion , dissemination and immune evasion 1–3 ., Dimorphic switching is governed by myriad signals in vitro and is co-regulated with virulence factors; the links between dimorphism and pathogenesis in vivo are further complicated by the complexity of signals and the potential for immune control of differentiation 4–6 ., Immunodeficiencies centered on either the innate or adaptive immune systems predispose for a number of opportunistic fungal infections with Candida spp ., 7 , 8 ., Lack of phagocyte NADPH oxidase ( Phox ) components causes chronic granulomatous disease ( CGD ) , a rare immunodeficiency associated with susceptibility to bacterial and fungal pathogens 9 ., Over 45 years ago , the specific cellular defect in CGD was determined to be an inability of CGD leukocytes to mount a respiratory burst and kill microbes upon in vitro stimulation , yet this may not explain why CGD patients suffer from symptoms beyond susceptibility to acute infection , such as hyperinflammation and B-cell deficits 9–11 ., Reactive oxygen species ( ROS ) produced during the respiratory burst are highly toxic to pathogens in vitro 12 , 13 , but it is now appreciated that ROS also impact many signaling pathways and cellular processes 9 , 14 ., Notably , both the phagocyte oxidase Phox and the dual-specific NADPH oxidase ( Duox ) have been implicated in promoting chemotaxis to specific stimuli and/or sites of inflammation , although it is not clear if either has a role in phagocyte recruitment to sites of infection 15–19 ., NADPH oxidases are important for immunity to many pathogens , although their roles in protection against C . albicans are not clear-cut ., Most in vitro experiments suggest Phox is important for killing C . albicans , and CGD mice are more susceptible to candidemia in the tail vein injection model 20 ., Other work suggests a more nuanced role for Phox , as candidemia is a rare cause of death in CGD patients 7 , 21 , Phox is not absolutely required for control of infection in the mouse 22 , and other phagocyte weapons can contain C . albicans both in vivo and in vitro 23–26 ., The nematode model of mucosal candidiasis suggests that Duox can also play an important role in protection , although this has yet to be tested in mammals 27 ., Current in vitro and in vivo models have not yet integrated these disparate data to explain the in vivo role ( s ) of NADPH oxidases in control of candidiasis ., The emerging larval zebrafish model provides a unique and powerful platform to discern how the in vitro activities of pleiotropic molecules such as ROS translate into in vivo roles during infection 28–30 ., We recently showed that a larval model of disseminated candidiasis shares key aspects of mammalian disease 31 ., We performed extended intravital imaging of live zebrafish to show that macrophages can inhibit germination of yeast into hyphae in vivo ., Additionally , we found that the phagocyte oxidase is important in limiting filamentous growth in vivo 31 ., Here we link these two observations to show that NADPH oxidase-dependent recruitment of phagocytes limits filamentous growth because it ensures that C . albicans is phagocytosed efficiently and is thus prevented from germination ., We demonstrate that both Phox and Duox are required for efficient phagocyte recruitment , phagocytosis , limiting filamentous growth , and survival ., We find that early immune recruitment is a strong and reliable indicator of eventual infection clearance ., We also implicate the EDT1-dependent dimorphic switching pathway in modulating both fungal containment and virulence of extracellular fungi ., These data identify a new dimension of NADPH oxidase-mediated immunity that strongly impacts fungal dimorphism in the host setting ., We recently provided the first demonstration of an in vivo role for the phagocyte NADPH oxidase in limiting filamentous growth of C . albicans 31 ., Here we sought to determine mechanistically how NADPH oxidase activity limits filamentation and susceptibility to infection ., Traditionally , the most important role for NADPH oxidase in immunity has been ascribed to its ability to create reactive oxygen species that directly damage or kill microbes 13 ., In fact , there is significant oxidative stress experienced by C . albicans when attacked by neutrophils or macrophages in vitro 32 , 33 ., Therefore , we first hypothesized that NADPH oxidase-derived oxidants in the phagosome might damage C . albicans and block intracellular germination in this infection to limit filamentous growth ., To determine if fungal cells were under oxidative attack in vivo , we used the OxYellow-T oxidative stress reporter strain ., This strain has the oxidative stress-induced CTA1 promoter driving EGFP expression and the constitutive ENO1 promoter driving dTomato expression ., Using this strain we find oxidative stress at 24 hpi but not at 4 hpi in control morphants , whereas there is no detectable oxidative stress at either 4 or 24 hpi in phagoctye oxidase morphants ( Fig . S1 ) , and using a similar strain we have previously published that there is no detectable oxidative stress at 6 hpi in this model 31 ., We also found no activation of the respiratory burst within phagocytes , as observable upon incubation of live infected fish with H2DCF-DA ( Fig . S2 ) , a cell-penetrating molecule that diffuses well into live zebrafish and fluoresces upon oxidation 31 , 34 ., Phagocyte oxidase-produced ROS have been demonstrated to drive localization of the autophagy reporter protein LC3 to the membrane of yeast-containing phagosomes in a process referred to as LC3-associate phagocytosis 35 , 36 ., To determine if a similar process occurs in vivo in zebrafish , we examined the localization of a GFP-LC3 fusion protein in phagocytes containing C . albicans ., We used a transgenic line of zebrafish for which GFP-LC3 localization has been shown to report on autophagic activity 37 ., In contrast to previously reported in vitro findings , we found very few phagosomes with GFP-LC3 localized to the phagosomal membrane in vivo , suggesting that ROS-mediated LC3 localization plays a less important role in this in vivo model than has been demonstrated in vitro ( Fig . S3 ) ., Further , two treatments recently shown by Huang et al . 35 to strongly inhibit phagosomal LC3 localization in vitro—blockade of NADPH oxidase activity with the pan-NADPH oxidase inhibitor diphenyleneiodonium ( DPI ) and treatment with the anti-oxidant α-tocopherol—mildly reduced but did not significantly affect GFP-LC3 localization to phagosomes ., Taken together , these data are not consistent with the idea that NADPH oxidase acts early to produce respiratory burst-derived oxidants that damage the fungi or traffic it to autophagosomes and thereby block filamentous growth ., Nevertheless , to determine if blockade of NADPH oxidase permitted germination of C . albicans within phagocytes in vivo , we examined the fungal morphotypes at 4 hpi with and without the pan-NADPH oxidase inhibitor DPI ., Regardless of NADPH oxidase activity , there was a striking difference in morphotype between intracellular yeast and extracellular filamentous growth , with filamentous cells found only outside of phagocytes ( Fig . 1A ) ., This difference in intra- vs . extracellular fungal morphology is statistically significant in both vehicle- and DPI-treated fish ( p<0 . 0001 by Fishers exact test ) ., Furthermore , there was no intracellular germination even upon blockade with DPI ( 0/204 for DMSO and 0/95 for DPI ) ., Thus , in contrast to our expectations , inhibition of NADPH oxidase did not permit germination and filamentous growth within phagocytes early during infection ., Instead , while some extracellular fungi switch morphotype and grow as filaments , phagocytosis can block germination even without NADPH oxidase activity ., This suggests that there are other host immune mechanisms besides NADPH oxidase that can control C . albicans growth within phagocytes ., Because time is a crucial axis of disease progression , we extended these studies to follow the fate of internalized C . albicans beyond 4 hpi to identify any later roles for NADPH oxidase in limiting germination ., To determine if phagocytes continue to control filamentous growth of internalized yeast up to 22 hpi , we took advantage of the Tg ( mpeg1:GAL4/UAS:Kaede ) photoswitchable macrophage line 38 ., We photoswitched macrophages in the hindbrain ventricle of representative fish at 4 hpi and followed them by time lapse every 2 hours until 22 hpi ( Fig . 1B ) ., Again , we found that internalized C . albicans yeast did not germinate within macrophages or neutrophils for the duration of the time-lapse , even in the presence of DPI ., In three independent experiments , a total of 279 fungi were followed in DMSO-treated fish ( 217 inside macrophages and 62 within neutrophils ) while in DPI-treated fish with limited engulfment , a total of 50 internalized fungi were followed ( 26 within macrophages and 24 within neutrophils ) ., Control experiments suggest that the lack of germination is not due to photoactivation itself or imaging-induced inhibition of filamentation ., Specifically , there is no inhibition of filamentous growth by frequent imaging in both green and red channels over the first six hours of infection and there is no clear defect in pathogenesis or immunity upon photoactivation ( Fig . S4 ) ., The lack of intracellular germination in these extended time-lapse experiments suggests that macrophages and neutrophils remain effective in suppressing filamentous growth of internalized fungi , even when NADPH oxidase activity is blocked ., Current in vivo models have not permitted extended assessment of Candida-phagocyte interactions at the infection site to characterize the dynamics of phagocyte migration ., To examine the role of NADPH oxidase activity in controlling immigration and emigration of macrophages , we analyzed time-lapse experiments performed by photoswitching Kaede-expressing macrophages at the infection site at 4 hpi ., We categorized the photoswitched and non-switched macrophage populations at the site of infection in the hindbrain ventricle between 4 hpi and 22 hpi ., In control fish , about half of the photoswitched ( red ) macrophages left the hindbrain within 12 hours but were replaced by new ( green ) macrophages from outside of the infection site ( Fig . 1C ) ., However , in fish treated continuously with DPI there was migration away from the hindbrain but no replacement with new macrophages , leading to uncontrolled growth of the C . albicans ( Fig . 1D ) ., Importantly , control uninfected fish treated continuously with DPI suffered no ill effects ., This suggests that the defects in immune infiltration associated with blockade of NADPH oxidase extend past 4 hpi and , if anything , are more severe when DPI treatment is continued to later times post-infection ., Furthermore , these experiments document for the first time the dramatic flux of phagocytes to and from the infection site for hours post-infection ., In mammals , macrophages are heterogeneous in function and can differentiate upon stimulation to promote diverse host responses , although it has not been possible to examine the dynamic roles of different subtypes in the context of C . albicans infection 39–41 ., Because our transparent model offers a unique tool to identify differential roles of individual phagocytes during C . albicans infection , we were able to quantify the divergent kinetics of egress from the hindbrain in two macrophage populations ., We found that macrophages that internalize yeast tend not to move away from the site of infection within the first 22 hpi , while most macrophages that do not engulf fungi move away from the infection site during this time ., Approximately 50% of non-phagocytic macrophages leave the hindbrain by 12 hours after photoswitching , while there is no bulk emigration of phagocytic macrophages within 18 hours after photoswitching ( Fig . 1E ) ., This suggests that phagocytosis of C . albicans is associated with reduced movement from the infection site ., Given that NADPH oxidase is not required for intracellular containment of C . albicans , we sought another mechanistic explanation for its requirement in limiting filamentous growth ., Our long-term timelapse experiments suggested that blockade of NADPH oxidase activity limited immune infiltration to the infection site ( Fig . 1 ) , and the Duox NADPH oxidase has been previously implicated in chemotaxis 19 , 42 ., To test if NADPH oxidase is required for early phagocyte chemotaxis to C . albicans , we treated fish with DPI and examined phagocyte dynamics in Tg ( mpx:GFP ) i114 transgenic zebrafish 43 , with EGFP-expressing neutrophils ., Over the first four hours of infection , we found a reduction in the level of immune infiltration to the site of infection and an even stronger decrease in the amount of intracellular containment of fungi ( Fig . 2A ) ., Time-lapse imaging confirms that lower phagocyte numbers are due to loss of recruitment , rather than failure to retain phagocytes at the infection site ., We quantified levels of infiltration and phagocytosis at 4 hours post-infection ( 4 hpi ) as the total number of EGFP-positive cells ( neutrophils ) combined with the number of EGFP-negative cells ( macrophages ) with internalized C . albicans and found that total infiltration was significantly decreased ( Fig . 2C ) ., The number of total phagocytes with C . albicans inside was also significantly lower , as measured by the total number of immune cells , regardless of EGFP expression , that had engulfed C . albicans ( Fig . 2C ) ., Quantification of the number of internal vs . extracellular fungi showed that overall levels of engulfed fungi were significantly decreased by DPI treatment ( Fig . 2D ) ., Inclusion of both EGFP-positive neutrophils and EGFP-negative phagocytes—unambiguously scorable with internalized C . albicans—permitted a more robust measurement of the phagocyte response ., Quantification of only the number of neutrophils at the infection site demonstrated a decreased number in DPI-inhibited fish , but the low overall number of EGFP-positive neutrophils at the site of infection led to differences that were not statistically significant ( Fig . S5 ) ., In contrast to the effects on leukocyte infiltration , NADPH oxidase blockade did not strongly affect the overall ability of phagocytosis by neutrophils at the site of infection , as the percentage of neutrophils with engulfed fungi was largely unchanged , highly variable and not significantly affected by DPI treatment ( Fig . S6 ) ., Taken together , these results indicate that short-term inactivation of NADPH oxidase activity evokes a significant deficiency in chemotaxis to the site of C . albicans infection ., Previous work has focused on NADPH oxidase-dependent neutrophil migration to the site of wounding , but macrophages also play an important role in response to C . albicans infection 31 ., To test if NADPH oxidase activity is required for macrophage chemotaxis , we took advantage of the new Tg ( mpeg1:GAL4/UAS:Kaede ) line of zebrafish with macrophages expressing the photoswitchable Kaede fluorescent protein 38 ., NADPH oxidase inhibition caused a significant decrease in macrophage migration to the infection site over the first 4 hpi , as shown by time-lapse microscopy ( Fig . 2B ) ., Quantifying these defects revealed significant reductions in total phagocyte infiltration and in total number of phagocytes with internalized fungi ( Fig . 2E ) , similar to results using the neutrophil transgenic ( Fig . 2C ) ., In addition , we confirmed the strong defect in containment of fungi by phagocytosis using this transgenic with marked macrophages ( Fig . 2F ) ., Quantification of only the number of macrophages at the infection site demonstrated a decreased number in DPI-inhibited fish , a statistically significant difference ( Fig . S5 ) ., The use of both neutrophil- and macrophage-specific transgenic lines allowed us to account for different types of phagocytes ( either neutrophils or macrophages , depending on the transgenic line ) that did not phagocytose fungi , as well as all phagocytes with intracellular fungi ., Because the results of these two complementary sets of experiments are comparable , this implicates NADPH oxidase in chemoattraction of both phagocyte types to the site of infection ., Our results suggest that NADPH oxidase-dependent leukocyte attraction then promotes phagocytosis primarily through efficient chemotaxis to the infection site rather than enhancement of engulfment at the infection site ., Serious tissue damage in the zebrafish larva elicits rapid neutrophil chemoattraction that is largely Duox-dependent 19 , 42 ., To test if tissue damage accounts for the NADPH oxidase-dependent attraction of neutrophils to the hindbrain ventricle upon C . albicans infection , we performed mock injections of buffer into vehicle ( DMSO ) or DPI-treated larva and measured neutrophil recruitment ., We found that there was a small increase in hindbrain ventricle neutrophils in mock-injected larvae ( from 0 . 75 to 2 neutrophils ) , although this was not significantly affected by DPI ( 2 . 05 vs 1 . 98 ) ( Fig . S7 ) ., Thus , in contrast to the NADPH oxidase-dependent phagocyte recruitment to infection , the minor recruitment induced by this injection method is not NADPH oxidase-dependent ., Our data demonstrate that NADPH oxidase ( s ) direct the early immune response to fungal infection in the zebrafish hindbrain ventricle , tissue in the central nervous system ., To test whether there is NADPH oxidase-dependent phagocyte recruitment and fungal containment in a localized infection in a different tissue , we infected the swimbladder of 4 dpf larvae with C . albicans ., We have recently shown that the presence of C . albicans in the larval swimbladder elicits similar immune responses to those seen in an in vitro reconstituted human epithelial infection model 44 ., Here , we modified the published protocol by injecting fungi directly into the swimbladder of 4 dpf larvae ., We pre-incubated larvae with DMSO ( vehicle ) or DPI , injected 5–20 fungi/fish , maintained treatment for 4 hours , and then scored neutrophil migration to the infection site ., We found that injection itself leads to a small but statistically significant increase in neutrophils at the site of infection ( Fig . S8 ) ., This injection-associated increase is presumably due to a small amount of damage due to the injection procedure itself ., Interestingly , this injection-related increase in neutrophil numbers is partially NADPH oxidase-dependent , as there is a small but significant reduction in neutrophil recruitment upon DPI treatment ( Fig . S6 ) ., However , while injection of fungi led to a strong increase in neutrophil migration to the swimbladder , this increase was not DPI inhibitible ., Thus , the early innate response to C . albicans infection in the swimbladder tissue at 4 dpf is different from the response in the hindbrain ventricle at 2 dpf , where in the swimbladder the early neutrophil response is more robust and not NADPH oxidase-dependent ., These differences may be due to tissue-specific or stage-specific immune responses ., We have shown that efficient engulfment of C . albicans in the hindbrain ventricle depends on NADPH oxidase-mediated phagocyte recruitment , and that internalization blocks switching from yeast to filamentous form ., We therefore speculated that the increased filamentous growth previously observed upon knockdown of p47phox 31 was due to defective early chemotaxis and containment ., We tested the requirement of p47phox for early immune response by measuring immune responses to infection in p47phox morphants , with reduced phagocyte oxidase activity ., Using the Tg ( mpx:GFP ) i114 line , we find that p47phox knockdown leads to chemotaxis deficits similar to that of DPI treatment ., This is seen both with time-lapse imaging ( Fig . 3A ) and upon quantitation of phagocyte behavioral phenotypes at 4 hpi ( Fig . 3B ) ., The similar immune deficits upon pan-NADPH oxidase inhibition and p47phox knockdown suggest that the phagocyte oxidase Phox is an important mediator of phagocyte migration and intracellular containment of C . albicans ., Further , this suggests that the previously described increase in filamentous growth seen at 24 hpi in p47phox morphants 31 is a direct consequence of early defects in fungal containment ., Our results indicate that the phagocyte oxidase is required for phagocyte chemotaxis , suggesting a requirement for NADPH oxidase activity in leukocytes ., Although not previously implicated in chemotaxis to microbes , the epithelial NADPH oxidase Duox is highly expressed in the brain and has a previously defined role in neutrophil chemotaxis to wounds 19 , 42 , 43 , 45 ., To test a role for Duox in phagocyte chemotaxis to C . albicans , we knocked down expression of duox , confirmed knockdown by rtPCR as described 42 , and verified that this morpholino eliminated all detectable transcript without causing gross developmental effects ( Fig . 4A ) ., To assess global and local neutrophil numbers within prim25 zebrafish , we counted EGFP-positive neutrophils in the head and caudal hematopoetic regions of duox and control morphants in a mock experiment , both 1 hour and 4 hours after injection with phosphate-buffered saline ., We found no difference in basal numbers of EGFP-positive neutrophils in either tissue of duox morphants at the stages of development most relevant to our studies ( Fig . 4B and 4C ) ., In contrast to our expectations , immune responses to infection in the duox morphants were severely impaired , similar to what we find with chemical inhibition and with knockdown of p47phox ., This is seen with time-lapse imaging ( Fig . 4D ) , quantitation of phagocyte behavioral phenotypes at 4 hpi ( Fig . 4E ) , and overall failure of phagocytosis ( Fig . 4F ) ., Interestingly , we do find a trend toward decreased phagocytosis on a per-cell basis for duox morphant neutrophils that is more consistent than trends seen for both DPI-treated and p47phox morphant neutrophils ( Fig . S2 ) ., This stronger phenotype suggests the possibility that Duox plays a more important role than p47phox in directing the phagocytic process , although the small number of neutrophils at the infection site in these treated fish makes such characterizations necessarily tentative ., In sum , the phenocopy of DPI treatment in both p47phox and duox morphants suggests that both the phagocyte-expressed Phox and the non-phagocyte-expressed Duox are required for bringing phagocytes to the site of infection , thus promoting efficient phagocytosis and inhibition of germination ., Our observations demonstrate that NADPH oxidases act quickly post-infection to attract phagocytes to C . albicans and limit its filamentous growth by internalization ., To understand the importance of these early immune responses , we sought to identify the consequences of poor initial infiltration and phagocytosis ., Our previous work established that failure to control filamentous growth at 24 hpi correlates with poor survival to 48 hpi 31 , and here we again exploited non-invasive imaging to ask if weak early phagocytosis is linked to extracellular filamentation and poor prognosis ., We characterized infected fish at 4 hpi as “low” or “high” responders , depending on whether they had greater or fewer than five extracellular fungi ., Surprisingly , there were fish of each type for each treatment group , including the control groups , indicating that there is some heterogeneity in immune competence among genetically identical individuals infected with the same doses ., However , consistent with our time-lapse results , there were more low responders in DPI-treated ( Fig . 5A ) , p47phox morphants ( Fig . 5B ) and duox morphants ( Fig . 5C ) than the comparable controls ., Thus , despite screening individual fish immediately post-infection to ensure consistent infectious doses , by 4 hpi the infections could be classified into two major categories dependent on the efficiency of intracellular containment ., As expected , the number of high-responder fish was strongly decreased by all treatments that blocked NADPH oxidase activity ., To determine if these early phenotypes are prognostic for survival , we assayed the fate of individual fish screened at 4 hpi ., Fish were imaged and scored for phagocyte response at 4 hpi , then kept in individual wells of a 24-well plate until 24 hpi to assess their fate ., As expected , low responders have a much worse prognosis than high responders , with approximately three-quarters succumbing to infection by 24 hpi ( Fig . 5D–5F ) ., Remarkably , though , the prognosis among low responders is comparable between controls and treatment groups , and the same is true for high responders ., Due to the role of phagocytosis in limiting germination , low responders have excessive filamentous fungal growth , and nearly all of the fish that die by 24 hpi are riddled with C . albicans filaments ( Fig . 5G ) ., The close correspondence of early phagocytosis with infection containment and survival highlights the crucial importance of early NADPH oxidase activity in protecting the host against C . albicans ., Considering the similar phenotypes between temporary chemical blockade and long-lasting morpholino knockdown , this suggests that early NADPH oxidase activity plays a more important role than later production of ROS in control of this acute disease ., Our demonstration of NADPH oxidase-dependent phagocyte recruitment is in contrast to what has been seen with other pathogens 15 , 18 , suggesting that C . albicans may have a special ability to counter ROS-independent chemotaxis ., Because the yeast-to-hyphal switch is an important virulence trait associated with genome-wide transcriptional remodeling 46 , 47 , we hypothesized that it may be required to limit NADPH oxidase-independent chemotaxis ., To test this idea , we examined the effects of NADPH oxidase inhibition on infections with the yeast-locked edt1Δ/Δ mutant ., We infected Tg ( mpx:GFP ) i114 fish with three different strains of dTomato-expressing C . albicans: wildtype , homozygous edt1Δ/Δ mutant , or heterozygous edt1Δ/EDT1 control ., We used the heterozygous edt1Δ/EDT1 mutant to control for potential artifacts due to transformation ., We treated infected fish with DPI or vehicle and performed time-lapse experiments to measure early immune response ., To our surprise , we found that a high proportion of DPI-treated , edt1Δ/Δ-infected fish elaborate a strong early immune response in which most of the fungi is internalized ( Fig . 6A ) ., Infections with the heterozygous edt1Δ/Δ/EDT1 control result in an intermediate phenotype , as is found frequently with mutants in the diploid C . albicans ., Quantitation of this response in even the limited number of fish examined by time-lapse microscopy suggests that there is a similar level of overall immune recruitment to the edt1Δ/Δ infection site , independent of NADPH oxidase inhibition ( Fig . 6B ) ., Internalization of edt1Δ/Δ is also apparently NADPH oxidase-independent , and a much higher percentage of yeast-locked fungi than wild type fungi are phagocytosed by 4 hpi ( Fig . 6C ) ., Percent phagocytosis is intermediate for the heterozygous edt1Δ/Δ/EDT1 strain , suggesting that there may be a partial haploinsufficiency phenotype ., Consistent with these high-resolution time-lapse results with a small sample size , we also find a large percentage of high responders in edt1Δ/Δ-infected , DPI-treated fish when large numbers of fish are screened at 4 hpi for their ability to contain the fungi ( Fig . 6D ) ., The significant difference in NADPH oxidase-independent phagocyte migration to the yeast-locked mutant in fungal containment at 4 hpi suggests that changes in C . albicans during the dimorphic switch may play an important role in limiting phagocyte chemotaxis ., Our data demonstrate that germination of extracellular C . albicans is enhanced by poor early immune response , which is associated with poor prognosis ., We therefore reasoned that genetic blockade of the C . albicans dimorphic switch would prevent mortality , even in conditions of poor phagocyte containment ., To investigate the contribution of the dimorphic switching program to virulence under these circumstances , we followed the fate of high and low responder fish infected with the yeast-locked edt1Δ/Δ mutant ., Although the majority of edt1Δ/Δ-infected fish internalize fungi successfully , the naturally heterogeneous early immune response among individuals allowed testing of our original hypothesis that extracellular germination is responsible for the poor prognosis after weak early chemotaxis ., As expected , the outcome of infections in low-responding fish diverges significantly between edt1Δ/Δ- and control-infected fish ., In contrast to the situation with control-infected fish , most of the low responders infected with edt1Δ/Δ manage to survive to 24 hpi and beyond ( Fig . 6E ) ., As is the case for other infections , there are no NADPH oxidase-dependent differences in mortality within high- and low-responder groups ., Thus , even when the early immune response fails to successfully contain the majority of edt1Δ/Δ mutant fungi , their inability to turn on the dimorphic switching pathway prevents pathogenesis ., These data point to the importance of the EDT1-dependent dimorphic switching pathway in both limiting early fungal containment and in exploiting a weak early response to grow extracellularly in filamentous form and cause mortality ., The advent of intravital imaging has begun to illuminate new aspects of host-pathogen interaction in the intact host ., Here , we exploited a transparent zebrafish model of candidemia to address mechanistic questions relevant to human primary immunodeficiency and immune response dynamics ., We describe a new role for NADPH oxidase in recruitment of phagocytes to the site of C . albicans infection , demonstrate that this early recruitment is a key event in control of infection , and provide evidence that the C . albicans dimorphic growth program impacts the ROS-dependence of early fungal containment ., The discovery of this unanticipated role of NADPH oxidase in phagocyte recruitment highlights the importance of early immune responses and points to a potentially new role of fungal dimorphism in regulating phagocyte activity ., In this study , we used a powerful in vivo model to demonstrate a role for NADPH oxidase-driven phagocyte containment of C . albicans ., Classically , ROS produced by the phagocyte oxidase and the dual-specific oxidase have been ascribed functions in direct chemical attack against systemic and epithelial insults 9 , 48 ., Although we find no evidence for a role of ROS in directly damaging intracellular C . albicans early during infection in vivo , we ascribe a novel role to these two NADPH oxidases in recruitment of leukocytes to the site of C . albicans infection ., In addition to our findings , abundant recent work challenges the narrow view of ROS as solely microbicidal and implicates NADPH oxidase-produced ROS in a range of other functions such as autophagy , neutrophil extracellular traps , tryptophan metabolism , kinase signaling , neutrophil recruitment to the endothelium and epithelium , and inflammasome activation 9 , 19 , 35 , 48–56 ., In this context , it is notable that we did not find frequent LC3-associated phagocytosis ( LAP ) of fungi , in contrast to what has been observed in vitro 35 ., Perhaps most relevant to our findings is recent work suggesting a role for the phagocyte NADPH oxidase or Duox in enabling neutrophil chemotaxis in vivo 16 , 19 , 42 , 52 .,

Introduction, Results, Discussion, Materials & Methods

Candida albicans is a human commensal and clinically important fungal pathogen that grows as both yeast and hyphal forms during human , mouse and zebrafish infection ., Reactive oxygen species ( ROS ) produced by NADPH oxidases play diverse roles in immunity , including their long-appreciated function as microbicidal oxidants ., Here we demonstrate a non-traditional mechanistic role of NADPH oxidase in promoting phagocyte chemotaxis and intracellular containment of fungi to limit filamentous growth ., We exploit the transparent zebrafish model to show that failed NADPH oxidase-dependent phagocyte recruitment to C . albicans in the first four hours post-infection permits fungi to germinate extracellularly and kill the host ., We combine chemical and genetic tools with high-resolution time-lapse microscopy to implicate both phagocyte oxidase and dual-specific oxidase in recruitment , suggesting that both myeloid and non-myeloid cells promote chemotaxis ., We show that early non-invasive imaging provides a robust tool for prognosis , strongly connecting effective early immune response with survival ., Finally , we demonstrate a new role of a key regulator of the yeast-to-hyphal switching program in phagocyte-mediated containment , suggesting that there are species-specific methods for modulation of NADPH oxidase-independent immune responses ., These novel links between ROS-driven chemotaxis and fungal dimorphism expand our view of a key host defense mechanism and have important implications for pathogenesis .

Over 45 years ago chronic granulomatous disease ( CGD ) was ascribed to a failure of neutrophils to mount a respiratory burst , and it is now known to result from primary genetic deficiencies in the phagocyte NADPH oxidase complex ., Recent work suggests that reactive oxygen species produced by NADPH oxidases have other important functions as diverse as maturing hormones and promoting protein kinase signal transduction ., Candida albicans is an opportunistic pathogen that preys on immunocompromised patients to cause lethal candidemia ., We used the transparent zebrafish larva to describe a novel function of both phagocyte oxidase and dual-specific NADPH oxidase in directing phagocyte recruitment to C . albicans infection foci ., We demonstrate that NADPH oxidase-dependent attraction of neutrophils and macrophages is instrumental in effective containment of yeast within phagocytes , which prevents the yeast-to-hyphal morphogenetic switch and limits mortality ., Remarkably , when the fungal morphogenetic switch is prevented by mutation , NADPH oxidase activity is no longer required for effective fungal containment ., Our study suggests that defects in CGD may extend beyond reduced microbial killing by superoxide to include impairment of chemotaxis , and provide a basis for exploring this alternative function in mammals .

journal.pntd.0001772

An Analytical Method for Assessing Stage-Specific Drug Activity in Plasmodium vivax Malaria: Implications for Ex Vivo Drug Susceptibility Testing

Malaria continues to pose a significant threat to human health globally ., Currently , as many as 2 . 6 billion people are at risk of P . vivax infection , with an estimated 72–390 million cases per year 1 , 2 ., Historically , malaria research has focussed on P . falciparum , due to its reputation as the most lethal human malaria parasite ., Although P . vivax is less pathogenic than P . falciparum , it still poses a serious public health burden and is being increasingly recognised as a cause of severe and fatal disease , particularly in children and pregnant women 2 , 3 ., A number of recent publications have highlighted the increasing recognition of the clinical importance of P . vivax and the renewed emphasis placed on research into this species 2 , 4 , 5 , 6 ., Further , the emergence of highly chloroquine ( CQ ) resistant P . vivax in Southeast Asia ( CQ remains a front-line treatment for vivax malaria as it is affordable , well tolerated and safe , and its long half-life ensures protection from early relapses 7 , 8 ) has created an urgent need for an improved understanding of the mechanisms of drug resistance in these parasites , the development of robust tools for defining the spread of resistance , and the discovery of new antimalarial agents ., To glean insights into the development of resistance in P . vivax , a modified form of the ex vivo Schizont Maturation Test ( SMT ) has been developed and applied to fresh isolates directly from patients 9 , 10 , 11 , 12 ., The central tenant of the SMT is that drug activity on susceptible parasites will completely stop or slow the growth in a dose-dependent manner , with reduced susceptibility being manifest by an ability of parasites to mature to schizont stages in the presence of higher concentrations of drug ., The standard assay is conducted for 30 hours , the time required for parasites to reach maturation without drug , and the proportion of schizonts at the conclusion of the assay is used as an indicator of parasite maturation ., The SMT was initially developed for testing drug susceptibility in P . falciparum 13 , 14 , where almost all parasites in the peripheral circulation are at the immature ring stage ., However , in infections due to non-falciparum species , trophozoite and schizont stages are commonly present in the peripheral circulation ., To accommodate this , a modified SMT has been developed in which the control wells are monitored until the number of schizonts exceeds 40% of parasites prior to harvest ( i . e . , assays are conducted for variable lengths of time ) 15 ., For the results of the SMT to be valid , a sample , irrespective of the drug resistance phenotype of the parasites , must have had sufficient exposure to the drug to affect a response ., The diversity in parasite life cycle stages in P . vivax infections creates a significant confounding factor , particularly since there is an apparent marked variation of drug susceptibility in different erythrocytic life cycle stages ., In previous work , it has been demonstrated that the trophozoite stages of P . vivax are almost completely resistant to CQ , and continue to mature no matter how high the concentration of drug 15 , 16 ., It follows that , if a drug only acts on ring stage parasites , but the sample contains a majority of trophozoites and schizonts , the parasite is likely to be erroneously categorised as resistant simply because there were no susceptible life cycle stages present in the assay ., In this study we develop a statistical methodology to identify stage specific drug activity in the SMT ., We use CQ against P . vivax as a case study and demonstrate that stage specific drug activity has profound consequences for the interpretation of SMT results ., We also examine stage-specific drug effects for other commonly used drugs and simulate the growth dynamics of P . vivax parasites within the SMT to provide recommendations on how to improve the reliability of SMT results ., Ethical approval for the collection of blood samples for drug susceptibility testing was obtained from the ethics committees of the National Institute of Health Research and Development , Ministry of Health ( Jakarta , Indonesia ) , and the Human Research Ethics Committee of NT Department of Health & Families and Menzies School of Health Research ( Darwin , Australia ) ., Written informed consent was obtained from adult patients and parents and/or guardians of enrolled children ., Blood samples were collected from patients attending outpatient clinics in Timika , Papua Province , Indonesia , as previously described 15 ., Only patients infected with a single species of Plasmodia were included in the study; the majority of samples contained P . vivax or P . falciparum , although a limited number of P . malariae and P . ovale isolates were also available 17 ., SMTs were conducted on these samples following World Health Organisation guidelines for drug susceptibility testing 18 , with modifications developed to aid the application of the test to P . vivax 15 ., Venous blood ( 5 mL ) was collected by venipuncture , and after removal of host white blood cells using a CF11 column , 800 µL of packed infected red blood cells ( IRBC ) were used for the SMT ., Assays conducted for up to 7 drugs: CQ , artesunate , amodiaquine , lumefantrine , mefloquine , piperaquine and pyronaridine 15 ., The proportion of parasite life cycle stages ( i . e . , rings , trophozoites and schizonts , as defined by Russell et al 12 , 15 ) in each isolate was assessed at 0 hours , 24 hours , and then at non-uniform times until 40% of the parasites in the control well reached mature schizonts ., At this point , the assay was terminated , and wells under serial drug concentrations were harvested ., The proportion of parasites in each stage of the erythrocytic cycle at time of harvest was reported for each drug concentration , as was the duration of the assay ., The estimated drug response ( R ) of each isolate was derived from the ratio between the proportion of schizonts at harvest in the treatment well compared to that in the control well ., Only data satisfying the following criteria were included in the dose response modelling: The sigmoid Emax dose-response curve ( equation, 1 ) was fitted to all available data simultaneously , with mixed-effects modelling used for CQ data ., Rij represents the drug mediated growth inhibition response ( the ratio between the proportion of schizonts at harvest in the treatment well compared to the control well ) for the ith isolate at the jth concentration , cij represents the drug concentration ., Emax and E0 represent the maxima and minima of the dose response curve and γ the slope of this curve ., The EC50 value represents the effective concentration at which 50% of the parasite population exhibit a response to the drug ., Inter-isolate variability was included for Emax , EC50 and γ ., The drug plate batch was also incorporated as a random effect , to control for batch-to-batch variability ., Predicted values for EC50 for each isolate were calculated from Empirical Bayes estimates ., EC50 values for parasite samples collected from April 2004 and May 2007 have previously been reported ( Russell 2008 ) ., However the methodology used to calculate EC50 for CQ differs between the previous report and the current study due to the use of mixed-effects modelling ., For each of the seven drugs tested , linear regression models were used to characterise the relationship between the EC50 values derived for each isolate and the following independent variables: assay duration , the proportion of rings at 0 hours , and delay between venepuncture and assay ., The EC50 values were not normally distributed , therefore ln ( EC50 ) was used as the dependent variable ., Where a relationship between one of the independent variables and ln ( EC50 ) was found a non-linear threshold model was constructed to determine the threshold at which the association ceased to exist ( Equation 2 ) ., a represents the rate of decline in EC50 when the duration of the assay is less than the threshold , b the mean EC50 values for samples where the duration exceeds the threshold , and c the threshold ., A threshold model was preferred over other non-linear models due to the greater interpretability of parameters ., A threshold model of the same form was also fitted to define the relationship between the proportion of rings at time 0 and EC50 ., The threshold models were fitted to the data using nonlinear regression ., A simulation approach was used to estimate the duration of each stage of the erythrocytic life cycle in both P . vivax and P . falciparum ., Estimates were also made of stage durations using the limited data available for P . malariae and P . ovale to validate the methodology ., Poisson distributions were selected to represent the duration of each stage , as random deviates produced by sampling the distribution are always positive , and the parameters of the distributions allow for biologically meaningful interpretation ., We assumed the duration of each stage could be drawn from a probability distribution , and estimate the mean of each Poisson distribution ( λ ) ., One hundred simulations of the growth dynamics within a culture well , each containing 200 parasites , were conducted ., It was assumed each parasite began life as a ring , transitioned to a trophozoite , then to a schizont ., The length of time spent in each stage was determined by sampling from the relevant probability distribution ., At selected time points , the proportion of parasites in each stage was calculated ., As parasites completed the schizont stage , they were assumed to die , and subsequently , proportions were calculated based only on the remaining surviving parasites ., A systematic search of the parameter space for the mean duration in each life cycle stage ( λ ) was conducted to determine the optimal values for each Poisson distribution ., The mean model fit from three simulation experiments ( totalling 300 simulations ) for each combination of parameters was used and the search examined potential parameters in increments of 0 . 5 hours ., The optimal fit was determined by minimising the sums-of-squares between the simulation results and data on proportion of parasite at each life cycle stage from the control wells , for a subset of the original field samples ., The subset of field samples used had, 1 ) 100% rings at 0 hours ,, 2 ) data for at least 3 time points , and, 3 ) an assay duration >42 hours ., This subset was used to ensure only samples with young ring stage parasites were included in the fitting process ., A penalty equivalent to a difference of 20 between data and simulation results was applied for any later time points where the death of the entire simulated parasite population meant that no direct comparison could be made to the data ., All statistical analyses and simulations were conducted using the R statistical computing software package 19 ., SMT results for parasites sourced from 784 patients with single-species infections of either P . vivax ( n\u200a=\u200a345 ) or P . falciparum ( n\u200a=\u200a439 ) were analysed; 289 ( 84% ) P . vivax and 331 ( 75% ) P . falciparum isolates met the inclusion criteria for statistical analysis ., Among these , 141 P . vivax ( 49% ) and 216 P . falciparum ( 65% ) isolates reached the 40% schizont threshold at harvest ., The time between venepuncture and start of the assay ( the ‘delay’ ) was significantly correlated with the duration of the assay for isolates of P . falciparum ( correlation co-efficient ( r ) =\u200a−0 . 211 , p<0 . 001 ) , but not P . vivax ( r\u200a=\u200a−0 . 065 , p\u200a=\u200a0 . 226 ) ., There was a significantly higher mean proportion of ring stage parasites in samples at the start of the assay ( 0 hours ) for P . falciparum ( 0 . 922 ) compared to P . vivax ( 0 . 588 ) ( Wilcoxon rank sum test , p<0 . 001 ) ., 87 . 5% ( 189/216 ) of P . falciparum isolates contained 100% ring stage parasites at 0 hours , compared to only 2 . 8% ( 4/141 ) of P . vivax isolates ., There was a significant negative association between the CQ assay duration ( hours ) and the ln ( EC50 ) values for P . vivax ( r2\u200a=\u200a0 . 219 , p<0 . 001; Figure 1a ) ., A similar , but weaker relationship was observed for P . falciparum ( r2\u200a=\u200a0 . 097 , p<0 . 001; Figure 1b ) ., Figure 1 suggests some bimodality in the distribution of results , but this is predominantly due to the lack of sampling between ∼32 and 40 hours ., The proportion of rings at the start of the assay was also significantly negatively associated with the ln ( EC50 ) values for both P . vivax ( r2\u200a=\u200a0 . 245 , p<0 . 001 ) and P . falciparum isolates ( r2\u200a=\u200a0 . 206 , p<0 . 001; Figure 2 ) ., In both species there was a significant negative correlation between the proportion of rings at the onset of assay and assay duration ( r2\u200a=\u200a0 . 621 , p<0 . 001; r2\u200a=\u200a0 . 185 , p<0 . 001 , for P . vivax and P . falciparum , respectively ) ., The threshold model for assay duration was successfully fit to the P . vivax data ( Figure 3 , Table 1 ) ., The threshold value , c , represents the point at which the assay duration was no longer significantly associated with estimates of EC50 ., We interpret this threshold as the point at which assay duration was sufficiently long to guarantee that the target parasite stage/s were present and exposed to the drug ., Although there was a relationship between assay duration and EC50 for P . falciparum we were unable to fit a threshold model as fitting procedures did not identify an appropriate non-linear model and associated threshold point ., A threshold model to determine the relationship between the initial proportion of rings and the EC50 was also only possible for P . vivax; we were unable to fit a threshold model to the P . falciparum data ., For P . vivax the proportion of rings at the onset of assay was found to have a significant non-linear relationship with EC50 ( Figure 4; Table 2 ) ., Samples with less than 65% ring stage parasites at time 0 had higher and more variable EC50 values than those isolates with a greater proportion of ring stage parasites ., Of the six other drugs tested , threshold models were successfully fitted to data for amodiaquine and mefloquine in P . vivax ., Confidence intervals around the estimated threshold points showed distinct overlap , suggesting no significant differences between the two drugs , or CQ ( Table 3 ) ., In the simulations of the growth dynamics under ex vivo assay conditions , the λ parameter for the Poisson distributions represented the duration of each parasite life cycle stage in P . vivax ., The mean λ was 19 . 1 hours ( 95% CI 11 , 29 ) for rings , 23 . 1 hours ( 95% CI 11 , 36 ) for trophozoites , and 2 . 2 hours ( 95% CI 0 , 7 ) for the schizonts ., The total duration of the P . vivax erythrocytic cycle was estimated as 44 . 5 hours ( 95% CI 29 , 62 ) ., Similar simulations of P . falciparum growth gave mean estimates of the λ parameter of 23 . 3 hours ( 95% CI 14 , 33 ) for ring stage parasites , 21 . 4 hours ( 95% CI 11 , 33 ) for trophozoites , and 3 . 7 hours ( 95% CI 0 , 10 ) for schizonts; estimates produce a total duration of 48 . 4 hours ( 95% CI 32 , 66 ) ., These estimates of an ex vivo life cycle do not include the age of the rings before the start of the assay and the growth time of schizonts after the end of the assay , thus the true life cycle is expected to be longer than reported ., To validate the methodology , the growth dynamics of a small number of SMT results were also simulated for P . malariae ( n\u200a=\u200a39 ) and P . ovale ( n\u200a=\u200a13 ) isolates ., The total duration of the erythrocytic cycle was estimated to be 75 . 6 hours ( 95% CI 59 , 93 ) in P . malariae and 53 . 9 hours ( 95% CI 34 , 79 ) in P . ovale ., Despite being developed initially for P . falciparum parasites , the Schizont Maturation Test ( SMT ) has seen considerable use in testing drug susceptibility of P . vivax ., However , little consideration has previously been given to whether the application of the SMT to P . vivax is appropriate ., In this study , we demonstrate that the stage-specific dynamics of drug activity may impact on the validity of SMT results ., If a particular drug targets ring stage parasites , but is tested in an isolate predominantly containing mature stage parasites , the drug will appear to be ineffective , resulting in an overestimate of resistance ., Assay duration and the proportion of rings in the initial sample both provide proxy indicators of the likelihood that early stage parasites will be exposed to drugs ., Previous experimental work has identified that trophozoite stage P . vivax parasites are insensitive to CQ 15 , 16; our results support this finding ., We apply statistical analysis techniques to show that the heterogeneity of erythrocytic life cycle stages present in peripheral blood samples taken from patients infected with P . vivax necessitates additional criteria be applied to the SMT to ensure the validity of the results ., The significant negative relationship between the time of venepuncture and start of the SMT and duration of the SMT for P . falciparum was not unexpected ., The onset of fever is usually associated with the start of a new erythrocytic cycle , meaning the sum of the time elapsed between establishment of the SMT and SMT duration will be more indicative of total duration of the erythrocytic cycle than the SMT duration alone ., Hence , parasites which have a delay between venepuncture and start of SMT will enter the SMT in a more advanced state , requiring less time to develop to 40% schizonts ., This effect is most likely more dominant in P . falciparum samples due to the high synchronicity in parasites when obtained from the patient , compared to P . vivax ., For both P . vivax and P . falciparum isolates , there were significant negative relationships between the duration of the assay and estimated EC50 values ( i . e . , short duration assays were associated with reduced susceptibility to drugs ) ., There are a number of possible explanations for these results , none of which is mutually exclusive and all of which are specific to the drug/parasite combination: When stage-specific drug activity is a consideration , as with CQ against P . vivax 15 , 16 , we hypothesised that samples more advanced in their development , as characterised by short assay duration or a low proportion of ring stage parasites in the initial blood sample , would appear to be less susceptible to a drug because a significant number of parasites present in the assay had developed beyond the target stage ., Support for this hypothesis is provided by the threshold modelling ., While the linear models we have described are appropriate for the P . falciparum SMTs , examination of the trends in P . vivax suggest a distinctly non-linear pattern for some drugs , including CQ ., The threshold modelling indicates that the EC50 for CQ in P . vivax parasites stabilises once the sample has been exposed to the drug for at least 33 . 7 hours ( CI 28 . 2 , 39 . 3 ) ., The simulation modelling of parasite development time suggests that P . vivax parasites spend an average of 25 . 3 hours as trophozoites and schizonts before termination of the assay ., If the SMT is terminated when the control well reaches 40% schizonts , and this occurs after 33 . 7 hours ( mean duration threshold ) , it follows that the SMT must have exposed sufficient ring stage parasites to the drug , resulting in EC50 reaching its minimum ., Combining the results from the threshold and life cycle modelling , an assay lasting 33 . 7 hours will have exposed 86 . 5% of the parasites to CQ for at least one hour at ring stage ., Using the upper 95% confidence limit for mean assay duration , a more conservative threshold estimate of 39 hours , we can predict that 95% of the parasites were exposed to CQ for at least 2 hours at ring stage ., We propose that this threshold represents the duration of the assay which is sufficient to guarantee that a significant proportion of the ring stage P . vivax parasites have been exposed to the drug ( i . e . there is no longer any association between duration and EC50 ) ., Assay durations shorter than this threshold expose a greater proportion of tolerant mature stage parasites to the drug , and thus the EC50 values derived from these samples will be artificially elevated ., Similar dynamics are apparent when examining the composition of the initial blood samples ., The disparity in the degree of developmental stage heterogeneity in the initial samples between P . vivax and P . falciparum is striking ., P . falciparum isolates are markedly more synchronous , presumably because the mature stages ( trophozoites and schizonts ) are sequestered in deep tissues and organs , rather than in the peripheral circulation ., Nearly all P . falciparum samples contain only rings , and are , therefore , ideal for SMT ., In contrast , P . vivax isolates tend to show significantly greater heterogeneity in the life cycle stages , with trophozoites and schizonts regularly occurring ., The presence of these advanced stages at the onset of SMT makes the interpretation of drug susceptibility results more difficult ., Our modelling suggests that the initial sample should contain a minimum of 66% ring stage parasites , preferably >90% ring stage parasites ( upper 95% confidence interval of the threshold parameter ) , to ensure the target life cycle stages are sufficiently present in a sample ., However , this significantly reduces the number of samples from which drug sensitivity data can be obtained potentially introducing a sampling bias ., It should be noted that , by selecting only those samples that achieve 40% schizonts , we also introduce bias against those parasites that do not grow well in culture ., Processes for synchronising parasitemia have also been proposed as a means of decreasing stage heterogeneity of Plasmodium isolates for ex vivo characterisation 20 , 21 ., Although such methods may have utility and permit testing of some field isolates that would otherwise be excluded from testing , the removal of mature trophozoite parasites inevitably results in a reduction in parasite count , which is itself another important parameter for reliable quantification of parasite growth ., An alternate approach to specifying a definitive threshold is to apply the same types of threshold models which we have developed here using all available data ., Such an approach would have three advantages ., First , it would allow all the field samples to be used , thus reducing the potential for bias in the SMT samples ., Second , it would allow the development of resistance to be monitored over time by looking for changes in the threshold duration and minimum EC50 ., Third , it can be used to look for stage-specific drug action in current and new antimalarial drugs ., Similar patterns and threshold values were found for CQ , amodiaquine and mefloquine , suggesting all of these drugs have their main effect on ring stage P . vivax parasites ., Such a relationship was not observed for the other antimalarials investigated ( i . e . , artesunate , lumefantrine , piperaquine and pyronaridine ) ., Differences between the stage specific activity of each drug and its variation between parasite species may prove highly informative in elucidating the mechanisms of drug action as well as innate and acquired drug resistance ., While it is always possible to investigate stage-specific drug activity using carefully planned laboratory experiments , as reported by Russell et al . 15 , the methodology presented here can identify stage specificity through far less laborious means , and can use previously collated results ., Our simulation of parasite development in the SMT and subsequent estimate for the duration of each life cycle stage is the first attempt to model parasite development times for P . vivax ., It is important to note that the estimates are relative to the parasite development in the restricted conditions of the SMT control well and may not represent the length of the life stages in vivo , or indeed in potential in vitro culture ., It should also be expected that the estimated duration of the ring stage underestimates the true duration due to the delay in obtaining the blood sample after parasite rupture and establishing the SMT ., More expansive sampling over the first 24 hours of the assay would likely reduce the confidence intervals of the estimated development times ., In summary , a threshold modelling approach was applied to data from a modified SMT to investigate resistance to CQ in P . vivax ., We identified patterns which suggest a non-linear relationship between drug susceptibility in the parasite and both the duration of an assay and the proportion of ring stage parasites in the initial sample , which signifies tolerance of late stage parasites to CQ ., Consequently , we recommend that P . vivax isolates should contain a minimum of 66% ring stage life cycle stages , and that assay duration should exceed 34 hours to ensure this stage-specific effect does not artificially inflate the reported EC50 ., More conservative thresholds would require a minimum of 90% ring stage parasites and a minimum assay duration of 40 hours ., An alternative approach would be to use the statistical methodology which has been developed ., For field researchers , this threshold modelling approach will allow for increased confidence in the reliability of resistance results ., This approach also provides a novel means of detecting stage-specific drug activity for new antimalarials , as demonstrated by our analysis of the susceptibility to amodiaquine and mefloquine .

Introduction, Methods, Results, Discussion

The emergence of highly chloroquine ( CQ ) resistant P . vivax in Southeast Asia has created an urgent need for an improved understanding of the mechanisms of drug resistance in these parasites , the development of robust tools for defining the spread of resistance , and the discovery of new antimalarial agents ., The ex vivo Schizont Maturation Test ( SMT ) , originally developed for the study of P . falciparum , has been modified for P . vivax ., We retrospectively analysed the results from 760 parasite isolates assessed by the modified SMT to investigate the relationship between parasite growth dynamics and parasite susceptibility to antimalarial drugs ., Previous observations of the stage-specific activity of CQ against P . vivax were confirmed , and shown to have profound consequences for interpretation of the assay ., Using a nonlinear model we show increased duration of the assay and a higher proportion of ring stages in the initial blood sample were associated with decreased effective concentration ( EC50 ) values of CQ , and identify a threshold where these associations no longer hold ., Thus , starting composition of parasites in the SMT and duration of the assay can have a profound effect on the calculated EC50 for CQ ., Our findings indicate that EC50 values from assays with a duration less than 34 hours do not truly reflect the sensitivity of the parasite to CQ , nor an assay where the proportion of ring stage parasites at the start of the assay does not exceed 66% ., Application of this threshold modelling approach suggests that similar issues may occur for susceptibility testing of amodiaquine and mefloquine ., The statistical methodology which has been developed also provides a novel means of detecting stage-specific drug activity for new antimalarials .

The schizont maturation test ( SMT ) was developed to monitor drug resistance in malaria parasites ., The SMT examines differences in the rate of parasite development when exposed to different drug concentrations , providing an estimate of drug efficacy ., While the assay is effective when examining resistance in Plasmodium falciparum , there are concerns regarding its suitability for testing other malaria species , particularly if the drug only targets particular life-cycle stages of the parasite ., Blood samples taken from Plasmodium vivax infected individuals exhibit significant heterogeneity in the parasite life-cycle stages present ., If a drug targets the early stage parasites , but only late stage parasites are present in the sample , the test will show an erroneously high degree of resistance ., In this study , we estimate thresholds which can be used to identify when test results can be considered accurate should the drug being tested only affect specific life stages of the parasites ., Chloroquine is used as a case study but the method developed also allows the identification of stage-specific activity in other malarial drugs in P . vivax ., For field researchers , this threshold modelling approach will allow for increased confidence in the reliability of P . vivax resistance results and provides a novel means of detecting stage-specific drug activity for new antimalarials .

medicine, infectious diseases, plasmodium vivax, global health, malaria, infectious disease modeling, parasitic diseases

journal.pcbi.1001064

Integrative Features of the Yeast Phosphoproteome and Protein–Protein Interaction Map

Protein phosphorylation is a reversible , ubiquitous , and fundamentally post-translational modification ( PTM ) that regulates a variety of biological processes; one of its critical roles is the control of protein signaling 1–3 ., Recent advances in mass-spectrometry ( MS ) –based technologies and phosphopeptide enrichment methods have enabled the use of high-throughput in vivo phosphosite mapping 4–7 to identify thousands of phosphoproteins ., To date , around 10 , 000 phosphosites of serine , threonine , or tyrosine residues have been identified in each of many organisms , including human 8–12 , mouse 13 and yeast 14–16 ., Many public databases , such as PHOSIDA 17 , Phospho . ELM 18 , and UniProt 19 , have been developed or expanded to catalog such phosphoproteome data ., Accordingly , the numbers of phosphoproteins that have been identified in various organisms now greatly exceed the numbers known to have roles in protein signaling ., This has raised the question of whether this intracellular phosphorylation , which occurs on such a large scale , has other major roles ., In modern biology , the use of high-throughput screening methods has enabled rapid progress in the disclosure of protein–protein interaction ( PPI ) networks in many organisms 20–27 ., Topological features common to PPI networks ( e . g . , scale-free and small-world properties ) are of prime importance in interpreting intracellular protein behavior and the evolutionary aspects of PPIs 28–31 ., PTM changes the physical characteristics of proteins ., It is therefore probable that reversible PTM has large effects on the dynamic states of intracellular protein-binding patterns and complex formation , and that it controls not only signal transduction but also many other cellular pathways ., However , the impact of PTM on the whole picture of the PPI network has not yet been described ., Here , we describe the intracellular global relationships between protein phosphorylation and physical PPI , as derived from the results of integrative and systematic data-mining of Saccharomyces cerevisiae multi-omics data ( Fig . 1 ) ., New phosphoproteome data on S . cerevisiae were initially obtained by MS–based analysis and unified with data on previously identified phosphoproteomes ., We superimposed the unified phosphoproteome data onto a S . cerevisiae PPI network with other multi-omics data on S . cerevisiae ., From the results , we infer that the tremendous numbers of phosphorylations within a cell have a large impact on PPI diversity , and that intracellular phosphorylation patterns are affected partly by simultaneous phosphorylation of physically bound proteins that is triggered by the action of single kinases ., On the basis of liquid chromatography ( LC ) -MS analysis , we initially identified 1 , 993 S . cerevisiae phosphoproteins containing 6 , 510 phosphosites ., Information on the identified phosphopeptides has been stored in PepBase ( http://pepbase . iab . keio . ac . jp ) ., We unified these new phosphoproteome data with the publicly available phosphoproteome datasets of Holt et al . 16 and UniProt 19 and obtained a total of 3 , 477 phosphoproteins containing 25 , 997 phosphosites ( Fig . 2; Supplementary Table S1 ) ., The pS/pT/pY ratios of this study , the study of Holt et al . , and UniProt were 72%/23%/5% , 72%/23%/5% , and 80%/18%/2% , respectively ., Among the unified phosphoproteome data , 343 phosphoproteins and 2 , 778 phosphosites were not found in the data of Holt et al . or UniProt ., Comparison with S . cerevisiae genomic information 32 revealed that 58 . 5% of the 5 , 815 known and predicted genes were phosphoprotein-encoding genes ( Supplementary Table S2 ) ., Although the use of current high-throughput technologies cannot disclose the entire phosphoproteome picture of a cell , these results imply that most intracellular proteins can be phosphorylated under the appropriate environmental conditions ., The unified phosphoproteome data were superimposed onto the PPI network to generate a “phospho-PPI” network ., PPI data were obtained via DIP ( Database of Interacting Proteins ) 33 and grouped into four categories according to the experimental method used for the PPI assay: all kinds of experimental methods ( “ALL” ) , yeast two-hybrid ( “Y2H” ) , co-immunoprecipitation ( “IMM” ) , and tandem affinity purification ( “TAP” ) ., Among all the protein nodes involved in every category of the phospho-PPI network , the proportion of phosphoproteins was also nearly 60% ( Supplementary Fig . S1 ) ., For example , the phospho-PPI network of the “ALL” category was composed of 4 , 945 proteins , including 2 , 934 phosphoproteins ( 59 . 3% ) and 17 , 215 physical interactions ., To explore specific characteristics of the phospho-PPI network , the number counts of interacting partners of phosphoproteins and nonphosphoproteins were analyzed ( note that throughout this study , the word “nonphosphoprotein” means a protein with no phosphosite identified to date ) ., We found that , in general , phosphoproteins had more interacting partners than nonphosphoproteins ., In each phospho-PPI network of the “ALL” and “Y2H” categories with enough protein nodes for the subsequent statistical analysis , the cumulative percentage distributions of node degrees ( or the number count of interacting partners ) of phosphoproteins and nonphosphoproteins were markedly different ( Fig . 3A and D ) ., For example , in the dataset of “ALL” , 47 . 6% of nonphosphoproteins had three or more interacting partners , but this was true for 67 . 9% of phosphoproteins ., Moreover , in both datasets , about twice as many phosphoproteins as nonphosphoproteins had 10 interacting partners ( Fig . 3B and E ) ., To analyze the statistical significance of this difference in the context of phosphorylation , we prepared randomly generated phospho-PPI networks by “node label shuffling” ( NLS ) , in which the node positions of phosphoproteins and nonphosphoproteins were randomly moved within the phospho-PPI networks ( for details , see Materials and Methods ) ., This demonstrated that the node degree of phosphoproteins was significantly higher than expected from a random distribution ( Fig . 3C and F ) ., Node degree in PPI networks has an exponential relationship with protein expression level 34–36 , perhaps because cellular proteins with more copies have a greater possibility of interacting with others by chance 36 ., Therefore , if the phosphoproteome data are biased by protein abundance and highly abundant proteins tend to be identified as phosphoproteins , there is a strong possibility that the relationship between phosphorylation and node degree is spurious , with no direct causal connection ., In fact , proteome abundance data obtained through a single-cell proteomic analysis combining high-throughput flow cytometry and a library of GFP-tagged yeast strains 37 showed that the number of phosphoproteins in the “ALL” phospho-PPI was skewed , especially among highly abundant proteins ( Fig . 4A and D ) ., However , we demonstrated that in the “ALL” phospho-PPI network there were still significant differences in the node degree levels of phosphoproteins and nonphosphoproteins of similar abundance , and that the differences could be explained independently of protein copy number ( Fig . 4B , C , E and F ) ., Similar results were derived from the phospho-PPI network generated only from the “Y2H” category ( Supplementary Fig . S2 ) ., We further compared the abilities to predict phosphoproteins by using node degree and protein abundance levels above given thresholds ., The predictive power of node degree was markedly higher than that of protein abundance , except in the case of proteins that were extremely abundant ( Supplementary Fig . S3 ) ., If this higher predictive ability were attributable to a spurious relationship associated with the actual intracellular proteome abundance , then the node degree of a protein given by PPI assays would appear to provide a better approximation of the intracellular protein copy number than would single-cell proteomic analysis , which is unlikely ., Protein disorder is also a typical feature of “hub” proteins in PPI networks 38–40 ., Parts of unstructured proteins lack fixed structure , and such disordered regions may have the ability to bind multiple proteins and to diversify PPI networks 38–40 ., Additionally , at the proteome level , phosphorylation occurs at high rates in the disordered regions of proteins 16 , 17 , 41–44 ., Therefore , it is highly likely that protein disorder affects the node degree difference between phosphoproteins and nonphosphoproteins ., For every S . cerevisiae protein registered in UniProt , we calculated the probability of harboring intrinsic disordered regions ( see Materials and Methods ) ., In the “ALL” phospho-PPI network , the ratio of phosphoproteins to nonphosphoproteins increased smoothly with increasing disorder probability level ( Fig . 4G ) ., However , in the same network , the node degree levels of phosphoproteins and nonphosphoproteins of the same disorder probability level were significantly different ( Fig . 4H and I ) ., Even between phosphoproteins that had a low disorder probability of <0 . 1 and nonphosphoproteins that had an extremely high disorder probability of >0 . 9 , the node degree level of the phosphoproteins was significantly higher than that of the nonphosphoproteins ( P\u200a=\u200a0 . 0043 ) ., Similar results were observed in the “Y2H” dataset ( Supplementary Fig . S2 ) ., These results imply that the higher node degree of phosphoproteins than of nonphosphoproteins is at least partly independent of the PPI network diversity produced by unstructured proteins ., Other factors that could influence the relationship between protein phosphorylation and interaction are protein size and protein groups with identical cellular function ., Larger proteins may have a greater chance of being phosphorylated and may provide more binding domains for interactions with other proteins ., However , similar to the results for protein abundance and disorder , statistical significance of the higher node degree of phosphoproteins was observed independently of protein length ( Supplementary Fig . S4 ) ., ( Phosphorylation probability was highly correlated with protein length; Supplementary Fig . S4 . ), In the event that both protein phosphorylation and interaction events occurring in a fraction of proteins confer a particular , identical cellular function , then the global difference in node degree levels of phosphoproteins and nonphosphoproteins would appear to be caused only by differences in function ., However , we found that , for most functional annotations of S . cerevisiae in GO Slim ( a higher level view of Gene Ontology ) , there was a higher node degree level for phosphoproteins than for nonphosphoproteins ( Supplementary Fig . S5 ) ., The average node degree of phosphoproteins is higher than that of nonphosphoproteins 45 , but it was unclear, 1 ) whether this characteristic was observable only in hub proteins or whether it existed broadly at the proteome level; and, 2 ) whether this was a spurious correlation that had emerged because of the presence of some third factor hidden in the complex and intertwining proteomes ., Our results show that , in many cases , this characteristic is present not only in hub proteins but also in proteins that have few interacting partners ., They also imply that these protein interactions or binding patterns are not the result of influence by a third factor but are caused by phosphorylation-dependent cellular activities ., The additive effect of kinase–substrate and phosphatase–substrate reactions is one possible model for interpreting this phenomenon in the phospho-PPI network ., If PPIs include many transient signaling reactions between kinases , phosphatases , and their substrates ( most of which are phosphorylated under certain conditions ) , then the signaling proteins may have interactions additional to the cohesive protein binding interactions in the PPI data ., Indeed , some enzyme–protein substrate interactions are surprisingly stable and can be captured in protein interaction assays 46 ., However , of the 795 yeast phosphorylation and dephosphorylation reactions for which information has previously been published 47 , only 3 . 9% , 1 . 6% , 2 . 4% , and 0 . 8% overlapped with those in our “ALL , ” “Y2H , ” “IMM , ” and “TAP” PPI datasets , respectively ( Supplementary Fig . S6 ) ., Note , however , that these values were significantly higher than those expected from negative controls of the corresponding PPI networks generated by “random edge rewiring” ( RER ) , and similar , significant overlaps between physical PPI and signaling network were obtained by another group 48; for details of RER , see Materials and Methods ., On the other hand , the node degree levels of at least 600 proteins ( >20% of phosphoproteomes in the “ALL” phospho-PPI network ) might have been related to , and affected by , phosphorylation , as evidenced by the cumulative percentage of phosphoproteins , which was more than 20% higher than that of nonphosphoproteins ( Fig . 3A ) ., In addition to this , many unidentified phosphoproteins are certain to be present in the nonphosphoprotein dataset ., Therefore , it is difficult to interpret such a large difference in the node degree of phosphoproteins and nonphosphoproteins only in terms of the additive effect of signaling reactions , which had such a small overlap with the PPI data ., Furthermore , among the GO Slim ontology groups within the “signal transduction” and “cell cycle” categories , which especially include many signaling proteins , there were no great distinctions between the node degree levels of phosphoproteins and nonphosphoproteins ( although the node degree levels for “cytokinesis” and “response to stress , ” like those for most of the other ontology groups , showed marked differences between phosphoproteins and nonphosphoproteins ) ( Supplementary Fig . S5 ) ., In the phospho-PPI network , phosphoproteins had a greater tendency than nonphosphoproteins to interact with proteins harboring phosphoprotein binding domains ( PPBDs ) ., Out of 10 known PPBDs—14-3-3 , BRCT , C2 , FHA , MH2 , PBD , PTB , SH2 , WD-40 , and WW 49—six ( BRCT , C2 , FHA , SH2 , WD-40 , and WW ) were present in the member proteins of the “ALL” phospho-PPI network , and the average probabilities that phosphoproteins would interact with proteins that had all PPBDs or each type of PPBD were higher than those for nonphosphoproteins ( Fig . 5 ) ., ( The gap between node degree levels of phosphoproteins and nonphosphoproteins was normalized; see Materials and Methods . ), Considering all of these results and perspectives , a reasonable and generalized model that can be used to interpret the higher node degree of phosphoproteins is that reversible and alternative phosphorylation reactions alter the physical characteristics of proteins under various environmental conditions; the interacting or binding partners of phosphoproteins are thereby more diversified than those of nonphosphorylated proteins ., Consistent with this interpretation , phosphoproteins harboring at least two phosphosites had more interacting partners than those with a single phosphosite in the phospho-PPI network ( Supplementary Fig . S7 ) , even though phosphoproteins follow a power-law distribution with regard to phosphosite number counts and only a small fraction of phosphoproteins have multiple phosphosites 50 ., Protein phosphorylation reactions therefore seem to make a large contribution to intracellular PPI diversity ., We further analyzed the phosphorylation patterns of protein pairs forming pair-wise interactions in the phospho-PPI network , and we found that both interacting proteins in each pair tended to be phosphorylated ., For every category of phospho-PPI network , three types of pair-wise interactions were counted , whereby “Both , ” “Either , ” or “Neither” of two interacting proteins were phosphorylated ., The “Both” and “Neither” types of protein interactions were significantly more common in the real phospho-PPI network than was expected from negative controls produced by RER , whereas the “Either” types of protein interactions were significantly less common than expected ( Fig . 6; Supplementary Fig . S8 ) ., Notably , this outcome was independent of whether the node degrees of the phosphoproteins were higher or lower than those of the nonphosphoproteins , because RER does not change the node degree of each protein in a given network 51 ., PPI data contain homodimer and heterodimer information that can be captured by experimental assays such as two-hybrid assays 52 ., Therefore , to check the possibility that the tendency of interacting proteins to have similar phosphorylation patterns was caused by protein interactions between structurally and sequentially homologous proteins with similar phosphosites , we conducted the same analysis as above but using “filtered” phospho-PPI networks , in which interactions between two homologous proteins were eliminated by E-value cut-offs of 1e–10 in the BLASTP program , but no marked change was observed ( Fig . 6; Supplementary Fig . S8 ) ., Proteins involved in signal transduction pathways tend to be phosphorylated , and this is reflected in the PPI data , although the overlaps between such signaling reactions and PPIs are limited ( see above and Supplementary Fig . S6 ) ., Another possible interpretation for the multitude of physical interactions between phosphoproteins is that physically binding proteins that are members of the same protein complex tend to be phosphorylated simultaneously by a single enzyme ., To search for the protein kinases potentially responsible for the co-phosphorylation of proteins forming the same complex , we analyzed a dataset of kinase–substrate relationships with PPI data of the “ALL” category ., In the following analysis , we used 85 and 65 kinases , respectively , from the experimental results of an in vitro kinase–substrate assay 53 and a literature-derived collection of yeast signaling reactions 47 , each having multiple substrates ( Supplementary Table S3 ) ., For each kinase , its multiple substrates were superimposed on the PPI network and the number of “interacting kinate modules” ( IKMs , triangle motifs composed of a kinase and its two physically interacting substrates ) ( Fig . 7A ) 53 was counted and compared with those estimated in negative controls of the PPI network produced by NLS and RER ., This analysis revealed that three kinases from the in vitro assay and 12 from the literature-based collection had significantly higher IKM formability than those expected from both NLS and RER ( P<0 . 05 ) ( Fig . 7B and C; Supplementary Table S3 ) ., Similar results were obtained by using the “filtered” phospho-PPI network ( Supplementary Fig . S9; Supplementary Table S3 ) ., Accordingly , we suggest that , when a protein complex and kinase are in close proximity within the intracellular environment , there is a high chance of simultaneous phosphorylation of member proteins participating in the complex ., This is consistent with the subcellular co-localization of signaling networks recently revealed through the systematic prediction of signaling networks by using phosphoproteome data with an integrated protein network information derived from curated pathway databases , co-occurring terms in abstracts , physical protein interaction assays , mRNA expression profiles , and the genomic context 48 , and by data analysis of time-course phosphoproteome data 54 ., IKMs may enhance the subcellular co-localization of signaling reactions , and/or vice versa ., The literature-derived signaling collection is presumably more enriched with well-investigated reactions and thus may more accurately reflect in vivo signaling ., This may explain why the collection harbored more kinases with high IKM formabilities ( 12 out of 65 ) than the in vitro kinase–substrate relationship data ( three out of 85 ) ., It is plausible that , in living cells , the diversity of protein interactomes ( not only of protein signaling but also of protein complex formation ) is essentially influenced by the large number of phosphorylation events; many reversible phosphorylations might control condition-specific protein binding interactions related to different subcellular processes and molecular machines ., On the other hand , protein phosphorylation patterns also seem to depend largely on intracellular protein interaction diversity ., It is possible that many of the proteins defined as nonphosphoproteins in this study can actually be phosphorylated under appropriate cellular conditions ., Even where this is true , however , the set we defined here as phosphoproteins should be enriched with proteins that are frequently phosphorylated under normal or many different cellular conditions , because the frequently phosphorylated proteins have a higher chance of being identified as phosphoproteins than do the rarely phosphorylated proteins ., Accordingly , the features and models discussed in this study should reflect the overall characteristics of phosphoproteins and nonphosphoproteins among a number of different cellular conditions ., This is supported by the finding that proteins that had two or more phosphosites physically interacted with more proteins than did those with only a single phosphosite ( Supplementary Fig . S7 ) ., Although the quality of current yeast PPI data is also not perfect and the data may include false positives , the observed features with statistical significance should be consequences of the actual behaviors of intracellular proteins , because the effects of such false positives on the statistical tests are supposedly random ., The integrative data-mining of yeast multi-omics data has now shed light on the macroscopic and large-scale relationships between phosphoproteomes and protein interactomes ., Future comprehensive analyses of the in vivo link between protein phosphorylation and physical interaction will yield more insights into the complex and intertwined molecular systems of living cells ., Saccharomyces cerevisiae strain IFO 0233 cells grown continuously on glucose medium 55 were used ., Pelleted cells were vacuum dried and frozen until further analysis ., A Bioruptor UCW-310 ( Cosmo Bio , Tokyo Japan ) was used to disrupt the pellets in 0 . 1 M Tris-HCl ( pH 8 . 0 ) containing 8 M urea , protein phosphatase inhibitor cocktails 1 and 2 ( Sigma ) , and protease inhibitors ( Sigma ) ., The homogenate was centrifuged at 1 , 500g for 10 min and the supernatant was reduced with dithiothreitol , alkylated with iodoacetamide , and digested with Lys-C; this was followed by dilution and trypsin digestion as described 56 ., Digested samples were desalted by using C-18 StageTips 57 ., Phosphopeptide enrichment by hydroxy acid–modified metal oxide chromatography ( HAMMOC ) was performed as reported previously 11 , 58 ., Briefly , digested lysates ( 100 µg each ) were loaded onto a self-packed titania-C8 StageTip in the presence of lactic acid ., After the samples had been washed with 80% acetonitrile containing 0 . 1% TFA , phosphopeptides were eluted by a modified approach using 5% ammonium hydroxide , 5% piperidine , and 5% pyrrolidine in series 59 ., An LTQ-Orbitrap XL ( Thermo Fisher Scientific , Bremen , Germany ) coupled with a Dionex Ultimate 3000 ( Germering , Germany ) and an HTC-PAL autosampler ( CTC Analytics AG , Zwingen , Switzerland ) was used for nanoLC-MS/MS analyses ., An analytical column needle with a “stone-arch” frit 60 was prepared with ReproSil C18 materials ( 3 µm , Dr . Maisch , Ammerbuch , Germany ) ., The injection volume was 5 µL and the flow rate was 500 nL/min ., The mobile phases consisted of ( A ) 0 . 5% acetic acid and ( B ) 0 . 5% acetic acid and 80% acetonitrile ., A three-step linear gradient of 5% to 10% B in 5 min , 10% to 40% B in 60 min , 40% to 100% B in 5 min , and 100% B for 10 min was employed throughout this study ., The MS scan range was m/z 300 to 1500 , and the top 10 precursor ions were selected in MS scans by Orbitrap with R\u200a=\u200a60 , 000 for subsequent MS/MS scans by ion trap in the automated gain control ( AGC ) mode; AGC values of 5 . 00e+05 and 1 . 00e+04 were set for full MS and MS/MS , respectively ., The normalized collision energy was set at 35 . 0 ., A lock mass function was used for the LTQ-Orbitrap to obtain constant mass accuracy during gradient analysis ., Both Mass Navigator v1 . 2 ( Mitsui Knowledge Industry , Tokyo , Japan ) and Mascot Distiller v2 . 2 . 1 . 0 ( Matrix Science , London , UK ) were used to create peak lists based on the recorded fragmentation spectra ., Peptides and proteins were identified by automated database searching using Mascot Server v2 . 2 ( Matrix Science ) against UniProt/SwissProt v56 . 0 with a precursor mass tolerance of 3 ppm , a fragment ion mass tolerance of 0 . 8 Da , and strict trypsin specificity , allowing for up to two missed cleavages ., Carbamidomethylation of cysteine was set as a fixed modification , and oxidation of methionines and phosphorylation of serine , threonine , and tyrosine were allowed as variable modifications ., Phosphopeptide identification and phosphorylated site determination were performed in accordance with a procedure reported previously 11 ., The false discovery rate was estimated to be 1 . 07% using a randomized database ., All annotated MS/MS spectra were stored in PepBase ( http://pepbase . iab . keio . ac . jp ) ., Saccharomyces cerevisiae phosphoproteome data were obtained from Dataset S1 of Holt et al . 16 ., Another collection of formerly identified phosphoproteins and their phosphosites was obtained from UniProt ( release 15 . 14; http://www . uniprot . org/ ) 19 ., All UniProtKB/Swiss-Prot protein entries identified to have at least one phosphosite in high-throughput phosphoproteomics studies were downloaded via the Protein Knowledgebase ( UniProtKB ) in XML format by querying the term scope: “PHOSPHORYLATION LARGE SCALE ANALYSIS AT” ., Some phosphoproteins registered in UniProt had multiple synonyms of UniProt accession ., For integrative analyses and comparisons of yeast multi-omics data , all identities of proteins and genes obtained from different data sources were standardized to UniProt accessions ., If objects ( e . g . gene names , ORF names , and/or locus names ) in a data source did not have UniProt accessions , the objects were standardized to their corresponding UniProt accessions according to the cross-reference list prepared from UniProtKB/Swiss-Prot protein entries obtained from UniProt ( release 15 . 14 ) ., In cases when an object corresponded to multiple synonyms of UniProt accessions , all accessions were used to identify its corresponding objects in other data sources ., The phosphoproteome data newly identified in this study and the former phosphoproteome datasets obtained from Holt et al . and UniProt were unified according to their UniProt accessions ., Positions of phosphosites and their amino acid residues in the unified phosphoproteome data were double-checked by using the proteome sequences obtained from UniProt ( release 15 . 14 ) ., From SGD ( Saccharomyces Genome Database; http://yeastgenome . org ) 32 , annotations of 5 , 815 known and predicted genes were obtained ., ORF names of genes were checked by using the unified phosphoproteome data to determine whether the encoded protein was identified as a phosphoprotein ., The S . cerevisiae PPI network was obtained as XML files ( Scere20081014 ) from DIP ( Database of Interacting Proteins; http://dip . doe-mbi . ucla . edu ) 33 ., We eliminated each interaction entry including three or more “interactors” ( e . g . , in which multiple prey proteins were detected for one bait protein in one experimental assay ) and used only those including two “interactors . ”, Every node in the PPI network was labeled by its corresponding UniProt ID provided in the same XML file ., For the PPI assay , PPI data were further grouped into four categories: all kinds of experimental methods ( “ALL” ) , yeast two-hybrid ( “Y2H” ) , co-immunoprecipitation ( “IMM” ) , and tandem affinity purification ( “TAP” ) ., A “filtered” PPI network was also prepared for each category by eliminating interactions between two similar proteins by using the BLASTP program and an E-value cut-off of 1e–10 ., Unified phosphoproteome data were mapped onto every category of PPI data prepared from DIP according to their UniProt accessions , and a phospho-PPI network was generated ., Throughout this study , proteins that did not correspond to phosphoproteome data were termed “nonphosphoproteins . ”, To prepare negative controls for PPI and phospho-PPI networks , two different processes ( as diagrammed in Fig . 1 ) were appropriately adopted on a case-by-case basis ., “Node label shuffling” ( NLS ) swaps the labels of two randomly selected nodes in a given network; it repeats this operation a sufficient number of times until all pair-wise interactions in the queried network have disappeared or until the number of iterations reaches 1 , 000 times the number of interactions ., “Random edge rewiring” ( RER ) randomly selects two edges in a given network and randomly rewires them ., During this process , each rewiring operation is retried if a pair of nodes redundantly wired by two edges occurs in the network; the iteration termination condition is the same as that of NLS ., Proteome abundance data for S . cerevisiae that were previously acquired through a single-cell proteomics analysis combining high-throughput flow cytometry and a library of GFP-tagged strains 37 were used to analyze the characteristics of protein expression in the phospho-PPI network ., These data were composed of proteome abundance data measured for cells grown in rich ( YEPD ) and synthetic complete ( SD ) medium ., For each cell growth condition , protein names were standardized to UniProt accessions , and protein abundance levels were log-transformed ( base 10 ) and superimposed on each of the phospho-PPI networks of “ALL” and “Y2H . ”, In this case , protein nodes for which the abundance levels were not provided in the abundance data were deleted from the phospho-PPI network ., The protein disorder level of every S . cerevisiae protein registered in UniProt ( release 15 . 14 ) was predicted by the POODLE-W program , which uses the support vector machine–based learning of amino acid sequences of structurally confirmed disordered proteins 61 ., For the analysis , we used the “disorder probability” ( i . e . the probability that a given protein is unstructured ) output by this program ., Saccharomyces cerevisiae gene annotations belonging to “molecular function , ” “biological process , ” or “cellular component” of GO Slim , a higher level view of S . cerevisiae Gene Ontology ( GO ) , were downloaded via the SGD ftp site ., Information on S . cerevisiae proteins , each of which has at least one of 10 known phosphoprotein binding domains ( PPBDs ) , namely 14-3-3 , BRCT , C2 , FHA , MH2 , PBD , PTB , SH2 , WD-40 , and WW 49 , was obtained according to the protein domain annotations of UniProt ( release 15 . 14 ) , which were provided by other protein databases ., To evaluate the tendencies of phosphoproteins and nonphosphoproteins to interact with proteins that had PPBDs , the normalized probabilities of such interactions were defined ., For each protein , the number of interacting protein partners that had PPBDs was divided by the number of all interacting partners ., To find possible IKMs , kinases previously reported to phosphorylate multiple substrates were obtained from data on in vitro substrates recognized by most yeast protein kinases that were measured with the use of proteome chip technology Supplementary Data 2 of Ptacek et al . 53 , as well as from a literature-derived collection of documented yeast signaling reactions Table S3 of Fiedler et al . 47 ., All gene names of substrates in the in vitro kinase–substrate relationship data and ORF name

Introduction, Results/Discussion, Materials and Methods

Following recent advances in high-throughput mass spectrometry ( MS ) –based proteomics , the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms ., Although a critical role of phosphorylation is control of protein signaling , our understanding of the phosphoproteome remains limited ., Here , we report unexpected , large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data ., First , new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data ., This revealed that nearly 60% of ∼6 , 000 yeast genes encode phosphoproteins ., We mapped these unified phosphoproteome data on a yeast protein–protein interaction ( PPI ) network with other yeast multi-omics datasets containing information about proteome abundance , proteome disorders , literature-derived signaling reactomes , and in vitro substratomes of kinases ., In the phospho-PPI , phosphoproteins had more interacting partners than nonphosphoproteins , implying that a large fraction of intracellular protein interaction patterns ( including those of protein complex formation ) is affected by reversible and alternative phosphorylation reactions ., Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells , the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level ., Moreover , analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells ., These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other .

To date , high-throughput proteome technologies have revealed that hundreds to thousands of proteins in each of many organisms are phosphorylated under the appropriate environmental conditions ., A critical role of phosphorylation is control of protein signaling ., However , only a fraction of the identified phosphoproteins participate in currently known protein signaling pathways , and the biological relevance of the remainder is unclear ., This has raised the question of whether phosphorylation has other major roles ., In this study , we identified new phosphoproteins in budding yeast by mass spectrometry and unified these new data with publicly available phosphoprotein data ., We then performed an integrative data-mining of large-scale yeast phosphoproteins and protein–protein interactions ( complex formation ) by an exhaustive analysis that incorporated yeast protein information from several other sources ., The phosphoproteome data integration surprisingly showed that nearly 60% of yeast genes encode phosphoproteins , and the subsequent data-mining analysis derived two models interpreting the mutual intracellular effects of large-scale protein phosphorylation and binding interaction ., Biological interpretations of both large-scale intracellular phosphorylation and the topology of protein interaction networks are highly relevant to modern biology ., This study sheds light on how in vivo protein pathways are supported by a combination of protein modification and molecular dynamics .

computational biology/systems biology

journal.pgen.1007486

Rad51 recruitment and exclusion of non-homologous end joining during homologous recombination at a Tus/Ter mammalian replication fork barrier

The stalling of replication forks at sites of abnormal DNA structure , following collisions with transcription complexes or due to nucleotide pool depletion—collectively termed “replication stress”—is a significant contributor to genomic instability ., Inherited mutations in genes that regulate the replication stress response cause a number of human diseases , ranging from developmental disorders to highly penetrant cancer predisposition syndromes 1–5 ., Replication stress is thought to be a near-universal phenomenon in tumorigenesis and some of the molecules that act upon the stalled fork are considered promising targets for cancer therapy 6 ., Replication fork stalling provokes a diverse set of cellular responses , including: stabilization of the stalled replisome; regulated replisome disassembly ( “fork collapse” ) ; protection of the fork from deleterious nucleolytic processing; remodeling of DNA structure at the stalled fork; and engagement of repair or “replication restart” 5 , 7–15 ., The S phase checkpoint and the homologous recombination ( HR ) systems are intimately involved in coordinating these responses , collaborating to suppress deleterious genome rearrangements at the stalled fork 2 , 16–20 ., However , the mechanisms governing this coordination remain poorly understood in mammalian cells ., DNA structure at the stalled fork is remodeled by topological stresses on the chromosome at the site of stalling and by the direct action of remodeling enzymes 5 , 12 , 21 ., The fork can be reversed to form a Holliday junction , generating a solitary DNA end which is extensively single stranded due to accompanying nascent lagging strand resection 20 , 22 , 23 ., Other forms of template switching can also occur in the vicinity of the stall site 18 , 24 , 25 ., Endonuclease-mediated fork breakage—either scheduled or unscheduled—can generate double strand breaks ( DSBs ) , which might be either one-ended or two-ended 5 , 20 ., The DNA structures generated by fork remodeling presumably limit the repair pathways that can be engaged ., Two-ended DSBs can potentially be repaired by end joining mechanisms as well as by recombination 26 , 27 ., In contrast , a one-ended DSB or a solitary DNA end lacks a readily available ligation partner for end joining , and may preferentially engage break-induced replication 28 , 29 ., Consistent with this , HR induced by a two-ended chromosomal DSB is subject to competition by classical non-homologous end joining ( C-NHEJ ) , whereas HR induced by a nicking enzyme ( “nickase” ) —in which the replication fork converts the nick into a one-ended DSB—is unaffected by deletion of C-NHEJ genes 30–32 ., Thus , in mammalian cells , the susceptibility of HR to competition by C-NHEJ in a particular cellular context is a useful “probe” with which to analyze the DNA structural intermediates of HR ., Since the stalled fork response entails the formation of diverse DNA structures and is not restricted to two-ended DSBs , repair pathway “choice” at a stalled fork may differ from that at a defined two-ended DSB ., Study of replication-coupled repair of a covalent DNA inter-strand crosslink ( ICL ) in Xenopus laevis egg extracts has revealed some of the fundamental steps of stalled fork processing and repair 2 , 20 ., The Fanconi anemia ( FA ) /BRCA pathway plays a key role in detecting and processing forks bidirectionally arrested at the ICL 33–35 ., The FANCD2/FANCI heterodimer orchestrates dual incisions of one of the sister chromatids on either side of the ICL ., Importantly , efficient incision of the bidirectionally arrested forks is suppressed until the two opposing forks have each stalled at the ICL 36 ., The resulting two-ended DSB is repaired by HR-mediated sister chromatid recombination , in which the BRCA gene products play canonical roles in promoting Rad51 loading and strand exchange functions 37–41 ., HR repair of such a two-ended DSB intermediate could , in principle , be subject to competition by C-NHEJ or other end joining systems ., However , recent evidence of fork reversal during ICL repair suggests that at least one of the two DSB ends is extensively single stranded 23 ., Competition between HR and C-NHEJ is not a major feature of DSB repair in yeast , since C-NHEJ is a relatively low-flux pathway ., Additionally , the Fanconi anemia pathway in yeast is limited to evolutionarily conserved homologs of FANCM 42 , 43 , suggesting that the innovation of FANCD2/FANCI-coordinated incision of bidirectionally arrested forks occurred relatively recently in evolution ., Thus , although certain “core” elements of DSB repair and stalled fork metabolism are conserved between yeast and vertebrates , there are likely significant inter-species differences that remain to be fully defined ., Studies in yeast , using non-enzymatic , locus-specific replication fork barriers ( RFBs ) , show that stalled fork HR can mediate both conservative and deleterious repair , the latter including gross chromosomal rearrangements and more localized copy number changes at the site of stalling 14 , 18 , 19 , 24 , 44–48 ., In contrast to the above-noted X . laevis ICL repair model , HR at an RTS1 RFB in Schizosaccharomyces pombe is not accompanied by evidence of DSB formation 19 , 24 ., Processing of the stalled fork in S . pombe may also trigger an aberrant form of “replication restart” , a rad22Rad52-dependent process in which the restarted fork is prone to collapse 45 ., This aberrant fork restart mechanism is reminiscent of break-induced replication ( BIR ) in Saccharomyces cerevisiae , which is characteristically unstable and mutation-prone 49 , 50 ., Indeed , current models of aberrant replication restart in S . pombe invoke a migrating bubble mechanism equivalent to the mechanism of BIR in S . cerevisiae 49 ., Rad52-dependent pathways have also been implicated in stalled fork repair in mammalian cells 51–53 ., To facilitate analysis of mammalian stalled fork metabolism and repair , we adapted the Escherichia coli Tus/Ter RFB for use in mammalian cells 54–58 ., A chromosomally integrated array of six 23 bp Ter sites mediates Tus-dependent , locus-specific replication fork stalling and HR on a mammalian chromosome , enabling direct quantitation of the repair products of mammalian replication fork stalling ., We showed that conservative “short tract” gene conversion ( STGC ) at Tus/Ter is positively regulated by BRCA1 , BRCA2 , Rad51 and the Fanconi anemia pathway—consistent with the idea that STGC represents a physiological HR response to fork stalling 56 , 58 ., In contrast , “long tract” gene conversion ( LTGC ) —an error-prone HR outcome in which a replicative mechanism copies several kilobases from the partner sister chromatid—is suppressed by BRCA1 and appears to be Rad51-independent ., We recently identified a novel product of stalled fork repair in primary mouse cells lacking the hereditary breast/ovarian cancer predisposition gene , Brca1: the formation of small ( 2–6 kb ) non-homologous or microhomology-mediated tandem duplications ( TDs ) 58 ., Tus/Ter-induced TDs in Brca1 mutant cells are mediated by a replication restart-bypass mechanism , which is completed by Xrcc4-dependent C-NHEJ ., This finding , together with previous observations , suggests that C-NHEJ can access DNA ends positioned close to the site of fork stalling 59 , 60 ., Notably , Tus/Ter-induced STGC is a product of bidirectional replication fork stalling 56 ., By analogy with the processing of forks bidirectionally arrested at an ICL in X . laevis , Tus/Ter-induced STGC might entail the formation of a two-ended DSB intermediate and might therefore be subject to competition by C-NHEJ ., To test this hypothesis , we have analyzed the impact of deletion of the C-NHEJ genes Xrcc4 and Ku70 on Tus/Ter-induced HR ., To determine whether C-NHEJ interacts with HR at Tus/Ter-stalled replication forks , we targeted a 6xTer-HR reporter as a single copy to the ROSA26 locus of mouse embryonic stem ( mES ) cells carrying biallelic conditional alleles of the C-NHEJ gene Xrcc4 ( here termed “Xrcc4fl/fl” ) , as described in Materials and Methods 56 , 61 ., The 6xTer-HR reporter contains an I-SceI target site adjacent to the 6xTer array ( Fig 1A ) ., Thus , transfection of Tus enables analysis of HR in the stalled fork response , while transfection of I-SceI in parallel samples enables analysis of DSB-induced HR ., The reporter also contains elements to distinguish short tract gene conversions ( STGC ) from long tract gene conversions ( LTGC ) , the latter being rare HR products in wild type cells 62 , 63 ., Although HR by either STGC or LTGC converts the cell to GFP+ , LTGC additionally converts the cell to RFP+ , by replicative duplication of an RFP cassette within the reporter ( Fig 1A ) 56 , 64 ., We transduced a ROSA26-targeted Xrcc4fl/fl 6xTer-HR reporter clone with adenovirally-encoded Cre recombinase and screened for derivative clones that had either lost ( Xrcc4Δ/Δ ) or retained ( Xrcc4fl/fl ) Xrcc4 ., Xrcc4 loss or retention was detected by PCR on genomic, ( g ) DNA and was confirmed in a subset of clones by western blotting ( Fig 1B and 1C ) ., We studied HR in five independent Cre-treated Xrcc4fl/fl 6xTer-HR reporter clones and five independent Cre-treated Xrcc4Δ/Δ 6xTer-HR reporter clones in response to either Tus or I-SceI—each transfected in parallel samples ( see Materials and Methods ) ., As expected , I-SceI-induced STGC and LTGC were elevated up to 4-fold in Xrcc4Δ/Δ cells in comparison to Xrcc4fl/fl cells ( Fig 1D and 1E ) 30 ., Interestingly , deletion of Xrcc4 stimulated STGC more strongly than LTGC; as a result , the proportion of I-SceI-induced HR events that resolved as LTGC was reduced from ~5% in Xrcc4fl/fl cells to ~2–3% in Xrcc4Δ/Δ cells ( Fig 1E ) ., The impact of Xrcc4 deletion on Tus/Ter-induced HR was quite different ., Tus/Ter-induced STGC was marginally reduced in Xrcc4Δ/Δ cells in comparison to Xrcc4fl/fl cells , while Tus/Ter-induced LTGC was unaffected by deletion of Xrcc4 ( Fig 1D and 1E ) ., These results suggest that the interaction between HR and C-NHEJ at a chromosomal DSB is not recapitulated in the regulation of HR at a stalled replication fork ., To determine whether the observed phenotypes are affected by re-expression of wtXrcc4 , we used lentiviral transduction to express N-terminal influenza haemagglutinin ( HA ) -tagged wild type mouse ( m ) Xrcc4 in Xrcc4Δ/Δ 6xTer-HR reporter clones #11 and #13 and in Xrcc4fl/fl 6xTer-HR reporter clones #8 and #39 ., Briefly , we adapted the lentiviral vector pHIV-Zsgreen 65 by replacing the Zsgreen cDNA with a bicistronic cDNA encoding the enzyme nourseothricin ( NTC ) acetyl transferase ( NAT ) 66 fused via a self-cleaving T2A peptide to the human ( h ) CD52 antigen ( S1A Fig ) 67 ., Transient expression of the empty pHIV-NAT-CD52 vector in mouse ES cells produced strong cell surface staining of hCD52 , as revealed by immunostaining using an anti hCD52-specific monoclonal antibody 68 ( S1B Fig ) ., Transduction of mES cells with the empty pHIV-NAT-CD52 vector , followed by selection in NTC , generated pools of transduced cells that stained strongly and specifically with anti-hCD52 , whereas transduction with pHIV-NAT ( i . e . , lacking hCD52 expression ) , followed by NTC selection , generated no CD52-specific cell surface signal ( S1B Fig ) ., CD52 expression levels in pHIV-NAT-CD52-mXrcc4-transduced , NTC-selected mES cells were lower than in control empty vector ( pHIV-NAT-CD52 ) -transduced controls , possibly reflecting constraints imposed by Xrcc4 expression from the multicistronic lentiviral expression cassette ., Nonetheless , exogenous wtXrcc4 was overexpressed in comparison to endogenous Xrcc4 , as revealed by RT-qPCR and by western blotting in lentivirally transduced Xrcc4fl/fl cultures ( Fig 2A and 2B ) ., As expected , re-expression of wtXrcc4 complemented the sensitivity of Xrcc4Δ/Δ cells to the radiomimetic drug phleomycin ( Fig 2C ) ., Xrcc4Δ/Δ 6xTer-HR reporter cells transduced with pHIV-NAT-CD52-Xrcc4 and selected in NTC revealed suppression of I-SceI-induced HR to levels equivalent to that observed in isogenic Xrcc4fl/fl 6xTer-HR reporter cells ( Fig 2D ) ., Indeed , I-SceI-induced STGC and LTGC were each restored to wild type levels and the ratio of LTGC:Total HR reverted from ~2% to ~4% in Xrcc4-transduced Xrcc4Δ/Δ cells ., Parallel cultures transduced with pHIV-NAT-CD52 empty vector and selected in NTC retained the original Xrcc4Δ/Δ phenotype ., These experiments confirm that Xrcc4 affects the balance between I-SceI-induced STGC and LTGC , suppressing STGC more strongly than LTGC ., In contrast , all measures of Tus/Ter-induced HR were unaffected by re-expression of wtXrcc4 in Xrcc4Δ/Δ cells ( Fig 2D ) ., To confirm these findings , and to minimize opportunities for cellular adaptation during complementation with wtXrcc4 , we used transient transfection to restore expression of wtXrcc4 in Xrcc4Δ/Δ cells ., Consistent with the above-noted findings , transient Xrcc4 expression strongly suppressed I-SceI-induced HR in Xrcc4Δ/Δ 6xTer-HR reporter cells , but had no significant impact on Tus/Ter-induced STGC or LTGC in these cells ( S2 Fig ) ., Taken together , these experiments show that Xrcc4 status has no impact on Tus/Ter-induced HR in mouse ES cells ., We showed previously that STGC at Tus/Ter-stalled forks is controlled by the HR proteins BRCA1 , CtIP , BRCA2 and Rad51 and by the structure-specific nuclease scaffold SLX4 56 , 58 ., In contrast , Tus/Ter-induced LTGC is suppressed by BRCA1 and is independent of BRCA2 or Rad51 ., We found that these relationships were unaffected by Xrcc4 status ( Fig 3A ) ., In the regulation of I-SceI-induced HR , we previously noted a specific role for BRCA1 and CtIP in suppressing an HR bias towards LTGC 64 ., In contrast , loss of BRCA2 or Rad51 had little impact on the LTGC/Total HR ratio in response to an I-SceI-induced DSB ., We observed similar effects on I-SceI-induced HR in Xrcc4Δ/Δ 6xTer-HR reporter cells ( Fig 3B ) ., Thus , although Xrcc4 deletion affects the ratio of LTGC:total HR in response to I-SceI , the interactions between HR mediators in execution of HR appear to be largely unaffected by loss of C-NHEJ ., DNA polymerase θ , encoded by the POLQ gene , has been implicated in an alternative end joining ( A-EJ ) pathway and in the prevention of genomic instability at sites of replication fork stalling 69–72 ., Polθ has also been found to suppress DSB-induced HR in some cell types 73 , 74 ., We therefore asked whether Polθ interacts with HR in mouse ES cells , either at a Tus/Ter RFB or in DSB repair ., Interestingly , siRNA-mediated depletion of Polθ modestly suppressed Tus/Ter-induced STGC in multiple clones , but in each case the effect failed reach statistical significance ( Fig 4 ) ., Depletion of Polθ had no impact on I-SceI-induced HR either in wild type or Xrcc4 null cells ., These findings raise the possibility that Polθ supports conservative STGC at stalled forks ., They also suggest that the previously reported competition between Polθ and HR in DSB repair is not a feature of mouse ES cells 73 , 74 ., The binding of the Ku70/Ku80 heterodimer to DNA ends is required for engagement of C-NHEJ 75 ., Ku has also been implicated in modulation of repair functions at forks stalled by the action of Topoisomerase I inhibitors , where one-ended breaks are thought to predominate 76 , 77 ., To determine whether Ku DNA end binding activity can influence Tus/Ter-induced HR independent of later steps of the C-NHEJ pathway , we targeted a single copy of the 6xTer-HR reporter to the ROSA26 locus of Ku70–/–mES cells 78 ., Nine independent ROSA26-targeted Ku70–/– 6xTer-HR reporter clones revealed wild type levels of Tus/Ter-induced HR but greatly elevated levels of I-SceI-induced HR ( Fig 5 ) ., To complement this phenotype , we co-transfected either Tus or I-SceI expression vectors with either empty vector or with a vector for expression of wt human KU70 ., Transient expression of wtKU70 suppressed I-SceI-induced HR and complemented phleomycin sensitivity of Ku70–/–cells , as expected ( Fig 6 ) ., In contrast , wtKU70 expression had no impact on Tus/Ter-induced HR ( Fig 6 ) ., In the processing of a conventional DSB , Ku binding to the DNA end is a barrier to DNA end resection ., DNA end resection activity , initiated by CtIP and the Mre11 nuclease , can displace Ku from the DNA end , providing a mechanism by which the HR machinery can overcome the barrier formed by Ku DNA end binding 79 ., To further search for evidence of Ku interaction with stalled fork HR , we determined the impact of siRNA-mediated CtIP depletion on HR in Ku70–/–cells either uncomplemented or transiently complemented with wtKU70 ., As previously reported , CtIP depletion reduced HR in response to Tus/Ter or to an I-SceI-mediated DSB 58 , and this effect was observed in both uncomplemented and Ku70-complemented Ku70–/–cells ( Fig 7A and 7B ) ., However , the proportional impact of CtIP depletion appeared less pronounced in uncomplemented I-SceI-transfected Ku70–/–cells than in the same cells complemented with wtKU70 ( Fig 7B ) ., We quantified this effect by calculating , for each test group , the induced HR in cells that received siCtIP as a proportion of induced HR in cells that received the control siRNA directed to luciferase ., Notably , for I-SceI-induced HR , this ratio was increased in uncomplemented Ku70–/–cells in comparison to wtKU70-complemented cells ( Fig 7C and 7D ) ., In contrast , for Tus/Ter-induced HR , this ratio was unaffected by Ku70 status ., We interpret these results as follows: at a DSB , Ku binding creates a barrier to end resection and CtIP plays a significant role in displacing Ku ., This Ku-displacing role of CtIP is not required in Ku70–/–cells , and the relative importance of CtIP in HR at a DSB in Ku70–/–cells is correspondingly less ., In contrast , at a Tus/Ter RFB , CtIP plays a significant role in HR that is fully independent of Ku70 ., Taken together with the above findings with regard to Xrcc4 , the data indicate that C-NHEJ does not compete with HR at a mammalian Tus/Ter RFB ., Rad51 loading onto ssDNA is a key step in HR ., In contrast to a DSB , where ssDNA is exposed following canonical DNA end resection , the stalled fork might present ssDNA for Rad51 loading through a number of different mechanisms ., To determine whether Rad51 accumulates at Tus/Ter-stalled forks , we used chromatin-immunoprecipitation to study Rad51 accumulation at the ROSA26 locus , in cells transfected with a DSB-inducing nuclease , Tus , or appropriate negative controls ., To induce a DSB at ROSA26 , we used either I-SceI or Cas9 targeted to the I-SceI target site by a sgRNA specific to the I-SceI site ., As a negative control for I-SceI and Tus , we transfected empty expression vector ., As a negative control for Cas9/I-SceI sgRNA , we co-transfected wtCas9 with a non-targeting sgRNA ., The chromatin-immunoprecipitation method is further described in Materials and Methods ., We assessed Rad51 recruitment at 24 and 48 hours following transfection , and assayed its enrichment near the 6xTer array or neighboring I-SceI site by quantitative real-time PCR , using primers at different positions within the ROSA26 gene ( Fig 8A ) ., 24 hours after transfection with either I-SceI or Cas9/I-SceI sgRNA , Rad51 was detected maximally at sites in close proximity to the I-SceI site , and this signal spread up to ~4 kb either side of the DSB ( Fig 8B ) ., By the 48 hour time-point , a specific DSB-induced Rad51 signal was no longer detectable ( Fig 8C ) ., The Rad51 response to a Tus/Ter RFB differed markedly ., Notably , Rad51 accumulation at Tus/Ter was more intense than in the response to a DSB , even though Tus/Ter consistently induces lower HR frequencies than I-SceI in our experiments ., A second striking difference was the distribution of Rad51 ., At the Tus/Ter RFB , Rad51 was strictly localized to within a few hundred base pairs of the RFB , with no spreading of the Rad51 signal detectable even 1 . 3 kb from the RFB ., Third , the Rad51 signal remained detectable at Tus/Ter up to 48 hours after transfection , at a time when the DSB-induced Rad51 signal had subsided ., These findings reveal that Rad51 accumulation at the Tus/Ter RFB is more intense , more sustained and more specifically localized than in the DSB response ., Taken together , these findings suggest that the major DNA structures that bind Rad51 at a Tus/Ter RFB are not conventional DSBs ., In contrast to HR induced by a chromosomal DSB , where C-NHEJ competes to repair the two-ended break , we show here that HR induced by a Tus/Ter RFB in mammalian cells is unaffected by the status of the C-NHEJ genes Xrcc4 or Ku70 ., This shows that the fundamental mechanisms of repair pathway “choice” at a stalled replication fork and a chromosomal DSB differ markedly ., The simplest explanation of these findings is that HR at Tus/Ter does not entail formation of a two-ended DSB intermediate ., We recently used High Throughput Translocation Sequencing ( HTGTS ) to study translocation-competent DNA lesions at Tus/Ter 58 ., In contrast to I-SceI-induced DSBs , where two-ended breaks predominate , the major lesions detected by HTGTS at Tus/Ter were solitary DNA ends ., However , it is possible that two-ended DSB intermediates of STGC arise at Tus/Ter but are not readily detected by HTGTS ., Indeed , in the X . laevis model of replication-coupled ICL repair , temporally coordinated dual incisions of one sister chromatid generate a two-ended DSB intermediate ., Bidirectional replication fork stalling is a critical step in this repair process , the arrival of both forks being required for replisome disassembly , asymmetrical fork reversal , nascent lagging strand resection and FANCD2/FANCI-coordinated incisions flanking the ICL 20 , 23 , 36 ., Significant parallels exist between Tus/Ter-induced STGC and the above-noted model of ICL repair , especially with regard to the role of bidirectional fork arrest ., We previously used Southern blotting to show that Tus/Ter-induced STGC products are of a fixed size , identical to products of I-SceI-induced STGC 56 ., In I-SceI-induced HR , where synthesis-dependent strand annealing ( SDSA ) is thought to be the dominant HR pathway , the fixed size of STGC products reflects the availability of a homologous second end of the two-ended break , which supports termination of gene conversion by annealing with the displaced nascent strand 26 , 27 ., Indeed , if I-SceI-induced STGC is denied a homologous second end , the STGC products retrieved are of variable size , reflecting termination of gene conversion at random sites within the reporter , without the assistance of homologous pairing/annealing 64 ., These aberrant STGCs are likely completed by end joining with the non-homologous second end of the DSB 80 ., In the case of Tus/Ter-induced HR , the stereotyped structure of the STGC products implies that a homologous second DNA end was available to enable termination of STGC by annealing ., This second end , we believe , must originate from the second ( opposing ) fork that stalls at Tus/Ter 56 ., In summary , the mechanism of STGC at Tus/Ter has paradoxical properties ., The structure of Tus/Ter-induced STGC products and its dependency on the Fanconi/BRCA/HR pathway is suggestive of SDSA of a two-ended break ., However , as shown here , C-NHEJ does not compete with Tus/Ter-induced HR ., Several possible models could reconcile these paradoxical properties ., In one model , the processing of the stalled fork might entail production of a conventional DSB , but the ability of Ku to access the DNA ends productively might be impaired ( Fig 9A ) ., Indeed , unproductive binding of Ku to presumptive solitary DNA ends at Topoisomerase I inhibitor-induced DNA lesions has been reported 76 , 77 ., Notably , in these studies , DNA end binding by Ku was shown to modulate repair activity and to influence the requirement for early end resection activities regulated by CtIP and Mre11 ., In contrast , in our experiments , deletion of Ku70 had no impact on Tus/Ter-induced HR and we found no evidence of an interaction between CtIP and Ku70 in the regulation of Tus/Ter-induced HR ., Thus , our findings do not fit readily with the idea that Ku binds unproductively to DSB intermediates during Tus/Ter-induced HR ., In an alternative model , protein complexes at the stalled fork might deny Ku access to a conventional two-ended DSB intermediate by an as yet undefined steric exclusion mechanism ., The process of V ( D ) J recombination in developing immune cells provides precedent for such a mechanism; the RAG protein recombination synapse both initiates incision of the recombining locus and helps to channel the DNA ends towards C-NHEJ , disfavoring engagement of alternative end joining pathways 81 , 82 ., However , none of our findings specifically support this model ., Although inactivation of the Fanconi anemia pathway has been reported to promote C-NHEJ-mediated toxic chromosome rearrangements 59 , 60 , we have not yet found any genetic context in which an interaction between C-NHEJ and Tus/Ter-induced HR is “unmasked” ., A notable problem with the above-noted models , which invoke a conventional DSB intermediate , is their failure to account for the distinctive pattern of Rad51 accumulation we observe at Tus/Ter ., We found that Rad51 accumulation at Tus/Ter is more intense , more sustained and more precisely localized than at a conventional DSB ., These findings strongly suggest that the major DNA structures that recruit Rad51 to the Tus/Ter RFB are not conventional DSBs ., We propose that Rad51 is recruited to non-DSB ssDNA structures at stalled forks and that the interaction of Rad51 with these structures accounts for the functional exclusion of C-NHEJ from stalled fork HR ., A major trigger to Rad51 loading at Tus/Ter may be ssDNA gaps on the arrested lagging strand , present immediately adjacent to the Tus/Ter RFB ( Fig 9B ) ., Such ssDNA gaps would be present , albeit transiently , within a normally processive fork ., However , fork stalling would render these same DNA structures abnormal , by virtue of their persistence ., A static ssDNA signal at the site of stalling could provide a stable platform for the loading of Rad51 ., By this model , Rad51 might act as an “early responder” during stalled fork repair , as has been suggested previously 83 ., If Rad51 deposition were a scheduled , early response to fork stalling , this might explain the intensity and localization of the Rad51 signal we observe at Tus/Ter ., Rad51 supports fork reversal in mammalian cells in response to a variety of DNA damaging agents 83 ., Rad51-mediated template switching at the site of stalling could drive limited reversal of the collapsed fork ., If initiated by Rad51-coated lagging strand gaps , this process would displace the unresected nascent leading strand as a 3’ ssDNA tail ( Fig 9B ) ., Rapid coating of the displaced ssDNA tail by RPA and Rad51 could render it inaccessible to binding by Ku and , hence , “invisible” to the C-NHEJ pathway ., The hypothetical limited fork reversal intermediate envisioned by this model might be subject to further processing , leading to more extensive fork reversal and potentially enabling HR initiation without formation of a DSB ., Alternatively , incision of the cruciate structure of the reversed fork could liberate a one-ended DSB with a long 3’ ssDNA tail formed by the displaced nascent leading strand ., It is not yet clear whether Tus/Ter-induced HR entails the formation of such a DSB intermediate ., In summary , a template switch/fork reversal model of HR initiation satisfies two of the key findings reported here: first , the intense , distinctively localized recruitment of Rad51 to the Tus/Ter RFB; second , the functional exclusion of C-NHEJ during Tus/Ter-induced HR ., This hypothetical model makes a number of additional predictions , which it will be relevant to test in future studies ., An interesting feature of I-SceI-induced HR was revealed in this study ., Specifically , although deletion of Xrcc4 elevated the frequencies of both STGC and LTGC , LTGC products as a proportion of all HR products were reduced from ~4% to ~2% in Xrcc4 null cells ., Xrcc4 deletion did not perturb the fundamental relationships of I-SceI-induced HR control reported previously for BRCA1 , CtIP , BRCA2 and Rad51 64 ., This suggests that Xrcc4 loss influences the balance between STGC and LTGC via an HR-independent mechanism ., I-SceI-induced LTGCs , generated by the HR reporter used here , can be considered a type of gap repair 26 ., Thus , I-SceI-induced LTGC might entail repair synthesis in one of two directions ., The first would entail Rad51-mediated invasion of the misaligned GFP copy while the second would entail Rad51-mediated invasion of the correctly aligned , unbroken I-SceI site-containing GFP copy ., ( In the latter case , wtGFP would be generated by annealing at the point of SDSA termination . ), In Xrcc4Δ/Δ cells , the loss of high flux error-free religation of I-SceI-induced DSBs might increase the proportion of cells in which I-SceI sites on both sister chromatids are broken simultaneously ., In such a circumstance , the second mechanism of LTGC noted above would be suppressed ., This , in turn , could lead to the observed reduction in the proportion of I-SceI-induced HR events that resolve as LTGCs in Xrcc4Δ/Δ cells ., The 6xTer-HR reporters used were assembled using standard cloning methods described previously for the 6xTer-HR reporter ( REF ) ., Stable Ter-containing plasmids were generated and manipulated in JJC33 ( Tus– ) mutant strains of E . coli ., All primers for conventional and quantitative PCR were purchased from Life Technologies ., All plasmids used for mouse embryonic stem ( ES ) cell transfection and 293T cell transfections were prepared by endotoxin-free maxiprep ( QIAGEN Sciences , Maryland , MD ) ., siRNA SMARTpools were purchased from GE Healthcare/Dharmacon ., Conditional Brca1 mutant mouse ES cell 1xGFP 6xTer reporters were previously described 58 ., Conditional Xrcc4 mutant mouse ES cells ( cells in which both Xrcc4 copies contained floxed Exon3 alleles ) 61 or Ku70 mutant mouse ES cells ( cells in which exon 4 and part of exon 5 is replaced with the neomycin resistance cassette 78 were thawed onto MEF feeders and subsequently maintained on gelatinized tissue culture plates in ES medium as described ., 20 μg of Kpn I linearlized 6xTer/HR reporter ROSA26 targeting plasmid was introduced by electroporation of 2 x 107 cells ., ES cells were plated onto 6-cm dishes containing Puromycin-resistant feeders and after 18 hours plates were supplemented with 4 μg/mL Puromycin for 24 hours ., Individual colonies were picked for expansion between 9 and 14 days later ., Multiple ROSA26 targeted lines were identified by PCR ., HR cassette ROSA26 integration and overall structure was verified for targeted lines by Southern blotting ., Multiple Xrcc4-deficient ES clones were generated by transient adenovirus-mediated Cre expression and excision of Xrcc4 Exon3 ., ROSA26 genotyping primers: ROSA26-sense- ( CAT CAA GGA AAC CCT GGA CTA CTG ) ; Ter-HR reporter antisense- ( cct cgg cta ggt agg gga tc ) ., KU70 status was verified by PCR: KU70 exon4 5’-sense- ( CCA GTA AGA TCA TAA GCA GCG ATC G ) ; KU70 exon5 3’-antisense- ( CTC TTG TGA CTC ATC TTG AGC TGG ) ; Exon 4/5-neo-deleted allele , KU70 3’- antisense- ( GCC GAA TAG CCT CTC CAC CCA AGC G ) ., Xrcc4 status was determined by PCR: Xrcc4 5’-sense- ( ttc agc taa cca gca tca ata g ) ; floxed allele , Xrcc4 3’-antisense- ( gca cct ttg cct act aag cca tct cac ) ; Exon 3-deleted allele , Xrcc4 3’- antisense- ( taa gct att act cct gca tgg agc att atc acc ) ., Exon3-deleted , Xrcc4-deficient mES cells were transduced with lentivirus expressing a single mRNA encoding nourseothricin acetyl transferase and human CD52 ( the CAMPATH antigen ) , with or without wild type , hemagglutinin-epitope tagged mo

Introduction, Results, Discussion, Materials and methods

Classical non-homologous end joining ( C-NHEJ ) and homologous recombination ( HR ) compete to repair mammalian chromosomal double strand breaks ( DSBs ) ., However , C-NHEJ has no impact on HR induced by DNA nicking enzymes ., In this case , the replication fork is thought to convert the DNA nick into a one-ended DSB , which lacks a readily available partner for C-NHEJ ., Whether C-NHEJ competes with HR at a non-enzymatic mammalian replication fork barrier ( RFB ) remains unknown ., We previously showed that conservative “short tract” gene conversion ( STGC ) induced by a chromosomal Tus/Ter RFB is a product of bidirectional replication fork stalling ., This finding raises the possibility that Tus/Ter-induced STGC proceeds via a two-ended DSB intermediate ., If so , Tus/Ter-induced STGC might be subject to competition by C-NHEJ ., However , in contrast to the DSB response , where genetic ablation of C-NHEJ stimulates HR , we report here that Tus/Ter-induced HR is unaffected by deletion of either of two C-NHEJ genes , Xrcc4 or Ku70 ., These results show that Tus/Ter-induced HR does not entail the formation of a two-ended DSB to which C-NHEJ has competitive access ., We found no evidence that the alternative end-joining factor , DNA polymerase θ , competes with Tus/Ter-induced HR ., We used chromatin-immunoprecipitation to compare Rad51 recruitment to a Tus/Ter RFB and to a neighboring site-specific DSB ., Rad51 accumulation at Tus/Ter was more intense and more sustained than at a DSB ., In contrast to the DSB response , Rad51 accumulation at Tus/Ter was restricted to within a few hundred base pairs of the RFB ., Taken together , these findings suggest that the major DNA structures that bind Rad51 at a Tus/Ter RFB are not conventional DSBs ., We propose that Rad51 acts as an “early responder” at stalled forks , binding single stranded daughter strand gaps on the arrested lagging strand , and that Rad51-mediated fork remodeling generates HR intermediates that are incapable of Ku binding and therefore invisible to the C-NHEJ machinery .

Genomic instability is a significant contributor to human disease , ranging from hereditary developmental disorders to cancer predisposition ., Two major triggers to genomic instability are chromosomal double strand breaks ( DSBs ) and the stalling of replication forks during the DNA synthesis ( S phase ) of the cell cycle ., The “rules” that govern mammalian DSB repair are increasingly well understood , and it is recognized that the two major DSB repair pathways—classical non-homologous end joining ( C-NHEJ ) and homologous recombination ( HR ) —compete to repair a mammalian DSB ., In contrast , we do not yet have equivalent insight into the regulation of repair at sites of mammalian replication fork stalling ., Here , we explore the relationship between C-NHEJ and HR at a defined chromosomal replication fork barrier in mammalian cells ., We show that , in contrast to DSB repair , repair at stalled forks does not entail competition between C-NHEJ and HR ., We find that Rad51 , a key mediator of HR , accumulates in an intense and highly localized fashion at the stalled fork ., Based upon these findings , we propose a model of HR initiation at the stalled fork in which a Rad51-mediated fork remodeling step prevents access of C-NHEJ to the stalled fork .

transfection, gene regulation, cloning, plasmid construction, analysis of variance, mathematics, dna replication, statistics (mathematics), dna construction, molecular biology techniques, dna, dna structure, research and analysis methods, small interfering rnas, mathematical and statistical techniques, gene expression, molecular biology, biochemistry, rna, nucleic acids, genetics, biology and life sciences, physical sciences, dna recombination, non-coding rna, statistical methods, macromolecular structure analysis

journal.pcbi.1004661

Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction

The computational prediction of protein structures from their amino acid sequence is an ongoing challenge that has occupied scientists for more than four decades ., While Anfinsen’s dogma 1 suggests that for most proteins the information contained in their amino acid sequence is sufficient to define their three-dimensional structure , the problem still remains largely unsolved ., For some small proteins ( <80 residues ) , current ab initio prediction methods are successful in predicting the corresponding 3D structures with high accuracy ., One such method is the Rosetta ab initio protocol , which assembles short fragments of known proteins by a Monte Carlo strategy 2 , 3 ., With increasing protein size however , sampling of the large conformational space becomes a major challenge 4 and combination with experimental data is required to achieve accurate protein models 5 , 6 ., As experimental data is not always available and may be difficult or costly to obtain , researchers have focused on reducing the search space of possible protein conformations in other ways , for instance by including evolutionary information found in patterns of correlated mutations in protein sequences ., The underlying assumption is that these correlated pairs indicate spatial proximity in the protein structure and can therefore be used to guide ab initio protein structure prediction 7 ., The idea has already been introduced in the early 1990s 8–11 , however , until recently , the accuracy of the predicted contacts was not sufficient to significantly improve structure prediction methods ., Pairs of correlated mutations have been calculated using ‘local’ statistical models , e . g . mutual information scores , which are not able to separate direct from indirect contact information ., While direct contacts reflect actual contacts in the protein structure , indirect contacts are false positives that arise from connections through a third residue ., These transitive ( indirect ) pair correlations greatly limit the accuracy of predicted residue-residue contacts 7 ., Recently , a substantial increase in prediction accuracy has been achieved by using ‘global’ statistical models 12–16 that are able to reduce these effects of transitivity by treating pairs of residues dependent on each other ., Another important factor for the recent boost in prediction accuracy is the rapid growth of available protein sequences due to advances in DNA sequencing technology 7 ., In 2011 , it has been shown that the information contained in maximum-entropy derived residue-residue contacts is sufficient to predict protein folds with explicit atomic coordinates quite accurately ( Cα-RMSDs of 2 . 7–4 . 8Å over at least two-thirds of the protein ) using the method EVFold 13 ., Since then , a lot of research focused on improving the contact predictions and new methods for residue-residue contact prediction emerge regularly 17–21 ., In addition to the initial predictions of mostly soluble proteins 13 , predicted contacts from evolutionary information have been used to predict protein-protein complexes 22–24 , and the structures of membrane proteins 25 , 26 ., While much effort is put into the improvement of contact predictions , there is also a substantial need to investigate how this information is best exploited in structure prediction ., The accuracy of contact predictions is limited by the statistical nature of the prediction methods , distracting sources of co-evolution ( e . g . active sites and protein-protein interaction sites ) , and limited numbers of homologous sequences ., Due to the noisy nature of the predicted residue-residue contacts , structure prediction protocols with a high tolerance against incorrect distance restraints are needed ., EVFold uses the CNS molecular dynamics software suite 27 , 28 for structure prediction ., It starts with the fully extended amino-acid sequence and folds the protein by applying standard distance geometry techniques and simulated annealing with bonded and non-bonded potentials 13 ., The fragment-based folding algorithm FRAGFOLD 29 , 30 was used in combination with the contact prediction method PSICOV 17 for ab initio structure prediction 31 ., The restraints were scored with a square well function with exponential decay ., Michel and coworkers applied the ab initio structure prediction protocol of the molecular modeling software suite Rosetta 32 with a smoothed square well restraint scoring function to predict structures within the PconsFold pipeline 33 ., A comparison between Rosetta and CNS indicated that with similar contact predictions , models of similar quality were generated 33 ., Improvements in structure prediction were mainly credited to improved residue-residue contact predictions obtained with the combined prediction method PconsC 34 ., The CONFOLD webserver uses the CNS suite 27 , 28 for a two-stage modeling approach ., Both , restraints derived from predicted contacts and secondary structure , are used and after the initial round of model generation , unsatisfied restraints are filtered out ., The method has been shown to be especially powerful when using true contacts 35 ., In this work we combine evolutionary information , obtained from predicted residue-residue contacts , with the Resolution-Adapted Structural RECombination approach RASREC 36 ( cf . Fig 1 ) ., RASREC is an iterative sampling protocol of Rosetta that carries out restraint-guided fragment assembly during six different resampling and refinement stages ., The main idea behind the protocol is the iterative recombination of frequently reoccurring structural features and promising strand pairings ., It has been shown previously that RASREC requires less data and is more robust against incorrect distance restraints than the standard Rosetta prediction protocol 5 , 6 , 36 ., These properties make RASREC the ideal starting point for developing a protocol for structure prediction guided by evolutionary restraints , the latter containing a fraction of incorrectly predicted protein-protein contacts ., For our method , evolutionary information was added to the RASREC protocol by translating the top scoring residue-residue contact pairs into sigmoidal distance restraints ., This initial RASREC prediction was furthermore followed by an additional refinement run using distance information from both the previous run and the predicted residue-residue contacts ., To investigate the performance of our method , we carried out a benchmark on 28 globular proteins using state-of the-art contact predictions ( generated using a pseudo-likelihood maximization approach ) ., To test the impact of increasing numbers of false restraints , we additionally predicted the structures of a smaller benchmark set using less accurate residue-residue contact predictions ( calculated with a mean-field direct coupling analysis ) ., In this manuscript we report the results of the benchmark using both types of residue-residue contact predictions and contrast the performance of our protocol with results obtained by the EVFold webserver using identical contact predictions ., We furthermore illustrate the contribution of the optional refinement run to the final results of our method and investigate the benefits of including predicted residue-residue contacts to the standard RASREC sampling method in general ., We have benchmarked our protocol on two previously published datasets , namely the 14 globular proteins from the EVFold benchmark set published in 13 and the 14 globular proteins used as test set for developing Pconsfold 33 ., The structures vary in sequence length between 58 and 247 residues and cover the three structured CATH classes i . e . mainly α , mainly β , and mixed α/β ., An overview of all targets in our benchmark set can be found in Table 1 ., In case of the EVFold benchmark set , the protein sequences of the models published in 13 ( available at http://evfold . org/evfold-web/datasets . do ) were used to enable a direct comparison between EVFold and our method ., For the Pconsfold dataset , the sequences deposited in the RCSB Protein Data Bank 37 were used ., FASTA sequences for all targets in our benchmark set are available in S2 File ., We used two sets of contact predictions , generated with the PLM ( pseudo-likelihood maximization ) and DI ( direct information/ mean field approximation ) scoring method , respectively ., The PLM method uses a pseudo-likelihood maximization approach 19 , 38 for finding the maximum entropy set of correlated interactions ., This approach is one of the most accurate prediction methods to date 20 ., Residue contacts based on this scoring method were predicted for the entire benchmark set using the EVFold webserver ( available at http://www . evfold . org/ ) with default parameters ., EVFold returns , along with the predicted 3D models , a list of all-by-all residue pairings computed with EVcouplings-PLM ., Restraints based on these contact predictions will be referred to as PLM-restraints in the remainder of this manuscript ., The DI method , as published in 13 , uses a less accurate mean field approximation ., The contact predictions used in 13 are provided as downloadable content on the EVFold website ., Restraints extracted from these contact predictions will be referred to as DI-restraints in the remainder of this manuscript ., In EVFold , contact predictions are further processed by applying several filters based on residue conservation , secondary structure prediction and cysteine pairings 13 before being translated to distance constraints ., In contrast , we used the predicted contacts without any filters to see how much information they provide by themselves ., For both restraint sets , the predicted contacts were ordered by their assigned confidence score and the L top-ranked contacts with a minimum distance of 5 residues were selected ( with L being the length of the protein sequence rounded down to the nearest multiple of 10 ) ., Unless mentioned otherwise , predicted residue contacts refer to these L top-ranked contacts ., The accuracy of the contact predictions was assessed in form of the positive predictive value ( PPV ) by comparing a potential contact to the actual Cβ-Cβ distance in the reference structure ., A contact was counted as a true positive if the Cβ-Cβ distance in the native structure is ≤ 8 Å ., To generate the three-dimensional structures , we used the RASREC protocol as described previously 36 ., For objective benchmarking and mimicking real application cases , homologous structures ( with a PSI-BLAST 39 e-score < 0 . 05 ) were excluded in creating the fragment library of each target ., Instead of using experimentally derived distance restraints , we used the predicted residue contacts as source of residue-residue distance information ., For this purpose , the L top scoring contact predictions were translated into Rosetta specific Cβ-Cβ distance restraints as described below ., To account for the fact that the predicted contacts might be noisy and might contain a varying number of incorrectly predicted contacts ( i . e . false positives ) , the distance restraints were scored with a shallow sigmoidal potential 23:, fSigmoid ( x ) =11+e−m⋅ ( x−x0 ) −0 . 5withx0=8 . 0andm=1, ( 1 ), Satisfied distance restraints ( Cβ-Cβ distance ≤ 8 Å ) add a bonus to the final energy term , while unsatisfied distance restraints are ignored ., This greatly reduces the influence of incorrectly predicted residue contacts and the structure prediction will not be misguided ., Using bounded restraints in this step instead , i . e . punishing each violated restraint with an energy penalty , often resulted in misfolded and unconverged structures in initial test runs ., As in 36 ,, the pool size of RASREC , specifying the number of best scoring models maintained during each iteration stage , was set to 500 ., The total number of models generated during a RASREC run depends on how fast the different iteration stages terminate and cannot be directly controlled ., For the EVFold benchmark set , the total number of generated models per target ranges from 13 , 000 to 65 , 000 ., For a detailed description of all options and parameters used , please refer to S1 Supporting Information and the Protocol Capture in S1 Text and S1 File ., RASREC requires substantial computer resources ., For the EVFold benchmark set , the average computation time was ~2600 cpu hours using 2 . 6 GHz AMD Opteron processors , see Fig A in S1 Supporting Information ., The computation time is dependent on several factors , which include sequence length , fold complexity , and instructiveness of the restraints ., The EVFold webserver offers to directly fold the protein of interest based on its predicted residue-residue contacts ., Structure prediction is accomplished using the CNS software 27 , 28 with the protocol described in 13 ., The webserver predicts structures for different amounts of filtered restraints , starting with only a few and increasing to L in 10 steps with L being the domain length ., As output , the 3D coordinates of all 50 predicted structures are provided ., We used the web interface to generate the models along with the predictions based on the PLM approach ., These models are referred to as EVFold-PLM models ., Further , we used the structures published in 13 ( available at http://evfold . org/evfold-web/datasets . do ) , which are based on the residue-residue contact predictions with the less accurate DI approach and are referred to as EVFold-DI models ., To evaluate the performance of our method , several different metrics were used: 1 ) Cα-RMSD calculated over all residues present in the reference structure ( RMSD ) , 2 ) Cα-RMSD calculated over all residues in secondary structural elements in the crystal structure as assigned by Stride 40 called RMSDSSE , and 3 ) TM-Score 41 over all Cα-atoms in the reference structure ., The template modeling score ( TM-Score ) evaluates the global fold similarity and is less sensitive to local structural variations than the RMSD ., It ranges from 0 ( random similarity ) to 1 ( perfect similarity ) 41 ., In contrast to e . g . RMSD values calculated with PyMOL 42 , which excludes outliers in a series of refinement cycles , these three metrics are easily reproducible and consider the same residues for each model evaluated ., Fig 2 shows the performance of our protocol ( ReRASREC-PLM ) compared to the one of the EVFold web server ( EVFold-PLM ) on the basis of three different metrics ., Our protocol converged ( fraction of converged residues > 0 . 5 in the 30 low-energy structures ) for 26 out of the 28 targets and correctly predicted the fold for each of the converged targets ( TMscore > 0 . 5 or RMSD < 5Å ) ., For the majority of the benchmark set , the final models were of high structural accuracy resulting in an average TM-score of 0 . 74 , an average RMSD of 4 . 4 Å , and an average RMSDSSE of 3 . 3 Å over all 26 converged targets ., The overall performance of our protocol was significantly higher than that of EVFold-PLM using identical contact predictions ( however not necessarily identical distance restraints , see section Structure Prediction with EVFold ) ., With an average TM-score of 0 . 72 over the entire benchmark set , ReRASREC-PLM lead to an improvement of 0 . 17 when compared to EVFold-PLM , whose average TM-score was only 0 . 55 ., ReRASREC-PLM furthermore increased the number of targets with a TM-score > 0 . 7 from 6 to 20 ., In terms of RMSD and RMSDSSE , using our method lead to an average improvement from 7 . 3 Å to 4 . 9 Å and from 5 . 7 Å to 3 . 7 Å respectively ., Moreover , EVFold-PLM failed to predict the correct fold for 6 out of 28 targets ( TM-score < 0 . 5 and RMSD > 5Å ) while our protocol predicted very accurate models ( TM-Score 0 . 62 ) with correct folds for all of these targets ., Based on our backbone convergence criteria ( see Materials and Methods ) our protocol failed for targets 2it6 and 3tgi ., Both targets consist of long loop regions ( fraction of secondary structural content is only 0 . 54 and 0 . 37 respectively ) and are therefore challenging for RASREC as it is mainly focusing on the recombination of reoccurring structural features such as secondary structure elements ., Fig 2 reveals that predictions for two converged targets , namely 5p21 and 1bdo , resulted in models with an RMSD > 10 Å ., The TM-Score is however above 0 . 5 in both cases , i . e . 0 . 65 and 0 . 58 , respectively , showing that the majority of the protein structure was predicted correctly ., The good accordance between the top-scoring models and the corresponding native structures can furthermore be seen in Fig B in S1 Supporting Information ., ReRASREC-PLM was not only able to predict the correct fold for a larger number of targets , but also significantly improved the accuracy within the set of targets with correctly predicted folds ., Excluding the 8 targets where either EVFold-PLM ( 6 ) or RASREC-PLM ( 2 ) had difficulties , ReRASREC-PLM still increased the average TM-Score by 0 . 18 over EVFold-PLM from 0 . 60 to 0 . 78 ., In terms of RMSD and RMSDSSE , RASREC-PLM improved them from 5 . 6 Å to 3 . 9 Å and from 4 . 2 Å to 2 . 9 Å , respectively ., We also compared the accuracy of ReRASREC-PLM with two other recently published methods ( PconsFold 33 and FRAGFOLD 31 ) on the subset of targets where each publication reported actual numbers on ., We found that , although both methods improve upon EVFold-PLM , ReRASREC-PLM still outperforms both ( Table A in S1 Supporting Information ) ., Fig 3 further indicates that the models generated with our protocol do not only have high accuracy in their backbones , but also a high rotamer recovery of core side-chain conformations ., A superposition of the lowest-energy model and the corresponding crystal structure of each target can be found in Fig B in S1 Supporting Information ., Table 2 shows that on average 84% of the converged core side chains in the RASREC models are in the same χ1 rotamer well , and 46% have the same set of rotamer states for all χ angles as the corresponding crystal structures ., An analysis of the single top-ranked models of EVFold-PLM and ReRASREC-PLM furthermore shows that ReRASREC-PLM predicts higher numbers of buried side chains with native χ1 romater assignment than EVFold-PLM , see Table B in S1 Supporting Information ., It has been shown previously 5 , 6 , 36 that RASREC is more robust against incorrect distance restraints than the standard Rosetta ab initio protocol ., A high tolerance against false positives is of special interest for proteins where only a limited number of homologous sequences are available ., In those cases , the fraction of false positives in the corresponding contact predictions is comparably high , hence making structure prediction for standard prediction methods difficult ., To investigate how our protocol performs with an elevated amount of incorrectly predicted residue contacts , we used it in combination with the contact predictions published in 13 ., These predictions were generated with the less accurate mean field approach ( DI–direct information ) and therefore contain an increased number of incorrectly predicted protein contacts as compared to the restraints obtained with the PLM approach ( see Table 1 ) ., With an average PPV of 0 . 51 , the accuracy of the DI-restraints drops by 0 . 17 compared to the average PPV of the PLM-restraints ., Given these restraints with a significantly lower accuracy , our protocol was able to converge for 12 out of 14 targets ( see Fig C in S1 Supporting Information ) and predicted the correct fold for all of the converged targets with an average TM-score of 0 . 70 and an average RMSD of 4 . 0 Å ( see Table 3 ) ., The results obtained with our protocol significantly outperform the top ranked results generated with EVFold using DI-restraints: Using our protocol lead to an increase in average TM-score of 0 . 17 when compared to the average TM-score of 0 . 47 of the corresponding EVFold results ., In terms of RMSD , the use of ReRASREC-DI improved the prediction from 7 . 2 Å to 5 . 6 Å ., For 6 targets , the top-ranked EVFold models furthermore displayed the incorrect fold ( TM-score < 0 . 5 and RMSD > 5 Å ) ., Using the less accurate DI-restraints had less of an impact on accuracy for ReRASREC than for EVFold; the average TM-score of the EVFold benchmark set decreased by 0 . 05 and by 0 . 1 points for ReRASREC and EVFold , respectively ( Table 3 ) ., While ReRASREC predicted the correct fold for all 12 converged targets with both restraint sets , EVFold increased the number of incorrect folds from 2 to 6 when using the less accurate DI-restraints instead of PLM-restraints ., This suggests that our protocol can predict structures with restraints of mediocre accuracy better than the CNS protocol used by EVFold ., For realistic application cases the ranking of the predicted structural models is of great importance as it will be the single criterion for selecting the final predicted models ., The models generated with our protocol were ranked with the full-atom energy function of Rosetta ., All-atom energy functions are very sensitive to correct packing of side chains due to the steep gradient of the Lennard-Jones repulsive term ., Correct packing of side chains is hard to achieve , in particular , if the backbone structure is not sufficiently accurate ., Selection based on this energy function is therefore only possible if the backbone accuracy is very high ., Fig 4 shows the full-atom energies and RMSD values for each model generated during the different stages of a single RASREC run for one exemplary target ., The energy funnel at the low RMSD area shows that the all-atom energy function is able to discriminate between correct and incorrect structural models ., This observation is further reinforced by comparing the lowest-RMSD models to the lowest-energy models ( Table C in S1 Supporting Information ) : The average TM-score of the lowest-RMSD models is with 0 . 77 only 0 . 05 higher than the one of the lowest-energy models generated by ReRASREC with 0 . 72 ., In contrast , EVFold ranks its models based on inherent geometrical properties and constraint satisfaction ., Choosing the lowest-RMSD models instead of the top ranked ones increases the average TM-score from 0 . 55 to 0 . 62 and improves the RMSD from 7 . 3 Å to 5 . 2 Å ., Investigating these results more closely , one can observe that the top ranked structures of EVFold-PLM adapt the incorrect fold ( RMSD > 5 Å and TM-score < 0 . 5 ) for two targets , namely 1bkr and 1o1z , although models with correct topologies were generated as well ., For those two targets , the ranking of EVFold-PLM therefore fails ., For ReRASREC-PLM using the full-atom score function , no such discrepancy was observed ., In this section , we analyze the accuracy of the models generated by EVFold-PLM and ReRASREC-PLM irrespective of their ranking schemes ., Therefore , we have compared the most accurate models ( average of the 10 lowest-RMSD models ) of ReRASREC to the single lowest-RMSD models generated by the EVFold web server within its 50 reported models ., As shown in Fig 5 , the ReRASREC models with lowest RMSD outperform the lowest-RMSD models of EVFold for each converged target ., Overall , the ReRASREC models show an increase in TM-score of 0 . 15 when compared to the average TM-score of 0 . 62 of the single most accurate EVFold models ., We have shown in the previous section that the difference in accuracy between the lowest-energy and lowest-RMSD models of ReRASREC-PLM is small ., The lowest energy models of ReRASREC-PLM are therefore more accurate than any models obtained with the EVFold webserver ( see Fig D in S1 Supporting Information ) ., On average , the lowest-energy models of ReRASREC-PLM lead to an increase in TM-score of 0 . 1 when compared to the TM-score of 0 . 62 of the single lowest-RMSD models of EVFold-PLM ., This shows that our method generates models of higher structural quality than EVFold-PLM ., If the backbone of the first RASREC run did not converge within 2 Å for over 90 percent of the residues , a refinement run ( see Materials and Methods ) was carried out ., To see to what extent the refinement run contributes to the final performance of our protocol , we compared the results of the initial RASREC run to the results obtained after the refinement run ( ReRASREC ) ., Fig 6A and 6B show that the accuracy of the top ten scoring models after the refinement run did not significantly improve ., However , Fig 6C indicates that the pairwise RMSD between all models in the ensemble of the 10 lowest-energy structures decreased by up to 1 . 4 Å after applying the refinement run , indicating better convergence ., On average , the pairwise RMSD decreased by 0 . 5 Å ., In addition , Fig 6D plots the average RMSD of the 10 lowest-energy models against their pairwise RMSD for both RASREC and ReRASREC ., In both cases , a similar correlation between RMSD and pairwise RMSD can be observed ., This shows that the refinement run does not lead to an artificial over-convergence but that the relation between both , as explored by RASREC individually , is kept ., This comparison shows that while the models have high accuracies after the initial RASREC run , the refinement run improves the overall prediction by increasing the precision and convergence of the final models ., Fig 6D shows that there is a reasonable correlation between the pairwise RMSD and the overall performance of each target ( pearson correlation coefficient of 0 . 83 and 0 . 73 for RASREC and ReRASREC respectively ) , meaning that low pairwise RMSD values correlate with low RMSD values and vice versa ., The same trend can be observed when relating the backbone convergence ( as defined previously ) of a prediction to its performance , see Fig E in S1 Supporting Information: High backbone convergence corresponds to low RMSD values with a pearson correlation coefficient of -0 . 77 ., These strong correlations indicate that the accuracy of our final models can be predicted by their convergence ., Highly converged structures ( low pairwise RMSD ) indicate an accurate prediction while a highly diverse ensemble suggests that the prediction is incorrect ., This observation further reinforces our choice deeming predictions with a convergence lower than 50% as unsuccessful ., To identify to what extent the RASREC protocol benefits from residue-residue contact information , we have compared RASREC runs without evolutionary information to RASREC runs including them in form of distance restraints for the 14 proteins of the EVFold benchmark set ., For this test , we considered the results after a single RASREC run without the optional refinement step ., As shown in Fig 7 , without the use of evolutionary contact information , RASREC only predicted the fold of 3 out of 14 proteins correctly ( TM Score > 0 . 5 or RMSD < 5Å ) with an average TM-score of 0 . 41 ., However , if restraints derived from predicted residue-residue contacts were included , RASREC improved the coordinate accuracy for all targets of the benchmark set significantly , yielding an average TM-score over all 14 targets of 0 . 69 ., This shows that the additional data provided by the predicted residue-residue contacts enables RASREC to predict models in a near-native conformation , which would not be possible otherwise ., To investigate to what extend the RASREC protocol uses the available contact information , we compared the fraction of satisfied restraints ( PPV ) , i . e . Cβ-Cβ distance ≤ 8 Å , in the top-scoring models of our protocol and the native structure ( Fig F in S1 Supporting Information ) ., On average , the fraction of satisfied restraints in the top-scoring models after the initial RASREC run ( 0 . 72 ) is very similar to the one of the native models ( 0 . 69 ) ., Overall , the RASREC models satisfy 88% of all restraints that are satisfied in the native structures , see Table D in S1 Supporting Information ., RASREC furthermore correctly violates 63% of the incorrect distance restraints ., The good correspondence between the PPVs on the native structure and the RASREC models , as well as the large fraction of satisfied “correct” restraints shows that RASREC is able to efficiently use the provided contact information ., However , ignoring a larger amount of incorrect distance restraints might improve the prediction even further ., Comparing the PPVs , calculated for the restraints used by EVFold , on the top-ranked EVFold models and the native structures suggests that EVFold does not use the provided contact information as well as RASREC , see Fig F in S1 Supporting Information ., In this study , we demonstrated that RASREC combined with evolutionary information is a powerful tool to predict the structures of globular proteins with high accuracy ., Tested on a benchmark set of 28 globular proteins , we showed that our protocol is able to outperform latest state-of-the-art methods by predicting structures to higher accuracies for the majority of the benchmark set ., We further showed that the combination of improved sampling and high error tolerance of RASREC enables structure prediction in cases where the accuracy of predicted contacts is comparatively low , e . g . dropping below 50 percent ., Robustness against erroneous distance restraints is of special interest for proteins for which only a limited amount of homologous sequences are known ., The accuracy of residue-residue contact prediction is highly dependent on the number of available sequences in the multiple sequence alignment ., For multiple sequence alignments with a small number of sequences , the accuracy is in general too low to significantly improve structure prediction using standard prediction protocols ., We find that our protocol is able to more efficiently use the sparse information contained in contact predictions with low accuracy , due to the error robustness and iterative sampling strategy of the underlying RASREC algorithm ., Our protocol should therefore be able to predict accurate models in cases where other currently published methods would most likely fail to predict the correct fold ., In addition , we have shown that integrating evolutionary information into the RASREC protocol is essential for accurate protein structure prediction for 9 out of 12 proteins in the EVFold benchmark set ., Even adding contact predictions with accuracies as low as 45% can be sufficient to predict high resolution models that would not be possible using RASREC alone ., The optional refinement run improves the prediction by increasing the precision of the final models ., Future work focusing on this step might further increase accuracy and convergence of the final models ., Overall , we have shown how evolutionary information can be efficiently used for predicting accurate protein structures ., The rapid growth of sequence information and the current advances in statistical sequence analysis have made protein structure prediction using evolutionary information highly relevant ., Finding a way to reliably and efficiently use the distance information contained in multiple sequence alignments will be a first step to fill the increasing gap between the large number of known protein sequences and the significantly smaller number of known protein structures .

Introduction, Materials and Methods, Results and Discussion

Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts ., Following this seminal result , much effort is put into the improvement of contact predictions ., However , there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions ., Here , we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta , called RASREC ., Compared to the classic Rosetta ab initio protocol , RASREC achieves improved sampling , better convergence and higher robustness against incorrect distance restraints , making it the ideal sampling strategy for the stated problem ., To demonstrate the accuracy of our protocol , we tested the approach on a diverse set of 28 globular proteins ., Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0 . 55 to 0 . 72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions ., Using a smaller benchmark , we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low ., This observation is of special interest for protein sequences that only have a limited number of homologs .

Recently , a breakthrough has been achieved in modeling the atomic 3D structures of proteins from their sequence alone without requiring any experimental work on the protein itself ., To achieve this goal , a database of evolutionary related sequences is analyzed to find co-evolving residues , giving insight into which residues are in close proximity to each other ., These residue-residue contacts can help to drive a computer simulation with an atomic-scale physical model of the protein structure from a random starting conformation to a native-like 3D conformation ., Although much effort is being put into the improvement of residue-residue contact predictions , their accuracy will always be limited ., Therefore , structure prediction protocols with a high tolerance against incorrect distance restraints are needed ., Here , we present a structure prediction protocol that combines evolutionary information with the iterative sampling approach of the molecular modeling suite Rosetta , called RASREC ., RASREC has been shown to converge faster to near-native models and to be more robust against incorrect distance restraints than standard prediction protocols ., It is therefore perfectly suited for restraints obtained from predicted residue-residue contacts with limited accuracy ., We show that our protocol outperforms other currently published structure prediction methods and is able to achieve accurate structures , even if the accuracy of predicted contacts is low .

journal.pntd.0004884

An Intradermal Inoculation Mouse Model for Immunological Investigations of Acute Scrub Typhus and Persistent Infection

Scrub typhus ( Tsutsugamushi disease ) is a potentially severe acute febrile illness transmitted through the bite of an infected feeding larval mite or chigger 1 ., It is caused by Orientia tsutsugamushi ( formerly known as Rickettsia tsutsugamushi before 1995 when it was reassigned to the new genus Orientia ) , a strictly intracellular , Gram-negative bacterium that resides free in the cytoplasm of the microvascular endothelium 2 ., Phylogenetically , Orientia is in the order Rickettsiales , family Rickettsiaceae 3 , 4 ., The vector and reservoir in nature of O . tsutsugamushi are Leptotrombidium spp ., mites or chiggers 5 , 6 , 7 ., However , these parasites in both nymphal and adult stages do not feed on vertebrates and are therefore irrelevant in transmission of human disease ., Scrub typhus is prevalent in a large geographic area , comprising approximately 13 , 000 , 000 km2 , stretching from the Russian Far East , China , Japan and Korea in the north , to northern Australia in the south , and to Afghanistan and Pakistan to the west 3 , 8 ., Many islands in the Pacific Ocean and Indian Ocean , including Taiwan , The Philippines , Indonesia and Sri Lanka among others , also have reported cases ., Accordingly , more than 1 billion people living in those areas are at risk of acquiring the infection , and overall more than 1 million cases are estimated to occur every year 9 ., Historically , this disease had a dramatic impact on U . S . troops during World War II and the Vietnam War and is now emerging as an important disease in the Far East 3 , 10 , 11 ., Scrub typhus is rated very high on the Armed Forces Medical Intelligence Center’s Global Severity Risk Index assessment of risk of naturally acquired infections of U . S . military personnel 12 ., In 1999 , the World Health Organization referred to scrub typhus as “probably one of the most underdiagnosed and underreported febrile illnesses requiring hospitalization in the region . ”, Scrub typhus is responsible for a large proportion of severe undifferentiated fevers , as well as up to 23% of all febrile episodes in rural endemic areas and has relatively high mortality rates 13 ., The average case-fatality rate is ~10% , but has been reported to be as high as 35% in some series , mostly due to delays in initiating effective antimicrobial treatment ., In the pre-antibiotic era , case-fatality ratios could be as high as 50% 14 ., However , the disease spectrum is extremely broad , and it is likely that disease severity in humans might depend , in part , on the strain of O . tsutsugamushi involved in human cases ., In fact , more than 70 strains of O . tsutsugamushi are currently described 15 ., Reinfection with the same genotype is possible in highly endemic areas ., Some individuals can progress to persistent infection even after antibiotic treatment 16 , and there are no effective vaccines for scrub typhus 4 , 17 ., Adaptive immunity or cross-protection following O . tsutsugamushi infection in humans appears strain-related and short-lived 13 , 18 , 19 , 20 , but the underlying mechanisms of waning immunity are largely unclear ., Non-human primates , especially Macaca fascicularis ( cynomolgus macaques ) and Presbytis cristatus ( silvered leaf monkeys ) , have been used to study the histopathology and immunological responses to O . tsutsugamushi 21 , 22 , 23 , 24 ., Walsh and colleagues have used cynomolgus macaques to evaluate the clinical manifestations and antibody responses , as well as histological features of eschars , a unique and localized pathological skin lesion often occurring in humans following inoculation of the organism at a cutaneous mite feeding site 22 ., More recently , Paris and colleagues have developed a cynomolgus macaque intradermal ( i . d . ) challenge model that closely mimics natural infection and eschars in humans and have used it to evaluate protective immune responses induced by p47-DNA vaccination against infection with O . tsutsugamushi Karp strain ( OtK ) 25 ., They have provided the first phenotypic correlations of immune protection in scrub typhus ., While non-human primates are the best models for human scrub typhus , they are not widely used in laboratories due to the high expense and other logistical issues ., Thus , there is a great need to develop murine models that mimic the natural entry route of organism inoculation and manifest certain immunological and pathogenic features of human scrub typhus ., Elucidation of pathogenic mechanisms or protective immunity to OtK has been hampered by the lack of availability of well-standardized rodent animal models that mimic the pathological features of the human disease ., Most publications report murine models that were initiated via the intraperitoneal ( i . p . ) inoculation route in outbred mouse strains , which resulted in diffuse peritonitis and severe mesothelial infection of the peritoneum , rather than disseminated , systemic infections following an incubation period 26 , 27 , 28 , 29 , 30 ., The newly developed model of intravenous ( i . v . ) inoculation in C57BL/6 mice leads to disseminated infection of endothelial cells , and lesions resembling the human pathology in cases of fatal scrub typhus 31 , 32 ., However , this model bypasses the natural route of infection since infections transmitted in nature follow i . d . entry of the organisms ., Several groups have explored other routes of infection via subcutaneous ( s . c . ) or i . d . needle inoculation or mite-based transmission; however , most of these reports have focused on the early phases of infection in BALB/c mice 33 , or in outbred mice which cannot be utilized for reproducible mechanistic studies 34 , 35 , 36 ., Here , we report a sub-lethal murine model for acute scrub typhus and persistent infection following i . d . inoculation of C57BL/6 mice with OtK ., This represents an advance that complements our recent development of a lethal scrub typhus model that used i . v . inoculation 31 , 32 ., Validation of this i . d . inoculation model should permit in-depth mechanistic studies related to the pathogenesis and specific immunological investigations of the host immune response following route-specific exposure to the bacteria , and it will open new avenues for future vaccine- or immune-based investigations for disease control ., Female wild-type C57BL/6 ( B6 , from Harlan Laboratories , Indianapolis , IN ) were used in this study ., Mice were maintained under specific pathogen-free conditions and used at 8–10 weeks of age , following protocols ( #9007082B and #1302003 ) approved by the Institutional Animal Care and Use Committee at the University of Texas Medical Branch ( UTMB ) in Galveston , TX ., All mouse infection studies were performed in the ABSL3 facility in the Galveston National Laboratory located at UTMB; tissue processing and analysis procedures were performed in the BSL3 or BSL2 facilities ., All procedures were approved by the Institutional Biosafety Committee , in accordance with Guidelines for Biosafety in Microbiological and Biomedical Laboratories ., UTMB operates to comply with the USDA Animal Welfare Act ( Public Law 89–544 ) , the Health Research Extension Act of 1985 ( Public Law 99–158 ) , the Public Health Service Policy on Humane Care and Use of Laboratory Animals , and the NAS Guide for the Care and Use of Laboratory Animals ( ISBN-13 ) ., UTMB is a registered Research Facility under the Animal Welfare Act , and has a current assurance on file with the Office of Laboratory Animal Welfare , in compliance with NIH Policy ., The identity of OtK bacterium was confirmed by sequencing of the Orientia 47-kDa gene ( accession #L31934 ) , prior to growth in Vero cells; oriential stock was prepared from heavily ( 80–100% ) infected Vero cell cultures , as previously described 37 ., To produce high-titer bacterial stocks and to avoid loss of virulence through laboratory passage adaptation , we prepared mouse lung-derived bacterial stocks by passages through B6 mice , as previously reported 31 ., Briefly , B6 mice were inoculated i . v . with Vero cell-propagated OtK and were euthanized when they were at the peak of illness ., Lung tissues were homogenized; bacteria were isolated , aliquoted for titer/quality analyses , stored at -80°C in sucrose-phosphate-glutamate ( SPG , 218 mM sucrose , 3 . 76 mM potassium phosphate monobasic , 7 . 1 mM potassium phosphate dibasic , 4 . 9 mM potassium glutamate ) buffer , or utilized to inoculate a naïve group of animals for further amplification ., The animal passages were performed 3–5 times to create high-quality bacterial stocks; the same lot of stock was used for all experiments described in this study ., Confluent monolayers of Vero cells in 6-well plates were inoculated with 200 μl of OtK stocks ( in serial 10-fold dilutions in triplicate ) ., After 2 h of incubation at 34°C with 5% CO2 , plates were triple rinsed with warm phosphate-buffered saline ( PBS ) to remove bacteria which did not adhere or invade the monolayer , and DNA was extracted for bacterial load analysis via quantitative PCR ( qPCR ) , as in our reports 31 , 32 ., Briefly , the 47-kDa protein gene was amplified via specific primers OtsuF630 ( 5′-AACTGATTTTATTCAAACTAATGCTGCT-3′ ) and OtsuR747 ( 5′-TATGCCTGAGTAAGATACGTGAATGGAATT-3′ ) ( IDT , Coralville , IA ) ., Serial 10-fold dilutions of known concentrations of single 47-kDa gene-containing plasmid were utilized to determine the copy number ., DNA isolated from bead-homogenized tissue samples were used to assess tissue bacterial loads ., Sample were normalized with the mouse GAPDH gene ( F , 5′-CAACTACATGGTCTACATGTTC-3′; R , 5′-CTCGCTCCTGGAAGATG-3′ , IDT ) ., Data are presented as 47-kDa copy numbers per 105 or 106 copies of GAPDH for tissues , or per μl of blood ., B6 mice were purchased at 7–9 weeks of age and allowed to acclimatize for 7 days prior to experimental use ., To select the experimental dose , mice were inoculated with OtK or PBS ( a sham control ) in the dermis of the lateral ear with a range of doses 6 x 105 to 6 x 101 viable organisms in 10–12 μl delivered via a 30G-needle and 25-μl Hamilton syringe ( Hamilton Company , Reno , NV ) ., Mice were monitored twice daily for 28 days for signs of illness ., Clinical signs of illness were consistently observed between 10–13 dpi with 103 to 105 viable organisms , whereas animals that received lower doses ( <103 total organisms ) had delayed , sporadic onset of clinical signs of illness that occurred between 14–16 dpi ., A dose of 6x104 was selected for subsequent experiments for the purposes of consistency of inoculation concentration and consideration that the dose of natural transmission of O . tsutsugamushi , although currently unknown , would not likely be as great as 105 or higher organisms ., Mice were inoculated as described with 6x104 viable organisms or PBS and monitored for signs of illness daily ( ruffled fur , lethargy , erythema , temperature change , and weight loss ) as in our previous studies 31 , 32 ., Body temperature and weight were monitored daily from 0–21 dpi , and then weekly during the remaining period of study ., During the 1st week of infection , a group of mice ( n = 4 ) was sacrificed daily for assessment of bacterial loads in blood and organs and for histology ., During the 2nd week of infection , mice were sacrificed every other day until 21 dpi and then once weekly until 84 dpi ., To determine if bacterial DNA detected in tissues represented viable organisms , we collected tissues at 81–84 dpi from 4 mice ( with IgG titers of 1:65 , 536 ) in the first experiment , and from 3 mice ( with IgG titers of 1:32 , 768 ) in the second ., Lung , kidney , and spleen/liver/lymph node samples were placed in DMEM , homogenized by using a 7-ml glass Dounce homogenizer in cold SPG buffer , and centrifuged at 700 x g for 10 min at 4°C ., Supernatants were saved , while pellets were subjected to another round of homogenization and centrifugation ., Bacteria in supernatants were harvested by centrifugation at 22 , 000 x g for 45 min at 4°C ., Enriched bacterial pellets were re-suspended and pooled in a total of 10 ml of SPG ., Each naïve mouse was inoculated via the i . p . route with a 250-μl aliquot ( 3–4 mice per tissue group ) ., Mice were monitored daily for signs of illness for 21 days ., At the desired time points , blood samples ( 500 μl ) were collected from each mouse into K2EDTA-coated microtainer tubes ( Becton Dickinson , Franklin Lakes , NJ ) ., Blood cell counts were measured by using a calibrated 950FS HemaVet apparatus ( Drew Scientific , Waterbury , CT ) ., Blood samples were analyzed by using the FS-Pak reagent kit , for measuring white blood cell count , differential leukocyte ( % ) count , hemoglobin , hematocrit , red blood cell count , red cell distribution width , platelet count , and mean platelet volume , respectively ., Antigen-coated , acetone-permeabilized 12-well slides were equilibrated to room temperature in PBS and then blocked in PBS containing 1% bovine serum albumin ( BSA ) and 0 . 01% sodium azide for 10 min ., Sera were diluted 2-fold starting at 1:64 and , if reactive , serially diluted to final end point titers in a solution of PBS containing 1% BSA , 0 . 1% Tween 20 , and 0 . 01% sodium azide ., Dilutions of sera were added to antigen-coated wells and incubated at 37°C for 30 min in a humidified chamber ., Slides were rinsed twice with PBS containing 0 . 1% Tween-20 for 10 min ., A secondary antibody , DyLight 488-conjugated anti-mouse IgG ( 1:15 , 000 , Jackson ImmunoResearch , West Grove , PA ) was added and incubated for 30 min at 37°C in a humidified chamber ., Slides were washed twice as before , with the final wash containing 1% Evans blue solution , mounted with DAPI fluoromount-G ( SouthernBiotech , Birmingham , AL ) and coverslipped ., Slides were observed under a fluorescence microscope at 400X magnification ( Leica Microsystems , Buffalo Grove , IL ) ., Blood was collected from mice at the time of euthanasia via cardiac puncture and sera were stored at -20°C until use ., Serum samples ( 100 μl ) were used for cytokine measurement in a Mouse Cytokine 23-Plex assay ( Bio-Rad Laboratories , Hercules , CA ) , according to the manufacturers instructions ., Samples with cytokine concentrations below the detection limits were assigned an averaged value between 0 and the lowest detectable levels in each assay; all samples were retained in the data set ., All tissues were fixed in 10% neutral-buffered formalin and embedded in paraffin , and sections ( 5-μm thickness ) were stained with hematoxylin and eosin or processed for antibody ( Ab ) staining , as in our previous reports 32 ., For IHC staining , all reagents were from Vector Laboratories ( Burlingame , CA ) , unless specified ., Briefly , slides were sequentially processed for antigen retrieval , deparaffinization , and rehydration ., Sections were blocked with 1X casein ( for endogenous IgG ) , a BLOXALL blocking solution ( for endogenous alkaline phosphatase ) , an avidin and biotin blocking solution , and normal goat serum ( for non-specific binding sites ) ., Sections were incubated with rabbit anti-OtK Ab ( 1:12 , 000 , produced in our laboratory ) at 4°C overnight , followed by incubation with biotinylated goat anti-rabbit IgG ( 1:200 ) for 30 min ., Signals were detected by incubation with alkaline phosphatase conjugate ( 1:200 ) and developed with an alkaline phosphatase substrate kit ., Slides were counterstained with hematoxylin , dehydrated , mounted with VectaMount , and examined under an Olympus BX53 microscope ., All slides were examined and scored blindly by two pathologists ( without knowledge of dpi or bacterial loads ) , following the below criteria ., For hepatic pathology scores , the diameters of clusters of inflammatory infiltrates were measured , and the average lesion size and number of lesions per 10 medium-power fields ( 100X ) were determined for each time point ., A liver inflammatory index was calculated as: number of lesions per 10 medium-power fields multiplied by the mean diameter of mononuclear infiltrative clusters ( μm ) ., For pulmonary pathology scores , all lung sections were examined sequentially ( according to dpi ) to obtain a general assessment of the histopathology and establish grading parameters; grades 1–4 were scored based on lesion spectra throughout the entire course of infection ., Grade 1+: widening of alveolar septa with scattered inflammatory cells in focal areas of pulmonary parenchyma , and focal inflammatory cells around bronchovascular bundles ., Grade 2+: grade 1 criteria plus multifocal clusters of inflammatory infiltrates around bronchovascular bundles ., Grade 3+: widening of alveolar septa with diffuse inflammatory cell infiltrates present in the pulmonary parenchyma , bronchovascular bundles , and focal areas of atelectasis ., Grade 4+: grade 3 criteria plus extensive areas of atelectasis ., Splenic histopathology was assessed based on changes in the white pulp , specifically expansion of the marginal zone and lymphoid activation in periarteriolar lymphoid sheaths ., Data were presented as mean ± standard errors of the mean ( SEM ) ., Statistical significance of differences between individual treatment and control groups was determined by using Student’s t test ., One-way ANOVA and Tukey’s post-test were used for multiple group comparisons ., Statistically significant values are designated as * , p < 0 . 05; ** , p < 0 . 01; *** , p < 0 . 001 , respectively ., Following i . d . inoculation in the ear , we observed no eschar formation at the inoculation site and no clinical signs of illness during the first 7 dpi , a finding similar to those in reported studies for i . d . inoculation in Swiss CD-1 outbred mice 35 and s . c . inoculation in BALB/c mice 33 ., Infected B6 mice had elevated body temperatures at 11 and 12 dpi ( 38 . 2–38 . 7°C ) , followed by hypothermia from 14 to 21 dpi ( Fig 1A ) , and recovered to normal body temperature by 28 dpi ., Infected mice showed an up to 13% difference in weight change compared to that in sham-inoculated controls ( Fig 1B ) ., Weight loss was most evident at 14–16 dpi , followed by gradual recovery starting at 17 dpi ., By 20 dpi , the body weight of infected mice had reached the pre-infection level , but it remained much lower than that of sham-inoculated controls ., To assess infection outcomes , we examined circulating OtK-specific IgG titers by IFA , as well as blood and tissue bacterial loads via PCR-based quantification of the OtK 47-kDa gene , from 1 until 84 dpi ., Seroconversion was observed as early as 5 dpi ( 1:128 in 25% of mice ) , reached the peak reciprocal endpoint titer of 1:65 , 536 by 13 dpi , and sustained high-titers ( 1:65 , 536 ) even at 84 dpi ( or 12 weeks ) ( S1 Fig ) ., Our findings were consistent with , but extended from , previous reports in human cases 38 , 39 ., Daily analyses of blood and tissue bacterial loads during the first 28 days of infection , as well as weekly analyses until 84 dpi , revealed the following consistent features ., The earliest peak of bacterial burden occurred in the ear , the site of inoculation , at day 9 pi ., At 1–7 dpi , bacterial loads were low or undetectable in the blood , lungs , liver , spleen ( Fig 2 ) , and brain ( S2 Fig ) ., In blood , bacteria were consistently detected at 9 dpi , followed by a peak mean bacteremia at 15 dpi ( 98 . 4 copies/μl blood ) ., In the lungs , bacterial loads peaked at 13 dpi ( 143 . 2 copies/105 GAPDH copies ) and remained detectable at 35 dpi ., The liver , spleen , and brain bacterial loads were lower , but were sustained longer ., In the liver , bacterial loads peaked at 13 dpi ( 7 . 8 copies/105 GAPDH copies ) and remained detectable at 63 dpi ., In the spleen and brain , bacterial loads peaked at 13 dpi and remained detectable at 70 dpi ., To confirm earlier reports of persistent Orientia infection in humans and animal models 16 , 40 , 41 , we selected the kidney as an additional organ to analyze during late infection ., Bacteria were observed in the kidney from 70–77 dpi ., The peaks of bacterial loads around 13–15 dpi corresponded with the time of occurrence of the greatest reduction of body weight in the infected mice ., The prolonged presence of OtK DNA in the ear , dLN , blood , liver , spleen , and brain ( 63–84 dpi ) , as well as in the kidneys ( 70–84 dpi , S2 Fig ) , led us to investigate whether viable infectious organisms were present ., At 81–84 dpi , we prepared homogenates from the kidney , lung , and spleen/liver/lymph nodes and used them respectively to inoculate naïve B6 mice via the i . p . route ( 3–4 mice per group ) , the most sensitive method to detect low quantities of Orientia 31 ., Our data from two independent experiments revealed that mice that were inoculated i . p . with kidney homogenates had the highest mortality rates and tissue bacterial loads; 57 . 1% ( 4 out of 7 ) mice died between 12–15 dpi , with 2x104 , 3 . 81x106 , 4 . 54x106 , and 1 . 25x107 Orientia 47-kDa copies per mg of kidney of the inoculated mice , respectively ., The remaining 3 mice inoculated with kidney homogenates all showed clinical signs of illness on 11 to 15 dpi , but recovered ., Mice inoculated with lung homogenates had a lower mortality rate and tissue bacterial loads , as 1 out of 3 tested mice ( 33% ) died at 14 dpi , with 7 . 07x105 Orientia 47-kDa copies per mg of lung of the inoculated mice ., Mice inoculated with spleen/liver/lymph node homogenates recovered from mild illness and were sacrificed around 21 dpi ., Together , we concluded that even though i . d . -inoculated mice had high titers of OtK-specific IgG ( 1:32 , 768 or 1:65 , 536 ) , they had infectious bacteria that persisted in the kidneys and other tissues , and , accordingly , these mice represent a model of observations of persistent infection in human scrub typhus 16 ., To better understand the clinical features of this i . d . model , we performed hematological and histopathological analyses ., While white blood cell counts remained relatively normal between 1–13 dpi , infected mice had transient , but consistent , alterations in the platelet and red blood cell counts and cell morphology ., The thrombocytopenia was transient , but most severe at 13 dpi during the peak of endothelial infection , likely reflecting deposition of platelets in foci of endothelial injury ( S3 Fig ) ., The transient increases in mean platelet volume ( at 13 dpi ) corresponded to the period of the bone marrow response for the release of immature platelets ., Similarly , the increased red cell distribution width on 21 dpi , and from then to the end of the study , may reflect the release of newly formed erythrocytes from the bone marrow in response to the development of anemia on 9 dpi ., Examination of the liver in infected animals showed foci of mononuclear inflammatory cells at 7 dpi , and a higher inflammatory index was statistically significant starting on 9 dpi ( p < 0 . 01 , Fig 3 ) ., The diameter and number of lesions in the liver peaked between 11 and 13 dpi ( p < 0 . 01 ) , which coincided with the highest bacterial loads in the liver ( Fig 2 ) ., After day 13 , the number of lesions in the liver decreased and then plateaued , but the inflammatory index remained elevated until 56 dpi , during which time the pathologic foci increased in size and then evolved from discrete clusters to confluent patches of mononuclear cellular infiltrates as the disease progressed ., Lesions in the spleen progressed similarly , as characterized by marked expansion of the marginal zone and lymphoid expansion in peri-arteriolar lymphoid sheaths , which were present even at 84 dpi ., Representative images of the kidney and spleen revealed persistent multifocal interstitial inflammatory infiltrates ( S4 Fig ) ., Examination of brain sections during the infection revealed no overt pathological lesions ., Lungs are the most important and severely affected organ in OtK infection following i . v . inoculation 31 , 32 ., Because i . d . -inoculated mice had the highest bacterial loads in the lungs , other than the inoculation site ( Fig 2 ) , we examined the location of oriential antigens in the lungs by OtK-specific IHC ., At 12 dpi , the lungs contained oriential antigens in endothelial cells lining septal capillaries ( Fig 4 ) ., Analysis of lung histopathology revealed substantial inter-animal variations in pathology scores during the course of infection , with most animals scoring between grades 2 and 3 ., The most severe pathology ( grade 4 ) occurred at the late stage of disease ( at 21 dpi ) ( Fig 5A–5D ) ., Histological lesions in the lungs did not show resolution even at 84 dpi ( Fig 5E ) ., We have previously reported that following i . v . inoculation of a lethal dose of OtK in B6 mice , the development of strong type 1 immune responses , with no IL-4/IL-13 production , contributes to mouse mortality 32 ., To define the immune responses in i . d . -inoculated mice , we measured cytokine profiles in the sera and lung homogenates by a BioPlex assay ., Serum cytokine analyses revealed two distinct patterns ( Fig 6 ) ., The early ( 9 dpi ) production of a set of cytokines and chemokines ( MCP-1/CCL2 , MIP-1α/CCL3 , and IL-10 ) during the incubation period was followed by the production of pro-inflammatory markers ( IL-6 , IL-12p40 , IFN-γ , G-CSF , RANTES/CCL5 , and KC/CXCL11 ) at 11–13 dpi , correlating with the onset of fever ., The production of other cytokines ( IL-1α/β , IL-2 , TNF-α , GM-CSF , MIP-1β/CCL4 , eotaxin/CCL11 , IL-9 , and IL-13 ) around 15–19 dpi , correlated with disease progression ., At 21–28 dpi , serum markers had returned to basal levels , except for IL-12p40 and RANTES/CCL5 ., At several time points between 35–77 dpi , relatively low , but statistically significant , elevations of MIP-1α/CCL3 , IL-1α/β , TNF-α , IL-2 , IL-12p40 , and IL-13 were detected , implying sustained immune responses ., Lung homogenates had similar cytokine and chemokine profiles as the sera ( Fig 7 ) ., The transient elevations of MCP-1/CCL2 , MIP-1β/CCL4 , IFN-γ , TNF-α , G-CSF , and KC/CXCL11 at 13 and/or 21 dpi were followed by increased concentrations of IL-1α/β , IL-2 , IL-12p40 , RANTES/CCL5 , MIP-1α/CCL3 , IL-13 , and GM-CSF at 21 and/or 28 dpi ., Of note , IL-9 levels in lung samples were significantly reduced compared with those from sham controls , which was in sharp contrast to the IL-9 production pattern in sera ., Scrub typhus , an endemic disease in the Asia-Pacific region , is an important acute febrile illness in the tropics 42 ., Development of an animal model that mimics the human histopathology and bacterial distribution is an important step toward understanding disease pathogenesis and immunity , as well as developing preclinical evaluation and interventions to prevent the infection and ameliorate its severity ., Mouse models available to study scrub typhus have employed mostly the i . p . inoculation route , which results in infection that does not resemble the human disease clinically , target organs and cells , and histopathology ., Our group recently developed an i . v . inoculation model of OtK infection , resulting in hematogenously disseminated endothelial infection mimicking human disease 31 ., This new model has permitted us to examine how endothelial stress and dysfunction 31 , 32 , or alarmin molecules such as IL-33 43 , contribute to oriential pathogenesis ., Since natural infections are initiated via mite feeding on the dermis of the skin , we sought to develop an i . d . inoculation model of scrub typhus using OtK ., In this report , we have shown that following a 10-day incubation period , i . d . -inoculated mice developed a systemic infection with body temperature changes , weight loss , and bacterial dissemination via the blood to other major organs ( Figs 1 and 2 ) ., While the clinical features of OtK-infected mice closely mimic the course of infection observed in human scrub typhus 44 , 45 , the duration of fever and hematological abnormality appeared to be much shorter or milder in these mice as compared to human cases 46 ., Interestingly , these mice developed a persistent infection with histologic lesions and infectious bacteria up to 84 dpi ., To the best of our knowledge , this is the first report of a murine i . d . model for acute and persistent infection , which will be of great value for future immunologically or vaccine-based studies ., Our bacteriologic and histopathological analyses have revealed several important features of the i . d . infection model of O . tsutsugamushi ( Figs 2–5 ) ., First , this inoculation route led to the establishment of infection with similar target cell tropism as scrub typhus , namely endothelial cells lining the microvasculature and macrophages , as in our previous report of the i . v . inoculation route and our study of human scrub typhus cases 2 , 31 ., Secondly , while the lungs were the major target organ for the infection , containing 10-fold more Orientia 47-kDa gene copies than the liver , spleen , and brain samples , we observed considerable differences in lesion distribution and magnitude of inflammatory infiltrates ., Bacteremia was sustained through 15 dpi , after the peak of parenchymal bacteria at 13 dpi , leading us to hypothesize that the delayed peak observed in the blood may be due to the release of Orientia from infected tissues ., This is suggested by the observation that by 15 dpi parenchymal tissue bacterial loads decreased , which coincided with a bacterial load increase in the circulating blood ., However , given that bacteremia kinetics mirror parenchymal tissue bacterial loads at the acute stage of the disease , further analysis with perfused organs would be informative ., Thirdly , the histologic pattern of lung lesions was that of acute interstitial pneumonitis , which resembles the histopathology in human scrub typhus patients and in experimentally infected , non-human primates 4 , 22 , 23 , 47 ., Histological lesions in the mouse lungs had not resolved completely even at 77–84 dpi , by which time most cytokines and chemokines had returned to their basal levels ., Finally , the liver inflammatory index is a reliable scoring system for comparative studies , especially during the acute stages of infection ., Detection of Orientia DNA in the liver , spleen and brain at 63–70 dpi poses the question as to the cell type ( s ) persistently infected in these organs and the long-term impact of persistent infection ., Such information will be important to understand the potential complications of persistent Orientia infection in humans 16 , 48 ., We detected a panel of Th1- and Th2-promoting , pro-inflammatory cytokines and chemokines in serum samples and lung homogenates ( Figs 6 and 7 ) ., Classical type 1 cytokines and chemokines ( e . g . , IL-2 , IL-12 , IFN-γ , TNF-α , MCP-1/CCL2 , MIP-1α/CCL3 , and RANTES/CCL5 ) were predominantly induced around the peak of bacterial infection , and then decreased as bacterial replication was controlled at 28 dpi ., Type 1 immune responses regulate migration of neutrophils , monocytes/macrophages , and T cells from the bloodstream across the vascular endothelium , as well as their effector function for bacterial control ., These cytokine/chemokine patterns resemble the observations in infected humans 49 , 50 , 51 and in i . d . -inoculated non-human primates 25 ., In this i . d . model , three immune modulatory cytokines were of particular interest to us ., First , IL-10 was one of the earliest cytokines , detected as early as 9 dpi in the blood , and one of the few cytokines with sustained and significant production for 8 days ., While IL-10 may contribute to minimizing host tissue damage , it will be important to further examine whether IL-10 also contributed to bacterial persistence in multiple organs in our model ., Additional studies with mice deficient in IL-10 or its receptor will provide new insight into the roles of IL-10 and its relevance to Orientia persistency ., Secondly , IL-13 was detected in both blood and lungs ,

Introduction, Materials and Methods, Results, Discussion

Scrub typhus is a neglected tropical disease , caused by Orientia tsutsugamushi , a Gram-negative bacterium that is transmitted to mammalian hosts during feeding by Leptotrombidium mites and replicates predominantly within endothelial cells ., Most studies of scrub typhus in animal models have utilized either intraperitoneal or intravenous inoculation; however , there is limited information on infection by the natural route in murine model skin or its related early host responses ., Here , we developed an intradermal ( i . d . ) inoculation model of scrub typhus and focused on the kinetics of the host responses in the blood and major infected organs ., Following ear inoculation with 6 x 104 O . tsutsugamushi , mice developed fever at 11–12 days post-infection ( dpi ) , followed by marked hypothermia and body weight loss at 14–19 dpi ., Bacteria in blood and tissues and histopathological changes were detected around 9 dpi and peaked around 14 dpi ., Serum cytokine analyses revealed a mixed Th1/Th2 response , with marked elevations of MCP-1/CCL2 , MIP-1α/CCL3 and IL-10 at 9 dpi , followed by increased concentrations of pro-inflammatory markers ( IL-6 , IL-12 , IFN-γ , G-CSF , RANTES/CCL5 , KC/CCL11 , IL-1α/β , IL-2 , TNF-α , GM-CSF ) , as well as modulatory cytokines ( IL-9 , IL-13 ) ., Cytokine levels in lungs had similar elevation patterns , except for a marked reduction of IL-9 ., The Orientia 47-kDa gene and infectious bacteria were detected in several organs for up to 84 dpi , indicating persistent infection ., This is the first comprehensive report of acute scrub typhus and persistent infection in i . d . -inoculated C57BL/6 mice ., This is a significant improvement over current murine models for Orientia infection and will permit detailed studies of host immune responses and infection control interventions .

Scrub typhus is a life-threatening disease that presents as a severe acute febrile illness ., It is caused by mite-transmitted Orientia tsutsugamushi , a Gram-negative , obligately intracellular bacterium ., Every year , approximately one million people are infected globally; however , there is no vaccine for the control of this infection ., Mechanistic studies of host immune responses have been few , partially due to the limited availability of suitable animal models and research facilities ., Here , we report our development of an intradermal inoculation mouse model that mimics the natural infection ., We also examined the kinetics of immune and inflammatory responses during acute and chronic stages of O . tsutsugamushi infection ., As the first comprehensive report of an intradermal inoculation murine model of scrub typhus , this study improves our understanding of host immune responses following cutaneous exposure to the bacteria and opens new avenues for future vaccine-based investigations .

typhus, medicine and health sciences, innate immune system, immune physiology, cytokines, pathology and laboratory medicine, body fluids, animal models of disease, pathogens, immunology, microbiology, animal models, bacterial diseases, developmental biology, model organisms, signs and symptoms, molecular development, bacterial pathogens, research and analysis methods, orienta tsutsugamushi, animal models of infection, infectious diseases, inflammation, animal studies, medical microbiology, microbial pathogens, mouse models, scrub typhus, hematology, immune response, immune system, diagnostic medicine, blood, anatomy, physiology, biology and life sciences

journal.pbio.2001894

Local introduction and heterogeneous spatial spread of dengue-suppressing Wolbachia through an urban population of Aedes aegypti

Dengue fever is the most common arboviral disease affecting humans 1 ., Over 2 , 500 , 000 , 000 people live in dengue-afflicted regions , and dengue incidence is increasing at an alarming rate in tropical and subtropical countries 2 ., A number of other arboviruses also represent emerging disease risks , including chikungunya and Zika , the latter being associated with a recent explosive epidemic in South America 3 , 4 ., The main approach to controlling these diseases has been suppression of the principal mosquito vector , Ae ., aegypti , either through source reduction or insecticide-based control programs ., Given the increasing incidence of Ae ., aegypti-associated human disease , it is clear that current control measures are insufficient ., In response to this problem , a number of new control approaches are currently being developed and tested 5 , 6 , 7 , 8 , 9 , 10 ., In contrast to control efforts that require repeated population suppression , the Eliminate Dengue Program ( http://www . eliminatedengue . com/program ) aims to modify populations using long-lasting local introductions of a dengue-inhibiting Wolbachia into naturally uninfected populations of Ae ., aegypti ., The strain , wMel , was transferred from Drosophila melanogaster into laboratory-raised Ae ., aegypti , who inherit the infection maternally 11 , 12 ., Following introgression of the infection into a native genetic background , Wolbachia-infected mosquitoes are released into the field to mate with wild uninfected mosquitoes , and wMel frequency increases through cytoplasmic incompatibility ( CI ) 13 , 14 ., CI describes the fact that uninfected females mated with Wolbachia-infected males produce inviable embryos ., In Ae ., aegypti , this is believed to occur in 100% of these incompatible crosses 11 ., In contrast , infected females can mate with either infected or uninfected males and produce almost 100% infected progeny ., CI greatly reduces the relative fitness of uninfected females when infected males are common and drives rapid establishment of Wolbachia in isolated mosquito populations 14 , given that there is no mating bias against wMel-infected Ae ., aegypti 15 ., Although wMel-infected females receive a frequency-dependent relative fitness advantage from CI , they also suffer from frequency-independent fitness costs , including decreases in fecundity and larval competitive ability 16 , 17 , 18 , 19 ., Thus , CI does not produce a net fitness advantage while wMel is rare , resulting in dynamics analogous to those produced by an Allee effect in ecology 20 , 21 and by natural selection on a locus ( or alternative karyotypes ) in which heterozygotes are less fit than either homozygotes ( i . e . , underdominance , 22 , 23 , 24 ) ., The interaction of the frequency-dependent advantage associated with CI and the frequency-independent cost ( s ) produces “bistable dynamics” with a threshold frequency of infection ( denoted p^ ) below which the infection will be locally eliminated and above which frequencies systematically increase 25 , 26 , 27 ., Curtis 23 first proposed transforming pest populations by introducing translocations that are expected to show bistable dynamics ( cf . 28 ) ., The bistable model for Wolbachia spread was introduced by Turelli and Hoffmann 29 to explain the rapid spread of wRi , a CI-causing Wolbachia variant , through California populations of Drosophila simulans ., Although this interpretation of wRi dynamics has now been challenged by more recent data on the spread of natural Wolbachia infections 30 , 3 lines of evidence nevertheless support bistability of wMel-transinfected Ae ., aegypti 31: ( 1 ) frequency dynamics from the original field releases 14 , ( 2 ) direct experimental evidence for lower fecundity and viability 19 , 32 , and ( 3 ) new data showing that persistent influx over 2 years of wMel-infected Ae ., aegypti into a relatively isolated population has not led to establishment of wMel there 31 ., In order for the invasion to spread spatially under bistability , new uncolonised areas must receive infected immigrants at a rate high enough to be pushed past the threshold frequency , p^ ., Under the dynamics produced by CI-inducing Wolbachia , spatial spread is expected in a habitat with relatively homogeneous population densities if p^ is below a critical value near 0 . 5 20 , 29 ., For wMel in Ae ., aegypti near Cairns , p^ is thought to be moderate ( p^≈0 . 2–0 . 35 ) because of its relatively low fitness costs and near-perfect maternal transmission 11 , 14 , 31 ., Previously , wMel-infected Ae ., aegypti released in 2 relatively isolated communities in Northern Queensland , Australia ( Gordonvale and Yorkeys Knob ) , colonised each area rapidly 14 , and the infection has persisted at high frequency ( >90% ) at both sites 18 ., Moreover , wMel continues to show strong blockage of dengue transmission in laboratory-challenged mosquitoes derived from field collections 33 ., Here we present data from 3 subsequent releases of wMel-infected Ae ., aegypti in Cairns , Northern Queensland , a city with about 150 , 000 residents that is located between the communities of Gordonvale and Yorkeys Knob ., These releases followed protocols similar to those of 14 , but the release zones were centred within suburban landscapes , providing a continuous habitat for Ae ., aegypti ., This study investigates the capability of the wMel infection to spread spatially through urban Ae ., aegypti populations and the stability of the infection in invaded regions over time ., Spread from localized releases to surrounding uninfected areas depends on mosquito dispersal and relative population densities ., Spatial spread can be slowed or stopped if densities are higher in surrounding uninfected areas 20 ., Dispersal of Ae ., aegypti varies with local environmental conditions ., Poor habitats generally induce larger dispersal distances as gravid females must travel further to find the relatively rare oviposition sites 34 , 35 , 36 ., Despite its global success as an invasive species in tropical habitats , presumably through dispersal of eggs and larvae 37 , adult Ae ., aegypti are generally considered weak dispersers ., Females usually remain within 50–150 m of their eclosion site 34 , 38 , 39 , 40 , 41 , 42 ., They appear to disperse poorly across highways 31 , 42 , 43 and through vegetated parkland 44 ., Occasional long-range dispersal , on the order of 0 . 5–1 km , has been observed 45 , 46 , 47 , 48 ., However , given the bistable dynamics of wMel in Ae ., aegypti , rare long-range dispersal will not accelerate Wolbachia spread because the infection will not increase locally from low initial frequencies 20 , 31 ., We document local wMel establishment and heterogeneous spatial spread from the 2 relatively large release areas ., Our new data demonstrate that local Wolbachia introductions can succeed , persist for at least 2 years , and produce slow spatial spread ., Using graphical summaries , we approximate the rate of spatial spread and the width of the spreading wave ., We also show that our field data are broadly consistent with simple mathematical models that depend critically on bistable frequency dynamics for wMel transinfected into Ae ., aegypti ., These models involve only 2 parameters , one describing the position of the unstable threshold point , p^ , and the other , σ , describing average Ae ., aegypti dispersal distance ., Both parameters can be estimated independently of spread data 31 ., We also present likelihood-based data analyses that fit simple curves to estimate the shape and speed of Wolbachia spread ., The shape of the advancing wave is summarized by wave width , defined as the inverse of the maximum slope in infection frequencies , averaged over the wave front 49 ., As discussed below , wave width provides an estimate of dispersal distance averaged over time ., Wave speed is defined as the average rate of movement of an intermediate infection frequency ( e . g . , 0 . 5 . ) The theory of bistable waves leads to a simple prediction for wave speed in terms of wave width and p^ , the threshold infection frequency above which local increases in infection frequencies ( p ) are expected 20 , 24 , 31 , 50 ., The observed speed of wMel spread in Cairns is broadly compatible with this prediction , and the estimated wave width is also consistent with independent estimates of dispersal ., Moreover , the lack of clear establishment or spread from our third , significantly smaller , release area ( only 0 . 11 km2 ) is consistent with the prediction for bistable dynamics that releases must be conducted over sufficiently large areas to initiate spatial spread ., Our likelihood analyses also quantify significant heterogeneity in rates of spatial spread that is apparent from our graphical representations ., We attempt to link this heterogeneity to easily measured habitat variables ., Heterogeneity in host population density is expected to strongly influence Wolbachia invasions subject to bistable dynamics , especially affecting wave speed and potentially restricting the extent of spread 20 ., Even if Ae ., aegypti disperse equally in all directions , heterogeneities in population density produce asymmetries in net migration ., This asymmetry accelerates spread from high-density patches to low-density patches and decelerates—or halts—spread out of low-density patches 20 ., Habitat variables such as shade , yard condition , and abundance of oviposition sites have been correlated with Ae ., aegypti abundance 41 , 51; the frequency of Wolbachia infection within the release zone in Gordonvale , Queensland , was higher in neighbourhoods with more brick and screened houses , which are associated with lower Ae ., aegypti abundance 32 ., This motivates our attempts to understand patterns of local spread by inferring local densities from easily measured habitat variables ., However , the variables we assessed did not predict observed heterogeneities in spread beyond the release zones ., Fig 1 shows the 3 areas in Cairns , Queensland , where Eliminate Dengue staff released Ae ., aegypti adults infected with the wMel strain of Wolbachia between January 10th and April 24th , 2013 ., The release zones , located in the suburbs of Edge Hill/Whitfield ( EHW ) , Parramatta Park ( PP ) , and Westcourt ( WC ) , were within 2 km of each other and encompassed 0 . 97 km2 , 0 . 52 km2 , and 0 . 11 km2 , respectively ., Mosquitoes were released evenly throughout each release zone at weekly intervals ., Total BG-Sentinel trap collections for EHW , PP , and WC are summarised in S1 Table ., Our collections continued for about 2 years and are summarized in 4 time intervals ., The first dry season D1 ( May 2013–October 2013 ) , began immediately after the releases , followed by the first wet season W1 ( November 2013–April 2014 ) , the second dry season D2 ( May 2014–October 2014 ) , and the second wet season W2 ( November 2014–April 2015 ) ., Weekly trap yields at EHW and PP decreased progressively from the onset of each dry season but rose again sharply at the beginning of each wet season ( Fig 2 ) ., Mosquitoes were caught in consistently higher numbers at PP than at EHW ( two-tailed Student t test: P < 0 . 001 ) , and onsite traps ( traps within the release zone ) collected mosquitoes at a faster rate than offsite traps ( traps outside the release zone; two-tailed Student t test: P < 0 . 001 ) ., When accounting for seasonal changes , yields of uninfected mosquitoes caught in offsite traps at both sites tended to decrease over time ( Fig 2 panel A ) ., At PP , there was a corresponding increase in infected mosquito numbers , while at EHW , infected mosquito yields were relatively consistent throughout ., Among onsite traps , yields of infected mosquitoes were consistent with seasonal expectations and were stable over time ( Fig 2 panel B ) ., The higher local infection frequencies onsite might lead to an assumption that uninfected mosquito numbers would decline more rapidly than those offsite , but this was not observed , with uninfected mosquito yields at both sites increasing sharply in W2 ., This proliferation was particularly surprising considering the 2-month-long periods at EHW in the previous season , D2 , during which no uninfected mosquitoes were caught onsite ( Fig 2 panel B ) ., EHW and PP were both invaded quickly , and by the time releases had finished , Wolbachia infection frequencies within each release zone had reached p = 0 . 85 ( S1 Fig ) ., Following the final releases , p remained relatively stable and near fixation within each release zone ., However , in W2 , onsite p at EHW dropped from 0 . 96 to 0 . 84 , the lowest recorded since monitoring began ., Considering Fig 2 panel B , it appears that this was due to neither imperfect maternal transmission of Wolbachia 26 nor increased mortality among infected mosquitoes , as their numbers increased to levels similar to those observed in W1 ., Rather , a sudden influx of uninfected mosquitoes seems most plausible ., Averaged across all 4 seasons , 0 . 88 of mosquitoes within the EHW release zone were infected , while 0 . 90 were infected at PP ., The WC release zone was invaded as quickly as EHW and PP ., However , beginning in September 2013 , onsite p dropped sharply to p < 0 . 7 , after which frequencies fluctuated ., While onsite p never dropped below any plausible value for p^ , at no point did the invasion at WC exhibit either the near-fixation values of p or the temporal stability observed at both EHW and PP ( see figures below and compare panel C of S2 Fig with panels A and B ) ., The changes of p with time at EHW , PP , and WC between 7 May 2013 and 30 April 2015 are displayed in Figs 3 , 4 and 5 , respectively , along with trap locations and yields ., The plots , based on spatial averaging ( ordinary Kriging as described in the Methods section , performed using ArcMap 10 . 2 . 2 52 ) , show considerable seasonal heterogeneity in the spatial structure of the invasions at EHW , PP , and WC ., At EHW ( Fig 3 ) after D1 , the infection was confined largely to the north and northeast , but by the end of W1 , the invasion had spread to the east , northeast , and southwest ., This pattern persisted through D2 , with a small retraction in the north and expansion in the east , though for this season , Kriging was affected by a small sample size ( N = 31 ) ., Kriging on W2 trap data demonstrated 3 main shifts from this pattern: the continued expansion to the north , northeast , and east; the successful invasion of the west; and the apparent reduction in p from p ≥ 0 . 8 to 0 . 65 ≤ p ≤ 0 . 8 at 3 traps in the centre of the release zone ., At PP ( Fig 4 ) , spread through D1 was confined mostly to the southeast , from the edge of the release zone up to Mulgrave Road ., In the following season ( W1 ) , infected mosquitoes were found south across Mulgrave Road and north of the release zone ., The infection persisted south of Mulgrave Road but only at below-threshold ( p ≤ 0 . 3 ) frequencies ., Over D2 , the invasion expanded in range , with high frequencies observed in the north and the southeast and moderate frequencies in the northwest ., At both EHW and PP , the area covered by the infection tended to increase over time ( Figs 3 and 4; summarized in panels A and B of S2 Fig ) , except for PP in W1 , in which the area within the p ≥ 0 . 8 contour decreased by 3% from D1 , and for EHW in D2 , in which the area within the p ≥ 0 . 8 and p ≥ 0 . 5 contours decreased by 7% and 1% , respectively ., Nevertheless , from D1 to W2 , the area enclosed by the p ≥ 0 . 8 contours grew by 85% at EHW and 77% at PP ., At WC ( Fig 5; S2 Fig panel C ) , establishment or spread of the infection was not observed ., Following D1 , the Wolbachia invasion failed to expand to the south or west of the release zone ., Within the release zone , a gradual retreat of the p ≥ 0 . 8 contour was observed , with several onsite traps in W2 registering p ≤ 0 . 3 ., The area covered by the infection at WC reached a peak at W1 , but by W2 the area enclosed by the p ≥ 0 . 8 and p ≥ 0 . 5 contours had decreased by 52% and 44% , respectively , from this maximum ( S2 Fig panel C ) ., Wolbachia also failed to spread from WC into PP ( or vice versa ) , but these areas are separated by parkland , which is likely to act as a barrier to movement and prevents ongoing monitoring there ., The time interval from D1 to W2 is around 1 . 5 years , which can be approximated as 15 generations , assuming about 10 generations per year ( explained below ) , or simply viewed as 548 days ., When containers are initially colonised and food is available for larvae , developmental time is likely to be rapid at 7–10 days ., However , larval populations can rapidly exceed the carrying capacity of the container and its food source ( typically leaves ) , and development is then slowed to 20–50 days 53; these variable conditions produce a range of adult body sizes that is typically found in field samples from Cairns 54 ., If we assume an intermediate value of 20 days in the field , along with time for adult maturation to mating and blood feeding ( 2–3 days post eclosion ) , blood-meal digestion and egg formation and oviposition ( 4 days ) , and egg embryonation ( 3 days ) 55 , this adds another 10 days of adult and egg developmental time ., In Cairns , a cooler winter period will lengthen developmental periods , while dry periods delay hatching ., Overall , 10 generations per year is likely to be a reasonable estimate ., S2 Fig provides approximations for the areas covered by wMel in different seasons after the releases ., For EHW , the area covered in which wMel has at least frequency 0 . 5 is about 1 . 3 km2 in D1 , and this rises to about 2 . 2 km2 in W2 ., We can calculate wave speed per generation ( assuming 10 generations a year ) or per day using alternative geometric approximations described in the Methods section: approximation 4 assumes a circular release area , approximation Eq ( 5 ) assumes a rectangle ( for which we approximate parameter y = 2 i . e . , a release area twice as long as wide ) , or approximation 6 assumes a rectangle in which spread does not occur ( or is not monitored ) in one direction ., ( Note that very little spread occurred to the south at EHW . ), The resulting estimates of wave speed per day are , respectively , cd = 0 . 35 m per day , 0 . 31 m per day , and 0 . 45 m per day ., If we assume 10 generations per year ( and so 15 generations separating D1 from W2 ) , the corresponding wave speeds per generation are: c = 12 . 9 , 11 . 2 , and 16 . 6 m/gen ., Assuming dispersal parameter σ ≈ 100 m/ ( gen ) 1/2 and unstable equilibrium p^≈0 . 3 ( see Turelli and Barton 31 ) , the cubic diffusion approximation for wave speed ( see Eq 2 ) , c=σ ( ½–p^ ) , predicts roughly 20 m/gen ., As discussed in the context of our likelihood analyses below , the discrepancy between the estimated speeds and this analytical prediction can be resolved by assuming longer generations , a higher unstable point , and/or long-tailed dispersal 31 ., For PP , the area covered in which wMel has at least frequency 0 . 5 is about 0 . 65 km2 in D1 , and this rises to about 1 . 17 km2 in W2 ., Using our geometric Models 4 , 5 and 6 ( with y = 2 , as for EHW ) , the resulting estimates of wave speed per day are , respectively , cd = 0 . 28 m per day , 0 . 25 m per day , and 0 . 37 m per day ., If we assume 10 generations per year ( and so 15 generations separating D1 from W2 ) , the corresponding wave speeds per generation are: c = 10 . 4 , 9 . 0 , and 13 . 4 m/gen ., The speed estimates for PP are systematically smaller than for EHW ., As discussed in the Methods section , both wave speed and wave width ( describing the distance over which infection frequencies change appreciably ) are proportional to average dispersal distances ., Thus , slower wave speed is expected if the higher adult densities observed at PP versus EHW translate into a more desirable habitat and consequently smaller average dispersal distances ( lower σ ) ., Consistent with this , we find a sharper wave at PP as quantified by smaller average distances between the 0 . 3 and 0 . 8 contours at PP than EHW; these distances average 326 m at EHW and only 252 m at PP ., Our likelihood analyses are independent of the graphical summaries produced by Kriging ., They rely on an approximate description of the expected shape of local spread and/or collapse ( see Eq 7 in the Methods section ) ., We present several successive analyses that summarize the rate and pattern of spatial spread of Wolbachia at EHW and PP ., Our summaries focus on 2 statistics: wave width and wave speed ., We start by analysing the data averaged over space and time , then present more detailed analyses that document heterogeneous spread ., We begin by analysing the data assuming that observed frequencies deviate from deterministic expectations only because of binomial sampling variation ., We then use a more complex probability model that accounts for additional sources of heterogeneity ., Finally , we explicitly test for directional heterogeneity in rates of spread , as documented visually in Figs 3 and 4 ., The details of the likelihood analyses are relegated to S1 Text ., Fig 5 illustrates the slow collapse of the wMel introduction at WC ., As shown in S2 and S5 Figs , in contrast to the rising infection frequencies outside the release zones at EHW and PP , p initially rises then slowly falls near the WC release ., A likelihood analysis of the pooled data , analogous to those presented in Table 1 , S3 Fig and Fig 6 , supports this conclusion ., The details of the analysis are given in S6 Table , with the results graphically summarized in Fig 7 ., Unlike the steady outward movement of the wave shown at EHW and PP , with the wave widths stabilizing at values near 400 m , Fig 7 shows that the estimated location , r0 , of the “wave” at WC retreats through time , while the wave width , w , steadily increases , corresponding to slow collapse of the wMel introduction ., From our likelihood analyses , the wave speed cd is approximately 0 . 5 m per day ( 186 m per year ) at EHW with wave width w about 460 m ., In contrast , we find a slower moving and sharper wave at PP with cd approximately 0 . 3 m per day ( 110 m per year ) and wave width w about 380 m ., These estimates are broadly consistent with our heuristic approximations ( from Eqs 4–6 ) obtained from the Kriging plots in Figs 3 , 4 and 5 ., As demonstrated by Turelli and Barton 31 , even with fast local dynamics and long-tailed dispersal , we can accurately approximate average local dispersal as σ = w/4 m/ ( gen ) 1/2 ( Eq 2 ) ., From this we infer σ ≈ 115 m/ ( gen ) 1/2 at EHW; in contrast , we obtain σ ≈ 95 m/ ( gen ) 1/2 at PP ., Given that the support intervals for the estimates of w at EHW and PP do not overlap ( Table 1 ) , we expect these results reflect differences in local dispersal ., Given that PP has consistently higher population densities , this difference may reflect less dispersal in a habitat where mosquito densities are higher ., However , this needs further testing against alternative hypotheses , such as more dispersal barriers surrounding the PP versus the EHW release areas ., It is notable that both the EHW and PP estimates of dispersal are consistent with values obtained from release–recapture experiments ( reviewed in 31 ) ., If we assume that the wave speed follows the cubic diffusion approximation c=σ ( ½–p^ ) , per generation and that generations are T days long , we can in principle reconcile observed wave speeds with expected wave speeds at each release site by choosing p^ and T appropriately , namely, T=σ ( 12−p^ ) /cd ,, ( 1 ), where σ is the local dispersal estimate and cd is the observed wave speed per day ., For instance , if we assume that at both EHW and PP , p^=0 . 3 , the observed and expected wave speeds can be reconciled if we assume that T = 46 days for EHW , whereas T = 63 . 3 days for PP ., Given that population densities are higher for PP , increased crowding may indeed produce longer generation times 53 ., These times are systematically larger than our conjecture of 10 generations per year , which we supported by an informal data review above ., These inferences assume that the cubic-diffusion prediction for wave speed ( c=σ ( ½–p^ ) per generation ) is accurate for these field populations ., However , as shown by Turelli and Barton 31 , long-tailed dispersal with fast local frequency dynamics ( as expected with complete cytoplasmic incompatibility , corresponding to sh = 1 in the models of 31 ) , can slow the expected wave speed by 20%–40% below the cubic-diffusion prediction ., If the expected wave speed is reduced by 30% , the observed wave speeds match the modified expectations with generation times reduced to 32 . 2 and 44 . 3 days at EHW and PP , respectively ., These times are closer to our conjecture of 10 generations per year ., In general , there seems to be reasonable quantitative agreement between the slow observed wave speeds and the predictions of simple models using parameter values that are consistent with the poorly known field biology of Ae ., aegypti and the deleterious fitness effects of wMel in Ae ., aegypti ., Despite many caveats , including uncertainty about parameter values and the imprecise meaning of the one-dimensional unstable point p^ for populations with overlapping generations and complex ecology 27 , the observed spread rates at EHW and PP are clearly consistent with approximation Eq ( 1 ) using plausible estimates of dispersal distance , the unstable point , and generation time ., In contrast to EHW and PP , the releases at WC did not lead to clear establishment and certainly did not produce spatial spread ( see Figs 5 and 7 ) ., Turelli and Barton 31 provide conditions on minimum release areas ( and maximum dispersal distances ) consistent with spatial spread , allowing for long-tailed dispersal and rapid local dynamics ., We expect that p^≈0 . 25–0 . 3 and σ ≈ 100 m/gen1/2 ., If these parameter estimates are accurate , the release area at WC is likely to be just below the minimum needed to produce successful local establishment and spread ( see Table 2 of 31 ) ., Moreover , the fact that the apparent collapse at WC is extremely slow is consistent with the slow dynamics expected near that critical size threshold for wave-establishing releases 31 ., Overall , the bistable dynamics of wMel in Ae ., aegypti will impose some minimum release size , and only WC is near a plausible minimum ., To rigorously test the minimum-release-area predictions of Barton and Turelli 20 and Turelli and Barton 31 , several more replicate releases in small areas would be needed ., Our data demonstrate that wMel can be stably established locally within urban areas surrounded by uninvaded but suitable habitat ., Hence , stable population replacement is not limited to small isolated habitats such as those where the initial releases and establishment of wMel in Ae ., aegypti took place ( cf . 14 ) ., Moreover , the temporal increase in infection frequency within the EHW and PP release zones was comparable to that seen in the isolated areas ., In contrast , the smallest release area , WC , did not show stable invasion ., This suggests that there is little impediment to the local establishment of Wolbachia in urban areas , provided the releases are conducted over sufficiently large areas ( e . g . , on the order of 0 . 5 km2 when dispersal distances are comparable to those in Cairns 31 ) ., These findings highlight the feasibility of patchy releases across large cities , suggesting that area-wide replacement can be produced gradually , with patchy releases complemented by natural local spread ., At EHW and PP , the area in which Wolbachia persists at high frequency roughly doubled after 2 years ( Figs 3 and 4 ) ., The failure of wMel to establish and spread at WC seems attributable to the small area of the release zone , as the habitat conditions in and around WC are similar to EHW and PP ., This is consistent with mathematical predictions concerning the minimum release zone radius , Rcrit 20 , 31 ., Based on the wider advancing wave front seen at EHW versus PP , we infer greater average dispersal distance at EHW ( which is likely to provide fewer feeding and breeding opportunities than PP ) ., Mosquito dispersal differences probably explain the faster spread observed at EHW versus PP ., In contrast , the slow temporal and spatial dynamics of local infection frequency at WC suggests that 0 . 11 km2 , the area of the WC release zone , may be very close to the minimum size needed to initiate spread , at least for the levels of dispersal typical of Cairns ., When contrasted against the successful spread at PP , we conclude that the critical release area under Cairns conditions is somewhere between 0 . 11 km2 and 0 . 52 km2 ., In tropical regions that support denser Ae ., aegypti populations , we expect lower dispersal distances ., This would allow successful local establishment using smaller release areas , but spatial spread would also be expected to be even slower than the 100–200 m per year observed at EHW and PP ., The heterogeneity in both the speed and patterns of the spatial dynamics at EHW and PP suggests that local environmental factors greatly influence the spread of Wolbachia transinfections ( such as wMel in Ae . aegypti ) that produce significant fitness costs ., Spread at each site exhibited strong spatial structure throughout the study , and the structure persisted across the monitoring period ., Areas that were easily invaded during the first dry season after the releases ( D1 , see Figs 3 and 4 ) generally stayed invaded in successive seasons , and the autocorrelation among mosquito numbers and infection frequencies increased as the study progressed ( S7 Table ) ., The invasion spread well beyond the initial release zones at EHW and PP , and our likelihood analyses ( Fig 6 ) suggest that slow but steady spread would continue in the absence of further releases until significant barriers to dispersal are encountered ., Barriers to spread can include both barriers to Ae ., aegypti dispersal and variation in Ae ., aegypti population density 20 ., At PP , the invasion spread south from the release zone immediately but never established to a high frequency south of Mulgrave Road ., Nevertheless , infected mosquitoes were caught at low frequencies south of Mulgrave Road from season W1 onwards ., These observations are consistent with the demonstration in Trinidad that roads represent partial barriers to Ae ., aegypti dispersal 43 ., At the very least , such barriers slow wave propagation 20 ., It remains unclear whether Mulgrave Road provides a sufficient barrier to stop the wave of Wolbachia , as is the case of the Bruce Highway at Gordonvale ., There , Wolbachia have failed to invade an area adjacent to the 2011 release zone for several years , despite persistent migration across the highway 31 ., Other evidence from mark-release experiments and genetic studies have pointed to potential barriers ( roads , rivers , forests ) to movement of Ae ., aegypti at a local scale 43 ., In W2 at EHW , there was an apparent drop in p in the southern half of the release zone ., This was unexpected given that in previous seasons traps in this region had recorded Wolbachia frequencies close to fixation ., It appears that the drop was due to a sudden increase in uninfected mosquito numbers onsite , which may represent the hatching of dormant uninfected eggs or an early influx of uninfected mosquitoes from an external source at the start of W2 ., One possibility is that Wolbachia infected larvae experienced a fitness cost under high-stress conditions prevailing at that time; such costs have been recently documented under stressful conditions that produce a range of adult sizes 19 , 56 similar to those seen under field conditions 54 , even though earlier studies su

Introduction, Results, Discussion, Methods

Dengue-suppressing Wolbachia strains are promising tools for arbovirus control , particularly as they have the potential to self-spread following local introductions ., To test this , we followed the frequency of the transinfected Wolbachia strain wMel through Ae ., aegypti in Cairns , Australia , following releases at 3 nonisolated locations within the city in early 2013 ., Spatial spread was analysed graphically using interpolation and by fitting a statistical model describing the position and width of the wave ., For the larger 2 of the 3 releases ( covering 0 . 97 km2 and 0 . 52 km2 ) , we observed slow but steady spatial spread , at about 100–200 m per year , roughly consistent with theoretical predictions ., In contrast , the smallest release ( 0 . 11 km2 ) produced erratic temporal and spatial dynamics , with little evidence of spread after 2 years ., This is consistent with the prediction concerning fitness-decreasing Wolbachia transinfections that a minimum release area is needed to achieve stable local establishment and spread in continuous habitats ., Our graphical and likelihood analyses produced broadly consistent estimates of wave speed and wave width ., Spread at all sites was spatially heterogeneous , suggesting that environmental heterogeneity will affect large-scale Wolbachia transformations of urban mosquito populations ., The persistence and spread of Wolbachia in release areas meeting minimum area requirements indicates the promise of successful large-scale population transformation .

Wolbachia are bacteria that live inside insect cells ., In insects that act as viral vectors , Wolbachia can suppress virus transmission to new hosts ., Wolbachia have been experimentally introduced into Aedes aegypti mosquito populations to reduce the transmission of dengue , Zika , and other arboviruses that cause human disease ., Wolbachia invade populations by causing cytoplasmic incompatibility , a phenomenon whereby embryos from crosses between infected males and uninfected females fail to hatch ., While Wolbachia have been shown to successfully invade and remain established in isolated Ae ., aegypti populations , outward spread from urban release zones has not been previously documented ., This is an important step in demonstrating that Wolbachia can be used to combat mosquito-borne infectious disease in cities ., Here we describe Wolbachia spread from 2 introduction areas within Cairns in northeastern Australia at a rate of about 100–200 meters per year ., Spread occurs only when introduction areas are sufficiently large ., The slow rates of observed spread are broadly consistent with mathematical predictions based on estimated Ae ., aegypti dispersal distances , Wolbachia dynamics , and effects seen in isolated populations ., Spread is uneven and likely depends on local characteristics ( e . g . , barriers ) that affect mosquito density and dispersal ., Our data indicate that Wolbachia can be introduced locally in large cities , remain established where released , and slowly spread from release areas ., These dynamics indicate that high Wolbachia infection frequencies can be established gradually across large urban areas through local releases .

invertebrates, medicine and health sciences, animals, wolbachia, seasons, mathematics, statistics (mathematics), population biology, insect vectors, bacteria, infectious diseases, geography, aedes aegypti, disease vectors, insects, arthropoda, approximation methods, population metrics, mosquitoes, statistical models, urban areas, earth sciences, geographic areas, biology and life sciences, species interactions, population density, physical sciences, organisms

journal.pntd.0004862

Th-1, Th-2 Cytokines Profile among Madurella mycetomatis Eumycetoma Patients

Mycetoma is a chronic subcutaneous infection caused by certain bacteria ( actinomycetoma ) or fungi ( eumycetoma ) 1 ., It is characterised by a slow progressive infection and a granulomatous inflammatory response that can result in severe soft tissue and muscle damage along with destruction of the underlying bone 1 , 2 ., Mycetoma is endemic in tropical and subtropical regions; however , it has been reported globally ., Eumycetoma in Sudan , is predominately caused by the fungus Madurella mycetomatis 2 ., The disease is characterised by extensive subcutaneous masses , usually with multiple draining sinuses and fungal grains 1 ., Mycetoma disease has significant negative medical health and socio-economic impacts on patients and communities , affects individuals of all ages , but is more frequently seen in adults who work outdoors ., The host defence mechanisms against fungi usually range from germline encoded immunity which present early in the evolution of microorganisms , to highly specialised and specific adaptive mechanisms that are induced by infection and disease ., The innate response to fungi serves two main purposes; a direct antifungal effector activity and activation or induction of specific adaptive immune responses ., In general , the direct antifungal effector activity mediates non-specific elimination of pathogens through either a phagocytic process with intracellular killing of internalised pathogens or through the secretion of microbiocidal compounds against undigested fungal molecules ., The activation and induction of the specific adaptive immune responses is accomplished by the production of pro-inflammatory mediators , including chemokines and cytokines , providing co-stimulatory signals to naive T cells , as well as antigen uptake and presentation to CD4+ and CD8+ T cells 3 , 4 ., Many individuals in mycetoma endemic areas are exposed to the causative aetiological agents , but only few develop the disease ., This may suggest variable responses of the host immune system towards the invading agent ., In this respect , the role of the innate immunity in host resistance to mycetoma infection has been studied in vitro and in animal models , but few studies have been performed in humans ., T cell–mediated immune response to eumycetoma fungi in humans was studied by Mahgoub and associates who suggest that patients with eumycetoma have a weak cell-mediated response as determined by skin reaction to dinitrochlorobenzene 5 ., Decreased lymphocyte proliferative response to phytohemagglutinin in those patients was also reported ., However , no evidence was provided to confirm whether this is a primary immune deficiency or a secondary response to a severe infection ., In addition , the same study showed high levels of IgA and IgM and low levels of IgG antibodies in mycetoma patients 5 ., In actinomycetoma , Gonzalez-Ochoa and Baranda 6 found that patients with severe lesions and extensive tissue destruction displayed a weak skin reaction to some pathogenic bacteria polysaccharides such as , Nocardia brasiliensis 6 ., However , it was not clear whether this represented a T-helper-1 ( Th-1 ) or T-helper-2 ( Th-2 ) response ., To date there has been limited data on the immune response to mycetoma infection and how patients can modulate their response against M . mycetomatis ., With this background , the present study aims to determine the Th-1 and Th-2 cytokines response of patients infected with M . mycetomatis and to find out the association between the measured Th-1 and Th-2 cytokine levels and the disease prognosis and outcome ., This descriptive cross-sectional hospital based study was conducted at the Mycetoma Research Centre , University of Khartoum , Khartoum , Sudan ., In this study 140 individuals were enrolled; 49 ( 35% ) were females and 91 ( 65% ) were males ( Table 1 ) , with an overall median age of 25 years ( range 12–70 years ) ., 70 patients with confirmed mycetoma infection due to Madurella mycetomatis were recruited ., The study population was divided into three groups; group I: healthy controls ( n = 70; median age 25 years ( range 12 to 70 years ) ) , matched for sex , age and locality with the patients group ., Group II: mycetoma patients without surgical excision ( n = 35 patients; median age 25 ( range 13 to 70 years ) ) , these patients were not treated with surgical excision and were under medical treatment ( 200 mg bd Itraconazole or 400 mg bd ketoconazole ) ., Group III: mycetoma patients who underwent surgical excision ( n = 35 patients; median age 25 years ( range 12 to 70 years ) and medical treatment ( 200 mg bd Itraconazole ) ., One hundred μl of blood were collected on filter paper ( Whatman qualitative filter paper , Grade 1 , circles , diam . 42 . 5 mm from SIGMA-ALORICH , KSA ) for cytokine’s determination ., The use of filter paper dried whole blood spots ( DBS ) for specimen collection was preferred to facilitate collection , storage and transportation of specimens in addition to being recommended by the World Health Organization ( WHO ) and also used in several previous studies 7–9 ., A hole puncher with a diameter of 6 mm was used for cutting out discs from the filter paper in the middle of the blood spot , where the blood was assumed to be evenly spread ., The discs were put in 10 ml tubes and 500 μl of PBS containing 0 . 05% Tween and 0 . 5% BSA was added ., The discs were then incubated for 2 hrs at room temperature on a shaker ., Finally , after vortexing the samples for 30 seconds , the supernatants ( eluted serum ) were collected with a Pasteur pipette and aliquoted in new 1 . 5 ml cryo tubes and stored at −20°C until analysis ., The extract corresponds to a serum dilution of ~1:100 ., This method was modified from a previous report by Mercader and colleagues 8 ., Measurements of cytokines were performed in sera by flow cytometry using Cytometric Bead Array ( CBA ) technology , as detailed by Cook and associates 10 ., Human Inflammation CBA kit ( BD Biosciences , San Jose , CA ) was used to quantitatively measure IFN-γ , TNF-α , IL-1β , IL-2 , IL-4 , IL-5 , IL-6 , IL-10 , and IL-13 levels ., The sensitivity of Human Inflammation CBA was comparable to conventional ELISA 11 ., Samples were analysed using a BD FACSCalibur flow cytometer ( BD Biosciences , San Jose , CA ) , according to the manufacturer’s instructions ., The data was managed by SPSS statistics software version 23 for Windows ( IBM , SPSS statistics ) ., The one-way analysis of variance ( ANOVA ) and Tukey’s test for post hoc analysis were used to compare mean levels of cytokines between various study groups ., The difference in cytokine levels across groups was analysed using ANOVA test ( Table 2 ) ., Linear regression models were used to predict each cytokine level ( Table 3 ) ., Unstandardised coefficient ( B ) regression is the determination of the statistical relationship between two or more variables 12 ., B analysis was adjusted for each cytokine according to gender ( Female = 0 and male = 1 ) , medical treatments ( Itraconazole = 0 and Ketoconazole = 1 ) , size of mass ( >10 cm = 1 ) , presence of grains ( No = 0 and Yes = 1 ) and age , as independent variables ., This study was approved by the Ethics Committee of Soba University Hospital , Khartoum , Sudan ., Written informed consent was obtained from the participants prior to their enrolment in the study ., Informed consent was also obtained from children and their guardians before participation ., The work described here was performed in accordance with the Declaration of Helsinki 13 ., A higher proportion of mycetoma patients were males ( 80% ) compared with females ( 20% ) ., Combined ( both with and without surgical intervention ) males and females among mycetoma patients groups were 56/70 and 14/70 , respectively ( p <0 . 001; Table 1 ) ., Patients with mycetoma received various antifungal drugs , which were used in combination with or without surgical excision ., Of the 70 individuals who received oral medication in this study , 46 patients ( 66% ) received Itraconazole ., Out of the patients who were treated with Itraconazole , Eleven patients ( 24% ) were treated without surgical excision and 35 patients ( 76% ) were surgically treated along with Itraconazole 200 mg bd ., Twenty four patients ( 34% ) received Ketoconazole 400 mg bd p value <0 . 001 and 95% confidence interval 95%CI; ( 0 . 55 to 0 . 80 ) ( Table 1 ) ., Ketoconazole 400 mg bd was only used among patients without surgical excision and not following surgery , whereas Itraconazole 200 mg bd was the only choice postoperatively p value <0 . 001 and 95% CI; ( 0 . 58 to 0 . 93 ) ., The proportion of lesions that were more than 10 cm in diameter were significantly higher in the surgically treated group compared to the non-surgically treated patients p value = 0 . 037 and 95% CI; ( -0 . 58 to 0 . 13 ) ( Table 1 ) ., Patients with mycetoma infection had significantly higher cytokine levels including IFN-γ , TNF-α , IL-2 , IL-4 , IL-5 , IL-6 , IL-10 and IL-13 , compared to the control group ( overall p value for each cytokine <0 . 001 ) ( Table 2 ) ., In contrast; no significant difference was observed in the levels of IL-1β between the study groups ( overall p value = 0 . 913 ) ( Table 2 ) ., Linear regression analysis showed significantly higher levels of Th-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) among mycetoma patients treated with surgical excision than in those treated without surgical intervention ., Unadjusted B ( 95% CI ) for: IFN-γ = 5 . 64; 95% CI ( 1 . 33 to 9 . 96 ) , p value = 0 . 011 ., For TNF-α = 14 . 58; 95% CI ( 11 . 56 to 17 . 60 ) , p value <0 . 001 ., For IL-1β = -0 . 36; 95% CI ( -0 . 67 to -0 . 05 ) , p value = 0 . 022 ., For IL-2 = 7 . 55; 95% CI ( 5 . 61 to 9 . 50 ) , p value <0 . 001 ( Table 3 ) ., When B was adjusted for gender , medical treatment , size of lesions and the presence of grains; similar statistical analysis indicated significantly higher levels of Th-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) among mycetoma patients treated with surgical excision than in those treated without surgical excision ., Adjusted B ( 95% CI ) for: IFN-γ = 6 . 62; 95% CI ( 1 . 42 to 11 . 81 ) , p value = 0 . 017 ., For TNF-α = 12 . 69; 95% CI ( 9 . 94 to 16 . 32 ) , p value <0 . 001 ., For IL-1β = -0 . 75; 95% CI ( -1 . 13 to -0 . 37 ) , p value <0 . 001 ., For IL-2 = 6 . 59; 95% CI ( 3 . 91 to 9 . 28 ) , p value <0 . 001 ( Table 3 ) ., In contrast , a similar linear regression analysis model for Th-2 cytokines showed significantly lower levels of Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) among mycetoma patients treated with surgical excision , compared to those treated without surgical excision ., Unadjusted B ( 95% CI ) for: IL-4 = -2 . 57; 95% CI ( -3 . 14 to -2 . 0 ) , p value <0 . 001 ., For IL-5 = -2 . 08; 95% CI ( -2 . 54 to -1 . 62 ) , p value <0 . 001 ., For IL-6 = -10 . 09; 95% CI ( -13 . 68 to -6 . 51 ) , p value <0 . 001 ., For IL-10 = -5 . 33; 95% CI ( -7 . 79 to -2 . 87 ) , p value <0 . 001 ( Table 3 ) ., When B was adjusted for gender , medical treatment , size of lesions and presence of grains , a similar statistical analysis model showed significantly lower levels of Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) among mycetoma patients treated with surgical excision compared to those treated without surgical excision ( Table 3 ) ., Adjusted B ( 95% CI ) for: IL-4 = -2 . 82; 95% CI ( -3 . 65to -1 . 99 ) , p value <0 . 001 ., For IL-5 = -2 . 38; 95% CI ( -3 . 04 to -1 . 72 ) , p value <0 . 001 ., For IL-6 = -7 . 66; 95% CI ( -12 . 88 to -2 . 44 ) , p value = 0 . 005 ., For IL-10 = -3 . 58; 95% CI ( -7 . 16 to -0 . 01 ) , p value = 0 . 05 ( Table 3 ) ., It is known that fungi release antigens ( Ag ) on the skin surface , and the antigens that penetrate the skin are subsequently captured by an antigen-presenting cell ( APC ) such as dendritic cells ( DCs ) 14 ., Fungal antigens can also play an important role in the DCs maturation ., Furthermore , production of inflammatory cytokines such as IFN-γ and TNF-α by other innate cells such as natural killer cells ( NK ) further enhance the activation of microbiocidal functions of phagocytic cells as well as maturation of DCs 15 ., In the present study , Th-1 cytokines ( IFN-γ , TNF-α , and IL-2 ) were found to be significantly higher in mycetoma patients than in controls ., Besides , the levels of Th-1 ( IFN-γ , TNF-α , IL-1β and IL-2 ) were significantly higher in mycetoma patients treated with surgical excision compared to those who were only medically treated ., These findings go a long way to explain the earlier findings of van de Sande and associates 16 , that neutrophils are attracted to the site of infection by mycetoma antigen , secrete TNF-α and IFN-γ cytokines in the presence of IL-17 17 ., Interestingly , in a previous study , Cassatella and colleagues suggested that neutrophils are multipurpose cells which play many roles , not only in inflammatory progressions but also in immune and antitumor processes 18 ., The same group had also added that , IFN-γ activated neutrophils release biologically active TNF-α related apoptosis-inducing ligand ( TRAIL/APO2 ligand ) , a molecule that exerts selective apoptotic activities towards tumours 18 ., Additionally , Elagab and associates , showed that , the peripheral blood mononuclear cells ( PBMC ) of mycetoma patients react differently to M . mycetomatis antigens than healthy controls 19 ., In general , when PBMCs produce IFN-γ upon stimulation with the antigen , no production of IL-10 was detected 19 ., There is also no significant differences between the cytokines TNF-α and TGF-β levels in patients and controls 19 ., The discrepancy between Elagab’s findings 19 and our findings may be explained by the differences in the study design ., IL-1 is an essential host defence cytokine against a broad range of pathogens , ranging from bacteria to parasites and fungi 20 ., IL-1β is primarily produced by innate immune cells such as monocytes , macrophages and dendritic cells upon activation , and is also an important cytokine for the control of fungal infection 21 ., It is also an important proinflammatory mediator whose production is controlled by multiprotein complexes called inflammasomes 22 , 23 ., Although IL-1β plays an active role in containing infection caused by different fungi , its role in controlling fungal infections remains unclear 24 ., The results of the current study has shown that higher levels of IL-1β cytokine are strongly associated with mycetoma patients treated with surgical excision , compared to those treated without surgical intervention ., It is of interest to note that , IL-1β can play a crucial role in the activation of complement protein-3 ( CR3 ) , dectin-1 as well as caspase-8 in coordinating cell death and inflammasome responses to β-glucans 25 ., Our findings led us to suggest that the observed higher levels of IL-1β cytokine play an important role in reducing the risk of M . mycetomatis infection ., However , more studies are needed to confirm farther this observation ., As mentioned earlier cytokine IL-2 exerts critical functions during immune homeostasis via its effects on Treg cells , and by optimising the effector lymphocyte responses of both T-cells and B-cells ., In addition , IL-2 receptors ( IL-2R ) were shown to be present on human neutrophils , and that IL-2-neutrophil interactions are believed to be important in both tumour rejection and increased susceptibility to bacterial infections 26 , 27 ., It is relevant to add that a previous study on mycetoma patients from an endemic area 16 , demonstrated that neutrophils are attracted to the site of infection by the mycetoma antigen ., In the current study IL-2 levels were significantly higher in mycetoma patients compared to controls ., In addition , IL-2 cytokine levels were elevated significantly in mycetoma patients treated with surgical excision , compared to those treated without surgical intervention ., We take this finding to indicate that , IL-2 cytokine plays a major role in the pathogenesis of mycetoma infection ., This novel finding on an association of IL-2 and neutrophils should pave the way to new avenues of research on IL-2-neutrophil interactions to better understand the response of patients to mycetoma infection ., The cytokines IL-4 , IL-5 , IL-13 and GM-CSF are produced by T-helper-2 cells at the site of inflammation but also they have important functions in haematopoiesis ., These cytokines , individually or collectively along with chemokines such as CCL11 , play a major role in coordinating the maturation and mobilisation of leukocytes ( Monocytes/Macrophages and Neutrophils ) and mast cell progenitors , ensuring the continued supply of leukocytes to the site of the inflammation 28 , 29 ., In present study , the in vivo effect of M . mycetomatis infection on the production of Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) was clearly reflected by the , significantly higher levels of Th-2 cytokines in mycetoma patients compared to controls ., Moreover , lower levels of Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) were significantly associated with mycetoma patients treated with surgical excision , compared to those treated without surgical intervention ., This finding is in line with the earlier hypothesis that Th-2 cytokines play an important role in the activation of the humoral immune response 28 , 29 ., It is well stablished that the type of cell-mediated immunity ( CMI ) is critical in determining resistance or susceptibility to fungal infection ., In general , Th1-type CMI is required for the clearance of fungal infections , while Th2 immunity usually enhances the susceptibility to infection and allergic responses 30 ., Additionally , Th-1 cells are concerned mainly with production of cytokines such as IFN-γ , and promote CMI and phagocyte activation , while in contrast , Th-2 cells predominantly produce cytokines such as IL-4 and IL-5 and tend to promote antibody production 30–32 ., Besides , IL-4 and IL-5 cytokines can play an important role in the activation of B-cells to differentiate to plasma cells that secrete IgM antibody and also generate memory B cells 33 ., A previous similar study found elevated levels of IgM antibody in mycetoma patients 5 ., Besides , another study on immune responses against mycetoma Sudanese patients , demonstrated the presence of immunoglobulins G , M and complement on the surface of the grains and on the filaments inside the grains of mycetoma lesions 34 ., Also , both neutrophils and macrophages were recruited into the lesion by complement and were involved in the fragmentation of the grains ., The cytokines profile in the lesion and regional lymph nodes was of a dominant Th-2 pattern ( IL-10 and IL-4 ) 34 , and these elevated levels of Th-2 cytokines in mycetoma patients may trigger the increased production of IgG , IgM and complement ., The significance of this phenomenon needs further investigations ., We noted with great interest higher levels ofTh-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) in mycetoma patients treated with surgical excision than in those patients treated without surgical intervention ., However , in contrast the Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) were significantly lower in patients treated with surgical excision compared to those treated without surgical intervention ., These results suggest that , the defence against the fungus M . mycetomatis is based on the adaptive effector phase and the duration of the infection as well as the size of the mycetoma mass and presence of grains ., The effects of CMI can also play a critical role in reducing the risk of localised infection in mycetoma patients treated with surgical excision compared to those treated without surgical intervention ., The essential role of the CMI response is to destroy the fungi and produce an immuno-protective status against infection ., At this moment the exact explanation of this finding is not clear and requires further investigation in mycetoma patients .

Introduction, Materials and Methods, Results, Discussion

Eumycetoma is a progressive and destructive chronic granulomatous subcutaneous inflammatory disease caused by certain fungi , the most common being Madurella mycetomatis ., The host defence mechanisms against fungi usually range from an early non-specific immune response to activation and induction of specific adaptive immune responses by the production of Th-1 and Th-2 cytokines ., The aim of this study is to determine the levels of Th-1 and Th-2 cytokines in patients infected with Madurella mycetomatis , and the association between their levels and disease prognosis ., This is a descriptive cross-sectional study conducted at the Mycetoma Research Centre , University of Khartoum , Sudan , where 70 patients with confirmed M . mycetomatis eumycetoma were enrolled; 35 with , and 35 without surgical excision ., 70 healthy individuals from mycetoma endemic areas were selected as controls ., The levels of serum cytokines were determined by cytometric bead array technique ., Significantly higher levels of the Th-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) were recorded in patients treated with surgical excision , compared to those treated without surgical excision ., In contrast , the Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) were significantly lower in patients treated with surgical excision compared to those treated without surgical excision ., In conclusion , the results of this study suggest that cell-mediated immunity can have a role to play in the pathogenesis of eumycetoma .

Madurella mycetomatis is the most common causative agent for eumycetoma , which is a progressive and destructive subcutaneous inflammatory disease ., It is a neglected tropical disease affecting the population in poor and remote endemic tropical and subtropical areas ., Currently , the susceptibility and resistance to mycetoma are not well defined , and many factors can be incriminated , including immunological , genetic , or environmental ones ., The current descriptive cross-sectional study was conducted to determine the Th-1 and Th-2 cytokine levels among 70 patients with Madurella mycetomatis eumycetoma and 70 healthy controls ., It aimed to find out the association between the disease prognosis and the level of these cytokines ., Significantly higher levels of the Th-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) were found in patients treated with surgical excision compared to those treated without surgical intervention ., However , the Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) were significantly lower in patients treated with surgical excision compared to those treated without surgical excision ., These findings suggested that , cell-mediated immunity has a prime role in the pathogenesis of eumycetoma .

innate immune system, medicine and health sciences, immune physiology, cytokines, pathology and laboratory medicine, mycetoma, immunology, tropical diseases, surgical and invasive medical procedures, developmental biology, fungi, signs and symptoms, molecular development, neglected tropical diseases, fungal diseases, infectious diseases, lesions, immune response, immune system, diagnostic medicine, surgical excision, physiology, biology and life sciences, organisms

journal.pgen.1003876

Eleven Candidate Susceptibility Genes for Common Familial Colorectal Cancer

Colorectal cancer ( CRC ) ( MIM 114500 ) is a major cancer type , with over one million new cases diagnosed worldwide each year ., It is the third most common malignancy 1 , and the second most common cause of cancer mortality 2 ., Inherited factors are estimated to play a crucial role in at least one third of all CRC cases 3 ., However , high-penetrance mutations in known CRC predisposing genes , such as the mismatch repair ( MMR ) genes , APC , MUTYH ( MYH ) , SMAD4 , BMPR1A , STK11/LKB1 , PTEN , AXIN2 , POLE , and POLD1 explain only around 5% of these cases 4–6 ., There are a few examples of rare variants in CRC predisposing genes conferring moderate or low carrier risk , such as APC ( I1307K ) 7 , BLM 8 and GALNT12 9 ., Of these , the APC I1307K variant has been most extensively studied and occurs almost exclusively in the Ashkenazi Jewish population 7 ., In addition to these , genome-wide association ( GWA ) studies have identified common low-penetrance variants at approximately 20 genomic loci associated with CRC susceptibility ., However , the identified common variants at these loci exert only a modest effect on CRC risk 10–12 ., Unknown variants of moderate or low penetrance are likely to explain at least part of the missing heritability in CRC ., CRC families with few affected individuals are an attractive patient group to search for such genetic factors , but tools for such work have been poor ., These families are relatively common but too small for linkage analyses , and the culprit variants are likely to be too diverse and rare to be detected in GWA studies ., One approach has been to study the additive contribution of low-penetrance variants on familial risk ., A previous study has estimated that ten known low-penetrance CRC variants collectively explain around 9% of the variance in familial risk 13 ., Advances in sequencing technologies have made exome sequencing a feasible approach to search for rare coding variants of varying penetrance ., In this study , we aimed at identifying variants predisposing to common familial CRC by performing exome sequencing on 96 independent familial CRC cases derived from a consecutive collection of unselected patients ., Here , familial CRC is characterized as having at least one first-degree relative diagnosed with CRC; indeed the great majority of the 96 familial cases displayed only one first-degree relative with CRC ., All patients were from Finland , known for its relatively homogenous population 14 , 15 ., This empowers the analysis since affected individuals are more likely to share ancestral predisposition mutations and haplotypes , stemming from a handful of founders ., To our knowledge , this is the largest effort to date where exome sequencing has been applied to familial forms of cancer to identify novel predisposing genes ., Exome sequencing analysis was performed on germline DNA from 96 independent familial CRC cases ., The clinical and histopathological features of the cases are summarized in Table 1 and in more detail in Table S1 ., The average read depth attained for target regions was 43 and at least 86% of the captured target regions were covered by four or more sequence reads for all the samples ., We identified a total of 76 , 487 nonsynonymous variants in the exome data ( Figure 1 ) ., Sequence data were first evaluated for known predisposing genes ( MLH1 , MSH2 , MSH6 , PMS2 , APC , MUTYH , SMAD4 , BMPR1A , LKB1/STK11 , PTEN , AXIN2 , POLE , and POLD1 ) ., No clear pathogenic mutations were found in these genes ., The following missense variants were identified ( not confirmed by Sanger sequencing ) ; MSH6 c . 2800G>C p . D934H , and PTEN c . 1016C>A P339Q ., However , the patients did not present typical clinical phenotypes; in the case of the MSH6 variant the tumor did not display microsatellite instability and in the case of the PTEN variant patient records revealed no features suggestive of Cowden syndrome ( MIM 158350 ) ., Thus , these variants remain of unknown clinical significance ., We hypothesized that predisposing germline variants would likely be rare in the general population , and predicted to truncate the protein product ., We therefore filtered the data to prioritize such variants ( Figure 1 ) ., First , variants had to be protein truncating with putative loss-of-function alteration; including nonsense , frameshift ( insertion and deletion ) or splice-site variants ( IVS +1 , +2 , −1 , and −2 ) ., A total number of 3 , 654 truncating variants were found in the exome data ., Second , variants were excluded if present in the 1000 Genomes Project 16 or population matched exome control data ( n\u200a=\u200a212 ) at minor allele frequency ( MAF ) >0 . 001 ., After control filtering , 2 , 090 truncating variants remained ., Third , genes with truncating variants in more than one familial CRC case were selected for further analysis ., There were a total of 588 such variants of which 422 were frameshift , 115 nonsense , and 51 splice site variants ( Figure 1 ) ., Frameshift variants were grossly overrepresented in the list of truncating variants due to sequencing artifacts ., Finally , manual filtering was performed on all variants to further remove artifacts due to duplicated regions , mapping errors , and systematic sequence specific errors ., The filtering procedure resulted in a shortlist of 29 genes with 46 truncating variants ., These were subsequently validated by Sanger sequencing ( Figure 1 ) ., Sanger sequencing was successful for all amplicons , and 23 truncating variants in 18 genes were confirmed ., Of these seven were frameshift , 12 nonsense , and four splice-site variants ., To further exclude neutral polymorphisms , the confirmed variants were screened in 310 Finnish population matched controls , of which approximately two-thirds were also regionally matched ., Variants with MAF>0 . 001 in the overall discovery phase control set ( including Finnish control exome data and Sanger sequenced controls ) were excluded ( Figure 1 ) ., In total , we identified 11 candidate predisposing genes with 14 truncating germline variants in at least two familial CRC cases ( Table 2 ) ; UACA , SFXN4 , TWSG1 , PSPH , NUDT7 , ZNF490 , PRSS37 , CCDC18 , PRADC1 , MRPL3 , and AKR1C4 ., A summary of all these variants and respective frequencies are presented in Table 2 ., Gene descriptions and proposed functions of the identified genes are listed in Table S2 ., Typically , the same truncating variant was detected in several patients ., However , three genes harbored two different types of truncating germline variants ( Table 2 ) ., Nine genes showed truncating variants in 2/96 familial cases ., Two genes had truncating variants in 3/96 cases; UACA ( uveal autoantigen with coiled-coil domains and ankyrin repeats ) ( 3/96 , 3 . 1% ) and SFXN4 ( sideroflexin 4 ) ( 3/96 , 3 . 1% ) ., In UACA , p . Q1116X was identified in two out of 96 familial cases and present in 522 Finnish population matched controls with a MAF of 0 . 001 ., UACA p . R1292X was found in one out of 96 cases and the variant was not found in controls ( Figure 2 ) ., In SFXN4 , three out of 96 cases had c . 32delC ., This variant had a MAF of 0 . 001 in population matched controls ., None of the other identified truncating variants were identified in population matched controls , except for c . 389_390insA in PSPH which was found in 1/502 controls ( MAF 0 . 001 ) ., To further explore the frequency of these variants in controls , we referred to the Exome Variant Server ( NHLBI GO Exome Sequencing Project ( ESP ) , Seattle , WA , http://evs . gs . washington . edu/EVS/ July 2013 ) ., Three of the identified germline variants , SFXN4 c . 32delC , NUDT7 c . 111T>A , and PRSS37 c . 176+1G>A , were reported , however , at a MAF of less than 0 . 0003 ., The exome data was also searched for missense variants in the 11 candidate predisposition genes; five missense variants were observed in five genes ( Table S3 ) ., All of the missense variants were present in one case only , except for p . Q83H in PSPH which was identified in two out of the 96 familial cases ., None of the missense variants were predicted to have a damaging effect on the protein by either of the prediction programs used ( Table S3 ) ., The identified missense variants were very rare in population matched controls ( MAF<0 . 001 ) ., Loss of heterozygosity ( LOH ) was examined in cancers of CRC cases with candidate predisposing germline variants ( Figure 1 ) ., The following genes displayed LOH in at least one cancer: UACA , TWSG1 , PSPH , and ZNF490 ( Table 2 ) ., Seven LOH events were observed and all targeted the wild-type allele ( P\u200a=\u200a0 . 0078 ) ., In UACA three out of six examined tumors showed loss of the wild-type allele and in TWSG1 ( twisted gastrulation protein homolog 1 ) both of the tumors showed loss of the wild-type allele ( Figure 2 ) ., Variants in genes showing loss of the wild-type allele in tumor tissue were genotyped in an independent set of validation phase samples ( Figure 1 ) ., This set included 954 Finnish population matched CRC cases and 586 Finnish population matched controls ., UACA p . Q1116X was identified in two additional unrelated CRC cases and one control ( Table 2 ) ., The ages at diagnosis were 67 and 58 years for the two cases ., In the overall set of Finnish population matched controls used in this study , two out of 1 , 108 controls had UACA p . Q1116X ( MAF\u200a=\u200a0 . 0009 ) ., UACA p . R1292X was found in one additional case ( diagnosis at the age of 61 ) and no controls were heterozygous for this variant ., The variant p . R350X in ZNF490 was found in one additional case ( diagnosed at the age of 58 ) and remained absent in controls ( Table 2 ) ., TWSG1 p . Q41X was not present in any additional cases or controls ., Genotyping was not successful for PSPH c . 389_390insA ., Next , LOH was analyzed in the tumors of the four additional cases with truncating variants ( Table 2 ) ., One of the additional cases with UACA p . Q1116X showed LOH involving the wild-type allele ( Figure 2 ) ., Segregation analysis of the identified truncating variants was performed for all the affected first degree relatives for whom samples were available ., In total , segregation was analyzed in seven families for five of the identified truncating variants; c . 32delC in SFXN4 , p . Q41X in TWSG1 , p . R350X in ZNF490 , c . 168+1G>A in PRADC1 , and c . 620delA in AKR1C4 ( Figure 3 and Figure S1 ) ., The following variants showed segregation; c . 32delC in SFXN4 , c . 168+1G>A in PRADC1 , and c . 620delA in AKR1C4 ., The variant p . Q41X in TWSG1 segregated in one family but not the other ( Figure 3 ) and p . R350X in ZNF490 did not segregate ( Figure S1 ) ., Exome sequencing is a powerful tool for discovering novel genetic variants that predispose to disease 17 ., To examine the genetic basis of common familial CRC we exome sequenced 96 independent cases ( Table 1 ) derived from a previously described population-based collection of patients 4 , 18 and from an additional unselected collection ( unpublished ) ., To our knowledge , this is the largest effort to date where familial CRC has been studied by exome sequencing to identify novel CRC predisposing genes ., Several strategies were applied to improve the power of gene discovery ., First , a large set of familial CRC cases ( at least one first-degree relative diagnosed with CRC ) was utilized , negative for any known high penetrance CRC mutation ., Second , the cases were from Finland , known for its isolated population with reduced genetic heterogeneity ., Such isolated populations are enriched for rare founder variants , facilitating identification of disease genes 15 ., Third , tumor tissue availability for all the CRC cases allowed for the assessment of somatic allelic imbalance , which gave important additional information related to pathogenicity of the variants ., Fourth , genotyping of selected variants was performed in a set of validation phase population matched samples , consisting of 954 cases and 586 controls ., In total , we identified 11 novel candidate CRC susceptibility genes with rare truncating variants in two or three familial CRC cases; UACA , SFXN4 , TWSG1 , PSPH , NUDT7 , ZNF490 , PRSS37 , CCDC18 , PRADC1 , MRPL3 , and AKR1C4 ( Table 2 and Table S2 ) ., They were absent or rare ( MAF≤0 . 001 ) in the general population ., The results fit with the “rare variant hypothesis” that proposes that a significant proportion of the missing heritability of complex diseases is due to a series of rare variants , each conferring a moderate increase in risk ., Typically , such risk alleles function dominantly and independently 19 , 20 ., The “rare variant hypothesis” is strongly supported by evolutionary theory , which argues that variants that promote disease are selected against and are therefore rare ., Another argument for the hypothesis comes from recent empirical population genetic data which shows that rare variants are enriched for deleterious mutations 21 ., The question remains whether the identified candidate genes act as classical tumor suppressors with second hits or show alternative characteristics , such as haploinsufficiency or dominant-negative effects ., Of the genes identified , four out of 11 showed loss of the wild-type allele in at least one tumor ., In total , seven LOH events were observed and none showed loss of the mutant allele ( P\u200a=\u200a0 . 0078 ) ., This suggests that complete inactivation of these genes seems to be preferentially selected for in tumor evolution and that these germline variants are prime candidates for CRC susceptibility ., Perhaps the strongest candidate predisposition gene , in view of the LOH data and case frequency , was the apoptosis-associated gene UACA ., Three of the 96 familial CRC cases were found to carry heterozygous truncating variants ( p . Q1116X and p . R1292X ) in UACA ( Table 2 ) ., We performed genotyping to screen the variants in a set of validation phase samples ., We identified three additional unrelated cases who were heterozygous for the variants encoding either p . Q1116X or p . R1292X ., Second hits by LOH involving the germline wild-type allele were found in three of the six tumors ( Figure 2 ) ., The average age of onset of CRC in the familial cases was 54 years ( 58 , 54 and 50 ) ( Figure 3 ) , younger than the mean age of onset of 71 in familial cases without the UACA truncating variants ( Table S1 ) ., UACA has recently been identified as a novel regulator of apoptosis ., It is known to reside within the Apaf-1/procaspase-9 complex and regulate apoptosis activating factor ( APAF-1 ) ., It also regulates the apoptotic pathway by controlling the activation of nuclear factor ( NF ) -κB 22 ., In addition , UACA gene expression has been shown to be down-regulated in non-small cell lung carcinoma ( MIM 211980 ) 23 ., Taken together , the loss of UACA in cancer cells might result in altered activation of apoptotic pathways , ultimately promoting genesis of CRC ., Another gene of particular interest was TWSG1 ., The detected truncating germline variant ( p . Q41X ) was present in two familial CRC cases ( 2/96 cases ) and completely absent in 1 , 039 Finnish population matched controls ( Table 2 ) ., Loss of the remaining normal TWSG1 allele was observed in both tumors indicating that the gene might act as a classical tumor suppressor gene ( Figure 2 ) ., The index case with Q41X in family 1 developed CRC at the age of 53 and segregation analysis showed that the variant was inherited from the affected mother ( Figure 3 ) ., The mother had developed CRC at the age of 68 and lung cancer at the age of 77 ., The variant did not segregate with CRC in family 2 ., Rare risk alleles of moderate penetrance are usually over-represented in familial cases; however co-segregation of disease is not always observed 19 ., Previous studies have shown TWSG1 to be a regulator of BMP-signaling 24 ., It is known to act downstream of TGF-β , inducing SMAD2 phosphorylation and mediating DNA binding on Smad3/4 consensus sites 25 ., TWSG1 functions in cellular pathways that are essential in genesis of CRC , however , its exact role in these pathways remains to be clarified ., In summary , exome sequencing is a well-justified strategy for discovering cancer predisposing variants ., The identification of predisposing variants has substantial implications for disease risk assessment and surveillance in family members ., Here , we identified eleven candidate predisposing genes with truncating variations in familial CRC ., A key challenge is how to identify predisposing variants in the background of non-pathogenic polymorphisms ., Screening the eleven genes in familial CRC cases representing different populations will be important to gain robust evidence for pathogenicity , as well as to characterize the natural history of the respective phenotypes ., This information , then , can be translated into tools for cancer prevention and early diagnosis in individuals carrying true predisposition alleles ., This study was reviewed and approved by the Ethics Committee of the Hospital district of Helsinki and Uusimaa ( HUS ) ., Signed informed consent or authorization from the National Supervisory Authority for Welfare and Health was obtained for all the study participants ., The Agilent SureSelect Human All Exon Kit v1 ( Agilent , Santa Clara , CA , USA ) was used to capture exomic regions ., Paired end short reads were sequenced on either Illumina GAII or HiSeq platform ( Illumina Inc . , San Diego , CA , USA ) ., Raw sequence data was received in FASTQ format and quality checked with FASTQC ( http://www . bioinformatics . bbsrc . ac . uk/projects/fastqc ) ., All exomes passed the quality control ., 3′ ends with high adapter similarity were removed by an in-house script whereafter reads were mapped to the human reference genome GRCh37 by BWA ( Burrow-Wheelers Aligner ) ., Duplicates were removed with Picard Tools ( http://picard . sourceforge . net ) MarkDuplicates ., Local realignment was done by Genome Analysis Toolkit ( GATK ) IndelRealigner to improve the detection of small insertions and deletions ., The initial single nucleotide variant ( SNV ) and indel calls needed for creating the GATK realignment intervals were made using samtools mpileup and downloaded from the 1000 genomes project Phase I indel calls ( 16the August 2010 release ) ., Final SNV and indel calls were made using the GATK UnifiedGenotyper with a low variant quality score threshold ( 1 . 0 ) ., Exome data analysis was performed in “Rikurator” , an in-house visualization and comparative analysis tool ( unpublished ) ., The tool allowed for simultaneous analysis of all the 96 exomes and interactive quality/control filtering ., The following quality filters were used:, ( i ) variants had to have a quality score ≥50 ,, ( ii ) coverage had to be ≥6 , and, ( iii ) the percentage of mutated reads had to be ≥30 ., Truncating variants , including nonsense , frameshifting insertion and deletion , or splice-site alteration IVS +1 , +2 , −1 , and −2 , were extracted ., Data was control filtered against population matched exome control data ( n\u200a=\u200a212 ) and data from the 1000 Genomes Project ( Phase 1 release ) 16 ., Variants were excluded if present in the 1000 Genomes Project or exome control data at MAF>0 . 001 ( Figure 1 ) ., Genes with truncating variants present in at least 2/96 cases were studied further ., Manual filtering was performed on all variants to further remove artifacts due to duplicated regions , mapping errors , and systematic errors ., Systematic errors , both position specific and sequence specific , in high-throughput sequence data have been described previously by Meacham et al 26 ., Finally , outputs were generated for Ensembl canonical transcripts ( Ensembl build 37 ) ., Potential loss-of-function variants were verified by Sanger sequencing from DNA extracted from normal tissue samples ., Sequencing primers were designed with the Primer3-program ( http://frodo . wi . mit . edu/primer3/ ) using NCBI37/Hg19 as the reference sequence ., The primer sequences can be found in Text S1 ., The fragments were amplified with the AmpliTaqGold enzyme ( Applied Biosystems , Foster City , CA ) ., The PCR products were purified using the ExoSAP-IT PCR purification kit ( USB Corporation , Cleveland , OH , USA ) ., Electrophoresis was run on a 3730xl DNA Analyzer ( Applied Biosystems at Institute for Molecular Medicine Finland , FIMM ) ., The sequencing reactions were performed utilizing the Big Dye Terminator v . 3 . 1 kit ( Applied Biosystems , Foster City , USA ) , Sanger sequencing was performed implementing the ABI3100×l technology ( Applied Biosystems ) , and the sequence graphs were visualized with the Chromas – software ( version 2 . 33 , Technelysium Pty Ltd , Helensvale , Australia ) ., The results were analyzed both manually and with the Mutation Surveyor –software ( version v3 , 30 , Softgenetics , State College , PA , USA ) ., Confirmed truncating variants were Sanger sequenced in 310 Finnish population matched healthy controls , of whom about two-thirds were regionally matched ., Sanger sequencing was performed as described above ., All variants that had a MAF>0 . 001 in the discovery phase control set were excluded ., Sanger sequencing was also performed on DNA extracted from tumor tissue in cases carrying validated truncating variants ., All tumors had been microscopically evaluated by a pathologist and all except one contained ≥50% of carcinoma tissue ., Loss of heterozygosity was analyzed by comparing allelic ratios of tumor and respective normal tissue DNA , as previously described 27 ., Peak heights were manually measured from sequence graphs based on which allelic ratios were calculated ., Variants in genes showing loss of the wild-type allele in tumor tissue were genotyped in a set of validation phase samples , comprising 954 population matched CRC cases and 586 population matched controls ., Genotyping was carried out by using the 7900HT Fast Real-Time PCR System ( Applied Biosystems ) and was performed at the Estonian Genome Center , University of Tartu ., The variant p . Q41X in TWSG1 was genotyped using massARRAY iPLEX Gold ( Sequenom , San Diego , CA ) and performed at the Institute for Molecular Medicine Finland ( FIMM ) , University of Helsinki ., The genotyping conditions and primers utilized can be found in Text S1 ., Genotyping success rates were over 90% for all the variants , except for PSPH where the genotyping assay failed ., All the variants identified by genotyping were further confirmed by Sanger sequencing ., The exome data was searched for missense variants at the 11 candidate predisposition loci ., The same filtering criteria were utilized as for truncating variants ., The variants were excluded if present in Exome Variant Server ( NHLBI GO Exome Sequencing Project ( ESP ) , Seattle , WA , http://evs . gs . washington . edu/EVS/ July 2013 ) with MAF>0 . 001 ., The functional effects of the identified missense variants were predicted by SIFT ( http://sift . jcvi . org/ ) and PolyPhen 2 ( http://genetics . bwh . harvard . edu/pph2/ ) ., Archived Formalin-fixed , Paraffin-embedded ( FFPE ) tissue samples were ordered for first degree relatives with CRC whenever possible ., Genomic DNA was extracted from all available FFPE samples ., Sanger sequencing was performed on identified truncating variants to test for segregation ., In total , segregation was analyzed in seven families for five of the identified truncating variants ., One-tailed exact binominal test was used for P-value calculations .

Introduction, Results, Discussion, Materials and Methods

Hereditary factors are presumed to play a role in one third of colorectal cancer ( CRC ) cases ., However , in the majority of familial CRC cases the genetic basis of predisposition remains unexplained ., This is particularly true for families with few affected individuals ., To identify susceptibility genes for this common phenotype , we examined familial cases derived from a consecutive series of 1514 Finnish CRC patients ., Ninety-six familial CRC patients with no previous diagnosis of a hereditary CRC syndrome were included in the analysis ., Eighty-six patients had one affected first-degree relative , and ten patients had two or more ., Exome sequencing was utilized to search for genes harboring putative loss-of-function variants , because such alterations are likely candidates for disease-causing mutations ., Eleven genes with rare truncating variants in two or three familial CRC cases were identified: UACA , SFXN4 , TWSG1 , PSPH , NUDT7 , ZNF490 , PRSS37 , CCDC18 , PRADC1 , MRPL3 , and AKR1C4 ., Loss of heterozygosity was examined in all respective cancer samples , and was detected in seven occasions involving four of the candidate genes ., In all seven occasions the wild-type allele was lost ( P\u200a=\u200a0 . 0078 ) providing additional evidence that these eleven genes are likely to include true culprits ., The study provides a set of candidate predisposition genes which may explain a subset of common familial CRC ., Additional genetic validation in other populations is required to provide firm evidence for causality , as well as to characterize the natural history of the respective phenotypes .

Many individuals with a family history of colorectal cancer have no detectable germline mutation in the known cancer predisposing genes ., We aimed to identify novel susceptibility genes for this common phenotype by performing exome sequencing on 96 independent cases with familial colorectal cancer ., Eighty-six patients had one affected first-degree relative , and ten patients had two or more ., None of the patients had a previous diagnosis of a hereditary syndrome ., We focused our search on genes with rare variants , predicted to truncate the protein product , since these are likely candidates for disease predisposition ., Using this approach we identified truncating germline variants in eleven genes , present in two or three independent familial colorectal cancer cases ., We analyzed the respective tumor DNAs and found loss of the wild-type allele in seven out of seven occasions , involving four genes ., No tumor showed loss of the mutant allele which provides us with additional evidence for disease causality ., Further studies are required to provide firm evidence for pathogenicity ., Genetic knowledge on confirmed predisposing genes can ultimately be translated into tools for cancer prevention and early diagnosis in individuals carrying predisposition alleles .

journal.pntd.0001045

Evaluation of Spatially Targeted Strategies to Control Non-Domiciliated Triatoma dimidiata Vector of Chagas Disease

Chagas disease , also called American trypanosomiasis , is caused by the protozoan parasite Trypanosoma cruzi , which is primarily transmitted to humans by blood-sucking bugs of the Triatominae subfamily ., The disease is endemic throughout Latin America , where it is one of the most important parasitic diseases with large socioeconomic impact ., According to various estimates , the prevalence rate in humans varies between 0 . 1 and 45 . 2% ( with an average of 1 . 4% ) , 8 to 15 million people are infected with T . cruzi ( with 40–50 , 000 yearly new cases ) , and 28–75 million individuals are at risk of infection 1–3 ., The disease causes about 12 , 500 deaths a year , and is responsible for premature disabilities of workers that are estimated to cost 670 , 000 disability-adjusted life years lost 4 ., Although international initiatives have been launched to reduce transmission of Chagas disease , especially through vector control and screening of blood or organ donors 5 , there are still large regions with active vector transmission 6 ., One of the main explanations for this is the transmission caused by non-domiciliated triatomines 7 ., These vectors are not able to reproduce and develop in the domestic habitat , and thus constitute typical ‘sink’ domestic populations sustained by peri-domestic and/or sylvatic ‘source’ populations 8 ., Non-domiciliated vectors tend to jeopardize the efficacy of vector control by insecticide spraying in the domestic habitat because of the re-infestation of treated houses 9 , 10 , 11 ., This situation has been described for several vector species of triatomines as T . brasiliensis and T . pseudomaculata in Brazil 12 , T . mexicana in central Mexico 13 and T . dimidiata in the Yucatan Peninsula of Mexico and Belize 14 , 15 ., Accordingly , the risk of transmission associated with non-domiciliated vectors is now identified as a major challenge for the future of Chagas disease control 16 , 17 , 18 , and a key objective is to evaluate the efficacy of classical or alternative control strategies to reduce their abundance ., Identifying optimal strategies can hardly be achieved through laboratory or field experiments , since testing a broad enough number of alternatives would require very large human and financial investments 11 , 19 ., Alternatively , mathematical models have proven to be very effective at evaluating the relative merit of various alternative strategies to control parasitic diseases 11; and references therein ., In addition , identifying optimal strategies clearly requires a detailed understanding of the vector spatial and temporal infestation dynamics ., Valuable insights into such spatio-temporal dynamics can be gained using the framework of meta-population theory combined with presence/absence data 19–21 ., Although appealing , the use of more elaborated models that include quantitative information on local population sizes requires even more data than the meta-population model sensus stricto 22 ., In previous contributions , we developed spatially explicit population dynamics models that were able to reproduce and to predict the spatial and temporal dynamics of T . dimidiata house infestation observed at the village scale in the Yucatan Peninsula , Mexico ., These models provided us with indirect estimates of the origin and characteristics of dispersal of these triatomines 23 , 24 ., Individuals found inside houses in the Yucatan Peninsula originated in similar proportions from both sylvatic and peri-domestic habitats , dispersed over rather small distances ( 40–60 m per displacement ) and were strongly attracted to houses 24 ., Remarkably , the observed and predicted dynamics showed an heterogeneity in transmission risk both in time , with a peak of vector abundance during March–June 14 , 25 , and in space , with much higher abundance of insects in the periphery of the village reflecting the influence of the sylvatic habitat 11 , 26 ., The temporal optimization of insecticide spraying with respect to this pattern has already been investigated at the scale of one house 11 , but the spatial micro-scale heterogeneity suggests that interventions could also be spatially targeted ., Such interventions would focus on the periphery of the village , where bugs were found more abundant ., While temporal heterogeneity adds constraints on control strategies ( i . e . the timing of intervention has to match the seasonality of house infestation , 11 ) , spatial heterogeneity could have beneficial consequences for control activities as it might allow to reduce the overall surface ( or number of houses ) to be treated and thus allow to reduce the cost associated with control ., Properly assessing whether such spatial design is relevant requires evaluating not only the efficacy of control in the treated areas , but also the impact of the control interventions in the untreated areas of the same village ., In this contribution , we aimed to build on our understanding of the temporal optimization of control strategies 11 , as well as our previous spatial modelling 23 , 24 to evaluate the potential of several strategies ., We first focus on conventional strategies — namely indoor insecticide spraying , use of door/window insect screens and peri-domicile management — that have been used to control vectors of different diseases as well as T . dimidiata 27–29 ., We further look at the potential of insect lethal traps that are currently extensively investigated for the control of a variety of vector species 30 , 31 ., Finally , since we have previously found that T . dimidiata was directly attracted to houses 24 , a control alternative could be to eliminate this house attractiveness , and the potential of such a strategy was also explored ., We aimed to set up a spatial population dynamics model able: ( 1 ) to reproduce and predict the temporal variations of vector abundance in all the houses of one village in the absence of control , and ( 2 ) to spatially represent various control strategies ., We adapted previous population dynamic models 23 , 24 , and combined them with a mathematical description of the control strategies that we aimed at evaluating ., The resulting model predicts the temporal variations in vector abundance in every house of the village as a function of survival , reproduction and dispersal of the triatomines , and the effect of the above control strategies on the demographic processes at each point of the village ., It was then used for the evaluation of the efficacy of spatially targeted interventions based on each of those strategies ., Model predictions in absence of control were fitted through a maximum likelihood approach to a first set of spatio-temporal data describing house infestation dynamics by T . dimidiata within a village in the absence of vector control ., We tested the predictive value of the resulting parameterized model on a replicate data set , corresponding to the infestation dynamics observed in the same village the following year ., The description of the effect of the different control strategies was then added to the model , and the resulting framework was used to explore the efficacy of control interventions whose spatial coverage was progressively increased from the border to the centre of the village ., The efficacy of each intervention was evaluated as the percentage of reduction in the yearly abundance of vectors in the village , in comparison with the expected abundance in the absence of control intervention that we evaluated from the model with no control ., Efficacy was also related to the consented effort , as measured by the number of households , where control strategies were applied ( either in the domestic or peri-domestic habitats ) ., We performed a sensitivity analysis to each survival , reproduction or dispersal parameter of the model to ensure the robustness of our conclusions on the efficacy of the various interventions within the confidence region associated with the maximum likelihood estimate of model parameters ., We further conducted a sensitivity analysis to different parameters of the model that described the efficacy of each of the strategies as measured by their impact on the survival , reproduction or dispersal of triatomines ., The spatio-temporal pattern of house infestation was observed in the rural village of Teya , Yucatan , Mexico over a two-year period from August 2006 to October 2008 26 ., All houses were identified and geo-referenced with a handheld global positioning system ( GPS ) ., Insects were collected by a standardized methodology based on community participation 32 , and data were imported into a geographic information system ( GIS ) database ( ArcView 3 . 2 -Environmental Systems Research Institute , Redlands , CA , USA ) to produce maps of observed triatomine abundance in the houses over 2-week intervals 32 ., Participating families provided oral consent prior to their participation , as written consent was waived because the study involved no procedures for which written consent is normally required outside of the research context ., Consent was logged in field notebooks ., All procedures , including the use of oral consent , were approved by the Institutional Bioethics Committee of the Regional Research Centre “Dr . Hideyo Noguchi” , Universidad Autonoma de Yucatan ., We set up a GIS-based Spatially Explicit Model ( GIS-SEM ) as such modelling provides a suitable framework to investigate spatial population dynamics in real landscapes by importing GIS data on a grid representing the area under study 33 ., Our GIS-SEM model was based on Cellular Automaton ( CA ) formalism 34 ., It consisted of a grid of cells representing the village of Teya , and allowed the calculation of the temporal variations of the vector abundance in cells , referred to as state variables , according to both local rules describing birth and death processes of bugs within cells , and dispersal rules that allow accounting for walking and flight movements between neighboring cells ., This model was similar in essence to the models built by Barbu et al . 24 , but with two necessary adaptations ., First , the local and dispersal rules were described in a deterministic rather than stochastic manner to reduce the complexity of the model and shorten the simulation time ., Second , the time unit of the model was changed from 15 days to a day to allow specifying the effect of control on a daily basis ., A deterministic CA such as the one intended here is defined as a quadruple Q\u200a= ( A , S , V , f ) , where A is the grid of cells arranged uniformly to represent the studied area; S is the set of values that can be taken by the state variables; V is the neighborhood function that allows identifying the set of neighboring cells V ( c ) that contribute to the change of the state variable of any given cell c by the mapping: ( 1 ) with v denoting the size of the neighborhood; and where f is the function describing the local and dispersal rules and thus specifies how the set of neighboring cells V ( c ) changes the state of the cell c from one time step to another: ( 2 ) with N ( c , t ) , the state variable that tracks the status of cell c at time t ., Maximum likelihood estimates ( MLE ) of the parameters of the model with no control were obtained using the spatio-temporal data sets describing T . dimidiata infestation dynamics of the village of Teya between mid-September 2006 and mid-September 2007 ., Model predictions were fitted to the observed number of bugs in each cell of the 24 maps describing the average biweekly distribution within the village ., The log likelihood ( LLH ) value was then calculated as follows: ( 6 ) where log denotes the natural logarithm , X ( c , t ) is the statistical variable corresponding to the number of adults in cell c , O ( c , t ) the observed abundance in this cell , and θ is a set of parameters of the model ., Probabilities were defined assuming a zero-inflated Poisson distribution to take into account an excess of null abundance in the data set 39 , possibly due to the non-participation of a proportion ( w ) of householders , with w\u200a=\u200a0 . 7 as before 24 ., The parameters θ of the model were identified using a genetic algorithm run at the super-computing centre ‘Institut du Développement et des Ressources en Informatique Scientifique ( IDRIS ) ’ located at Orsay , France ( http://www . idris . fr/ - Project IDRIS 112290 ) ., Genetic algorithms search for solutions using techniques inspired by natural evolution ., The interested reader can find a detailed description of such methods and the typical terminology we adopted below in 40 ., The algorithm considered the 8 parameters of the model ( Sd , Sp , d , Kp , Ks , D , σ and H ) to be estimated as independent quantitative traits with a continuum of alleles representing possible trait values within biologically relevant domains ., The fitness function corresponded to the LLH value defined with respect to the GIS-SEM model with no control described above ., The fittest individuals were selected to produce offspring through free recombination and unbiased mutations ., The variance of the effect of the mutations was dynamically adapted to the variance in the parental population ., All codes were written in C/MPI ., Confidence intervals were calculated by establishing the profile likelihood for each parameter , and by using these relationships to determine the 1−α confidence region defined as: ( 7 ) where is the MLE of parameter and stands for the ( 1−α ) th quantile of the distribution on 1 degree of freedom 41 ., The ability of the parameterized model to predict other infestation dynamics was tested by comparing its prediction to the spatio-temporal distribution of bug abundance in a second year of infestation of the same village ., A Poisson regression between observed and predicted abundances was performed after data were pooled over 3-month periods ( starting in mid-September ) and within three distance categories: 0–80 m , 81–200 m and >200 m from the bush area outside the villages 24 , 26 ., The McFaddens likelihood ratio index was used as a pseudo R-squared ., Because the spatial distribution of bugs follows a spatial gradient with higher abundance at the periphery of the village 24 , 26 , the control strategies were applied to a ring of cells located at the border of the village , the size of this ring increasing progressively until the intervention covered the whole village ( Figure 1 ) ., The efficacy of any given spatially targeted strategies was measured in terms of yearly bug abundance both in the whole village and in the different concentric rings ., This allowed us to quantify the relationship between the effort in terms of control coverage and the global efficacy , and to simultaneously assess the consequences of interventions in the various parts of the village ., The efficacy of intervention was evaluated using the set of parameters estimates providing the best fit to the data ., It was complemented by a sensitivity analysis of the corresponding results to the parameters estimates ., Each parameter was then independently set to the boundary values of its confidence interval , i . e . and , while keeping the others to their MLE ., We evaluated the efficacy of five types of control strategies applied individually , including indoor insecticide spraying , door and window insect screens , peri-domicile cleaning , triatomine lethal traps located in the peri-domestic habitat , and housing improvement to reduce house attractiveness to bugs ., The effect of each strategy on bug survival , reproduction and/or dispersal was modelled as described below ( see also supplementary methods — Text S1 — for the mathematical changes that were made to the model to include these effects ) ., Indoor insecticide spraying was modelled by reducing vector survival in each treated house as before 11 ., The control-induced mortality was calculated with respect to the residual dose of insecticide that we adjusted daily , and to the lethality of the dose as expected from a typical sigmoid dose-response relationship ., Assuming that the control-induced and natural mortalities act independently ( i . e . to survive one of the two causes of death does not affect the probability to survive the second one ) , we combined them multiplicatively to define the overall survival probability ., We considered a spray rate of 50 mg . m−2 of pyrethroid insecticide at the beginning of the infestation season ( since it was previously shown to be the optimal timing for spraying 11 ) , the half-life of the insecticide was set to 38 days , and the lethal doses 50% and 90% were fixed to 32 . 2 mg . m−2 and 182 . 4 mg . m−2 11 ., A sensitivity analysis to insecticide dose was performed predicting the effect of spraying at 100 , 200 and 300 mg . m−2 ., Door and window screens were considered as physical barriers impeding the arrival of a proportion of the non-domiciliated vectors into the domestic habitat , and were thus modelled by lowering immigration into the houses by a factor of bug exclusion r set at 85% and constant over time 11 , 42 ., Again , a sensitivity analysis was conduced by considering r equals to 70 , 80 and 90% ., Because the efficacy of screens is likely to depend on the behavioral response of dispersal bugs failing to enter houses because of screens , and because no information was available in the literature about such a response , we considered three alternative assumptions ., Bugs that could not enter into houses were considered: ( 1 ) to stop dispersing and die , or ( 2 ) to stop dispersing for one day before starting again with no learning in their dispersal behavior ( and thus possibly attempting to enter the same house ) , or ( 3 ) to go on dispersing while avoiding the house they could not enter ., Peri-domicile cleaning was assumed to eliminate all bug colonies established in this habitat for the rest of the current year ., This reduced immigration from the cleaned sites , but did not have any effect on individuals that originated from other areas and may pass through the peri-domiciles where this control strategy was applied ., In addition , we performed a sensitivity analysis by considering that cleaning removes only 60% and 80% of insects established in the peri-domestic habitat ., Manipulation of houses attractiveness to bugs was achieved by decreasing H from its estimated value to 1 , the value for which houses are no more attractive than the peri-domestic and sylvatic habitats ., This represents the strongest possible effect and allows evaluating the maximal potential for this strategy; a sensitivity analysis for the intermediate values of H was then performed ., Triatomine lethal traps in the peri-domestic habitat were assumed to attract and kill triatomines into the cells where they are positioned according to an additional parameter Htrap that measured the trap attraction ., As for the study of the control of houses attractiveness we first wanted to evaluate the maximal potential of this strategy ., The density of traps was then fixed at 2 traps per household , and attraction was set to a constant level Htrap\u200a=\u200a12 , almost twice the attraction of houses ., Sensitivity analysis was then performed for different density of traps , in the range 5 traps per household to 1 trap for 10 households , and trap attraction , in the range 1 to 50 ., The model predictions fitted very well the yearly spatio-temporal dynamics of infestation observed in the village of Teya between mid-September 2006 and mid-September 2007 ., The correlation between observed and simulated spatio-temporal data indicated that the model reproduced well both the seasonal variations in triatomine densities , and the spatial spread of bugs from the border to the centre of the village ( Figure 2A , McFaddens likelihood ratio index\u200a=\u200a0 . 93 ) ., Importantly , the model parameterized with the data on this first year was able to predict the observed spatial and temporal dynamics of bug abundance in the following year ( Figure 2B , McFaddens likelihood ratio index\u200a=\u200a0 . 67 ) ., We note that while our model tends to predict well high abundances , predictions at lower vector abundances seem less precise ., However , this is rather inconsequential since predicting fine variations in space and time at low abundances is of little relevance for our ultimate objective of evaluating control strategies ., The convergence of the presented results with a previous study , that used a stochastic model 24 , also showed that the selected local and dispersal rules ( see Definition of the function f including the local and dispersal rules ) were reliable in their ability to both reproduce and anticipate the spatio-temporal dynamics of these non-domiciliated vectors ., Likelihood profile confidence intervals gave further information on the estimated parameters of these rules ( Table 1 ) ., Those confidence intervals were quite narrow around the MLE ., The lower and upper boundaries were typically located at less than 30% of the MLE of each of the parameters , indicating that larger changes in one of the parameter estimates would no longer allow properly reproducing the data ., The survival rates in the domestic and peri-domestic habitats were very close to 0 . 2 and 0 . 9 , respectively; the numbers of insects immigrating from the colonies established in the sylvatic and peri-domestic habitats were in the range 150–260 insects for 15 days; there was nearly a 1∶1 ratio between immigration from the sylvatic and peri-domestic habitats; the attraction to the house was always at least 5 times higher than attraction to the peri-domestic area , and the optimal ( and mean ) distance of dispersal was between 50 and 60 meters ( Table 1 ) ., All of those results were consistent with and supported our previous conclusions that insects found in houses came in roughly similar proportion from the sylvatic and peri-domestic habitats and that they disperse over rather small distances and with a strong attraction to the domestic habitat 24 ., Overall , our spatial model with no control thus offered a good framework where spatially targeted control strategies could be evaluated ., We investigated the efficacy of the five strategies considered independently by applying them to concentric rings defined from the border of the village and whose size was increased until a complete coverage of the village was reached ., For each strategy , we calculated its efficacy , measured as the post-intervention reduction of bugs abundance in the whole village , in function of the extent of village zones treated , i . e . the effort in terms of the control intervention ( Figure 3A–E ) ., We also calculated the effect of the interventions in each concentric village area , including those without control intervention ( Figure 3F–J ) ., Finally , we performed a sensitivity analysis to the parameter values by independently replacing the MLE with the upper and lower values of each profile likelihood based confidence interval ( Table 1 ) ., The first key point is that all the results obtained with each of the five strategies were only weakly sensitive to changes in demographic parameters values ., Such changes indeed lead to no qualitative change in the form of the relationships ( Figure 3 ) ., As expected , parameters with the strongest effect depend on the control strategy considered ., Maximal changes were obtained when changing survivals ( Sp , Sd ) for insecticide spraying , immigration rates ( d ) for screens and outdoor traps , houses attraction ( H ) for the control of houses attractiveness and the number of individuals leaving colonies ( Kp , Ks ) for peri-domestic cleaning ( results not shown ) ., However , these effects were systematically lower than 5% on both treated ( Figure 3A–E ) and untreated areas ( Figure 3F–J ) ., The results obtained are thus very robust to variations of the parameters of the model with no control , and we will thus further describe only the results obtained with the MLEs ., Indoor insecticide spraying in the whole village allowed the reduction of total bug abundance over a year by about 70% for one year ( Figure 3A ) ., The relationship between the proportion of treated houses and global efficacy was a slightly convex diminishing return curve , so that half of the maximal decrease could be obtained by spraying only the first two external zones of the village ( a third of the houses ) ., We also evaluated the local efficacy of insecticide in untreated village zones at the forefront of the treated areas ., Independently of the number of village areas sprayed , the use of indoor insecticide only reduces the vector abundance in the treated area; it has a negligible effect on neighboring untreated areas ( Figure 3F ) ., To increase the dose applied allowed the predicted levels of vector reduction to reach higher levels ( doses of 100 , 200 and 300 mg . m−2 lead to a 79% , 85% and 87% maximal control efficacy , respectively; data not shown ) , with no change in the main conclusion: Insecticide spraying in only the first two outer zones allowed for half of the maximal control efficacy ., Door and window insect screens applied to all the houses of the village decreased the total vector abundance by about 80% when bugs that could not enter into houses were assumed to go on dispersing ( assumptions 2 and 3 , the former including possible attempts at entering again the house they just failed to infest ) ( Figure 3B ) ., As for insecticide spraying , there was a slightly convex diminishing return between the number of treated zones and efficacy ., Accordingly , limiting the intervention to the first two zones at the periphery of the village ( a third of the village houses ) again led to half of the maximal reduction in abundance ., Under the two assumptions not including the death of the insects failing to enter the houses 2–3 , the analysis of insect screens local efficacy indicated that while infestation was well controlled in houses with screens , the control had a detrimental effect on the immediate non-equipped neighbor: an increase of up to 40% in vector abundance was estimated in the most proximate untreated village zone ( Figure 3G ) ., This negative effect on neighboring areas disappeared for untreated areas more than 3 zones away from the treated one ., On the other hand , when the vectors were assumed to die when failing to enter a house ( assumption 1 ) , the effects of screens were significantly different ., In this case , vector abundance was reduced slightly further ( up to 90% ) when screens were used in all the houses of the village ( Figure 3B upper dotted black line ) , and the control strategy then had no negative effect on untreated neighboring houses ( Figure 3G upper dotted black line ) ., To vary the efficacy of screens produced only small linear changes in the global efficacy ., Under assumptions 2 and 3 , a reduction factor r of 70% , 80% and , 90% lead to a 51% , 64% and 80% maximal control efficacy , while under assumption 1 , a reduction factor r of 70% , 80% and 90% led to a 73% , 82% and 91% maximal control efficacy; data not shown ., The above conclusions are consequently very robust to variations of r , which is thought to be in the range 80–90% in the field 42 ., Peri-domicile cleaning reduced total bug abundance by up to 62% for one year when performed in the whole village ( Figure 3C ) ., The increase in efficacy with increasing coverage was a concave relationship with a slightly increasing return ., Because of the lower efficacy of peri-domicile cleaning at the periphery of the village , intervention in at least the first 3 zones ( 60% of the village peri-domestic surface ) was required to reach half of the maximal reduction in abundance ., Interestingly , when peri-domicile cleaning was performed only in some parts of the village it had an important beneficial effect on untreated neighboring houses ., The vector abundance in the two closest non-treated zones was reduced by 40% and 15% respectively ( Figure 3H ) ., Lowering the rate of colonies destruction by peri-domicile cleaning , which was initially set to 100% , lowered the total efficacy in an almost perfectly linear way , but again had no effect on the above qualitative conclusions ., Typically , assuming that only 80% or 60% of colonies are removed by cleaning peri-domiciles allowed for a maximal control efficacy of 50% ( ≈62%×80% ) and 37% ( ≈62%×60% ) , and in both cases intervention in the first 3 zones was needed to get half of these outcomes ., Manipulation of houses attractiveness was found 60% effective when applied to the whole village and when such attraction was completely eliminated , so that domestic habitat was no more attractive than the peri-domestic and sylvatic habitats ( H\u200a=\u200a1 ) ( Figure 3D ) ., Half of the maximal efficacy could be reached by an intervention targeted on the first two zones of the village representing a third of the village houses ., However , such strategy had an important negative impact on the abundance of bugs in non-manipulated neighboring houses when applied to parts of the village ( Figure 3I ) ., Indeed , the lack of attraction of manipulated houses resulted in an increase of over 50% and 30% in bug abundance in the next two untreated village zones ., Importantly , sensitivity analysis of intermediate values of reduction in house attractiveness indicated that efficacy of the intervention was rapidly lost as H was incompletely reduced: the maximal efficacy was of 40% , 17% and less than 5% , for H values of 2 , 4 , and 6 , respectively ( Figure 4 ) ., Insect lethal traps were found potentially able to reduce global vector abundance by up to 72% when considering a high density ( two traps per household ) and a high attractiveness ( Htrap\u200a=\u200a12 , nearly twice the attractiveness of houses ) and 100% of lethality ( Figure 3E ) ., Under these conditions , an important diminishing return was observed since to install traps in the first zone of the village ( 27% of the peri-domestic surface ) allowed to attain half of the maximal efficacy ., Furthermore , this strategy had substantial positive effects on the 4–5 neighboring areas without traps , where insects abundance was decreased by 50% , 30% , 15% and 7% , respectively ( Figure 3J ) ., However , reducing the attraction factor of each trap had an important effect at the village scale as the global control went down from 72% to 55% when attraction of individual traps was reduced from Htrap\u200a=\u200a12 to Htrap\u200a=\u200a5 , a value similar to the attractiveness of houses ( Figure 5 ) ., On the contrary , to increase attraction to higher levels had almost no effect whatever the number of traps considered ., To lower the number of traps also had a strong detrimental effect , and the reduction of bug abundance due to control was never found larger than 30% when the number of traps was dropped to 1 trap for 10 households ( Figure 5 ) ., A nearly 100% control efficacy at the village scale was reached only when more than 2 , 500 traps were used in the village , which represent about 5 traps per household within the village ., Although the elimination of transmission of Chagas disease was targeted by the WHO for the year 2010 4 , there are still large regions with active vectorial transmission mostly due to non-domiciliated triatomines 6 ., These vectors do not constitute permanent colonies inside houses , so that domestic populations actually are typical ‘sinks’ sustained by peri-domestic and/or sylvatic ‘source’ populations 8 ., The risk of transmission associated with these non-domiciliated vectors is thus now identified as a major challenge for the future of Chagas disease control 5 , and a key objective is to evaluate the efficacy of classical or alternative control strategies to reduce their abundance ., Since non-domiciliated insects infesting houses typically come from the sylvatic and peri-domestic habitat 11 , to evaluate the potential of various strategies requires a good understanding of the village infestation dynamics in absence of

Introduction, Materials and Methods, Results, Discussion

Chagas disease is a major neglected tropical disease with deep socio-economical effects throughout Central and South America ., Vector control programs have consistently reduced domestic populations of triatomine vectors , but non-domiciliated vectors still have to be controlled efficiently ., Designing control strategies targeting these vectors is challenging , as it requires a quantitative description of the spatio-temporal dynamics of village infestation , which can only be gained from combinations of extensive field studies and spatial population dynamic modelling ., A spatially explicit population dynamic model was combined with a two-year field study of T . dimidiata infestation dynamics in the village of Teya , Mexico ., The parameterized model fitted and predicted accurately both intra-annual variation and the spatial gradient in vector abundance ., Five different control strategies were then applied in concentric rings to mimic spatial design targeting the periphery of the village , where vectors were most abundant ., Indoor insecticide spraying and insect screens reduced vector abundance by up to 80% ( when applied to the whole village ) , and half of this effect was obtained when control was applied only to the 33% of households closest to the village periphery ., Peri-domicile cleaning was able to eliminate up to 60% of the vectors , but at the periphery of the village it has a low effect , as it is ineffective against sylvatic insects ., The use of lethal traps and the management of house attractiveness provided similar levels of control ., However this required either house attractiveness to be null , or ≥5 lethal traps , at least as attractive as houses , to be installed in each household ., Insecticide and insect screens used in houses at the periphery of the village can contribute to reduce house infestation in more central untreated zones ., However , this beneficial effect remains insufficient to allow for a unique spatially targeted strategy to offer protection to all households ., Most efficiently , control should combine the use of insect screens in outer zones to reduce infestation by both sylvatic and peri-domiciliated vectors , and cleaning of peri-domicile in the centre of the village where sylvatic vectors are absent ., The design of such spatially mixed strategies of control offers a promising avenue to reduce the economic cost associated with the control of non-domiciliated vectors .

Chagas disease is one of the most important parasitic diseases in Latin America ., Since the 1980s , many national and international initiatives have contributed to eliminate vectors developing inside human domiciles ., Todays challenge is to control vectors that are non-adapted to the human domicile , but still able to transmit the parasite through regular short stay in the houses ., Here , we assess the potential of different control strategies applied in specific spatial patterns using a mathematical model that reproduces the dynamic of dispersion of such ‘non-domiciliated’ vectors within a village of the Yucatan Peninsula , Mexico ., We show that no single strategy applied in the periphery of the village , where the insects are more abundant , provides satisfying protection to the whole village ., However , combining the use of insect screens in houses at the periphery of the village ( to simultaneously fight insects dispersing from the garden and the forest ) , and the cleaning of the peri-domicile areas of the centre of the village ( where sylvatic insects are absent ) , would provide a cost-effective control ., This type of spatially mixed strategy offers a promising way to reduce the cost associated with the repeated interventions required to control non-domiciliated vectors that permanently attempt to infest houses .

infectious diseases/neglected tropical diseases, ecology/spatial and landscape ecology, infectious diseases/protozoal infections, ecology/theoretical ecology, ecology/population ecology, infectious diseases/epidemiology and control of infectious diseases

journal.pntd.0000834

Metabolomics-Based Discovery of Diagnostic Biomarkers for Onchocerciasis

Onchocerciasis , commonly referred to as “river blindness” is classified by the World Health Organization ( WHO ) as a neglected tropical disease , afflicting approximately 37 million people in Africa , Central and South America and Yemen , with 89 million more at risk 1 ., Symptoms of the disease include acute dermatitis and blindness , the result of which is the loss of 1 million disability-adjusted life years ( DALYs ) annually 2 ., The causative agent , the filarial nematode Onchocerca volvulus , is transmitted in its larval stage between human hosts through the bite of a Simulium ( sp . ) black fly ., Once these parasites have matured into the adult form , they can live for approximately 14 years in subcutaneous nodules within a human host 3 ., The drug ivermectin ( Mectizan ) has served as the principal means of onchocerciasis control 4 , however , after initially reducing the number of microfilariae , within a year , the microfilariae return to levels of 20% or higher than that prior to treatment 5 ., The combination of the lack of effect of annual ivermectin treatment on adult worm survival and the fecundity of adult females , along with significant fly and human migration patterns has helped to perpetuate the disease ., In Africa , where onchocerciasis control programs have been in place since the founding of the Onchocerciasis Control Programme in West Africa ( OCP , 1974–2002 ) and are currently being conducted by the African Programme for Onchocerciasis Control ( APOC , 1995-present ) , diagnosis is an essential aspect of the determination of treatment and distribution of medication ., In the Western hemisphere , accurate and robust diagnostics are essential for attaining the goal of disease elimination ., Twice yearly dosage of ivermectin , through the efforts of the Onchocerciasis Elimination Program for the Americas ( OEPA , 1992-present ) , has lead to a minimization of infection to 13 foci within six countries in Central and South America ., Although mass treatment of onchocerciasis foci in the Western hemisphere is slated to be suspended in 2012 6 , achieving the goal of elimination is contingent upon continued surveillance of the disease ., However , proper surveillance is directly dependent on the availability of robust diagnostic technologies used for infection assessment ., This need is further underscored in studies of antibiotic treatments being investigated for targeting Wolbachia endosymbiotic bacteria 7–10 as well as reports of sub-optimal response to ivermectin treatment 11 , 12 ., In both of these cases an accurate diagnostic is critical for the analysis of drug efficacy and patient drug response ., Currently , multinational control and elimination programs primarily rely on various techniques for diagnosis including: entomological studies of Simulian flies , Ov specific antigen tests , antibody tests , analysis of microfilariae in skin snips , nodule palpation and quality of those nodules that can be excised ., There are a number of technical concerns with each technique including: a lack of sensitivity and reproducibility , invasiveness , and the inability to distinguish past from present infection or between filarial diseases 13–15 ., A small molecule/metabolite based test has the potential for reflecting a more accurate picture of infection status , as it is a comprehensive measure of the effects of posttranslational modification and regulation ., Furthermore , small molecules are frequently constitutively produced ( e . g . , excretory-secretory products ) , diffuse easily and are inherently non-immunogenic in vivo , thus avoiding some of the technical challenges associated with DNA and protein-based diagnostics ., Although adult O . volvulus worms do not reside directly in the blood , the highly vascularized subcutaneous nodules of the human host allow for the potential diffusion of adult parasite-derived compounds into the blood where compounds involved in host response to infection might also be present ., Since the microfilariae ( mf ) and third infective larval stage ( L3 ) of the O . volvulus life cycle do come in contact with the vascular system during vector transmission , it is additionally possible that some mf or L3 produced compounds might also be localized to this biological sample ., Certainly , as a starting point , the blood matrix serves as an easy to obtain , chemically complex data rich matrix for metabolite analysis 16 , 17 ., However , a technical challenge of analyzing a large number of metabolites stems from the shear size and complexity of the resulting data set ., Initially devised and applied to the analysis of highly dimensional gene micro-array data , a number of machine learning approaches have been expanded and used for identifying patterns of biomarkers resulting from the multidimensional analysis of genes , proteins , and metabolites that can be linked to early detection 18 , survival prediction 19 , and disease outcomes 20 ., Although identification of a single biomarker “smoking gun” is perceived as the ideal scenario , more attention is being focused on the use of multiple markers for improving overall diagnostic accuracy 18 , 21 , 22 and model stability 23 ., Herein , we report a liquid chromatography-mass spectrometry ( LC-MS ) based approach to the discovery of a set of molecules that , in combination , provide a statistically relevant characteristic of onchocerciasis infection ., An initial untargeted analysis was applied to the profiling of O . volvulus infected and uninfected blood plasma and serum samples representing a variety of geographic regions and disease states , including other tropical diseases ., This analysis resulted in a set of statistically significant mass features identified for their potential as onchocerciasis-specific biomarkers ., Using multivariate statistics and machine learning algorithms , these metabolic signatures were further evaluated for their ability to discriminate O . volvulus-infected and uninfected individuals , therefore , creating the basis of a small molecule-based diagnostic for onchocerciasis ., The use of human serum and plasma samples in the study was approved by the Scripps Health Human Subjects Committee ., Samples with geographic origins outside of the United States of America ( USA ) consisted of pre-existing , unidentifiable diagnostic specimens collected with written informed consent and in cases of illiteracy , a literate witness signed and a thumbprint was made by the participant ., These samples were determined by the Scripps Health institutional review board ( IRB ) to be exempt from formal review under 45 CFR 46 101 ., O . volvulus negative controls from the USA consisted of serum and plasma samples and were obtained with written informed consent from healthy donors through The Scripps Research Institute Normal Blood Donor Service and approved by the Scripps Health IRB ., All patient codes have been removed in this publication ., Onchocerciasis positive samples were collected in characterized endemic areas and their status confirmed by either positive skin snip ( mf + ) or nodule palpation ( nodule + ) ., Several sample groups used in this analysis were collected during previously published studies including serum from Liberia 24 , 25 and Ghana collected in 2003 9 ., The Ghana sera collected in 1986 and 1991 were obtained from the College of Public Health , University of South Florida ., Cameroon samples were obtained as part of a nodulectomy campaign conducted in villages surrounding Kumba , Cameroon in 2006 and consist of plasma from O . volvulus-positive individuals ( nodule + with nodules containing live females ) , O . volvulus-negative individuals ( skin snip - volunteers with no current or prior symptoms of O . volvulus infection ) , and ambiguous samples ( nodules contained either dead , calcified worms or lipomas with no evidence of worms , or for which there were no particular disease symptoms recorded ) ., Guatemala sera were obtained as part of a nodulectomy campaign conducted by the Guatemala Ministry of Health and the Centro de Estudios en Salud , Universidad del Valle de Guatemala in several villages within the Guatemalan Central Endemic Zone from 2007–2008 ., Nodules were surgically removed from all individuals sampled , and nodule dissection was conducted to assess worm viability on nodules from five of the 21 individuals whose serum was analyzed in this study ., Of those dissected , no live worms were found ., Leishmaniasis positive , Chagas disease positive , and onchocerciasis negative sera were obtained from the Centro de Estudios en Salud , Universidad del Valle de Guatemala ., Indian lymphatic filariasis positive plasma samples were obtained from the Laboratory of Parasitic Diseases , U . S . National Institutes of Health ., A detailed summary of the samples analyzed in this study is presented in Table 1 ., Solvents used were of high performance ( HPLC ) grade ., A methanol precipitation of proteins was conducted by adding 400 µl aliquots of ice cold methanol to 100 µl aliquots of serum and plasma samples ., The samples were immediately vortexed for 30 sec and allowed to rest on ice for 20 min ., After centrifugation at 13 , 780×g for 5 min , the metabolite containing supernatent was removed from the precipitated protein pellet and transferred to fresh tubes ., The supernatent samples were dried in a GeneVac EX-2 Evaporation System ( GeneVac Inc . , Valley Center , New York , USA ) at ambient temperature and then resuspended to a 50 µl volume in water: acetonitrile ( 95∶5 ) , vortexed for 30 sec and then centrifuged again at 13 , 780×g for 5 min ., After being transferred to LC vials , samples were stored at 4°C and transferred to the LC-MS thermostated autosampler ( 6°C ) , typically within 48 h of their preparation ., In order to minimize instrumental drift , sample sequences were composed of a single injection of each sample in randomized order ., To monitor any potential instrument irreproducibility and to confirm the absence of sample carry over within the chromatographic run , a mobile phase blank and an external standard were injected every 24 h throughout the duration of the analysis ., Experiments were performed with an electrospray-ionization time-of-flight ( ESI-TOF ) MS ( Agilent 1200 LC , TOF 6210 , Agilent Technologies , Santa Clara , CA , USA ) ., Each sample analysis consisted of an 8 µl injection of extracted sample with chromatographic separation across a reverse phase C18 column ( Zorbax 300SB C18 Capillary , 3 . 5 µm , 1 mm×150 mm; Agilent Technologies , Santa Clara , CA , USA ) at a capillary pump flow rate of 75 µl/min ., Mobile phase A was composed of water with 0 . 1% formic acid , and mobile phase B was acetonitrile with 0 . 1% formic acid ., Each sample was analyzed over a 60 min run time with a gradient consisting of a 45 min linear gradient from 5% to 95% B and 15 min isocratic hold at 95% B . Between sample injections a wash step was used to minimize carry over ., It consisted of a saw-tooth linear gradient beginning with a hold at 95% A for 10 min ., Then , linear ramping between 5% and 98% B for 5 minute increments throughout the 35 min wash cycle was followed by a 20 minute final re-equilibration of the column with an isocratic hold at 95% A . Consistent mass accuracy ( <2 ppm ) was maintained through the constant infusion ( 2 µl/min ) of reference masses via a second nebulizer ., Data were collected in positive electrospray ionization ( ESI ) mode scanning in centroid mode from 75 to 1 , 100 m/z with a scan rate of 1 . 0 spectrum per second in 2 GHz extended dynamic range ., The capillary voltage was 3 , 500 V; the nebulizer pressure , drying gas flow and gas temperature were set to 20 psig , 12 l/min and 350°C , respectively ., All mass spectral data was collected in ., d format and converted to ., mzData using the Mass Hunter Qualitative Analysis software version B . 03 . 01 ( Agilent Technologies , Santa Clara , CA , USA ) ., XCMS 26 software was used for peak matching , non-linear retention time alignment and quantitation of mass spectral ion intensities across all ., mzData mass spectral files ., Statistical comparison of the intensity data was conducted using the XCMS built in Welchs t-test ., False discovery rate ( FDR ) analysis was conducted with the q-value program 27 in R version 2 . 9 . 0 28 ., Principal Components Analysis ( PCA ) was conducted with Statistica software version 8 . 0 ( StatSoftInc . , Tulsa , OK , USA ) , machine learning algorithms were implemented using Weka Explorer version 3 . 6 . 0 29 with 10 fold cross-validation settings ., The molecular formula assignment made for the 10 selected small molecule biomarkers was conducted through a combination of LC-MS/MS fragmentation using a quadrupole- TOF MS ( QTOF 6510 , Agilent Technologies , Santa Clara , CA , USA ) and sub-2 ppm accurate mass measurements using a Bruker Daltonics Apex II 7 . 0 Tesla Fourier transform ion cyclotron ( FT-ICR ) MS ( Bruker Daltonics . , Billerica , MA , USA ) ., For the QTOF analysis chromatographic conditions were identical to those reported for the profiling experiment and serum plasma samples from either the Scripps normal blood or pooled patient samples were used for the analysis ., The average m/z and retention times of each of the biomarkers obtained through XCMS analysis , were used for targeted MS/MS analysis with a starting collision-induced dissociation energy of 20eV ., Fragmentation patterns were analyzed with the Agilent Mass Hunter Qualitative Analysis software version B . 03 . 01 using the targeted MS/MS and formula generation algorithms and compared with the MS/MS fragment data in the METLIN database 30 ., The FTMS system was equipped with a custom machined electrospray source with two nebulizers for dual spray ionization ., The main orthogonal nebulizer was used for LC-eluent , while the second nebulizer was used to introduce a calibration mixture containing two compounds ( aminoantipyrine at 204 . 1132 m/z and quinidine 325 . 1911 m/z ) at 3 mM concentration mixed with 1∶10 dilution of Agilent low concentration tune mix ., A linear calibration fit was used in the narrow range to internally calibrate individual mass spectra ., The chromatographic conditions were identical to those reported for the profiling experiment with an additional analysis using a smaller i . d . column with the same stationary phase composition ( Zorbax 300SB C18 Capillary , 3 . 5 µm , 0 . 3 mm×150 mm; Agilent Technologies , Santa Clara , CA , USA ) at a capillary pump flow rate of 4µl/min ., Pooled serum and plasma samples from either the Scripps normal blood or patient samples were used for the analysis ., The most important aspect of any clinical analytical study resides with the quality of the samples used; here representative serum and plasma samples from a variety of subject populations were incorporated to minimize the effects of non-relevant metabolic variation ( e . g . , nutrition , sex , age , race ) and magnify those metabolic differences that are not only statistically significant between specific populations , but relevant in identifying the changes in metabolism that can be directly attributable to infection ., One of the analytical limitations with an untargeted LC-MS metabolomics approach is that of inter-sequence reproducibility ( i . e . , sample preparation , instrument drift , column and mass spectral baseline variation ) when comparing samples directly between analytical sequences ., Such inter-sequence variability can introduce shifts in ion intensities that can interfere with the accuracy of downstream statistical analysis ., Therefore , this study was conducted with single injections of each sample , analyzed in randomized order consecutively within one analytical sequence ( Figure 1 ) ., Due to such analytical constraints , small groups of representative samples were selected from various sample classes ( e . g . , O . volvulus-infected and uninfected individuals from various geographic regions and individuals infected with other parasitic diseases ) ., XCMS analysis of the sample mass spectral data files ( n\u200a=\u200a136 ) resulted in the measurement of a total of 2 , 350 mass features ., Testing the overall reproducibility of the analysis , the coefficient of variation ( CV ) was found to be 15 . 9% as calculated from all mass feature intensity values compared across triplicate injections of a single plasma sample analyzed throughout the analytical sequence ., This value is comparable to previous studies of analytical variation within plasma and serum analysis by our laboratory and consistent with a number of other LC-MS based metabolomics studies 33 , 34 ., Statistical comparison between all onchocerciasis positive samples ( n\u200a=\u200a76 ) and all onchocerciasis negative samples ( n\u200a=\u200a56 ) , including those infected with other tropical diseases , by Welchs t-test resulted in 194 features with a p<1×10−4; with a false discovery rate FDR of 54% ., To reduce the number of potentially erroneous markers and focus on those mass features with the most potential in distinguishing disease , the top 35 mass features ( p<1×10−7 ) were chosen for more stringent analysis through assessment of the quality of the resulting extracted ion chromatograms ( EICs ) ( Figure S1 ) ., While XCMS pre-processing software contains a robust retention time correction and peak alignment algorithm , an important aspect of this study is the statistical quantitation of biomarkers , therefore any features with questionable quantitation , observed as imperfect alignment or inconsistent peak boundaries across samples were ruled out of further analysis ., Additionally , since several mass features may redundantly describe one chemical metabolite due to the presence of in-source fragments , adducts , or multiply charged species and overlapping retention time ., The features were separated into unique peak groups and representative ions with the highest overall abundance were included in a subset of 14 features for further analysis ( Table 2 ) ., Interestingly , the majority of these features were detected at lower levels in infected individuals relative to those without onchocerciasis ., Analysis of the selected biomarkers with MS/MS and FTMS analysis has provided molecular masses and assigned molecular formulas that could be used to classify the biomarkers into distinct chemical classes; of the 14 markers identified 10 were small molecules and four were protein fragments or small peptides ., Beginning with a subset of the larger sample set , the mass spectral data for the top 14 candidate biomarkers were investigated for their ability to discriminate O . volvulus infected individuals ( n\u200a=\u200a55 ) from healthy controls ( n\u200a=\u200a18 ) from the African serum and plasma samples ., PCA of the effect of these 14 biomarkers was used to visualize the variation between these samples groups ( Figure 2A ) ., A distinct clustering of the O . volvulus infected versus the healthy individuals was observed across the x-axis of the PCA score plot , implying that principal component 1 ( PC1 ) contained the variance of the data set required to distinguish these two sample groups ., The next greatest amount of variation within the data set appeared to have little effect on discriminating infection or even geographic differences , but appears to be more representative of the heterogeneity present among healthy controls ., The top 14 candidate biomarkers were also applied to a larger sample set comprised of multiple geographic regions including O . volvulus-infected individuals ( n\u200a=\u200a76 ) and healthy and disease controls ( n\u200a=\u200a56 ) ., PCA of these 14 biomarkers ( Figure 2B ) revealed the inherent complexity encountered when employing a metabolite profiling approach to diagnostic development ., As with the initial African samples , there is general clustering of the onchocerciasis positive individuals with the variance contained in PC1 having good discriminatory power ., The disease and healthy controls cluster separately from the onchocerciasis positive individuals , however , there is some overlap between one of the Chagas disease and two of the leishmaniasis positive individuals ., Interestingly , the lymphatic filariasis samples , infected with the closely related filarial parasite Wuchereria bancrofti , cleanly cluster with the healthy controls ., Ideally serum and plasma samples would not be directly compared against each other as the two matrices have distinct chromatographic differences ( Figure S2 ) ., However , given the nature of onchocerciasis sample banks that have been collected over the past 20 years , it was important to determine if the resulting biomarker results would be biased to one biological sample type over another ., Importantly , our results show that the plasma samples from Cameroon as well as the Indian lymphatic filariasis plasma samples consistently align as expected with the multi-region serum sample set in distinguishing onchocerciasis infected from uninfected individuals ., As evidenced in Figure 2C , there is little clustering of the Guatemalan individuals initially classified as onchocerciasis positive; rather there appears to be a continuum of onchocerciasis disease variation within those samples ., However , dissections of excised nodules at the time of nodulectomy revealed no live worms , as opposed to the results of the Cameroon samples where infection status was confirmed by the extraction of live O . volvulus worms ., Although tools such as PCA provide a graphical means of distinguishing between sample groups , they do not have the ability to provide a quantitative diagnostic assessment as would be needed nor are they intended to be used for field applications of an onchocerciasis diagnostic ., Alternatively , machine learning algorithms do provide the necessary binary output , as well as calculate confidence intervals of a given classification ., The mass spectral intensity values for the onchocerciasis serum and plasma data set were used as inputs in a collection of machine learning algorithms ., The algorithms were chosen to provide a survey of the various types of machine learning algorithms that could be used with mass spectral data in diagnostic assessments , either alone or in combination in more sophisticated algorithms ., Results of this analysis are summarized in Table 3 where sensitivity ( true positive rate ) and specificity ( 1–false positive rate ) are displayed ., The receiver operating characteristic ( ROC ) areas present a numerical value description of the relationship between sensitivity and specificity for a given diagnostic test 35 , 36 ., In the context of a binary classification problem as presented here , a value of 0 . 5 indicates there is no discrimination within the test and shows any result is essentially the same as a random guess , while a value of 1 . 0 indicates a perfect test prediction ., Based upon the data , it is clear that the inclusion of the Guatemala samples within the sample analysis dramatically increases the number of reported false positives , compromising the accuracy of the test overall ., However , it is important to note that within the context of the Africa sample set , the ROC area approaches , or is equal to , a perfect test prediction in numerous cases , and in the case of the functional trees classification tree algorithm , perfect sensitivity and specificity can be achieved ., Metabolomics , or the measurement of all the metabolites present in an organism , and metabolite profiling , in which a smaller subset of metabolites are measured , have become established as useful tools in the “real-time” measurement of organismal metabolism ., For infectious disease , previous metabolomics approaches have included mice challenged with the protozoan parasites Trypanosoma brucei brucei 37 and Plasmodium berghei 38 , trematode parasites Echinostoma caproni 39 and Schistosoma mansoni 40 and some viruses 41 ., This study represents the first investigation of a metabolomic approach to the discovery of biomarkers and creation of a diagnostic test for identifying and classifying onchocerciasis infection ., Through the use of multivariate statistics and machine learning algorithms , the potential of metabolomic analysis has been demonstrated for uncovering biomarkers for specific determination of not only onchocerciasis infection but holds promise for the diagnosis of other parasitic diseases ., Specifically , this was demonstrated by the clustering of the W . bancrofti infected samples with those individuals that were not infected with O . volvulus in the multivariate PCA ., This clustering showed the potential specificity of the biomarkers for the discrimination of onchocerciasis from other filarial diseases ., Although this analysis consists of only four representative lymphatic filariasis samples , the distinct clustering of these samples with uninfected individuals is noteworthy and argues for future analysis that includes other filarial disease pathogens ( e . g . , Brugia malayi , Loa loa ) ., The 14 candidate biomarkers showed excellent performance in the African specific sample set with up to 99–100% sensitivity and specificity when examined with the single machine learning algorithms ., With 99% of onchocerciasis disease prevalence in Africa 42 and the presence of multiple regions of ongoing transmission 43 , this is the most clear test of the biomarker strategy ., When applied to a multi-region sample set , the multivariate PCA of the biomarker analysis resulted in a wide spread of results across the range of infected and uninfected individuals ., This observation raises several questions regarding the unique epidemiological challenges of measuring onchocerciasis in the Americas ., In the context of the PCA , the Guatemalan patients did not classify as expected if nodule presence alone is used as an indicator of infection ., However , nodule presence as a diagnostic is known to have exceedingly poor sensitivity and specificity ., A possible explanation of this data is that the observed heterogeneity is related to microfilarial load ., Unfortunately , skin snip samples with mf counts were not collected for the Guatemala sample set ., Nonetheless , if this observed spread of data were correlated with variation in the presence of the mf , then in a region such as the Guatemalan Central Endemic Zone ( CEZ ) where biannual dosage of ivermectin reaches high coverage levels 44 , mf should be nearly absent and we would expect to see no spread of the data but rather a distinct cluster with or near the uninfected individuals ., Alternatively , the observation that a quarter of the nodules from these infected individuals from the Guatemalan CEZ did not contain living worms , indicates that these biomarkers may be sensitive to not only the presence , but also the viability of the infective worms ., The results of this PCA are consistent with an increasing body of evidence that biannual ivermectin treatments , as are received in the Guatemalan CEZ , have an effect on the viability of adult female worms and ultimately on the elimination of parasites 45–47 ., Since the Guatemalan O . volvulus positive samples do not segregate along clear lines with the clinically confirmed samples from Africa , it is possible that the continuum seen in the PCA plot reflects a range of infection that could be correlated qualitatively or quantitatively to the health of the worms ( e . g . , live healthy , dying , and dead ) in vivo ., Given that an individual with dead or dying worms does not need further treatment in the context of ivermectin mass drug administration , this finding is particularly valuable in the context of onchocerciasis elimination progress ., Ideally , a biomarker determination study would involve independent sample sets for training , validation , and testing ., Due to sample limitations inherent to onchocerciasis and many neglected tropical diseases in general , we have chosen to use an approach that trains on the majority of the sample set , and through the 10-fold cross validation machine learning analyses , conduct tests on small subsets of the full sample set 48 ., In this study , we report only those features detected in positive ion mode with the highest statistical significance and the most accurate intensity values by XCMS analysis ., Consistent among these 10 small molecule features is that they are all fatty acids and related fatty acid derivatives ., Further investigations into the biological roles of these fatty acids and fatty acid sterols in onchocerciasis disease progression and potential interaction with the down-regulated proteins is of distinct interest , not only in the development of a diagnostic but also to more clearly understand the biology of this disease ., Almost certainly , other biomarkers could be discovered and validated simply by altering the chromatographic ( e . g . , HILIC ) and/or ionization conditions ( e . g . , negative mode ESI , APCI ) ., It is possible that additional markers can be eventually be added to the repertoire of biomarkers used for onchocerciasis detection , further increasing assay specificity ., The achievement of the goals of elimination and eradication of onchocerciasis and of the neglected tropical diseases in general , ultimately depends upon the ability to measure and track the progress of disease elimination and recrudescence ., Our study highlights advantages of a metabolomics based diagnostic over onchocerciasis diagnostics currently implemented including: sensitivity , reproducibility , invasiveness , and the potential for multiplexing with biomarkers for other filarial and/or neglected tropical diseases ., Fine calibration of this test in the Western Hemisphere would require characterized samples from individuals with confirmed active infection ., Unfortunately , these samples are rapidly becoming a rarity due to the success that has been achieved by OEPA ., Further refinement and validation of this metabolomic based diagnostic approach calls for an expansion of the mass spectral analysis with larger sample sets , while inclusion of a greater demographic representation will allow for further validation of the test in specific populations ( e . g . , children , adults , different genetic backgrounds ) ., Eventually , the optimized biomarkers can be ported into field-based technologies ( e . g . , immuno-chromatographic or micro-fluidic-based tests ) for use as a point-of-care diagnostic , a determinant for the distribution and duration of treatment , and ultimately for long-term disease surveillance .

Introduction, Methods, Results, Discussion

Development of robust , sensitive , and reproducible diagnostic tests for understanding the epidemiology of neglected tropical diseases is an integral aspect of the success of worldwide control and elimination programs ., In the treatment of onchocerciasis , clinical diagnostics that can function in an elimination scenario are non-existent and desperately needed ., Due to its sensitivity and quantitative reproducibility , liquid chromatography-mass spectrometry ( LC-MS ) based metabolomics is a powerful approach to this problem ., Analysis of an African sample set comprised of 73 serum and plasma samples revealed a set of 14 biomarkers that showed excellent discrimination between Onchocerca volvulus–positive and negative individuals by multivariate statistical analysis ., Application of this biomarker set to an additional sample set from onchocerciasis endemic areas where long-term ivermectin treatment has been successful revealed that the biomarker set may also distinguish individuals with worms of compromised viability from those with active infection ., Machine learning extended the utility of the biomarker set from a complex multivariate analysis to a binary format applicable for adaptation to a field-based diagnostic , validating the use of complex data mining tools applied to infectious disease biomarker discovery and diagnostic development ., An LC-MS metabolomics-based diagnostic has the potential to monitor the progression of onchocerciasis in both endemic and non-endemic geographic areas , as well as provide an essential tool to multinational programs in the ongoing fight against this neglected tropical disease ., Ultimately this technology can be expanded for the diagnosis of other filarial and/or neglected tropical diseases .

Onchocerciasis , caused by the filarial parasite Onchocerca volvulus , afflicts millions of people , causing such debilitating symptoms as blindness and acute dermatitis ., There are no accurate , sensitive means of diagnosing O . volvulus infection ., Clinical diagnostics are desperately needed in order to achieve the goals of controlling and eliminating onchocerciasis and neglected tropical diseases in general ., In this study , a metabolomics approach is introduced for the discovery of small molecule biomarkers that can be used to diagnose O . volvulus infection ., Blood samples from O . volvulus infected and uninfected individuals from different geographic regions were compared using liquid chromatography separation and mass spectrometry identification ., Thousands of chromatographic mass features were statistically compared to discover 14 mass features that were significantly different between infected and uninfected individuals ., Multivariate statistical analysis and machine learning algorithms demonstrated how these biomarkers could be used to differentiate between infected and uninfected individuals and indicate that the diagnostic may even be sensitive enough to assess the viability of worms ., This study suggests a future potential of these biomarkers for use in a field-based onchocerciasis diagnostic and how such an approach could be expanded for the development of diagnostics for other neglected tropical diseases .

chemical biology/small molecule chemistry

journal.pcbi.1002506

Filament Compliance Influences Cooperative Activation of Thin Filaments and the Dynamics of Force Production in Skeletal Muscle

Striated muscle contraction is a Ca2+ dependent process ., Ca2+ binding to troponin initiates thin filament activation , defined as exposure of sites along F-actin to which myosin can bind and form a cross-bridge ( XB ) ., In turn , XB binding can promote additional thin filament activation 1 , 2 ., The increase in force production with increasing Ca2+ is highly non-linear , suggesting there is coupling between Ca2+-dependent and XB-dependent processes to augment thin filament activation and force production ., The highly structured organization of the myofilament lattice ( Figure S1 ) has led many investigators to suspect a role for spatial interactions between neighboring thin filament regulatory units ( 1 RU\u200a=\u200a7 actin monomers+1 troponin complex+1 tropomyosin molecule ) and/or neighboring XBs along the myofilaments to cooperatively augment thin filament activation 3–20 ., Experiments have identified some possible forms of cooperativity between RUs along thin filaments and from XBs binding to actin ., However a detailed picture of the Ca2+-dependent and XB-dependent cooperative mechanisms remains unclear because multiple cooperative processes are almost certainly coupled as muscle fibers contract ., Recent computational efforts have identified several potential mechanisms of cooperativity 21–27 ., To study how these mechanisms rely upon the spatial and mechanical framework of the contractile filament lattice , we recently developed a spatially-explicit model that included Ca2+ regulation of individual RUs along thin filaments 27 ., This modeling paradigm demonstrated a mechanical form of cooperativity that arises from compliant thick and thin filaments: XB binding to actin results in realignment between myosin heads and binding sites along the thin filament , which leads to additional XB recruitment as force develops ( XB-XB cooperativity ) 23 , 25 , 27 ., However , our previous models did not account for kinetic properties of thin filament RU activation being influenced by XB binding ( XB-RU cooperativity ) or the activation state of neighboring RUs ( RU-RU cooperativity ) ., In this study we developed computational algorithms that allow thin filament RU activation rates to vary throughout a simulation , depending upon the spatial and biochemical states of neighboring RUs and XBs ( Figure 1 ) ., This approach permitted a systematic investigation of potential cooperative mechanisms that , individually or in combination , influence Ca2+-sensitive force production in skeletal muscle ., We simulate measurements of cooperative force production and illustrate relative contributions from spatial , kinetic , and mechanical characteristics of the half-sarcomere that determine cooperative activation of the thin filament in skeletal muscle ., Because thin filament RUs are linked end-to-end via tropomyosin head-to-tail overlap , Ca2+ binding to a troponin and subsequent tropomyosin movement may activate more than 7 actins within a structural RU ., We 4 and others 28 estimated this thin filament activation span to be 10–12 actins for skeletal muscle by using experimental approaches to titrate the number of functional troponin complexes along the length of thin filaments ., If this Ca2+ activation span is correct , this would make Ca2+ binding to troponin capable of partially activating a region of the neighboring structural RUs ., To simulate these experimental findings we co-varied model parameters RUspan and ρTn , which control the length of RU activation along a thin filament ( Table 1 ) and the functional troponin density , respectively ., At the Ca2+ that yields maximal steady-state behavior ( pCa 4 . 0 ) with ρTn\u200a=\u200a1 , force and fractional thin filament activation were 973±14 pN and 0 . 993±0 . 004 , consistent with our previous results 27 ., These maximal values were not significantly different as RUspan varied from 7–14 actins , suggesting complete activation of thin filaments can occur at pCa 4 . 0 ., As ρTn was increased from 0 to 1 at pCa 4 . 0 , steady-state force increased linearly with ρTn when RUspan was 7 actins , but became increasingly convex as RUspan increased to 9 , 11 , and 14 actins ( Figure 2A ) ., Although a RUspan of 14 actins produced the greatest non-linear increases in force as ρTn increased , RUspan values of 9 and 11 actins predicted behaviors most consistent with skeletal muscle force measurements from our laboratory 4 ., As RUspan increased from 7 to 9 to 11 actins , the Ca2+ sensitivity of the force-pCa relationship ( pCa50 ) progressively increased by roughly 0 . 2 pCa units with little change in cooperativity ( nH ) ( Figure 2B and Table 2 ) , for simulations implementing all kinetic and mechanical forms of cooperativity with parameter values: ρTn\u200a=\u200a1 , kxb\u200a=\u200a3 pN nm−1 , kfil\u200a=\u200a1X ., The model predicted similar relationships for fractional thin filament activation versus pCa ( data not shown ) , with slightly lower pCa50 and nH values for thin filament activation level than force ( Table 2 ) ., In combination , these results suggest a RUspan of 14 actins produces supra-physiological activation and contractile responses , but a RUspan value of 9 actins best describes skeletal muscle measurements from our laboratory 4–8 ., Therefore , we used a RUspan of 9 actins for all further simulations ., To investigate the effect of individual versus combined mechanisms of cooperativity on Ca2+-sensitivity and cooperativity of force production , we systematically assessed the influence of each kinetic form of cooperative thin filament activation ( i . e . all possible combinations of source-target cooperativity illustrated in Figure 1A and further described the Materials & Methods ) ., All simulations ( Figure 3 ) used standard parameter values fixed at RUspan\u200a=\u200a9 actins , kxb\u200a=\u200a3 pN nm−1 , and kfil\u200a=\u200a1X ., Neighboring activated RUs ( TF3 as the source of RU-RU cooperativity ) provided the greatest influence on the force-pCa relationship , followed by low-force XBs at neighboring RUs ( XB2 as the source of XB-RU cooperativity ) , and finally high-force XBs at neighboring RUs ( XB3 as the source of XB-RU cooperativity ) ., This demonstrates a hierarchy of influence on the force-pCa relationship for the three kinetic sources of cooperativity: TF3>XB2>XB3 ., Throughout a simulation there are more RUs activated than there are XBs bound , which likely promotes this hierarchy ., In addition , the finding that low-force bearing XBs ( XB2 in Figure 1A ) may contribute more to cooperative thin filament activation than high-force bearing XBs ( XB3 in Figure 1A ) is an intriguing prediction that supports a role for low-force ( weak binding ) XBs in the activation process 29 , 30 as well as the idea of a Ca2+-dependent equilibrium between low-force and high-force XBs in modulating thin filament activation 3 , 5 , 10 , 11 , 13 ., There is also a hierarchy of influence for the thin filament transition rate ( s ) being targeted by a cooperative mechanism ., Targeting rt , 12 or rt , 23 in combination ( Figure 3B and S2G–I ) produced a greater cooperative response than rt , 12 alone ( Figure 3A and S2A–C ) , both which produced greater responses than rt , 23 alone ( Figure S2D–F ) ., This demonstrates a synergistic effect of targeting rt , 12 and rt , 23 in combination that was consistent across all simulations , even for the least influential source of cooperativity ( XB3 ) ., The results suggest XB binding may play an important role in preventing tropomyosin moving back to an inhibitory position , stabilizing RU activation and augmenting thin filament activation throughout the half-sarcomere ., This set of simulations supports the hypothesis that RU-RU cooperativity is the dominant source of cooperativity in skeletal muscle , and could influence thin filament transition rates that are downstream from Ca2+ binding of troponin C , such as the troponin C-troponin I interaction , to facilitate greater tropomyosin mobility ., To isolate the effects of XB binding from other sources of cooperativity on thin filament activation we investigated the fractional activation of thin filaments by Ca2+ , in the presence and absence of XB binding ., XB binding provided the greatest increases in thin filament activation at submaximal pCa values ( Figure 4A ) when all possible mechanisms ( kinetic and mechanical ) of cooperativity were implemented ., In contrast , in the absence of kinetic forms of cooperativity , XB binding had a minimal effect on thin filament activation across the entire pCa range ( Figure 4A ) ., Moreover , the activation and force traces shown in Figure 4B illustrate that including kinetic forms of cooperative thin filament activation dramatically slows the rate of thin filament activation ( kTF , act ) ., The full kTF , act-pCa relationships are shown in Figure S3 ., These simulations ( Figure 4B ) also demonstrate a significant XB-dependent increase in the magnitude of thin filament activation when kinetic forms of cooperativity were implemented , consistent with the steady-state results shown in Figure 4A ., To investigate how the mechanical properties of XBs and the myofilaments influence cooperative force production , rate of force development ( kdev ) , or rate of XB turnover ( ATPase ) , we varied XB , thick filament , and thin filament spring constants ( kxb , km , and ka , respectively ) ., Standard filament stiffness values ( kxb\u200a=\u200a3 pN nm−1; kfil\u200a=\u200akm\u200a=\u200aka\u200a=\u200a1X ) resulted in maximal force and kdev values of 973±14 pN and 32 . 6±0 . 1 s−1 , consistent with results shown in Figure 2 ., Decreasing kxb decreased maximal force and kdev ( Figure 5A–B ) , diminished cooperativity ( Figure 5C ) and Ca2+ sensitivity ( Figure 5F ) of the force-pCa relationship , and elevated XB cycling ( Figure 5I ) ., Increasing kxb , however , produced more heterogeneous dynamics ., A kxb of 10 pN nm−1 increased maximal kdev by 20% , but resulted in minimal shifts in the force-pCa relationship and a small decrease in nH ., Further increasing kxb to 30 pN nm−1 increased maximal kdev by 6% , but produced a small ‘left-shift’ in the force-pCa relationship , slightly increasing pCa50 to 5 . 96 ., For all simulations that varied kxb , stiffer XBs led to slower rates of XB turnover ( Figure 5I ) due to decreased ability of myosin to diffuse to a binding site ., While these changes in maximal force production and ATPase are consistent with our previous observations 27 , the findings that XB stiffness can influence the cooperative nature of the force-pCa relationship and kdev reveals a new role for mechanics of filaments and XBs in cooperative binding processes ., With kxb fixed at 3 pN nm−1 , decreasing thin filament stiffness ( ka ) 10-fold decreased maximal force by 25% ( Figure 5D ) , slowed kdev by 70% ( Figure 5E ) , and reduced pCa50 by ∼0 . 5 pCa unit ( Figure 5F ) ., Conversely , increasing ka 10-fold decreased maximal force by 20%and slightly increased pCa50 , but similarly slowed kdev , albeit by only 9% ., Comparable decreases in thick filament stiffness ( km ) reduced maximal force by 12% , slowed kdev by 14% , and reduced pCa50 , while a 10-fold increase in km produced relatively minimal changes in the force-pCa response ., Thus , these results suggest changes in thin filament stiffness ( relative to thick filament stiffness ) have a larger influence of on Ca2+ mediated activation and force development ., However , there is a coupling mechanism that arises from the relative stiffness values between the thin and thick filaments , as simultaneously reducing both ka and km 10-fold ( kfil\u200a=\u200a0 . 1X ) resulted in a 50% reduction in maximal force ( Figure 5G ) , a relatively large increase in nH and pCa50 compared to the other simulations ( Figure 5C , F ) , a 70% reduction in kdev ( Figure 5H ) , and a doubling in the XB turnover rate ( Figure 5I ) ., In contrast , simultaneously increasing both ka and km 10-fold ( kfil\u200a=\u200a10X ) produced minimal shifts in the force-pCa relationship ( Figure 5G ) or XB turnover rate , and a minor ( 5% ) increase in maximal kdev ( Figure 5H ) ., Even though kxb was fixed throughout these simulations , shifts in the force-pCa relationship arise from a redistribution of the bound XB populations throughout a simulation ., This illustrates the influence of mechanical forms of cooperativity due to strained compliant filaments as force develops throughout a simulation , causing realignment of binding sites along the thin filament with respect to XB locations along the thick filaments ., This realignment leads to additional XB recruitment ( i . e . XB-XB cooperativity ) and affects the force-pCa relationship when combined with kinetic forms of cooperative thin filament activation ., Importantly , these stiffness-dependent shifts in kdev and pCa50 were not seen in previous analyses 27 ., In summary , decreases in XB or myofilament stiffness increase XB-XB cooperativity , increasing rates of XB turnover , but diminishing force , nH , pCa50 , and kdev ., These results support our previous simulations 27 , illustrating the counter-intuitive influence of XB-XB cooperativity on the dynamics of force development , where a more compliant filament lattice reduces transmission of force production and slows the apparent rate of force development even though there is increased XB recruitment and turnover ., These simulations predict a fascinating relationship between mechanical and kinetic forms of cooperativity that are coupled to regulate cooperative thin filament activation and force development in a muscle fiber ., In previous experiments we reduced cooperative interactions between neighboring RUs along thin filaments in demembranated rabbit psoas muscle fibers by extracting native troponin C ( TnC ) , then reconstituting troponin complexes with varying mixtures of native TnC and a mutant TnC ( D27A , D63A ) that cannot bind Ca2+ at N-terminal sites I and II 4 ., Those results showed that reduced spatial coordination between neighboring RUs along the thin filament can limit force production ., By varying the number of actins that become available for myosin binding upon Ca2+ activation of a RU in silico ( via the model parameter RUspan ) , simulation results agreed best with our prior measurements when the RUspan was set at 9–11 actins ( Figure 2 ) ., Simulations also predict that Ca2+ binding to troponin is unlikely to activate a thin filament span greater than two structural RUs ( ∼14 actin monomers ) , because this leads to a hypersensitive force response ., An estimated RUspan of 9–11 actins agrees well with other studies 17 , 19 and our previous estimate of 10–12 actins 4 , although shorter than estimates from single molecule 31 or early muscle fiber 14 studies ., Therefore , our empirical and computational results suggest that the functional activation span of a RU is greater than the 7 actin monomers of a structural RU , but remains bracketed by the pair of neighboring troponin molecules surrounding a troponin complex ., Thus coupling between neighboring RUs along the thin filament can influence spatial and kinetic processes of activation , albeit at relatively local regions along the thin filament ., The current model allowed us to explore the relative influence on thin filament activation of various mechanisms , such as the number of proximal activated RUs or bound XBs that augment activation kinetics at neighboring RU ( Figure 1A ) ., We find that adjacent activated RUs ( state TF3; Figure, 1 ) provided the greatest source of cooperative thin filament activation , with RU-RU cooperativity producing the largest increases in pCa50 and nH of the force-pCa relationship ., The next most effective contributors to cooperative thin filament activation were neighboring , bound XBs: states XB2 and XB3 , respectively ., In addition , combining multiple forms of RU-RU and XB-RU cooperativity that jointly targeted multiple thin filament activation rates ( rt , 12 and rt , 23 , Figure, 1 ) influenced the force-pCa relationship more greatly than individual forms of kinetic cooperativity ., These findings suggest that the physiological force-pCa relationship arises from multiple , cooperative processes at the molecular level that coordinate thin filament activation and XB binding ., The cooperative force-pCa relationship may be more sensitive to RU activation than XB binding because the fractional pool of activated RUs is always greater than the fractional pool of bound XBs ( ∼100% vs . ∼15% at pCa 4 . 0 ) ., Within this 15% , roughly 2/3 of the bound XB population resides in the low-force bearing state throughout a simulation , which may explain the why low-force XBs ( XB2 ) contribute more greatly to cooperative activation than high-force XBs ( XB3 ) ., Therefore , the relative sensitivity to multiple sources of cooperativity may vary as RU activation and XB kinetics vary with fiber type and taxa ., The spring constants of the XBs and the myofilaments are important determinants of the force-pCa relationship and the rate of force development ( kdev ) ., Compliance in the filament lattice leads to a spatial redistribution of binding sites in response to local XB force generation ., That redistribution , in turn , may increase recruitment of additional XBs and may influence RU activation ., These varied cooperative dynamics would not occur within a system of inextensible filaments , because there would be no heterogeneity in the transmission of forces throughout the filaments as occurs herein with varied values for XB , thin- or thick-filament stiffness ( kxb , ka or km , respectively ) ., Interestingly , isolated decreases in kxb , ka or km generally reduce maximal force , pCa50 , and nH , indicating a diminished cooperative force response ., In contrast , simultaneous decreases in ka and km ( kfil\u200a=\u200a0 . 1X ) increased nH and pCa50 , indicating a more cooperative force-pCa relationship ., Thus , the relative influence of multiple forms of cooperativity depends upon the relative stiffness difference between thick and thin filaments , where greater divergence between thick and thin filament stiffness values or increased XB flexibility diminishes the potency with which cooperative mechanisms augment force development ., We note that the thick versus thin filaments stiffness cannot deviate too much in their relative stiffness values , otherwise nearly all of the realignment will reside in the more compliant set of filaments , which reduces the capacity for cooperative force production ., Because there are twice as many thin filaments as thick filaments , variations in ka alone affected the cooperative force response more than comparable changes in km , alone , making effective variations in myofilament lattice stiffness more sensitive to ka than km ., In contrast , simultaneous decreases in ka and km reduce the relative stiffness difference between the filaments , allowing them to undergo comparable levels of compliant realignment to facilitate mechanical and kinetic forms of cooperative force production at mid-pCa levels , even though maximal force values at pCa 4 . 0 may be significantly compromised ., This implies that the most efficient levels of cooperative force production may arise from relatively stiff thick and thin filament values , at the same order of magnitude , consistent with physiological observations in vertebrates 32 , 33 ., Our spatially-explicit models provide the unique ability to explore how spatial , kinetic , and mechanical characteristics of thin filament activation and XB binding throughout the half-sarcomere influence cooperative activation ., As summarized below , our findings are consistent with results from previous studies 24 , 34–37 , which continue to suggest that multiple cooperative mechanisms are almost always required , in combination , to simulate physiological measurements of cooperative force production ., Consistent with our observations , RU-RU cooperativity has been the most potent form of cooperative thin filament activation in previous computational studies 24 , 34–36 ., However , XB binding consistently contributes a synergistic role that maintains and augments thin filament activation to recruit additional XBs as force develops 34 , 35 ., The kinetic forms of cooperativity significantly slow the apparent rate of activation and force development ( kTF , act , kdev , or ktr , depending upon the kinetic parameter in question ) because these cooperative mechanisms increase the pool of activated RUs and bound XBs as force develops over time 22 , 26 ., Therefore , the kinetic transition rates underlying thin filament activation and XB cycling may differ greatly from the apparent rates of cooperative force development and relaxation throughout a simulation or a muscle contraction 22 , 34 , 35 ., Recently , Geeves et al . 37 combined solution kinetic measurements of myosin binding to regulated thin filaments and a continuous flexible chain model of RU activation , rather than assuming a rigid RUspan value as modeled here and elsewhere 27 , 35 ., Their measurements suggest that strong XB binding can cooperatively activate RUs along the thin filament , even though the rate of myosin binding is regulated by the position of tropomyosin along the thin filament ., Consistent with our model of thin filament activation , their results illustrate that the rate of myosin binding to actin may be limiting force production , rather than the rate of RU activation because dynamic movement of tropomyosin is more rapid than troponin I detachment from actin ., Predictions from their continuous flexible chain model also suggest Ca2+-binding or XB binding may lead to ‘clusters’ of force bearing XBs along the thin filament ( over a length of 25–50 nm ) , particularly near the onset of contraction at low Ca2+ ., This distance agrees well with our estimates of RUspan and supports the idea that cooperative activation occurs at relatively local regions along the thin filament , consistent with clustered islands of XB binding throughout the half-sarcomere that have been demonstrated by previous spatially-explicit models 23 , 26 ., These simulations show that RU-RU cooperativity occurs rapidly and dominates filament activation early in the contractile process , while the influence of XB-RU and XB-XB cooperativity occurs more slowly , becoming increasingly important as force continues to develop ., Moreover , the mechanical characteristics of the XBs and the myofilaments greatly influence these mechanisms ., The relative speed and influence of these various cooperative mechanisms favors the interpretation that rapid and complete activation of skeletal muscle thin filaments leads to maximal force production in a fiber , thereby allowing graduated recruitment of motor units to dictate contractility of the whole muscle ., In contrast , every muscle cell in the heart contracts during each heartbeat , which may require a redistribution in the hierarchy of influence from multiple kinetic and mechanical forms of cooperativity ., For instance , the relative contribution of XB-RU cooperativity may increase in cardiac muscle to provide for more ‘local’ regulation of force development within a RU as XB binding events enhance Ca2+ binding to troponin or maintain RU activation ., Consistently , the dominant influence of RU-RU cooperativity may diminish , as the RUspan in cardiac muscle appears to be less than the 7 actins of a structural RU 28 , 38 ., As discussed within previous computational studies , cardiac muscle may also involve a ‘negative’ or ‘anti’ cooperativity that favors rapid thin filament deactivation to locally control contraction throughout each heartbeat 34–36 ., The computational methods developed herein provide unique tools for examining and discerning kinetic and mechanical differences between skeletal and cardiac muscle contraction at the molecular level , and importantly , how regulation of contraction may be altered with damage or disease ., Similar to a finite element model , Ca2+-activated thin filament regulatory processes and thick-to-thin filament XB interactions are simulated within a network of linearly elastic springs ., Within this network , forces and deformations occur along the axial direction of the filaments ( Figure 1D ) , providing a linear system of equations that represents a one dimensional instantaneous force balance throughout the half-sarcomere ( Eq . 1 ) ., Individual thick or thin filaments consist of 61 or 91 elastic spring elements , respectively , linked end-to-end at ‘nodes’ about which forces balance ( 60 thick filament nodes and 90 thin filament nodes ) ., Similar to our previous simulations 23 , 25 , 27 , thick and thin filament spring constants are km\u200a=\u200a6060 and ka\u200a=\u200a5230 pN nm−1 for resting ( unstrained ) elements of length 14 . 3 and 12 . 3 nm , respectively , which constitute half-sarcomere long thick and thin filaments of ∼860 ( =\u200a60×14 . 3 nm ) and ∼1110 ( =\u200a90×12 . 3 nm ) nm ., Node locations coincide with model structures that represent myosin XBs along thick filaments and actin monomers along thin filaments ., Stoichiometrically this leads to 6 myosins every ∼43 nm of thick filament that are co-linearly aligned with , and may bind to , 3 actin monomers every ∼37 nm of thin filament 27 ., Because different ratios and arrangements of the thick and thin filaments can lead to different levels of XB recruitment and turnover 27 , it is plausible that different model geometries or varied stoichiometry could influence kinetic and mechanical forms of cooperativity investigated in this study ., As further discussed below , Ca2+ regulation of contraction stems from a sub-set of thin filament nodes that are co-located with model structures representing troponin ., The XB spring constant ( kxb ) was primarily fixed at 3 pN nm−1 to be consistent with parameter ranges used in previous simulations and recent estimates from cellular experiments 39–41 ., Collapsing this geometry into a matrix of spring constants ( K ) , and a vector of boundary conditions ( V ) allows us to solve the instantaneous balance of forces within the elastic network to determine a vector of unknown node locations ( P ) given the state of all XBs 23 , 25–27: ( 1 ) We assume that inertial and viscous interactions are negligible under isometric conditions 42 ., Some simulations scaled the value of km , ka , or kxb independently , while other simulations simultaneously scaled the values of km and ka ., The scalar multiple affecting individual filament stiffness values precedes X , such as km\u200a=\u200a10X or ka\u200a=\u200a10X to represent either km or ka becoming 10 times stiffer ., Simulations where both km and ka varied simultaneously are listed as kfil\u200a=\u200a0 . 1X , for example , if both thick and thin filament stiffness values decreased 10 fold ., Compared to our previous model 27 , the current model has an additional parameter representing the fraction of functional troponin molecules along thin filaments ( ρTn , i . e . the density of troponin capable of binding Ca2+ ) ., Throughout any single simulation ρTn is set at the beginning of each simulation via Monte Carlo algorithms that randomly ‘knocked out’ troponin complexes along each thin filament ., RUspan is another new model parameter , representing the length of thin filament near a troponin molecule that becomes available for myosin binding upon Ca2+ activation of a RU ( Figure 1B–D ) ., Because thin filaments are modeled as a discrete set of thin filament nodes or ‘actin binding sites’ along thin filaments , RUspan effectively takes on discrete values ( Table 1 ) ., Thus , ρTn and RUspan collectively establish the total number of actin nodes available to bind myosin XBs , simulating Ca2+-regulation by troponin and tropomyosin ., The kinetic state at a troponin site is applied to actin nodes within the distance of RUspan ., Therefore , when RUspan assumes the distance of a structural RU or 7 actins , each troponin will control the state of all actin nodes within a single RU ., However , as RUspan increases , there become regions for overlap where a single thin filament node may be influenced by multiple , adjacent troponins along one of the two helices making up a thin filament ., In these cases , we apply the most activated state ( i . e . TF3>TF2>TF1 ) between the two influential troponin molecules to represent the state of the thin filament node in question ., This spatially-explicit thin filament activation algorithm differs from our prior models 27 , 43 that effectively assumed a RUspan of 1 structural RU or 7 actins , which dictates no possible overlap between the ‘spatial regions of influence’ among adjacent RUs along the thin filament ., Thin filament activation and XB kinetics are controlled through two coupled , three-state cycles ( Figure 1A ) , similar to our previous model 27 ., Thin filament states represent troponin without Ca2+ bound ( TF1 ) , Ca2+ bound to troponin ( TF2 ) , and tropomyosin movement to a position permitting myosin binding with actin ( TF3 ) ., Thin filament states TF1 and TF2 represent inactivated RUs where myosin cannot bind with actin ., XB states are unbound ( XB1 ) , bound pre-power stroke ( XB2 ) , or bound post-power stroke ( XB3 ) ., XB1 represents an unbound state that does not bear force ., The bound states represent low-force ( XB2 ) and high-force ( XB3 ) bearing conformations ( although the specific force borne by any XB depends upon XB strain and stiffness ) ., Model kinetics are stochastically driven with Monte Carlo algorithms by drawing a random number ( n ) from a uniform distribution over the open interval ( 0 , 1 ) ., Any single transition probability ( pij ) from state i to state j depends upon the transition rate ( rij ) and time-step ( dt\u200a=\u200a1 ms ) : pij\u200a=\u200arijdt ., Transition probabilities are calculated each time-step to determine whether the Markov process underlying behavior of each node undergoes a forward transition , reverse transition , or remains as is: ( 2 ) Basic thin filament transition rates are listed in Table 3 and position-dependent XB transition rates are shown in Figure 6 for kxb\u200a=\u200a3 pN/nm ., A state transition will occur within a single time-step if pij≥1 , as would occur for basic model parameters rt , 12 or rt , 13 if Ca2+ exceeded 2 mM ( pCa≈1 . 7 ) ., If corresponding thick and thin filament nodes representing an attached XB become unfavorably aligned a number of XB transitions could take place within a single time-step ( if rx , ij ( x ) >1000 s−1in Figure 6 ) ., Within any simulation where cooperative thin filament activation kinetics were implemented , thin filament transition rates varied with the state of neighboring thin filament or XB nodes within an adjacent RU ( Figure 1A ) ., This introduces kinetic forms of cooperativity where rt , 12 and rt , 23 take on one of two values: their basic value if neighboring RUs are in state TF1 or TF2 and/or neighboring actin nodes within an adjacent RU do not have a XB attached , or their cooperative value if neighboring RUs are activated to state TF3 and/or neighboring actin nodes within adjacent RUs have a XB attached ( Table 3 ) ., This creates pairs of ‘source-target’ cooperativity ( Figure 1A ) where behavior within adjacent RUs becomes the source of the cooperativity ( either states TF3 , XB2 , or XB2 ) , which can augment thin filament activation of the RU in question ( either rt , 12 and/or rt , 23 become the target ) ., This approach permits the relative strength of these pathways to increase , allowing us to examine sources of cooperativity stemming from proximal , activated RUs or proximal bound XBs ., These kinetic forms of cooperativity can be combined from any RU-RU or XB-RU cooperative pathway outlined by the dashed arrows in Figure 1A , where individual pathways use TF3 , XB2 , or XB3 as the sources that can target RU activation rates rt , 12 and/or rt , 23 together or in combination ( which we simply refer to as the RU target ) ., Therefore , individual pathwa

Introduction, Results, Discussion, Materials and Methods

Striated muscle contraction is a highly cooperative process initiated by Ca2+ binding to the troponin complex , which leads to tropomyosin movement and myosin cross-bridge ( XB ) formation along thin filaments ., Experimental and computational studies suggest skeletal muscle fiber activation is greatly augmented by cooperative interactions between neighboring thin filament regulatory units ( RU-RU cooperativity; 1 RU\u200a=\u200a7 actin monomers+1 troponin complex+1 tropomyosin molecule ) ., XB binding can also amplify thin filament activation through interactions with RUs ( XB-RU cooperativity ) ., Because these interactions occur with a temporal order , they can be considered kinetic forms of cooperativity ., Our previous spatially-explicit models illustrated that mechanical forms of cooperativity also exist , arising from XB-induced XB binding ( XB-XB cooperativity ) ., These mechanical and kinetic forms of cooperativity are likely coordinated during muscle contraction , but the relative contribution from each of these mechanisms is difficult to separate experimentally ., To investigate these contributions we built a multi-filament model of the half sarcomere , allowing RU activation kinetics to vary with the state of neighboring RUs or XBs ., Simulations suggest Ca2+ binding to troponin activates a thin filament distance spanning 9 to 11 actins and coupled RU-RU interactions dominate the cooperative force response in skeletal muscle , consistent with measurements from rabbit psoas fibers ., XB binding was critical for stabilizing thin filament activation , particularly at submaximal Ca2+ levels , even though XB-RU cooperativity amplified force less than RU-RU cooperativity ., Similar to previous studies , XB-XB cooperativity scaled inversely with lattice stiffness , leading to slower rates of force development as stiffness decreased ., Including RU-RU and XB-RU cooperativity in this model resulted in the novel prediction that the force-Ca2+ relationship can vary due to filament and XB compliance ., Simulations also suggest kinetic forms of cooperativity occur rapidly and dominate early to get activation , while mechanical forms of cooperativity act more slowly , augmenting XB binding as force continues to develop .

In striated muscle myosin binds to actin and converts chemical energy from ATP hydrolysis into force , work , and power ., Myosin cross-bridge binding is regulated by Ca2+ and the thin filament proteins troponin and tropomyosin ., Cooperative interactions between actin , myosin , troponin , and tropomyosin greatly influence spatial and kinetic properties of thin filament activation , thereby affecting muscle mechanics and contractility ., Such cooperative interactions are complex and individual contributions from the different contractile and regulatory proteins are difficult to separate experimentally ., However , a few theoretical models have explored interactions between the spatial , kinetic , and mechanical processes that affect cooperative cross-bridge binding to actin ., Building on our prior spatially-explicit computational models , we investigated the relative contributions of thin filament regulatory proteins and cross-bridges to cooperatively amplify skeletal muscle force production ., We find that Ca2+-dependent contraction in skeletal muscle is dominated by neighboring regulatory protein interactions along the thin filament , while cross-bridge binding is critical for maintaining or stabilizing thin filament activation as force develops ., Moreover , we reveal that variations in filament and cross-bridge stiffness can alter Ca2+-sensitivity and cooperativity of skeletal muscle force production ., In conclusion , these simulations show that multiple cooperative mechanisms combine to produce physiological force responses measured from muscle cells .

biotechnology, bioengineering, biological systems engineering, biology, computational biology, biophysics, engineering

journal.pcbi.1006052

Epigenetic regulation of cell fate reprogramming in aging and disease: A predictive computational model

Aging is associated with profound changes in the epigenome involving large disturbances of the epigenetic landscape and genome architecture 1 , 2 ., Studies in model organisms have not only revealed the complex changes occurring in chromatin structure and functioning during aging , but also the remarkable plasticity of age-associated epigenetic marks 3–5 ., Thus , whereas epigenetic alterations in DNA methylation , post-translational modification ( PTM ) of histones and chromatin remodelling are considered highly conserved hallmarks of aging 4 , 6 , the ability of cellular reprogramming-driven epigenetic remodelling to ameliorate age-associated phenotypes has been described recently ., This finding unequivocally supports the causative role of epigenetic dysregulation as a driver of aging 7 ., The reversible nature of epigenetic regulation of aging is receiving increasing attention as it might offer a revolutionary strategy to simultaneously delay or reverse a spectrum of diseases , including cancer , clustered in older individuals 8 , 9 ., A mechanistic understanding of the dependence and inter-relationship between aging and the functional status of specific epigenetic modifiers , for example histone demethylases ( HDMs ) and histone deacetylases ( HDACs ) , is largely lacking ., There is an increasing awareness of the relationship between epigenetic modifiers and metabolism ., Common metabolites of intermediary metabolism , such as acetyl-CoA , NAD+ , α-ketoglutarate , succinate , FAD , ATP or S-adenosylmethionine , drive epigenetic processes by directly regulating epigenetic modifiers ., The usage of these intermediates as substrates and regulators of chromatin-modifying enzymes provides a direct link between the metabolic state of the cell and epigenetics 10–17 ., However , it remains intriguing how aging-related changes in cellular metabolism ( e . g . , loss of NAD homeostasis 18–20 ) might control the layers of epigenetic instructions that influence cell fate without involving changes in the DNA sequence ., The capacity of the chromatin structure to affect cellular identity and cellular state transitions can differ as a function of metabolic conditions that change during aging ., However , the possibility that cellular aging might result from the stochastic translation of metabolic signals into cellular epigenetic states has not been formally evaluated ., In this paper , we explore the causative relationship between cofactor ( e . g . metabolite ) variability and chromatin modification state underpinning the aging-associated loss of epigenetic resilience , which leads to a gain of more plastic cell and tissue features ., This fact might predispose aging tissues to cancer 21 , 22 ., To this end , we generated an ensemble of epigenetic regulation ( ER ) systems by means of Approximate Bayesian Computation ( ABC ) whose heterogeneity reflects the inhomogeneous abundance of cofactors used by epigenetic modifiers ., By analysing the robustness of ER systems in response to the regulation of HDM and HDAC activity , we present a model of ER capable of formulating strategies aimed at modifying the aging process and the aging-dependency of cancer , based on the control of epigenetic resilience and plasticity ., Recent advances in experimental determination of the mechanisms of ER have triggered an interest in developing mathematical models capable of reducing their intrinsic complexity to essential components such as ER of gene expression 17 , 23–27 and epigenetic memory 24 , 25 , 27–32 ., For comprehensive reviews , we refer the readers to 25 , 27 ., In order to put our model into context , we briefly summarise the current state of the art in ER modelling ., Models of ER were originally formulated in order to shed light onto the mechanisms of epigenetic memory; since DNA during cell cycle is duplicated and , therefore , the epigenetic marks diluted , early ER models were aimed at explaining how epigenetic-regulatory states remain stable upon cell division and transmitted to daughter cells ., Such models must satisfy two essential properties , namely , they must be bistable , i . e . , each steady state corresponding to an alternative epigenetic state , and the basin of attraction of such states must allow that large perturbations of the ER systems undergoing DNA replication should not change the epigenetic state thus allowing mitotic heritability 29 ., Dodd et al . 28 developed the first of such ER models ., The authors considered a region of DNA consisting of N nucleosomes , each assumed to be in either of three states , namely unmodified ( U ) , methylated ( M ) , and acetylated ( A ) ., Because modifying and de-modifying enzymes carry out nucleosome modifications and removal of marks , a crucial ingredient of the model by Dodd et al . 28 is that histone-modifying enzymes are recruited by modified nucleosomes , thereby providing the necessary positive feed-back for the system to be bistable ., However , recruitment based on next-neighbours interactions is not enough to produce robust bistability ., Long-range correlations are necessary ., The model by Dodd et al . 28 has been modified and extended in several ways 31 ., Sneppen and Dodd have successfully applied the same ideas 32 to modelling the patterns of epigenetic regulation in CpG islands 33 ., Another interesting feature of the model developed by Sneppen and Dodd 31 is that medium-length correlations are provided by the size of nucleosomes , which allows relaxing the requirement for recruited demethylation ., Angel et al . 30 have proposed an ER model to explain quantitative epigenetic control associated with the phenomenon of vernalisation , i . e . the perception and epigenetic memory of a period of cold temperatures to initiate flowering later ., This model is capable of reproducing both the patterns of flowering locus C ( FLC ) and the quantitative dependence with respect to the duration of the exposition to low temperatures ., Besides the issue of maintaining stable epigenetic memory , recent efforts have been dedicated to the study of the regulation of epigenetic modifications by transcription factors 23 , 26 ., Based on the experimental observation that transcription factors ( TFs ) can recruit histone-modifying enzymes , Sneppen et al . 23 proposed a model where transcription factors are coupled to ER ., A similar approach , although with rather significant differences , has been recently proposed by Berry et al . 26 ., An essential feature of this model is the proposed feedback between transcription and epigenetic chromatin modification: activation of transcription depends on the balance between positive and negative modifications , and , in turn , each passage of RNA polymerase II , which is modelled as a discrete event , causes demethylation ( see 26 for details ) ., An important feature that distinguishes this model from its predecessors is the assumption of next-neighbour recruitment as exclusively opposed to long-distance recruitment ., Bintu et al . 24 have recently proposed a more phenomenological ER model capable of explaining experimental data obtained by using a reporter gene that expresses a fluorescent protein with induced recruitment of a number of epigenetic-modifying enzymes ., The model by Bintu et al . 24 considers active , reversible silent , and irreversible silent states and is able to predict the rates of transition between states ., The stochastic model of epigenetic regulation is formulated in terms of the associated Chemical Master Equation ( CME ) , which , in general , is given by:, ∂ P ( X , t ) ∂ t = ∑ i ( W i ( X - r i ) P ( X - r i , t ) - W i ( X ) P ( X , t ) ) ( 1 ), where X = ( X1 , … , Xn ) is the vector containing the number of molecules of each molecular species at time t , Wi ( X ) is the transition rate corresponding to reaction channel i and ri is a vector whose entries denote the change in the number of molecules of each molecular species when reaction channel i fires up , i . e . P ( X ( t + Δt ) = X ( t ) + ri|X ( t ) ) = Wi ( X ) Δt ., Our model ( see Table 1 ) is based on the stochastic models by Dodd et al . 28 and Menéndez et al . 34 ., Dodd et al . 28 consider that direct transitions between M and A are very unlikely ., Instead , they assume that transitions occur in a linear sequence given by M ⇌ U ⇌ A . They further put forward the hypothesis that such nucleosome modifications are of two types , namely , recruited and unrecruited ., Mathematically , recruited modifications are represented by non-linear dependence on the number of M-nucleosomes and A-nucleosomes of the corresponding transition rates ( see Table 1 ) ., Specifically , the reactions involved in our model are: All these reactions can be both recruited or unrecruited ., The associated reactions rates are reported in Table 1 ., We consider the scenario where both hyper- ( hypo- ) abundance of A ( M ) marks allows for genes to be expressed , insofar the associated transcription factors are present 10 ., On the contrary , we associate hypo- ( hyper- ) abundance of A ( M ) marks with silent states where genes are not expressed even in the presence of the appropriate transcription factors ., We here focus on the conditions for bistability to arise and the robustness of the associated open and closed states particularly in connection with the abundance or activity of HDMs and HDACs ., Our aim is to analyse the effects of varying the concentration of these enzymes as well as possible synergies between them ., In more detail , we focus our analysis on plastic behaviour of the epigenetic regulatory states when the activity of histone-modifying enzymes ( HMEs ) is down-regulated against the background of heterogeneity due to variability in the pool of cofactors for chromatin-modifying enzymes ., We proceed by first defining a base-line scenario ( which we categorise as normal cell ) in which the associated epigenetic regulatory system is such that , for average values of HDM and HDAC activities , the differentiation-promoting gene ER is open and the pluripotency-promoting gene ER is closed ., We then proceed to generate an ensemble of ER systems that satisfy the requirements imposed by this base-line scenario; the necessary variability to generate this ensemble is provided by heterogeneity in abundance of epigenetic cofactors ., Analysis of this ensemble reveals that the requirements of the base line scenario restrict the values of a few parameters only , leaving ample flexibility to fix the rest of them ., This behaviour is typical of the so-called sloppy models 35 , where available data constrains a limited number of parameters ( or parameter combinations ) , the system being robust to the choice of a large number of model parameters ., In our case , this feature is absolutely essential since , nested within this heterogeneous ensemble of ER systems , there exists a sub-ensemble of plastic ER systems ., In order to gain some insight into the behaviour of the stochastic ER model , we analyse its mean-field limit regarding time scale separation and the quasi-steady state approximation ., For a full account of the technicalities we refer the reader to our previous work 36 , 37 ., The mean-field equations , which describe the time evolution of the ensemble average of the variables Xi , associated to the stochastic system with rates given in Table 1 are:, d Q i d t = ∑ j = 1 16 r j , i W j ( Q ) ( 2 ), where Q is a vector whose entries , Qi , are Qi ≡ 〈Xi〉 ., In order to proceed further , we assume that the variables describing the system are divided into two groups according to their characteristic scales ., More specifically , we consider the situation where the subset of chemical species Xi , with i = 1 , 2 , 3 , scale as Xi = Sxi , where xi = O ( 1 ) , whilst the remaining species are such that Xi , with i = 4 , 5 , 6 , 7 , scale as Xi = Exi , where xi = O ( 1 ) ., Key to our approach is the further assumption that S and E must be such that ϵ = E S ⪡ 1 ., The averaged variables , Qi , are similarly divided into two groups: slow variables , i . e . Qi = Sqi ( i = 1 , 2 , 3 ) , and fast variables , i . e . Qi = Eqi ( i = 4 , 5 , 6 , 7 ) ., Under this rescaling , we define the following scale transformation for the transition rates in Table 1: Wj ( Q ) = k4S2Eωj ( q ) ., We further rescale the time variable so that a dimensionless variable , τ , is defined as τ = k4SEt ., It is now straightforward to verify that , upon rescaling , the mean-field equations become:, d q i d τ = ∑ j = 1 16 r j , i ω j ( q ) , i = 1 , 2 , 3 , ( 3 ) ϵ d q i d τ = ∑ j = 1 16 r j , i ω j ( q ) , i = 4 , 5 , 6 , 7 ., ( 4 ), with ϵ = E/S ., If ϵ = E/S ≪ 1 holds , Eqs ( 3 ) and ( 4 ) naturally display multiple scales structure , which we will exploit to simplify our analysis by means of a quasi-steady state approximation ( QSSA ) 38 , which is given by:, d q 1 d τ = e H D M ( κ 1 + q 3 ) ( κ 3 + κ 6 q 3 ) q 2 ( κ 2 + κ 3 ) + ( κ 1 + q 3 ) q 2 + ( κ 5 + κ 6 ) q 3 + e H D A C ( κ 9 + κ 12 q 2 ) ( κ 11 + κ 14 q 2 ) q 3 ( κ 10 + κ 11 ) + ( κ 9 + κ 12 q 2 ) q 3 + ( κ 13 + κ 14 ) q 2 - ( κ 8 q 2 + κ 7 + κ 16 q 3 + κ 15 ) q 1 ( 5 ), d q 2 d τ = - e H D M ( κ 1 + q 3 ) ( κ 3 + κ 6 q 3 ) q 2 ( κ 2 + κ 3 ) + ( κ 1 + q 3 ) q 2 + ( κ 5 + κ 6 ) q 3 + ( κ 8 q 2 + κ 7 ) q 1 ( 6 ), d q 3 d τ = - e H D A C ( κ 9 + κ 12 q 2 ) ( κ 11 + κ 14 q 2 ) q 3 ( κ 10 + κ 11 ) + ( κ 9 + κ 12 q 2 ) q 3 + ( κ 13 + κ 14 ) q 2 + ( κ 16 q 3 + κ 15 ) q 1 ( 7 ), q 4 = e H D M κ 2 + κ 3 + ( κ 5 + κ 6 ) q 3 ( κ 2 + κ 3 ) + ( κ 1 + q 3 ) q 2 + ( κ 5 + κ 6 ) q 3 ( 8 ), q 5 = e H D M ( κ 1 + q 3 ) q 2 ( κ 2 + κ 3 ) + ( κ 1 + q 3 ) q 2 + ( κ 5 + κ 6 ) q 3 ( 9 ), q 6 = e H D A C κ 10 + κ 11 + ( κ 13 + κ 14 ) q 2 ( κ 10 + κ 11 ) + ( κ 9 + κ 12 q 2 ) q 3 + ( κ 13 + κ 14 ) q 2 ( 10 ), q 7 = e H D A C ( κ 9 + κ 12 q 2 ) q 3 ( κ 10 + κ 11 ) + ( κ 9 + κ 12 q 2 ) q 3 + ( κ 13 + κ 14 ) q 2 ( 11 ), where the re-scaled parameters κj are defined in Table 2 , and the conservation laws q4 ( τ ) + q5 ( τ ) = eHDM and q6 ( τ ) + q7 ( τ ) = eHDAC hold ., These conservation laws account for the fact that the total number of enzyme molecules , i . e . the enzyme molecules in their free form and those forming a complex must be constant ., Hence , the quantities eHDM and eHDAC are defined as e H D M = z 0 E and e H D A C = v 0 E , respectively , where z0 and v0 are the numbers of HDM and HDAC enzyme molecules , respectively ., E is the characteristic scale ( i . e . average ) of abundance of the histone-modifying enzymes which , for simplicity , has been taken to have the same value for both HDMs and HDACs ., This result opens interesting avenues to investigate , since both oncometabolic transformation and aging appear to reduce the number of both types of enzymes ., Our theory thus allows us in a natural manner to explore the effects of these anomalies on the stability of epigenetic regulatory states ., We first focus on a bifurcation analysis of the mean-field QSSA Eqs ( 5 ) – ( 11 ) , to investigate the qualitative behaviour of the ER system as the relative abundances of HDMs and HDACs are varied ., Results are shown in Fig 3, ( a ) and 3, ( b ) ., In particular , the phase space of both ER systems obtained by varying the parameters eHDM and eHDAC ., Both these diagrams display three differentiated regions: one in which the only stable steady-state is the one associated with a silenced gene , another one in which the only stable steady-state is the corresponding to an open gene , and a third one where the system is bistable ., Fig 3, ( a ) is associated with the differentiation-promoting gene , and Fig 3, ( b ) corresponds to the pluripotency-promoting gene ( parameters as per Table A , Table B in S1 File , respectively ) ., In order to clarify the three regions ( open , closed and bistable ) displayed in Fig 3, ( a ) , a 3D plot is shown in Fig 4, ( a ) , where the vertical axis shows the level of positive marks ( q3 ) ., This plot shows that the system dysplays bistable behaviour: depending on the parameter values eHDM and eHDAC , the system may be both in the open state ( high levels of q3 , top of the plot ) , or in the closed state ., Fig 4, ( b ) displays the projection on the xy-plane of the plot shown in Fig 4, ( a ) , where we can clearly identify the three regions described in Fig 3, ( a ) ., A more detailed picture of the situation illustrated in Figs 3, ( a ) and 4 is given in Fig 3 ( c ) , which shows the bifurcation diagram where eHDM , i . e . HDM concentration , is taken as the control parameter , whilst keeping eHDAC constant ., In particular we show the steady state value of q3 , i . e . the variable with positive marks , as a function of HDM concentration ., This allows to distinguish the three regions displayed in Fig 3, ( a ) ., We observe , that a decrease in HDM makes the corresponding gene inaccessible to the transcription machinery ( corresponding to the closed region , Fig 3, ( a ) ) ., As HDM concentration recovers , the system enters a bistable regime where both the active and silent states coexist ( region marked as bistable in Fig 3, ( a ) ) ., Further increase of the demethylase concentration drives the system through a saddle-node bifurcation , beyond which the only stable steady-state is the active state ( region labelled as open in Fig 3, ( a ) ) ., It is noteworthy that these results are in agreement with the oncometabolic transformation scenario associated with IDH mutations proposed by Thompson and co-workers 10 , 42 in which downregulation of HDM activity locks differentiation genes into a silenced state which favours reprogramming of the differentiated state of somatic cells into a pluripotent phenotype 17 ., The association between IDH mutations and cancer progression has been well established in the case of glioblastomas and acute myelogenous leukaemia 43–46 ., In Fig 3 ( e ) , we show the bifurcation diagram associated with fixing eHDM and varying eHDAC ., Within the scenario we are considering , i . e . the epigenetic regulation of a differentiation-regulating gene , reduced HDAC concentration recovers the base-line state where the epigenetic regulatory machinery is set to the open state ., As HDAC concentration recovers , the system enters a bistable regime in which both the active and silent states coexist ., Further increase in HDAC activity locks the system into the close chromatin state so that the gene is silenced ., This implies that reduced HDAC activity may help to rescue differentiation-regulating genes from the effects of IDH mutation ., Numerical results which verify the predictions of the bifurcation analysis are presented and discussed in Section I in S1 File ., We now proceed to analyse in more detail the implications of the bifurcation analysis , regarding robustness of the epigenetic regulatory state ., In Fig 3, ( d ) , which shows the phase diagram of both modes of epigenetic regulation ( differentiation- and pluripotency-promoting ) in the same phase space , the region between the solid red line and the dashed blue line represents the part of the phase space where the differentiation genes are open and the pluripotency genes are closed ( region marked as Normal Cell in Fig 3, ( d ) ) ., This sub-space is therefore associated with normal , differentiated somatic cells ., As we have previously shown 17 , efficient reprogramming requires both closed differentiation genes and open pluripotency genes ., Such situation is not viable under the scenario shown in Fig 3, ( d ) because these two conditions cannot hold simultaneously , which we therefore dubb as the refractory scenario ., By contrast , Fig 3 ( f ) corresponds to a plastic scenario , where , under appropriate conditions , cells become poised for reprogramming ., The main difference with the refractory scenario is the intersection between the bistability regions of both the differentiation regulator and the pluripotency gene ., In Fig 3 ( f ) , the regime where both bistability regions overlap is the one between the red solid line and the blue dashed line ( region marked as Rep . in Fig 3 ( f ) ) ., Within this region , since both genes are in the bistable epigenetic regulatory regime , it is possible to find the differentiation gene in its closed state and the pluripotency gene in the open state ., Such situation makes reprogramming much more likely to occur 17 and therefore we identify this feature of the phase space with plastic behaviour ., By driving the ER system into this region by means of down-regulation of both HDM and HDAC activity , cells become epigenetically poised to undergo reprogramming ., This is consistent with evidence according to which both oncometabolic transformation ( e . g . IDH mutation leading to down-regulation of JHDM activity 10 , 42 ) and aging ( e . g . down-regulation of SIRT6 5 , 19 , 47 ) induce loss of HDM and HDAC activity thus facilitating reprogramming ., In order to study the robustness of the refractory and plastic scenarios with respect to variations of the model parameters , kj ( see Table 1 ) , we first generate an ensemble of parameter sets θ = ( kj , j = 1 , … , 16 ) compatible with simulated data for the epigenetic regulation systems ., Such ensemble is generated using Approximate Bayesian Computation 48 ( for further details see Section III in S1 File ) ., Our approach is as follows ., For each mode of epigenetic regulation , we have generated simulated data ( denoted as “raw data” in Fig 2 ) using the stochastic simulation algorithm on the model defined by the transition rates Table 1 ., This simulated data will play the role of the experimental data , x0 , to which we wish to fit our model ., We consider two different data sets x 0 d and x 0 p , corresponding to the differentiation gene ( reaction rates from Table A in S1 File ) and the pluripotency gene ( reaction rates from Table B in S1 File ) , respectively ., Each data set consists of 10 realisations and 25 time points per realisation ., For each time point , ti , we consider two summary statistics: the mean over realisations , x ¯ ( t i ) , and the associated standard deviation , σ ( ti ) ., We then run the ABC rejection sampler method until we reach an ensemble of 10000 parameter sets which fit the simulated data , x0 , within the prescribed tolerances for the mean and standard deviation ., Fig 2, ( a ) & 2, ( b ) shows results comparing the reference ( raw simulated ) data to a sub-ensemble average ( full posterior distributions are shown in Fig . C in S1 File , differentiation-promoting gene , and Fig . D in S1 File , pluripotency-promoting gene ) ., The above procedure provides us with an ensemble of parameter sets that are compatible with our raw data , i . e . such that they fit the data within the prescribed tolerances ., The heterogeneity associated with the variability within this ensemble has a clear biological origin ., The rates kj are associated with the activity of the different enzymes that carry out the epigenetic-regulatory modifications ( HDMs , HDACs , as well as , histone methylases ( HMs ) and histone acetylases ( HACs ) ) , so that variation in these parameters can be traced back to heterogeneity in the availability of cofactors , many of them of metabolic origin such as NAD+ , which are necessary for these enzymes to perform their function ( as illustrated in Fig 1 ) ., We first consider the differentiation ER system ., In particular , we focus on the sub-ensemble of the 400 parameter sets that best fit the raw data ., Within such sub-ensemble , we proceed to evaluate the robustness of the different scenarios we study ., We consider that a particular scenario is sensitive to a specific parameter , kj , if its distribution is significantly different from the uniform distribution 49 ., We first analyse the base-line scenario for the epigenetic regulation of a differentiation-regulated gene , namely ,, ( i ) when eHDM = eHDAC = 1 , the regulatory system is mono-stable ( only the open chromatin state is stable ) , and, ( ii ) for eHDM < 1 , eHDAC < 1 there exists a region of bistability ., Out of all the parameter sets of the considered sub-ensemble , only 94 fulfill these requirements ., We refer to these as the viable set ., The remaining 307 are bistable at eHDM = eHDAC = 1 , and they will be referred to as the non-viable set ., In Fig 5 , we present the cumulative frequency distributions ( CFDs ) of each kj within both sets ., The rationale for looking into this is that the requirements upon system behaviour associated with both sets should reflect themselves on the corresponding CFDs ., Regarding the viable set , we seek to assess which kinetic constants have distributions which deviate in a statistically significant manner from the uniform distribution 49 ., Such parameters are deemed to be the essential ones for the ER system to exhibit the behaviour associated with the viable set ., We perform this analysis by means of the Kolmogorov-Smirnov ( KS ) test 50 , 51 , which we use to compare our samples with the uniform distribution ., According to such analysis , the kinetic constants k1 , k3 , k6 , k7 , k12 , k14 , and k16 are not uniformly distributed ( p-values are reported in Table E in S1 File ) ., Nested within the viable set , there are parameter sets which exhibit plastic behaviour , as characterised by a phase diagram as per Fig 3 ( f ) ., We thus continue by studying the plastic subset regarding both its frequency within the viable subset and further restrictions imposed on parameter variability ., We first check the number of the plastic parameter sets within the viable set relative to the pluripotency-gene ER system defined by Table D in S1 File ., Somehow unexpectedly , the plastic scenario is rare , but not exceptional: amongst the 94 parameter sets that we have identified as viable , 10 exhibit plasticity ( see Fig 5 for their CFDs ) ., Further restrictions on parametric heterogeneity imposed by the plastic scenario are analysed regarding the variation of the CFDs of kinetic constants when compared to those associated with the whole viable subset ., The results of KS analysis performed on the data shown in Fig 5 show that only the distributions of k1 ( associated with recruited demethylation ) , k9 ( unrecruited deacetylation ) , and k14 ( recruited deacetylation ) are significantly modified by the plasticity requirement ( p-values reported in Table G in S1 File ) ., From a more mechanistic perspective , we observe that , within the plastic set , the mass of the CFDs of k1 , k9 and k14 is displaced towards the large-value end of their intervals with respect to their behaviour within the full viable set ., In other words , k1 , k9 and k14 tend to be larger for plastic ER systems than for non-plastic , viable ER systems ., In essence , we observe that ER systems exhibiting plastic behaviour tend to have increased activity in the enzymes performing histone deacetylation ., This is consistent with recent evidence that aging decreases histone acetylation and promotes reprograming 5 , 19 , 47 ., The same analysis has been conducted regarding the ensemble of parameter values generated using ABC for the pluripotency gene ER system ( full posterior distribution in Fig . D in S1 File ) ., The results of this analysis are shown in Fig, 6 . Detailed analysis using the KS test of the ensemble viable pluripotency ER systems shows that k3 , k8 , k12 , k14 , k15 , and k16 are significantly constrained by the requirements of such scenario ( i . e . their CDF departs significantly from the uniform distribution , as shown by the p-values from Table F in S1 File ) ., We then move on to investigate further restrictions within the plastic set ., We observe that only the CDFs associated with k2 and k6 are significantly different ( p-values reported in Table H in S1 File ) ., In both cases , values of k2 and k6 associated with plasticity are larger than in the general viable population ., Both parameters are associated with demethylation activity ., Our ensemble analysis thus provides a rationale for the coupling between variations in the size of the pool of epigenetic cofactors and increased reprogramming in a heterogeneous cell population ., A notable case in point is provided by metabolic changes during aging: those cells where key metabolites such as acetyl-CoA and NAD+ are less abundant lose acetylation capability ( in our model , this is reflected through the dependence of histone-modifying enzyme activity on the concentration of these cofactors ) , leading to cells poised for reprogramming ., This analysis provides a rationale for a strategy to interfere with the epigenetic regulatory system , regarding the ability to either drive the system away from plastic behaviour or to drive it to the plasticity scenario , while keeping it functional ( i . e . within the restrictions of the base-line scenario ) ., An example illustrating the effectiveness of this strategy is shown in Fig, 7 . Consider the viable set of the ER differentiation-promoting gene , Fig 5 , which is neutral with respect to the value of k9: k9 remains uniformly distributed within the viable subset ., By contrast , when plasticity is required , the admissible values of k9 accumulate mostly towards the large-value end ., This suggests that decreasing the value of k9 might be a viable strategy to restore resilience ., To check this , we consider the parameter set , θ = kj/k4 , j = 1 , … , 16 , that gives rise to the plastic behaviour depicted in Fig 3 ( f ) ( Table C in S1 File , for the differentiation-promoting gene ) ., We then analyse the effect of modifying the value of k9 for the differentiation-promoting gene on system behaviour ., The new parameter set , θ ′ = k j ′ / k 4 , j = 1 , … , 16 , is such that k 9 ′ = k 9 / 4 and k j ′ = k j for all j ≠ 9 ( kj values as per Table C in S1 File ) ., Parameter values for the pluripotency gene remain unchanged ( as per Table D in S1 File ) ., The corresponding phase space is shown in Fig 7, ( a ) ., We observe that by reducing deacetylase activity in this fashion , the ER system reverts to resilient behaviour ., This suggests that , by regulating the abundance of cofactors associated with ( de ) acetylation , we can drive the system off the plastic regime into the base-line behaviour ., Similarly , we can seek for complex , combined strategies to increase the robustness of plastic behaviour ., An example of such strategy is shown in Fig 7, ( b ) ., Based on the results of the KS test for the differentiation-promoting gene , we observe that deacetylation-related rates k9 and k14 are significantly increased in plastic scenarios ., Taking parameter sets from a resilient scenario ( Tables A & D in S1 File , which lead to a combined phase diagram qualititatively similar to that shown in Fig 3, ( d ) ) and modifying k9 and k14 for the differentiation-promoting gene so that k 9 ′ = 3 k 9 and k 14 ′ = 3 k 14 while keeping all the others at the same value , the resulting ER system corresponds to a plastic system ., Futhermore , this combined strategy results in more robust plasticity ( as compared to e . g . the case shown in Fig 3 ( f ) ) , as measured by the area of the phase space region where reprogramming is feasible ., This indicates that by combining the strategies suggested by the statistical analysis of the plastic sub-ensemble , we can find conditions for optimal conditions to achieve robust reprogramming ., This , in turn , highlights the importance of cofactor levels , since as it has been shown in Fig 7 , depending on its availability , the same ER system can be driven to the plastic or resilient state ., These strategies require close attention to be payed to the correlations between parameters ., Parameters in complex systems biology models exhibit strong correlations which confer the system with essential properties such as sloppiness , which refers to the property exhibited by many multi-parameter systems biology models , whereby the system’s behaviour is insensitive to changes in parameter values except along a small number of parameter combinations 35 ., In order to quantify such corr

Introduction, Materials and methods, Results, Discussion

Understanding the control of epigenetic regulation is key to explain and modify the aging process ., Because histone-modifying enzymes are sensitive to shifts in availability of cofactors ( e . g . metabolites ) , cellular epigenetic states may be tied to changing conditions associated with cofactor variability ., The aim of this study is to analyse the relationships between cofactor fluctuations , epigenetic landscapes , and cell state transitions ., Using Approximate Bayesian Computation , we generate an ensemble of epigenetic regulation ( ER ) systems whose heterogeneity reflects variability in cofactor pools used by histone modifiers ., The heterogeneity of epigenetic metabolites , which operates as regulator of the kinetic parameters promoting/preventing histone modifications , stochastically drives phenotypic variability ., The ensemble of ER configurations reveals the occurrence of distinct epi-states within the ensemble ., Whereas resilient states maintain large epigenetic barriers refractory to reprogramming cellular identity , plastic states lower these barriers , and increase the sensitivity to reprogramming ., Moreover , fine-tuning of cofactor levels redirects plastic epigenetic states to re-enter epigenetic resilience , and vice versa ., Our ensemble model agrees with a model of metabolism-responsive loss of epigenetic resilience as a cellular aging mechanism ., Our findings support the notion that cellular aging , and its reversal , might result from stochastic translation of metabolic inputs into resilient/plastic cell states via ER systems .

Cell reprogramming , a process that allows differentiated cells to re-acquire stem-like properties , is increasingly considered a critical phenomenon in tissue regeneration , aging and cancer ., In light of the importance of metabolism in controlling cell fate , we designed a computational model capable of predicting the likelihood of cell reprogramming in response to changes in aging-related metabolites ., Our predictive mathematical model improves our understanding of how pathological processes that involve changes in cell plasticity , such as cancer , might be accelerated or attenuated by means of metabolic reprogramming .

cell physiology, medicine and health sciences, enzymes, cancer risk factors, enzymology, cell metabolism, oncology, stem cells, enzyme metabolism, epigenetics, enzyme chemistry, chromatin, cell potency, chromosome biology, proteins, animal cells, gene expression, pluripotency, biochemistry, cell biology, genetics, biology and life sciences, cellular types, cofactors (biochemistry), aging and cancer

journal.pgen.1004845

Association Mapping across Numerous Traits Reveals Patterns of Functional Variation in Maize

Natural phenotypic variation arises from a combination of genetic effects , environmental effects , and gene-by-environment interactions ., A major goal of modern genetics is to tease apart these components , and especially to identify the genetic loci that govern variation in traits ., In the past decade , genome-wide association studies ( GWAS ) have become a major tool to advance our understanding of genetic variation ., While many genome-wide association studies ( GWAS ) focus on disease phenotypes , especially in humans ( e . g . , 1–5 ) , it is also important to identify the genetic nature of normal functional variation in populations—that is , all genetic variation which has a discernible phenotypic effect ., There is also increasing evidence that differences in gene regulatory regions plays a significant role in functional variation 6–8 , although the exact balance between regulatory variation versus protein-coding variation is still unsettled ., Because of the ability to create controlled crosses , model organisms provide powerful platforms to dissect this natural genetic variation ., In recent years , large artificial populations have been created using several different organisms to leverage this power to dissect genetic traits ( e . g . , the mouse Collaborative Cross 9 and the Arabidopsis Multiparent Advanced Generation Intercross population 10 ) ., Currently , the largest such population is the maize Nested Association Mapping ( NAM ) population 11 ., Maize is an excellent genetic model for understanding natural variation due to the large phenotypic and genetic diversity available in its collections ., NAM was designed to capture a large fraction of this variation by crossing 25 diverse founder lines to the reference line , B73 , and generating 200 recombinant inbred lines ( RILs ) from each cross 11 ., The hierarchical design of NAM provides both the high power of traditional linkage analysis and the high resolution of genome-wide association ., We leveraged the strengths of the NAM population to perform high-resolution GWAS across 41 diverse phenotypes to identify the general patterns of functional variation in maize ., These traits were gathered from several individual studies on the NAM population ( Table 1 ) and span the range of relatively simple metabolic traits up to highly complex traits such as height and flowering time ., Our intent was not to re-identify regions influencing any specific trait , but rather to determine properties that make variants in general more likely to have a functional impact ., We expect to have very high resolution for these hits because of the speed with which linkage disequilibrium ( LD ) decays in maize ., An empirical calculation of LD decay in NAM shows that most LD decays to below background levels within 1 kilobase of a given polymorphism , though the variance is large since some alleles are segregating in only one or two families ( S1 Figure ) ., Due to this rapid LD decay , the high density of polymorphisms we used , and the high statistical power gained by using the NAM population , we expect that many of the polymorphisms we identified will be extremely close ( within a few kb ) to the causal polymorphism , and in many cases may even be the causal polymorphisms themselves ., We find that a large amount of functional variation is located outside of protein-coding genes , presumably in regulatory regions , and that these non-genic variants often have large phenotypic effects ., We also find that genes identified by association analysis are enriched for regulatory functions and for paralogs; this latter implies that gene duplication followed by functional divergence ( e . g . , subfunctionalization or neofunctionalization ) is likely to be a strong driver of normal functional variation ., The majority of phenotype data in this analysis was taken from existing studies on the maize Nested Association Mapping population ( Table 1 ) 12–19 ., These existing phenotypes cover various plant architecture , developmental , and disease resistance traits ., In addition , we also obtained trait data for 12 different metabolites in leaves: Chlorophyll A , Chlorophyll B , Fructose , Fumarate , Glucose , Glutamate , Malate , Nitrate , Starch , Sucrose , Total amino acids , and Total protein ., ( Details of data acquisition are in the Methods section . ), An in-depth analysis of these metabolites and the variants associated with each of them is forthcoming ( Zhang et al . , in preparation ) ; for this paper , we used them primarily to expand our pool of available phenotypes ., Both raw metabolite data and best linear unbiased predictors ( BLUPs ) for each NAM line are included in S1 Dataset ., Single-nucleotide polymorphisms ( SNPs , also including short indels of <15 base pairs ) were taken from Maize Hapmap1 20 and Hapmap2 21 , for a total of 28 . 9 million segregating SNPs ., We also used the raw Hapmap2 read depth counts to identify ∼800 , 000 putative copy-number variants ( CNVs ) as done previously 21 ., These 29 . 7 million total segregating polymorphisms were then projected onto the 5 , 000 RIL progeny based on low-density markers obtained through genotyping-by-sequencing ( GBS ) 22 ., We then performed forward-regression GWAS to identify which of these variants associated with the different phenotypes ., Full details are in the Methods section; in brief , the forward-regression model iteratively scans the genome , each time adding only the most significant SNP to the model until no SNPs pass the significance threshold ., We ran 100 such genome-wide associations for each trait with a random 80% of lines subsampled each time ., The random subsampling allows us to filter based on how many of these 100 iterations a SNP appears in , a measure of the strength and stability of the association ., After filtering to remove hits that showed up in <5 iterations 12 , 13 , we identified 4 , 484 SNPs and 318 CNVs that were significantly associated with at least one phenotype ., These variants are referred to as the “GWAS dataset” for the rest of this article , in contrast to the input dataset of ∼30 million variants ., The number of polymorphisms identified for each trait varies widely and broadly matches prior assumptions based on the genetic complexity of the traits ( Fig . 1 ) ., Comparing our results with those of published studies in NAM shows good agreement with the locations of known QTL ( S2 Figure ) ., To classify each polymorphism , we used the Ensembl Variant Effect Predictor ( VEP ) 23 to identify the potential effect of each SNP in both the input and GWAS datasets ., Since most SNPs are likely not causal but just linked to the causal polymorphism , these annotations serve primarily to identify the region a SNP lies in and the types of SNPs most frequently identified by GWAS across our dataset ., After classification , we analyzed the distribution of VEP classes and copy-number variants ( CNVs ) for enrichment in GWAS hits relative to the input dataset ( Fig . 2 ) ., Intergenic regions ( >5 kb away from the nearest gene ) are strongly depleted for GWAS hits , causing almost all other categories to show significant enrichment ( Fig . 2B ) ., Part of this depletion may be due to transposon activity in intergenic regions altering the physical location—and thus the projected genotype—of sequences in some founder lines ., After controlling for intergenic regions , both genic SNPs and CNVs are still strongly enriched for GWAS hits ( Fig . 2C ) ., This agrees with the recent findings of Schork et al . 24 , who found similar enrichment patterns of GWAS hits close to genes ., Of the enriched classes , large CNVs show the most enrichment , while the most enriched SNP category is for synonymous mutations ., Some of the enrichment for synonymous sites is probably due to synthetic associations 25 , 26 , where the signals from several low-frequency causal SNPs combine to make a nearby , higher-frequency SNP appear associated with the trait ., ( This is different from the normal situation in GWAS where the associated SNPs are assumed to be linked to causal loci that werent sampled but that would show up if they had been . ), Such associations are probably not the sole explanation for the enrichment of synonymous SNPs , however , because synonymous SNPs are also significantly enriched over intronic SNPs ( p\u200a=\u200a2 . 80×10−8 by Chi-square test ) despite having similar site frequency spectra ( S3 Figure ) and being in similar LD structures ( due to the small size of maize introns , which have a median size of only ∼150 base pairs in quality-filtered genes ) ., This implies a legitimate enrichment for synonymous SNPs ., Some ( and possibly most ) of that enrichment is probably due to linkage with nearby causal SNPs; this may also result in the enrichment of synonymous over intronic SNPs , since synonymous ones will on average still be in tighter LD with nonsynonymous SNPs than will those in introns ., The remainder of the enrichment is likely due to the ( unknown ) fraction that are causal themselves but act through mechanisms other than protein sequence ( e . g . , altering mRNA stability , protein binding sites , or local translation rates 27 ) ., Although genic regions are the most strongly enriched in GWAS , the majority ( ∼70% ) of our hits still fall outside of annotated genes , as defined by their transcriptional start and stop sites ., Plotting the distances from non-genic SNPs to the nearest gene on a log scale reveals a bimodal distribution , with a peak at ∼1–5 kb away from genes that is not reflected in the input dataset ( Fig . 3 ) ., This corresponds with likely positions of promoters and other short-range regulatory elements ., Finding enrichment at this scale provides evidence for the high resolution and biological relevance of the GWAS hits in this study ., The second peak , which follows the null distribution , probably reflects elements that are not correlated with gene distance ( e . g . , long-range regulatory elements , unannotated transcripts , etc . ) ., For example , using a list of 316 maize noncoding RNAs from Gramene ( available at http://ftp . gramene . org/release39/data/fasta/zea_mays/ncrna/ ) that were not included in the Ensembl annotations reveals that intergenic hits are significantly enriched for polymorphisms within 5 kb of these RNAs ( n\u200a=\u200a13 , expected\u200a=\u200a1 . 07 , p\u200a=\u200a1 . 3×10−10 by two-sided exact binomial test ) ., Alternatively , some of these “intergenic” hits may actually be tagging legitimate genes that are simply not present in the reference genome due to the high amount of presence-absence variation in maize 21 ., Identifying the nature of these hits should be possible as more information about the maize pan-genome becomes available ., We also determined the relative effect each polymorphism class has on phenotype ., We classified all SNP hits by whether they fell within genes ( genic ) , within 5 kb of a gene ( gene-proximal ) , or more than 5 kb away ( intergenic ) , and compared the variance explained among traits for these classes and for CNVs ( Fig . 4A ) ., Genic and gene-proximal SNPs explain the most unique variance , meaning the proportion of variance explained when the specified category is added last to a model ., However , examining the minor allele frequency ( MAF ) and effect size distributions for each class reveals a more complex picture ( Figs . 4B & 4C ) ., Both MAF and effect size strongly influence variance explained , and in our dataset they are negatively correlated ., Similar results were found in a previous study of inflorescence traits 12 ., This negative correlation is probably due to both biological factors ( e . g . , large-effect mutations are more likely to be detrimental to overall fitness 28 , 29 and thus kept at low frequency ) and also statistical limitations ( e . g . , GWAS can only identify rare variants if they have large effects ) ., At the extremes , intergenic variants have the largest median effect size but the lowest allele frequencies , while CNVs are the reverse ., Thus many large phenotypic effects tend to occur outside of genes ( presumably in regulatory elements , unannotated transcripts , or the like ) , but they also tend to be rare and so make only minor contributions to total variance explained ., This inverse relationship between allele frequency and effect size holds across polymorphism classes ( Fig . 5 ) , implying a general pattern across polymorphisms ., Since large-effect polymorphisms are exactly the sort of mutation breeders often look for in selecting germplasm for breeding programs , these data may prove useful for future breeding efforts ., Since the annotation of single nucleotides in genic regions is more straightforward than in intergenic regions , we also identified common characteristics of genes that were tagged by genic or gene-proximal GWAS hits ., First , an analysis of expression levels using RNA-seq data from the Maize Gene Atlas 30 reveals a small ( ∼20% ) but highly significant depletion of low-expressed genes ( p\u200a=\u200a1 . 30×10−22 by Mann-Whitney test and ≈0 by Kolmogorov-Smirnov test ) ( Fig . 6 ) ., The expression level of these genes is even lower than most transcription factors , which are themselves usually only expressed at a low level , and their depletion among GWAS hits may reflect a lower probability of such rarely expressed genes altering plant phenotype ., Second , Gene Ontology ( GO ) term analysis revealed significant enrichment ( ∼34% ) in terms relating to regulatory activity , especially protein kinase activity and transcription factor activity , and depletion ( ∼71% ) among several core metabolism and signaling terms ( Table S2 ) ., These terms are fairly broad , probably because the diverse phenotypes in this study make it so that the only terms that are significantly changed are those general enough to be involved across many different phenotypes ., Nonetheless , the enrichment of regulatory terms across such a broad phenotypic spectrum implies that changes in gene regulation are a frequent driver of functional variation ., Conversely , the depletion of core metabolic terms speaks to the difficulty of altering these functions without causing detriment to the organism ., The depletion in core metabolic terms is especially striking because the studied traits include 12 metabolic traits ., Finally , we found that genes with GWAS hits in their primary transcripts are ∼50% more likely to have a paralog than expected by chance ( 36 . 4% of 970 GWAS-hit genes vs 24 . 2% of 39 , 656 total genes in the maize AGPv2 filtered gene set; p\u200a=\u200a3 . 79×10−17 by two-sided exact binomial test and 1 . 06×10-17 by Fishers exact test ) ., Paralogous genes do not appear to have significant differences from non-paralogous genes in either allele frequency or LD structure , and the marginally lower density of SNPs in them would seem to disfavor their selection by GWAS , all other things being equal ( Fig . 7 ) ., Thus the enrichment for paralogous genes is probably due to the benefits of gene duplication , since having redundant copies of a gene allows one of them to more easily take on altered ( and phenotypically significant ) roles through either subfunctionalization or neofunctionalization 31 ., Also , we did a parallel analysis looking only at paralogs resulting from maizes most recent genome duplication to see if they followed a different distribution ., The resulting enrichment ratio and p-value are nearly identical to the analysis with all paralogs ( 30 . 7% paralogous in GWAS versus 20 . 0% in the maize filtered gene set , p\u200a=\u200a2 . 91×10−17 by exact binomial test ) , so we conclude that for this analysis the source of paralogs does not play a significant role ., Taken together , the large number and effect sizes of hits outside genes and the enrichment for copy-number variants indicate that while variation in gene sequence is important , a large portion of functional variation in maize probably stems from differences in copy number and gene regulation rather than in protein-coding sequence ., These results corroborate similar findings in other organisms 6–8 , indicating that this pattern will likely hold for many other species ., One caveat , however , is that our filtering for robust GWAS hits intrinsically skews the results toward more common alleles; rare variants may follow different patterns ., Also note that since intergenic regions were enriched for rare variants , we may still be underestimating their contribution to the various traits ., Our results also imply that the cost-saving measure of genotyping individuals by sequencing only the exome may be of limited utility for GWAS , at least for organisms like maize where LD decays rapidly ., This is in direct contrast with the conclusions of Li et al . 32 , who determined that 79% of the explained variation in their maize dataset could be encompassed by genic and promoter ( <5 kb upstream ) regions ., We suspect that this difference is chiefly due to choice of input polymorphisms ., Li et al . used ∼290 , 000 SNPs derived from RNA-seq data and ∼775 , 000 SNPs from Maize Hapmap1; the former is obviously biased toward genic regions , while the latter has a similar ( albeit smaller ) bias due to using methyl-sensitive restriction enzymes to construct genomic libraries 20 ., In contrast , the majority ( ∼92% ) of our input polymorphisms come from Maize Hapmap2 , where sequencing libraries were created by random shearing and thus show much smaller bias toward genic regions 21 ., Ultimately , the goal of modern crop genetics is to design crops for rapidly changing environments ., Doing so requires accurate information about which genomic regions contribute to trait qualities ., The fact that most of our hits ( 70% ) lie in poorly annotated regions outside of annotated genes and that these hits often have large phenotypic effects argues for an urgent need to identify the genetic features in these regions ., Such efforts are already underway for humans and several model animals 33–35; similar work should be extended to plants and especially to important crops like maize ., The low cost of current sequencing would even make it possible to , for example , combine GWAS with expression profiling across several thousand individuals to identify both regulatory regions and their effects on phenotype ., Identifying these features and including them in prediction models will further not only basic genetics , but also help breeders craft better crops and help improve food security for the global population ., Unless otherwise stated , all analyses were performed with in-house bioinformatics pipelines written in SAS , R , Perl , or Java ., Source code for the various scripts is included in S3 Dataset ., All analyses were done with using the maize B73 genome ( version AGPv2 ) as reference ., The maize filtered gene set was taken from maizesequence . org and is available at ftp://ftp . gramene . org/pub/gramene/maizesequence . org/release-5b/filtered-set/ZmB73_5b_WGS_to_FGS . txt ., verified 13 Oct 2014 Phenotype data for GWAS analysis was taken from previous studies by our lab and others on a variety of traits , along with the metabolite data included herein ( Table 1 ) ., In the majority of cases phenotypic data had already been processed by fitting a joint-linkage model 45 with 1 , 106 high-confidence SNP markers across NAM ., Chromosome-specific residuals were then determined by fitting a model that included as covariates all identified quantitative trait loci ( QTL ) except those on the given chromosome ., For traits without precomputed residuals , the same process was followed but with an updated list of ∼7 , 000 SNPs derived from genotyping-by-sequencing 22 ., All genotypes are available at http://www . panzea . org; chromosome-specific residuals are included in S2 Dataset ., Forward-regression genome-wide association was then performed with the NamGwasPlugin in TASSEL version 4 . 1 . 32 46 ., This plugin was created specifically to run stepwise forward regression on the Maize NAM population , and takes as input the chromosome-specific residuals , a genetic map , anchor genotypes in the progeny , and founder genotypes to be imputed ., Each chromosome was analyzed separately for each phenotype via 100 forward-regression iterations , each of which excluded a random 20% of NAM lines to destabilize spurious associations 47 ., The cutoff for polymorphism inclusion in the model was a raw p-value <9 . 50×10−8 , which was empirically determined by permutation testing with the days to anthesis phenotype to correspond to a genome-wide Type I error rate of 0 . 01 ., The resample model inclusion probability ( RMIP ) 47 of each polymorphism was determined as the proportion of iterations in which a specific polymorphism was called as significant; only polymorphisms with an RMIP ≥0 . 05 are considered in this study ., The input SNPs were a union of all SNPs in Hapmap1 and Hapmap2 ., A small portion of SNPs are duplicated between the datasets—that is , they were independently discovered in both studies—but in almost every case they have different ( and sometimes conflicting ) allele calls ., Thus we made no attempt to merge such SNPs and instead let each original call be tested individually ., After running GWAS , we found a single case of ambiguity in determining which SNP had been chosen by the model , due to two SNPs having identical positions and allele codings ., In this case we retained both to maintain consistency with the input dataset ., Putative CNVs were determined by two methods ., First , Hapmap2 sequencing reads aligned to the maize genome were counted in 2 kb-windows and compared to a high-coverage B73 sample with edgeR 48 ., This procedure had been done previously 21 , and our analysis was primarily to update the results to a newer version of the Zea mays reference genome ( AGPv2 ) ., The B73 sample from Hapmap2 itself served as the null distribution to determine the cutoff corresponding to an empirical , genome-wide Type I error rate of 0 . 05 ., CNVs that had been previously identified within annotated genes by the same method 21 were also included in the analysis but with updated gene coordinates based on their stable Ensembl gene identifiers ., Independently , the mapped reads were also analyzed by CNVnator 49 to identify putative CNVs based on shifts in mean read depth across 500 bp bins ., Interestingly , although many CNVnator CNVs showed consistent segregation across the NAM founders , GWAS hits came almost exclusively from the edgeR-derived CNVs ., Looking at the characteristics of each , this disparity is probably due to two factors: ( 1 ) the edgeR-derived CNVs are generally much smaller than those found by CNVnator , and smaller CNVs have previously been shown to have more significant GWAS hits in this population 21; and ( 2 ) edgeR also detects many more CNVs than CNVnator to begin with , presumably because small CNVs are more common than large ones ., Since there were a total of three separate sources of CNVs in this analysis , a single genome region could potentially contribute to multiple CNVs and thus be tested multiple times per GWAS run ., The contribution to each set of CNVs will be different , however , since each one depends on all regions within its limits and is then collapsed to a single score of 0 or 1 ., Putative SNP effects were determined by running all 28 . 9 million SNPs through the Ensembl Variant Effect Predictor ( VEP ) 23 using a local copy of the Zea mays Ensembl database ( version 68 ) ., Since the VEP annotates effects relative to any gene model ( not just quality-filtered ones ) , it was run with both the “–most-severe” and “–per-gene” options to get lists of the worst overall effect per SNP and the worst per gene , respectively ., ( Note that the VEP considers that changing an existing amino acid is more severe than in-frame insertions and deletions , so small indels that do both get classified as “missense . ”, These make up <0 . 1% of the input polymorphisms and only 3 GWAS-hit ones , however , so altering the annotation would not significantly affect the results . ), The two results were then combined with in-house Perl scripts to create a list of the worst overall SNP effect with respect to only those genes in the Zea mays 5b . 60 filtered gene set ( available at http://www . gramene . org ) ., Polymorphisms classes were tallied for both the input SNPs dataset and for the GWAS-hit SNPs ., Using the input dataset as the null , we then removed any categories with <5 expected counts in the GWAS dataset ., Total counts in the remaining groups were then tested for significance by a Chi-square test using the Stats package in R 50 ., Individual categories were then tested for enrichment by a two-sided exact binomial test , also in R . Due to the possibility that linkage disequilibrium could distort the results from the above test , we also ran 1 million circular permutations of the hits to generate a null distribution of what would be expected by chance ., Circular permutation in this case refers to keeping the order of all elements intact while randomly changing the “start” location along the chromosome ., This maintains the structure of the original data while randomizing its relationship to genomic features ., The resulting counts formed a normal distribution , which was used to extrapolate the p-values in S1 Table ., Marginal variance explained by polymorphisms classes ( genic , gene-proximal , intergenic , and CNVs ) was calculated by fitting linear models to each trait and comparing the difference in variance explained ( adjusted R2 ) between a model with all identified SNPs and a model with all SNPs except those in the chosen category ., Standardized effect sizes for each polymorphism were determined by first taking all effect sizes the NAM-GWAS model identified for each trait and fitting an empirical cumulative distribution function with ecdf ( ) in R 50 ., This function was then used to determine the quantile of each effect ., Mean quantile scores were then calculated for each polymorphism that passed RMIP≥0 . 05 filtering ., Each point in the distribution thus represents a specific trait-polymorphism combination ., Gene Ontology term analysis was performed with agriGO 51 using all genes with GWAS hits within 5 kb of their annotated transcript ., Statistical analysis was performed in R 50 via a two-sided Fishers exact test with Benjamini-Yekutieli control of the false discovery rate ( FDR ) to analyze for both enrichment and depletion ., Maize paralogs were taken from an existing list 52 ( available at http://genomevolution . org/CoGe ) ., The number of genes with paralogs in the GWAS hit dataset was compared to those in the maize filtered gene set and significance of the difference tested by a two-sided exact binomial test in R 50 .

Introduction, Results, Discussion, Methods

Phenotypic variation in natural populations results from a combination of genetic effects , environmental effects , and gene-by-environment interactions ., Despite the vast amount of genomic data becoming available , many pressing questions remain about the nature of genetic mutations that underlie functional variation ., We present the results of combining genome-wide association analysis of 41 different phenotypes in ∼5 , 000 inbred maize lines to analyze patterns of high-resolution genetic association among of 28 . 9 million single-nucleotide polymorphisms ( SNPs ) and ∼800 , 000 copy-number variants ( CNVs ) ., We show that genic and intergenic regions have opposite patterns of enrichment , minor allele frequencies , and effect sizes , implying tradeoffs among the probability that a given polymorphism will have an effect , the detectable size of that effect , and its frequency in the population ., We also find that genes tagged by GWAS are enriched for regulatory functions and are ∼50% more likely to have a paralog than expected by chance , indicating that gene regulation and gene duplication are strong drivers of phenotypic variation ., These results will likely apply to many other organisms , especially ones with large and complex genomes like maize .

We performed genome-wide association mapping analysis in maize for 41 different phenotypes in order to identify which types of variants are more likely to be important for controlling traits ., We took advantage of a large mapping population ( roughly 5000 recombinant inbred lines ) and nearly 30 million segregating variants to identify ∼4800 variants that were significantly associated with at least one phenotype ., While these variants are enriched in genes , most of them occur outside of genes , often in regions where regulatory elements likely lie ., We also found a significant enrichment for paralogous ( duplicated ) genes , implying that functional divergence after gene duplication plays an important role in trait variation ., Overall these analyses provide important insight into the unifying patterns of variation in traits across maize , and the results will likely also apply to other organisms with similarly large , complex genomes .

genome-wide association studies, cereal crops, quantitative trait association studies, statistical analysis of genetic association, genomics, plant science, crop science, genome analysis, crops, maize, genetics, biology and life sciences, family-based association studies, plant genetics, computational biology, crop genetics, agriculture

journal.pntd.0004790

Performance Testing of PCR Assay in Blood Samples for the Diagnosis of Toxoplasmic Encephalitis in AIDS Patients from the French Departments of America and Genetic Diversity of Toxoplasma gondii: A Prospective and Multicentric Study

The protozoan Toxoplasma gondii is a cosmopolitan parasite that virtually infects all warm-blooded animals , including humans who become infected postnatally by ingesting tissue cysts from undercooked meat , consuming food contaminated with oocysts , or by accidentally ingesting oocysts from the environment 1 ., The genetic diversity of this parasite is limited to a few successful clonal lineages in North America , Europe , Africa and China but is considerably higher in tropical South America 2 ., In non-immunocompromised persons , toxoplasmosis is usually asymptomatic or limited to a mild symptomatology except in tropical South America ., The prevalence of acquired and congenital ocular toxoplasmosis is much higher in Brazil and Colombia than in another place in the world and the Amazonian toxoplasmosis is a disseminated infection that requires management in intensive care units even in otherwise healthy adults 3 , 4 ., There is more and more evidence that the greater severity of toxoplasmosis in South America results from poor host adaptation to the genetically diverse T . gondii strains from this region 5 ., Toxoplasmic encephalitis ( TE ) in patients with AIDS is a life-threatening disease mostly due to reactivation of Toxoplasma gondii cysts in the brain ., TE can be inaugural of AIDS in patients who are not aware of their HIV seropositivity , but poor compliance with cotrimoxazole prophylaxis in patients with CD4 cell counts <200/μL is the major event leading to TE 6 ., The incidence of TE in AIDS patients has greatly decreased since the introduction of HAART , but HIV-associated toxoplasmosis hospitalizations remain substantial , even in the United States 7 , 8 ., TE must be treated with specific anti-toxoplasmic therapy as soon as the diagnosis of TE is clinically and radiologically suspected 9 ., Cerebral biopsy showing T . gondii tachyzoites is the only way to make a definite diagnosis of TE but is rarely undertaken in AIDS patients at baseline ., The clinical and radiological response to specific therapy is still the gold standard for confirming a posteriori the diagnosis of TE in AIDS patients ., This presumptive diagnosis has important limitations since up to 40% of AIDS patients with suspected TE and treated with specific therapy could not have in fact TE 10 ., Laboratory investigations are considered not helpful in the diagnosis of TE ., The majority of patients have positive IgG and negative IgM against T . gondii , simply indicating that they acquired toxoplasmosis in the past , mostly during childhood ., However , the negative predictive value of a negative serologic testing for toxoplasmosis is high because it is estimated that < 3% of patients with AIDS have no demonstrable antibodies to T . gondii at the time of diagnosis of TE 11 ., The diagnostic performance of PCR tests in various biological samples , mostly CSF and blood , was regularly assessed since the early years of the AIDS pandemic 12 ., Blood samples are the only ones that can be easily obtained from the patients without invasive procedures ., Although specificity was high , the use of PCR testing in blood samples for the diagnosis of TE has been limited by its poor sensitivity in the studies conducted in Europe 12–16 ., A few studies have been conducted in tropical South America and the results of sensitivity were highly controversial 17–20 ., Considering that T . gondii strains from tropical South America have substantial genetic and pathogenic differences with those from USA and Europe , it is therefore important to re-evaluate the performance of the PCR assay in blood samples for the diagnosis of TE in AIDS patients from this region ., The main objective of this study was to evaluate the performance of real-time PCR assay in peripheral blood samples for the diagnosis of TE in AIDS patients from the French departments of America ., The French departments of America ( DFA ) are French tropical overseas departments that include French Guiana in mainland South America and the French West Indies islands of Martinique and Guadeloupe in the Caribbean ., The secondary objective was to collect and genotype T . gondii isolates from these patients ., This study was approved by the French Ethics Committee “Comité de Protection des Personnes du Sud-Ouest et Outre-Mer 4” on April 4 , 2008 , with the reference number cpp08-006a ., The Toxo-DFA study is registered in the ClinicalTrials . gov database ( Identifier: NCT00803621 ) ., All patients included in the study were adult and provided written informed consent ., The Toxo-DFA study is an epidemiological , prospective , and multicentric study to validate the real-time PCR assay in peripheral blood samples for the diagnosis of TE in AIDS patients from a tropical area ., It was conducted between September 16 , 2008 , and December 30 , 2011 , in four hospital centers of the French departments of America: two in French Guiana ( Cayenne and Saint-Laurent du Maroni ) and two in the French West Indies ( Fort de France in Martinique and Pointe à Pitre in Guadeloupe ) ., Study participants had to meet all of the following criteria: age >18 years , informed written consent , positive serologic test for HIV , and clinical and radiological suspicion of TE with start of specific antitoxoplasmic therapy ., Patients legally protected or uncovered by social insurance , or with a specific antitoxoplasmic therapy already initiated since 72h or more were excluded from the study ., The collection of data was done in an online secured case report form with CS-ONLINE from CAPTURE SYSTEM software ., Patients were assessed clinically at baseline , between days 6–8 , 15–21 , and 42–56 ., Neuroradiographic scans by computed tomography ( CT ) or magnetic resonance imaging ( MRI ) were performed at baseline , between days 15–21 , and 42–56 ., The gold standard for diagnosing TE was based on the clinical and radiological responses to specific antitoxoplasmic therapy after suspicion of cerebral toxoplasmosis ., A validation committee of independent experts reviewed and classified the cases in 4 categories ., TE was considered definite when there was a complete or significant clinical and radiological response to specific therapy ( with not necessarily disappearance of radiological and clinical lesions ) , and no elements for an alternative diagnosis ., TE was considered probable when radiological lesions were compatible but only partial improvement was observed with specific therapy ( due to non-optimal treatment or incomplete follow-up ) , and no elements for an alternative diagnosis ., Absence of TE was considered definite when there was no improvement or worsening of lesions with specific therapy or absence of T . gondii in cerebral biopsy samples and presence of elements for an alternative diagnosis ., Absence of TE was considered probable when there was no response to specific therapy and no elements for an alternative diagnosis ., Two different centers were involved in the laboratory investigations: the Limoges center in metropolitan France and the Cayenne center in French Guiana ., A Neighbor-Joining tree was reconstructed from microsatellite data to examine the relationships between strains collected from human cases of toxoplasmosis in South America and the Caribbean ., The tree was constructed with Populations 1 . 2 . 32 ( http://bioinformatics . org/populations/ ) based on Cavalli-Sforza and Edwards chord-distance estimator 26 and generated with MEGA 6 . 05 ( http://www . megasoftware . net/history . php ) software ., The software used for statistical analyses was SAS 9 . 3 and the significant threshold of the p value was 0 . 05 ( SAS Institute , Cary , USA ) ., Median and interquartile intervals were given for quantitative variables while qualitative variables were presented as sample size and percentages ., Nonparametric tests were used for comparing variables between patients with TE and those without TE: a Fisher exact test was performed for the qualitative variables and a Mann-Whitney test was used for the quantitative variables ., To evaluate the diagnostic performance of the PCR assay in detecting TE in peripheral blood samples from AIDS patients in the French West Indies and Guiana , the reliability and the validity of the test were assessed ., For these analyses , cases reviewed by the validation committee with definite or probable TE were coded positive and those with definite or probable absence of TE were coded negative ., The reliability of the PCR assay was estimated by the Cohen’s Kappa coefficient of agreement and its 95% confidence interval between results of the test in Cayenne and Limoges centers ., The validity of the PCR assay was estimated by the sensitivity and the specificity of the test in detecting definite and probable cases of TE that had been identified by the validation committee ., The 95% confidence intervals of sensitivity and specificity were estimated by the exact method ., A study of the false negative results was carried out to search explanatory factors according to the sample size of this sub-group by using a logistic regression model with the false negative status as the response variable and the potential associated factors as the explicative variables ., A total of 46 patients were included in this study: 17 in French Guiana ( 8 in Cayenne and 9 in Saint-Laurent du Maroni ) and 29 in the French West Indies ( 23 in Guadeloupe and 6 in Martinique ) ., The validation committee reviewed the cases as follows: 2 cases were not assessable because of insufficient data ( both included in the center of Saint Laurent du Maroni in French Guiana ) , 36 cases were classified in the TE group and 8 in the non-TE group ., In the TE group , 30 were classified as definite TE and 6 as probable TE ., In the non-TE group , 6 were classified as definite absence of TE and 2 as probable absence of TE ., The demographic , laboratory and clinical baseline characteristics of the 44 patients classified in TE and non-TE groups by the validation committee are available in Table, 2 . There was no statistical difference between patients with TE and those without TE with respect to the variables listed in Table 2 except for the place of birth and the results of T . gondii serology ., Two different classifications were used for clustering the 44 patients into two groups according to their place of birth ., In the first classification , the first group gathered the 29 patients who were born in the French West Indies and Guiana ( 6 in French Guiana , 17 in Guadeloupe , and 6 in Martinique ) while the 15 patients who were born elsewhere were put together in a second group ( 7 in Haiti , 3 in Brazil , 2 in Suriname , 1 in Dominica , 1 in Dominican Republic , and 1 in Spain ) ., Being born in the French West Indies and Guiana was significantly more common in patients without TE than in those with TE ( p = 0 . 04 ) because all patients without TE ( n = 8 ) were born in the French West Indies and Guiana ( Table 2 ) ., The second classification was based on geography with 32 patients born in the Caribbean ( 17 in Guadeloupe , 7 in Haiti , 6 in Martinique , 1 in Dominica , and 1 in Dominican Republic ) , 11 in South America ( 6 in French Guiana , 3 in Brazil , 2 in Suriname ) , and 1 in Europe ., According to this second classification , being born in the Caribbean was not statistically different between patients with TE and those without TE ( Table 2 ) ., Of the 41 patients with available data on T . gondii serology , only 2 had negative test results for IgG and IgM against T . gondii , and both of them had definite absence of TE ( Table 2 ) ., The blood samples of the remaining 39 patients tested positive for IgG and negative for IgM , indicating past immunization against T . gondii ., At presentation , only 7 patients were receiving systemic antiprotozoal prophylaxis: trimethoprim-sulfamethoxazole ( cotrimoxazole , n = 5 ) , pyrimethamine ( n = 1 ) , and atovaquone ( n = 1 ) ., Of the 5 patients with cotrimoxazole prophylaxis , good compliance was reported in only one patient ., The choice of specific empirical antitoxoplasmic first-line therapy was based on routine practice of each center: all patients from French Guiana ( n = 15 ) , five out of six patients from Martinique and only one patient from Guadeloupe were treated with trimethoprim-sulfamethoxazole ( cotrimoxazole ) whereas 22 out of 23 patients from Guadeloupe were given a pyrimethamine-based combination with either sulphadiazine ( n = 16 ) or clindamycin ( n = 6 ) ., One patient was treated with a combination of pyrimethamine plus atovaquone in Martinique ., Five ( 11 . 4% ) patients , all with TE , died within the first 12 weeks after antitoxoplasmic therapy was begun ., Of the 44 patients , the PCR assay tested positive in blood samples of 9 patients with TE and tested negative in 35 patients ( 27 in the TE group and 8 in the non-TE group ) ., The sensitivity was 25 . 00% ( 95% CI 12 . 12–42 . 20 ) which is low , and the specificity was 100% ( 95%CI 63 . 06–100 . 00 ) which is maximal ., Of the 9 blood samples with a positive PCR test , only 4 were detected simultaneously in both Limoges ( Parasites/mL blood: 0 . 01–0 . 13 , 0–0 . 42 , 4 . 67–6 . 49 , 2 . 30–8 . 80 ) and Cayenne ( Parasites/mL blood: 1 . 46–1 . 83 , 0 . 45–0 . 53 , 15 . 38–22 . 25 , 2 . 15–9 . 83 , respectively ) laboratories , whereas 4 were detected in the Cayenne laboratory alone ( Parasites/mL blood: 0 . 02–0 . 24 , 0 . 01–0 . 18 , 0 . 37–0 . 98 , 4 . 80–8 . 30 ) and 1 in the Limoges laboratory alone ( Parasites/mL blood: DNA: 0–1 . 16 ) ., The sensitivity was 11 . 11% ( 95% CI 3 . 11–26 . 06 ) , 13 . 89% ( 95% CI 4 . 67–29 . 50 ) , and 22 . 22% ( 95% CI 10 . 12–39 . 15 ) when positive PCR tests were observed in both centers , in the Limoges center , and in the Cayenne Center , respectively ., The Cohens kappa coefficient used to estimate the agreement between results of PCR tests performed in both centers was 0 . 5528 ( 95% CI 0 . 2112–0 . 8945 ) , which indicates moderate agreement 27 ., From these data , we can conclude that the majority of PCR results were close to the limits of detection and the difference in detection of T . gondii DNA at the two centers were due to differences in the analytical sensitivity of the PCR assay at each site ., The relationship between the risk of having a false negative result with the PCR assay in the blood for the diagnosis of TE and different baseline variables is shown in Table, 3 . Altered level of consciousness and being born in the French West Indies and Guiana were the only two variables that were associated with significantly decreased risk of false negative results with the PCR assay according to multivariate logistic regression analysis ., Five blood samples that tested positive with the PCR assay in the Limoges laboratory were inoculated into mice and only one strain was isolated ., This strain was isolated from a patient who was living in Guadeloupe but born in Haiti ., The blood sample of this patient tested positive with the PCR assay in both laboratories of Limoges and Cayenne , and corresponded to the sample with the highest parasite load ( Parasites/mL blood: 4 . 67–6 . 49 and 15 . 38–22 . 25 , respectively ) ., This strain was designated HTI01 in this study and cryopreserved at the Toxoplasma BRC with the denomination code TgH40001 ., The genotype of this strain with 15 microsatellite markers was compared with the genotypes of the three reference type I , II , and III strains and with those of 43 strains collected in human cases of toxoplasmosis from South America and the Caribbean region ( Table 1 ) ., The neighbor-joining analysis clustered the HTI01 strain collected in the present study with the type II reference strain in the unrooted tree ( Fig 1 ) ., The 43 strains collected from human cases of toxoplasmosis in the Caribbean and in South America by the French national reference center for toxoplasmosis were clustered as follows:, i ) strains collected in patients from the French West Indies were highly structured in only two groups: 2 strains clustered with the HTI01 strain in the type II group and 8 strains were grouped into a separate cluster called Caribbean group;, ii ) the anthropized strains from French Guiana were also highly structured into three groups: 2 strains were assembled in the type I group , 2 in the type III group , and 1 in the Caribbean group;, iii ) the 18 wild strains from the Amazonian forest of French Guiana were found on separate long branches and were highly divergent from all the other strains , as reported previously 24 , 25;, iv ) of the 10 Brazilian strains , six were structured in one separate group called Brazilian group and four strains were divergent and found on separate long branches like wild strains from French Guiana ., There is a need of treatments and diagnostic tools for TE adapted to AIDS patients in the specific context of tropical areas ., For example , the standard therapy of TE is the combination of pyrimethamine and sulfadiazine 9 , 28 ., However this treatment has several limitations , such as its cost , the high frequency of adverse reactions in AIDS patients , the absence of intravenous formulation and its frequent unavailability in poor resource settings ., For these reasons , cotrimoxazole is often preferred as a first line therapy of TE in AIDS patients in tropical areas because it is efficacious , cheap , better tolerated , with an intravenous formulation and , most of all , is widely available in developing countries 29 , 30 ., Diagnosis of TE is not straightforward because the majority of clinicians rely initially on an empiric diagnosis based on clinical and radiographic improvement to specific anti-T ., gondii therapy in the absence of a likely alternative diagnosis 28 ., In tropical areas , many patients are diagnosed with HIV only after developing opportunistic infections such as TE and the differential diagnosis of focal neurological disease in patients with AIDS can be complex in the context of poor-resource settings 29 ., Under-diagnosis is likely to be the consequence of the difficulties with diagnosing TE in tropical areas 31 ., In this study , we aimed at evaluating the diagnostic performance of TE with the real-time PCR assay in peripheral blood samples from AIDS patients in a tropical setting ., A total of 44 patients , 36 with TE and 8 without TE , from the French West Indies and Guiana in the French departments of America were included in the present study ., The standards of healthcare are close to those of mainland France but this region is a crossroads for poor Caribbean and South American people who emigrate there for socio-economic reasons 32 ., In this region , the HIV epidemic is a major public health problem and TE is a leading cause of death among HIV-infected adults 33 , 34 ., All patients without TE tested negative with the PCR assay in blood samples and all patients with a positive PCR result had TE ., The 100% specificity of the PCR assay in blood samples for the diagnosis of TE in patients with AIDS in our study confirms the very high specificity of this test reported in the literature ( median 99% , IQR 93 . 1%–100% ) 12–18 , 20 ., However , with a sensitivity of 25% , the capacity of the PCR assay to detect TE in blood samples from patients with AIDS is low in a tropical area like the French departments of America ., In fact , the sensitivity of this test to diagnose TE in blood samples from patients with AIDS seems to vary with geography in the literature ., According to 5 studies conducted in the 1990s 12–16 , the sensitivity ranged from 13 . 3% to 29 . 5% in Europe ( median 24 . 3% , IQR 17 . 9–25 . 6 ) which is similar to the result of our study although this latter was conducted in a tropical area ., In contrast , 3 studies from tropical South America in the 2000s showed contradictory results with a sensitivity of 1 . 2% in north-east Brazil , 18 . 8% in Colombia , and 80% in south-east Brazil 17 , 18 , 20 ., It is difficult to compare these studies from Europe and South America with our study in the French departments of America because the volumes of blood samples ( 1 , 5 , or 10 mL ) , DNA extraction protocols ( buffy coat versus whole blood ) , DNA targets ( REP529 , B1 , TgRE1 , and rDNA repetitive gene ) , and even primers for the same DNA target ( B1 or REP529 ) were different ., The 6 oldest studies performed a conventional PCR 12–17 whereas real-time PCR was done in the present work and in the most recent studies 18 , 20 ., The Brazilian study with the highest PCR sensitivity ( 80% ) used a volume of 10 mL of blood sample and a conventional PCR assay targeting the B1 gene with primers B22 and B23 17 ., The DNA extraction step is essential for detecting T . gondii in blood samples ., A recent study in the animal model showed that it was preferable to use buffy coat rather than whole blood , but stressed the importance of the volume of blood sample to increase the sensitivity of PCR assay 35 ., The volume of blood sample in most studies that evaluated the performance of the PCR assay in AIDS patients with TE was 5 or 10 mL 12–15 , 17 except in 2 studies that used only 1 mL 18 , 20 ., Using a limited volume of blood sample might explain , in part , the poor sensitivity of the PCR assay reported in one Brazilian study 19 ., The choice of the DNA target for the PCR assay is also important because studies that tested different targets showed different sensitivity results according to the target 14 , 15 ., Experts generally recommend the use of REP529 DNA target and real-time PCR for reaching the highest sensitivity but they stress the importance of the proficiency of the laboratory performing the diagnosis and the need for optimization of PCR conditions 36 ., It is therefore better to use a well-optimized PCR assay targeting the B1 gene with a conventional PCR assay in a reference laboratory rather than a non-optimized PCR assay targeting REP529 with a real-time PCR assay in an inexperienced laboratory 37 ., The use of different primers for the same target may also lead to different results of sensitivity , as suggested in one study 15 ., In our study , the target was REP529 in Limoges and Cayenne laboratories but , because each center independently developed their own laboratory-optimized PCR assay for routine diagnosis of toxoplasmosis , primers were different and the sensitivity in each center was also different ( 13 . 89% and 22 . 22% , respectively ) ., However , it is also true that identical primers can give variable results of sensitivity depending on the laboratory , which underlines , once again , the crucial importance of PCR optimization 36 ., What makes consensus is the systematic use of uracyl DNA N-glycosylase ( UDG ) to avoid false-positive results caused by carry-over contaminations and an internal positive control ( IPC ) to avoid false-negative results caused by PCR inhibitors of PCR 38 ., Such basic precautions were taken in the present study , in 3 studies from Europe , and in 1 study from Colombia 12 , 14 , 15 , 20 ., One study in Europe and one in Brazil reported the use of IPC but not UDG with sensitivities of 20% and 1 . 2% , respectively 13 , 18 ., The two studies that performed PCR without IPC and UDG reported a sensitivity of 25% in Europe and 80% in Brazil 16 , 17 ., Altogether , it seems that the methodological issues raised here cannot entirely explain the huge difference between sensitivities of the PCR assay in blood samples for diagnosing TE in AIDS patients from 4 tropical areas: 1 . 2% in patients from Recife , north-east Brazil 18 , 18 . 8% in Colombia 20 , 25% in the French West Indies and Guiana ( this study ) , and 80% in São Paulo , south-east Brazil 17 ., In the present study , the geographic origin of patients was likely to influence the sensitivity of the PCR assay because being born in the French West Indies and Guiana was a variable significantly associated with a decreased risk of false negative results according to multivariate logistic regression analysis ., The first hypothesis to explain the link between geography and sensitivity of T . gondii DNA detection in blood samples is the strain hypothesis because the hotspot of T . gondii genetic diversity is in tropical South America and because the genotype of T . gondii strains is strongly linked to the presumed geographical origin of infection in immunocompromised patients 2 , 39 ., Most cases of TE result from local reactivation of brain cysts without parasitemia which explains the absence of detection of T . gondii in most blood samples ., If some strains are more likely to disseminate in blood flow than others , this would have a strong effect on sensitivity of PCR in blood samples ., For example , the sensitivity of PCR in blood samples is very low for diagnosing ocular toxoplasmosis in immunocompetent patients from France and T . gondii DNA is detectable only in ocular fluid samples 40 , 41 ., In contrast , T . gondii genotypes involved in ocular toxoplasmosis in south and south-east Brazil were not characterized from ocular fluid samples but from peripheral blood , and this prolonged parasitemia was confirmed by direct microscopic observation of tachyzoites in some blood samples 42–44 ., Brazil is a big country with a complex T . gondii population structure and it is possible that such differences also exist at a regional scale in Brazil ., If strains from south and south-east Brazil are more likely to disseminate in blood flow than those from the north-east , this could explain the regional variation of PCR sensitivity in blood samples for diagnosing TE in AIDS patients from Brazil 17 , 18 ., Another example of disseminating disease is the Amazonian toxoplasmosis whose diagnosis is always confirmed by a positive result of the PCR assay in blood samples despite the fact that the patients are not immunocompromised 4 ., Little is known about the genetic background that characterizes disseminating strains but , based on what is known from wild strains of the Amazonian rainforest , the genotypes of these atypical strains are found on separate long branches in neighbor-joining trees and are highly divergent from the genotypes of all other strains , especially from the clonal type II and III strains that are common in Europe 24 ., However , in the present study , we found little evidence that the effect of geographic origins on PCR sensitivity in blood samples for the diagnosis of TE in AIDS patients was caused by differences in T . gondii strains ., Unfortunately , we isolated only one T . gondii strain in a patient who was not born in the French departments of America but in Haiti ., The genotype of this strain was not atypical but rather related to type II which represents >95% of strains in Europe where the sensitivity of PCR assay is low in blood samples ., The other patient who was not born in the French West Indies and Guiana and who had a positive PCR result was born in Spain and therefore also likely infected by a type II strain ., If the strain hypothesis were true in our study , we would have expected positive PCR results in blood samples of the 3 patients from Brazil but none of them tested positive ., In fact , the proportion of positive PCR results was higher in patients born in the French West Indies and Guiana ( 7/29 , 24% ) than in those born elsewhere ( 2/15 , 13% ) ., We included in the analysis the genotyping data of 43 T . gondii strains collected by the French national reference center from patients infected in tropical South America and the Caribbean ., Strains that infect humans in the French West Indies and anthropized areas of French Guiana were not found on separate long branches in the neighbor-joining tree like wild strains from the Amazonian rainforest or some strains from Brazil but were highly structured like in Europe ., Type II and III strains that are common in Europe are also common in the French departments of America ., The difference with Europe is the predominance of an endemic lineage called Caribbean group that comprises the Caribbean 1 , 2 and 3 genotypes already described in domestic animals from the anthropized area of French Guiana and in immunocompromised patients from the French West Indies 24 , 25 , 39 ., Although we did not isolate strains in patients born in the French departments of America in this study , it is likely that they were also infected by T . gondii strains belonging either to the types II and III lineages or to the Caribbean group but not to highly divergent strains that could have explained the better detection of T . gondii in blood samples for these patients ., In the absence of a clear explanation by differences in T . gondii strains , the effect of geography on the sensitivity of PCR in blood samples of AIDS patients remains to be elucidated ., The main result of our study is that the sensitivity of PCR in blood samples increases with the severity of TE ., The main severity factors of TE in AIDS patients are profound immunodepression and impaired consciousness ., In a study conducted in AIDS patients with TE at admission in intensive care units , the factors independently associated with a poor outcome were a Glasgow coma scale ≤8 and a CD4 cell count <25/μL 45 ., In our study , a CD4 cell count <25/μL was not associated with a decreased risk of false negative results with the PCR assay ., However , altered level of consciousness was the second variable significantly associated with a decreased risk of false negative results according to multivariate logistic regression analysis ., Of the 8 patients with altered level of consciousness and TE in our study , 5 ( 62 . 5% ) tested positive in blood samples ., All patients ( n = 3 ) with a Glasgow coma scale ≤9 had a positive test result with the PCR assay in blood samples ., The high PCR sensitivity of 80% in blood samples of the 64 patients from São Paulo , Brazil , could be explained by a high number of severe TE cases in this study but clinical data were not available 17 ., In conclusion , the PCR assay in blood samples is not recommended for diagnosing TE in the tropical setting of the French departments of America areas because of a poor sensitivity ., The only interest of PCR would be in the most severe forms of TE with altered consciousness because PCR is more likely to be positive ., Even in these cases , it seems difficult to reach a good sensitivity with the PCR assay because the concentration of T . gondii DNA is very low ., PCR protocols have to be perfectly optimized because positive PCR results rely on high Ct values that are at the limit of the detection of the method which jeopardizes a good agreement between diagnostic laboratories , as showed in our study ., There is no argument that the PCR sensitivity could be influenced by the genetic background of T . gondii strains in this area even if the geographic origin of patients is likely to play a role for unclear reasons ., We believe that our results can be expanded in any tropical setting with the exception of other parts of tropical South America , especially Brazil where T . gondii strain diversity is far more complex than in the French West Indies and the anthropized areas of French Guiana ., Other studies are needed in Brazil to know whether genetic-based differences in the capacity of hematogenous dissemination of locally acquired T . gondii strains are likely to explain the considerable regional variations of the sensitivity of the PCR assay in blood samples of AIDS patients from this country .

Introduction, Methods, Results, Discussion

Toxoplasmic encephalitis in patients with AIDS is a life-threatening disease mostly due to reactivation of Toxoplasma gondii cysts in the brain ., The main objective of this study was to evaluate the performance of real-time PCR assay in peripheral blood samples for the diagnosis of toxoplasmic encephalitis in AIDS patients in the French West Indies and Guiana ., Adult patients with HIV and suspicion of toxoplasmic encephalitis with start of specific antitoxoplasmic therapy were included in this study during 40 months ., The real-time PCR assay targeting the 529 bp repeat region of T . gondii was performed in two different centers for all blood samples ., A Neighbor-Joining tree was reconstructed from microsatellite data to examine the relationships between strains from human cases of toxoplasmosis in South America and the Caribbean ., A total of 44 cases were validated by a committee of experts , including 36 cases with toxoplasmic encephalitis ., The specificity of the PCR assay in blood samples was 100% but the sensitivity was only 25% with moderate agreement between the two centers ., Altered level of consciousness and being born in the French West Indies and Guiana were the only two variables that were associated with significantly decreased risk of false negative results with the PCR assay ., Our results showed that PCR sensitivity in blood samples increased with severity of toxoplasmic encephalitis in AIDS patients ., Geographic origin of patients was likely to influence PCR sensitivity but there was little evidence that it was caused by differences in T . gondii strains ., ClinicalTrials . gov NCT00803621

Diagnosis of toxoplasmic encephalitis ( TE ) in patients with AIDS is not straightforward because clinicians rely initially on an empiric diagnosis based on clinical and radiographic improvement to specific anti-Toxoplasma gondii therapy ., There is therefore a need for biological tools to improve the diagnosis of TE , especially in tropical areas where this diagnosis is likely to be underestimated ., The use of PCR testing in blood samples for the diagnosis of TE has been limited by its poor sensitivity in the studies conducted in Europe ., In tropical South America , the results of PCR sensitivity in blood samples were controversial ., Considering that T . gondii strains from tropical South America have substantial genetic and pathogenic differences with those from USA and Europe , it is therefore important to re-evaluate the performance of the PCR assay in blood samples for the diagnosis of TE in AIDS patients from this region ., Our results showed that the only interest of PCR would be in the most severe forms of TE with altered consciousness because PCR is more likely to be positive ., We also provided important genotyping data on T . gondii strains isolated in human cases of toxoplasmosis in the Caribbean and in South America .

medicine and health sciences, toxoplasma gondii, geographical locations, parasitic diseases, parasitic protozoans, ethnicities, protozoans, toxoplasma, molecular biology techniques, research and analysis methods, infectious diseases, artificial gene amplification and extension, south america, aids, protozoan infections, molecular biology, brazil, french people, toxoplasmosis, people and places, diagnostic medicine, polymerase chain reaction, biology and life sciences, population groupings, viral diseases, organisms

journal.pcbi.1005036

Improved Metabolic Models for E. coli and Mycoplasma genitalium from GlobalFit, an Algorithm That Simultaneously Matches Growth and Non-Growth Data Sets

Metabolism is the best understood large cellular system ., Genome-scale metabolic models that largely rely on constraints for mass balance ( i . e . , all internal metabolites that are produced must also be consumed ) are routinely applied to predict a wide range of metabolic phenomena 1 ., The most widely-used of these constraint-based methods , Flux Balance Analysis ( FBA ) , has been successfully applied to predict a range of biological phenomena such as gene knockout effects 1 and the evolutionary adaptation of microbial strains 2–4 , and has been employed to predict drug targets 5 and to design microbial strains for bioengineering 6 ., Network models are reconstructed by supplementing genomic annotation with information from biochemical characterizations and the organism-specific literature 7 ., The resulting draft reconstructions often contain gaps: the modeled organism or its gene knockout strain can grow in vivo , while the model is unable to produce biomass in silico in the same metabolic environment ( false-negative predictions , FNp ) ., Gap filling methods have been introduced to resolve individual FNp through a minimal number of network changes , making irreversible reactions reversible or adding reactions from a database 8–11 ., A second type of inconsistencies is the erroneous prediction of growth where the experiment shows no growth ( false-positive predictions , FPp ) ., Such cases can be rectified by deleting reactions , making reversible reactions unidirectional , or adding metabolites to the biomass ( all reactions necessary for the production of a given metabolite become essential once this metabolite is added to the biomass ) ., GrowMatch 12 , the current state-of-the-art in automatic network refinement , uses bi-level optimization to identify reactions that must be deleted or modified for each FPp ., GrowMatch also allows to add to the biomass products and/or substrates of reactions that are experimentally essential but are blocked in the model 12 ., All currently available methods for network refinement based on growth data are greedy algorithms , solving one inconsistency between model and experiment at a time 8–15 ., While each individual set of network changes is minimal , the union of these sets can become larger than a minimal set of changes that solves all inconsistencies simultaneously ., Reactions considered essential or model changes introduced early may make the reconciliation of FNp or FPp considered later impossible ( for an example , see our application to Mycoplasma genitalium below ) ., Furthermore , experimental errors that happen to be consistent with the initial model can severely bias the results ., Moreover , previous methods only alter the biomass equation independently of other network modifications 12 , 16 and may miss solutions that combine biomass and network changes ., We present GlobalFit , a novel bi-level optimization method capable of comparing flux-balance analysis ( FBA ) 17 model predictions to measured growth across all tested environments and gene knockouts ( or subsets thereof ) simultaneously ., Allowed model changes are, ( i ) removals or, ( ii ) reversibility changes of existing reactions;, ( iii ) additions of reactions to the model from a database of potential reactions;, ( iv ) removals of metabolites from the biomass; and, ( v ) additions of metabolites to the biomass ., GlobalFit does not change gene-protein-reaction associations ( GPRs ) , and thus isoenzymes should be identified and included in the model as a preprocessing step ., The algorithm is first formulated as a bi-level linear problem , where each condition is represented by separate metabolites and fluxes ( see the detailed method description in Methods ) ., To ensure in silico growth for conditions with experimentally demonstrated growth , the biomass production for these conditions must be greater than a predefined threshold ., For non-growth phenotypes , the inner optimization problem maximizes the biomass production to check whether it stays below a non-growth threshold ., The outer optimization problem jointly minimizes the number of model changes and the number of experiments that are incorrectly predicted by the final model ., The penalties for individual network changes can be set independently ., This allows , for example , to prefer reversibility changes over reaction additions , to preferentially remove reactions not associated with a gene , or to preferentially include additional reactions from metabolic network reconstructions of close relatives ( see some suggestions for setting these penalties in the S1 Table ) ., The bi-level problem can be re-formulated as a single-level optimization problem 18; a corresponding implementation of GlobalFit , integrated with the sybil toolbox for constraint-based analyses 19 , is freely available from CRAN ( http://cran . r-project . org/web/packages/GlobalFit/ ) ., While GlobalFit is designed to find globally optimal network modifications by considering all experimental data simultaneously , the corresponding MILP problem rapidly becomes prohibitively large when considering high-throughput gene knockout data ., For example , simultaneously considering all possible 1366 E . coli knockouts 20 with 4000 allowed network modifications would result in a matrix with 13 million columns by 37 million rows , a problem size not addressable with current computing infrastructures ., However , when searching for model changes that rectify a FPp , trivial but unhelpful solutions such as the deletion of essential reactions are already avoided by simultaneously requiring growth in one or more specified true positive cases ., When searching for model changes that rectify a FNp , overly generous changes ( such as the removal of metabolites from the biomass ) are avoided by simultaneously requiring non-growth in one or more specified true negative cases ., Thus , while a globally optimal solution is only guaranteed when simultaneously considering all experimental growth data , a good approximation may be found by solving subsets of inconsistencies ., We explore this “subset strategy” below in our application to the E . coli genome-scale model ., We suggest contrasting each individual FPp with a wild-type growth case ( or , if growth was assayed on different media , with a small set of wild-type growth cases ) ., FNp may first be solved alone ., However , if a suggested solution for a FNp or a FPp converts other previously correct predictions to false predictions ( TPp to FNp or TNp to FPp ) , the originally considered case should be solved again , this time contrasting it with the complete set of these conflicting cases ., This last step must be repeated until no more additional false predictions occur ( or until no solution is found ) ., The runtime of MILP solvers depends crucially on the number of binary variables ., Importantly , this number depends only on the number of allowed changes ( plus a single binary variable for the inclusion/exclusion of each growth/non-growth case ) ., Thus , a MILP strategy that considers n possible model changes for a single growth/non-growth case solves a problem with n binary variables ., In comparison , the number of binary variables in a GlobalFit run that considers n possible model changes and contrasts m growth and non-growth cases is n+m ., The number of binary variables can be further reduced by a set of preprocessing steps ( Methods ) ., When reconciling a metabolic network with experimental data , the most parsimonious network modifications are not always those that best describe the true metabolic system ., GlobalFit can also provide a specified number of alternative optimal or sub-optimal solutions ( using the integer cut method ) ., Thus , users can choose the solution ( s ) that best agree with available evidence , or design additional experiments that distinguish between competing network modifications ., In cases where all suggested alternatives appear excessive or unrealistic , users may also consider modifying individual GPR rules ., The runtime for n alternative solutions is approximately n times the runtime for a single optimum ., In the test cases reported below , we only examined a small range of alternative solutions and did not consider manual modifications ., We first applied GlobalFit to the genome-scale metabolic network of Mycoplasma genitalium 21 , using the same gene knockout essentiality data 22 as the initial reconstruction with GrowMatch ( reported by 21 to have a global accuracy of 87 . 3% , corresponding to a Matthew’s correlation coefficient , a more balanced measure of classification quality 23 , of MCC = 0 . 56; Table 1 ) ., The growth medium used for the knockout experiments was chemically undefined 22 ., When applying GlobalFit , we thus allowed the uptake of all nutrients for which transport reactions are included in the model ., All other FBA parameters were set to the values used in 21 ., The initial network obtained from 21 was not able to produce biomass; to rectify this problem , we had to convert three irreversible reactions ( ZN2t4 , INSK , LYSt3 ) to reversible reactions ., With these modifications , the original model 21 has an accuracy of 85% and a Matthews’ correlation coefficient MCC = 0 . 44 ., False predictions mainly occurred in the form of FPp , i . e . , by incorrectly establishing growth in silico where a lethal phenotype was observed in vivo ( Table 1 ) ., To construct a database of potential additional reactions , we started from all reactions contained in metabolic networks provided by the BiGG database 24 ., We removed globally blocked reactions , i . e . , those reactions of the database that were not able to carry any flux in a supernetwork containing all reactions ., Reversible reactions were represented as two independent irreversible reactions , corresponding to forward and backward directions ., The database is provided as S2 Database of the supplementary material ., In our first analysis , we used a very restrictive , conservative set of potential network changes:, ( i ) addition of reactions from other network reconstructions that are catalyzed by enzymes with significant sequence similarity to the M . genitalium genome ( BLAST e-value <10−13 ) ;, ( ii ) conversion of irreversible to reversible reactions for reactions that are at least classified as reversible with uncertainty in the E . coli model 25;, ( iii ) removal of reactions ( separately for individual reaction directions for reversible reactions ) ;, ( iv ) removal of biomass components; and, ( v ) addition of biomass components that occur in the biomass of other network reconstructions 16 , 20 , 24 ., In this application , we assigned the same penalty ( 1 . 0 ) for all changes ., However , as the growth medium used in the knockout experiments was undefined , we assigned a lower penalty ( 0 . 1 ) for the removal of exchange reactions ., Thus , removal of a metabolite from the representation of the undefined medium ( corresponding to the removal of an exchange reaction ) was preferred to the removal of the corresponding transporter ., To test the applicability of GlobalFit’s subset strategy to larger models , we next applied it to the most recent genome-scale metabolic reconstruction for E . coli , iJO1366 20 ., Again , we employed the same gene knockout essentiality data 30 , 31 as used in the initial reconstruction ., For all FBA simulations , we used the same parameters as described in 20 ., The maximal influx of all nutrients in the defined growth media was set to 10 mmol gDW-1h-1 ., The lower bound of the non-growth associated maintenance reaction ( ATPM ) was set to 3 . 15 mmol gDW-1h-1 ., Gene essentiality was then calculated by FBA , considering any flux larger than 5% of the optimal biomass core reaction as growth ., For the published iJO1366 model , we obtained the same accuracies as reported originally 20: a combined global accuracy of 90 . 8% calculated across knockout experiments on glucose and on glycerol media , corresponding to a Matthew’s correlation coefficient MCC = 0 . 67 ( Table 3 ) ., In the application of GlobalFit to the iJO1366 model , we only allowed conservative network modifications ( as defined for the M . genitalium model ) ., However , as the growth medium used in the E . coli experiments was chemically defined , we did not allow the removal of exchange reactions ., We constructed a database of potential new reactions as for M . genitalium ( S2 Database ) ., The knockout data for E . coli includes growth data on two different media that contained either glucose or glycerol as carbon sources 30 , 31 ., Accordingly , we solved all FPp against two wild-type growth cases , one on glucose and one on glycerol ., While this increases the number of continuous variables compared to using only a single wild-type growth case , the number of binary variables is still the same as in algorithms that only consider a single non-growth case at a time 12 ( note that we don’t allow the exclusion of any growth/non-growth case in this application ) ., We tested if the order in which false growth/non-growth predictions are considered in GlobalFit’s subset strategy affects the final result; this was not the case ., By applying the network modifications suggested by GlobalFit , we could strongly increase the quality of predictions for growth on both glycerol and glucose ( Table 3 ) ; for the experiments on glucose and on glycerol combined , accuracy increased from 90 . 8% to 95 . 4% , while Matthew’s correlation coefficient increased from 0 . 67 to 0 . 84 ., The detailed model changes are outlined below ., In this work , we describe and implement a novel algorithm to automatically modify metabolic network models based on growth/non-growth data ., The algorithm can utilize data from different growth environments and/or different gene knockouts ., In contrast to previous approaches , the “global” mode of GlobalFit does not reconcile the network model with inconsistent experiments iteratively , but finds a globally minimal set of network changes that resolves all inconsistencies simultaneously ( in so far as the inconsistencies are resolvable with the allowed model modifications ) ., To make GlobalFit applicable to large metabolic network reconstructions , we also explored a subset strategy , where individual false predictions are solved simultaneously with small subsets of growth/non-growth cases ., We demonstrate the utility of these approaches through applications to the previously published network models of M . genitalium 21 ( optimizing model predictions for gene knockout data from Ref . 22 ) and E . coli 20 ( utilizing gene knockout data from Ref . 30 , 31 ) ., Allowing only highly conservative network changes ( e . g . , only adding reactions catalyzed by enzymes that are homologous to genes of the species studied ) , we were able to halve the number of false growth predictions in each case ., Overall , GlobalFit improved the accuracy of growth/non-growth predictions for M . genitalium from 87 . 3% to 93 . 6% ( MCC from 0 . 56 to 0 . 68 ) and for E . coli from 90 . 8% to 95 . 4% ( MCC from 0 . 67 to 0 . 84 ) ., If we allow a much wider range of possible network modifications—which is routinely done in alternative approaches 12 , 21–even higher accuracies can be achieved ., Importantly , GlobalFit can enumerate alternative optimal or sub-optimal solutions , such that expert knowledge or additional experiments can help select the biologically most realistic modifications ., For some inconsistencies , we found solutions that improved accuracy on one medium while decreasing accuracy on the other ., For example , adding selenium to the biomass reaction of E . coli would resolve three FPp on glycerol , while converting four TPp to FNp on glucose ., Thus , the accuracy achievable for one growth medium could be further improved by sacrificing the accuracy for the other medium , albeit at a likely loss of biological correctness ., This observation emphasizes the utility of combining gene knockout data across different nutritional environments to avoid problems of overfitting ., In other cases , several genes whose products act together in a protein complex had contradictory experimental results: in the same medium , some were found to be essential , while the rest was declared non-essential ., Such contradictions may be caused either by experimental errors , by erroneous assignment of genes to reactions ( incorrect GPRs ) , or by a residual function of the enzyme complex even with some of its components missing ., GlobalFit may suggest a solution in this case , but this will simultaneously distort one or more true predictions ., For example , the FPp for the E . coli gene b3560 ( the α-subunit of glycine tRNA synthetase ) could be resolved by adding the charged and uncharged glycine tRNA to the biomass reaction as substrate and product , respectively ., This modification would at the same time transform the TPp of b3559 ( the β-subunit ) to a FNp , and would thus not improve accuracy ., In the applications of GlobalFit , we adopted the in silico growth cutoffs used in the original model publications , i . e . , one third of the mean growth rate for M . genitalium 21 and 5% of the optimal biomass core reaction for E . coli 20 ., A more general way to resolve FPp would be to treat the cutoff that distinguishes in silico growth from non-growth as an additional variable in the optimizations ., For example , the knockout of E . coli ATPS4rpp reduced the biomass yield in glycerol below 10% of the wild-type yield ., Such a substantial reduction in growth rate may explain why 6 out of 8 knockouts for the genes involved in the corresponding enzyme complex were labeled as essential in the experiment; however , following 20 in considering 5% biomass production as growth , we regarded these knockouts as FPp in this study ., An adjustable growth threshold might have rectified these FPp cases without any model changes ., It is not clear a priori which in silico cutoff corresponds best to a given set of experimental data ., Thus identifying the cutoff value that minimizes the necessary model changes seems most appropriate ., In this paper , we have explored the application of GlobalFit to the improvement of existing metabolic network reconstructions and showed that it can substantially reduce the number of false growth predictions even when restricted to conservative network changes ., It is conceivable that GlobalFit can also be employed for other tasks related to metabolic model refinement ., One possible such application is the initial reconstruction of a metabolic network model starting from a computer-generated template that is based on genome annotation ( such as provided , e . g . , by the SEED algorithm 33 ) ., GlobalFit might also be used to remove thermodynamically impossible energy-creating cycles , which sometimes plague initial network reconstructions ., While we only score growth and non-growth , GlobalFit could also be applied using yield data by choosing appropriate thresholds ., Finally , we envisage future usage of GlobaFit for strain optimization in metabolic engineering applications that combine gene knockouts 34 with gene additions ., GlobalFit compares flux-balance analysis ( FBA ) 17 model predictions to measured growth across all tested environments and gene knockouts simultaneously ., Allowed model changes are, ( i ) removals or, ( ii ) reversibility changes of existing reactions;, ( iii ) additions of reactions to the model from a database of potential reactions;, ( iv ) removals of metabolites from the biomass; and, ( v ) additions of metabolites to the biomass ., We thus solve the following bi-level problem:, min→δ ( ∑y∈M ( δyRF+δyRB ) ×wyR+∑x∈IδxI×wxI+∑z∈Dδzadd×wzadd+∑j∈ASδjAS×wjAS+∑k∈APδkAP×wkAP+∑l∈BSδlRS×wlRS+∑m∈APδmRP×wmRP+∑g∈GδgG×wgG+∑h∈NδhN×whN ), ( 1 ), subject to:, ∀g∈GS×vg=0, ( 2 ), ∀h∈GS×vh=0, ( 3 ), ∀y∈M , g∈G∪N\xa0vymin× ( 1−δyRB ) ≤\xa0vyg≤\xa0vymax× ( 1−δyRF ), ( 4 ), ∀x∈I , g∈G∪N−1000×δxI≤vxg, ( 5 ), ∀z∈D , g∈G∪N\xa00≤\xa0vzg≤1000×δzadd, ( 6 ), ∀y∈M , g∈G∪N∑l∈BS ( 1−δlRS ) ×clRS+∑j∈ASδjAS×cjAS→vBiog∑m∈BP ( 1−δmRP ) ×cmRP+∑k∈APδkAP×ckAP, ( 7 ), ∀g∈G ( vBiog+1000×\xa0δBioiG≥\xa0Tg ), ( 8 ), ∀h∈N ( v^Bioh−1000×\xa0δBioiN≤\xa0Th ), ( 9 ), with:, Inner\xa0Problem:v^Bioh∶=\xa0maxv→hvBioh ,, ( 10 ), subject to: Eqs ( 3 ) – ( 7 ) and to the definitions following below ., Line ( 7 ) defines the flux through the biomass reaction , vBiog , for condition g ., The sets used in this system of equations are listed in Table 9 , while the parameters are defined in Table 10 ., For binary variables , 1 corresponds to TRUE ( i . e . , a model change is executed ) , while 0 corresponds to FALSE ( no change compared to the initial network ) ., What is the purpose of each of the lines in the above system of equations ?, The network must be in a steady state ( i . e . , no concentration changes to internal metabolites ) in all conditions g ∈ G Eq ( 2 ) and h ∈ N Eq ( 3 ) that are to be solved simultaneously ., Lines ( 4 ) – ( 6 ) convert the binary variables for the removal or reversibility change of existing reactions , and for the addition of new reactions from the database , into constraints for the respective fluxes ., In Eq ( 4 ) , if δyRB=0 ( i . e . , no change ) , then the lower limit for reaction y in all conditions g ( vyg ) remains at the predefined limit vymin; setting δyRB=1 instead sets the lower flux limit to 0 , i . e . , removes the backwards reaction ., Similarly , setting δyRF=0 keeps the upper flux limit for reaction y at the predefined limit vymax , while setting δyRF=1 sets the upper flux limit to 0 , i . e . , removes the forward reaction ., Line ( 5 ) sets the lower flux limit to -1000 for reaction y in all conditions g if δxI=1 , i . e . , it makes an irreversible reaction ( with flux vxg≥0 ) reversible in this case ., Line ( 6 ) allows non-zero ( positive ) flux for reactions that are not part of the original ( input ) model if δzadd=1 ., Note that in the database of additional potential reactions , we consider bidirectional reactions as two separate reactions corresponding to forward and backward directions ( both with fluxes ≥0 ) ., Metabolites can be removed from both sides of the biomass reaction ( flux vBiog ) , and additional metabolites can be added Eq ( 7 ) with pre-specified stoichiometric coefficients c ., To ensure in silico growth for conditions with experimentally demonstrated growth , the biomass flux for these conditions must be greater than a predefined threshold Tg in all conditions g ∈ G Eq ( 8 ) ., Conversely , to ensure in silico non-growth for conditions with experimentally demonstrated non-growth , the biomass flux for these condition must be less than a predefined threshold Th in all conditions h ∈ N Eq ( 9 ) ., The thresholds Tg and Th can be set separately for each phenotype , e . g . , to account for estimates of experimental errors ., For non-growth phenotypes , a simple condition that forces the biomass production to be lower than a threshold is not sufficient , though , as a trivial solution with v→h=0 would satisfy this condition ., To overcome this problem , the inner optimization problem maximizes the biomass production of non-growth cases Eq ( 9 ) , and this maximum is compared against the non-growth threshold ., Line ( 1 ) describes the outer optimization problem ., GlobalFit aims to find a solution that is able to correctly predict all growth and non-growth cases with a minimal number of network changes ( indicated by values 1 for the binary variables ) :, δyRF , δyRB , δxI , δzadd , δjAS , δkAP , δlRS , δmRP , δgG , δhN, The penalties for each type of network change , and even for each individual change , can be set independently ., This allows , for example , to prefer reversibility changes over reaction additions , or to preferentially include new reactions with stronger genomic evidence , or reactions from metabolic network reconstructions of close relatives ., Users should choose appropriate penalties based on the details of the network reconstruction and the proposed changes ., As a starting point , we include a list of suggested penalty values in S1 Table ) ., To guarantee a feasible solution , even if inconsistent growth cases are used , we implemented additional binary variables that allow the exclusion of individual growth ( δgG Eq ( 8 ) ) and non-growth cases ( δhN Eq ( 9 ) ) from the growth threshold conditions ., In our application to the M . genitalium network , we penalize these condition exclusions with very high values wgG and whN; thus , any network modification that explains additional cases is preferred over the exclusion of conditions , regardless of the number of required changes ., Instead , the penalties can be set to smaller values , so that the exclusion of potentially erroneous experiments is preferred over excessive network changes ., Metabolic network reconciliation with large-scale experimental data usually incorporates a manual curation stage , where experts for the physiology and biochemistry of the organism under study review network changes suggested by automated methods ., To support this process , GlobalFit can put out not just one best solution , but , e . g . , the five best solutions that can then be reviewed to identify the changes most compatible with existing knowledge ., To speed up the calculations , network changes can also be limited to a maximal number ., No efficient software tools for general bi-level optimization problems are available ., Solving the inner problem for each possible combination of network changes would be computationally too slow ., We adapt the “Reduction Ansatz” of Section 4 . 3 . 4 in 18 to eliminate the inner problem in line ( 9 ) ., In this approach , the optimality conditions of the inner optimization problem are expressed as equality and inequality conditions using additional “dual” variables ., For fixed δ→ and h , the inner problem is simply a linear program; thus , the assumptions in 18 are trivially satisfied ., Because of the use of binary variables , algorithms to solve this type of optimization problem are termed mixed integer linear programming ( MILP ) ., MILP is NP hard 35; while no known algorithms can guarantee to find a solution efficiently , algorithms that work well for many practical problems exist in software solvers ., We used the solver of IBM ILOG CPLEX 12 . 5; to avoid trickle flow , we implemented indicator constraints ., Alternatively , our implementation of GlobalFit also allows using the GUROBI solver ., Academic users can obtain both CPLEX and GUROBI free of charge ., The search for a globally minimal set of network changes is a computationally very intensive task ., To speed up this process , it is advisable to restrict the examined conditions to a maximal consistent ( “feasible” ) set , i . e . , a maximal set of conditions that can all be correctly predicted with the same modified metabolic network ( regardless of the type and number of modifications ) ., To identify such feasible condition sets , GlobalFit provides a simple mode , which only minimizes the number of erroneous predictions of growth regardless of the number of network changes ., To speed up the calculation of a feasible condition set , it is possible to first solve individual wrong predictions against a “control” condition , thereby identifying conditions that cannot be reconciled with the network with the allowed modifications ., We applied this strategy for the pre-processing of the M . genitalium data ( see Results ) ., Furthermore , the number of binary variables can be reduced by a set of additional preprocessing steps ., First , binary variables for changes to the network not allowed ( such as reversibility changes to reactions strictly considered irreversible ) should be constrained to zero ., Second , we can consider a “supermodel” that encompasses the input model with all allowed reactions converted to reversible reactions and all reactions from the database of potential additional reactions ., We can then reduce the number of binary variables further by, ( i ) excluding all reactions that are blocked in this supermodel ,, ( ii ) constraining to zero the binary variables for the removal of reactions that are essential in this supermodel ., GlobalFit can optionally calculate a user-defined number n of alternative optimal or suboptimal solutions ., The search for alternative solutions is executed using the integer cuts method ., Thus , the complexity for each additional alternative solution is only increased through a single linear constraint ., Consequently , the runtime for n alternative optimal or suboptimal solutions is approximately n times the runtime for a single optimum ., We provide an implementation of GlobalFit , integrated with the sybil toolbox for constraint-based analyses 19 , which runs in the R environment for statistical computing 36 ., The source code and documentation is available free of charge from CRAN ( http://cran . r-project . org/web/packages/GlobalFit/ ) ., The optimized models for E . coli and M . genitalium are provided as SBML files that can be read , e . g . , by sybil 19 and the COBRA toolbox 37 .

Introduction, Results, Discussion, Methods

Constraint-based metabolic modeling methods such as Flux Balance Analysis ( FBA ) are routinely used to predict the effects of genetic changes and to design strains with desired metabolic properties ., The major bottleneck in modeling genome-scale metabolic systems is the establishment and manual curation of reliable stoichiometric models ., Initial reconstructions are typically refined through comparisons to experimental growth data from gene knockouts or nutrient environments ., Existing methods iteratively correct one erroneous model prediction at a time , resulting in accumulating network changes that are often not globally optimal ., We present GlobalFit , a bi-level optimization method that finds a globally optimal network , by identifying the minimal set of network changes needed to correctly predict all experimentally observed growth and non-growth cases simultaneously ., When applied to the genome-scale metabolic model of Mycoplasma genitalium , GlobalFit decreases unexplained gene knockout phenotypes by 79% , increasing accuracy from 87 . 3% ( according to the current state-of-the-art ) to 97 . 3% ., While currently available computers do not allow a global optimization of the much larger metabolic network of E . coli , the main strengths of GlobalFit are already played out when considering only one growth and one non-growth case simultaneously ., Application of a corresponding strategy halves the number of unexplained cases for the already highly curated E . coli model , increasing accuracy from 90 . 8% to 95 . 4% .

Mathematical models that aim to describe the complete metabolism of a cell help us understand cellular metabolic capabilities and evolution , and aid the biotechnological design of microbial strains with desired properties ., Draft models are frequently improved through adjustments that increase the agreement of growth/non-growth predictions with observations from gene knockout experiments ., Automated methods for this task typically correct one erroneous prediction after the other ., We present GlobalFit , a novel method that can consider all experiments and all possible changes simultaneously to identify model modifications that are globally optimal ( i . e . , that correct the largest possible number of wrong predictions while introducing sets of changes that are most compatible with existing knowledge ) ., This becomes computationally very hard when considering large metabolic models; however , a reduced application of GlobalFit that only looks at small subsets of experiments simultaneously works very well in practice ., Allowing only changes that are conservative ( e . g . , introducing new reactions only if supported by significant genomic evidence ) , GlobalFit halves the number of wrong growth/non-growth predictions for the state-of-the-art metabolic models of E . coli and Mycoplasma genitalium , increasing prediction accuracy to 95 . 4% and 93 . 0% , respectively ., By additionally allowing less conservative changes , we are able to improve accuracy further to 97 . 3% for the M . genitalium model .

medicine and health sciences, chemical compounds, metabolic networks, gene knockout, carbohydrates, organic compounds, glucose, genomic databases, optimization, monomers (chemistry), metabolites, mathematics, network analysis, genome analysis, molecular biology techniques, pharmacology, drug metabolism, research and analysis methods, polymer chemistry, computer and information sciences, biological databases, chemistry, artificial genetic recombination, molecular biology, pharmacokinetics, biochemistry, organic chemistry, database and informatics methods, glycerol, genetics, monosaccharides, biology and life sciences, physical sciences, genomics, metabolism, computational biology

journal.ppat.1006951

Host shifts result in parallel genetic changes when viruses evolve in closely related species

Host shifts–where a pathogen jumps into and establishes in a new host species–are a major source of emerging infectious diseases ., RNA viruses seem particularly prone to host shift 1–4 , with HIV , Ebola virus and SARS coronavirus all having been acquired by humans from other host species 5–7 ., Whilst some pathogens may be pre-adapted to a novel host , there are increasing numbers of examples demonstrating that adaptation to the new host occurs following a host shift 8 , 9 ., These adaptations may allow a pathogen to enter host cells , increase replication rates , avoid or suppress the host immune response , or optimise virulence or transmission 10 , 11 ., For example , in the 2013–2016 Ebola virus epidemic in West Africa , a mutation in the viral glycoprotein gene that arose early in the outbreak and rose to high frequency was found to increase infectivity in human cells and decrease infectivity in bats , which are thought to be the source of Ebola virus 12 , 13 ., Likewise , a switch of a parvovirus from cats to dogs resulted in mutations in the virus capsid that allowed the virus to bind to cell receptors in dogs , but resulted in the virus losing its ability to infect cats 14 , 15 ., In some instances adaptation to a novel host relies on specific mutations that arise repeatedly whenever a pathogen switches to a given host ., For example , in the jump of HIV-1 from chimps to humans , codon 30 of the gag gene has undergone a change that increases virus replication in humans , and this has occurred independently in all three HIV-1 lineages 5 , 16 ., Similarly , five parallel mutations have been observed in the two independent epidemics of SARS coronavirus following its jump from palm civets into humans 17 ., Similar patterns have been seen in experimental evolution studies , where parallel genetic changes occur repeatedly when replicate viral lineages adapt to a new host species in the lab ., For example , when Vesicular Stomatitis Virus was passaged in human or dog cells , the virus evolved parallel mutations when evolved on the same cell type 18 ., Likewise , a study passaging Tobacco Etch Potyvirus on four plant species found parallel mutations occurred only when the virus infected the same host species 19 ., These parallel mutations provide compelling evidence that these genetic changes are adaptive , with the same mutations evolving independently in response to natural selection 20 ., These studies have only used a limited number of hosts , and so do not provide information on how viral evolution occurs across a wide phylogenetic breadth of host species ., The host phylogeny is important for determining a pathogens ability to infect a novel host , with pathogens tending to replicate most efficiently when they infect a novel host that is closely related to their original host 2 , 21–34 ., Here , we asked whether viruses acquire the same genetic changes when evolving in the same and closely related host species ., We experimentally evolved replicate lineages of an RNA virus called Drosophila C Virus ( DCV; Discistroviridae ) in 19 species of Drosophilidae that vary in their relatedness and shared a common ancestor approximately 40 million years ago 35 , 36 ., We then sequenced the genomes of the evolved viral lineages and tested whether the same genetic changes arose when the virus was evolved in closely related host species ., To examine how viruses evolve in different host species we serially passaged DCV in 19 species of Drosophilidae ., In total we infected 22 , 095 adult flies and generated 173 independent replicate lineages ( 6–10 per host species ) ., We deep sequenced the evolved virus genomes to generate over 740 , 000 300bp sequence reads from each viral lineage ., Out of 8989 sites , 584 contained a SNP with a derived allele frequency >0 . 05 in at least one viral lineage , and 84 of these were tri-allelic ., None of these variants were found at an appreciable frequency in five sequencing libraries produced from the ancestral virus , indicating that they had spread though populations during the experiment ( Fig 1 ) ., In multiple cases these variants had nearly reached fixation ( Fig 1 ) ., We next examined whether the same genetic changes occur in parallel when different populations encounter the same host species ., Of the 584 SNPs , 102 had derived allele frequencies >0 . 05 in at least two viral lineages , and some had risen to high frequencies in multiple lineages ( Fig 1 ) ., We estimated the genetic differentiation between viral lineages by calculating FST ., We found that viral lineages that had evolved within the same host were genetically more similar to each other than to lineages from other host species ( Fig 2; P<0 . 001 ) ., Furthermore , we found no evidence of differences in substitution biases in the different host species ( Fisher Exact Test: p = 0 . 14; see methods ) , suggesting that this pattern is not driven by changes in the types of mutations in different host species ., To examine the genetic basis of parallel evolution , we individually tested whether each SNP in the DCV genome showed a signature of parallel evolution among viral lineages passaged in the same host species ( i . e . we repeated the analysis in Fig 2 for each SNP ) ., We identified 56 polymorphic sites with a significant signal of parallel evolution within the same host species ( P<0 . 05; significantly parallel sites are shown with a red asterisk in Fig 1; the false discovery rate is estimated to be 17% 37 ) ., We investigated if viruses passaged through closely related hosts showed evidence of parallel genetic changes ., We calculated FST between all possible pairs of viral lineages that had evolved in different host species ., We found that viral lineages from closely related hosts were more similar to each other than viral lineages from more distantly related hosts ( Fig 3A ) ., This is reflected in a significant positive relationship between virus FST and host genetic distance ( Fig 3B , Permutation test: r = 0 . 15 , P = 0 . 002 ) ., We lacked the statistical power to identify the specific SNPs that are causing the signature of parallel evolution in Fig 3 ( false discovery rate >0 . 49 for all SNPs ) ., Two of the most striking examples of parallel evolution in related species are in Scaptodrosophila pattersoni and S . lebanonensis , which show two high frequency parallel mutations ., These are a synonymous mutation in the 2C replicase protein at position 1901 and a triallelic non-synonymous mutation in a viral capsid protein at position 8072 ., However , the wider pattern of parallel evolution is not driven by these two examples , as the results remained significant after viruses that had evolved in these two species were removed from the dataset ( within species parallelism: P<0 . 001; between species parallelism: P = 0 . 013 ) ., When a pathogen infects a novel host species , it finds itself in a new environment to which it must adapt 4 , 8 , 10 , 44 ., When DCV was passaged through different species of Drosophilidae , we found the same genetic changes arose repeatedly in replicate viral lineages in the same host species ., Such repeatable parallel genetic changes to the same host environment are compelling evidence that these changes are adaptive 20 ., We then examined whether these same genetic changes might occur in closely related host species , as these are likely to present a similar environment for the virus ., We found that viruses evolved in closely related hosts were more similar to each other than viruses that evolved in more distantly related species ., Therefore , mutations that evolve in one host species frequently arise when the virus infects closely related hosts ., This finding of parallel genetic changes in closely related host species suggests that when a virus adapts to one host it might also become better adapted to closely related host species ., Phylogenetic patterns of host adaptation may in part explain why pathogens tend to be more likely to jump between closely related host species ., This pattern is seen in nature , where host shifts tend to occur most frequently between closely related hosts , and in laboratory cross-infection studies , where viruses tend to replicate more rapidly when the new host is related to the pathogens natural host 2 , 21–34 ., For example , in a large cross-infection experiment involving Drosophila sigma viruses ( Rhabdoviridae ) isolated from different species of Drosophila , the viruses tended to replicate most efficiently in species closely related to their natural hosts 34 ., This suggests that these viruses had acquired adaptations to their host species that benefitted them when they infected closely related species ., Our results demonstrate that this pattern is apparent at the level of specific nucleotides , and can arise very shortly after a host shift ., While the susceptibility of a novel host is correlated to its relatedness to the pathogens’ original host , it is also common to find exceptions to this pattern ., This is seen both in nature when pathogens shift between very distant hosts 45 , 46 , and in laboratory cross-infection experiments 33 , 34 ., This pattern is also seen in our data where we also observe parallel genetic changes occurring between more distantly related hosts ., For example , a mutation at position 8072 was not only near fixation in most of the lineages infecting two closely related species , but also occurred at a high frequency in replicate lineages in a phylogenetically distant host ( Fig 1 ) ., The function of these mutations is unknown , but in other systems adaptations after host shifts have been found to enhance the ability of the virus to bind to host receptors 11 , increase replication rates 16 or avoid the host immune response 8 , 10 , 47 ., Of the high frequency significant SNPs ( shown in Fig 1 with red asterisk ) nine occur in the non-structural proteins ( ORF1: RNA dependant RNA polymerase , the putative protease and helicase proteins ) , and eleven occur in the capsid proteins ( ORF2 ) ., Interestingly , none were in DCV-1A , which suppresses the host antiviral RNAi defences 48 ., It will be of interest to examine the functions of the parallel mutations we detected , and characterise phenotypically how they affect viral infectivity and replication ., One mutation rose to a high frequency across all the host species ( Fig 1 , position 214 in the 3’ un-translated region ) ., This is unlikely to be an error in the genome sequencing , as it did not occur when we sequenced the ancestral virus ., This may have been due to natural selection favouring this change in all species , perhaps because there was a strongly deleterious mutation at this site in the virus we cloned or due to the virus going from cell culture to being passaged in vivo ., Previous studies have elegantly demonstrated parallel evolution following host shifts ( eg 18 ) ., However , these are often in cell culture , and so do not reflect the heterogeneity of tissue and cell types in whole animals that occur in studies in vivo ( although see 49 that suggests otherwise ) ., The complex nature of different tissue types in vivo coupled with a limited number of generations may explain why some parallel SNPs have remained at a low frequency in this study ., Following a host shift , viruses must sometimes acquire specific mutations that allow them to be transmitted in their new host 9 , 10 ., As we artificially inoculated the virus , this aspect of adaptation to a new host is missing from our study ., In conclusion , we have found that host relatedness can be important in determining how viruses evolve when they find themselves in a new host ., This study suggests that while some genetic changes will be found only in specific hosts , we frequently see the same changes occurring in closely related host species ., These phylogenetic patterns suggest that mutations that adapt a virus to one host may also adapt it to closely related host species ., Therefore , there may be a knock-on effect , where a host shift leaves closely related species vulnerable to the new disease ., DCV is a positive sense RNA virus in the family Discistroviridae that was isolated from D . melanogaster , which it naturally infects in the wild 50 , 51 ., To minimise the amount of genetic variation in the DCV isolate we used to initiate the experimental evolution study , we aimed to isolate single infectious clones of DCV using a serial dilution procedure ., DCV was produced in Schneider’s Drosophila line 2 ( DL2 ) cells 52 as described in 53 ., Cells were cultured at 25°C in Schneider’s Drosophila Medium with 10% Fetal Bovine Serum , 100 U/ml penicillin and 100 μg/ml streptomycin ( all Invitrogen , UK ) ., The DCV strain used was isolated from D . melanogaster collected in Charolles , France 54 ., DL2 cells were seeded into two 96-well tissue culture plates at approximately 104 cells in 100 μl of media per well ., Cells were allowed to adhere to the plates by incubating at 25°C for five hours or over-night ., Serial 1:1 dilutions of DCV were made in complete Schneider’s media , giving a range of final dilutions from 1:108–1:4x1014 ., 100 μl of these dilutions were then added to the cells and incubated for 7 days , 8 replicates were made for each DCV dilution ., Each well was then examined for DCV infection of the DL2 cells , and a well was scored as positive for DCV infection if clear cytopathic effects were present in the majority of the cells ., The media was taken from the wells with the greatest dilution factor that were scored as infected with DCV and stored at -80°C ., This processes was then repeated using the DCV samples from the first dilution series ., One clone , B6A , was selected for amplification and grown in cell culture as described above ., Media containing DCV was removed and centrifuged at 3000 x g for 5 minutes at 4°C to pellet any remaining cell debris , before being aliquoted and stored at -80°C ., The Tissue Culture Infective Dose 50 ( TCID50 ) of the DCV was 6 . 32 x 109 infectious particles per ml using the Reed-Muench end-point method 55 ., We passaged the virus through 19 species of Drosophilidae , with 6–10 independent replicate passages for each species ., We selected species from across the phylogeny ( that shared a common ancestor approximately 40 million years ago 35 , 36 ) , but included clades of closely related species that recently shared common ancestors less than 5 million years ago ( Fig 1 ) ., All fly stocks were reared at 22°C ., Stocks of each fly species were kept in 250ml bottles at staggered ages ., Flies were collected and sexed , and males were placed on cornmeal medium for 4 days before inoculation ., Details of the fly stocks used can be found in the supplementary materials ., 4–11 day old males were infected with DCV using a 0 . 0125 mm diameter stainless steel needle ( 26002–10 , Fine Science Tools , CA , USA ) dipped in DCV solution ., For the first passage this was the cloned DCV isolate in cell culture supernatant ( described above ) , and then subsequently was the virus extracted from the previous passage ( described below ) ., The needle was pricked into the pleural suture on the thorax of flies , towards the midcoxa ., Each replicate was infected using a new needle and strict general cleaning procedures were used to minimise any risk of cross-contamination between replicates ., Species were collected and inoculated in a randomised order each passage ., Flies were then placed into vials of cornmeal medium and kept at 22°C and 70% relative humidity ., Flies were snap frozen in liquid nitrogen 3 days post-infection , homogenised in Ringer’s solution ( 2 . 5μl per fly ) and then centrifuged at 12 , 000g for 10 mins at 4°C ., The resulting supernatant was removed and frozen at -80°C to be used for infecting flies in the subsequent passage ., The remaining homogenate was preserved in Trizol reagent ( Invitrogen ) and stored at -80°C for RNA extraction ., The 3 day viral incubation period was chosen based on time course and pilot data showing that viral load reaches a maximum at approximately 3 days post-infection ., This process was repeated for 10 passages for all species , except D . montana where only 8 passages were carried out due to the fly stocks failing to reproduce ., Each lineage was injected into a mean of 11 flies at each passage ( range 4–18 ) ., Experimental evolution studies in different tissue types have seen clear signals of adaptation in 100 virus generations 18 ., Based on log2 change in RNA viral load we estimate that we have passaged DCV for approximately 100–200 generations ., After passaging the virus , we sequenced evolved viral lineages from 19 host species , with a mean of 9 independent replicate lineages of the virus per species ( range 6–10 replicates ) ., cDNA was synthesised using Invitrogen Superscript III reverse-transcriptase with random hexamer primers ( 25°C 5mins , 50°C 50mins , 70°C 15mins ) ., The genome of the evolved viruses , along with the initial DCV ancestor ( x5 ) were then amplified using Q5 high fidelity polymerase ( NEB ) in nine overlapping PCR reactions ( see supplementary Table S2 for PCR primers and cycle conditions ) ., Primers covered position 62-9050bp ( 8989bp ) of the Genbank refseq ( NC_001834 . 1 ) giving 97% coverage of the genome ., PCRs of individual genomes were pooled and purified with Ampure XP beads ( Agencourt ) ., Individual Nextera XT libraries ( Illumina ) were prepared for each viral lineage ., In total we sequenced 173 DCV pooled amplicon libraries on an Illumina MiSeq ( Cambridge Genomic Service ) v3 for 600 cycles to give 300bp paired-end reads ., FastQC , version 0 . 11 . 2 56 was used to assess read quality and primer contamination ., Trimmomatic , version 0 . 32 57 was used to removed low quality bases and adaptor sequences , using the following options: MINLEN = 30 ( Drop the read if it is below 30 base pairs ) , TRAILING = 15 ( cut bases of the end of the read if below a threshold quality of 15 ) , SLIDINGWINDOW = 4:20 ( perform a sliding window trimming , cutting once the average quality within a 4bp window falls below a threshold of 20 ) , and ILLUMINACLIP = TruSeq3-PE . fa:2:20:10:1:true ( remove adapter contamination; the values correspond in order to: input fasta file with adapter sequences to be matched , seed mismatches , palindrome clip threshold , simple clip threshold , minimum adapter length and logical value to keep both reads in case of read-through being detected in paired reads by palindrome mode ) ., To generate a reference ancestral Drosophila C Virus sequence we amplified the ancestral starting virus by PCR as above ., PCR products were treated with exonuclease 1 and Antarctic phosphatase to remove unused PCR primers and dNTPs and then sequenced directly using BigDye reagents ( ABI ) on an ABI 3730 capillary sequencer in both directions ( Source Bioscience , Cambridge , UK ) ., Sequences were edited in Sequencher ( version 4 . 8; Gene Codes ) , and were manually checked for errors ., Fastq reads were independently aligned to this reference sequence ( Genbank accession: MG570143 ) using BWA-MEM , version 0 . 7 . 10 {Li , 2009 #1605} with default options with exception of the parameter–M , which marks shorter split hits as secondary ., 99 . 5% of reads had mapping phred quality scores of >60 ., The generated SAM files were converted to their binary format ( BAM ) and sorted by their leftmost coordinates with SAMtools , version 0 . 1 . 19 ( website: http://samtools . sourceforge . net/ ) 58 ., Read Group information ( RG ) was added to the BAM files using the module AddOrReplaceReadGroups from Picard Tools , version 1 . 126 ( https://broadinstitute . github . io/picard ) ., The variant calling was then performed for each individual BAM using UnifiedGenotyper tool from GATK , version 3 . 3 . 0 ., As we were interested in calling low frequency variants in our viruses , we assumed a ploidy level of 100 ( -sample_ploidy:100 ) ., The other parameters were set to their defaults except—stand_call_conf:30 ( minimum phred-scaled confidence threshold at which variants should be called ) and—downsample_to_coverage:1000 ( down-sample each sample to 1000X coverage ) We used a trimmed version of a phylogeny produced previously 33 ., This time-based tree ( where the distance from the root to the tip is equal for all taxa ) was inferred using seven genes with a relaxed molecular clock model in BEAST ( v1 . 8 . 0 ) 43 , 59 ., The tree was pruned to the 19 species used using the Ape package in R 60 , 61 ., We examined the frequency of alternate alleles ( single nucleotide polymorphisms: SNPs ) in five ancestral virus replicates ( aliquots of the same virus stock that was used to found the evolved lineages ) ., SNPs in these ancestral viruses may represent pre-standing genetic variation , or may be sequencing errors ., We found the mean SNP frequency was 0 . 000923 and the highest frequency of any SNP was 0 . 043 across the ancestral viruses ., We therefore included a SNP in our analyses if its frequency was >0 . 05 in any of the evolved viral lineages ., For all analyses we included all three alleles at triallelic sites ., As a measure of genetic differentiation we estimated FST between all the virus lineages based on the heterozygosity ( H ) of the SNPs we called 62:, FST=Hb−HwHb, ( Eq 1 ), where Hb is the mean number of differences between pairs of sequence reads sampled from the two different lineages ., Hw is mean number of differences between sequence reads sampled from within each lineage ., Hb and Hw were calculated separately for each polymorphic site , and the mean across sites used in Eq ( 1 ) ., Hw was calculated separately for the two lineages being compared , and the unweighted mean used in Eq ( 1 ) ., To examine whether there had been parallel evolution among viral lineages that had evolved within the same fly species , we calculated the mean FST between lineages that had evolved in the same fly species , and compared this to the mean FST between lineages that had evolved in different fly species ., We tested whether this difference was statistically significant using a permutation test ., The fly species labels were randomly reassigned to the viral lineages , and we calculated the mean FST between lineages that had evolved in the same fly species ., This was repeated 1000 times to generate a null distribution of the test statistic , and this was then compared to the observed value ., To identify individual SNPs with a signature of parallel evolution within species , we repeated this procedure separately for each SNP ., We next examined whether viral lineages that had evolved in different fly species tended to be more similar if the fly species were more closely related ., Considering all pairs of viral lineages from different host species , we correlated pairwise FST with the genetic distance between the fly species ., To test the significance of this correlation , we permuted the fly species over the Drosophila phylogeny and recalculated the Pearson correlation coefficient ., This was repeated 1000 times to generate a null distribution of the test statistic , and this was then compared to the observed value ., To identify individual SNPs whose frequencies were correlated with the genetic distance between hosts we repeated this procedure separately for each SNP ., We confirmed there was no relationship between rates of molecular evolution ( SNP frequency ) and either genetic distance from the host DCV was isolated from ( D . melanogaster ) or estimated viral population size ( see supplementary S1 and S2 Figs ) using generalised linear mixed models that include the phylogeny as a random effect in the MCMCglmm package in R 63 as described previously 34 ., We also examined the distribution of SNPs and whether they were synonymous or non-synonymous ( see supplementary results ) ., To test whether there were systematic differences in the types of mutations occurring in the different host species , we classified all the SNPs into the six possible types ( A/G , A/T , A/C , G/T , G/C and C/T ) ., We then counted the number of times each type of SNP arose in each host species at a frequency above 5% and in at least one biological replicate ( SNPs in multiple biological replicates were only counted once ) ., This resulted in a contingency table with 6 columns and 19 rows ., We tested for differences between the species in the relative frequency of the 6 SNP types by simulation 64 ., Sequence data ( fastq files ) are available in the NCBI SRA ( Accession: SRP119720 ) ., BAM files , data and R scripts for analysis in the main text are available from the NERC data repository ( https://doi . org/10 . 5285/4434a27d-5288-4f2e-88ac-4b1372e4d073 ) .

Introduction, Results, Discussion, Methods

Host shifts , where a pathogen invades and establishes in a new host species , are a major source of emerging infectious diseases ., They frequently occur between related host species and often rely on the pathogen evolving adaptations that increase their fitness in the novel host species ., To investigate genetic changes in novel hosts , we experimentally evolved replicate lineages of an RNA virus ( Drosophila C Virus ) in 19 different species of Drosophilidae and deep sequenced the viral genomes ., We found a strong pattern of parallel evolution , where viral lineages from the same host were genetically more similar to each other than to lineages from other host species ., When we compared viruses that had evolved in different host species , we found that parallel genetic changes were more likely to occur if the two host species were closely related ., This suggests that when a virus adapts to one host it might also become better adapted to closely related host species ., This may explain in part why host shifts tend to occur between related species , and may mean that when a new pathogen appears in a given species , closely related species may become vulnerable to the new disease .

Host shifts , where a pathogen jumps from one host species to another , are a major source of infectious disease ., Hosts shifts are more likely to occur between related host species and often rely on the pathogen evolving adaptations that increase their fitness in the novel host ., Here we have investigated how viruses evolve in different host species , by experimentally evolving replicate lineages of an RNA virus in 19 different host species that shared a common ancestor 40 million years ago ., We then deep sequenced the genomes of these viruses to examine the genetic changes that have occurred in different host species that vary in their relatedness ., We found that parallel mutations–that are indicative of selection–were significantly more likely to occur within viral lineages from the same host , and between viruses evolved in closely related species ., This suggests that a mutation that may adapt a virus to a given host , may also adapt it to closely related host species .

invertebrates, microbial mutation, medicine and health sciences, pathology and laboratory medicine, pathogens, microbiology, animals, viruses, animal models, parallel evolution, drosophila melanogaster, model organisms, experimental organism systems, genome analysis, microbial genetics, drosophila, research and analysis methods, genomic libraries, genomics, medical microbiology, microbial pathogens, viral replication, evolutionary genetics, insects, arthropoda, eukaryota, virology, viral pathogens, genetics, biology and life sciences, computational biology, evolutionary biology, evolutionary processes, organisms

journal.ppat.1004957

The Herpes Simplex Virus Protein pUL31 Escorts Nucleocapsids to Sites of Nuclear Egress, a Process Coordinated by Its N-Terminal Domain

Morphogenesis of herpesviral capsids is an intricate process initiated in the infected nucleus 1 ., A fragile procapsid is formed and packaged with one copy of the viral genome that is generated by cleavage of replicated concatameric DNA molecules ., During this process , the rather spherical procapsids change their conformation and mature into the icosahedral and more stable C capsids ., These accumulate in large numbers in capsid assembly sites and in the nucleoplasm ., Over time , the infected nuclei are enlarged , concurrently the capsids get dispersed , the host chromatin is marginalized , and the nuclear lamina is partially disintegrated 2–5 ., How mature capsids are released from sites of assembly , and how they translocate from there to the nuclear envelope is not completely understood , and their mode of transport to the nuclear periphery is discussed controversially 5–9 ., With a diameter of 125 nm , herpesviral nucleocapsids exceed the nuclear pore diameter forcing them to take a different route out of the nucleus ., Nuclear egress involves primary envelopment of capsids at the inner nuclear membrane ( INM ) resulting in a transiently enveloped perinuclear particle followed by de-envelopment at the outer nuclear membrane ( ONM ) and release of capsids to the cytoplasm 10 , 11 ., Nuclear egress of all herpesviruses is mediated by a group of conserved viral proteins ., In Herpes simplex virus type 1 ( HSV-1 ) , pUL31 , a nucleo-phosphoprotein 12 , and pUL34 , a type II membrane protein 13 , are recruited to the INM where they form the nuclear egress complex ( NEC; 13 , 14 ) ., Both proteins are required for nuclear egress of capsids out of the nucleus since deletion of either NEC component leads to their nuclear retention concomitant with a defect in viral propagation 15 , 16 ., Moreover , the NEC recruits several viral and cellular kinases to partially disintegrate the major host barriers , namely the chromatin and the nuclear lamina , and to provide access of capsids to the INM 17–21 ., Current data on pUL34 and pUL31 interaction ( s ) support a temporally regulated and orchestrated sequence of events at the INM , e . g . docking of capsids at the nucleoplasmic face , initiation of membrane curvature , wrapping of capsids by the INM , completion of budding by membrane scission and release of enveloped capsids into the perinuclear space 22–27 ., In vivo , co-expression of the two NEC proteins in absence of any other viral protein is sufficient to form and accumulate empty vesicles in the perinuclear space 28 , 29 ., Recently , insights into the membrane-associated NEC activity have been obtained by in vitro systems 30 , 31 ., Recombinant HSV-1 pUL31 and pUL34 form ordered coats on artificial membranes and can induce membrane curving , invaginations , and vesicle formation ., Thus , the NEC represents the minimal virus-encoded membrane-budding machinery with an intrinsic activity to drive membrane budding and scission of vesicles 30 , 31 ., During infection , the situation is more complex due to the presence of other viral and cellular factors and their spatio-temporal regulation ., Among them are the nonessential HSV-1 protein kinase pUS3 32 , the viral proteins pUL47 33 and ICP22/pUS1 34 as well as numerous host factors 33 ., In addition to its well documented role in primary envelopment of nucleocapsids 18 , 25 , 26 , 35 , pUL31 may assist in viral genome cleavage/packaging 15 , 36–41 and thus link capsid maturation to nuclear egress ., Several studies have reported a preferred nuclear egress of C capsids over A or B capsids ( 10 , 11; and references therein ) ; however , the molecular mechanism of these sorting events is poorly understood ., The minor capsid proteins pUL25 and pUL17 that physically interact with pUL31 38 , 39 , 41 , 42 are candidates to contribute to this quality control of nuclear capsid egress 10 , 11 , 27 , 43 , 44 ., Orthologous pUL31 proteins share several features ., The larger C-terminal domain can be divided into four conserved regions CR1 to 4 45 , 46 with CR1 of all pUL31 orthologs containing a binding site for the respective pUL34 ortholog ( Fig 1A; 45–47 , and references therein ) ; however , additional binding sites are likely to exist in their C-terminal domain 25 , 26 , 48–50 ., In contrast , the smaller N-terminal domains are variable and enriched in basic residues clustered in several patches ( red in Fig 1B ) ., Furthermore , a putative classical bipartite nuclear localization signal ( NLS; 45 , 50–52 ) has been identified by in silico analysis ( Fig 1A , grey in Fig 1B ) ., To characterize the functions of the N-terminal domain of pUL31 reported to be phosphorylated by the US3 protein kinase 18 , we generated a series of HSV-1 mutants with a particular focus on the basic patches ( Fig 1C ) ., We identified a classical bipartite NLS embedded in the N-terminal domain that was however not required for nuclear import of pUL31 during HSV-1 infection ., Furthermore , we show here that pUL31 and pUL34 entered the nucleus independently of each other via separate routes ., pUL31 lacking the N-terminal domain associated with capsids in the nucleoplasm but was unable to support nuclear egress and viral replication ., A considerable amount of pUL31ΔN was retained in the cytoplasm if co-expressed with pUL34 suggesting that these proteins had prematurely interacted , and that the N-terminal domain of pUL31 controls the interaction with pUL34 ., Interestingly , while the C-terminal domain of pUL31 was sufficient to interact with nucleocapsids , the N-terminal domain was required for translocation of capsids from the nucleoplasm to the nuclear envelope and for viral propagation ., Together , our data suggest a highly regulated sequence of events during nuclear egress: pUL31 is initially targeted to nuclear sites of capsid assembly and then escorts the nucleocapsids to the nuclear envelope for primary envelopment , a process coordinated by the N-terminal domain of pUL31 ., Hep2 ( ATCC-No . CCL-23 ) , HeLa ( ATCC-No . CCL-2 ) and Vero cells ( ATCC-No . CCL-81 ) were cultured as described previously 53 ., HSV1 ( 17+ ) Lox was used for all experiments 54 , 55 ., The HSV-1 strain 17+ ( kindly provided by D . J . McGeoch ) and pHSV1 ( 17+ ) Lox 54–56 were used for PCR amplification ., HSV-1 propagation , titration and kinetics were done as described previously 13 , 53 ., Plasmid transfection was performed using Effectene Transfection Reagent ( Qiagen ) , while BAC transfection was done using Lipofectamine 2000 ( Invitrogen ) ., The yeast 2-hybrid method ( Y2H; 53 , 57 ) , the NEX-TRAP assay 58 and the LUMIER assay 59 , 60 were described previously ., Cloning was performed by classical restriction or Gateway recombination according to the manufacturer’s protocol ( Gateway , Invitrogen ) ., Single base pair exchanges were introduced using the QuikChange Site-directed Mutagenesis Kit ( Stratagene ) and verified by sequencing ., Constructs encoding pUL31 or mutants thereof were cloned into pCR3-N-myc destination vectors ., Constructs encoding maltose-binding protein ( MBP ) -UL34 were cloned into the pCR3-MBP destination vector similar to the plasmid encoding Strep-pUL34 described previously 13 ., The plasmids encoding bp1 ( basic patch 1 ) , bp2 or bp1bp2 were cloned using the plasmid EYFP ( Clontech ) ., Primers used to generate plasmids encoding EYFP-UL31-bp1bp2 , -bp1 , -bp2 , and pUL31-mp1 ( mutant patch 1 ) , -mp2 , -mp1mp2 , -hbpmp1mp2 , pSV40NLS-UL31-mp1mp2 , pUL31ΔN , and pSV40NLS-UL31ΔN ( Table 1 ) are described in Table 2 ., The plasmid EYFP-Nuc ( Clontech ) was used as control ., Plasmids used for the yeast 2-hybrid ( Y2H ) and the LUMIER assays 59 and the plasmid EYFP-FRB-pUL31 58 have been described before ., The HSV-1 UL31 mutants were generated using pHSV1 ( 17+ ) Lox 54–56 and a modified galK positive counterselection scheme essentially as described ( 13 and Striebinger et al . , in revision ) ., First , the non-overlapping coding region of UL31 ( Nucleotides 9 to 865 ) was replaced by a galK-kan cassette , which had been amplified using the pGPS-galK-kan plasmid and primers equipped with 50bp homologies flanking the UL31 locus ( Table 3: H5-UL31/galk and H3-UL31/galk ) ., In a second step , the galK-kan cassette was substituted with a UL31 region encoding either wild type ( wt ) pUL31 , pUL31ΔN , pSV40NLS-UL31ΔN , pUL31-mp1 ( mutant patch 1 ) , pUL31-mp2 , pUL31-mp1mp2 , or pUL31-hyperbasic patch 1 ( hbp1 ) mp1mp2 ( Fig 1C; Tables 1 , 3 and 4 ) ., To rescue the ΔUL31/galk intermediate , the galK-kan cassette was replaced by the wt UL31 sequence ., To reverse the pHSV1 ( 17+ ) Lox-UL31-mp1mp2 to a pUL31 wt sequence , a two-step recombination process was applied resulting in pHSV1 ( 17+ ) Lox-UL31-mp1mp2 revertant ( rev ) ., For PCR amplification , mutant plasmids that had been generated by site-directed mutagenesis using specific primers ( Table 2 ) were used as templates ., Details of BAC mutants are presented in Tables 1 and 4 ., Direct insertion of the SV40NLS coding sequence at the 5´end of UL31 would have perturbed the 3´coding sequence of UL32 ., To leave the UL32 coding sequence intact , a BAC was generated in which galK-kan was inserted into the UL31 locus while the original start site of UL31 was inactivated without changing the amino acid sequence of pUL32 ., Upon insertion of the coding sequence of pSV40NLS-UL31-mp1mp2 , the overlapping 8 bp of UL31 and UL32 were duplicated ., To reverse the pHSV1 ( 17+ ) Lox-SV40NLS-UL31-mp1mp2 to a UL31 wt sequence , a two-step recombination process was applied resulting in pHSV1 ( 17+ ) Lox-SV40NLS-UL31-mp1mp2 revertant ( rev ) ., This revertant still carries the 8 bp duplication of the 5´ UL31 region as well the mutated original start codon of UL31 ( Tables 3–4 ) ., All BAC sequences were validated by sequencing of the DNA regions targeted by mutagenesis and by restriction pattern analysis of the entire BAC backbone ., The pHSV1 ( 17+ ) Lox strains were reconstituted by transfecting BAC DNA into Vero cells using Lipofectamine 2000 according to the manufacturer´s instructions ( Invitrogen ) ., Hep2 , HeLa or Vero cells grown on coverslips , either transfected or infected , were fixed with 2% formaldehyde/PBS ( 15 min , room temperature ) and permeabilized with 0 . 5% Triton X-100 ( 5 min , 4°C ) ., Binding of antibodies to the HSV-1 Fc-receptor like proteins gE/gI was blocked with human blood sera of HSV-1 negative individuals/PBS for at least 3 h at room temperature 13 ., Mouse monoclonal antibodies anti-myc ( clone 9E10; kindly provided by J . von Einem ) , anti-MBP ( NEB ) , anti-ICP0 ( Santa Cruz ) , anti-ICP8 ( kindly provided by R . Heilbronn ) and anti-VP5 ( clone 8F5; kindly provided by J . Brown ) as well as rabbit anti-pUL31 and anti-pUL32 antibodies ( kindly provided by B . Roizman and J . Baines 49 ) , anti-gM antibodies ( kindly provided by T . Mettenleiter ) , and anti-pUL34 antibodies 13 , 53 were used ., Anti-mouse and anti-rabbit fluorescently labelled secondary antibodies were from Invitrogen ., Cells were examined using a confocal laser scanning microscope ( LSM710; Zeiss , Oberkochen , Germany , or TCS SP5; Leica , Mannheim , Germany ) ., Pictures were processed using Adobe Photoshop ( Adobe ) and Zen-Lite ( Zeiss , Oberkochen , Germany ) ., Fluorescence was measured along a 1 pixel thick and 6 μm long line using the plot profile tool of the software ImageJ ( version 1 . 48K ) on 8 bit images ( Zeiss LSM710 ) taken with a 63x objective , NA 1 . 4 , a pinhole aperture of 1 Airy unit , and a pixel size of 78 x 78 nm ., To evaluate complex formation between pUL31 and pUL34 , 3 . 5 x 106 HeLa cells were transfected with single plasmids encoding MBP-pUL34 , myc-pUL31 or myc-pUL31ΔN , or a combination of a plasmid encoding MBP-pUL34 with one either encoding myc-pUL31 or myc-pUL31ΔN using Effectene according to the manufacturer´s protocol ( Qiagen ) ., Twenty-four hours post transfection ( hpt ) , the cells were washed with ice-cold PBS , incubated for 20 min with ice-cold lysis buffer ( 20 mM Tris-HCl pH8 , 150 mM NaCl , 10% ( v/v ) glycerol , 0 . 5% ( v/v ) Triton X-100 , 2 mM EDTA , with complete Protease-Inhibitor Cocktail ( Roche ) ) ., The lysates were pre-cleared by centrifugation ( 4°C , 12 000 rpm , 10 min ) and incubation with Protein A Sepharose beads ( GE Healthcare ) for 10 min at 4°C ., Following centrifugation ( 4°C , 5300 rpm , 10 min ) , the lysates were incubated with prewashed Amylose Resin ( NEB ) ., After incubation for 1 hour at 4°C on a rotating wheel , the supernatant was removed , and the beads were washed 3x using ice-cold lysis buffer ., Proteins were released from the resin by incubation with 4x Lämmli buffer ( room temperature , 15 min ) and analyzed by SDS-PAGE followed by Western blotting using anti-MBP antibodies and anti-myc antibodies and peroxidase-conjugated secondary antibodies ., Vero cells were seeded onto coverslips 1 day prior to infection ., The cells were pre-cooled for 20\u2009min on ice , and incubated with HSV-1 at 1\u2009pfu/cell in CO2-independent medium containing 0 . 1% ( w/v ) BSA for 2\u2009h on ice on a rocking platform as described previously 54 , 61 ., The cells were then shifted to regular growth medium at 37°C and 5% CO2 for 1\u2009h ., Non-internalized virus was inactivated by a short acid wash for 3\u2009min ( 40\u2009mM citrate , 135\u2009mM NaCl , 10\u2009mM KCl , pH\u20093 ) , and the cells were transferred back to regular growth medium ., After another 12 h , the cells were fixed with 2% ( w/v ) glutaraldehyde in 130\u2009mM cacodylate buffer at pH\u20097 . 4 containing 2\u2009mM CaCl2 and 10\u2009mM MgCl2 for 1 h at room temperature ., Subsequently the cells were washed and postfixed for 1\u2009h with 1% ( w/v ) OsO4 in 165\u2009mM cacodylate buffer at pH\u20097 . 4 containing 1 . 5% ( w/v ) K3Fe ( CN ) 6 , followed by 0 . 5% ( w/v ) uranyl acetate in 50% ( v/v ) ethanol overnight ., The cells were embedded in Epon , and 50\u2009nm ultrathin sections were cut parallel to the substrate ., Images were taken with an Eagle 4k camera at a Tecnai G2 electron microscope at 200\u2009kV ( FEI , Eindhoven , The Netherlands ) ., For quantitation , images were taken at low magnification ( 6000x ) and merged ( Adobe Photoshop ) to cover the whole cell area ., The capsids in the nucleus and in the cytoplasm were counted and the areas of the nucleus and the cytoplasm were measured ( ImageJ ) ., Capsid numbers were calculated per area in mm2 ., Bioinformatic analysis revealed two patches of positively charged residues composed of RRRSR ( basic patch 1; bp1 ) and RRASRK ( basic patch 2; bp2 ) separated by a linker region within the first 42 residues of HSV-1 pUL31 which resemble a classical bipartite NLS ( http://www . expasy . org/; Fig 1A and 1B; 45 , 50–52 ) ., To be classified as an NLS , a given sequence has to target an unrelated cytoplasmic protein to the nucleus ., In addition , it should mediate physical interaction with transport factors of the importin α/β family 62 , and its mutagenesis should result in a cytoplasmic localization while re-addition should restore the nuclear residence 62 ., EYFP-pUL31-bp1bp2 comprising only residues 21 to 42 of pUL31 ( grey in Fig 1B ) fused to EYFP was as efficiently targeted to the nucleus as EYFP-SV40NLS ( Fig 2A ) ., Both bp1 and bp2 of pUL31 were able to individually target EYFP to the nucleus although less efficiently than the combination of both ( Fig 2A ) while EYFP alone was located to both cytoplasm and nucleus ., Yeast 2-hybrid ( Y2H; Fig 2B ) and LUMIER experiments ( S1A Fig ) furthermore demonstrated a physical interaction of pUL31 with transport factors of the importin α family 62 ., While pUL31 , pUL31-mp1 ( mutant patch 1; Fig 1C ) as well as pUL31-mp2 ( mutant patch 2; Fig 1C ) interacted with importins ( Fig 2B ) , pUL31-mp1mp2 did not ( Fig 1C; Fig 2B; S1A Fig ) ., Thus , the residues 21 to 42 of pUL31 constitute a classical bipartite NLS that can mediate nuclear import ., Its relevance for nuclear import of pUL31 was analyzed by transient expression of myc-tagged pUL31 or mutants thereof ( Fig 2C and 2D ) ., pUL31 was exclusively located to the nucleus ( Fig 2C and 2D ) consistent with previous results 25 , 35 , 49 , 63 , 64 ., Mutant pUL31 with either the first ( pUL31-mp1 ) or the second basic patch ( pUL31-mp2 ) mutated were also located to the nucleus ( Fig 2C ) ., pUL31-mp1mp2 , with three basic residues in each of the two patches being replaced by neutral residues showed a more pancellular distribution , while adding an SV40NLS to its N-terminus restored its nuclear localization ( Fig 2C ) ., An additional exchange of a single residue G10R generated a hyperbasic patch ( hbp ) identical to residues 21 to 25 ( Fig 1C ) ., The resulting pUL31-hbpmp1mp2 was located to both cytoplasm and nucleus , similar to pUL31-mp1mp2 ( Fig 2C ) , indicating that such an artificial basic patch did not rescue nuclear import ., pUL31ΔN that lacked the N-terminal 44 residues ( Fig 1C ) remained cytoplasmic; again its nuclear import was rescued by adding an SV40NLS ( Fig 2D; 62 ) ., To reveal any potential export activity of pUL31 , we used the NEX-TRAP ( nuclear export trapped by rapamycin ) assay 58 ., EYFP-FRB-pUL31 was exclusively located in the nucleus both in the absence or presence of rapamycin ( Fig 2E ) ., pUL31 was unable to reach the cytoplasmic gM-FKBP for rapamycin-induced dimerization at the TGN and thus lacked any export activity ( Fig 2E ) , a finding further corroborated by the interspecies heterokaryon assay 58 ., In summary , we conclude that HSV-1 pUL31 harbors an import activity within the N-terminal variable domain , but no export activity ., The import activity of pUL31 is composed of a classical bipartite NLS and an unrelated import activity that together mediate the very efficient nuclear import of pUL31 ., Next we determined the subcellular distribution of the different pUL31 variants in the presence of pUL34 ., Strep-tagged pUL34 expressed alone was located in cytoplasmic structures and the nuclear envelope ( Fig 3A , left; 13 , 35 ) ., Upon co-expression with pUL31 , pUL34 was exclusively targeted to the nuclear envelope while pUL31 was predominantly located in the nucleoplasm ( Fig 3A , right ) consistent with previous reports 13 , 35 ., Co-expression of pUL31-mp1mp2 and pUL34 resulted in localization of both proteins in the cytoplasm ( Fig 3A , right ) ., pSV40NLS-UL31-mp1mp2 co-expressed with pUL34 however was targeted to the nucleoplasm indicating its nuclear import ( Fig 3A , right ) ., In contrast , upon co-expression of pUL31ΔN or pSV40NLS-UL31ΔN with pUL34 , both proteins were predominantly located in the cytoplasm ( Fig 3A , right ) ., Thus , while the addition of an SV40NLS restored nuclear localization of pUL31ΔN in the absence of pUL34 ( Fig 2D ) , this was not the case in the presence of pUL34 ( Fig 3A , right ) ., The nucleoplasmic distribution of pUL31 even upon co-expression with pUL34 ( Fig 3A , right ) suggested that the interaction of pUL31 and pUL34 might be regulated ., pUL34 is a tail-anchored membrane protein while pUL31 per se is free to move between cytoplasm and nucleus ., The current model for transport of integral membrane proteins to the INM 65 predicts that once anchored in the membrane of the endoplasmic reticulum ( ER ) , pUL34 would be transported laterally along the ER membranes to the outer nuclear membrane ( ONM ) , and the pore membrane ( POM ) , eventually passing the peripheral nuclear pore channels to reach the INM ., Transmembrane proteins with cytoplasmic domains above 60 kDa are too large to pass the peripheral nuclear pore channels 65 ., To determine the mode of nuclear import of pUL34 , pUL34 was fused N-terminally to the maltose-binding protein ( MBP ) thereby enlarging its cytoplasmic domain to about 60 kDa ., Similar to Strep-pUL34 ( Fig 3A , left ) , transiently expressed MBP-pUL34 was targeted to cytoplasmic structures resembling the ER ( Fig 3B , left ) ., As shown above , pUL31 expressed alone was exclusively located in the nucleus , pUL31ΔN essentially remained cytoplasmic while pSV40NLS-UL31ΔN was also nuclear ( Fig 2D; Fig 3B , left ) ., Upon co-expression of MBP-pUL34 and pUL31 , MBP-pUL34 remained cytoplasmic whereas pUL31 was exclusively localized in the nucleus ( Fig 3B , right ) ., Thus , MBP-pUL34 was inserted into membranes already in the cytoplasm , but could not enter the nucleus due to its enlarged cytoplasmic domain ., In contrast , pUL31 was efficiently imported into the nucleus ., Interestingly , a different situation developed upon co-expression of MBP-pUL34 with pUL31ΔN or pSV40NLS-UL31ΔN ( Fig 3B , right ) ., With or without an NLS , a considerable amount of either pUL31 protein was retained in the cytoplasm , a finding reminiscent of the results obtained with Strep-pUL34 ( Fig 3A , right ) ., This suggested that in the wild type situation , the interaction between pUL34 and pUL31 is prevented in the cytoplasm ., In absence of the N-terminal domain however , pUL31ΔN interacted prematurely with pUL34 and/or other components thereby retaining both proteins in the cytoplasm ., To gain further insight into the interaction of pUL31 with pUL34 , we used Y2H ( Fig 3C ) and LUMIER assays ( S1B Fig ) ., As expected , pUL31 physically interacted with pUL34 ( Fig 3C; S1B Fig ) ., The same was true for pUL31-mp1mp2 and pUL31ΔN ( Fig 3C; S1B Fig ) consistent with the notion that pUL31-CR1 and potentially other regions of the C-terminal domain contribute to the assembly of the NEC complex 25 , 26 , 48 , 49 ., Interestingly , the N-terminal domain of pUL31 also interacted with pUL34 , either alone or in co-operation with the neighboring CR1 of pUL31 ( Fig 3C ) ., To determine whether pUL31 and pUL31ΔN interacted directly with MBP-pUL34 , co-affinity purification was performed ., pUL31 or pUL31ΔN were transiently expressed either alone or together with MBP-pUL34 ., Both myc-pUL31 and myc-pUL31ΔN were co-purified with MBP-pUL34 but not with the Amylose resin alone ( Fig 3D ) ., Thus , both proteins had retained the ability to interact with MBP-pUL34 and did so in a specific manner ., Interestingly and consistent with previous reports 66 , in absence of pUL34 or if spatially separated from it , pUL31 appeared unstable ( Fig 3D ) while this seemed different with pUL31ΔN ( Fig 3D ) ., Taken together , these data show that the NEC proteins pUL34 and pUL31 utilize different transport routes to the nucleus ., Most importantly , the presence of a functional N-terminal domain prevents pUL31 from interacting prematurely with pUL34 in the cytoplasm ., Previous data suggested a role of the N-terminal domain of pUL31 in viral replication 18 ., To analyze the function of the pUL31 N-terminal domain in the context of an HSV-1 infection , we generated pHSV1 ( 17+ ) Lox-ΔUL31 , Lox-UL31ΔN , Lox-SV40NLS-UL31ΔN , Lox-UL31-mp1 , Lox-UL31-mp2 , Lox-UL31-mp1mp2 , Lox-SV40NLS-UL31-mp1mp2 , and Lox-UL31-hbpmp1mp2 using BAC mutagenesis ( Fig 4A , 4C and 4E ) ., A rescue mutant was generated for HSV1 ( 17+ ) Lox-ΔUL31/galK-kan ( Fig 4B ) , and revertants were made for Lox-UL31-mp1mp2 as well as Lox-SV40NLS-UL31-mp1mp2 resulting in Lox-UL31-mp1mp2 rev ( Fig 4D ) and Lox-SV40NLS-UL31-mp1mp2 rev ( Fig 4F ) , respectively ., All mutations were verified by restriction digest and sequencing of the mutated regions of the BAC DNAs ., Next , the BAC-DNAs of the respective mutants or the parental pHSV1 ( 17+ ) Lox were transfected into Vero cells ( Fig 5A ) ., pHSV1 ( 17+ ) Lox readily formed plaques surrounded by cells expressing the HSV-1 immediate early protein ICP0 ( Fig 5A ) ., Consistent with an essential function of HSV-1 pUL31 15 , 34 , 67 , transfection of pHSV1 ( 17+ ) Lox-ΔUL31 resulted in single cells expressing ICP0 while no plaques were formed ( Fig 5A ) ., Transfection of the pHSV1 ( 17+ ) Lox-UL31ΔN or Lox-UL31-mp1mp2 gave similar results ( Fig 5A ) , and the N-terminal addition of an SV40NLS did not compensate the growth defect of either mutant ( Fig 5A ) ., In contrast , the revertants pHSV1 ( 17+ ) Lox-UL31-mp1mp2 rev and Lox-SV40NLS-UL31-mp1mp2 rev formed plaques as efficiently as the parental strain thus indicating the integrity of the BAC backbone ( Fig 5A ) ., These results furthermore demonstrate that the N-terminal addition of the SV40NLS had not impaired pUL31 function ., The 3´coding region of the essential UL32 gene overlaps with the 5´coding region of UL31 ( Fig 4A; 68 ) ., When Vero cells had been transfected with pHSV1 ( 17+ ) Lox , Lox-ΔUL31 , or Lox-UL31-mp1mp2 , expression of UL32 was comparable and replication compartments appeared normal as indicated by the subcellular localization of ICP8 , the HSV-1 single strand DNA binding protein ( S2 Fig; 3 , 5 , 8 ) ., Thus , pUL31 mutagenesis had not affected pUL32 expression and function ., Nevertheless , the UL31 mutant strains were unable to spread and to form plaques ., Together these data show that the N-terminal domain of pUL31 and its basic patches are essential for plaque formation ., Most importantly , the addition of the SV40NLS to pUL31-mp1mp2 or pUL31ΔN did not restore plaque formation , suggesting that the N-terminal basic patches of pUL31 convey additional functions beyond merely mediating nuclear import ., To further analyze the role of the N-terminal basic patches the mutants pHSV1 ( 17+ ) Lox-UL31-mp1 or Lox-UL31-mp2 were generated ( Fig 1C ) and transfected into Vero cells; the resulting plaques were comparable to those of the parental BAC ( Fig 5A ) ., Thus , either of the authentic single basic patches was sufficient for virus replication ., While the addition of the SV40NLS did not compensate the pUL31-mp1mp2 mutation ( Lox-SV40NLS-UL31-mp1mp2 in Fig 5A ) , Lox-UL31-hbpmp1mp2 with the single mutation G10R that generated a sequence identical to basic patch 1 formed plaques ( Fig 5A ) , although they were considerably smaller than those of the parental strain ( Fig 5A ) ., Thus , the G10R exchange partially complemented pUL31-mp1mp2 and restored function ., Viral reconstitution showed that Lox-UL31-mp1 and Lox-UL31-mp2 replicated to parental titers , while the titers for Lox-UL31-hbpmp1mp2 were at least 2 logs lower ( Fig 5B ) ., Taken together , a single basic patch was sufficient to partially restore the crucial functions harbored within the N-terminal region of pUL31 ., Since the artificial SV40NLS did not compensate , the relative position within the N-terminal domain and the exact amino acid sequence are apparently important for the essential function of pUL31 ., To further decipher the function ( s ) of the pUL31 N-terminal domain , Vero cells transfected with the parental pHSV1 ( 17+ ) Lox or the mutant BACs ( Fig 1C; Tables 1 and 4 ) were analyzed 20 hours post transfection ( hpt ) using monoclonal antibodies recognizing mature hexon capsid epitopes ( mAb 8F5 69 , 70 in combination with antibodies directed against pUL31 ( 49; Fig 6A; S3A Fig ) or ICP8 ( Fig 6B ) ., Confocal fluorescence microscopy analysis showed that all forms of pUL31 were targeted to the nucleoplasm ( Fig 6A; S3A Fig ) ., Thus , during HSV-1 infection , both the N-terminal authentic and the SV40NLS were dispensable for nuclear targeting of pUL31 ., There was no labeling in Vero cells transfected with Lox-ΔUL31 demonstrating the specificity of the anti-pUL31 antibodies ( S3A Fig ) ., After transfection with parental pHSV1 ( 17+ ) Lox , Lox-UL31ΔN or Lox-UL31-mp1mp2 ( Fig 6A ) , the subnuclear localization of pUL31 appeared punctuate and correlated with the capsid protein VP5 detected by antibodies to mature hexon epitopes ( Fig 6A; 69 ) ., While wt pUL31 was located to both nucleoplasm and nuclear envelope , pUL31ΔN or pUL31-mp1mp2 co-localized with capsids in the nucleoplasm , but not with the nuclear rim ( Fig 6A ) ., In contrast , ICP8 , a marker of replication compartments 8 , had a different subnuclear localization than the capsids ( Fig 6B ) ., Line histograms revealed that pUL31 and capsids largely co-localized while this was not the case for ICP8 and capsids ( Fig 6A and 6B , right panels ) ., Thus , pUL31 , pUL31ΔN and pUL31-mp1mp2 could associate with capsids ., Upon transfection with pHSV1 ( 17+ ) Lox-UL31ΔN or Lox-SV40NLS-UL31ΔN , pUL31ΔN or SV40NLS-UL31ΔN were partially retained in the cytoplasm ( S4A Fig ) , reminiscent of their localization after co-expression of pUL31ΔN or pSV40NLS-UL31ΔN with pUL34 ( Fig 3A and 3B ) ., Thus , the absence of the amino-terminal domain conferred partial cytoplasmic retention of pUL31 while nuclear import and targeting to capsids still occurred ., Taken together , pUL31 was targeted to capsids present in the nucleoplasm and the C-terminal domain of pUL31 was sufficient to mediate this association ., To analyze the subcellular distribution of pUL34 upon mutagenesis of the N-terminal domain of pUL31 , Vero cells were transfected with pHSV1 ( 17+ ) Lox or the UL31 BAC mutants ( S3B Fig; S4B Fig ) ., As expected , in cells transfected with the parental Lox , pUL34 was located to the nuclear envelope ., In cells transfected with Lox-ΔUL31 , Lox-UL31-mp1mp2 , Lox-UL31ΔN , or Lox-SV40NLS-UL31ΔN , pUL34 was also targeted to the nuclear envelope although its distribution seemed more patchy ( S3B Fig; S4B Fig ) ., This suggested that in addition to pUL31 , other viral and host factors contribute to targeting of pUL34 to the nuclear envelope , a finding also supported by other recent reports 9 , 33 , 34 ., A single amino-acid exchange within pUL31-mp1mp2 resulting in pUL31-hbpmp1mp2 rescued the functions of the N-terminal domain ( Fig 5B ) ., To further define these functions , Vero cells were infected with HSV1 ( 17+ ) Lox or Lox-UL31-hbpmp1mp2 at an MOI of 1 ( Fig 7A–7G ) ., About 80% of the cells infected with either HSV1 ( 17+ ) Lox or Lox-UL31-hbpmp1mp2 expressed ICP0 at 4 hours post infection ( hpi ) ( Fig 7C ) ., Nevertheless , three phenotypes could be distinguished ( Fig 7A and 7B ) : cells with capsids condensed in the nucleoplasm ( category I ) , cells devoid of cytoplasmic capsids but with a dispersed and speckled appearance of nuclear capsids ( category II ) , and cells with both nuclear and cytoplasmic capsids ( category III ) ., To quantify these phenotypes , cells were analyzed at 8 hpi ( Fig 7D ) , 10 hpi ( Fig 7E ) , 12 hpi ( Fig 7A , 7B and 7F ) or 16 hpi ( Fig 7G ) with a total of 60 infected cells for each condition ( Fig 7D–7G ) ., In the majority of cells infected with the parental strain , the nucleocapsids were dispersed throughout the nucleus with a considerable number of cytoplasmic capsids already at 8 hpi ( Fig 7D ) ., Only a few cells fell into category I or II while category III dominated , and this phenotype was further enhanced at later time points ., In contrast , upon infection with Lox-UL31-hbpmp1mp2 , the majority of cells belonged to category I at 8 hpi ( Fig 7D ) ., At 10 and 12 hpi , cytoplasmic capsids were detected in about 50 and 60% , respectively , of the cells ( category III; Fig 7E and 7F ) ., At 16 hpi , the percentage of cells in category III remained rather constant , at the same time , nuclei containing dispersed capsids ( category II ) increased , a phenotype rarely observed with the parental virus ( Fig 7G ) ., For closer inspection , Vero cells infected with the parental virus were compared to cells infected with Lox-UL31-hbpmp1mp2 ( Fig 8A and 8B; S5 Fig ) ., pUL31-hbpmp1mp2 had also been targeted to nucleocapsids ( Fig 8A ) , whereas the nuclear replication compartments containing ICP8 did not co-localize with the nuclear sites of capsid assembly ( Fig 8B ) ., Line histograms clearly showed that the capsid protein VP5 co-localized well with pUL31-hbpmp1mp2 but not with ICP8 ( Fig 8A and 8B , right panels ) ., Depending on the stage of infection , pUL31-hbpmp1mp2 co-localized with capsids enriched in speckles in close association with the nuclear envelope ( Fig 7A and 7B; S5 Fig ) ., However , unlike in cells infected with the parental virus , a clear nuclear rim localization of pUL31 could not be detected 38 , 39 .

Introduction, Materials and Methods, Results, Discussion

Progeny capsids of herpesviruses leave the nucleus by budding through the nuclear envelope ., Two viral proteins , the membrane protein pUL34 and the nucleo-phosphoprotein pUL31 form the nuclear egress complex that is required for capsid egress out of the nucleus ., All pUL31 orthologs are composed of a diverse N-terminal domain with 1 to 3 basic patches and a conserved C-terminal domain ., To decipher the functions of the N-terminal domain , we have generated several Herpes simplex virus mutants and show here that the N-terminal domain of pUL31 is essential with basic patches being critical for viral propagation ., pUL31 and pUL34 entered the nucleus independently of each other via separate routes and the N-terminal domain of pUL31 was required to prevent their premature interaction in the cytoplasm ., Unexpectedly , a classical bipartite nuclear localization signal embedded in this domain was not required for nuclear import of pUL31 ., In the nucleus , pUL31 associated with the nuclear envelope and newly formed capsids ., Viral mutants lacking the N-terminal domain or with its basic patches neutralized still associated with nucleocapsids but were unable to translocate them to the nuclear envelope ., Replacing the authentic basic patches with a novel artificial one resulted in HSV1 ( 17+ ) Lox-UL31-hbpmp1mp2 , that was viable but delayed in nuclear egress and compromised in viral production ., Thus , while the C-terminal domain of pUL31 is sufficient for the interaction with nucleocapsids , the N-terminal domain was essential for capsid translocation to sites of nuclear egress and a coordinated interaction with pUL34 ., Our data indicate an orchestrated sequence of events with pUL31 binding to nucleocapsids and escorting them to the inner nuclear envelope ., We propose a common mechanism for herpesviral nuclear egress: pUL31 is required for intranuclear translocation of nucleocapsids and subsequent interaction with pUL34 thereby coupling capsid maturation with primary envelopment .

Herpesviral capsid assembly is initiated in the host nucleus ., Due to size constraints , newly formed nucleocapsids are unable to leave the nucleus through the nuclear pore complex ., Instead herpesviruses apply an evolutionarily conserved mechanism for nuclear export of capsids called nuclear egress ., This process is initiated by docking of capsids at the inner nuclear membrane , budding of enveloped capsids into the perinuclear space followed by de-envelopment and release of capsids to the cytoplasm where further maturation occurs ., Two viral proteins conserved throughout the herpesvirus family , the membrane protein pUL34 and the phosphoprotein pUL31 form the nuclear egress complex that is critical for primary envelopment ., We show here that pUL31 and pUL34 enter the nucleus independently of each other ., pUL31 is targeted to the nucleoplasm where it binds to nucleocapsids via the conserved C-terminal domain , while its N-terminal domain is important for capsid translocation to the nuclear envelope and for a coordinated interaction with pUL34 ., Our data suggest a mechanism that is apparently conserved among all herpesviruses with pUL31 escorting nucleocapsids to the nuclear envelope in order to couple capsid maturation with primary envelopment .

journal.pcbi.1000819

A Cell-Based Model for Quorum Sensing in Heterogeneous Bacterial Colonies

Bacteria have evolved signaling networks enabling them to sense the environment by producing , exporting and importing small signaling molecules called autoinducers ., By using autoinducers that can rapidly diffuse across cell populations and accumulate over time , bacterial cells can receive information about the cellular density in the surrounding environment ., The information can then be used to generate decentralized population-wide responses at high enough cell densities ., This phenomenon , known as quorum sensing ( QS ) , has been shown to be important for several biological mechanisms since the initial discovery of it as a regulator of bioluminescence 1–3 ., In particular , it appears to be a key regulator of several bacterial phenotypes with medical implications , e . g . virulence factor production , biofilm development , and synthesis of antibiotics 4–6 ., Typically , quorum-sensing Gram-negative bacteria use largely homologous quorum-sensing networks 3 , wherein the autoinducers are acylated homoserine lactones ( AHL ) , detected and regulated via the genetic circuits similar to the LuxIR circuit in Vibrio fischeri ( Figure 1A ) ., The lux operon in V . fischeri is positively regulated by AHL , and apart from controlling bioluminescence , it upregulates the expression of the AHL-synthase LuxI ., This creates a positive feedback loop that increases AHL production in an AHL-sensitive fashion ., LuxR is an AHL-dependent luxI activator , whose dimerized complex with AHL leads to transcriptional activation of the operon 7 , 8 ., LuxR has also been implicated in regulation of its own expression 9–11 , providing an additional positive feedback loop in the system ., The lux operon circuit may be regarded as the central network for controlling QS behavior , but other regulatory mechanisms have also been identified ( see e . g . 12 , 13 ) ., Studying QS in detail at a population level introduces some interesting complications ., The internal concentration of the autoinducer is dependent not only on its production and degradation , but also on the permeability of the bacterial cell wall as well as on the diffusive properties of the surrounding medium ., While the response switch from a low lux gene expression state ( off state ) to its high expression state ( on state ) is easily predictable in experiments where the exogenous autoinducer concentration is controlled , the cell response in the presence of autoinducer auto-regulation is more complex to analyze and understand ., For instance , waves of QS signaling might develop or be arrested , depending on the mutual location of signaling cells , as the probability for a cell to be induced might depend on the transport properties of the medium and the signaling levels of the neighboring induced cells ., The intracellular switch of the QS network is dependent on the autoinducer concentration just outside the cell , and since this concentration increases with the number of nearby cells even if they are in the basal “off” state , the QS can be switched at high densities of bacteria ., However , the autoinducer levels are highly dependent not only on the population size , but also on the degree of local cell clustering and on the geometry of the environment in which the bacteria are growing ., Since these parameters are not controllable by the individual bacteria , there is an ongoing discussion as to whether the main benefit derived by cells in QS is from measuring cell density ( or reaching a “quorum” ) , the diffusion of autoinducer away from the cell ( diffusion sensing , DS ) or the potential efficiency of a process metabolically more expensive than secretion of AHL ( efficiency sensing , ES ) 14 , 15 ., QS can be beneficial from a population perspective , but since there is a cost associated with ensuring a new beneficial trait for the colony , it is exploitable by the so-called cheater cells , e . g . , those that do not contribute to the production of autoinducer or expression of the QS-regulated operon , but still take advantage of whatever benefit the QS response provides to the colony 15–20 ., This has recently been highlighted in experiments with mixed populations 21 , 22 ., These experiments have measured the relative fitness of cheater cells , depending on the initial ratio of producer and cheater cells within the colony 21 ., In particular , an example of Simpsons paradox was seen 22 , wherein QS signal producing cells taken together have a net advantage if cell populations form groups with different initial ratios of producing and cheater cells , even though the producing cells are at a disadvantage in each of the individual groups ., Several mathematical models have been used to describe the molecular network central for quorum sensing 23–26 ., The models have all used networks with single or double positive feedback loops , and assumed different regulatory mechanisms of luxI via the AHL-LuxR complex ., Despite the differences , the models converge in their predictions of a bistable switch-like behavior dependent on the external concentration of the autoinducer ., Although the models have provided information on how the intracellular QS-network behaves , the effect at the population level have thus far been excluded in all computational investigations ., To be able to investigate the behavior of quorum sensing in a bacterial colony where the autoinducer is produced within the colony , we introduce a model that explicitly includes growing bacteria interacting with each other and the surrounding environment via both molecular and mechanical interactions ., The model assumes two positive feedback mechanisms where a dimerized LuxR-AHL complex activates both LuxI and LuxR production similar to recently published models 25 , 26 ( Figure 1A ) ., We use a combination of analytical and numerical investigations of the model to explore how for example colony size , local clustering , and confinement , affects the behavior both on the single-cell level as well as on the colony level ., In mixed population simulations we investigate the competition between autoinducer producing cells and non-producing cheater cells ., First we analyzed the single-cell system described in Equations 1–4 without transport , i . e . and are set to zero ., At the steady state , all derivatives are equal to zero , which gives a set of algebraic equations , which in turn can be simplified into a single equation ( Equation 6 in Methods ) , which can have one or multiple positive real roots ., Equation 6 was solved numerically to create bifurcation diagrams in the model parameters ( Figure 1B and C ) ., It is clear that within a certain parameter region the system has multiple stable solutions , but this region can be complex with several surrounding monostable regions ., Adding intercellular transport and external diffusion is expected to affect the parameters in Equation 6 , so that the system trajectory would be able to move into and out of the multistability region ( s ) ( cf . Figure 1B and C ) ., Several single-cell QS models have predicted an -dependent switch-like response of the QS network 23–26 ., To address the effect of communicating AHL with the cell environment , we first assumed a constant and added transport terms to see how this would affect the equilibrium behavior ., This generated two important differences as compared to the non-transport analysis above ., The transport out of the cells ( term in Equation, 1 ) has the same form as the degradation ( ) , so an increase in outwards transport moves the state of the system towards a monostable “off” state in our equilibrium analysis ( upwards in in Figure 1C ) ., The transport into the cells ( ) gives an dependent constant contribution and will hence effectively increase the constant which will move the state towards a monostable “on” state ( right in Figure 1C ) ., Hence , the addition of transport terms affects the control parameter values and results in changes with opposite effects ., At low extracellular AHL concentrations the outflux can dominate the influx and thus drive the bacteria towards an “off” state , whereas high extracellular AHL concentrations are expected to drive the bacteria towards the “on” state ., Note that the analysis above was only for the equilibrium behavior and to investigate the dependence on the external autoinducer concentration in a dynamically growing cell-based model , we next performed simulations wherein first slowly increased , and then decreased ( Video S1 ) ., As expected , the colony displayed QS response hysteresis ( Figure 1D ) ., For the parameters used here the transition between states was fairly smooth , but for other parameter values the transition can be steeper and can even be irreversible ( Figure S1 ) ., The requirement to have the switching capability in the QS network due to changes in external does not put severe constraints on the model ., As long as an “off” state is available at low concentrations , a sufficient increase in will always lead to a switch to an “on” state , due to the dependent increase of the constant term in Equation 1 ( , cf . Figure 1C and Equation 8 in Methods ) ., In nature , however , the QS switching is more restricted since it is the bacteria themselves that produce the autoinducers and there is an upper limit of how high the concentrations of can reach within the colony ., Furthermore , the switch threshold needs to be reached while the bacteria are still in the “off” state ., The production of AHL cannot be so high as to allow a single-cell to switch by itself , but it must be high enough , so that at high enough densities the colony is able to reach the threshold in ., A simplified equilibrium analysis of Equations 1–5 including a single external compartment , but multiple cells , leads to a single change from the non-AHL-transport analysis above ., The dependent term is changed: , where is the number of bacteria and is the diffusion out from the extracellular milieu ( see Methods ) ., As discussed above , an increase of the parameter moves the state of the bacteria towards a stable “off” state , upwards in Figure 1C , and this simplified model shows that adding diffusive interaction with the extracellular domain can only drive the bacteria towards that monostable “off” state ., The change due to the addition of transport is bounded ( ) , wherein the lower bound is for ., Thus the effect of increasing the population size , , corresponds to decreasing or going downwards in Figure 1C ., However , since the contribution is bounded from below , the system can never move beyond , or below , the initial state ., Hence , this simplified equilibrium analysis predicts that , in order to have a QS response of the colony , the parameters must be chosen such that a single bacterium without AHL transport is in an “on” state , but close enough to the multistable region to allow inclusion of the transport terms to “move” the single bacterium into its “off” state ., The analysis presented is of course for a very simplified description of the QS , and to be able to investigate how QS works in a more realistic non-equilibrium environment , we used a cell-based model and a spatially meshed extracellular domain with dynamically diffusing AHL ., We simulated the system of growing communicating bacteria , starting with a single bacterium that grows , divides and communicates with the environment ( see Methods ) ., The overall simulation domain was assumed to be a thin rectangular layer , of the same thickness as the bacteria , and with on the boundary ., This assumption , in addition to simplifying visualization and analysis of the results , corresponds to the experimental design used to analyze bacterial colony growth in microfluidic devices , thus providing a potential model validation platform 27 ., The colony displayed a quorum-sensing behavior with a clear unanimous switch in and at a specific population size ( Figure 2A , see also Figure S2 and Video S2 ) ., When the number of cells was small the colony was in an “off” state ., At a threshold population size , cells in the spatial center of the colony started to switch on , leading to a short time period of an inhomogeneous colony with cells both in “on” and “off” states , but the switching propagated quickly and soon virtually all cells of the colony were switched on ., A reason for the homogeneity of responses of all the cells in the colony is the positive feedbacks ensuring that the production of AHL is much higher in cells that are turned on compared to the constitutive basal production ., As soon as a few cells turn the signaling on , the amount of AHL in the environment can quickly rise , driving the fast propagation of the switching throughout the colony ., However , the low constant production of AHL is necessary for the initiation of the switching behavior ., To investigate the system dependence on the model parameters , we performed a parameter scan and studied how parameter variation affected the colony behavior ( Figures S3 , S4 , S5 ) ., In Figure 2B we show the effect of varying the three transport parameters ( , , and ) ., We observed that the colony response moves into and out of the bistability region at different population sizes ., Specifically , we found that , at low diffusion rates , the system was inclined to switch , whereas at higher diffusion rates the colony was no longer able to accumulate sufficient amount of AHL to make the switch possible , gray line in Figure 2B ., This is in accordance with the simplified equilibrium analysis above , wherein changing affected via ., Hence , at low , the system is essentially in the situation without AHL transport , whereas at high , we get which might be enough of a change in the effective AHL removal rate to move the system into the “off” state ., From the same simplified model it is also clear that and should effectively change in opposite directions , which is also exactly what one observes in Figure 2B ., Thus far we have shown that the switching mechanism of QS in individual cells is dependent on the extracellular AHL concentration , and that for a bacterial colony this concentration depends on the net loss of local ., Our simplified analysis showed that this loss can be approximated by a change in , given by addition of the term , which explicitly shows that this depends on the outflux ( or loss ) in the exterior ( ) and the density of bacteria ( ) , with the individual bacteria thus not being able to distinguish whether or is changed in the environment ., The simulations of the colony growth ( Figure, 2 ) also showed that being in the center of a dense population facilitates the QS switch ., Taken together , these results demonstrate that the model confirms that bacteria cannot measure cell density , exterior loss of autoinducer , and spatial clustering independently , in agreement with prior qualitative arguments 15 ., It has been shown that bacteria often actively seek out small cavities and populate them to very high densities 28 , 29 ., To see how local density and confinement might affect colony behavior , we compared dense cell population simulations with simulations of a sparsely populated colony ., The populations were simulated with open boundaries as before ( Figure 3A ) ., We also considered colonies confined in a small cavity with a single small outlet ( Figure 3A ) ., In the simulations , we fixed the population size at different values and examined the resulting QS ., The system switched once the population reached a certain number of cells , and the system switch occurred at lower cell number in the dense population than in the sparse population ( Figure 3B , see also Figure S6 ) ., This result demonstrated that although QS is generally a population-size effect , it can be facilitated by local clustering of bacteria 15 ., We also observed that confinement of the sparse population makes its switching behavior similar to that of the dense population ., Thus , not only the local density and the number of bacteria matters for the response of the colony but also the geometry of the surrounding environment ., In light of the results in Figure 3B the strategy of populating cavities makes sense as a way of facilitating the onset of quorum sensing ., However , the geometry of the cavity may also affect the ability of the colony to perform the switch in concert , e . g . by controlling the escape of AHL ., To address this possibility more directly , we performed simulations of colony growth and QS in a cavity geometry similar to previously used microfluidic chambers 27 , but with variable number of outlets ., Simulations were initiated with a single bacterium and simulations were run until the expanding colony completely filled up the cavity ( Figure 4A and Videos S3 , S4 , S5 , S6 ) ., At sufficiently high values of the population only partially switched states ( Figure 4A ) ., Typically , it is only at the regions furthest away from the exits that the colony was able to accumulate sufficient levels of to undergo the switch ., Figure 4B shows the fraction of cells in the “on” state as a function of time , with the clear result of an organized population-dependent behavior ., At first no cells are in the on state , but at a colony size determined by the number of outlets , parts of the population make a sudden sharp switch and reaches a new stable configuration ( cf . Videos S3 , S4 , S5 , S6 ) ., The bacteria furthest away from the exits are those that initiate the switching behavior ., In Figure 4C the and concentrations of individual bacteria are plotted as a function of the spatial position along the horizontal axis for Chamber 2 ., At positions far away from the exits the bacteria are homogeneously in the “on” state , while closer to the exits the population is less homogeneous due to the loss of AHL at the exits ( the upper leg marked with in Figure 4C ) ., Note also that the signaling in the chamber legs between two exits is tightly concentrated around ( e . g . in Figure 4C ) ., The system can show multi-stable responses and the cells in these legs are clearly at the stable fixed point wherein the production has switched on while the production has not ., Taken together our simulations show a complex behavior with the switching of each bacterium being dependent on its location within the chamber , and with local subpopulations with high signaling homogeneity created ., In natural habitats , bacteria live in environments with a mixture of different bacterial strains ., This property can affect the QS behavior and lead to a problem of emergence of cheater cells that can exploit the “common good” produced by the QS population ., The phenomenon was recently studied in controlled environments for bacteria 21 , 22 ., These cheater cells do not produce the autoinducer ( or other QS resulting common good molecules ) themselves but do take advantage of the metabolically expensive QS signaling by the rest of the population ., By not participating in the generation of QS response , cheater cells can instead use metabolic energy to more rapidly grow and divide ., We considered this situation by modeling cheater cells as the other bona fide signaling cells , but with no production of ( ) ., Furthermore , we assumed that once the normal cells switch on and thus increase their QS response , their growth rate slows down ( see Methods ) ., Data from simulations in a confining chamber starting from different initial states are presented in Figure 5A where we tracked the population dynamics in the mixed colonies ., Initially the growth rates of the producer and cheater sub-populations were equivalent , but once some of the producer cells switched states , the cheater cell population rapidly started to dominate the chamber ( cf . Video S7 ) ., Note that although the fraction of producing cells that turned on was quite small ( about 10% , dashed-dotted line in Figure 5A ) , this was sufficient to break the symmetry and give the cheater cells a clear advantage ., The domination of cheater cells leads to a dilution of producing cells which lowers the AHL concentration in the chamber ., This resulted in a decrease in the number of producer cells that were switched on and thus diminished the advantage of the cheater cells ., In the end the relative cell numbers of the two sub-populations can stabilize ., The simulations in the other chambers displayed similar behaviors ( see Figure S7 ) ., The dynamics of the colonies ( Figure 5A ) clearly showed that whether or not the cheater cells were at an advantage , depended on the composition of the mixture of cheater cells and normal cells 21 ., To investigate this further we performed simulations wherein the initial colony consisted of different ratios of cheater and producer cells ., In these simulation we added the assumption that the producing cells could provide the population with some advantage or “common good” , a property beneficial for the survival and growth of the population as a whole ., In the model this was simplified by assuming an autoinducer dependent growth rate ( see Methods ) ., In Figure 5B the resulting relative fitness of the cheater cells is displayed , indicating decreasing advantage for increasing initial ratios of cheater cells , as seen in experiments 21 ., The model predicts that the advantage of cheater cells is directly related to the number of producing cells that are in the “on” state , which in turn is dependent on the number and location of the cheater cells ., This leads to an effective negative feedback , so that the producer cells are not completely overtaken by the cheater cells in any of the cases in Figure 5B ., In fact , the addition of the AHL-dependent growth does not alter the relative fitness behavior in the individual simulations ( data not shown ) but actually leads to a total increase of producer cells if all initial colony configurations are summed up ( Figure 5C ) ., Although cheater cells always have a local advantage and never grow slower than the producing cells , the colonies with more producer cells will grow faster and this is sufficient for generating more producer cells in total ., This has recently been reported for synthetic bacteria strains and is referred to as the Simpsons paradox 22 ., The simulations with mixed populations show that cheater cells may have a local advantage , but a negative feedback via the colony growth and dilution of producing cells leads to a situation where this advantage is only transient ., Quorum sensing is a key example of the ability of unicellular bacteria to act not only as individual cells but also as an ensemble , resembling in many respects a multicellular organism ., This collective cell behavior phenomenon is important for various biological behaviors , with considerable implications for the physiology and pathology of plants and animals 13 ., Hence it merits further understanding both for a better appreciation of the fundamental properties of cell-cell communication and for its applications ., With the increasing amount of quantitative data for the molecular networks at the center of the cellular QS signaling , the use of mathematical models has emerged as an important tool for understanding how the molecular network structure with its multiple feedbacks can explain the complex behavior of the population ., Previous models have mainly discussed the intracellular network with the underlying QS switch , and have treated the extracellular environment as a boundary condition 23–26 ., An exception is the static model briefly described in Hense et al . 2007 15 ., Recent development of microscopy techniques together with the increased use of microfluidic devices have increased the ability to study cell colony behaviors at a cellular resolution 30 ., Here we have presented a model explicitly taking into account individual growing bacteria as well as the transport and geometry of the extracellular milieu ., This resulted in a model framework with the results directly comparable with data from cell-based experiments in microfluidic devices and other experimental settings , and allowed for an explicit investigation of how population-level behavior emerges from single-cell mechanisms ., In this report , we presented simulations investigating cell-to-cell variations in homogeneous populations as well as the behavior of mixed populations ., An equilibrium analysis of the model was used to find the parameter values capable of population-size dependent QS switching and the analysis highlighted the differences between a situation where autoinducer levels are tuned extracellularly and when bacteria themselves are the only source of the autoinducer ., In the former case , we showed that QS switching was not very constrained ., However , in the latter case , the effect of adding the autoinducer transport boiled down to variation of a single parameter of the model: the effective degradation of the autoinducer ., The variation of the effective degradation was shown to be dependent on the transport parameters characterizing the autoinducer and the cell medium , and on the number of bacteria present , and was shown to be bounded by the rate of autoinducer transport out of the cells ., Hence , the ability of QS switching is only ensured if this bounded parameter can change so that the systems can visit both “on” and “off” states ., A clear prediction from this analysis is that if autoinducer membrane transport is blocked , the cells would have be to be in an “on” state ., Simulations of growing and proliferating bacteria showed a population-size dependent switching behavior , wherein although it is the bacteria in the center of the colony that initially switch on , the whole colony quickly follows creating a very homogeneous behavior ., This is mainly due to the strong positive feedback in the signaling system , ensuring that the autoinducer production greatly increases in the cells that are switched on ., A scanning of the model parameters orders of magnitudes around their initial values showed that the main QS feature , the population switching , is very robust , while the actual population size where the switch happens is quite dependent on parameter values ., We further found , as expected , that the switching of the population is driven by the external autoinducer concentration ., This is dependent on the population size , but also on how much autoinducer is lost from the colony , which depends on the local density ( clustering ) and the confinement of the external geometry; parameters that to a large extent are beyond the control of single cells ., We explored these parameters explicitly in our model simulations showing that growing dense populations in small confined cavities facilitates population switching , a potentially common strategy 28 , 29 ., This relates to the discussion of the evolutionary fitness advantage provided by a collective cell population behavior , with the quorum sensing , diffusion sensing and efficiency sensing have been suggested as different explanations 15 ., Our model suggests that cells can sense different aspects of their environment through determination of the value of a single , albeit complex parameter ( ) , comprising all these different possibilities ., Additionally , the model suggests that a possible evolutionarily selectable strategy of populating small cavities as a means to control diffusion , local density , and confinement in order to facilitate the onset of quorum sensing ., Bacteria live in environments where different biological organisms compete ., It has been noted that a QS behavior can be exploited by strains of cheater cells that do not participate in some aspects of QS , but still take advantage of the benefits this provides ., The corresponding advantages of this behavior for cheater cells have recently been investigated in controlled experiments 21 , 22 ., The cell-based approach allowed us to investigate competition between autoinducer producing and non-producing cells by adding a growth reduction for producing cells that are in their “on” state ., We showed that the cheater cells did have an advantage as soon as producing cells switched on ., This advantage , however , led to a dilution of producing cells , and hence the amount of autoinducer per cell , within the mixed population as the cheater cells increased their relative number ., The decrease in autoinducer further led to producing cells switching “off” , which diminished the cheater cell advantage ., Hence , the growth dynamics in these mixed populations creates a feedback that disallows a cheater strain to fully overtake a population ., If we assumed also that the growth was dependent on the production of autoinducer or the corresponding beneficial population trait ( e . g . , the ability to cause the host to provide nutrients ) , we could observe situations where populations initiated with different ratios of cheater cells generated an overall advantage for producing cells , although in each individual local sub-population , cheater cells were never at an disadvantage ., The phenomenon is known in statistics as the Simpsons paradox , and was recently demonstrated for synthetic bacterial strains 22 ., The number of molecules , including members of the transcription machinery present in bacteria can be very low ., Hence it is expected that effective transcription and reaction rates might be noisy , and segregation of the transcription factor molecules into the daughter cells at cell division can be inhomogeneous 31 ., Interestingly , a test with complete random placement of all molecular species at division had very minor effects for the cell population ( data not shown ) ., This shows a model robustness of the population behavior to molecular fluctuations in individual cells , but it also points out a limitation of our deterministic approach ., In the deterministic model , a switch from a low stable state to a high stable state does not spontaneously happen in the bistable region ., Hence , to get a switch in the simulations a change of condition ( e . g . increasing the number of cells ) will need to move the system into the monostable high region of the state space ., A fluctuation in concentrations at division will then quickly move back to the only stable state ., In a stochastic model , on the other hand , it could be enough to be in the bistable region where switching between high and low states could be initiated by fluctuations in concentrations ., Given the number of bacteria , external compartments , and reactions in our simulations , a complete stochastic treatment may be out of reach , but an interesting future improvement would be to add stochasticity to the model , for example via adding noise terms to the ODEs ., Recent experimental developments have changed our ability to quantify cell states , from the population averages to the dynamics of single cells ., The presented work is important since it represents the same development for the mathematical models used to analyze cell-based behavior ., The combination of high-resolution experiments where colonies are grown in regulated environments , and models where single cells are growing to form colonies will help understanding of how population dependent behaviors , such as quorum sensing , can be derived from single cell molecular networks ., Following earlier efforts 27 , 32 each cell is modeled as an individual object , described as two semi-spheres attached at opposite sides of a cylinder ., The dynamics of the bacteria is governed by a potential , where the different contributions describe cell-cell interactions , cell-wall interactions and the internal potential respectively ., We further assume that the dynamics of the colonies is dominated by viscous friction so the equations of motion for a given cell is described bywhere and are the two coordinates , chosen as the centers of the two semi-spheres , is the friction coefficient and denotes the derivative with respect to and respectively ., For the friction coefficient , we assume a generalization of Stokes formula 33where is the distance between

Introduction, Results, Discussion, Methods

Although bacteria are unicellular organisms , they have the ability to act in concert by synthesizing and detecting small diffusing autoinducer molecules ., The phenomenon , known as quorum sensing , has mainly been proposed to serve as a means for cell-density measurement ., Here , we use a cell-based model of growing bacterial microcolonies to investigate a quorum-sensing mechanism at a single cell level ., We show that the model indeed predicts a density-dependent behavior , highly dependent on local cell-clustering and the geometry of the space where the colony is evolving ., We analyze the molecular network with two positive feedback loops to find the multistability regions and show how the quorum-sensing mechanism depends on different model parameters ., Specifically , we show that the switching capability of the network leads to more constraints on parameters in a natural environment where the bacteria themselves produce autoinducer than compared to situations where autoinducer is introduced externally ., The cell-based model also allows us to investigate mixed populations , where non-producing cheater cells are shown to have a fitness advantage , but still cannot completely outcompete producer cells ., Simulations , therefore , are able to predict the relative fitness of cheater cells from experiments and can also display and account for the paradoxical phenomenon seen in experiments; even though the cheater cells have a fitness advantage in each of the investigated groups , the overall effect is an increase in the fraction of producer cells ., The cell-based type of model presented here together with high-resolution experiments will play an integral role in a more explicit and precise comparison of models and experiments , addressing quorum sensing at a cellular resolution .

Unicellular organisms have the ability to communicate with each other via signaling molecules , leading to correlated behaviors resembling that of higher organisms ., This process , called quorum sensing , allows the cells to monitor the population size or density in a decentralized fashion and perform a common task when these parameters exceed predefined threshold values ., The quorum sensing mechanism has been implicated in diverse functions such as producing bioluminescence , virulence factors , and initiating biofilm formation ., Complex emergent behaviors , such as quorum sensing , can be hard to analyze and understand without the assistance of mathematical and computational models ., Here , we present a cell-based model of proliferating bacterial microcolonies and investigate how population-level responses can emerge from the signaling and mechanical properties of individual cells ., We study both signaling variations within homogeneous ( homotypic ) bacterial populations as well as signaling and competition in mixed heterotypic populations ., We investigate in particular how population size , local cell density , and spatial confinement affect colony growth and predict strategies for facilitating quorum sensing ., We also show that the interplay between “honest” quorum sensing signal producing bacteria and non-producing “cheaters” can lead to emergent feedback regulation via differentiated growth that provides only a transient benefit for cheating cells .

biophysics/theory and simulation, computational biology/systems biology, cell biology/cell signaling

journal.pcbi.0030181

Organization and Evolution of Primate Centromeric DNA from Whole-Genome Shotgun Sequence Data

Alpha-satellite is the only functional DNA sequence associated with all naturally occurring human centromeres ., Alpha satellite consists of tandem repetitions of a 171-bp AT-rich sequence motif ( called a monomer ) ., In humans , two distinct forms of alpha-satellite are recognized based on their organization and sequence properties ., In humans , a large fraction is arranged into higher-order repeat ( HOR ) arrays ( also known as chromosome-specific arrays ) where alpha-satellite monomers are organized as multimeric repeat units ranging in size from 3–5 Mb 1 ., While individual human alpha satellite monomer units show 20%–40% single-nucleotide variation , the sequence divergence between higher-order repeat units is typically less than 2% 2 , 3 ( Figure 1 ) ., The number of multimeric repeats within any centromere varies between different human individuals and , as such , is a source of considerable chromosome length polymorphism ., Unequal crossover of satellite DNA between sister chromatid pairs or between homologous chromosomes during meiosis is largely responsible for copy-number differences and is thought to be fundamental in the evolution of these HOR arrays ., The organization and unit of periodicity of these arrays are specific to each human chromosome 4 , 5 , with the individual monomer units classified into one of five different suprafamilies based on their sequence properties 5 , 6 ., Interestingly , studies of closely related primates , such as the chimpanzee and orangutan 2 , 7 indicate that these particular associations do not persist among the centromeres of homologous chromosome , implying that the structure and content of centromeric DNA changes very quickly over relatively short periods of evolutionary time ., In addition to higher-order arrays , large tracts of alpha-satellite DNA have more recently been described that are devoid of any HOR structure 6 , 8–11 ., The individual repeats within these segments show extensive sequence divergence and have been classified as “monomeric” alpha-satellite DNA ., Such monomeric tracts are frequently located at the periphery of centromeric DNA 9 , 11 , 12 ., Consequently , unlike higher-order arrays , some of these regions have been accurately sequenced and assembled because they localize in the transition regions between euchromatin and heterochromatin ., Phylogenetic and probabilistic analyses suggest that the higher-order alpha-satellite DNA emerged more recently and displaced existing monomeric repeat sequence as opposed to having arisen by unequal crossing-over of local monomeric DNA 8 ., Centromeres and pericentromeric regions are frequently poorly assembled in primate whole-genome sequence assemblies 13–15 ., These regions are generally regarded as too difficult to accurately sequence and assemble strictly from whole-genome shotgun ( WGS ) sequence ., However , most WGS sequencing efforts include substantial amounts of alpha-satellite repeat sequence ., Indeed , as much as 2%–5% of the sequence generated from the underlying WGS consists of centromeric satellite sequences—such data most often remain as unassembled in public database repositories ., In this study , we develop computational methods to systematically identify and classify alpha-satellite sequences from primate WGS sequence ., We predict novel HOR structures from uncharacterized primate genomes and define the phylogenetic relationship of these sequences within the context of known human HOR satellite sequences ., Finally , we take advantage of publicly available cloned resources to experimentally validate the dispersal of these newly described alpha-satellite sequences within various primate genomes ., The data provide the first genome-wide sequence analysis of alpha-satellite DNA among primates from WGS data and a framework to identify and characterize more repeat-rich , complex regions of genomes as part of genome sequencing projects ., We took advantage of the extensive annotation of human centromeric DNA in the literature to initially construct a non-redundant database of HOR monomeric repeat sequences ., We then retrieved WGS sequence data from four primate genomic libraries , identified alpha-satellite monomers using RepeatMasker , and extracted all alpha-satellite repeat units of ∼171 bp in length ( Table 1 ) ., Our analysis indicated that approximately 1%–5% of all end-sequenced clones generated as part of the WGS libraries represented potential centromeric subclones ., Although each library represents only 0 . 05–0 . 3 sequence coverage for each genome , human higher-order alpha-satellite arrays are typically 3–5 Mb in length , with hundreds to thousands of copies of each individual unit per chromosome ., Consequently , each human HOR unit would be expected to be represented multiple times despite the relatively low coverage of the sequence library ., We compared human WGS alpha-satellite sequences identified within the WIBR2 library to the non-redundant set of HOR sequences by pairwise alignment 16 and Hamming distance 17 ., A total of 70% ( 132 of 188 ) of human HOR sequences were specifically identified within WGS sequence data ( at most 4-bp mismatches ) , with an average representation of 240 reads per HOR monomer unit ., We note that the representation of particular classes was variable and less than the expected number ( R2 = 0 . 13–0 . 09 ) as predicted by published minimum and maximum length of each array ( Tables S1 and S4 , Figure S1 ) ., In several cases ( e . g . , D8Z1 , D9Z1 , and D16Z1 ) , sequence corresponding to the published HOR arrays was not discovered once within the library ( Table 2 ) ., We repeated this analysis with additional sources of human WGS sequence and obtained similar results ( Tables S1 and S4 ) ., The underrepresentation of particular sequences may indicate subcloning biases , variation in copy number , and/or sequence variation between centromeric HOR and published canonical alpha-satellite sequences ., We performed a pairwise analysis of all 135 , 816 human monomers retrieved from the human WIBR2 library ( see Methods ) ., Based on this self-comparison and the sequence similarity to published human HORs , we classified each monomer into one of three categories: ( 1 ) those that clustered with our dataset of published higher-order centromeric satellites; ( 2 ) those that clustered with each other but did not intersect those in ( 1 ) ; and ( 3 ) those that failed to cluster ., Since our goal was to recover novel HOR sequences , clusters were established where all members showed at maximum 4-bp differences with any other member in a cluster ., This target threshold was chosen because individual alpha-satellite sequences typically exhibit <2% sequence divergence with other paralogous members within a tandem array 18 ., By these criteria , 23 . 3% ( 31 , 691 of 135 , 816 ) of the recovered monomers clustered with known HORs , with an equivalent proportion ( 26 . 2% or 35 , 499 ) grouping into 142 HOR clusters not apparently represented in our original dataset ., The remaining 68 , 214 ( 50% ) alpha-satellite monomers represent divergent HOR sequences or putative monomeric alpha-satellite lacking higher-order structure ., WGS sequence reads corresponding to each cluster ( type 2 , as discussed above ) were then retrieved , and each related sequence read was encoded based on its cluster composition ( Figure 2 ) ., We would expect different monomeric units within different arrays to cluster if they are organized as HOR units ., Based on the average read length , a typical WGS read should , then , consist of approximately three distinct HOR monomers ., Encoded read compositions were then grouped into larger pattern sets based on a reiterative clustering algorithm ., As expected , the pattern set ultimately looped as a result of tandem repetition of the array ., We created sequence assemblies ( PHRAP; default parameters , -forcelevel=10 ) 19 , 20 for all pattern sets that included 30 or more independent WGS sequence reads ., A total of 18 distinct sequence contigs were created where the array length ( k ) ranged from 3–20 subunits ., Each assembled sequence contig was searched against GenBank ( nr database ) by BLAST ( default parameters , p = blastn ) ., We found that 3 of 18 patterns sets corresponded to higher-order alpha-satellite arrays , which had not been included in the original HOR set as part of our literature survey , while another 14 pattern sets showed sequence similarity to other human HOR but were discrepant with respect to published reports either in being more sequence divergent or incomplete with respect to the structure ( e . g . , D12Z3 , D17Z1 , D18Z1 , etc ) ., In the end , all but one computationally predicted HOR pattern set from the human WGS could be reconciled with published datasets ( literature or GenBank ) ., Our analysis predicted one potentially novel 8-mer HOR unit ( HSAHOR8; Table, 3 ) with 92% sequence similarity and 99% query coverage to a clone from Chromosome 22 , and only 85% sequence similarity and 94% query coverage to published alpha-satellite sequence D2Z1 ( Figure 3 ) ., In order to validate its structure , we performed a number of computational and experimental analyses ., As a measure of homogeneity , we computed an adjacency statistic that simply calculates the number of times a specified monomer within the WGS sequence read maps adjacently to another specified monomer within the predicted HOR unit ( Figure 2 ) ., If this repeat were organized as a multimeric tandem array , we would expect encoded monomers to map adjacently at a high frequency ., This adjacency statistic for this novel HOR repeat ranged from 97%–100% , indicating considerable homogeneity in the organization of the repeat unit ( Figure 3B ) ., Next , we analyzed mate–pair information associated with the WGS sequence reads ., In our model , we would predict that HOR units should be repeated hundreds of times to form a large array of centromeric sequence typically several megabases in length ., Consequently , corresponding end sequences from human fosmid clones should both map to the same encoded pattern set even though the two ends are separated by more than 40 kb ., For 155 of 156 end-sequence pairs , we observed both the forward and reverse WGS sequences mapping to the same ( encoded pattern set ) or HOR unit , confirming long-range tandem repeat organization within the clone ., As a final test , we performed fluorescence in situ hybridization ( FISH ) analyses using five different 40-kb fosmid clones representative of this new HOR array , using each as a probe in metaphase hybridizations ( Figure 3C ) ., FISH confirmed a typical centromeric HOR pattern , with signals observed on Chromosomes 14 and 22 ( Figure 3C ) for each of the five probes ., Our initial analysis was biased by triaging alpha-satellite sequences that clustered with known HOR units ., As such , we favored accurate reconstruction of these by partitioning the sequence complexity ., As a test of de novo alpha-satellite HOR reconstruction , we repeated our computational prediction of new higher-order arrays without excluding repeat units that map to HOR sequence ( Table 4 ) ., In this blind test , we accurately predicted 12 of 24 known higher-order arrays with more than 92% sequence similarity ., If we increase the maximum allowed Hamming distance from 4 to 6 , we recover two more arrays with sequence identity greater than 92% ( Table 4 ) ., This is likely a reflection of underrepresentation of particular classes of HOR sequence within WGS data ( Table S1 ) ., Although not all classes of human HORs could be recovered , this analysis suggested that the approach could be implemented to discover a subset of previously undescribed HOR structures in uncharacterized genomes ., In an effort to discover novel centromeric HOR units and to compare centromeric DNA in other primate genomes , we repeated our analysis for publicly available chimpanzee , gibbon , and macaque fosmid and bacterial artificial chromosome ( BAC ) end sequences ., We extracted and classified all monomeric alpha-satellite DNA into two groups: monomeric ( lacking HOR structure by our criteria ) or HOR ( evidence for HOR structure within WGS data ) ( Table, 1 ) for each species ., We identified encoded pattern sets in each species and assembled potential higher order repeats ( Table 3 ) ., Upon analysis of macaque “higher-order” arrays , all potential multimeric repeat units collapsed into a core dimeric repeat structure ( see Figure S2 ) ., While adjacent monomers showed 30%–45% sequenced divergence , pairwise sequence comparisons of dimeric repeats showed between 2%–5% sequence divergence ( Table S5; Kimura 2 parameter ) ., Similar values were obtained based on comparisons between the encoded pattern sets , suggesting considerable homogeneity in the structure and organization of macaque centromeric satellites ( as predicted by restriction digest analysis 21 ., In contrast , the chimpanzee encoded pattern set showed considerably more diversity in structure , more reminiscent of human centromeric DNA architecture ( Table 4 ) ., The average chimpanzee paired-end statistic for these pattern sets ( 37 . 21% ) was similar to accurately predicted HORs in humans , predicting the presence of HORs in chimpanzees ., Interestingly , the assembled chimpanzee sequences showed >12% sequence divergence when aligned to human HOR sequences ( maximum sequence identity between 78%–88% between human and chimpanzee HORs; Table S3 ) ., As a test of our in silico prediction of HOR structure , we retrieved a chimpanzee fosmid clone corresponding to seven of the chimpanzee alpha-satellite HORs ., We designed a specific restriction enzyme assay to digest once and only once within the chimpanzee higher-order array ( not including the fosmid polylinker multiple-cloning site ) ., Partial and complete restriction enzymatic digestions confirmed the presence of an alpha-satellite HOR structure in all subclones ., In six of seven cases , the observed fragment sizes were consistent with that expected based on in silico analyses ( Figure 4 and Table 3 ) ., Presence of distinct dimeric ladder-sized bands in complete digests suggests a lack of homogeneity or a more degenerate structure in chimp HOR arrays ., Similarly , restriction digests of macaque fosmid clones confirmed multiples of the basic dimeric repeat pattern ., As a final test , we selected a fosmid clone representing each of the chimpanzee and macaque HOR units and assessed its chromosomal distribution by metaphase FISH analysis ., In humans , it has been shown that centromeric HOR units are grouped into suprafamilies , and that subsets of nonhomologous chromosomes share monomer alpha-satellite sequences from the same suprafamily ., Consequently , probes representing a specific HOR unit can cross-hybridize to centromeres from nonhomologous chromosomes under low stringency hybridization conditions ., For the chimpanzee HOR , we observed each of the predicted HOR hybridizing to the centromeres of a set of nonhomologous chromosomes ( Table 3 and Figure 5A and 5B ) ., Unlike human HORs , we noted several secondary signals mapping to pericentromeric locations on chimpanzee chromosomes ., Moreover , even under high-stringency conditions , a single signal to a specific chromosome was seldomly observed ., As predicted 2 , 5–7 , hybridization of the chimpanzee probes against human metaphases mapped to the centromeres and pericentromeric regions of nonorthologous chromosomes ( Figure S3 ) ., We note that not all chimpanzee centromeres were identified in this analysis , indicating that only a fraction of the HORs have been successfully identified ., Furthermore , some chromosomes ( e . g . , Chromosomes 19 and 20 ) were common to a large number of the probes ., Interestingly , even in cases where the FISH patterns appeared virtually identical ( PTRHOR 3 and PTRHOR 8 ) , a sequence comparison revealed that the two HORs shared only 78 . 6% sequence identity , suggesting the presence of two different HOR units on the same chromosome ., Fosmids that were used as probes were required to have end sequences matching to the same pattern matching set ., We did not FISH those where one end mapped to HOR and the other did not ., Such fosmid clones may represent edges of arrays with diverged alpha-satellite ., In contrast to the human and chimpanzee , each probe isolated from the macaque and baboon libraries cross-hybridized equally well to all chromosomes ( with the exception of the Y chromosome; Figure 5C and 5D ) 21 , 22 ., Reciprocal experiments ( where baboon probes were hybridized to macaque , and vice versa ) confirmed a long-standing , predominant pancentromeric signal distribution in both species ( Figure S3 ) ., Despite numerous experiments , no probe could be unambiguously assigned to a specific chromosome in these species ., These data suggest fundamental differences in the structure and organization of centromeric DNA between the Old World and great ape primate lineages 2 , 21 , 22 ., In an effort to assess the evolutionary history of primate alpha-satellite sequence , we examined the phylogenetic relationship between both monomeric and higher-order alpha satellite sequences extracted from primate WGS sequence data ., In these analyses , we included all higher-order alpha satellite consensus sequences from human , chimpanzee , and gibbon centromeric regions; dimeric alpha-satellite sequences from macaque and baboon; monomeric alpha satellite sequences from New World monkey 6; and monomeric alpha-satellite sequence located at the periphery of Chromosome 8 8 ., In light of the large number of sequence taxa of limited length , we performed 100 bootstrap tests for each phylogenetic analysis ., Our analysis reveals a tripartite evolutionary relationship among these primate sequences; Old World monkey , ape higher-order , and human monomeric alpha-satellite are each evolutionarily distinct ( Figure 6 ) ., The data show clear introgression of our predicted chimpanzee HORs , with human suprafamily designations , while our limited survey of gibbon sequences suggest the possibility of a distinct origin from a common set of ape ancestral HOR sequences ., The dimeric repeat structure is the fundamental unit of macaque centromeric DNA ( Figure 6B ) ., Random sampling , as well as testing of alpha-satellites mapping to encoded pattern sets from the macaque , all show a distinct bifurcation ( Figure 6 , Figure S4 , and unpublished data ) ., Analysis of alpha-satellite sequences identified from random BAC end sequences of the colobus , African green monkey , and baboon confirm that the dimeric repeat structure is common to all Old World monkey species ( Figure 6C ) ., The current model of primate centromere DNA organization has been developed almost exclusively from FISH and restriction enzyme studies of the human genome in the last 25 years 4 , 5 , 23 ., These efforts required the systematic cloning and sequencing of heterochromatic DNA , frequently from chromosome-specific reagents ., Our understanding of the extent of sequence and structural diversity among nonhuman primates is much more limited 2 , 11 , 21 , 22 , 24–26 ., We developed an algorithm to identify , categorize , and reconstruct HOR structures from genome-wide sequence data ., In this study , we analyzed more than 1 . 42 Gb of sequence primarily from three species to identify 265 , 868 ( Table, 1 ) alpha-satellite repeat units corresponding to an estimated 100 , 000 BAC and fosmid clones ., Our results provide a genome-wide perspective on the evolution and structure of these regions and a clone framework for further evolutionary , cytogenetic , and sequence characterization ., We have demonstrated that it is possible to reconstruct known HOR alpha-satellite organization in humans via an algorithm that exploits the multimeric tandem repeat organization and the extensive intrachromosomal sequence homogenization of alpha-satellites ., Although many human HOR sequences could be identified ( Tables 2 and S1 ) , not all were recovered from analysis of WGS sequence ., Although restriction enzyme and subcloning biases are most likely responsible for this , our analysis of different human genome libraries of various insert size , vector type , and subcloning strategies ( including WGS from randomly sheared DNA ) showed virtually identical biases ( Table S1 ) ., In addition , not all of those correctly identified as human HORs could be properly assembled into a pattern set that completely corresponded to the known sequence array ( Table S2 ) ., Due to these limitations , our approach should be viewed as opportunistic at this point , as opposed to comprehensive ., Advances in sequencing technology that obviate the need for subcloning may lead to better characterization of centromeric DNA 27 ., The most important factor in correctly predicting HOR pattern sets was the Hamming distance choice for clustering of repeats ., There is a tradeoff between sensitivity and specificity ., A Hamming distance estimate that is too low will fail to cluster related repeats , while increasing the value will lead to overcollapse and a concomitant loss of power to accurately distinguish HOR pattern sets ., In humans , we optimally set the Hamming distance to 4 based on paralogous sequence divergence between multimeric units within the human HOR arrays ., In a blind study of human WGS sequence , we estimate that approximately 12 of 24 ( Table, 2 ) multimeric units can be partially or fully reconstructed at this distance ., The heuristics described to merge pattern sets may also impose problems in HOR array prediction ., If there exists two different HOR sets that include monomers of high sequence identity ( <2% divergent ) , the pattern-merging scheme may generate chimeric higher-order structures ., For this reason , we only use the HOR structures that are experimentally verified as part of our phylogenetic analysis ., In addition , all the HOR structures reconstructed using human WGS reads are either identical to previously published HOR arrays , or validated experimentally ., Similarly , all but one computationally predicted HOR structure in the chimpanzee can be experimentally validated ., The availability of paired-end sequence data and corresponding clone reagents provide additional tools for confirmation ., Our analysis of human WGS data , for example , identified a previously undescribed HOR sequence structure ( HSAHOR8 ) and corresponding clones for testing ., Mate-pair data from human fosmid ends ( 40-kb inserts ) confirm that 99 . 35% of the pairs map to the same pattern set , confirming tandem reiterations of this multimeric repeat unit ., FISH analysis of a corresponding fosmid clone from the library ( Figure 3 ) map the novel higher-order sequence to the primary constriction of Chromosomes 14 and 22 ., Similarly , analysis of chimpanzee fosmid paired-end sequence data identified seven novel HOR units of various lengths ( Table 3 ) , and FISH analysis assigned each of these to specific centromeres on chimpanzee chromosomes ( Figure 5A and 5B ) ., Phylogenetic analyses confirm that human and chimpanzee HOR alpha-satellites share a common origin 23 that is evolutionarily distinct from the flanking peripheral monomeric sequences ., Every major human alpha-satellite suprachromosomal family shares homologous sequences with chimpanzee ( Figures 6A and S5 ) , despite the fact that they map to nonorthologous chromosomes between the two species ( Table 3 ) ., A comparison of gibbon alpha-satellites reveals only limited introgression with human–chimpanzee sequence clades ., These data suggest that gibbon HORs evolved , in large part , independently from that of the human and chimpanzee ., It should be noted however , that the number of gibbon sequences is significantly fewer ( Table 1 ) ., In addition , the gibbon sequences are derived from a large-insert BAC library where restriction enzyme subcloning biases are thought to be more pronounced ., Additional sequencing of the gibbon genome in smaller insert libraries may reveal other , yet unreported sequences and phylogenetic relationships ., Comparisons between ape and Old World monkey alpha-satellite DNA confirm two radically distinct patterns of centromeric organization and chromosome distribution 21 , 22 , 25 ., Almost all ( 80% of all monomers at Hamming distance = 10 ) macaque alpha-satellite sequences are organized around a distinct dimeric repeat structure configuration ( Figure 6B ) ., Sampling of different Old World monkey species ( including colobus , African green monkey , macaque , and baboon ) confirm that the dimeric structure is ancient ( 15–20 million years old ) based on the estimated evolutionary divergence of these species 28 ., FISH analysis with either baboon or macaque probes reveal a pancentromeric distribution on metaphase chromosomes ( testing of representative clones from each of the ten HOR pattern sets showed no difference; Figure 5 ) ., Unlike the great ape higher-order alpha-satellite , HOR structures cannot be assigned to a specific chromosome in these species ., These data provide compelling evidence that intrachromosomal homogenization of alpha-satellite DNA has predominated in humans and apes , while transchromosomal exchanges have been the dominant mode among all Old World monkey species ., In summary , we have shown that we can systematically extract evolutionary data regarding centromeric DNA structure and organization from the 2%–5% of WGS sequence data that is typically excluded as part of genome sequencing projects ., We provide one of the first genome-wide analyses of centromere structure and evolution from human , chimpanzee , and macaque ., Fundamental differences in the structure and organization of centromere DNA between ape and Old World monkey lineages are confirmed 21 , 22 ., The availability of these clone reagents provides a resource for further functional and sequence characterization of primate centromeres and pericentromeric transition regions 29 ., We constructed a nonredundant reference set of 254 monomer units from published human higher-order alpha-satellite DNA sequences 6 , tracking their suprafamily designation 5 , 6 ., We classified 188 units as canonical human HOR sequence and distinguished an additional 66 as divergent HOR units due to their association with atypical or more divergent centromeric arrays ( e . g . , Y chromosome and short arm of acrocentric chromosomes ) ., An additional ∼270 , 000 alpha-satellite monomers were obtained from WGS sequences from various published primate genomic sequencing projects 14 , 15 , 30–32 ., Sequence and corresponding paired-end sequence annotation was obtained from the National Institutes of Health trace repository ( http://www . ncbi . nlm . nih . gov/Traces/trace . cgi ) from two human library sources ( Fosmid library WIBR2 31 ) and WGS data from Celera 30 ) and three nonhuman primate libraries , including chimp ( Pan troglodytes ) fosmid library ( CHORI-1251 ) 15 , rhesus macaque ( Macaca mulatta ) fosmid library ( MQAD ) 14 , and Northern white-cheeked gibbon ( Nomascus leucogenys ) BAC genomic library ( CH271 ) ., A small subsample ( 300–500 alpha-satellite monomers per species ) was obtained from randomly end-sequenced BAC clones from various Old World monkey species , including olive baboon ( Papio hamadryas anubis; RPCI-41 ) , vervet monkey ( Cercopithecus aethiops; CH252 ) , and black-and-white colobus monkey ( Colobus guereza; CH272 ) ., We would expect to recover more alpha-satellite sequences from Old World Monkey genomes ., However , restriction bias limits subcloning of particular regions , especially in the case of BAC subclones ., It is also the likely reason we do not recover all HOR sequences in humans ., As a representative of human monomeric DNA lacking higher-order structure , we extracted ( 360 monomers ) from a previously described genomic clone mapping peripherally of higher-order alpha-satellite DNA ., We also extracted 71 monomers from another genomic clone mapping peripherally of higher-order alpha-satellite DNA on Chromosome 19 to further validate the phylogenetic relationship of monomeric versus HOR alpha-satellite sequences ., Alpha-satellite DNA sequences were retrieved from WGS data from human , chimpanzee , gibbon , and macaque fosmid and BAC end sequences used as part of genome sequencing projects ( Table 1 ) ., Reads containing alpha-satellite sequences were initially identified by BLAST sequence similarity searches ( p = blastn , v = 10 , 000 ) , and individual monomer units were extracted using a customized RepeatMasker library 33 with higher-order alpha-satellite consensus sequences in 6 ( parameters: -no_is –nolow –lib ‘hor . fa ) ., We extracted alpha-satellite monomers with the same begin and end positions based on RepeatMasker coordinates 33 ., This procedure generated a total of 265 , 868 alpha-satellite monomer repeat units ., For each species , we constructed all possible pairwise alignments for each monomer pair and computed the aligned Hamming distance ( defined as the minimum number of substitutions required to change one string into the other ) between each pair 17 ( not counting indels ) as follows: Hamming distance computation is solvable in O ( n ) time for a pair of sequences of length n ., Here , we compute Hamming distance of pairwise alignments; thus , computation of aligned Hamming distance takes O ( n2 ) time for a pair of sequences , and O ( k · m · n2 ) time for m repeat units against k alpha-satellite ., To compute the aligned Hamming distance faster , we exploited the fact that the divergence of any pair of alpha-satellite sequences is less than 40% ., We first built the multiple sequence alignment of all 188 sequences in the HOR set via Clustal W 34 and used the computed consensus sequence of the alignment as a centroid , where it is aligned pairwise with all WGS repeat units ., This step is reminiscent of the “center-star multiple alignment” method described in 35 ( pp . 348–350 ) ., If any gaps are inserted to the centroid as a result of a pairwise alignment with a WGS repeat unit si , the bases in si that correspond to a gap in the centroid are removed ., Thus all the sequences are converted to a new version of the sequence , where all the sequences are of equal length , and the bases that can be optimally aligned to the consensus ( therefore conserved in most monomers ) are readjusted to the same location within the sequence ., As stated above , we only count the number of substitutions during the Hamming distance computation , and indels are not penalized ., Any bases inserted in a monomer but not present in the consensus ( thus a specific insertion for that monomer ) would induce gaps to the other monomer when pairwise alignments are performed ., This method of normalizing the sequences precipitates the removal of such bases inserted in a monomer that would not be counted in any case , while aligning the conserved regions ( along with substituted bases ) to the same coordinates ., This ensures that the Hamming distance of any two alignments of repeat units against the centroid would be the same as their aligned Hamming distances ., The pairwise alignment of m repeat units and k higher-order consensus sequences with the centroid is completed in O ( ( m + k ) · n2 ) time , Hamming distances for all pairs of sequences take O ( k · m · n ) time , and the overall distance computation time is thus reduced to O ( ( m + k ) · n2 + k · m · n ) ., Once the Hamming distance was computed for each pair , we classified monomers into one of three categories: ( 1 ) repeat units that have aligned Hamming distance at most four to at least one of the consensus sequences in HOR or divergent HOR unit sets ( typical divergence of monomers within an array is <2%; we therefore set the typical Hamming distance to 171 × 2% = 3 . 41 ≈ 4 ) subset have aligned Hamming distance of at most four ( potential new HOR units ) ; and ( 3 ) the remaining repeat units that fail to cluster by this threshold cutoff ., In the human genome , it is usually possible to

Introduction, Results, Discussion, Methods, Supporting Information

The major DNA constituent of primate centromeres is alpha satellite DNA ., As much as 2%–5% of sequence generated as part of primate genome sequencing projects consists of this material , which is fragmented or not assembled as part of published genome sequences due to its highly repetitive nature ., Here , we develop computational methods to rapidly recover and categorize alpha-satellite sequences from previously uncharacterized whole-genome shotgun sequence data ., We present an algorithm to computationally predict potential higher-order array structure based on paired-end sequence data and then experimentally validate its organization and distribution by experimental analyses ., Using whole-genome shotgun data from the human , chimpanzee , and macaque genomes , we examine the phylogenetic relationship of these sequences and provide further support for a model for their evolution and mutation over the last 25 million years ., Our results confirm fundamental differences in the dispersal and evolution of centromeric satellites in the Old World monkey and ape lineages of evolution .

Centromeric DNA has been described as the last frontier of genomic sequencing; such regions are typically poorly assembled during the whole-genome shotgun sequence assembly process due to their repetitive complexity ., This paper develops a computational algorithm to systematically extract data regarding primate centromeric DNA structure and organization from that ∼5% of sequence that is not included as part of standard genome sequence assemblies ., Using this computational approach , we identify and reconstruct published human higher-order alpha satellite arrays and discover new families in human , chimpanzee , and Old World monkeys ., Experimental validation confirms the utility of this computational approach to understanding the centromere organization of other nonhuman primates ., An evolutionary analysis in diverse primate genomes supports fundamental differences in the structure and organization of centromere DNA between ape and Old World monkey lineages ., The ability to extract meaningful biological data from random shotgun sequence data helps to fill an important void in large-scale sequencing of primate genomes , with implications for other genome sequencing projects .

primates, homo (human), computational biology, evolutionary biology, molecular biology

journal.pcbi.1004210

Particle Simulation of Oxidation Induced Band 3 Clustering in Human Erythrocytes

The clustering of membrane proteins plays an important role in various cellular processes , ranging from signal transduction to cell migration 1 ., In human red blood cells ( RBCs ) , oxidation induced clustering of anion exchanger 1 ( band 3 ) greatly contributes to determining the timing of cell removal , by generating a high affinity site for autologous antibody binding ., Although enhanced band 3 clustering has been closely associated with certain RBC disorders causing hemolytic anemia , such as glucose-6-phosphate dehydrogenase ( G6PD ) deficiency 2 , 3 , malaria 4 , 5 , and sickle-cell disease 6 , as well as critical biochemical changes during blood storage 7 , 8 , the details of its molecular mechanisms remain poorly understood ., Band 3 is an integral membrane protein that accounts for approximately 25% of the RBC membrane surface ., It has a number of functions , including the aid of anion transport across the membrane 9 , 10 , regulation of the glycolytic pathway 11 , 12 , stabilization of the membrane structure 13 , 14 , and control of RBC lifespan 15 , 16 ., At normal state , several band 3 are bound to a cytoskeletal network of spectrin , a long , flexible rod protein , and have limited or confined diffusion ., However , elevation of oxidative stress levels , promotes the oxidation 17 , phosphorylation 18 , and dissociation of band 3 from the spectrin cytoskeleton 19 , resulting in enhanced mobility ., This subsequently leads to the formation of band 3 clusters , which form a neoantigen that binds autologous immunoglobulin ( IgG ) and complement , thus allowing opsonization or direct recognition by phagocytes 20 , 21 ., Several experimental studies in the past have attempted to characterize the chain of reactions leading to band 3 clustering , using in vitro models 3 , 4 ., Increased oxidation , phosphorylation , and clustered band 3 levels have been observed in such experiments of RBCs treatment with oxidizing agent diamide ., While inhomogeneous distributions of band 3 have also been observed in other studies 3 , 7 , 22–25 , there has been no framework to integrate such spatial and temporal data , to enable the simultaneous monitoring of multiple factors during oxidative treatment ., Moreover , utilizing single-particle tracking techniques using fluorescent protein tags for direct measurement of its reaction dynamics , such as in other protein clustering studies 26 , 27 , remains difficult because of the lack of protein synthesis machinery and large quantity of band 3 in RBCs ., Thus , details of how these clusters form , and how they behave under different conditions remains largely unexplored ., Computational models have the advantage of enabling observation of the time profile of multiple components , and the effects of changes in multiple individual parameters on the total system ., Due to the pivotal role of RBCs in our bodies and the simplicity of their systems , a number of models of human RBC biochemical pathways have been published in the past 28–32 ., Previously , we developed a computational model to assess the changes in RBC metabolism during oxidation by hydrogen peroxide 33 , however spatial information and reactions representing alterations in band 3 were excluded ., To mechanistically describe oxidation induced band 3 clustering , a novel strategy that incorporates deterministic algorithms to model metabolic reactions , and stochastic algorithms with particle reaction-diffusion processes to model band 3 behavior is needed ., By integrating a kinetic model of RBC antioxidant metabolism with a model of band 3 diffusion , we introduce a particle simulation model that enables the prediction of oxidation induced band 3 cluster formation at single molecule resolution ( overview illustrated in Fig 1 ) ., We show that our model reproduces the time-dependent changes of glutathione ( GSH ) and clustered band 3 levels , as well as band 3 distribution in human RBCs treated with diamide , observed in experimental studies ., Through parameter analysis , we predict that the formation of these transient and densely clustered regions of band 3 , are dependent on high affinity between clustered molecules and irreversible state transitions of band 3 ., In addition , we simulate the responses of band 3 under repeated oxidative perturbation , to predict how clustering could contribute to in vivo erythrophagocytosis ., Finally , we extend the model to include to the effects of cytoskeletal components , to observe how functional impairment of the membrane cytoskeleton could affect band 3 clustering ., Our model was developed on E-Cell Simulation Environment Version 3 ( E-Cell 3 ) 34–36 installed with Spatiocyte 37 ., Spatiocyte is a lattice-based Monte-Carlo simulation method that can model complex reaction-diffusion mediated cellular processes at single molecule resolution ., To represent cell compartments and to rapidly resolve molecular collisions , the method discretizes the three-dimensional space into hexagonal closed-packed lattice ., Each molecule randomly walks voxel-to-voxel in a time step , calculated from its diffusion coefficient ., Molecular collisions can take place between each walk ., Collisions between two reactive species molecules can generate one or two product molecules with a probability , p that is computed from the rate of reaction ., Immobile lipid molecules represent surface compartments such as cellular and nuclear membranes ., Stochastic reactions involving homogeneously distributed species in a compartment are performed using a modified Next Reaction method 37 , 38 ., In this work , we have further integrated the Spatiocyte method with the ordinary differential equation ( ODE ) solvers of E-Cell 3 to simultaneously perform Michaelis-Menten and mass action reactions involving large number of metabolites ., The integration of stochastic , deterministic and particle reactions with the diffusion processes of Spatiocyte was supported by the multi-algorithm , multi-timescale method of E-Cell 3 ., A schematic representation of our developed model and example images of RBCs exhibiting inhomogeneous distribution of band 3 in presence of diamide are shown in Fig 2 ., Reaction rates and parameters of the antioxidant pathways were extracted from a previous whole human RBC metabolism model 39 ., The full set of the ODEs , parameter list , and initial conditions for these reactions are provided in S1 Text ., Since the focus of this study is to reproduce the effects of oxidation at single molecule resolution , rather than precisely predict the metabolic changes , a minimal amount of metabolic pathways was implemented into the model ., Reaction-diffusion of band 3 was modeled using Spatiocyte ., The series of band 3 related reactions is given in Table 1 ., The initial model parameters and reactions rates for these reactions are given in S1 Table ., Di represents the diffusion coefficient of molecule i , k represents intrinsic rate constants , and p represents the probability for a reactive collision between reactants ., Reaction rates and schemes for band 3 oxidation , phosphorylation and clustering were determined by manually fitting the GSH depletion and reversible band 3 clustering curve in control RBCs , as measured by a previous experiment 3 ., Cluster images , and cluster size estimates 23 from previous experimental studies , were also used to estimate the parameters that were not available from literature ., All simulation results represent the average for stochastic simulations of 100 runs ., Our model is composed of largely three components of the band 3 clustering process; oxidation , phosphorylation , and clustering ., The process of oxidation is represented by the formation of oxidized band 3 ( Band3oxi; Table 1 equation 1 ) , which is based on previous evidence of oxidized—SH groups in band 3 in the presence of diamide 40 ., Further , we have also included diamide induced rapid conversion of GSH to oxidized glutathione ( GSSG ) ( 41 , Table 1 equation 2 ) and the formation of mixed disulfides between GSH and other protein—SH groups to form S-glutathionylated proteins ( PSSG , 42 , 43; S1 Text ) in the model ., Band 3 oxidation is modeled as a reversible reaction , as it has been previously reported that GSH also promotes the reduction of oxidized band 3 , returning it to the normal state ( Table 1 equation 3 ) ., The binding and phosphorylation of the band 3 cytoplamic region by Syk tyrosine kinase , has been previously shown to follow oxidation ( Table 1 equation 4 ) ., In the model we have simplified the formation of phosphorylated band 3 ( Band3phos ) to be independent of Syk concentration , based on the assumption that Syk exists in sufficient amounts throughout the interior of the RBC ., As band 3 has previously been identified as a target for SHP-2 tyrosine phosphatase 44 , we also included its dephosphorylation reaction ( Table 1 equation 5 ) ., In the actual RBC , it has been reported that band 3 bound to the spectrin cytoskeleton , detaches from spectrin upon phosphorylation , and gains greater mobility ., In our main model we have represented this with an increase in diffusion coefficient , as observed by an experimental study ., 19 ., We mimicked cluster formation by creating an immobile cluster species ( Band3cluster ) that forms as phosphorylated band 3 molecules collide ., The immobility of Band3cluster is based on previous observations of the static clustered proteins 26 ., The clustering process was separated into two parts; the initial nucleation of the cluster ( Table 1 equation 6 ) , and the following growth of the cluster ( Table 1 equation 7 ) ., As reversible clustering has been described in previous works , we also included a reaction that allows clustered molecules to dissociate from the cluster at certain rates ( Table 1 , equation 8 ) ., Furthermore , in addition to these basal reactions , we added a scheme for the attachment of hemichrome , a degraded product of hemoglobin ( Hb ) oxidation ( Table 1 equation 9–15 ) ., This will be further discussed in the following sections ., All simulations were carried out on a Linux cluster , running E-Cell 3 and Spatiocyte ., A single molecule was set to represent a dimeric band 3 in its natural form 45 ., During initialization , all band 3 molecules were randomly placed on the cuboid compartment surface with a dimension of 1 . 06 μm length , 1 . 06 μm width , and 89 nm height ( S1 Table ) , which represents approximately one-thousandth volume of an actual RBC and the corresponding surface area ., Simulations were performed using voxels of radius 3 . 62 nm , and for this setup with 4800 diffusing molecules , it took approximately five hours of actual time to simulate six hours ., To compare the localization of multiple clusters on a whole RBC surface , additional simulations were also run on a RBC-like biconcave compartment ( S1 Text ) with one-hundredth volume of the actual RBC and the corresponding surface area ., To ensure steady state before diamide addition , we also simulated the values of several antioxidant pathway related metabolites in the absence of diamide addition ( S1 Text ) ., We simulated the response of healthy and G6PD deficient RBCs at 30% hematocrit after treatment with 0 . 25 mM diamide at t = 0 min , to fit the conditions of the previous experimental study 3 ., G6PD catalyzes the first reaction of the pentose phosphate pathway , and is a key enzyme that helps maintain high levels of antioxidant metabolites such as GSH and reduced nicotinamide adenine dinucleotide phosphate ( NADPH ) in the cell ., Therefore G6PD deficient RBCs have been known to have lower levels of these metabolites and thus are more susceptible to maintain homeostatic levels of these compounds after oxidative treatment 46 ., The patient-specific parameters are given in Table 2 47 ., In addition to these conditions , we also traced clustered band 3 levels of the control RBC model during treatment with pulses of diamide with varying intervals ( from 1 to 60 min ) , and concentrations ( from 0 to 0 . 25 mM ) , to assess the properties of clustering under repeated oxidation ., To study the interaction of the RBC membrane cytoskeletal network and its role in band 3 clustering , the above model was further extended to include compartments for positioning of spectrin and spectrin-bound band 3 molecules ( S1A Fig ) ., The model structure is explained in detail in S1 Text ., In the extended model , the spectrin molecules were placed in contiguous rows to represent filaments ., The filaments would intersect to form a group of equilateral triangles , and the spectrin-bound band 3 molecules were located at the intersection points ., Such six-fold triangular structure has been used previously in several studies to represent the RBC spectrin cytoskeleton 48–51 ., The edge lengths were set to 100 nm , as in the actual RBC membrane ., Initial band 3 were defined as two different species; spectrin-bound band 3 which localizes at the intersection of spectrin filaments ( BoundBand3 ) , and free band 3 which has slightly confined diffusion compared to our non-spectrin model because of the physical spectrin barriers ., The reaction schemes and rates for the oxidation induced changes in band 3 were left the same ., To validate model accuracy , the position of a single freely diffusing molecule was tracked every 0 . 22 ms for 700 ms , and compared to the diffusional behavior of band 3 undergoing confined diffusion between compartments from previous experiments 57 ., Furthermore , simulation of 0 . 25 mM diamide treatment in RBCs with different amounts of initial spectrin ( S2 Table ) was carried out to assess the effects of cytoskeletal defect on band 3 clustering ., Spatiocyte performs diffusion of molecules at predefined intervals , computed from the voxel radius and the diffusion coefficient ., The deterministic and stochastic reactions , however , are executed with event-driven step intervals ., Phosphorylated band 3 molecules require an interval of 12 μs before walking to a neighbor voxel , whereas unphosphorylated band 3 molecules diffused much slower with an interval of 1 . 2 ms . For all reactions in the RBC model that does not consider the spectrin cytoskeleton ( control model ) , we calculated the average interval for a reaction event to take place during steady state cluster formation ( from t = 1000 s to t = 2000 s ) ., Clustering of phosphorylated band 3 molecules , which is a diffusion-limited reaction , was the fastest , with an average interval of 337 μs ., The slowest diffusion-influenced reaction was the dimerization of hemiBand3phos , which nucleates the hemiBand3cluster , at 400 ms on average ., The fastest Gillespie’s next reaction was the first-order reaction that reduces Band3cluster to Band3phos , each event taking place at 0 . 7 ms intervals ., The dephosphorylation of hemiBand3phos to hemiBand3oxi was the slowest stochastic reaction , with an average interval of 152 s ., The fastest ODE reaction was S-glutathionylation that takes place every 39 ms , whereas G6PDH was the slowest deterministic reaction , with an average interval of 104 s ., For all diffusion-limited reactions in the model , the highest probability for a bimolecular reaction to occur upon collision is unity ( Table 1 ) ., Therefore , the situation where the molecules stop diffusing until a reaction event takes place , does not arise ., In all diffusion-influenced reactions , the diffusion step interval is shorter than the average reaction interval ., For example , in the case of phosphorylated band 3 species , the diffusion interval is 12 μs whereas its shortest reaction interval is 337 μs ( Band3phos + Band3cluster → Band3cluster + Band3cluster ) ., As a result , the Band3phos molecule performs random walk on average 337/12 = 28 times before it collides with Band3cluster and reacts with unit probability ., We use the modified Gillespies next reaction to perform reactions involving a diffusing species such as band 3 and a chemical species such as diamide ., In Diamide + Band3 → Band3oxi for example , when the reaction time is up , a single band 3 molecule is selected randomly and is converted to band3oxi while the diamide molecule number is decremented ., To evaluate the diffusion behavior of band 3 molecules , we measured the effective diffusion coefficient of unphosphorylated band 3 molecules under various conditions ., We first diffused 100 band 3 molecules devoid of reactions and spectrin on a membrane with periodic boundary condition at the edges ., We measured an average diffusion coefficient of 1 . 09×10-14 m2s-1 , which agrees well with the specified diffusion coefficient ( 1 . 0×10-14 m2s-1 ) in the model ., Increasing the number of molecules to 4800 , which is the total number of band 3 in the control model , resulted in 8 . 1×10-15 m2s-1 ., This slowdown in diffusion is attributed to the excluded volume effect brought by the crowded band 3 molecules on the membrane ., Next , we added all of the reactions in the control model but removed diamide to prevent clustering ., We obtained the same effective diffusion coefficient of 8 . 1×10-15 m2s-1 , which demonstrates that the reactions do not affect the diffusion behavior ., Adding diamide to the model generated band 3 clustering and resulted with an effective diffusion of 7 . 2×10-15 m2s-1 during steady-state clustering ( from t = 1000 s to t = 2000 s ) ., However , measuring only the freely diffusing band 3 molecules gave the same coefficient of 8 . 1×10-15 m2s-1 ., Therefore , the slower diffusion coefficient during clustering is attributed to the fixed band 3 molecules in the clusters ., Finally , when spectrin cytoskeleton was added to the model , its cage-like effect caused the diffusion coefficient of band 3 to drop drastically to 6 . 0×10-16 m2s-1 , when measured for 100 s ., To study band 3 spatial distribution during diamide exposure , we utilized a technique that labels band 3 via the lysine-430 of their cytoplasmic domain 53 ., Mouse RBCs were washed in 5% PBS glucose solution with 1 mM EDTA solution for preparation ., For the diamide samples , RBCs were incubated in 0 . 125 mM diamide ( from Sigma , St . Lewis , Missouri , USA ) for 30 minutes at 37°C ., Control and diamide-treated RBCs were then fixed using 4% paraformaldehyde , labeled with 0 . 5 mM eosin-5-maleimide ( EMA; from Molecular Probes , Eugene , Oregon USA ) , and washed three times with PBS ., Images were collected using an inverted microscope ( Ti-U , Nikon ) with a 100x 1 . 49 NA oil-immersion objective lens , an EMCCD camera with 512x512-pixel chip ( iXon3 , Andor Technology , Belfast , UK ) and a fiber-coupled 488 nm laser ., Eosin was excited by a 488 nm laser and fluorescence was collected through a 525/45 nm bandpass filter ., NIS elements software ( Nikon , Tokyo , Japan ) was used for image acquisition and ImageJ software was used for image processing and final figure preparation ., To better understand the mechanics of band 3 clustering , we developed a model that represents the changes in metabolites and band 3 after diamide treatment ., When we treated RBC with 0 . 125 μM diamide for 30 min , the rate of cells exhibiting bright puncta was significantly increased compared with control condition ( Fig 2A and S2 Fig ) , indicating that oxidation stress by diamide induces the rearrangement of band 3 in RBCs ., The experimental result showed good agreement with previously published experimental data 3 ., Our simulation results also produced similar patterns to our microscopy images ., As illustrated in Fig 3A , transient decrease of GSH and increase of clustered band 3 levels were observed in the control RBC following diamide treatment ., In addition , progressive and irreversible decrease of GSH and increase of clustered band 3 levels were observed in GP6D deficient RBC ., In the visuals ( Fig 3B ) , relatively larger clusters were observed in the G6PD deficient RBC , compared to those of the control RBC ., The mean for the percentage of clustered molecules at t = 15 , 30 , 60 , and 120 min for 100 simulation runs was 7 . 61% , 10 . 2% , 7 . 42% , and 2 . 47% for the control , and 15 . 3% , 32 . 4% , 41 . 0% , and 41 . 3% for the G6PD deficient RBC , respectively ., The differences in cluster size and distribution in our biconcave shape model results , were similar to a previous comparative imaging study of control and oxidant susceptible RBCs with visceral lesihmaniasis , ( Fig 3C; 24 ) ., Furthermore , clusters displayed growth until 30 min and then exhibited shrinkage in the control RBC , whereas clusters grew continuously in G6PD deficient RBCs ., To determine the reactions that contribute to cluster formation , we assessed the influence of discarding several model specifications we had incorporated in our developed model ., For example , we found that modifying the reaction settings for cluster dissociation rates resulted in greatly different morphological characteristics , even under the same simulation conditions of 0 . 25 mM diamide treatment ., In our developed model , we had modeled cluster breakdown so that the dissociation rate of cluster-associated band 3 exponentially decreases with the addition of a neighboring molecule ( Fig 4A , 54 ) ., Hence , the more bound sites there are , the harder it is for the molecule to dissociate from the cluster ., This bound-site dependent dissociation rate scheme always resulted in the formation of large , dense and stable clusters , closely representative of experimentally reported protein clusters ( Fig 4B right ) ., However , when representing this process with a first order reaction , where clustered band 3 is converted back to its freely phosphorylated state independent of its position , several sparse , static clusters were formed ( Fig 4B , left ) ., The difference was also quantified by comparing the number of bound sites of each clustered molecule in the two models ( Fig 4B ) ., At 30 min , the peak timing for clustered levels , a large population of molecules were bound to 1 to 3 molecules , whereas in the latter model , most were bound to 4 to 6 molecules ., Therefore it could be implicated that affinity between clustered molecules is closely correlated with cluster shape formation dynamics ., In developing our model , we attempted to reproduce two main properties of band 3 clustering in control RBCs observed from the experimental data 3; the transient clustering of band 3 , and the maintenance of low levels of clustered band 3 over a long period following diamide exposure ., During this process , we found that cluster reversibility is greatly affected by the presence of irreversible state transitions of band 3 ., When representing the sequential reactions leading to cluster formation with only a fully reversible scheme ( Fig 5A , top ) , we found that a high rate of reverse reactions ( reduction , dephosphorylation , and cluster dispersion ) compared to forward rates , was required to reproduce the transient behavior ( Table 1 ) ., However , the high rate also resulted in the complete breakdown of the cluster at t = 60 min in the control RBC ( Fig 5B ) ., As this contradicted the second property , we speculated that a portion of the clustered band 3 undergo irreversible transition into a state that inhibits its breakdown ., A number of previous studies have pointed out the role of hemichrome , a product of hemoglobin ( Hb ) denaturation , in promoting band 3 clustering 19 , 55 , 56 ., Hb is oxidized by molecular oxygen to form ferric methemoglobin ( metHb ) ., Although metHb can be reduced back to Hb by a nicotinamide adenine dinucleotide ( NADH ) -dependent reducing system , it has been shown that the oxidation process can be followed by the transformation of metHb into hemichrome ., The hemichrome then binds to the cytoplasmic domain of band 3 , forming an insoluble copolymer , which is suggested to play a key role in the control of damaged and aged cells in the blood circulation ., In the developed model , we added a reaction where hemichrome formed by strong autoxidation is attached to cluster-associated band 3 molecules to disable them from returning to their original freely diffusing state ( Fig 5A; Table 1 equation 9–15 ) ., As a result , clustered levels remained present even after 60 min ( Fig 5B ) ., Similarly , whereas without the hemichrome formation reaction G6PD deficient RBCs exhibited transient clustering of band 3 molecules , the addition of the reaction resulted in formation of permanent clusters ( Fig 5B ) ., These results suggest that oxidation induces formation of hemichrome associated clusters , which extend cluster lifespan and prolong its effects , even hours after the oxidative insult ., Human RBCs have a lifespan of 120 days , during which they are continuously exposed to small amounts of oxidative stress from superoxides and hydrogen peroxide 52 ., Since previous experiments have only assessed band 3 clustering after a single oxidative event , we applied the model to observe the consequences of multiple oxidative perturbations , to mimic a more physiological situation of oxidative stress ., Dosages of 0 . 25 μM diamide were added to the control RBC model at 10 second intervals , and clustered band 3 levels were traced for seven days ., Interestingly , although previously cluster formation was shown be reversible in control RBCs , irreversible gradual increase of clustered band 3 was observed for the first few days , followed by an exponential increase and saturation of levels after day 6 ( Fig 6A ) ., To closely investigate the elevation of clustered levels , we ran simulations with longer time intervals ( 30 min and 60 min ) and a higher concentration of 0 . 125 mM ., For both simulations , transient clustering was accompanied with each pulse , and the basal level of the clustered band 3 was increased over time ( Fig 6B ) ., In the 60 min simulation , the clustered levels did not decline after approximately t = 200 min , resulting in irreversible cluster growth ., Diamide pulses appeared to enhance cluster growth , rather than the increase of cluster number ., Analysis of the clustering behavior with varying diamide concentrations and addition intervals , showed that addition of higher concentration of diamide with shorter intervals lead to rapid saturation of clustered levels ., At t = 120 min approximately 44 . 3% of the simulation conditions resulted in the saturation of clustered band 3 levels ( 100% clustered levels ) , and 35 . 2% of them resulted in clustered levels of less than 10% of total band 3 ( Fig 6C ) ., Saturation of clustered levels became more apparent as time proceeded ( S3 Fig ) , however the portion of conditions that resulted in 40 to 80% of band 3 to become clustered did not change over time ., At normal state , the RBC membrane cytoskeleton takes the form of a hexagonal lattice , which is held together by junctions where spectrin and other membrane-spanning proteins such as band 3 interact 58 ., Thus , spectrin is suggested to play an important role in regulating the diffusion of band 3 ., In our original model we had allowed band 3 to be homogeneously distributed during initialization , so here we extended the original model by including additional RBC membrane components and related reactions ( Fig 7A; S1 Text ) ., A number of prior studies have shown that band 3 which are not attached to the cytoskeleton exhibit a “hop-diffusion” like movement , where they diffuse freely within the spectrin barriers , and occasionally when the spectrin tetramers transiently dissociate thereby creating a gap in the “fence” , they are able to diffuse to a separate neighboring compartment 52 , 59 , 60 ., To reproduce this , we simulated and compared the trajectory of free band 3 in normal state with spectrin present , with the trajectories obtained from a prior experimental study 52 ., The locus of diffusing band 3 molecules simulated for 200 sec ( example shown in Fig 7B ) was similar to those previously observed in the experiment ., Simulation of the temporal position ( coordinates ) predicted that band 3 hops to an adjacent mesh at an average of every 347 . 2 ms . This was in good agreement with the experimentally obtained residency times for each domain , with an average of 350 ms 52 ., Finally , as an application of our band 3 clustering spectrin model , we predicted the behavior of clustering in RBCs with a membrane cytoskeletal disorder , namely spectrin deficiency ., In the RBC , the spectrin-based membrane skeleton is responsible for the unique flexibility and mechanical stability of the cell ., Spectrin defect is known to result in the loss of membrane stability , leading to surface area loss and hereditary spherocytosisa , a common hemolytic anemia characterized by the production of irregular sphere-shaped RBCs 61 ., From our simulations , we found that band 3 clustering levels were more pronounced in the spectrin deficient RBC models by roughly two-folds ( Fig 7C ) ., Also , clusters of larger size were observed in the spectrin deficient RBCs , whereas in the controls , cluster size was partially confined within the spectrin filament physical barriers ( Fig 7D ) ., In this study , we developed a computational model of biochemical changes in human RBCs to study band 3 distribution during oxidative treatment ., We integrated the cytoplasmic biochemical reactions and diffusion-influenced reactions on the RBC membrane and parameterized the model using time-course data and fluorescent images from previous works ., From the model , we were able to speculate the factors that contribute to the remodeling of band 3 clustering behavior observed in previous experimental studies , and predict how cluster formation is affected by the conditions that the cell is put under ., It has long been discussed that oxidation induced clustering of band 3 is an essential process in RBC senescence ., Although experimental approaches have described the macroscopic effects on the RBC during oxidative treatment , the properties of cluster formation and how it behaves under various conditions have remained limited ., Specifically , questions such as what causes the irreversibility of clustering in G6PD deficient cells , how are band 3 clusters formed in vivo during aging , and how does the physical architecture of the membrane contributes to cluster formation , are yet to be answered ., Prior experimental and computational studies of the changes in RBC metabolism during oxidative treatment have suggested a decline in essential antioxidant metabolites such as GSH and NADP , and in turn an increase in GSSG and reduced nicotinamide adenine dinucleotide phosphate ( NADPH ) , following oxidation ., As reduced levels of antioxidants have been observed in RBCs with disorders associated with short RBC lifespan , using these models it is possible to predict the consequences of oxidative stress on RBC function and lifespan , to a certain degree ., However , several reports showing that the decrease of enzyme activity and metabolite levels are nonlinear with cell age 21 , 62 , suggest that metabolites levels alone are not sufficient to allow the direct assessment of RBC health ., Our work , for the first time focuses on modeling beyond the changes in metabolism , hence the effects of oxidation on the RBC membrane ., Since the clustering of band 3 proteins induced by oxidative stress is a process that directly promotes cell removal , our work enables a more detailed and visual description of the oxidative events that occur during the RBC life cycle ., The simulations of diamide treatment with our models were consistent with the reported characteristics of the cells in vitro ., The levels of clustered band 3 rose to a peak at about 30 min , and then declined in the control , and progressively increased in the G6PD deficient RBC ( Fig 3A ) ., Visualization of the simulation results also showed good agreement to the previously described band 3 clusters ( Fig 3B and 3C; 3 , 22–25 ) , and revealed larger clusters in the G6PD deficient cell compared to the control ., The fact that the clusters are greater in s

Introduction, Materials and Methods, Results, Discussion

Oxidative stress mediated clustering of membrane protein band 3 plays an essential role in the clearance of damaged and aged red blood cells ( RBCs ) from the circulation ., While a number of previous experimental studies have observed changes in band 3 distribution after oxidative treatment , the details of how these clusters are formed and how their properties change under different conditions have remained poorly understood ., To address these issues , a framework that enables the simultaneous monitoring of the temporal and spatial changes following oxidation is needed ., In this study , we established a novel simulation strategy that incorporates deterministic and stochastic reactions with particle reaction-diffusion processes , to model band 3 cluster formation at single molecule resolution ., By integrating a kinetic model of RBC antioxidant metabolism with a model of band 3 diffusion , we developed a model that reproduces the time-dependent changes of glutathione and clustered band 3 levels , as well as band 3 distribution during oxidative treatment , observed in prior studies ., We predicted that cluster formation is largely dependent on fast reverse reaction rates , strong affinity between clustering molecules , and irreversible hemichrome binding ., We further predicted that under repeated oxidative perturbations , clusters tended to progressively grow and shift towards an irreversible state ., Application of our model to simulate oxidation in RBCs with cytoskeletal deficiency also suggested that oxidation leads to more enhanced clustering compared to healthy RBCs ., Taken together , our model enables the prediction of band 3 spatio-temporal profiles under various situations , thus providing valuable insights to potentially aid understanding mechanisms for removing senescent and premature RBCs .

In order to maintain a steady internal environment , our bodies must be able to specifically recognize old and damaged red blood cells ( RBCs ) , and remove them from the circulation in a timely manner ., Clusters of membrane protein band 3 , which form in response to elevated oxidative damage , serve as essential molecular markers that initiate this cell removal process ., However , little is known about the details of how these clusters are formed and how their properties change under different conditions ., To understand these mechanisms in detail , we developed a computational model that enables the prediction of the time course profiles of metabolic intermediates , as well as the visualization of the resulting band 3 distribution during oxidative treatment ., Our model predictions were in good agreement with previous published experimental data , and provided predictive insights on the key factors of cluster formation ., Furthermore , simulation experiments of the effects of multiple oxidative pulses and cytoskeletal defect using the model also suggested that clustering is enhanced under such conditions ., Analyses using our model can provide hypotheses and suggest experiments to aid the understanding of the physiology of anemia-associated RBC disorders , and optimization of quality control of RBCs in stored blood .

journal.pntd.0006363

Role of maternal health and infant inflammation in nutritional and neurodevelopmental outcomes of two-year-old Bangladeshi children

159 million children under five years old , or 23 . 8% of the world’s population in this age range , are stunted ( length-for-age Z score LAZ < -2 standard deviations SD ) 1 ., In a pooled analysis , stunting conferred a hazard ratio of 2 . 28 for mortality prior to five years of age with severe stunting ( LAZ < -3 SD ) having a hazard ratio of 5 . 48 2 ., It has also been suggested that growth deficits early in life lead to obesity , type II diabetes , and metabolic disturbances later in life 3 ., Height has been positively associated with earnings suggesting that early life adversity affecting growth leads to an immense loss of “human capital” throughout much of the world 4 ., Additionally , stunting and infection have been associated with neurodevelopmental deficits , compounding the loss to productivity 3 ., Both growth and neurodevelopment are multifactorial , which makes designing effective interventions difficult , especially in low- and middle- income countries ( LMICs ) where a variety of interconnected insults are present ., Systemic inflammation , febrile episodes , lack of primary vaccines , lower socioeconomic status , and poor sanitation have all been directly associated with stunting in children from LMICs 5–11 ., Environmental enteric dysfunction ( EED ) , hallmarked by enteric inflammation , has also been shown to be associated with deficits in linear growth 6 , 12–14 ., Importantly , EED has been identified as a distinct entity from diarrheal disease although pathogen carriage may play a role in its development 15–18 ., In addition to postnatal factors such as nutrition and inflammation , prenatal and maternal factors have also been correlated with growth ., Maternal anthropometrics and maternal education have both been associated with stunting in children living in LMICs 7 , 9 , 19 , 20 ., In a multinational study , a 1 cm increase in maternal height was associated with a 1 . 2% decrease in risk of child mortality 21 ., Additionally , birth anthropometry is a strong predictor of postnatal growth suggesting prenatal insults effect stunting 6 , 9 ., Associations between aspects of childhood life in LMICs and neurodevelopmental outcomes have also been described ., Lower neurodevelopmental scores have been associated with diarrheal disease in some studies 22–24 but not in others 25 ., The effect of enteric infection on neurodevelopment may be pathogen-specific as deficits have been associated with giardiasis and cryptosporidiosis specifically 23 , 26 ., Early childhood systemic inflammation and febrile illness have also been associated with poor neurodevelopmental outcomes 27 , 28 ., On a population level , average national IQ was associated with overall burden of infectious diseases suggesting an inflammatory pathway mediating effects on neurodevelopment 29 ., Additionally , stunting has been associated with poor neurodevelopmental outcomes although the nature of this relationship remains undefined 3 , 30–33 ., Different types of insults affect separate aspects of neurodevelopment 34 ., Prenatal and maternal factors including maternal malnutrition have been associated with decreased problem solving and motor function 35 , 36 ., Early life anthropometrics have been associated with cognitive and language function 34 ., In one study breastfeeding was associated with improved language skills but not social-emotional/behavioral skills 37 ., Neonatal sepsis has been linked to decreased motor and cognitive function but not social-emotional function 38 , 39 ., Systemic inflammation in animal models and increased intestinal permeability in humans has been linked to social-emotional function 40 , 41 ., The combination of these findings suggest a need to assess neurodevelopment directly and with subscale analysis ., Many of the variables associated with poor growth and neurodevelopment are not independent but rather interdependent 42 , 43 ., This has made analysis of their individual importance in these outcomes difficult ., Our objective was to clarify which aspects of childhood in LMICs are the strongest predictors of both growth and neurodevelopment ., The Performance of Rotavirus and Oral Polio Vaccines in Developing Countries ( PROVIDE ) study is a longitudinal study of Bangladeshi infants ., The primary objective of the PROVIDE study was to determine if there was an association between EED and the underperformance of oral vaccines with the analysis described here being secondary ., Detailed methods of this study have been published elsewhere 6 , 44 ., Briefly , 700 children were enrolled within one week of birth ., Participants were randomized to receive the Rotarix oral rotavirus vaccine or not and all children received the oral polio vaccine ., A rolling admission spanned from May 2011 through November 2014 ., Results of this study examining the association of biomarkers of EED and oral polio vaccine failure , rotavirus vaccine failure , growth through one year of age , small intestine bacterial overgrowth , the impact of enteropathogens on oral rotavirus and polio vaccination , and the association of Rotarix vaccination and serum zinc levels with severe rotavirus diarrhea have been published elsewhere 6 , 45–47 ., PROVIDE was conducted in the urban borough of Mirpur in Dhaka , Bangladesh ., The area is densely populated with a mean of 5 people living in 1 . 5 rooms ., Over 95% of the construction is of tin or mud brick ., Uncovered sewer drains flow throughout the area and abut 59% of dwellings ., Our subjects tended to come from the lower socioeconomic strata of Mirpur due to the area in which recruitment occurred and the location of our study clinic ., All children received vaccines , administered by the study staff , included in the Bangladesh Expanded Programme on Immunization , including the oral polio vaccine ., Children were randomized after enrollment to receive the oral rotavirus vaccine Rotarix ( GlaxoSmithKline plc . , Middlesex , UK ) or not ., Children randomized to the Rotarix vaccine arm received two doses at 10 and 17 weeks of age ., Rotavirus vaccination was assessed as a dichotomous predictor , either receiving the vaccine or not ., Stool and serum for biomarker analysis was collected within 7 days of the scheduled study visit and were immediately stored at 4°C ., Samples were transported from our field office to The International Centre for Diarrhoeal Disease Research , Bangladesh Parasitology Laboratory and stored at -80 °C within 4 hours of collection ., They were then pulled and analyzed in batches ., Plasma activin , plasma ferritin , plasma anti-lipopolysaccharide ( LPS ) antibody , plasma retinol binding protein ( RBP ) , plasma soluble cluster designation 14 ( sCD14 ) , plasma zinc , fecal myeloperoxidase , fecal calprotectin , fecal alpha-1 antitrypsin , fecal neopterin , fecal Reg 1B , plasma vitamin D , plasma C-reactive protein ( CRP ) , and Cytomegalovirus ( CMV ) status were assessed via commercially available ELISA kits ., CMV status was dichotomized to positive or negative per manufacturer’s specifications ( Abcam , inc . Cambridge , Ma USA ) ., Mannitol recovery was assessed via urine high performance liquid chromatography after giving children a standardized mannitol load ., Plasma cytokine analysis was conducted via a commercially available Human BioPlex Pro assay 6 , 44 ., Plasma activin , plasma cytokines , and plasma CRP were chosen as markers of systemic inflammation ., Plasma ferritin , RBP , and zinc were chosen as both acute phase reactants and key nutritional variables ., Vitamin D was also selected as a key nutritional measure ., Fecal myeloperoxidase , fecal calprotectin , fecal alpha-1 antitrypsin , fecal neopterin , and fecal Reg 1B were selected as markers of enteric inflammation and damage ., Mannitol recovery , anti-LPS Ab , and sCD14 were selected as markers of intestinal permeability ., Diarrheal surveillance to calculate days of diarrhea was conducted by field research assistants who visited the homes of the participants twice per week ., Maternal and socioeconomic data were collected at the time of enrollment via questionnaire ., Mother’s level of education achieved was collected then dichotomized to any formal education or no formal education ., Presence of an open drain directly outside the home , family’s use of a septic tank or toilet ( as opposed to slab latrines , pit latrines , open latrine , hanging latrines , or open defecation ) , use of a toilet shared by other families , and use of any method of improved water treatment were also assessed via questionnaire ., Anthropometric assessment ( including maternal anthropometry ) was conducted by study physicians trained in the procedure using measuring boards , measuring tape , and calibrated scales as appropriate for size and age ., Anthropometry was measured at enrollment and at 16 scheduled study visits throughout the 2-year study period ., A trained psychologist assessed neurodevelopmental scores at two years of age using a version of the Bayley Scales of Infant and Toddler Development , Third Edition ( Bayley-III ) that was adapted to be culturally appropriate to Bangladeshi children ., Despite cultural adaptation , this version was not normalized to the Bangladeshi population ., This version of the Bayley III has been used in other studies by our group and was shown to have high short term ( within 7 days ) retest reliability ( r > 0 . 80 ) and high inter-observer reliability ( r = 0 . 99 ) 27 , 28 ., LAZ , weight-for-age Z score ( WAZ ) , and weight-for-height Z score ( WHZ ) were calculated using the World Health Organization software WHO Anthro ( version 3 . 2 . 2 ) ., CRP was measured at 4 time points ( 6 , 18 , 40 , and 53 weeks ) ., The variable “CRP index” was created as a measure of sustained inflammation ., For each measurement , if a child was in the top 50th percentile for that time point , they were given a score of 1 ., “CRP Index” was created by summing the scores given for all four CRP measurements and thus ranged from 0 to 4 ., Cytokines were discretized into <50th percentile , the 50th– 75th percentile , and >75th percentile ., All other variables were either dichotomous or continuous based on the nature of the variables ., Separate datasets were created for anthropometric and neurodevelopmental outcomes ., Any child with an incomplete data set for the specified outcome was removed from the analysis ., Outliers in predictors , defined as any value > 5 SD from the mean , were excluded from the analysis ., 28 subjects for anthropometric analysis and 22 subjects for neurodevelopmental analysis were excluded ., Outliers were not assessed in outcome measurements ., Differences in enrollment characteristics between the remaining subjects and the original cohort who had complete enrollment data were assessed via Mann-Whitney U tests and χ2 tests as appropriate ., Pearson correlation for all predictive variables was calculated using the dataset constructed for anthropometry ., Hierarchical clustering to examine relationships between variables was performed and depicted as a cluster dendrogram ., A dissimilarity index of 1 . 75 was chosen for the clustering cutoff in order to describe how larger groups of the variables were related ., Each variable was color-coded based on which of the three clusters it was in and this color-coding was used to identify variables in the random forests plots ., Outcomes of interest for the predictive models included LAZ at two years of age , the change in LAZ from enrollment to two years ( ΔLAZ ) , and the four components of the Bayley-III ( cognitive , language , motor , and social-emotional ) ., A separate random forests analysis was conducted for each of our outcomes of interest to select and rank predictive variables ., Conditional random forests analyses were performed to account for the correlations between predictors with a threshold of Pearson’s correlation coefficient ≥0 . 2 ., Variable importance values ( VIMP ) were calculated for all predictors and then scaled based on the predictor with the highest VIMP in that analysis ( sVIMP ) ., In order to determine the direction of the association between variables and outcomes , dependence plots between predictors and outcomes of interest were generated for the top 15 predictors ., As a validation , penalized linear regression analyses with SCAD penalty were performed on the same datasets ., For SCAD analyses , dummy variables for cytokine measurements were created and if either the 50th– 75th or the >75th percentile was selected , the other was forced into the model ., To assess the predictability from the random forests analyses , for each outcome , a mean squared error was calculated using the predicted value from the random forests model and the observed values , and then percentage of variation explained by the predictors was calculated ., Percentage of variation explained was also calculated from the SCAD model ., All analyses were done using R software ., The statistical package ‘party’ version 1 . 2–2 from February 27 , 2017 was used for conditional random forests ., The statistical package ‘grpreg’ version 3 . 0–2 from July 11 , 2016 was used for variable selection with SCAD ., The PROVIDE study was approved by the Research Review and Ethics Review Committees at The International Centre for Diarrhoeal Disease Research , Bangladesh and by the Institutional Review Boards at the University of Virginia and the University of Vermont ., Informed consent was obtained from parents for their child’s participation in this study ., All data analyzed were anonymized ., The hierarchical cluster analysis demonstrated three distinct major clusters , similar to previous analysis of this data 6 ( S1 Fig ) ., Systemic cytokines continued to cluster tightly as in the previous analysis ., However , enrollment anthropometry , as opposed to week 18 anthropometry used previously , more closely correlated with sanitation ., Economic status ( income and expenditure ) closely clustered with biomarkers of enteric inflammation ., CRP index was also in this cluster ., Overall , variables from each cluster tended to represent that cluster across our random forests analyses ., sVIMP values from the conditional random forests analyses are depicted for the top ranked variables in Fig 2 and dependence plots for all outcomes in S2–S7 Figs ., For LAZ at two years as a static measure , maternal weight ( index sVIMP , 1 . 0 ) and LAZ at enrollment ( sVIMP 0 . 57 ) were substantially stronger predictors than the remainder ., There was a substantial drop in sVIMP between LAZ at enrollment and the next highest predictor , which was mannitol recovery at week 12 ( sVIMP 0 . 15 ) ., For ΔLAZ from enrollment to two years , LAZ at enrollment became the strongest predictor ( index sVIMP , 1 . 0 ) , followed by maternal weight ( sVIMP 0 . 33 ) ., Overall , birth anthropometry and maternal weight far surpassed all other variables in terms of their ability to predict anthropometry at two years and growth ( Fig 2A & 2B ) ., In analysis of Bayley-III outcomes , in general , inflammation was of greater importance ., For cognitive score WAZ at enrollment was the top predictor ( index sVIMP , 1 . 0 ) , followed by income ( sVIMP 0 . 77 ) , and LAZ at enrollment ( sVIMP 0 . 71 ) ., Inflammatory variables such as TNFα at 18 weeks ( sVIMP 0 . 41 ) , sCD14 at week 18 ( sVIMP 0 . 33 ) , and ferritin at week 18 ( sVIMP 0 . 27 ) , as well as the economic variable expenditure ( sVIMP 0 . 36 ) were weaker predictors ( Fig 2C ) ., Language scores were predicted by presence/absence of the rotavirus vaccine ( index sVIMP , 1 . 0 ) , IL 5 at week 18 ( sVIMP 0 . 87 ) , sCD14 at week 18 ( sVIMP 0 . 75 ) , maternal weight ( sVIMP 0 . 64 ) , TNFα at week 18 ( sVIMP 0 . 63 ) , male gender ( sVIMP 0 . 60 ) , and WHZ at enrollment ( sVIMP 0 . 56 ) ., Language scores were influenced by a diverse combination of factors ranging across all three groups of our cluster analysis ( Fig 2D ) ., For motor function , predictors included calprotectin at 12 weeks ( index sVIMP , 1 . 0 ) followed by WAZ at enrollment ( sVIMP 0 . 95 ) , neopterin at week 12 ( sVIMP 0 . 79 ) , CRP index ( sVIMP 0 . 68 ) , TNFα at week 18 ( sVIMP 0 . 56 ) , days of diarrhea at week 18 ( sVIMP 0 . 51 ) , IL 5 at week 18 ( sVIMP 0 . 50 ) , sCD14 at week 18 ( sVIMP 0 . 49 ) , alpha-1 antitrypsin at week 12 ( sVIMP 0 . 47 ) , and mannitol recovery at week 12 ( sVIMP 0 . 40 ) ., Motor score was strongly predicted by variables that grouped tightly in our hierarchical cluster analysis and included biomarkers of enteric inflammation ., Biomarkers of systemic inflammation and birth anthropometry were also strong predictors ( Fig 2E ) ., The highest-ranking predictor of social-emotional function was sCD14 ( index sVIMP , 1 . 0 ) followed by income ( sVIMP 0 . 95 ) , WAZ at enrollment ( sVIMP 0 . 77 ) , LAZ at enrollment ( sVIMP 0 . 69 ) , and TNFα at week 18 ( sVIMP 0 . 47 ) ., Social-emotional score appeared to be influenced by gut barrier integrity , economic means , systemic inflammation , and birth anthropometrics ( Fig 2F ) ., The estimates of the percentage of variance explained in our outcomes at two years of age from the conditional random forests analyses were 35 . 4% and 34 . 3% for LAZ and ΔLAZ respectively; 42 . 7% for cognitive score , 28 . 1% language score , 40 . 8% for motor score , and 37 . 9% for social-emotional score ., In order to identify risk factors that independently predicted outcomes , variable selection utilizing SCAD was done ., Overall , SCAD selected 18 of 23 ( 78% ) of predictors that random forests assigned sVIMP values >0 . 50 ., For anthropometry alone , SCAD selected 3 of 3 ( 100% ) of predictors with sVIMP values >0 . 50 ., This included LAZ at enrollment for both LAZ at two years and ΔLAZ from enrollment to two years ., For LAZ at two years it also included mother’s weight ., For Bayley-III outcomes alone , SCAD selected 15 of 20 ( 75% ) of variables with random forests sVIMP values >0 . 50 ., For the cognitive component both WAZ at enrollment and income were selected while LAZ at enrollment was not ., For language , only WHZ at enrollment was not selected ., SCAD confirmed the importance of rotavirus vaccine status , IL 5 , sCD14 , mother’s weight , TNFα , and male gender in predicting language scores ., SCAD analysis of Bayley-III motor scores selected calprotectin , WAZ at enrollment , neopterin , TNFα , and IL 5 , all of which random forests selected with sVIMP >0 . 50 ., However SCAD failed to select CRP index and days of diarrhea at week 18 ., For social-emotional predictors , SCAD overlapped with random forests on 3 of 4 variables with sVIMP >0 . 50 including sCD14 , income , and WAZ at enrollment ., LAZ at enrollment was not selected ( Table 3 ) ., Linear regression models created by SCAD for each outcome produced estimates of variability for each outcome that were 18 . 0% for LAZ at two years , 17 . 9% for ΔLAZ , 17 . 5% for cognitive score , 12 . 9% for language score , 13 . 6% for motor score , and 17 . 6% for social-emotional score ., The key discovery of this work was ranking the importance of putative predictors of infant growth and neurodevelopment and demonstrating that they were different ., LAZ at two years of age was predicted predominantly by maternal and birth anthropometrics ., In contrast developmental scores were most prominently predicted by inflammatory biomarkers ., These data suggest that interventions aimed to improve growth and neurodevelopment need to be directed at both improvements in maternal and neonatal nutrition and reduction of gut and systemic inflammation ., The finding that perinatal child and maternal anthropometry predicted linear growth reaffirms several studies showing birth anthropometrics are strong predictors of ΔLAZ , suggesting that catch up growth in children born small for gestational age or with intrauterine growth restriction is insufficient 48–51 ., Additionally , our findings support previous work showing maternal anthropometry to influence infant growth 21 , 50 ., Fecal calprotectin and alpha-1 antitrypsin were positively associated with growth ., Both markers can be elevated due to intestinal inflammation but have also been shown to be increased in breastfeeding children and thus may be a surrogate marker of improved nutrition in our analysis 52 , 53 ., However , our data showed that markers of systemic or enteric inflammation were not the strongest predictors of poor growth although studies have repeatedly shown an association 3 , 15 , 16 , 54–59 ., As random forests ranks predictors in order of their importance , it may be that the association between inflammation and growth noted in other studies is valid but that inflammation is not as important a driver of growth when compared with maternal or prenatal factors ., Our results suggest that future investigation into the complex pathogenesis of growth stunting should include study of the prenatal period ., Our analysis of Bayley-III outcomes demonstrates differences in cognitive , language , motor , and social-emotional development pathways and suggests that different insults may influence separate aspects of neurodevelopment ., Cognitive development was strongly affected by perinatal anthropometrics and economic variables although systemic inflammation also played a role ., Although there is literature suggesting birth LAZ is predictive of cognitive outcomes 60 , several studies have shown associations between LAZ as a static measure at other ages and cognitive function 31 , 61–64 ., However , these studies did not examine birth anthropometry as a confounder ., Our results suggest that birth anthropometry may influence both future LAZ/growth and cognitive performance ., Work showing that nutritional supplementation in early childhood had minimal or no effect on cognition support a maternal , prenatal , or non-nutritional ( i . e . possibly inflammatory ) cause of cognitive deficits 65–67 ., However , a recent meta-analysis showed certain nutrients given postnatally including iron can affect cognitive development ., Maternal nutritional supplementation in the first trimester was also associated with improved cognition 68 ., The presence of ferritin and birth anthropometry as important predictors of cognitive function in our analysis supports these findings ., Our analysis is consistent with previous work by our group that suggests systemic inflammation negatively effects cognitive development and work by others showing infectious diseases in early childhood were associated with lower cognitive function 28 , 30 , 61 , 69 ., Language scores in our analysis were predicted in part by vaccination against rotavirus , systemic inflammation , mother’s weight , and gender ., In this study , the vaccine was shown to have an efficacy of 73 . 5% against severe rotavirus diarrhea 70 ., As only days of diarrhea until 18 weeks was entered into our models , it may be that rotavirus vaccination was a marker of decreased diarrhea over a longer period , which contributed to improved language ability , possibly through a decrease in systemic inflammation ., Studies of meningitis in children have repeatedly shown sensorineural hearing loss leading to language deficits to be associated with inflammation in the central nervous system 71 ., As we did not measure hearing in our cohort , it is uncertain if the association of systemic inflammation and language deficits has a similar pathogenesis in children from LMICs ., Our finding that male gender was associated with decreased language function is consistent with a large body of literature showing females to progress faster in language development 72 , 73 ., However , Rotavirus vaccination , maternal weight , and markers of systemic inflammation were all stronger predictors than gender ., This would suggest that in addition to the direct effects of Rotavirus vaccination on diarrheal disease , downstream effects on development may be an additional benefit of adding the Rotavirus vaccine to national campaigns ., Motor score was associated with markers of systemic immune activation including TNFα , ferritin , CRP , and sCD14 ., Additionally calprotectin and neopterin were strongly associated with motor function and SCAD revealed a direct relationship ., While these markers of enteric inflammation have been associated with poor linear growth in other studies , it may be that their anti-inflammatory effects are significant enough to limit a systemic effect of enteric inflammation and thus are neuroprotective ., Anthropometry in older children has been associated with poor motor function but , again , our work would suggest birth anthropometrics to be a confounder in these analyses ( 45 , 48 ) ., Social-emotional function was predicted by a diverse set of variables spanning all three groups in our cluster analysis with sCD14 , income , and birth anthropometrics being the highest ranking ., Mannitol recovery and fecal calprotectin were also negatively associated with social-emotional scores ., Inflammation and specifically enteric inflammation has been associated with poor socio-emotional function in other settings including in studies of attention-deficit-hyperactivity-disorder and autism 41 , 74–77 ., Zinc levels were negatively associated with social-emotional score , which was a surprising and unexplained finding ., Our study reaffirms the findings from the first year analysis of this data that our measured predictors cluster into three distinct groups 6 ., While systemic inflammation still clustered tightly , CRP index was more closely correlated with maternal , socioeconomic , and enteric inflammatory variables ., This variation is likely due to use of the CRP index in our analyses instead of weeks 6 and 18 CRP values used in the previous work ., Additionally , we used enrollment anthropometrics instead of week 18 anthropometrics ., While week 18 values clustered with maternal anthropometrics , enrollment values clustered tightly with markers of sanitation 6 ., This supports the findings from our random forests and SCAD analyses showing that maternal anthropometry is an important driver of postnatal growth ., Birth anthropometrics appear more closely linked to risk factors with potential water , sanitation and hygiene ( WASH ) interventions ., Given the prominence of birth anthropometry in all outcomes of interest in this study , future investigation is warranted to determine the effects of prenatal WASH interventions in expecting mothers , which may have high yield in mitigating the adverse effects of the LMIC environment on childhood growth and development ., Our study has several strengths ., First was our relatively large sample size and the ability to collect multiple predictors related to complex biologic processes such as poverty , maternal health , enteric inflammation , and systemic inflammation ., Additionally children were followed closely in semi-weekly household visits for two years to obtain neurodevelopmental and anthropometric data ., Finally , we were able to utilize two distinct statistical methods , which had significant overlap in findings ., There are several procedural limitations that should be considered when examining this work ., First , not all of the original 700 children in the cohort had all of the biomarkers measured ., While comparison of enrollment characteristics showed no difference between the children included in the original cohort and those analyzed except for maternal education , the possibility remains of selection bias ., Second , while our Bayley-III assessment was culturally adapted , it was not normalized to the Bangladeshi population ., This limits our ability to compare the absolute values to an international population and define the extent of the neurodevelopmental delays documented by comparison ., Third , information regarding the children’s home environment as it relates to home education and stimulation was not collected nor was information regarding dietary intake ., These variables are known to affect scores on neurodevelopmental assessments and may represent unexamined confounders in our analysis 78 ., Fourth , several variables collected including ferritin , RBP , and zinc may be difficult to interpret since they are both acute phase reactants and nutritional markers 79 ., Finally , for biomarkers of inflammation other than CRP , a limited number of time points were sampled ., This limits our ability to assess if we are measuring acute or chronic inflammation , which would improve our understanding of the inflammatory insult on our outcomes ., Previous work has shown that birth anthropometry , maternal education , infection , inflammation , and poverty can impact growth and neurodevelopment 27 , 34 , 60 , 64 , 80 ., Our analyses suggest that there are several different pathways leading to poor linear growth and neurodevelopment which are likely interrelated ., Given the prominence of maternal and prenatal factors in our analyses , future efforts to study linear growth and neurodevelopmental deficits in LMICs should include data collection on these variables ., However , to fully assess factors affecting neurodevelopmental outcomes , postnatal effects including those from EED and infection will need to be considered as well .

Introduction, Methods, Results, Discussion

Previous studies have shown maternal , inflammatory , and socioeconomic variables to be associated with growth and neurodevelopment in children from low-income countries ., However , these outcomes are multifactorial and work describing which predictors most strongly influence them is lacking ., We conducted a longitudinal study of Bangladeshi children from birth to two years to assess oral vaccine efficacy ., Variables pertaining to maternal and perinatal health , socioeconomic status , early childhood enteric and systemic inflammation , and anthropometry were collected ., Bayley-III neurodevelopmental assessment was conducted at two years ., As a secondary analysis , we employed hierarchical cluster and random forests techniques to identify and rank which variables predicted growth and neurodevelopment ., Cluster analysis demonstrated three distinct groups of predictors ., Mother’s weight and length-for-age Z score ( LAZ ) at enrollment were the strongest predictors of LAZ at two years ., Cognitive score on Bayley-III was strongly predicted by weight-for-age ( WAZ ) at enrollment , income , and LAZ at enrollment ., Top predictors of language included Rotavirus vaccination , plasma IL 5 , sCD14 , TNFα , mother’s weight , and male gender ., Motor function was best predicted by fecal calprotectin , WAZ at enrollment , fecal neopterin , and plasma CRP index ., The strongest predictors for social-emotional score included plasma sCD14 , income , WAZ at enrollment , and LAZ at enrollment ., Based on the random forests’ predictions , the estimated percentage of variation explained was 35 . 4% for LAZ at two years , 34 . 3% for ΔLAZ , 42 . 7% for cognitive score , 28 . 1% for language , 40 . 8% for motor , and 37 . 9% for social-emotional score ., Birth anthropometry and maternal weight were strong predictors of growth while enteric and systemic inflammation had stronger associations with neurodevelopment ., Birth anthropometry was a powerful predictor for all outcomes ., These data suggest that further study of stunting in low-income settings should include variables relating to maternal and prenatal health , while investigations focusing on neurodevelopmental outcomes should additionally target causes of systemic and enteric inflammation .

Children from low-income settings experience linear growth faltering and neurodevelopmental delay that have been associated with maternal , socioeconomic , and infectious/inflammatory variables ., Given the interdependent nature of these associations , understanding which variables are the best predictors of poor outcomes has been difficult ., We conducted a longitudinal study of Bangladeshi children from birth to two years and collected predictors assessing maternal , inflammatory , and socioeconomic aspects of early childhood in Bangladeshi children ., We conducted a random forests analysis to rank predictors associated with growth and neurodevelopment ., Linear growth was best predicted by birth anthropometry and maternal weight ., Cognitive function was predicted by birth anthropometry , socioeconomic status , and systemic inflammation ., The receipt of the rotavirus vaccine and a combination of systemic inflammatory , maternal , and socioeconomic variables predicted language score ., Motor score was predicted by systemic inflammation with enteric inflammatory markers having a reverse relationship ., Social-emotional development was predicted by systemic inflammation , birth anthropometry , and economic means ., This work demonstrates that specific pathways are responsible for different aspects of growth and development ., Our data suggest that studies investigating pediatric stunting in low-income settings should focus on maternal and prenatal variables while those focused on neurodevelopmental outcomes should additionally target causes of systemic and enteric inflammation .

children, medicine and health sciences, pathology and laboratory medicine, pathogens, immunology, microbiology, social sciences, neuroscience, reoviruses, vaccines, viruses, age groups, developmental biology, cognitive psychology, signs and symptoms, rna viruses, artificial intelligence, infectious disease control, morphogenesis, families, language, infectious diseases, computer and information sciences, inflammation, anthropometry, rotavirus, medical microbiology, microbial pathogens, neurodevelopment, immune response, people and places, machine learning, psychology, diagnostic medicine, anatomy, viral pathogens, biology and life sciences, population groupings, cognitive science, organisms

journal.ppat.1006004

Zika (PRVABC59) Infection Is Associated with T cell Infiltration and Neurodegeneration in CNS of Immunocompetent Neonatal C57Bl/6 Mice

Zika virus ( ZIKV ) is an emerging mosquito-borne pathogen that belongs to the Flavivirus genus of the Flaviviridae family , which includes globally relevant arthropod-transmitted human pathogens such as dengue ( DENV ) , yellow fever ( YFV ) , West Nile ( WNV ) , Japanese encephalitis ( JEV ) , and tick-borne encephalitis viruses ., The first strain of ZIKV ( MR 766 ) was isolated in 1947 from a febrile sentinel rhesus monkey in the Zika forest near Entebbe , Uganda after the virus underwent intracerebral passage in Swiss albino mice1 ., For the next 50 years infections with ZIKV were reported sporadically in different regions of Africa and Asia , but were associated with mild symptoms consisting of skin rashes , conjunctivitis , fever and headaches2 ., In 2007 ZIKV started spreading west , first with an outbreak in Island of Yap where it infected over 70% of the population , followed in 2013 by an outbreak in French Polynesia3 ., This last outbreak was associated with a sharp increase in cases of Guillain Barre Syndrome ( GBS ) , an autoimmune disease characterized by weakening and even paralysis of the limbs and face 4 , 5 ., In 2015 Zika spread to South and Central America , infecting thousands of people in Brazil and Colombia , where it associated with an increase in GBS rates as well as a significant increase in severe fetal abnormalities that include spontaneous abortion , stillbirth , hydrocephaly , microcephaly , and placental insufficiency6–9 ., The temporal association of the viral outbreak and increased incidence of GBS and birth defects did not necessarily imply causality , however recent studies showing infection of the CNS in utero as well as infections of human neural progenitor and neural stem cells leading to cell cycle arrest and death 10 , 11 lend credence to the causality of ZIKV in microcephaly 12 , 13 ., While a majority of ZIKV infections in adults are asymptomatic or result in skin rashes , conjunctivitis ( nonpurulent ) , muscle pain and joint pain ( small joints of hands and feet ) , or a headache lasting for 2–7 days , the recent spread of ZIKV and its association with increased rates of neurological disorders has created an urgent need for animal models to examine the pathogenesis of the disease and explore the efficacy of potential therapeutics and vaccines ., In recent weeks , studies showed that young or adult immunocompetent mice are not susceptible to infection , but prepubescent mice lacking the capacity to produce or respond to interferons ( IFNs ) , including A129 ( type I IFNAR KO ) , Interferon Regulatory Factor ( IRF ) 3/5/7 triple KO , and AG129 ( type 1 and type 2 IFN KO ) , develop neurological disease and succumb to infection with high viral loads in the brain , spinal cord , and testes 14 , 15 ., Interestingly , in seeking a model that was less dramatically challenged Lazear et al infected mice deficient in IRF3 , IRF5 , or Mitochondrial Anti-Viral Signaling ( MAVS ) , which mediate the signaling of pattern recognition receptors for ssRNA RIG-I and MDA5 , however none of these mouse models developed disease 14 ., While these IFN deficient models provide useful data , the profound immunological defects in these strains may skew our understanding of the pathophysiology of the disease as the impaired IFN response can , for example , modify the susceptibility to infection of specific tissues ., Moreover , previous studies on the pathology of flavivirus suggest that pathogenicity may be determined not just by the effect of the virus but by the immune response it elicits 16 ., Thus an immunocompetent mouse model is urgently needed to understand the host response and pathogenesis of the disease and to test and compare the potency of potential therapeutic approaches ., More recently several studies have shown that mice from immunocompetent strains can be infected in utero provided a high titer infection is achieved in the dam or antibodies neutralizing interferon are administered 17–20 ., These infections , when performed early in pregnancy ( E5-E8 ) , result in increased fetal resorption and altered brain and eye development ., The relative contribution of virus-induced placental insufficiency versus direct deleterious effect of the virus on the cells of the fetal CNS is unclear 20 , 21 ., In mice , the stage of CNS development of neonatal pups has been equated to a human mid-term fetus 22 ., Neonatal rodents have shown to be highly susceptible to many neurotropic viral infections , including Herpes , Bornavirus , Tacaribe arenavirus and more recently Chikungunya , that present with meningoencephalitis 23–26 ., In this study we establish a new model of subcutaneous ( SC ) ZIKV infection in neonatal ( 1 day old ) , immunocompetent C57BL/6 ( B6 WT ) mice and compare its clinical progression , virus distribution , immune response , and neuropathology with C57BL/6-IFNAR-/- ( IFNAR KO ) mice , which are deficient in type 1 IFN responses ., We show that immunocompetent mice , when infected at day 1 of age ( P1 ) develop unsteady gait , loss of balance , kinetic tremors , severe ataxia and seizures beginning around 13 days post infection ( dpi ) that subside 2 weeks later ., Infection-induced IFN responses appear to reduce but not completely abrogate CNS infection in B6 WT mice ., Further , whereas the response to the virus in the CNS of B6 WT mice is characterized by cellular infiltration consisting predominantly of CD8+ T cells and is associated with increased expression levels of T cell effector molecules such as IFNg , granzyme B and perforin , the CNS in IFNAR KO mice show infiltration predominantly by neutrophils and macrophages as well as higher levels of inflammatory cytokines ., Lastly the CNS of B6 WT mice shows evidence of neurodegeneration that is less prominent in IFNAR KO mice ., This model does not address transplacental transmission of virus from mother to fetus or adult transmission , however it offers an immunocompetent symptomatic mouse model for ZIKV infections that may prove useful to understand the long term effects of ZIKV infection ., In addition , it avoids transplacental infection and consequent placental insufficiency as a confounding factor in the development of the brain ., Lastly , this model may help understand the clinical consequences for infections in late pregnancy or early childhood ., While ZIKV infections in early pregnancy have catastrophic consequences , there is concern that infections at later stages of gestation or early childhood may result in long term neurodevelopmental issues that we are as yet unaware of ., Recent studies showed that mice defective in interferon responses are susceptible to infection and develop a lethal disease ., To explore whether IFNAR KO mice are susceptible to infection with the contemporary ZIKV PRVABC59 strain , 10 day old ( P10 ) IFNAR KO mice were challenged subcutaneously ( sc ) with 2 x 103 PFU of ZIKV ., The mice remained asymptomatic and maintained their weight gain for the first 4 dpi ( Fig 1B ) ., Beginning late on 4 dpi the mice demonstrated reduced movement , tremors , bilateral hind limb paralysis , and died within 24 hours of disease onset ( Fig 1 ) ., These results were consistent with those reported for 3 week old: A129 , IRF-3/5/7 -/- and AG129 ( IFNa/b/g KO ) mice challenged with the African MR766 or MP1751 strains , or the more recent H/PF/2013 strain 27–29 ., We next examined whether the ZIKV PRVABC59 strain could be used to challenge B6 WT mice ., Reports on susceptibility to infection in utero30 , 31 , ZIKV’s ability to infect developing neurons in vitro 10 , 17 and prior studies in TCRV25 , 32 , Sindbis ( manuscript in preparation ) , and Chikungunya 26 suggested that a challenge with ZIKV very early in life , when the central nervous and immune systems are not fully mature may yield a productive infection ., Thus we explored whether B6 WT mice were susceptible to ZIKV if challenged one day after birth ( P1 ) ., As shown in Fig 1 , WT mice infected on P1 with PRVABC59 ( 2x103 PFU ( sc ) ) remain asymptomatic for 12 days , except for a decreased rate of weight gain ., After 2 weeks the mice develop unsteady gait with widening stance , hyperactivity and ataxia ., This is followed by reduced mobility , intermittent alternating collapse of the hind limbs , loss of balance and seizures ( S1–S3 Movies ) ., Interestingly , unlike IFNAR KO mice , these mice do not develop flaccid hind limb paralysis or succumb to the infection ( Fig 1 and S4 Movie ) ., Indeed , the observed symptoms diminish over the course of 2 weeks and most mice survive the challenge and recover ., Additional studies will be needed to assess the long term consequences of infection ., To determine whether the difference in clinical presentation was due to the age of the mice at the time of challenge , IFNAR KO mice were challenged at P1 and P3 ., The clinical presentation in mice challenged on P1 or P3 was similar to that of mice challenged on P10 as they developed bilateral paralysis and succumbed to disease by 5 dpi ( S1 Fig ) ., Conversely , B6 wt mice challenged on P3 or P10 do not develop signs of disease ., This indicated that the virus was inducing fundamentally different pathology in immunocompromised and immunocompetent mice ., Given that the clinical development of the disease was similar for mice challenged on P1 , P3 or P10 , all the ensuing experiments used P10 infections for the IFNAR KO model so that the peak of disease coincides in age with that of the B6 model ( P15 ) ., Previous reports show that in mice defective in IFN responses ( A129 , AG129 , IRF-3/5/7 triple KO ) the virus distributes systemically , with detectable ZIKV present in the brain and spinal cord , testes , spleen , liver , kidney and serum 28 ., Our challenge model with PRVABC59 in IFNAR KO mice confirms these results showing high virus titers in the CNS ( 4 . 4 x 107 TCID50 ) , as well as spleen ( 9 x 105 TCID50/0 . 5 g of tissue ) , and liver ( 1 . 5 x 105 TCID50/0 . 5 g of tissue ) at 5 dpi ( Fig 2A ) ., In comparison , WT mice challenged at P1 show relatively lower viral loads in the CNS ( 9x104 TCID50/0 . 5 g of tissue ) at 15 dpi , the time when the animals displayed peak neurological deficit ., Moreover , the B6 WT mice do not show evidence of viral infection in spleen or liver , indicating selective infection of the CNS ., Similarly , quantitative real-time PCR showed detectable levels of viral RNA in the CNS of B6 WT mice starting at day 3 and increasing through day 9 of infection ( Fig 2B and 2C ) ., Of note the levels of viral RNA in CNS did not reach those evident in IFNAR KO mice at peak of disease ( 105 ZIKV RNA copies/mL vs 108 ZIKV copies/mL ) ( Fig 2A and 2B ) ., No RNA was detectable in liver or spleen of B6 mice ( Fig 2B ) ., The presence of more than 105 ZIKV RNA copies /mL in liver and spleen of IFNAR KO mice suggests that type I IFNs play a key role in controlling the virus in the peripheral organs and peripheral infection may play a role in lethality in these models ., In contrast , infection in B6 WT appears to be restricted to the CNS possibly due to lower levels of IFNs in response to the virus in CNS as previously shown 33 , 34 ., Given that both strains were positive for ZIKV in the CNS but displayed different clinical presentation , we next explored whether the distribution of the virus in the CNS was similar in B6 WT and IFNAR KO mice ., Immunohistochemistry of the CNS using a mouse monoclonal antibody ( clone D1-4G2-4-15 ) that has been shown to react with ZIKV and other members of the flavivirus family , demonstrated detectable viral antigen in the CNS of both B6 WT ( 15 dpi ) and IFNAR KO ( 5 dpi ) mice ( Fig 2D ) ., As expected , age-matched , uninfected control sections were negative for the virus stain ( Fig 2D ) ., Consistent with the higher virus titers , IFNAR KO mice showed stronger and broader staining of the virus by immunohistochemistry that extended to the cortex ., In contrast , the CNS of B6WT mice showed virus predominantly in the cerebellar white matter and granular layers as well as in the hippocampus region ( S2 Fig ) , but not in the frontal cortex ., Colocalization of stains for ZIKV and neurofilament heavy chain in the areas of cerebellum ( Fig 2E ) and hippocampus ( S2 Fig ) indicates that ZIKV infects neurons ., We next determined whether the immune response to the virus in the CNS was similar between IFNAR KO and B6 WT mice ., Both strains showed profound up-regulation of the expression of genes linked to inflammation and cellular infiltration ., These included a significant up-regulation of Ccl2 , Ccl5 , Cxcl10 and Cxcl11 with the corresponding increases in genes linked to the recruitment and activation of neutrophils ( PMN ) and monocyte/macrophages including increases in MHC , Cd80 , Cd86 , Cd68 and Cd40 as well as marked increases in Csf1 and Csf2 ( Fig 3 ) ., These were accompanied by significant increases in the expression of IFNb , Tnfa , Il6 , Il1 , Ifng , C3 and Cox2 , all indicating a severe inflammatory response in the CNS ., In all , 49 of the 96 genes screened showed at least a 5 fold increase in expression in both strains ., Interestingly , the magnitude of the up-regulation for several of these genes was markedly different between the models ., For example , IFNAR KO mice showed significantly higher levels of Csf2 , Csf3 , Sele and Selp , while B6 WT mice showed relatively higher levels of genes linked to antigen presentation such as H2-Eb1 and B2m ., B6 WT mice also showed significantly higher levels of Cd45 , Ccr7 and Cxcr3 , suggesting increased infiltration of peripheral leukocytes , while IFNAR KO mice showed higher levels of Ccr4 likely expressed on microglia and astrocytes ( Fig 3 ) ., Among the genes linked to inflammation , the expression of Ifna , Ifnb , Cox2 , Il1 , and Il6 were significantly higher in the IFNAR KO mice potentially due to higher virus titers in the CNS and the deficient IFN response ., In contrast , the CNS of B6 WT mice had relatively higher expression of ISGs OAS1 and ISG15 , as well as genes corresponding to T cells including CD3 , CD4 and CD8 , and markers of Th1 and cytolytic responses such as GzmB and Prf1 , Il2 , Ifng , and STAT1 ., These data would indicate that ZIKV enables a significant T cell response in the CNS of B6 WT mice that is not evident in IFNAR KO mice ., The data above suggested that the CNS infection in both strains was accompanied by microglial/macrophage activation and immune cell infiltration ., To explore this further , we isolated cells from the CNS of infected animals at 15 dpi and studied the cell populations using flow cytometry ., As predicted by the increased expression of genes related to T cells and antigen presenting cells , there was significant cellular infiltration of CD45hi cells in the CNS of both strains ., In B6 WT mice the majority of infiltrating cells were T cells , with CD8+ T cells comprising 45% and CD4+ T cells comprising 20% of the CD45hi infiltrating population ., The remaining cell types consisted of F4/80+CD11b+ macrophages ( 15% ) , NK1 . 1+ Natural Killer cells ( 3% ) and CD19+CD45R+ B cells ( 5% ) ( Fig 4A ) ., IFNAR KO mice showed even higher levels of cellular infiltration , however in these mice the infiltrating cells corresponded to CD11b+Ly6G+ and CD11b+F4/80+ consistent with PMN and macrophages respectively , with only a minor population of T cells and NK cells ( Fig 4A ) ., These data together with the analysis of gene expression suggests that the inflammatory and immune processes that follow ZIKV are fundamentally different in IFNAR KO and B6 WT mice , with IFNAR KO mice showing significant inflammation of the CNS accompanied by infiltration by PMN and granulocytes , while IFN-sufficient animals mount a T cell driven response characterized by CD8+ T cells and high levels of IFNγ and granzyme B . Previous studies had shown inflammatory and degenerative changes in the brains of IFN deficient mice challenged with ZIKV ., These include the presence of scattered nuclear fragments , perivascular cuffing , and PMN infiltrating gray and white matter 27 ., To determine whether the B6 WT mice would show similar evidence of inflammatory and degenerative changes , we stained sagittal brain sections collected from Zika-infected B6 WT mice at 15 dpi and age-matched control animals ., Fluorescence immunohistochemistry confirmed the presence of CD45+ immune cells in the parenchyma of the CNS in both B6 WT and IFNAR mice and showed that these cells concentrate in the white matter and granular layers of the cerebellum ( Fig 4B ) , consistent with previous studies in IFN deficient mice 27 ., The clinical presentation of ZIKV infected B6 WT mice , along with the previously described tropism of ZIKV for neurons suggested that the virus infects and damages the CNS of B6 WT mice ., To test this , we stained sections adjacent to those used to detect virus and inflammation with the Fluoro-Jade C , a stain that specifically labels degenerating neurons ., As expected , uninfected , age-matched controls showed no staining with the Fluoro-Jade C stain ., In contrast , infected B6 WT mice showed foci of Fluoro-Jade C positive neurons in all layers of the cerebellum , but predominantly in the granular and PC layers ( Fig 5 ) ., Interestingly , there were fewer Fluoro-Jade C+ cells in granular and Purkinje layers of the cerebellum of IFNAR KO mice ., The presence of infiltrating CD8+ T cells and higher levels of fluorojade C positive cells in the CNS of B6 WT mice suggests a possible role for CD8+ T cells in the pathology ., ZIKV belongs to the Flavivirus genus that includes several etiological agents of viral encephalitis , the most significant being Japanese encephalitis virus , West Nile virus , and tick-borne encephalitis virus ., As with other flaviviruses , the majority of infected individuals will not develop disease , but a minority will develop a severe illness with a significant chance of permanent neurological damage , congenital malformations , or death ., The factors that determine this are likely numerous , involving complex interactions between virus and host that are yet to be uncovered ., Animal models can help us understand the pathophysiology of the virus , identify therapeutic targets , and explore the safety and efficacy of new therapeutics and vaccines ., This study shows that neonatal B6 WT mice challenged with ZIKV develop a slow onset non-lethal encephalitis that is characterized by unsteady gait , kinetic tremors , severe ataxia , loss of balance and seizures ., The virus localizes to the CNS where it elicits a strong IFN response , T cell infiltration with increased expression of RNA coding for Ifng , granzymeB , perforin1 and Il-2 ., In addition , these mice show evidence of neurodegeneration in particular affecting the Purkinje and granular cell layers of the cerebellum as evidenced by Fluoro-Jade C staining ., Our data suggests that innate and adaptive responses can limit viral expansion but may also play a role in pathological changes in the CNS ., In response to the outbreak of ZIKV and its association with increased congenital and neurological disease , animal models are being developed with unprecedented celerity to understand the pathogenesis of the disease and test possible therapeutics and vaccines ., Early studies in mice had suggested that ZIKV can replicate and cause injury in cells of the central nervous system 1 , 35 , 36 but used the prototype MR 766 strain of ZIKV , which had undergone extensive passage in suckling mouse brains ., Several new mouse models of ZIKV were developed over the past 6 months using current viral isolates ., Using a low passage Cambodian isolate of ZIKV , Rossi et al showed that 3 week old A129 and AG129 mice , which lack type I or I & II IFN respectively , develop paralysis and succumb to disease by 7 dpi while older mice showed viremia and weight loss but recovered after day 815 , 29 ., Similar results were observed by Lazear et al , using either 4–6 week old A129 or Irf3/5/7 triple knockout mice challenged with ZIKV ( H/PF/2013 ) from French Polynesia , as well as African ZIKV strain MR 76628 ., Our studies advance on these findings by characterizing the immune response to ZIKV in the CNS and key peripheral organs ., We show that in the CNS of mice with deficient IFN responses the virus in the brain was localized predominantly to the cerebellum at the peak of disease and elicits a marked inflammatory response characterized by significant increases in the mRNA expression of complement ( C3 ) , Cox2 , Il1a , Il1b , and Il6 ., The increased mRNA expression of Sele , Selp , Csf2 and Csf3 is consistent with the observed increase in infiltrating neutrophils and macrophages evident by flow cytometry ., Together this pattern of expression suggests the activation of microglia and/or infiltration of activated macrophages , which has been shown to lead to uncontrolled inflammation and neuronal death in other models of flavivirus encephalitis such as mice infected with Japanese encephalitis virus 37 ., Interestingly , despite significant upregulation of markers for inflammation and the evidence of infiltrating neutrophils and macrophages , the infected IFNAR KO mice did not show significant increase in the expression of genes linked to apoptosis ( including BCL2 , Bax , Agtr2 or Bcl2l1 ) ( S3 Fig ) ., This differs from studies showing a role for caspase 3 in the apoptosis of infected neurons in vitro 38 ., Similarly we found no evidence of widespread neurodegeneration in the CNS , although this could be due to the rapid progression of the disease , or result from the absence of IFN responses , which could sensitize the tissue to apoptosis and support CD8+ T cell mediated cytotoxic responses 39 ., Alternatively , recent studies suggest that ZIKV infection results in the formation of autophagosomes that facilitate virus replication 40 ., Since autophagy is negatively correlated with Type I interferon production 41 , it is possible that the increase in viral load and lack of infiltrating T cells in IFNAR KO mice may be secondary to increased autophagy , reduced antigen clearance by programmed necrosis , and/or reduced presentation ., Additional studies will be needed to assess the effects of infiltrating neutrophils and macrophages in the CNS of infected IFNAR KO mice and determine whether they would constitute a therapeutic target in the human disease ., Lastly , given the limited neurodegeneration observed in the CNS of IFNAR KO mice at peak of disease and the rapidity of death in the IFNAR KO mice , it is possible that infection of the peripheral organs contribute to the lethality of ZIKV infection in IFNAR KO mice , and thus although the mice show clear signs of neurological damage , additional studies will be needed to establish the cause of death in the infected IFNAR KO mice ., The neonatal B6 WT model presents several striking differences with the IFNAR KO model used in these studies ., In addition to the differences in the pace ( prodrome of 13 vs 5 days ) and survival , the B6 WT model shows no evidence of virus spread into spleen or liver at the peak of the disease , following subcutaneous infection ., This may be secondary to the effect of increased levels of ISG expression resulting in protection of the peripheral organs as well as lower levels of virus in the CNS of B6 WT compared to IFNAR KO mice ., In other words , it is conceivable that the absence of virus in the periphery of B6 WT mice reflects the preferential homing of the virus to the CNS or the more efficient IFN-mediated protection of virus in other organs ., Indeed the levels of mRNA for type I IFNs at the peak of clinical disease are strikingly low and suggest that the virus may interfere with the IFN response as reported for other viruses 42 , 43 ., Additional studies will clarify whether the virus in the B6 WT model exclusively infects the nervous system or whether infections extend to peripheral tissues where it is rapidly cleared and assess whether it extends to the eye as has been reported in the IFNAR KO models ., The B6 WT model also differs from the IFNAR KO model in the type of cellular infiltration and immune response in the CNS at the peak of infection , with IFNAR KO displaying extensive infiltration of neutrophils and macrophage as well as upregulation of inflammatory genes such as IL-6 , TNFa and IL-1 , whereas B6 WT mice show cellular infiltrates composed primarily of T cells and upregulation of genes associated with Th1 CD4+T cells and IFN-driven cytotoxic CD8+ T cell responses ., Indeed , the differential influx of immune cells evident in the CNS of B6 WT and IFNAR KO mice may underlie the differential clinical outcome ., It is possible that the direct damage that Zika causes to the tissue is only one component of its pathogenesis , while the immune response it elicits may also contribute to the pathology ., In recent studies we showed that mice infected with Tacaribe virus develop a meningoencephalitis that is driven by the CD4+ and CD8+ T cell response to the virus as mice lacking T cells do not develop disease despite high levels of virus in the CNS32 ., Similar observations were made in mice infected with Sindbis or West Nile virus 44–46 ., While it is possible that the influx of T cells into the CNS of B6 WT plays a key role in controlling the virus and aids in the survival of the host , the presence of foci of degenerating neurons suggests that the influx of CD8+ T cells may be driving the observed neuropathology ., Understanding the role the different immune cells play in pathogenesis and anti-viral responses will be critical for rational development of therapeutic and preventive approaches ., Type I IFNs play a key role in the earliest responses to viral infections and it is well known that several Flaviviruses interfere with IFN production or activity 47 , 48 , however other factors such as age , neuronal and astrocyte maturation are likely important factors for the establishment of productive infections as observed with other neurotropic RNA viruses including Sindbis , Chikungunya and Tacaribe virus , where neonate but not adult mice develop productive infections 25 , 26 , 49 ., Similar resistance to challenge was recently reported in studies with ZIKV in IFN deficient mice 28 ., In our studies , the virus replicates in neonatal neural tissue in both B6 WT and IFNAR KO mice , but was present in peripheral tissues only in IFNAR KO mice suggesting that:, i ) interferons play a role in limiting ZIKV spread but the virus can infect other tissues if the innate immune response to the virus is deficient and, ii ) the virus can infect immature nervous tissue even in immunocompetent animals ., Detailed studies of the kinetics of infection and clearance in other organs of B6 WT and IFNAR KO mice are underway to understand whether the virus can establish and/or clear productive infections in other immunoprivileged sites ., These studies establish and characterize the first contemporary ZIKV animal model in immunocompetent neonatal B6 WT mice ., The loss of balance and altered gait observed are consistent with the evidence of neurodegeneration and infiltration by cytotoxic CD8+ T cells in the cerebellum of B6 WT mice ., The presence of infiltrating CD8+ and CD4+ T cells also suggests that the virus could induce an immune response that triggers a neurodestructive inflammatory response in the CNS ., The B6 WT model will allow for studies into the immunopathology of the virus in a milieu that does not exclude one of the main immune paths for resistance to virus infection ., Given the breadth of knock out and transgenic strains available on a C57BL/6 background , it will facilitate detailed investigations into the pathogenesis of the disease as well as mechanistic studies for possible therapeutics ., While the need to infect mice at day 1 of life may limit its utility in assessing the direct protective effect of vaccines , it allows for conducting vaccination studies in pregnant mice followed by challenges in the offspring ., Lastly , since this model entails a 13 day prodromal phase , it provides an opportunity for the testing of potential therapeutics and its non-lethal outcome allows for studies assessing the long term effects of the infection , and offers the option of testing conditions that may lead to reactivation of the disease ., C57BL/6 ( B6 ) and C57BL/6-IFNAR-/- ( IFNAR KO ) mice used in this study were bred as homozygous breeding pairs ( >20 generations ) ., Mice were housed in sterile microisolator cages under 12-hour day/night cycle and given food and water ad libitum in the specific pathogen-free , AAALAC accredited animal facility of the U . S . Food and Drug Administration’s Division of Veterinary Medicine ( Silver Spring , MD ) ., This study was carried out in strict accordance with the recommendations in the Public Health Service Policy on Humane care and Use of Laboratory Animals ., All protocols involving animals were approved by the Animal Care and Use Committee at US-FDA ( Protocol Number: #2016–14 ) ., Zika virus PRVABC59 ( Puerto Rico strain ) used in this study is a contemporary strain that was isolated by CDC from the serum of a ZIKV infected patient who travelled to Puerto Rico in 2015 ., The complete genome sequence is published ( Ref . Gene bank accession # KU501215 ) ., The virus stocks used for these studies had a titer of 7 . 2 log10 pfu/mL ., Virus stocks from CDC were kindly provided by Maria Rios ( Food and Drug Administration ) ., All newborn mice were born from pathogen-free parents and inoculated with 2000 PFU or 20 , 000 PFU as indicated by subcutaneous ( s . c . ) inoculation ., IFNAR KO mice were inoculated at 10 days of life ( P10 ) and C57BL/6 mice were inoculated one day after birth ( P1 ) ., In some experiments IFNAR KO mice were infected on P1 and P3 and C57BL/6 mice were infected on P10 ., For experiments tracking survival following ZIKV infection , mice were monitored daily for clinical signs of pathology and weighed every other day to minimize handling ., Moribund ( unable to access nutrition due to severe paresis and/or respiratory distress ) animals were euthanized in accordance with the FDA IACUC guidelines ., Mice were examined daily for signs of infection and weighed on alternate days ., Examination included appearance , stance , and motility ., Fig 1C includes a description of the changes observed including the evidence of tremors , hyperactivity ( increased motor exertion and excitability ) , stance ( increased spread of hind legs while standing or walking ) , staggered march ( evidence of unusual pauses during movement ) , limb collapse ( refers to the momentary collapse of the limb under the weight of the body ) , seizures ( partial loss of voluntary movement with evidence of stiffness and/or tonic contraction of the hind legs ) , flaccid paralysis ( loss of muscle tone with collapse of the lower extremities ) ., For brain homogenates , infected mice were euthanized by CO2 asphyxiation and exsanguinated by trans-cardiac perfusion ., Brains were removed aseptically , placed in 2 ml of cold RPMI media ( ThermoFisher , Carlsbad , CA ) and manually disrupted with ice cold Tenbroeck glass grinders ( Wheaton , Millville , NJ ) until uniform homogenates were obtained ., The cellular fractions were pelleted by centrifugation at 400 x g for 15 min ., The supernatants were collected and stored at -80°C prior to virus assay ., The pelleted ( cellular ) fraction was used for flow cytometry analysis ( see below ) ., In some experiments the homogenates were directly stored at -80°C and centrifuged after thawing ., The supernatants were then used for the assay ., Infectious ZIKV levels were measured as TCID50/0 . 5g of tissue on Vero monolayers using an end-point dilution assay as previously described 50 , 51 ., ZIKV RNA levels were measured using quantitative one step reverse transcriptase PCR to amplify ZIKV genome position 1087 to 1163 based on ZIKV MR 766 strain ( GenBank accession no . AY632535 ) Zika virus RNA transcript levels in the samples were quantified by comparing to a standard curve generated using dilutions of an RNA transcript cop

Introduction, Results, Discussion, Materials and Methods

The recent spread of Zika virus ( ZIKV ) and its association with increased rates of Guillain Barre and other neurological disorders as well as congenital defects that include microcephaly has created an urgent need to develop animal models to examine the pathogenesis of the disease and explore the efficacy of potential therapeutics and vaccines ., Recently developed infection models for ZIKV utilize mice defective in interferon responses ., In this study we establish and characterize a new model of peripheral ZIKV infection using immunocompetent neonatal C57BL/6 mice and compare its clinical progression , virus distribution , immune response , and neuropathology with that of C57BL/6-IFNAR KO mice ., We show that while ZIKV infected IFNAR KO mice develop bilateral hind limb paralysis and die 5–6 days post-infection ( dpi ) , immunocompetent B6 WT mice develop signs of neurological disease including unsteady gait , kinetic tremors , severe ataxia and seizures by 13 dpi that subside gradually over 2 weeks ., Immunohistochemistry show viral antigen predominantly in cerebellum at the peak of the disease in both models ., However , whereas IFNAR KO mice showed infiltration by neutrophils and macrophages and higher expression of IL-1 , IL-6 and Cox2 , B6 WT mice show a cellular infiltration in the CNS composed predominantly of T cells , particularly CD8+ T cells , and increased mRNA expression levels of IFNg , GzmB and Prf1 at peak of disease ., Lastly , the CNS of B6 WT mice shows evidence of neurodegeneration predominantly in the cerebellum that are less prominent in mice lacking the IFN response possibly due to the difference in cellular infiltrates and rapid progression of the disease in that model ., The development of the B6 WT model of ZIKV infection will provide insight into the immunopathology of the virus and facilitate assessments of possible therapeutics and vaccines .

The recent spread of Zika virus ( ZIKV ) and its association with increased rates of neurological disorders and congenital defects created an urgent need for animal models to examine the pathogenesis of the disease and explore the efficacy of potential therapeutics and vaccines ., We describe the first symptomatic PRVABC59 ( ZIKV ) animal model in immunocompetent B6 WT mice showing that a subcutaneous challenge in 1 day old mice leads to non-lethal neurological disease that is characterized by unsteady gait , kinetic tremors , severe ataxia and seizures that subsides after 2 weeks ., ZIKV infects neurons in cerebellum of mice and elicits the infiltration of lymphocytes into the brain ., The immune response protects mice from death but may also contribute to neurodegeneration as mice with defective interferon responses have increased virus loads in brain and peripheral organs , succumbing to the disease in 5–6 days , but have fewer signs of neurodegeneration ., This mouse model bypasses transplacental transmission and consequent placental insufficiency and will facilitate detailed investigations into the pathogenesis of the disease as well as mechanistic studies for possible therapeutics and vaccines ., Lastly , its non-lethal outcome allows for studies assessing the long term effects of the infection , and exploring conditions that could lead to disease reactivation .

blood cells, medicine and health sciences, immune cells, pathology and laboratory medicine, nervous system, pathogens, immunology, microbiology, brain, animal models, viruses, model organisms, rna viruses, signs and symptoms, cerebellum, research and analysis methods, white blood cells, inflammation, animal cells, proteins, medical microbiology, microbial pathogens, t cells, mouse models, immune response, biochemistry, diagnostic medicine, anatomy, central nervous system, flaviviruses, cell biology, viral pathogens, interferons, biology and life sciences, cellular types, cerebral cortex, organisms, zika virus

journal.ppat.1007507

Zika virus infection at mid-gestation results in fetal cerebral cortical injury and fetal death in the olive baboon

Originally isolated from a febrile sentinel rhesus monkey in the Zika forest in Uganda in 1947 , Zika virus ( ZIKV ) belongs to the Flaviviridae family , genus Flavivirus , which includes dengue ( DENV ) , West Nile ( WNV ) , yellow fever ( YFV ) , and Japanese encephalitis virus ( JEV ) 1 , 2 ., Zika virus infection during pregnancy has now been firmly linked to an increased incidence of newborns with microcephaly and a variety of other congenital anomalies , collectively referred to as Congenital Zika Syndrome ( CZS ) 3–6 ., It was recently shown that one in seven children born from women with confirmed or possible ZIKV infection during gestation in Puerto Rico had a birth defect or neurodevelopmental abnormality ., 7 In addition to the spectrum of congenital malformations , ZIKV infection in pregnancy is associated with intrauterine fetal demise and increased incidence of miscarriage 3 , 4 ., The development of animal models that faithfully recapitulate the complex pathogenesis of ZIKV infection including trans-placental passage of the virus resulting in CZS anomalies is essential for developing and testing vaccines and anti-viral strategies ., Although mice have been widely used to study ZIKV infection and fetal outcome , in order for pregnant mice to be infected with ZIKV , interferon ( IFN ) signaling must be blocked raising questions regarding the translational application of findings to humans 8–10 ., Alternatively , fetal ZIKV infection in mice has been achieved via direct viral inoculation of the fetus , neonate or uterus/placenta 11–15 ., These studies verified that ZIKV infection results in a range of fetal pathologies including fetal demise , intrauterine growth restriction and fetal CNS pathologies ., While mouse models have provided insight into ZIKV pathogenesis , non-human primates ( NHPs ) are the best-documented animal reservoirs for Zika and related flaviviruses ., ZIKV infection has been characterized in male and non-pregnant female rhesus macaques ( Macacca mulatta; 16–22 ) cynomolgus macaques ( Macacca fascicularis; 23 , 24 ) and baboons ( Papio anubis; 25 ) following the standard subcutaneous ( sc ) route of inoculation ., Successful infection of rhesus macaques has also been described following intra-vaginal/intra-rectal 26 , 27 , oropharangeal mucosal 28 or mosquito bite 29 routes of inoculation , and seroprevalence of ZIKV has been reported in wild African Green Monkeys ( Chlorocebus aethiops ) and baboons 30 ., Zika virus infection of pregnant rhesus macaques 16 , 31–36 , pigtail macaques ( Macacca nemestrina; 37 , 38 ) , and marmosets ( Callithrix jacchus; 39 ) , has been achieved to model pregnancy outcomes and feto-placental pathologies in NHPs ., Similar to humans , intrauterine fetal death and/or miscarriage has been reported as a common ( 26% ) outcome following ZIKV infection in macaques 40 ., While microcephaly has not been reported in macaques infected with ZIKV during gestation , a range of fetal or infant neuropathologies has been documented ., An initial study of four rhesus macaques , sc inoculation ( mid-1st or early 3rd trimesters ) with the French Polynesian strain of ZIKV 31 , resulted in no overt fetal brain pathology by late gestation , although ocular and lung pathology was observed ., Subsequently 32 , only subtle effects on fetal brain structure were noted in 2/5 fetuses from pregnant rhesus macaques following sc inoculation ( 1st or 2nd trimesters ) with the Puerto Rican strain of ZIKV ., However , significant fetal neuropathology was reported in a single pigtail macaque following multi-site sc infection 37 with the Cambodian strain of ZIKV , albeit the dose of ZIKV was artificially high ( 5x107 pfu ) ., In this study , the most consistent CNS pathology was loss of fetal non-cortical brain volume with white matter and ependymal epithelium injury with gliosis ., However , the authors noted normal cortical folding and found no evidence of cortical malformations ., A subsequent study from this group using the same inoculating dose of either the Cambodian ( 2 macaques ) or Brazilian ZIKV strain ( three macaques ) confirmed the initial findings and additionally reported a decrease in late neuroprogenitor cells ( NPCs ) in the subgranular zone ( SGZ ) of the hippocampal dentate gyrus and subventricular zone ( SVZ ) of the temporal cortex 38 ., Interestingly , the Cambodian ZIKV strain is not associated with adverse pregnancy outcome or CZS in humans ., The most severe fetal neuropathology in NHPs was recently reported in a study of six rhesus macaques following sc inoculation with the Brazilian strain of ZIKV ( 1x103 pfu ) early in pregnancy which resulted in one in utero fetal death/abortion while the remaining five infants ( at birth ) exhibited smaller brain size and CNS lesions including calcifications , hemorrhage , necrosis , vasculitis , gliosis and apoptosis of NPCs 34 ., In addition , these authors found significant placental pathology , potentially contributing to the more extensive infant CNS pathology observed in this study ., Another study of four pregnant rhesus macaques ( late 1st to late 2nd trimester ) circumvented the need for vertical transfer by simultaneous ZIKV ( Brazilian strain ) inoculation via both intra-amniotic and maternal intravenous routes 33 ., These authors noted reduced NPCs in the SGZ of the hippocampal dentate gyrus but not in the cortical SVZ coupled with areas of calcification and gliosis ., Subcutaneous inoculation of two marmosets with ZIKV resulted in utero fetal death and miscarriage at 16–18 dpi ., Although fetal CNS pathology was observed in one fetal marmoset , it is not clear if this was in response to ZIKV or as an outcome of in utero fetal death 39 ., Cumulatively , these studies confirm that macaques have a high ( 100% ) rate of vertical transfer of maternally delivered ZIKV with a diverse range of fetal/infant neuropathology ., It is less clear if the high rate of vertical transmission of ZIKV in macaques is inherent to these species of primates or is related to the uniquely prolonged maternal viremia in pregnant macaques ( routinely a month or longer ) that may lead to continued or episodic exposure of the fetus to ZIKV over long periods of gestation , despite the development of neutralizing antibodies ., Despite these elegant studies in pregnant macaques , there is still a clear need to develop additional NHP models to study pregnancy and fetal outcome from ZIKV infection , in particular addressing the early events of vertical transfer ., In the present study , we developed the olive baboon ( Papio anubis ) as an alternative NHP model to study ZIKV infection and pathogenicity during pregnancy that can be compared/contrasted with ZIKV infection in human and other NHP pregnancies ., Unlike the studies in macaques that focused on late gestation fetal or infant neuropathological outcome , our focus was on the timing of transplacental ZIKV passage and the early mechanistic events of ZIKV induced pathogenesis of the fetal brain ., The olive baboon is similar to humans in terms of size , genetics , reproduction , brain development and immune repertoire which makes the baboon an excellent translational NHP model to study ZIKV infection and for vaccine and therapeutics development 41–43 ., The baboon has been used as a NHP model for assessing safety and efficacy of vaccines in adults , pregnant females and their infants 42 , 44 ., The baboon is permissive to flavivirus infection and replication , including ZIKV , and produces a virus-specific immune response 25 , 43 ., Herein , we describe infection of four timed-pregnant olive baboons at mid-gestation with a contemporary French Polynesian strain of ZIKV ( H/PF/2013 ) ., The French Polynesian ZIKV strain contains a single point mutation in the prM protein that dramatically increases ZIKV infectivity in both human and mouse NPCs compared to the ancestral African/Asian ZIKV strains 45 ., This mutation was conserved during the ZIKV spread through the Americas and is associated with adverse fetal outcomes , including increased microcephaly in French Polynesia 46–49 ., We report that the pregnant olive baboon is susceptible to ZIKV infection during gestation including vertical transfer of virus to the fetus resulting in both fetal death as well as fetal cerebral cortical pathologies ., All ZIKV infected dams had minor weight loss during the study period ( Dam 1: 16 . 4 start , 16 . 2 kg end 7 dpi; Dam 2 16 . 0 start , 15 . 6 kg end 14 dpi; Dam 3: 21 start , 20 . 9 kg end; 14 dpi; Dam 4: 13 . 8 start , 13 . 6 kg end 21 dpi ) , however , none of the dams exhibited inappetence ., Dam 1 exhibited a mild rash on day three post-infection in the axillary and inguinal regions as well as conjunctivitis that cleared by day seven post-infection; Dam 2 exhibited a mild rash in the axillary and inguinal regions and minor conjunctivitis by day three post-infection which expanded to moderate to severe maculopapular rash on the abdomen and inguinal regions with mild rash on the chest and back of both arms with mild conjunctivitis by day seven that resolved by day 14 post-infection ., Dam 3 developed a mild rash in the axillary region and a moderate rash in the inguinal region with moderate conjunctivitis that resolved by day seven post-infection ., Dam 4 exhibited a mild rash on day three post-infection in the axillary and inguinal regions as well as mild conjunctivitis that progressed to a mild to moderate rash by day seven-post infection that included the abdomen , chest and backs of arms that resolved by 14 dpi ., Body temperatures obtained under ketamine sedation did not show any fever greater than 1°C above day 0 over the course of the study for each animal ., Normal fetal heart rates were obtained for Dams 1 , 3 and 4 from the day of inoculation through pregnancy termination on days 7 , 14 and 21 post-infection respectively ., Dam 2 exhibited normal fetal heart rate through day 7 post-infection ., However , on day 14 , no fetal heart rate was found and upon subsequent necropsy , fetal demise likely occurred within the preceding 24 hours ., There was rupture of the fetal membranes in this pregnancy and signs of meconium staining ., For Dams 1 , 2 and 4 , ZIKV RNA was not detected in whole blood on day three but was detected on day seven post-infection ., In Dams 2 and 4 , viremia was resolved by day 14 post-infection ( Fig 1A ) ., In Dam 3 , ZIKV RNA was detected on days 3 through 14 post-infection ( study termination , Fig 1A ) , albeit the peak viremia was observed at 5 dpi in this dam and had declined by ~3 orders of magnitude by day 14 post-infection ., ZIKV RNA was detected in saliva from Dams 1 and 3 at day seven post-infection , and from Dam 2 on day 14 post-infection ( Fig 1B ) ., ZIKV RNA in urine was only detected in Dam 2 on day 14 post-infection ( day of necropsy ) ( urine was not collected from Dam 3; Table 1 ) ., Only Dam 4 had ZIKV RNA in CSF ( day 7 post-infection ) ., None of the dams had ZIKV RNA in vaginal swabs at any time point ., Reproductive tissues ( cervix , uterus , ovaries ) , cerebral cortex and cerebellum were examined for ZIKV RNA from the dams from tissues taken at the time of necropsy ., ZIKV RNA was only detected in the uterus of Dam 2 ( 14 dpi ) ., Fetuses are coded to match the dams ( eg . Dam 1 = Fetus 1 ) ., We did not detect ZIKV in cord blood obtained at necropsy from any of the four fetuses ., Fetus 2 ( 14 dpi ) had ZIKV RNA in placenta , cerebral cortex , lung , spleen and ovary ( Table 1 ) ., ZIKV RNA was found in Fetus 4 ( 21 dpi ) in placenta , cerebral cortex , lung , spleen , intestine and ovary ., ZIKV RNA was detected in amniotic fluid for both Fetus 2 and 4 ( Table 1 ) ., Fetus 3 ( 14 dpi ) had ZIKV RNA in the placenta and Fetus 1 ( 7 dpi ) did not have detectable ZIKV in any tissue examined or amniotic fluid ., Upon standard H&E staining , none of the three fetuses from ZIKV infected dams ( with available CNS tissue ) for histology had gross pathology of the cerebral cortex or other brain structures ( Fetus 2 had extensive autolysis of the brain after in utero death ) ., Histological examination of the frontal cortex ( CNS region with abundant ZIKV RNA; 1x104 copies/mg ) of Fetus 4 , which exhibited vertical transfer of virus at 21 dpi , revealed no major gross pathological lesions , calcifications , signs of vascular collapse or vasculitis or decreased cortical volume compared to the control fetus or the two fetuses collected at days 7 and 14 post-infection with no evidence of vertical transfer of ZIKV ., Immunofluorescence ( IF ) for GFAP , a classical marker for radial glia ( RG ) and astrocytes in the developing cortex , revealed a pronounced difference in the ZIKV infected frontal cortex compared to the control ( or day 7 or 14 post-infection fetuses without vertical transfer of virus ) ., In the control fetus and fetuses with uninfected brains ( Fetus 1 , 3 ) , the anticipated pattern of dense glial fibers projecting from the ventricular zone ( VZ ) to the marginal zone ( MZ ) was observed ( Fig 3B–3E ) ., However , in the frontal cortex from the ZIKV infected fetus , there was a pronounced decrease in GFAP+ fibers , in particular in the subplate ( SP ) and intermediate zone ( IZ; Fig 3F ) ., Image analysis demonstrated that Fetus 4 had ~10% of the RG fibers in the SP/IZ ( Fig 3F ) compared to the control fetus and the two fetuses from ZIKV infected dams that did not have detectable ZIKV in the brains or other fetal tissues ( Fetus1 , 3 ) ., Concurrent with the loss of RG fibers , a noted increase in the density in astrocytes was observed in the IZ/SP regions of Fetus 4 ( ~5-fold increase; Fig 3G ) compared to the cortex of the control fetus and the two fetuses from infected mothers not exhibiting vertical ZIKV transfer ., In the uninfected fetal frontal cortices , GFAP-IF revealed a pattern of sporadic RG/astrocytes with few astral branches representing maturing astrocytes with normal growing processes typical of this gestational age ( Fig 3B–3D ) ., In order to determine if ZIKV infection targeted NPCs or reduced cortical neurons we performed IF for NeuN ( neurons , differentiating neurons ) and Nestin ( NPCs ) ., In the control fetal cortex and the cortices of Fetus 1 and 3 ( no detection of ZIKV in the fetus ) , NeuN+ IF positive neurons were observed in long organized tracks of migration in the IZ/SP through the CP , while in the ZIKV infected cortex ( Fetus 4 ) , the pattern of NeuN+ neurons appeared disorganized and not in the characteristic tracks , even in the CP ( Figs 4 and 5 ) ., In addition , the number of NeuN+ neurons in the CP of Fetus 4 were approximately 50–60% of those observed in the control fetus and the two fetuses from ZIKV infected dams not exhibiting viral RNA in the fetus ( Fig 4 ) , indicating that the neuronal migration to the CP had been disrupted after loss of the RG fibers ., Immunofluorescence for Nestin revealed a different pattern in the frontal cortex of Fetus 4 compared to control or Fetus 1 and 3 , in particular in the IZ/SP ( Fig 5 ) ., When observed in the 21 day ZIKV positive cortex , Nestin+ IF cells were typically clustered , however there were regions within the IZ/SP that had fewer or were devoid of Nestin+ cells in the ZIKV infected Fetus 4 ., Overall , there appeared to be similar numbers of Nestin+ NPCs in the IZ/SP of Fetus 4 , however their distribution was highly altered , again possibly related to the loss of RG fibers noted above ., In order to determine if ZIKV infection causes white matter damage in term pregnancies and postnatally as reported in ZIKV+ fetuses/infants in human population 5 and observed in the 3rd trimester pigtail macaque fetus with ZIKV positive brain 37 , 38 , we performed IF for O1 , a marker for immature oligodendrocytes and the only cell population that matures to oligodendrocytes that are responsible for myelinating the axons ., O1 IF showed abundant O1+ immature oligodendrocytes in the SP of the control fetal brain ( and the cortices of the fetuses without vertical transfer of ZIKV ) that exhibited numerous processes typical of immature oligodendrocytes ( Fig 6 ) ., In Fetus 4 , although the numbers of immature OLs were similar in the SP compared to the control fetus , the immature oligodendrocytes were largely without multiple processes and were not evenly distributed as observed for the control fetus ( Fig 6 ) ., In the IZ , smaller immature oligodendroctyes were observed in the control fetus; these immature oligodendrocytes were largely without processes in the IZ and numerous ., In the IZ of Fetus 4 , O1 staining revealed that the immature oligodendrocytes were located in the deeper region of the IZ indicating possible disruption of migration and appeared to be in the process of degeneration ( Fig 6 ) ., The cerebral cortices of Fetus 1 ( 7dpi ) and Fetus 3 ( 14dpi ) appeared similar to the control brain ., The 21 day ZIKV infected cortex ( Fetus 4 ) exhibited increased neuroinflammation with an approximate 7-fold increase in Iba1 immunoreactive microglia ( Fig 7A–7E ) and IL-6 ( Fig 7E–7H ) immunoreactive cells ( proinflammatory cytokine ) compared to the control fetus or Fetus 1 ( 7 dpi , no vertical ZIVK transfer ) ., Fetus 3 ( 14 dpi ) had an approximate 4-fold increase in both Iba1 and IL-6 immunostaining in the frontal cortex ., While not detecting ZIKV in tissues ( including cortex ) in this fetus , there was ZIKV RNA and protein in the placenta of this pregnancy ., Neuroinflammation was not observed in the day seven post-inoculation frontal cortex ( Fetus 1 ) ., This fetus did not have ZIKV RNA detected in any fetal tissue or the placenta ., There were little to no apoptotic cells in the control frontal cortex in any region of the developing cortex ( Fig 8 ) ., There were notable apoptotic cells in the cortex in the day 21 post-infection cortex compared to the control cortex , primarily in the IZ/SP region ( Fig 8 ) ., The cortex of Fetus 1 was similar to the Control fetus with few apoptotic cells , while the day 14 post-infection fetus exhibited a similar amount of TUNEL staining compared to the ZIKV infected 21 day post-infection fetus ., Immunofluorescence for ZIKV ( pan-flavivirus ) revealed focal presence of ZIKV in the frontal cortex of Fetus 4 but not in Fetus 1 , 3 or the control fetus , confirming localization of ZIKV in Fetus 4 with high vRNA burden in the frontal cortex ., The viral IF was most notable in the subventricular zone ( SVZ ) and intermediate zone ( IZ ) ( Fig 9 ) ., Routine H&E staining showed only minor evidence of inflammation in Dam 1 , while the placenta of Dam 3 was histologically similar to the control placenta , despite having one cotyledon positive for ZIKV RNA ., Similarly , Dam 4 , which had vertical transfer of ZIKV to the placenta and fetus , exhibited only minor indices of inflammation ., The placenta of Dam 2 ( intrauterine fetal death ) exhibited significant placental pathology , with extensive fibrin deposition in the intervillous space with nearly uniform degenerated villi with frequent necrosis and acute inflammation ., The control Dam and Dam 1 ( 7 dpi ) were negative for ZIKV IF ., ZIKV IF ( pan flavivirus ) in the placentas of Dams 2 and 3 ( 14 dpi; both positive for ZIKV RNA ) demonstrated the presence of ZIKV , localized primarily in the syncytial layer with regions exhibiting greater intensity ( Fig 10 ) ., Dam 2 , which had fetal demise and ZIKV RNA detected in fetal tissues , exhibited the most intense ZIKV IF and also had IF signal in villous cores , in either stromal or enodothelial cells ., Dam 4 ( Fig 10; 21 dpi; placenta and fetus ZIKV RNA positive ) also exhibited ZIKV IF in the syncytial layer , although the signal was not as widespread as observed in the 14 dpi placentas potentially indicating a decrease in viral replication in the placenta by 21 dpi ., The cytokine/chemokine response was highly variable between dams ., Dam 1 was notable in that no discernable change in any cytokine/chemokine was observed on day three or seven post-infection ( study termination , Fig 11A ) ., Dam 2 had an increase in IL-1β , IL-2 , IL-6 , IL-7 , IL-12 , IL-15 , IL-16 and IL-17A , peaking on day 14 post-infection for all but IL-12 and IL-15 which peaked on day 7 post-infection ( Fig 11B ) ., Dam 3 exhibited an increase above baseline in IL-2 , IL-6 , IL-7 , and IL-15; notably , all cytokines increased on day 7 and returned to basal by day 14 post-infection ( Fig 11C ) ., Dam 4 exhibited an increase above baseline in IL-1β , IL-2 , IL-6 , IL-7 , IL-15 and IL-16 ( Fig 11D ) , and similar to Dam 2 , peak levels of these cytokines were on day 14 post-infection with the exception of IL-15 ( day 7 ) ; by 21 dpi , most cytokines had returned to baseline or were lower than peak levels ., Similar to that seen for cytokines , Dam 1 did not display any notable increase in plasma chemokine levels post-infection ( Fig 12A ) ., Dam 2 had notable increases in plasma levels of Eotaxin , MCP-1 and MCP-4 ( Fig 12B ) , and similar to cytokines for this Dam , chemokines exhibited a progressive increase from Day 0 through study termination on Day 14 ., Dam 3 exhibited small transient increases in Eotaxin and IL-8 on day 7 ( Fig 12C ) ., Dam 4 had increases in plasma levels of Eotaxin ( small ) , IL-8 and MCP-4 peaking primarily on day 14 post-infection ( Fig 12D ) ., In this study , we describe ZIKV infection in four olive baboons at mid-gestation ( 97–107 dG; term ~183 dG ) following sc delivery of a relatively modest dose ( 1x104 ffu ) of the French Polynesian isolate resulting in vertical transfer of the virus to the fetus associated with fetal demise in one pregnancy and significant fetal CNS pathology in a second pregnancy ., We chose the French Polynesian isolate since the mutation in the prM protein ( S139N ) of the ancestral Asian ZIKV strain arose prior to the French Polynesian outbreak , and has been stably maintained in the strains circulating in the Americas ., This mutation was shown to significantly enhance infectivity in human NPCs and yielded a more significant microcephaly in mice 45 ., A retrospective study reported an increase in microcephaly and CZS following the ZIKV epidemic in French Polynesia 49 ., Following ZIKV infection , all four pregnant dams exhibited viremia within the first week post-infection and all presented with rash and conjunctivitis varying from mild to moderate , similar to that we described for male and non-pregnant female baboons 25 ., Most clinical signs in pregnant humans , including rash , resolve within a week but may last up to two weeks ., Description of rash following ZIKV infection in humans has been variable with estimates ranging from relatively infrequent ( ~1 in 5 ) 4 to greater than 2/3rds of definite ZIKV cases in a Brazilian pregnancy cohort 50 ., As such , the presence and duration of rash and conjunctivitis in our pregnant baboons resemble that observed in human pregnancy ., While the magnitude of viremia achieved in the pregnant baboons was similar to that described in our prior study of male and non-pregnant female baboons 25 , and pregnant and non-pregnant macaques receiving a similar dose and route of delivery of ZIKV ( French Polynesian or other strains ) , the onset of viremia in three of the four pregnant baboons was slightly delayed ( detected at 7 but not 3 dpi ) compared to male and non-pregnant female baboons where peak viremia was obtained routinely at 3 to 4 dpi and typically resolved by 7 to 10 dpi ., The course of viremia in the pregnant baboons was also different than that described for pregnant macaques , which characteristically show viremia that initiates very early post-inoculation ( 1–2 days post-inoculation ) , but is unusual in that it is routinely reported to be prolonged with viral RNA detectable in blood for several weeks up to 70 dpi 16 , 31 , 34–36 ., Viremia was observed in one dam to 14 dpi , albeit declining by >3 orders of magnitude from the peak at 5 dpi , although it is possible that viremia might have been prolonged for this dam if the time frame of her study had been extended ., It is noteworthy that this dam also exhibited early viremia , detected at 3 dpi ., In humans , viremia following ZIKV infection is usually short-lived ( 3–7 days ) with occasional longer durations of up to 10 to 14 days 4 ., While prolonged viremia ( 46 to 53 days ) has been reported in pregnant women 51 , it should be noted that this was restricted to five cases after a search of the entire U . S . Zika Pregnancy Registry and as such , prolonged viremia during pregnancy in women appears rare ., These authors did not find a correlation between prolonged viremia and an increased incidence of CZS ., Vertical transfer of ZIKV described thus far in macaques appears to be very efficient ( 100% ) ., We observed vertical transfer to the placenta in three of the four pregnant baboons infected at mid-gestation , two at 14 dpi and the third at 21 dpi ., Further vertical transfer to the fetus was observed in two dams , in one dam , vertical transfer of ZIKV was associated with intrauterine fetal death by day 14 post-infection ( Dam/Fetus 2 ) , while in a second dam , vertical transfer associated with significant cerebral cortical neuropathology in the fetus at day 21 dpi ( Dam/Fetus 4 ) ., The latter fetus was otherwise healthy with no congenital anomalies or signs of growth restriction ., ZIKV RNA was detected in both fetuses in cerebral cortex , lung , spleen , and ovaries , and additionally in the intestine in Fetus 4 at 21 dpi ., Unfortunately , in utero fetal death precluded meaningful cortical histopathology of Fetus 2 ., ZIKV RNA was detected in the amniotic fluid of both of these pregnancies as well ., We sampled three to four separate sites ( cotyledons ) of each placenta since a recent study in macaques indicated that ZIKV infection of the placenta might be localized and not diffuse 32 ., In Dam 2 ( 14 dpi ) , ZIKV was observed in two cotyledons , in Dam 3 , ZIKV RNA was detected in one cotyledon while three cotyledons were positive for ZIKV RNA in Dam 4 ., Immunofluorescence for ZIKV ( pan flavivirus ) verified the presence of ZIKV in the placenta of all three dams with ZIKV RNA ., Of interest , IF revealed ZIKV in the syncytiotrophoblast layer of all three placentas to varying intensities with the day 14 post infection placenta exhibiting the most uniform IF in the syncytiotrophoblast layer while there was restriction of the IF signal by 21 dpi indicating potential resolution of placental infection by this time post-infection ., In situ hybridization for ZIKV RNA has been shown in macaque placental villi , collected at 14 dpi , consistent with our IF findings 34 ., Other studies in macaques have focused on long-term infection with collection of the fetus and placenta at late gestation ( or delivery ) and as such , our findings are of the first to show the early infection of the placenta and targeting of the syncytiotrophoblast by ZIKV ., While we did not detect ZIKV RNA in the placenta of Dam 1 ( study terminated at 7 dpi ) , it can be argued that terminating study in this dam at 7 dpi may have precluded placental infection ( vertical transfer ) and suggests that transfer of the virus to the placenta and fetus occurs at the 2–3 week post-infection period ., It is possible that viral escape from the placenta to the fetus and/or amniotic fluid could have been delayed in Dam 3 as well , since we detected ZIKV RNA in the placenta and this dam also had the longest duration of viremia ., Based on our observations , vertical transfer in baboons would appear to take place at some point between peak maternal viremia ( 7 dpi ) and 21 dpi , and as such , a relatively early event during ZIKV infection ., In support of our findings of a rapid transfer of virus to the fetus , Hirsch et al 32 observed vertical transfer in two late 2nd trimester rhesus monkeys within 20 dpi and vertical transfer with fetal death was noted in an additional study of rhesus macaques at approximately three weeks post-infection 34 ., However , this does not preclude vertical transfer from occurring at a later time point post-infection since it has been suggested in pregnant women that vertical transfer may take up to 5 weeks ., Similarly , in marmosets , sc infection during early gestation led to vertical transfer with fetal death and miscarriage at 16–18 dpi 39 ., In macaques , vertical transfer may also occur over an extended period of time post-infection since the duration of viremia in macaques is unusually prolonged with widespread infection of maternal tissues including immune-privileged sites such as the LN’s and the CNS which could be potential viral reservoirs for future infections ., This could provide longer periods for virus to cross the placental barrier as suggested by Nguyen et al 31 , since these studies were mostly done in early and mid-gest and taken to term or near-term pregnancy ., Clearly , future studies are needed to follow ZIKV infected pregnant baboons for longer periods post-infection ., To our knowledge this is the first study in NHPs where the primary focus was on the early post-infection time course of vertical transfer of ZIKV ., Fetal death , miscarriage and preterm birth have been attributed to ZIKV infection in humans 3 ., In symptomatic women , a 5 . 8% miscarriage rate and 1 . 6% stillbirth was reported for women infected with ZIKV in the first trimester 52 ., However , this is likely an underestimate since a majority of ZIKV infections ( ~60–80% ) are asymptomatic 53 ., A recent aggregate communication between National Primate Research Centers concluded that fetal death occurred in 26% of ZIKV infected pregnant macaques 40 ., Our finding of one case of fetal death in the four infected baboon pregnancies is consistent with that reported for macaques and supports studies in macaques that miscarriage and fetal demise are likely considerably higher in women than currently estimated ., In our case , fetal demise occurred at two weeks post-infection similar to the timing of fetal death noted in two macaque studies and in a study in marmosets 34 , 35 , 39 ., It is of interest that the pregnant baboon in the present study with in utero fetal death exhibited a potentially delayed or suboptimal immune response to ZIKV with low IgM titers found at day 14 and an absence of IgG titers against ZIKV at day 14 post-infection ., This failure to mount an immune response in spite of the highest viremia may have contributed to the rapid vertical transfer and fetal demise ., While this dam also exhibited a notable systemic cytokine/chemokine response , it is unclear if this cytokine response played a role in fetal demise or was a result of placental pathology and fetal death ., Two other dams studied to 14 and 21 days elicited an IgM response by 14 dpi as well as having IgG titers and robust ZIKV neutralizing capacity by day 14 ( Dam, 3 ) and day 21 ( Dam, 4 ) post-infection ., Again , it is tempting to suggest that the earlier neutralizing IgG response in Dam 3 may have provided some protection to vertical transfer while the delayed IgG response in Dam 4 was insufficient to prevent vertical transfer even though there was efficient transfer of the IgG to the fetus ., Unlike the case with fetal demise , Dam 3 exhibited a rather restricted cyto-chemokine response that resolved by day 14 ., The systemic cytokine response in Dam 4 , that also exhibited vertical transfer of virus to the fetus , was delayed , peaking at day 14 post infection and returning to baseline by 21 dpi ., Dam 1 was noteworthy in that there was no noted increase in plasma cytokines at either day 3 or 7 ( study terminated ) despite a robust viremia and rash ., While the fold increases in these cytokines is of similar magnitude as that described in humans in response to ZIKV in the acute phase of infection 54 , the individual cytokine response to ZIKV infection in pregnant baboons appears quite variable ., Similar to what has been described in macaques , rupture of the fetal membranes was noted in the pregnancy with fetal demise ( Dam 2 ) , consistent with ZIKV RNA in both amniotic fluid and urine .

Introduction, Results, Discussion, Materials and methods

Zika virus ( ZIKV ) infection during pregnancy in humans is associated with an increased incidence of congenital anomalies including microcephaly as well as fetal death and miscarriage and collectively has been referred to as Congenital Zika Syndrome ( CZS ) ., Animal models for ZIKV infection in pregnancy have been developed including mice and non-human primates ( NHPs ) ., In macaques , fetal CZS outcomes from maternal ZIKV infection range from none to significant ., In the present study we develop the olive baboon ( Papio anubis ) , as a model for vertical transfer of ZIKV during pregnancy ., Four mid-gestation , timed-pregnant baboons were inoculated with the French Polynesian ZIKV isolate ( 104 ffu ) ., This study specifically focused on the acute phase of vertical transfer ., Dams were terminated at 7 days post infection ( dpi; n = 1 ) , 14 dpi ( n = 2 ) and 21 dpi ( n = 1 ) ., All dams exhibited mild to moderate rash and conjunctivitis ., Viremia peaked at 5–7 dpi with only one of three dams remaining mildly viremic at 14 dpi ., An anti-ZIKV IgM response was observed by 14 dpi in all three dams studied to this stage , and two dams developed a neutralizing IgG response by either 14 dpi or 21 dpi , the latter included transfer of the IgG to the fetus ( cord blood ) ., A systemic inflammatory response ( increased IL2 , IL6 , IL7 , IL15 , IL16 ) was observed in three of four dams ., Vertical transfer of ZIKV to the placenta was observed in three pregnancies ( n = 2 at 14 dpi and n = 1 at 21 dpi ) and ZIKV was detected in fetal tissues in two pregnancies: one associated with fetal death at ~14 dpi , and the other in a viable fetus at 21 dpi ., ZIKV RNA was detected in the fetal cerebral cortex and other tissues of both of these fetuses ., In the fetus studied at 21 dpi with vertical transfer of virus to the CNS , the frontal cerebral cortex exhibited notable defects in radial glia , radial glial fibers , disorganized migration of immature neurons to the cortical layers , and signs of pathology in immature oligodendrocytes ., In addition , indices of pronounced neuroinflammation were observed including astrogliosis , increased microglia and IL6 expression ., Of interest , in one fetus examined at 14 dpi without detection of ZIKV RNA in brain and other fetal tissues , increased neuroinflammation ( IL6 and microglia ) was observed in the cortex ., Although the placenta of the 14 dpi dam with fetal death showed considerable pathology , only minor pathology was noted in the other three placentas ., ZIKV was detected immunohistochemically in two placentas ( 14 dpi ) and one placenta at 21 dpi but not at 7 dpi ., This is the first study to examine the early events of vertical transfer of ZIKV in a NHP infected at mid-gestation ., The baboon thus represents an additional NHP as a model for ZIKV induced brain pathologies to contrast and compare to humans as well as other NHPs .

Zika virus is endemic in the Americas , primarily spread through mosquitos and sexual contact ., Zika virus infection during pregnancy in women is associated with a variety of fetal pathologies now referred to as Congenital Zika Syndrome ( CZS ) , with the most severe pathology being fetal microcephaly ., Developing model organisms that faithfully recreate Zika infection in humans is critical for future development of treatments and preventions ., In our present study , we infected Olive baboons at mid-gestation with Zika virus and studied the acute period of viremia and transfer of Zika virus to the fetus during the first three weeks after infection to better understand the timing and mechanisms of transfer of ZIKV across the placenta , leading to CZS ., We observed Zika virus transfer to fetuses resulting in fetal death in one pregnancy and in a second pregnancy , significant damage to the frontal cortex of the fetal brain at a critical period of neurodevelopment in primates ., Thus , the baboon provides a promising new non-human primate model to further compare and contrast the consequences of Zika virus infection in pregnancy to humans and other non-human primates .

medicine and health sciences, reproductive system, pathology and laboratory medicine, maternal health, obstetrics and gynecology, nervous system, pathogens, microbiology, vertebrates, brain, animals, mammals, primates, viruses, developmental biology, womens health, rna viruses, pregnancy, pregnancy complications, old world monkeys, frontal lobe, embryology, monkeys, baboons, placenta, medical microbiology, microbial pathogens, fetal death, macaque, eukaryota, anatomy, flaviviruses, central nervous system, viral pathogens, biology and life sciences, amniotes, cerebral cortex, organisms, zika virus

journal.ppat.1002064

Endemic Dengue Associated with the Co-Circulation of Multiple Viral Lineages and Localized Density-Dependent Transmission

Dengue is the most important mosquito-borne viral disease of humans , annually responsible for approximately 40 million cases and some 20 , 000 deaths in tropical and subtropical regions 1 ., Dengue is caused by one of four single-stranded positive-sense RNA viruses ( DENV-1 to DENV-4 , also referred to as serotypes ) of the genus Flavivirus ( family Flaviviridae ) ., Despite the large burden of dengue disease , and considerable research effort , there are currently no licensed vaccines or specific therapies ., The challenge of effective and safe dengue vaccination is increased by the possibility that imperfect cross-protective vaccination could enhance DENV infection , or even virulence 2 , and that lineages within individual DEN viruses , particularly different ‘genotypes’ , may also differ in antigenicity 3–6 ., In addition , the population dynamics of DENV within individual localities are complex , involving the birth-and-death of viral lineages that may also differ in both virulence and fitness 7–13 , as well as the intricate patterns of gene flow , at both the local and international scales 7 , 14 , 15 ., DENV transmission among humans is largely caused by the urban adapted and anthropophillic Aedes aegypti mosquito ., Spatial and temporal patterns of dengue prevalence are likely driven by multiple factors including the immune status of human hosts 16 , their age 17 , 18 , virus traits 13 , 19 , 20 , the mosquito vector , and environmental variables including aspects of climate such as levels of precipitation 21 , 22 ., Human movement must also be an important , but poorly understood , contributor to viral transmission dynamics , and is obviously responsible for the increasingly widespread and complex distribution of the four DEN viruses at the global scale ., On a local scale , how much DENV transmission within a specific population is due to the local movement of infected human hosts rather than of mosquitoes is unclear ., Understanding the spatial and temporal dynamics of dengue transmission in endemic dengue populations is therefore central to the rational deployment of vector control activities and the design of intervention strategies ., In this respect it is critical to determine the spatial structure of DENV within endemic populations , the rate at which DENV lineages diffuse through space ( particularly in the face of a partially immune population ) , whether specific lineages are spreading more rapidly than others and indicative of enhanced fitness , and the likely contribution of mosquitoes and humans to local transmission patterns ., To address these questions we employed a fine-scale molecular approach to characterize the virus population dynamics of a recent DENV-1 outbreak in southern Viet Nam , a region of high dengue endemicity ., Between 2006–2008 the estimated incidence of DENV-1 infection in the southern twenty provinces of Viet Nam ranged from 86–190 cases/100 , 000 13 , markedly higher than during the preceding six-year period when it ranged from 1–28 cases/100 , 000 ., The causes of this increased incidence are unknown ., To determine the patterns and dynamics of dengue transmission we utilized an expansive data set of DENV-1 whole genome sequences sampled prior to and during the peak in DENV-1 prevalence over a period of six years ( 2003–2008 ) ., We inferred the dynamics of viral transmission within individual communities , between communities , and between neighboring countries , using recently developed Bayesian phylogenetic methods that utilize both the temporal and spatial information of the sampled sequences ., Uniquely , these time-calibrated phylogenetic methods provide the ability to reveal the complex interplay of spatial , genetic and epidemiological dynamics at the local , regional and global scales , and have the ability to consider individual viral lineages , whereas epidemiological approaches based on the analysis of incidence data are at best only able to distinguish among the four DEN viruses ., We determined the consensus DENV-1 genome sequence ( minimum sequence from nt 70–10 , 400 ) in acute plasma samples collected from 751 hospitalized patients in urban Ho Chi Minh City ( HCMC ) ( n\u200a=\u200a575 sampled between 2003–2008 ) and rural Dong Thap Province in the Mekong Delta region ( n\u200a=\u200a176 sampled between 2006–2007 ) ., The majority of viruses were sampled from 2006 to 2008 during which DENV-1 was the most prevalent serotype in circulation ( Figure S1 ) ., To determine the evolutionary relationships of DENV-1 in Viet Nam in the context of surrounding countries we analyzed the envelope ( E ) gene sequences from these locations ( Figure 1A ) ., The 751 DENV-1 sequences sampled from Viet Nam fell into one of five clades within the broader Genotype I cluster of viruses 23 ., Four of the five clades consistently clustered within the diversity of Cambodian viruses with good support ( posterior probability ranging from 0 . 81 to 1 . 0 ) ., This phylogeographic evidence , coupled with Cambodia and Viet Nams shared border , is compatible with Cambodia acting as the major source of Vietnamese DENV-1 ., A caveat to this is the lack of contemporaneous DENV-1 sequences from nearby Thailand , which has previously been shown to harbor substantial DENV diversity and importation into Viet Nam 13 ., Clearly , wider sampling in both time and space is needed to test this hypothesis ., The majority of the clades largely comprised viruses from HCMC , with the exception of clade 1 , which was found to be Dong Thap dominant ., The timing of these inferred introductions were gauged from the age of the most recent common ancestor ( TMRCA ) of each clade ( Table 1 ) ., The period in which these different viral clades emerged in southern Viet Nam ranged from late 2001 to mid-2005 ., Apart from clade 1 , which was found to be the most recent introduction , the mean ages of clades 2–5 did not differ significantly , suggesting that different viral lineages were imported over short or similar time-scales , and then co-circulated ., These clades were chosen for more detailed phylogeographic analysis ., Finally , genome-wide rates of nucleotide substitution – at ∼1×10−3 nucleotide substitutions per site , per year ( Table, 1 ) – were the same among clades and highly consistent with those previously determined for DENV 24 , 25 ., For the clades identified as being within Viet Nam , a discrete spatial model 26 was employed to reveal the migration between the sampling locations ., The results are shown in Figure 1B , in which branches are colored by the most probable state location ., In four of the five clades HCMC was the most likely viral source , with viruses exported to the rural area of Dong Thap ., The non-HCMC isolates in these clades were interspersed among the HCMC sampled isolates , which strongly suggested that the DENV-1 epidemics in southern Viet Nam mainly emerged first in HCMC ., The exception was clade 1 , which was dominated by Dong Thap viruses and where Dong Thap was inferred to be the most likely place of origin ., Moreover , the HCMC viruses in clade 1 did not form a monophyletic group , supporting the view that clade 1 viruses were imported into HCMC on multiple occasions from Dong Thap ., To determine whether the viral migration rates varied between urban and rural epidemics , we compared the spatial dynamics between clades 1 and 4 ( Table 2 ) ., When focusing on the number of transitions from the inferred source location , a symmetrical pattern was observed between the two clades ., For instance , the transmission rate between HCMC and Dong Thap was higher in the HCMC dominant clade 4 , while for the reverse direction ( Dong Thap to HCMC ) it was greater in Dong Thap dominant clade 1 ., Hence , once a virus became established in a location , rural or urban , the rate of viral exportation was found to be greater than the rate of viral importation ., The geographical coordinates of the patients residential address in HCMC ( n\u200a=\u200a381 ) or Dong Thap Province ( n\u200a=\u200a175 ) was known for 556 cases and this information was employed to reconstruct the fine-scaled dispersion of the individual viral lineages within the sampling areas using a continuous spatial diffusion model with non-homogenous dispersion rates 27 ., The average viral dispersion rate ( km/year ) was calculated for each clade , and separately for HCMC and/or Dong Thap data subsets , as if the epidemic in these regions derived from a single introduction ( Table 2 ) ., We define virus dispersion rate as a measure of how quickly a virus lineage spreads geographically , given the inferred root location and final sampling locations ., Even though we only had one estimate of the average dispersion rate of DENV-1 in Dong Thap , a clear disparity was observed when compared to the rates from HCMC lineages ( Table 3 ) ., Specifically , the viral lineages from clade 1 in Dong Thap spread approximately 2–3 times faster than any lineage from HCMC ., This is indicative of a fundamental difference in the epidemiological dynamics of DENV-1 in the two areas ., A further dissection of the dispersion rates through time in HCMC ( clades 2 , 4 and 5 ) and Dong Thap ( clade, 1 ) revealed interesting patterns in the rate of viral spread in the two locations ., In HCMC ( Figure 2A and B ) , the monthly incidence of DENV-1 showed a similar trend as in Dong Thap , with corresponding regular fluctuations and an increasing overall trend ., However , there was no clear association between genetic diversity , incidence , and dispersion rate observed in the urban environment demonstrated by the roughly horizontal relationship in Figure 2B and the overlapping 95% HPD ( highest posterior density ) intervals ., Hence , although the DENV-1 clades were introduced independently into HCMC , they had spread at similar and effectively constant rates ., For Dong Thap , clade 1 was the only one clearly derived from a distinct single importation and of a sufficient size for analysis ., The dispersion rate of DENV-1 appeared to be associated with the fluctuations in genetic diversity and monthly incidence in Dong Thap ( Figure 2C and D ) ., The two peaks in relative genetic diversity of clade 1 in Dong Thap coincided with the two major peaks in the monthly incidence , indicating that DENV-1 epidemic in Dong Thap is largely driven by this lineage ., To investigate whether these dispersion rate estimates in HCMC were simply a reflection of the geographic constraint of our samples , they were re-estimated by randomizing the tip location for each clade ( Table 4 ) ., The results indicated what the maximum dispersion rate could be given the sampled locations , which were found to be 2–3 times greater than the empirical estimates , with wide HPD intervals ( Table 4 ) ., The spatial reconstruction of the viral spread at different stages of the epidemics showed that these viral lineages had co-circulated in the same place at the same time ( Figure 3 ) ., This observation is of fundamental importance as it suggests that the number of susceptible hosts to DENV-1 had not been saturated in HCMC , and could potentially have supported additional DENV-1 lineages in this area ., To determine whether transmission routes within HCMC varied according to population density , we employed a non-reversible discrete phylogeography model applied to district level data ., Importantly , the more densely populated inner city districts ( above 30 , 000 people per km2 ) were found to contribute significantly to DENV-1 transmission compared to the suburban districts ( Figure 4 ) ., Moreover , the most densely populated region , District 5 , had the highest number of connections , providing compelling evidence that this area might be a major hub in the city ., At the scale of South-East Asia , the observation that there is a strong clustering by country indicates that there is a far higher level of DENV-1 gene flow within than between countries ., Such a phylogeographic pattern is compatible with relatively short transmission distances for DENV as a whole , including that meditated by mosquitoes ., This rather limited spatial movement also sits in marked contrast to that observed in respiratory borne pathogens such as influenza , where there is relatively little clustering by place of isolation even on a global scale 28 ., Each of the five clades of DENV-1 we identify has a very recent common ancestry , dating only shortly before the appearance of that clade ., Given that dengue is endemic in southern Viet Nam , with DENV-1 circulating there for at least 23 years 29 , such recent common ancestry suggests that there is a rapid and continual turnover of viral lineages , as has been increasingly described for this and other DEN viruses 8–11 , 30 , 31 ., Less clear is whether these instances of lineage turnover are due to fitness differences between the lineages in question , such that natural selection is preferentially able to favor one lineage over another , or whether there is simply a stochastic die-off ., That the three major clades we detect in HCMC co-circulate in the same spatial region with overlapping ranges , and possess broadly equivalent levels of relative genetic diversity , suggests that they are of similar fitness and hence that there is little , if any , competition between them ., Consistent with this , we did not observe differences in early plasma viremia levels between patients infected with viruses belonging to the different clades ( Figure S2 ) ., Indeed , we suggest below that HCMC is likely characterized by a large number of susceptible hosts , which would in turn reduce the extent of selective competition between lineages ., More generally , these results indicate that although a specific viral serotype may appear to be endemic in a specific geographic region for an extended period , this does not mean that the same viral clades are involved throughout this period ., A striking result from this study is that the ‘virus dispersion rates’ we estimate appear to be very low , and particularly in HCMC where mean rates were universally <20 km/year ., Such low rates are especially noteworthy given the rapidity and geographic scale with which DENV-1 re-emerged as the dominant serotype in southern Viet Nam 13 ., We therefore interpret these low rates to mean that urban centers like HCMC are characterized by sufficiently high numbers of susceptible hosts such that the virus does not have to move very far to infect a new host ., Such a notion is supported by the fact that higher virus dispersion rates are observed in Dong Thap , which is characterized by an approximately ten-fold lower population density ( 495 persons/km2 ) relative to HCMC ( 3024 persons/km2 ) , although more estimates are clearly needed from this locality ., In addition , the highest levels of viral movement were found in and out of the most densely populated region of HCMC ( District 5 ) , suggesting that this well-connected locality acts as a focal point for dengue dispersion within the city ., Hence , it is not that DENV-1 moves slowly at a spatial scale in HCMC , but rather that it does not have to move far geographically to continue its transmission ., Although our sample of genome sequences is biased toward those from HCMC , our analysis indicates that DENV-1 generally diffuses from HCMC to Dong Thap ., Again , this observation is suggestive of a gravity model of viral transmission , in which spatial diffusion occurs over a gradient of population density , and is compatible with our observation that dispersion rates are associated with the numbers of susceptible hosts ., A similar gravity-dependent pattern of virus dispersion was recently suggested for DENV-2 in Viet Nam 14 , although the use of a strictly reversible phylogeographic model in that case meant that directionality could not be ascertained with certainty ., Combined , these studies strongly suggest that the density of the human host population plays a fundamental role in determining the transmission dynamics of endemic dengue ., Typically , adult A . aegypti mosquitoes travel short distances of less than ∼100 m during their average life-span of a few weeks 32–34 ., The very short distances traveled by DENV-1 , particularly in HCMC is consistent with mosquitoes , rather than humans , being responsible for the majority of the spatial spread in HCMC , which is again in part a function of the high density of susceptible hosts ., A similarly limited movement of dengue has been reported by recent studies that focused on smaller geographic areas , reflecting the restricted spatial range of mosquito vectors , and corroborating the highly focal pattern of DENV transmission observed in HCMC 15 , 35 ., It is also notable that the geographical range of the three major clades in HCMC changed little from 2003–2008 ., As such , the full geographic range of these clades is established very early on as the virus is able to spread rapidly through a susceptible host population ., Upon the introduction of a new dengue serotype into Iquitos , Peru , it was noted that early-confirmed cases were scattered throughout the city , suggesting a rapid establishment of the virus when entering a completely naïve population 36 ., This observation gives added weight to our conclusion that the dispersion rates of DENV-1 in southern Viet Nam are largely a function of the availability of susceptible hosts ., These results have a number of important implications for the future control of dengue ., Most generally , that DENV tends to spread relatively slowly on a spatial scale ( such that DENV phylogenies exhibit a strong spatial structure both nationally and internationally ) suggests that any future vaccine escape or drug resistance mutations would also spread relatively slowly ., In addition , that the dispersion rates of DENV appear to largely reflect the density of human host population , including movement from Ho Chi Minh City to Dong Thap , suggests that future control measures , including mosquito spraying , should be directed toward the densest host populations and preferentially to urban over rural areas ., The dengue patients from whom DENV whole genome sequences were determined were enrolled in one of two prospective studies at the Hospital for Tropical Diseases in Ho Chi Minh City , Viet Nam or at Dong Thap Hospital , Dong Thap Province , Viet Nam ., The median age of these patients was 12 years ( interquartile range 7–17 years ) and 51% were male ., Serological investigations ( IgM and IgG capture ELISAs ) were performed using paired plasma samples using methods described previously 37 ., DENV serotype and viraemia levels were determined using an internally-controlled real-time RT-PCR assay that has been described previously 38 ., Viral genomes were sequenced using the Broad Institutes capillary sequencing ( Applied Biosystems ) directed amplification viral sequencing pipeline http://www . broadinstitute . org/scientific-community/science/projects/viral-genomics-initiative ) ., This sequencing effort was part of the Broad Institutes Genome Resources in Dengue Consortium ( GRID ) project ., Viral RNA was isolated from diagnostic plasma samples ( QIAmp viral RNA mini kit , Qiagen ) and the RNA genome reverse transcribed to cDNA with superscript III reverse transcriptase ( Invitrogen ) , random hexamers ( Roche ) and a specific oligonucleotide targeting the 3′ end of the target genome sequences ( nt 10868 to 10890 , AGAACCTGTTGATTCAACAGCAC ) ., cDNA was then amplified using a high fidelity DNA polymerase ( pfu Ultra II , Stratagene ) and a pool of specific primers to produce 14 overlapping amplicons of 1 . 5 to 2 kb in size for a physical coverage of 2-fold across the target genome ( nt 40 to 10649 ) ., Amplicons were then sequenced in the forward and reverse direction using primer panels consisting of 96 specific primer pairs , tailed with M13 forward and reverse primer sequence , that produce 500–700 bp amplicons from the target viral genome ., Amplicons were then sequenced in the forward and reverse direction using M13 primer ., Total coverage delivered post amplification and sequencing was 8-fold ., Resulting sequence reads were assembled de novo using the Broad Institutes AV454 assembly algorithm ( Henn et al . 2011 . in review ) and a reference-based annotation algorithm ., All whole genome sequences newly determined here have been deposited in GenBank and assigned accession numbers ( Table S1 ) ., A data set of DENV-1 sequences was collated to include isolates from countries in Southeast Asia that were likely linked to Viet Nam via migration ., An alignment of the envelope ( E ) gene ( 1485 nt ) was assembled for the Southeast Asian and Vietnamese isolates ( n\u200a=\u200a134 and 751 , respectively ) to include the broadest range of locations ., An initial neighbor-joining tree was constructed in PAUP* 39 , using a HKY85 nucleotide substitution model with gamma-distributed rates ., This allowed us to make an initial identification of the major clades of DENV-1 in Viet Nam ., These Vietnamese isolates were then subsampled ( n\u200a=\u200a101 ) to explore their phylogeography in context of the South East Asian isolates ., Isolation dates for the South East Asia data set were obtained from GenBank annotations and via personal communication ., Where specific dates were not available in terms of day and month , a mid-point of the year of isolation was used ., The spatial dynamics of DENV-1 in Southeast Asia were investigated with a discrete diffusion model 26 using Bayesian Monte Carlo Markov Chain ( MCMC ) method implemented in BEAST 40 ., The phylogeography analysis was executed with a codon-structured SDR06 substitution model 41 , a relaxed uncorrelated lognormal clock 42 and a Gaussian Markov Random Field ( GMRF ) coalescent prior 43 over the unknown phylogeny ., The discrete diffusion model used the country of isolation of the sampled sequences to reconstruct the ancestral location states of the internal nodes from the posterior time-scaled tree distribution ., The MCMC was run for 50 million generations , sampling every 5000th state , and executed multiple times to ensure adequate mixing and stationarity had been achieved ., Major clades of Vietnamese DENV-1 identified from the broad-scale South East Asian analysis were selected for further study to examine the spatial and temporal variation in Viet Nam ., In clades with appreciable numbers of sequences from Dong Thap and HCMC , isolates from these locations were analyzed independently to gauge the regional variation in viral transmission patterns ., For the fine-scale analysis , a continuous diffusion model based on a lognormal relaxed random walk 27 was employed to model the DENV-1 spatial dynamics in Viet Nam ., For each isolate , the specific sample date and location information in terms of the longitude and latitude of the patients household were used ., Isolates that were identical in sample date and location information were down-sampled so as to reduce the potentially biasing effect of over-sampling of epidemiologically-linked cases ., The MCMC runs were evaluated as previously described , and the chain lengths ranged from 50 to 100 million generations , and were sampled regularly to yield 10 , 000 trees from the posterior distribution ., The viral dispersion rates ( km/yr ) for each data set were calculated across the tree ( i . e . total straight-line distance travelled divided by the total time ) and biannually to consider the spatial heterogeneity in a time-scaled framework ., Plots of relative genetic diversity over time were reconstructed using the GMRF coalescent prior to reveal the association between the genetic diversity of each group in terms of their evolutionary history 43 ., Further discrete phylogeography analyses were performed with the robust counting method 44 , 45 to determine the extent of viral migration between Dong Thap and HCMC and whether this varied when the lineage originated in a rural or urban area ., In this case , the discrete states were represented by either the isolate being sampled from HCMC , Dong-Thap or neither ( non-Dong Thap or HCMC ) ., For the limiting case of a freely mixing ( non-spatially structured ) epidemic in HCMC , dispersion rates were estimated whilst randomizing the tip locations during the tree proposal in the MCMC , whilst co-estimating the rates for each independent lineage and the joint DENV-1 diffusion rate ., To determine the viral transmission network within HCMC , a non-reversible discrete phylogeography model was applied to all the HCMC isolates , using the district of isolation for the discrete states ., The analysis was performed and evaluated as described above with the addition of implementing Bayesian Stochastic Search Variable selection ( BSSVS ) to identify significant transition rates between locations 26 ., The transition rates supported by a Bayes factor of at least 3 were examined further by looking at the number of in-degree and out-degree per district ., The number of connections was normalized by the number of samples from the source location in order to reduce the bias from under-represented locations in our data set ., Patients ( or their parents/guardians ) gave written informed consent to participate in each of the studies ., The study protocols were approved by the Hospital for Tropical Diseases and the Oxford University Tropical Research Ethical Committee .

Introduction, Results, Discussion, Methods

Dengue is one of the most important infectious diseases of humans and has spread throughout much of the tropical and subtropical world ., Despite this widespread dispersal , the determinants of dengue transmission in endemic populations are not well understood , although essential for virus control ., To address this issue we performed a phylogeographic analysis of 751 complete genome sequences of dengue 1 virus ( DENV-1 ) sampled from both rural ( Dong Thap ) and urban ( Ho Chi Minh City ) populations in southern Viet Nam during the period 2003–2008 ., We show that DENV-1 in Viet Nam exhibits strong spatial clustering , with likely importation from Cambodia on multiple occasions ., Notably , multiple lineages of DENV-1 co-circulated in Ho Chi Minh City ., That these lineages emerged at approximately the same time and dispersed over similar spatial regions suggests that they are of broadly equivalent fitness ., We also observed an important relationship between the density of the human host population and the dispersion rate of dengue , such that DENV-1 tends to move from urban to rural populations , and that densely populated regions within Ho Chi Minh City act as major transmission foci ., Despite these fluid dynamics , the dispersion rates of DENV-1 are relatively low , particularly in Ho Chi Minh City where the virus moves less than an average of 20 km/year ., These low rates suggest a major role for mosquito-mediated dispersal , such that DENV-1 does not need to move great distances to infect a new host when there are abundant susceptibles , and imply that control measures should be directed toward the most densely populated urban environments .

Although dengue is a major cause of morbidity in many tropical and subtropical regions of the world , little is known about how the causative virus ( dengue virus , DENV ) spreads through endemic populations ., To address this issue we undertook a phylogeny-based analysis of 751 complete genome sequences of DENV-1 sampled from patients in southern Vietnam during 2003–2008 ., We show that multiple viral lineages co-circulate within the urban area of Ho Chi Minh City ( HCMC ) , and spread at approximately equivalent rates through overlapping geographical areas , suggesting that they are of equivalent fitness ., We also observed that DENV-1 within HCMC tended to disperse from more to less densely populated regions , and that this city was the source population for DENV-1 in the rural area of Dong Thap ., Despite the high prevalence of DENV-1 in southern Vietnam , viral dispersion rates were relatively low , especially in HCMC where they averaged less then 20 km/year ., Such a low rate is consistent with predominantly mosquito-borne spatial dispersal of DENV-1 in this urban setting containing a large number of susceptibles ., Together , these results suggest that dengue control measures such as insecticide spraying should be directed toward the most densely populated regions of localities where the virus is endemic .

sequence analysis, phylogenetics, emergence, evolutionary biology, population genetics, biology, computational biology, gene flow, evolutionary systematics, evolutionary processes, evolutionary genetics

journal.ppat.1006954

HSV-1-induced disruption of transcription termination resembles a cellular stress response but selectively increases chromatin accessibility downstream of genes

Transcription termination is an essential process in gene expression that is coupled to all parts of RNA metabolism including transcription initiation , splicing , nuclear export and translation ( reviewed in 1 , 2 ) ., It results in the release of RNA polymerase II ( Pol II ) and the nascent transcript from the chromatin , determines the general fate of individual transcripts and plays a crucial role in limiting the extent of pervasive transcription of the genome ., Herpes simplex virus 1 ( HSV-1 ) efficiently modulates cellular RNA metabolism and both cellular and viral gene expression to facilitate lytic infection 3–9 ., Using 4-thiouridine- ( 4sU ) -tagging followed by sequencing ( 4sU-seq ) , we recently reported that lytic HSV-1 infection results in the disruption of transcription termination ( DoTT ) of the majority but not all cellular genes 10 ., This was dependent on de novo protein synthesis and already became broadly detectable by 2-3h of infection , which is before the release of the first newly generated virus particles at around 4h post infection ( p . i . ) ., At 7-8h p . i . , about 50% of all 4sU-seq sequencing reads mapping to the human genome originated from intergenic regions ( compared to <10% in uninfected cells ) ., Previously , we referred to transcription beyond poly ( A ) sites due to DoTT as ‘read-out’ ., As this term has led to confusion , we now use the term ‘read-through’ to refer to transcription that extends beyond poly ( A ) sites ., Transcription into a downstream gene arising from read-through from an upstream gene is referred to as ‘read-in’ ., For more than half of expressed cellular genes , poly ( A ) read-through affected >35% of their transcription ., Read-in transcription into downstream genes was responsible for the seeming induction of about 1 , 100 cellular protein-coding and non-coding genes late in infection ., In addition , it resulted in chimeric transcripts spanning two or more genes as evidenced by intergenic splicing events that connect exons of neighboring cellular genes ., Subsequently , two other studies reported on the disruption of transcription termination in cellular stress responses and cancer 11 , 12 ., Transcription downstream of genes ( DoG ) was observed in the osmotic stress response in human neuroblastoma cells , which was independent of de novo protein synthesis but appeared to at least partially rely on inositol-1 , 4 , 5-trisphosphate receptor ( IP3R ) activation and calcium signaling 11 ., In addition , pervasive transcription read-through was identified in renal cell carcinoma 12 ., This was dependent on the loss of histone methyltransferase SETD2 , consistent with the role of epigenetic factors in RNA processing ., Similar to HSV-1 infection , novel RNA chimeras were observed ., Invasion of oncogenes by polymerases that initiated at upstream genes indicated a novel link between aberrant expression of oncogenes and chimeric transcripts prevalent in cancer ., Taken together , these findings raise important questions regarding the underlying molecular mechanisms and functional roles of DoTT/DoG transcription in HSV-1 infection , cellular stress responses and cancer ., DoG transcription during osmotic stress was identified by Vilborg et al . upon exposure to 80mM KCl for 1h ( from now on referred to as ‘salt stress’ ) in a human neuroblastoma cell line ( SK-N-BE ( 2 ) C ) by RNA-seq on nuclear , RiboMinus-treated RNA 11 ., This revealed about 2 , 000 human genes to be affected ., In addition , DoG transcription was also observed following heat stress ( 44°C ) 11 ., Recently , Vilborg et al . also reported on DoG transcription upon oxidative stress and found significant similarities but also clear stress-specific differences between the three stressors 13 ., In our primary study , we analyzed newly transcribed RNA purified using 4sU-seq in one hour intervals of the first 8h of lytic HSV-1 infection of primary human foreskin fibroblasts ( HFF ) ( Fig 1A ) ., Under these conditions , the HSV-1 infected cells only start to lyse around 16 to 24h of infection ., This allowed us to directly assess and quantify the relative frequency of transcripts experiencing DoTT as well as the extent of read-through transcription occurring within one hour intervals during the first eight hours of infection 10 ., Throughout this manuscript , we refer to HSV-1-induced disruption of transcription termination as ‘DoTT’ to differentiate it from stress-induced DoG transcription ., It is important to note here that transcription in intergenic regions downstream of genes was almost exclusively observed on the sense strand in relation to the upstream gene ., This clearly distinguishes read-through from the recently reported activation of antisense transcription of the host genome during lytic HSV-1 infection 14 ., Although DoTT was much more prominent at late times ( 7-8h p . i . ) of HSV-1 infection than in salt or heat stress , we wondered whether the two phenomena might reflect the same cellular mechanism ., We thus performed a detailed comparison and characterization of HSV-1-induced DoTT and DoG transcription triggered by salt and heat stress using 4sU-seq in the same cell type , namely HFF ., This showed clear similarities in read-through between HSV-1 infection and the different stresses but also clear context- and condition-specific differences ., Furthermore , we performed ATAC-seq ( transposase-accessible chromatin using sequencing 15 ) to compare chromatin accessibility before and during HSV-1 infection and stress ., Strikingly , HSV-1-induced DoTT was accompanied by a strong increase in chromatin accessibility downstream of the affected poly ( A ) sites , which essentially matched the region of read-through transcription ., This did not cause but rather required DoTT as well as a high level of transcriptional activity into downstream genomic regions ., Interestingly , this effect was specific to HSV-1 and not observed in salt or heat stress ( up to 2h ) indicating that other mechanisms by which HSV-1 perturbs RNA processing contribute to this unexpected gene-specific alteration in the host chromatin landscape ., To directly compare HSV-1-induced DoTT with DoG transcription during cellular stress responses , we performed 4sU-seq analysis ( 60min 4sU-tagging followed by RNA sequencing ) of HFF exposed to either salt ( 80mM KCl ) or heat stress ( 44°C ) for 1 and 2h ( see Fig 1B ) ., Two biological replicates of each condition as well as 2 untreated samples for each stressor were analyzed ., 4sU-seq data for the first 8h of HSV-1 infection in HFF were obtained from our previous study 10 ., A visual inspection of mapped reads for marker genes with either strong ( SRSF3 , SRSF6 ) or no ( GAPDH , ACTB ) DoTT/DoG transcription already indicated a striking similarity between presence or absence of DoTT/DoG transcription in the three conditions ( Fig 1C and 1D; Fig A in S3 File , links to UCSC genome browser sessions showing read coverage for all cellular genes and samples separately for both replicates can be found at www . bio . ifi . lmu . de/HSV-1 ) ., As previously reported for HSV-1 infection 10 ( Fig 1E ) , the percentage of reads mapping to intergenic regions downstream of gene 3’ ends increased substantially during salt and heat stress in HFF ( Fig 1F ) ., Intergenic read counts were highest directly downstream of gene 3’ ends and gradually decreased with increasing distance to gene 3’ ends ., Furthermore , downstream intergenic transcription occurred almost exclusively in the same orientation as the upstream gene in all conditions ( Fig B in S3 File ) ., The low levels of antisense reads downstream of genes increased with increasing distance from gene 3’ ends as a consequence of read-through transcription for genes expressed from the opposite DNA strand outside of the 100kb downstream window considered ., The gradual decrease in read levels downstream of genes was not due to differences in the length of read-through between genes , but was also observed at the level of individual genes ( Fig B in S3 File and Fig C in S3 File ) ., It could be approximated reasonably well by a linear fit at least late in HSV-1 infection and at 2h salt and heat stress , but the slope of the linear fit differed between genes ( Fig C in S3 File ) ., As a consequence of this gradual decrease and in contrast to regular mRNAs , 3’ ends of poly ( A ) read-through transcripts are not clearly defined 10 , 11 ., As the extent of read-through for individual genes gradually increased throughout infection , read-through transcripts extended further and further downstream of the gene ., To compare the extent of DoTT/DoG transcription between the three conditions , we focused on the 9 , 404 protein-coding and lincRNA ( long intergenic non-coding RNA ) genes whose expression was well detectable ( fragments per kilobase of exons per million mapped reads ( FPKM ) ≥1 ) in all uninfected/untreated 4sU-seq samples ., We then applied our previous approach 10 of dividing expression in the 5kb downstream of genes by the gene expression ( FPKM ) value ( see methods ) ., This measure ( denoted as percentage of downstream transcription ) is independent of any normalization to sequencing depth , which is canceled out in the division ., As 4sU-tagging provides newly transcribed RNA from defined intervals of infection and stress , the obtained ratios quantify the percentage of transcripts newly transcribed in this interval that experience poly ( A ) read-through ., To avoid confounding effects due to transcription of neighboring genes , we only included genes separated from neighboring genes on the same strand by at least 5kb on either side ( 5 , 928 genes ) ., Although the restriction to the first 5kb downstream of a gene is relatively arbitrary , using a larger window of e . g . 10kb resulted in highly correlated values of downstream transcription ( Spearman correlation Rs>0 . 95 ) but would exclude an additional 737 genes ( 12 . 4% ) from the analysis ., To account for small levels of downstream transcription in uninfected and untreated cells ( mean = 4 . 2% and 0 . 06% , respectively ) , we calculated read-through as the difference in the percentage of downstream transcription between infected/stressed and uninfected/untreated samples ( see methods ) ., Read-in was quantified in the same way by first quantifying transcription in the 5kb upstream of genes relative to gene expression and then subtracting levels in uninfected/untreated samples ., Since our previous study indicated that genes with read-in were more prone to read-through , we only used genes for the comparative analysis with at most 10% read-in in both HSV-1 infection and salt and heat stress ( 3 , 682 genes , Table A in S1 File ) ., With the exception of the first three hours of HSV-1 infection where DoTT was hardly detectable , read-through values were highly correlated between replicates ( Fig D in S3 File; Rs≥0 . 85 ) ., The induction of DoG transcription upon salt and heat stress was reflected in median read-through levels of 6 to 15% ( Fig 2A; Fig D in S3 File for individual replicates ) ., Consistent with the recent report by Vilborg et al . 13 , global read-through levels peaked at 1h of salt stress , but required 2h to reach comparable levels in heat stress ., At the highest level , read-through in both salt and heat stress was comparable to read-through at 4-5h post HSV-1 infection , but considerably lower ( ~3-fold ) than at the end of our HSV-1 infection time-course ( 7-8h p . i . ) ., Median read-through levels in all conditions were highly correlated ( Rs\xa0=\xa00 . 99 ) to the overall perturbation of gene expression ( measured as standard deviation of FPKM log2 fold-changes; Fig 2B ) ., Here , results for salt and heat stress fitted very well to a curve estimated from our HSV-1 time-course ., At single gene level , however , read-through showed only a weak positive correlation with fold-changes in gene expression for HSV-1 infection ( after the first 3h ) , salt and heat stress ( Fig E in S3 File; Rs≤ 0 . 37 ) ., Vilborg et al . 13 also only found weak correlations between fold-changes in DoG transcription and fold-changes in expression of the respective genes ( Rs\xa0=\xa00 . 12 ) ., The even lower correlations observed by Vilborg et al . may be explained by their use of nuclear RNA , which also contains RNA produced before stress ., This underestimates gene expression changes for genes with low basal RNA turnover 16 ., It should be noted that gene expression fold-changes estimated from RNA-seq data ( even after normalization to house-keeping genes as performed here ) only indicate changes in the relative , but not absolute , abundance among all expressed genes ., As the overall transcription levels decline during lytic HSV-1 infection 17 , positive fold-changes do not necessarily indicate actual transcriptional induction but only less down-regulation compared to other genes ., In our previous study , we reported that DoTT-induced read-through was increased for genes without the canonical AAUAAA poly ( A ) -signal upstream of the gene 3’end ., Similarly , Vilborg et al . found several 6-mers to be depleted ( including AAUAAA ) or enriched downstream of genes with pan-stress DoG transcription ., However , their analysis focused on the total frequency of the 6-mers downstream of all pan-stress DoG genes instead of the frequency for individual genes ., We now aimed to identify 6-mers whose abundance in the 100nt up- or downstream of individual gene 3’ends was significantly correlated to read-through ( FDR adjusted p-value <0 . 0001 for at least one condition or time-point , see methods ) ., Strikingly , AAUAAA was the only 6-mer whose abundance upstream of gene 3’ends was significantly correlated with read-through in both stresses and HSV-1 infection ( Fig 2C ) and its absence upstream of gene 3’ ends was associated with significantly higher read-through ( Wilcoxon rank sum test , p<0 . 0001; Fig 2D ) ., Other 6-mers were only significantly correlated to read-through in HSV-1 infection and showed no significant differences in read-through in salt or heat stress ( Fig F in S3 File ) ., Upstream of gene 3’ends , negative correlations were found for a 6-mer overlapping the AAUAAA sequence as well as two C-rich motifs ., Downstream of gene 3’ends , this included a number of G-rich motifs ., Only one motif downstream of genes was positively correlated to read-through ( AUUUUU ) , but only in HSV-1 infection ., This sequence resembles binding motifs of a number of RNA binding proteins 18 , 19 , including HNRNPC ( Heterogeneous Nuclear Ribonucleoprotein C ) , which has been shown to influence poly ( A ) site usage ., To directly compare HSV-1-induced DoTT to DoG transcription , we calculated Spearman rank correlations of read-through values between each pair of conditions and time-points ., This compares the ranking of genes with regard to read-through , i . e . whether top- and lowest-ranked genes tend to be the same between samples ., Read-through mostly correlated extremely well ( Rs>0 . 8 ) between adjacent time-points for the same condition apart from the first three hours of HSV-1 infection where DoTT was hardly noticeable ( Fig 3A ) ., Moderate but comparable correlations were observed between salt stress and either heat stress or HSV-1 infection at 4-5h p . i . ( Rs\xa0=\xa00 . 45-0 . 51 ) ., In contrast , read-through in heat stress was slightly better correlated to salt stress than to HSV-1 infection ( Rs\xa0=\xa00 . 4 ) ., Since we observed a weak correlation between read-through and gene expression fold-changes in all conditions , we also calculated correlations after excluding genes with highest fold-changes ( ≥2 in any sample ) ., This aimed to exclude genes for which differences in read-through between conditions might be explained by changes in transcriptional activity ., However , correlations for the remaining 2 , 601 genes did not increase , which is probably explained by the observation that gene expression fold-changes were also well correlated ( Fig G in S3 File ) ., Thus , differences between conditions in DoTT/DoG transcription cannot be explained by differential alterations in transcriptional activity ., Next , we performed hierarchical clustering of genes based on read-through ( average of replicates ) for each condition ( Fig 3B ) ., This identified a large cluster of 1 , 368 genes ( 37% ) with read-through in all conditions ( marked in blue ) as well as a number of clusters with differences between conditions ., It furthermore highlighted the prevalence of DoTT/DoG transcription with only 102 genes ( 3% ) showing no DoTT/DoG transcription ( defined as ≤5% read-through ) in any infected/stress sample ., Overrepresentation analysis for Gene Ontology ( GO ) terms using DAVID 20 found an enrichment of genes with extracellular regions ( 25 genes ) and heparin binding ( 6 genes ) among these 102 genes ., However , no functional categories were overrepresented for the 1 , 368 genes with read-through in all conditions ., Interestingly , the only gene experiencing ≥75% read-through already after 2-3h p . i . HSV-1 infection and in all stress conditions was interferon regulatory factor 1 ( IRF1 ) ( Fig H in S3 File ) ., IRF1 is an important mediator of both type I and II interferon signaling and studies with IRF1-deficient mice have shown an important role for IRF1 in the immune response against viruses 21–23 ., Furthermore , even a relatively small reduction in IRF1 expression , e . g . mediated by cellular miR-23a , is sufficient to measurably augment HSV-1 replication in cell culture 24 ., Notably , ribosome profiling data from our previous study revealed a >4-fold drop in IRF1 translation during HSV-1 infection despite an >1 . 8-fold increase in total RNA at 8h p . i . 10 ., This presumably reflects the negative effects of DoTT on IRF1 translation and suggests that HSV-1 exploits DoTT to evade the host immune response ., A striking characteristic of HSV-1-induced DoTT was the associated increase in aberrant splicing 10 ., In particular , this comprised novel intragenic and intergenic splicing events as well as splicing associated with nonsense-mediated decay ( NMD ) ., Intergenic splicing joins known exons of neighboring genes and confirms transcription of large chimeric transcripts spanning two or more cellular genes ., It can be observed as early as 3-4h p . i . in HSV-1 infection ., One of the most prominent examples connects SRSF2 and JMJD6 ., We also observed intergenic splicing in the two stress conditions , but the few examples did not cluster with intergenic splicing events in HSV-1 infection ( Fig 3C ) ., Analysis of induced splicing events upstream of gene 3’ ends , however , showed similar characteristics in both HSV-1 infection and salt and heat stress ., In all three conditions , induced intragenic splice junctions were enriched for novel splice junctions and junctions found only in processed transcripts ( containing no ORF but not classified as long or short non-coding RNAs ) or in NMD-associated transcripts ( Fig 3D; examples in Fig I in S3 File ) ., Genes with induced intragenic splicing events showed increased read-through in all three conditions ( Fig I in S3 File ) , but read-through was also observed in genes without induced splicing events ., Thus , aberrant splicing upstream of gene 3’ ends more likely resulted from , rather than is responsible for DoTT/DoG transcription ., One possible explanation for the association of aberrant splicing with DoTT/DoG transcription may be that all serine and arginine rich splicing factor ( SRSF ) genes included in our analysis ( SRSF2 , SRSF3 , SRSF5 , SRSF6 , SRSF7 , SRSF10 , SRSF11 ) showed DoTT/DoG transcription in at least two , but mostly all three conditions ., All of these SRSF genes showed a >2-fold greater drop in translation at 8h p . i . HSV-1 infection in the ribosome profiling data than expected from the changes in their total RNA levels ., Vilborg et al . reported that salt stress-induced DoG transcription in SK-N-BE ( 2 ) C cells depends on IP3R activation , Ca2+ release from intracellular stores and downstream kinases 11 ., HSV-1 entry into cells is dependent on the activation of Ca2+ signaling pathways and triggers Ca2+ release from intracellular stores 25 , 26 ., In addition , HSV-1 infection results in an increasing loss of stable , resting Ca2+ at late times of infection indicating a bimodal role of Ca2+ signaling in HSV-1 infection 27 ., Before assessing the effect of Ca2+ signaling inhibitors on DoTT in HSV-1 infection of HFF , we first aimed to reproduce the results by Vilborg et al . in salt stress ., HFF were exposed to 80mM KCl for 1h in presence of, ( i ) an inhibitor of IP3R signaling ( 2-APB ) ,, ( ii ) the membrane permeable Ca2+ chelator BAPTA-AM , or, ( iii ) inhibitors of the downstream kinases Ca2+/calmodulin-dependent protein kinase II ( CaMKII ) and protein kinase C/protein kinase D ( PKC/D ) ( KN93 and Gö6976 , respectively ) ., DoG transcription was first quantified by qRT-PCR on total RNA for DDX18 , which shows strong read-through in HSV-1 infection as well as salt and heat stress ., Consistent with the previous report , BAPTA-AM prevented DoG transcription while the other inhibitors resulted only in a moderate ( 25–65% ) reduction ( Fig 4A ) ., We thus aimed to assess the effect of BAPTA-AM on DoTT in HSV-1 infection ., To avoid the described detrimental effects of BAPTA-AM on virus entry and the onset of productive infection 25 , 26 , we only added BAPTA-AM to the cell culture media of HFF at 1h p . i . ( MOI = 10 ) when viral gene expression is already well initiated ., To first determine its effect on viral gene expression , we quantified immediate-early ( ICP0 ) , early ( ICP8 ) and true late ( ICP5 ) gene expression at 8h p . i . by qRT-PCR ., Strikingly , BAPTA-AM treatment was highly detrimental to viral gene expression of all three kinetic classes resulting in a >1 , 000-fold drop in viral mRNA levels ( Fig 4B ) ., Considering this strong reduction in viral gene expression , we hypothesized that depletion of intracellular Ca2+ by BAPTA-AM in HFF might globally impair Pol II activity rather than specifically interfere with DoTT/DoG transcription ., We thus analyzed the effect of 1h of BAPTA-AM treatment of uninfected cells on transcriptional activity of three cellular genes ( SRSF3 , IRF1 and DDX18 ) ., For this purpose , we labeled newly transcribed RNA by adding 500μM 4sU to the cell culture medium for 1h ., Following isolation and purification of the 4sU-labeled newly transcribed RNA ( 4sU-RNA ) from a fixed amount of biotinylated total RNA per condition ( 60μg ) , transcriptional activity of these genes was quantified using qRT-PCR on 4sU-RNA ., BAPTA-AM indeed induced a drop in transcriptional activity that was at least as strong as observed upon inhibition of Pol II using actinomycin D ( Act-D; Fig 4C ) ., In addition , global 4sU incorporation rates into total cellular RNA were substantially reduced upon BAPTA-AM treatment ( Fig 4D ) ., This indicated that BAPTA-AM might not only interfere with Pol II but also with rRNA synthesis ( Pol I and III transcription ) , which contributes about 50–60% of 4sU-RNA in HFF as estimated from our RNA-seq data 10 ., We thus quantified transcription rates from 4sU-RNA for a Pol I transcript ( 18S rRNA ) , a Pol III transcript ( 5S rRNA ) in addition to four genes transcribed by Pol II ( GAPDH , SRSF3 , IRF1 and DDX18 ) upon exposure of HFF to 80mM KCl for 1 and 2h and BAPTA-AM ( Fig 4E ) ., In addition , we tested whether the combined exposure of cells to Gö6976 and KN93 , which also diminished salt stress-induced DoG transcription in total RNA , also globally affected transcriptional activity ., While salt stress alone already resulted in a drop in transcription rates for Pol I ( ≈1 . 5-fold ) , II ( 3- to 5-fold ) and III ( ≈1 . 4-fold ) transcripts , BAPTA-AM impaired transcriptional activity of all three polymerases ., This suggests that global inhibition of cellular RNA polymerases by BAPTA-AM rather than a specific effect on transcription termination is responsible for the loss of salt stress-induced DoG transcripts ., As BAPTA and its derivatives share a high selectivity for Ca2+ over Mg2+ ( >105 stronger binding ) , the observed effects did not result from the co-depletion of intracellular Mg2+ 28 ., Interestingly , combined Gö6976/KN93 treatment also globally impaired Pol I , II and III transcription , albeit to a lesser degree ( 2- to 10-fold ) , thereby explaining the slight reduction in DoG levels in total RNA ( Fig 4A ) ., In contrast , 2-ABP treatment , which had shown no effect on DoG transcription when analyzing total cellular RNA , did not impair polymerase activity ., Finally , we quantified read-through transcription for the three DoG genes SRSF3 , IRF1 and DDX18 in 4sU-RNA ( Fig 4F ) ., Neither KN93/Gö6976 nor 2-ABP treatment had any effect on the induction of the respective DoG transcripts ., Unfortunately , BAPTA-AM treatment did not allow to reliably measure read-through transcription due to the impaired transcription ( very low copy numbers or even negative PCR results ) ., We conclude that the reduced levels of DoG transcripts upon inhibition of Ca2+ signaling do not result from direct effects on DoG transcription but from global inhibitory effects on cellular transcription in general ., To our knowledge , this strong inhibitory effect of BAPTA-AM treatment on RNA polymerase activity has not been appreciated so far and should be considered when interpreting results obtained using BAPTA-AM to inhibit calcium signaling ., Vilborg et al . initially reported that DoG transcripts ( DoGs ) were strongly enriched at the chromatin 11 and that one of the more abundant DoGs , doSERBP1 ( downstream of SERBP1 ) , remained at the site of synthesis ., However , they subsequently also observed DoGs in the nucleoplasma of cells when searching for them by confocal microscopy with increased sensitivity 13 ., To assess the fate of the transcripts arising from DoTT in HSV-1 infection , we separated cell lysates ( uninfected cells and 8h p . i . ) into cytoplasmic , nucleoplasmic and chromatin-associated fractions 29 , 30 and analyzed all three fractions as well as total cellular RNA by RNA-seq ( 2 replicates ) ., The efficient separation of the cytoplasmic from the nuclear RNA fraction was confirmed by the enrichment of well-described nuclear lincRNAs ( MALAT1 , NEAT1 , MEG3; Fig J in S3 File ) in nucleoplasmic and chromatin-associated RNA as well as cytoplasmic enrichment of reported cytoplasmic lincRNAs ( LINC00657 , VTRNA2-1; Fig J in S3 File ) ., In addition , overrepresentation of intronic reads in chromatin-associated RNA compared to nucleoplasmic RNA ( >5-fold higher ) demonstrated the efficient separation of these two RNA fractions ( Fig J in S3 File ) In uninfected cells , only chromatin-associated RNA showed notable levels of downstream transcription ( median 7 . 2%; Fig 5A ) , consistent with the standard model of transcription termination in eukaryotic cells 1 ., At 8h p . i . , substantial read-through was observed in all fractions except for cytoplasmic RNA ( Fig 5B , Table B in S1 File ) , indicating that read-through transcripts are not efficiently exported to the cytoplasm ., When we grouped genes according to their extent of read-through in 7-8h p . i . 4sU-RNA , we observed a strong increase during infection in the enrichment of the respective mRNAs ( counting only the exonic regions upstream of gene 3’ ends ) in both nucleoplasmic ( Fig 5C ) and chromatin-associated RNA ( Fig J in S3 File ) depending on the extent of read-through ., While no change in nuclear enrichment was observed for genes without read-through , genes with >75% read-through were on average >2 . 5-fold more enriched at 8h p . i . than in uninfected cells ., In particular , IRF1 was >6 and 4-fold more enriched in nucleoplasmic and chromatin-associated RNA , respectively , at 8h p . i . than in uninfected cells ., Further evidence for an inefficient export of read-through transcripts is provided by intergenic splicing events , which are mostly absent in cytoplasmic RNA at 8h p . i . despite their considerable abundance in the other subcellular RNA fractions ( Fig 5D ) ., This also explains our previous observation based on ribosome profiling that RNA chimeras and consequently genes induced by read-in transcription arising from DoTT are not , or only poorly translated 10 ., We conclude that DoTT leads to nuclear retention of the respective read-through transcripts and thereby notably contributes to HSV-1 induced host shut-off ., The similar overall level and high gene-specific correlation ( Rs\xa0=\xa00 . 8 ) of read-through in nucleoplasmic and chromatin-associated RNA indicates that transcripts resulting from HSV-1-induced DoTT are generally released from the chromatin , i . e . the site of synthesis , into the nucleoplasm ( see e . g . Fig 5F ) ., Nevertheless , we identified 18 genes ( Table C in S1 File ) for which these transcripts appeared to remain at the chromatin ( ≤5% read-through in nucleoplasmic and cytoplasmic RNA , but ≥25% in chromatin-associated RNA; examples in Fig K in S3 File ) ., Interestingly , there was a modest correlation ( Rs\xa0=\xa00 . 32-0 . 53 ) between the percentage of downstream transcription observed in chromatin-associated RNA of uninfected/unstressed cells and read-through upon stress or HSV-1 infection ( Fig 5E ) ., This suggests that genes with a relatively high extent of downstream transcription in uninfected/unstressed cells might be predisposed for DoTT/DoG transcription ., To exclude that this was an artifact of read-through being calculated from downstream transcription , we calculated ‘mock’ read-through values from the two biological replicates for the same time-point ( see methods ) ., For mock read-through , the correlation was much lower at only ~0 . 13 ., This suggests a link between downstream transcription detectable in chromatin-associated RNA in uninfected/untreated cells and read-through in stress/infection ., A possible explanation might be that the respective poly ( A ) sites are weaker and thus more prone to further disruption by HSV-1 or stress-related mechanisms ., Fig 5F illustrates this for IRF1 , for which downstream transcription in chromatin-associated RNA of uninfected cells was 14% and covered ~5kb ., Interestingly , the correlation between downstream transcription in chromatin-associated RNA in uninfected cells and read-through during infection was highest at early time-points , i . e . at 1h for salt/heat stress and 2-3h p . i . for HSV-1 infection ( Fig 5E and 5G ) ., At late stages of HSV-1 infection , even cellular genes with very little downstream transcription in chromatin-associated RNA from uninfected cells showed read-through transcription ( Fig J in S3 File ) ., Based on publicly available DNase hypersensitive and ATAC-seq data for unstressed murine fibroblasts , Vilborg et al . recently reported that , even prior to stress , pan-DoG genes are already characterized by a chromatin signature indicative of an open chromatin state ., However , due to the lack of respective data following salt or heat stress , they could not assess the consequences of read-through on cellular chromatin ., We thus performed ATAC-seq in HFF at 0 , 1 , 2 , 4 , 6 and 8h of HSV-1 infection and 1 and 2h of salt and heat stress ( n = 2 ) ., For all ATAC-seq samples , open chromatin regions ( OCRs ) were enriched around promoters , thereby confirming the high quality of the data ( Fig L in S3 File ) ., Both length and score of OCRs at gene promoters correlated with gene expression in uninfected cells ( Rs\xa0=\xa00 . 42 and 0 . 4 , respectively; Fig L in S3 File ) ., In contrast to the findings by Vilborg et al . , we did not observe a positive correlation between DoTT/DoG transcription and the presence of OCRs in the 5kb downstream of genes in unstressed/uninfected cells ( Fig M in S3 File ) ., However , we noted a weak positive correlation ( Rs≤0 . 25 ) between the presence of downstream OCRs ( dOCRs ) and the expression level of the co

Introduction, Results, Discussion, Materials and methods

Lytic herpes simplex virus 1 ( HSV-1 ) infection triggers disruption of transcription termination ( DoTT ) of most cellular genes , resulting in extensive intergenic transcription ., Similarly , cellular stress responses lead to gene-specific transcription downstream of genes ( DoG ) ., In this study , we performed a detailed comparison of DoTT/DoG transcription between HSV-1 infection , salt and heat stress in primary human fibroblasts using 4sU-seq and ATAC-seq ., Although DoTT at late times of HSV-1 infection was substantially more prominent than DoG transcription in salt and heat stress , poly ( A ) read-through due to DoTT/DoG transcription and affected genes were significantly correlated between all three conditions , in particular at earlier times of infection ., We speculate that HSV-1 either directly usurps a cellular stress response or disrupts the transcription termination machinery in other ways but with similar consequences ., In contrast to previous reports , we found that inhibition of Ca2+ signaling by BAPTA-AM did not specifically inhibit DoG transcription but globally impaired transcription ., Most importantly , HSV-1-induced DoTT , but not stress-induced DoG transcription , was accompanied by a strong increase in open chromatin downstream of the affected poly ( A ) sites ., In its extent and kinetics , downstream open chromatin essentially matched the poly ( A ) read-through transcription ., We show that this does not cause but rather requires DoTT as well as high levels of transcription into the genomic regions downstream of genes ., This raises intriguing new questions regarding the role of histone repositioning in the wake of RNA Polymerase II passage downstream of impaired poly ( A ) site recognition .

Recently , we reported that productive herpes simplex virus 1 ( HSV-1 ) infection leads to disruption of transcription termination ( DoTT ) of most but not all cellular genes ., This results in extensive transcription beyond poly ( A ) sites and into downstream genes ., Subsequently , cellular stress responses were found to trigger transcription downstream of genes ( DoG ) for >10% of protein-coding genes ., Here , we directly compared the two phenomena in HSV-1 infection , salt and heat stress and observed significant overlaps between the affected genes ., We speculate that HSV-1 either directly usurps a cellular stress response or disrupts the transcription termination machinery in other ways with similar consequences ., In addition , we show that inhibition of calcium signaling does not specifically inhibit stress-induced DoG transcription but globally impairs RNA polymerase I , II and III transcription ., Finally , HSV-1-induced DoTT , but not stress-induced DoG transcription , was accompanied by a strong increase in chromatin accessibility downstream of affected poly ( A ) sites ., In its kinetics and extent , this essentially matched poly ( A ) read-through transcription but does not cause but rather requires DoTT ., We hypothesize that this results from impaired histone repositioning when RNA Polymerase II enters downstream intergenic regions of genes affected by DoTT .

cellular stress responses, classical mechanics, gene regulation, cell processes, dna-binding proteins, vertebrates, mechanical stress, dogs, animals, mammals, dna transcription, epigenetics, chromatin, chromosome biology, proteins, thermal stresses, gene expression, histones, physics, biochemistry, eukaryota, cell biology, transcriptional termination, genetics, biology and life sciences, physical sciences, amniotes, organisms

journal.pcbi.1004679

Capabilities and Limitations of Tissue Size Control through Passive Mechanical Forces

The mechanisms underlying tissue size control during embryonic development are extremely robust ., There are many cases where the rates of proliferation , growth , or death are perturbed significantly yet patterns are maintained or repaired during later stages of development ., For example , even after 80% of the material in a mouse embryo is removed , accelerated growth can give rise to correctly proportioned , albeit non-viable offspring 1 ., In fruit fly embryos , overexpressing the maternal effect gene bicoid leads to stark overgrowth in the head region , but the excess tissue is removed during later stages of development through apoptosis ( programmed cell death ) , leading to viable adults 2 ., Tetraploid salamanders of the species Amblystoma mexicanum have half the number of cells as their diploid counterparts , yet are the same size 3 ., The robustness of tissue size control relies on tight coordination of cellular processes including growth , proliferation , apoptosis and movement at a tissue level ., However , the fundamental mechanisms underlying such coordination remain largely unknown ., In particular , the mechanical implementation of tissue size control is not well understood ., The regulation of cellular mechanical properties is known to play a key role during morphogenetic events , such as tissue folding , elongation and cell sorting 4 , 5 ., For example , upregulation of myosin II generates tension that helps to straighten compartment boundaries in the Drosophila wing imaginal disc 6 , while controlled cell death provides the tension required for invagination during Drosophila leg development 7 ., It has been illustrated theoretically how mechanical feedback might facilitate uniform growth in epithelia in the face of morphogen gradients 8 ., Could mechanical forces also play a significant role in robustly maintaining tissue size ?, To explore questions of pattern repair , we develop a computational model of a patterned epithelium , with application to the segments of the Drosophila embryonic epidermis ( Fig 1 ) ., These tissues define the body plan along the head-tail axis ., They are first defined during stage 6 of embryonic development and are visible as stripes in the epidermis of the larva 9 ., The segments are subdivided into anterior ( A ) and posterior ( P ) compartments , which are marked by distinct gene expression patterns ., In particular , cells in the P compartment express the gene engrailed 10 ( Fig 1D ) ., While the initial specification and establishment of segments is relatively well studied 11 , maintenance of segment identities have received much less attention ., However , it is known that compartment dimensions can be robustly restored in the presence of genetic manipulations made during earlier developmental patterning events 2 , 12–14 ., Both the conserved epidermal growth factor receptor ( EGFR ) and Wnt/Wingless ( WG ) pathways have been implicated in regulating apoptosis to achieve pattern repair for perturbations made in each of the compartments and are known to antagonize each other 2 , 14 ., A major strength of Drosophila as a model organism is the availability of genetic tools that enable the ectopic expression of gene products or RNA interference ( RNAi ) constructs to manipulate cell growth , proliferation and signaling in a spatio-temporally controlled manner 15–17 ., For example , the bipartite GAL4-UAS system can be used to drive expression of ectopic genes in embryos through a cross of one line containing a tissue-specific enhancer driving expression of the heterologous yeast transcription factor GAL4 with a second line that activates expression of a transgene upon binding of GAL4 to the UAS promoter region ., Using this approach , Parker 14 investigated P compartment size using the GAL4 driver line as the control genotype engrailed-GAL4 , UAS-GFP , in the following referred to as wt ( wild type ) ., This was compared to various perturbations ( Fig 1B ) ., In particular , these included crosses between the driver line and UAS-CyclinE ( which we shall term en>CycE ) and UAS-dacapo lines ( further specified as en>dap ) , which perturbed the amount of final proliferation events towards the end of the normal range of proliferation in the epidermis ( Fig 1A ) ., Parker 14 observed an increase in final cell number of more than 30% ( Fig 1E , right bar ) in the P compartments of en>CycE embryos , which exhibited an additional round of cell division ., However , the size of the P compartment was much less affected by this perturbation ( Fig 1F , right bar ) , as measured in first instar larvae 14 ., Conversely , in en>dap embryos that exhibited a loss of one round of cell division , Parker 14 observed a reduction in cell number of 25% while , again , the compartment size was relatively unchanged ( Fig 1E and 1F , middle bars ) ., Parker’s findings also suggest that epidermal growth factor receptor ( EGFR ) signaling , through the activating ligand Spitz , patterns apoptosis inside the P compartment ., Spitz is released by a column of cells inside the anterior ( A ) compartment that is directly adjacent to the P compartment ., Identifying cell death events through TUNEL staining 20 , Parker 14 observed apoptosis much more frequently in the ‘front’ ( more anterior ) half of the P compartment , away from the Spitz source ( Fig 1G ) , than the ‘back’ half ., These numbers differed by a factor of nearly 40 in wt 14 ., Counter-intuitively , inhibiting apoptosis by expressing the caspase inhibitor p35 inside the P compartment of en>CycE embryonic segments resulted in compartment shrinkage by nearly 10% ., The above findings shed light on tissue size control in the Drosophila embryonic epidermal tissues , suggesting a reliance on the regulation of apoptosis rather than proliferation ., However , the cell-level interactions governing size control remain poorly understood ., In particular , potential roles of cellular mechanics in augmenting or repairing growth defects in patterned tissues remain unexplored ., To address this , we develop a vertex model of an embryonic segment to test hypotheses about the emergence of size control ., Comparing the model to previously published data across wt and genetic perturbations , we investigate the extent to which passive mechanical forces might suffice to explain the observed size control and asymmetries in cell death frequencies across the P compartment ., Our results suggest that the basis of size control can rely to a significant degree on the passive mechanical responses of cells ., However , the observed spatial asymmetry in cell death frequencies requires patterning of mechanical properties by inter-cellular communication ., These results also provide a basis for differentiating experimentally how extracellular signaling pathways like EGFR and WG might impact cellular decision making processes through predictions of observable cellular morphologies , and tissue behaviour after cell bond ablation ., Vertex models approximate cells in epithelial sheets as polygons ., The polygons represent the cells’ apical surfaces , where most inter-cellular forces originate 4 ., The terms in the model account for the mechanical effect of the force-generating molecules that accumulate in the apical surface of the cells , such as actin , myosin , and E-cadherin ., Vertices correspond to adherens junctions , and their positions are propagated over time using an overdamped force equation , reflecting that adherens junctions are not associated with a momentum ., The force equation takes the form, μ d x i d t = - ∇ i E ., ( 1 ), Here , μ is the friction strength ( which we assume to take the same constant value for all vertices ) , t is time , xi is the position vector of vertex i , and E denotes the energy of the whole system ., The total number of vertices in the system may change over time due to cell division and apoptosis ., The symbol ∇i denotes the gradient operator with respect to the coordinates of vertex i ., The forces act to minimise a phenomenological energy function , based on the contributions thought to dominate epithelial mechanics 22:, E = ∑ α K 2 ( A α - A 0 , α ) 2 + ∑ ⟨ i , j ⟩ Λ l i , j + ∑ α Γ 2 p α 2 ., ( 2 ), Here , the first sum runs over every cell in the sheet , Aα denotes the apical surface area of cell α and A0 , α is its preferred area , or target area ., This energy term penalises deviations from a target area for individual cells , thus imposing cellular bulk elasticity ., The second sum runs over all edges 〈i , j〉 in the sheet and penalizes long edges ( we choose Λ > 0 ) , thus representing the combined effect of E-cadherin , myosin , and actin at the binding interface between two cells ., The third sum also runs over all cells , and pα denotes the perimeter of cell α ., This term models the effect of a contractile acto-myosin cable along the perimeter of each cell 22 ., The parameters K , Λ , and Γ together govern the strength of the individual energy contributions ., Although this description of cell mechanics is phenomenological , a variety of previous studies have demonstrated its ability to match observed junctional movements and cell shapes in epithelial sheets through validation against experimental measurements 6 , 22 , 25 ., In contrast to many previous vertex model applications , we allow the mechanical parameters Λ , Γ , and A0 to vary between cells as a consequence of underlying tissue patterning ., In particular , we consider A0 to be a function of cell generation and introduce the parameter, R A = A 0 , daughter / A 0 , mother ( 3 ), as the ratio of target areas of daughter cells to mother cells ., To ensure that the target areas of all cells add up to the total size of the spatial domain , which is assumed to be fixed , we choose the value RA = 0 . 5 unless stated otherwise ., Throughout the study , variation of the parameter RA is used to account for cellular growth of daughter cells as well as changes in total target area upon division ., In each simulation , the initial area of each cell , As , equals its initial target area , A 0 s , with A s = A 0 s = 121 μm2 ( see discussion below for the choice of length scales in the model ) ., In S1 Text and S4 Fig we analyse the extent to which deviations of cell target areas may affect the simulation results by increasing A 0 s ., The simulated P compartment sizes and cell numbers are not strongly affected by such changes in initial condition , except for an increase in apoptosis for the en>CycE perturbation ., In contrast to several previous applications 22 , 25 of the vertex model the spatial domain in this study is constrained due to the fact that there is no net organism growth during embryogenesis ., In addition to evolving vertex positions in accordance with Eq ( 1 ) , we must allow for cell intercalation and cell removal through topological rearrangements ., One such topological rearrangement is a T1 swap , which simulates cell neighbour exchanges ., In a T1 swap an edge shared by two cells is removed and the cells are disconnected , while a new perpendicular edge is created that then connects the cells that were previously separated by the edge ( see Fig 2B ) ., In our implementation T1 swaps are executed whenever the length of a given edge decreases below a threshold lmin = 0 . 11 μm , which is 100 times smaller than the approximate length of a cell at the beginning of the simulation ., The length of the new edge , lnew = 1 . 05lmin , is chosen to be slightly longer than this threshold in order to avoid an immediate reversion of the swap ., A summary of the frequency of T1 swaps occurring in model simulations is provided in S1 Table ., There are very few cell intercalation events in our simulations , with no T1 swaps observed for wt , in line with experimental observations of germ-band retraction 28 ., A second topological rearrangement in vertex models is a T2 transition , during which a small triangular cell is removed from the tissue and replaced by a new vertex ( see Fig 2B ) ., In our implementation any triangular cell is removed if its area drops below a threshold Amin = 0 . 121 μm2 , which is 100 times smaller than the initial area of each cell ., The energy function Eq ( 2 ) in conjunction with T2 transitions can be understood as a model for cell removal: cells are extruded from the sheet by a T2 transition if they become mechanically unstable ., Note that we do not discriminate between cell removal by cell death or by delamination , since this distinction is immaterial for our purposes ., However , delamination has been shown to provide an alternative way of cell removal from epithelial sheets that is distinct from apoptosis 29 ., Rates of cell removal predicted by previous vertex model applications have coincided with experimental measurements in the Drosophila wing imaginal disc 22 and notum 29 ., All simulations start with N P s = 24 cells in the P compartment and N A s = 40 cells in the anterior compartment , to approximately match observed cell numbers 14 and to ensure that there are similar amounts of anterior tissue on either side of the P compartment ., In the case of a wt embryonic segment each cell divides once , with cell cycle times drawn independently from the uniform distribution on 0 to t ˜ w t = 600 time units ., This corresponds to the duration of the sixteenth division cycle in the epidermis , which occurs during late stage 10 and early stage 11 and takes roughly 50 minutes 18 ., After the round of divisions is complete , the system is allowed to relax for 200 more time units , corresponding to a total simulation time of twt = 800 time units ., For an en>CycE embryonic segment , the first round of divisions is implemented as for wt , but each cell in the P compartment then has a probability PCycE = 0 . 54 of dividing a second time once the first round of divisions is complete , with cell cycle times drawn independently from the uniform distribution from t ˜ C y c E = 600 to t ˜ C y c E = 1200 time units ., This probability is inferred from published data on the en>CycE+p53 perturbation 14; in this case apoptosis is blocked , allowing us to infer the average number of cell division events ., The second period of 600 time units corresponds to the duration of the ectopic divisions in the en>CycE embryos , which occur during late stage 11 and early stage 12 14 ., After the second round of divisions is complete , the system is allowed to relax for 200 more time units , corresponding to a total simulation time of tCycE = 1400 time units ., For an en>dap embryonic segment , each cell in the P compartment has a fixed probability Pdap = 0 . 6 of not participating in the single round of divisions ., This probability is inferred from published data on the en>dap perturbation 14 ., As with wt , divisions occur during the first t ˜ w t = 600 time units , after which the system is allowed to relax for 200 more time units , corresponding to a total simulation time of twt = 800 time units ., These simulation times are chosen such that the system is at quasi-steady state at each time point ., This quasi-steady state assumption is commonly used in vertex models 6 , 22 , 29 , 30 and reflects the fact that the times associated with mechanical rearrangements ( seconds to minutes ) are an order of magnitude shorter than typical cell cycle times ( hours ) 22 ., At each cell division event , a new edge is created that separates the newly created daughter cells ., The new edge is drawn along the short axis of the polygon that represents the mother cell 31 ., The short axis has been shown to approximate the division direction ( cleavage plane ) of cells in a variety of tissues 32 , including the Drosophila wing imaginal disc 33 ., The short axis of a polygon crosses the centre of mass of the polygon , and it is defined as the axis around which the moment of inertia of the polygon is maximised ., Each daughter cell receives half the target area of the mother cell upon division unless stated otherwise ., In order to simulate the subsections of the P compartment we consider a spatial domain comprising two adjacent cell populations , the cells in the P compartment and parts of the adjacent tissue in the anterior compartment on either side of it ., Sample simulation images are shown in Fig 2A and 2C ., For simplicity , we assume that cells initially have regular hexagonal shapes ., We analyse the sensitivity of P compartment sizes and cell numbers to this choice of initial condition in S1 Text and S3 Fig . Motivated by the repeated pattern of A and P compartments along the anterior-posterior axis of the embryo , as well as by the fact that single P compartments stretch farther dorso-ventrally than the simulated region , doubly periodic boundary conditions are applied ( Fig 2A ) ., These boundary conditions keep the simulated region of interest at a fixed size ., Compartment size changes are analysed as changes in the relative proportions of the anterior and posterior compartment within the simulated region ., An analysis of the sensitivity of P compartment sizes and cell numbers to this choice of boundary condition is provided in S1 Text and S1 Fig . The precise choice of boundary condition imposed in the model simulations does not significantly affect predicted compartment sizes and cell numbers ., To enable comparison of cell death rates in the front and back halves of the P compartment ( see Fig 1G ) , a cell is defined to be in the front or back half if its centroid is located to the anterior ( ‘left’ ) or posterior ( ‘right’ ) side of the centre of the tissue , respectively ., The tissue centre is defined to be the horizontal midpoint of the sheet at time t = 0 and is held fixed at all times ., When computing measures of cell shape in our analysis of simulation results , we define the area and perimeter of a cell to be those of the associated polygon in the vertex model , while ‘cell elongation’ is defined as the square root of the ratio of the largest to the smallest eigenvalues of the moment of inertia of that polygon ., This latter measure provides a robust way to measure elongations of arbitrary shapes 31 and is comparable to the ratio of the lengths of the long and short axis of the best fit ellipse to a cell ., Unless stated otherwise , the line tension along the compartment boundaries is set to Λb = 2Λ , twice the value of the line tension in the remainder of the tissue ., High tension along compartment boundaries is known to promote cell sorting and boundary straightness 6 , 30 , and the presence of myosin cables that can generate this tension between A and P compartments in the Drosophila embryonic epidermis has been reported 34 ., S2 Fig shows that while the increase in line tension along compartment boundaries does affect the straightness of the boundary between A and P compartments in the model simulations , it does not significantly affect compartment sizes or cell numbers ., To investigate the consequences of asymmetries in cell mechanical properties on P compartment size control and patterning of apoptosis , we consider three distinct cases ., In the first case , we allow for asymmetry in cell target areas in the P compartment ., This is implemented by modifying the target area of each cell in the P compartment to take the form, A 0 ′ = ( R A ) g ( 1 ± λ A ) , ( 4 ), where RA = 0 . 5 as listed in Table 1 , g ∈ {0 , 1 , 2} denotes the generation of the cell , and the − and + signs apply to cells located in the front and back halves of the compartment , respectively ., We refer to the parameter λA as the area asymmetry ., In the second case , we allow for asymmetry in line tensions in the P compartment ., This is implemented by modifying the line tension of each cell-cell interface ( edge ) inside the P compartment to take the form, Λ = Λ r ( 1 ± λ l ) , ( 5 ), where Λr is the value of the line tension when no asymmetry is imposed ., The + sign applies to all edges between P compartment edges whose midpoint is the front half of the compartment , while the − sign applies to all edges whose midpoint is in the back half of the compartment ., We refer to the parameter λl as the line tension asymmetry ., In the third case , we allow for asymmetry in perimeter contractility in the P compartment ., This is implemented by modifying the perimeter contractility of each cell in the P compartment to take the form, Γ = Γ r ( 1 ± λ p ) , ( 6 ), where Γr is the value of the perimeter contractility when no asymmetry is imposed , and the + and − signs apply to cells in the front and the back halves of the P compartment , respectively ., We refer to the parameter λp as the perimeter asymmetry ., The asymmetry parameters are all fixed at 0 in Figs 3 , S1 and S2 , and are varied in Figs 4 , 5 and 6 ., Prior to solving the model numerically , we non-dimensionalise it ., Non-dimensionalising reduces the number of free parameters in the system and facilitates comparison of parameter values to previous implementations of the vertex model 22 ., Rescaling space by the characteristic length scale L and time by the characteristic time scale T , Eqs ( 1 ) and ( 2 ) become, μ L T d x i ′ d t ′ = - 1 L ∇ i ′ E , ( 7 ) E = ∑ α K L 4 2 ( A α ′ - A 0 , α ′ ) 2 + ∑ ⟨ i , j ⟩ Λ L l i , j ′ + ∑ α Γ L 2 2 p α ′ 2 , ( 8 ), where x′i , A α ′ , A 0 , α ′ , l i , j ′ and p α ′ denote the rescaled ith vertex positions , the rescaled cell area and cell target area , the rescaled edge length between vertices i and j , and the rescaled cell perimeter , respectively ., The symbol ∇ i ′ denotes the gradient with respect to the rescaled ith vertex position ., Multiplying the first equation by T/μL , we obtain, d x i ′ d t ′ = - ∇ i ′ T μ L 2 E , ( 9 ) E ′ = T μ L 2 E = ∑ α T K L 2 μ 1 2 ( A α ′ - A 0 , α ′ ) 2 + ∑ ⟨ i , j ⟩ Λ T L μ l i , j ′ + ∑ α Γ T μ 1 2 p α ′ 2 ., ( 10 ), Finally , by introducing the time scale T = μ/KL2 , and the rescaled mechanical parameters Λ ¯ = Λ T / ( L μ ) = Λ / K L 3 , Γ ¯ = Γ T / μ = Γ / ( K L 2 ) the non-dimensionalised equations read, d x i ′ d t ′ = - ∇ i ′ E ′ , ( 11 ) E ′ = ∑ α 1 2 ( A α ′ - A 0 , α ′ ) 2 + ∑ ⟨ i , j ⟩ Λ ¯ l i , j ′ + ∑ α Γ ¯ 2 p α ′ 2 ., ( 12 ), We choose the characteristic length scale L = 11 μm such that L2 is the mean cell area in the P compartment at the start of the simulation period , i . e . 121 μm2; the P compartment occupies a total area of 2 . 76×103 μm2 14 and is initialized with 24 cells ., The precise value of the characteristic time scale T depends on tissue properties ( μ and K ) and could be inferred from the duration of vertex recoil after laser ablation experiments , for example ., In the non-dimensionalised model , cell shapes are governed by the rescaled target area of each cell and the rescaled mechanical parameters , Λ ¯ and Γ ¯ ., For these parameters we use previously proposed values 22 , unless stated otherwise ., A complete list of parameters used in this study is available in Table 1 ., To solve Eqs ( 11 ) and ( 12 ) numerically we use an explicit forward Euler scheme:, x i ′ ( t ′ + Δ t ′ ) = x i ′ ( t ′ ) - ∇ i ′ E ′ ( t ′ ) Δ t ′ ., ( 13 ), The time step used in the forward Euler scheme is 0 . 01 rescaled time units and is manually chosen to ensure that the numerical scheme converges and that a further reduction in the time step does not change the results ., We implement the model within Chaste , an open source C++ library that provides a systematic framework for the simulation of vertex models 31 , 35 ., All code used to implement model simulations and to generate results presented in this work is provided ( see S1 Software ) ., As an initial study , we analyse simulations where compartment size is governed solely by passive mechanical properties of individual cells , and no further regulatory mechanism for size control is assumed ., In particular , all cells in the tissue are specified to have the same mechanical properties , with the exception of interfaces shared by cells at the compartment boundary ., As we shall show , such passive mechanical interactions are sufficient to explain the robustness of compartment size to hyperplastic manipulations ., Fig 3A shows snapshots of individual simulations of wt , en>dap and en>CycE embryonic segments ., We observe cells that are larger but fewer in number in en>dap than in wt , while the en>CycE compartment contains more smaller cells ., Generating statistical distributions by running 100 simulations in each case , we obtain the summary statistics visualized in Fig 3B and 3C ., To allow for comparison with observed values we superimpose on each panel in Fig 3B and 3C either the upper and lower bounds in observed P compartment areas 14 across the three perturbations ( shaded gray ) or the upper and lower limits in cell numbers for each perturbation separately ( blue , green , red for wt , en>CycE , and en>dap , respectively ) ., We do not plot the distinct shaded regions in the case of P compartment areas since the regions for individual perturbations overlap ., Fig 3B shows that , for wt and en>dap , the average P compartment sizes and cell numbers at the end of the final round of divisions predicted by the model closely match observed values ., The difference in cell number between simulation and experiment for en>CycE is statistically significant ( 17% ) , indicating that the model underestimates the number of cell deaths in this perturbation ., These simulation results were achieved using literature values of the parameters Λ ¯ and Γ ¯ 22 , and by assigning daughter cells to have half the target area of their mother cells ( RA = 0 . 5 ) ., Although the model is a drastic simplification of epithelial compartment size homeostasis , the in silico results provide a close match to experimental values without any parameter tuning ., The model thus provides a simple explanation for the emergence of P compartment size control 14: size control can be achieved through passive mechanical forces without any further regulation of cellular properties through signaling gradients ., To explore how robust the observed size control is to the model parameters , we performed a single parameter sensitivity analysis while fixing the remaining parameters at their values listed in Table 1 ( Fig 3C ) ., For most parameter values considered , the simulation results fall within the bounds of experimentally observed values , except for values of the target area ratio RA smaller than 0 . 4 and larger than 0 . 9 , and for values in Λ larger than 0 . 2 ., Focusing on the results of en>CycE simulations , the model exhibits some counter-intuitive behaviour ., In particular , uniformly low perimeter contractility , Λ ¯ , or high line tension , Γ ¯ , leads to mechanically induced P compartment shrinkage ., In addition , an increase or decrease of RA away from 0 . 5 will increase compartment sizes for the en>CycE perturbation ., We may interpret these results as follows ., Mechanically induced P compartment shrinkage can be understood as a result of the balance between the energy terms in Eq ( 2 ) ., The perimeter contractility and line tension terms act to minimise edge lengths and perimeters of cells ., These force contributions can be counteracted by the area term , which acts to keep the cell close to its target area , or by stretching forces exerted by neighbouring cells ., Upon division , a new edge is created , which adds an inward contractile force that any expansive forces must counteract ., Therefore , daughter cells occupy a smaller area than their mother cell once they reached mechanical equilibrium ., The observation that an increased rate of cell division leads to tissue shrinkage is counter-intuitive , yet not unrealistic; data from 14 for en>CycE and en>CycE+p53 embryonic compartments show a similar trend , in which the more cells are present , the smaller the compartment area ., Inhibition of cell death in the en>CycE+p53 leads to more cells , but smaller compartments ., Further , this counter-intuitive experimental result , which cannot be explained by a simpler hypothesis where EGFR signaling leads to size control through direct patterning of apoptosis and growth , may be explained by a simple mechanical argument ., A similar mechanism explains the dependence of the size of the en>CycE compartment on the target area ratio , RA ., Mitosis induced shrinkage is a result of the perimeter contractility and line tension terms in the mechanical model ., If we choose a value for RA that is not equal to 0 . 5 , then the target areas of all cells will no longer add up to the total area of the tissue , and more cells have areas that are far away from their actual target areas ., This increases the absolute value of the area elasticity term in the energy equation , and hence reduces the relative strength of the perimeter contractility and line tension terms ., As the relative strength of these two terms decreases , the extent of mitosis-induced shrinkage is also reduced ., In the case RA<0 . 5 , the additional line tension and perimeter force due to the new edge during division are not strong enough to stretch the cells surrounding the division further away from their target area , and if RA>0 . 5 the forces originating from the new edge are not strong enough to further oppose the strength of the target area terms of the new cells ., Hence , mitosis-induced shrinkage occurs only if RA ≈ 0 . 5 ., In our simulations , P compartment size is relatively robust to the value of RA , despite the fact that the areas of many cells differ widely from their target values ., The bulk elasticity energy term in Eq ( 2 ) varies quadratically with deviations between cell area and cell target area ., Thus , one might expect significant changes in P compartment areas or cell numbers when target areas are perturbed upon proliferation ., Our simulation results suggest that P compartment areas or cell numbers are not affected by such changes in total tissue energy ., A further counter-intuitive result shown in Fig 3C is that increasing the line tension parameter Λ ¯ and increasing the perimeter contractility parameter Γ ¯ have opposing effects on P compartment size in the en>CycE perturbation ., Increasing line tension results in a stronger contractile force on the cell , resulting in more T2 transitions and hence a smaller P compartment ( Fig 3C , central panel ) ., In contrast , although increasing perimeter contractility also results in a stronger contractile force for each cell , in this case the mechanical interactions between adjacent cells ( a contracting cell acts to stretch its neighbours ) result in fewer T2 transitions and hence a larger P compartment ., All the observed changes in P compartment sizes and cell numbers remain within experimentally measured values ( Fig 3 , shaded regions ) , the exception being the P compartment cell numbers for the en>CycE perturbation ., The discrepancy between observed values and in silico results for the P compartment cell numbers in en>CycE is insensitive to parameter variation ., The robustness of the simulation results in Fig 3B to parameter values provides further confirmation that size control is a natural outcome of passive mechanical cellular interactions in our model ., Size control is preserved in the face of small amounts of cell growth or shrinkage ( variations in RA ) or perturbations of cellular mechanical properties ( variations in Λ ¯ and Γ ¯ ) ., However , this model fails to capture the observed asymmetry in cell death locations , as measured by the ratio of accumulated cell death occurrence between the front and the back half of the P compartment across multiple embryos ., The third row of Fig 3C shows that the total number of cell deaths across 100 simulations is the same between the front half and the back half of the P compartment ., Here we only plot the cell death occurrences of the en>CycE simulations , since no cell deaths were observed in any wt or en>dap simulations ., This is in close agreement with experimental results 14 , where only 0 . 7 ( wt ) or 0 . 2 ( en>dap ) cell deaths where identified by TUNEL staining per embryo ., We draw

Introduction, Materials and Methods, Results, Discussion

Embryogenesis is an extraordinarily robust process , exhibiting the ability to control tissue size and repair patterning defects in the face of environmental and genetic perturbations ., The size and shape of a developing tissue is a function of the number and size of its constituent cells as well as their geometric packing ., How these cellular properties are coordinated at the tissue level to ensure developmental robustness remains a mystery; understanding this process requires studying multiple concurrent processes that make up morphogenesis , including the spatial patterning of cell fates and apoptosis , as well as cell intercalations ., In this work , we develop a computational model that aims to understand aspects of the robust pattern repair mechanisms of the Drosophila embryonic epidermal tissues ., Size control in this system has previously been shown to rely on the regulation of apoptosis rather than proliferation; however , to date little work has been done to understand the role of cellular mechanics in this process ., We employ a vertex model of an embryonic segment to test hypotheses about the emergence of this size control ., Comparing the model to previously published data across wild type and genetic perturbations , we show that passive mechanical forces suffice to explain the observed size control in the posterior ( P ) compartment of a segment ., However , observed asymmetries in cell death frequencies across the segment are demonstrated to require patterning of cellular properties in the model ., Finally , we show that distinct forms of mechanical regulation in the model may be distinguished by differences in cell shapes in the P compartment , as quantified through experimentally accessible summary statistics , as well as by the tissue recoil after laser ablation experiments .

Developing embryos are able to grow organs of the correct size even in the face of significant external perturbations ., Such robust size control is achieved via tissue-level coordination of cell growth , proliferation , death and rearrangement , through mechanisms that are not well understood ., Here , we employ computational modelling to test hypotheses of size control in the developing fruit fly ., Segments in the surface tissues of the fruit fly embryo have been shown to achieve the same size even if the number of cells in each segment is perturbed genetically ., We show that simple mechanical interactions between the cells of this tissue can recapitulate previously gathered data on tissue sizes and cell numbers ., However , this simple model does not capture the experimentally observed spatial variation in cell death rates in this tissue , which may be explained through several adaptations to the model ., These distinct adaptations may be distinguished through summary statistics of the tissue behaviour , such as statistics of cell shapes or tissue recoil after cutting ., This work demonstrates how computational modelling can help investigate the complex mechanical interactions underlying tissue size and shape , which are important for understanding the underlying causes of birth defects and diseases driven by uncontrolled growth .

journal.pntd.0005180

Schistosoma mansoni Infection Can Jeopardize the Duration of Protective Levels of Antibody Responses to Immunizations against Hepatitis B and Tetanus Toxoid

It is estimated that globally over 240 million people have schistosomiasis , with the bulk of cases occurring in sub-Saharan Africa 1 , 2 ., A vast majority of those infected in the region harbor either Schistosoma mansoni , S . haematobium or both 3 , with an estimated 122 million cases occurring in east Africa 4 ., In western Kenya , near Lake Victoria where this study takes place , S . mansoni infections are common in schoolchildren ., Prevalence in this population often reaches 50% or higher but decreases as distance from the lake increases 5 ., There is a paucity of information on schistosomiasis prevalence levels in Kenyan adults ., However , recent studies in Western Kenya suggest that prevalence in 9–12 year olds , is an excellent predictor of the prevalence in adults 6 ., Thus , schistosomiasis is an ongoing community level public health problem in western Kenya ., The current study is designed to determine if this situation influences standard adult immunizations in those who have or do not have S . mansoni infections at the time of their immunizations 7 ., Helminths , including schistosomes , are remarkable in their ability to modulate immune responses in their host , presumably to promote their own survival ., Their modulation of immune responsiveness has been shown to affect both responses to schistosome antigens and to bystander antigens 8–12 ., Helminth infections have also been implicated in diminished or altered immune responses to a number of other infectious diseases including malaria 13 14 , Helicobacter pylori 15 , HIV 16 , 17 , and Mycobacterium ., tuberculosis 18 ., Also , S . mansoni and hepatitis B co-infection has been associated with more severe liver disease 19 ., In murine models , harboring a helminth infection at the time of immunizations has been shown to skew immune responses to vaccine antigens against diphtheria 20 , HIV 21 , pneumococcus 22 , and hepatitis B 23 ., In human populations , diminished responses to tetanus vaccination have been reported in individuals with schistosomiasis 24 , lymphatic filariasis 25 , and onchocerciasis 26 ., Helminth infections have also been implicated in the diminished efficacy of other established vaccines , such as Bacille Calmette-Guerin ( BCG ) 27–30 and cholera vaccines 31 , 32 ., They have also been reported to lower responses to an experimental Plasmodium falciparum vaccine 33 and it is hypothesized that they may impede progress on the development of other experimental vaccines against malaria , HIV , and helminths 9 , 10 , because clinical trials of these vaccine candidates often need to be done in areas where these diseases are co-endemic with helminth infections ., However , in contrast to the studies cited above , a study in Gabonese children infected with S . hematobium showed no detrimental responses to tetanus toxoid ( TT ) boost 34 and females harboring helminth infections in Uganda and Tanzania responded as well as controls to the human papillomavirus ( HPV ) vaccine 35 , 36 ., Hepatitis B vaccination in Egyptian adults infected with S . mansoni showed mixed results ., When plasma derived vaccine was used , individuals with hepatosplenic schistosomiasis did not respond to the vaccine series 37 but individuals with less severe schistosomiasis did respond ., However , even for those who did respond , 5 years after completion of the vaccine series , antibody responses had dropped below protective levels in 38% of those who completed follow-up and less than optimal protection levels were maintained in another 23% 38 ., When recombinant hepatitis B vaccine was used in another study of Egyptian males , who were treated for their schistosomiasis after the first boost , responses were robust immediately following the completion of the series ., However , no uninfected control group was included in the study for comparison nor were responses of schistosome-infected individuals followed after completion of the vaccine series 39 ., The Global Vaccine Action Plan seeks to greatly increase vaccine coverage in the developing world 40 ., While these efforts are primarily focused on children and pregnant women , there is a distinct need to immunize all African adults in terms of preventing infections that can cause epidemics such as meningococcal disease 41 or cholera 42 and to protect healthcare workers 43 ., To evaluate the potential influence schistosomiasis may have on responses by young adults to different vaccines , we investigated primary responses to hepatitis B vaccination and secondary responses to TT in individuals who did or did not have S . mansoni ., We found that individuals with schistosomiasis at the time of the initiation of the vaccinations responded to the hepatitis B vaccine series and TT boost , surpassing the minimum antibody levels associated with protection ., However , the schistosomiasis positive group’s median antibody responses to hepatitis B surface antigen were generally lower following the second and third doses of the vaccine series , and for tetanus boost many of them had dropped below 1 IU/ml the level considered optimal for long term protection 44 ., In light of these findings , we believe that having schistosomiasis at the time of vaccination may be detrimental to the maintenance of long term antibody responses to hepatitis B and tetanus vaccines in some young adults ., Study participants were recruited from Kisumu Polytechnic College ( KPC ) located in Kisumu , Kenya ., Study inclusion criteria were as follows: participants were required to be staff or students of KPC , be healthy men or women ≥18 years of age , and available to participate in the study for approximately 10 months ., Inclusion in the study also required that participants attend a voluntary counseling and testing ( VCT ) clinic to ascertain their HIV status before we could evaluate their blood for the presence of antibodies to HIV ., All study participants gave written informed consent prior to enrollment ., Study procedures were approved by the institutional review boards of the University of Georgia ( UGA ) , the Centers for Disease Control and Prevention ( CDC ) , the Scientific Steering Committee of the Kenya Medical Research Institute ( KEMRI ) , and the KEMRI/National Ethics Review Committee of Kenya ., Details of the study protocol are available in the STROBE Checklist ( S1 Fig ) ., Participants were required to submit 3 stools on 3 consecutive days in order to ascertain helminth infection status ., Stool samples were collected and processed on the same day using the Kato-Katz thick smear method 45 with 2 slides per stool for the quantitative determination of S . mansoni and the detection of the soil transmitted helminths ( STH ) ., Slides were read within an hour of preparation by 2 trained microscopists in order to detect hookworm , Ascaris lumbricoides , and Trichuris trichiura eggs ., The microscopists re-examined the slides at least 24 hours after preparation to ascertain the presence of S . mansoni eggs ., Results were recorded as the number of eggs per gram of stool ( EPG ) for S . mansoni and positive or negative for STH infections ., Under the supervision of the KPC student medical clinic , individuals positive for S . mansoni eggs were treated with 40mg/kg of praziquantel ., Individuals positive for hookworm , A . lumbricoides , and T . trichiura eggs were treated with 500 mg of albendazole ., This study took place in an urban university setting with students potentially coming from diverse geographical regions of Kenya ., As the students were living now in an urban university setting with access to piped drinking water , showers and flush toilets their exposure to S . mansoni and STH most likely occurred previously at their family homes ., To assist in assessing possible previous exposure to these infections , a structured , validated questionnaire was given to participants at baseline by trained interviewers to ascertain participants’ home districts , socio-economic status ( SES ) and water exposure ., SES was determined using 5 proxy measures including family land ownership , source of household drinking water , type of household toilet , and type of flooring and roof construction in residence ., Water exposure was determined by asking about participant contact with water from lakes , ponds , rivers , or streams for work or routine daily activities ., The SES and water exposure questionnaire is presented as S2 Fig . The vaccines used in this study were those employed in Kenya Ministry of Health vaccination programs ., Hepatitis B vaccine ( Elovac- B , HBI , division of Indian Immunologicals , Limited ) was administered in a 3 dose series as recommended by the manufacturer at time 0 , 1 month and 6 months ., A small subset of individuals were unavailable to receive the final dose of hepatitis B vaccine at 6 months but were available to provide a final blood sample at 8 months ., Those individuals were offered and administered the third dose at the time of the final blood draw ., A booster dose of tetanus toxoid vaccine ( Tetanus Vaccine ( Absorbed ) I . P . Serum Institute of India ) was administered at time 0 as well ., All vaccinations were given by a Kenya Ministry of Health nurse in conjunction with the Kenya Ministry of Health ., Venous blood was collected at baseline , 7 weeks , and 8 months ., At each time point , thick and thin blood smears were made and slides read by well-trained microscopists to detect malaria infection ., Individuals with positive malaria blood smears were referred to the KPC student medical clinic for appropriate treatment ., Hemoglobin was measured at baseline and 8 months using hemocuvettes ( EFK Diagnostics , Poland ) and the Hemo Control reader ( EFK Diagnostics , Germany ) ., Individuals with hemoglobin levels below 8 g/dL were referred to the KPC student medical clinic for appropriate care ., HIV screening was performed using the Determine HIV-1/2 test ( Abbott Laboratories Abbott Park , IL ) on baseline plasma samples of participants whose attendance of a VCT clinic had been confirmed ., Participant plasma samples for each time point were collected and stored at -20C until being thawed and transferred using ViveST sample transport matrices ( ViveBio Alpharetta , GA ) for transport to the University of Georgia for further analyses ., Circulating levels of T regulatory cells ( Tregs ) were determined at each time point for S . mansoni and STH positive individuals as well as a similar number of S . mansoni and STH negative controls utilizing a method described in detail previously 46 ., Briefly , whole blood was stained with Alexa Fluor anti-CD3 clone UCHT1 , PerCP anti-CD4 clone RPA-T4 , and PE anti-CD25 clone BC96 ( all from Biolegend , San Diego , CA ) ., Samples were run on a FACS Calibur 4 color flow cytometer ( BD Biosciences , San Jose , CA ) and analyzed using FlowJo version 9 ( Ashland , OR ) ., Tregs were defined as the CD3+/CD4+/CD25high population ., Using fluorescence minus one ( FMO ) controls a gate was set to distinguish CD25neg cells from CD25med cells ., CD4/CD25 dot plots were examined for all individuals in order to establish the threshold for the CD25hi population ., Cytokine responses to vaccine antigens were determined at each time point for S . mansoni and STH positive individuals as well as a similar number of S . mansoni and STH negative controls ., Under sterile conditions , whole blood was diluted 1:5 with cell culture media ( RPMI 1640 , 1% L-glutamine , 1% Penicillin-streptomycin ) and cultured with phytohemagglutinin ( PHA ) ( Sigma-Aldrich , St . Louis , MO ) at a final concentration of 2 . 5 μg/ml , tetanus toxoid ( TT ) antigen ( Mass Biologics , Boston MA ) at a final concentration of 5 μg/ml , or hepatitis B surface antigen ( HbSAg ) ( Reagant Proteins of Pfenex Dan Diego , CA ) at a final concentration of 0 . 1 μg/ml ., The cultures were allowed to incubate for 72 hours at 37C in 5% CO2 at which time culture supernatant fluids were collected and stored at -20C ., IL-10 , IL-5 , and IFN-γ production in response to PHA , TT , and HbSAg were measured using Duoset ELISA kits ( R&D systems , Minneapolis , MN ) as recommended by the manufacturer ., Hepatitis B surface antigen antibody ( anti-HBs ) levels were quantitatively measured using Monalisa Anti-HBs EIA and Monalisa Anti-HBs calibrator kits ( EIA ) ( BioRad , Redmond , WA ) in baseline , 7 week and 8 month time point plasmas ., Total antibody levels to hepatitis B nucleocapsid antigen ( core ) were qualitatively measured in baseline plasma samples using the Monalisa Anti-HBc Enzyme Immunoassay ( EIA ) ( BioRad Redmond , WA ) ., All assays were run as recommended by the manufacturer ., We retrospectively allocated participants to the following categories ., Previous exposure and recovery from hepatitis B infection or previous immunization with hepatitis B vaccine was defined as a baseline anti-HBs level above 10 mIU/ml 47 ., Prior immunization would be very unlikely due to the relative expense and general lack of vaccine availability for adults in western Kenya ., Potential hepatitis B chronic infection was defined as baseline antibody positive to hepatitis B core antigen , anti-HBs titers below 10 mIU/ml and failure to respond to the vaccine series 48 , 49 ( levels never rose above 10 mIU/ml ) ., Initial susceptibility to hepatitis B infection was defined as those individuals who at baseline were antibody negative to core antigen , had anti-HBs titers below 10 mIU/ml , and developed anti-HBS levels above 10 mIU/ml after vaccination 47 ., Finally , persons with false positive core antibody responses were defined as individuals with baseline antibodies against core antigen , anti-HBs levels below 10mIU/ml , and the development of anti-HBs responses above 10 mIU/ml after vaccination 50–53 ., Antibodies to TT were quantitatively measured using a commercially available tetanus toxoid IgG ELISA ( Genway Platinum , San Diego , CA ) ., Antibodies to TT were measured in baseline , 6 week and 8 month time point plasmas ., Data was entered into Microsoft Access 2010 databases ., Individual datasets were generated using IBM SPSS version 23 ., GraphPad Prism version 5 . 04 for windows ( Graphpad Software , San Diego , CA ) was used for statistical analyses as well as for preparing graphs ., The non-parametric Mann-Whitney test was used to compare anti-HbS levels , anti-TT levels , cytokine levels in response to vaccine antigen stimulation of whole blood , and to compare circulating CD3+CD4+CD25high T-regulatory cells between controls and S . mansoni positive individuals at each of the time points- baseline , week 6 and month 8 ., To determine differences in CD3+CD4+CD25high T-regulatory cell levels within group and over 3 time points a Friedman test followed by Dunn’s multiple comparison test was used ., A chi square test was utilized to compare differences between the proportions of control versus S . mansoni positive individuals regarding social economic status ( SES ) , water contact , hepatitis B virus exposure status before vaccination , and hepatitis B vaccine responder status after the 2nd vaccine dose ., A Fisher’s exact test was utilized to determine differences in the proportion of controls versus S . mansoni positive individuals maintaining TT antibody levels above 1 IU/ml at 6 weeks after boost and 8 months after boost ., This study took place in western Kenya from July 2013 until January 2015 at Kisumu Polytechnic College located in the center of Kisumu ., The study location was selected because the staff and enrolled students were primarily from western Kenya , an area endemic for schistosomiasis so they were potentially infected with the parasite as children or adolescents ., However , as the staff and students were living at or near the college in an urban setting , they were unlikely to be infected or re-infected with schistosomes after treatment during the 8 month follow-up period ., The study timeline and number of persons lost to follow up is outlined in Fig 1 ., A total of 376 individuals signed consent forms and of those , 179 individuals gave a baseline blood sample ., From this group , 162 ( 90 . 5% ) study participants received the tetanus booster , at least 2 of the 3 doses of the hepatitis B vaccine series and gave a follow-up blood sample 2 weeks after the 2nd hepatitis B vaccine dose ., Twelve individuals were excluded from analysis for failing to show a VCT card by the end of the study period ( proof they knew their HIV status ) and 3 study participants ( 2% ) were HIV-positive and excluded from analysis ., One participant with a Hb level below 8 mg/dL at baseline was excluded from the study ( Fig 1 ) ., Thus , data from a total of 146 individuals who gave at least 2 blood samples and met the study requirements were available to contribute to the final analyses ., Of the 146 individuals available for analysis ( summarized in Table 1 ) 53% were female and the median age was 21 years with a range of 18-57years ., Malaria prevalence by smear positivity was 3% at baseline and remained below 3% throughout the study ., Median hemoglobin ( Hb ) levels for participants were 13 mg/dL or above at each of the follow-ups ., A total of 38 individuals ( 26% ) were S . mansoni egg positive , at the time of their initial immunizations , as determined by Kato-Katz stool assay done on three consecutively collected stools , with two Kato-Katz slides per stool ., The average arithmetic mean egg burden for those infected was 159 . 5 eggs per gram ( EPG ) of stool ., Of the 38 individuals who were stool positive for S . mansoni 4 were also stool positive for Trichuris trichiura and 1 was stool positive for hookworm ., In addition , 9 individuals who were S . mansoni egg negative were egg positive by Kato-Katz for a soil transmitted helminth ( STH ) infection with 4 hookworm positive , 2 T . trichiura positive , 2 Ascaris lumbricoides positive , and 1 both A . lumbricoides and T . trichiura positive ., Finally , 99 individuals were egg negative for S . mansoni and STHs by Kato-Katz and made up the control , uninfected group ., Age , sex , malaria , HIV , Hb levels and SES , as determined by the SES/water questionnaire ( S2 Fig ) , did not differ significantly between the egg negative uninfected controls ( controls ) and the schistosomiasis egg positive group ( Sm+ ) ., However , water contact did differ significantly ( Chi square p < 0 . 001 ) between the 2 groups with the Sm+ group being more likely to have worked , bathed , washed items , and or collected water from Lake Victoria while in their home village ., In sub-Saharan Africa hepatitis B virus is most commonly acquired during early childhood 54 ., The prevalence of this virus in Kenya has been estimated to be greater than 8% 55 , with hepatitis B vaccination of infants in Kenya becoming standard after 2001 56 ., All of our participants were born prior to this time and less than 2% of participants self-reported having received all or part of the hepatitis B vaccine series ., Therefore , hepatitis B exposure status at baseline before vaccination was defined by looking first at baseline antibody responses to hepatitis B core and surface antigens and then evaluating how individuals responded to the vaccine series ., A total of 145 individuals ( 98 controls , 38 Sm+ , and 9 STH+ ) received at least 2 doses of hepatitis B vaccine during the study and provided baseline ( before vaccination ) and post-vaccination blood samples ( 2 weeks after the second hepatitis B dose , which was 1 week post-praziquantel treatment for Sm+ group ) as shown in the study timeline ( Fig 1 ) ., Of these , 104 completed the 3 dose vaccine series and provided a final blood sample at 8 months ( 2 months after final immunization ) ., We allocated participants to the following categories as described in the methods section: a total of 36 individuals ( 25% ) were assigned to the previous exposure and recovery from hepatitis B category; 8 individuals ( 5% ) were assigned to the category of possible chronic hepatitis B infection and excluded from analysis; 85 individuals ( 59% ) were considered unexposed and susceptible to hepatitis B virus at baseline and were included in the final analysis; and 16 individuals ( 11% ) were considered false positives to core antigen and therefore unexposed and susceptible to hepatitis B infection ., There were no significant differences in the proportion or number of control , Sm+ , or STH+ individuals assigned to each of the hepatitis B exposure categories as determined by Chi square analysis ( p = 0 . 4 ) ( Table 2 ) ., Of the 101 individuals deemed likely to be susceptible to hepatitis B virus infection at baseline , 6 individuals ( 6% ) failed to achieve an anti-HBs response above 10 mIU/ml after receiving the full vaccination series and were classified as vaccine non-responders ., This is within the expected range of non-response rate to hepatitis B vaccination of 4–10% 57 , 58 and these vaccine non-responders were excluded from the hepatitis B vaccine response analysis ., The remaining 95 individuals ( 63% controls , 31% Sm+ and 6% STH+ ) all produced more than10 mIU/ml anti-HBs antibody levels after receiving the second or third dose of the vaccine series , indicating that they had achieved or exceeded the minimum level of protection ., However , as blood concentration of anti-HBs antibodies have been shown to correlate with the maintenance of protective responses over time 59 , we asked if study participants with S . mansoni infection at baseline ( n = 29 ) or controls ( n = 60 ) demonstrated differences in their median anti-HBs levels after 2 or 3 doses of vaccine ., Median anti-HBs levels at 2 weeks after the second dose of hepatitis B vaccine were significantly lower ( p = 0 . 038 ) in the Sm+ group as compared to the controls ( Fig 2 ) ., By 2 months after the 3rd dose of the vaccine ( 8 months from the initial dose and 7 months following treatment ) the median response by those who had had schistosomiasis was still lower than those who were uninfected with the lower 25th percentile being 157 . 8 mIU/ml compared to 560 . 5 mIU/ml for the controls ., However , this difference was no longer statistically significant ( p = 0 . 09 ) ( Fig 2 ) ., We found that during the 6 months needed to complete the full hepatitis B immunization regimen approximately 30% of the participants were lost to follow-up or unavailable for 3rd dose of the hepatitis B vaccine ., Therefore , we examined what percentage of participants receiving 2 doses did not produce more than 10 mIU/ml of specific antibody , the minimum level needed for protection ., After the 2nd dose of vaccine 18% of the control group failed to mount a protective vaccine response ( anti-HBs < 10 mIU/ml ) as compared to 38% of the Sm+ group , and while 30% of the controls produce more than 100 mIU/ml of anti-HBs antibody ( the highest category of protection ) , only 17% of the Sm+ group did so ., While these differences failed to reach statistical significance ( p = 0 . 10 by chi square analysis ) , they imply that individuals with schistosomiasis may be at a greater disadvantage than uninfected individuals if they do not to complete the full vaccine series ., STH+ individuals ( n = 6 ) antibody responses to hepatitis B vaccination are shown in S3 Fig . While the sample size for each was too small to run statistical analysis on ( A . lumbricoides n = 2 , hookworm n = 3 , and T . trichuria n = 1 ) , the median values of the STH+ group more closely resembled the controls than the Sm+ group ( S3 Fig ) ., Vaccination against tetanus has been part of the extended program of immunizations in Kenya since 1980 ., It is also given to pregnant women as part of standard prenatal care 59 as well as individuals seeking care for injuries in a hospital settings ., In our study , a total of 146 Sm+ , STH+ and uninfected controls received the TT vaccine booster and gave a baseline and 7 week blood sample , with 113 participants giving a final blood sample at 8 months ., Only 34% of participants recalled having ever received a tetanus booster vaccination , however all participants’ baseline TT titers were above 0 . 01 IU/ml ( minimum level of protection ) 44 , indicating they had received at least one tetanus vaccination in the past ., In order to account for the variation in participant tetanus vaccination histories as well as the impact that existing levels can have on recall responses 60 , 61 , we analyzed participants according to three categories based on their baseline TT antibody titers: below 0 . 1 IU/ml; 0 . 1 to 1 IU/ml; and above 1 IU/ml 44 ., For TT antibody titers below 0 . 1 IU/ml immediate immunization is recommended; 24 ( 16 . 5% ) of all participants fell into this category ., Sixty-three ( 43% ) participants were categorized as needing immunization within 1 to 2 years ( values of 0 . 1–1 . 0 IU/ml ) , and 59 ( 40 . 5% ) individuals had levels greater than 1 IU/ml , for which immunization is recommended after 2 or more years ., When the anti-TT response of our participants in need of immediate boost were analyzed with respect to their S . mansoni infection status , the median antibody levels of the Sm+ group were significantly lower compared to the controls at 6 weeks ( p < 0 . 002 ) after vaccination and relatively lower 8 months after receiving TT immunization ( p = 0 . 07 ) ( Fig 3A ) ., For those categorized as needing a booster dose in 1–2 or more than 2 years , median antibody levels overlapped and did not differ between the Sm+ group and the control group ( Fig 3B and 3C ) ., As long term protection against tetanus is defined as having antibody concentrations above 1 IU/ml , we determined what percentage of participants produced this antibody level 6 weeks after booster vaccination and maintained that level for at least 8 months ., At 6 weeks after vaccination , 97% of uninfected controls versus 84% of the schistosomiasis group had concentrations above 1 IU/ml ., These differences were significant by Fishers exact test ( p < 0 . 01 ) ., At 8 months after vaccination , 82% of uninfected controls maintained antibody levels above 1 IU/ml , compared to only 62% of the schistosomiasis group ., These differences were significant by Fishers exact test ( p < 0 . 03 ) ., Taken together , these data show that as a group , people with schistosomiasis are capable of responding to the tetanus toxoid boost but their antibody responses are not as robust as those of the control group and decline faster ., This could leave them less protected over time and thus in need of more frequent TT immunization ., STH+ individuals’ ( n = 9 ) antibody responses to TT booster vaccination are shown in S4 Fig and again these were kept separate due to the small sample size ., At baseline , all STH+ individuals were categorized as either needing immunization in 1 to 2 years or 2+ years ., All STH+ participants responded to the booster vaccination with only 1 individual’s concentration dropping below 1 IU/ ml at 8 months ( S4 Fig ) ., Robust responsiveness to hepatitis B vaccination has been previously associated with cytokine production in response to in vitro HBsAg stimulation 50 ., In light of this finding , we evaluated IFN-γ , IL-5 , and or IL-10 production in whole blood cultures in response to HbsAg stimulation before and after hepatitis B immunization and also analyzed if cytokine production was associated with antibody responses to the immunizations ., We observed no differences in median levels of IFN-γ or IL-10 ( Table 3 ) produced by the two groups in response to stimulation at any of the time points studied , nor did cytokine levels correlate with antibody responses ., IL-5 was not produced by either group at any time point in response to HbSAg stimulation ( Table 3 ) ., We also examined cytokine responses to TT stimulation in whole blood cultures from individuals from the schistosomiasis and control groups at baseline , 6 weeks , and 8 months after immunization ., We observed no differences in IFN-γ responses to TT antigen between participants with or without schistosomiasis at the time of TT immunization ., Median IFN-γ levels were similar between the two groups as was the percentage of responders; i . e . , proportion of individuals who produced IFN-γ in response to TT stimulation at baseline , 6 weeks and 8 months after vaccination ( Table 3 ) ., However , there were differences in the IL-5 levels produced in response to TT stimulation ., Median IL-5 levels were somewhat higher in the schistosomiasis group as compared to the uninfected controls at 6 weeks after vaccination , and significantly higher 8 months after immunization ( p < 0 . 03 ) ( Table 3 , Fig 4 ) ., Furthermore , when IL-5 responses were examined in terms of individuals maintaining long-term antibody protection ( above 1 IU/ml ) at 8 months , we saw that individuals in the schistosomiasis group that had antibody titers above 1 IU/ml were more likely to produce IL-5 in response to TT stimulation ., Fifteen out of 21 ( 71 . 4% ) of long-term antibody producers mounted an IL-5 response compared to 4 out of 13 ( 30 . 8% ) individuals whose anti-TT antibody titers had fallen below 1 IU/ml ( p < 0 . 03 ) ., IL-10 was not produced in response to TT stimulation at any of the time points in either group ( Table 3 ) ., Among the schistosomiasis group 24 ( 63% ) were characterized as having a light infection ( mean EPG less than 100 ) , 11 ( 29% ) had moderate infection ( mean EPG 100–400 ) and only 3 ( 8% ) heavy infection ( mean EPG above 400 ) 62 ., Previously , it has been reported that S . mansoni infection intensities influenced immune responses to TT vaccination 24 ., However , in this study S . mansoni infection intensity , represented by EPG , failed to correlate with anti-HbS responses after 2 doses of vaccine and after the vaccine series was completed and median antibody levels did not differ significantly at each of the time points ( S5A and S5B Fig ) ., Infection intensities also failed to correlate with anti-TT titers at 6 weeks and 8 months after the booster vaccination was administered , with individuals with light , moderate or heavy infections being fairly evenly spread among those maintaining long-term protection at both 6 weeks and 8 months ( S5C and S5D Fig ) ., CD3+ CD4+ CD25hi T regulatory cells ( Treg ) are elevated in individuals with schistosomiasis 46 , leading us to ask if those in our study had elevated levels of CD3+ CD4+ CD25hi Treg , and if their Treg levels correlated with their antibody responses and for those who were Sm+ if their levels changed after treatment with praziquantel ( PZQ ) ., The S . mansoni group had significantly higher levels of CD3+ CD4+ CD25hi Treg at baseline ( before treatment ) ( p <0 . 005 ) and at 1 week after treatment ( p < 0 . 0001 ) when compared to the uninfected control group , which remained steady throughout the study ., By 7 months after treatment the elevated Treg levels in the S . mansoni group had then declined to levels seen in the uninfected control group ( Fig 5A ) ., For those members in the control and schistosomiasis groups for whom we had CD3+ CD4+ CD25hi T-regulatory cells percentages at each time point we looked to see if those levels changed significantly between time points using the Freidman test followed by Dunn’s multiple comparison test ., We saw no significant changes in Treg levels at each of the time points for the control group ( Fig 5B ) ., For the schistosomiasis group we saw significant differences in Treg levels with Treg levels increasing significantly between baseline and 1 week post praziquantel treatment and decreasing significantly 7 months following treatment ( p < 0 . 0001 ( Fig 5C ) ., We also determined if an individual’s Treg levels correlated with their antibody responses to hepatitis B and TT immunizations ., Treg individual levels did not correlate with individual antibody responses to either of the vaccines at any time points in either the schistosomiasis or control group , or with infection intensities in the schistosomiasis group ( S6 and S7 Figs ) ., This study evaluated the immune responses of people with or without

Introduction, Materials and Methods, Results, Discussion

Schistosomiasis is a disease of major public health importance in sub-Saharan Africa ., Immunoregulation begins early in schistosome infection and is characterized by hyporesponsiveness to parasite and bystander antigens , suggesting that a schistosome infection at the time of immunization could negatively impact the induction of protective vaccine responses ., This study examined whether having a Schistosoma mansoni infection at the time of immunization with hepatitis B and tetanus toxoid ( TT ) vaccines impacts an individual’s ability to achieve and maintain protective antibody levels against hepatitis B surface antigen or TT ., Adults were recruited from Kisumu Polytechnic College in Western Kenya ., At enrollment , participants were screened for schistosomiasis and soil transmitted helminths ( STHs ) and assigned to groups based on helminth status ., The vaccines were then administered and helminth infections treated a week after the first hepatitis B boost ., Over an 8 month period , 3 blood specimens were obtained for the evaluation of humoral and cytokine responses to the vaccine antigens and for immunophenotyping ., 146 individuals were available for final analysis and 26% were S . mansoni positive ( Sm+ ) ., Schistosomiasis did not impede the generation of initial minimum protective antibody levels to either hepatitis B or TT vaccines ., However , median hepatitis B surface antibody levels were significantly lower in the Sm+ group after the first boost and remained lower , but not significantly lower , following praziquantel ( PZQ ) treatment and final boost ., In addition , 8 months following TT boost and 7 months following PZQ treatment , Sm+ individuals were more likely to have anti-TT antibody levels fall below levels considered optimal for long term protection ., IL-5 levels in response to in vitro TT stimulation of whole blood were significantly higher in the Sm+ group at the 8 month time period as well ., Individuals with schistosomiasis at the start the immunizations were capable of responding appropriately to the vaccines as measured by antibody responses ., However , they may be at risk of a more rapid decline in antibody levels over time , suggesting that treating schistosome infections with praziquantel before immunizations could be beneficial ., The timing of the treatment as well as its full impact on the maintenance of antibodies against vaccine antigens remains to be elucidated .

Vaccines are a mainstay for the prevention of morbidity and mortality to numerous infectious diseases ., Concurrent schistosomiasis infection at the time of immunizations has been implicated in the impairment of protective immune responses to vaccines ., We asked if schistosomiasis at the initiation of the hepatitis B vaccine series and tetanus toxoid boost in adults would impact the subsequent immune responses to those vaccines ., We found that Schistosoma mansoni infection did not block the production of antibodies to either tetanus toxoid or hepatitis B vaccine ., However , the kinetics of the antibody responses differed between the schistosomiasis-infected and control groups , with lower median antibody titers to hepatitis B vaccine and a more rapid decline of antibodies against tetanus toxoid in the S . mansoni-positive group ., The data indicate that this could put the individuals who are positive for S . mansoni at the start of primary or secondary immunizations at risk for losing protective antibody levels more quickly than those without schistosomiasis .

schistosoma, invertebrates, schistosoma mansoni, medicine and health sciences, immune physiology, enzyme-linked immunoassays, helminths, immunology, tropical diseases, parasitic diseases, animals, liver diseases, vaccines, preventive medicine, infectious hepatitis, hepatitis, gastroenterology and hepatology, neglected tropical diseases, antibodies, vaccination and immunization, immunologic techniques, antibody response, research and analysis methods, public and occupational health, immune system proteins, infectious diseases, proteins, immunoassays, immune response, biochemistry, helminth infections, schistosomiasis, hepatitis b, physiology, biology and life sciences, viral diseases, organisms

journal.pcbi.1002561

Computing with Neural Synchrony

Neuronal synchronization is ubiquitous in the nervous system 1 , 2 ., In the retina , neighboring cells are often synchronized at a fine timescale 3 , 4 , and relative spike timing carries information about visual stimuli 5 ., Visual and somatosensory stimulation also elicits synchronized activity in the thalamus 6–8 , which impacts target cortical neurons 9–12 ., In olfaction , fine odor discrimination relies on transient synchronization between specific neurons 13 ., In the auditory system , phase locking in brainstem neurons 14 produces fine stimulus-driven correlations in spike timing which are determinant for sound localization 15 ., At cellular level , modeling and experimental studies show that correlated inputs are more likely to make neurons fire 16–19 , and synaptic plasticity mechanisms favor correlated synaptic inputs 20 , 21 , so that developed neural circuits should be very sensitive to correlations ., These findings suggest that neural synchronization is functionally important in early sensory pathways , but it is not clear what it implies in terms of computation ., In many theoretical studies of spiking neural networks , spike timing and neural heterogeneity are treated as noise to be averaged out in the activity of “neural masses” 22 ., One theory , reservoir computing , assigns a computational role to neural heterogeneity , that of representing sensory stimuli in a high-dimensional space where decoding is easier 23 , but it does not assign a specific role to spike timing or synchrony ., Thus , although many authors have advocated the idea that the brain may use precise spike timing to process sensory information 24 , 25 , there are few general theories of spike-based computation ., One such theory postulates that the rank order of spikes carries information 26 ., This is supported by experimental evidence in the retina 5 , but physiologically decoding this information is not entirely straightforward , as it would involve rather specific circuits of inhibition and excitation ., In addition , although it seems to be a metabolically efficient way of processing information , the advantages in terms of computational power are not obvious ., On the other hand , synchrony can be easily decoded by neurons , by means of coincidence detection 27 , and is compatible with Hebbian learning theories , in which correlated inputs tend to be strengthened 20 ., In this article , I focus on synchrony induced by the stimulus ( rather than by coupling between neurons 28–31 ) and I address the two following questions: what does synchrony mean ?, how and what can neurons compute with synchrony ?, It appears that neural heterogeneity , which is considerable in the nervous system 32 , is the key ingredient that makes synchrony computationally interesting , because synchrony then reveals sensory invariants , which play a central role in psychological theories of perception ., For synchrony to be computationally useful , it must be stimulus-dependent ., To illustrate this idea , let us consider neurons which spike after being hyperpolarized ( “rebound spiking” ) , because of the presence of voltage-activated conductances ( Fig . 1; simple neuron models are used in this and all other figures; see Methods for details ) ., Neurons with rebound spiking have been found for example in the superior paraolivary nucleus of the auditory brainstem , a structure involved in encoding the temporal structure of sounds 33; and in the pyloric network of lobsters , involved in the generation of rhythmic motor patterns 34 ., Fig . 1 shows a minimal neuron model with this property ( but it is only meant as an illustration ) ., The model includes a slow outward current , modeling K+ channels , which activates at low voltages ( half-activation voltage −70 mV ) ., This current prevents the neuron from spontaneously spiking ., When the neuron is inhibited for a few hundred ms ( Fig . 1A , top ) , the K+ channels slowly close ( the conductance decreases , Fig . 1A , bottom ) ., When inhibition is released , the negative K+ current is smaller than at rest , which makes the neuron spike ., The latency of the rebound spike depends on the value of the K+ conductance when inhibition is released , and therefore on the duration of inhibition: if the neuron is inhibited for a shorter duration , K+ channels are still partially open when inhibition is released and the neuron spikes later ., If inhibition is very short , the neuron may not spike ., Thus , the timing of the rebound spike is negatively correlated with the duration of inhibition ., Fig . 1B shows this relationship for two different model neurons A and B , which have the same rebound spiking property but quantitatively different parameter values ( spike threshold and time constant of K+ channels ) ., The receptive field of a neuron can be defined as the set of stimuli which elicit a response in the neuron: in this example , stimuli are inhibitory pulses with duration varying between 0 and 1000 ms , and the receptive fields of neurons A and B are inhibitory pulses lasting more than 200 ms . Therefore , the individual receptive fields of the neurons convey little information about duration ., I now define the synchrony receptive field ( SRF ) of two neurons as the set of stimuli which elicit synchronous firing in these two neurons ., For neurons A and B in Fig . 1B , the SRF is found at the intersection of the duration-latency curves: the two neurons fire in synchrony when the stimulus lasts about 500 ms . At this point , I make three remarks ., First , the SRF reveals information about the stimulus that may not be available from individual receptive fields ( here , both neurons fire one spike to all stimuli lasting more than 200 ms ) ., Second , this additional information can only be available when neurons have heterogeneous properties ( otherwise , the SRF is the set of all stimuli ) ., Third , the SRF is specific of a pair ( possibly group ) of neurons: the duration-latency curve of neuron A will generally intersect that of another neuron C at a different point , or may not intersect it at all ( and the SRF is empty ) ., Therefore , in a heterogeneous population of neurons , any given stimulus will trigger a specific synchrony pattern ., How can this synchrony pattern be decoded ?, Consider a postsynaptic neuron receiving excitatory inputs from neurons A and B ( Fig . 1C ) ., The neuron also receives inputs from other sources , which are modeled as background noise ., If this neuron is sensitive to coincidences , then it will fire more when the two inputs are synchronous , that is , when the stimulus is in the SRF of A and B . As a result , the firing rate of this neuron will be tuned to the duration of the stimulus , although its inputs are not ( Fig . 1D ) ., The model used in Fig . 1D is a simple integrate-and-fire neuron with background noise ( time constant τ\u200a=\u200a5 ms ) ., As shown in 19 ( elaborating on ideas proposed by Abeles 16 ) , the key ingredient for the neuron to be sensitive to coincidences is that the average background input is subthreshold ., In this regime , the neuron is said to be “fluctuation-driven”: it fires to large fluctuations above the mean potential ., This property can be understood in terms of signal detection theory 35 ., In vivo intracellular recordings show that in many areas , the membrane potential distribution p, ( v ) peaks well below threshold , indicating that neurons are indeed fluctuation-driven ( e . g . in auditory cortex 36 , visual cortex 37 , barrel cortex 38 , frontal cortex 39 ) ., This distribution is represented in Fig . 1E ( “background” ) , which we consider as noise with standard deviation σ ., When coincident spikes depolarize the neuron by an amount Δv ( =\u200anw for n coincident postsynaptic potentials ( PSPs ) of size, w ) , this probability distribution is shifted by Δv ( Fig . 1E , “signal” ) ., The neuron spikes when the membrane potential exceeds the spike threshold θ , which implements the decision threshold to detect these coincidences over the background ., The neuron will respond to coincidences ( hits ) but also to background activity ( false alarms ) , with some probability called the “hit rate” ( HR ) and “false alarm rate” ( FR ) ., Both rates decrease when the threshold increases ., For a given value σ of the noise , HR and FR are linked by a curve named the receiver-operating characteristic ( ROC ) , obtained by varying the threshold ., ROC curves are shown in Fig . 1F for a noisy integrate-and-fire neuron with exponentially decaying PSPs , with three noise levels ( black curves ) ., The rates are calculated as the probability of firing within one integration time constant τ when the neuron receives a PSP of size Δv ( HR ) and when it does not ( FR ) ., That is , the FR is the product Fτ , where F is the spontaneous firing rate ., Each ROC curve is calculated ( with numerical simulations ) by varying the spike threshold while keeping the same noise level ., Higher thresholds correspond to lower rates ., When the noise is very high , this ROC curve is a diagonal ( dashed ) , meaning that coincidences cannot be distinguished from background ., As the noise decreases , the ROC curve shifts toward the upper left corner , meaning that spikes indicate coincidences more reliably ., In signal detection theory , this relationship between hit rate and false alarm rate is quantified by the sensitivity index d′ , which , for normal distributions , is the spread between the distributions in units of the noise standard deviation: ., Red curves in Fig . 1F show the theoretical ROC curves for the noise values used in the simulations ., Thus , d′ quantifies the ability to detect coincidences while the value of the spike threshold corresponds to a particular trade-off between hit rate and false alarm rate ., For example , in the case of two coincident spikes , one simple choice is θ\u200a=\u200a2w ( relative to the mean membrane potential ) , which ensures a HR of 50% when the two input spikes are synchronous , and a lower FR for background activity ( Fig . 1F , horizontal dashed line ) ., More generally , the ratio between HR and FR increases when the FR decreases: this implies that , to detect coincidences , the false alarm rate should be set to a low level ., For an integrate-and-fire neuron with spontaneous firing rate F and integration time constant τ , we have defined the FR as F ., τ ., Some experimental evidence indicates that this quantity is indeed low in vivo: the membrane time constant is short in vivo ( e . g . around 5 ms in the frontal cortex 39 ) , because of the large total synaptic conductance 40; average firing rates are low , possibly smaller than 1 Hz 41 ., Although the latter point is controversial , the product F ., τ remains small even with larger estimates of F . In addition , we note that the temporal window of integration is in fact shorter than the membrane time constant , because of spike threshold adaptation 42 , 43 , and because of coordinated inhibition 44 ., This ensures that the ratio HR/FR is high , even for small d′ ( small depolarization ) ., Thus , neurons can detect coincidences above background noise , but an important question is the temporal resolution of coincidence detection ., We can use signal detection theory again to address this question ., Consider two input spikes delayed by a time δ , each one producing an exponential PSP of size w and decay time τ ( Fig . 1G ) ., When the spikes are synchronous ( Fig . 1G , left ) , the membrane potential at peak time is 2w , plus the background noise ., When they are delayed ( Fig . 1G , right ) , the peak membrane potential is , plus the noise ., To detect between these two possibilities , we need to distinguish between two random variables with means differing by and standard deviation σ ( Fig . 1H ) ., This corresponds to a sensitivity indexand for short delays ( ) : ., This can be described as the product of the signal-to-noise ratio ( w/σ ) with the delay in units of the time constant ., We can now define the temporal resolution of coincidence detection using the concept of “just noticeable difference” ( JND ) , defined as the delay for which spikes can be correctly distinguished from synchronous spikes with 75% probability ( assuming 50% correct answers for ) ., This corresponds to a d′ of 1 . 35 35 , which gives for short delays:Thus , the temporal resolution of coincidence detection is proportional to the integration time constant , and inversely proportional to the signal-to-noise ratio ., Note that the approximation corresponds here to , i . e . , low noise ., The precise expression using the original formula for d′ is:This expression is only defined with relatively low noise , when : this is because above this value , it is not possible to correctly distinguish between synchronous and asynchronous spikes ( ) with 75% probability ., Let us come back to the specific example of duration selectivity I have presented above ., The postsynaptic neuron receives input spikes from neuron A and neuron B , at latencies LA ( D ) and LB ( D ) , where D is the duration of the stimulus ., The latency curves intersect at some duration D* ( 500 ms in Fig . 1B ) ., The timing difference between the two spikes is ., We approximate it near the intersection point as , and we obtain this approximate expression of the JND in duration:The term quantifies how different the latency curves are near the intersection point ., This formula indicates that the detection of duration is more accurate when the properties of the presynaptic neurons are heterogeneous ., We can now apply these principles to decode synchrony patterns at the population level ., Consider a population of neurons with rebound spiking properties but heterogeneous parameters ., For example , in Fig . 2A , the membrane time constant varies randomly across neurons between 10 and 50 ms , and the K+ channel time constant varies between 300 and 500 ms ( see Text S1 for a justification of this choice of parameter values ) ., For a given stimulus , for example an inhibitory pulse with duration 300 ms , we can look at the synchrony pattern in the neural population ., In Fig . 1E ( top left ) , neurons represented with the same color produce a rebound spike at the same time ( with a 2 ms precision ) , that is , the stimulus is in the SRF of neurons with the same color ., Thus , the neuron population can be divided in groups of synchronous neurons ( possibly containing just one neuron ) ., I call this partition of the neural population the synchrony partition ( mathematically , it is the neural partition defined by synchrony , which is an equivalence relation ) ., This definition mirrors the definition of the SRF: the SRF describes the set of stimuli for which a given group of neurons are synchronous , the synchrony partition describes the groups of neurons that are synchronous for a given stimulus ., Fig . 2A shows the synchrony partition in a population of 25 heterogeneous neurons for three stimuli: inhibitory pulses of 300 ms , 400 ms and 500 ms . Each stimulus produces a different synchrony partition: for example , the three neurons colored in green for the 300 ms stimulus are not synchronous for the 400 ms stimulus ., Decoding synchrony patterns is now straightforward ( Fig . 2B ) ., For each synchrony partition ( each stimulus ) , we assign a population of postsynaptic neurons , one neuron for each group in the partition ( colored neurons in Fig . 2B ) ., Presynaptic neurons in the same group ( same color ) make excitatory synapses onto the same postsynaptic neuron ., In this figure , the peak size of PSPs was set as the difference between threshold and mean potential divided by the number of neurons in the presynaptic group: this choice means that the hit rate should be 50% ( only approximately , since input synchrony is not perfect ) ., Therefore , the postsynaptic neural assembly maximally fires for a specific synchrony partition , that is , for a specific stimulus ( Fig . 2C ) ., In this way , synchrony partitions are mapped to patterns of postsynaptic activity , and SRFs are mapped to standard receptive fields ., We note in Fig . 2C a few deviations from the ideal scenario described above ., First , the maximum firing probability is generally lower than 0 . 5 ., This is because a synchronous group was defined as a group of neurons that fire within 2 ms of each other , rather than at the exact same time ., With more encoding neurons , groups could be defined with a better precision ( i . e . , finer synchrony partitions ) ., Second , the duration selectivity curves are non-symmetrical , with more spikes produced at longer durations ., This is because there is more heterogeneity in spike latency at short durations ( where latency curves diverge , see Fig . 1B ) than at long durations ( where latency curves are constant ) ., Making the integration time constant of coincidence detectors shorter would reduce this phenomenon ., As a consequence of these two facts , selectivity curves do not peak exactly at the expected duration ., The ideal scenario corresponds to the limit case where stimuli are encoded by many neurons ( allowing fine synchrony partitions ) and synchrony patterns are decoded with a fine resolution ( short time constant of coincidence detectors ) ., Decoding synchrony patterns requires that neurons are sensitive to coincidences ( in the sense that they fire more when their inputs are coincident ) , but it does not rely on specific neural properties , as is shown in Fig . 3 ., Varying the amount of internal noise quantitatively changes the neuron sensitivity to coincidences ( the sensitivity index d′ in the signal detection theory perspective ) but it does not change the qualitative properties ( Fig . 3A ) ., In Fig . 3B , inputs to the neurons were modeled as excitatory synaptic conductances ( exponentially decaying with time constant τe\u200a=\u200a2 ms ) ., The main difference is that the size of PSPs now depends on the driving force ( synaptic reversal potential minus membrane potential ) ., However , as argued in 19 , for an excitatory synapse , the driving force is restricted to a rather small range below spike threshold ( 50–80 mV ) , so that it has little impact on PSP size and on coincidence detection properties ., In Fig . 3C , the coincidence detector neurons are modeled in the same way as the presynaptic neurons , with rebound spiking ( with time constants τ\u200a=\u200a10 ms and τKLT\u200a=\u200a400 ms , see the Methods for details ) ., That is , neurons of the same type encode the stimuli and decode the synchrony patterns ., The results are qualitatively unchanged ., I have shown an explicit construction of the decoding circuit , but how can this circuit spontaneously emerge ?, As explained above , a simple condition for a neuron to be sensitive to coincidences is to ensure that its firing rate is low ., This can be implemented by a homeostatic principle ., Two physiologically plausible mechanisms are intrinsic plasticity , where excitability ( e . g . spike threshold or membrane resistance ) changes with activity 45 , and synaptic scaling , where synaptic weights change with pre- and/or post-synaptic activity 46 ., In the context of signal detection theory ( Fig . 1E–H ) , homeostasis can be seen as the process of setting the decision threshold so as to maintain a low false alarm rate ., I consider a simple synaptic scaling mechanism in which synaptic weights continuously increase , independently of pre- and post-synaptic activity , and each postsynaptic spike reduces all synaptic weights:This multiplicative form corresponds to experimental observations 47 and it also has theoretical advantages:, 1 ) it is equivalent to a change in spike threshold ,, 2 ) it leaves the relative strengths of the synapses unchanged and, 3 ) it keeps the weights positive , without imposing a hard boundary ., Weights are stable when ( where F is the postsynaptic firing rate ) , that is , when ., Thus this mechanism maintains a target firing rate F . Homeostasis acts on the decision threshold but is not synapse-specific ( that is , it does not improve the sensitivity index d′ ) ., In the circuit shown in Fig . 2 , the postsynaptic neuron fires when the presynaptic neurons belong to the same ( stimulus-specific ) synchronous group ., To develop such circuits requires a synaptic plasticity mechanism that selectively strengthens synapses that are co-activated with the postsynaptic neuron , in a short temporal window corresponding to the precision of the synchrony partition ., This is consistent with the properties of long-term potentiation in spike-timing-dependent plasticity ( STDP ) seen at excitatory synapses onto excitatory neurons 48 , and theoretical studies have shown that STDP favors correlated inputs 20 , 21 , 49 ., In addition to homeostasis , I consider an STDP rule in which the synaptic weight modification depends on the difference in timing tpost-tpre of a pre- and post-synaptic spike ( Fig . 4A ) : The synaptic modifications induced by all pairs of pre and post spikes are added , but in this context where firing rates are low ( around 1 Hz ) , the precise way in which pairs interact does not make a difference ., The time constant is set equal to the membrane time constant τ ., I also choose a small value for aLTP , so that the average firing rate is mainly determined by the homeostatic mechanism while the relative strengths of synapses are determined by the correlations between the synaptic inputs and the neuron output ., It is not necessary to impose a boundary on the synaptic weights , because stability is ensured by the homeostatic mechanism ., In the same way , long term depression ( LTD ) is unnecessary here , and it is ignored for simplicity ., I consider a group of presynaptic neurons ( 100 were simulated ) and postsynaptic neurons as in Fig . 2 , connected by random synapses , with an average of 5 synapses per postsynaptic neuron ( Fig . 4B ) ., The synaptic weights are initially random between 0 and 1 ( 1 is the spike threshold ) , and they evolve through homeostasis and STDP while 5000 stimuli with random duration are sequentially presented ., Fig . 4C shows the selectivity curves of 5 postsynaptic neurons , before ( top ) and after learning ( bottom ) , as in Fig . 2C ., Initially , neurons tend have high-pass properties , that is , they fire when the stimulus is longer than a given duration ., This mirrors the properties of the inputs ( Fig . 1B ) ., In one case ( green curve ) , the neuron almost never fired to any stimulus ., After learning , most neurons have a peaked selectivity curve , with a preferred duration ., Fig . 4D shows the evolution of synaptic weights during learning for the postsynaptic neuron corresponding to the blue curves in Fig . 4C ., It appears that most synaptic weights decay , except two of them which stabilize at 0 . 5 ( half the distance to spike threshold ) and one weaker synapse ., The properties of these synapses are shown in Fig . 4E ., Each curve represents the spike latency of the presynaptic neurons for the neuron considered in Fig . 4D ( as in Fig . 1B ) , and are the two strongest synapses are displayed in red ., It appears that these two curves intersect at a duration of about 430 ms , which is the best duration of the neuron shown in blue in Fig . 4C ., This illustrates the idea that the postsynaptic neuron fires when the stimulus is in the synchrony receptive field of its presynaptic neurons ., Fig . 4F shows that the learning mechanism selects synapses in the same way as I described in Fig . 2 , that is , it selects synapses that are synchronously active for a specific stimulus duration ., Each color corresponds to a postsynaptic neuron ( same color code as in Fig . 4C ) and each dot represents the weight of one synapse vs . the spike latency of the corresponding presynaptic neuron , at the best duration of the postsynaptic neuron ., For example , for the green neuron , the two strongest synapses are synchronously active ( same spike latency ) at the best duration ( about 420 ms , Fig . 4C ) , while the other synapses are activated at diverse latencies ., Similar observations can be made for the two other neurons ., An interesting point is that the blue and green neurons have the same best durations ( about 420–430 ms , Fig . 4C ) but respond at different latencies ( about 25 ms and 55 ms; strongest synapses in Fig . 4F ) ., This corresponds to two different groups of the synchrony partition in Fig . 2 ( neurons shown with two different colors in the same column ) ., Thus , the proposed decoding circuit ( Fig ., 2 ) can emerge in an unsupervised way , through a combination of homeostasis and STDP ., I introduced the concepts of synchrony receptive fields and synchrony partition with an elementary example , duration selectivity , where stimuli are one-dimensional ., Real world stimuli , on the other hand , vary along many dimensions , which makes computation much more difficult 50 ., To understand synchrony patterns in this more general setting , I describe neuron responses in the following simplified way ( Fig . 5A , top ) : a stimulus S is transformed through a linear or non-linear filter N , which represents the ( standard ) receptive field of the neuron , then the filtered stimulus N ( S ) is mapped to a spike train through a non-linear spiking transformation ( for example , N ( S ) is the input to a spiking neuron model ) ., Note that although this description appears to be feedforward , the computation of the filter N may or may not rely on a feedforward circuit ., Assuming that two neurons A and B fire in synchrony when they receive the same dynamic input NA ( S ) and NB ( S ) , the SRF of A and B is the set of stimuli S such that NA ( S ) =\u200aNB ( S ) ., In mathematical terms , this is a manifold of stimulus space; if the neural filters are linear , it is a linear subspace of stimuli ., For example , in two dimensions , the SRF is a line ( Fig . 5B , left ) ., In contrast , a neuron fires when the filtered stimulus exceeds some threshold , N ( S ) >θ , that is , in two dimensions , when the stimulus is on one side of a line ( Fig . 5B , right ) ., In higher dimension , a neuron fires when the stimulus is on one side of a hyperplane , while two neurons fire in synchrony when the stimulus is close to a hyperplane ( assuming linear filtering ) ., This makes computation with synchrony qualitatively different from rate-based computation , with interesting computational properties , for example SRFs are unchanged by linear scaling of the stimulus ( i . e . , intensity change ) ., I will describe these qualitative differences in more details in the next section , but first I will comment on the hypothesis that two neurons fire in synchrony when they receive the same dynamical input ., First , this should not be true if the neurons have different intrinsic properties ( for example , spike threshold or resistance ) ., Therefore I consider that the heterogeneity in intrinsic properties is implicitly included in the description of the receptive field ( or filter ) N . For example , the membrane resistance can be included as a gain applied to the filter N ( N→R . N ) rather than in the spiking transformation; the membrane time constant can be included as a low-pass filter ., Thus the hypothesis really means that two identical neurons fire in synchrony in response to identical time-varying stimuli ., In vitro experiments have demonstrated that a single cortical neuron responds identically ( at a millisecond timescale ) to repeated time-varying currents 51 ., As for coincidence detection properties , the main condition is that the neuron is in a fluctuation-driven regime , with a subthreshold average input 52 , 53 ., This property is illustrated with neuron models in Fig . 5C–E , which shows the response of a spiking neuron model to a fluctuating input ( Fig . 5C ) over repeated trials , with a subthreshold mean ., The same current is presented in all trials , with an additional independent noise ( red ) ., This noise represents both the intrinsic noise and the difference in inputs between trials ., If the noise level is low enough , spike timing is reproducible at a fine timescale , as shown by the shuffled autocorrelogram ( SAC , see 54 ) ( Fig . 5D , right ) ., A very important property is that the precision of synchrony between trials , as estimated by the width of the SAC ( Fig . 5E; see Methods ) , reflects the similarity of the input signals ( measured by the signal to noise ratio ) , rather than the intrinsic timescale of the signal fluctuations ( seen in the autocorrelation of the signal in Fig . 5C , right ) ., In particular , when noise level goes to 0 , precision converges to 0 ms rather than to the timescale of input fluctuations ( Fig . 5E , left ) ., Therefore , when two identical neurons receive inputs NA ( S ) and NB ( S ) , their degree of synchrony reflects the degree of similarity between NA ( S ) and NB ( S ) ., This is related to the mechanism used by Brody and Hopfield 55 , 56 in a previous model of odor recognition based on spike timing , where constant inputs are added to an external oscillation , but it is more general ., That oscillation-based mechanism works only in a limited input range ( see Fig . 1 in 55 ) because it relies on 1∶1 phase-locking ( one spike per period of the oscillation ) in a mean-driven regime ( average input above threshold ) , which is less robust than the mechanism shown here 53 ( phase locking is also more robust in the fluctuation-driven regime 57 ) ., This reproducibility of spike timing has been demonstrated in vitro 51 and in vivo in early sensory pathways such as the retina 5 and the auditory brainstem 58 , but it could be argued that it is an unrealistic assumption in other neural structures ., However , synchrony-based computation does not critically rely on reproducible spike timing but rather on reproducible synchrony ., Specifically , network activity may introduce inter-trial variability that is shared by neurons , as seen in the auditory cortex 59 , degrading the reproducibility of absolute spike timing but not of relative spike timing ., This is shown in Fig . 6 , where three model neurons receive a stimulus-driven input , identical in all trials , and a shared external input , variable between trials ., In addition , each neuron has a private source of noise ., Neurons A and B receive the same stimulus-driven input , meaning the stimulus is in the SRF of A and B , and neuron C receives a different input ( Fig . 6A ) ., It appears that spike-timing reproducibility is low for all neurons ( Fig . 6B , C ) , but that A and B are reliably synchronized in all trials ( Fig . 6D , cross-correlogram ) ., The peak of the cross-correlogram depends on the signal-to-noise ratio , defined between the shared and private components of the noise ( Fig . 6E , F ) ., This dependence can be quantified in exactly the same way as in Fig . 5E , where the signal is the sum of the stimulus and of the shared noise , while the noise corresponds to the private noise ., Therefore , the mechanism used here does not critically rely on reproducible spike timing , but rather on reproducible stimulus-dependent synchrony ., In this framework , a random stimulus cannot produce tightly synchronous responses in neurons with different receptive fields ., Therefore , synchrony must reflect some non-randomness or “structure” in the stimulus ., Fig . 7 illustrates the relationship between synchrony and structure with a few sensory examples ., A classical example is binaural hearing ( Fig . 7A ) ., Leaving sound diffraction aside for the moment ( see last section of the Results ) , the sound S ( t ) produced by a source on the left of the animal will arrive first at the left ear , then at the right ear , with propagation delays dL and dR , respectively ., Therefore the two monaural signals are SL ( t ) =\u200aS ( t−dL ) and SR ( t ) =\u200aS ( t−dR ) , respectively ., The interaural time difference

Introduction, Results, Discussion, Methods

Neurons communicate primarily with spikes , but most theories of neural computation are based on firing rates ., Yet , many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing ., Among potential spike-based codes , synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations ., However , it is unclear what role synchrony may play in neural computation , and what functional advantage it may provide ., With a theoretical approach , I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties ., In this context , the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field , the set of stimuli which induce synchronous responses in a group of neurons ., In a heterogeneous neural population , it appears that synchrony patterns represent structure or sensory invariants in stimuli , which can then be detected by postsynaptic neurons ., The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity ., Using examples in different sensory modalities , I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli , for example to identify a fluctuating odor in the presence of distractors ., This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance , and suggests new types of neural network models for sensory processing with appealing computational properties .

How does the brain compute ?, Traditional theories of neural computation describe the operating function of neurons in terms of average firing rates , with the timing of spikes bearing little information ., However , numerous studies have shown that spike timing can convey information and that neurons are highly sensitive to synchrony in their inputs ., Here I propose a simple spike-based computational framework , based on the idea that stimulus-induced synchrony can be used to extract sensory invariants ( for example , the location of a sound source ) , which is a difficult task for classical neural networks ., It relies on the simple remark that a series of repeated coincidences is in itself an invariant ., Many aspects of perception rely on extracting invariant features , such as the spatial location of a time-varying sound , the identity of an odor with fluctuating intensity , the pitch of a musical note ., I demonstrate that simple synchrony-based neuron models can extract these useful features , by using spiking models in several sensory modalities .

computational neuroscience, biology, sensory systems, neuroscience

journal.pntd.0003098

Non-Participation during Azithromycin Mass Treatment for Trachoma in The Gambia: Heterogeneity and Risk Factors

Trachoma is a leading cause of preventable blindness in endemic areas 1 ., Control is through the SAFE strategy 2 , of which a key component is mass drug administration ( MDA ) with the antibiotic azithromycin ., Entire communities are targeted during MDA in order to reach both pre-school and school aged children who form the reservoir of infection for Chlamydia trachomatis , the causative bacterial agent for trachoma 3 ., There is renewed commitment from the World Health Organization ( WHO ) , donors of funding for disease control and research and also pharmaceutical companies to support efforts to eliminate Neglected Tropical Diseases ( NTDs ) , including trachoma , by 2020 4–6 ., The success of MDA for NTDs is thought to depend heavily on adequate population coverage in affected areas and participation amongst those offered treatment 7 , 8 ., With increasing provision of MDA for trachoma , prevalence is expected to fall so that endemic areas will , over time , become low prevalence settings on a trajectory towards the endgame of elimination 4 ., In such settings , MDA participation amongst those at highest risk of infection is important ., If spatial clusters , or hotspots , of non-participation occur during MDA and correlate with hotspots of infection , it is possible that reservoirs of infection could remain to facilitate continued transmission 9 ., This would in turn increase the time needed to reach elimination goals ., Identification of factors associated with persistent non-participation in low prevalence settings could therefore provide important clues about how to minimise non-participation ., Determining whether infected individuals are amongst non-participators in previous annual MDAs may also provide information regarding the importance of non-participation in low prevalence areas and the potential need for resources to improve participation ., C . trachomatis infection , follicular trachoma ( TF ) and non-participation with azithromycin MDA have all been found to cluster within communities and also within households 10–16 ., Limited data on non-participation in trachoma control suggest that non-participation is associated mainly with household level decision-making factors , related to knowledge and awareness of trachoma control and also mode of delivery ( for example , perception of community drug distributors ) ., A case-control study in Tanzania found household level risk factors such as guardians of children reporting better health in themselves , increased burden due to poor family health , more children per household and younger guardians 3 ., At community level , enhanced effort to increase coverage during implementation of MDA was successful in achieving higher participation rates ., Studies in Nigeria and South Sudan identified prior household head knowledge of trachoma control and prior notification of MDA as factors associated with better participation but no association with age or gender 17 , 18 ., In a cluster randomised trial ( CRT ) in Ethiopia , women and younger children were more likely to be non-participators 15 ., For CRTs evaluating the impact of MDA intervention , non-participation can be problematic as it can reduce power to detect intention-to-treat effects 19 and lead to bias in results if there is systematic or heterogeneous non-participation due to reasons also associated with the outcome 20 , 21 ., In the Partnership for Rapid Elimination of Trachoma ( PRET ) CRT in The Gambia 22 , 23 which represents a hypo-endemic setting ( prevalence of TF of 10–20%7 , 24 ) , MDA took place over a three year period to evaluate the effectiveness of different frequency and coverage MDA delivery strategies on C . trachomatis infection and TF in children aged 0–5 years ., The aims of this study are to quantify non-participation amongst children aged 1–9 years during PRET , to identify factors associated with non-participation of different types at child , household and community level , to investigate the presence of heterogeneity of non-participation at household and , or community level and determine if any observed household or community heterogeneity is spatially clustered ., Approval was obtained from the London School of Hygiene & Tropical Medicine Ethics Committee , and The Gambia Government/Medical Research Council Unit , The Gambia Joint Ethics Committee ., Written informed consent was obtained from a parent or guardian prior to examination for all children ., In PRET , 48 communities ( enumerations areas , or EAs ) were randomised in a 2×2 factorial design to MDA delivery strategies 11 , 22 , 23 ., A frequency strategy allocation resulted in either three annual MDAs of all community members in 24 communities or MDA at baseline only in the remaining 24 communities ., A coverage strategy allocation ( 24 communities per arm ) was either standard ( one day visit to each community by the treatment team of National Eye Health Program ( NEHP ) in The Gambia ) or enhanced ( two visits to each community to achieve higher coverage ) ., At the end of the trial , the overall prevalence of TF was around 3% and of C . trachomatis , less than 1% ., All community members in treated EAs were eligible to receive azithromycin , with the exception of pregnant women and children under six months old who were offered tetracycline ointment if needed ., The study took place in two adjacent districts on the northern Bank of the River Gambia and two adjacent districts on the southern Bank ( Figure, 1 ) identified for azithromycin MDA ., Twelve EAs per district were randomly selected so that only one EA within settlements of more than one EA was chosen ., A restricted randomisation of EAs within districts to trial arms was performed by the trial statistician , such that all EA within larger settlements of multiple EA received the same allocation to avoid contamination ., Every six months , between baseline and 36 months inclusive , a complete census was taken ., Following this , a random sample of children aged 0–5 years was taken from each community in order to measure trachoma outcomes ( the primary outcomes of PRET; presence of TF and C . trachomatis infection ) ., Full details of survey methods , sampling strategies and measurement of trachoma outcomes are published elsewhere 11 , 22 ., MDA took place within approximately one month of the examination rounds ., Treatment receipt for each individual was recorded against the census ., A central treatment station was set up in each community during MDAs ., Adults aged 14 years or above received 1 g of azithromycin and height was used as a surrogate for weight for childrens dosing on the basis of 20 mg/kg 25 ., Treatment was administered and directly observed by NEHP treatment teams and the number of tablets or ml of suspension recorded within pre-printed fields included in census forms ., NEHP staff attended the initial training workshop for the PRET trial ., Prior to each MDA , treatment team leaders received training about recording treatment status on census forms from the trial coordinator and about dosing from NEHP ., Team leaders trained their team ., Data review and feedback took place throughout MDAs ., Communities were sensitised to MDA by the trial field team before fieldwork started ., During the census prior to treatment , the study was again explained to households , and the expected dates for examination and treatment teams visits were explained ., Supervisory field visits were conducted by the NEHP to ensure appropriate distribution ., Treatment team members were given per diems to cover food and accommodation for days spent in the field , as a single payment at the end of the fieldwork based on the expected number of days needed ., For each MDA , treatment receipt and eligibility were categorised according to one of the following categories: Two binary outcomes were analysed for each MDA;, 1 ) PNT versus treated and, 2 ) EBA versus treated ., EA level variables included coverage allocation , North or South river bank and district , EA type ( single settlement , multi-settlement , or segment of a settlement ) and population size ( small: <600 , medium: 600–800 , large: >800 individuals ) ., For households , variables included size ( small: <11 , medium: 11–16 , large: >16 individuals ) , latrine access , time to primary water source , recall of community health education , years of education of household head , a diagnosis of TF for a child aged 0–5 years in the household during the survey immediately prior to the MDA and treatment status of the household head ., Child level variables were gender , age , participation in a previous ocular examination survey and treatment status at previous MDAs ., Latitude and longitude coordinates were measured for each household ., Data were analysed using Stata , version 13 Special Edition 26 and SaTScan 27 and mapped using Quantum GIS 28 ., All EAs were treated at baseline and 24 EAs at year one and year two ., All available data for children aged 1–9 at the time of each MDA in treated EAs were used to analyse non-participation in this sub-study of the PRET trial ., Children with unknown ( missing ) outcome data were excluded ., The number ( % ) of children treated , PNT or EBA was summarised overall and by characteristics of interest for each MDA , treating each as a cross-sectional survey ( Table S1 ) ., Using random effects logistic regression , multivariable models were developed for both outcomes using the baseline data ., EA level random intercepts were included in all models and household level random intercepts for EBA versus treated comparisons ., PNT children were too few to include a household level random effect ., Factors associated with the outcome by a likelihood ratio test ( LRT ) p-value of <0 . 1 in univariable analyses were included in a step-wise model building approach to obtain a final multivariable model ., Coverage delivery allocation was included in all multivariable models a priori since by design the enhanced allocation was intended to increase participation ., The same multivariable models were fitted to the year one and two MDA data for validation ., Treatment status at previous treatment rounds was added to each of these final models a priori ., Tests for interaction with coverage allocation were pre-specified if an association between coverage allocation and the outcome was found ., Intracluster correlation coefficients ( ICCs ) with corresponding 95% confidence intervals were obtained from final multivariable models ., Considering the study areas north and south of the River Gambia separately , spatial point patterns were investigated using Kulldorfs scan statistic 29 for each MDA round ( baseline , year one and year two ) , in order to test whether PNT and EBA cases were randomly distributed over space compared to treated children and to identify the location of any significant spatial clusters ., Within SatScan software , a circular window is moved systematically throughout the geographic space to identify clusters by centring the window on each household location with a window size of 0% to 50% of the study population , to allow detection of small and large clusters ., Clusters containing more than 50% of the population are ignored ., A LRT test for a Poisson based model was conducted for each location and size of scanning window to test the hypothesis of an increased rate of non-participator type compared with the distribution outside the window ., P-values corresponding to the most likely and secondary clusters are determined using Monte Carlo replications of the dataset ., Spatial clusters of PNT and EBA children were added to maps showing the location of children and their treatment status ., The locations of infected children at year three are shown on the map for the year two MDA for visual inspection ., Treatment status was unknown for 403 ( 3 . 6% ) , 88 ( 1 . 6% ) and 187 ( 3 . 0% ) eligible children at baseline , year one and year two , respectively ., Participation was high overall during each MDA ., The overall prevalence of non-participation at baseline was 6 . 2% ( 604/9777 ) with 1 . 0% ( 99/9777 ) of children being PNT and 5 . 2% ( 505/9777 ) of children EBA ( Table S1 ) ., The distribution of treatment status was similar at year one ., Over the three MDAs , the percentage of EBA children appeared to increase and the percentage of PNT children to decrease ., By year two , overall non-participation increased to 10 . 4% ( paired t-test of EA summary data p<0 . 01 ) due to the increase in EBA children ., Reductions in PNT non-participation were not significant ., Of 1626 households eligible for treatment in 24 annually treated communities , one household ( 0 . 1% ) had PNT children in all three MDAs and 34 ( 2 . 1% ) had EBA children in all three MDAs ., Persistent EBA households were generally larger and within EAs comprised of multiple settlements ., The persistent PNT household was further from water , without latrine access and with a household head with no recall of health education or education ., Univariable analyses of baseline data are presented in Table 1 ., The final multivariable model for being PNT versus treated at baseline included coverage allocation , time to water , household size , household head treatment status and district ( Table 2 ) ., Children residing in a medium or large household compared to small ( p<0 . 001 ) and within 15 minutes of primary water source ( p<0 . 001 ) were less likely to be PNT ., A child was more likely to be PNT if the household head was untreated ( p<0 . 001 ) ., An association with district was also found ( p\u200a=\u200a0 . 002 ) , due to a difference between districts south of the River Gambia ., No effect of coverage allocation was found ( p\u200a=\u200a0 . 842 ) ., A TF diagnosis in the household during the baseline examination round , approximately one month prior to treatment , was associated with lower odds of being PNT in univariable analyses ( Table 1 ) but not after adjustment for other factors in the final model ., The same final model was fitted to the year one and year two data , adding previous treatment status ., For these follow-up MDAs , the fixed term for district was removed due to zero PNT cases north of the river ., Treatment status one year previously was an important predictor of non-participation at both years one and two , with children who were PNT at the previous round being more likely to be PNT again the following year ( baseline treatment status at year one MDA: p\u200a=\u200a0 . 034 , year one treatment status at year two MDA: p\u200a=\u200a0 . 032 , Table 2 ) ., Treatment status at baseline was not associated with being PNT at year two ( p\u200a=\u200a0 . 656 ) ., The final multivariable model for being EBA versus treated at baseline ( Table 3 ) suggested being EBA was more likely for children who were not included in the baseline examination round ( p<0 . 001 ) , aged 3–5 or 1–2 years compared to 6–9 years ( p<0 . 001 ) , whose household head was also EBA compared to treated , who resided in households further from water ( p\u200a=\u200a0 . 018 ) and possibly for those whose household head could not recall community health education ( p\u200a=\u200a0 . 060 ) ., Coverage allocation was not associated with being EBA ( p\u200a=\u200a0 . 166 ) ., Children who were EBA at each previous round were more likely to be EBA at later time points ( Table 3 ) ., Results also suggest that children who were ineligible at both previous treatment rounds were more likely to be EBA at year two ., In the EBA versus treated comparisons ICCs suggested substantially more variation was present between households within EAs , than between EAs ( Table 3 ) ., ICCs from PNT models at EA level were closer to the ICCs estimated at household level for EBA children , possibly because between-household variation could not be determined due to the very low prevalence of PNT non-participation ., GPS coordinates were missing for 11 out of 1626 households , excluding 23 children from spatial analyses ., Spatial clusters of PNT and EBA children were detected at baseline in study areas on each side of the river ( Table 4 ) ., No PNT children were reported in year one or year two in the northern river bank districts ., Spatial clusters of PNT and EBA children reduced in size in each subsequent MDA and by year two , clusters included either single households or a small group of adjacent households ( Figures 2 and 3 ) ., Cases of C . trachomatis infection in annually treated communities at year three ( n\u200a=\u200a14 ) were found within three kilometres of Senegal in all but one child ., In Senegalese districts adjacent to The Gambia , MDA had not yet taken place ., Infections were detected amongst children who were ineligible or treated during the three prior MDAs , apart from one child residing on the north side of the river who was persistently EBA during the MDAs ., Two cases were located in an EA with households within a year two EBA cluster on the south side of the river ( Figures 2 and 3 ) ., In the two EAs with households in this spatial cluster , approximately 15% of 1–9 year olds were EBA during the year two MDA ., In this large study of non-participation during azithromycin MDA from a low prevalence trachoma setting , we demonstrate further evidence of heterogeneity of non-participation in children aged 1–9 years , particularly at household level , in line with studies in higher prevalence settings ., We also observed persistent non-participation over time in annual MDAs , as seen elsewhere in a CRT setting 3 ., Geographical clustering of non-participation represents a new finding and we found two different types of non-participators ., We found circumstantial rather than statistical evidence of an association between infection and non-participation during a previous MDA , however , the overall prevalence of infection and TF in 0–5 year olds at the end of PRET was below a level requiring any SAFE interventions ., Detection of infection in communities close to untreated areas 22 , relatively high EBA rates in those communities during the previous MDA and literature from The Gambia and elsewhere linking travel with re-infection 30 , 31 together , suggest the observed infections could have resulted from exposure to untreated persons ., Travel plans could have been set prior to notification of MDA timing and therefore could have been unrelated to intentional non-participation , although intentional decision making to miss treatment is a possibility ., Household level variables were associated with greater likelihood of being PNT and EBA ., Household head non-participation and their type of non-participation predicted PNT and EBA status in children , implying household decision making with respect to MDA participation behaviour ., The finding that children in households further from their primary water source were more likely to be PNT or EBA is probably indicative of some other unmeasured risk factor , for example , marginalisation within the community due to either household head or community leader choice , or a mixture of the two ., Non-participation during MDA subsequent to participation in a previous MDA has been found to be associated with possible markers of marginalisation in another CRT 32 ., Active trachoma has been found to be associated with lower socio-economic status ( SES ) and isolation of households from the community 33 so access to , or participation in , trachoma control activities could also be affected by these unmeasured factors ., Smaller household size was important for predicting PNT status but not EBA , compared to treated children , which could represent some effect of lower SES ., Recent migration into the community could also mean less access to community decision making and activities ., Participation in a previous TF examination survey could be indicative of increased awareness and acceptance of control activities in annually treated communities , however , a proxy effect cannot be concluded in case of potential bias introduced by households more willing to take part in all control and assessment activities ., Results from the Gambian setting suggest that enhanced efforts to increase coverage of mass treatment programs , by means of an extra treatment team visit to the community do not improve participation , in contrast to the PRET trial conducted in Tanzania 3 ., Studies of MDA participation in Africa for onchocerciasis and lymphatic filariasis , other NTDs for which control is through mass community-wide treatment , have also linked non-participation to household level decision making factors , for example , a perception of low disease risk or lack of family or household support 34–36 ., The Gambia has relatively high childhood immunisation coverage 37 , elimination of trachoma by 2020 is attainable 24 and non-participation was higher in the districts south of the river where the prevalence of TF was consistently lower during PRET 22 ., It is perhaps plausible therefore that a household level decision based on a perceived lack of need for treatment could apply in this low prevalence setting , although we do not have data from each community to assess this ., Reasons for being EBA in this setting could be logistic and independent of participation choices , for example , population movement and travel where children are sent away for weaning which is common practice in The Gambia , or farming related activities ., PNT and EBA comparisons to treated children were performed separately as it was hypothesised that there may be differences in reasons for non-participation that may or may not be related to refusal of treatment or a perceived lack of need for treatment ., The data do suggest some differences between PNT and EBA children but further information is unavailable to determine if and why there was an active decision to refuse treatment ., Due to the very low prevalence of TF and infection in both MDA frequency arms ( annual and baseline only MDA ) throughout the original trial , it is unlikely that the heterogeneous non-participation observed here had an additional negative effect on power to detect differences between arms in intention-to-treat analyses in the PRET trial ., It is also unlikely that heterogeneous non-participation introduced bias in comparative analyses given the low prevalence of TF and infection ., We found a geographical effect on non-participation and on trachoma outcomes 22 ., Infections did occur in one part of the study area with notable EBA non-participation at the previous MDA , however , even if all PNT and EBA children at the year two MDA had been found to have infection and TF , the overall prevalence of each outcome at year three would have been less than 5% and thus still below MDA continuation thresholds for TF ., Therefore , for the Gambian national trachoma control program , efforts and resources to address non-participation are not required ., For national control programs in low and medium prevalence settings , heterogeneous non-participation linked to increased risk of infection could present challenges for elimination efforts ., Links between infection and non-participation in prior MDA rounds could undermine MDA where corresponding prevalence levels for TF meet criteria for continued MDA at the time of impact assessment ., Identification of hotspots of infection and non-participation , along with modifiable risk factors for non-participation could take place during impact assessment following repeated MDA ., The results could then aid control program managers working towards elimination goals in low and medium prevalence settings , by enabling them to target delivery resources for continued MDA and to improve coverage in areas with a greater threat of continued transmission .

Introduction, Methods, Results, Discussion

There is concern that untreated individuals in mass drug administration ( MDA ) programs for neglected tropical diseases can reduce the impact of elimination efforts by maintaining a source of transmission and re-infection ., Treatment receipt was recorded against the community census during three MDAs with azithromycin for trachoma in The Gambia , a hypo-endemic setting ., Predictors of non-participation were investigated in 1–9 year olds using random effects logistic regression of cross-sectional data for each MDA ., Two types of non-participators were identified: present during MDA but not treated ( PNT ) and eligible for treatment but absent during MDA ( EBA ) ., PNT and EBA children were compared to treated children separately ., Multivariable models were developed using baseline data and validated using year one and two data , with a priori adjustment for previous treatment status ., Analyses included approximately 10000 children at baseline and 5000 children subsequently ., There was strong evidence of spatial heterogeneity , and persistent non-participation within households and individuals ., By year two , non-participation increased significantly to 10 . 4% overall from 6 . 2% at baseline , with more , smaller geographical clusters of non-participating households ., Multivariable models suggested household level predictors of non-participation ( increased time to water and household head non-participation for both PNT and EBA; increased household size for PNT status only; non-inclusion in a previous trachoma examination survey and younger age for EBA only ) ., Enhanced coverage efforts did not decrease non-participation ., Few infected children were detected at year three and only one infected child was EBA previously ., Infected children were in communities close to untreated endemic areas with higher rates of EBA non-participation during MDA ., In hypo-endemic settings , with good coverage and no association between non-participation and infection , efforts to improve participation during MDA may not be required ., Further research could investigate spatial hotspots of infection and non-participation in other low and medium prevalence settings before allocating resources to increase participation .

As the target year for Global Elimination of Trachoma ( GET2020 ) approaches , the scale up of mass drug administration ( MDA ) with azithromycin will lead to more endemic areas becoming low prevalence settings ., In such areas , identification of those at highest risk of Chlamydia trachomatis infection and at highest risk of non-participation during MDA could inform control planning , especially if correlation is present ., We investigated non-participation in children aged 1–9 years during three annual MDAs in The Gambia , a low prevalence setting ., We found evidence that non-participation is associated with household membership and decision-making , as seen in medium and high prevalence settings in East Africa ., In addition , we demonstrate geographical heterogeneity ( spatial clustering ) of non-participation , persistent non-participation behaviour over time and different non-participator types ., Between the first and third MDA , non-participation increased significantly overall from 6 . 2% to 10 . 4% , whilst spatial clusters became smaller with non-participation more focused in single households or small groups of households ., There was no evidence of association between infection and non-participation ., In low prevalence settings with no evidence to suggest non-participation as a risk factor for infection , resources to improve participation may not be required ., Spatial hotspot analysis could address this research question in areas with more infection .

medicine and health sciences

journal.pcbi.1005589

Leveraging functional annotations in genetic risk prediction for human complex diseases

Achieving accurate disease risk prediction using genetic information is a major goal in human genetics research and precision medicine ., Accurate prediction models will have great impacts on disease prevention and early treatment strategies 1 ., Advancements in high-throughput genotyping technologies and imputation techniques have greatly accelerated discoveries in genome-wide association studies ( GWAS ) 2 ., Various approaches that utilize genome-wide data in genetic risk prediction have been proposed , including machine-learning models trained on individual-level genotype and phenotype data 3–8 , and polygenic risk scores ( PRS ) estimated using GWAS summary statistics 9 , 10 ., Despite the potential information loss in summary data , PRS-based approaches have been widely adopted in practice since the summary statistics for large-scale association studies are often easily accessible 11 , 12 while individual-level data are more difficult to acquire , deposit , and process ., However , prediction accuracies for most complex diseases remain moderate , which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium ( LD ) 13 ., Explicit modeling and incorporation of external information , e . g . pleiotropy 7 , 8 and LD 10 , has been shown to effectively improve risk prediction accuracy ., Recent advancements in integrative genomic functional annotation , coupled with the rich collection of summary statistics from GWAS , have enabled increase of statistical power in several different settings 14–16 ., To our knowledge , the impact of functional annotations on performance of genetic risk prediction has not been systematically studied ., Here , we introduce AnnoPred ( available at https://github . com/yiminghu/AnnoPred ) , a principled framework that integrates GWAS summary statistics with various types of annotation data to improve risk prediction accuracy ., We compare AnnoPred with state-of-the-art PRS-based approaches and demonstrate its consistent improvement in risk prediction performance using both simulations and real data of multiple complex diseases ., AnnoPred risk prediction framework has three main stages ( Methods ) ., First , we estimate GWAS signal enrichment in 61 different annotation categories , including functional genome predicted by GenoCanyon scores 17 , GenoSkyline tissue-specific functionality scores of 7 tissue types 14 , and 53 baseline annotations for diverse genomic features 18 for each trait analyzed ., Second , we propose an empirical prior of SNP effect size based on annotation assignment and signal enrichment ., In general , SNPs located in annotation categories that are highly enriched for GWAS signals receive a higher effect size prior ., Finally , the empirical prior is adopted in a Bayesian framework in which marginal summary statistics and LD matrix estimated from a reference panel are jointly modeled to infer the posterior effect size of each SNP ., AnnoPred PRS is defined by, PRS=∑j=1MXjEA ( βj|β^ , D^ ), where Xj and βj are the standardized genotype and effect size of the jth SNP , respectively , β^ is the marginal estimate of β , D^ is the sample LD matrix , and EA ( βj|β^ , D^ ) denotes the posterior expectation of effect sizes under an empirical prior based on annotation assignment for all SNPs when adjusting for LD matrix estimated from a reference panel ( Methods ) ., We first performed simulations to demonstrate AnnoPred’s ability to improve risk prediction accuracy ., We compared AnnoPred with four popular PRS approaches ( Methods ) , including PRS based on genome-wide significant SNPs ( PRSsig ) , PRS based on all SNPs in the dataset ( PRSall ) , PRS based on tuned cutoffs for p-values and LD pruning ( PRSP+T ) , and recently proposed LDpred 10 ., Mean correlations between simulated and predicted traits were calculated from 100 replicates under different simulation settings ( Methods ) ., AnnoPred showed the best prediction performance in all settings when the causal SNPs are highly enriched in annotated regions ( Table 1 , S2 Table and S2 Fig ) ., In general , performance of PRSsig , PRSP+T , LDpred , and AnnoPred all improved under a sparser genetic model and higher trait heritability ., PRSall showed comparable performance between sparse and polygenic models but its prediction accuracy was consistently worse than other methods ., Sample size in the training set was also crucial for risk prediction accuracy ., Increasing sample size could lead to continuous improvement in prediction accuracy under different settings ( Fig 1 ) ., To illustrate the improved risk prediction performance in real data , we applied AnnoPred to five human complex diseases—Crohn’s disease ( CD ) , breast cancer ( BC ) , rheumatoid arthritis ( RA ) , type-II diabetes ( T2D ) , and celiac disease ( CEL ) ., We first estimated GWAS signal enrichment in different annotation categories ( Methods ) ., Enrichment pattern varies greatly across diseases ( Fig 2A; S1 Table ) , reflecting the genetic basis of these complex phenotypes ., Functional genome predicted by GenoCanyon was consistently and significantly enriched for all five diseases ., Blood was strongly enriched for three immune diseases , namely CD ( P = 8 . 9×10−12 ) , CEL ( P = 7 . 0×10−15 ) , and RA ( P = 9 . 9×10−6 ) , while gastrointestinal ( GI ) tract was enriched in CD ( P = 2 . 6×10−5 ) and CEL ( P = 1 . 4×10−4 ) , both of which have a known GI component ., For BC , epithelium ( P = 7 . 4×10−4 ) , GI ( P = 5 . 9×10−3 ) , and muscle ( P = 6 . 1×10−3 ) were significantly enriched ., A few studies have shown that breast cancer could arise from epithelial cells 19 , 20 ., The connections between breast cancer and muscle as well as GI tract have also been previously suggested 21 , 22 ., In addition , studies have suggested that GI can be used as diagnostic and treatment target for type-II diabetes , Crohn’s disease , and celiac disease 23–25 ., Furthermore , the connection between immune system and Crohn’s disease , celiac disease and rheumatoid arthritis have been extensively studied in literature 26–28 ., Next , we evaluated the effectiveness of proposed empirical effect size prior in three diseases ( i . e . CD , CEL , and RA ) with well-powered testing cohorts ( N>2 , 000 ) ., Interestingly , despite the highly variable enrichment results in training datasets , integrative effect size prior could effectively identify SNPs with large effect sizes and consistent effect directions in independent validation cohorts ( Fig 2B and 2C ) ., Correlations between the calculated PRS and disease status ( COR ) for different approaches are summarized in Table 2 ., AnnoPred showed consistently improved prediction accuracy compared with all other methods across five diseases ., Notably , PRSsig and PRSall showed suboptimal performance in these datasets , reaffirming the importance of modeling LD and other external information ., A likelihood ratio test was used to test for the difference in the prediction accuracy between models comparing the likelihood of a logistic regression fitting PRS of one method to that of a logistic regression fitting PRS of two methods jointly ( S11 Table ) ., From the test , AnnoPred with 61 annotations performed significantly better than LDpred ( p = 1 . 2E-22 for CD , p = 0 . 045 for BC , p = 4 . 2E-7 for RA , p = 3 . 3E-4 for T2D and p = 1 . 3E-3 for CEL ) ., Reversing the order of test ( that is , comparing the likelihood of model using annotations with model using and not using annotations jointly ) results in non-significant p-values for most tests ( S11 Table ) , which further demonstrates that PRS incorporating functional annotations mostly encompasses the information of PRS without annotations ., To test different methods’ ability to stratify individuals with high risk , we compared the proportion of cases among testing samples with high PRS ., AnnoPred outperformed all other methods in CD , CEL , RA , and T2D ( S1 Fig ) ., Next , we tested AnnoPred’s performance using only the 53 baseline annotations and observed a substantial drop in prediction accuracy for all diseases ( S3 Table ) ., AnnoPred with GenoCanyon and GenoSkyline annotations only ( nine annotation tracks in total ) yields better performance than the 53 baseline annotations ( S10 Table ) ., For CD and T2D , by using these 9 categories AnnoPred even achieved higher accuracy than the model with all 61 annotation tracks added ., These results highlight the importance of annotation quality in genetic risk prediction , and also demonstrate GenoCanyon and GenoSkyline’s ability to accurately identify functionality in the human genome ., Since different diseases have various enrichment patterns , we also run AnnoPred with significantly enriched annotations ( enrichment test p value less than 0 . 05 ) for each disease ( S10 Table ) ., In general , using only the significantly enriched annotations indeed improved the performance in most diseases ., Tissue specificity plays an important role in genetic risk prediction ., Integrating more functional annotations with higher tissue and cell type specificity may further increase risk prediction accuracy , especially when the tissue type that is biologically relevant to the disease is not well characterized by the seven available tissue tracks in our current analyses ., To explore how these factors will affect the AnnoPred model , we performed a few follow-up analyses ., We have recently expanded our GenoSkyline annotations to more than 100 tissue and cell types from the Roadmap Epigenomics Project 29 ., We investigated the performance of AnnoPred after integrating 66 annotation tracks representing a spectrum of adult tissue and cell types ., As shown in S10 Table , incorporating more annotations into the model does not always further improve risk prediction accuracy compared with AnnoPred with fewer annotations in the model ., This may be due to the overlap between functional regions ( e . g . functional annotations for slightly different brain regions ) when incorporating too many annotation tracks into the model , which will cause numerically unstable heritability estimates ., This is because annotation-stratified LD score regression , the method we used to empirically estimate the informative prior for SNPs’ effect sizes , is a multiple linear regression model that regresses SNP-level summary statistics against annotation-stratified LD scores ., When two functional annotation tracks are similar , the corresponding LD scores will also be correlated by definition ., It is well understood that if multi-collinearity ( i . e . correlation among covariates ) in multiple regression leads to numerically unstable estimates for regression coefficients 30 ( the heritability parameters in our case ) ., In order to study the effect of highly associated SNPs ( e . g . SNPs in MHC regions for immune traits ) , we repeated the analysis on CD , RA , BC and T2D after removing the SNPs in MHC region ( chr6: 28 , 477 , 797–33 , 448 , 354 bp ) ., Re-analysis of CEL was unnecessary since the training summary statistics of CEL does not contain any SNP in the MHC region ., After removing SNPs in MHC regions , the prediction accuracies for RA drops dramatically for all methods and AnnoPred remained to be the method with the best performance ( S9 Table ) ., For the rest diseases , results varied little from the original analysis ., Besides COR , we also included AUCs for all the analysis performed ( S2 , S6 , S9 and S10 Tables ) , all of which showed consistent patterns ., Due to distinct allele frequencies and LD structures across populations , risk prediction accuracy usually drops when the training and testing samples are from different populations ., In order to investigate the robustness of AnnoPred against population heterogeneity , we applied AnnoPred to three non-European cohorts for breast cancer and type-II diabetes while training the model using summary statistics from European-based studies ., The CORs and AUCs are summarized in S6 and S7 Tables ., As expected , we observed a drop in prediction accuracy for all methods ., However , AnnoPred still performed the best in all three trans-ethnic validation datasets ., Our work demonstrates that functional annotations can effectively improve performance of genetic risk prediction ., AnnoPred jointly analyzes diverse types of annotation data and GWAS summary statistics to upweight SNPs with a higher likelihood of functionality , which lead to consistently better prediction accuracy for multiple complex diseases ., Our method is not without limitation ., First , despite the consistent improvement compared with existing PRS-based methods , accuracies for most diseases remain moderate ., In order to effectively stratify risk groups for clinical usage , our model remains to be further calibrated using large cohorts with measured environmental and clinical risk factors 1 ., Second , accurate estimation of GWAS signal enrichment and SNP effect sizes requires a large sample size for the training dataset ., This could potentially be improved by new estimators for annotation-stratified heritability 19 ., A few Bayesian models combining GWAS summary statistics with functional annotations have been proposed for the purpose of fine-mapping functional variants 16 , 20 , 21 ., Whether these models could be adapted to benefit risk prediction accuracy remains to be investigated in the future ., Importantly , the rich collection of publicly available integrative annotation data , in conjunction with the increasing accessibility of GWAS summary statistics , makes AnnoPred a customizable and powerful tool ., As GWAS sample size continues to grow , AnnoPred has the potential to achieve even better prediction accuracy and become widely adopted as a summary of genetic contribution in clinical applications of risk prediction ., GenoCanyon is a statistical framework to predict functional regions in the human genome through integrative analysis of ENCODE epigenomic data and multiple conservation metrics 17 ., Later we have further extended the model and developed GenoSkyline , which aimed to predict tissue-specific functionality 14 ., In the AnnoPred model , we incorporated GenoCanyon general functionality scores , GenoSkyline tissue-specific functionality scores for seven tissue types ( brain , gastrointestinal tract , lung , heart , blood , muscle , and epithelium ) , and 53 LDSC baseline annotations that covered a variety of genomic features 18 ( S1 Table ) ., We smoothed GenoCanyon scores by a 10Kb window , a strategy previously shown to improve robustness of functionality prediction 22 ., The smoothed GenoCanyon annotation and raw GenoSkyline annotations of seven tissue types were dichotomized based on a cutoff of 0 . 5 ., The regions with GenoCanyon or GenoSkyline scores greater than the cutoff were interpreted as non-tissue-specific or tissue-specific functional regions in the human genome ., Such dichotomization has been previously shown to be robust against the cutoff choice 14 ., Notably , the AnnoPred framework allows users to specify their own choice of annotations ., We assume throughout the paper that both the phenotype YN×1 and the genotypes XN×M are standardized with mean zero and variance one ., We assume a linear model, YN×1=XN×MβM×1+εN×1, X , β and ε are mutually independent ., We also assume that β is a random effect and effects of different SNPs are independent ., A key idea in the AnnoPred framework is to utilize functional annotation information to accurately estimate SNPs’ effect sizes ., In order to achieve that , we first partition trait heritability by annotations using LD score regression 18 ., Since genotypes are standardized , per-SNP heritability is defined as the variance of βi for the ith SNP , and is used to quantify SNP effect sizes ., More specifically , assume there are K + 1 pre-defined annotation categories , denoted as S0 , S1 , … , SK with S0 representing the entire genome ., Under an additive assumption for heritability in overlapped annotations , we have βi∼N ( 0 , ∑j:i∈Sjτj ) , where τ0 , τ1 , … , τK , quantify the contribution to per-SNP heritability from each annotation category ., Denote the estimated marginal effect size of the ith SNP as β^i=XiTYN , then we have the following approximation, E ( Nβ^i2 ) ≈ ( N−1 ) ∑kτkl ( i , k ) +1, where l ( i , k ) is the annotation-stratified LD score and N denotes the total sample size ., Regression coefficients τk are estimated through weighted least squares ., The estimated heritability of the ith SNP is then Var^ ( βi ) =∑j:i∈Sjτ^j ., Based on per-SNP heritability estimates , we propose two different priors for SNP effect sizes to add flexibility against different genetic architecture ., For the first prior , we assume that SNP effect size follows a spike-and-slab distribution, βi∼p0N ( 0 , σ^i2p0 ) + ( 1−p0 ) δ0, where p0 is the proportion of causal SNPs in the dataset , and δ0 is a Dirac function representing a point mass at zero ., The empirical variance of each SNP , i . e . σ^i2 , is determined by the annotation categories it falls in ., More specifically , we assume σ^i2=c ( ∑j:i∈Sjτ^j ) , where c is a constant calculated from the following equation, ∑iσ^i2=H^2 ., We do not directly use ∑j:i∈Sjτ^j as the empirical variance prior because it is estimated in the context where all SNPs in the 1000 Genomes Project database are included in the model 18 ., Such per-SNP heritability estimates cannot be extrapolated to the risk prediction context where many fewer SNPs are analyzed 23 ., Therefore , we rescale the heritability estimates to better quantify each SNP’s contribution toward chip heritability ., Following 24 , we use a summary statistics-based heritability estimator that approximates the Haseman-Elston estimator:, H^2= ( χ¯2−1 ) Nl−, where χ¯2 and l¯ denote the mean Nβ^i2 and mean non-stratified LD score , respectively ., In the first prior , we assumed the same proportion of causal SNPs but different effect sizes across annotation categories ., We now describe the second prior that assumes different proportions of causal SNPs but the same effect size across annotation categories ., To be specific , we assume the causal effect size to be Var ( βcausal ) = V , the total number of SNPs to be M0 , and the overall proportion of causal SNPs to be p0 ., The total heritability H02 can then be written as H02=p0M0V ., For the ith SNP , use Ti= ( ⋂j:i∈SjSj ) ∩ ( ⋂k:i∉SkSkc ) to denote the collection of SNPs that share the same annotation assignment with the ith SNP , and let MTi=|Ti| , i . e . the number of SNPs in the set ., Then , the total heritability of SNPs in Ti is HTi2=pTiMTiV , with pTi denoting the proportion of causal SNPs in Ti ., Following these notations , we have, βi∼pTiN ( 0 , V ) + ( 1−pTi ) δ0, where V=H0p0N0 and pTi=p0M0HTi2MTiH02 ., We use H^2 to estimate H02 , and the following formula to estimate HTi2 ., Finally , p0 is treated as a tuning parameter for both prior functions in our analysis ., By Bayes’ rule , the posterior distribution of β is:, f ( β|β^ , D^ ) ∝f ( β^|β , D^ ) f ( β ), where D^=1NXTX is the sample correlation matrix and β^=1NXTY is the marginal effect size estimates ., Given β and D^ , β^ follows a multivariate normal distribution asymptotically with the following mean and variance, E ( β^|β , D^ ) =1NE ( XTXβ|β , D^ ) +E ( XTε|β , D^ ) =D^β, Var ( β^|β , D^ ) =Var ( 1NXTε|β , D^ ) =1N ( 1−hg2 ) D^ ., However , D^ is usually non-invertible and has very high dimensions ., We thus study the posterior distribution of a small chunk of β^ instead ., Let β^b be the estimated marginal effect size of SNPs in a region b ( e . g . a LD block ) and the corresponding genotype matrix is Xb and sample correlation matrix is D^b ., Then the conditional mean and variance of β^b are, E ( β^b|βb , D^b ) =1NE ( XbTXβ|βb , D^b ) +E ( XbTε|βb , D^b ) =D^bβb, Var ( β^b|βb , D^b ) =1N2var ( XbTXbβb+XbT ( X−bβ−b+ε ) |βb , D^b ) =1N2var ( XbT ( X−bβ−b+ε ) |βb , D^b ) =1N2XbTvar ( X−bβ−b+ε|βb , D^b ) Xb=1N ( 1−hb2 ) D^b, where hb2=∑i∈bσi2 is the heritability of SNPs in region b , and X−b and β−b denote the genotype matrix and effect sizes of SNPs not in region b ., The conditional distribution of βb is:, f ( βb|β^b , D^b ) ∝N ( D^bβb , 1N ( 1−hb2 ) D^b ) ∏i∈bf ( βi ) ∝{N ( D^bβb , 1N ( 1−hb2 ) D^b ) ∏i∈b p0N ( 0 , σi2p0 ) + ( 1−p0 ) δ0 , underthefirstpriorN ( D^bβb , 1N ( 1−hb2 ) D^b ) ∏i∈b pTiN ( 0 , V ) + ( 1−pTi ) δ0 , underthesecondprior, Although it is difficult to derive E ( βb|β^b , D^b ) from the joint conditional distribution of βb , each element of βb follows a mixed normal distribution conditioning on β^b , D^b , and all other elements in βb ., Therefore , we apply a Gibbs sampler to draw samples from f ( βb|β^b , D^b ) and use the sample mean as an approximation for E ( βb|β^b , D^b ) ., We further performed a sensitivity analysis on the choice of the size of block b ( S6 Fig ) ., Specifically , we ran AnnoPred on the data of Crohn’s disease with different sizes of block and found that the results were robust to the sizes ., In practice , the size of block b is specified by the total number of variants divided by 3 , 000 ., PRS is calculated using the following formula, PRS=∑j=1MXjEA ( βj|β^ , D^ ) ,, where EA denotes the posterior expectation as described above ., In practice , the individual-level genotype matrix is not available and we use the LD matrix estimated from a reference panel or the validation samples to substitute D^ ., We apply the same standard of choosing the size of b as described in 10 ., Choices of prior and p0 can be tuned in an independent cohort ., For the data analysis described in this work , we adopted a cross-validation scheme to select tuning parameter due to the challenge in finding multiple independent cohorts without overlapping with the training GWAS summary statistics ., The training datasets in our real data analyses and simulations are always fixed , i . e . GWAS summary statistics ., We did not perform a classical cross-validation by using different subsets of the complete data to train and test our prediction model ., The purpose of cross-validation in our study is purely parameter tuning ., To select a suitable tuning parameter , we divide the independent testing dataset ( individual level genotype and phenotype data ) into two equal parts ( A and B ) , and select the tuning parameters by optimizing prediction accuracy on dataset A . We then evaluate prediction accuracy using the remaining half of testing data , i . e . dataset B . Finally , we repeat the analysis one more time by choosing the tuning parameter on dataset B while evaluating the prediction accuracy on dataset A . Results from these two separate analyses are averaged to quantify model performance ., For T2D where multiple independent cohorts are available ( phs000237 and phs000388 ) , we used an independent cohort for parameter tuning and the other for evaluating performance ( S12 Table ) ., The results are consistent with the cross-validation ., We compared AnnoPred with several commonly used risk prediction methods based on summary data of association studies ., PRSsig and PRSall were both calculated as the inner product of marginal effect size estimates and the corresponding genotypes ., PRSall used all the SNPs that are shared between training and testing datasets while PRSsig only used SNPs with p-values below 5 × 10−8 in the training set ., PRSP+T used SNPs passing both LD pruning and p-value thresholding ., The thresholds are tuned in an independent dataset over a grid ( 0 , 0 . 1 , 0 . 2 , … 0 . 9 for LD; 1 , 0 . 3 , 0 . 1 , 0 . 03 , 0 . 01 , 3E-3 , 1E-3 , 3E-4 , 1E-4 , 3E-5 , 1E-5 , 1E-6 , 1E-7 , 5E-8 , 1E-8 for p-value ) ., LDpred can be viewed as a special case of AnnoPred , assuming the whole genome as the only functional annotation ., This is because when enrichment is constant ( i . e . causal variants are uniformly distributed across the genome ) , per-SNP heritability estimates would be nearly constant and therefore results in similar performance to LDpred ., We have performed an additional simulation to demonstrate this using WTCCC genotype data with ~15K individuals and ~330K variants ., We randomly divided the genome into two parts ( two annotations ) and uniformly selected causal SNPs ., Then the traits were simulated in a similar way as other simulations in this paper ., We estimated per-SNP heritability using LDSC in the two annotation categories , respectively ., We ran the procedure for 100 times and the distributions of estimated per-SNP heritability in both regions are summarized in the figure below ( the dashed line denotes the true per-SNP heritability , added as S4 Fig in the manuscript ) , which indicates that the per-SNP heritability estimates are uniform across the genome under constant enrichment ., Therefore , AnnoPred would be mathematically equivalent with LDpred with enrichment is constant ., We downloaded python code for PRSP+T and LDpred from Github ( https://github . com/bvilhjal/ldpred ) ., All the tuning parameters were tuned through cross-validation as we did for AnnoPred ., Besides all these PRSs , we also compared AnnoPred with a evaluating method used in 5 , which uses 1E-1 , 1E-2 , … , 1E-5 as p-value threshold to select SNPs and report the accuracy for the best performed threshold ( S4 and S5 Tables ) ., Given that many large-scale GWAS summary statistics have included almost all available cohorts for a disease of interest , it is challenging to find independent datasets with individual-level genotype and phenotype information and sufficient sample sizes ., We were able to identify ideal validation datasets for the five diseases we analyzed in this paper ., The performance of different methods on more traits shall be evaluated when we get access to more data in the future ., We simulated traits from WTCCC genotype data , which contain 15 , 918 individuals genotyped for 393 , 273 SNPs after filtering variants with missing rate above 1% and individuals with genetic relatedness above 0 . 05 ., We first generated two annotations and each annotation was simulated by randomly selecting 10% of the genome , denoted as A1 and A2 , which we assume are known when applying AnnoPred ., Denote the heritability of the trait as hg2 ( 25% or 50% ) and the number of causal variants as m ( 300 or 3 , 000 ) ., Causal variants were generated as follows: m3 causal variants were selected from A1 , m3 from A2 and the rest from ( A1UA2 ) C corresponding to a high enrichment of signals in A1 and A2 ., Effect sizes of causal variants were sampled from N ( 0 , hg2m ) ., For each simulation , we used 70% of the data to calculate the training summary statistics and randomly divided the rest 30% into two parts for parameter tuning ., We also randomly selected half of the training data to calculate summary statistics in order to study the effect of sample size on prediction accuracy ., In order to evaluate the improvement in accuracy , we performed a permutation test to compare the CORs of AnnoPred and LDpred ., Suppose the CORs of LDpred and AnnoPred in simulations are x1 , x2 , … , xn and y1 , y2 , … , yn , respectively ., And the hypothesis we want to test is, H0:μx=μy↔H1:μx≠μy, where μx and μy represent the population mean of accuracies of LDpred and AnnoPred ., We used |x¯−y¯| as the test statistics and the p value can be calculated as p=Pr ( |x¯−y¯ ) >|x¯obs−y¯obs||H0 ) , in which x¯−y¯ represents the random variable and x¯obs−y¯obs represents the actually observed values ., We used permutation to approximate the distribution of ( x¯−y¯ ) when H0 is true ., Specifically , we first pooled xi′s and yi′s together ., Then x˜1 , x˜2 , … , x˜n and y˜1 , y˜2 , … , y˜n were sampled from the pooled data for N = 106 times and we calculated ( x˜¯−y˜− ) for each x˜i′s and y˜i′s sampled , which formed the empirical distribution of ( x¯−y¯ ) under H0 ., And the p value could be approximated by p^=∑k=1NI{|x˜¯k−y˜¯k|>|x¯obs−y¯obs|}N , in which x˜¯k−y˜¯k represents the sampled test statistic of the kth permutation ., To further study the effect of sample size on prediction performance , we simulated traits using SNPs of chromosome 1 , chromosomes 1 and 2 , chromosomes 1 to 4 and the whole genome while keeping the same proportion of causal variants and heritability to mimic the situation of increasing sample size ., The corresponding relative sample sizes ( NMMs , where N is the number of individuals , M is the total number of variants and Ms is the number of variants used in simulation ) for the four scenarios are ~135K , ~67K , 37K and ~11K ., For each effective sample size , we simulated traits under four settings: h2 = 0 . 25 , p = 0 . 001; h2 = 0 . 25 , p = 0 . 01; h2 = 0 . 5 , p = 0 . 001; h2 = 0 . 5 , p = 0 . 01 , where p represents the proportion of causal variants ( Fig 1 ) ., The study was approved by YALE UNIVERSITY HUMAN INVESTIGATION COMMITTEE with approval number 100 FR1 and 100 FR27 ., We trained AnnoPred using publicly accessible GWAS summary statistics and evaluated risk prediction performance using individual-level genotype and phenotype data from cohorts independent from the training samples ., Only SNPs shared between training and testing datasets were kept in our analyses ., Details for each training and testing dataset are provided in S1 Text and S8 Table ., For Crohn’s disease , we trained the model using summary statistics from International Inflammatory Bowel Disease Genetics Consortium ( IIBDGC; Ncase = 6 , 333 and Ncontrol = 15 , 056 ) 25 ., Samples from the Wellcome Trust Case Control Consortium ( WTCCC ) were removed from the meta-analysis and used as the validation dataset ( Ncase = 1 , 689 and Ncontrol = 2 , 891 ) 26 ., For breast cancer , we trained the model using summary statistics from Genetic Associations and Mechanisms in Oncology ( GAME-ON ) study ( Ncase = 16 , 003 and Ncontrol = 41 , 335 ) 27 , and tested the performance using samples from the Cancer Genetic Markers of Susceptibility ( CGEMS ) study ( Ncase = 966 and Ncontrol = 70 ) 28 ., Shared samples between CGEMS and GAME-ON were removed ., We used samples from the CIDR-GWAS of breast cancer for trans-ethnic analysis ( Ncase = 1 , 666 and Ncontrol = 2 , 038 ) 29 ., For rheumatoid arthritis , we used summary statistics from a meta-analysis with 5 , 539 cases and 20 , 169 controls to train the model 30 ., WTCCC samples were removed from the meta-analysis and used for validation ( Ncase = 1 , 829 and Ncontrol = 2 , 892 ) 26 ., For type-II diabetes , the training dataset is Diabetes Genetics Replication and Meta-analysis ( DIAGRAM ) consortium GWAS with 12 , 171 cases and 56 , 862 controls 31 ., We used samples from Northwestern NUgene Project for validation ( Ncase = 662 and Ncontrol = 517 ) 32 ., Samples from Institute for Personalized Medicine ( IPM ) eMERGE project are used for trans-ethnic analysis ( African American: Ncase = 517 and Ncontrol = 213; Hispanic: Ncase = 477 and Ncontrol = 102 ) 33 ., The training dataset for celiac disease is from a GWAS with 4 , 533 cases and 10 , 750 controls 34 ., Samples in the National Institute of Diabetes and Digestive and Kidney Diseases ( NIDDK ) celiac disease study were used for validation ( Ncase = 1 , 716 and Ncontrol = 530 ) 35 ., AnnoPred software and source code are freely available online at https://github . com/yiminghu/AnnoPred .

Introduction, Results, Discussion, Methods, Ethics statement, Data access

Genetic risk prediction is an important goal in human genetics research and precision medicine ., Accurate prediction models will have great impacts on both disease prevention and early treatment strategies ., Despite the identification of thousands of disease-associated genetic variants through genome wide association studies ( GWAS ) , genetic risk prediction accuracy remains moderate for most diseases , which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes in the presence of linkage disequilibrium ., In this paper , we introduce AnnoPred , a principled framework that leverages diverse types of genomic and epigenomic functional annotations in genetic risk prediction for complex diseases ., AnnoPred is trained using GWAS summary statistics in a Bayesian framework in which we explicitly model various functional annotations and allow for linkage disequilibrium estimated from reference genotype data ., Compared with state-of-the-art risk prediction methods , AnnoPred achieves consistently improved prediction accuracy in both extensive simulations and real data .

Genetic risk prediction plays a significant role in precision medicine ., Accurate prediction models could have great impact on disease prevention and early treatment strategies ., For example , mutations in BRCA1 and BRCA2 have been used to evaluate women’s breast cancer risk and as a guideline for early screening ., However , genetic risk prediction models also present important challenges , including extreme high-dimensionality , limited access to and efficient computational methods for individual-level genotype data ., To make use of rich GWAS summary statistics , we propose a novel method to address these challenges by integrating genomic functional annotations , which have been successfully applied in GWAS to generate biological insights ., We demonstrate the improvement in accuracy in both extensive simulation studies and real data analysis of breast cancer , Crohn’s disease , celiac disease , rheumatoid arthritis and type-II diabetes .

genome-wide association studies, rheumatology, medicine and health sciences, breast tumors, functional genomics, immunology, cancers and neoplasms, oncology, diabetes mellitus, rheumatoid arthritis, clinical medicine, endocrine disorders, mathematics, forecasting, statistics (mathematics), genome analysis, genome annotation, research and analysis methods, mathematical and statistical techniques, arthritis, endocrinology, statistical methods, breast cancer, metabolic disorders, clinical immunology, genetics, autoimmune diseases, biology and life sciences, physical sciences, genomics, genetics of disease, computational biology, human genetics

journal.pntd.0000658

The Genome Sequence of Trypanosoma brucei gambiense, Causative Agent of Chronic Human African Trypanosomiasis

Trypanosoma brucei subsp ., gambiense is the causative agent of Human African Trypanosomiasis ( HAT ) , or sleeping sickness , which is a vector-borne disease restricted to rural areas of sub-Saharan Africa ., Trypanosomiasis in humans and livestock imposes substantial morbidity , representing a major impediment of agricultural production in the affected areas 1 , and is fatal where untreated ., The World Health Organization estimated in 1998 that up to 60 million people are at risk in approximately 250 distinct foci 2 , although under-reporting has been estimated as high as 40% in some foci 3 ., T . b ., gambiense is the most clinically relevant sub-species , causing over 90% of all human disease ., The gambiense disease is typically chronic , often lasting several years with few severe signs and symptoms until the late stage of nervous system involvement ., T . b ., gambiense is sensitive to treatment with pentamidine ( early stage ) and eflornithine ( late stage ) , drugs which are frequently ineffective against T . b ., rhodesiense 4 , although the underlying biochemical reasons for these differences are unknown ., Combination therapies against the late stage disease have performed encouragingly 5 but few drugs are available ., Furthermore , unpleasant and in some cases severe side effects often result in poor patient compliance ., Hence , new molecular targets are required to supply current drug discovery programmes 6 ., T . brucei is subdivided into three subspecies based on infectivity to humans , pathogenicity and geographical distribution ., T . b ., gambiense and T . b ., rhodesiense are human pathogens , causing Human African Trypanosomiasis ( HAT ) in West/Central and East Africa respectively ., T . b ., brucei cannot by definition infect humans and is found in a wide range of wild and domestic mammals ., The human pathogens have also been found in various animal species and HAT caused by T . b ., rhodesiense in East Africa is recognized as a zoonosis ., T . b ., gambiense comprises two groups; a genetically homogeneous group to which the majority of isolates belong ( group 1 ) , and a second represented by a handful of isolates from West Africa ( group 2 ) ., Group 1 T . b ., gambiense strains have the smallest genomes in the T . brucei species complex , having 71–82% of the highest DNA content measured for T . b ., brucei 7–8 ., Pulse-field gel analysis of T . b ., gambiense chromosomes shows that few if any mini-chromosomes are present compared to the estimated 100 in T . b ., brucei and T . b ., rhodesiense , and the mini-chromosomes are also of a smaller size–average 25 kb in T . b ., gambiense compared to 100 kb in T . b ., brucei and T . b ., rhodesiense 8–9 ., Perhaps as a consequence of this reduced genome , T . b ., gambiense also has a restricted repertoire of Variant Surface Glycoprotein ( VSG ) genes 8 , 10–12 ., At any time , bloodstream form trypanosomes possess a surface glycoprotein coat formed through the expression of a single gene from a large archive of VSGs 13 ., This coat obfuscates the host immune system by shielding the invariant surface epitopes from view and , when an immune response is inevitably raised against the VSG monolayer and the active VSG is replaced by another , it allows parasites expressing the novel variant to escape the immune response 13 ., This periodic VSG ‘switching’ , or in situ activation , is facilitated by transposition of inactive VSG into a dedicated expression site at the telomeres by gene conversion 13–15 ., Although VSG repertoire is clearly very large 16–17 , it is not known how VSG diversity accumulates over time and between strains ., The SRA gene encodes a truncated VSG-like protein 18; it is located within one specific VSG expression site and is expressed in Human serum-resistance clones of T . b ., rhodesiense only 19 ., Innate immunity to trypanosomes in Humans is conferred by a trypanolytic factor , apoL1 20 and SRA has acquired a role in neutralizing the toxic effects of this protein 21 ., Hence , when transcriptionally activated , SRA enables particular T . b ., rhodesiense clones to infect Humans 22 ., SRA is absent in T . b ., gambiense 23 and the underlying basis for the trait of human infectivity here is as yet unknown ., In T . b ., gambiense , as yet the only example of a subspecies-specific gene is TgsGP , which encodes a 47 kDa VSG-like receptor protein , and is expressed in the flagellar pocket of bloodstream stage cells 24 ., However , TgsGP is not associated with human infectivity in T . b ., gambiense 25 ., We produced an improved , high-quality draft genome sequence for T . b ., gambiense DAL927 with the twin aims of identifying subspecies-specific genomic features that might contribute to our understanding of phenotypic variation and assessing the scale of genomic variation across T . brucei ., This was achieved through comparison with the T . b ., brucei 927 reference genome and we sought to evaluate the proficiency of this reference , ahead of the next generation of genome sequencing projects that will compare multiple isolates to scrutinize genetic divergence and genomic rearrangements in relation to disease ., Our analyses show that the genome sequence of T . b ., gambiense corresponds closely in gene order and content to the T . b ., brucei 927 genome ., Intraspecific genomic variation is largely associated with tandem or segmental duplications , among which we identify several subspecies-specific isoforms ., Our final objective was to compare the VSG repertoires of T . b ., brucei and T . b ., gambiense , and so provide the first global perspective of how VSG diversity evolves on a genome scale ., Details of the genome project describing the ‘Minimum Information for Genome Sequences’ are available online ( http://genomesonline . org/GOLD_CARDS/Gi00917 . html ) ., The sequence of the Trypanosoma brucei gambiense genome has been submitted to the EMBL database under accession numbers FN554964- FN554974 inclusive ., The T . b ., gambiense strain MHOM/CI/86/DAL972 was isolated from a patient in Côte dIvoire in 1986 and has been used routinely in laboratory studies since this time 26 ., Bloodstream form trypanosomes were fed to tsetse in vitro and procyclics from infected midguts were established in culture and subsequently optically cloned ., Procyclic form trypanosomes were grown in Cunninghams medium supplemented with 10% v/v heat-inactivated foetal calf serum , 5 µg/ml hemin and 10 µg/ml gentamycin at 27°C ., High molecular weight DNA was purified by standard methods of phenol-chloroform extraction and alcohol precipitation ., T . b ., gambiense DNA was randomly sheared , size-selected DNA purified and subcloned into pUC19 plasmids ( 1 . 4 kb–4 kb inserts ) , as well as BAC vectors as previously described 27 ., Inserts were sequenced by random sequencing using dye-terminator chemistry on ABI 3730 sequencing machines from both ends to generate paired end reads ., There were 369 , 043 passed paired-end reads , producing roughly eight-fold coverage of the whole genome ., Sequence reads were assembled using Phrap ( www . phrap . org; P . Green , unpublished ) ., Automated in-house software ( Auto-Prefinish ) was used to identify primers and clones for additional sequencing to close physical and sequence gaps by oligo-walking ., Manual base checking and finishing was carried out using Gap4 ( http://www . mrc-lmb . cam . ac . uk/pubseq/manual/gap4_unix_1 . html ) ., Regions containing repeat sequences or with an unexpected read depth were manually inspected ., The assembled contigs were iteratively ordered and orientated against the T . brucei 927 genome sequence , with manual checking ., Aided by information from orientated read-pairs , together with additional sequencing from selected large insert clones , we re-examined regions with apparent breaks in chromosomal colinearity for potential assembly errors ., The human-curated annotation of the T . b ., brucei 927 reference genome was transferred to the assembled T . b ., gambiense genome on the basis of BLASTp matches and positional information using custom perl scripts ., Subsequently , gene structure and functional annotation were manually inspected and further edited , where appropriate , using the Artemis software 28 , as previously detailed 27 ., The annotation of the T . b ., gambiense genome can be viewed and searched via GeneDB ( http://www . genedb . org/ ) and comparative chromosome maps for T . b ., brucei and T . b ., gambiense are available at TritrypDB ( http://tritrypdb . org; 29 ) ., Chromosomal sequences have been submitted to EMBL with the following accession numbers: FN554964-FN554974 inclusive ., The T . b ., gambiense capillary shotgun reads were aligned against the T . b ., brucei 927 reference genome using SSAHA2 ( http://www . sanger . ac . uk/Software/analysis/SSAHA2/ ) ., We discarded reads that mapped to more than one location on the reference genome , as well as pairs of reads that did not map in the correct orientation or to within 20% of the expected insert size of the library ., In-house perl scripts were used to identify single nucleotide polymorphisms ( SNPs ) from the SSAHA alignments that adhered to a modified version of the Neighbourhood Quality Standard ( NQS , 30 ) ; we term this AltNQS ., According to NQS , an acceptable SNP ( or fixed difference ) has a phred quality score of ≥23 and the 5 bases on either side of the SNP position have a quality score of ≥15 ., However , these strict criteria do not allow for multiple mismatches within the 11 bp window ., To accommodate the higher levels of polymorphism , our AltNQS adheres to the same rules as NQS but allows for multiple SNPs within the 11 bp alignment window as long as the base quality of each SNP has a phred score of at least 23 ., To identify regions with significantly high SNP density on each chromosome , non-overlapping windows of 10 kb with at least 50% of read coverage were selected for analysis ., For these windows , SNP density was calculated as the number of SNPs divided by the number bases covered in that 10 kb window ., Using random sampling we estimated the mean and 97 . 5% confidence limit of mean SNP density ., Regions with a value above the 97 . 5% quantile were identified as having significantly high SNP density values ., Tandem gene arrays in the T . b ., brucei 927 genome with >3 gene copies have previously been defined , and are known to contain polymorphism that is affected by recombination 31 ., We assessed the variation among tandem gene duplicates to identify subspecies-specific genes ., For each of these arrays , the coding and 3′ UTR sequences were gathered from the corresponding regions of the T . b ., gambiense genome sequence ., The downstream limit of the 3′ UTR was defined by the polypyrimidine termination motif 32 ., All T . b ., brucei and T . b ., gambiense sequences were aligned in ClustalX 33 and manually adjusted ., Those arrays showing no variation or only corresponding isoforms in both subspecies ( i . e . , simple orthology ) were discarded , leaving just those cases where a disparity in sequence diversity was apparent ., To detect any ambiguity in phylogenetic relationships among sequences , each of these alignments was analyzed using SplitsTree v4 . 3 34 , which applies a Neighbour-Net method 35 ) to estimate a phylogenetic network ., Genetic distances were corrected for variation in base composition after excluding phylogenetically-uninformative characters ., Each alignment was also analyzed using the pair-wise homoplasy index ( PHI ) test 36 that can detect multiple phylogenetic signals within an alignment and is robust in the presence of rate heterogeneity ., A third method , the genetic algorithm for recombination detection ( GARD , 37 ) was applied to estimate the number and placement of recombination breakpoints along each alignment ., 1258 predicted VSG protein sequences encoded in the T . b ., brucei genome were compared with the T . b ., gambiense 972 read library using pair-wise BLASTp searches ., These included 36 VSG-related ( VR ) sequences that are structurally distinct from the bulk of canonical VSG 17 ., Initially , all VSG-like sequences were extracted from the T . b ., gambiense read library using BLASTx against whole VSG protein sequences ., Each T . b ., brucei VSG protein sequence was then individually BLAST-searched against this subset of VSG-like reads to determine its closest match in T . b ., gambiense ., A reciprocal comparison was carried out to confirm the relationship ., To determine if a given gene was most closely related to a paralog in T . b ., brucei or to an ortholog in T . b ., gambiense , each T . b ., brucei VSG protein sequence was also compared a combined database of VSG gene models and VSG-like reads using BLASTp ., BioLayout Express 3D 38 was used to visualize the relative genetic distances between the 1258 T . b ., brucei VSG sequences , using the BLAST scores derived from comparisons of each gene with all others , and a 70% cutoff to simplify the resulting network graph ., To determine if VSG diversity is sub-structured according to life stage , nine VSG sequences known to be associated with metacyclic expression sites were BLAST-searched against all other ( bloodstream-expressed ) VSG and added to the network ., The draft genome assembly consists of 1768 contigs larger than 2 kb , amounting to 32 . 6 Mb of data ., Of these , 281 contigs , totaling 22 . 1 Mb , were ordered and orientated against the T . b ., brucei 927 reference genome ., The remaining contigs encode additional copies of tandemly arrayed gene families as well as genes typically associated with subtelomeres such as expression site associated genes ( ESAGs ) , variant surface glycoprotein ( VSG ) genes and the ingi transposable element ., The gene models and annotation of an initial set of 9898 coding sequences located on core chromosomes ( i . e . , not in subtelomeres ) were transferred to the T . b ., gambiense genome on the basis of BLASTp matches and positional information using custom perl scripts ., When compared , the T . b ., brucei and T . b ., gambiense genome sequences are very similar in terms of content , gene order and sequence identity ., The absence of potentially gambiense-specific sequences was confirmed by examining a Phrap assembly of those capillary reads that did not map against the T . brucei 927 reference genome ., Analysis of ∼40 , 000 unmapped sequence reads using BLASTx showed that among them were features homologous to VSG , ESAG and RHS genes , as well as ingi retrotransposons , but no additional coding sequences that were missing from T . b ., brucei ., We examined the divergence of coding sequences and a frequency histogram of percentage nucleotide identity ( Fig . 1 ) shows that 86 . 4% of genes vary by less than 1% from their T . b ., brucei ortholog ( mean average nucleotide identity =\u200a99 . 2% ) ., Non-coding regions were more divergent , which is unsurprising given that they are probably under weaker purifying selection , but still remained 95 . 4% identical on average ., However , against this general background of correspondence there are 69 pairs of orthologs that display significantly greater evolutionary change , ( i . e . , they are among the 5% most divergent orthologs with a nucleotide identity <95 . 2% ) ., 35 of these gene pairs are VSG sequences; these surface glycoproteins are exposed to frequent gene conversion and evolve rapidly 16–17 , so naturally , they display lower sequence identities of ∼60–85% ., However , they still display reciprocal top BLAST hits with T . b ., brucei sequences ., Also among these divergent gene pairs are 17 uncharacterized genes , 10 of which are predicted to encode cell-surface targeted proteins ., For example , Tb927 . 5 . 4010/Tbg972 . 5 . 4300 ( 92 . 7% identical ) and Tb10 . 70 . 1280/Tbg972 . 10 . 6310 ( 93 . 7% identical ) are both located at strand-switch regions and encode hypothetical proteins with predicted signal peptides and GPI anchor sites ., These genes , which appear to be evolving very quickly , are not found in either Leishmania major or T . cruzi , indicating that they are specific to African trypanosomes ., A source of variation with potentially important functional consequences is allelic polymorphism ., We detected high-confidence SNPs and fixed differences by mapping the T . b ., gambiense reads to the T . brucei 927 reference sequence ., Our analysis focused on the non-repetitive component of the genome as firstly , non-identical repeats can appear indistinguishable from SNPs and secondly , repeated regions may be subject to unusual selective pressures ( see below ) ., After excluding these sequences , we identified a total of 224 , 568 putative fixed differences from 19 . 4 Mb of non-repetitive sequence , i . e . a diversity ( π ) of 0 . 0116 nucleotides per site ., 92 , 794 of these differences were in coding regions , 49% of which were non-synonymous ., To confirm that the variation identified when mapping the T . b ., gambiense reads against the T . b ., brucei 927 were not in fact false-positives due to heterozygosity within the T . b ., brucei 927 reference sequence itself we also used the available capillary read data from the T . b ., brucei 927 genome project to identify polymorphism within the published “haploid” consensus ., Unfortunately , this was only possible for the four chromosomes ( 1 , 9–11 ) that were originally produced by shotgun sequencing , ( rather than a clone walking strategy ) , since these contain data from two homologous chromosomes at a given locus ., From the SSAHA alignments , we identified 23 , 804 SNPs in 10 . 8 Mb of map-able sequence ( π\u200a=\u200a0 . 0022 ) , of which 1 , 187 had the same heterozygous alleles in both the T . b ., brucei 927 and the T . b ., gambiense genome , indicating a false-positive rate of 5% ., We identified 298 regions exhibiting higher than average diversity along the megabase chromosome ., It is noteworthy , that this analysis excluded all telomere proximal regions owing to their highly repetitive nature ., Whereas telomeres are well established in many species as sites of sequence variation and rearrangement 39–40 , the presence of interstitial regions of high diversity in addition to the sub-telomeres is striking ., On rare occasions the otherwise consistent chromosomal colinearity is disrupted by sequence inversions and insertion-deletion events ( indels ) ., In many cases indels coincided with sequence gaps , making it difficult to confirm genuine rearrangements ., Nevertheless , chromosome 10 provides two examples , between 275–330 kb and 3250–3350 kb , of 55 and 110 kb segmental inversions respectively ., Gene order within these inverted regions remains conserved ., Typically , indels have two principal causes: transposable elements and internal VSG ‘islands’ ., Transposable elements such as ingi and RIME sequences recombine in trypanosome genomes and are responsible for several rearrangements 27 ., On chromosome 9 , a 7 kb insertion occurs in T . b ., brucei due to an ingi element ( at 1 . 24 Mb ) not present in T . b ., gambiense ., Similarly , a 29 kb indel follows Tb11 . 02 . 5830 where an expression site-associated gene ( ESAG ) and a trans-sialidase gene have been inserted into T . b ., gambiense at the corresponding position to a RIME sequence in T . b ., brucei ., By their nature , such rearrangements frequently occur in repetitive regions of the genome and , consequently , are difficult to resolve in genome assemblies ., This therefore does not preclude that further events will be identified in the future ., Another source of genomic variation concerns core chromosomal VSG and ESAG genes ., VSG genes are predominantly found in subtelomeric arrays , on intermediate or mini-chromosomes 27 , 13 ., In addition , VSG/ESAG genes are less commonly found non-telomerically as ‘islands’ , often on the opposing strand to neighbouring loci ., These genes ( or pseudogenes ) may be:, ( i ) atypical VSGs that do not encode all elements for accurate folding or post-translational modification;, ( ii ) VR genes; or, ( iii ) canonical VSG genes , imported from the subtelomere or mini-chromosome through segmental duplication ., An example of the latter is a segmental insertion including 8 VSG genes that affects chromosome 9 in T . b ., gambiense ( Tbg972 . 2 . 570–640 ) , since the VSG sequences are unrelated to each other and therefore , have not resulted from recent tandem duplications ., In total , 17 such VSG/ESAG islands were noted in both genomes , only 6 of which were unique to one subspecies or other , including a segmental duplication in T . b ., brucei of an atypical VSG combined with an insertion or deletion of ESAGs ( Supplementary Fig . S1 ) ., Clearly , VSG/ESAG islands are among the more dynamic features of core chromosomes , yet where they are conserved between T . b ., brucei and T . b ., gambiense they contain orthologous gene sequences , indicating that they not exposed to frequent gene conversion processes like VSGs elsewhere ., Beyond transposable elements and VSG ‘islands’ , other differences in gene order are caused by a class of small , putative coding sequences of unknown function ( 103 cases ) ., These genes encode hypothetical proteins with a predicted length of 151–274 amino acids and which have no database matches to any experimentally characterized protein ., Transcriptomic data ( George Cross , Rockefeller University , unpublished data; Veitch et al . , University of Glasgow , submitted ) suggest that some of these putative genes are at least transcribed , although no product has yet been identified in proteomic assays to date ( Aswini Panigrahi , SBRI , pers . comm . ) ., Regardless of which genome encodes the putative gene , homologous sequences of high identity are found in the other genome at the corresponding positions , but without the open reading frame ., Hence , they may be non-coding RNA genes or other non-coding conserved elements of undiscovered function ., These features are annotated to ensure completeness , and they may yet reveal functional importance , but our view is that they are unlikely to produce proteins and will not be considered further ., Our comparative analysis identified only a single coding sequence , a putative iron-ascorbate oxidoreductase ( Tb09 . 211 . 4990 ) , which is absent from the genomic repertoire of T . b ., gambiense ., We did not identify the TgsGP locus , which is known to be unique to T . b ., gambiense 24 because it is located in the subtelomere and these regions were not fully assembled ., However , sequence identical to TgsGP was identified among the unassembled reads ., Thus , it is possible that other subspecies-specific genes exist within the subtelomeres that are not recorded here ., Tb09 . 211 . 4990 is preceded upstream on chromosome 9 by a strand-switch region and downstream by both retrotransposon-like proteins and the splice-leader RNA tandem array ., This region is conserved in T . b ., gambiense , but the oxidoreductase is absent ., The gene is absent from the more distantly related kinetoplastids Leishmania major and T . cruzi , as well as 9 out of 11 other T . b ., brucei strains and a representative group 2 T . b ., gambiense ( STIB 386 ) that we examined with PCR primers specific to this oxidoreductase ( data not shown ) ., When compared phylogenetically with other iron-ascorbate oxidoreductases in T . brucei , ( principally the tandem gene array at the right-hand terminus of chromosome 2 , e . g . Tb927 . 2 . 6180 ) , this protein is clearly structurally distinct ( only 80% amino acid identity ) and constitutes an evolutionarily old lineage ., This suggests that Tb09 . 211 . 4990 is gained and lost at the population level , and that it provides additional functionality to T . b ., brucei 927 and two other T . b ., brucei strains in which it has been found ., The comparison of gene content did not identify widespread subspecies-specific loci , and found no obvious differences that could explain the distinct phenotypes of T . brucei subspecies ., For example , ornithine decarboxylase , the target of eflornithine to which T . b ., gambiense is uniquely sensitive , is present in single , diploid copy in both genomes and displays only a single non-synonymous substitution ( N137I ) ., We did , however , detect substantial variation within families of certain uncharacterized genes that could have important functional consequences ., Such differences in co-linearity involve either the expansion of a single-copy gene in one subspecies to a tandem pair in the other , or a difference in the number of duplicates where there is a tandem array in both subspecies ., Current methods of genome assembly tend to detect the first scenario ( i . e . , single copy vs . many ) but have limitations in accurately quantifying copy number and in distinguishing between copy number and allelic variation ., In fact , while the number of repeat units assembled can be arbitrary , the variation among tandem gene duplicates can be accurately assessed from genome sequence data for the two subspecies ., In 20 cases , a single-copy feature ( be it a single gene or chromosomal segment ) in T . b ., gambiense exists in multiple , tandem copies in T . b ., brucei , while 8 cases of the converse were observed ( Table 1 ) ., For the majority of these cases , the tandem duplicates were identical and the duplication did not result in any novel , unique sequence ., But in 8 cases in T . b ., brucei , the extra duplicates contained sequence variation that might represent subspecies-specific isoforms ., In four additional cases , the would-be unique sequences were found among sequence reads of the apparently single-copy subspecies , indicating that it had been omitted from the assembly ( marked by an asterisk in Table 1 ) ., The genes involved in these T . b ., brucei-specific segmental duplications are as yet uncharacterized , but their features suggest that they are potential sources of subspecies-specific factors and interesting opportunities for further research ., They are evolutionarily novel since they are not conserved in either T . cruzi or L . major; several encode proteins with predicted cell surface roles; and some are among the fastest evolving of all T . brucei genes ., For example , a tandem gene array of hypothetical genes encoding cysteine-rich secretory proteins is shown in Supplementary Fig . S2; these are homologous to a single gene ( Tbg972 . 3 . 6170 ) at the corresponding position on chromosome 3 in T . b ., gambiense ., From the relative strength of BLAST hits between homologs , it is clear that the first gene in the array and the singleton in T . b ., gambiense are orthologs , while the additional copies in T . b ., brucei ( absent from the T . b . gambiense read library ) are unique paralogs ., Indeed , they have evolved considerably , sharing only 55 . 1% amino acid identity with the upstream orthologs ., Similarly , Figure 2 shows a single segment on chromosome 9 in T . b ., gambiense ( Tbg972 . 9 . 4160 , 4140 and 4130 ) that corresponds to five tandem repeats in T . b ., brucei ., Among gene duplicates of the second and third coding sequences , which encode hypothetical transmembrane and GPI-anchored proteins respectively , there is considerable sequence variation ( average nucleotide identities of 51 . 2% and 59 . 1% respectively ) ., As in Supplementary Fig . S2 , the 5′-most segment in T . b ., brucei is orthologous to the T . b ., gambiense genes , but the downstream copies are structurally divergent ., A third example of segmental duplication with subsequent divergence of tandem copies occurs on chromosome 6 and concerns a hypothetical protein with a predicted signal peptide and GPI anchor ( Supplementary Fig . S3 ) ., Such segmental duplications provide rare examples of subspecies-specific gene paralogs or isoforms ., It remains to be seen how common , and how ephemeral , such copy number variation is among T . brucei strains generally ., But these cases are especially interesting because they do not simply concern gene dosage ., In fact , with divergence in protein sequence often between 30–40% among paralogs , the effects on protein function could be considerable ., Not only have these genes multiplied in number in very recent evolutionary time , this has been accompanied by rapid structural divergence in their predicted cell surface gene products , suggesting a role for adaptive change ., Such protein isoforms could contribute to the observed differences between group 1 T . b ., gambiense and other T . brucei isolates in the host-parasite relationship , both in the mammalian and insect hosts ., Tandem gene arrays in the T . b ., brucei genome usually contain sequence variants and analysis of tandem duplicate variation using T . b ., brucei sequences alone showed that divergence frequently results in sequence mosaics and concerted evolution within genomes 31 ., After discounting the minority of invariant tandem arrays in T . b ., gambiense , 35 tandem gene arrays that contained sequence variation were compared with their T . b ., brucei homologues , demonstrating that 27 arrays contained subspecies-specific gene copies ( Table 2 ) ., In 5/49 instances subspecies-specific copies displayed unique sequence motifs , suggesting differential assortment of the ancestral gene repertoire between the daughter subspecies ., Elsewhere , subspecies-specific copies were recombinants of other duplicates ., Tests for recombination carried out on multiple alignments of gene copies from both subspecies demonstrated that sequence mosaics occurred in 31/35 data sets as exemplified by the array of invariant surface glycoproteins on chromosome 2 ( ISG; Tb927 . 2 . 3270–3320 ) ( Fig . 3 ) ., The ISG array comprises 6 and 12 gene copies in T . b ., brucei and T . b ., gambiense , respectively ., GARD analysis detected at least five recombination breakpoints ( Fig . 3a ) and the recombinant nature of ISG is reflected in a highly reticulated phylogenetic network ( Fig . 3b ) ., This also identifies potential subspecies-specific recombinants , for instance , the proximity of ‘Tbg7’ to ‘Tbg10’ reflects the overall similarity of these copies , but closer inspection shows that small sections of homology exist with other copies , i . e . , ‘Tbg8/9’ ( Fig . 3c ) ., Similarly , the intermediate position of Tbg1 reflects its affinities with multiple , unrelated sequences ( Fig . 3d ) ., Some of the hardest genome regions to reliably assemble are subtelomeres , since they usually contain numerous high-copy gene families , as well as simple and complex sequence repeats ., The fluidity of subtelomeric assemblies perhaps reflects some reality about the true mutability of subtelomeric regions , since they are known to vary widely in length between trypanosome strains 41 ., In comparing ∼1 . 3 Mbp of subtelomeric sequence immediately contiguous to the chromosomal cores between the two subspecies , it is clear that they are highly similar in composition and gene order ., In both T . b ., brucei and T . b ., gambiense the largest component of subtelomeric genes comprises VSGs ( 67 . 8% and 44 . 4% , respectively ) , followed by ESAGs ( 13 . 4% , 15 . 8% ) , and transposable element-related genes ( 7 . 6% , 13 . 5% ) ., Adenylate cyclases ( 2 . 2% , 3 . 0% ) and glycosyltransferases ( 1 . 1% , 1 . 5% ) are also prominent features in both genomes ., Beyond these subtelomeric regions , previous comparisons of telomeric VSG expression sites in various T . brucei strains and subspecies have established that the essential components are ubiquitous 42–43 ., Hence , although T . brucei telomeres are known to evolve rapidly and display widespread karyotypic variation , the composition of regions beyond core chromosomes remains consistent across the species ., As the relative divergen

Introduction, Methods, Results/Discussion

Trypanosoma brucei gambiense is the causative agent of chronic Human African Trypanosomiasis or sleeping sickness , a disease endemic across often poor and rural areas of Western and Central Africa ., We have previously published the genome sequence of a T . b ., brucei isolate , and have now employed a comparative genomics approach to understand the scale of genomic variation between T . b ., gambiense and the reference genome ., We sought to identify features that were uniquely associated with T . b ., gambiense and its ability to infect humans ., An improved high-quality draft genome sequence for the group 1 T . b ., gambiense DAL 972 isolate was produced using a whole-genome shotgun strategy ., Comparison with T . b ., brucei showed that sequence identity averages 99 . 2% in coding regions , and gene order is largely collinear ., However , variation associated with segmental duplications and tandem gene arrays suggests some reduction of functional repertoire in T . b ., gambiense DAL 972 ., A comparison of the variant surface glycoproteins ( VSG ) in T . b ., brucei with all T . b ., gambiense sequence reads showed that the essential structural repertoire of VSG domains is conserved across T . brucei ., This study provides the first estimate of intraspecific genomic variation within T . brucei , and so has important consequences for future population genomics studies ., We have shown that the T . b ., gambiense genome corresponds closely with the reference , which should therefore be an effective scaffold for any T . brucei genome sequence data ., As VSG repertoire is also well conserved , it may be feasible to describe the total diversity of variant antigens ., While we describe several as yet uncharacterized gene families with predicted cell surface roles that were expanded in number in T . b ., brucei , no T . b ., gambiense-specific gene was identified outside of the subtelomeres that could explain the ability to infect humans .

Sleeping sickness , or Human African Trypanosomiasis , is a disease affecting the health and productivity of poor people in many rural areas of sub-Saharan Africa ., The disease is caused by a single-celled flagellate , Trypanosoma brucei , which evades the immune system by periodically switching the proteins on its surface ., We have produced a genome sequence for T . brucei gambiense , which is the particular subspecies causing most disease in humans ., We compared this with an existing reference genome for a non-human infecting strain ( T . b . brucei 927 ) to identify genes in T . b ., gambiense that might explain its ability to infect humans and to assess how well the reference performs as a universal plan for all T . brucei ., The genome sequences differ only due to rare insertions and duplications and homologous genes are over 95% identical on average ., The archive of surface antigens that enable the parasite to switch its protein coat is remarkably consistent , even though it evolves very quickly ., We identified genes with predicted cell surface functions that are only present in T . b ., brucei and have evolved rapidly in recent time ., These genes might help to explain variation in disease pathology between different T . brucei strains in different hosts .

genetics and genomics/gene discovery, genetics and genomics/comparative genomics, evolutionary biology/evolutionary and comparative genetics, infectious diseases/neglected tropical diseases, evolutionary biology/genomics, genetics and genomics/genome projects, infectious diseases/protozoal infections

journal.pgen.1002223

Global Chromosomal Structural Instability in a Subpopulation of Starving Escherichia coli Cells

Copy number variations ( CNVs ) are regions of DNA either deleted or duplicated/amplified relative to a reference genome ., CNVs constitute the most ubiquitous differences between individual or personal human genomes 1 , can be associated with many Mendelian and complex human diseases 2 because de novo events cause a significant fraction of sporadic birth defects 3 and are responsible for the selected rapid evolutionary changes accompanying animal domestication ( e . g . 4 ) ., In human , CNV arises either through non-allelic crossing-over between repeated sequences , giving recurrent end-points , or at non-recurrent positions ., Non-recurrent events show two conspicuous features: many of them show complexity 5 , often in the form of lengths of nearby sequence inserted at the novel junction , and second , the junctions tend to show microhomology of a few base-pairs , not sufficient to allow homologous recombination to occur ( reviewed by 6 , 7 ) ., We and others have reported similar properties in our studies of amplification in Escherichia coli , namely that some of the events are complex , and the junctions show microhomology at the site of the joint making E . coli a useful model for studying the mechanisms that underlie human CNV 8 , 9 , 10 ., Amplification at lac in the Lac assay system on an F′-plasmid in E . coli requires DNA polymerase I ( Pol I ) but not excision repair ( also involving Pol I ) , placing the event at replication forks 10 , 11 ., Parenthetically , in yeast both break-induced replication ( BIR ) 12 and CNV 13 require the non-essential DNA polymerase subunit pol32 ., Furthermore , in E . coli amplification is enhanced by 3′ single-stranded DNA ends , suggesting priming of DNA synthesis 10 ., Based on these observations we proposed the long-distance template-switch model , in which the 3′ primer-end at a stalled replication fork switches template to a different replication fork and anneals at a site of microhomology 10 ., Repeated switches would explain the complexity at the junctions , and a template switch to a region already replicated would produce a duplication that could be expanded into amplification by unequal crossing-over ., However , amplification also requires TraI 14 , an endonuclease that nicks the F-plasmid at the origin of transfer , oriT , and this requirement is suppressed by double-strand cutting near lac on the F′-plasmid 14 ., Taking these findings together with the report that BIR repair of collapsed ( broken ) replication forks in yeast shows frequent template switching 15 , we proposed that microhomology-mediated ( MM ) events might occur by a modification of BIR ( MMBIR ) whereby repair is achieved by annealing of the 3′-tail at a collapsed fork with any nearby single-stranded DNA 6 ., Annealing would have lower homology requirements than homologous recombination , and hence explain the microhomology junctions ., Another possible explanation for recombination at sites of microhomology is non-homologous end-joining ( NHEJ ) ., NHEJ requires two double-strand breaks to make every heterologous junction , and consequently complex events would require multiple DNA double-strand breaks ., NHEJ fails to explain the requirement for DNA polymerase I or the involvement of 3′ DNA ends in amplification ., For these reasons we do not favor NHEJ as a mechanism for adaptive amplification in the Lac assay , nor is it our preferred mechanism to explain microhomology observed at human genomic deletion rearrangements with a single junction; the latter being explained more parsimoniously by a single template switch 1 , 5 ., In the Lac assay in E . coli 16 , stationary phase Lac− cells carrying a +1 frameshift mutation are spread on lactose minimal medium ., Lac+ colonies arise over days from the starving cells ., The colonies carry either amplified arrays of the leaky lac allele or a compensating frame-shift mutation ( point mutants ) 17 ., The point-mutant Lac+ colonies are found to carry secondary unselected mutations at a high frequency ( up to 10−2 for some loci ) 18 , 19 , 20 ., Starved cells on the same plate that did not mutate to Lac+ carry a much lower frequency of unselected mutations 19 ., Thus , some or all Lac+ colonies arise from a hypermutating subpopulation ( HMS ) while the majority of the starved cells do not take part in hypermutability ., The HMS is defined by the stress responses that are activated in given cell 21 , 22 ., It has not been established whether or not amplified Lac+ colonies arise from a chromosomally unstable subpopulation , though it has been shown that they do not arise from the HMS 17 ., This study reports the use of array comparative genomic hybridization ( aCGH ) to analyze genome-wide changes in copy number ., We sought , first , evidence of secondary unselected cell-wide chromosomal structural instability in those cells that carry amplification at lac ., Evidence of secondary chromosomal structural change in amplified isolates that is not seen in controls constitutes evidence of a physiological difference that affects genome stability between cells undergoing amplification and those that do not ., We found a significantly higher occurrence of unselected events that would not have bestowed a growth advantage among amplified isolates compared with stressed Lac− control cells ., This demonstrates that amplification is happening in a differentiated subpopulation undergoing general chromosomal structural change , suggesting that this differentiation might be mediated by stress responses ., Second , we sought further evidence that amplification in E . coli shows similar complexity to human non-recurrent CNV events ., We found complexity in the amplification events in over 7% of amplified isolates , mostly in the form of inverted duplications within the amplicons ( units of amplification ) , confirming that there is a tendency for events that mediate chromosomal structural change to be complex ., The most common complexity was an inverted duplication embedded in the amplified region ( Figure 2a , PJH1490 ) ., This was found in 16 of 300 amplified isolates ( 5 . 3% ) ( Table 1 ) ., The same configuration was found to be common in the study by Kugelberg et al . with the Lac assay in Salmonella enterica 8 ., In all 16 cases , the lac region was included in the embedded duplication ., Detailed study of these events showed that the embedded inverted duplications vary in size from 5 . 2 to 42 . 6 kb ., Two novel junctions were found in each case ., The junctions showed microhomology of 3 to 30 bp ( Table 1 ) ., We interpret these events as two inverted template switches that generate an inverted triplication , followed by unequal crossing-over that generates the amplified array ( Figure 3 , see Discussion ) ., We identified two other inverted regions that generated a distinct pattern on aCGH data where part of the amplicon appears to be detached from the rest on the map of the parental strain based on the standard map of E . coli ( PJH39 and PJH2122 ) ( one example , PJH39 , is indicated in Figure 2b by an open arrow ) ., When the map is corrected to include this inversion , the amplicon is seen to be contiguous ., These events show only two novel junctions , the right end of the inversion and the amplification being the same junction ., We therefore regard the inversion and the duplication as parts of the same event , and explain them below as a pair of inverted template switches followed by unequal crossing over ( Figure 3c , 3d ) ., Another event of the same type , PJH2058 , that did not involve inversion or duplication of lac ( apart from the amplification ) is shown in Figure 2c ., There is a short sequence within amplicon that is present in 2-fold less copy number than the rest of the amplicon ( open arrow in Figure 2c ) ., This can also be explained by 2 switches , but neither of them is inverted ( Figure 3e , 3f , see below ) ., A very large tandem duplication ( about 300 kb ) was found in an isolate ( PJH1475 ) in which the F′-factor was integrated into the chromosome , so that part of the F′ including lac , and part of the chromosome was duplicated ( Figure 1 ) ., We have confirmed the HFR status of this isolate by showing that conjugational transfer of proAB , which is on the F′-plasmid in FC40 , is RecA-dependent in this isolate , whereas it would not be if it were situated on a plasmid ., The duplication is flanked by IS5 sequences , and therefore was presumably formed by homologous recombination ( Table 1 ) ., Similarly , the integration of the F′-plasmid occurred by homologous recombination between sequences that are in common between the chromosome and F′128 , because aCGH detected no other copy number change ., Two other large duplications , PJH1477 and PJH1487 , were found that included lac and had one or both ends outside the chromosomal sequence on the F′ ., The junctions were not found in the IS3 elements that span chromosomal sequence on the F′-plasmid as has been observed previously 8 , 9 ., The same two events contained duplications within the amplified segment ., The junction sequences of both duplications were found to be recalcitrant to amplification by PCR ., Multiple primer pairs were used in all pair-wise orientations , but no product or only unspecific product was found ., Similar results have been reported for some human non-recurrent copy number changes ( e . g . 25 ) ., It is possible that these represent translocations , further unanticipated orientational complexities at the breakpoint junctions , or insertions of large genomic sequences/structures between the designed primers that do not correspond to a preconceived notion based on a reference genome sequence used for primer design ., Array CGH provides copy number information , but neither positional nor orientational information ., We were unable to characterize these further ., These data establish that , like in human , a significant proportion of events of chromosomal structural change that generate amplification are complex in that more than one structural change occurred , apparently within the same event ., This applies to 19 of 300 events resolved by our approach ( omitting large duplications that might have assisted amplification , but might not be part of the same event ) ., In the same sample of 300 amplified isolates , we also found six that included a chromosomal structural change that was not apparently directly involved in the amplification ., None was seen in the 240 stressed control isolates ., The null hypothesis that the amount of that unrelated chromosomal structural change does not differ between amplified and stressed non-amplified isolates , can be rejected ( p\u200a=\u200a0 . 036; Fishers exact test 26 , 27 ) ., Using the Peto Odds Ratio we can estimate the odds ratio ( OR\u200a=\u200a6 . 2 ) and a corresponding 95% confidence interval ranging from 1 . 2 to 31 . 0 ., 24 ., Duplications should be unstable , so it is not surprising that we saw none that did not duplicate lac and thereby provide selection for maintenance of the duplication ., Four of the unselected events were deletions ( 1 . 33% of 300 events ) : two on the F′-plasmid and two on the chromosome ., One of the deletions ( PJH1474 ) was flanked by non-identical IS elements , and so might have occurred by homeologous recombination or alternatively might have utilized the shorter homology stretches to mediate a template switch ., The other three show microhomology junctions ( 1 to 4 bp ) , and so probably happened by events similar to those generating amplification ., The chromosomal deletions were 0 . 8 and 1 . 6 kb long , and are situated at about 1 . 4 and 1 . 6 megabases on the standard reference E . coli map ( PJH2116 and PJH1482 respectively ) ., Deletions of 0 . 2 and 7 . 5 kb long ( PJH2030 and PJH1482 respectively ) were found on the F′ at about 44 kb and 50 kb from lac respectively ( Figure 1 ) ., An example , PJH1474 , is shown in Figure 2D ., We found one inversion because it made an apparent separation of the amplicon into two parts ( based on the standard map ) ( Figure 2e , PJH1479 ) ., The endpoints of the inversion and the amplification are different , so we see no evidence that the events are related ., The inversion presumably happened before the amplification , and the amplification then included part of the inverted region ., Because most inversions would not be detected by aCGH , we searched all 300 amplified and 240 stressed control isolates for inversion within 20 kb to either side of lac by unidirectional PCR ( Figure 4 ) ., When PCR primers point in the same direction , there is no PCR product unless the sequence at one of the primer binding sites has been inverted ., We found one further inversion in an amplified isolate ( PJH1465 ) and none in the controls ., These two inversions are described in Figure 4 ., It is interesting that , although the exchanges were almost reciprocal , the junctions are not exactly in the same position , so that a mutation of a small deletion or insertion is made at either end of both inversions ., Kugelberg et al 8 , 9 , studying the Lac assay in Salmonella enterica , have proposed that amplification at lac is not induced by the stress of starvation , but is a product of selection for more β-galactosidase expression with parameters within those established for chromosomal structural changes in growing cells of E . coli ., We regard these amplification events as stress-induced because it was not pre-existing 17 and has been shown to require two stress response regulators: the general and stationary-phase stress-response regulator σS ( RpoS ) 28 , 29 and the periplasmic misfolded protein stress-response regulator σE ( RpoE ) 30 ., The strong requirement for σS would appear to be definitive , except that a few RpoS-controlled functions are expressed in growing cells 31 , so one might argue that it is growth-dependent functions that are required ., This idea is refuted by the demonstration that the growth phase level of expression of σS is insufficient for adaptive mutation 14 ., The strong requirement for the RpoE stress-response is for both formation and maintenance of amplification 30 ., The requirement for two of the cells major stress-response regulators is a strong argument for stress-induction of amplification ., We report that a significant number of amplification events are complex in that they show more than one novel junction , indicating more than one non-homologous recombination event ., The case that amplification events in the Lac assay reflect template switches during replication has been made in detail elsewhere 6 , 7 ., The events described here are readily interpretable in terms of template-switching mechanisms , and support the concept ., Figure 3 describes the template switch processes that we propose to have occurred to explain the complex events that we see , based on either the long-distance template switch model 10 or the MMBIR model 6 ., Figure 3a and 3b show how two inverted template switches form an inverted triplication interspersed with direct and inverted duplications ., Non-allelic homologous recombination ( or unequal crossing-over ) between directly duplicated regions will generate the complex amplicon that we see ., Kugelberg et al . 8 use a very similar pattern of events to explain this configuration , which was also common in their data for amplification in S . enterica ., Figure 3c and 3d shows how a different configuration , amplification overlapping an inverted region , which we saw twice ( Figure 2b , PJH39 and PJH2122 ) , can be derived very similarly from two inverted template switches followed by unequal crossing-over ., The difference is only in the relative positions of the two template switches ., If an inverted template switch occurs , the product will not be a viable Lac+ clone under the conditions of these experiments unless there is a second inverted template switch ., This is because a single inverted switch will generate an incomplete F′-plasmid ., However , this requirement for a second inversion cannot be the explanation for all complexity because , as shown in Figure 3e and 3f , we also see evidence of double template switches in direct orientation , where no consideration of viability exists ., In the case portrayed in Figure 3e , PJH2058 , the amplicon consists of a direct duplication that has a deletion between the repeats ., The events depicted in Figure 3e and 3f differ from those interpreted above that include inversions only in the positions and orientations of the switches ., To determine whether a secondary structural change might play a role in the amplification process , we screened for loss of amplification in one strain carrying a secondary inversion ., When this strain was used in a starvation-induced mutation experiment , the rate of amplification was unchanged from the control strain FC40 ( Figure S2 ) suggesting that there is no functional reason for the occurrence of this inversion in a strain that carries amplification at lac ., Taking the four deletions and the two inversions that did not share a junction with amplification as events secondary to amplification , we see a significant occurrence of secondary events in cells that underwent amplification compared with starved cells not showing amplification ( p\u200a=\u200a0 . 036; Fishers exact test ) ., This is clearly an under-estimate of structural changes because duplications would be expected to be unstable , so it is not surprising that we saw none that did not duplicate lac ., Those duplications that include lac presumably provide selection for maintenance of the duplication 8 , 9 ., We only found inversions that were close to lac because we did not look elsewhere ., Using aCGH , we would detect only those unrelated inversions that overlapped the amplicon , and we found one of these ., We also looked for inversion by unidirectional PCR , but only in the 40 kb surrounding the lac locus ., The meaning of the finding that the sample of amplified isolates differs in the frequency of secondary events from non-amplified cells from the same plate is important ., First , it means that amplifying cells differ from other cells in their propensity to undergo chromosomal structural change ., Second , it shows that this happened in a subpopulation of starved cells rather than in the whole population of starved cells , because starved cells that did not undergo amplification provide the basis of comparison ., The identification of chromosomal structural mutations that are secondary to the selected event is analogous to the finding in the Lac assay that lac+ point mutation is correlated with an elevated frequency of other unselected secondary point mutations 18 , 19 , 20 ., Third , the discovery in the same cells of events that are apparently separate from the amplification events shows that the structural changes occurring during starvation on lactose medium are not targeted specifically or exclusively to the lac locus ., The existence of this chromosomally unstable subpopulation is compatible with the concept of stress-induced differentiation to a condition permissive for chromosomal structural change and genomic instability , and is incompatible with models that seek to explain these events as normal change and selection in slowly growing cells ., We suggest that this subpopulation is differentiated to a physiological condition that allows chromosomal structural change ., This is suggested by the finding that some of the secondary events occurred on the chromosome , indicating that a diffusible factor is involved ., Further , we suggest that this differentiation was induced by the stress of starvation ., We made the unexpected finding that some inversions involve almost , but not quite , reciprocal non-homologous recombination so that the junctions show insertions or deletions of a few tens of base-pairs ., We suggest that this might occur as follows: If a template switch occurred because a replication fork was stalled by secondary structure forming in template DNA , then the complementary sequence on the other template would also be capable of forming a similar secondary structure ., If two such sites occurred in a short interval , within the dimensions of a single replication fork , then the series of template switches portrayed in Figure 5 might explain how the almost reciprocal recombination occurred to form the inversion ., Uncoupling of lagging-strand synthesis , after leading-strand synthesis is stalled by secondary structure ( labeled “1” in Figure 5 ) , might allow both structures to form on both strands , and so expose one to two kb of single-stranded sequence within the same replication fork ., An inverted switch of the nascent leading strand from “1” to where lagging-strand synthesis is blocked ahead of the second secondary structure “2” is followed by synthesis in inverted orientation as far as the complementary secondary structure to the first blockage “1R” ., The second template switch is to downstream of the complement to the second structure “2R” , thus completing the not quite reciprocal exchanges that flank the inversion ., This allows replication to escape the blockage imposed by secondary structures ., Figure 5 shows the secondary structures that could form in the regions involved in one of the inversions ., All four junction sequences are in positions that can form a stem or a stem/loop of secondary structure ., We suggest that at least these two inversion events formed by template switches 32 , 33 within a replication fork induced by secondary structures in DNA ., REP is a pseudopalindromic sequence of about 38 bp that occurs in clusters in intergenic regions 34 ., Kugelberg et al . have noted that there is a tendency for junctions to occur at REP sequences 8 , 9 , and interpret this as evidence of homologous recombination ., We found that 22 of 90 sequenced novel junctions ( 24% ) occur at REP sequences ( Table 1 and Table S1 ) ., Of these , 14 are too short for homologous recombination ( 5 to 20 bp ) and 8 are in a range that might or might not allow homologous recombination ( 29 to 32 bp ) 35 , 36 , but could also allow microhomology-mediated template switches as has been proposed for Alu repetitive sequences in the human genome 5 ., REP clusters are rich in potential to form secondary structures ., Figure S1 shows secondary structure predicted in a cluster of REP sequences near lac that is involved in 20% of the junctions listed in Table 1 and Table S1 ., We suggest that the propensity of the region to form secondary structures , rather than homology , is instrumental in forming this hotspot ., Study of the positions of novel junctions of amplification show a preference for the stem of potential stem-loop structures ., For 40 amplification junctions that we sequenced ( Table S1 ) , 16 are REP sequences , and therefore rich in potential secondary structures ., Analyzing potential secondary structures in the regions close to the junctions of the other 24 amplicons , only one junction sequence is confined to predicted unstructured sequence , and only one is confined to a potential hairpin loop ., Those in the commonest class ( 10/24 ) occur on the stems of predicted secondary structures , and 9/24 are on both a stem and a loop ., We considered that secondary structure might target amplification by blocking the progress of replication forks , or it might function to provide single-stranded DNA to which a primer could anneal during template switching ., Because a minority of junctions are situated on a predicted hairpin loop where single-stranded DNA might occur , we favor the hypothesis that secondary structures target amplification by blocking replication ., Others have reported that secondary structures in DNA are involved in chromosomal structural change 37 ., Direct evidence of fork stalling at inverted repeats in vivo strongly suggests that stalling is mediated by hairpin formation on the lagging-strand template at replication forks 38 ., We have suggested above that some inversions are formed by template switching within a replication fork ., Template switching is much more difficult to apply to other events reported here because most switches cover tens of kb , well beyond the 1 . 5 kb dimensions of a replication fork in E . coli ., For this reason , we suggested previously that template switches occur between different replication forks: the long distance template-switch model 10 ., Based on the evidence that double-strand breaks are involved in amplification at lac , we later suggested that the mechanism was a modification of break-induced replication ( BIR ) at collapsed replication forks , namely that in place of RecA-mediated strand invasion , the broken end annealed by microhomology to nearby single-stranded DNA ( MMBIR ) 6 ., We know from experiments that use I-SceI endonuclease to make double-strand cuts near to lac that double-strand breaks increase amplification at lac 14 ., From this we suggested that nicking at oriT by TraI provides a discontinuity in the DNA template that leads to replication fork collapse 6 followed by MMBIR ., We present evidence that , among stressed cells , a small proportion enters a state of heightened genomic instability during which multiple chromosomal structural changes might occur anywhere in the genome ., Many such changes would be expected to be disadvantageous , but rarely a change occurs that allows escape from the stress ., Because the events that we studied here in a bacterial model system are similar to those described for copy number changes in human , this conclusion might apply generally throughout biology ., This view suggests that genome evolution might occur in bursts of multiple simultaneous chromosomal changes induced by stress ., This view also has implications for understanding cancer progression in the stressful tumor microenvironment and the stresses imposed by chemotherapy , both of which might induce showers of chromosomal structural changes ., Escherichia coli cells of strain SMR4562 39 , isogenic with FC40 16 carry the conjugative plasmid F′128 with a leaky lac +1 frameshift mutation were initially grown to stationary phase for 3 days at 32° 14 ., We then followed the standard procedure 40 for adaptive mutation experiment in the Lac assay 16 ., Lac+ colonies arise over several days , and are marked daily ., Amplification was distinguished from point mutation by its instability as seen by blue and white sectoring of colonies grown on rich medium with X-gal ., 284 Lac+ colonies from day 7 together with 16 previously published amplified strains 10 were collected for further study ., We also studied 60 Lac+ colonies arising on day 7 that carried point mutations reverting the lac mutation ., 180 Lac− stressed FC40 control cells were collected by taking plugs from the same lactose plate on day 5 ., Sixty colonies derived from unstressed control cells were taken from the initial stationary phase culture ., These 584 new isolates are identified by strain numbers PJH1458–PJH1642 and PJH2025–PJH2425 ., Those described previously 10 are strains PJH2 , PJH5 , PJH6 , PJH7 , PJH19 , PJH20 , PJH22 , PJH26 , PJH27 , PJH39 , PJH59 , PJH64 , PJH79 , PJH80 , PJH81 and PJH165 ., Total genomic DNA was extracted from exponential culture in M9 lactose medium for Lac+ isolates or M9 glycerol medium for Lac− isolates by using the QIAGEN DNA Purification kit ., E . coli custom high-resolution genomic microarray ( 4×44K ) containing 44 , 000 unique sequence oligonucleotides spaced at about 100-bp intervals were obtained from Oxford Gene Technology ( OGT ) ., Probe labeling and hybridization were performed following the manufacturers protocol ( Agilent Oligonucleotide Array-based CGH for Genomic DNA Analysis ) ., Slides were scanned on a GenePix 4000B Microarray Scanner ( Axon Instruments ) ., Data extraction , normalization and visualization were achieved by using Agilent Feature Extraction Software A . 7 . 5 . 1 ., Extraction data were analyzed for copy number differences by using Microsoft Excel software ., All occurrences of two or more adjacent probes showing 2-fold or more increase or decrease in copy number relative to the reference FC40 DNA were investigated further , except those that mapped to repetitive elements or prophages ., All deletion , inversion and duplication junctions were further validated by PCR and sequencing ., Inward-facing primers for deletions and inversions and outward-facing primers for tandem duplication were designed based on sequence from National Center for Biotechnology Information ( NCBI ) Escherichia coli K-12 substr ., MG1655 ., Long-range PCR was performed using LongAmp™ Taq Master Mix ( New England Biolabs ) ., The PCR products were purified with either a QIAquick PCR Purification Kit ( QIAGEN ) or a QIAEX II Gel Extraction Kit ( QIAGEN ) following the manufacturers instructions , and sequenced by Lone Star Labs ( Houston , Texas , United States ) ., DNA sequences were analyzed by comparison to reference sequences with the use of BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) ., Possible secondary structures in DNA were found by use of DNAMAN version 6 ( Lynnon Biosoft ) ., Deamplified lines were derived from amplified isolates by screening for sectors in colonies of amplified strains that showed a low level of β-galactosidase as seen on medium containing X-gal , but yet retained the ability to grow without proline .

Introduction, Results, Discussion, Materials and Methods

Copy-number variations ( CNVs ) constitute very common differences between individual humans and possibly all genomes and may therefore be important fuel for evolution , yet how they form remains elusive ., In starving Escherichia coli , gene amplification is induced by stress , controlled by the general stress response ., Amplification has been detected only encompassing genes that confer a growth advantage when amplified ., We studied the structure of stress-induced gene amplification in starving cells in the Lac assay in Escherichia coli by array comparative genomic hybridization ( aCGH ) , with polymerase chain reaction ( pcr ) and DNA sequencing to establish the structures generated ., About 10% of 300 amplified isolates carried other chromosomal structural change in addition to amplification ., Most of these were inversions and duplications associated with the amplification event ., This complexity supports a mechanism similar to that seen in human non-recurrent copy number variants ., We interpret these complex events in terms of repeated template switching during DNA replication ., Importantly , we found a significant occurrence ( 6 out of 300 ) of chromosomal structural changes that were apparently not involved in the amplification event ., These secondary changes were absent from 240 samples derived from starved cells not carrying amplification , suggesting that amplification happens in a differentiated subpopulation of stressed cells licensed for global chromosomal structural change and genomic instability ., These data imply that chromosomal structural changes occur in bursts or showers of instability that may have the potential to drive rapid evolution .

Much of the difference between individual humans is in the number of copies of genes and lengths of genome ., The mechanisms by which copy number variation arises are not well understood ., We sought information on copy number change mechanisms by extensive use of array comparative genomic hybridization of whole genomes in bacteria selected for amplification of part of the genome ., We report that about 10% of amplified isolates carried other chromosomal structural changes associated with the amplification , a result comparable to that seen in human copy number variants ., Importantly , we found a significant occurrence of structural changes that were not involved in the amplification event ., These were not seen in a control sample of stressed cells not carrying amplification ., This establishes that chromosomal structural change happens in a subpopulation of cells apparently licensed to undergo these changes ., Because the changes occur under the stress of starvation and require two of the cells stress-response systems , we propose that licensing for cell-wide structural change in this subpopulation is a component of response to stress ., This idea has implications for the mechanisms of evolution and cancer progression , suggesting that changes occur in a shower of events rather than as isolated random events .

cancer genetics, genetics, molecular genetics, biology, genetics of disease, genetics and genomics

journal.ppat.1002784

The Rhoptry Proteins ROP18 and ROP5 Mediate Toxoplasma gondii Evasion of the Murine, But Not the Human, Interferon-Gamma Response

Toxoplasma gondii is a widespread intracellular parasite capable of infecting most warm-blooded animals and is an important opportunistic pathogen for immunocompromised individuals and unborn fetuses ., Toxoplasma resides within a non-fusogenic parasitophorous vacuole and has three apical secretory organelles , the micronemes , rhoptries and dense granules , which secrete proteins into the host cell during invasion that mediate important host-pathogen interactions 1 ., In general , an asymptomatic but chronic infection is established in immunocompetent humans ., However , in rare cases Toxoplasma can cause severe disease even in immunocompetent people ., Diverse disease outcomes may be due to genetic differences between infecting strains 2 ., Toxoplasma has a partially clonal population structure of 12–15 3 , 4 haplogroups with the majority of North American and European isolates belonging to the canonical types I , II and III strains 5 , 6 , although haplogroup 12 has been recently shown to be prevalent in wild animals in North America 6 ., In mice , these strains differ in virulence , with type I strains having an LD100 of just one parasite , compared to the LD50 of ∼103 or ∼105 parasites for types II and III strains , respectively 7 , 8 ., Type I strains may also be more virulent in humans , as they are more frequently isolated from cases of congenital or severe ocular toxoplasmosis than from animals 5 , 9 ., Interestingly , in South America , more genetically diverse strains are isolated , while the canonical strains are rarely found 10 ., Some of these strains are associated with high mortality rates in mice 11 ., Additionally , there are high rates of ocular toxoplasmosis in humans in South America 12 , 13 , and some strains isolated from French Guiana have been reported to cause severe disseminated toxoplasmosis even in healthy individuals 14 ., The determinants of canonical strain-specific differences in murine virulence are well studied , but the same determinants for non-canonical strains or for human infection remain unknown ., Mice and humans use divergent immune mechanisms to resist Toxoplasma ., Interferon-γ ( IFNγ ) is essential to murine Toxoplasma resistance , and IFNγ-deficient mice die after infection even with avirulent strains 15 ., Some of the important downstream effectors of this immune activation are the IFNγ-inducible immunity-related GTPases ( IRGs ) , which belong to the dynamin family of GTPases and can cooperatively oligomerize to vesiculate membranes ., Mice deficient in individual members of the IRG family die of toxoplasmosis , but at different stages of infection , and expression of the IRGs is required even in non-hematopoietic cells , suggesting IRGs have non-redundant , crucial roles in the innate immune response against Toxoplasma 16–18 ., Different IRGs are sequentially and cooperatively loaded onto the parasitophorous vacuole membrane ( PVM ) with Irgb6 and Irgb10 initiating and stabilizing the loading of the other members 19 ., The IRGs are able to disrupt the PVM and kill the parasite 20 , 21 ., While mice have 23 IRG genes , humans have only two IRG genes: IRGC which is expressed only in the testis and IRGM which is expressed independently of IFNγ induction and has a truncation in the nucleotide-binding G-domain 22 ., Despite these differences , IRGM plays a role in autophagy-mediated destruction of Mycobacterium tuberculosis and Salmonella typhimurium in human cells , and some variants are associated with increased risk for Crohns disease 23 , 24 ., Thus , IRGM may have an immune role , but its lack of GTPase activity suggests a distinct mechanism of action in humans ., Humans do have other known IFNγ-mediated mechanisms of resistance to Toxoplasma ., For instance , IFNγ-induced indoleamine 2 , 3-dioxygenase ( IDO1 ) degrades cellular tryptophan for which Toxoplasma is auxotrophic , thereby inhibiting Toxoplasma growth 25 , 26 ., The NALP1 inflammasome also mediates the innate immune response to Toxoplasma , and NALP1 was recently identified as a susceptibility locus for human congenital toxoplasmosis 27 ., Toxoplasma strain differences in evasion of murine immune responses exist ., For instance , type I strains are able to prevent the accumulation of IRGs on the PVM , while types II and III strains are susceptible to killing by the IRGs even when co-infecting the same cell as a type I parasite 28 ., Because strain-specific evasion of the IRGs is correlated with increased virulence in the mouse , it is likely that the genetic determinants of IRG evasion will also be associated with virulence ., Quantitative Trait Locus ( QTL ) mapping analyses of the virulence of F1 progeny derived from type I×II , I×III and II×III crosses have identified the genetic loci associated with virulence , and subsequent experiments have identified the causative genes within these loci ., ROP18 , a highly polymorphic rhoptry protein kinase , was identified as a virulence locus in the II×III QTL study and the only virulence locus in the I×III cross 7 , 29 ., ROP18 is highly expressed in types I and II strains but an insertion in the promoter prevents expression in type III strains ., Addition of a type I or II copy of ROP18 into an avirulent type III strain makes that strain become virulent 7 , 11 ., Recently , it was shown that type I ROP18 can phosphorylate a conserved threonine in the G-domain of Irga6 and Irgb6 , disrupting their accumulation on the PVM 30 , 31 ., However , type II strains have the highest percentage of vacuoles coated with IRGs 19 , 28 despite the fact that a type II copy of ROP18 is also able to make a type III strain virulent , suggesting that other polymorphic proteins are involved in IRG evasion 7 ., ROP18 was also shown to promote the degradation of the endoplasmic reticulum-associated transcription factor ATF6-β , compromising CD8 T cell-mediated adaptive immune responses 32 ., Importantly , ROP18-mediated ATF6-β degradation occurs in human as well as murine cells ., The ROP5 locus , which consists of a family of 4–10 tandem duplicates of highly polymorphic genes encoding for rhoptry pseudokinases that localize to the PVM , is another important virulence determinant in mice 33 , 34 ., Deletion of ROP5 in a type I strain significantly attenuates virulence ., Furthermore , ROP5 was the only significant virulence locus identified in the recent I×II QTL analysis and was the main virulence locus in the II×III QTL study 7 , 34 ., Both types I and III strains have a virulent ROP5 locus , but the mechanism by which ROP5 affects virulence and which of the three major ROP5 isoforms , A , B or C , 33 are necessary to complement the virulence of type II are not known ., A third virulence locus , identified in the II×III QTL study , contains the rhoptry protein kinase ROP16 , which in types I and III strains leads to sustained phosphorylation and activation of STAT3/6 35 ., It was recently shown that ROP16 and the dense granule protein GRA15 , suggested to be the fourth virulence locus in the II×III QTL study 36 , affect the accumulation of p65 guanylate binding proteins ( GBPs ) on the PVM in infected murine cells 37 ., Because GBPs are also dynamin family members and were found on the same vacuoles as the IRGs , ROP16 and GRA15 might also affect the accumulation of the IRGs on the PVM ., Furthermore , since the GBPs are present in humans , ROP16 and GRA15 could possibly affect survival in IFNγ-stimulated human cells ., Because the murine and human immune responses to Toxoplasma are so different , it cannot be assumed that ROP18 , ROP5 , ROP16 and GRA15 , which determine Toxoplasma virulence in mice , similarly affect survival in human cells ., Furthermore , it is currently unknown for most of these proteins what effects they have outside the clonal lineages from which they were identified ., Many of the exotic strains are highly virulent in mice , but because they are so divergent from the canonical strains and the exotic strains have not been used in QTL or gene manipulation studies , it is not known what factors drive virulence in these strains ., For example , IRG evasion has not been measured for the exotic strains , and it may be that this is strictly a type I phenotype ., In this study , we find that ROP18 can only inhibit accumulation of the IRGs on the PVM of strains that also express virulent ROP5 alleles ., Expression of ROP18 in strains that do not express virulent ROP5 alleles does not affect IRG accumulation or in vivo virulence ., In contrast , specific ROP5 alleles can reduce IRG coating even in the absence of ROP18 expression and directly interact with Irga6 to inhibit its oligomerization ., Non-canonical strains exhibit differences in evasion of IRG-mediated killing as well , and the allelic combination of ROP18 and ROP5 also correlates with strain differences in IRG evasion and virulence for these strains ., However , neither ROP18 nor ROP5 markedly affect parasite survival in IFNγ-activated human cells ., Type II strains have the highest percentage of IRG-coated vacuoles compared to types I and III strains 19 , 28 even though they possess a ROP18 allele capable of conferring virulence to a type III strain 7 ., To determine if , like ROP18I 30 , 31 , the increased virulence due to ROP18II is correlated with reduced IRG coating in a type III background , we measured the percentage of vacuoles coated with Irgb6 by immunofluorescence in IFNγ-stimulated mouse embryonic fibroblasts ( MEFs ) infected with type I , II , III , III + ROP18I , or III + ROP18II ( Figure 1A ) ., Indeed , transgenic expression in the type III strain CEP of either ROP18I or ROP18II decreased the average number of vacuoles coated with Irgb6 from 45% to 23% ( P\u200a=\u200a0 . 001 ) for ROP18I or 29% ( P\u200a=\u200a0 . 003 ) for ROP18II ( Figure 1B ) ., Although it is generally assumed that once the PVM is coated , it will eventually lead to killing of the parasite inside , it has also been shown that Toxoplasma can escape a coated vacuole and invade a new cell 37 , 38 ., Therefore , to measure killing of Toxoplasma , 100 parasites were seeded on a monolayer of MEFs , either previously stimulated for 24 hours with IFNγ or left untreated , and the number of plaques that form after 4–7 days of growth was determined ., Type III had an average of 45% plaque loss when comparing plaques formed on IFNγ-stimulated MEFs to unstimulated MEFs ., This percentage plaque loss was similar to the percentage of vacuoles coated with Irgb6 , suggesting that coated vacuoles are eventually destroyed ., Furthermore , plaque loss is drastically reduced in Atg7 deficient MEFs ( Figure S1 ) in which the IRGs are misregulated as previously reported for Atg5 deficient MEFs 19 , 39 , suggesting the killing observed is indeed due to the IRGs ., Similar to the decrease in Irgb6 coating , the plaque loss of type III + ROP18I or ROP18II was significantly decreased to 18% ( P\u200a=\u200a0 . 0002 ) and 21% ( P\u200a=\u200a0 . 0004 ) , respectively ( Figure 1B ) ., The 23% PVM coating and 18% killing of type III + ROP18I is similar to the 25% coating and 35% plaque loss of the type I strain GT1 ., Thus , ROP18 expression can likely explain most of the difference in IRG coating and killing between type I and type III strains ., Despite the ability of ROP18II to reduce IRG coating of type III strain vacuoles and subsequent killing of the parasite , type II strains are still very susceptible to the IRGs , with 70% Irgb6 coating and 73% plaque loss for Pru ( type II ) ( Figure 1B ) ., Thus , there must be at least one other gene involved in IRG evasion that is shared between types I and III but different in type II ., It was recently demonstrated that the ROP5 cluster of pseudokinases accounts for most of the variation in virulence between types I and II strains and between types II and III strains , with types I and III strains possessing a virulent ROP5 locus 33 , 34 ., Therefore , the ROP5 locus is an excellent candidate for explaining strain differences in IRG evasion ., We tested a potential role of ROP5 in mediating ROP18-independent strain differences in IRG evasion by using the S22 strain , an avirulent F1 progeny from a II×III cross 40 which possesses the avirulent ROP18III and ROP5II alleles ., We compared the percentage plaque loss and percentage of Irgb6 coated vacuoles between S22 and an S22 transgenic strain carrying the cosmid LC37 , which contains the ROP5 locus from the RH ( type I ) genome and was previously shown to have significantly increased virulence 33 ., Expression of ROP5I significantly reduced the Irgb6 coating from 48% to 28% ( P<0 . 001 ) , and the plaque loss from 38% to 27% ( n . s . ) ( Figure 2A ) ., Thus , ROP5I can function independently of ROP18I/II to prevent IRG accumulation on the PVM and subsequent killing of the parasite ., While ROP5 can function independently of ROP18 in reducing IRG accumulation on the PVM of S22 + LC37 vacuoles , type II strains , which have a virulent allele of ROP18 and an avirulent ROP5 locus , have a high percentage of IRG-coated vacuoles ., This suggests that either ROP18 cannot function independently of ROP5 , or that ROP18 is inhibited in the type II background ., We expressed ROP18II in S22 and in S22 + LC37 to determine if ROP18II can function in the absence of virulent ROP5 alleles ., ROP18II only slightly reduced Irgb6 coating in S22 from 47% to 41% ( n . s . ) and plaque loss from 39% to 24% ( n . s . ) ., However , ROP18II significantly reduced Irgb6 coating from 31% to 7% ( P<0 . 001 ) and plaque loss from 27% to 9% ( P<0 . 01 ) when expressed in S22 + LC37 ( Figure 2A ) ., Together , this suggests that ROP18 needs the virulent ROP5 locus for its function ., That the Irgb6 coating and plaque loss in S22 + LC37 + ROP18II are similar to those in RH ( type I ) signifies that these two genes are sufficient to complement IRG evasion and plaque loss in the S22 background ., To determine if the interactive effect of ROP18 and ROP5 on parasite survival also occurs in vivo , we infected outbred CD-1 mice by intraperitoneal injection with S22 , S22 + ROP18II , S22 + LC37 or S22 + LC37 + ROP18II tachyzoites expressing firefly luciferase and followed parasite growth and dissemination using in vivo imaging ., On the third day after infection , the parasite burden in S22 + LC37 and S22 + LC37 + ROP18II-infected mice was 10-fold higher than in S22 or S22 + ROP18II-infected mice ., By day six , both strains containing the LC37 cosmid had disseminated throughout the peritoneal cavity , but S22 + LC37 + ROP18II-infected mice had 35-fold higher luciferase activity than S22 + LC37-infected mice ( P\u200a=\u200a0 . 03 ) , which in turn had 10-fold higher activity than S22 + ROP18II-infected mice ( P\u200a=\u200a0 . 1 ) and 30-fold higher activity than S22-infected mice ( P\u200a=\u200a0 . 06 ) ., While S22 + ROP18II had a greater parasite burden than S22 , this was not significant ( P\u200a=\u200a0 . 27 ) ., Indeed , S22 + LC37 + ROP18II killed 100% of the mice in the acute stage of infection at both a low and high dose ( Figure 2B and C ) ., Likewise , in keeping with the increased IRG evasion of S22 + LC37 but not S22 + ROP18II , S22 + LC37 showed increased virulence compared to S22 , but S22 + ROP18II-infected mice survived the infection and did not show significant differences compared to S22 infected mice ( Figure 2D ) ., Thus , overall these results suggest that ROP18 only affects virulence in the context of a virulent ROP5 locus ., Although mouse virulence has been determined for many non-canonical strains 11 , it is unknown what factors determine virulence in these strains ., We wondered if virulent non-canonical strains could also evade IRG-mediated killing , or if IRG evasion is specific to type I strains ., We measured the percentage plaque loss in IFNγ-stimulated MEFs as well as percentage Irgb6-coated vacuoles for strains from haplogroups 1–11 6 , 41 ., In general , IRG evasion correlates with virulence as strains that have a mortality rate of greater than 90% in CD-1 mice also have 25% or less Irgb6-coated vacuoles and plaque loss ( Figure 3A ) ., However , some exceptions are CASTELLS and COUGAR , which exhibit greater than 50% Irgb6 coating and plaque loss in IFNγ-stimulated MEFs , despite a high mortality rate in mice 11 ., These strains may have a different mechanism underlying their virulence in mice besides IRG evasion ., For most strains , the Irgb6 coating and plaque loss correlates with their ROP18 allele ( Figures 3A and S2 ) 11 ., For example , CASTELLS and P89 , as well as the type III strains CEP and VEG , have between 40% and 50% Irgb6 coating , and all of these strains do not express ROP18 because they have a ROP18III-like allele that contains an insertion in the promoter 11 ., The strains that express a type I-like allele of ROP18 , with the exception of BOF , display 25% or less Irgb6 coating ., Type II strains and COUGAR are highly susceptible to the IRGs with 70% and 53% Irgb6 coating respectively , despite having the virulent ROP18II allele ., For type II strains , the avirulent ROP5 locus likely explains the high degree of Irgb6 coating , but it is unknown what versions of ROP5 are present in the non-canonical strains ., For most of the strains mentioned above , Irgb6 coating correlates with their ROP18 allele , suggesting that they also have a virulent ROP5 locus , as this is necessary for ROP18 to function ( Figure 2 ) ., It is currently unknown what determines the virulence and IRG evasion properties of the ROP5I/III locus because both copy number and amino acid sequence of the individual copies differ between the canonical strains 33 ., To identify differences that may be associated with virulence or IRG evasion , we sequenced the different ROP5 isoforms of strains from haplogroups 1–11 ( GenBank JQ743705-JQ743783 ) ., Based on the Toxoplasma genome sequence ( www . ToxoDb . org ) and our own genome sequencing of seven non-canonical Toxoplasma strains ( Minot et al . , submitted ) , we identified four distinct ROP5 open reading frames that we amplified and sequenced separately using isoform specific primers ., Sequence chromatograms indicated that two or more alleles were present for the second ROP5 reading frame ., We therefore cloned the PCR product from this ROP5 gene and sequenced multiple clones to obtain sequences from the different alleles , but some alleles may still be missing ., Sequences from this expanded paralog matched what has previously been called both ROP5-B ( minor ) and C ( major ) genes ( Figure 3B ) 33 , 34 ., We could not differentiate B and C alleles for all strains if they were not similar to the canonical strains , so we refer to them here as B copies ., We determined that besides the three major ROP5 copies that were previously described , 2 other highly divergent ROP5 isoforms exist that we call ROP5L-A and ROP5L-B ( Figures S3 and S4 ) ., Interestingly , ROP5L-A and ROP5L-B are highly conserved between strains , but we find that these isoforms are not expressed in tachyzoites ( Figure S3 ) so they will not be discussed further ., The previously described ROP5 genes ( A , B and C ) 33 are highly divergent with strong evidence for diversifying selection ( Figure 3C ) ., , especially in surface exposed residues in the kinase domain 42 In general , for ROP5-A and for ROP5-B and C , which cluster together , alleles can be divided into distinct groups with the BOF , P89 , CAST and GPHT strains grouping with the virulent types I and III alleles ( Figure 3B ) ., A second allelic group consists of the strains VAND , RUB , GUY-KOE , GUY-DOS and GUY-MAT ., The ability to confer virulence of this allelic group is unknown but because these strains are all highly virulent 11 and able to evade the IRGs , these alleles are likely virulent ., A third very divergent group of alleles contains the strains MAS , CASTELLS and TgCatBr5 , but there is less diversity in the ROP5-A , B and C isoforms present in these strains ., The COUGAR allele is most similar to but divergent from the second group , but interestingly , COUGAR has only one B/C allele ., The avirulent ROP5 locus from type II is also divergent , and a phylogenetic analysis of all ROP5 alleles indicates that the type II ROP5-B and C genes are more closely related to ROP5-A than to ROP5-B or C of the other strains ., These results suggest that ROP5-B and/or C could be important for IRG evasion and virulence since type II strains and COUGAR have high levels of IRG coating ( Figure 3A ) and seem to have either ROP5 alleles that are all ROP5-A-like ( type II ) or are missing ROP5-C ( COUGAR ) ( Figure 3B ) ., Next , we tested whether differences in ROP5 expression or copy number could account for strain differences in IRG evasion ., For example , BOF has virulent ROP18 and ROP5 alleles but is highly coated by Irgb6 ( Figure 3A–B ) ., To estimate copy number differences between the strains we have sequenced , we plotted the sequencing coverage of the ROP5 locus versus the average genome coverage , as this was previously shown to be a good estimate for copy number 43 ., Most of the strains had about twice as many reads at ROP5-A and B as the rest of the genome , while MAS and TgCatBr5 have 3–5 copies of each gene ( Figure 3D ) ., However , coincident with our inability to amplify ROP5-A , we found that BOF is missing ROP5-A and has only one copy of ROP5-B ., We also looked at ROP5 expression levels determined using RNA-Seq data from 24 hour infections of murine bone marrow derived macrophages with each strain ( Figure 3D ) ., BOF has barely detectable expression of ROP5-B and no expression of ROP5-A , likely explaining its high Irgb6 coating despite having a similar ROP5-B/C amino acid sequence to types I and III ., Indeed BOF + LC37 has virtually no Irgb6 coating ( 0 . 33% ) compared to BOF ( 40% Irgb6 coating , P\u200a=\u200a0 . 001 ) ( Figure 4A ) ., ROP5 expression levels can also likely explain many intra-haplogroup strain differences where ROP18 and ROP5 coding sequence are the same; for example , VEG has higher ROP5 expression levels compared to the other type III strain CEP , and VEG has slightly reduced IRG coating compared to CEP ., Thus , higher ROP5 expression is correlated with reduced IRG coating , suggesting a non-enzymatic , dose-dependent role for ROP5 in IRG evasion ., Because the LC37 cosmid that reduced Irgb6 coating and plaque loss in S22 and BOF contains ROP5-A , B and C it is unknown which of these isoforms ( or which combination ) is important for IRG evasion ., However , the fact that type II ROP5 alleles are less divergent and more similar to ROP5-A suggests type II is missing ROP5-B and C . Additionally , ROP5-C was previously described as the major allele with A and B as minor alleles when trace reads were assembled for the ROP5 coding region of types I , II and III 34 ., Therefore , we tested if ROP5-AIII , ROP5-CIII or LC37 , which contains the entire ROP5 locus , could complement IRG evasion in the type II background ., Although some of the effects we see in the type II background will be due to an interaction with ROP18 , because ROP18 is present in all backgrounds , we can still compare the effects of individual ROP5 genes ., We find , as expected , that expression of ROP5-AIII in the type II strain Pru led to only a slight but significant reduction in Irgb6 coating ( 51% , P<0 . 05 ) , but expression of ROP5-CIII in Pru led to a significant reduction of IRG coating ( 36% , P<0 . 001 ) similar to that of Pru + LC37 ( 38% , P<0 . 001 ) compared to a heterologous control ( 62% ) ( Figure 4B ) ., The 36% IRG coated vacuoles in Pru + ROP5-CIII is comparable to the 25% IRG coated vacuoles for GT1 , suggesting that the lack of ROP5-C may account for the excessive IRG accumulation on type II vacuoles ., To see if ROP5-CIII can also increase the survival of type II parasites in vivo , we infected CD-1 mice with Pru , Pru + ROP5-AIII , Pru + ROP5-CIII or Pru + LC37 ., The growth and dissemination of Pru and Pru + LC37 was determined by in vivo imaging of luciferase activity ., On the third day post infection , Pru + LC37-infected mice had twice the parasite burden of Pru-infected mice ( Figure 4C and D ) ., By day six , there was 50 fold higher luciferase activity in Pru + LC37-infected mice ( P<0 . 0001 ) , and the parasites had disseminated throughout the peritoneal cavity ., Indeed , 100% of Pru + LC37-infected mice died within 11 days of infection even at the lowest dose ( Figure 4E ) ., Mice infected with Pru parasites expressing only ROP5-AIII or ROP5-CIII survived the infection ( Figure 4E ) but Pru + ROP5-CIII-infected mice had more ruffled fur and lost significantly more weight ( Figure 4F ) than Pru-infected mice throughout the course of infection ( P\u200a=\u200a0 . 01 at 15 days post infection ) while Pru + ROP5-AIII-infected mice continued to gain weight ., Together , these results suggest that while expression of ROP5-CIII can reduce Irgb6 coating of type II parasites , ROP5-CIII only partially enhances the survival of type II parasites in vivo , and the whole ROP5 locus is required to significantly increase virulence in mice ., It is not clear how ROP5 inhibits IRG accumulation at the PVM , but other pseudokinases have been shown to serve as protein scaffolds or to regulate the activity of kinases 44 ., Since ROP18 requires ROP5 for fully efficient IRG evasion , and there is an interactive effect of adding ROP18 and ROP5 to the S22 strain , it is possible that ROP5 and ROP18 interact directly ., To test this hypothesis , we immunoprecipitated ROP5 and ROP18II-HA from MEFs infected with CEP or CEP + ROP18II-HA for one hour with or without previous IFNγ stimulation ., We were unable to detect by western blot co-immunoprecipitation of ROP18 and ROP5 ( Figure 5A ) ., Furthermore , when recombinant , tagged ROP18 kinase domain ( Lim et al . , submitted ) is added to cell lysates from IFNγ-stimulated or unstimulated MEFs infected for one hour with Pru + ROP5-CIIIHA , and ROP5 is immunoprecipitated with anti-HA , we do not co-immunoprecipitate ROP18 ( Figure S5A ) indicating that there is no direct interaction between ROP5-CIII and the ROP18 kinase domain ., Next we tested the hypothesis that ROP18 is only active in the presence of virulent ROP5 alleles by immunoprecipitating ROP18II-HA from MEFs infected with S22 , S22 + ROP18IIHA , and S22 + LC37 + ROP18IIHA for one hour with or without previous IFNγ stimulation for use in an in vitro kinase assay ., We found that there was no difference in the activity of ROP18 immunoprecipitated from parasites with or without a virulent ROP5 , as measured by the phosphorylation of an optimized substrate ( Lim et al . , submitted ) in vitro , ( Figures 5B and S5B ) ., This established that ROP18 was active in all backgrounds and indicated that there are no irreversible effects of ROP5 on ROP18 kinase activity ., Because ROP5 does not directly interact with or irreversibly affect ROP18 kinase activity , we next tested the hypothesis that ROP5 directly interacts with one or more IRGs ., We immunoprecipitated HA-tagged proteins from IFNγ-stimulated or untreated MEFs infected for one hour with Pru , Pru + ROP5-AIII-HA , Pru + ROP5-CIII-HA , or RH + GRA15II-HA and lysed in the presence or absence of GTPγS ( a non-hydrolyzable form of GTP ) ., Co-immunoprecipitated proteins were separated by SDS-PAGE and identified by mass-spectrometry ., We did not recover any ROP18 peptides , again suggesting that ROP5 does not directly interact with ROP18 ., We did , however , recover 13 peptides ( 38% sequence coverage ) from Irga6 only in the Pru + ROP5-CIII-HA infected samples lysed in the presence of GTPγS ( Figure 5C ) suggesting a specific interaction between ROP5-C and Irga6 because the other HA-tagged , PVM associated proteins did not co-immunoprecipitate Irga6 under these conditions ., Under different buffer conditions and in the absence of GTPγS , we also recovered 4 peptides of Irga6 and 2 peptides ( 9 . 8% sequence coverage ) of Irgb10 only in the Pru + ROP5-CIII-HA infected samples ( data not shown ) ., Because ROP5 lacks kinase activity 42 but reduces IRG localization to the PVM , we wondered if Irga6 binding by ROP5 could inhibit Irga6 oligomerization , which is necessary for its activity ., To test this hypothesis , we measured the GTP-mediated oligomerization of recombinant Irga6 by dynamic light scattering in the presence of recombinant maltose binding protein ( MBP ) -tagged ROP5 or MBP alone ., We found the predicted hydrodynamic radius of Irga6 to be reduced in the presence of ROP5 but not MBP ( Figure 5D ) ., Thus , we find that ROP5-CIII binds and inhibits the oligomerization of at least one IRG ., It was recently reported that p65 guanylate-binding proteins ( GBPs ) , members of the dynamin superfamily that includes the IRGs , also accumulate on the Toxoplasma PVM alongside the IRGs 37 ., Because ROP16 and GRA15 were shown to affect GBP coating , we were interested to see if ROP16 and GRA15 also affect IRG coating ., We measured the effect of ROP16 and GRA15 on IRG coating and IRG-mediated killing in types I , II and III genetic backgrounds ., In a type I background , the deletion of ROP16 , the transgenic expression of GRA15II , or both in combination did not significantly alter IRG coating or killing ( Figure 6A and not shown ) ., Likewise , type IIΔgra15 , type II transgenically expressing ROP16I , and type III transgenically expressing GRA15II showed no statistical differences in Irgb6 coating or plaque loss compared to their parental strains ., Thus , while these genes may affect GBP coating , they do not significantly alter Irgb6 accumulation ., Not all of the F1 progeny in the I×II cross that have the type I ROP5 are as virulent as type I in mice 34 indicating that there are other genes besides ROP5 and ROP18 that affect virulence ., While the genetic location of the dense granule protein GRA2 has not been verified as a QTL affecting virulence , an RHΔgra2 strain is one of the few type I knockouts that have reduced mouse virulence 45 ., While the reason for this reduced virulence is unknown , it is known that GRA2 functions in the formation of the tubulovesicular network in the Toxoplasma PVM 46 , which creates negative curvature in the PVM that might help to attract Toxoplasma proteins secreted into the host cell back to the PVM 47 ., Indeed , it has been shown that the RHΔgra2 strain has reduced ROP18 localization to the tubulovesicular network in the Toxoplasma PVM 47 ., We therefore hypothesized that this GRA2-dependent ROP18 and ROP5 localization and/or localization of other proteins , would be important for IRG evasion ., Indeed , the RHΔgra2 strain has significantly increased IRG coating to 36% ( P<0 . 001 ) and increased plaque loss on IFNγ-stimulated MEFs to 24% ( P\u200a=\u200a0 . 08 ) ( Figure 6B ) ., Therefore , a protein required for the formation of the PVM structure also affects IRG accumulation ., We wondered if there are strain differences in the survival of Toxoplasma in IFNγ-stimulated human cells since strain differences in virulence have been primarily studied in mice , and human cells lack the multitude of IRGs present in murine cells ., We measured the percentage plaque loss of different types I , II and III strains as well as of non-canonical strains in human foreskin fibroblasts ( HFFs ) pre-stimulated for 24 hours with IFNγ ( Figure 7A ) ., In general , the percentage plaque loss in IFNγ-stimulated HFFs is higher than in IFNγ-stimulated MEFs ., The type I strains RH and GT1 have plaque losses of 54% and 63% , respectively , while the type II strains ME49 and Pru have plaque losses of 73% and 96% , respectively and the type III strains CEP and VEG have plaque losses of 90% and 67% , respectively ., The non-canonical strains range in plaque loss from 47% ( GUY-DOS ) to 67% ( CASTELLS ) ., Thus , strain susceptibility to IFNγ-mediated killing in human cells does not correlate with that of murine cells ., As we have shown , ROP18 and ROP5 are responsible for most of the strain differ

Introduction, Results, Discussion, Materials and Methods

The obligate intracellular parasite Toxoplasma gondii secretes effector proteins into the host cell that manipulate the immune response allowing it to establish a chronic infection ., Crosses between the types I , II and III strains , which are prevalent in North America and Europe , have identified several secreted effectors that determine strain differences in mouse virulence ., The polymorphic rhoptry protein kinase ROP18 was recently shown to determine the difference in virulence between type I and III strains by phosphorylating and inactivating the interferon-γ ( IFNγ ) -induced immunity-related GTPases ( IRGs ) that promote killing by disrupting the parasitophorous vacuole membrane ( PVM ) in murine cells ., The polymorphic pseudokinase ROP5 determines strain differences in virulence through an unknown mechanism ., Here we report that ROP18 can only inhibit accumulation of the IRGs on the PVM of strains that also express virulent ROP5 alleles ., In contrast , specific ROP5 alleles can reduce IRG coating even in the absence of ROP18 expression and can directly interact with one or more IRGs ., We further show that the allelic combination of ROP18 and ROP5 also determines IRG evasion and virulence of strains belonging to other lineages besides types I , II and III ., However , neither ROP18 nor ROP5 markedly affect survival in IFNγ-activated human cells , which lack the multitude of IRGs present in murine cells ., These findings suggest that ROP18 and ROP5 have specifically evolved to block the IRGs and are unlikely to have effects in species that do not have the IRG system , such as humans .

Toxoplasma gondii can infect any warm-blooded animal and is transmitted orally by consumption of tissue cysts ., To facilitate transmission , the parasite must balance induction and evasion of host immune responses to allow parasite growth and persistence , while avoiding excessive parasite burden , which can kill the host before infectious cysts are formed ., Different strains of Toxoplasma have likely evolved specific effector molecules to modulate the immune responses of different hosts ., In many mammals , including mice but not humans , the cytokine interferon gamma ( IFNγ ) induces the immunity-related GTPases ( IRGs ) , which are essential to the murine immune response to Toxoplasma ., They function by binding to and disrupting the parasite-containing vacuole ., However , some Toxoplasma strains prevent the IRGs from disrupting the parasitophorous vacuole ., It was previously shown that the secreted Toxoplasma kinase ROP18 promotes virulence in mice by phosphorylating the IRGs , leading to their inactivation ., We report that ROP18 requires another virulence factor , the secreted pseudokinase ROP5 , to prevent IRG accumulation , and these two proteins determine the majority of strain differences in IRG evasion , even for divergent strains for which virulence determinants have not been studied ., Additionally , we show that ROP18 and ROP5 do not affect Toxoplasma survival in IFNγ-stimulated human cells ., Thus , ROP18 and ROP5 are strain- and host-specific determinants of Toxoplasma immune evasion .

genetics, immunology, biology, microbiology, molecular cell biology, genetics and genomics

journal.ppat.0040029

A Mouse Model for Chikungunya: Young Age and Inefficient Type-I Interferon Signaling Are Risk Factors for Severe Disease

Chikungunya virus ( CHIKV ) was first isolated in Tanzania in 1953 1 , and has recently emerged in islands of the Indian Ocean in 2005 , and engendered the largest Chikungunya fever epidemic on record 2 ., The most affected region was the island of La Réunion , where CHIKV infected approximately a third of the islands inhabitants ( i . e . , ∼300 , 000 ) 3–5 ., The outbreak , which now also involves India with an estimated 1 . 3 million cases 6–8 , has a significant potential to spread globally given the wide distribution of its arthropod vector 9 , 10 ., CHIKV is a member of the genus Alphavirus in the family of Togaviridae ., Alphaviruses are small , enveloped viruses with a message-sense RNA genome that encodes four non-structural proteins ( nsP1–4 ) and three structural proteins ( C , E1–2 ) ., This arbovirus is maintained in nature by uninterrupted cycles of transmission between mosquitoes and vertebrate hosts such as macaques 11–13 ., Several alphaviruses cause disease in humans , primarily as a result of epizootic infections ., These include the American encephalitic alphaviruses and several species in the Semliki Forest Virus group , principally the Afro-Asian CHIKV , the African ONyong-Nyong virus , as well as the Australasian Barmah Forest virus and Ross River virus 14 ., CHIKV infection is characterized by fever , arthralgia , myalgia , rash and headache ., During the La Réunion Island outbreak , previously unreported severe forms of Chikungunya infection were observed in adults , complicated by encephalopathy and hemorrhagic fever ., These severe cases almost exclusively occurred in adults with underlying conditions such as diabetes , alcoholic hepatopathy or impaired renal function 3 , 15 ., Moreover , while CHIKV-associated fatalities had not been reported prior to this outbreak , at least 213 persons infected with CHIKV died in La Réunion Island 16 , 17 ., Finally , although never described before , per-partum mother-to-child CHIKV transmission has been observed and is associated with severe neonatal disease characterized in more than half of the cases by encephalopathy 18 , 19 ., The pathophysiology of human CHIKV infection has so far remained essentially unknown , in part because of the lack of a permissive small animal model ., In order to gain a better understanding of CHIKV-associated pathophysiology , we have developed a mouse model of infection ., Using this model system and comparing it to human samples , we have uncovered the following pathophysiological features of CHIKV infection:, ( i ) viral dissemination and disease severity are strongly increased during the neonatal period ,, ( ii ) type-I IFN signalling plays a critical role in the control of the infection and is associated with severe infection when deficient ,, ( iii ) symptomatic organs are those infected by CHIKV , and, ( iv ) fibroblasts of connective tissues are prominent cell targets in vivo , in permissive mice as well as in humans ., Together , this study offers the first in depth in vivo analysis of CHIKV cellular tropism and offers a validated small animal model that may prove useful for the development and testing of novel vaccine and therapeutic strategies ., In order to study the pathophysiology of CHIKV infection in the adult and neonatal hosts , we aimed to develop a small animal model of infection ., We first inoculated a series of classical laboratory mouse strains: outbred OF1 mice and inbred C57BL/6 and 129s/v mice ., Intra-dermal ( ID ) injection of 106 PFU of CHIKV-21 , isolated from an individual from La Reunion with central nervous system ( CNS ) symptoms 4 , showed that WT adult OF1 C57BL/6 and 129s/v mice are resistant to CHIKV infection ., Neither morbidity nor mortality was observed following infection and no infectious virus could be recovered from tissues ( unpublished data ) ., In contrast , neonatal C57BL/6 mice exhibited an age-dependent lethality to CHIKV infection ( Figure 1A ) : six-day-old and 9-day-old mice all developed flaccid paralysis on D6 or D7 post infection ( pi ) , and all 6-day-old mice died before D12 pi , whereas more than half of 9-day-old infected mice recovered ., Strikingly , by day 12 of life , C57BL/6 mice showed neither morbidity nor mortality following infection ., We investigated the kinetic of virus replication in tissues of 9-day-old mice at D3 and D5 pi and at the onset of symptoms ( D7 pi ) ., Infectious virus was detected at low level at D3 pi in serum and at D5 and D7 pi in liver ( Figure 1B ) ., Strikingly , at all time points analyzed , infectious CHIKV was detected very abundantly in muscle , joint and skin and to a lower extent in the brain ( Figure 1B ) ., Together , these results establish that , as observed in humans , CHIKV pathogenicity is strongly age-dependent in mice , and that in less-than 12 day-old mouse neonates , CHIKV induces a severe disease ., Of note , in the infected neonatal mice , CHIKV is abundantly detected in the same organs than those symptomatic in humans , particularly infected neonates and babies under the age of one year 18 ., That neonatal mice but not adult mice were permissive to CHIKV infection ruled out the existence of an intractable species barrier in the mouse ., It also questioned the nature of mouse adult non-permissiveness ., Guided by the well-established ability of CHIKV and other alphaviruses to trigger type-I IFN synthesis and their sensitivity to type-I IFN responses 20–24 , we tested the permissiveness of adult IFN-α/βR knockout ( IFN-α/βR−/− ) mice towards CHIKV ., In contrast to WT adult mice , all infected IFN-α/βR−/− adult mice developed a severe disease characterized by muscle weakness of the limbs ( i . e . , loss of muscle tone ) and lethargy and died at D3 pi ( Figure 2A ) ., Whereas no mortality was observed in WT adult mice inoculated with 106 PFU of CHIKV-21 , the lethal dose 50 ( LD50 ) was of 3 PFU in adult IFN-α/βR−/− adult mice , with an average survival of 3 ± 0 . 2 days ., Similar results were obtained when infecting IFN-α/βR−/− adult mice with two other isolates from La Réunion ( CHIKV-27 and −115 ) and one African isolate from Congo ( CHIKV-117 ) ( unpublished data ) ., Together , these results indicated that the basis for the resistance of WT adult mice to CHIKV is linked to type-I IFN signalling , which thus stands as a key player in the control of CHIKV infection ., We next investigated CHIKV replication in tissues of WT and IFN-α/βR−/− adult mice inoculated via the ID route ., As IFNAR-α/βR−/− adult mice are highly susceptible to CHIKV infection , we inoculated them with about 10 LD50 , i . e . , 20 PFU , whereas we inoculated WT mice with 106 PFU , and determined the viral load in mouse tissues at different time points pi ., CHIKV was not isolated from tissues of WT mice at H3 , H16 , H32 , D3 pi , or D6 pi ( Figure 2B and unpublished data ) ., In IFN-α/βR−/− mice at H16 pi , infectious virus was detected only in the liver ., At D3 pi , it was abundantly detected in muscles , joints , skin , and brain with high viral titers also present in serum , liver and spleen ( Figure 2B ) ., Similar results were obtained with the Congolese isolate CHIKV-117 ( unpublished data ) ., In search for a model of non-lethal CHIKV infection that would reflect the mild disease predominantly observed in human adults , we evaluated disease pathogenesis in IFN-α/βR+/− adult mice ., In IFN-α/βR+/− adult mice –which express IFN-α/βR to a similar level found in WT animals , as assessed by FACS analysis ( unpublished data ) – , no mortality nor lethargy was observed upon infection with 106 PFU of CHIK-21 ( Figure 2A ) ., However , in contrast to what was observed in WT adult mice , infectious virus was recovered from liver as early as H16 , at titers similar to that of IFN-α/βR−/− infected mice ( Figure 2B ) ., Strikingly , at D3 pi , infectious virus was confined to muscles and joints ( Figure 2B ) after which the virus was cleared and reached undetectable levels by D6 pi ( unpublished data ) ., These data suggest that a dose effect of type-I IFN receptor gene controls the level of permissiveness of adult mice towards CHIKV , with IFN-α/βR+/− and IFN-α/βR−/− adult mice developing a mild and severe infection , respectively ., As observed in human clinical cases , the mild form of the disease corresponds to a peripheral infection targeting predominantly skeletal muscles and joints , whereas the severe form is also associated with viral dissemination to other organs , including the CNS ., We next investigated the cell tropism of CHIKV in liver at H16 pi and in peripheral infected tissues , namely , liver , skeletal muscles , joints and skin at D3 pi ., In liver of infected adult IFN-α/βR−/− mice at H16 pi , a weak labelling of CHIKV antigens was detected in sinusoidal capillary endothelial cells ( Figure S1A–S1C ) , as well as in some F4/80 labelled mature macrophages ( Figure S1D–S1F ) , whereas at D3 pi , consistent with a sharp increase in liver and serum viral loads , CHIKV immunolabeling was more intense and diffuse , and co-localized within and around sinusoid capillaries ( Figure S1G–S1I ) ., Confirmatory findings were obtained by transmission electron microscopy analysis of sections from the same infected livers ., These images reveal numerous CHIKV particles budding at the surface of sinusoid capillary endothelial cells ( Figure S2A ) ., A lower number of viral particles were also found associated with ( arrow ) or budding at the surface ( arrowhead ) of Kupffer cells ( Figure S2B ) ., In order to further investigate viral replication in this cell type , we isolated primary liver macrophages and infected them in vitro ., Although only low levels of viral replication were observed , we do conclude that liver macrophages are capable of being infected by CHIKV ( Figure S1J ) ., In contrast , brain-derived microglial cells were refractory to infection suggesting that macrophages are not a general target of infection ( Figure S1J ) ., In spleen , the immunolabelling for viral antigen was exclusively observed in the red pulp , notably in F4/80-positive cells ( unpublished data ) ., In skeletal muscles of infected adult IFN-α/βR−/− mice , and to a lower intensity of IFN-α/βR+/− mice , CHIKV immunolabelling was predominantly observed in connective tissue , particularly in the external region , the epimysium ( also called muscle fascia ) , and to a lower extent in the perimysium and endomysium ( Figure 3A–3C ) ., Consistent with these observations , viral load was higher in the epimysium than in the perimysium/endomysium ( unpublished data ) ., The main target cells in muscles were fibroblasts , as shown by the morphology of the cells and their co-immunolabelling with anti-vimentin and anti-CHIKV antibodies ( Figure S3 ) , and the absence of basal lamina surrounding the immunolabeled cells ( Figure 3A–3C ) ., Few immunolabeled F4/80-positive macrophages were also observed in the epimysium and to a lesser extent in the perimysium where they predominated around middle size arteries and veins in a close contact with fibroblasts ( unpublished data ) ., Consistent with a recent study performed on human material 25 , rare satellite cells were also immunolabeled ( arrowhead in Figure 3B ) , readily recognizable as small mononucleated cells located beneath the muscle fiber basal lamina ., In skeletal muscles , myofibers were not immunolabelled with anti-CHIKV antibodies ., In IFN-α/βR−/− adult mice at D3 pi , fibroblasts of the joint connective tissue located beneath the synovial wall were also infected ( Figure 3F–3H ) , but both deep articular and osseous tissues ( i . e . , chondrocytes and osteocytes and osteoblasts ) were uninfected ., In the skin , viral antigens were also observed in fibroblasts of the deep dermis ( Figure 3D , 3E ) ., In IFN-α/βR+/− adult mice at D3 pi , viral antigens were also detected in fibroblasts of the connective tissue of joints ( Figure 3I ) and skin ( unpublished data ) but to a lower level than in IFN-α/βR−/− mice ., Viral cell tropism in infected peripheral tissues of neonates , including muscle , joint and skin was similar to that of adult mice , with a pronounced tropism for fibroblasts ( Figure 4A–4C ) ., A notable difference was the presence of severe necrotic myositis consistent with severe myofiber necrosis and inflammation manifested by the infiltration of lymphocytes and monocytes/macrophages ( Figure 4D , 4E ) ., Importantly , our in vitro experiments with primary mouse and human muscle fibroblasts confirm the high permissiveness of this cell type towards CHIKV ( unpublished data ) ., To investigate whether blood leukocytes were a target for CHIKV in vivo , peripheral blood mononucleated cells from infected IFN-α/βR−/− mice obtained in the course of the infection ( D1 to D3 pi ) were double-stained with an anti-CHIKV and a pan-murine haematopoietic cell marker ( CD45 . 2 antibody ) and analyzed by flow cytometry ., No infected leukocyte was detectable in the blood of infected mice , indicating that blood leukocytes do not represent a significant cell target for CHIKV in vivo ( unpublished data ) , as also reported in the in vitro context 26 ., Together , these data show that in infected peripheral tissues of adult IFN-α/βR+/− and IFN-α/βR−/− mice , as well as in WT neonates , fibroblasts constitute a prominent target cell of CHIKV ., We next investigated the histopathology and CHIKV infection of the CNS ., The only histopathological finding in IFN-α/βR−/− mice at the CNS level was a severe vacuolization of choroid plexus epithelial cells and often of the adjacent ependymocytes ( Figure 5A ) ., Choroid plexuses , ependymal wall , and lepto-meningeal cells , including external cells in the Virchow-Robin spaces , were strongly stained for CHIKV , whereas the brain parenchyma did not show significant labelling ( Figure 6 ) ., We could observe no CHIKV immunolabelling in microglial cells and astrocytes , including those forming the glia limitans ( unpublished data ) ., The choroid plexuses , which form the blood-cerebrospinal fluid ( CSF ) barrier , were infected ( Figure 6D ) ., In contrast , microvascular endothelial cells that constitute the blood-brain barrier ( BBB ) were not ( Figure 6A , arrowheads ) ., Viral titer in the meninges of infected IFN-α/βR−/− was 5-fold higher than in the total brain ( Figure 5B ) ., In the CNS of infected WT mouse neonates , CHIKV infection was also detected at the leptomeningeal level ( Figure 4F ) , but here again , no infection was detected in the brain parenchyma ., To determine whether CHIKV infection altered the permeability of the BBB , we administrated intravenously horseradish peroxydase ( HRP ) , which does not diffuse to the brain parenchyma when the BBB is intact , but leaks into the brain parenchyma in case of BBB disruption 27 ., HRP in brains of infected IFN-α/βR−/− adult mice , was confined to the lumen of brain microvessels , as observed in the brains of mock-infected mice ( Figure 5C ) ., Thus , despite a strong infection of the meninges and of the Virchow-Robin spaces , the barrier function of the brain microvessels was preserved upon infection ., These in vivo findings were confirmed in in vitro BBB systems 28 , 29 ., Primary choroid plexus epithelial cells were highly susceptible to infection via the apical route ( Figure 7A ) and to a lesser extent via the basal route ( Figure 7B ) , suggesting that CHIKV accesses the cerebrospinal fluid through the choroid plexuses , and may also secondarily infect choroid plexus epithelial cells via their apical surface , thus amplifying viral titers in the CSF ., In sharp contrast , primary brain microvessel endothelial cells were fully resistant to CHIKV infection ( Figure 7C ) ., Together , these findings suggest that CHIKV gets access to the CNS via the choroid plexuses , and exhibit a marked tropism for the meninges , whereas it does not infect the brain microvessels and parenchyma and does not induce tissue alteration at the brain parenchyma level ., Given the observations made during the La Réunion outbreak that CHIKV can be vertically transmitted from viremic mothers to their newborns , we investigated maternal-fetal transmission of CHIKV in pregnant IFN-α/βR−/− mice infected with CHIKV-21 via the ID route at D16–18 of gestation ., At D2 pi , animals were sacrificed and viral titers determined in maternal serum as well as in placentas and fetuses ( Figure 8A ) ., As expected , viral load in maternal serum was elevated ., In contrast , placenta viral titers were at least 2 orders of magnitude lower and fetuses were uninfected ( Figure 8A ) ., Moreover , no CHIKV immunolabeling could be observed in these placentas ( unpublished data ) ., The non-permissiveness of the placental barrier towards CHIKV was confirmed in vitro , by the observation that the human syncytiototrophoblastic cell line BeWo is refractory to infection ( Figure 8B ) ., In order to test the relevance for human of our in vivo and in vitro studies , we investigated CHIKV cell and tissue tropisms in biopsy samples of CHIKV-infected humans with acute CHIKV infection ., We developed a sensitive and specific immunohistochemistry assay ( see Materials and Methods ) to detect CHIKV antigens in available human tissue samples from a fatal neonatal case ., In the tissues that are the classical sites of symptoms in the human disease , namely the skeletal muscles , joints and skin , CHIKV antigens were detected , and viral infection appeared to be confined to fibroblasts of the joint capsule , of skeletal muscle fascia and of the dermis ( Figure 9A–9C ) ., As brain human samples were not available , we could not investigate CHIKV dissemination to the CNS ., However , studies in experimentally infected Cynomolgus monkey , who develop a severe CHIKV infection , indicate that CHIKV disseminates to the CNS , where it targets the choroid plexus and the leptomeninges , but not the brain microvessels and parenchyma ( Roques et al . personal communication ) ., Together , these results demonstrate that in humans , CHIKV is present in symptomatic organs , and that the fibroblast is the privileged cell target in these organs ., Moreover , and in agreement with the frequent positivity of CHIKV RT-PCR in the CSF of humans with CNS symptoms 30 , CHIKV is able to reach the CNS via the choroid plexuses and preferentially target the leptomeninges in Cynomolgous monkey ., Here we have combined in vivo , in vitro , and histopathology approaches to gain a better insight in Chikungunya disease pathophysiology ., Using the mouse as a model , we show that in the neonatal host , as well as in adult mice harboring one or two copies of IFN-α/βR null allele , CHIKV exhibits a marked tropism for skeletal muscles , joints and skin , which constitute the classical symptomatic organs in the human disease ., This shows that , in contrast to other acute viral infections in which symptoms may predominantly reflect the systemic immune response rather than viral organ dissemination ( e . g . , influenza ) , classical symptoms of Chikungunya disease closely reflect CHIKV tissue tropism ., Indeed , our study provides direct evidence that in the mouse adult and neonate models , as well as in humans , muscles , joints and skin are privileged CHIKV targets ., We demonstrate here that CHIKV infection severity is critically dependent on two host factors: age and functionality of type-I IFN signaling , thus underlining similarities between CHIKV and the prototypic alphavirus Sindbis 23 , 31 ., In the neonatal host as well as in the adult mouse with a totally abrogated type-I IFN signaling , CHIKV-associated disease is particularly severe , and this severity correlates with higher viral loads and dissemination to the CNS ., Importantly , similar findings have been reported in human neonates and adults with severe disease 32 ., The reasons why the neonatal status and a defect in type-I IFN signaling favor severe CHIKV infection may partly overlap , but specific neonatal factors may also be involved ., Indeed , a number of physiological variables differentiate the neonatal and adult hosts , including the relative proportion of tissue fibroblasts , the rate of cell division , and the maturity and effectiveness of the innate immune system 33 ., Future work will have to focus on the similarities and differences between the neonatal and adult hosts with respect to type-I IFN triggering , signaling and responses ., Nevertheless , the basic characteristics of CHIKV cell and tissue tropisms are conserved in these two complementary models , and their similarity with what observed in humans strongly argues in favor of their pathophysiological relevance ., With the IFN-α/βR+/− mice , we also provide a model for the benign CHIKV human infection ., This animal model should prove very helpful in the development of future vaccine and therapeutic strategies ., Importantly , that the gene copy number of IFN-α/βR strictly influences the viral load and tissue distribution as well as the severity of the disease is a strong indication that the strength of type-I IFN signaling likely plays a critical role in the control of CHIKV replication ., The significance of CHIKV specific tissue tropism is emphasized by the observation that tissue fibroblasts constitute the principal CHIKV cell target in all these infected peripheral organs ., This in vivo finding is consistent with the in vitro observation that primary mouse muscle fibroblasts are susceptible to CHIKV infection ( unpublished data ) as well the recent finding by Sourrisseau and colleagues that cultured human lung and mouse skin fibroblasts are permissive to CHIKV 26 ., The molecular basis for this prominent in vivo tropism for fibroblasts is unknown and may indicate that fibroblasts could be , relative to other cell types , either, ( i ) in a hyper-permissive status towards CHIKV entry/replication , and/or, ( ii ) in a hypo-sensitive status to type-I IFN-mediated viral interference , making them a target of choice for CHIKV ., Interestingly , it is proposed that fibroblasts of connective tissue of dermis , joint capsules 34–36 , and muscles have in common the property to form a reticular network of cells interconnected by gap junctions ., Whether this characteristic contributes to the selective in vivo hyper-susceptibility of connective fibroblasts to CHIKV infection , and if it plays a role in viral cell-to-cell dissemination deserves future investigations ., Of note , similarly to CHIKV in mouse neonates , Ross River virus , an alphavirus closely related to CHIKV and associated with muscle and joint pathology , has been shown to induce myositis in adult mice 23 , 37 , 38 ., The absence of myositis in CHIKV-infected IFN-α/βR−/− adult mice could be linked to their early lethality , while it could be linked to the rapid recovery of CHIKV-infected IFN-α/βR+/− adult mice ., Before reaching its target organs , CHIKV undergoes an early burst of viral replication in the liver in IFN-α/βR+/− and IFN-α/βR−/− mice ., Indeed , in these mice , the liver is the first and only detectably infected tissue until H32 pi , and CHIKV antigens are primarily detected in sinusoidal capillary endothelial cells and to a lesser extent in Kupffer cells ., By D3 pi , and only in IFN-α/βR−/− mice , there is a sharp increase in viremia , with CHIKV antigen detectable in the red pulp of the spleen ., This is in contrast with what has been observed with Sindbis virus infection , in which a high level of infection is detected in the spleen of IFN-α/βR−/− mice as early as D1 pi with a subsequent dissemination to the liver at D2 pi 23 , 37 , 38 ., In the spleen as in the liver , macrophage-dendritic like cells are thought to be the main target cells of Sindbis virus , although by D3 pi in the liver , “sinusoid-lining cells” considered as Kupffer cells and/or endothelial cells are also infected 23 ., These in vivo differences between Sindbis ( and other alphaviruses such as SFV , EEV or Ross River virus ) and CHIKV could reflect the non-permissiveness of dendritic cells to CHIKV ( unpublished data and 26 ) ., We also found that , in contrast to mouse liver macrophages , CHIKV is not detected in mouse blood leukocytes in vivo ., These data are consistent with previous experiments that showed that human primary monocytes are not permissive to CHIKV , whereas human primary macrophages are 26 ., Earlier studies on Sindbis and Ross River also identified the connective tissues of joints and skeletal muscles as sites of viral replication , although the cell type targeted by these viruses in these tissues has not been formally identified 23 , 37 , 38 ., We show here that this connective tissue tropism also extends to CHIKV ., It is thus likely that these shared tropisms and symptoms highlight a common viral pathogenic property , the understanding of which should provide critical clues to the pathophysiologic properties of alphaviruses responsible for arthralgia ., Interestingly , both joint and muscle connective tissues contain a high amount of nociceptive nerve-endings 39 that may account for the muscle and joint pain characterizing disease caused by alphaviruses associated with muscle and joint pathology ., In the case of severe CHIKV infection , we found that CHIKV disseminates to the CNS , as also observed in human 30 and non-human primates ( Roques et al . personal communication ) ., It is noteworthy that all CHIKV isolates tested exhibited a similar ability to reach the CNS ., CHIKV dissemination to the CNS does not correspond to non-specific spreading due to an overwhelming viral multiplication ., Indeed , CHIKV is not detected at the brain microvessel and parenchyma levels , but gets access the CNS exclusively via the choroid plexus route , and undergoes a step of viral amplification at the ependyma and leptomeningeal levels ., In agreement with these findings , CHIKV is detected in the CSF in humans with severe human Chikungunya disease associated with CNS symptoms 30 ., Thus , in contrast to what has been observed for American encephalitic alphaviruses , CHIKV does not appear to be intrinsically encephalitogenic and is associated with reversible CNS symptoms in humans , in line with a virus that does not invade the brain parenchyma nor infect neurons ., That CHIKV does not target the brain endothelium and is not detectable at the brain parenchyma level also contrasts with what has been observed with the more closely related Semliki Forest virus , which targets the brain microvessels and also infects neurons 40 , 41 ., Leptomeningeal tissues are , like fibroblasts , of mesenchymatous origin , and exhibits common features with peripheral fibroblastic connective tissue capsules , as they also play a common “envelop” function and form an interconnected multicellular network that acts as a regulatory interface between cerebrospinal fluid and the surface of the brain and between arterioles within the brain and the surrounding neural tissue ( Virchow-Robin spaces ) 42 ., These common tissue organizations and functions may play a role in CHIKV dissemination ., Given its public health and pathophysiological importance , we also investigated CHIKV infection in the pregnant host ., By use of the most permissive model we have developed , the IFN-α/βR−/− mice , we show that the placenta does not constitute a privileged target for CHIKV ., Indeed , no infected cells can be detected when observing placental tissue sections from infected mice ., This is in line with our investigations carried out in human placentas obtained from viremic mothers , in which no infected cell could be detected by mean of immunohistochemistry either 18 ., This suggests that viral titers detected in mouse and human placentas rather correspond to a contamination from remaining maternal serum than to an actual placental infection ., This is supported by our in vitro finding that human syncytiotrophoblast is refractory to infection , and by the observation that ante-partum fetal contamination is exceptional in humans 18 ., This provides an explanation for why all cases of vertical transmission of CHIKV in the recent outbreak in La Réunion were observed in per-partum , at a time when highly viremic maternal blood can get in contact with the fetal circulation , particularly in the setting of the uterine contractions of the labor , which are known to induce placental barrier breaches ., These observations appear to contrast with what observed with Ross River virus , for which actual placental infection and transplacental dissemination have been described both in mice and humans 43 , 44 ., In conclusion , we have developed a mouse animal model for CHIKV infection , allowing us to uncover the viral tissue and cell tropism of this re-emerging alphavirus ., We have shown that , in this model , as well as in humans , the fibroblast is the cell type chiefly targeted by CHIKV , and that this accounts for its tropism for muscles , joint and skin connective tissues ., The molecular basis for this tropism is currently unknown but may combine specific virus-host cell and tissue interactions as well as an intrinsic relatively lower ability of this cell type to control CHIKV infection ., We have also identified two critical factors influencing viral replication , which are the neonatal status , and a defective type-I IFN signaling ., Whereas it is clear that an increased neonatal susceptibility is also observed in humans , the relevance of type-I IFN defect as a basis for severe infection in humans remains to be demonstrated ., However , the fact that severe infections in humans are exclusively observed in individuals with underlying conditions renders this hypothesis attractive 3 , 15 ., This may indicate that type-I IFN could be of interest to prevent severe disease in adults , as well as in exposed neonates ., In addition , the use of neutralizing antibodies also appears interesting , given the strong correlation between viral load and disease severity ., A better understanding of the pathophysiology of CHIKV infection and the ensuing development of therapeutic strategies are both critical in the context of a possible globalization of the current CHIKV epidemic ., CHIKV isolates were obtained from individuals during the 2005–06 CHIKV outbreak in La Réunion Island and amplified on mosquito C6/36 cell as described 4 ., CHIKV-21 was isolated from the serum of a newborn male with CHIKV-associated encephalopathy; CHIKV-27 was isolated from the CSF of another new-born male with encephalopathy; CHIKV-115 from the serum of a 24-year old female with classical CHIK symptoms ., CHIKV-117 was isolated at the Institut de Médecine Tropicale du Service de Santé des Armées ( IMTSSA ) , Marseille , France during the 2000 CHIKV outbreak in Democratic Republic of the Congo from the serum of a person presenting classical CHIK symptoms ., Titers of virus stocks were determined by standard Vero cell plaque assay and are expressed as PFU per ml ., Primary choroid plexuses and brain microvascular endothelial cells were obtained , purified and cultured as described 28 , 29 ., The Bewo cell line was obtained from the ATCC ., Cells were infected with CHIKV at a multiplicity of infection ( MOI ) of 10 ., Human tissue samples were obtained from biopsy specimens collected in the course of the clinical care of people with CHIKV infection in La Réunion ., Outbred OF1 mice , and inbred C57BL/6 and 129s/v mice were obtained from Charles River laboratories ( France ) ., IFN-α/βR−/− 129s/v mice were given by F . Tangy with permission from M . Aguet 45 ., Mice were bred according to the Institut Pasteur guidelines for animal husbandries and were kept in level-3 isolators ., Mice were inoculated by ID in the ventral thorax with 50 μl of a viral suspension diluted with PBS for adult mice and with 30 μl for neonates ., Mock-infected mice received PBS alone ., Mice were anesthetized with isoflurane ( Forene , Abbott Laboratories Ltd , United-Kingdom ) ., Blood was collected by cardiac puncture after which each mouse was perfused via the intracardiac route with 40 ml of PBS at 4 °C before harvesting of organs ., Tissues were homogenized , and virus tite

Introduction, Results, Discussion, Materials and Methods

Chikungunya virus ( CHIKV ) is a re-emerging arbovirus responsible for a massive outbreak currently afflicting the Indian Ocean region and India ., Infection from CHIKV typically induces a mild disease in humans , characterized by fever , myalgia , arthralgia , and rash ., Cases of severe CHIKV infection involving the central nervous system ( CNS ) have recently been described in neonates as well as in adults with underlying conditions ., The pathophysiology of CHIKV infection and the basis for disease severity are unknown ., To address these critical issues , we have developed an animal model of CHIKV infection ., We show here that whereas wild type ( WT ) adult mice are resistant to CHIKV infection , WT mouse neonates are susceptible and neonatal disease severity is age-dependent ., Adult mice with a partially ( IFN-α/βR+/− ) or totally ( IFN-α/βR−/− ) abrogated type-I IFN pathway develop a mild or severe infection , respectively ., In mice with a mild infection , after a burst of viral replication in the liver , CHIKV primarily targets muscle , joint , and skin fibroblasts , a cell and tissue tropism similar to that observed in biopsy samples of CHIKV-infected humans ., In case of severe infections , CHIKV also disseminates to other tissues including the CNS , where it specifically targets the choroid plexuses and the leptomeninges ., Together , these data indicate that CHIKV-associated symptoms match viral tissue and cell tropisms , and demonstrate that the fibroblast is a predominant target cell of CHIKV ., These data also identify the neonatal phase and inefficient type-I IFN signaling as risk factors for severe CHIKV-associated disease ., The development of a permissive small animal model will expedite the testing of future vaccines and therapeutic candidates .

Chikungunya virus ( CHIKV ) is transmitted by mosquito bites ., CHIKV has recently re-emerged and is responsible for a massive outbreak in the Indian Ocean region and India ., It has also reached Italy , indicating that CHIKV has a great potential to spread globally ., Infection from CHIKV typically induces a mild disease in humans , characterized by a flu-like syndrome associated with muscle and joint pain and rash ., Cases of severe infection involving the central nervous system ( CNS ) have recently been described , notably in neonates ., We have developed the first animal model for CHIKV infection and studied the pathophysiology of the resulting disease ., We show here that mouse neonates are susceptible to CHIKV and neonatal disease severity is age-dependent ., Adult mice with a partial or complete defect in type-I interferon pathway develop a mild or severe infection , respectively ., In mice with a mild infection , CHIKV primarily targets muscle , joint and skin fibroblasts , a cell and tissue tropism similar to that observed in biopsy samples of CHIKV-infected humans ., In case of severe infections , CHIKV also disseminates to the CNS ., Our work indicates that CHIKV-associated symptoms perfectly match viral tissue and cell tropisms , and demonstrate that the fibroblast is a prominent target cell of CHIKV ., It also identifies the neonatal phase and inefficient type-I interferon signaling as risk factors for severe CHIKV-associated disease ., The development of a permissive small animal model will expedite the testing of future vaccines and therapeutic candidates .

viruses, infectious diseases, virology, microbiology, mus (mouse), homo (human)

journal.pntd.0001820

G Protein-Coupled Receptor Kinase 2 Promotes Flaviviridae Entry and Replication

Although a century has passed since yellow fever virus ( YFV ) , which gave flaviviruses their name , was shown to cause a human disease , flaviviral epidemics still represent a serious threat worldwide ., Viruses such as dengue virus ( DENV ) and YFV have undergone a major geographic expansion in the past two decades 1 , 2 ., Currently , there are no approved treatments and few preventives for flaviviral diseases ., Effective vaccines have been developed only against YFV , tick-borne encephalitis virus and Japanese encephalitis virus 3–5 ., The YFV vaccine was generated in 1937 6 and its use decreased occurrence of the disease significantly ., Spread of the mosquito vectors range , increased urbanization , and , importantly , lack of vaccination in at-risk regions , however , has led to the reemergence of yellow fever 7 ., Thus , there is a critical need for the development of anti-flaviviral treatments ., The molecular details of host-virus interactions taking place during flaviviral infection remain mostly unknown ., Excitingly , the recent development of genome-scale siRNA screens to identify host factors required for viral propagation has shed light on the complexity of these interactions 8 , 9 ., We sought to identify host factors shared by different flaviviruses , as these may represent attractive targets for the development of broad-spectrum anti-flaviviral therapies ., Thus , we conducted genome-scale siRNA screens to investigate human host factors required for YFV propagation and identified hundreds of candidate genes ., Among these candidates are two members of the G protein-coupled receptor kinase family ( GRK ) , GRK2 and GRK4 ., The GRK proteins have been primarily described for their role in G protein-coupled receptor ( GPCR ) phosphorylation leading to receptor desensitization 10 , 11 , but they also interact with , and presumably phosphorylate , dozens of non-receptor cellular factors 12 , 13 ., Here , we showed that GRK2 is required for efficient propagation of viruses from the Flaviviridae family and that a decrease in GRK2 level alters both virus entry and RNA genome amplification ., All cell lines were cultured at 37°C , 5% CO2 in Dulbeccos Modified Eagle Medium ( DMEM , Invitrogen ) supplemented with 10% Fetal Bovine Serum ( FBS , Gemini ) ., GRK2−/− , GRK2−/− , bGRK2 ( which stably express bovine GRK2 in a GRK2−/− background ) , GRK6 −/− , and β-arrestin 1/2-KO MEFs were kindly provided by Dr . R . J . Lefkowitz ( Duke University ) ., In immuno-fluorescence assays , DENV was detected with the anti-E mouse monoclonal antibody 4G2 ( isolated from the DI-4G2-4-15 hybridoma , American Type Culture Collection ) used at a 1∶2000 dilution ., YFV-17D was detected with the mouse ascites fluid from animals harboring a hybridoma line produce after immunization with YFV ( YF-mAF ) ( available through ATCC in 2005 under the reference NIAID V-525-701-562 ) used at a 1∶2000 dilution , or with anti-E antibody described above ., HCV was detected with the anti-core primary antibody ( C7-50 , Abcam ) used at a 1∶500 dilution ., The Alexa488-conjugated anti-mouse secondary antibody ( Invitrogen ) was used at a 1∶2000 dilution ., Nuclei were stained with Hoescht 33342 ( Sigma ) ., For western blotting assays , proteins were extracted in 100 mM Tris-HCl pH 7 . 5 , 4 mM EDTA , 1% Triton X100 ., GRK2 was detected using the GRK2 C-15 antibody ( Santa Cruz Biotechnology ) at a 1∶500 dilution ., ß-actin was detected with the β-actin C4 antibody ( Santa Cruz Biotechnology ) at a 1∶4000 dilution ., Western blots were analyzed using secondary antibodies and Odyssey infrared scanner from Li-Cor ., The dengue replicon constructs DRrep and DRrep-RdRPmut were a generous gift from Dr . E . Harris ( UC , Berkeley ) and are described in 14 ., DRrep and DRrep-RdRPmut DNA constructs were digested by XbaI and purified using the QIAquick PCR purification kit ( QIAGEN ) ., RNA templates were produced by in vitro transcription using the MEGAscript T7 kit ( Ambion ) following the manufacturers protocol for synthesis of capped RNAs ., Few modifications were made: 2 µg of DNA template were used and the cap analog was m7G ( 5′ ) ppp ( 5′ ) A ( New England BioLabs ) ., The reactions were incubated for 6 hr at 37°C and subsequently treated with TURBO DNase for 15 min at 37°C ., The Fluc construct was created by insertion of the Fluc open reading frame , amplified by PCR from pGL3 ( promega ) , into pTNT ( Promega ) using XbaI and XhoI ., RNA templates were transcribed in vitro using the MEGAscript T7 kit following the manufacturers protocol ., All the transcribed RNAs were purified using the RNeasy kit ( QIAGEN ) ., Total RNA was isolated from infected HuH-7 cells using TRI Reagent ( Sigma ) ., cDNA was generated using High-Capacity cDNA Reverse Transcription kit ( Applied Biosystems ) ., Quantitative PCR ( qPCR ) was performed using Power SYBR Green PCR master mix ( Applied Biosystems ) and the StepOnePlus real-time PCR system ( Applied Biosystems ) ., The primers used to detect YFV-17D were 5′-GGGCGAAGGAGTATCCCAGT-3′ and 5′-ACGCTAACCAGCATCATCAGGAGT-3′ ., Each sample was normalized based on the amount of beta-actin detected using the primers 5′-GCTCGTCGTCGACAACGGCTC-3′ and 5′-CCTCGTCGCCCACATAGGAATC-3′ ., The relative amount of YFV-17D cDNA present in the different samples was calculated as described in 15 ., Preparation of DEN2-NGC and YFV-17D stocks has been previously described 8 ., Infectious stocks of JFH1 HCV were generated and titrated by foci forming assay using HuH-7 . 5 cells as described in 16 ., Titers of stocks and experimental samples were determined by foci forming assays ., These assays were performed as described in Sessions et al . 8 with the following modifications: 2×105 Vero cells/well were plated in 24-well plates ., Following virus adsorption , 0 . 5 mL of 1∶1 1 . 2% Tragacanth Gum ( Sigma ) /2× EMEM ( Lonza ) , supplemented with 5% FBS and 10 mM HEPES ( Invitrogen ) was added per well ., The primary antibodies 4G2 and YF-mAF were used to detect DEN2-NGC and YFV-17D , respectively ., Alexa488-conjugated anti-mouse antibody was used as secondary and foci were detected by immuno-fluorescence ., The protocol used to screen the Human genome siRNA library ( QIAGEN ) has been described in detail in Barrows et al . 17 ., Briefly , HuH-7 cells were reverse transfected with a total siRNA concentration of 15 . 4 nM , each siRNA being present at 7 . 7 nM ., Approximately 51 hr after transfection , the siRNA-treated cells were infected with YFV-17D at a M . O . I . of 0 . 1 ., Forty two hours post-infection , the HuH-7 cells were fixed with 4% PFA and stained with the 4G2 primary antibody followed by the Alexa488-conjugated secondary antibody ., Each assay well was imaged using the Cellomics Array Scan VTI system ( Cellomics ) ., To determine the percentage of infection , the acquired images were analyzed using the vHCS Scan version 5 . 1 . 2 ., When siRNAs are used individually , the same protocol as described above was used except that HuH-7 cells were reverse transfected with 7 . 7 nM of siRNA ., 22 , 909 genes were interrogated by the Qiagen Human Genome siRNA Library ., 21 , 529 genes passed the criteria that the cell density in all 4 sets ( GS1AB , GS1CD , GS2AB , GS2CD ) exceeded our minimum threshold ., Within each set the percent infection values were ordered low to high , and a rank was assigned ., The ranks were summed for each gene generating the sum rank 4SR ., The statistical limit to identify a putative YFV host factor from the genomic screen was set , a priori , at a p-value≤0 . 00135 ., 395 hits were identified as required for the propagation of YFV and are presented in Table S3 ., The 4SR values were normalized to the mean and standard deviation for a simulated population distribution and the Z-statistic and associated p-value are reported in Table S1 ., For a detailed description of the 4SR statistical interpretation see Figure S1 ., The entry assay was performed following protocols in 18 , 19 ., HuH-7 cells treated with siRNA control or siRNA targeting GRK2 for 48 hr were incubated with YFV-17D ( M . O . I . =\u200a5 ) at 37°C ., After 2 hr cells were placed at 0°C and washed three times with chilled PBS ., Cells were then incubated with chilled 1 M NaCl , 50 mM Na2CO3 , pH9 . 5 for 3 min ., This solution removes the surface bound virus without affecting the internalized virus ., Cells were washed again three times with chilled PBS and finally treated with TRI Reagent ( Sigma ) to collect total RNA ., HuH-7 cells treated with siRNA control or siRNA targeting GRK2 for 48 hr were trypsinized and wash once with DPBS ( Invitrogen ) , and resuspend in 0 . 5 mL of OPTI-MEM I ( Invitrogen ) to a concentration of 5×106 cells per mL ., 15 µg of replicon RNA , 3 µg of Fluc RNA control and 80 pmol of siRNA were added to the cells ., The mixture was transferred to a 0 . 2 cm cuvette ( Bio-Rad ) for electroporation and kept on ice ., Electroporation was performed at room temperature using the Gene pulser II apparatus ( Bio-Rad ) set at 270 V and 200 µF ., Immediately after the pulse , 1 mL of media was added to the cells ., Later the electroporated cells were diluted in 11 . 5 mL of DMEM , 10% FBS media and 0 . 5 mL/well was plated in 24-well plate ., At indicated times , cells were washed once with DPBS and lysed into 100 µL of passive lysis buffer ( Promega ) ., The luciferase activities were measured using the Dual-Luciferase Reporter assay kit ( Promega ) following the manufacturers protocol ., Rluc activity from the replicons was normalized to the mean of Fluc values detected during the first 26 hr ., Statistical analyses were performed using GraphPad Prism 5 . 0 software as described in the figure legends ., To identify candidate human host factors required for efficient propagation of YFV , we performed two identical genome-wide siRNA screens ., Performing duplicate screens , five months apart , also represented a unique opportunity to analyze the reproducibility of siRNA-based whole-genome screens , as reported in Barrows et al . 17 ., The screens were done in HuH-7 cells using the YFV vaccine strain 17D ( YFV-17D ) as summarized in Figure 1A ., Infected cells were visualized by immuno-fluorescence staining of the viral envelope protein and quantified using high-content imaging and analysis ., We screened using a 2×2 pooled siRNA library format which has the advantage of interrogating each gene with two independent siRNA sets each containing two siRNAs ( siRNA A and siRNA B ( set AB ) ; siRNA C and siRNA D ( set CD ) ) ( Figure 1A and 17 ) ., As indicated in Figure 1A , the percent infection from the four siRNA sets , GS1AB , GS1CD , GS2AB and GS2CD , were each ordered lowest to highest and attributed a rank from 1 to n ( n\u200a=\u200atotal number of genes analyzed ) ., For each gene , the four ranks were summed and genes having a low Sum Rank ( SR ) were considered as YFV host factor candidates ., The use of 4SR analysis minimized the number of false positive off-target candidates since a gene having a low 4SR was likely the result of a gene scoring with both independent siRNA sets in both screens ., We used the 4SR described above to derive a hit list composed of 395 members ( Table S1 ) ., Figure 1B and 1C show how candidate host factors distributed in the overall GS1 and GS2 population , respectively ., In order to assess the quality of the primary screening data and of the 4SR analysis , we tested the four unique siRNAs ( A , B , C , and D ) individually for a subset of candidates present in this 395 hit list ., The effect of four individual siRNAs targeting 98 candidate genes was assayed in triplicate ., The ranks of the 98 genes tested are shown in Figure S2 ., Two siRNAs were used as negative controls: siGFP , which targets the transcript coding for the Green Fluorescent Protein ( GFP ) , and a non-silencing control siRNA , siNSC , described by Quiagen as an siRNA with no homology to any known human gene ., An siRNA was considered validated if it decreased the percentage of infection by at least 2 fold compared to infection observed with siGFP ., The siGFP was used as a reference because it was the more conservative of the two negative controls: 76 . 6% infection in siGFP treated cells versus 84 . 5% infection in siNSC treated cells ., In this validation , 88 out of 98 genes scored ( 89 . 8% ) with two siRNAs or more showing a 2-fold decrease of the level of infection and an associated p-value≤5 . 8×10−6 ( Table S2 ) ., This level of validation shows that when a 2×2 pooled siRNA library format is used , a high confidence list of gene candidates can be directly derived from the primary screen data ., It should be noted that the validation rate for a particular candidate may vary depending on its SR ( Figure S2 ) ., We compared the 395 candidate host factors required for YFV propagation to those identified for WNV and DENV 8 , 9 ., We also considered a sub-genomic screen performed by Drs ., P . W . Mason and F . Santa Maria Guerra , which interrogates a subset of 5500 genes to look for WNV host factors 20 ., The commonalities between the siRNA screens looking at flavivirus host factors are presented in Table S3 ., Previously described host factors such as subunits of the vacuolar ATPase complex ( V-ATPase ) 21–23 were identified by all three different screens ., Interestingly , members of the GRK family , mostly known for their role in GPCR phosphorylation , were also represented among the shared factors ., GRK6 was identified as a WNV host factor and GRK2 and 4 were identified as YFV host factors ., Both GRK2 and GRK4 were identified as YFV host factor candidates ., To explore further the role of the GRK family in YFV-17D propagation , we focused our experiments on GRK2 , which is ubiquitously expressed in contrast to mainly expression in testis for GRK4 24 , 25 ., To confirm the screen results and to validate GRK2 as a YFV host factor , GRK2 was silenced in HuH-7 cells using the four siRNAs from the screened library individually ., In this experiment , we used two negative control siRNAs: the siGFP and the siAllStars which is a newer version of non-silencing siRNA provided by Qiagen and that replaces the original siNSC described earlier ., We also used a positive control siRNA , siV0C , which targets the transcript coding for ATP6V0C , a subunit of the V-ATPase complex previously shown to be required for flavivirus infection 8 , 9 ., The four siRNAs targeting GRK2 led to a 3 to 7 fold knockdown of the protein ( Figure 2A , lanes 4 to 7 ) compared to the three siRNA controls ( Figure 2A , lanes 1 , 2 and 3 ) ., The siRNA treated cells were infected with YFV-17D at a M . O . I . of 0 . 1 and the level of infection was assayed by immuno-fluorescence after 42 hr ., Representative images are presented in Figure 2B ., The quantification of those images showed that , as expected , in cells transfected with the positive control siV0C , the level of infection was dramatically reduced ( Figure 2C ) ., In cells transfected with siGRK2_1 , siGRK2_2 and siGRK2_6 , the extent of YFV-17D infection was reduced by 2 to 3 fold compared to infection in cells treated with negative controls siRNAs ., These observations validate GRK2 as a protein required by YFV-17D to replicate efficiently in HuH-7 cells ., Unexpectedly , cells treated with siGRK2_5 in which the GRK2 protein level was decreased did not show a reduction of YFV-17D infection ., This may be due to an off-target effect of this siRNA targeting not only GRK2 transcript but also a host factor which knockdown counteracts GRK2 knockdown effects ., To test whether the decrease of infection observed by immuno-fluorescence correlates with a diminution of infectious particle release , we measured the viral titer of culture media collected at the end point of the assay described above ., Media from siRNA-treated HuH-7 cells infected with YFV-17D were used in focus forming assays ., In the media from the GRK2 knocked down cells in which the YFV infection was altered , the viral titer was decreased by 2 . 4 to 6 . 3 fold compared to the control siRNA-treated cells ( Figure 2D ) ., To further confirm a role of GRK2 in YFV infection , a siRNA-independent validation was also carried out using mouse embryonic fibroblasts ( MEFs ) in which the GRK2 gene was knocked out ( GRK2−/− MEFs ) ., YFV-17D infection in GRK2−/− MEFs was compared to infection in GRK2−/− , bGRK2 MEFs which stably express bovine GRK2 in a GRK2−/− background ( Figure 3A ) ., These two cell lines were infected with YFV-17D at a M . O . I . of 1 , 5 and 10 ., After 36 hr of infection the cells were immuno-stained for YFV-17D , and the percentage of infected cells was quantified ., Representative images are presented in Figure 3B ., In the GRK2−/− MEFs , the infection was decreased by 7 . 8 to 19 . 8 fold at a M . O . I . of 1 and 5 , respectively , compared to the infection in GRK2−/− , bGRK2 cells ( Figure 3C ) ., At the highest M . O . I . of 10 , low level of infection could be detected in the GRK2−/− cells , indicating that GRK2 was not absolutely essential for infection but was required for robust infection in MEFs ., In media from GRK2−/− MEFs , the viral titer was also reduced ., A 45 fold decrease was observed between the GRK2−/− , bGRK2 and the GRK2−/− MEFs ( Figure 3D ) ., Taken together , these results confirmed GRK2 as a YFV host factor both in human and murine cell lines ., GRK6 was identified by Krishnan et al . as a WNV host factor 9 suggesting that members of the GRK family may be pan-flaviviral host factors ., To test this hypothesis we asked whether GRK2 is also required for DENV propagation and if GRK6 is required for YFV-17D and DENV propagation ., First , GRK2 was knocked down in HuH-7 cells using two validated siRNAs individually ., As in the previous experiment , siAllStars , siGFP , and siV0C were used as siRNA controls ., The siRNA treated cells were infected with DEN2-NGC 48 hr later ., After 42 hr of infection , the cells were fixed and immuno-stained for the presence of E protein ., Quantification of the infected cells showed a 2 to 5 fold decrease in cells treated with siGRK2 compared to cells treated with control siRNAs ( Figure 4A ) ., DENV2 infection was also measured in GRK2−/− and GRK2−/− , bGRK2 MEFs ., Both cell lines were infected with DEN2-NGC at a M . O . I . of 1 , 5 or 10 ., Thirty-six hours later the cells were fixed , immuno-stained , and imaged ( Figure 4B ) ., In the GRK2−/− MEFs the infection was decreased by 28 to 33 fold compared to the infection in the GRK2−/− , bGRK2 ( Figure 4C ) ., Secondly , we tested another GRK , GRK6 , which was required for WNV infection 9 ., A GRK6 requirement was assessed using MEFs in which the GRK6 gene was knocked out , GRK6 −/− MEFs ., GRK6 +/+ MEFs derived from littermates were used as control ., GRK6 MEFs were infected with YFV-17D or DEN2-NGC at a M . O . I . of 1 , 5 or 10 ., Quantification of the infected cells after immuno-staining showed a 4 to 5 fold decrease of YFV-17D infection and a 2 to 3 fold decrease of DEN2-NGC infection in the GRK6 −/− MEFs ( Figure 3C and Figure 4C ) ., These results suggested that GRK6 , like GRK2 , was needed by both YFV and DENV to establish a high level of infection ., A recent siRNA “kinome” screen looking for factors required for hepatitis C virus ( HCV ) entry also identified GRK2 as a host factor 26 ., HCV is a distinct member of the Flaviviridae that exclusively infects humans and establishes chronic infection of the liver ., In order to verify this requirement in our experimental conditions , GRK2 was knocked down in HuH-7 . 5 cells 27 and the siRNA treated cells were subsequently infected with the replication-competent JFH1 strain of HCV 28 ., After 72 hr of infection , the cells were fixed and immuno-stained for the presence of HCV core protein ., Quantification of the infected cells showed a 2 to 8 fold decrease in cells treated with siGRK2 compared to cells treated with control siRNAs ( Figure 4D ) ., Of note , in contrast to YFV-17D and DEN2-NGC , we also observed that HCV infection did not require the ATP6V0C subunit to propagate ., In conclusion , our results indicated that the GRK family , specifically GRK2 and GRK6 , was required for two different flaviviruses , YFV-17D and DEN2-NGC , to propagate efficiently in both human and murine cell lines ., GRK2 was also required for HCV propagation in human cells ., Taken together , our results suggested that GRK2 is required by multiple members of the Flaviviridae ., Decrease of infection observed upon reduction of GRK2 expression may be due to an alteration of virus entry , viral RNA translation , viral RNA synthesis , and/or infectious particle assembly and release ., It has recently been proposed that GRK2 might be a host factor for HCV entry 26 ., Therefore , to test whether GRK2 participates to the early steps of the flaviviral life cycle , we examined the ability of YFV-17D to enter HuH-7 cells treated with siRNA control or targeting GRK2 ., To assess entry , HuH-7 cells treated with siNSC or siGRK2_2 were incubated with YFV-17D for 2 hr at 37°C which allows binding and internalization of the virus ., To remove membrane-associated virus and ensure that only the internalized virus was measured , cells were treated with high-salt alkaline buffer ., After additional washes , total RNA was collected and analyzed by qRT-PCR ., This analysis indicated that the internalization of the virus was decreased by 2 . 5+/−0 . 5 fold in cells treated with siGRK2_2 compared to cells treated with siRNA control ( Figure 5A ) ., These results suggest that GRK2 plays a role in YFV-17D entry into HuH-7 cells ., We cannot exclude , however , that the lower amount of YFV-17D RNA detected in cells treated with siGRK2 is the result of a lower stability of the viral genome when GRK2 is absent ., It has been previously shown that flaviviruses use clathrin-coated vesicles to enter cells 29 , 30 ., They share this property with GRK-phosphorylated GPCRs , which are internalized by a clathrin-dependent mechanism ., After binding to ligands , GPCRs are phosphorylated by GRKs , and subsequently bind β-arrestins which interact directly with the clathrin complex and initiate receptor internalization 31 ., In this process , β-arrestins are essential to clathrin-dependent internalization 11 , 31 , 32 ., The arrestin family consists of four members but only β-arrestin 1 and β-arrestin 2 are ubiquitously expressed ., Participation of β-arrestins in flavivirus infection was tested using MEFs in which both β-arrestin 1 and 2 genes were knocked out ., Wild type ( WT ) MEFs derived from littermates were used as control ., β-arrestin 1/2-KO and WT MEFs were infected with either YFV-17D or DEN2-NGC at a M . O . I . of 1 , 5 and 10 ., After 36 hr MEFs were fixed and immuno-stained to assess infection ., Quantification of infected cells showed no consistent differences between WT and β-arrestin 1/2-KO MEFs , with either YFV-17D or DEN2-NGC ( Figure 5B and C ) ., These results indicated that GRK2 mechanism of action in flaviviral entry is independent of the β-arrestins ., To investigate whether GRK2 also plays a role in later stages of infection we used DENV replicons expressing renilla luciferase ( Rluc ) in place of the structural proteins ( Figure 6A ) ., Because replicons were electroporated and do not express the structural proteins , any differences observed upon GRK2 knockdown would be independent of changes in viral entry or assembly and egress of infectious particles ., Wild type replicon ( DRrep ) or replicon which cannot synthesize RNA due to a mutation in the NS5 RdRP ( DRrep-RdRPmut ) were electroporated in HuH-7 cells treated with siNSC or siGRK2_2 , and Rluc activity was measured overtime ., DRrep-RdRPmut construct was used to assess translation of the input RNA ., Early Rluc activity was similar in cells treated with control or GRK2 siRNA ( Figure 6B ) indicating that GRK2 knockdown did not affect translation of the incoming viral RNA ., The same results were observed with the DRep replicon ., GRK2 knockdown , however , resulted in a 5-fold decrease of Rluc activity at later time points , when newly synthesized viral RNA is produced ., This result suggested that GRK2 plays a role at the level of viral RNA synthesis ., Altogether , these mechanistic data indicate that GRK2 acts as a key factor in propagation of flaviviruses by controlling multiple stages of the viral life cycle ., Moreover , the assays utilized here suggest that GRK2 regulates viral entry and genome amplification independently ., We performed genome-scale siRNA screens to identify host factor candidates required for YFV-17D propagation in human cells ., In recent years , there has been a concern that published hit lists from RNAi screens contain false positives due to sequence-dependent off-target effects ., To address this concern , it was suggested that every published hit should be validated by at least two distinct siRNAs 33 ., To meet this requirement , in most of the studies using RNAi screens , hits identified in a primary screen are submitted to a second round of screening using either alternative sequences or individual sequences if a siRNA pool was used 34 ., In contrast , we chose a 2×2 pool screen format that interrogated each gene by two distinct siRNA pools at the first pass of screening ., This screening format allows generation of gene candidate lists containing low number of false positives without having to run a second round of screening ., It must be noted , however , that in case where only two of the four siRNAs are effective and happen to be in the same pool , the corresponding gene would not be considered as a candidate factor and this would be a false negative ., Nonetheless , we posit that the early elimination of false positives outweighs the slight increase in false negatives , and we believe that the data produced using this screening strategy has generated a robust hit list of YFV host factors ., We previously found that hit list composition differs depending on the algorithm used to analyze data and on the defined threshold 17 ., Therefore , it is valuable if entire datasets generated by siRNA genomic screens were made available to the scientific community for future re-analysis ., To this end , we provide the entire datasets from two YFV screens as supplemental material ( Table S1 ) so that other researchers can both assess and mine the data ., To our knowledge , this is the first time that the GRK family is validated as a Flaviviridae host factor and that GRK2 is described as a factor affecting the efficiency of both viral entry and viral RNA synthesis ., Only the V-ATPase complex has been previously proposed as host factor able to control multiple stages of infection , release of the viral genome and egress of viral particles 22 , 35 ., This is new evidence that a single host factor can control independently distinct steps of the flaviviral life cycle ., These two different functions in viral propagation might be explained by the ability of GRK2 to interact with and phosphorylate many different cellular proteins 36 , 37 ., GRKs are known as kinases that phosphorylate GPCRs such as the CC chemokine receptor 5 ( CCR5 ) 38 ., CCR5 is a well-described example of a human GPCR involved in viral infection and which mediates HIV-1 entry 39 , 40 ., Similarly , GRK2 could regulate a GPCR and therefore control YFV entry ., GPCRs regulated by GRK2 , such as the β2-adrenergic receptor 41 , 42 , or the P2Y purinergic receptors ( P2YR ) 43 , were identified as YFV host factor candidates ., Interestingly , the P2YR10 was also identified as a WNV host factor ( Table S3 , 20 ) ., GRK2 function in entry could also be independent of receptor phosphorylation and could affect directly the endocytic machinery ., In fact GRK2 interacts with various endocytic factors such as clathrin which was shown to be involved in DENV entry 29 , 30 , 44 ., It would be interesting to test if the expression of a GRK2 mutant in which the interaction with clathrin is disrupted affects viral entry ., In this study , mechanisms that support viral RNA synthesis are also controlled by GRK2 ., Even though the translation of the incoming RNA is not affected by GRK2 knockdown , the translation of the newly synthesized RNA , which may require a different subset of factors , may be regulated by GRK2 ., It has been described that GRK2 can phosphorylate the ribosomal protein P2 ( RPLP2 ) 45 which , together with the ribosomal proteins P0 ( RPLP0 ) , P1 ( RPLP1 ) and L12 ( RPL12 ) , forms the stalk of the ribosome 46 ., RPLP1 and RPLP2 are not required for general translation , but it has been suggested that they may regulate , depending on their phosphorylation state , the translation of a specific subset of transcripts 46–48 ., We can hypothesize that the newly synthesized viral RNA may require phosphorylated RPLP2 to be efficiently translated ., Intriguingly , we noted that RPLP1 , RPLP2 and RPL12 were among YFV host factor candidates ., It would be then interesting to test if RPLP1 and RPLP2 knockdowns affect the luciferase activity from the dengue replicon at late time points , as observed upon GRK2 knockdown ., In addition to providing mechanistic insights , future studies on the role of GRKs in flaviviral propagation could pave the way for much needed therapies to treat diseases caused by these viruses ., The identification of a well-studied family of protein kinases as flaviviral host factor represents an attractive target for the development of anti-flaviviral drugs ., Indeed members of the GRK family , which overexpression has been linked to heart failure , have been targeted by therapeutics for the treatment of cardiovascular diseases 49 ., These drugs could represent starting points for the development of novel anti-flaviviral compounds .

Introduction, Materials and Methods, Results, Discussion

Flaviviruses cause a wide range of severe diseases ranging from encephalitis to hemorrhagic fever ., Discovery of host factors that regulate the fate of flaviviruses in infected cells could provide insight into the molecular mechanisms of infection and therefore facilitate the development of anti-flaviviral drugs ., We performed genome-scale siRNA screens to discover human host factors required for yellow fever virus ( YFV ) propagation ., Using a 2×2 siRNA pool screening format and a duplicate of the screen , we identified a high confidence list of YFV host factors ., To find commonalities between flaviviruses , these candidates were compared to host factors previously identified for West Nile virus ( WNV ) and dengue virus ( DENV ) ., This comparison highlighted a potential requirement for the G protein-coupled receptor kinase family , GRKs , for flaviviral infection ., The YFV host candidate GRK2 ( also known as ADRBK1 ) was validated both in siRNA-mediated knockdown HuH-7 cells and in GRK−/− mouse embryonic fibroblasts ., Additionally , we showed that GRK2 was required for efficient propagation of DENV and Hepatitis C virus ( HCV ) indicating that GRK2 requirement is conserved throughout the Flaviviridae ., Finally , we found that GRK2 participates in multiple distinct steps of the flavivirus life cycle by promoting both entry and RNA synthesis ., Together , our findings identified GRK2 as a novel regulator of flavivirus infection and suggest that inhibition of GRK2 function may constitute a new approach for treatment of flavivirus associated diseases .

The Flavivirus genus includes several emergent and reemergent viruses , such as dengue and yellow fever viruses , which cause severe diseases in humans for which there is no approved treatment ., Flaviviruses are transmitted to humans by arthropods and they rely on scores of vertebrate and invertebrate factors to replicate in these disparate hosts ., Identifying the host factors involved in viral propagation is critical to understanding the molecular mechanisms of infection and the development of new therapeutics ., To identify human host factors required for yellow fever virus propagation , we completed two genome-scale siRNA screens ., Among the candidates discovered were the G protein-coupled receptor kinases GRK2 and GRK4 ., We focused on the protein GRK2 , a kinase first identified for its role in cellular signal transduction ., We found that GRK2 was a host factor needed for productive infection by yellow fever , dengue and hepatitis C viruses and was required for both viral entry and efficient replication of the viral genome ., GRKs , which are considered druggable , may be used as targets to develop broadspectrum anti-flavivirals .

virology, biology, microbiology

journal.pntd.0006657

Stratified sero-prevalence revealed overall high disease burden of dengue but suboptimal immunity in younger age groups in Pune, India

Dengue disease is an important emerging public health problem in countries of tropical and subtropical regions ., 1–3 Estimated annual global burden of disease is approximately 390 million infections , 96 million clinical cases , and 20 thousand deaths , with almost 34% of total dengue cases occurring in India . 4, According to recent estimates , 2·9 million dengue episodes and 5906 deaths , with an economic burden of $950 million occur annually in Southeast Asia ( SEA ) alone . 5, It is known that disease intensity and disease burden is highly variable between different places within a country or region . 6, In India , dengue is a reportable disease and all confirmed cases are expected to be reported to government of India through NVDCP , Delhi . 7, Recent studies using various models have suggested gross underreporting of dengue cases ., It is estimated that each case reported may be multiplied by 200 to get fair estimate . 8 , 9, There are 4 antigenically distinct DENV serotypes ( DENV 1–4 ) ., Dengue can result from infection with any one of four viral serotypes ., Infection with one serotype provides long-term protection to that serotype , but not to others ., Thus , DENV seropositive individuals could be monotypic due to primary infection or multitypic due to secondary infections ., Presence of certain serotypes , including primary infection with DENV-3 from the SEA region and secondary infection with DENV-2 , DENV-3 , and DENV-4 also from the SEA region , as well as DENV-2 and DENV-3 from non-SEA regions , increased the risk of severe dengue infections . 10, Thus , age specific distribution for different serotypes and their contributions in monotypic and multitypic cases are worthy of special consideration ., Dengue infection results into subclinical disease in majority of the cases and clinical disease in about 25% cases ., Proportions of asymptomatic , mild cases and severe cases are highly variable in different areas ., Differential diagnosis between clinically similar diseases caused by DENV , Chikungunya virus and other febrile illnesses is almost impossible in resource limited countries like India ., Therefore clinical surveillance data which already suffers with tremendous reporting bias is inadequate to estimate true burden of disease ., In such situations , properly designed seroprevalence studies may adequately quantify and characterize the extent of transmission ., Currently there is no effective drug for treatment of dengue ., Sustained effective vector control has become impractical in developing countries ., Therefore vaccination has become focus of attention in management of dengue ., Several vaccines are in different phases of developments and clinical trials ., The first live attenuated ( recombinant ) tetravalent dengue vaccine , Dengvaxia , produced by Sanofi Pasteur , has been licensed for use in some countries in Asia and Latin America ., World Health Organization ( WHO ) Strategic Advisory Group of Experts ( SAGE ) recommends that countries consider introduction of this dengue vaccine only in populations where epidemiological data indicate a high burden of disease ., In order to maximize public health impact and cost effectiveness , the populations to be targeted for vaccination , as measured by seroprevalence , should be approximately 70% or greater in the age group targeted for vaccination . 11, Seroprevalence typically increases with age , and countries may choose to target vaccination to the youngest age ( 9 years or older ) for which seroprevalence exceeds the recommended 70% threshold . 12, Since such data is not available for most of the endemic places in India , well designed serosurveys are recommended to support decision making for vaccine introduction for public health as well as for conducting clinical trials with dengue vaccines ., In view of these concerns , a stratified serosurvey was conducted in Pune city , Maharashtra , India ., Pune is fast growing city , chosen under Smart Cities Mission scheme of the Prime Minister of India for speedy and orderly infrastructure development ., The city has been experiencing seasonal , annual dengue outbreaks ., It is pertinent to generate data on epidemiological determinants including disease burden estimates for proper planning of dengue management ., Pune , the second largest city in the state of Maharashtra after Mumbai and the seventh most populous city in the country is situated 560 meters above sea level on the Deccan plateau ., Pune is the administrative headquarters of Pune district and is one of the fastest growing cities in the Asia-Pacific region ., It lies between 18° 32 North latitude and 73° 51 East longitude ., Pune is 149 kilometers , southeast of Mumbai by road ., Average temperatures ranges between 19 to 33°C ., Pune experiences three seasons: summer , monsoon , and winter ., Typical summer months are from mid-March to June with maximum temperatures sometimes reaching 42°C ., The monsoon lasts from June to October , with moderate rainfall and temperatures ranging from 22 to 28°C ., Most of the 722 mm of annual rainfall in the city falls between June and September , and July is the wettest month of the year ., In winter , the daytime temperature hovers around 26°C while night temperature is around 10–14°C , sometimes dropping to 5 to 6°C ., The population of the Pune city is 3 , 124 , 458 and Pune Urban Agglomeration is 5 , 057 , 709 as of the 2011 census . 13, Annual exponential growth rate of population was 2 . 08 per year ( for 2001–2011 ) , with birth rate of 19 . 3 live births per thousand of population per year . 13 , 14, In 2017 , the estimated population of Pune is 3 . 99 million . 15, Pune city is divided into 5 administrative zones , having 15 administrative units called wards ., Each ward has one or 2 clinics managed by Pune Municipal Corporation , many private clinics managed by General Practitioners , and some tertiary care hospitals ., A cross sectional , stratified , facility based , multistage cluster sampling was conducted in Pune city between May 4 and June 27 , 2017 , following the principles of WHO guidelines . 12, The dengue season in this area is typically from July to December ., The present survey was planned to capture activity of dengue from the previous 2016 dengue season ., Medical clinics are the first contact point between febrile cases and health seeking facilities ., In all 15 wards , a corporation clinic was chosen as first point for sampling ., Additional 3 clinics of general practitioners were chosen in such a manner to provide fair representation to the ward ., This ratio was based on assumption that about 25% of the primary healthcare in the city is provided by the corporation clinics and the rest by the private practitioners ., Fig 1 shows approximate locations of the collection sites ( health facility ) ., The data on dengue prevalence in Pune city were not available ., However , dengue prevalence of 59% was reported from an urbanized village near Pune city . 16, Assuming that prevalence in Pune city will be higher than the adjoining urbanized village , for the purpose of sample size calculations we assumed 65% prevalence in Pune city ., The minimum sample size of 1 , 396 participants was calculated under the assumption of 65% prevalence for dengue infection , α ± 5% error , Confidence level 95% ., Accounting for the multistage sampling , the sample size considered a design effect of 4 . 0 ., Sample allocation to each ward and age groups was in proportion to the population of the ward and age group with respect to the Pune population ., Allowing 5% additional samples to meet contingencies like insufficient sample , leakage and spoilage we targeted 1 , 465 samples ., A team visited each health facility ., Each non-febrile patient and/or the person accompanying them visiting the facility and resident of the same ward were invited to participate in the study ., The willing persons were enrolled until the target sample collection was achieved for that site ., Each enrolled person was requested to provide a blood sample following administration of ethical consent/assent approved by the Institutional Ethics Committee of the University ., We collected blood samples from a total of 1 , 434 participants , 31 less than the original 1 , 465 sample target ., About 5 mL blood was collected from each participants in anti-coagulant free vacutainer tubes ( BD Bioscience ) by trained phlebotomists and kept overnight at 4°C ., Serum samples were separated by centrifugation at 3 , 000 rpm for 10 minutes and stored at -80°C ., Each serum sample was tested for dengue IgG antibodies by ELISA using the commercial Panbio Dengue IgG Indirect ELISA kit ( Panbio Diagnostics , Brisbane , Australia , Cat no . 01PE30 ) according to manufacturer’s instructions ., The presence of detectable IgG antibodies indicates past exposure to dengue infection ., Panbio units were calculated by dividing the sample absorbance by the cut-off value and then multiplying this value by 10 ., Samples were considered positive if Panbio units were >11 , <9 Panbio units were considered negative and if Panbio units were between 9 to 11 , samples were considered equivocal and retested to confirm the result ., An anti-dengue IgG-capture ELISA ( Panbio Diagnostics , Brisbane , Australia , Cat no . 01PE10 ) was performed according to the manufacturer’s instructions ., Anti-dengue IgG Panbio units were calculated by dividing the sample absorbance by the cut-off value and then multiplying this value by 10 ., Using this criteria , a value of >22 Panbio units was used to identify secondary infection ., <18 Panbio units were considered negative for secondary infection and if Panbio units were between 18 to 22 , samples were considered equivocal and retested to confirm the result for secondary infection . 17, High Panbio units are indicative of elevated levels of IgG antibodies which suggest that the patient has been recently exposed to dengue virus due to secondary infection ., As WHO recommends use of PRNT90 titers to minimize serum cross-reactivity with other dengue serotypes and flaviviruses prevalent in DENV endemic areas 12 , 18 , we opted for PRNT90 method for this study ., Due to resource constraint , we decided to process 120 indirect ELISA positive samples for PRNT ., The selection of samples was based on Panbio units of IgG-positives ( Indirect ELISA ) arranged at the interval of 5 units and represented comparable proportions of total positives in each category ., We followed WHO guidelines for the PRNT90 test ., However , since we were interested in assessing neutralizing antibodies ( NAbs ) against the currently circulating Indian strains , necessary modifications were made ., The DENV strains used were DENV-1 ( S19 ) ( Accession no . MG053115 ) , DENV-2 ( S15 ) ( Accession no . MG053142 ) , DENV-3 ( S111 ) ( Accession no . MG053151 ) and DENV-4 ( 1028 ) ( Accession no . MG272272 ) isolated during 2016 in Pune city 19 ., The viruses actually used for PRNT were passaged 4–5 times , titrated using plaque assay and stored at -80°C at smaller aliquots ., The test included two controls in duplicates; cell control without any virus or serum and virus control for different serotypes , without serum were used in the assay ., For the test , early passage Vero cells ( CCL-81 , ATCC ) were seeded at the density of 1 x 105 cells/mL in Minimum Essential Medium ( MEM ) ( GIBCO ) with 10% Fetal Bovine Serum ( FBS , GIBCO ) in 24-well plate ( 1mL/well ) ., The following day , serum samples ( diluted 1:5 in MEM with 2% FBS ) were heat inactivated at 56°C for 30 min and then serially diluted 4-fold in the same diluent in 96-well microtiter plates ., Serially diluted serum samples were mixed with an equal volume i . e , 1:2 of diluted virus that gives 40–100 plaques/control well with each serotype ., The final serum dilutions were 1:10 to 1:2560 ., After incubation for 1 hr at 37°C , 5% CO2 incubator , the medium was removed from 24-well plate and 100µl of each dilution of serum/virus mixture was added onto the cells in duplicate ., The plates were then incubated for 1 hr for DENV-1 , 2 , 3 and 2 hr for DENV-4 at 37°C , 5% CO2 incubator to allow virus adsorption ., After adsorption , 1ml of overlay media containing 1% Aquacide-II ( Calbiochem ) were added onto the cells and incubated for 3 days at 37°C , 5% CO2 incubator ., Three days post infection , the overlay medium was discarded from the plates , and the cell monolayer was fixed with formalin for 30minutes at RT and permeabilized with 0 . 2% Triton X-100 in PBS for 5min ., The cells were washed three times with PBS-T ( 0 . 02% Tween-20 in PBS ) and stained with HB112 pan-flavivirus mouse monoclonal antibody ( D1-4G2-4-15 , ATCC ) at 1:500 dilution in PBS for 2 hr ., Cells were washed three times with PBS-T and incubated with goat anti-mouse IgG horseradish peroxidase ( HRP ) at 1:1500 dilution in PBS for 1 hr ., After washing three times with PBS-T and two times with PBS , cells were stained with True Blue peroxidase substrate ( KPL , Sera Care , MA , USA ) and blue color staining of virus infected cells were counted as plaques ., PRNT90 titer was calculated using NIH LID Statistical Web tool . 20, PRNT90 titer ≥ 1:10 to one dengue serotype at least was considered seropositive ., A monotypic response was defined by the presence of NAbs against only one of the four DENV serotypes ., A multitypic response was defined as a concomitant detection of NAbs against more than 1 serotype ., Statistical analyses were performed using ‘R’ Version 3 . 4 . 1 . , Microsoft windows Excel 2010 , SPSS v . 17 . 0 ( SPSS Inc . , USA ) and Graphpad Prism v . 7 . 0 ( Graphpad Software USA ) . 21, The logarithm ( Log10 ) values of antibody titers of the serotypes were used for analysis and graphical representation ., The statistical comparison of the means of the antibody titers of the serotypes was performed using analysis of variance ( ANOVA ) ., The association between the numbers of DENV serotypes ( one , two and three simultaneous serotype infections ) and mean age was performed using ANOVA with POST HOC Least significant difference ( LSD ) test , whereas their association with gender was tested through χ2 ( chi-square ) test for trend ., Mann-Whitney U test was performed to check the association of PRNT90 titers against all 4 serotypes across different age groups ., Studies were conducted at Interactive Research School for Health Affairs ( IRSHA ) , a constituent unit of Bharati Vidyapeeth ( deemed to be University ) , Pune ., The study was approved by the Institutional Ethics Committee ( IEC/2017/04 ) ., Written consent/assent to participate in the study , reviewed and approved by the Ethics committee , was administered to each participant or to their legal guardian ., All data were handled anonymously and confidentially ., In this study , 1434 participants were recruited from 15 wards of Pune city ., Of these , 723 ( 50 . 4% ) were men and 711 ( 49 . 6% ) women , 401 ( 28 . 0% ) were children ≤18 years and 1033 ( 72 . 0% ) were adults >18 years ., The age ranged from 1 month to 85 years with a mean of 31 . 2 years and a median of 29 years ( Table 1 ) ., Ward-wise sample seropositive for anti-DENV IgG antibody by indirect IgG ELISA is presented in Fig 2 ., Overall percent seropositivity was 81% ., The median age of seropositives was 33 ., The percent seropositivity between wards was significantly different ( p< 0 . 001 ) ., The proportion of seroprevalence varied among the wards from moderate high in Aundh ( 61 . 8% ) to very high in Wanawadi ( 94 . 9% ) ., The difference in percent seropositivity between males ( 81 . 5% ) and females ( 80 . 7% ) was not significant ( p = 0 . 745 ) ., Similarly , there was no significant difference ( p = 0 . 786 ) in percent seropositivity between the participants visiting GP clinics ( 80 . 96% ) and Corporation clinics ( 82 . 12% ) ., Only 92 of 1 , 205 seropositive individuals ( 7 . 6% ) could remember having dengue in the past ., Distribution of seropositive samples in different age group is presented in Table 1 ., There was an increasing trend with age , from 21 . 6% among < 36 months group to 77 . 3% in age group 16–18 years ., The positivity was significantly different ( p<0 . 001 ) in different age groups in children ≤ 18 years but not significantly different in adults ( Fig 3A and 3B ) ., In adults > 70 yrs ( n = 42 ) all the persons were seropositive ., A third order linear polynomial model is best fit to the overall data ( R2 = 0 . 97 ) ., Our estimated seroprevalence at 9 years age ( SP9 ) was 54 . 17% ( 95% CI: 49 . 13% - 58 . 97% ) , which is classified as a low-to-moderate DENV transmission intensity ., This test is designed to detect high levels of anti-DENV IgG antibodies indicative of a secondary infection ., A total of 150 of 1 , 363 samples tested were positive ( 11 . 01%; 95% CI: 9 . 3%-12 . 6% ) ., Overall seropositivity was highly variable between wards , ranging from 2 . 91% in Kondhwa to 20 . 95% in Hadapsar ( S1 Table and S1 Fig ) ., Only 4 of 229 children in age group ≤ 10 ( 1 . 7% ) were seropositive suggesting a very low rate of secondary infection in young children ., The seropositivity in older age groups varied between 11 . 2 and 15 . 9% ., Overall distribution of positive proportions was non-linear suggesting age independent phenomenon ( Fig 4 ) ., A total of 120 indirect IgG ELISA positive samples were tested for the presence of neutralizing antibodies by PRNT ., Of these , 119 samples were confirmed to be seropositive via the presence of neutralizing antibodies and PRNT90 titers of ≥ 10 ., One sample had a PRNT90 titer of <10 against all 4 DENV serotypes and was considered seronegative ( Table 2 ) ., Over 69 . 2% samples were positive for DENV 1–4 followed by 11 . 7% samples which were positive for 3 serotypes , DENV2 , DENV-3 and DENV-4 ., There was significant difference in the percent positivity for different serotypes ( p<0 . 01 ) ., Percent PRNT positives for all DENV serotypes in different age groups are shown in Table 3 ., Amongst PRNT positives , DENV-2 was the most prevalent serotype across all age groups ( 94 . 4–100% ) ., Only 5–6% individuals of age group up to 15 years were susceptible to DENV-2 and all individuals > 44 years of age were seropositive to this virus ( Table 3 ) ., DENV-3 and DENV-4 follow age dependent linear distribution suggesting endemic nature of these serotypes for long duration but introduced late in comparison to DENV-2 ., DENV-1 was also prevalent across all the age groups in comparatively lower proportion and follows time independent distribution suggesting recent introduction ., There is significant difference for percent positivity among different DENV serotype for age group 15 to 44 years ( p = 0 . 006 ) and age group 60 years and above ( p = 0 . 026 ) ( Table 3 ) ., The sample size for PRNT was not enough for serotype specific model building for force of infection ., Higher titers of neutralizing antibodies ( log10 PRNT90 ) were detected in individuals infected with DENV-2 ( 2 . 524; 95% CI: 2 . 407–2 . 641 ) compared to other serotypes ., The titer for DENV-4 was lowest among four serotypes ( 1 . 943; 95% CI: 1 . 844–2 . 041 ) ., There was significant difference between overall neutralizing antibody titer of the four serotypes ( p<0 . 05; F = 23 . 568 ) ., Post hoc ( LSD ) test showed that this difference is because of neutralizing antibody titer of DENV-2 ( 2 . 524; 95% CI: 2 . 407–2 . 641 ) which was significantly higher than the titers of all other serotypes ( p<0 . 05 ) and there is a significant difference between titer of DENV-3 ( 2 . 109; 95% CI: 1 . 987–2 . 231 ) and DENV-4 ( 1 . 943; 95% CI: 1 . 844–2 . 041 ) ( Fig 5 ) ., The titer of DENV-2 was highest across all age groups followed by DENV-3 ., In younger age groups , DENV-1 exhibited lower titer and in higher age groups DENV-4 showed lowest titer ., However , differences in the titers across all age groups were not significant ( Fig 6 ) ., To estimate the transmission intensity of dengue , two catalytic models were fitted to the age-specific seroprevalence for indirect ELISA data , time constant ( model A ) and time varying ( model B ) forces of infection ( Fig 7 ) ., As per model A , dengue naive children seroconverted at the rate of 7 . 81% per year ( 95% CI: 7 . 24%-8 . 43% ) ., Under model B , annual rate of seroconversion was 8 . 68% ( 95% CI: 7 . 52%-9 . 95% ) in younger population ≤ 18 years and 7 . 51% ( 95%CI: 6 . 87%-8 . 20% ) in individuals > 18 years ., The LR test showed non-significant association to favor any model B ( p = 0 . 091 ) ( Table 4 ) ., We also fitted model B with 15 and 12 years age break point without any significant difference in FOI ., Thus the model B was consistent with a significantly higher force of infection during the period 2000–2016 ( λ = 0 . 868; 95%CI: 0 . 752–0 . 099 ) ., Our estimated basic reproductive number ( R0 ) for dengue in Pune is 4 . 23 ., These estimates assume endemic circulation of 4 serotypes and were derived using the FOI estimates and census data ., The sample size was not enough to calculate FOI for individual wards ., We pooled data by zone ., Each zone consists of 3 adjoining wards ., The mean R0 for Pune was 4 . 23 ( 95% CI: 3 . 58–4 . 87 ) , lowest 3 . 41 in zone 3 and highest 5 . 25 in zone 1 ( S2 Fig ) ., For estimation of the burden of disease , we have taken FOI as 8 . 68% for primary infection and 7 . 51% for secondary infections ( Table 4 ) ., Accordingly in Pune city with estimated population of ~3 . 99 million in 2016 , we estimate that this leads to approximately 65 , 800 ( 95% CI: 57009–75507 ) primary infections and 242 , 716 ( 95% CI: 222 , 032–265 , 016 ) secondary infections per year ., Assuming that 69% immune population is positive for all 4 serotypes ( Table 2 ) , only 31% of secondary cases are likely to give rise to active dengue cases ., Therefore , 75 , 242 secondary infections only are considered potentially secondary infections for estimation of dengue cases ., The ratio between in-apparent and symptomatic dengue cases is highly variable , ranging from 1:1 to 3:1 ., Considering a ratio of 3:1 , Pune city is burdened by about 47 , 000 symptomatic dengue cases each year ., We found 11 . 1% seropositivity in Capture IgG ELISA ., This test is designed to detect high levels of anti-DENV IgG antibodies indicative of secondary infection ., This translates to 358 , 741 secondary infections; 111 , 210 potential secondary cases and 59 , 000 dengue cases each year in Pune ., Dengue was first reported in India from Calcutta in 1912 . 27 Now , it is a well established endemic disease in majority of Indian cities with occasional epidemics . 28 , 29, In Pune city , sporadic cases were reported in 1970s and 80s ., Seasonal outbreaks have been recorded from 90s in different localities of the city with hemorrhagic involvement in some cases . 30, In spite of high prevalence of clinical disease , limited information is available on prevalence and incidence of the disease in India ., Overall 81% IgG positivity by indirect ELISA with ~ 100% positivity in age groups > 45 years reported by us is much higher than 43% and 59% reported from 2 villages near Pune ., In our study , seroprevalence of dengue was 50% in children of 6–10 years age group ., In the same age group , high positivity is reported in Mumbai ( 80% ) , Delhi ( 60 . 2–66 . 5% ) , Wardha ( 69% ) , Bangalore ( 62% ) , Hyderabad ( 58% ) and low positivity in Kalyani ( 23% ) . 31, The seropositivity of 79 . 3% in age group from 5–40 years is lower than 93% reported in Chennai . 32, Seropositivity of 11% by Capture ELISA and 81% by Indirect ELISA in our study is similar to the report from Hyderabad . 33, High seropositivity is also reported in Asian countries like Thailand , Bangladesh , Indonesia etc ., 34–36 Human population density is reported to be an important variable associated with a high historical incidence of dengue ., 37–40 The level of seroprevalence seems to be also associated with the population size of the city ., Small places like a village near Pune , population ( 2 , 621 ) and Kalyani in WB ( population 100 , 575 ) reported low seroprevalence ., Hyderabad and Pune similar in population pattern have nearly similar dengue prevalence; Chennai , Mumbai and Delhi , the metropolitan cities reported higher seroprevalence ., The lowest FOI and R0 for Indian subcontinent reported was based upon the data from Andamans island collected in 1988–89 . 6 , 41 Population of this region was also very small on individual islands ., In our study , only 7 . 6% participants could recall having the disease which is suggesting of a high frequency of unapparent infection or mild undifferentiated fever in agreement with other epidemiological studies ., 42–45 We estimated FOI , seroconversion rate , 8 . 68% in younger age group ≤ 18 years and 7 . 51% in older age groups ., This is very different from 23% in Chennai ., The reported seroconversion is highly variable in different places ., In Sri Lanka , 8% seroconversion was reported in children ≤ 12 years age , 11 to 17% among children aged 2 to 15 years in Vietnam , 2 . 1 to7 . 9% in Thailand , 10% in Bangladesh , 13 . 1% primary infection per year in children in Indonesia , 17% in children aged 3 years in Salvador , Brazil ., 22 , 32 , 35 , 46–52 Our estimated R0 for dengue , 4 . 3 is lower than 5 . 3 estimated in Chennai and is comparable to estimates in hyperendemic settings in Thailand and Brazil . 6 , 53 , 54, As reported in other places , we also found significant heterogeneity between different wards ., In India , this is the first study to provide data on PRNT90 , the test recommended by WHO for survey for neutralizing antibodies ., Over 69% indirect ELISA positive samples were positive for all 4 serotypes followed by 11 . 7% positive for 3 serotypes , DENV-2 , DENV-3 and DENV-4 ., DENV-2 was the most prevalent ( 94 . 4% ) serotype across all age groups ., This suggests widespread circulation of all the serotypes in Pune for quite some time ., In another study , we reported active circulation of all the serotypes in Pune during 2016 dengue season . 19, Only other report from India , based on PRNT50 titers in children of 5–10 years age groups , reported overall positivity of 97 . 2% for at least one serotype , 79 . 7% for all four serotypes ., DENV-1 was dominant serotype in Delhi; DENV-2 in Mumbai , Wardha , Bangalore and Hyderabad; DENV-3 in Kalyani ., There is ample evidence that all 4 serotypes have been circulating in majority of the Asian countries . 55 , 56, For analysis of neutralization tests , some investigators used PRNT60 , others PRNT50 31 , 51 making it difficult to compare the results of different studies ., Following WHO recommendations , 12 we used PRNT90 ., In case of DENV-2 , the multitypic response was positively associated with age because of the diversity of antibodies generated as a result of ongoing exposure ., Highest neutralizing antibody titers observed for the DENV-2 suggests ongoing activity of this virus over the years and a multitypic response caused by the booster effect . 52 , 57 , 58, One of the limitations of the present study is that PRNT was performed in a subset of individuals since it is expensive and laborious ., Therefore the study population may not be true representative of the entire city ., However , in spite of limitations our results provide valuable data on previous immunity at population level ., For estimation of number of dengue cases , whether average seropositivity ( 11 . 1% ) in capture ELISA for estimation of secondary cases in population can be extrapolated or not is an important issue ., According to the manufacturer of the Panbio kit and others an IgG result of 22 Panbio units correlates with an HI titer of 1:1280 , the cut-off used to distinguish between primary and secondary dengue infection ., 59–61 Therefore , percent positivity in capture ELISA was used for estimation of secondary dengue under assumption that the high titered antibodies wane to below 22 Panbio units within a year and before next dengue season ., However , there is a need to generate region specific data on decay pattern of these high titered antibodies in population ., Further , in absence of testing for IgM antibody , possibility of primary infection in some cases cannot be ruled out ., Currently , the use and deployment of vector control as part of dengue outbreak response strategies is managed by public health in Pune city ., It is highly unreliable and unsustainable due to limited resources and difficulties in management of human resources involved in vector control measures ., There is no impact assessment in place for such a measure ., There is also growing evidence that vector control is not a logical solution for control of dengue in large cities . 62, In the absence of specific drugs and limited usefulness of vector control measures , suitable vaccines are eagerly awaited . 63, Dengvaxia , a live attenuated ( recombinant ) tetravalent vaccine is a licensed vaccine for dengue in several countries for children 9 years of age or older living in DENV endemic areas having high endemicity among 9 year-olds ., Children who are seronegative at the time of first vaccination may be primed for future risk of severe dengue illness in areas of low to moderate ( SP9 = 30%-50% ) and even moderate to high ( SP9 = 50%-70% ) endemicity . 11, Therefore it was suggested that average seropositivity of 70% may be minimum requirement for introduction of the vaccine because of variability from locality to locality ., With estimated average SP9 = 54% in present study ., This vaccine is not suitable for Pune at this stage for the specified age group ≥ 9 ., It has been well-documented that passive surveillance involving case notifications does not accurately reflect the burden of dengue in most of locations ., Cohort studies in different provinces of Thailand and in Nicaragua had revealed higher numbers of prospectively determined dengue incidences as compared with national reported figures , with a discrepancy of 8 to 21 . 3-folds ., 64–66 According to Shepard et . al . ( 2014 ) disease burden of dengue in India is 282 times the reported number per year , substantially more than captured by officially reported cases . 9, In this study , we estimated 47 , 000 to 59 , 000 cases per year in Pune city alone ., As per official government report , only 6 , 792 cases of dengue were reported from whole of the Maharashtra in 2016 ., Therefore , it is strongly recommended that for a disease like dengue , serosurveys should be conducted periodically ., It could shed light on the true dengue infections in the population and can be a good tool to monitor impact of interventions at population level .

Introduction, Methods, Results, Discussion

In India , dengue disease is emerging as the most important vector borne public health problem due to rapid and unplanned urbanization , high human density and week management of the disease ., Clinical cases are grossly underreported and not much information is available on prevalence and incidence of the disease ., A cross sectional , stratified , facility based , multistage cluster sampling was conducted between May 4 and June 27 , 2017 in Pune city ., A total of 1 , 434 participants were enrolled ., The serum samples were tested for detection of historical dengue IgG antibodies by ELISA using the commercial Panbio Dengue IgG Indirect ELISA kit ., Anti-dengue IgG-capture Panbio ELISA was used for detection of high titered antibodies to detect recent secondary infection ., We used this data to estimate key transmission parameters like force of infection and basic reproductive number ., A subset of 120 indirect ELISA positive samples was also tested for Plaque Reduction Neutralizing Antibodies for determining serotype-specific prevalence ., Overall , 81% participants were infected with dengue virus ( DENV ) at least once if not more ., The positivity was significantly different in different age groups ., All the adults above 70 years were positive for DENV antibodies ., Over 69% participants were positive for neutralizing antibodies against all 4 serotypes suggesting intense transmission of all DENV serotypes in Pune ., Age-specific seroprevalence was consistent with long-term , endemic circulation of DENV ., There was an increasing trend with age , from 21 . 6% among <36 months to 59 . 4% in age group 10–12 years ., We estimate that 8 . 68% of the susceptible population gets infected by DENV each year resulting into more than 3 , 00 , 000 infections and about 47 , 000 to 59 , 000 cases per year ., This transmission intensity is similar to that reported from other known hyper-endemic settings in Southeast Asia and the Americas but significantly lower than report from Chennai ., Our study suggests that Pune city has high disease burden , all 4 serotypes are circulating , significant spatial heterogeneity in seroprevalence and suboptimal immunity in younger age groups ., This would allow informed decisions to be made on management of dengue and introduction of upcoming dengue vaccines in the city .

Dengue disease , transmitted through the bite of DENV infected mosquitoes , is an increasing health problem in the Asian subcontinent , including India ., Dengue ranges from mild undifferentiated fever to circulatory shock and potentially death ., Clinical disease gives an incomplete picture of the magnitude of dengue , because many infections are asymptomatic ., Presence of antibodies to DENV provides evidence of past infection ., This study provides the first estimate of the prevalence and incidence of dengue , based on the data collected from a well-designed , comprehensive serosurvey ., By studying age–wise antibody prevalence , we estimated the force of DENV infection by applying a catalytic model to our serosurvey data ., Over 81% individuals were positive for DENV antibodies suggesting intense DENV transmission in Pune city ., We estimate that 8 . 68% of the susceptible population gets infected by DENV each year resulting into more than 3 , 00 , 000 infections and about 47 , 000 to 59 , 000 cases per year ., The estimated seroprevalence at 9 years age ( SP9 ) , taken as benchmark for introduction of Dengvaxia vaccine by WHO , was 54 . 17% suggesting moderate transmission intensity of dengue , making introduction of the vaccine unsuitable in younger children .

dengue virus, medicine and health sciences, enzyme-linked immunoassays, immune physiology, pathology and laboratory medicine, pathogens, immunology, geographical locations, microbiology, india, viruses, vaccines, age groups, rna viruses, infectious disease control, antibodies, immunologic techniques, research and analysis methods, public and occupational health, immune system proteins, infectious diseases, proteins, medical microbiology, immunoassays, microbial pathogens, people and places, biochemistry, asia, flaviviruses, physiology, viral pathogens, population groupings, biology and life sciences, organisms

journal.pcbi.1004412

Inferring Growth Control Mechanisms in Growing Multi-cellular Spheroids of NSCLC Cells from Spatial-Temporal Image Data

In early development , tumors grow up to 1–2mm in diameter , nourished by the nutrients and oxygen provided by the existing vasculature ., Either 2D or 3D cell culture systems are utilized as biological models to study that phase , or aspects usually occurring in later phases of tumor growth and development ., Current 2D cell culture approaches are only of limited use to investigate tumor progression in these stages , as they neglect crucial histo-morphological and functional features of these avascular micro-metastases or inter-capillary micro-regions of solid in vivo tumors ., During the last decades , great effort has been undertaken to generate biological 3D models that describe the early phases of tumor development in a tissue context more accurately ., They can thus serve as intermediate systems between traditional 2D cell culture and complex in vivo models ( 3 , 4 ) ., Of these approaches , Multicellular Tumor Spheroids ( MCTS ) offer easy handling and fast generation , even for larger batches , and automation ( 5 , 6 ) ., MCTS as a model system can be well characterized and have been shown to reproduce the spatial organization and micro-environmental factors of in vivo micro tumors , such as relevant gradients of nutrients and other molecular agents and deposition of Extracellular Matrix ( ECM ) ( see Fig, 1 ) ( 7 , 8 ) ., Furthermore , gene expression studies revealed substantial differences in both the baseline profiles and profiles after stimulation between 2D and MCTS cultures ., The latter is decisively closer to patients profile gene expression ( 9–11 ) ., Consequently , MCTS have now been established as experimental systems for both basic research and high throughput screening of clinically relevant drugs ( reviewed by: 12 , 13 ) ., As already explained in 14 the organization of the different cell phenotypes within a MCTS ( growing , quiescent and dead ) is supposed to be radial , and to be controlled by different factors: growth promoters ( GP ) , viability promoters ( VP ) , growth inhibitors ( GI ) and viability inhibitors ( VI ) ., In the case of spheroids the promoters are mainly delivered from the growth medium surrounding the tumor ( with exception of ECM ) while inhibitors are generally assumed to be produced by the tumor itself ., As a consequence , the local composition and interplay of those factors favor different transitions between cell phenotypes at different distances from the tumor border ., To understand the dynamics of avascular tumor growth , several mathematical models were proposed linking the growth kinetics on the multi-cellular level ( radius/volume in time ) with mechanisms on cell or subcellular scales ( cell growth , contact-inhibition , nutrient limitation etc . ) ., They can be classified in two main approaches: ( 1 ) continuum models of the different components densities that evolve in time and space following PDEs ( see e . g . 15–18 ) , ( 2 ) agent-based models that describe each cell individually and how it grows , divides , moves and dies ( see e . g . 19–21 ) ., When cells are modeled as agents , oxygen , nutrients and/or growth factors or inhibitors are often modeled by continuum models 1 , 2 , 22–31 or with simplified assumed profiles 32 , 33 ., Hybrid models on the other hand combine within the same framework the two model types for the cells , depending on the tumor zone ( e . g . 26 ) ., Despite the large variety of models , identification of a plausible mechanistic model and its quantitative parameterization able to quantitatively explain a large set of data and predict the outcome of experiments that were not used to calibrate the model remains a difficult task ., Issues are the large number of parameters and the lack of validation of the underlying mechanisms ., Different models have successfully been fitted to the growth dynamics of cell populations ( e . g . 2 , 22 consider only the population size but not the diameter , 1 , 34 consider both ) relying on different mechanisms but leading to the same growth curves ., For example the transition from exponential to linear radius growth phase can be due to contact-inhibition or nutrient-limitation ., Based on the growth curves alone , model selection could not be made , indicating that development of a mathematical model only relying on growth curves is insufficient ., In this paper we pursue a quantitative image-based approach based on bright field micrographs ., This is in-line with a recent trend in large-scale simulations of brain tumors based on magnetic resonance imaging ( MRI ) 35 , 36 following inspiring work by Swanson and co-workers 37 ., Histological information has been used recently by Frieboes et . al . 38 , who included histological staining measurements in a partial-differential-equation tissue model of Non-Hodgkin lymphoma growth , and by Macklin et . al . 39–41 who developed a multiscale model , mimicking cells as individual agents subject to forces , to predict ductal carcinoma growth in individual patients , including histological information ., In this paper we study mathematical model development and model parameterization by comparison with experimental data for different oxygen and glucose concentrations in the medium for the non-small cell lung cancer ( NSCLC ) cell line SK-MES-1 ., These data consist in the growth kinetics and the corresponding spatial staining patterns for nuclei , different cell states and cell environment , namely , HOECHST for cell nuclei , KI67 for proliferation , TUNEL for dead cells , collagen IV for ECM ., We study in how far mechanisms that have not been directly assessed can be inferred by simultaneous matching of simulation results with experimental results on many experimental observables ., Our strategy is stepwise: we first develop a model for one growth condition only , and then expand the model to capture additional growth conditions after verifying that the previous ( simpler ) model stage was incapable of explaining the added growth data ., For this , we perform many computer simulations with the “previous” model varying each model parameter within its physiological range ., Finally we arrive at a model that can almost completely be projected to the experimentally derived scheme on spheroid growth ( compare Fig 1 to reference 8 ) ., By such a stepwise strategy involving experiments , imaging , image analysis and modeling , an order mechanism during liver regeneration could be identified 42 , indicating that such a strategy may be powerful in unveiling interplaying mechanisms in multi-cellular organization ., In order to study the influence of environmental conditions on the growth dynamics of tumor spheroids ( Fig 2 ) , SK-MES-1 cells were cultivated in-vitro as multi-cellular tumor spheroids under different nutriment conditions , with the hanging drop method ., Then , at different points in time , spheroid size was determined with bright field microscopy and some of the spheroids were frozen , cryosectioned , stained and imaged with fluorescence microscopy ( Fig 3 ) ., For a detailed description see the Materials and Methods section ., Our objective is to explain the experimentally observed growth pattern for different glucose and oxygen medium concentrations within one mathematical model ., For this purpose we first searched for a minimal model ( in the sense specified in the abstract ) explaining the experimental tumor growth observations for medium concentrations of G = 25mM and O = 0 . 28mM , and then stepwise extended this model to capture the other growth conditions ( for illustration of the stepwise model development strategy , see Fig S3 in S1 Document ) ., We based our choice of possible control mechanisms upon prior knowledge guided by published information and own experiments ., We have chosen the condition of maximum glucose and oxygen medium concentration as the similarity of the growth kinetics for G = 25mM and O = 28mM versus G = 5mM and O = 28mM suggests that for the former condition neither glucose nor oxygen may be limiting ( see also Discussion below ) ., This line of argument is supported by findings of Freyer and Sutherland for another cell type at almost the same oxygen and glucose medium conditions ( compare with 1 ) ., We fit at each model development step all parameters again ., So the fits shown in this article were the best we could obtain for the respective model ., However , due to the large search space and duration of simulation of at least one day ( reference computer: Intel ( R ) Xeon ( R ) processor X5680 3 . 33GHz 12M cache 6-core and 144 GB DDR3-RAM 1333 MHz ) it cannot completely be excluded that further parameter searches could give additional slight improvements ., In order to promote readability , we enumerate the model at each development level ., Usually we performed for each parameter set only a single simulation i . e . , a single realization of the stochastic growth process ., This can be justified by observing that the growth process , that starts with about 10000 cells as in the experiment , is self-averaging such that the variations for different realizations of the growth process for the same parameters are negligible ( Fig S11 in S1 Document ) ., Our basic model considers each cell individually within an agent-based model i . e . each individual cell is represented by an agent ., Molecules , which finally are glucose , oxygen and lactate , as well as extra-cellular matrix and “waste” material released by dying cells , are represented by their local concentrations ., We use the term “molecules” in what follows to generically describe these environmental factors that affect the cells ., The model is three-dimensional ., In the following sections we will introduce briefly the main model components ., A detailed description of the model as well as the biological processes mimicked can be found in the material and method section and in the supporting information ( S1 Document ) ., A number of parameters related uniquely to cells or molecules were either taken or estimated from literature ( e . g . molecular diffusion coefficients , consumption rates , cell cycle time distribution ) ., Others could be inferred from the data presented in this article either directly ( e . g . initial conditions , cell size ) or by sensitivity analysis ( e . g . cellular division , cell death and lysis rates ) ( see Table S1 in S1 Document ) ., The identification of the main mechanisms coupling the cellular and molecular kinetics and their parameterization ( see Fig 5 ) was subject to the model comparison with data explained below ., The labeling patterns in Figs 3 and 4 confirm a border distance-dependent “zonation” as discussed in the introduction ( compare also 8 and Fig 1 ) ., In the following we will study the influence of cell-cell-contacts , extra-cellular matrix and metabolic compounds ( nutrients/metabolites ) to infer the corresponding model mechanisms ., For the latter ( metabolism ) we will compare four different hypotheses ( model 1–4 ) ., A summary of the equations used to calculate the transition rates and probabilities depicted in Fig 5 for all models studied in the following sections can be found in Table 2 ., In this paper we inferred a mathematical model of tumor spheroid growth for the non-small cell lung cancer cell line SK-MES-1 from image data of growing tumor spheroids ., Cell nuclei , proliferating cells , extra-cellular matrix and dying cells ( by either necrosis or apoptosis ) were labeled at different points in time and under different oxygen and glucose medium concentrations ., The model was built by an iterative procedure , which we propose as a general template for modeling tissue organization processes ., We started by developing a minimal model for one growth condition only , then stepwise extending this model by further mechanisms whenever the previous simpler model turned out to be insufficient to reproduce the experimental observations for an additional growth condition ., Before adding a new mechanism to an existing model version we verified by extensive computer simulations ( usually hundreds of runs ) , that within the parameter range for each parameter of the existing model no satisfying agreement between model and data could be achieved ., Minimal is here to be understood as sufficient to explain the data and containing as least mechanisms as possible , whereby the building blocks of the model were chosen from those mechanisms that have already been described somewhere for any cell population ., A similar iterative strategy was pursued for liver regeneration after drug induced damage predicting a previously unrecognized and subsequently validated order mechanisms 65 ., We studied four different combinations of glucose and oxygen in the medium ., To explain the growth kinetics , the proliferation , ECM , and cell death for the condition with high glucose and high oxygen medium concentration ( G = 25mM , O = 0 . 28mM ) , the second with intermediate concentration of glucose and high oxygen concentration ( G = 5mM , O = 0 . 28mM ) , we needed to assume that the cell cycle progression is possible only above a critical local production rate of ATP ( = mM/h ) ., A second necessary condition was , that the local density of extra-cellular matrix had to be higher than a critical value ( 0 . 003 ) ., This is in accordance to literature , where dependence of cancer progression on the ECM has been shown for skin cancer 66 , breast cancer 67 and NSCLC 68 , where Collagen IV can regulate crucial cell signaling ., If both conditions ( enough ATP and ECM ) were fulfilled , cells could reenter the cell cycle after a cell division ., Here , cells , which were closer to the spheroid surface and thus needed less energy in order to expand , had an increasing chance to continue proliferation and not to become quiescent ., Interestingly the decision whether a cell in a certain condition became quiescent , had to be stochastic ., This introduced some heterogeneity in subsets of cells in the same conditions ., A deterministic scenario could not have explained the smooth transition from proliferating to quiescent zones ., The production rate of ATP depended on the local oxygen and glucose concentrations ., Thereby , the ratio between both dictates to which extent a cell is in the aerobic Krebs cycle or the anaerobic lactate fermentation ., Warburg stated in 69 that all cancer cells suffer from an injured respiration and thus have an exclusively anaerobic metabolism ., In opposition , Zu and Guppy 70 disproved this hypothesis due to the lack of evidence and rather claimed the metabolism in cancer cells to be functional , but mainly glycolytic due to hypoxia ., Here we come to a partially different conclusion: if cells are sufficiently supplied with glucose ( independent from the oxygen supply ) , the metabolism will remain glycolytic ( 90% ) , and only if the glucose supply is getting short , the metabolism will favor the aerobic Krebs cycle ( see Fig 8 ) ., Besides lactate acidity ( > 20mM ) , the depletion of carbon sources to maintain a critical ATP production ( = 900mM/h ) and not hypoxia were the main reasons of death ., The latter was also recently suggested by Kasinskas et al . 71 , while , in contrast to our assumptions , they excluded lactate as source of acidity and instead assumed it to be an important secondary metabolic resource ., However , either growth adverse or death promoting effects were described for high lactate concentrations 59 ., So here further clarification of the dominating role of lactate would be necessary ., The functional forms of the oxygen and glucose consumption rates were inferred from experimental findings of Freyer , Sutherland and co-workers in EMT6/Ro cells ., The lactate and ATP production rates were then directly derived from those rates by the single assumption that cells transform the consumed glucose in an optimal way with respect to ATP output ., For wide ranges of glucose and oxygen concentrations the ATP production rates remain stable between 80…130 × 10−17 mol/cell/s or 1000…1700mM/h respectively , assuming a reference cell volume of 2700μm ., In literature values can be found between 4 . 6…15 . 3 × 10−17 mol/cell/s ( 72–75 ) ., The difference could be either due to differences in energy needs between different cell types , or to the model simplification that glucose in our model is exclusively used for metabolism ., Interestingly and importantly , the model , despite only having been calibrated with two of the four growth conditions , were subsequently able to correctly and quantitatively predict the growth phase of the other two growth conditions ( G = 1mM , O = 0 . 28mM and G = 25mM , O = 0 . 07mM , respectively ) ., This indicates that the model did capture the functionalities necessary to explain the data for different glucose and oxygen conditions ., To further permit independent validation of our model , we performed additional simulations for other glucose and oxygen medium concentrations ( Fig S12 in S1 Document ) ., However , all growth curves showed saturation and partially even shrinkage after some time ., The saturation phase could be largely captured by adding the potential effect of a waste produce being released in the extracellular space from cells undergoing lysis ., Shrinkage could be added if dying cells at the border detach and enter the growth medium; however , we did not consider this process , as it was not observed in the experiments ( for example , for A549 cells , another NSLC cell line , a massive detachment of cells from the spheroid could be observed in the experiments ) ., Interestingly , model simulations with a lysis rate of 0 . 35/h , a typical value in-vivo , turned out to be incompatible with the in-vitro data ., A lysis rate of a few hours as observed in-vivo would lead to a very fast removal of dying cells and thus almost no dead cells in the tumor center , in sharp contast to the in-vitro experiments ., We obtained a much smaller value of about 0 . 01/h by comparison of model simulation results and the spatial cell death and proliferation profiles i . e . , only such a small lysis rate permits the occurrence of a “necrotic core” as observed in the in-vitro experiments ., For such a low lysis rate we found that the apoptosis—if present—would need to be very slow , as it affects also cells in the viable rim in order to agree with the experimental observation of only very little dead cells in the viable rim ., For this reason , apoptosis could be neglected in explaining the experimental results in this paper ., The small value of the lysis rate , even though surprising on a first view , may be explained by noticing that stromal cells ( such as e . g . macrophages ) digesting dead cells are not present in-vitro ., Hence lysis might be expected to be slower in-vitro than in-vivo ., Contact inhibition seems to be a crucial element ., Suppressing contact inhibition with varying combinations of the other mechanisms in each case leads to complete failure of match between data and model simulations ( see Fig S6 in S1 Document , where the parameters of model 4 has been used ) ., This observation supports the view expressed previously in the paper that a mechanical growth inhibition plays an important role in multicellular spheroids ., We moreover tested the possibility that cells may actively migrate towards the necrotic zone by necrotaxis ( Figs S9 , S10 in S1 Document ) ., As to keep a sufficiently large necrotic core as experimentally observed the lysis rate had to be small , significant migration could not be observed ., On the other hand , if the lysis rate was chosen large , then significant migration of cells could be observed but the necrotic core was too small , as cells in the center were too quickly eliminated by lysis ., In the latter case , the necrotic core with increasing migration rate became smaller ( Figs S9 , S10 in S1 Document ) ., We concluded that migration driven by morphogens towards the central necrosis in SK-MES-1 cells is small ., Interestingly the final model emerging from this stepwise , image-guided inference strategy closely resembles the hypothesis on growth control of MCTS by growth promoters ( GP ) , growth inhibitors ( GI ) , viability promoters ( VP ) and inhibitors ( VI ) ( Fig 11 ) ., In order to permit validation of our model , we simulated a number of predictions ( Fig S13 in S1 Document ) ., We predicted the spatial temporal growth dynamics for G = 1mM , G = 3mM , O2 = 0 . 28mM , and G = 25mM , O = 0 . 07mM ., In this context we would like to remind that our model was able to predict the growth kinetics ( L ( t ) ) for G = 1mM , O2 = 0 . 28mM , and G = 25mM , O2 = 0 . 08mM correctly ., In order to quantify the goodness of the fits shown in Figs 7 , 9 and 10 we calculated the log-likelihoods for subsets of curves ( see Table 3A ) as well as the ensemble of all curves ( see Table 3B ) ., We assumed that the measurement error is additive , normally distributed as well as independent and identically distributed ( i . i . d . ) ., Accordingly , the likelihood of the measured data mean μ given the parameter θ and the corresponding model prediction x ( θ ) is given by L ( θ ) = ∏ i 1 2 π σ i 2 e x p ( ( x ( θ ) i - μ i ) 2 2 σ i 2 ) , where the index i runs over the data points ., The uncertainty of the data points is determined by the standard deviation , σ ., Accordingly , points with large uncertainties σ are weighted less ., Despite some deviations in single profiles , the final model considering ATP , lactate and waste is found to be the most likely to explain the experimental data ., On the other hand , an increase of the likelihood correlates with the increasing number of model parameters and the risk of over-fitting ., Especially model 2 and 3 have a small relative difference in log-likelihood ., As a measure for model quality accounting for the number of parameters we used the Akaike information criterion ( AIC ) ( see Table 3D ) , which also confirms the final model to be the best choice ., We note that the number of parameters had no influence on the ordering of the models as the absolute differences of their log-likelihoods ΔlnL is many orders of magnitude larger than the difference in number of parameters Δk ( see Table 3B and 3C ) ., To conclude , we would like to stress the key message demonstrated in this paper: quantitative comparison of spatial profiles observed from cells states in different experimental conditions and time-points , generates information so rich that one may infer even molecular control mechanisms and parameters of spatio-temporal growth and death patterns ., Hence , careful imaging , image processing and image analysis may serve as an important source of information to infer mechanistic knowledge on tissue growth and organization processes ., Such an approach would gain to be more fully explored ., Our model is hybrid ., It integrates as separate components the cell and molecules , and as functional components a mechanical form of contact inhibition , a metabolic component comprising oxygen , glucose , ATP , lactate , and waste , several of the molecules acting as morphogens ., It would be interesting to see in how far the same model can capture the growth behavior of other cell lines and of other cell types ., We think that the possible imaging techniques and image analysis software in combination with modeling could permit a screening of growth dynamics and subsequent quantitative classification of multicellular spheroids ., For example , EMT6/Ro cells ( 14 ) show a very similar but not equal growth phenotype as SK-MES-1 cells: ( 1 ) detachment of cells is rare ( as opposed to , for example , A549 cells , that reveal significant detachment in-vitro ( and in-vivo ) ) , ( 2 ) under sufficient oxygen supply , the growth of the outer spheroid diameter remains unaffected ( or almost unaffected ) by glucose , while ( 3 ) reduction of oxygen from 0 . 28mM to 0 . 07mM reduces growth dramatically in SK-MES-1 cells even if glucose medium stays high , while in EMT6/Ro cells no reduction is observed as long as nutrient medium concentration stays high: this demarcates a difference between the EMT6/Ro and SK-MES-1 cells ., On the other hand , glucose affects the size of the necrotic core ., Reduction from 16 . 5mM glucose to 0 . 8mM glucose medium concentration in EMT6/Ro cells ( at 0 . 28mM oxygen ) increases the necrotic core ( as one can infer from comparing the cell count with the diameter , see 34 ) , which can also be observed in SK-MES-1 cells if glucose is reduced from 25mM to 5mM ( at 0 . 28mM O2 ) ., However , in SK-MES-1 cells the necrotic core for richer glucose ( G = 25mM vs . 5mM ) occurs later but at about 24 days is about the same size ., Models can provide a quantitative framework to test how far such differences can be attributed to parameters with the same model , or whether “another” model needs to be used by adding or dropping mechanisms ., For example in the first case , can the same model be used to capture a wide range of cell lines with regard to their MCS growth behavior by only adjusting its parameters—indicating only quantitative changes , or , in the 2nd case , does one need to implement mechanisms for one cell type that are not observed within the physiological range of parameters for another cell type ?, Given how much multicellular spheroids are still in use as biological model system , we think it would be of fundamental interest to do such an analysis as a community effort , even though this might be considered as on the first view as a “step-back” as the growth dynamics of multi-cellular spheroids could have been measured 20—30 years ago ., Modern technology could largely permit automated analysis if pipelines were constructed for that purpose , hence avoiding the largely manual and tedious analysis applied for the work in this paper ., Adding more and more cell lines would permit to refine the model one starts with , and zoom into the so far still highly simplified representation of mechanisms without the threat of having a far to large number of fit parameters that cannot be controlled ., In this way , identification of necessary model components and adjustment of parameters linking the components could be achieved ., NSCLC cell line SK-MES-1 used in this study was obtained from ATCC ( Manassas , VA , USA ) and cultivated in a humidity controlled incubator at 37 C and 5% CO2 in 150cm2 tissue culture dishes ( TPP ) in DMEM ( Dulbecco’s modified Eagle’s medium , LONZA , Verviers , Belgium ) supplemented with 10% FCS ( fetal calf serum , Southern America , GIBCO , Germany ) and 1% Penicillin/Streptomycin ( Biochrom AG , Berlin , Germany ) ., Cells were used between passages 10 and 30 and passaged at a split ratio of 1:4 to 1:6 ., Cultures were routinely tested for mycoplasm contamination as described by Stacey and Doyle 1997 and always found to be negative ., Additional medium for the test cultures was DMEM w/o Glucose ( GIBCO , Germany ) supplemented with 10%FCS and 1mM Glucose ( Carl Roth GmbH , Germany ) , and DMEM with 1 . 0 g/L glucose w/o L-Glutamine ( LONZA , Verviers , Belgium ) supplemented with 10% FCS and 25mM L-Glutamin ( SIGMA , Germany ) ., Additionally , cells were kept in a humidity controlled incubator at 37 C and 5% CO2 and either normal atmospheric 20% O2 ( corresponding to 0 . 28mM ) or 5% O2 ( corresponding to 0 . 07mM ) ., This choice permits comparison to classical work in literature ( e . g . 12 ) and takes into account that lung is rich in oxygen ., NSLC cells originate from lung epithelium having at least partially direct contact to the inhaled air so are at least initially not limited to the blood oxygen level ., The images acquired ( see above ) are raw data ., They consist of a set of pixels with position ( x , y ) ∈ N 2 and color intensities for the three color channels red , green and blue defined by, I c h a n n e l x , y ∈ 0 , 1 ) , I c h a n n e l : N 2 → R , ( 12 ), where channel ∈ {red , green , blue} ., In the following , the tools used to preprocess the raw images ( e . g . to reduce noise ) and to identify or segment objects ( e . g . cell nuclei , spheroid border ) will be introduced ., Then the preprocessed images have been analyzed quantitatively as described subsequently ., The cellular automaton model , which extends on previous work ( 43 , 47 ) , is defined by a set of rules: Glucose and oxygen are among the main metabolites of most biological cells ., In normal tissue they are provided mainly by the vascularization ., Tumor spheroids as avascular tumors ( in-vivo ) are mainly fed by nutrients diffusing into the interior from the border ., Above a certain size they display regions lacking glucose ( hypo-nutrition ) and/or oxygen ( hypoxia ) ., This occurs if the nutrients entering the tumor via its borders are consumed completely before reaching the center ., We modeled the diffusion and uptake of glucose and oxygen by a reaction-diffusion equation:, ∂ t u = ∇ · ( D u ( σ ) ∇ u ) + r u ( σ , u ) , ( 19 ), where Du is the molecule diffusion coefficient and ru ( σ , u ) denotes the reaction term , u ∈ {G , O} ., The diffusion coefficient was chosen differently in the nutrient medium than inside the tumor spheroid ., The reaction term mimicked the cells consumption with the choice, r u ( σ , u ) = - q u ( u ) σ = - q u ( u ) ∑ k δ ( r - r k ) ., ( 20 ), The cellular dynamics was modeled by a master equation describing the time evolution of the probability of the multi-cellular configuration ., The molecular dynamics was modeled by a system of deterministic partial differential equations .

Introduction, Results, Discussion, Materials and Methods

We develop a quantitative single cell-based mathematical model for multi-cellular tumor spheroids ( MCTS ) of SK-MES-1 cells , a non-small cell lung cancer ( NSCLC ) cell line , growing under various nutrient conditions: we confront the simulations performed with this model with data on the growth kinetics and spatial labeling patterns for cell proliferation , extracellular matrix ( ECM ) , cell distribution and cell death ., We start with a simple model capturing part of the experimental observations ., We then show , by performing a sensitivity analysis at each development stage of the model that its complexity needs to be stepwise increased to account for further experimental growth conditions ., We thus ultimately arrive at a model that mimics the MCTS growth under multiple conditions to a great extent ., Interestingly , the final model , is a minimal model capable of explaining all data simultaneously in the sense , that the number of mechanisms it contains is sufficient to explain the data and missing out any of its mechanisms did not permit fit between all data and the model within physiological parameter ranges ., Nevertheless , compared to earlier models it is quite complex i . e . , it includes a wide range of mechanisms discussed in biological literature ., In this model , the cells lacking oxygen switch from aerobe to anaerobe glycolysis and produce lactate ., Too high concentrations of lactate or too low concentrations of ATP promote cell death ., Only if the extracellular matrix density overcomes a certain threshold , cells are able to enter the cell cycle ., Dying cells produce a diffusive growth inhibitor ., Missing out the spatial information would not permit to infer the mechanisms at work ., Our findings suggest that this iterative data integration together with intermediate model sensitivity analysis at each model development stage , provide a promising strategy to infer predictive yet minimal ( in the above sense ) quantitative models of tumor growth , as prospectively of other tissue organization processes ., Importantly , calibrating the model with two nutriment-rich growth conditions , the outcome for two nutriment-poor growth conditions could be predicted ., As the final model is however quite complex , incorporating many mechanisms , space , time , and stochastic processes , parameter identification is a challenge ., This calls for more efficient strategies of imaging and image analysis , as well as of parameter identification in stochastic agent-based simulations .

We here present how to parameterize a mathematical agent-based model of growing MCTS almost completely from experimental data ., MCTS show a similar establishment of pathophysiological gradients and concentric arrangement of heterogeneous cell populations as found in avascular tumor nodules ., We build a process chain of imaging , image processing and analysis , and mathematical modeling ., In this model , each individual cell is represented by an agent populating one site of a three dimensional un-structured lattice ., The spatio-temporal multi-cellular behavior , including migration , growth , division , death of each cell , is considered by a stochastic process , simulated numerically by the Gillespie algorithm ., Processes on the molecular scale are described by deterministic partial differential equations for molecular concentrations , coupled to intracellular and cellular decision processes ., The parameters of the multi-scale model are inferred from comparisons to the growth kinetics and from image analysis of spheroid cryosections stained for cell death , proliferation and collagen IV ., Our final model assumes ATP to be the critical resource that cells try to keep constant over a wide range of oxygen and glucose medium concentrations , by switching between aerobic and anaerobic metabolism ., Besides ATP , lactate is shown to be a possible explanation for the control of the necrotic core size ., Direct confrontation of the model simulation results with image data on the spatial profiles of cell proliferation , ECM distribution and cell death , indicates that in addition , the effects of ECM and waste factors have to be added to explain the data ., Hence the model is a tool to identify likely mechanisms at work that may subsequently be studied experimentally , proposing a model-guided experimental strategy .

carbohydrate metabolism, cell death, chemical compounds, oxygen, cell cycle and cell division, cell processes, carbohydrates, glucose metabolism, organic compounds, glucose, oxygen metabolism, cellular structures and organelles, extracellular matrix, chemistry, biochemistry, cell biology, organic chemistry, apoptosis, monosaccharides, biology and life sciences, physical sciences, metabolism, chemical elements

journal.pcbi.1000645

Hydrogen-Bond Driven Loop-Closure Kinetics in Unfolded Polypeptide Chains

The formation of contacts between pairs of residues in an unfolded polypeptide chain is one of the earliest steps in in vitro protein folding and is considered to determine the so-called protein folding speed limit 1 ., Evidence exists for unfolded states being compact under native conditions 2–11 , although it is unclear whether these states contain specific secondary structures 5 , 9 or whether compaction is a nonspecific hydrophobic-driven effect 10 , 12 ., The former scenario is consistent with a hierarchical mechanism of folding 13–15 , in which secondary structures that are local in sequence form first ( e . g . , the diffusion-collision model 16 ) ., Non-specific compaction is non-hierarchical , requiring condensation for the formation of secondary structures ( e . g . , the hydrophobic-collapse 12 , 17 , 18 and nucleation-condensation 19 models ) ., The hierarchical and non-hierarchical models may represent two extremes of a continuum of mechanisms 20 , 21 , and the position of any given protein on the continuum may depend on , for example , the intrinsic propensity of its amino acid sequence to form secondary-structural elements ., A strategy for obtaining insight into early folding events that is receiving sustained attention from experiment 22–30 , theory 31–36 and atomistic computer simulation 37–39 is the study of loop-closure kinetics in unfolded peptides under physiological conditions ., The experimental study of loop-closure kinetics requires spectroscopic probes that report on van der Waals contact formation ., An effective method is to monitor selective energy transfer such as tryptophan triplet quenching by a cysteine residue 24 , 35 ., However , since quenching by cysteine at contact is not instantaneous , the quenching rate observed experimentally is less than the rate of actual contact formation ( the quenching process is not diffusion-limited , but reaction-controlled 37 , 38 ) ., Therefore , extrinsic labels providing faster quenching processes , that have been previously shown to be diffusion limited , can be used 23 , 25 , 27 , 30 , 40–42 ., These labels include oxazine fluorophores that are quenched by tryptophan in single-molecule experiments 30 , 40–42 ., The experiments are amenable to interpretation using atomistic molecular dynamics ( MD ) simulations since the time scales involved are fast ( ns- ) , thus allowing bridging of the timescale gap between simulation and experiment that has hitherto existed 43–51 ., The molecules studied here are glycine-serine ( GS ) based peptides ., Due to their high chain flexibility , their solubility and the absence of a stable folded structure 23 , 25 , 27 , 30 , 52 these have been shown to be valuable model systems for studying end-to-end contact formation in “unstructured” polypeptide chains under native conditions , hence providing insight highly relevant to our fundamental understanding of the very first steps of protein folding ., Recent FRET experiments and MD simulation have provided evidence for intrachain interactions in these systems in water 28 , 39 ., However , whether and how the loop closure kinetics is affected by these interactions is still an open question ., Poly-GS peptides exhibit exponential kinetics for end-to-end loop-closure with time constants in the 10–100 nanosecond time range depending on the number of GS units 23 , 25 , 27 , 30 ., The end-to-end loop closure rates in peptides with more than 10 peptide bonds exhibit power law scaling as a function of the number of peptide bonds ., However , this scaling breaks down for shorter peptides , which exhibit slower rates than obtained by extrapolation of the longer-chain behaviour 25 , 27 , 30 ., The origin of this “rollover” to slower kinetics is unclear ., It has been suggested that the rollover is due to the shorter chains being intrinsically stiffer than the longer ones 27 , 53 , although it has not been ruled out that the rollover might be an artefact due to perturbation by the extrinsic reporter system 33 , 35 , 36 ., Here , a combined experimental and computational study is presented with a twofold aim: understanding the role of intra-peptide hydrogen bonds in the loop-closure kinetics and unveiling the origin of the observed rollover to slower kinetics for the shorter peptides ., Loop-closure kinetics in GS peptides of various lengths , labelled with the oxazine derivative MR121 ( the fluorescent dye ) and tryptophan ( the specific quencher ) at the terminal ends ( MR121- , with n ranging from 2 to 15 ) , are investigated using fluorescence correlation spectroscopy ( FCS ) at the single-molecule level with nanosecond time-resolution and MD simulation on the timescale ., Excellent agreement is found between the simulated and experimental rate constants , allowing the loop-closure processes to be understood at atomic detail and the role played by intra-peptide hydrogen bonds to be determined ., Understanding the loop-closure dynamics of unfolded peptides provides valuable insight into early steps in in vitro protein folding ., Here , loop closure of poly-GS peptides is characterized by combining fluorescence correlation spectroscopy with atomistic molecular dynamics simulation ., The experimentally-derived end-to-end loop-closure rate constants are found to decrease with increasing chain length in longer peptides ( ) , while they become almost independent of chain length for the shorter peptides , as has been previously observed in other experiments that make use of extrinsic probes 25 , 27 ., Analysis of the simulations reveals that the observed rollover at short chain lengths is due to a perturbation by the extrinsic reporter system ., The experimental rate constants of the short chains are found to be determined by transitions to the closed state from open-state conformations containing hydrogen bonds between the MR121 fluorophore and the backbone ., However , for peptides with negligible perturbation of the chain dynamics on the experimentally-detectable timescale by the reporter system is seen , as demonstrated by the very good agreement between loop-closure rate constants in peptides with and without the dye reporter system and by the similarity of the corresponding open states ., These results resolve the existing ambiguity regarding the experimentally-determined rollover at short chain lengths in favour of a perturbation effect by the extrinsic reporter systems ., Nevertheless , evidence for an intrinsic stiffness of the shorter chains is also provided ., The observation of a rollover to slower kinetics and the absence of intra-peptide hydrogen bonds for the shorter unlabelled peptides ( i . e . , the GS repeats without the extrinsic MR121 dye attached ) clearly show that there is , indeed , an intrinsic stiffness in the short polypeptide chains ., The MD simulations allow the dynamical processes driving the end-to-end loop closure to be determined ., The nanosecond closing time constants for peptides containing more than 10 peptide bonds correspond to transitions to the closed conformations from open state configurations possessing intra-backbone hydrogen bonds with a broad range of lifetimes ., As the chain length increases , the probability of formation of -sheet elements increases , leading to the experimentally-detectable length-dependent end-to-end loop-closure time constants on the 20–100 ns timescale , which are determined by the lifetimes of the secondary-structural elements ., Early secondary-structure formation in unstructured chains , as found here , is in principle not restricted to -sheet formation and could also , possibly , involve formation of -helices , depending on the aminoacid sequence ., The scaling with length of the loop-closure rate constants for chains with more than 10 peptide bonds is found here , as well as in previous studies 24 , 25 , 27 , to be consistent with predictions for Gaussian chains ., However , again in line with previous work 34 , 39 , 61 , the presence of partial structuring in unfolded states found here shows that random-coil statistics are not a unique signature of structureless polypeptide chains ., Partial structuring of unfolded states of peptides and proteins has potentially dramatic consequences for the thermodynamics and kinetics of folding 15 ., The results presented here reveal structuring in unfolded polypeptide chains driven by backbone hydrogen bonding , also involving transient ( of the order of few nanoseconds ) -sheet segments ., What is most striking , however , is the finding that formation of these peptide hydrogen bonds accelerates end-to-end contact formation by lowering the free energy barrier to closure ., In an unfolded polypeptide chain this corresponds to the acceleration of the search for “productive” folding contacts between distant residues ., Structuring in poly-GS peptides found here is thus not only consistent with hierarchical models of protein folding , that highlight the importance of secondary structure formation early in the folding process 13–16 , but is also shown to speed up the search for productive folding events ., MD simulations of a set of MR121- peptides ( , 3 , 5 , 7 and 9 ) in water were performed with the GROMACS software package 62 and the GROMOS96 force field 63 ., Partial atomic charges for the dye MR121 were taken from Ref ., 54 ., One peptide molecule was solvated with water and placed in a periodic rhombic dodecahedral box large enough to contain the peptide and at least 1 nm of solvent on all sides at a liquid density of 55 . 32 mol/l ( 1 ) ( the starting peptide conformation was taken at the end of a 10 ns-long MD simulation in explicit water in which the peptide was initially modelled in an extended conformation ) ., Water was represented with the simple point charge ( SPC ) model 64 ., Simulations were performed at the experimental temperature of 293 K in the NVT ensemble and isokinetic temperature coupling 65 was used to keep the temperature constant ., The bond lengths were fixed 66 and a time step of 2 fs for numerical integration was used ., Periodic boundary conditions were applied to the simulation box and the long-range electrostatic interactions were treated with the Particle Mesh Ewald method 67 using a grid spacing of 0 . 12 nm combined with a fourth-order B-spline interpolation to compute the potential and forces in between grid points ., The real space cut-off distance was set to 0 . 9 nm ., The C-terminal end of the peptides was modeled as consistent with the experimental pH of 30 ., No counter ions were added since the simulation box was neutral ( one positive charge exists on the MR121 ) ., A second series of simulations was performed for the unlabelled peptides ( , 2 , 3 , 5 , 7 , 9 and 12 ) under the same conditions as the labelled peptides ., For these simulations the MR121-dye was replaced by an N-terminal NH group ., Simulation lengths of the different systems are 1 . 2 , 1 . 5 , 2 . 5 , 3 . 2 and 3 . 8 for MR121- peptides with , 3 , 5 , 7 and 9 , respectively , and 0 . 6 , 0 . 8 , 1 . 0 , 1 . 9 , 2 . 5 , 3 . 3 and 4 . 0 for peptides with , 2 , 3 , 5 , 7 , 9 and 12 respectively ., The total number of atoms in the simulation box varies between 1366 and 8643 , the number of water molecules between 443 and 2821 and the volumes between 13 . 3 and 84 . 7 depending on the peptide length ., To test the dependence of the sampled backbone conformations on the force field used , two additional simulations of 500 ns of the and peptides were also performed with a different force field , namely the OPLS-AA 68 force field ., Agreement between the two force fields is found in the hydrogen bonding properties , namely % of the structures populating the open state of the peptide contains short -sheet segments , while these are absent in the shorter peptide ., Details on the experimental methods , setup and some of the results are reported elsewhere 30 ., The relaxation process of the radiative emission of a fluorescent probe can be analysed via the second-order autocorrelation function of the fluorescence signal I ( t ) 30: ( 1 ) where the angle brackets denote average over all starting times ., Assuming an all-or-none transition between the fluorescent and non-fluorescent states , the following model was used to fit both the experimental- and simulation-derived autocorrelation functions in the labelled peptides: ( 2 ) where is the equilibrium constant between the open and closed states and is the mean relaxation time ., From and , as obtained by Eq ., 2 in both the experiments and simulation , average opening , , and closing , , times can be derived as follows: ( 3 ) The criterion for quenching employed in analysing the present simulations is that a non-fluorescent state occurs when the minimum distance between an atom of the conjugated rings of the MR121 and an atom of the rings of the Trp is nm ( I ( t ) ) while the state is fluorescent otherwise ( I ( t ) ) ., For the non-labelled peptides the minimum distance between the Trp ( the rings and the C-terminal group ) and the N-terminal group is taken to define if the chain is in a closed ( distance nm ) or open ( distance nm ) state ., The cut-off values ( 0 . 45 and 0 . 58 nm for labelled and unlabelled peptides , respectively ) were chosen by constructing for each simulation the probability-density-based free energy profile as a function of the end-to-end minimum distance and by taking as the cut-off value the distance at which the profile exhibits a free energy barrier for escaping from the global minimum at short distances ( a representative example is given in Figure 7 ) ., For the definition of a -sheet segment the program Dictionary of Protein Secondary Structure ( DSSP ) with the default hydrogen-bond cutoff parameter of 0 . 5 kcal was used 69 .

Introduction, Results/Discussion, Methods

Characterization of the length dependence of end-to-end loop-closure kinetics in unfolded polypeptide chains provides an understanding of early steps in protein folding ., Here , loop-closure in poly-glycine-serine peptides is investigated by combining single-molecule fluorescence spectroscopy with molecular dynamics simulation ., For chains containing more than 10 peptide bonds loop-closing rate constants on the 20–100 nanosecond time range exhibit a power-law length dependence ., However , this scaling breaks down for shorter peptides , which exhibit slower kinetics arising from a perturbation induced by the dye reporter system used in the experimental setup ., The loop-closure kinetics in the longer peptides is found to be determined by the formation of intra-peptide hydrogen bonds and transient β-sheet structure , that accelerate the search for contacts among residues distant in sequence relative to the case of a polypeptide chain in which hydrogen bonds cannot form ., Hydrogen-bond-driven polypeptide-chain collapse in unfolded peptides under physiological conditions found here is not only consistent with hierarchical models of protein folding , that highlights the importance of secondary structure formation early in the folding process , but is also shown to speed up the search for productive folding events .

In studies of protein folding evidence exists for early compaction in the unfolded state , although it is unclear whether these compact conformations contain specific secondary structures ( through hydrophilic interactions ) or whether compaction is a non-specific hydrophobic-driven effect ., Here we combine single-molecule fluorescence spectroscopy and molecular dynamics simulation to demonstrate peptide hydrogen-bond-driven polypeptide-chain collapse involving secondary structure formation as the key process in the early stage of folding ., Partial structuring in unfolded polypeptide chains is shown to lead to faster contact formation kinetics than would be expected if the unfolded state were populated by featureless random-coils .

biochemistry, biophysics/protein folding, biophysics/experimental biophysical methods, biochemistry/protein folding, biophysics/theory and simulation, computational biology/molecular dynamics, biochemistry/theory and simulation, computational biology, biophysics

journal.pcbi.1007024

Predicting three-dimensional genome organization with chromatin states

The human genome contains about 2 meters of DNA that is packaged as chromatin inside a nucleus of only 10 micrometers in diameter 1 ., The way in which chromatin is organized in the three-dimensional space , i . e . , the chromatin structure , has been shown to play important roles for all DNA-templated processes , including gene transcription , gene regulation , DNA replication , etc 2–4 ., A detailed characterization of chromatin structure and the physical principles that lead to its establishment will thus greatly improve our understanding of these molecular processes ., The importance of chromatin organization has inspired the development of a variety of experimental techniques for its characterization ., For example , using a combination of nuclear proximity ligation and high-throughput sequencing , chromosome conformation capture and related methods quantify the interaction frequency in three-dimensional space between pairs of genomic loci 5 , 6 , and have revealed many conserved features of chromatin organization ., A consistent picture that is emerging from these experiments is the formation of chromatin loops and topologically associating domains ( TADs ) at the intermediate scale of kilobases to megabases , and the compartmentalization of chromatin domains that are millions of base pairs apart in sequence 7–11 ., Many of the findings from these cross-linking experiments are now being validated and confirmed with microscopy imaging studies that directly probe spatial contacts 12–20 ., Polymer modeling has played a critical role in our understanding of the genome organization and in interpreting features of Hi-C contact maps 21 ., In particular , due to its deviation from the value of an equilibrium globule 6 , the power-law exponent of the contact probability between pairs of genomic segments as a function of the genomic separation has attracted the attention of numerous research groups 22–28 ., Of the many mechanisms that have been proposed , the non-equilibrium extrusion model 29–31 , which assumes that cohesin molecules function as active enzymes to inch along the DNA and fold the chromatin until encountering bound CTCF molecules , has gained wide popularity 32 ., Notably , this model succeeds in explaining the flanking of CCCTC-binding factor ( CTCF ) and cohesin binding sites at the boundaries of chromatin loops and TADs 7 , 9–11 , 33 ., On the other hand , phase separation , which is emerging as the key mechanism for organizing numerous membraneless organelles 34–36 , has been suggested as the driving force for chromosome compartmentalization 37–39 ., Since polymer molecules that differ in chemical compositions are known not to intermix 40 , micro-phase separation can contribute to the formation and compartmentalization of chromatin domains with distinct histone modification profiles ., Finally , besides these mechanism-based modeling strategies , data-driven approaches have also been quite successful in reconstructing chromosome structures directly from Hi-C data and revealing structural features of both interphase and metaphase chromosomes 41–45 ., In parallel , bioinformatics studies have provided powerful tools in addressing potential biases in Hi-C data 46–48 , and offered numerous insights in our understanding of genome organization ., In particular , correlating one-dimensional genomics and epigenomics data with 3D contacts has been rather informative and has led to many proposals on the molecular mechanism of chromatin folding 4 , 49–54 ., Furthermore , using advanced machine learning techniques , numerous groups have developed predictive models to identify specific contacts between regulatory elements 55–58 ., Though not able to construct the whole contact map and 3D chromosome structures , these machine learning approaches have achieved the level of resolution and specificity needed to study functionally important contacts within a TAD ., On the other hand , it remains challenging to quantitatively study such functionally important contacts using polymer modeling approaches , though significant progress towards that direction is being made 39 , 59–63 The difficulty in predicting contacts between specific regulatory elements using polymer models is at least twofold ., First , existing phase separation models based on A/B compartments or six subcompartments are inadequate for such purposes , despite their success in recapitulating the long-range block-wise patterns observed in Hi-C ., As chromosome compartments are defined based on contact patterns revealed by Hi-C at a coarse resolution from 50kb to 1 Mb , they tend to group many regulatory elements together as one “active” type and fail to capture the distinction among them 6 , 7 , 47 ., The ambiguity of these compartments significantly limits the accuracy of polymer models built upon them ., To study enhancer-promoter interactions , one must introduce new chromatin types at a higher resolution to achieve the required specificity ., How to define these types and how many types are needed remain unclear ., Secondly , even with our current understanding of chromatin folding mechanisms , developing a quantitative polymer model to predict contact probability between pairs of genomic loci is still a non-trivial task ., In particular , robust and efficient schemes are needed to derive parameters of polymer models to ensure their accuracy ., In this paper , we report the development of a predictive and transferable polymer model to simulate the structure and dynamics of chromosomes at five kilo base resolution ., This model takes combinatorial patterns of epigenetic marks and genomic location and orientation of CTCF binding sites as input , and can be parameterized from Hi-C data with a robust and efficient maximum entropy approach 64 , 65 ., A key innovation of this model is its use of chromatin states to capture the wide variety of regulatory elements and to probe their interactions ., Computer simulations of this model provide a high-resolution structural characterization of chromatin loops , TADs , and compartments , and succeed in quantitatively reproducing contact probabilities and power-law scaling of 3D contacts as measured in Hi-C and super-resolution imaging experiments ., Many significant enhancer-promoter contacts can be captured in simulated contact maps as well ., As the model incorporates ingredients from both the extrusion and the phase separation mechanism , its success in quantitative predictions of genome organization provides strong support for such mechanisms ., In the meantime , detailed analysis of the model parameters further reveals a significant difference between the interactions that stabilize TAD and those that drive compartmentalization , providing additional insight into chromatin folding not appreciated in existing modeling efforts ., Finally , we demonstrate that the model is transferable across chromosomes and cell types , setting the stage for de novo prediction of the structural ensemble for any given chromatin segment using only one-dimensional sequencing data that is available for hundreds of cell types ., We introduce a predictive model to study cell-type specific 3D chromatin folding ., This model takes a sequence of chromatin states derived from genome-wide histone modification profiles and a list of CTCF binding sites as input ., We selected these genomic features due to their known roles in organizing the chromatin at various length scales ( Fig 1A ) ., At the core of this model is an energy function—a force field—that is sequence specific and ranks the stability of different chromatin conformations ., Starting from the input for a given chromatin segment , we use molecular dynamics simulations to explore chromatin conformations dictated by the energy function and to predict an ensemble of high-resolution structures ., These structures can be compared directly with super-resolution imaging experiments or converted into contact probability maps for validation against genome-wide chromosome conformation capture ( Hi-C ) experiments ., As shown in Fig 1B , a continuous genomic segment is represented as beads on a string in this model ., Each bead accounts for five-kilo bases in sequence length and is assigned with a chromatin state derived from the underlying combinatorial patterns of 12 key histone marks ., Chromatin states are known to be highly correlated with Hi-C compartment types 39 , 54 , 66 and , therefore , will help model large-scale chromosome compartmentalization ., In the meantime , chromatin states can go beyond traditional A/B compartments or subcompartments to provide polymer models with the specificity needed for studying interactions between regulatory elements ., We define a total of 15 chromatin states , identified using a hidden Markov model 67 , to distinguish promoters , enhancers , heterochromatin , quiescent chromatin , etc ( see Methods ) ., Detailed histone modification patterns for these chromatin states are shown in Fig 1C ., We note that 15 is large enough to capture the diversity of epigenetic modifications while still being small enough to ensure a sufficient population of each state for a robust inference of interaction parameters between them ( Figure A1 in S1 Supporting Information ) ., We further studied a hidden Markov model with 20 states , and found that further increasing the number of states does not lead to a discovery of additional epigenetic classes with significant populations ( Figure A2 in S1 Supporting Information ) ., A polymer bead is further labeled as a CTCF site to mark chromatin loop boundaries if both CTCF and cohesin molecules are found to be present in the corresponding genomic region ., We define the orientation of these CTCF sites by analyzing the underlying CTCF motif and the relative position of CTCF molecules with respect to cohesin ., Details for the definition of CTCF binding sites are provided in Methods ., The potential energy for a given chromatin configuration r is a sum of three components , and UChrom ( r ) = U ( r ) + UCS ( r ) + UCTCF ( r ) ., U ( r ) is a generic polymer potential that is included to ensure the continuity of the chromatin , and to enforce excluded volume effect among genomic loci ., UCS ( r ) is a key innovation of the chromatin model , and is crucial to capture the formation of TADs and compartments ., It quantifies the chromatin state specific interaction energies between pairs of loci ., As detailed in Section: Physical principles of chromatin organization and Methods , we used a general form for UCS ( r ) to capture its dependence on genomic separation ., UCTCF ( r ) is inspired by the loop extrusion model 29–31 , and facilitates the formation of loop domains enclosed by pairs of CTCF binding sites in convergent orientation ( Fig 1A ) ., Both UCS ( r ) and UCTCF ( r ) contain adjustable parameters that can be derived from Hi-C data following the optimization procedure developed by one of the authors 64 , 65 ., Segments of chromosomes 1 , 10 , 19 and 21 from GM12878 cells were used for parameterization to ensure a sufficient coverage of all chromatin states ( see Figure A1 in S1 Supporting Information ) ., Detailed expressions for the potential energy , and the parameterization procedure are provided in Methods and in the S1 Supporting Information ., Using the parameterized energy function , we simulated the ensemble of chromatin structures and determined the corresponding contact probability map for a 20 Mb region of chromosome 1 from GM12878 cells ., As shown in Fig 2A , the simulated contact map is in good agreement with the one measured by Hi-C experiments from Ref ., 7 and reproduces the overall block-wise checkerboard pattern that corresponds to the compartmentalization of chromatin domains ., A zoomed-in view along the diagonal of the contact map provided in Fig 2B and 2C further suggests that chromatin TADs and loops are also well reproduced ., Similar comparisons for other chromosomes used in parameterizing the model are provided in Figure B in S1 Supporting Information ., We note that the length 20 Mb was chosen for computational efficiency , but the model can be easily generalized to longer chromatin segments ( see Figure C in S1 Supporting Information ) ., To go beyond the visual inspection and quantify the correlation between simulated ( GM-Sim ) and experimental ( GM-Exp ) contact maps , we calculated the Pearson correlation coefficient ( PCC ) between the two for chromosome 1 and found that it exceeds 0 . 96 ., Importantly , this number is higher than the PCC ( 0 . 94 ) between GM-Sim and Hi-C data from IMR90 cells ( IMR-Exp ) ., Breaking down the PCC at different genomic separations also supports that GM-Sim is more correlated with GM-Exp at all ranges than with IMR-Exp ( Figure D in S1 Supporting Information ) ., In addition , we also determined the stratum-adjusted correlation coefficient ( SCC ) that takes into account the distance-dependence effect of contact maps by stratifying them according to the genomic distance 68 , and obtained 0 . 7 for GM-Sim/GM-Exp , and 0 . 66 for GM-Sim/IMR-Exp ., Therefore , SCC analysis also validates our model’s ability in reproducing Hi-C contact maps and in capturing the distinction between cell types ., We note that the magnitude of SCC can be sensitive to the smoothing parameter used in its calculation and should be interpreted with caution ( Figure E in S1 Supporting Information ) ., We further examined the agreement between simulated and experimental contact maps using multiple feature-specific metrics ., First , we define the contact enhancement for a pair of genomic loci as the ratio of their contact probabilities over the mean contacts averaged over a locally selected background region ( see Figure F1 in S1 Supporting Information ) ., The contact enhancement for chromatin loops from chromosome 1 is always larger than one , indicating a strong enhancement of spatial colocalization between loop anchors ., Furthermore , over 74% of the loop pairs exhibit a contact enhancement that is larger than the 90th percentile of the distribution for random genomic pairs ., These random pairs are selected regardless of CTCF occupancy but with comparable sequence separations as those found in chromatin loops ., Therefore , if we use the 90th percentile of the random distribution as a threshold ( 1 . 16 ) and predict every convergent CTCF pairs as loops , the prediction will have a false negative rate of 26% , and a false positive rate less than 10% ., The false positive value is an upper bound since most of the random pairs are not flanked with convergent CTCF ., The sensitivity of chromatin loop predictions on the threshold is shown in Figure F2 in S1 Supporting Information ., It is worth pointing out that the contact enhancement for chromatin loops calculated using Hi-C data is in general larger than simulated values and separates better from that for random pairs ( Figure F3 in S1 Supporting Information ) ., The overlap between the two distributions in our simulation is due to that random pairs include a significant fraction of convergent CTCF pairs whose contacts are enhanced as a result of the potential UCTCF ( r ) ., Many of these pairs , however , are not recognized as loops in Hi-C , and more advanced algorithms than simple binding site orientations are probably needed to identify loop forming CTCF pairs 69 ., To go beyond CTCF mediated contacts and evaluate our model’s ability in reproducing strong interactions between genomic loci , we selected statistically significant contact pairs from simulated and experimental contact maps for chromosome 1 using the software Fit-Hi-C 48 ( Figure G in S1 Supporting Information ) ., As a quantitative metric , we define the matching score as the percent of experimental pairs that can be found in the list extracted from simulation ., The reverse matching score can be similarly defined as the percent of simulated pairs found in the experimental list ., The matching score for the top 1000 chromatin contacts is determined to be 46% and 52% for the reverse matching ., To examine specific interactions between regulatory elements , we performed a similar analysis by selecting the top 100 enhancer ( state: EnhW1 ) -promoter ( state: PromD1 ) pairs with highest contact probabilities based on simulated and experimental contact maps ., We find that over 70% of experimental pairs are captured in our simulation for chromosome 1 ., These results suggest that our model based on chromatin states and CTCF mediate interactions is able to reproduce a large fraction of significant contacts detected in Hi-C experiments ., Further improving the model’s ability in predicting functionally important pairs would potentially require considering the effect of other proteins , such as YY1 that are known to mediate chromatin interactions 70 , and will be an interesting future direction ., We next determined the correlation coefficients between the top five eigenvectors for simulated and experimental contact matrices ., As shown in Figure H in S1 Supporting Information , the contact maps reconstructed using only these eigenvectors recapitulate the formation of TADs and compartments observed in the original maps ., The high correlation between simulated and experimental eigenvectors ( with PCC at approximately 0 . 8 ) supports that the corresponding features are well captured by the computational model , and confirms the qualitative observations from Fig 2 and Figure B in S1 Supporting Information ., To more closely examine the quality of simulated TADs , we calculated the insulation profile by sliding a uniform 500kb × 500kb square along the diagonal of the contact matrix and averaging over all contacts within the square ., The minima of this profile can be used to identify TAD boundaries as inter-TAD contacts are sparser compared to intra-TAD contacts , resulting in a drop in the insulation score profile as the sliding window crosses TAD boundaries 71 ., The PCC between experimental and simulated insulation profiles for chromosome 1 is 0 . 7 ., We find that the matching score for TAD boundaries is 80% and 100% for the reverse matching ., As another independent validation , we determined TAD boundaries using the software TADbit 43 , and found that the simulated results again match well with experimental ones ( see Figure I in S1 Supporting Information ) ., To demonstrate the transferability of the computational model across chromosomes and cell types , we performed additional simulations for chromosomes from GM12878 , K562 , and Hela cells , whose Hi-C data were not included during the parameterization procedure ., As shown in Fig 3 and Figure J in S1 Supporting Information , these de novo predictions are in good agreement with experimental results as measured by PCC ( Fig 3B ) and SCC ( Fig 3C ) between experimental and simulated contact maps , matching score between TAD boundaries detected from the insulation profile ( Fig 3D ) and from TADbit ( Figure K1A in S1 Supporting Information ) , PCC between experimental and simulated insulation profiles ( Figure K1D in S1 Supporting Information ) , matching score between significant contacts detected using Fit-Hi-C ( Fig 3E ) , matching score between interacting enhancer-promoter pairs ( Figure K2C in S1 Supporting Information ) , correlation coefficients of the top five eigenvectors ( Fig 3F and Figure H in S1 Supporting Information ) , and false negative rate of loop predictions ( Fig 3F ) ., Furthermore , the model succeeds in revealing the cell-type specificity of Hi-C contact maps , and the simulated contact maps are always more correlated with the corresponding experimental data from the same cell type than with those from IMR90 cells ( light colors in Fig 3B and 3C ) ., The matching scores between experimental and simulation results are also significantly higher than those calculated between experimental and control data ( light colors in Fig 3D and 3E ) , which were obtained by randomly shuffling the size of loops/enhancer-promoter pairs/TADs along the chromosome while keeping their total number unchanged ., The success of these de novo predictions supports that the chromatin-state-based model introduced here provides a consistent description of the 3D genome organization across cell types ., We next analyze the simulated 3D structural ensembles to gain additional insights on chromatin organization ., Consistent with previous experimental and theoretical studies 37 , 72 , 73 , our model reproduces the clustering of active chromatin state and their preferred location at the exterior of chromosomes ( Figure L in S1 Supporting Information ) ., Super-resolution imaging experiments probe chromatin organization in 3D space to quantify spatial distances between genomic segments ., These 3D measurements can be compared directly with simulated chromatin structures , and thus provide a crucial validation of the computational model parameterized from Hi-C experiments with independent datasets ., To understand the overall compactness of various chromatin types , we selected a set of active , repressive and inactive chromatins and determined their radiuses of gyration from the ensemble of simulated structures ., These different chromatin types are identified using two key histone marks H3K4me2 and H3K27me3 ( Fig 4A ) ., The complete list of chromatin domains with their genomic locations is provided in the Extended Data Sheet ., As shown in Fig 4B , the radius of gyration increases at larger genomic separation following a power law behavior in all cases with exponents of 0 . 34 , 0 . 31 and 0 . 23 for the three chromatin types respectively ., These scaling exponents are in quantitative agreement with imaging measurements performed for Drosophila chromosomes 12 and support the notion that active chromatins adopt less condensed conformations to promote gene activity ., Consistent with the imaging study performed on chromosome 21 from IMR90 cells 13 , 20 , we also observe a strong correlation between Hi-C contact probabilities and spatial distances for pairs of genomic loci ( Fig 4C ) ., One of the most striking features revealed by high-resolution Hi-C experiments is the formation of chromatin loops anchored at pairs of convergent CTCF sites 7 , 10 , 74 , 75 ., Microscopy studies that directly visualizes 3D distances using fluorescence in situ hybridization ( FISH ) methods further find that these loops are dynamic , and despite their high contact frequencies , loop anchors are not in close contact in every cell 16 , 41 , 76 ., Consistent with their dynamic nature , chromatin loops in our simulation adopt flexible conformations as well ., As shown in Fig 5A , for the loop formed between chr1:39 . 56–39 . 73 Mb , we observe a large variance in the probability distribution of its end-to-end distances ., Additional results for other loop pairs are provided in Figure M in S1 Supporting Information ., Two example configurations of the loop domain with distance at 0 . 08 and 0 . 24 μm are shown in the inset ., A systematic characterization of all the loops identified in Ref ., 7 for the simulated chromatin segment shows that the conformational flexibility is indeed general , though there is a trend in decreasing variance for loops with larger contact probabilities ( Fig 5B ) ., We also emphasize that though higher contact probabilities , in general , corresponds to smaller end-to-end distances , their relationship is not strictly monotonic ., The opposite correlation can be seen in numerous cases in Fig 5B ., Such seemingly paradoxical observations have indeed been found in previous experimental studies that compare 3C with FISH experiment 16 , 77 , and can naturally arise as a result of dynamical looping or loop extrusion 78 ., Compared to chromatin loops , TADs are longer and are stabilized by a complex set of interactions 79 ., The analysis of their structural ensemble is less straightforward , and the end-to-end distance may not be sufficient for a faithful description of their conformational fluctuation 80 ., It is desirable to analyze TAD structures using reaction coordinates that not only help to distinguish different clusters of chromatin conformations , but can also provide insight into the mechanism of TAD folding and formation ., Borrowing ideas from protein folding studies , we approximate these reaction coordinates using collective variables with slowest relaxation timescales as determined following the diffusion map analysis 81 , 82 ., Progression along these variables approximates well the most likely transition between two sets of structures and can , therefore , shed light on the pathway for conformational rearrangements ., Diffusion map analysis has been successfully applied to a variety of systems to provide mechanistic insights on the conformational dynamics involved in protein folding , ligand diffusion , etc ., 83 , 84 ., We applied the diffusion map technique to the predicted structural ensemble of the genomic region chr1:34–38 Mb from GM12878 cells that consists of three visible TADs ., As shown in Fig 6 , several basins are observed in the probability distribution of chromatin conformations projected onto the first two reaction coordinates , suggesting the presence of multiple stable TAD structures , rather than a unique one ., Conformational heterogeneity in TADs has indeed been observed in a recent super-resolution imaging study that characterizes single cell chromatin structures 20 ., To gain physical intuition on the reaction coordinates and insight on the transition between TAD structures , we calculated the corresponding contact maps at various values of these coordinates ., As shown in the top panel , reaction coordinate one captures the formation of contacts between TAD1 and TAD3 while the structures for all three TADs remain relatively intact ., On the other hand , progression along reaction coordinate two ( left panel ) leads to significant overlaps between TAD1 and TAD2 ., Interaction between TAD2 and TAD3 can also be observed along a third coordinate as shown in Figure N in S1 Supporting Information ., Example structures for the three TADs in various regions are also provided on the right panel ., These results are consistent with the notion that TADs are stable structural units for genome organization 79 , but also suggest the presence of significant cross-talk among neighboring TADs 85 ., Though the exact molecular mechanism and driving force for chromatin folding remain elusive , it is becoming increasingly clear that different molecular players are involved in organizing the chromatin at various length scales 49 , 60 , 86 , 87 ., For example , transcription factors and architectural proteins are critical in stabilizing the formation of chromatin loops and TADs 4 , 33 , 79 ., On the other hand , nuclear compartments , such as the nucleolus and the nuclear envelope , contribute to chromatin compartmentalization and mediate contacts among chromatin domains separated by tens of Mb in sequence 50 , 88 ., We expect that these different molecular mechanisms will give rise to distinct interaction energies at various genomic length scales ., For example , for the same pair of chromatin states , as the genomic separation between them is varied , the interaction energy that stabilizes their contact should vary ., In the following , we examine the dependence of inferred contact energies on genomic separation to reveal the principles of genome organization ., Fig 7A presents the derived contact energies among chromatin states UCS ( r ) at various genomic separations ( 500kb , 1 . 5 Mb , 4 Mb and 10 Mb from left to right ) , with blue and red for attractive and repulsive interactions respectively ., A notable feature for all four length scales is the clear partition of chromatin states into at least two groups that correspond to well-known active and repressive chromatins respectively ., For example , attractive interactions are observed among the top half chromatin states that include promoters ( PromD1 , PromU ) , enhancers ( TxEnh5 , Enhw1 ) and gene body ( Tx ) , and for the bottom half that includes inactive chromatin ( Quies ) , polycomb repressed domain ( ReprPC ) and heterochromatin ( Het ) ., The unfavorable interactions among active and repressive chromatins will drive their phase separation shown in Fig 2D and Figure L in S1 Supporting Information ., Partitioning of chromatin states into active and inactive groups is also evident from the dendrogram shown in Fig 7B , and the eigenvectors for the largest in magnitude eigenvalue of the interaction matrices shown in Fig 7C ., Despite their overall similarities , the interaction energies at various genomic separations differ from each other ., To quantify their differences , we determined the pairwise Pearson correlation coefficients between the interaction matrices ., As shown in Fig 7C , the interactions that are responsible for TAD formation ( ~ 1 Mb ) indeed differ significantly from those that lead to chromatin compartmentalization ( ~ 10 Mb ) , as evidenced by the low correlation among them ., Strikingly , the correlation coefficient between interaction matrices at 4 Mb and 10 Mb exceeds 0 . 9 , indicating the convergence of chromatin interactions at large genomic separation ., We further compared the complexity of the interaction matrices by calculating the ratio of the first n eigenvalues over the sum of all eigenvalues ., Fig 7D plots this complexity measure as a function of n , and absolute values of the eigenvalues were used to calculate the measure ., For all three matrices with genomic separation larger than 1 Mb , we find the top first six eigenvectors can explain a large fraction of their complexity ( over 80% ) ., This observation is consistent with the success of our previous effort in modeling chromatin organization with six compartment types 37 ., However , more eigenvectors are needed , especially for short range in sequence interactions , to capture the full matrix complexity ., These results together highlight the presence of distinct mechanisms that fold the chromatin at various genomic separations , and argues the importance of using sequence length dependent contact energies ., We introduced a novel computational model for studying 3D genome organization by integrating bioinformatics analysis with polymer modeling ., This integration brings together the best of both worlds and results in a powerful predictive tool ., Similar to bioinformatics approaches , our model succeeds in identifying cell-type specific interactions between regulatory elements ., As in polymer modeling , the availability of 3D chromosome conformations makes it possible to characterize contacts between any genomic segments and construct the whole contact map , to study global properties of the genome organization that involve many-body interactions , and to explore the physical mechanism and driving force of genome folding ., This predictive model presents a significant improvement from our previous effort in simulating chromatin structures 37 by switching the input from compartment types to chromatin states ., In particular , unlike compartment types that are results from clustering Hi-C contact matrices 7 , chromatin states are defined as combinational patterns of histone modification profiles ., Uncoupling the input from Hi-C data is critical to ensure that the model is genuinely predictive ., Furthermore , chromatin states allow us to model chromatin structures at a much higher resolution ( 5kb ) to provide a detailed structural characterization of chromatin loops and TADs , and to resolve long-range specific contacts between promoters and enhancers ., On the other hand , chromatin models based on compartment types are inherently limited to 50kb 37 , 39 , a resolution at which compartment types can be robustly derived from Hi-C data 7 ., Finally , as shown in Fig 7 , the novel sequence-separation dependent contact potential developed here enables a rigorous assessment of the number of “types” needed for modeling chromatin structures , and suggests that the six compartment types are insufficient for an accurate

Introduction, Results, Discussion, Methods

We introduce a computational model to simulate chromatin structure and dynamics ., Starting from one-dimensional genomics and epigenomics data that are available for hundreds of cell types , this model enables de novo prediction of chromatin structures at five-kilo-base resolution ., Simulated chromatin structures recapitulate known features of genome organization , including the formation of chromatin loops , topologically associating domains ( TADs ) and compartments , and are in quantitative agreement with chromosome conformation capture experiments and super-resolution microscopy measurements ., Detailed characterization of the predicted structural ensemble reveals the dynamical flexibility of chromatin loops and the presence of cross-talk among neighboring TADs ., Analysis of the model’s energy function uncovers distinct mechanisms for chromatin folding at various length scales and suggests a need to go beyond simple A/B compartment types to predict specific contacts between regulatory elements using polymer simulations .

Three-dimensional genome organization is expected to play crucial roles in regulating gene expression and establishing cell fate , and has inspired the development of numerous innovative experimental techniques for its characterization ., Though significant progress has been made , it remains challenging to construct chromosome structures at high resolution ., Following the maximum entropy approach pioneered by Zhang and Wolynes , we developed a predictive model and parameterized a force field to study chromatin structure and dynamics using genome-wide chromosome conformation capture data ( Hi-C ) ., Starting from one-dimensional sequence information that includes histone modification profiles and CTCF binding sites , this model predicts chromosome structure at a 5kb resolution , thus establishing a sequence-structure relationship for the genome ., A significant advantage of this model over comparable approaches is its ability to study long-range specific contacts between promoters and enhancers , in addition to building high-resolution structures for loops , TADs and compartments ., Furthermore , the model is shown to be transferable across chromosomes and cell types , thus opens up the opportunity to carry out de novo prediction of genome organization for hundreds of cell types with available epigenomics but not Hi-C data .

chromosome structure and function, histone modification, chromosome mapping, mathematics, materials science, algebra, epigenetics, molecular biology techniques, macromolecules, structural genomics, chromatin, research and analysis methods, polymers, polymer chemistry, gene mapping, chromosome biology, gene expression, chemistry, chromatin modification, molecular biology, eigenvectors, cell biology, linear algebra, genetics, biology and life sciences, physical sciences, genomics, materials, chromosomes

journal.pcbi.1002590

Learning with Slight Forgetting Optimizes Sensorimotor Transformation in Redundant Motor Systems

The motor system exhibits tremendous redundancy 1 ., For example , an infinite number of muscle activation patterns can generate a desired joint torque because multiple muscles span a single joint; moreover , several combinations of neuronal activity in the motor cortex can achieve exactly the same muscle activation pattern ., Nevertheless , strongly stereotypical patterns are observed in the activity patterns of neurons in the primary motor cortex ( M1 ) 2–6 as well as those of the muscles 7–11 ., How , then , does the motor system select such stereotypical behavior from an infinite number of possible solutions ?, The hypothesis that the brain selects a solution that minimizes the cost of movement has long been proposed 12 , 13 ., Recent studies have indicated that various aspects of motor control , such as trajectory formation and the selection of a muscle activation pattern , can be reproduced when the motor command is constructed to minimize the cost J 10 , 14 , 15 , as expressed by: ( 1 ) With regard to the movement accuracy , it is widely accepted that information on movement error is available to the brain 16–21 ., In contrast , there is no evidence indicating that the brain explicitly computes the cost of motor effort across a vast number of neurons and muscles ( i . e . , the sum of the squared activity ) 22 ., Some theoretical studies have proposed that the brain can implicitly minimize the motor effort cost by minimizing the variance of motor performance in the presence of signal-dependent noise ( SDN ) 23 , 24 ., This theory has attracted widespread interest because the minimization of variance is more biologically plausible than the explicit minimization of the motor effort cost ., However , there is still no evidence indicating that a statistical quantity such as variance is represented in the brain 25 , 26 ., Thus , it is unknown how the optimization process that minimizes the cost function J is implemented in the brain ., It should be noted that these conventional optimization studies tacitly assume that the brain somehow constructs a motor command that theoretically minimizes the cost function , and largely ignored the underlying trial-by-trial learning process 8–13 , ., In contrast , recent studies that focused on the trial-by-trial modification of motor commands suggested that forgetting ( i . e . , synaptic weight decay ) is helpful for minimizing the motor effort cost without an explicit calculation of a complex quantity ( i . e . , sum of squares ) 29–35 ., Although the “weight decay method” has been used as a technical method in the machine-learning community since the 1980s to suppress irrelevant connections in a neural network and to improve the networks generalization ability 36–39 , it is only recently that its potential for solving the redundancy problem in the context of motor control began to be investigated ., Importantly , Emken et al . 32 demonstrated that trial-by-trial error-feedback learning with forgetting minimizes a cost function that is the weighted sum of motor error and motor effort ., However , since the authors formulated their motor learning scheme with only a single lumped muscle ( i . e . , a non-redundant actuator ) , their model cannot predict the activation patterns of individual muscles ., Burdet et al . and Franklin et al . also proposed a similar but more elaborate algorithm ( the V-shaped learning function ) and showed that it could predict the evolution of the activity of individual muscles that was actually observed when human subjects learn to perform movements in novel dynamic environments 29 , 34 , 35 ., This algorithm has been also used to realize human-like adaptive behavior in robots 40 , 41 ., However , it is unknown whether the decay algorithm could minimize the cost ( J ) in a highly redundant neural network that includes M1 neurons and whether it can predict the activation patterns of M1 neurons ., Neurophysiological studies reported that the preferred direction ( PD; the direction in which the neuron is maximally active ) of M1 neurons was stereotypically biased toward a specific direction 2–6 ., Although a conventional optimization study suggested that the bias is a result of the minimization of the cost ( J ) 27 , it is unclear how the two terms of the cost function ( i . e . , error and effort ) are minimized on a trial-by-trial basis and how the PD bias of M1 neurons is formed during the optimization process ., To gain insight into these mechanisms , we conducted computer simulations of motor learning by applying the “feedback-with-decay” algorithm to a redundant neural network model for sensorimotor transformation ., First , we used a simple linear model to gain a firm theoretical understanding of the effect of the decay on the minimization of the cost ( J ) and the formation of the PD bias ., Then , using a non-linear network model with realistic musculoskeletal data , we examined numerically whether this algorithm could predict the PD bias reported in various motor tasks ., Our simulations revealed that the “feedback-with-decay” algorithm could consistently reproduce the PD distribution observed during various motor tasks , including a 2D isometric torque production task and a reaching task , and even a 3D reaching task ., As a simple example of a redundant motor task , we considered a task that requires the production of torque in a two-joint system with redundant actuators ( Figure 1A , B ) ., To demonstrate clearly the effect of weight decay , we initially used a simple linear feed-forward neural network that transforms the desired torque ( input layer ) into actual torque ( output layer ) through an intermediate layer that consisted of 1000 neurons ( Figure 1B ) ., Each neuron in the intermediate layer received a desired torque vector ( τ ) from the input layer with a synaptic weight ( W ) that could be modified with learning ., The activation level ( r ) was linearly dependent on the input torque vector ( i . e . , r\u200a=\u200aWτ ) , indicating that it obeys cosine tuning ., Each neuron generated its own 2D torque vector ( mechanical torque direction vector: MDV ) that was predetermined by its connection strength ( M ) with the output layer ., The total output of the network ( T ) was the vector sum of the output from all neurons ., The MDVs were biased toward the first and third quadrants in the torque space ( dots for M in Figure 1C ) ., The network was trained to produce appropriate output torque by randomly presenting 8 target torques ( Figure 1A ) over 40 , 000 trials ., An error back-propagation algorithm 42 was successively used to modify the synaptic weight ( W ) , while the MDV matrix ( M ) was held constant ., First , we considered the case where the synaptic weights are solely modified to reduce the error , according to the following equation: ( 2 ) where α is the learning rate and Je is the error cost , as calculated by the error vector ( e\u200a=\u200aT−τ ) between the output and the desired torque: Je\u200a=\u200a1/2eTe ., The error gradually decreased and approached zero at around the 500th trial ( Figure 2A ) ., Once the error converged to zero , further synaptic modifications did not occur in this model ( i . e . , the PDs did not change after the 500th trial , Figure 2E ) , as schematized in Figure S1A ., Thus , the cost of the motor effort ( the sum of the squared neural activity ) did not achieve an optimal level , and the converged states depended on the initial settings for the synaptic weight ( Figure 2C; the different colors represent the different initial states ) ., The distribution of the PDs in the converged state also depended on the initial synaptic weight ( see polar histograms in Figure 2C ) ., When uncertainty was introduced into the system ( i . e . , the existence of noise in execution and synaptic modification ) , the results were almost identical ( Figure S2A–C ) ., The synaptic weights randomly moved back and forth along a null trajectory satisfying zero movement error ( Figure S2D ) , which is the natural consequence of redundancy in the motor system 43 ., However , the situation was considerably different when modification of the synaptic weights based on error feedback was not perfect , but incorporated weight decay , as follows: ( 3 ) where β indicates the decay in motor learning and has a small positive value ( β\u200a=\u200a1 . 0×10−4 ) ., In this model , the sum of the squared neural activity converged at an optimal value regardless of the initial synaptic weight ( Figure 2D ) ., Importantly , the distribution of the PDs also converged on the same distribution ( Figure 2D , * indicates a significant bimodal distribution revealed by the Rayleigh test , P<0 . 05 ) ., Why did such a convergence occur ?, Intuitively , but not mathematically rigorous , this was because , even after error convergence , the synaptic decay term ( -βWij ) continued to induce a very small error ., To reduce this small error , the error-feedback term continuously and gradually modified the synaptic weight; as a result , the neuron PDs ( Figure 2F ) and the distribution of the PDs ( polar histogram in Figure 2D ) continued to change , until the synaptic weight converged on the optimal state ., In mathematical terms , the modification of the synaptic weights based on the feedback-with-decay rule ( Eq ., ( 3 ) ) is similar to the gradient descent rule for minimizing the cost function J , which is the weighted sum of the error cost ( Je ) and the motor effort cost ( Jm ) : ( 4 ) as the gradient descent rule for minimizing J is expressed by: ( 5 ) However , it should be noted that Eqs ., ( 3 ) and ( 5 ) do not necessarily minimize the expected value of the cost J ( i . e . , EJ ) ., The reason why we should consider EJ rather than J itself is that the optimal solution for the biological system should globally minimize the cost J for whole movement directions ( see Supporting Text S1 ) ., Hereafter , the optimal solution means that it minimizes EJ ., In this study , we theoretically proved that the modification rule Eq . ( 3 ) leads to optimal synaptic weight among many solutions that satisfy zero error under several necessary conditions ( see Supporting Text S1 ) : first , the decay rate ( β ) must be much smaller ( i . e . , slower ) than the learning rate ( α ) ( condition #1 ) ; second , there must be a large number of neurons , each of which generates a quite small output relative to the desired torque magnitude ( condition #2 ) ; and third , more than two different and independent targets must be practiced ( condition #3 ) ., Furthermore , we have also proven that the synaptic weight matrix ( W ) converges to a unique pseudo-inverse of the matrix M that consists of the MDVs from all of the actuators ( see Supporting Text S1 ) ., As the synaptic weight matrix determines the PDs of the neurons , the inverse relationship between W and M indicates that the distribution of the PD vectors ( PDVs ) was orthogonal to that of the MDVs ., Therefore , when the distribution of the MDVs is biased toward the 1st and 3rd quadrants , the distribution of the converged PDVs should be biased toward the 2nd and 4th quadrants ( Figure 1C ) ., The above results indicate three important points regarding the “feedback-with-decay” rule ., First , the optimal solution can be obtained using only trial-based error information , without the explicit calculation of the sum of the squared neural activity ., Second , the biomechanical properties of the actuators ( i . e . , MDVs ) necessarily determine the neuronal recruitment pattern ( i . e . , PDVs ) ., Third , the optimal PD bias is steadily formed during the minimization of the motor effort ., Another interesting observation regarding the formation of the bias of the PDs is that when the initial synaptic weight is relatively small ( see cyan trace in Figure 2C ) , even the “feedback-only” rule predicted a PD bias that is similar to the optimal PD bias predicted by the “feedback-with-decay” rule ( Figure 2D ) ., By assessing the underlying mechanism mathematically , we found that if a large number of neurons participate in the task ( condition #2 ) , the “feedback-only” rule leads the synaptic weight W to converge on:where A ( ≠0 ) is a matrix that never increases |W ( 0 ) | and always satisfies MAW ( 0 ) =\u200a0 ( see Supporting Text S1 ) ., This result indicates that if the initial synaptic weight matrix ( W ( 0 ) ) is considerably smaller than the pseudo-inverse matrix ( MT ( MMT ) −1 ) ( condition #4 ) , the converged PD bias is dominated by the PD bias of the pseudo-inverse ., Thus , if conditions #2 and #4 are satisfied , even the “feedback-only” rule can predict the approximate direction of the optimal PD bias , even though the converged synaptic weight matrix is not optimal ., In summary , in the linear neural network model , the “feedback-with-decay” rule consistently leads to the optimal synaptic weight and the optimal PD bias , whereas the “feedback-only” rule only predicts the approximate direction of the optimal PD bias in limited conditions ., Next , we examined whether these aspects hold true in non-linear neural network models that additionally include a muscle layer whose activity ( a ) was constrained as positive ( i . e . , muscles do not push ) ( Figure 3A ) ., Here , it is assumed that the 2nd neural layer consists of corticospinal neurons in M1; however , since M1 actually includes inhibitory interneurons , the layer cannot be regarded as a real M1 ., Nevertheless , we modeled the neural network incorporating the properties of actual M1 neurons to gain an insight into how the corticospinal neurons are recruited under the feedback-with-decay rule ., Firstly , each corticospinal neuron receives the desired movement parameters from the input layer and their firing rate obeys cosine tuning 44 ., Secondly , each corticospinal neuron innervates multiple muscles 45–48 ., Considering that there are two types of corticospinal neurons 49 , one type has direct connections with motoneurons ( i . e . , cortico-motoneuronal neurons ) while the other type indirectly influences motoneurons through spinal interneurons , the innervation weight from the neurons to the muscles ( Z ) is allowed to take positive and negative values ., At present , it is assumed that innervation is random and does not have any bias to specific muscles ., It is also assumed that the innervation weight ( Z ) is constant through time 50 , although this is controversial 51 ., These assumptions considerably simplified the model and allowed us to gain a clear insight into the formation of neuronal PDVs relative to the MDVs ., Thirdly , the mechanical pulling direction vectors of muscles ( M ) were determined by the muscle parameters ( e . g . , moment arm ) derived from a realistic musculoskeletal model 52 , 53 ., M was also kept constant because we only examined the static aspect of movement , e . g . , isometric force production or the initial ballistic phase of reaching movements ., By simulating these tasks with this network model , we examined whether the feedback-with-decay rule accounts for the reported activation patterns of muscles and M1 neurons ., It has long been hypothesized that well-organized stereotypical movements are achieved by minimizing the cost ( J ) , which includes the motor error and the variables related to the motor effort ( e . g . , jerk , torque change , sum of squared muscle activity , and variance of error ) 12–15 , 23 ., It has also been shown that such an optimization model can predict the bias of the PDs of muscles and M1 neurons observed in primate and human experiments 8–10 , 24 , 27 ., However , most of the previous optimization studies have examined only the resultant state obtained by the optimization process and largely ignored the underlying trial-by-trial learning process ., Therefore , it is unclear how the cost function ( J ) is minimized on a trial-by-trial basis and how the PD biases are formed during optimization ., A small number of previous studies have proposed a mechanism for how the cost of the motor effort is minimized in the brain on a trial-by-trial basis ., Kitazawa 26 proposed the “random work hypothesis” in which , in the presence of SDN , the system gradually approaches the optimal state only by successively feeding back trial-based error information ., However , there is no guarantee of convergence with the optimal state , especially for highly redundant systems ., Indeed , in our highly redundant neural network ( n\u200a=\u200a1000 ) with SDN , but without synaptic decay , the synaptic weights were captured at a suboptimal level ( Figure S2B ) ., Even when the system was small ( n\u200a=\u200a2 ) , consistent convergence did not occur ( Figure S2D ) ., In contrast , recent studies have suggested that forgetting might be useful to minimize the motor effort 29–35 ., Emken et al . 32 demonstrated that trial-by-trial error-feedback learning with forgetting is mathematically equivalent to the minimization of error and effort by formulating the force adaptation task during gait , although their formulation was limited to the case of a single lumped muscle system ( i . e . , a non-redundant actuator system ) ., An important prediction from this scheme is that the motor system continuously attempts to decrease the level of muscle activation when the movement error is small 30–33 ., Such a decrease in muscle activity was actually observed when human subjects learned to perform movements in a novel force field environment; initially , muscle activity was increased to reduce the movement error produced by the force perturbation , but once the error decreased to a small value , the muscle activity was gradually decreased 56 , 57 ., Burdet et al . and Franklin et al . 29 , 34 , 35 showed that a simple learning rule that incorporates the decay of muscle activity can precisely predict such a specific pattern of change in individual muscle activity during adaptation to various force fields ., The present study further applied the “feedback-with-decay” algorithm to the sensorimotor transformation network , which includes M1 neurons ., We initially used a linear neural network and theoretically derived the necessary conditions for convergence on the optimal state ., Importantly , these conditions seem to be satisfied in the actual brain ., First , the decay rate is known to be much smaller than the learning rate 58; second , a very large number of M1 neurons actually participate in a single motor task; and third , multiple targets are practiced in real life ., Furthermore , using a more realistic non-linear network model , we also confirmed consistent convergence that was irrespective of the initial synaptic weight and spatial bias of the movement directions during practice ., These results indicate that weight decay is a more promising process than SDN for a motor system to resolve the redundant actuator problem ., The “feedback-with-decay” rule can be considered as biologically plausible in that it does not need to explicitly calculate the sum of the squared neural activity ( total effort cost ) by gathering activity information from a vast number of neurons ., Since weight decay in each synapse could occur independently of other synapses , a global summation across all neurons would not be needed ., Using a framework of weight decay , it would be possible for the CNS to minimize even the motor effort cost during movement of the whole body ., One may argue that since we perceive tiredness , the brain must compute the total energetic cost ( or motor effort cost ) ; however , to the best of our knowledge , individual neurons that encode the total energetic cost have not been discovered ., It is rather likely that such a physical quantity is represented by a large number of distributed neurons in the brain and this distributed information may be perceived as tiredness ., Since it is unclear whether the total energetic cost could be readout from such distributed information , decay would be a more promising mechanism for minimizing motor efforts ., Furthermore , our simulation results indicate that the formation of an optimal PD distribution pattern for M1 neurons was not necessarily accompanied with the realization of a nearly optimal muscle activation pattern ( compare Figure 4D with Figure 4H ) , suggesting that optimization of motor effort at the neural level could not be accomplished by minimization of muscle effort by monitoring the metabolic energy consumption in the muscles ., Although we referred to the “feedback-with-decay” algorithm as biologically plausible , it should be noted that our simulation algorithm is not fully biologically plausible because it still depends on an artificial calculation ( i . e . , error back-propagation ) ., Although it is well established that error information is available to the cerebellum 16–21 , it is unclear how such information is used to modify the activity of individual M1 neurons in the next trial; that is , it is unclear how gradients of error are calculated ., Determining a biologically plausible model that does not depend on an artificial calculation remains a major challenge in the field of motor control and learning ., The important point of the present study is that we theoretically proved that the “feedback-with-decay” rule consistently leads the PDs of M1 neurons to converge at a distribution that is orthogonal to the MD distribution ., Although Guigon et al . 27 reproduced the skewed PD distribution of M1 neurons for 2D movements , they did not theoretically describe the inverse relationship between the PD and MD distributions , which is probably because they adopted only complex non-linear models and needed to rely only on numerical simulations for solving the optimization problem ., In contrast , the present study , which is based on the theoretical background of the linear model , further numerically showed that the inverse relationship also persisted in the non-linear models too ., Importantly , the non-linear model combined with the realistic musculoskeletal parameters can reproduce the non-uniform PD distribution of M1 neurons observed during various motor tasks ., The origin of the PD bias has been a hotly debated topic in neurophysiology 59 , 60 ., Although it has been pointed out that the PD bias observed in 2D postural and reaching tasks emerges as a consequence of the neural compensation of the biomechanical properties 2 , 3 , the PD bias observed in 3D reaching has been considered to be derived from use-dependent plasticity ( i . e . , the frequent reaching toward the biased directions accompanying feeding behavior ) 4 , 5 ., One of the reasons for this conflict between the two groups is that they adopted different movement tasks , i . e . , one group insisted that 2D tasks with a robotic exoskeleton are advantageous for the comparison of neural activity with accurately measured mechanical variables such as joint motion and joint torque 59 , while the other group insisted that unconstraint 3D movements are necessary to reveal the nature of neural activity 60 ., The present study is the first to try to resolve this issue ., By using a realistic 3D biomechanical model , we found that the PD bias observed in 3D reaching movements by monkeys 4 , 5 corresponds to the direction toward which few muscles contribute to the acceleration of the fingertip; the PDs tend to be biased toward the direction according to the weight decay hypothesis ., It was also demonstrated that the feedback-with-decay rule always leads the PDs to be biased toward the same direction , irrespective of the spatial bias of the reaching directions during practice ., Thus , the weight decay hypothesis suggests that the PD distribution reflects the inverse of the biomechanical properties of the musculoskeletal system ( i . e . , muscle anatomy and limb configuration ) ., Although it remains to be clarified whether weight decay is actually used for optimization in the brain , the present study provides a unifying framework to understand stereotypical activation patterns of muscles and M1 neurons during 2D and 3D reaching movements ., Another interesting finding is that even the “feedback-only” rule predicts the skewed PD distribution of M1 neurons approximately if the two following conditions are satisfied: a large number of neurons participate in the task ( condition #2 ) and the initial synaptic weight is considerably smaller than the pseudo-inverse matrix ( MT ( MMT ) −1 ) ( condition #4 ) ., This finding indicates that the PD bias itself is not direct evidence of the minimization of effort , as has been thought previously 2 , 61 ., Nevertheless , we believe that the fact that the optimal PD bias was consistently observed in various motor tasks may reflect the consequence of the minimization of effort because there is no assurance that condition #4 is always satisfied ., Thus , theoretically assessing the effects of the error feedback and decay separately , the present study convincingly showed that the decay is essential to reproduce consistently the PD bias observed in the experiments ., To verify whether the motor effort is actually minimized and whether weight decay is used during minimization , future studies need to examine the changes in the activity of a large number of neurons for a long period of time ., According to our mathematical consideration , the weight decay rate must be substantially lower than the learning rate ( see Supporting Text S1 ) ., This necessary condition is biologically very plausible because the strength modulation of the synaptic connections , which is mediated by long-term potentiation and/or long-term depression , is known to decay slowly 58 ., It was also demonstrated that , when the decay rate was relatively large , the bias in the PD distribution was not formed and considerable error remained ( Figure S3 ) ., This clearly indicates that the slightness of the decay is necessary for the formation of the non-uniform PD distribution of M1 neurons ., The present scheme also implies that motor learning has two different time scales: a fast process associated with error correction and a slow process associated with optimizing efficiency through weight decay ( Figure 2B , 2D ) ., Due to the coexistence of both time scales , the neural network can assume various unstable states even after motor performance appears to have been achieved 43; however , after adequate training is conducted to completely learn the task by the slow process , the network should converge to a more stable unique state 62 ., The two time scales can be also observed in muscle activity during motor adaptation ., While muscle activity rapidly increased in response to the initial large errors caused by a novel perturbation , it was slowly reduced once the error fell below a threshold 34 , 56 , 57 , 63 ., The present study suggests that the slow reduction of muscle activity is the result of the optimization process with weight decay ., This slow optimization may explain why prolonged training , even after the performance level appears to have reached a plateau , is important 64 ., Due to its simplicity , our model provided clear insights into the role of weight decay on optimization; however , of course , it has several limitations ., First , the model considered only corticospinal neurons , although M1 also includes inhibitory interneurons ., However , it is noteworthy that our model could predict the PD distribution of M1 neurons recorded from non-human primates , suggesting that most of the neurons recorded in previous experiments were corticospinal neurons ., Indeed , considering the large size of corticospinal pyramidal neurons , it is likely that the chance of recording these neurons is relatively high because stable isolation over an extended period of time is required in such experiments 65 ., To confirm this possibility , future studies need to examine the PD distribution while distinguishing between interneurons and pyramidal neurons using recently described techniques 66 , 67 ., Second , a uniform distribution was assumed for the neuron-muscle connectivity ( Z ) ., As there are no available data for Z , assuming a uniform distribution is reasonable as a first attempt ., This assumption results in the distribution of neuron MDs having the same bias as that of muscle MDs ., Interestingly , irrespective of such a simple assumption , the model accounted for the PD distribution in various tasks ., Since this connectivity depends on the recording site , to resolve this issue , it is necessary to examine the innervation weights of each neuron to the muscles by using a spike triggered average technique as well as the PD of each neuron ., Thirdly , the model only considered static tasks ( i . e . , isometric force production ) and an instantaneous ballistic task ( i . e . , the initial phase of the reaching movement ) ., Such a single time point model is unrealistic for reaching movements in that it ignores the change of limb posture , posture-dependent changes in the muscle moment arms , multi-joint dynamics during motion , and the deceleration phase ., This limitation prevents us from predicting the essential features of movement such as trajectory formation and online trajectory correction 12 , 13 , 15 , 23 that arise from the optimization of a series of motor commands by taking into account the multi-joint dynamics that change according to the limb configuration 68 , 69 ., However , it is not that our model completely ignores multi-joint dynamics; indeed , we incorporated instantaneous multi-joint dynamics at the initial limb configuration by dealing with the linear acceleration of the fingertip rather than the muscle torque ( see Methods ) ., In addition , considering that the CNS does not plan an entire trajectory of movement at the time of movement onset 15 , 70 , 71 , it is likely that the activity of corticospinal neurons just before reach initiation would be largely for the production of the initial acceleration ., Thus , the comparison between the neural activity in our model and that recorded during the reaction time period is justified to some extent ., However , of course , the present model ignores the effect of events occurring after the initial ballistic phase on the modification of the synaptic weight for the next movement ., Finally , the single time point model cannot predict the change of the movement representation in the motor areas that was observed during the course of sensorimotor transformations 72–74 ., In the future , we need to extend the decay theory to the more dynamic problem of controlling eye or limb movements , including temporal trajectories through motor planning and execution phases 23 , 27 , 75–78 ., This dynamic task presents the next major challenge for understanding the neural control of movement ., First , we used a linear neural network to transform the desired torque ( input layer ) into the actual torque ( output layer ) through an intermediate layer that consisted of 1000 neurons ( n\u200a=\u200a1000 ) ( Figure 1B ) ., Each neuron in the intermediate layer received a desired torque vector ( τ ∈ ℜ2 ) from the input layer with a synaptic weight ( Wi ∈ ℜ2 ) that could be modified with learning ., The activation level ( ri ) was linearly dependent on the input torque vector ( i . e . , ri\u200a=\u200aWiTτ ) , indicating that it obeys cosine tuning 44 ., The activation vector for all of the neurons ( r ∈ ℜn ) is expressed as r\u200a=\u200aWτ , where W ∈ ℜn×2 is the synaptic weight matrix for all neurons , expressed as:The output vector for each neuron ( Ti ∈ ℜ2 ) is determined by its activation level ( ri ) multiplied by its mechanical pulling direction vector ( MDV ) ( Mi ∈ ℜ2 ) : Ti\u200a=\u200aMi ri ., The total output of the network ( T ∈ ℜ2 ) is expressed as the vector sum of the output of all neurons: T\u200a=\u200aMr , where M ∈ ℜ2×n is the matrix of MDVs for all neurons , expressed as:The distribution of the directions of the MDVs was biased toward the first and third quadrants (

Introduction, Results, Discussion, Methods

Recent theoretical studies have proposed that the redundant motor system in humans achieves well-organized stereotypical movements by minimizing motor effort cost and motor error ., However , it is unclear how this optimization process is implemented in the brain , presumably because conventional schemes have assumed a priori that the brain somehow constructs the optimal motor command , and largely ignored the underlying trial-by-trial learning process ., In contrast , recent studies focusing on the trial-by-trial modification of motor commands based on error information suggested that forgetting ( i . e . , memory decay ) , which is usually considered as an inconvenient factor in motor learning , plays an important role in minimizing the motor effort cost ., Here , we examine whether trial-by-trial error-feedback learning with slight forgetting could minimize the motor effort and error in a highly redundant neural network for sensorimotor transformation and whether it could predict the stereotypical activation patterns observed in primary motor cortex ( M1 ) neurons ., First , using a simple linear neural network model , we theoretically demonstrated that:, 1 ) this algorithm consistently leads the neural network to converge at a unique optimal state;, 2 ) the biomechanical properties of the musculoskeletal system necessarily determine the distribution of the preferred directions ( PD; the direction in which the neuron is maximally active ) of M1 neurons; and, 3 ) the bias of the PDs is steadily formed during the minimization of the motor effort ., Furthermore , using a non-linear network model with realistic musculoskeletal data , we demonstrated numerically that this algorithm could consistently reproduce the PD distribution observed in various motor tasks , including two-dimensional isometric torque production , two-dimensional reaching , and even three-dimensional reaching tasks ., These results may suggest that slight forgetting in the sensorimotor transformation network is responsible for solving the redundancy problem in motor control .

It is thought that the brain can optimize motor commands to produce efficient movements; however , it is unknown how this optimization process is implemented in the brain ., Here we examine a biologically plausible hypothesis in which slight forgetting in the motor learning process plays an important role in the optimization process ., Using a neural network model for motor learning , we initially theoretically demonstrated that motor learning with a slight forgetting factor consistently led the network to converge at an optimal state ., In addition , by applying the forgetting scheme to a more sophisticated neural network model with realistic musculoskeletal data , we showed that the model could account for the reported stereotypical activity patterns of muscles and motor cortex neurons in various motor tasks ., Our results support the hypothesis that slight forgetting , which is conventionally considered to diminish motor learning performance , plays a crucial role in the optimization process of the redundant motor system .

neural networks, anatomy and physiology, neuroscience, learning and memory, motor systems, computational neuroscience, neurological system, coding mechanisms, robotics, musculoskeletal system, biology, central nervous system, physiology, musculoskeletal anatomy, neurophysiology

journal.pcbi.1005039

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Synergistic advances in pharmacogenomics , genome-wide association studies ( GWAS ) and next-generation sequencing bring promise to future applications of personalized medicine ., Exploring the mechanistic link between human sequence variation and responses to drug therapy is likely to shed light on why certain drugs show a reduced or even harmful effect on specific individuals ., For example , if an individual has a specific polymorphism or rare variant , the consequences of administering a given drug are potentially immense if a life-threatening gene-drug association has not yet been identified 1 ., While numerous harmful gene-drug associations have been identified from GWAS ( and those with significant side effects now have warnings on pharmaceutical labels 2 ) , screening genome-wide associations across the broad scope of available pharmaceutical compounds is currently limited by both the cost of carrying out such studies 3 as well as a lack of statistical power due to the rarity of deleterious mutations ., To address these limitations , a number of recent studies have developed mechanistic , computational analyses and the construction of omics-based workflows that identify , for example , the mode of action of common drug side effects 4 ., Genome-scale modeling enables the analysis of disease-causing mutations in mechanistic detail ., Genome-scale models of metabolism ( GEMs ) encompass the known interactions of diverse biological components , or the reactome of a target organism , into a unified , functional framework ., This framework contains all known metabolic reactions , the genes that encode each enzyme , and all metabolites in a given organism and therefore provides a direct mapping from genes , to gene products , to the phenotypic responses of cellular activity ., Mapping sequence variations in a gene to changes in the biological states of an entire metabolic network enables characterizing the effects of sequence variation in simplified cellular systems , such as the human erythrocyte 5 , 6 ., Furthermore , a recently updated version of the erythrocyte metabolic model ( iAB-RBC-283 ) , based on the global reconstruction of the human metabolic network ( Recon 2 ) 7 has been used to study the response of the cell to deleterious single nucleotide polymorphisms ( SNPs ) as well as drugs with known targets 5 , 8 , 9 ., Predicting the wide range of possible effects that SNPs and single nucleotide variations ( SNVs ) can have on structure-function relationships in proteins requires extending a systems-level description to include details from physics-based approaches , such as molecular dynamics simulations ., To this end , three-dimensional structures of proteins provide complementary data for further elucidating changes in drug-protein interaction networks ., Much attention has been placed on developing bioinformatics tools for the statistical analysis of large-scale data sets , ( which contain information on non-synonymous , exonic mutations on individual proteins ) , and generating hypotheses that explain how mutations affect stability , protein-protein interactions , ligand binding , or catalytic function 10 ., Atomistic simulations have been used as a complement to experimental methods to assess changes in relative binding affinities of potential lead compounds to key enzymatic targets 11 ., While these approaches are rich in molecular-level details , they are limited in their ability to address how significant the observed changes are in the context of an entire biochemical pathway or , ultimately , a whole cell ., This limitation thus motivates the need to develop novel workflows that integrate systems-level and molecular-level details to characterize biological processes at graded levels of chemical detail 12–14 ., The growing field of structural systems biology brings promise to the integration of systems and molecular sciences , enabling applications in personalized medicine 13 , 15–17 , drug discovery 18–20 , understanding off target binding 21–23 or mechanisms of action , 24–26 and also to enhance pharmacokinetic/pharmacodynamic models 27 ., Here , we build upon previous studies which integrate protein structural information into GEMs 22 , 23 , 28 , by developing a multi-scale framework to analyze the effects of sequence variation on drug responses in human erythrocyte metabolism ( Fig 1 ) ., Using genome-scale modeling approaches , we identify key proteins in erythrocyte metabolism that are perturbed in the presence of, ( i ) pharmaceutical drugs and, ( ii ) sequence variants ., Using atomistic simulations , we characterize changes in structure and function relationships for different metabolic proteins in the form of drug or metabolite binding differences resulting from reported sequence variants ., Finally , we integrate the knowledge gained from these simulations into a detailed genome-scale model of the erythrocyte , allowing for both constraint-based and kinetic methods of analysis to understand the systems-wide effect of these variants ., We were interested in quantifying the number of proteins in the human erythrocyte metabolism that, ( i ) are known pharmaceutical targets and, ( ii ) have been documented with both disease and non-disease causing mutations ( Fig 2 ( A ) ) ., The erythrocyte presents a valuable and tractable model system for studying the effects of human genetic variation on drug metabolism ., First , it is widely appreciated that the erythrocyte possesses drug metabolizing capabilities such that extracts of erythrocyte enzymes are commonly used as a general measure of enzyme activity 31 , 32 ., Second , genetic changes that occur in cells other than the erythrocyte are often manifested in the erythrocyte , assuming correct isoforms and similar genetic control 33–36 ., The ease of collection of human erythrocyte samples and subsequent purification of enzymes of interest motivates the study of the erythrocyte as an in silico model that can be tested against ., Lastly , the erythrocyte outnumbers any other cell type in the human body ( 85% of the total cell count ) 37 ., Starting from the set of metabolic genes in the genome-scale model , iAB-RBC-283 8 , we mapped gene identifiers to cross-referenced information from dbSNP 38 , OMIM 39 , and UniProt 40 ., We find that for 6800 exon coding SNPs in genes which are expressed in the erythrocyte , the majority ( >90% ) are missense SNPs as opposed to frameshift or insertion/deletion variations ., These SNPs map to 247 of the 281 genes ( 88% ) in the erythrocyte model ., The majority of these annotated as “disease-causing” map to enzymes within the heme biosynthesis , glycolysis , and galactose metabolism pathways , which is consistent with hemolytic dysfunction ., Other non-disease causing SNPs , ( or SNPs with unknown associations ) , occur in nucleotide metabolism ., Harmful mutations also tend to alter the type of amino acid much more than non-disease causing SNPs ., For instance , mutations from a hydrophobic residue to another hydrophobic residue are quite common , but disease causing SNPs greatly increase this type of amino acid change to a polar , non-polar , or positive amino acid ( Fig D in S1 Text ) ., Our pipeline also identifies variants that potentially influence drug-binding capabilities of respective proteins ., Of the metabolic proteins in the erythrocyte , 143 are found to be potential targets for pharmaceutical action ., We find 343 drugs ( approved , experimental , withdrawn drugs , or drug metabolites ) that bind to different proteins in the model 41 , 42 ., In addition , mapping to the PharmGKB database , we find 274 deleterious SNP-drug associations , or documented adverse reactions ( i . e . , pharmaceutical complications ) in patients ( referred to herein as SNP-drug association ) ., To summarize , our systems pharmacological database provides details on all documented missense SNPs in erythrocyte metabolism , whether they are causal for disease or cause pharmaceutical complications in a significant percentage of the human population with a sequence variation 29 ., In addition , our dataset contains information on drug-binding capabilities of all proteins in the model ., This combined source of information for genetic and pharmacological information within the erythrocyte allows for the selection of interesting targets to further analyze with both molecular and systems simulations ., To address the structural implications of changes to sequence or drug-binding capacity , we were interested in mapping all protein-encoding genes within the metabolic network of the erythrocyte to their three-dimensional ( 3D ) macromolecular structures ., Integration of protein structural data and GEMs has previously been described through the construction of GEnome-scale models of Metabolism with PROtein structures ( GEM-PRO ) ., The established pipelines for constructing a GEM-PRO have been recently updated 28 ., Applying this procedure for the human erythrocyte metabolic model , we start from the existing GEM , iAB-RBC-283 8 , and the final outcome is a mapping of all protein-encoding genes to the 3D structures of their catalyzing enzymes ., The selected protein structures have been quality-controlled and ranked to ensure the highest quality structures are retained ., The new GEM-PRO model , iNM-RBC-283-GP , initially contained structural coverage for 181 of the 346 proteins in the metabolic network ( Fig 2 ( A ) ) , and includes a total of 1766 unique PDB entries ( the original GEM is comprised of 281 genes which encode 346 unique proteins ) ., In addition , 312 homology models were obtained for proteins from existing homology model databases 43 , using the I-TASSER suite of programs 44 ., Our QC/QA pipeline identifies experimental structures and homology models that can be used with high confidence in molecular modeling simulations 28 ., Several quality metrics are used to rank-order structures , including:, ( i ) coverage of the wild-type amino acid sequence ( with a wild-type being defined as the canonical UniProt sequence ) ;, ( ii ) X-ray structure resolution;, ( iii ) number of missing or unresolved parts of the structure ., The final QC/QA statistics indicate that 36% of proteins in the GEM model ( 125/346 ) have high quality structural information , whereas the remaining 64% ( 221/346 proteins ) can be represented by template-based and ab initio generated homology models ( see Fig C in S1 Text for detailed statistics on subsystem coverage ) ., Interestingly , when we combine the structural data and the pharmacogenomic data , we are able to assess SNP data in the context of protein structural information and derive new association ., For example , we find that , on average , disease causing SNPs are 4 Å closer to annotated enzyme active sites than non-disease causing SNPs ., All structural annotations , mapped database information , and quality statistics are included as a supplementary database ( S1 Database ) ., One of the main advantages of assembling a structural systems pharmacological dataset for the erythrocyte is that it can be used to address questions requiring multi-scale perspectives , such as “Can mutating a single amino acid in a protein influence network-level perturbations , and , ultimately lead to disease phenotypes ? ”, Considering the availability of information ( pharmacogenomic and structural ) that emerged from our mapping efforts , we were interested in focusing on several specific cases that could be studied in greater molecular detail , using a combined systems and molecular modeling approach ., To this end , we assessed the available experimental , pharmacogenomic , protein structural and metabolic information available for all proteins in the erythrocyte model ., Given the data collected from publically available datasets ( described above ) , we classified proteins based on:, ( i ) availability of experimental protein structure , drug or metabolite binding information ,, ( ii ) known harmful gene-drug associations and, ( iii ) if the knockout of this gene within the context of erythrocyte caused significant changes in metabolite import and export ( see Methods ) , resulting in four different classes of proteins based on these criteria ( Fig 2 ( B ) ) ., This categorization mainly aids in the next steps of our contributed workflow , in studying the effects of SNVs on metabolite and drug binding using all-atom molecular simulations ., As shown in Fig 2 ( B ) , Class I targets have the most information available , including 3D protein structures ( some in complex with a metabolite , drug or analogue ) , known drug-protein interactions , gene-drug associations , and clinically relevant phenotypic responses to a drug therapy ., This group of proteins includes six proteins: catechol-O-methyltransferase ( COMT ) , aldehyde dehydrogenase ( ALDH3A1 ) , adenosine deaminase ( ADA ) , glucose-6-phosphate dehydrogenase ( G6PD ) , glutathione peroxidase 1 ( GPX1 ) , and uridine 5-monophosphate synthase ( UMPS ) ., Class II targets provide case-studies amenable to experimental testing SNV or drug-induced effects ., Class III & IV targets are proteins found to be important in the genome-scale model , but do not have other sources ( structural or pharmacogenomic ) of information available , and therefore constitute examples of where our molecular modeling framework is useful for filling in missing information ( Table B in S1 Database ) ., Here , we focus the rest of this study on three distinctive proteins in erythrocyte metabolism ( Fig 3 ) :, ( i ) catechol-O-methyltransferase ( COMT ) , a class I protein ( according to our above classification scheme ) ;, ( ii ) glucose-6-phosphate dehydrogenase ( G6PD ) , a class I protein;, ( iii ) glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) , a class II protein ., For the purpose of validation , we study the class I proteins , which have ample experimental , structural and pharmacological data associated with their roles in metabolism ., To assess the predictive value of this workflow , we study the class II protein , a rare variant where population data was not available to understand the impact of documented sequence variants ., Such an example serves as a demonstration for how this structural systems biology framework can be used in the absence of experimental and pharmacological data ., The targets chosen for this study and their pharmacogenomic importance are outlined in Table 1 ., The next stage of our proposed workflow builds on previous methods 22 , 23 , 45 , 46 and leverages systems modeling with molecular dynamics ( MD ) simulations ., How SNPs/SNVs affect structure/function relationship is a question that requires analysis beyond a comparison of crystal structures ., Here , we take advantage of using an ensemble of protein conformations , generated from explicit solvent MD simulations , to study the effects of clinically relevant SNVs/SNPs on drug and/or native metabolite binding ( Fig 4 ( A ) ) ., While understanding protein-drug interactions provides information on how sequence variation changes protein structure and reactivity , evaluating the downstream effects of these changes requires a systems-level perspective ( Fig 5 ( A ) ) ., Changes in metabolic networks can be assessed using a variety of systems methods including constraint-based and kinetic modeling techniques 5 , 77–79 ., To test the susceptibility of the metabolic network of the human erythrocyte to the harmful variants detailed above , we utilized both constraint-based modeling of the iAB-RBC-283 model 8 and a recently developed in silico kinetic rate law model derived from the Mass Action Stoichiometric Simulation ( MASS ) approach 80 , 81 ., For a number of proteins , disease causing mutations can cause systemic changes within the metabolic network or in the transport of certain metabolites 8 , 82 ., With regards to the erythrocyte , understanding these differences in metabolite transport can be correlated with changes in metabolite concentrations within biofluids , which potentially expands the use of this model as a diagnostic tool for human disease ., Similar perturbations can also be linked to the specific phenotypic responses of the erythrocyte , such as to drug treatments , or the ability to respond to changes in oxidative ( rate of NADPH use in order to combat oxidants ) or energy ( rate of ATP use ) load 5 ., Here , we propose a framework for mapping protein structural information to genome-scale models of human erythrocyte metabolism for the characterization SNP-drug associations ., Three case studies presented in this contribution point to the complexity of pharmacogenomic associations and being able to conduct integrated in silico simulations that extend from the molecular scale to the systems level ., Using parameters from molecular simulations to guide genome-scale modeling , we are able to study how changes in protein structure and binding affinity influence the phenotypic states of an entire metabolic network ., We find that the union of genome-scale modeling and molecular , physics-based methods , presents , to the best of our knowledge , the first workflow capable of systematically integrating data from pharmacogenomics research , in conjunction with 3D high resolution protein structural information , to model changes on both the pathway ( i . e . metabolic network ) and molecular ( i . e . protein ) scales ., The information gained through molecular modeling simulations can be utilized to supply parameters to both kinetic models and constraint-based modeling approaches and has been found to be amenable to the study of other enzymopathies 5 , 91 ., Our findings indicate that there is consistency between experimental and computational trends in substrate and drug compound binding in wild-type versus mutant proteins ., Currently , most systems biology approaches lack the ability to utilize insights from structure-based analyses related to metabolite and/or drug binding ., Fortunately , atomistic molecular simulations have evolved to become powerful tools for the characterization of binding mechanisms and as such constitute valuable assets for systems modeling ., Extending analysis beyond crystallographic structures through the use of ensemble confirmations substantially enhances the predictive scope of docking methods by identifying alternative binding modes for a drug molecule 56–60 ., Ensembles of the thermodynamically accessible states of a protein , generated from molecular dynamics , allows for the mechanistic characterization of how sequence and structural variation may influence metabolite or drug binding 92 ., The scalability of this workflow is mainly limited, ( i ) to the documentation and experimental analysis of exonic SNVs/SNPs , and, ( ii ) by the execution of molecular dynamics simulations , which takes a significant manual effort and requires high performance computing resources ., For the second point , certain efforts have already shown that high-throughput simulations using classical MD can be performed on large numbers of proteins 93 , 94 ., However , performing high accuracy computations on a systems scale is currently intractable , due to the intense computational and time requirements of quantum-based simulations or free energy calculations ., Therefore , a trade-off between accuracy and cost must be considered ( see Fig B in S1 Text and recent reviews on the subject 95–97 ) ., In light of these limitations , we find that the additional information gained from protein structure greatly contribute to our understanding of causal mutations and can assist in selecting protein targets for more detailed molecular studies ., Thus , when combined with other developing frameworks 4 and experiments 98 , the contributed workflow provides a first step in the translation of Big Data in the pharmaceutical industry to practical therapeutic applications and is expected to have a positive transformative impact on the fields of systems medicine , population studies and drug discovery efforts ., The techniques used here are a consolidation of 4 previous methods to add protein structural information to genome-scale models 22 , 23 , 99 , 100 , and described in detail in 28 ., To do so , the SBML model of the erythrocyte genome-scale model was first obtained from the BiGG Models website ( http://bigg . ucsd . edu/models/iAB_RBC_283 ) 101 , and all gene IDs were mapped to their corresponding amino acid sequences ( UniProt and RefSeq entries ) ., This model differed from the construction of previous GEM-PROs due to the appearance of protein isoforms , and required additional manual mapping to ensure correctness ., Gene isoforms led to inconsistencies between database entries and additional difficulty linking to available homology models ( discussed in the section “Homology Modeling” ) ., Additional QC/QA steps were taken in order to ensure the correct sequence was being retrieved , as described below ., Previous work was done to map data from the Online Mendelian Inheritance in Man ( OMIM ) database in order to find disease causing mutations that could map to erythrocyte proteins 8 ., We also collected all known SNPs from dbSNP , and filtered them down to variations in exons that could be studied utilizing protein structure information ., Information was additionally cross-referenced with UniProt variant annotations 109 ., There are a number of drug target databases that were queried for this study ., DrugBank was used in a previous study to gather drug targets based on sequence 8 ., In order to be as comprehensive as possible , we also obtained data from ChEMBL 110 and MATADOR 42 , with MATADOR providing annotations for indirect interactions ., With this , we were able to verify targets that appeared in all 3 databases ., Drug adverse effects due to variation were mainly gathered from the PharmGKB , a pharmacogenomics database with information from clinical studies , research articles , and individual cases 111 ., The PharmGKB further annotates for the significance of an association , as well as details of the clinical trial or GWAS study carried out ., Finally , the DrugBank contains a simple list of SNP-drug associations in their SNP-ADR and SNP-FX sub-databases 41 , which was cross-referenced with all information found in the PharmGKB ., As a final source of parameters for validation of our model , experimentally determined kinetic values for binding of a drug or inhibitor to a target ( wild-type as well as mutant ) were obtained from BRENDA and the BindingDB 112 , 113 ., As expected , information for this step was much sparser than the previous information , which indicates the need for experimental assays if we are to validate the predictions made from this model ., For the targets in this study , we also manually searched for additional information from published biochemical studies ., Finally , for the selection of interesting targets to study with molecular and systems modeling techniques , we also wanted to understand the essentiality of each gene within the erythrocyte model ., Gene knockouts were performed for each gene contained within iAB-RBC-283 , as per 8 ., A gene was marked as interesting to study within the context of the erythrocyte if there were significant changes within the reaction fluxes of metabolite import and export through the membrane using flux variability analysis ( FVA ) simulations 114 ., In order to detect these significant differences , all reaction fluxes were compared to the normal “wild-type” state of the cell ., Specifically , similar procedures to Shlomi et al . and Bordbar et al . were followed 8 , 82 ., Changes in exchange fluxes were categorized into, i ) activation/inactivation ,, ii ) shift to a fixed direction ,, iii ) a change in magnitude of flux , or, iv ) no change ( refer to 8 , Fig 5 ) ., For changes in magnitude of flux , if the new flux span ( defined as maximum flux—minimum flux ) was less than 40% of the original flux span , it was considered to be a significant change ., Experimental PDB structures or homology models representing the genes of interest in this study were taken from the GEM-PRO data frame following ranking and QC/QA ., Mutant forms of the enzymes were either taken directly from the PDB , if available , or modeled by point mutations of the structure ., Next , the general approach for each target was to first understand the binding position and energetics of either the native metabolite or a drug of interest to a wild-type protein structure and its corresponding mutant ., Flexible docking simulations using DOCK6 were carried out with default parameters and binding sites defined when known 115 ., Furthermore , simulations were conducted with and without cofactors , to account for competitive binding drugs or cases where the order of substrate binding was not known ., To compare flexible docking results to ensemble docking , simulations were repeated under different random seeds for a total of 500 docking runs ., Molecular dynamics simulations were run utilizing the PMEMD module of the AMBER14 toolkit 116 ., Initial parameterization of ligands and cofactors were carried out utilizing the Gaussian 09 software 117 or obtained from previously published data sets ( see S1 Text for protein-specific methods and S2 Database for parameter sets ) ., For generating topologies as input to AMBER , 99SB force field charges and atom types were then used and then solvated in a periodically repeated TIP3P 12 Å water box with counterions being added as needed ( Na+ or Cl- ) ., Minimization was carried out under constant volume conditions at while being heated to 300 K . Structures were then equilibrated under constant temperature and pressure conditions with restraints being released ., Finally , the structures were run in production phase of 75 ns or more under a Langevin thermostat and Particle Mesh Ewald ( PME ) cutoff of 12 Å ., At least 4 separate MD simulations ( representing WT and SNP structures in cofactor unbound and bound states , more for additional cofactor bound states ) were carried out on each enzyme ( see Tables D-F in S1 Text for all simulation information ) ., Every 100 frames from these trajectories were utilized as input for ensemble docking of the substrate of interest ., All docked positions were clustered into 5 representative poses based on the distances from known binding residues ., Specifically , distances from 3 known binding or interacting residues to the atoms of the drug or metabolite were calculated for each extracted frame , and k-means clustering of the Euclidean distance separated these frames into 5 distinct binding modes for use in further simulation ., These docked positions were subject to additional MD production runs of 10 ns each , in order to examine the stability of the bound position and if they would converge into one distinct pose ., We conducted free energy calculations for each of the ligands in the cofactor bound state of the WT and SNP enzymes ., MM-PBSA calculations were carried out to predict the difference in free energies of binding ( ΔΔG ) ., The binding energies of all 5 representative conformations were averaged per ligand , and the resulting value indicates if the ligand is more favorable to bind to WT ( negative ΔΔG ) or SNP ( positive ΔΔG ) structures ., MM-GBSA/MM-PBSA calculations utilizing the MMPBSA . py script available in the AMBER14 toolkit were carried out on the 10 ns simulated receptor-ligand complexes 61 ., The first nanosecond of simulations was discarded before running calculations to account for initial stabilization of the docked ligand ., Thermodynamic integration ( TI ) calculations were calculated utilizing the Simulated Annealing with NMR-derived Energy Restraints ( SANDER ) module within AMBER14 118 ., The dual topology paradigm was utilized with a three step alchemical transformation , with state 0 representing a wild-type enzyme and state 1 the mutant form ., Step 1 carried out the decharging of the WT utilizing 10 λ points and simulations of 1 ns each ., Step 2 transformed the residue atoms of the WT to the SNP again utilizing 10 λ points and simulations of 1 ns each ., Step 3 carried out the recharging of the mutant residue atoms with the same number of λ points and simulation time ., This was run for both ligand bound and unbound states ., Finally , the change in potential energy of the system with ligand bound was calculated by integration over the λ points and subtracted from the ligand unbound state ., For full information on docking , MD , MM-PBSA , and TI parameters , please refer to the section entitled “Molecular modeling simulations” in S1 Text ., The constraint-based modeling approach was carried out for all enzymes in this study by simulating a normal ( wild-type ) and perturbed ( mutant ) erythrocyte condition utilizing FVA followed by a Markov chain Monte Carlo ( MCMC ) based sampling approach 83 , 91 , 119 ., Previous simulations for identifying biomarkers have simulated perturbed states by setting the upper and lower bounds of flux through affected enzymes of the cell to 0 , effectively mirroring a full gene inhibition , and then analyzing the exchange conditions 8 , 82 ., For the purposes of this study , we are now able to understand the relative differences in native metabolite catalysis utilizing the ratio of differences in the binding affinity between wild-type and mutant forms of the enzymes ., This ratio was then converted into a ratio of flux in wild-type to mutant enzymes , assuming equal concentration of substrate and enzyme ( see Equation S3 ) ., From this , the determined normal wild-type minimum and maximum fluxes through the corresponding reaction were adjusted to a perturbed mutant state , and both FVA and MCMC simulations were then run with the goal of analyzing, 1 ) the flux differences through the exchange reactions ( import/export of metabolites ) of the erythrocyte ( as described above in the section “Genetic variation , drug-target interactions , and essential genes” ) and, 2 ) significant flux shifts within the internal network ., In this way , hypotheses for the altered phenotypic state of the erythrocyte and its impact on the body could be deduced based on the differences of uptake or secretion of metabolites or large-scale internal network changes ., For MCMC simulations , significant shifts in the distribution of fluxes were considered ( p-value < 0 . 05 ) ., Additional information on MCMC sampling is included in the section entitled “Systems modeling” in S1 Text ., With the kinetic rate law model , we are able to directly integrate the predicted Km and experimental Kcat values as well as simulate the cell under oxidative or energy load conditions ., This detailed model was utilized for the simulations of normal and perturbed G6PD and GAPDH enzymes ., Simulation of COMT within the kinetic model was not available due to the current model being limited to core metabolic enzymes ., We utilize the model to also understand the erythrocyte’s capability to withstand oxidative stress or increased energy needs and compare wild-type to mutant states ., Oxidative stress is simulated as an increase in the rate of NADPH usage , to mirror the fact that a cell under stress requires NADPH to neutralize reactive oxygen species ., Energy load is simulated as an increase in the rate of ATP usage ., The normal , wild-type cell was first simulated and the maximum oxidative and energy loads were determined for comparison to the mutant state ., Integration of the predicted Km without any change in Kcat was then simulated for the mutant state , to understand if only changes in binding affinity led to a change in maximum tolerable oxidative or energetic load ., Finally , changes from predicted Km , experimental Km , and experimental Kcat were fully integrated to investigate the model’s accuracy to the known phenotype .

Introduction, Results and Discussion, Methods

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies ., Recently , genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins ., The integration of these molecular-level details , such as the physical , structural , and dynamical properties of proteins , notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes ., In this study , we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network ., Using this approach , we can understand how genetic variation , which impacts the structure and reactivity of a protein , influences both native and drug-induced metabolic states ., As a proof-of-concept , we study three enzymes ( catechol-O-methyltransferase , glucose-6-phosphate dehydrogenase , and glyceraldehyde-3-phosphate dehydrogenase ) and their respective genetic variants which have clinically relevant associations ., Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins ( and their mutant variants ) in complex with their respective native metabolites or drug molecules ., We find that changes in a protein’s structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules , and inflicts large-scale changes in metabolism .

Structural systems pharmacology is an emerging field of computational biology research that aims to merge network and molecular views of biology ., Genome-scale models are in silico , network models of metabolism , and by integrating the detailed knowledge we can gain from molecular simulations with these models , we can begin to understand whole cell phenotypes at a more complete scale ., In this study , we use and integrate a variety of simulation tools at both the network and molecular levels to allow us to understand how a mutation can change an enzyme’s ability to bind to drugs or metabolites ., We look at three different enzymes within red blood cell metabolism , and find that these computational tools reflect what we know about them relatively well , and also potentially serve as a workflow for understanding other traits in the overall theme of personalized medicine .

blood cells, medicine and health sciences, protein metabolism, enzymology, red blood cells, enzyme metabolism, protein structure, protein structure databases, pharmacology, drug metabolism, enzyme chemistry, research and analysis methods, animal cells, proteins, biological databases, molecular biology, pharmacokinetics, biochemistry, biochemical simulations, cell biology, database and informatics methods, biology and life sciences, cellular types, computational biology, drug interactions, metabolism, macromolecular structure analysis

journal.pcbi.1004130

Neuroblastoma Tyrosine Kinase Signaling Networks Involve FYN and LYN in Endosomes and Lipid Rafts

Neuroblastoma arises from cells of the neural crest , a population of multipotent , migrating cells that differentiate into neurons in the peripheral nervous system , melanocytes , and structural cells 1 ., Neuroblastoma represents 7–10% of childhood cancers and about half of all infant cancers ., Positive prognosis ranges from 95% to 10% depending on age , markers expressed in tumor cells , and stage of progression ., 70% of neuroblastomas are already metastatic at diagnosis ., There is compelling evidence that stalled or incomplete cell differentiation is the primary defect that gives rise to this cancer 2–6 ., Neural crest cells appear to restrict their range of cell fate choices in sequential steps 7 , 8 , and the profound heterogeneity in neuroblastoma is caused by a failure to differentiate at different stages ., Neuroblastoma tumors and cell lines thus represent a snapshot of failed differentiation at different stages in the neural crest sympathoadrenal lineage 2 , 4 , 7 , 8 ., Anaplastic lymphoma kinase ( ALK ) , a receptor tyrosine kinase ( RTK ) , is frequently mutated and activated in both familial and spontaneous neuroblastomas , suggesting that this receptor can prevent a key differentiation step in neural crest cells 9–15 ., Incompletely differentiated cells may give rise to a proliferating population when mutations occur that allow checkpoints in the cell division cycle and mechanisms of programmed cell death to be bypassed ., The tragic outcome is too often a metastatic cancer with poor prognosis ., To address this clinically challenging problem , a greater understanding of the signaling mechanisms that are active in neural crest and neuroblastoma is required ., Tyrosine kinase signaling networks play a major role in governing cell differentiation , including in neuroblastoma 16 ., There are 90 tyrosine kinases in the human genome; 58 of these are receptor tyrosine kinases 17 , 18 , many of which have unknown functions ., Src Homology 2 ( SH2 ) domains ( and one-fifth of phosphotyrosine-binding or PTB domains ) mediate selective protein–protein interactions with proteins phosphorylated on tyrosine residues , and thus mediate assembly of phosphotyrosine signaling networks 19 ., The metazoan evolution of multicellular organisms coincided with expansion of tyrosine kinases , protein tyrosine phosphatases , and SH2 domains , which suggests that tyrosine kinase signaling mechanisms play a major role in cell differentiation 20–22 ., Unfortunately , the system isn’t foolproof , and cancer results when the dynamic assembly of signaling complexes goes awry 23 ., Thus , the complexity of kinase-substrate and other protein-protein interactions in tyrosine kinase signaling pathways is important to understand because these pathways govern the choice between differentiation and cancer ., Tyrosine kinase signaling mechanisms are intimately intertwined with mechanisms that govern protein interactions in endocytosis ., Src Homology 3 ( SH3 ) domains are among the most abundant protein domain modules encoded by eukaryotic genomes; over 300 SH3 domains are found in 213 human proteins 24 , 25 ., SH3 domain-containing proteins , which typically bind to proline-rich motifs 26 , are functionally linked to both endocytosis and tyrosine kinase signaling pathways 24 ., SH3-domain-containing proteins play a role in endocytosis that is conserved in yeast , worms , and humans 26 , 27 ., SH3 proteins may also contain other domains ( e . g . , kinase , phosphatase , GTP exchange , GTPase activating ) to perform conserved functions in endocytosis and cytoskeletal dynamics , and , in metazoans , RTK signaling 28 , 29 ., 36 human proteins contain one SH2 domain and one or more SH3 domain ( s ) ( SH2-SH3 proteins ) 25 ., Most SH2-SH3 proteins are phosphorylated on multiple sites on tyrosine as well as serine and/or threonine residues ., Half of them also have tyrosine kinase domains , e . g . , the SRC-family kinases ( SFKs ) ., Interactions between proteins that contain SH2 and SH3 domains indicate that tyrosine kinase signaling and endocytosis are linked , and there is good evidence that endocytosis and signal transduction in general are integrated 30 , 31 ., To identify patterns in tyrosine phosphorylation in neuroblastoma , we acquired phosphoproteomic data from 21 neuroblastoma cell lines and cell fractions including endosomes and detergent-resistant lipid rafts as previously characterized 32 , 33 ., New approaches were devised to analyze these data ., We previously experimented with different dimensionality reduction and clustering techniques and validated methods that effectively resolve clusters from lung cancer phosphoproteomic data 34 ., An important first step is to represent missing values as “data not available” instead of zero in spectrometry data ., By combining pattern recognition techniques with gene ontology ( GO ) and protein-protein interaction ( PPI ) data , we learned that clusters that contain interacting proteins are likely to indicate functional signaling pathways 34–40 ., Here , we extend methods that employ graph theory and pattern recognition algorithms to introduce techniques to visualize data structure , namely a cluster-filtered network ( CFN ) and co-cluster correlation network ( CCCN ) ., We focussed primarily on proteins containing tyrosine kinase , tyrosine phosphatase , SH2 and SH3 domains , which collectively we call phosphotyrosine network control proteins ( PNCPs ) ., To identify patterns in tyrosine phosphorylation in neuroblastoma , we analyzed tyrosine phosphoproteomic data acquired from 21 neuroblastoma cell lines using immunoprecipitation of tyrosine phosphorylated peptides as previously described 41 , 42 ., Four cell lines SH-SY5Y , LAN-6 , SMS-KCN , and SK-N-BE ( 2 ) were selected for further studies because of their different point mutations in ALK , p53 status , RTK expression , morphology , and growth patterns ., These cells were fractionated to isolate endosomes and detergent-resistant lipid rafts 32 , 33 , and analyzed under different conditions that changed the state of their signaling pathways ., Quantification of immunoprecipitated phosphopeptides was obtained from the peak intensity of each peptide ( from the MS1 spectrum of the intact peptide before fragmentation for MS/MS analysis ) 41 , 42 ., We experimented with different ways to analyze the mass spectrometry data ( described in detail in Materials and Methods ) ., For the first analysis described below , phosphopeptide amounts were summed for each protein in each sample , with the exception of the SRC-family kinases ( SFKs ) , where the C-terminal inhibitory phosphorylation was summed separately and given the names SRC_i; LYN_i; FYN_i; and YES1_i ., This provided an overview of which proteins were present and phosphorylated together in the same samples ., For the second analysis , phosphopeptides were summed into individual phosphorylation sites , which were then clustered ., Clustering data were obtained by treating all samples mathematically as different states in the neuroblastoma system ., We describe analysis of the whole dataset first , then subsets of the data , focusing on signaling proteins in endosomes and detergent-resistant membranes ., 1622 phosphorylated proteins were identified in all neuroblastoma samples ( S1 Fig; S3 Dataset ) ., 1203 of these were tyrosine phosphorylated , identified from peptides immunoprecipitated using an anti-phosphotyrosine antibody ., 557 proteins were identified from phospho-AKT substrate immunoprecipitation; of these 419 were unique , and 138 were dually phosphorylated proteins also found in the phosphotyrosine data ., Due to limits in mass spectrometric detection of peptides 43–47 , these data were not an exhaustive determination of all phosphorylated proteins in all samples ., To ask whether these data were complete enough for analysis of signaling pathways , we employed graph theory , which describes the properties of networks 35 , 38 ., S1 Fig shows a network constructed using proteins identified in neuroblastoma phosphoproteomic data as nodes , and protein-protein interaction ( PPI ) edges merged as described 34 ., We found that the entire neuroblastoma phosphoproteomic network of 1622 proteins and 18728 interactions is dense enough to have the structure and properties expected of biological networks , including clusters that can be usefully interpreted ( S2 Fig ) ., PPI databases are biased towards proteins best studied in the scientific literature 36–38 , and not all protein-protein interactions in PPI databases may occur in neuroblastoma cells ., Nevertheless , PPI network analysis indicates that the phosphoproteomic data are complete enough to examine further to gain insight into signal transduction pathways that are active in neuroblastoma ( S2 Fig ) ., We hypothesize that proteins containing tyrosine kinase , tyrosine phosphatase , SH2 and SH3 domains ( PNCPs ) will collectively initiate and control phosphotyrosine signaling pathways 19 , 24 ., In neuroblastoma phosphoproteomic data , we detected 31 phosphorylated RTKs out of 58 in the human genome ( S3 Fig ) ; 41 of 110 SH2-domain-containing proteins; 12 out of 38 ( or 107 possible , based on open reading frames in the human genome ) proteins containing the tyrosine phosphatase ( PTPc ) domain; and 61 out of the 216 human SH3-domain containing proteins ., There are 36 proteins in the human genome that contain both SH2 and SH3 domains and 17 of these were detected in neuroblastoma phosphoproteomic data ., These data indicate that neuroblastoma cell lines express and phosphorylate a large fraction of the PNCPs in the human genome ., This remarkable diversity in phosphotyrosine signaling pathways likely represents a snapshot of signaling pathways activated in the sympathoadrenal lineage of neural crest that gives rise to neuroblastoma at different stages of development 2–6 ., The robust expression of RTK pathways that are known to function in neural crest differentiation suggests the hypothesis that neuroblastoma cells might be multipotent despite being selected for proliferation in culture ., To test this hypothesis we transplanted neuroblastoma cells in to the developing neural tube of live chick embryos and indeed found that they were capable of both migration and terminal differentiation ( S4 Fig ) ., Notably , four different transplanted human neuroblastoma cell lines LAN6 , SK-N-BE ( 2 ) , SMS-KCN , and SH-SY5Y migrated to neural crest target sites , incorporated into the developing ganglia , and expressed neuronal markers specific to mature afferents ( S4 Fig ) ., The potential to migrate along the stereotypical neural crest migration pathways , and differentiate into most neural-crest-derived cell types , suggests that many of the RTK signaling pathways that control differentiation and migration were generally functional in these neuroblastoma cell lines ., Thus , our phosphoproteomic data has relevance to pathways active in neural crest from which neuroblastoma is derived , and warrants detailed analysis ., We developed new methods to analyze proteomic data based on the hypothesis that data structure can be described using a combination of graph theory and pattern recognition techniques ., The first key step was to recognize that missing data , which are common in mass spectrometry data due to stochastic variation in phosphopeptide detection , should not have a value of zero 34 ., The next key step was to represent different statistical relationships by proximity on two- or three-dimensional graphs using an effective dimension reduction , or embedding , technique , t-distributed stochastic neighbor embedding ( t-SNE ) 48 , 49 ., Clusters were identified by proximity on resulting three-dimensional data structures ( embeddings ) using a minimum spanning tree , single linkage method 34 , 50 ., 75–80 clusters were identified from each embedding based on dissimilarity calculated in different ways ( S1 Movie; S1 Dataset ) ., Clusters were evaluated internally , based on the primary data , and externally , using PPI and gene ontology ( GO ) databases ( S5 Fig ) ., These evaluations confirm that these methods effectively resolve meaningful clusters as previously described 34 ., We experimented with different approaches to use these clusters to define signaling pathways active in neuroblastoma ., One approach was to apply a “hard” filter , or exclusive approach to identify groups of proteins that co-cluster from two or more dissimilarity representations ., This exclusive approach separates groups of proteins that are most likely to define core units of signaling pathways 34 ., Alternatively , an inclusive approach treats clusters derived from different embeddings as equally valid and therefore allows overlap between cluster membership ., This inclusive approach recognizes that signaling pathways use common effectors ., We show results from each of these approaches in turn ., For the first , exclusive cluster analysis , we focused on PNCPs and proteins whose phosphorylation pattern was statistically most similar determined by both Euclidean distance and Spearman correlation ( Figs 1 and S6 ) ., Heat maps ( Fig 1 and S6 , right ) indicate that the phosphorylation patterns in the primary data are reasonably consistent within each cluster ., The RTK , ALK , clustered with two other RTKs ( FGFR1 , PDGFRA ) , activated FYN , and LYN phosphorylated on the C-terminal inhibitory site ( LYN_i; Fig 1A ) ., The tyrosine kinase , FAK ( PTK2 ) , and the adaptor molecules BCAR1 , SHC1 and CBLB were included in this group of PNCPs ., We also noted other clusters that suggest interactions among phosphorylated tyrosine kinases: IGF1R with LYN , FER , the phosphatase PTPN11/SHP-2 , and the tyrosine kinase TNK2 , whose interactions with other proteins in this group have not been previously characterized ( Fig 1B ) ., In addition , we found that EGFR and EPHB3 clustered with inhibited FYN and SRC as well as the SH3 , SH2 containing tyrosine kinase , ABL1 , and MPP5 , a protein with PDZ , SH3 , and guanylate kinase domains whose interactions are not characterized ( Fig 1C ) ., Examples of other clusters identified using this hard filter are shown in S6 Fig . These clusters define phosphorylated proteins most commonly phosphorylated together in the same samples in this data set , which suggests possible interactions among signaling proteins that were previously unknown ., Assignment of proteins to one cluster should not be viewed as evidence for excluding it from participating in a signaling pathway identified in another cluster , however 34 ., An alternative , inclusive approach is to recognize possible relationships defined by different measures of statistical similarity ., Clusters derived from t-SNE applied to Spearman , Euclidean , and hybrid Spearman-Euclidean ( SED ) embeddings were typically overlapping but not identical , yet reasonably close in their ability to resolve meaningful clusters as determined by external and internal evaluations ( S5 Fig; 34 ) ., This suggests that statistical relationships independently defined by Euclidean distance or Spearman correlation are equally valid ., Using this inclusive method that recognizes clusters derived from different embeddings had the advantage that it allows overlap between cluster membership , which makes sense biologically for these data because signaling pathways overlap and converge ., We employed the inclusive clustering strategy to filter protein interaction edges to obtain the cluster-filtered network ( CFN ) shown in Fig 2 ., In this graph , only edges among proteins that co-clustered based on Spearman , Euclidean , or hybrid Spearman-Euclidean ( SED ) dissimilarity are shown ., This CFN data structure is useful because graph layouts that treat edges like springs ( edge-weighted , spring embedded; force-directed ) aggregate proteins that share a statistical relationship and interact with one another , so nearest neighbors are likely to represent functional groups ( regions highlighted in Fig 2 ) ., An alternative visualization of data structure is a co-cluster correlation network ( CCCN; S7 Fig ) ., In this graph , edges represent positive ( yellow ) or negative ( blue ) correlation , filtered to show only edges among proteins that clustered together and have a Spearman correlation coefficient greater than the absolute value of 0 . 5 ., The networks in Figs 2 and S7 are complementary because they apply a different filter to clustering results ., Proteins that interact with one another may not tightly correlate , and co-clustered proteins that do tightly correlate may not have been studied previously for evidence of interactions ., These filtered networks thus prune cluster members that have no evidence for interaction and do not tightly correlate with others in the group , yet allow potential interactions among pathways to be studied because overlapping cluster membership is defined by different embeddings ., Exploration of these networks reveals potential functional interactions among signaling proteins defined by the structure of neuroblastoma phosphoproteomic data ., We noted two groups of highly phosphorylated RTKs that clustered together ( Fig 3 ) ., Networks in Fig 3 show only positive correlation ( yellow ) and PPI ( grey ) edges between RTKs and co-clustered effector proteins , with proteins that link to three or more receptors grouped in the center of the graphs ( Fig 3 ) ., The similarity in phosphorylation patterns for proteins in these groups can be seen in heat maps of the primary data ( S8 Fig ) ., Co-clustering of ALK with PDGFRA , FGFR1 , and IGF1R ( through co-clustering with FGFR1 ) is indicative of a collaborative relationship ( Fig 3A ) ., Similarly , EGFR co-clusters with PDGFRB , EPHA2 , EPHB3 , and DDR2 ( Fig 3B ) , indicating that these RTKs form a separate collaborative group ., While different RTKs within these collaborative groups share a number of co-clustering downstream proteins in common , the only effector proteins in common between these two collaborative groups are PIK3R2 , FYN , and the SFK scaffold protein , PAG1 51 ., The following general conclusions can be made from these analyses so far ., Clusters that contain proteins that interact with one another , identified using statistical relationships from phosphoproteomic data , likely indicate functional signaling pathways ., New potential interactions are suggested when strong clustering is observed among proteins whose physical interactions have not been previously characterized ( e . g . , TNK2 and MPP5 in Fig 1 ) ., Common patterns of phosphorylation in neuroblastoma samples suggests collaboration among RTKs within functional groups ( Fig 3 ) ., Since activation of different RTKs was associated with different states of activation and inhibition of different SFKs , particularly FYN and LYN ( Figs 1 and 3 ) , we next examined how stimulation or inhibition of RTKs affected phosphorylation of other tyrosine kinases ., RTK activation affects other RTKs , SFKs , and other tyrosine kinases ., To examine the effects of RTK stimulation on other tyrosine kinases , we compared phosphoproteomic data from cells treated to influence RTK activity , or not treated , in the same experiment ., Fig 4A shows tyrosine kinases whose total phosphorylation changed more than two-fold under experimental conditions where RTKs were stimulated by ligand or ALK was inhibited ., For example , NGF treatment caused a more than twofold increase in total phosphorylation of DDR2 , and more than fivefold decrease in phosphorylation of PDGFRA in both LAN-6 and SH-SY5Y cells ., EGF treatment of SK-N-BE ( 2 ) cells activated EGFR and stimulated EPHA3 phosphorylation about 3-fold ( Fig 4A ) ., These data indicate that stimulation of one RTK affects the phosphorylation state of other RTKs in neuroblastoma cell lines ., Changes in inhibitory phosphorylation of LYN and SRC were also observed ( Fig 4A , LYN_i; SRC_i ) , so individual phosphorylation sites on SFKs and other kinases were examined further ., Phosphopeptides were assigned to phosphorylation sites based on peptide sequence homology ( see Materials and Methods ) ., The data revealed that both activating ( SFK Y411-426 ) and inhibitory ( SFK Y508-531 ) phosphorylation sites on the SFKs LYN , FYN , YES1 , and SRC were significantly affected in different ways by treatments that influence RTK activity ( Fig 4B ) ., For example , the LYN inhibitory phosphorylation ( LYN 508 ) was reduced by NGF treatment and increased by EGF treatment ., In contrast , FYN inhibitory phosphorylation ( FYN 531 ) was increased by NGF in two cell lines ( Fig 4B ) ., These data suggest the hypothesis that activation and inhibition of LYN and FYN distinguishes responses to different RTKs ( Figs 1 and 3 ) ., Tyrosine phosphorylation of RTKs is generally thought to be a measure of activation , but differences in different RTK phosphorylation sites were seen in these experiments ., For example , NGF treatment both increased and decreased phosphorylation on different sites on EGFR , RET , IGF1R , ALK , and other RTKs in LAN-6 and SH-SY5Y cells ( S9A Fig ) ., Some variations in individual phosphorylation site responses to treatments were also observed for other tyrosine kinases ( S9B Fig ) , but they were not as dramatic as those of SFKs ( Fig 4B ) ., These data indicate that different RTKs initiate signaling mechanisms to cause distinct phosphorylation patterns on other tyrosine kinases , including RTKs and SFKs ., Combined with the clustering patterns shown in Figs 1 and 3 , the data suggest the hypothesis that SFKs , particularly FYN and LYN , discern and integrate signals from different RTKs ., We hypothesized that functional interactions among these signaling proteins may occur in specific intracellular locations , namely endosomes and lipid rafts , and therefore we performed phosphoproteomic analyses on these fractions ., We asked whether particular signaling proteins were enriched in endosomes and detergent-resistant membranes ( DRMs ) ., RTKs are present in endosomes that can be distinguished from other types of receptors by size and density ( S10 Fig ) 32 ., Phosphoproteomic analysis was also performed on detergent-resistant and-sensitive fractions distinguished by extraction with non-ionic detergent ( S10 Fig ) 33 , 52 ., Endosomes from three neuroblastoma cell lines were characterized by phosphoproteomic analysis ., In all endosome fractions from three cell lines ( LAN-6 , SMS-KCN , SK-N-BE ( 2 ) ) , 908 proteins were detected , including 22 RTKs , 10 tyrosine phosphatases; 30 SH2- and 44 SH3-domain-containing proteins ., The most highly phosphorylated RTKs in neuroblastoma were those identified in Fig 5A by large yellow nodes that indicates large amounts detected in endosome fractions ( e . g . , DDR2 , ALK , KIT , RET , EGFR , PDGFA , FGFR1 ) ., FYN and LYN containing both activating and inhibiting phosphorylations were also prominent in endosomes , along with PAG1 , inhibited SRC ( SRC_i ) , the SH3 adaptor protein BCAR1 , several other adaptor proteins , two tyrosine phosphatases ( PTPN11/SHP-2 and PTPRN ) , and PLCG1/PLCγ1 , which was found previously in endosomes in PC12 cells 53 ., Notably , 26 out of the 55 SH3-domain-containing proteins in the human genome that were predicted to have a function in endocytosis based on orthologous interactions in C . elegans were found in neuroblastoma endosome fractions , and 2 of the 55 were detected in lysosome fractions 24 ., We asked whether particular phosphorylated proteins were enriched in endosomes and DRMs by calculating the ratio between amounts in those fractions compared to proteins in all other samples from the same cell line ., ALK , FGFR1 , RET , PDGFRA , DDR2 , EGFR , and IGF1R were enriched in endosomes from two or more neuroblastoma cell lines , but there were profound differences among cell lines ( Fig 5B ) ., In Fig 6 , enrichment was graphed in PPI networks as big yellow nodes for positive enrichment and small blue nodes for de-enrichment ( defined as lower amounts in that fraction compared to elsewhere ) ., In LAN-6 cells , most RTKs were enriched in endosomes , except EPHA2 and ROR1 , which were enriched in DRMs ( Fig 6A and 6B ) ., In SK-N-BE ( 2 ) cells made to over-express NTRK1/TrkA , this receptor was enriched in endosomes and de-enriched in DRMs , whereas its related receptor , NTRK2/TrkB , had the opposite pattern , being enriched in DRMs and de-enriched in endosomes ( Fig 6C and 6D ) ., The SFKs , FYN and LYN were localized differently , with LYN ( and LYN_i ) being enriched in DRMs in LAN-6 and SK-N-BE ( 2 ) cells , and FYN ( and FYN_i ) being enriched in endosomes in LAN-6 cells , but not in SK-N-BE ( 2 ) cells ( Fig 6 ) ., PAG1 was enriched in endosomes in LAN-6 cells ( Fig 6A ) and , in contrast , in DRMs in SK-N-BE ( 2 ) cells ( Fig 6D ) ., We noted differences in distribution of SFK and PAG1 phosphorylation on individual phosphorylation sites between the two cell lines ( Fig 5C ) ., For example , PAG1 81 was consistently phosphorylated in endosomes , and PAG1 317 was consistently phosphorylated in DRMs in both cell lines , yet PAG 359 and other sites were highly phosphorylated in LAN-6 , but not SK-N-BE ( 2 ) endosomes ( Fig 5C ) ., These data suggest a relationship between SFK and PAG1 phosphorylation on specific sites and intracellular localization ., These data suggest the hypothesis that stimulation of different RTKs should affect the activity and intracellular localization of FYN and LYN ., We used a cell fractionation approach to assay intracellular localization after stimulation of ALK with PTN and KIT with SCF ( Fig 7 ) ., Amounts of FYN and LYN increased with PTN and SCF treatment in organelles whose migration on velocity sedimentation gradients overlaps with Rab7 and acid phosphatase 32 , markers for late endosomes and lysosomes ( Fig 7A–7D , fractions 4–7 ) ., SCF also induced increases mainly in FYN localization to fractions 8–11 ( Fig 7B and 7D ) , which contain endosomes marked by Rab4 and Rab5 32 ., LYN and FYN also increased in fractions 16–22 in response to both ligands ( Fig 7A–7D ) ., These fractions contain soluble , cytoplasmic proteins , and signaling particles , which were previously resolved on gradients centrifuged with greater force 52 ., FYN and LYN were robustly associated with membranes that floated to the density of endosomes on floatation equilibrium gradients , and amounts increased in organelles of higher sedimentation velocity ( E1 ) after PTN treatment ( Fig 7E ) ., Both FYN and LYN were predominantly phosphorylated on their activating sites in these membranes ( Fig 7F ) ., Differences between FYN and LYN localization to detergent-resistant and-soluble fractions were also observed ., FYN’s response to PTN ( enhanced DRM and diminished P1M association; Fig 7G ) was different from that to SCF ( reduced DRM , enhanced P1M association ) ., In contrast , LYN’s response to both ligands was similar ( reduced DRM , increased P1M association; Fig 7G ) ., The magnitude of ligand-induced changes in FYN and LYN in organelle fractions were distinct in response to PTN and SCF ( Fig 7H ) ., Increased FYN and LYN in faster sedimenting organelles ( lys and E1 fractions ) likely reflects migration to multivesicular bodies , late endosomes , and possibly lysosomes 32 ., These data are consistent with the hypothesis that RTK activation regulates FYN and LYN localization and activity in neuroblastoma cells in a manner that distinguishes responses to individual RTKs ., These data motivated further higher resolution interrogation of the relationships between individual protein phosphorylation events ., We investigated the relationships among phosphorylation sites by clustering phosphorylation sites ( summed from homologous phosphopeptides ) and visualizing data structure as a co-cluster correlation network ( CCCN ) ., The edge-weighted , spring-embedded layout of this network showed several distinct groups of sites with statistical relationships to other groups ( S11 Fig ) ., The data were interrogated with a focus on the most highly phosphorylated sites on RTKs , SFKs , and PAG1 to ask if phosphorylation sites cluster together ., Two distinct clusters are shown in Fig 8 ., ALK was detected in 22 distinct phosphopeptides in neuroblastoma samples , which could be collapsed into 13 distinct phosphorylation sites based on sequence homology ., Fig 8A shows that the ALK phosphorylation site , ALK 1507 , which was most frequently seen in neuroblastoma samples , was associated with inhibited LYN ( LYN 508 ) , and activated FYN ( FYN 420; LCK 394; SRC 419; YES1 426; this site was assigned to FYN in total phosphorylation calculations because other FYN phosphopeptides were detected in the same samples; see Materials and Methods ) ., Co-clustered phosphorylation sites on several other proteins in this cluster resemble the cluster in Fig 1A ., Fig 8B shows that other ALK phosphorylation sites ( ALK 1096 and 1604 ) clustered with the most prominently detected phosphorylation site on DDR2 ( DDR2 481 ) , along with activated LYN ( LYN 411 ) , and inhibited FYN and SRC ( FYN 531; YES1 537 and SRC 530 ) ., Also co-clustered with the group in Fig 8B were phosphorylation sites from other RTKs represented in the cluster in Fig 1B ., Strikingly , a number of phosphorylation sites on PAG1 were detected , but none were statistically clustered with activated FYN and inhibited LYN ( Fig 8A ) , while the most prominent PAG1 phosphorylation sites clustered with activated LYN and inhibited FYN and SRC ( Fig 8B ) ., The data suggest a mutually antagonistic relationship between different SFKs , particularly LYN and FYN , so that when one is activated , the other is inhibited ., Phosphorylation of PAG1 , which recruits SFKs and their inhibitory kinase , CSK , to bind to it 51 , appears to be associated with the state where LYN is activated , and FYN and other SFKs are inhibited ( Fig 8B ) ., The data suggest that RTK phosphorylation does not occur on all sites at once under all conditions , resulting in different phosphorylation sites on ALK and other RTKs clustering separately from one another ., RTKs phosphorylated on different sites also fractionated to endosomes and DRMs selectively ( S12 Fig ) ., For example , some ALK and KIT phosphorylation sites were enriched in endosomes , while others were enriched in DRMs , with differences between the two cell lines examined ( S12 Fig ) ., In contrast , all EGFR and RET phosphopeptides were consistently enriched in endosomes ., Phosphorylation on selected sites would be consistent with RTKs acting as effectors as well as initiators of signal transduction ., Phosphorylation by other tyrosine kinases , such as other RTKs or SFKs , may favor particular sites , and thus influence intracellular location , providing different contexts for signaling pathways to influence cell responses ., There is considerable interest in tyrosine kinase signaling mechanisms because of their roles in tumor initiation and metastasis ., Tyrosine kinase signaling mechanisms arose during evolution when multicellular organisms evolved 19 , 54 , and many RTKs are known to be involved in governing cell behaviors such as cell division , cell death , differentiation , and migration ., Acquisition of phosphoproteomic data from a migratory , multipotent tumor cell type was motivated by these considerations ., The complexity of the data forced us to develop new approaches to understand signaling mechanisms that involve tyrosine phosphorylation ., Indeed , modeling dynamic complex systems and their interacting macromolecules remains a general challenge that lags far behind large-scale acquisition of biological data 35 , 55 ., To make progress , we found it useful to apply techniques from the fields of pattern recognition and graph ( network ) theory and combine them with external PPI and GO data 34 , thus extending the concept of using a variety of statistical techniques for exploratory data analysis 56 ., Exploratory data analysis is inherently descriptive in its initial stage

Introduction, Results, Discussion, Materials and Methods

Protein phosphorylation plays a central role in creating a highly dynamic network of interacting proteins that reads and responds to signals from growth factors in the cellular microenvironment ., Cells of the neural crest employ multiple signaling mechanisms to control migration and differentiation during development ., It is known that defects in these mechanisms cause neuroblastoma , but how multiple signaling pathways interact to govern cell behavior is unknown ., In a phosphoproteomic study of neuroblastoma cell lines and cell fractions , including endosomes and detergent-resistant membranes , 1622 phosphorylated proteins were detected , including more than half of the receptor tyrosine kinases in the human genome ., Data were analyzed using a combination of graph theory and pattern recognition techniques that resolve data structure into networks that incorporate statistical relationships and protein-protein interaction data ., Clusters of proteins in these networks are indicative of functional signaling pathways ., The analysis indicates that receptor tyrosine kinases are functionally compartmentalized into distinct collaborative groups distinguished by activation and intracellular localization of SRC-family kinases , especially FYN and LYN ., Changes in intracellular localization of activated FYN and LYN were observed in response to stimulation of the receptor tyrosine kinases , ALK and KIT ., The results suggest a mechanism to distinguish signaling responses to activation of different receptors , or combinations of receptors , that govern the behavior of the neural crest , which gives rise to neuroblastoma .

Neuroblastoma is a childhood cancer for which therapeutic progress has been slow ., We analyzed a large number phosphorylated proteins in neuroblastoma cells to discern patterns that indicate functional signal transduction pathways ., To analyze the data , we developed novel techniques that resolve data structure and visualize that structure as networks that represent both protein interactions and statistical relationships ., We also fractionated neuroblastoma cells to examine the location of signaling proteins in different membrane fractions and organelles ., The analysis revealed that signaling pathways are functionally and physically compartmentalized into distinct collaborative groups distinguished by phosphorylation patterns and intracellular localization ., We found that two related proteins ( FYN and LYN ) act like central hubs in the tyrosine kinase signaling network that change intracellular localization and activity in response to activation of different receptors .

journal.pntd.0007230

Strongyloides stercoralis infection: A systematic review of endemic cases in Spain

Strongyloidiasis is a disease caused by soil-transmitted helminths , mainly by the species Strongyloides stercoralis ., This intestinal nematode infects an estimated 300 million people worldwide , although this is probably underestimated ., It is one of the most neglected of the neglected tropical diseases ( NTD ) and is widely distributed 1–2 ., Although it generally occurs in subtropical and tropical countries , transmission is also possible in countries with temperate climates ., Autochthonous cases have been described in Spain , possibly infected long time ago ., It remains uncertain whether S . stercoralis is currently endemic in Spain ., Still , some authors consider this country and some other southern European countries as endemic 3 ., The life cycle of S . stercoralis is complex and follows multiple routes , including a complete life cycle outside the human host ., The most frequent mechanism of infection is percutaneous entry of the filariform larvae ., In healthy people , most of the cases are asymptomatic , although it can cause intermittent symptoms that mainly affect the intestine , the lungs or the skin 4 ., About criterion used to establish the diagnosis of strongyloidiasis is not homogenized among the centers ., The diagnostic laboratory criterion of strongyloidiasis is the observation of larval stages ., However , in chronic infection , larvae excretion may be low and fluctuating , and microscopic observation is not sensitive enough and multiple stool specimens should be analyzed to increase the sensitivity of the method ., The clinical criterion is a patient with epidemiological antecedents and any of the associated clinical manifestations , especially if it is an immunosuppressed patient ., These methods are laborious , time consuming , and in the case of fecal culture , requires well trained technicians in order to differentiate S . stercoralis ., Several immunological tests have also been described ( ELISA , IFAT and Western blot ) with variable sensitivity and specificity depending on the population tested among other factors 1 ., Alternative diagnostic methods , such as molecular biology techniques ( mostly polymerase chain reaction , PCR ) have been implemented ., However , PCR might not be suitable for screening purpose , whereas it might have a role as a confirmatory test , since it still misses a relevant proportion of infected people 5 ., Due to the subtle symptoms , low sensitivity of diagnostic techniques and the complex lifecycle that can cause asymptomatic autoinfection for decades , the prevalence of S . stercoralis is thought to be severely underestimated ., Typically risk factors for severe infection include immunosuppression , certain malignancies , human T-cell lymphotropic virus type 1 infection , and alcoholism ., Likewise , S . stercoralis has been associated with agricultural or mining activities ., In Germany , it was recognized as a parasitic professional disease in miners 6 ., S . stercoralis infection has also been linked to low socioeconomic factors and infrastructure , indicating that it as a disease of disadvantage 7–8 ., In recent years the number of diagnosed cases has been increasing in high income countries due to the growing number of immigrants , travelers and refugees 9–10 ., To provide information on this topic , a systematic review of the cases of endemic strongyloidiasis in Spain was carried out , as well as the description of the epidemiological characteristics of these patients ., Aiming to assemble all scientific articles based on endemic strongyloidiasis diagnosed in Spain , a systematic review was carried out ., Relevant articles were retrieved from PubMed , EMBASE , Scielo , ISI Web of Knowledge , and Cochrane Library databases using combinations of the search terms adapted to each database ., Additionally , Gray Literature in the form of communications presented at national congresses was performed , as well as OpenGrey ., As a secondary source , Google Scholar and free internet search was used for non-indexed articles ., The keywords were “Strongyloides stercoralis” , “soil-transmitted helminthiasis” , “endemic” , and “Spain” ., The following combinations of MeSH were used in PubMed: ( Strongy* MeSH AND Spain ) , ( Strongyloidiasis MeSH AND Spain NOT imported NOT immigrant ) , and ( Strongyloidiasis MeSH AND endemic AND Spain ) ., The selection criteria were articles published in any language until May 31st 2018 that contained the description of at least one human case of infection with S . stercoralis acquired in Spain without a history of travel to endemic areas ., No restrictions were applied based on the study design or data collection ., Human filter was applied ., A manual search of the bibliographical references cited in the relevant articles was carried out ., All potential articles were analyzed by two researchers to assess compliance with the selection criteria ., In situations of missed information , the corresponding author of the paper was contacted to gather the information ., If the author answered the required information to fulfill the inclusion criteria , those articles were considered ., If not , they were excluded because they could not ensure the endemic acquisition ., The exclusion criteria included: animal studies , cases in which endemic infection could not be assured , cases of foreigners from an endemic country for S . stercoralis , native people with trips to endemic or probably endemic countries in the past ( e . g . Italy , France or Portugal ) , transplanted people in which this contagion route could not be excluded , and duplicated cases ., Based on these criteria the articles were reviewed in two stages ., In the first stage , articles were selected by titles and abstracts according to selection criteria ., In the second stage , the full text of the articles was analyzed ., Finally , the articles that met the selection criteria were included in the study ., From each study the following data was extracted: the study period , year of publication and number of endemic cases described ., The following epidemiological data from patients described in the studies was collected: age , gender , geographical origin , medical comorbidities and concomitant treatments , occupation ( or hobbies if relevant ) , other risk factors , year of diagnosis , diagnostic technique used for diagnosis , presence of eosinophilia and clinical symptoms ., Thirty-six studies were included describing a total of 1083 patients with endemic strongyloidiasis in Spain ( see Tables 1 and 2 ) 11–46 ., The average age of the described cases was 68 . 35 years , ranging from 17 to 100 years old ., Two hundred and eight of the 251 ( 82 . 9% ) patients in whom gender was reported were male , and most of them had current or past dedication to agriculture ., The province in whom most cases were described was Valencia , with 1049 people diagnosed ., Alicante had 13 and Murcia 5 , eventually describing cases in provinces of coastal oceanic climate with abundant rainfall most of the year and temperatures below 22°C ( Cantabria , Asturias , and Pontevedra ) ., See Fig 1 ., Regarding the number of diagnosed cases per year , a decreasing trend is observed since the beginning of this decade ., The year with higher number of diagnosed cases was 2003 , with 82 patients ., Since 2011 , no more than 10 cases have been reported annually ( Fig 2 ) ., The technique that led to the diagnosis of strongyloidiasis was described in 743 patients from twenty-six different articles ., In some cases , different techniques were used for the same diagnosis ., In 692 patients ( 93 . 1% ) , the technique used for the definitive diagnosis of strongyloidiasis was the fresh stool examination , specific fecal culture , the Baermann test , the Ritchie technique or the Harada Mori technique ., In 39 patients ( 5 . 2% ) the diagnosis was made by the sputum or bronchoalveolar lavage examination ., In 6 cases ( 0 . 8% ) the diagnosis was made by serological techniques and in another 6 cases ( 0 . 8% ) the diagnosis was made by histopathological analysis ., In 26 of the 37 patients individually described , comorbidities were reported ., Out of those , most frequent were diseases that associate the use of corticosteroids such as: chronic obstructive pulmonary disease ( COPD ) , asthma , and inflammatory bowel diseases or immunosuppressive conditions due to advanced HIV infection ( AIDS stage ) or malignancies ., In all patients diagnosed with COPD , severity of airflow limitation ( FEV1 ) was according to the Global Initiative for Chronic Obstructive Lung Disease ( GOLD ) criteria at least moderate GOLD 2 ( 50% ≤ FEV1 < 80% predicted ) if not severe: GOLD 3 ( 30% ≤ FEV1 < 50% predicted ) ., Overall , 70 . 3% of these patients had at least one comorbidity ., In patients in whom blood test results were reported , 41 out of the 50 ( 82% ) exhibited eosinophilia ., The median eosinophil count in patients with eosinophilia was 4 , 057 eosinophil/mm3; considering 24 individual reported counts ., Strongyloidiasis prevalence may be underestimated in many countries ., With the data provided by this review it is likely that underestimation could have been a reality for the last five decades in Spain ., The main cause would be the lack of clinical suspicion ., But also the subtle symptoms , the decades-long persistence of infection in untreated hosts and the absence of a diagnostic test of choice with high sensitivity and specificity would ultimately contribute ., An important finding of our work is that almost 97% of all published infections occurred in the province of Valencia ., The fact that most cases diagnosed and published are in the province of Valencia , can respond to various reasons ., Firstly , the area had the perfect combination of temperature and humidity , population exposed to S . stercoralis for occupational reasons such as rice farmers or irrigation ditch cleaners ( activities that were characteristically carried out barefoot ) and hygiene factors of rural areas during the 1960s ( lack of drinking water and toilets in some homes ) ., It is noteworthy that no cases of strongyloidiasis have been reported in other areas with similar climatology and population equally dedicated to the cultivation of rice fields , such as the Delta del Ebro in Tarragona province ., We consider highly probable that there has been transmission in other areas outside those described ., Secondly , health care professionals in the area of Valencia probably had a greater awareness of the infection , with a higher suspicion and therefore a higher number of diagnoses ., Although we concur that the estimated prevalence of S . stercoralis by one highly cited article is not representative of the entire country , we disagree that Spain should not be considered an endemic country 17 ., However , autochthonous cases have been anecdotal in the last decade , as indicated by Martinez-Perez 47 ., The results of the individuals diagnosed showed an average age close to 70 years old ., Given the known characteristics of the disease the contagion probably took place decades before the diagnosis , coinciding with the postwar period where hygienic conditions and infrastructure were affected ., On one hand , factors of unavoidable mention that directly affect the transmission of this helminth are the improvement in hygienic conditions and the mechanization of agricultural work ., On the other hand , the increase of awareness by health care workers , especially from the most affected communities , may have led to the diagnosis of new cases in recent years ., An overall higher incidence rate in male gender is described , which is consistent with previous studies 15 , 17 , 21 , 27 ., This might be explained due to a gender biased; since some articles focus on screening high risk population ( farmers or smokers with COPD ) , traditionally associated with gender roles ., Regarding the diagnostic techniques used , there is great heterogeneity among the different studies ., The sensitivity of techniques based on microscopy is not good enough , particularly in chronic infections ., Serology is a useful tool but could overestimate the prevalence of the disease due to cross-reactivity with other nematode infections and its difficulty distinguishing recent and past ( and cured ) infections ., However , current serological tests are specific enough and negativization or a decrease in the titers could be observed 6–12 month after treatment , making this tool very useful 48 ., There are some limitations that have to be mentioned ., Inevitably there are cases of strongyloidiasis that have not been written for publication ., In addition , ten articles had to be excluded due to lack of information about travel history or did not comply with the minimum information required ., Therefore , it is highly probable that there were more than 1083 cases ., Lastly , given the characteristics of this review , it is possible that there are some duplicate cases in multiple description articles and described individually by another researcher ., In summary , there are still new diagnoses of autochthonous cases of strongyloidiasis in Spain every year , especially as occupational hazard in a specific Spanish region ., Although the number of diagnoses is much lower than in the past decade , it is highly probable that the infection remains undiagnosed due to low clinical suspicion among Spanish population without recent travel history ., Epidemiological studies in at risk areas based on serological techniques could give more information about the real situation of autochthonous cases of strongyloidiasis in Spain .

Introduction, Methods, Results, Discussion

Strongyloides stercoralis infection , a neglected tropical disease , is widely distributed ., Autochthonous cases have been described in Spain , probably infected long time ago ., In recent years the number of diagnosed cases has increased due to the growing number of immigrants , travelers and refugees , but endemically acquired cases in Spain remains undetermined ., We systematically searched the literature for references on endemic strongyloidiasis cases in Spain ., The articles were required to describe Strongyloides stercoralis infection in at least one Spanish-born person without a history of travel to endemic areas and be published before 31st May 2018 ., Epidemiological data from patients was collected and described individually as well as risk factors to acquisition of the infection , diagnostic technique that lead to the diagnosis , presence of eosinophilia and clinical symptoms at diagnosis ., Thirty-six studies were included , describing a total of 1083 patients with an average age of 68 . 3 years diagnosed with endemic strongyloidiasis in Spain ., The vast majority of the cases were described in the province of Valencia ( n = 1049 ) ., Two hundred and eight of the 251 ( 82 . 9% ) patients in whom gender was reported were male , and most of them had current or past dedication to agriculture ., Seventy percent had some kind of comorbidity ., A decreasing trend in the diagnosed cases per year is observed from the end of last decade ., However , there are still nefigw diagnoses of autochthonous cases of strongyloidiasis in Spain every year ., With the data provided by this review it is likely that in Spain strongyloidiasis might have been underestimated ., It is highly probable that the infection remains undiagnosed in many cases due to low clinical suspicion among Spanish population without recent travel history in which the contagion probably took place decades ago .

S . stercoralis is a soil transmitted helminth that is common in many subtropical and tropical countries , but is also found in other regions of the world ., In this study we reviewed all published material on endemic infection of S . stercoralis acquired in Spain issued before 31st May 2018 ., We collected data from these articles and reported clinical and epidemiological characteristics of patients ., Our systematic review of the articles showed a clear geographical pattern; nearly 97% of the cases described had been acquired in the Valencia province ., Most of them ( 82 . 9% ) were male , and most had current or past dedication to agriculture ., Our results showed that 70 . 3% had at least one condition or treatment that could have made them more vulnerable to suffer a severe form of this helminthic disease ., Our data suggests that S . stercoralis infection probably remains underdiagnosed in Spanish population ., Due to the scarce information available about endemic strongyloidiasis in Spain until now , we believe that the present work will be relevant and the conclusions derived from it might raise awareness about underdiagnosis ., Transmission risk factors described in the people diagnosed may be key for prevention and control strategies implementation .

invertebrates, medicine and health sciences, european union, pathology and laboratory medicine, tropical diseases, geographical locations, parasitic diseases, animals, pulmonology, chronic obstructive pulmonary disease, diarrhea, signs and symptoms, gastroenterology and hepatology, neglected tropical diseases, strongyloides stercoralis, strongyloides, spain, soil-transmitted helminthiases, pain, people and places, helminth infections, professions, eukaryota, diagnostic medicine, strongyloidiasis, nematoda, biology and life sciences, population groupings, europe, organisms, abdominal pain, agricultural workers

journal.pcbi.1006421

Emergence of spontaneous assembly activity in developing neural networks without afferent input

Developing nervous systems exhibit ongoing neural activity even in the absence of sensory stimulation 1 ., With recent advances in imaging technology , this spontaneous activity has been shown to be highly organised at the population level 2 , and often consists of a number of structured neural assemblies; i . e . , groups of neurons that tend to fire together ., Neural assemblies have been increasingly interpreted as the basic units of cortical computation and coding , and their presence in spontaneous activity has led to speculation about the contribution of spontaneous activity to neural computation 1 ., For instance , spontaneous activity has been hypothesised to interact with evoked activity to affect the representation or processing of information 3 , 4 ., Spontaneously active neural assemblies can also resemble the population responses evoked by sensory stimuli 5 , 6 , and thus could contribute to probabilistic inference by acting as a Bayesian prior over possible stimuli in the external environment 5 ., The mechanisms surrounding the emergence of structured spontaneous activity have yet to be fully elucidated ., Theoretical progress has been made towards understanding how plasticity-driven self-organisation can explain some of the statistical properties of synaptic wiring in cortex 7–13 , and on the development and dynamics of structured spontaneous activity in computational models of neural circuits 11 , 14–17 ., Recently it has been shown how multiple forms of synaptic plasticity and homeostasis can interact to develop neural assemblies from repeated sensory stimulation 18 , and how trained memories can be retrieved as the activation of neural assemblies both spontaneously 18 and by partial cues 15 in detailed circuit models ., Other models based on spike-timing-dependent plasticity rules have analysed the complementary problem of memory retention ., One study of balanced random networks , for example , established that membership in the set of strongest synapses decays exponentially with time 19 ., Similarly , receptive field structure in networks with feedforward excitation and lateral inhibition is unstable , with an autocorrelation that decays to zero despite continued stimulation with the same set of stimuli 20 ., Surprisingly , however , even when animals are deprived of sensory stimuli during development , spontaneous activity still exhibits a highly structured form 21 , 22 ., While computational analyses have investigated assembly formation under sensory stimulation , the mechanisms underlying the development of assemblies in systems with no structured afferent input remain poorly understood ., Here we focus on constructing a simple computational model that explains how neural assemblies can emerge over developmental timescales in the absence of external input , as recently seen in the developing zebrafish 21 , 22 ., We describe how a Hebbian plasticity rule that reinforces synchronous neural activity can interact with a simple normalisation rule to reorganise the structure of neural networks into a highly modular state where assemblies activate spontaneously ., Rather than focusing on plasticity at a millisecond timescale , we instead consider timesteps on the order of one second ., This matches the order of magnitude for both burst-timing-dependent plasticity rules 23 and the temporal resolution of much calcium imaging data for large neural populations , and makes it computationally feasible to track plasticity over developmental timescales ., We relate our model to calcium imaging of in vivo spontaneous assembly activity , showing how simple mechanisms can explain the emergence and dynamics of structured neural assemblies in the developing brain ., Recent studies of population activity in the optic tectum of the larval zebrafish have revealed the presence of recurrent spontaneous assembly activity 21 , 22 ., Fig 1 shows a comparison of typical assembly activity that emerges in the tectum with and without afferent input , reanalysed from ref ., 21 ., We applied a spectral clustering-based assembly detection algorithm that extracted neural assemblies from the calcium activity 21 , and sorted the fluorescence raster according to the detected assemblies ., In normally-reared zebrafish this revealed spontaneous and synchronous bursts of fluorescence from assemblies of neurons , a modular correlation structure , and a small number of spatially structured assemblies ( Fig 1A–1C ) ., Remarkably , zebrafish bilaterally enucleated at 24 hours postfertilisation showed qualitatively similar patterns of spontaneous assembly activity in the tectum despite the absence of afferent input ( Fig 1E–1H ) , a result also demonstrated in ref ., 22 ., Thus , the basic structure of the neural assemblies formed in the zebrafish optic tectum does not depend on afferent input ., This raises the question of what mechanisms endogenous to a neural population such as the tectum could explain assembly formation ., To address this question we simulated spontaneous activity in a recurrent network of binary units ( Fig 2A and 2B ) , where the activities x i E and x j I of the excitatory and inhibitory neurons respectively updated according to threshold rules, x i E ( t + 1 ) = Θ ( ∑ j w i j EE x j E ( t ) - ∑ j w i j EI x j I ( t ) + β i , t E - θ ) x i I ( t + 1 ) = Θ ( ∑ j w i j IE x j E ( t ) - ∑ j w i j II x j I ( t ) + β i , t I - θ ), where Θ is the heaviside step function , θ is the activition threshold , and each β i , t X is a random variable that drives background spontaneous activity ., β i , t X takes the value 1 + θ ( with probability p i X ) or 0 ( with probability 1 - p i X ) , and to allow for some variation in baseline firing rates we sampled p i X from a Gaussian distribution with mean μ and standard deviation σ ( see Methods for parameter values ) ., In order to draw comparisons between the experimental data and our model , the network initially consisted of 100 excitatory and 25 inhibitory units , which roughly matched the number of neurons recorded experimentally ., We also identified each timestep as one second , which matched the order of magnitude of the temporal resolution of the calcium imaging experiments ., As the kinetics of genetically-encoded calcium indicators operates on the order of seconds 24 , we replaced the rise and decay of the calcium fluorescence by the instantaneous activation of a binary neuron ., This also ensured that the network model was tractable enough to simulate for periods of days ., Before studying the effects of Hebbian learning on spontaneous neural activity , we first explored the structure of fixed network wiring required to produce neural assemblies given these activity dynamics ., In computational models of neural networks , structured spontaneous activity can result from strongly connected groups of spontaneously active neurons 25 ., One approach to characterising the presence of such modular structure in a network is with the graph modularity , a measure that describes how strongly a network can be divided into disjoint modules by comparing the strengths of connections within modules to the expected strengths of connections outside of modules if the network had weights chosen at random 26 ( see Methods ) ., We artificially partitioned the set of model neurons into clusters of equal size ( Fig 2B ) and defined a parameter α to control the separation of within-cluster to between-cluster connection strengths ., We generated the within-group synaptic weights by sampling at random from U ( 0 , 1 ) , the uniform distribution over ( 0 , 1 ) , the between-group synaptic weights from U ( 0 , 1 − α ) , and then normalising the weights according to the normalisation procedure described below ., As we increased α we generated networks of greater modularity and greater within-cluster strengths ( Fig 2C and 2D ) , which strengthened the embedding of the artificial assemblies ., The modularity of a network is closely related to the structure of the eigenvalue spectrum of its weight matrix 27 ., Networks with high modularity have more strongly embedded communities , and this is reflected in the spectra of their weight matrices as the separation of eigenvalues into two groups: a continuous “spectral band” comprised of most eigenvalues , and a group of outliers , the number of which is often used to estimate the number of communities present in the network 27 ., The leading eigenvalues gradually separated from the spectral band as we strengthened the embedding of the assemblies in the network ( Fig 2E ) ., The distance Δλ between the real parts of the eigenvalues in the spectral band and the outliers has previously been related to the presence of assembly activity in balanced networks of integrate-and-fire neurons 28 ., Here we show that this relationship also holds for the simplified model dynamics ( Fig 2E and 2F ) ., The increasing modularity , within-assembly connection strengths , and separation of eigenvalues caused an amplification of the recurrent excitation within the embedded assemblies and led to their spontaneous activation ( Fig 2G–2I ) ., We repeated the analysis for the division of the network into 3 to 9 assemblies and characterized the assembly activation frequency as a function of the modularity ( Fig 2F ) , revealing the existence of a threshold in the graph modularity that a network must exceed before neural assemblies will activate spontaneously ., We next investigated how increases in modularity might arise as a result of self-organisation ., We initialised the neural network with random synaptic strengths and modified the excitatory synapses during spontaneous activity according to the covariance learning rule 29, Δ w i j EE = η ( x i E ( t ) - ⟨ x i E ⟩ ) ( x j E ( t ) - ⟨ x j E ⟩ ) ,, which updated the strength of the connection w i j EE between excitatory units i and j at rate η ( where 〈⋅〉 denotes averaging over time ) ., We enforced a lower saturation constraint to prevent the excitatory connections from becoming negative , and then defined a normalisation rule to regulate the growth of the synaptic weights ., A common consequence of Hebbian plasticity is the emergence of rich-get-richer dynamics 10 , where a small fraction of neurons acquire increasingly stronger outgoing connections and come to dominate the population activity ., To prevent this we presynaptically normalised the connections leaving each neuron j by setting w ˜ i j EE = w i j EE / ∑ k w k j EE ., In our model θ was the proportion of the total possible synaptic input that a neuron needed to activate , so we next postsynaptically normalised the modified connections with w i j EE = w ˜ i j EE / ∑ k w ˜ i k EE to ensure that this proportion was constant across all iterations ., All connections other than EE-type connections were kept fixed throughout the simulation ( see Discussion ) ., The interaction between the covariance learning rule and the pre- and post-synaptic normalisation rule caused the spontaneously activating neurons to organise into assemblies that activated in synchronous bursts ( Fig 3A and 3C ) ., The synaptic weight matrix of the neural network reveals how the plasticity rule reshaped the structural connectivity of the network into distinct modules ( Fig 3B and 3D ) ., We then extracted modules from the weight matrix using a modularity-based community detection algorithm ( the Louvain method 30 ) ., To confirm that the modules detected from the synaptic weight matrix coincided with the assemblies visible in the event raster , we compared the ability of a standard assembly detection algorithm based on independent components analysis 31 with the Louvain method in a task to recover artificially defined assemblies of varying size and with a range of imposed modularities ., We generated weight matrices that embedded assemblies with increasing modularity using the separation parameter α ., For each generated weight matrix we simulated 10 , 000 seconds of spontaneous activity and applied ICA to recover the artificially embedded assemblies from the activity raster , and the Louvain method directly to the weight matrix ., To measure how well the recovered assemblies corresponded to the embedded assemblies we used a variant of the best match score 32 , which has been used previously to estimate the accuracy of assembly detection algorithms 21 ., We measured the performance of an assembly detection method by comparing the detected assemblies to the artificial assemblies with the best match score , where the assembly detection algorithm perfectly recovered the embedded assemblies if they shared a best match score of 1 ., In every condition , the Louvain method operating on the weight matrix performed at least as well as ICA in recovering the predefined assemblies ( Fig 3E ) , confirming that extracting modules directly from the weight matrix via the Louvain method is an effective method for detecting assembly structure ., We tracked the development of the network’s structural properties as we simulated spontaneous activity ( Fig 3 ) ., The evolution of the network tended to display two phases , consisting of an initial assembly formation phase where the network modularity increased monotonically and the leading eigenvalues separated from the spectral band ( Fig 3F and 3G ) indicating that assemblies were being embedded into the network , followed by a stable phase where the number of assemblies remained approximately constant ( Fig 3H ) and assembly activity corresponded to the attractor states of a multistable dynamical system 33 ., We used the coefficient of variation ( CV ) to estimate the dispersion of the set of assembly sizes that the network generated ., A CV near 1 indicates that the assembly sizes are highly heterogeneous , whereas a CV near 0 indicates a homogeneous distribution of assembly sizes ., For a single simulation the assembly size CV followed the basic trajectory of the number of assemblies , decreasing monotonically before reaching a stable range of values ( Fig 3I ) ., We recorded the CV for 1000 independent simulations of 100 , 000 time steps of network development ( approximately 28 hours model time ) , starting from randomly generated synaptic weight matrices each trial ., This revealed the variability in the dispersion measures for the assembly sizes ( Fig 3J ) ., At 100 , 000 time steps the model had a mean assembly size CV of 0 . 19 , suggesting that , while there is some variability , most assemblies tended to be of a similar size ., We then performed a more complete analysis of the structural properties of the network during its development ., The learning rate η controlled the rate of structural modification in the system , so one could expect that greater learning rates should allow the network to approach a stable modularity more rapidly ., We found that greater η induced greater numbers of assemblies with higher modularity and stronger within-assembly weights ( Fig 4A ) , but with little effect on the rate of network development; i . e . , how quickly the network converged to a stable number of assemblies ., The spontaneous activation rates p i X are drawn from a normal distribution with a mean rate μ ., Surprisingly , when we varied μ we found that structural properties such as the modularity , number of assemblies , and within-cluster weights were unaffected in their stable values , but were strongly affected in their time required to stabilize ( Fig 4B ) ., The delayed learning time for small μ is likely a result of the fewer activations providing fewer opportunities for the Hebbian learning rule to modify the network structure , causing a prolonged assembly formation phase , but without affecting the stable structural properties ., In our model a neuron’s excitability is inversely related to the activation threshold θ ., We studied the development of spontaneous activity when θ deviated from its default value of 0 . 1 ., More excitable networks had greater mean event frequencies ( Fig 4C ) but developed fewer assemblies that tended to be more weakly connected ., Thus η and θ regulated the structural properties of the mature architecture ( number of assemblies , assembly size , modularity , within-assembly synaptic strengths ) with only minor effects on the temporal evolution of the network , while μ governed the timescale of the network evolution , leaving the stable structural properties relatively unaffected ., Spontaneous activity typically degrades structured patterns of connectivity in neural networks with plastic synapses ., However , it was recently shown that spontaneous activity can be critical to reinforcing a learned network architecture 18 , 34 ., Observing that the number of assemblies in our network model became approximately constant ( Fig 4 ) , we asked whether the spontaneously developed neural assemblies were stable over time , or if they degraded and were continuously replaced by new assembly structures ., Remarkably , we found that despite the roughly constant number of assemblies , the composition of each assembly was constantly changing ( Fig 5A ) ., We recorded the set of assemblies present at regular intervals and defined an index to measure the similarity of the assemblies present at two different time points , which we refer to as the autosimilarity ., The autosimilarity was defined as a function of the time Δt between sampling timepoints via the best match score , and took values between 0 ( if the mean best match score between assemblies sampled with an interval Δt is 0 ) and 1 ( if the mean best match score is 1 , see Methods ) ., The autosimilarity decayed exponentially as assemblies were sampled at greater intervals ( Fig 5B ) ., We considered whether assemblies eventually became randomly reorganised , or if there were ‘core’ groups of neurons that were maintained despite the reorganisation ., Following the initial assembly formation period , we simulated the network for 200 days ( model time ) and sampled the assemblies every 2 days ., This sampling interval was sufficiently long to allow the autosimilarity to decay to its baseline asymptote ., We then recorded how many times every pair of neurons was assigned to the same assembly in a co-membership matrix ( Fig 5C and 5D ) ., The Louvain method for community detection failed to identify any strong modular structure in the matrix , suggesting that the network does not retain subsets of neurons during its reorganisation ., For each autosimilarity decay curve we fit an exponential of the form, y = β baseline + β amplitude exp ( - Δ t / β decay ), and tracked the time constants βdecay as we varied the model parameters ., We found that the time taken for a set of assemblies to decay was highly sensitive to the learning rate , baseline rate of activation , and excitability ( Fig 5E–5G ) , sometimes changing by an order of magnitude with a small deviation in a single parameter ., While high learning rates generated more assemblies with greater within-assembly connection strengths , the increased rate of structural modification due to large η led to more rapid assembly degradation ( Fig 5E ) ., When we examined the autosimilarity decay for μ = 0 . 001 we saw a substantial increase in the decay time constant ( Fig 5F ) , due to structural modification being greatly prolonged at low rates of baseline activity ( Fig 4B ) ., As we increased the baseline rate of activation the emergent assemblies maintained similar within-assembly connection strengths , but degraded much more rapidly ( Fig 5F ) as a result of the synaptic structure being modified at a much higher frequency caused by the increase in the frequency of synchronous events ., Next , we studied how network excitability affected autosimilarity via the threshold parameter θ ., At low θ the network was highly excitable , which increased the event frequency due to the influence of lateral connections , causing rapid and continual modification to the assembly structure ( Fig 5G ) ., High levels of θ reduced the influence of the recurrent input , and thereby reduced the frequency of synchronous events , which greatly extended the autosimilarity decay time ( Fig 5G ) ., What are the detailed temporal dynamics of assembly activity in the mature network ?, To investigate this we simulated the development of spontaneous activity in the network for 100 , 000 timesteps , and then froze the synaptic weights and probed the assembly activation process in the fixed network ., Fig 6A shows a typical assembly activation event ., Here the coincidental activation of two neurons within an assembly initiated a sequence of activity that resulted in the activation of the complete assembly ., We characterised this assembly activation process by calculating the average number of neurons active preceding the event onset ( Fig 6B ) ., This revealed a steady build-up of activity within the assembly prior to the event onset , without recruiting many neurons from other assemblies ., We then considered whether the neurons that were active leading up to the assembly events were persistently active , in which case the set of active neurons would simply accumulate until the event onset , or whether distinct groups of neurons were active at each timestep ., We calculated the number of neurons that were active at both timesteps t − 1 and t as we varied t over the time window preceding each assembly event ., This showed that neurons tended to not stay persistently active during the build up to the assembly event , but rather mostly disjoint groups of neurons activated in sequence up until the last two timesteps ( Fig 6C , c . f . Fig 6A ) ., In order to establish how many neurons were required to trigger a complete assembly event we next disabled spontaneous background activity in the network and manually activated sets of neurons ., We targeted in turn all possible combinations of 1 , 2 or 3 neurons within an assembly and recorded the ratio of these combinations that resulted in the complete activation of the assembly within an interval of 20 timesteps ( Fig 6D ) ., While only 0 . 2% of individual neurons could trigger an assembly event , this rose to 21% and 85% for neuron pairs and triples respectively , indicating that most combinations of 3 neurons were sufficient ., We then compared these model results to the experimental data ., In particular , we computed the average number of active neurons preceding an assembly event in the zebrafish recordings in analogy to Fig 6B ( see Methods for the definition of event onset times ) ., The experimental data was qualitatively similar to the simulated data ( Fig 6E ) ., In the tectum , assembly events had a prolonged build-up and recruited increasing numbers of within-assembly neurons prior to the event onset , with little recruitment of neurons outside of the assembly ( Fig 6E ) ., This occurred on a timescale comparable to the activation process in the simulated data , which provides support for the choice of timescale in the computational model ., An analogue to Fig 6C is difficult to obtain from the experimental data since the kinetics of the calcium indicator caused active neurons to remain active for several seconds , inflating the estimates of how many neurons were active at successive timesteps ., The locally structured spatial organisation of assemblies in the optic tectum could potentially result from nearby neurons receiving correlated input due to the topography of the retinotectal projection 35 ., However , the persistence of spatiotemporal assembly structure in systems without afferent input suggests that this emergent spatial pattern could also be the result of an endogenous tectal mechanism ., In the mammalian nervous system , lateral connections are hypothesised to substantially influence the development and structure of cortical maps , and many theoretical studies of cortical map formation model the source of excitatory synaptic input by neurons at anatomically short distances 36 , 37 ., We therefore investigated local excitatory connectivity as a mechanism to explain the emergence of the spatial structure of neural assemblies ., We assigned each excitatory neuron coordinates in a two-dimensional grid and constrained the strength of their connections by a Gaussian function of the distance between them ( see Methods ) ., In order to improve the visualisation of the network’s spatial properties we increased the size of the network to 1024 excitatory neurons and 256 inhibitory neurons ., Inhibitory neurons were not assigned spatial locations and innervated every excitatory neuron , similar to the dense and non-specific patterns of inhibitory connections observed in cortex 38 ., Additionally , to eliminate edge effects we imposed periodic boundary conditions ., We found that enforcing these local excitatory interactions caused neurons to self-organise into spatially structured assemblies that were spontaneously active ( Fig 7A and 7B ) ., We verified that the larger model with spatial constraints exhibited a developmental profile in line with our earlier results , such as convergence to a stable range of values for various network metrics ( Fig 7C–7E ) , and had an autosimilarity decay time constant within the range previously identified ( Fig 7F , c . f . Fig 5E–5G ) ., For some assemblies the activation sequence was more prolonged than in the smaller network ( Fig 7A ) ., The emergent assemblies tiled the two-dimensional surface ( Fig 7G ) , resembling the organisation of assemblies in the optic tectum ( Fig 1 ) ., Our model can thus explain how a simple , biologically plausible mechanism leads to the emergence of realistic assembly structure ., Inspired by recent data from the zebrafish optic tectum , we have proposed a theoretical account of the emergence of assembly structure in developing neural systems without afferent sensory input ., We characterised the presence of assembly structure in the synaptic weight matrix using the graph modularity , a measure often used to identify communities in large social networks ., Consistent with theory on the eigenvalue spectra of random graphs , we found that the development of assembly structure was reflected in the gradual separation of eigenvalues in the spectrum of the synaptic weight matrix ., By coupling a standard Hebbian learning rule with a simple weight normalisation rule , we demonstrated how a recurrent neural network could transform independent spontaneously activating neurons into strongly connected assemblies that activated in synchronous bursts ., The network exhibited an initial assembly formation phase where the modularity increased monotonically and the leading eigenvalues separated from the spectral band , followed by a more stable phase where the number of assemblies was approximately constant ., The simplicity of our model allowed us to isolate fundamental parameters that govern assembly formation and modification over long timescales , and we showed how these parameters affect the network structure and timescale of development ., We characterised the assembly activation process in the mature network and showed how this qualitatively resembled assembly activation in the experimental data ., Previous models of assembly formation have focused on identifying crucial physiological processes at the millisecond timescale needed for stable assembly formation from correlated input ., The model of ref ., 18 produced spontaneous assembly activity , and found realistic STDP , homeostatic inhibitory plasticity , and balanced excitation and inhibition to be critical elements of the model , which addressed the transient spontaneous reactivation of trained assemblies in cortical networks that reflected previously experienced stimuli ., A similar model 15 showed that multiple forms of plasticity operating in concert could embed stable assemblies that could be retrieved over long timescales ., However , in this model one of the trained assemblies is always active , and the network switches discretely between these high activity states ., While the spontaneous formation of coactive groups of neurons from spike-timing plasticity rules was reported in a previous model 39 , our study differs from this model in several important ways ., In ref ., 39 , a neuron was a member of a neuronal group ( analogous to an assembly ) if it was driven to spike by the synchronous arrival of spikes from presynaptic neurons already belonging to the group , starting from an initial fixed neuron ., In this way the activation of neuronal groups was sequential ., Neuronal group identification was then performed combinatorially by cycling through candidate neurons and checking the membership criterion ., By contrast , in our model assembly activation was synchronous , and our characterisation of assembly structure with the synaptic modularity naturally lended itself to modularity-based algorithms for assembly extraction ., Importantly , the emergent structure of spontaneous activity in our model resembled experimentally observed patterns of neural activity in zebrafish larvae ., Synaptic normalisation is a basic computation observed across a variety of brain areas and nervous systems 40 ., Experimental work has shown the conservation of total synaptic weight accompanies activity-dependent potentiation or depression in , for example , synaptic inputs to intercalated neurons of the amygdala 41 and in hippocampal slice cultures 42 ., Our model relies on the pre- and postsynaptic redistribution of connection strengths to conserve the total quantity of synaptic weight in the network , constrain the growth of individual connection strengths , and encourage competition among synapses ., Earlier models of assembly formation either performed excitatory postsynaptic normalisation 18 or enforced hard constraints on synaptic weights to control their growth 15 ., Another previous study examined how feedforward chains of activity ( in contrast to synchronous assembly activity ) can develop from initially random connections by combining STDP with a pre- and postsynaptic normalisation rule in a network of binary neurons similar to ours 43 ., The timescale of the covariance plasticity rule could be most closely related to the time constants involved in NMDA receptor activity , and our plasticity rule could loosely correspond to recently discovered rules for burst-dependent plasticity in the lateral geniculate nucleus 44 and cerebellum 45 which appear to operate on the order of seconds 23 ., Our model does not incorporate recently studied rules for homeostatic inhibitory plasticity 46 , which , in earlier models of assembly formation 15 , 18 , were required to maintain network stability while assemblies were being embedded ., The temporal resolution of our model allowed the network to maintain stability without homeostatic mechanisms like inhibitory plasticity that act at a timescale on the order of milliseconds; however , such mechanisms may be important for models based on more physiologically detailed neurons ., Our model shows that assemblies can form from endogenously generated spontaneous activity in the absence of any afferent input ., In vitro cultures of dissociated cells certainly operate in this regime ., Networks of dissociated hippocampal neurons spontaneously release glutamate at excitatory synapses , and a number of recent studies have recorded the emergence of coordinated spontaneous activity in the development of hippocampal cultures 47–50 ., It is , however , currently unclear the extent that afferent input is fully absent in any particular in vivo system ., In the larval zebrafish example ( Fig 1 ) , while early bilateral enucleation removed the main afferent input to the tectum , it has recently been shown that there are also tectal inputs from the lateral line and auditory systems 51 ., Afferent input could be definitively removed by explanting the tectum and recording the development of tectal spontaneous activity ex vivo ., This would be challenging , however , as tectal removal would have to occur before the first retinal axons enter the tectum at approximately 2 dpf , and possibly earlier depending on other sources of afferent input ., E

Introduction, Results, Discussion, Materials and methods

Spontaneous activity is a fundamental characteristic of the developing nervous system ., Intriguingly , it often takes the form of multiple structured assemblies of neurons ., Such assemblies can form even in the absence of afferent input , for instance in the zebrafish optic tectum after bilateral enucleation early in life ., While the development of neural assemblies based on structured afferent input has been theoretically well-studied , it is less clear how they could arise in systems without afferent input ., Here we show that a recurrent network of binary threshold neurons with initially random weights can form neural assemblies based on a simple Hebbian learning rule ., Over development the network becomes increasingly modular while being driven by initially unstructured spontaneous activity , leading to the emergence of neural assemblies ., Surprisingly , the set of neurons making up each assembly then continues to evolve , despite the number of assemblies remaining roughly constant ., In the mature network assembly activity builds over several timesteps before the activation of the full assembly , as recently observed in calcium-imaging experiments ., Our results show that Hebbian learning is sufficient to explain the emergence of highly structured patterns of neural activity in the absence of structured input .

Even in the absence of sensory stimulation , the developing brain can exhibit highly organised patterns of neural activity ., This activity often takes the form of structured assemblies of neurons ., Here we draw on calcium imaging experiments in zebrafish larvae to construct a computational model of assembly formation in neural networks without correlated input ., Our model shows how a simple learning rule can explain the emergence and dynamics of patterned neural activity in the early nervous system , and predicts a continual reorganisation of assemblies despite maintaining stable statistical properties .

fish, medicine and health sciences, neural networks, brain, vertebrates, neuroscience, animals, animal models, osteichthyes, model organisms, mathematics, algebra, network analysis, experimental organism systems, neuronal plasticity, neuroimaging, research and analysis methods, computer and information sciences, imaging techniques, animal cells, superior colliculus, calcium imaging, zebrafish, cellular neuroscience, eukaryota, cell biology, anatomy, linear algebra, neurons, biology and life sciences, cellular types, physical sciences, eigenvalues, organisms

journal.pcbi.1007319

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline

The standardization and automation of genome characterization and editing techniques has been accompanied by a rapid increase in the number of prokaryotic and eukaryotic microbial organisms available for engineering for overproduction of target commodity metabolites ., With annotated genomes and CRISPR-Cas toolboxes consolidated in organism-specific biofoundries 1–5 , the demand for biologically robust genetic intervention strategies for target metabolite overproduction has also increased ., This has created a need for a standardized computational workflow capable of reliably predicting phenotype based on genetic intervention strategies ., Traditional stoichiometric models of metabolic networks and integer programming design algorithms such as OptKnock 6 have provided insight into metabolic state as a function of genetic perturbation ., These tools provide information on how an organism may behave under a specific genetic condition ., However , the types of information that can be gleaned from them is limited to what can be deduced from reaction flux distributions , and flux ranges predicted via stoichiometry-based models are generally broad and subject to variability based on a user-defined cellular objective ( i . e . maximum butyric acid production 7 , maximum biomass 8 , MOMA 9 ) ., In recent years kinetic models of metabolism have ( re ) -emerged as a promising modeling paradigm offering a number of advantages over their stoichiometric counterparts albeit with a much higher effort associated with their construction ., Kinetic models incorporate the mechanistic details of enzyme catalyzed reactions in metabolic networks to characterize a metabolite concentration/reaction flux pair as a function of physiological state ., Kinetic models developed to date have primarily focused on characterizing either metabolic pathway behavior 10–13 or core metabolic function 14–17 , as the computational burden and data needs associated with parameter estimation has been a limiting factor in both the rate of kinetic model development and scale-up of metabolic network ., A number of kinetic formalisms and parameterization methods have been used to characterize and predict the dynamic behavior of metabolic systems ., Mass action 18–20 , S-system 21 , 22 , and log-transformed kinetic 10 , 23–25 models have used canonical kinetic rate expressions to describe enzyme-catalyzed reactions ., A number of models have also used mechanistic or approximate mechanistic expressions to characterize behavior of metabolic pathways 11–13 , 26 and central carbon metabolism 14 , 16 , 17 ., Both gradient-based 27–29 and stochastic 14 , 30 , 31 optimization methods have been developed for in silico identification of optimal sets of kinetic parameters ., However , probabilistic 12 and meta-heuristic 30 parameterization methods have been at the forefront of recent kinetic model development 12 , 14–16 , 32 to bypass the computational challenges arising from the nonconvexity of the constraints and interdependence of kinetic parameters ., Off the shelf solvers are ill-equipped to address the kinetic parametrization problem for these reasons , as the non-linearity of algebraic equality constraints required to ensure conservation of mass makes finding even an initial feasible point challenging ., Furthermore , evaluation of mutant strain metabolite concentration , enzyme level , and reaction flux requires integration of a system of ordinary differential equations that tends to be stiff and prone to failure ., The ensemble modeling paradigm 30 was introduced to address these challenges , and incorporated mechanistic rate expressions ., However , application of this method to large metabolic networks requires very significant computational resources for parametrization rendering follow-up analysis of parameter robustness and sensitivity analysis prohibitive ., This is due to the costly integration step needed each time a new steady-state is evaluated and the many thousands of recombination operations needed for convergence due to the non-inclusion of gradient information to guide the search ., Greene et al . 33 have demonstrated how conservation and stability analysis on kinetic models in an ensemble can significantly improve parameterization time in the ensemble modeling paradigm by reducing both the number of model evaluations required to parameterize a kinetic model and the time required for a single model evaluation ., Their complete methodology , however , has been limited in application to toy networks and a core kinetic model ., Lee et al . 34 have used first order partial derivatives with the ensemble modeling paradigm to characterize the robustness of synthetic metabolic pathways by perturbing Michaelis-Menten ( Km ) and maximum rate of reaction ( Vmax ) parameters across all models in an ensemble and determining the probability of failure ., Their analysis , however , was limited to systems with less than 20 reactions and did not require experimental training data ., Kinetic model development has been further hindered by a lack of experimental datasets to use in parametrization ., Fluxomic data ( in the case of ensemble modeling paradigm ) across a range of single or multiple gene knockout conditions , and coverage across the entire metabolic network considered in a kinetic model , is required to generate a set of kinetic parameters capable of predicting metabolic state for any given condition ., Mechanistic microbial kinetic models have been developed for core 14 , 17 and genome-wide 32 metabolism of E . coli as well as core metabolism for C . thermocellum 16 , while canonical models have been shown to be scalable to genome scale size 18 ., A prominent tool for characterizing reaction flux distribution in living cells is 13C metabolic flux analysis ( 13C-MFA ) 35–41 ., The workflow for 13C-MFA is carried out in experimental and computational stages ., The experimental stage is performed by first introducing an isotopically labeled substrate to a growing cell culture ., Then the labeling distribution of mass isotopmers of labeled metabolites produced by the cell is measured using gas chromatography-mass spectrometry 42 , liquid chromatography-mass spectrometry 43 , or nuclear magnetic resonance spectroscopy 44 ., Proteinogenic amino acid fragments and metabolites from central carbon metabolism are prominently featured in isotopic labeling datasets 42 , 43 , 45 ., The 13C-MFA computational workflow consists of a least-squares fitting problem , whereby a metabolic flux distribution is estimated by minimizing the variance weighted sum of squared residuals ( SSR ) between the experimentally measured isotopic labeling distribution and a predicted isotopic labeling distribution inferred via the estimated flux distribution 46 ., Application of 13C-MFA has yielded quantitative core metabolic characterization of a plethora of prokaryotic and eukaryotic organisms and cell types 37 , 47–52 ., It has also shed light on flux redirection under genetically and environmentally perturbed conditions 53–55 and revealed previously unknown pathway activity usage 56 , 57 ., Elucidation of atom mappings for peripheral carbon pathways and more elegant methods for mapping carbon flow ( i . e . the EMU framework 58 ) has allowed for scale-up of 13C-MFA to the genome-scale in three organism: E . coli 40 , Synechocystis PCC 6803 59 , and Synechococcus PCC 7923 60 ., In order to accelerate the emergence of kinetic metabolic models as a viable tool for use in microbial strain design , we have developed a pipeline for rapid kinetic parameterization ., By coupling 13C-MFA and kinetic parameterization computational methods using the same metabolic network , we acknowledge the intrinsic dependence of kinetic modeling on the metabolic network and 13C-glucose labeling datasets used to elucidate the flux distributions required for kinetic parametrization ., We also provide a customizable framework for generating kinetic models that are consistent with reported flux ranges and applicable to any microbial metabolic network for which a set of isotopic labeling data across multiple genetic or environmental conditions can be procured ., Our workflow for rapidly generating kinetic models of metabolic networks was carried out in two phases: flux elucidation was carried out via 13C-MFA and kinetic model parameterization using the gradient-based K-FIT algorithm developed by Gopalakrishnan et al . 61 ., K-FIT differs from previously developed elementary decomposition approaches to kinetic parameterization by optimizing the model on the space of wild-type enzyme fractions and reverse elementary fluxes ., Net fluxes and concentrations for the mutant networks are then recovered based on an iterative decomposition approach ., The inclusion of gradient information in K- FIT also allows for the direct assessment kinetic parameter sensitivities ., Taking advantage of 13C labeling data available for E . coli generated using glucose feedstock labeled at the first two carbon positions ( 1 , 2-13Cglucose , known to yield precise flux estimations in E . coli core metabolism by 13C-MFA when compared to other single-tracer experiments 62 ) , we have applied our seamless workflow to the development of a kinetic model of E . coli core metabolism ., Our model can predict metabolite pool size and metabolic flux distribution , satisfies flux distributions for wild-type and seven single gene deletion mutants from upper glycolysis , PP pathway , and Entner-Doudoroff ( ED ) pathway under mid-exponential growth conditions , and recapitulates carbon uptake kinetics ., We elucidated flux distributions and 95% confidence ranges for wild-type and seven single gene deletion mutant strains of E . coli ( Δfbp , Δedd , Δeda , Δpgi , Δrpe , Δzwf , and Δgnd ) ., For strains Δfbp , Δedd , and Δeda , the flux distributions were similar to the wild-type strain , with statistically insignificant variations from the wild-type strain in glucose uptake rate ., Strains Δpgi , Δrpe , Δzwf ( glucose-6-phosphate dehydrogenase ( G6PDH2r ) knock-out ) , and Δgnd each exhibited flux redirections compared to the wild-type strain ., Given the obtained flux datasets , we then parameterized a core kinetic model using a metabolic network identical to that used for flux elucidation with 74 reactions , 61 metabolites , and 55 substrate-level inhibitions ., Although activation is a prevalent regulatory mechanism in metabolism 63 , it was not included in the model due to the absence of complete cofactor balance and known inaccuracy of energy metabolism representation in core metabolism flux distributions ., The use of identical metabolic networks for both flux elucidation and kinetic parameterization safeguards against information loss in the form of feasible flux distributions due to flux projection from a core model to a larger model stemming from incomplete atom mapping and stoichiometric information ., Kinetic parameterization time was reduced by approximately 80% over the ensemble modeling ( EM ) method employed by Khodayari et al . 14 from more than a week to 36 hours ( real time , due to evaluation of locally unstable parameter sets ) for a core kinetic model ., The average parameterization time per random initialization during k-ecoli74 parameterization was approximately four hours ., The model constructed in this study ( k-ecoli74 ) predicted 86% of reaction fluxes within a single standard deviation ( SD ) , 95% within two SDs , and 99% within three SDs of 13C-MFA estimated flux values for mutant strains used in fitting ., k-ecoli74 was validated , and its predictive capabilities tested by comparing product yields for seven metabolites produced by nine engineered strains with experimental yield values and those reported for the previously developed k-ecoli457 kinetic model 32 ., k-ecoli74 predicted product yields well overall , significantly outperforming the predictive capabilities of k-ecoli457 for malate and acetate production by engineered strains ., This was due to similarity in experimental conditions between the strain engineering studies and those used for 13C-labeling data generation in this study ( i . e . glucose-rich batch culture , mid-exponential growth phase ) ., Metabolites not included in k-ecoli74 were systematically overpredicted due to the use of central carbon metabolism drains as proxies for pathways not included in the model ., For example , 2 , 3-butanediol was over-predicted due to the use of pyruvate dehydrogenase ( PDH ) as a proxy for the heterologous 2 , 3-butanediol synthesis pathway ( not included in k-ecoli74 ) ., When flux data generated using a simplified metabolic network was used to parameterize a kinetic model with a metabolic network identical to k-ecoli74 , discrepancies in flux predictions were observed in amino acid metabolism ., In particular , there were significant differences in both the magnitude and directionality of reactions in serine , glycine , threonine , and glutamate metabolism ., A significant decrease in predictive capability was observed when the kinetic model parameterized using simplified flux dataset was used to predict metabolite yields for the nine validation strains ., The model generated with the reduced flux set was only able to predict feasible flux distributions for five of nine validation strains tested , and four out of those five strains yielded predictions similar to or worse than k-ecoli74 predictions ., The method developed in this study provides a framework for constructing kinetic models of metabolic networks from experimental data that ensures all pathways with resolvable flux ranges are accounted for in parameter estimation , and carbon and energy balance are characterized as accurately as possible ., In addition , the relative computational tractability of the kinetic parameterization method used in this approach allows for the a posteriori analyses on kinetic parameter identifiability and sensitivity ., Application of kinetic parameterization pipeline developed in this study to any organism or metabolic network requires a set of 13C labeling data , an identical metabolic network for flux elucidation and kinetic parameterization , a 13C-MFA software package for flux elucidation , and the K-FIT algorithm for kinetic parametrization ., The developed workflow for kinetic model construction relies on identical metabolic networks for flux elucidation and kinetic parameterization ., This circumvents the information loss in the form of feasible solutions associated with the projection of the core model flux distributions onto the genome scale metabolic model and allows for the seamless integration of biomass yield information on precursor pathway drains ., A pictorial representation of the kinetic parameterization pipeline is presented in Fig 1 ., The steps for constructing a kinetic model using the pipeline are as follows: first the stoichiometric model ( Fig 1 , step 1B ) and corresponding atom mapping model are assembled ( Fig 1 , step 1A ) ., Then they are used for flux elucidation of wild-type and genetic mutant strains of the organism of interest via 13C-MFA from 13C-isotopic labeling data ( Fig 1 , step 2 ) ., Finally , using the constructed stoichiometric model , the elucidated flux ranges , and any substrate level regulatory events identified in literature or inferred via computational methods ( e . g . SIMMER 41 or model-based identification 64 ) , the kinetic model is parameterized using the K-FIT kinetic parameterization algorithm ( Fig 1 , step 3 ) ., The core metabolic network used for 13C-MFA in this study ( Fig 2 ) contains 74 reactions and 61 metabolites ., The metabolic network and atom mapping model developed by Leighty and Antoniewicz 42 was used as a basis with the addition of L-serine deaminase ( SERD-L ) ., Pyruvate kinase ( PYK ) was also allowed to carry reverse flux to account for the significant flux converting pyruvate to phosphoenolpyruvate by the terminal phosphotransferase in the PTS system observed in vivo 65 and phosphenolpyruvate synthase activity ., Atom transitions for SERD-L were taken from the imEco726 genome-scale atom mapping model 40 ., The network included glycolytic , pentose phosphate ( PP ) pathway , and tricarboxylic acid ( TCA ) cycle pathways , as well as anaplerotic and cataplerotic reactions , lumped amino acid synthesis pathways , glycine cleavage , energy metabolism , acetate metabolism , and a biomass sink reaction ., The metabolic network used for kinetic parameterization included identical reactions to those used for 13C-MFA ., However , the biomass sink reaction was decomposed into individual metabolite sinks for each biomass precursor ., A total of 55 substrate level regulations on central carbon metabolism reactions curated from the BRENDA 66 and EcoCyc 67 databases were included in the kinetic model , and are depicted in Fig 3 ., Substrate level regulations included competitive , uncompetitive , and noncompetitive inhibition ., The reactions , metabolites , allosteric regulations , and atom mapping model used in this study are provided in S4 File ., 13C isotopic labeling datasets for wild-type and seven single gene deletion mutant strains with glucose feedstock labeled at the first two carbon positions ( 100% 1 , 2-13C glucose ) generated by Long and Antoniewicz 68 was used as input data for the kinetic parameterization pipeline ., Mass isotopomer distributions for 22 metabolite fragments derived from 10 amino acids ( alanine , glycine , valine , leucine , serine , threonine , phenylalanine , aspartate , glutamate , tyrosine ) and two sugar phosphates ( ribose 5-phosphate , glucose 6-phosphate ) were included in each labeling dataset ., The seven mutant strains with available 13C isotopic labeling data included pgi , fbp , zwf ( glucose-6-phosphate dehydrogenase ( G6PDH2r ) knock-out ) , gnd , rpe , edd , and eda knockout strains ., Fig 2 shows the location of reactions in upper glycolysis , PP pathway , and ED pathway inactivated by genetic knockouts in strains use for parameterization ., Metabolite yield data from a series of genetically engineered overproducing strains was procured from literature 69–78 and used for model validation and testing predictive capabilities under conditions not included in the training data ., Model validation strains included both up and downregulation of central carbon metabolism reactions as well as genetic knockouts ., Genetic perturbation strategies , metabolites whose yields were tested , and experimental yield values are listed in S9 File , and a visual representation of perturbation strategies are provided in Fig D in S3 File ., Strains designed to overproduce malate , acetate , L-valine , naringenin , lactic acid , 2 , 3-butanediol , and glucaric acid were included in the validation set ., The malate overproduction strain was characterized by a downregulation of phosphotransacetylase ( PTAr ) and upregulation of phosphoenolpyruvate carboxylase ( PPC ) ., The acetate overproduction strain was characterized by a downregulation of ribose-5-phosphate isomerase ( RPI ) ., Two naringenin overproducing strains were considered , one characterized by succinyl-CoA synthetase ( SUCOAS ) knockout and fumarase ( FUM ) downregulation , and the other malate dehydrogenase ( MDH ) knockout and SUCOAS downregulation ., Two lactic acid overproduction strains were also included , one characterized by acetate kinase ( ACKr ) downregulation and the other by ACKr knockout ., One 2 , 3-butanediol overproduction strain was characterized by PYK overexpression , and one glucaric acid overproduction strain was characterized by NAD transhydrogenase ( NADTRHD ) overexpression ., Flux elucidation and 95% confidence interval estimation was performed for wild-type and each of the seven mutant strains using 1 , 2-13Cglucose isotope tracer data ., The atom mapping model assembled from the atom transitions gleaned from Leighty and Antoniewicz 42 and the imEco726 model 40 was used to construct the elementary metabolite unit ( EMU ) network ., An EMU is a subset of carbon atoms of any metabolite included in the stoichiometric model , and the EMU network characterizes how these subsets of carbon travel through the reactions in the network ., The EMU network allows for characterization of the mass isotopmoer distribution of each metabolite in the metabolic network based on the isotope labeling scheme of the substrate upon estimation of a steady-state flux distribution 58 ., Strain-specific biomass composition and acetate yields determined by Long et al . 79 were used for flux fitting ., EMU decomposition , flux elucidation , and confidence interval estimation were performed according to the procedure outlined by Gopalakrishnan and Maranas 40 , and glucose uptake was normalized to 100 flux units as a basis for each fitting ., A summary of the 13C-MFA computational procedure is provided in S1 File ., In order to ensure the best flux distribution was selected for use in the kinetic parameterization procedure , 100 randomly initialized multi-starts were performed for each strain ., The minimized 13C-MFA objective was the variance weighted SSR between experimentally measured mass isotopomer distributions and mass isotopomer distributions inferred using the EMU network and steady-state flux distribution ., A solution was accepted only if the algorithm converged to that solution at least 50% of the time ., This does not guarantee convergence to the true global minimum but provides a practical safeguard against accepting local minima as solutions ., Kinetic parameterization was performed using the flux distributions estimated via 13C-MFA for wild-type and all mutant strains as training data ., The gradient-based K-FIT algorithm 61 was used to parameterize the kinetic model ., The wild-type flux distribution was used to estimate a set of elementary kinetic parameters ( i . e . a set of kinetic parameters satisfying the wild-type flux distribution was generated ) ., This was done to ensure the set of elementary parameters corresponds to a feasible steady-state solution in the wild-type strain ., The elementary kinetic parameters were then used to estimate mutant flux distributions , and calculate the variance weighted sum of squared residual error ( SSR ) between all 13C-MFA mutant flux distributions and mutant flux distributions predicted using the estimated elementary parameters ., Kinetic parameters were updated using gradient-based optimization , and the process was repeated until a local minimum was reached ., To ensure reactions carrying little flux with narrow flux ranges were not over-weighted in the objective function , standard deviation used for weighting of residual errors was defined as the maximum value of either 1 . 0 , five percent of the corresponding flux value , or the standard deviation value calculated according to the 13C-MFA 95% confidence interval ., A summary of the K-FIT optimization algorithm is included in S1 File ., Due to the nonconvexity of the resultant optimization model , 500 randomly-initialized multi-starts were performed ., A threshold for change in concentration of any metabolite with respect to time was set at 10−6 flux unit to ensure strict adherence to the pseudo-steady state assumption 80 ., All fluxes used as training data were scaled by the ratio of absolute mutant glucose uptake rate to absolute wild-type glucose uptake rate ., Model acceptance criteria was based on the SSR value , flux distribution reproducibility , and ability to predict flux distributions for genetic conditions not used for parameterization ., In order for a model to be selected as a best model , two criteria had to be satisfied: first , the model had to yield the lowest SSR with at least one other model yielding a local minimum SSR value within 10% of the best model’s value ( to ensure a reproducible solution ) ., Because elementary kinetic parameters are the product of two distinct optimization variables ( reverse elementary reaction flux and enzyme complex fractional abundance ) , local minima with similar SSR values but different elementary parameters may exist ., Selecting a model with SSR similar to other local minima ( i . e . within 10% of optimal SSR value ) allows for the assessment of the sensitivity of Km and Vmax parameters using models yielding similar flux distributions through comparison of parameters generated by different models with similar fitness to data ., The second condition for model acceptance was the capability to estimate steady-state flux distributions under genetic conditions not used for parameterization 81 ., A total of 896 elementary kinetic parameters and 78 inhibitor constants were estimated corresponding to the 74 reactions , 34 biomass precursor sink reactions , and 55 substrate-level regulations in the metabolic network ., Fluxes corresponding to central carbon metabolism , amino acid synthesis and degradation , and biomass formation were fitted ., Fructose bisphosphatase ( FBP ) and phosphofructokinase ( PFK ) fluxes were excluded from fitting due to unresolvability ( i . e . , very wide ranges ) stemming from simplifications made to energy metabolism in the core model ., Energy metabolism and nutrient uptake reactions were also disregarded in the fitting due to simplifications and unavoidable inaccuracy of energy metabolism fluxes due to the nature of core metabolism 13C-MFA 40 ., A total of 94 fluxes were fitted per mutant strain ., The best-fitting model across seven mutant strains that also exhibited model stability across all strains for which metabolite yields were predicted had a SSR of 338 and an average weighted squared residual per mutant reaction flux of 0 . 52 ( SSR was calculated for the seven single gene deletion strains used for training . The nine non-inclusion strains used to validate the model and evaluate predictive capability were not included in the calculation of SSR or evaluation of model fitness ) ., Two additional models at neighboring local minima yielded SSR values within 10% of the optimal SSR value ., The average percent error for reactions whose SD was within 20% of the experimental flux value ( 210 of 665 reactions ) was 5 . 7% ., Of those reactions whose SD was greater than 20% of the experimental flux value ( 455 of 665 reactions ) , 91% of predicted values differed from the corresponding experimental value by less than 1 mmol/100 mmol wild-type glucose uptake , and the average deviation was 0 . 36 mmol/100 mmol wild-type glucose uptake ., The contribution of each strain to overall lack of fitness is shown in Fig, 4 . Δeda was the worst fitting strain , and contributed 36% of the overall SSR , while Δedd contributed to 23% of the overall SSR ., Δpgi contributed to 19% of the total SSR , and no other strain contributed more than 8% of the total SSR ., The best fitting strain was Δzwf , and contributed only 2% of overall SSR ., Fig 5 shows a comparison of model-predicted flux values and 13C-MFA-estimated flux values ., The plotted data yielded a Pearson correlation coefficient of 0 . 997 , indicating a strong positive correlation between model predictions and 13C-MFA values ., No single flux in the metabolic network deviated from the 13C-MFA value by more than a single SD for more than three fitted strains ., Five predicted fluxes deviated from 13C-MFA values by more than a single SD across three strains ., Lower glycolytic reaction PDH deviated by more than a single SD in Δpgi , Δedd , and Δeda ., Fig E in S3 File depicts the relative contribution of each reaction in each strain to the total SSR ., Acetate exchange , deviated by more than a single SD in Δpgi , Δeda , and Δgnd , and TCA cycle reactions citrate synthase ( CS ) and aconitase ( ACONT ) deviated by more than a single SD in Δedd , Δeda , and Δfbp , while isocitrate dehydrogenase ( ICDHyr ) and alpha ketoglutarate dehydrogenase ( AKGDH ) , deviated by more than a single SD in Δpgi , Δedd , and Δeda ., Fig 6 shows the number of fluxes falling within one , two , three , or four SDs of 13C-MFA values across strains ., The results indicate 86% of all fluxes fitted fell within a single SD , 96% fell within two SD , and 99% within three SDs of their corresponding 13C-MFA values ., Glycolytic reaction fluxes were underpredicted in Δpgi due to the misdirection of flux towards the biomass sink reaction for ribose 5-phosphate and through glycine cleavage ( GLYCL ) instead of through L-serine deaminase ( SERD-L ) ., k-ecoli74 predicted Δpgi succinyl-CoA synthetase ( SUCOAS ) flux in the opposite direction of the 13C-MFA flux ., Because SUCOAS flux was in the positive direction in all other strains used for parameterization , the fitted kinetic parameters were incapable of delivering reverse flux through SUCOAS ., Serine hydroxymethyltransferase ( SHMT ) and GLYCL flux were also overpredicted by more than one SD in Δpgi ., k-ecoli74 underpredicted SERD-L flux , while serine synthesis flux was predicted higher than 13C-MFA flux to satisfy biomass precursor demand ., GLYCL , therefore , served as a sink for carbon that should have been delivered back to glycolysis ., Most Δpgi PP and ED pathway fluxes deviated from 13C-MFA values by less than a single SD ., k-ecoli74 predicted a 98% reduction in atp concentration and a 76% reduction in nadh concentration ( competitive inhibitors of G6PDH2r ) in Δpgi relative to the wild-type strain ., This caused an increase in enzyme available to catalyze the G6PDH2r reaction , and k-ecoli74 was , therefore , able to successfully re-direct the entirety of carbon flux through the PP pathway ., Acetate exchange was a significant carbon sink in all strains except for Δpgi ., The optimal set of kinetic parameters , therefore , were suited for delivering significant flux towards acetate secretion , and that flux was overpredicted in the Δpgi strain ., Due to ED pathway irreversibility , the effect of Δeda and Δedd knockouts on the predicted flux distributions were almost identical , despite having different 13C-MFA flux distributions when scaled by absolute glucose uptake rate ., In Δedd , reactions whose predicted fluxes were greater than a single SD from the corresponding 13C-MFA value ( and contributed most to SSR ) were primarily found in glycolysis and the TCA cycle ., Fluxes were overpredicted in both pathways ., Δedd glucose uptake rate when scaled to 100 mmol of wild-type glucose uptake was 8 . 7 mmol/100 mmol wild-type glucose uptake lower than wild-type , thus fluxes were overpredicted ., In the Δeda strain , TCA cycle reactions , acetate formation and excretion , and PDH contributed most to SSR ., Scaled Δeda glucose uptake rate was 2 . 4 mmol/100 mmol wild-type glucose uptake greater than in the wild-type strain ., Despite their differences in glucose uptake rates from the wild-type strain and each other , glucose uptake rate did not contribute significantly to SSR in either strain compared to the aforementioned reactions ., This is because standard deviations for glucose uptake rate in both strains was large , ensuring glucose uptake was not a primary source of error ., In Δfbp , only TCA cycle predicted fluxes deviated from 13C-MFA values by more than a single SD ., Specifically , upper TCA cycle fluxes were overpredicted by the model ., Because CS flux was increased , ACONT flux was increased as well ., Isocitrate lyase ( ICL ) showed increased activity compared to the experimental data , redirecting carbon flowing through the TCA cycle ., Glutamate dehydrogenase ( GLUDy ) also had a higher predicted flux than the 13C-MFA value in order to drain the excess carbon ., While FBP and PFK reactions were not used to fit the kinetic model because of their unresolvability across all other strains during 13C-MFA , leaving them out of the fitting did not have an impact on the resulting fitness of the Δfbp strain ., FBP flux was fixed to zero in Δfbp , and the flux through PFK was constrained by the other reactions in the network producing and consuming f6p ( glucose-6-phosphate isomerase ( PGI ) , transaldolase ( TALA ) , fructose bisphosphate aldolase ( FBA ) ) to ensure conservation of mass ., Because flux throu

Introduction, Materials and methods, Results, Discussion

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction ., The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models ., In this study , we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations ., The first step involves the elucidation of all intracellular fluxes in a core model of E . coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis ( 13C-MFA ) ., Here , fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis , pentose phosphate pathway and the Entner-Doudoroff pathway ., The computed flux ranges are then used to parameterize the same ( i . e . , k-ecoli74 ) core kinetic model for E . coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm ., The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations ., k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values ., By performing both tasks using the same network , errors associated with lack of congruity between the two networks are avoided , allowing for seamless integration of data with model building ., Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities ., Using this workflow , the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated .

Microbial production hosts are used for production of a wide range of commodity chemicals ., Improving the conversion efficiency of microbial strains is critical to the economic viability and the continued push towards the use of environmentally neutral bioprocesses as a means for producing the chemicals society depends on ., Metabolic models have played a key role in helping us to predict metabolic behavior in response to environmental and genetic perturbation that can maximize efficiency ., Recently , kinetic models of metabolism have re-emerged as a means for characterizing metabolism , offering improvements over their stoichiometric counterparts in both the type of information that can be gleaned from them , and in prediction accuracy ., Despite recent developments , a lack of raw experimental data needed for flux elucidation and , subsequently , kinetic parameterization , and high computation cost have prevented the development of a uniform workflow for construction of the most informative kinetic models ., Here , we have incorporated raw 13C-isotopic labeling data and a computationally inexpensive parameterization algorithm into a kinetic parameterization pipeline to ensure that the resulting kinetic model ( k-ecoli74 ) conforms to experimental data ., We show how the use of an identical metabolic network for flux elucidation and kinetic parameterization influences predictive capabilities .

carbohydrate metabolism, chemical compounds, metabolic networks, enzymology, carbohydrates, glucose metabolism, organic compounds, glucose, mutation, metabolites, network analysis, enzyme metabolism, molecular biology techniques, enzyme chemistry, research and analysis methods, cell labeling, computer and information sciences, mutant strains, metabolic pathways, chemistry, molecular biology, biochemistry, metabolic labeling, organic chemistry, monosaccharides, genetics, biology and life sciences, physical sciences, metabolism

journal.pgen.1004434

Background Selection as Baseline for Nucleotide Variation across the Drosophila Genome

The causes of the variation observed within natural populations have been a long-standing question in evolutionary and genetic studies ., Particular insight into these causes can be gained by analyzing the distribution of nucleotide diversity across genomes , where species- and population-specific parameters such as the number of individuals , environmental factors , or demography are constant ., A number of population genetics models have been put forward to explain this intra-genomic variation in diversity , often including the predicted consequences that selection acting at a genomic site impinges on genetically linked sites , either neutral or under selection themselves ( i . e . , models of ‘selection at linked sites’; 1–4 and references therein ) ., Although there is general agreement that selection at linked sites can play a role shaping levels of variation , there is still intense debate and research on the selective nature of the mutations causing such effects ( e . g . , beneficial or deleterious ) and whether the same causes can be applied to different species 4–6 ., Strongly beneficial mutations rise rapidly to fixation and hitchhike adjacent sites , causing a fingerprint of reduced intra-specific variation around the selected site known as a ‘selective sweep’ ( the HHss model; 2 , 3 , 7–11 ) ., A qualitatively similar outcome can be generated by another model of selection and hitchhiking effects at genetically linked sites without requiring adaptive changes , just as a result of the continuous input of strongly deleterious mutations and their removal by natural selection ( the background selection ( BGS ) model 1 , 4 , 12–16 ) ., Both models also predict that the consequences of selection removing adjacent diversity diminish when genetic recombination increases , a general pattern that has been observed in many species when comparing genomic regions with high and low ( or zero ) recombination rates ( reviewed in 1 , 3–5 ) ., The magnitude and distribution of recombination rates across genomes play key roles in predicting the consequences of selection on adjacent variation ., In humans , for instance , the presence of large recombination cold spots raised the possibility that BGS could reduce polymorphism levels at specific genomic regions ., In agreement , recent analyses using models of purifying selection rather than purely neutral ones suggest that patterns of nucleotide diversity across the human genome are consistent with BGS predictions 17–21 ., In the model system D . melanogaster , low-resolution recombination maps described limited or absent recombination near sub-telomeric and –centromeric regions whereas recombination outside these sub-telomeric and –centromeric regions ( i . e . , across trimmed chromosome arms ) has been often assumed to be both high and homogeneously distributed ., As a consequence , variation in nucleotide diversity across trimmed chromosome arms has been mostly attributed to positive selection and selective sweeps ( 4 , 5 , 22–29; but see 30 ) ., There are , however , several reasons to believe that BGS effects could be significant in D . melanogaster as well ., First , compared with humans , D . melanogaster has a more compact genome and a larger effective population size ( Ne ) , predicting tighter genetic linkage between genes and stronger purifying selection , respectively , and both factors forecast greater BGS effects ., Second , recent whole-genome studies of recombination rates in D . melanogaster exposed extensive heterogeneity in the distribution of crossover rates even after removing sub-telomeric and centromeric regions 31 ., This high degree of variation in recombination rates across D . melanogaster chromosomes is observed when recombination is obtained from a single cross of two specific strains 31 , 32 as well as when analyzing a species average obtained from combining genetic maps from crosses of several natural strains 31 ., The presence of coldspots of recombination embedded in chromosomal regions assumed to have high recombination rates , therefore , provides the opportunity for BGS to play a more significant role across broader genomic regions than previously anticipated 31 ., Finally , Charlesworth 33 has recently showed that BGS effects are predicted to be detectable in the middle of recombining chromosome arms in D . melanogaster ., The consequences of BGS at a given nucleotide position in the genome ( focal point ) can be described by the predicted level of neutral nucleotide diversity when selection at linked sites is allowed ( π ) relative to the level of diversity under complete neutrality and free recombination between sites ( π0 ) , with B\u200a=\u200aπ/π0 12–15 ., Therefore , B∼1 would indicate negligible BGS effects whereas B<<1 would suggest very strong BGS and a substantial reduction in levels of neutral diversity ., B can also be understood in terms of a reduction in Ne , and variation in B forecasts differences in levels of diversity within species but also differences in the efficacy of selection , which can be approximated by the product of Ne and the selection coefficient s ., Note , however , that the prediction about reduced efficacy of selection is a qualitative one since there is no simple scalar transformation of Ne influenced by selection at linked sites that allows estimating probabilities of fixation of selected mutations 34 , 35 ., Thus , a comprehensive study of the predictive power of BGS to explain natural variation across genomes needs to show that ,, 1 ) conditions exists across a genome to generate significant overall effects reducing B ,, 2 ) B varies across the genome , and, 3 ) regions with reduced B are associated with reduced levels of polymorphism and efficacy of selection ( e . g . , detectable on rates of protein evolution ) ., Here , we investigated what is the fraction of the D . melanogaster genome that is influenced by BGS and how much of the observed variance in patterns of intra-specific variation and rates of evolution across this genome can be explained by BGS alone ., Importantly , to obtain a sensible BGS baseline that could be used to test for positive selection and other departures from neutrality , we investigated BGS models that are purposely simple and independent of nucleotide variation data ., Additionally , we studied whether our conclusions are sensitive to parameters of the BGS model ., To this end , we expanded approaches previously applied to investigate human diversity 17 , 18 to now estimate BGS effects across the D . melanogaster genome ., In all , we generated a detailed description of the consequences of purifying selection on linked sites at every 1 kb along D . melanogaster chromosomes under a variety of BGS models ., Our results show that BGS likely plays a detectable role across the entire genome and that purifying selection alone can explain a very large fraction of the observed patterns of nucleotide diversity in this species ., Notably , we show that these conclusions are robust to different parameters in the BGS models ., The use of a BGS baseline also uncovers the presence of regions with the signature of a recent selective sweep and , less expected , numerous instances of balancing selection ., Furthermore , analyses of rates of protein evolution suggest that the recombination landscape has changed recently along the D . melanogaster lineage thus generating disparity between short- and long-term Ne at many genomic positions ., We discuss the advantages of incorporating BGS predictions across chromosomes and the potential consequences of temporal variation in recombination landscapes when estimating demographic and selective events ., BGS expectations ( i . e . , estimates of B ) were obtained for every 1-kb region across the whole genome as the cumulative effects caused by deleterious mutations at any other site along the same chromosome ( see Materials and Methods for details ) ., These estimates of B were based on BGS models that include our current knowledge of genome annotation at every nucleotide site of the genome and high-resolution recombination landscapes in D . melanogaster that distinguish between crossover and gene conversion rates 31 ., These models also incorporate the possibility that strongly deleterious mutations occur at sites that alter amino acid composition as well as at a fraction of sites in noncoding sequences ., The inclusion of deleterious mutations in noncoding sequences allows taking into account the existence of regulatory and other non-translated functional sequences , either in introns and 5′- and 3′-flanking UTRs , or in intergenic regions 22 , 33 , 36–38 ., For each category of selected sites ( nonsynonymous , intronic , UTR , or intergenic ) we used the proportion of constrained sites ( cs ) estimated for D . melanogaster 22 , 37 , 38 as the fraction of sites with deleterious fitness consequences when mutated 33 ., In terms of recombination rates , we studied BGS predictions following the standard approach of including crossover as the sole source of recombination ( hereafter models MCO ) and also when combining the effects of crossover and gene conversion events ( models MCO+GC ) to better quantify the true degree of linkage between sites in natural populations ., The distribution of deleterious fitness effects ( DDFE ) and the diploid rate of deleterious mutations per generation ( U ) are parameters that have direct implications on estimates of B but are more difficult to establish experimentally ., Although a gamma distribution has been proposed a number of times for deleterious mutations 39–44 , a log-normal DDFE allows capturing the existence of lethal mutations and fits better D . melanogaster polymorphism data 45 , 46 ., Additionally , a log-normal DDFE predicts a higher fraction of mutations with minimal consequences removing linked variation than a gamma DDFE and , ultimately , weaker BGS effects ( see Materials and Methods ) ., Therefore , the use of a log-normal DDFE can be taken as a conservative approach when inferring the magnitude of BGS effects ., Direct estimations of deleterious mutation rates are still fairly limited ., In D . melanogaster , initial analyses of mutation accumulation lines estimated a mutation rate for point mutations and small indels ( u ) of ∼8 . 4×10−9/bp/generation and a diploid rate of deleterious mutations per generation ( U ) of ∼1 . 2 47 ., Nevertheless , one of the lines used in this study had an unusually high mutation rate 48 and more recent studies suggest u∼4–5×10−9 ( U∼0 . 6 ) for point mutations and small indels 48–50 ., These lower estimates , however , do not include the possible presence in natural populations of genotypes with high mutation rates or the deleterious consequences of transposable element ( TE ) insertions ., In fact , TEs are very abundant in natural populations of D . melanogaster 51–60 and have been proposed to be an important source of BGS in this species 30 ., Therefore , U∼0 . 6 represents a lower boundary for the deleterious mutation rate when inferring the consequences of BGS ., To include the consequences of TE insertion in our BGS models , we obtained an approximate diploid insertion rate of UTE≥0 . 6 based on a detailed description of TE distribution in D . melanogaster 60 and mutation-selection balance predictions ( see Materials and Methods for details ) ., Thus , a genome-wide diploid deleterious mutation rate of ∼1 . 2 per generation is a reasonable approximation that captures the consequences of point mutations , small indels and the insertion of transposable elements ., To assess how robust our results and conclusions are to the parameters of the BGS model , we obtained genome-wide landscapes for B under eight different models , with DDFE following a log-normal or a gamma distribution ( models MLN and MG , respectively ) , with deleterious mutations rates that include or not TE insertions ( models MStdMut and MLowMut , respectively ) , and with recombination taking into account crossover and gene conversion events or only crossovers ( models MCO+GC and MCO , respectively ) ., Unless specifically noted , we report results based on the BGS model that is most consistent with our current knowledge of gene distribution across the genome , a log-normal DDFE , a genome-wide diploid deleterious mutation rate of U\u200a=\u200a1 . 2 , and recombination rates that include crossover and gene conversion events ( i . e . , our default model is MLN , StdMut , CO+GC ) ., Table S1 summarizes the results from the BGS models and Table S2 provides the full distribution of B estimates across all chromosomes ., Genome-wide estimates of B show a median of 0 . 591 and indicate that the predicted influence of BGS across the D . melanogaster genome would reduce the overall Ne substantially relative to levels predicted by evolutionary models with free recombination ( see Figure 1A ) ., The study of the distribution of B along chromosomes shows that the reduction in neutral diversity is severe in a large fraction of the genome , with 19% of all 1-kb regions with B<0 . 25 and a lower 90% CI for B of 0 . 005 ( Figure 1B and Figure 2 ) ., Importantly , the distribution of B across trimmed chromosomes is also highly heterogeneous ., As expected , estimates of B are strongly influenced by variation in local crossover rates ( c ) , with a Spearmans rank correlation coefficients ( ρ ) between B and c of 0 . 792 for trimmed chromosomes ., As shown in Figure 3 , however , there is detectable variance in B for a given local c that exposes the additional effects of long-range distribution of recombination rates and genes when estimating B at a focal point ., Median B across trimmed chromosome arms is 0 . 643 , with a minimum estimate of 0 . 19 ., Significant and variable BGS effects are , therefore , expected in D . melanogaster not only due to sub-telomeric and -centromeric regions but also across trimmed chromosomes ( see also 33 ) ., This general conclusion does not vary qualitatively when considering BGS models with other parameters ( Table S1 ) ., As expected , a model with a DDFE following a gamma distribution ( MG ) predicts stronger BGS effects and lower estimates of B across the genome than when the DDFE follows a log-normal ( as in our default model ) ., Under model MG , CO+GC the median B is 0 . 428 and the lower 90% CI for B is 0 . 001 ( median B across trimmed chromosomes is 0 . 493 , with a minimum estimate of 0 . 007 ) ., Also anticipated , models with a lower deleterious mutation rate ( models MLowMut ) generate higher estimates of B than when TE insertions are taken into account ( models MStdMut ) ., For instance , median B increases from 0 . 591 ( MLN , StdMut , CO+GC ) to 0 . 769 ( MLN , LowMut , CO+GC ) , and from 0 . 428 ( MG , StdMut , CO+GC ) to 0 . 654 ( MG , LowMut , CO+GC ) ., In addition , the comparison of predictions under models with and without gene conversion shows that the standard approach of considering crossover as the only source of recombination between sites would overestimate linkage effects ., Median estimates of B are 20 and 21% lower for models MLN , CO and MG , CO than for MLN , CO+GC and MG , CO+GC , respectively ., The use of only crossover rates in BGS models skews estimates of B particularly in regions with intermediate rates ( ∼0 . 2–2 cM/Mb ) , mostly across trimmed chromosomes ., Both crossover and gene conversion data , therefore , need to be considered to obtain accurate estimates of the consequences of selection on linked sites and , in this case , the magnitude of BGS effects ., Finally , it is worth noting that although the different BGS models predict different point estimates and ranges of B across the chromosomes , all models generate B estimates across the genome that have virtually the same relative ranking ( i . e . , monotonically related; Table S3 ) ., Pairwise Spearmans ρ between estimates of B from different BGS models range between ρ\u200a=\u200a0 . 9856 ( comparing the two most distinct models MLN , StdMut , CO and MG , LowMut , CO+GC ) and ρ>0 . 9999 ( for the four comparisons between models differing in the deleterious mutation rate ) ., In this study , all sites along a chromosome were allowed to potentially play a role adding up BGS effects at any focal region of the same chromosome ., To investigate the size of the genomic region causing detectable BGS effects in D . melanogaster , we estimated the size of the region surrounding a focal 1-kb needed to generate 90% of the total BGS effect obtained when considering the complete chromosome ( DB90 in either genetic or physical units ) ., Equivalently , we also obtained DB75 and DB50 as the size of the genomic region needed to generate 75 and 50% , respectively , of the total BGS effect obtained when considering the whole chromosome ., The study of complete chromosomes shows a median genetic DB90 , DB75 and DB50 of 5 . 5 , 1 . 2 and 0 . 15 cM , respectively ., In terms of physical distance , the median DB90 , DB75 and DB50 is 2024 , 477 and 76 kb , respectively ( Figure 4A ) ., Although the overall effects of BGS are reduced along trimmed chromosomes compared to whole chromosomes , the size of the region playing a significant role in the final magnitude of B at a focal point is fairly equivalent , with 6 . 9 , 1 . 8 and 0 . 21 cM for DB90 , DB75 and DB50 , respectively ( 2 , 412 , 640 and 84 kb for DB90 , DB75 and DB50 , respectively ) ., This genetic and physical scale , moreover , increases with crossover rates ( Figure 4B ) ., This analysis , therefore , suggests that the extent of BGS at most genes and intergenic sites across the D . melanogaster genome is influenced by the cumulative effects of many sites and include numerous other genes ., Thus , accurate estimates of B in D . melanogaster require the study of genomic regions at the cM or Mb scale , ideally full chromosomes ., Otherwise , BGS can be severely underestimated , and inferences about demographic events or other types of selection may be unwarranted ., These results are also in agreement with the previous observation that all intergenic sequences and introns across the genome are predicted to be influenced by BGS ., A second goal of this study was to estimate how much of the observed levels of neutral diversity across the D . melanogaster genome can be explained by a BGS landscape obtained independently from variation data ., A strong positive correlation would not only indicate that BGS should not be ignored in population genetic analyses but also that our estimates of B are likely suitable as baseline to infer additional types of selection and/or demographic events ., Because our best experimentally-obtained whole-genome recombination maps for crossover and gene conversion have a maximum resolution and accuracy at the scale of 100-kb 31 , the predictive nature of the B baseline was first investigated at this physical scale ., To obtain levels of neutral diversity across the D . melanogaster genome , nucleotide diversity per bp ( πsil ) at introns and intergenic sequences was estimated from a sub-Saharan African population ( Rwanda RG population 62; see Materials and Methods for details ) ., The comparison of estimates of B generated by our BGS models and levels of πsil across the genome reveals a strikingly positive association ( Table 1 and Figure 5 ) ., For autosomes , the correlation between B and πsil is ρ\u200a=\u200a0 . 770 ( 965 non-overlapping 100-kb regions , P<1×10−12 ) , and increases up to ρ\u200a=\u200a0 . 836 ( P<1×10−12 ) along individual autosome arms ., Equivalent results are obtained when silent diversity is analyzed separately at intergenic and intronic sites , with ρ\u200a=\u200a0 . 736 between B and πintergenic , and ρ\u200a=\u200a0 . 741 between B and πintron ( P<1×10−12 in both cases ) ., The study of individual autosome arms shows a positive association up to ρ\u200a=\u200a0 . 799 and 0 . 800 for intergenic and intronic sites , respectively ( P<1×10−12 in both cases ) ., The predictive nature of the B landscape in D . melanogaster remains remarkably high along trimmed autosomes where BGS has been often assumed to play a minor role explaining variation in levels of polymorphism ., The correlation between B and πsil is ρ\u200a=\u200a0 . 529 , ranging up to ρ\u200a=\u200a0 . 655 when trimmed chromosome arms are analyzed separately ( P<1×10−12 in all cases ) ., Additionally , the BGS models investigated generate a stronger association between B and πsil than between estimates of local crossover ( c ) and πsil , particularly along trimmed chromosomes ( ρ\u200a=\u200a0 . 677 and ρ\u200a=\u200a0 . 397 for complete and trimmed autosomes , respectively ) ., This last result exposes the limitations of using local c as an estimate of the overall strength of linked selection at a given genomic position , and highlights the importance of including long-range information of recombination rates and gene structures ., Altogether , these results show the high predictive value of simple BGS models , with almost 60% of the observed variance in πsil across 100-kb autosomal regions explained by BGS , a percentage that is as high as ∼70% when investigating variation in nucleotide diversity along individual chromosome arms ( see Table 1 ) ., The robustness and high predictive power of the BGS models to explain qualitative trends of nucleotide diversity across the genome , suggest that we can investigate the presence of other forms of selection by searching for regions that depart from BGS expectations ., We , therefore , compared observed πsil and levels of diversity predicted by B , and parameterized departures by using studentized residuals ( πsil-R; see Material and Methods ) ., Overall , the distribution of πsil-R does not show a significant departure from normality ( χ2\u200a=\u200a28 . 9 , d . f . =\u200a23 , P\u200a=\u200a0 . 183 ) thus validating the approach ., Nevertheless , there are 58 outlier regions with nominal P<0 . 05 , 24 regions with significantly negative πsil-R ( revealing a deficit in πsil relative to BGS expectations ) and 34 regions with significantly high πsil-R ( revealing a relative excess of πsil ) ., Regions with a relative deficit of πsil are candidate regions for recent adaptive events 2 , 8 , 63 and our data confirms the presence of several regions with the fingerprints of a recent selective sweep across the D . melanogaster genome 22 , 23 , 25 , 26 , 29 ., The strongest signal of selection at this 100-kb scale , and the only region that shows a departure that remains significant after correction for multiple tests P<1 . 6×10−6 , with false discovery rate ( FDR ) q-value<0 . 10 , suggests a recent selective sweep at position 8 . 0–8 . 1 Mb along chromosome arm 2R ., This genomic region includes gene Cyp6g1 and also showed the strongest signal of directional selection and selective sweep in large-scale population genetic analyses of North American 26 and Australian D . melanogaster 64 populations ., Not all regions with a severe reduction in πsil across the trimmed genome , however , may need recent adaptive explanations ., A number of regions with πsil much smaller than the median ( e . g . , 0 . 002 relative to a median of 0 . 008; see Figure 6A ) also show estimates of B of 0 . 25 or smaller and , thus , the observed πsil would be close to the predicted level of neutral diversity when a BGS context is taken into account ., Because the genome-wide recombination maps used in this study were generated to have good accuracy at the scale of 100-kb , our BGS models assumed homogeneous rates within each 100-kb region ., Notably , these models predict variation in B across 100-kb regions due to the heterogeneous location of genes and exons within these regions and the differential effects of proximal and distal flanking regions ., Nevertheless , detailed analyses of a few small genomic regions have revealed recombination rate variation at a smaller scale in Drosophila 32 , 68 ., Therefore , outliers from BGS expectations at scales smaller than 100-kb can reveal the localized fingerprints of other types of selection ( directional or balancing selection ) but the possibility of uncharacterized heterogeneity in recombination within these regions cannot be formally ruled out ., That said , the study of the relevant size of the genomic region adding up BGS effects at a given focal point ( with DB75>200 kb; see above ) suggests that very local recombination variation may play a limited role influencing B at a focal point ., With these caveats and considerations in mind , we investigated the presence of outliers at the scale of 10 and 1-kb to identify candidate regions under positive or balancing selection using the approach discussed for 100-kb regions ., The strong relationship between B and the observed level of silent diversity is maintained when analyzing smaller regions ( Figure 5 ) ., At 10-kb scale , B remains a very good predictor of πsil along complete autosomes ( ρ\u200a=\u200a0 . 678 , 8 , 883 regions; Figure 6B ) whereas ρ is 0 . 551 ( 55 , 467 regions ) at the finest scale of 1-kb ( P<1×10−12 in both cases ) ., The use of BGS models with different parameters ( MG , StdMut , MG , LowMut or MLN , LowMut ) generate virtually equivalent results , with ρ between estimates of B and πsil ranging between 0 . 678 and 0 . 682 for analyses at the 10-kb scale , and with ρ ranging between and 0 . 551 and 0 . 554 for analyses at the 1-kb scale ., As observed before , B along the X chromosome shows reduced association with πsil than for autosomes also at 10- and 1-kb resolution ., For X-linked regions , the correlation between B and πsil is ρ\u200a=\u200a0 . 397 ( 1 , 979 regions ) and 0 . 295 ( 12 , 680 regions ) for 10- and 1-kb regions , respectively ( P<1×10−12 in both cases ) ., Another prediction of the models of selection and linkage ( either HHss or BGS ) is that , parallel to a reduction in intra-specific variation , there will be a reduction in efficacy of selection ( i . e . , the Hill-Robertson effect 10 , 70–76 ) ., This general prediction has been previously confirmed in Drosophila using local low-resolution crossover rates as indirect measure for the magnitude of Hill-Robertson effects acting on a gene ., These studies showed weak but highly significant associations between crossover rates and estimates of codon usage bias or rates of protein evolution 71 , 74 , 75 , 77–85 ., To investigate whether B landscapes also capture differences in efficacy of selection , we focused on selection against amino acid substitutions along the D . melanogaster lineage , after split from the D . simulans lineage ( less than 5 mya 86 ) ., To this end , we obtained the ratio of nonsynonymous to synonymous changes ( ω , ω\u200a=\u200adN/dS ) for 6 , 677 protein encoding genes and , more informatively , the variation in ω after controlling for selection on synonymous mutations based on residual analysis ( ωR; see Materials and Methods for details ) ., When each gene is analyzed as a single data point , there is a negative association between B and ωR ( ρ\u200a=\u200a−0 . 086 , P\u200a=\u200a2×10−12; Table 2 ) ., Interestingly , and contrary to the results of nucleotide diversity , the X chromosome shows a tendency for a stronger effect of B on ωR than autosomes: ρ\u200a=\u200a−0 . 189 ( P\u200a=\u200a3 . 4×10−8 ) and ρ\u200a=\u200a−0 . 071 ( P\u200a=\u200a5 . 7×10−8 ) for X-linked and autosomal genes , respectively ., An equivalent but more clear pattern is observed at the scale of the resolution of our recombination maps ( 100 kb ) , where estimating the average ω and ωR for all genes within each region also allows for reducing idiosyncrasies of different genes influencing rates of protein evolution ( e . g . , gene expression breadth and levels , protein length , etc . ; see 84 ) ., At this scale , variation in B is negatively associated with ωR along autosomes ( ρ\u200a=\u200a−0 . 160 , P\u200a=\u200a6 . 1×10−6 ) and the X chromosome ( ρ\u200a=\u200a−0 . 367 , P\u200a=\u200a1 . 5×10−6; Table 2 ) ., Again , the association between estimates of B and rates of protein evolution is robust to different BGS models and parameters ., Equivalent ρ are obtained for all eight BGS models investigated , and this is observed when analyzing individual genes ( ρ between B and ωR ranging between −0 . 0856 and −0 . 0874; P≤3×10−12 ) and when using the average ωR for genes within 100-kb regions ( ρ ranging between −0 . 187 and −0 . 193; P≤5 . 9×10−9 ) across the whole genome ., The association between B and rates of amino acid substitution along the D . melanogaster lineage , although highly significant in terms of associated probability , is much weaker than that for levels of polymorphism at silent sites ., Heterogeneity in overall evolutionary constraints among proteins is expected to add substantial variance when investigating the consequences of B on ω relative to studies of B on πsil because πsil is only influenced by local Ne and the mutation rate ., Nevertheless , temporally variable recombination rates at a given genomic location would also reduce the association between B and ω ., Indeed , the high degree of intra-specific variation in crossover genetic maps within current D . melanogaster populations 31 together with differences in genetic maps between closely related Drosophila species 87–89 support the notion that recombination landscapes vary within short evolutionary scales , at least across trimmed chromosomes ., Under this scenario , extant recombination rates and the corresponding estimates of linkage effects would be poor predictors of interspecific rates of protein evolution , even between closely related species ., In this study across the D . melanogaster genome , the use of recombination rates obtained experimentally would provide only an approximation for the relevant B along the lineage leading to D . melanogaster populations 84 ., These estimates of B would be an even weaker predictor of ωR ( or ω ) along the D . simulans lineage after split from the D . melanogaster lineage ., In agreement , we observe no significant relationship between B and ωR estimated along the D . simulans lineage ( ρ\u200a=\u200a−0 . 009 based on the default BGS model whereas the other BGS models generate ρ ranging between −0 . 014 and +0 . 011; P>0 . 25 in all cases ) ., A similar result has been obtained in comparisons of local crossover rates and rates of protein evolution between two other closely related Drosophila species , D . pseudoobscura and D . persimilis 88 ., In species where BGS plays a significant role , temporal fluctuations in recombination landscapes could influence a number of analyses of selection that assume constancy in Ne , including estimates of the fraction of adaptive substitutions ( α 6 , 90–92 ) ., Several studies have shown that the bias in estimating α can rapidly reach considerable values as a consequence of demographic changes , with α being overestimated when Ne influencing polymorphism is larger than Ne influencing divergence ( Ne_Pol>Ne_Div; 34 , 91 ) ., We propose that temporal changes in recombination at a given genomic position would generate an equivalent scenario , with a B influencing polymorphism ( B_Pol ) that differs from long-term B influencing divergence ( B_Div ) , or the corresponding terms for local Ne ., Because long term Ne can be approximated by its harmonic mean 93 , temporal fluctuations of recombination landscapes ( and of local B and , therefore , local Ne ) would also predict a tendency for local Ne_Pol≥Ne_Div ., Such scenario would allow amino acid changes to make a larger relative contribution to divergence than to polymorphism , particularly in regions where recombination has recently increased , and thus bias estimates of α upward ., Precise quantitative predictions of the potential bias in α would minimally depend on the rate , magnitude and physical scale of the variation in recombination landscapes along lineages ., To obtain initial insight into the effects of temporal changes in recombination rates on estimates of α , we investigated a rather simple and conservative scenario with forward po

Introduction, Results, Discussion, Materials and Methods

The constant removal of deleterious mutations by natural selection causes a reduction in neutral diversity and efficacy of selection at genetically linked sites ( a process called Background Selection , BGS ) ., Population genetic studies , however , often ignore BGS effects when investigating demographic events or the presence of other types of selection ., To obtain a more realistic evolutionary expectation that incorporates the unavoidable consequences of deleterious mutations , we generated high-resolution landscapes of variation across the Drosophila melanogaster genome under a BGS scenario independent of polymorphism data ., We find that BGS plays a significant role in shaping levels of variation across the entire genome , including long introns and intergenic regions distant from annotated genes ., We also find that a very large percentage of the observed variation in diversity across autosomes can be explained by BGS alone , up to 70% across individual chromosome arms at 100-kb scale , thus indicating that BGS predictions can be used as baseline to infer additional types of selection and demographic events ., This approach allows detecting several outlier regions with signal of recent adaptive events and selective sweeps ., The use of a BGS baseline , however , is particularly appropriate to investigate the presence of balancing selection and our study exposes numerous genomic regions with the predicted signature of higher polymorphism than expected when a BGS context is taken into account ., Importantly , we show that these conclusions are robust to the mutation and selection parameters of the BGS model ., Finally , analyses of protein evolution together with previous comparisons of genetic maps between Drosophila species , suggest temporally variable recombination landscapes and , thus , local BGS effects that may differ between extant and past phases ., Because genome-wide BGS and temporal changes in linkage effects can skew approaches to estimate demographic and selective events , future analyses should incorporate BGS predictions and capture local recombination variation across genomes and along lineages .

The removal of deleterious mutations from natural populations has potential consequences on patterns of variation across genomes ., Population genetic analyses , however , often assume that such effects are negligible across recombining regions of species like Drosophila ., We use simple models of purifying selection and current knowledge of recombination rates and gene distribution across the genome to obtain a baseline of variation predicted by the constant input and removal of deleterious mutations ., We find that purifying selection alone can explain a major fraction of the observed variance in nucleotide diversity across the genome ., The use of a baseline of variation predicted by linkage to deleterious mutations as null expectation exposes genomic regions under other selective regimes , including more regions showing the signature of balancing selection than would be evident when using traditional approaches ., Our study also indicates that most , if not all , nucleotides across the D . melanogaster genome are significantly influenced by the removal of deleterious mutations , even when located in the middle of highly recombining regions and distant from genes ., Additionally , the study of rates of protein evolution confirms previous analyses suggesting that the recombination landscape across the genome has changed in the recent history of D . melanogaster ., All these reported factors can skew current analyses designed to capture demographic events or estimate the strength and frequency of adaptive mutations , and illustrate the need for new and more realistic theoretical and modeling approaches to study naturally occurring genetic variation .

arthropoda, organisms, invertebrates, drosophila melanogaster, natural selection, biology and life sciences, population genetics, evolutionary theory, evolutionary biology, drosophila, evolutionary processes, animals, genetic drift, evolutionary genetics, insects

journal.pcbi.1003403

A Disorder-Induced Domino-Like Destabilization Mechanism Governs the Folding and Functional Dynamics of the Repeat Protein IκBα

NF-κB , a mammalian multi-domain transcription factor family , regulates immune responses and the expression of several genes associated with cell growth and diseases through a tightly regulated signaling pathway 1 ., In its inactive state , NF-κB dimer is tightly bound to IκBα thus localizing the complex in cytoplasm and inhibiting transcription ., Upon the presence of appropriate signals , IκBα is degraded releasing NF-κB that enters nucleus and initiates the transcription of hundreds of essential genes 1 , 2 ., Apart from its fundamental role in inhibiting transcription , IκBα is also known to bind at least 10 different proteins with the affinities for some complexes varying by over 3 orders of magnitude 3 , 4 ., From a biological regulatory standpoint , IκBα thus plays a critical role in the proper functioning of the cellular machinery and its response to external stimuli as it is also implicated in Hodgkins lymphoma 5 ., Structurally , IκBα is composed of the N-terminal signal response region that targets the protein for ubiquitination , the central 6 ankyrin repeat ( AR ) domain 6 , and the C-terminal disordered PEST region 7 ., True to its promiscuous binding and central role in signaling , the domain composed of the 6 ARs ( from hereon referred to as IκBα; Figure 1A ) displays a complicated solution behavior and resists crystallization in its free-state ( i . e . in the absence of binding partners ) unlike its counterpart in Bcl-3 8 and short half-life within cells ( <10 minutes ) 9 ., An array of ensemble biophysical measurements on IκBα from the group of Komives and co-workers indicate a malleable conformational behavior with large ANS binding ( molten-globule like character ) , low thermodynamic stability ( melting temperature ∼315 K compared to the physiological growth temperature of 310 K ) 10 , repeat-specific hydrogen/deuterium exchange pattern and hence different stabilities with repeats 5 and 6 significantly disordered 10–12 , and significant roll-over in the denaturant dependence of relaxation rates indicative of multiple intermediate states during ( un ) folding 13 ., Recently , ensemble experiments have also been buttressed by some elegant single molecule Förster Resonance Energy Transfer ( smFRET ) experiments on IκBα with the donor/acceptor placed at different repeats ( AR 1–4 , 2–5 , 2–6 and 3–6 ) thus monitoring the overall distances between the different repeats that can be used as a proxy for the degree of unfolding ., These experiments reveal that the distance between the repeats evolve in an intricate manner with the number of peaks , their mean FRET value and broadness , dependent on not only the identity of the repeats but also the temperature 14 , 15 ., Though the experiments are comprehensive and probe a variety of conformational features , there are several questions that remain unanswered that includes the reason for its low stability , the unique behavior of the individual repeats , structural properties of the intermediates , the magnitude of the barriers separating them , the possible conformational states that are populated in smFRET experiments , and in essence the nature of the entire folding free energy landscape ., Identifying the key conformational states are necessary as they directly impinge on the unique folding behavior of IκBα and the possible reasons for its promiscuous binding role ., A related issue is the structure of IκBα that is characterized by both ordered and disordered regions – the so-called supertertiary structure 4 – that goes beyond the conventional modeling procedures developed for purely ordered or intrinsically disordered proteins ., In other words , any model dealing with IκBα has to adequately capture both these distinct behaviors while simultaneously reproducing experiments ( including smFRET ) to make predictions of value ., Here we address these issues by employing the structure-based Ising-like statistical mechanical Wako-Saitô-Muñoz-Eaton model 16–18 ( WSME ) with electrostatics 19 and solvation terms 19 , 20 that has a higher level of resolution and predictive capability 19 , 21 compared to other Ising-like studies on repeat proteins 22–27 ., We predict in a semi-quantitative fashion several details of the landscape and show how the lack of structure in a single repeat can have a dramatic effect on the resulting landscape with implications on extending the functional repertoire of disordered regions ., The Wako-Saitô-Muñoz-Eaton ( WSME ) model is a structure-based Ising-like statistical mechanical model 16–18 that is consistent with the minimal frustration principle of the energy landscape theory 28 , i . e . native contacts are assumed to determine the folding mechanism ., Briefly , the basic surmise is that the conformational space of every residue can be represented as binary variables 0 and 1 , for unfolded and folded subspace , respectively , with multiple nucleation sites distributed along the chain ., The large cost involved in fixing an unfolded residue in a native-like conformation is taken into account by the entropic parameter ΔSconf ., In the exact solution formulation of the WSME model , the contribution to the partition function from a very large ensemble of 2N microstates is taken into consideration ., The effective stabilization free-energy of every microstate is represented as a sum of van der Waals interaction energy ( ξ ) , electrostatics ( based on the Debye-Hückel model ) and solvation free-energy ( that depends on the heat capacity change ΔCp ) ( equations S . 2–S . 6 in the supporting text S1 ) ., A detailed description of the method and energetic terms can be found in two recent works 19 , 21 and also in the supporting information file ( supporting text S1 ) ., The global partition function can be calculated from a transfer-matrix formalism ( equations S . 7–S . 8 ) , which enables the calculation of both free-energy as a function of number or ordered residues ( by accumulating partial partition functions ) and the global probability of finding a residue folded ( equation S . 9 ) ., It is important to note that though the WSME model is similar to the analytical model of repeat proteins developed by Ferreiro et al . 24 , it is different in several aspects ., Firstly , the WSME model has not only been employed for repeat proteins but also globular proteins 18 , to predict mechanisms of folding 20 , 29 , 30 , to engineer stabilities through mutations 21 and even in the analysis of pulling experiments 31 , 32 and in characterizing the effects of confinement 33 ., Secondly , the WSME model in the current version includes contributions from 2N microstates ( where N is the protein length ) with detailed mean-field energetics ( van der Waals , electrostatics and solvation ) while the model of Ferreiro et al . 24 does not make these distinctions and employs a much smaller ensemble of just 2R microstates where R is the number of repeats ., However , some of the predictions are quite similar from these models despite these differences and we discuss them below ., In the current work , van der Waals interaction partners are identified with a 6 Å cut-off excluding the nearest neighbors ., The effective dielectric constant ( εeff ) is fixed to 29 that has been successful in reproducing the equilibrium and dynamic behavior of four different homologous families and also the thermodynamic effect of 138 single point mutations involving charged residues from 16 different proteins 19 , 21 ., Charges on IκBα were assigned according to the experimental pH 7 . 0 protonation state and the ionic strength value was fixed to 0 . 05 M 10 ., The partition function and the overall probability of finding a residue folded was calculated using the methodology of Wako and Saitô 16 , 17 ., The final parameters are: ΔSconf =\u200a−18 . 1 J mol−1 K−1 per residue ( at a reference temperature of 385 K 34 ) , ξ\u200a=\u200a−70 . 1 J mol−1 per native contact and =\u200a−0 . 358 J mol−1 K−1 per native contact 19 ., Chemical denaturation effects are introduced by a phenomenological constant ( mcont ) that decreases the stabilization free-energy linearly with denaturant concentration ( equation S . 10 ) ., The Single Sequence free-energy landscapes are calculated by algorithmically enumerating the statistical weights of single stretches of folded residues employing the same parameters as above ., Though this dramatically reduces the complexity of the folding landscape , it provides a simple minimalistic way to visualize it ., Moreover , such a representation has already been successful in identifying experimentally consistent intermediates in the folding of bovine lactalbumin 19 ., Since this landscape is characterized by single-stretches of folded-like protein their structure can be directly obtained by editing the PDB file 29 that enables one to calculate experimental observables ( number of hydrogen bonds , contacts , contact-order , secondary structure ) for a zeroth-order approximation of the possible signal along the one-dimensional free-energy profile ., A similar methodology was employed to calculate distances between the labeled fluorophores from the crystal structure ( PDB id: 1NFI ) for the structured regions ., Since the number of unstructured residues in the disordered regions are known it is possible to approximate the distance from the freely-jointed chain model 35 that is commonly employed to interpret single-molecule FRET measurements 36:where the left hand side corresponds to the mean squared distance between residues i and j , lp is the persistence length ( fixed to 4 Å ) and b is the bond-length ( fixed to 3 . 8 Å ) ., The energy transfer efficiency ( E ) is then calculated fromwhere R0 is the Förster radius ., Equilibrium experiments on free ( unbound ) IκBα reveal that ∼20% of the total transition amplitude can be accounted for by a steep pre-transition that has been termed as ‘on-cooperative’ 37 ., This can be seen as a subtle version of a related observation in an archaeal AR protein in which an equilibrium intermediate is populated 38 or a variant of the near-continuous transitions observed in alpha-helical downhill-folding proteins 29 , 39 , 40 ., Evidence from hydrogen-exchange measurements , mutational studies and tryptophan fluorescence suggest that the pre-transition originates from the weak structure of AR domains 5 and 6 37 ., Accordingly , we systematically eliminated interactions between those repeats and the rest of the protein ( bound IκBα; Figure 1B ) until it was possible to reproduce the magnitude of the pre-transition observed in equilibrium experiments ( Figure 1C ) while at the same time maintaining agreement with differential scanning calorimetry ( DSC ) measurements 10 that enable a precise estimate of the sharpness or co-operativity of the global unfolding transition ( Figure S1A ) ., The resulting contact-map ( Figure 1B ) lacks long-range interactions beyond 15 residues in the ARs 5 and 6 ( i . e . dominated entirely by local interactions ) and thus serves as a physical template that accounts for the presence of order and disorder in the same structure ., This is further validated by the fact that we are able to capture the m-value for chemical denaturation upon characterizing just the thermal denaturation data ( Figure S1 ) ., Global thermodynamic analysis of IκBα folding therefore points to a two-state-like picture with sharp transitions in equilibrium experiments albeit with a significant pre-transition ., However , kinetic experiments on IκBα and its variants indicate that at least 4 thermodynamic macrostates – two intermediates and two end-states ( native and unfolded ) - are necessary to explain the dependence of relaxation rates on denaturant , i . e . the chevron plot , though the magnitude of barriers and the identity of the intermediates are unknown 13 ., To reconcile these two observations we calculated the one-dimensional ( 1D ) free-energy profiles as a function of temperature with the number of structured residues as an order parameter ( Figure 2A ) ., Under folding conditions , the lowest free energy is observed at a value of ∼170 structured residues that corresponds to structured ARs 1–5 with the 6th completely unfolded ( the native state; N ) ., With increasing temperatures , the unfolded ( U ) and intermediate states get progressively more stabilized and a Hammond-like movement of the barrier tops is also evident ., A closer look at the free-energy profile near the transition mid-point indicates at least two intermediate states as required by chemical-four-state models to reproduce denaturant-dependent kinetics ., The major intermediates , I1 and I2 , are centered at values of ∼94 and ∼139 structured residues ( Figure 2B ) while an early pseudo-intermediate ( I′ ) at ∼74 structured residues also appears as a shoulder to the first barrier ., A simple calculation from the primary sequence boundaries of IκBα indicates that I′ , I1 and I2 correspond to 2 , 3 and 4 folded ARs while the primary barrier ( following the unfolded state ) is located at ∼54 structured residues suggesting that ∼1 . 5 ARs ( i . e . one AR together with the interface with the subsequent AR ) have to be folded to nucleate the folding mechanism in line with a previous computational study 24 ., For both the intermediates and the primary barrier , it is not clear as to what combination of repeats are involved looking at just the 1D free-energy profile ( see below ) ., This analysis is evidence that observing a single sharp transition in experiments is no proof for the lack of intermediates in the folding pathway ., The populations of the native and unfolded macrostates ( Figure 2C ) display a near-sigmoidal transition though the former is significantly broader ., The population of the intermediate I2 shows a non-monotonic behavior and is maximally populated at 310 K ( ∼70% ) indicating that only ∼4 repeats are fully structured under physiological conditions ., Observation of roll-overs in chevron plots is generally seen as evidence to the presence of intermediates or changes in the rate-limiting step though both are interlinked 41 , 42 ., As the model naturally predicts the presence of two major intermediates in IκBα , we sought to reproduce the chevron plot by describing the relaxation kinetics as diffusion on the predicted 1D free-energy profile 43 ., In experiments , the folding kinetics was monitored from the fluorescence of a sole-tryptophan engineered into the second AR of IκBα ( A133W/W258F ) 13 ., Instead of arbitrarily choosing a signal switch along the free-energy profile we introduce here a simple method to estimate a possible 1D signal for tryptophan fluorescence for this coordinate ., We first constructed a minimal 2D structural ensemble , the SSA ( Single Sequence Approximation ) landscape 19 , represented by an ensemble of partially structured states with only a single stretch of folded-like residues ., For the 213-residue IκBα this is characterized by 22 , 791 structured microstates ( from N* ( N+1 ) /2 , where N is the number of residues in the protein ) ., The interactions between tryptophan and every other residue within a contact cut-off ( 6 Å ) was calculated for each of the microstates ( Figure S2 ) and then projected onto the order parameter thus approximating the tryptophan signal ( continuous red line in Figure 2B ) ., The simulated relaxation kinetics using this signal is predicted to be quasi-exponential in the folding limb but bi-exponential at concentrations close to the midpoint in the unfolding limb ( 3 . 5–5 M in Figure 2D and Figure S3 ) ., The slower phase and the onset of roll-over at ∼5 M urea agree well with experiments despite calibrating the model with purely equilibrium experiments ., Bi-exponential kinetics apart from cis-trans proline-isomerization phases has been reported in other AR proteins at precisely the same conditions 23 , 44 ( i . e . close to the midpoint ) highlighting the robustness of the calculation ., In an alternate calculation , a simple signal switch was employed at an order parameter value corresponding to the position maximal barrier ( 54 structured residues; dashed line in Figure 2B ) ., The simulated relaxation kinetics in this scenario is quasi-exponential under most conditions ( Figure S3C ) mirroring experimental observations while matching with the predictions of the previous calculation ( Figure 2D ) ., The agreement between experimental and predicted relaxation kinetics ( in both the shape and relative magnitude ) suggests that the order parameter –number of structured residues – can also serve as a good reaction coordinate ., Can this free-energy profile then be employed to capture and rationalize the smFRET studies on IκBα ?, 14 , 15 Accordingly , we simulated native state dynamics on the 1D free-energy profile by single-bond flip Monte Carlo ( MC ) dynamics ., The simulated MC dynamics reveals an increasingly complex behavior in equilibrium with increasing temperatures ( Figure 3A and 3B ) ., At 298 K , only RC values corresponding to 4 ( I2 ) and 5 ( N ) structured repeats are populated while at 310 K the conformational heterogeneity increases to the extent of populating even 3 ( I1 ) and occasionally 2 structured repeats ( I′ ) apart from I2 and N . To compare against smFRET experiments , distance information is required at the microscopic level , which is generally challenging from the viewpoint of simple models ., It should however be much easier for repeat proteins due to the linear nature of their domain organization ., To this end , we introduce a novel but simple avenue to reproduce smFRET data by combining the calculated distances between specific residues ( as in the experimental construct ) for the microstates in the 2D structural ensemble and combining them with distances for the unstructured regions employing the freely-jointed chain model 35 ( Figure S4 ) ., The distances are then projected onto the RC for each of the construct used in experiments as was done for the estimation of tryptophan fluorescence signal ( Figure 3C ) ., Combining the dynamical information ( Figure 3A–B ) with the effective distances ( Figure 3C ) and converting the distances to FRET values using a R0 of 51 Å ( for the Alexa 555 – Alexa 647 pair ) and without invoking any other assumptions we are able to calculate the temperature-dependent FRET dynamics that resemble experiments to a remarkable degree ., For example , for donor/acceptor probes located at AR 1 and AR 4 and at 298 K , a broad peak centered at high FRET values is seen ( blue in Figure 3D ) indicating that the protein is well-folded though with fluctuations from sampling partially-structured conformations ( I2; Figure 3A ) ., At 310 K , the predicted mean FRET is lower and broader as a result to populating more disordered regions as represented by increasing distances for lower values of RC , exactly as observed in experiments 15 ., Though there is a correlation between experiments and simulations on the mean FRET values of the first and second peak ( Figure 3H ) we do not capture the experimental amplitude , i . e . the relative counts , due to the simplicity of the model and the assumptions involved in calculating the distance along the 1D profile ., Despite these shortcomings the semi-quantitative agreement is evidence that the conformational heterogeneity predicted by the model is real and provides a simple and rational explanation to the experimental findings: the dynamics at 310 K can encompass the sampling of as few as three structured repeats ( just ∼40% of the protein is folded ) and this manifests in the form of changing mean FRET values , increased broadness and extra peaks , relative to the observation at 298 K . The complex nature of kinetics and smFRET experiments suggests that IκBα should display a correspondingly intricate thermodynamic behavior when monitored at higher detail ( residue/atom level ) despite displaying two-state-like global thermodynamics ., As expected , unfolding curves predicted by the model point to differences in the thermodynamic stabilities of individual repeats evident in the form of variable transition mid-points ( Figure 4A ) ., We find that AR6 is completely unfolded under all conditions while AR5 undergoes a non-cooperative transition from ∼80% folded at 298 K to just ∼35% folded under physiological conditions ( 310 K ) ., AR4 is also partially destabilized due to minimal interactions with AR5 ( Figure 4A ) while the repeats 1–3 are reasonably well structured at 298 and 310 K . This suggests that the partial of unfolding of AR repeats 4 and 5 primarily contribute to the steep pre-transition observed in equilibrium experiments , An alternate procedure to visualize the difference in stability is to plot the mean residue probability ( a proxy to stability without a recourse to two-state model fitting ) as a function of repeat index ( Figure 4B ) ., This clearly reveals the difference in stability between repeats and their temperature dependence ., It also shows that AR 1 is less stable than ARs 2 and 3 due to end-effects as one of its interfaces is completely exposed to the solvent in contrast to the central repeats , while ARs 5 and 6 are significantly unfolded in agreement with experiments 37 ., The thermodynamic manifestation of this observation should be captured in hydrogen/deuterium exchange ( HX ) experiments that can report on the degree of exchange of amide protons arising from structural fluctuations ., In fact , HX experiments at 298 K reveal the following order of protection - AR 2>3 , 4>1>5>6 11- and is highly correlated with the predicted mean residue probabilities of individual repeats at both 298 and 310 K ( Figure 4C ) ., Repeat proteins are generally symmetric both structurally and at the level of sequences ., At the structural level , every repeat has two inter-repeat interfaces while the end-repeats ( at the N- and C-terminii ) are characterized by one interface ., At the sequence level , there is a high degree of similarity that enables the repeats to adopt the ankyrin fold ., This has been exploited to engineer super-stable AR proteins simply by tuning the agreement with consensus sequences 37 , 45–48 ., What happens when this symmetry is broken or when one of the terminal repeats is ‘designed’ to be less structured than the rest ?, In other words , what is the origin , at the sequence level , of this unique conformational behavior in IκBα ?, Sequence alignment and mutational studies indicate that AR 6 lacks a particular consensus motif ( TPLHLA ) that makes it less stable and prone to disorder 12 ., We find that this evolutionarily selected lack of structure in AR 6 translates into a loss of one inter-repeat interface for AR 5 ., This in turn destabilizes AR 5 and hence AR 4 to a lesser extent ( as shown in Figure 4B ) highlighting the critical nature of inter-repeat interactions and is in agreement with expectations based on analytical and coarse-grained models 24 , 49 ., In other words , we identify a novel domino-like effect of disordered regions in IκBα involving successive destabilization of nearby repeats that might be functionally important ( see below ) arising from simple symmetry-breaking design by Nature ., A control simulation with ARs 5 and 6 fully folded reveals only marginal destabilization of repeats and is seen only in ARs 1 and 6 due to the lack of one contact interface , i . e . the end effects ( also seen in AR 1 of IκBα; Figure 4B ) ., From a thermodynamic perspective , the disorder induced domino-like destabilization mechanism drives the dynamics under native conditions with ARs 2 and 3 displaying minimal equilibrium fluctuations relative to other repeats ( Figure 4 ) ., Accordingly , from the viewpoint of folding , the AR 2 or 3 should fold first , followed by ARs 4 , 1 , 5 and 6 in that order ( on average ) as inferred from HX experiments akin to the nucleation-condensation mechanism ., It is , however , important to note that AR 5 is not fully folded even at 298 K while AR 6 is completely unstructured under all conditions ., The offshoot of the disorder-induced domino effect is an intricate conformational landscape characterized by several valleys and ridges and hence numerous metastable states that can be accessible by equilibrium fluctuations ( Figure 5A and 3A–B ) ., The valleys or local minima in free-energy ( dark blue in Figure 5A ) along the SSA landscape correspond to the intermediate states as also seen in the 1D free-energy profile ( Figure 2A and 2B ) ., Since there is a direct structural interpretation , we map the corresponding minima to specific partially structured states as shown in Figure 5B and 5C ., The intermediate state 1 ( I1 ) corresponds to structure in three repeats ( ARs 123 , 234 , 345 in that order of decreasing probability ) while I2 involves contributions from ARs 1234 and 2345 ., This near-continuous residue-level SSA landscape is very similar to the experimentally constructed discrete ( at the repeat level ) landscape of the notch AR protein 50 but with the associated barriers apart from the intermediates ., Eliminating the equilibrium pre-transition by assuming ARs 5 and 6 to be fully folded ( i . e . similar to the crystal structure ) dramatically alters the landscape that becomes much simpler with the population of very few intermediate species ( control in Figure 4B Figure S5B ) ., Experimentally , the half-life of mutant IκBα with fully structured ARs 5 and 6 is increased to 23 minutes ( compared to 7 minutes for the wild-type ) while remarkably binding weaker to NF-κB thus affecting the NF-κB signaling module in vivo 12 ., This is indirect evidence that disorder in repeat 6 and the related landscape and dynamics that we extract here might be required for proper functioning of IκBα ., In effect , our theoretical analysis of the IκBα folding behavior provides a structural view of the populating intermediates and explains the diverse and sometimes conflicting dynamic and thermodynamic experimental observations ., We have , in parallel , devised a methodology to model disorder in systems that fold upon binding by modifying the contact map guided by equilibrium experimental observables and model smFRET experimental data in linear systems in combination with elementary polymer physics treatments ., The resulting conformational landscape of IκBα is predicted to be complex with multiple unstructured states populated even in equilibrium ., This directly explains the short half-life of IκBα inside cells as proteins with unstructured regions ( in this case arising from intermediates or metastable states ) are targeted for degradation thus temporally regulating their biological activity 51 ., The inability of the simple kinetic model to capture the roll-over in the folding arm of the chevron and over-prediction of relaxation rates on either side of the denaturation midpoint is evidence that a single diffusion coefficient might not be sufficient to explain the dynamics due to either the changing nature of the equilibrium ensemble with conditions ( as shown in the one-state downhill folder BBL 52 ) or due to the effect of parallel/competing pathways and stable compact intermediates 48 , 53–55 ., Promiscuous binding in single domain proteins can be possibly achieved by downhill-folding systems that respond to stimuli/conditions by changing the dimensions of the ensemble through gradual unfolding 29 , 56 , 57 ( the ‘molecular rheostat’ hypothesis 29 ) ., An ensemble approach to multi-domain systems suggests that the degree and magnitude of coupling between individual domains can result in a variety of functional behaviors 58 ., Analytical models of repeat proteins further predict a delicate balance between intra- and inter-repeat interactions that enable them to sample functionally relevant conformations 24 ., We find a similar feature in IκBα wherein the dimensions , nature and dynamics of the ensemble are determined by a disorder-induced domino effect that can selectively tune the degree of unfoldedness of individual repeats ., This , we propose , is critical for its promiscuous binding as a heterogeneous native ensemble under physiological conditions can enable IκBα to bind several different partners and possibly explains the large differences in binding constants of characterized IκBα protein pairs ( Kd ∼40 pM –217 nM ) 4 , 9 ., The folded ARs are generally seen as scaffolds on to which the more functionally relevant and weakly structured ARs are linked 59 ., The disorder induced destabilization mechanism of repeats we propose goes beyond the mere scaffold functionality and points to a simple strategy employed by Nature to load multiple functionalities into repeat proteins while at the same time imposing a tight temporal control through disorder .

Introduction, Methods, Results, Discussion

The stability of the repeat protein IκBα , a transcriptional inhibitor in mammalian cells , is critical in the functioning of the NF-κB signaling module implicated in an array of cellular processes , including cell growth , disease , immunity and apoptosis ., Structurally , IκBα is complex , with both ordered and disordered regions , thus posing a challenge to the available computational protocols to model its conformational behavior ., Here , we introduce a simple procedure to model disorder in systems that undergo binding-induced folding that involves modulation of the contact map guided by equilibrium experimental observables in combination with an Ising-like Wako-Saitô-Muñoz-Eaton model ., This one-step procedure alone is able to reproduce a variety of experimental observables , including ensemble thermodynamics ( scanning calorimetry , pre-transitions , m-values ) and kinetics ( roll-over in chevron plot , intermediates and their identity ) , and is consistent with hydrogen-deuterium exchange measurements ., We further capture the intricate distance-dynamics between the domains as measured by single-molecule FRET by combining the model predictions with simple polymer physics arguments ., Our results reveal a unique mechanism at work in IκBα folding , wherein disorder in one domain initiates a domino-like effect partially destabilizing neighboring domains , thus highlighting the effect of symmetry-breaking at the level of primary sequences ., The offshoot is a multi-state and a dynamic conformational landscape that is populated by increasingly partially folded ensembles upon destabilization ., Our results provide , in a straightforward fashion , a rationale to the promiscuous binding and short intracellular half-life of IκBα evolutionarily engineered into it through repeats with variable stabilities and expand the functional repertoire of disordered regions in proteins .

It is well recognized that unstructured or disordered proteins play a vital role in the cell ., How does this disorder translate into function , and what effect does it have when linked to ordered regions ?, We attempt to answer this question by computationally characterizing the repeat protein IκBα , a central player in the NF-κB signaling module that possesses both structured and disordered domains ., Upon constraining a structure-based statistical mechanical model with equilibrium experiments , we are able to successfully predict both the ensemble kinetic and single-molecule behaviors ., Functionally , we unearth a unique mechanism in which the effect of disorder propagates , even into ordered regions , in a domino-like fashion , thus rendering the entire structure highly flexible ., In other words , the evolutionarily designed disorder in IκBα places it on a functional precipice that can be either recruited for binding to transduce external stimuli or just be degraded to shut down the inhibitory effect , reconciling both functional and folding behaviors in a single framework .

journal.pcbi.1005895

Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting

Malaria is a worldwide problem of clinical significance causing an estimated 483 , 000 deaths , with a disproportionate percentage occurring in children less than 5 years of age , according to the World Health Organization1 ., Additionally , 1 . 2 billion people are at high risk of contracting the infection1 ., Plasmodium is a challenging organism to understand and treat , since it has a complex life cycle2 and can remain latent within hosts ., Indeed , current antimalarials target the symptomatic Plasmodium life cycle stages , while , allowing ample time for transmission before symptoms are seen ., The use of experimental model organisms , such as mice , has provided a wealth of knowledge about the various life cycle stage in the Plasmodium genus; however many differences between rodent- , primate- , and human-infecting species remain incompletely understood ., Thus , to identify effective means to eradicate malaria , there is a need to understand its biological capabilities as it relates to drug targeting in different stages of its life cycle and also across the different species ., Among potential drug targets , metabolic genes are of particular interest , since many anabolic and catabolic processes are critical for cellular growth and survival ., Furthermore , methods have been developed to identify vulnerabilities in human pathogens by accurately predicting essential metabolic genes in genome-scale metabolic network reconstructions 3–14 ., Here , we present detailed genome-scale metabolic network reconstructions of five life cycle stages of Plasmodium falciparum ( P . falciparum ) ., We used these stage-specific genome scale metabolic models GeMMs to characterize functional metabolic features of each stage as well as to predict essential targets whose inhibition would interfere with malaria growth across asexual , sexual ( transmission ) and mosquito stages ., Moreover , we reconstructed GeMMs for four additional Plasmodium species that infect rodents , non-human and human primates ( including P . vivax , berghei , cynomolgi , and knowlesi ) ., We used these Plasmodium GeMMs to investigate cross-species similarities and differences with particular focus on characterizing functional metabolic differences between rodent- and human-infecting species ., Results provide a means to rank order and stratify established and new malaria treatment targets in addition to providing key insights into differences between rodent versus primate specific infections and implications for the interpretation of experimental animal models ., A manually curated and quality-controlled15 metabolic network reconstruction of P . falciparum ( Fig 1 , Step 1 ) , iAM-Pf480 , was constructed to interrogate the parasitic metabolic capabilities throughout the life cycle stages of malaria ., iAM-Pf480 ( Fig 2A ) was built using the genome annotation of P . falciparum ( Plasmodb . org , v24 ) , the Malaria Parasite Metabolic Pathway ( MPMP ) Database ( http://mpmp . huji . ac . il/ ) , and specific biochemical and genetic characterization studies from 332 primary and review literature reference articles ( Table A in S1 Tables ) ., The metabolic network of the P . falciparum accounts for 1083 reactions , 617 unique metabolites and 480 genes localized to their respective intracellular compartments and organelles , including the cytoplasm , mitochondrion , the plastid-like apicoplast , endoplasmic reticulum , Golgi apparatus , and lysosome ., Gene-protein-reaction ( GPR ) associations could be defined for 480 genes and 68% of all enzymatic reactions ( Fig 2A ) ., In order to validate ( Fig 1 , Step, 2 ) iAM-Pf480 predictions , we tested if iAM-Pf480 could correctly predict gene essentiality ( Fig 2B ) ., To accomplish this , we compiled a list of experimentally confirmed gene knockouts ( n = 21 , Table B in S1 Tables ) and phenotypes resulting from targeted inhibition of enzymatic activities with drugs ( n = 59 , Table C in S1 Tables ) in P . falciparum16 ., Under standard growth conditions , iAM-Pf480 correctly predicted 95% and 71% for the single gene knockouts and drug inhibition phenotypes , respectively ( Fig 2B , Table B and C in S1 Tables ) ., We also compared iAM-Pf480 gene essentiality predictions to iTH36617 , iPfa18 and iPfal1719 , using our set of experimentally validated targets ( Table B and C in S1 Tables ) , which revealed that iAM-PF480 accounts for a larger scope of genomic content , a larger biochemical complement , and functionally outperformed previously published P . falciparum models ( see supplementary material , Fig A and Table L in S1 Text , Table B and C in S1 Tables ) ., iAM-Pf480 flux predictions were validated against available rapid stable-isotope labeling data to assess the metabolic flux changes in glycolysis of wild-type ( WT ) versus apicoplast pyruvate dehydrogenase knockout ( PDHapi-KO ) P . falciparum parasites20 ., Glucose and hypoxanthine uptake rates21 , 22 were used to constrain the model ( Table D and E in S1 Tables ) ., Model-predicted glycolytic flux rates for both WT and PDHapi-KO showed good correlation ( Pearson correlation coefficients 0 . 77 and 0 . 78 for WT and PDHapi-KO , respectively ) with published experimentally measured fluxomic data20 ( Fig 2C and 2D ) ., Validation through fluxomic , single gene knockout , and drug targeting enzymatic assays provided confidence in the content and predictive capabilities of the metabolic model , setting the stage for further investigation into the consequences and capabilities of the parasites metabolic architecture ( Fig 1 , Step 3 ) ., Only approximately 1% of the asexual parasites develop into male and female gametocytes in response to yet unknown cues23 ., However , most current therapies are targeted against the blood stages , which result in clinical infections2 ., Thus , there is a pressing need to investigate potential targets that are critical for both the asexual and gametocyte stages to suppress both malarial transmission and active infection23; identification of such targets require understanding the stage-specific metabolic capabilities of P . falciparum ., Towards this end , stage-specific models of metabolism throughout malaria’s life cycle were constrained using multiple data types ( Fig 1 , step 3i ) ., Stage-specific growth rates24 , glucose and lactate secretion rates25 , as well as stage-specific transcriptomic data26 were used to constrain iAM-Pf480 producing five distinct stage-specific models; trophozoite ( T ) , schizont , early gametocyte ( GII ) , late gametocyte ( GV ) and ookinete ( ook ) ( Fig 3A ) ( see Table F in S1 Tables and Methods for details ) ., In the early gametocyte stage ( GII ) , biomass precursors production was permitted; however in the late , metabolically quiescent23 , 25 mature gametocyte stage ( GV ) , ATP generation was optimized27 without associated net biomass accumulation ., Generic , canonical reaction groupings into pathways do not inform functional states ., However , GeMM-based simulations can be used to calculate groups of reactions with highly correlated metabolic fluxes under a set of condition ( s ) , i . e . , correlated reaction sets ( co-sets ) , that in turn can yield insight into metabolic capabilities28 , 29 and also reduce network size into functionally correlated modules of reactions ., Artificial Center Hit and Run30 sampling was used to determine the steady state flux distributions of the stage-specific models , and these were used to compute co-sets across different conditions ( Table G in S1 Tables ) ( see Methods ) ., 612 reactions were involved in co-sets with 3 or more reactions spanning pathways related to anabolic and catabolic processes for amino acids , fatty acids , and biomass production ( Table G in S1 Tables ) ., A quantitative assessment of flux magnitude as well as co-set size across the different stages are visualized as Voronoi plots ( Fig 4A ) ., Comparison across the different stages reflects that the biomass constraint provides the greatest influence on the size and modularity of the metabolic network , thus the late gametocyte , with the most relaxed biomass constraint , had the most modularity where high modularity reflects networks with co-sets that are similar in size , whereas low modularity refers to networks that have one or two co-sets that are very large in comparison to the rest of the co-sets in the network ( Fig 4 ) ., Across the different stages , there were considerable changes in central carbon metabolism , based on the co-sets ( Fig 4 ) ., The metabolic simulations predicted splitting of glycolysis into independent upper and lower branches to divert biomass ( nucleic acids , lipids , glycosylated proteins ) required for proliferating parasite stages , whereas the late gametocyte stage is non-proliferative , accordingly , both the upper and lower glycolytic branches were in one co-set ., Contrary to what was believed about the pentose phosphate pathway ( PPP ) deploying the oxidative arm only during the early stages of the parasite intra-erythrocytic development cycle ( IDC ) and the non-oxidative arm in the later stages of the IDC 31 , simulations with stage-specific models showed that:, 1 ) both the oxidative and non-oxidative arms were active with low fluxes in the early stages ( trophozoite , schizont and GII ) ,, 2 ) the non-oxidative arm operated in the backward direction to produce glycolytic intermediates in the early stages while the oxidative arm produced NADP+ and ribose-5-phosphate ( r5p ) , and, 3 ) only the non-oxidative branch of PPP was active in the GV stage in the backward direction and was correlated with inositol metabolism ( Fig, 4 ) underscoring the importance of inositol metabolism across the transmissible stage of malaria32, 4 ) similar to the GV stage , only the non-oxidative arm was active in the ookinete stage , albeit in the forward direction using fructose-6-phosphate ( f6p ) and glyceraldehyde-3-phosphate ( g3p ) supplied by glycolysis , thus maximizing the production of r5p ( Fig 4B ) ., We constructed a comprehensive map of druggable targets for P . falciparum ( Fig, 5 ) using our curated list of experimentally validated targets ( Table B and C in S1 Tables ) ., This was used to compare to predictions made by the stage-specific P . falciparum ., Selecting stage-specific targets spanning the parasite’s life cycle could promote the design of strategies for potential multi-stage targets or combination of existing drugs ., The color scheme of highlighted reactions denotes model prediction classification across all stages ., The red group in Fig 5 highlights reactions sensitive in the proliferative stage as well as the late gametocyte ( GV ) stage; this is of particular importance since the late gametocyte is of high clinical interest to target and represent a large percentage of the parasitic load that is not targeted by some of the more commonly used treatment drugs ., Reactions associated with genes from experimentally validated single gene targets ( Fig 2B ) are highlighted in the colored rectangles in Fig 5 ., There are several gene deletion associated reactions for which drugs have not been developed; these highlight potential targets for new drug development ., We also note that the yellow group identifies reactions that were missed by the models , and highlights areas for future model refinement ., By overlaying stage-specific model predictions on top of the experimental ( single-stage ) validated drug targets , the network map aids in the prioritization of drug target characterization ., The use of experimental model organisms , such as mice , has yielded a wealth of knowledge about the Plasmodium genus ., However , there has been relatively limited investigation into species-specific differences in Plasmodium metabolism33–35 ., While there is 94% ( 422/448 ) homology among the metabolic genes of the different species ( Table H in S1 Tables ) , it is unclear how they differ in their metabolic capabilities ., Further , differences have been observed between rodent- and human-malaria infecting species to certain drug inhibitors36 but no mechanistic explanation was attributed to these differences ., Beginning with the iAM-Pf480 GeMM we systematically studied the functional metabolic differences between 5 different Plasmodium species ( Fig 1 , Step 3ii ) : Plasmodium falciparum 3D7 ( Pfal ) , Plasmodium vivax ( Pviv ) Sal-1 ( iAM-Pv461 ) , Plasmodium berghei ( Pber ) ANKA ( iAM-Pb448 ) , Plasmodium cynomolgi ( Pcyn ) strain B ( iAM-Pc455 ) , and Plasmodium knowlesi ( Pkno ) strain H ( iAM-Pk459 ) ., The core metabolic content reflects the intersection of the genes , reactions , and metabolites of all five reconstructions , whereas the pan metabolic content is the union of these entities ., The core Plasmodial metabolic content is comprised of 1064 reactions and 422 orthologous genes ( Fig 6A ) and the pan metabolic capabilities include 1083 reactions corresponding to 448 orthologous genes ( Table H in S1 Tables ) , reflecting a considerable level of conservation ., However there are multiple functional differences cross the metabolic GeMMs ( Fig 6B , Table H and I in S1 Tables ) ., The differences in metabolic reaction content across the five reconstructed species predominantly involve co-factor metabolism ( 4 reactions ) , phospholipid metabolism ( 4 reactions ) , and purine/pyrimidine metabolism ( 3 reactions ) ( Fig 6B ) ., We performed in silico single gene deletion analysis for the set of 448 orthologous genes shared among the five species ( Table H in S1 Tables ) ., The deletion of 15% ( 67/448 ) of these orthologous genes caused a 100% reduction in growth across all species ( Table H in S1 Tables ) ., These genes spanned several metabolic subsystems with the majority involved in isoprenoid biosynthesis , phospholipid metabolism , as well as purine and pyrimidine metabolism ., Interestingly , 19 genes out of this set have already been targeted by drug inhibitors ( Table C in S1 Tables ) while the remaining 48 orthologous genes represent overlooked novel druggable vulnerabilities in malaria ., 14% ( 61/448 ) of the orthologs differed in their essentiality across the 5 Plasmodium species ., Reactions with the most striking differences in essentiality across malaria species were between the rodent and non-rodent species , namely: thiamine pyrophosphokinase ( TPK ) , and choline kinase ( Table H in S1 Tables ) ., Both genes were predicted to be essential in P . berghei only , while their deletion had no effect on growth in any of the non-rodent species ., Thiamine analogs36 and choline kinase ( CK ) inhibitors37 have been tested as antimalarial therapeutics both in plasmodium species that infect human ( in vitro ) and rodents ( in vivo ) ; however , it is not clear from these studies whether an equally potent antimalarial effect is observed in both species ., Our multi-species reconstructions revealed three key thiamine ( Vitamin B1 ) biosynthesis enzymes: phosphomethylpyrimidine kinase ( PMPK ) , hydroxyphosphomethylpyrimidine kinase ( HMPK1 ) , and thiamine-phosphate pyrophosphorylase ( TMPPP ) that are absent in rodent malaria , but present in non-rodent malarial species ( Fig 6C ) ., Consequently , TPK was predicted to be essential for rodent malaria , but non-essential in non-rodent species , which can replenish thiamine pyrophosphate through the HMPK1-PMPK1-TMPPP pathway ( Fig 6C ) ., In silico deletion of choline kinase ( CK ) , the first enzyme in the Kennedy pathway ( CDP-choline pathway ) for synthesis of phosphatidylcholine ( PC ) ( Fig 6D ) , inhibited growth of the rodent species while causing only marginal reduction in growth ( 3% ) in the primate and human species ., The model simulations revealed the difference in essentiality of CK was the result of the lack of phosphoethanolamine N-methyltransferase ( PMT ) in P . berghei , which in turn rendered it incapable of de novo PC synthesis from ethanolamine ( Fig 6D ) ., Thus , efforts to perturb PC for malaria treatments will require different strategies in human species than in non-human-infecting species since decreased potency is expected when perturbing choline metabolism in the non-rodent relative to rodent-infecting species ., A comparative analysis of stage-specific models of human and rodent species ( see Methods and supplementary material for details ) , based on co-sets and in silico single gene deletion experiments , showed that pantothenate metabolism was not essential for growth in any of the life cycle stages of P . berghei ., In contrast , pantothenate metabolism was essential for growth during the asexual and early gametocyte stages of P . falciparum ( Fig 6E , Table K in S1 Tables ) ., In line with recent evidence38 , our stage- and species-specific models predicted that the pantothenate transporter activity was essential in human malaria , but was mostly dispensable in rodent parasites ., Pantothenate is a precursor of the enzyme cofactor coenzyme A ( CoA ) and the capability of de novo synthesis of CoA distinguishes P . falciparum asexual forms from its sexual counterparts as well as from the rodent and avian malaria parasites , thus challenging the assumption that rodent and human malaria parasites utilize similar nutrient acquisition strategies39 ., Therapeutic drugs that target multiple stages of the parasite , including sexual and asexual stages , will facilitate eradication of malaria ., Developing effective medications will require understanding basic biological mechanisms , particularly the limitations of experimental animal models that are used as surrogates for understanding human Plasmodium pathogenesis ., Systems analysis are thus needed to interpret and integrate multiple , large disparate datasets to unravel the complex life cycles of these pathogens ., In this study we use genome-scale metabolic modeling to interrogate malaria stage- and species-specific metabolic capabilities ., Through the integration of high-throughput data , careful manual curation , and model prediction validation , we reconstructed detailed stage-specific models that span five distinct stages of the life cycle of P . falciparum ., Since GeMMs allow condition-specific analyses , we were able to simulate the effects of reaction inhibition across different stages of the parasite’s life cycle and to identify drugs that are effective across more than one stage ., Moreover , we detected stage-dependent metabolic redirection of flux in central carbon metabolism of the parasite that stage-matched proliferation requirements ., The overall genome organization and content across Plasmodium species is highly conserved , with about 4000 conserved syntenic genes located within the central core regions of the 14 chromosomes40 ., Subsequently , it is frequently assumed that findings from the animal models will directly translate to the human-infecting species , particularly in areas of essential , core metabolism , given the high degree of homology across the malaria species ( 94% ) ., However , our GeMMs for multiple Plasmodium species highlight important metabolic differences ., The GeMM simulations provide a mechanistic explanation for why P . berghei would be more sensitive to thiamine analogs as well as to drugs interfering with PC metabolism36 and not in human infecting parasites ., Additionally , differences in pantothenate metabolism were revealed in stage-specific analysis of P . falciparum and P . berghei , further highlighting potential differences between the metabolism of human- versus rodent-infecting species ., This finding suggests a potential use of an auxotrophic mutant of P . falciparum defective in the de novo biosynthesis of pantothenate for vaccination in analogy to Mycobacterium tuberculosis41 ., These multi-species GeMMs have enabled us to make informed predictions about specific differences between rodent and non-rodent metabolic capabilities , underscoring the fact that the metabolic architecture and nutritional requirement of a rodent malaria species does not necessarily predict that of a human malaria species39 ., Furthermore , in addition to using the models to make predictions and gain systems-level insights to malaria metabolism in relation to drug targeting , these models can be used as a foundational structure upon which additional high-throughput data can be analyzed and predictive simulations can be conducted , thus leading to improved understanding , testable hypotheses and increased knowledge42 ., The genome sequence and genome annotations for P . falciparum were downloaded from Plasmodb . org ( release 26 ) ., A list of P . falciparum metabolic pathways was built based on current genome annotation of P . falciparum ( Plasmodb . org ) , the Malaria Parasite Metabolic Pathway ( MPMP ) Database ( http://mpmp . huji . ac . il/ ) , and malaria-specific biochemical characterization studies ( Table A in S1 Tables ) ., The stoichiometric matrix was constructed with mass and charge balanced reactions in the standard fashion and flux balance analysis was used to assess network characteristics and perform simulations44 ., Linear programming calculations were performed using Gurobi ( Gurobi Optimization , Inc . , Houston , TX ) and MATLAB ( The MathWorks Inc . , Natick , MA ) with the COBRA Toolbox45 , 46 ., We tested iAM-Pf480-predicted flux rates against kinetic flux data ( rapid stable-isotope labeling ) of glycolysis in wild-type ( WT ) and pyruvate dehydrogenase ( PDH ) deficient P . falciparum parasites cultured in vitro20 ., The generic iAM-Pf480 model was allowed to uptake metabolites available in standard in vitro growth conditions ( RPMI 1640 , 25 mm HEPES , 2 mm l-glutamine supplemented with 50 μm hypoxanthine and 10% A+ human serum20 ) ( Table D in S1 Tables ) ., Uptake rates for glucose and hypoxanthine were obtained from literature21 , 22 ., For validation of in silico single gene deletion essentiality predictions , we compiled a curated list of experimentally validated gene knock-outs ( n = 21 , Table B in S1 Tables ) and phenotypes resulting from targeted inhibition of enzymatic activities with drugs ( n = 59 , Table C in S1 Tables ) in P . falciparum based on our recently published list16 of targeted chemical compounds in MPMP ., An experimentally measured growth rate ( lower bound of 0 . 045 mmol/gDW/h corresponding to approximately 15 hours24 was imposed on the biomass function ) ., Lactate secretion for asexual stages ( 93% of uptake glucose ) was applied to the iAM-Pf480 model simulating in vitro growth conditions ( see fluxomics data section ) ., For the gametocyte stages , we developed two models ., The first model ( GII ) simulated early gametocyte stage II which is metabolically active and hence , the objective function was set to maximize the production of biomass precursors ., The constraint on the lower bound of the biomass function was relaxed to 0 mmol/gDW/h since it’s expected that the early gametocyte stages will exhibit a lower growth rate compared to the asexual stages ., Lactate secretion was set to a minimum of 80% of glucose uptake rate25 ., The second model ( GV ) represents mature , metabolically quiescent gametocyte stages ., The objective function in the GV model was to set to optimize ATP production23 and while no flux was allowed in the biomass function ( lower bound = 0 and upper bound = 1e-9 ) ., Uptake of N-acetyl glucosamine ( GlcNAc ) was allowed in both gametocyte models since GlcNAc induces gametocytogensis47 ., For the ookinete model , the glucose uptake was constrained to 10% of the asexual stages glucose uptake rate since the mosquito gut is a glucose-rare environment25 ., P . falciparum 3D7 life cycle stage-specific RNA-Seq data was downloaded from SRA archive ( SRP009370 ) 26 ., SRA files were converted to fastq files using the sra-toolkit48 ., Tophat249 was used for the alignment ( —library-type fr-unstranded ) libraries ., PICARD ( http://broadinstitute . github . io/picard/ ) and samtools50 were used for processing the aligned reads and HTSeq51 was used to produce read counts ( —stranded = no ) ., The normalized read counts were then used to further constrain the stage-specific models ( Fig B in S1 Text ) ., Validation in part was performed from growth rate predictions of overall biomass production rates in each life cycle stage of P . falciparum ( Table F in S1 Text ) ., The predictions showed an overall qualitative agreement with the experimentally observed growth phenotypes of the parasite during the asexual and sexual stages ., Specifically , our models successfully predicted significant increase of the growth rate ( FDR < 0 . 05 ) by 1 . 8 and 20 fold in the trophozoite relative to the early and late gametocyte stages , respectively ( Table F in S1 Text ) ., The ookinete model was the only malaria stage-specific model that was able to grow in absence of glucose ( although the reduction in growth was 93% ) , which is in line with the glucose-rare medium in the mosquito gut where this stage develops25 ., Stage-specific model predictions were compared against differential gene expression ( DEG ) following a previously published workflow52 , outlined in Fig B in S1 Text ., Briefly , differential gene expression analysis was carried out between every two stages and the lists of significantly differentially expressed genes ( FDR < 0 . 05 and ( > 75th or < 25th percentile of the log2 fold change in expression ) ) were later used for evaluation of stage—specific models’ predictions ., The network flux states were sampled and significantly different reactions ( FDR < 0 . 05 and ( > 75th or < 25th percentile of the log2 fold change in reaction fluxes ) ) were identified following removal of loop reactions ., The corresponding genes were selected using gene-protein-reaction relationships and were compared against the list of significantly differentially expressed genes ., Correlated reaction sets ( co-sets ) were calculated using the sampled steady state solution points for the iAM-Pf480 stage-specific models ( COBRA toolbox46 ‘identifyCorrelSets’ with a correlation cutoff threshold of 0 . 95 ) ., Only co-sets containing 3 or more reactions were labeled , since these co-sets generally represent transport of individual metabolites and not biochemical pathways per se ., Sampled reaction fluxes in the pentose phosphate pathway were compared across the different stages and differential flux activity was acknowledged if the flux distributions were significantly different following multiple hypothesis correction , as previously described52 ., The modularity of the co-sets was assessed using the ratio of the mean size of co-sets divided by the maximum size of the co-sets for each stage ., Voronoi plots were generated using TreeMap ( v . 3 . 8 . 3 ) using the co-sets annotation ( Table G in S1 Tables ) and sampled flux distribution of each reaction in the corresponding co-set ., Genome-scale metabolic models were reconstructed for five Plasmodium species ( P . falciparum ‘Pfal’ , P . knowlesi ‘Pkno’ , P . vivax ‘Pviv’ , P . cynomolgi ‘Pcyn’ , and P . berghei ‘Pber’ ) ., The details of the procedure for building Plasmodium multi-species genome-scale metabolic models are outlined in ( Fig C in S1 Text ) .

Introduction, Results, Discussion, Materials and methods

Several antimalarial drugs exist , but differences between life cycle stages among malaria species pose challenges for developing more effective therapies ., To understand the diversity among stages and species , we reconstructed genome-scale metabolic models ( GeMMs ) of metabolism for five life cycle stages and five species of Plasmodium spanning the blood , transmission , and mosquito stages ., The stage-specific models of Plasmodium falciparum uncovered stage-dependent changes in central carbon metabolism and predicted potential targets that could affect several life cycle stages ., The species-specific models further highlight differences between experimental animal models and the human-infecting species ., Comparisons between human- and rodent-infecting species revealed differences in thiamine ( vitamin B1 ) , choline , and pantothenate ( vitamin B5 ) metabolism ., Thus , we show that genome-scale analysis of multiple stages and species of Plasmodium can prioritize potential drug targets that could be both anti-malarials and transmission blocking agents , in addition to guiding translation from non-human experimental disease models .

Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite ., Plasmodial infections are difficult to treat for a myriad of reasons , but the ability of the organism to remain latent in hosts and the complex life cycles greatly contributed to the difficulty in treat malaria ., Genome-scale metabolic models ( GeMMs ) enable hierarchical integration of disparate data types into a framework amenable to computational simulations enabling deeper mechanistic insights from high-throughput data measurements ., In this study , GeMMs of multiple Plasmodium species are used to study metabolic similarities and differences across the Plasmodium genus ., In silico gene-knock out simulations across species and stages uncovered functional metabolic differences between human- and rodent-infecting species as well as across the parasite’s life-cycle stages ., These findings may help identify drug regimens that are more effective in targeting human-infecting species across multiple stages of the organism .

medicine and health sciences, parasite groups, plasmodium, gametocytes, tropical diseases, vertebrates, plasmodium falciparum, parasitic diseases, parasitic protozoans, animals, parasitology, mammals, germ cells, developmental biology, apicomplexa, protozoans, pharmacology, drug metabolism, malarial parasites, animal cells, life cycles, pharmacokinetics, rodents, eukaryota, cell biology, biology and life sciences, cellular types, malaria, amniotes, organisms, parasitic life cycles

journal.ppat.1002861

Exon Level Transcriptomic Profiling of HIV-1-Infected CD4+ T Cells Reveals Virus-Induced Genes and Host Environment Favorable for Viral Replication

CD4+ T cells – the primary cellular target of HIV-1 – are progressively depleted over the course of infection ., This long-term process culminates in the onset of AIDS , a condition in which the immune system is too weak to efficiently mount an effective defence against opportunistic pathogens ., Yet , HIV-1 uses only 15 proteins to disable the natural immune defences and harness the host cell machinery to complete its replicative cycle ., To do so , viral proteins interact with multiple cellular proteins , perturbing the normal flow of cellular processes ., Moreover , the virus influence extends beyond the cells it infects ., Indeed , the apoptosis rate of uninfected bystander CD4+ T cells is elevated in individuals carrying HIV-1 1 ., The dichotomy between uninfected bystander and HIV-1-infected CD4+ T cells is an important topic to study , as a deeper understanding of HIV-1 pathogenesis mechanisms might lead to new therapeutic approaches ., Powerful technologies developed in recent years have provided high-throughput tools to study cellular dynamics ., Among them , microarrays allow for the quantification of expression levels of thousands of genes at once ., Since the inception of this technology , few studies have used microarrays to characterize the effect of HIV-1 on various cell types that compose the immune system ., However , productive infection rates in primary human cells such as CD4+ T lymphocytes are very low ., As microarray technology captures the average transcriptomic profile of a cell population , achieving a high level of purification of subpopulations of interest is crucial to accurately quantify any possible virus-mediated changes in the host transcriptome 2 ., We recently developed a new reporter virus system that allows the efficient separation of HIV-1-infected cells from their uninfected bystander counterpart in vitro 3 ., In the current work , we used this unique and versatile tool to define the HIV-1-induced modulation of host gene expression in one of its most worrisome cellular reservoir ., We hereby provide the first time-course comparative and comprehensive study of the effect of HIV-1 in productively infected versus uninfected bystander primary human CD4+ T cells using Affymetrix Human Exon arrays ., We pinpoint understudied genes which seemingly play important roles in the subset of CD4+ T cells preferentially infected by HIV-1 and provide an overview of splicing events found in this subpopulation ., We next performed a comparative microarray analysis of mock-infected , uninfected bystander ( HSA− ) and HIV-1-infected primary human CD4+ T cells ( HSA+ ) in an attempt to identify both virus-induced genes and intracellular environment most permissive to productive infection ., To this end we magnetically separated virus-infected cells from three different donors on the basis of HSA expression from the whole cell population exposed to the R5-tropic reporter virus for 24 , 48 or 72 h ( Figure 1C ) ., We next confirmed productive HIV-1 infection in isolated cell fractions using a spliced Tat-specific qRT-PCR based on the idea that such a sensitive technique allows the quantification of early expressed viral transcripts without detection of input viral RNA ., Data shows a strong signal in the fractions containing HSA-expressing cells ( called HIV Pos ) , while uninfected bystander cell fractions ( called HIV Neg ) and mock-infected ( called Mock ) displayed an almost complete absence of spliced Tat , thus indicating a very high degree of cell purification ( Figure 1D ) ., Thereafter , we extracted total RNA from the studied cell fractions and performed transcriptome profiling using Affymetrix Human Exon arrays , which interrogate over 5 million probes spanning all exons in the human genome ., A careful analysis of differentially expressed genes ( DEGs ) using a false discovery rate of 1% and a cut-off of 1 . 7 fold showed no effect of HIV-1 on the transcriptome of uninfected bystander cell population compared with mock-infected cells at any studied time point or in aggregate ( Figure 2A , left panels ) ., Comparison between mock-infected and HIV-1-infected cells revealed 287 , 236 and 176 DEGs at 24 , 48 and 72 h post-infection , respectively ( Figure 2A , middle panels ) while the aggregate comparison of all time points yielded 289 genes ., Given that no DEG was identified in the uninfected bystander cell population , we then compared cells productively infected with HIV-1 to uninfected bystander cells to improve our statistical power , the rationale being that the separation procedure allows to isolate the small percentage of virus-infected cells by removing them from the uninfected bystander cell fraction ( constituting the majority of cells ) , creating a better signal differential than the one obtained comparing with the mock-infected fraction ., By doing so , we obtained 502 , 366 and 323 DEGs ( at 24 , 48 and 72 h ) in HIV-1-infected cells while the aggregate comparison yielded 464 DEGs ( Figure 2A , right panels ) ., Proportional Venn diagrams depict the relative distribution of DEGs and their evolution through time for these two comparisons ( Figure 2B ) ., An overview of all modulated genes is presented in Figure 2C as a hierarchical cluster ., It can be concluded that most DEGs identified in HIV-1-infected target cells ( Figure 2C ) are stable at least during the studied period , whereas expression of a small cluster of genes increases significantly over time ., Complete lists of DEGs that were found to be modulated are depicted in Dataset S1 ., We broadly defined the characteristics of the dataset using the metadata clustering engine DAVID , which identifies enriched biological themes within a list of genes using various biological annotation sources 7 ., Using the combined list of 835 DEGs identified in all comparisons performed , we found the following statistically over-represented categories: immune system process , cytokine-receptor interaction , regulation of leukocyte activation , Map kinase phosphatases , FOS/JUN related genes , positive/negative regulation of apoptosis and p53 pathways ( Figure 3 ) ., The first three identified categories overlap significantly and contain markers for Th1 ( i . e . IFN-γ , TNF-α , TNF-β , IL-1A , IL-3 , and TBX21/T-bet ) , Th2 ( i . e . IL-4 , IL-5 , IL-10 , and IL-13 ) and Th17 profiles ( i . e . IL-17A , IL-17F , IL-21 , IL-22 , IL-23R , and IRF4 ) , all of which are over-expressed in primary human CD4+ T cells productively infected with HIV-1 ( Figure 3 , top left ) ., The expression patterns associated with these different profiles suggest that Th1 and Th17 cells are slightly more susceptible to infection with the studied R5-tropic reporter virus , as virus-infected cells express more IFN-γ and IL-17A than other cytokines ( Figure S1 ) ., Other molecules associated with the effector phenotype and activated T cells in general are also differentially expressed ( i . e . IL4R , IL18R , MCSF , GMCSF , ICAM-1 , OX40 , OX40L , CD27 , and its counterpart CD70 , CD80 , CTLA4 , CD69 , CD40LG , IL2RA , FASLG , IL12RB2 , IL18R1 , IL18RAP , and IL-9 ) ., Their expression pattern is consistent with highly activated TEM cells being preferentially infected by HIV-1 ., Several Map kinase phosphatases involved in T-cell activation are also modulated in virus-infected cells ( Figure 3 , bottom right ) ., Fos and Jun bind together to form the AP-1 transcription factor 8 , which is essential for the differentiation and proliferation of lymphocytes ., Multiple AP-1 binding sites are found in the HIV-1 LTR 9 and it has been demonstrated that this transcription factor promotes viral transcription 10 ., The DAVID analysis pinpointed a cluster of over-expressed Fos-related genes ( i . e . Fos , Jun and related genes BATF3 , ATF3 , FOSB , FOSL1 , and FOSL2 ) in virus-infected cells ( Figure 3 , bottom left ) ., Interestingly , a promoter analysis of the genes modulated in HIV-1-infected cells reveals that 57% of DEGs identified in this study and 70% of core genes ( differentially expressed at 3 time points ) contain at least one binding site for AP-1 ( Figure S2 ) , confirming that this transcription factor is one of the core determinants of the cellular environment that is most favorable to productive HIV-1 infection in primary human CD4+ T cells ., Among DEGs found precisely in HIV-1-expressing cells are genes involved in both positive and negative regulation of apoptosis ( Figure 3 , top right ) ., Most of these genes appear to be an integral part of the genetic programme engaged in virus-infected cells and are stable through time ., However , expression a subset of genes increases rapidly at 48 and 72 h post-infection , thus suggesting that such DEGs are a consequence rather than the determinant of viral gene expression ., Further analysis shows that these genes are implicated in p53-dependent apoptosis – specifically CDKN1A/p21 , MDM2 , GADD45A , GADD45G , TNFRSF10B ( TRAIL ) , ATM , ZMAT3 , PMAIP1 ( NOXA ) , BCL2L11 , PERP , TP53I3 , RRM2B , and SESN3 ( Figure 3 , bottom right ) ., Additionally , the p53 pathway is identified by the DAVID analysis as the most significantly over-represented ontology category among the 180 genes exclusive to the 48 h and 72 h time points ( p<0 . 0001; data not shown ) ., The expression pattern of these genes over time is in agreement with the numerous reports of p53-related apoptosis in HIV-1-infected cells following DNA damage caused by the virus integration process 11 , 12 , 13 , 14 , 15 ., However , patterns of differential expression of p53-regulating genes emerge as early as 24 h , before HIV-1-induced apoptosis occurs ., For example , MDM2 , a factor responsible for the degradation of p53 16 , is over-expressed in virus-infected cells ., On the other hand , ATM , a gene involved in phosphorylation and inactivation of MDM2 17 , is under-expressed during this same period ., This suggests that cells with low potential for p53 activation are more susceptible to productive HIV-1 infection , perhaps due to their slower reaction time for triggering p53-dependent apoptosis following viral integration , giving the virus more time to replicate actively ., The DAVID analysis is useful for broad categorization but is ultimately insufficient to fully extract biological significance from the dataset ., A bibliography analysis was thus performed to visualize known relationships between genes present in the dataset ., Bibliosphere ( Genomatix ) extracts relationships from co-citation of gene names ( including synonyms ) in abstracts from Medline ., The resulting network illustrated in Figure 4A is consistent with the previous analysis , as the already identified over-represented categories cluster together ., Moreover , the analysis uncovered numerous DEGs closely related to these clusters , but missed by the DAVID analysis ., For example , it pinpointed CDC25A , CDC25C , and CDC14A near the p53 genes cluster ., The pattern of expression of these genes is consistent with cells being most permissive to HIV-1 infection when entering in the M-G2 phase of the cell cycle 18 ., The graph can also help identify small clusters of genes that would otherwise have gone unnoticed ., Notably , furin and PCSK5 proteases , which are both known to participate in the cleavage of viral glycoprotein gp160 19 , 20 , 21 , 22 , show opposite expression patterns ., Indeed , furin is over-expressed in virus-infected cells , while PCSK5 is under-expressed at the 24 h time point ., This implies that , although both proteases can cleave the viral envelope in vitro , furin is favored in acute HIV-1 infection studies ., The graph file is available as Dataset S2 and can be explored dynamically by using graph visualization software Gephi ( http://gephi . org/ ) ., It was recently suggested that poorly characterized genes deserve a more careful analysis 23 ., We performed a careful literature analysis and found 178 unconnected genes for which no known relationship with other genes in the dataset is currently described and/or little information is available ( Figure 4A ) ., We repeated the DAVID analysis on this subset of genes and found two over-represented protein domains , i . e . Krüppel associated box ( KRAB ) and GTPase regulator activity ( Figure 4B ) ., The KRAB domain is mainly found in KRAB-ZNFs , a large family of mammalian transcription factors responsible for negative regulation of transcription through chromatin remodelling via their association with KAP1 24 ., They have been recently implicated in control of endogeneous retroelements 25 ., Most of the modulated KRAB-ZFPs are under-expressed in virus-infected cells , which might indicate that their absence creates a permissive environment for HIV-1 , probably by playing a role in T-cell regulation ., Two of the identified KRAB-ZFPs can modulate crucial components of HIV-1 and lymphocyte biology ., Indeed , overexpression of ZNF383 inhibits the transcriptional activities of AP-1 26 , while ZNF675 can suppress TRAF6-induced activation of NF-κB and c-Jun N-terminal kinase 27 ., Interestingly , the two members that are over-expressed in virus-infected cells show mutations in the MLE motif of their KRAB domain ( i . e . ZNF79 and ZNF282 ) ( Figure S3 ) , a pattern associated with loss of repression potential for KRAB-ZFPs 28 ., It should be noted that ZNF79 is also a known p53 target 29 ., The second over-represented category contains GTPase regulators , mainly of the RHO/RAC family ., These influence a variety of cellular processes such as endocytic trafficking , actin dynamics and cell growth by affecting the rate of GTP hydrolysis by GTPases 30 ., Many of the identified members either participate in T-cell signaling responses or directly influence proteins known to be important for HIV-1 entry in human primary CD4+ T cells , such as HERC1 ( under-expressed in virus-infected cells ) that is known to regulate ARF6 31 , 32 ., Amongst the list of DEGs in HIV-1-infected cells , most genes showing high levels of differential expression are poorly studied ., Since we isolated cells productively infected with HIV-1 from the total population ( primarily composed of uninfected bystander cells ) , under-expressed genes potentially impede HIV-1 at some step of its replicative cycle , while the reverse holds true for over-expressed genes ., For example , TRIM22 , known for its role in inhibition of HIV-1 transcription 33 , is under-expressed in virus-infected cells ., Of note TRIM22 RNA levels progressively return to normal , suggesting that HIV-1 can overcome its effects over time ( Table 1 ) ., Transcription factors known to repress HIV-1 transcription such as YY1 34 and BCL11B/CTIP2 35 are also found to be under-expressed in virus-infected cells ., MARCH8 , a regulator of vesicular transport of proteins between cellular compartments , was recently identified in a large scale siRNA screen as a top candidate for the inhibition of HIV-1 infection 36 and is found under-expressed in virus-infected cells in the present dataset ., We hereby present a list of the most promising understudied genes in regard to their importance for HIV-1 pathobiology and T lymphocyte biology ., For example the transcription factor ZBED2 is highly over-expressed in HIV-1-infected cells ., Although little is known about its function , this gene has been reported to be over-expressed in differentiated T cells 37 , 38 ., Its precise direct and indirect roles in the regulation of HIV-1 expression or lymphocyte differentiation remain to be more clearly defined ., GJB2 and GJB6 are connexins that are members of the gap junction protein family involved in the formation of cell-cell channels 39 ., It is possible that they play a role in viral entry or cell-cell transmission ., GSDMB and DFNA5 are two members of the gasdermin protein family – DFNA5 has been recently shown to participate in p53-dependent apoptosis 40 ., MYOF is a membrane-associated protein involved in both caveolin and clathrin-mediated endocytosis pathways along with membrane resealing after damage 41 ., This gene is highly over-expressed in HIV-1-infected cells and could participate in the repair of the plasma membrane following the budding of a multitude of virions , allowing the cell to live longer and to produce more viruses ., The expression pattern of CLC ( also called galectin-10 ) differs from that of other modulated genes ., CLC mRNA levels are significantly diminished in virus-infected cells whereas they steadily increase over time in uninfected bystander cells ( although not reaching statistical significance ) ., CLC has been shown to be a crucial determinant of Treg suppressor function 42 ., PCR quantification for the aforementioned genes reveals excellent concordance with the microarray data ( Table 1 ) ., We are currently investigating the precise role of some of these genes in the HIV-1 infection process and T-cell biology ., Affymetrix Exon arrays allow for the detection of alternative splicing events ., Using the PECA-SI algorithm 43 , we detected 323 , 129 and 107 differential splicing events in transcripts of virus-infected versus uninfected bystander CD4+ T cells at 24 , 48 and 72 h post-infection , respectively ( Figure 5A and Dataset S3; see Methods for filtering parameters ) ., Similarly to gene expression , more events were detected when comparing HIV-1-infected to uninfected bystander cells than to mock-infected ones , and no alternative splicing events were detected in the uninfected bystander population ., We looked for a splicing event known to occur in HIV-1-infected cells ., In T lymphocytes , CD45 isoforms are a marker of naive ( CD45RA ) and memory phenotypes ( CD45RO ) – the latter being preferentially infected by HIV-1 44 ., This is indeed reflected in the data , as exon 4 of CD45 ( corresponding to CD45RA ) is under-expressed in virus-infected cells , implying that this fraction contains more CD45RO ( Figure 5C , top ) ., We next focused our attention on events susceptible to be important in the biology of both HIV-1-infected and CD4+ T cells ., Multiple events are detected in the C-terminal portion of inositol 1 , 4 , 5-triphosphate receptor ( ITPR1 ) , yielding a short isoform containing only the calcium-channel domains of the protein ( Figure 5C , center ) ., ITPR1 plays a role in lymphocyte activation 45 and this isoform could represent a constitutionally active form of the receptor present in highly activated and/or memory cells ., RUNX1 ( AML1/EVI1 ) is a master regulator of hematopoietic development important in T-cell differentiation 46 and is known to have multiple isoforms arising from alternative promoter usage 47 ( Figure 5C , bottom ) ., We identify a short isoform of RUNX1 being enriched specifically in virus-infected cells ., The isoforms of RUNX1 have been implicated in different stages of haematopoiesis 47 ., However their precise role in T cells or potential interaction with HIV-1 are still unclear ., Other interesting alternative splicing events include an isoform of LEF1 , a transcription factor implicated in the regulation of HIV-1 transcription 48 , and EVI5 , an oncogene implicated in cell cycle control 49 – both are RUNX1 interaction partners ., qRT-PCR confirmation for all the aforementioned events was performed and results are highly consistent with microarray-acquired data ( Figure 5B ) ., In this manuscript , we provide the first comparative analysis of exon-level transcriptomic profiles between HIV-1-infected primary human CD4+ T cells and their uninfected bystander cell counterparts ., By doing so , we define the virus-induced genes and microenvironment most favorable to allow productive HIV-1 infection and show that even within a population of activated CD4+ T cells , the permissive environment for HIV-1 infection is very specific ., The profile of virus-infected cells is consistent with activated/effector memory CD4+ T cells expressing high levels of cytokines ., We found that Th1 and Th17 were to some extent more permissive to virus infection in this specific in vitro experimental setting ., The expression patterns of most genes identified in this study are in agreement with the current literature in regard to HIV-1 and lymphocyte activation status , which provide significance to our observations ., However , it must be emphasized while we rediscovered many well-studied genes and pathways known to be important for HIV-1 , the precise function of a large number of other genes that were identified in this work is still currently poorly described ., We believe that efforts should be made to understand their function , as yet unexplored avenues might allow deepening our understanding of the interplay between HIV-1 and lymphocyte biology ., The design of our microarray experiment captures both the transcriptomic portrait of highly permissive cells and the changes induced by the virus itself ., While a clear discrimination between theses two events is complicated by confounding factors such as asynchronous infection and a potential cell death of some CD4+ T cells , a longitudinal analysis of the data allows us to achieve a comparison of gene expression patterns between HIV-1-infected and uninfected bystander cells ., We concluded that genes for which levels change significantly over time in virus-infected cells are directly modulated by HIV-1 ., As an example , the p53 apoptosis pathway is clearly induced in virus-infected cells at 48 and 72 h post-infection ., However , the vast majority of differentially expressed genes are relatively stable over time in virus-infected CD4+ T cells and thus define the transcriptomic programme of a subpopulation preferentially infected by HIV-1 ., In this context , over-expressed genes are potential viral permissiveness factors , while underexpressed genes are candidate restriction factors ., Functional assays are currently underway to determine which of the newly identified candidate genes play a key role in HIV-1 and/or lymphocyte biology ., One of our primary objectives was to identify changes occurring in uninfected bystander CD4+ T cells exposed to HIV-1 , as previous studies have reported dramatic effects in cells exposed to virions or its components ., To this end , we purposely left the initial viral inoculum in our purified CD4+ T cell populations to allow for putative virus-mediated signal transduction events ., We were surprised to find no significant changes in the transcriptome of the uninfected bystander cell population at least at early time points after HIV-1 infection ( i . e . 24 , 48 and 72 h ) ., It is possible that the presence of cells other than CD4+ T lymphocytes is required to mediate changes in gene expression in uninfected bystander cells ., We nonetheless detected an increase of apoptosis in uninfected bystander cells at late time points ( data not shown ) ., This could imply that gene regulation is not necessary for apoptosis induction ., For example , direct caspase activation induced by FAS or TRAIL ligation could explain this phenomenon ., It should also be noted that we used an R5-tropic variant of HIV-1 , as this tropism is more representative of early infection events ., However , it cannot be excluded that an X4-tropic virus would have an effect on the transcriptome of uninfected bystander cells , as this variant characteristic of late-stage infection is known to have higher apoptosis-inducing activity 50 ., On a similar note , a somewhat different HIV-1-mediated gene expression pattern might be obtained when using a distinct R5-tropic virus ., Additional experiments are needed to solve these issues ., As we demonstrated in this work , the isolation of human primary CD4+ T cells productively infected with HIV-1 is a powerful approach which amplifies the power of transcriptome analysis ., We believe that further dissection of virus-infected CD4+ T cell subtypes could yield even more information , as the profile we obtained is undoubtedly an average of different types of susceptible subpopulations ., While observations made in this manuscript describe the relationship between HIV-1 and CD4+ T cells , it is in the absence of the multitude of other factors influencing dynamics of infection in vivo ., Therefore , transcriptomic analysis of virus-infected cells in more complex experimental settings such as total peripheral blood mononuclear cells or humanized mice models would provide additional insight in the intricate relationship between the virus and its host environment ., We hope that the data provided here can serve as a roadmap to focus efforts on neglected aspects of T-cell and HIV-1 biology , leading to a better understanding of the complex relationship between the virus and its host ., Human peripheral blood mononuclear cells were obtained from healthy blood donors , in accordance with the guidelines of the Bioethics Committee of the Centre Hospitalier de lUniversité Laval Research Center , by density-gradient centrifugation on Ficoll-Hypaque ( Wisent , St-Bruno , QC ) ., All blood donors were informed and agreed to a written consent prior to blood donation ., Cells were plated in 75-cm2 flasks at 15×106/mL for 2 h ., The non-adherent cells from the supernatant were enriched in CD4+ T cells with the human CD4+ T Cell Enrichment Kit ( Stemcell Technologies Inc . , Vancouver , BC ) ., The purity obtained was routinely higher than 98% of CD3+CD4+ T cells ., Cells were cultured at 2×106/ml in RPMI-1640 medium ( Invitrogen , Burlington , ON ) supplemented with 10% foetal bovine serum ( Invitrogen ) , L-glutamine ( 2 mM ) ( Wisent ) , penicillin G ( 100 U/ml ) , streptomycin ( 100 µg/ml ) ( Wisent ) and primocine ( Amaxa Biosystems , Gaithersburg , MD ) ., Cells were rested for 24 h after isolation and treated with phytohemagglutinin-L ( PHA ) ( 1 µg/ml ) ( Sigma-Aldrich , St-Louis , MO ) and recombinant human IL-2 ( rhIL-2 ) ( 30 U/ml ) ( AIDS Research and Reference Reagent Program , Germantown , MD ) for 2 days at 37°C under a 5% CO2 atmosphere prior to HIV-1 infection and rhIL-2 was refreshed when the medium was changed ., Human embryonic kidney 293T cells were maintained in DMEM ( Invitrogen ) supplemented with 10% FBS , L-glutamine , penicillin G and streptomycin ., NL4-3 BAL-IRES-HSA virions were described previously 3 and produced in 293T cells using a commercial calcium phosphate kit ( CalPhos Mammalian Transfection kit , Clontech , Palo Alto , CA ) ., Cell-free supernatants were ultracentrifuged to eliminate free p24 ., Finally , samples were aliquoted before storage at −85°C ., A homemade ELISA test was used to normalize the p24 content in all viral preparations 51 ., Virus infection was achieved by inoculating primary human CD4+ T cells with a fixed amount of reporter virus standardized in term of p24 ( i . e . 10 ng of p24 per 1×105 target cells ) ., All virus preparations underwent a single freeze-thaw cycle before initiation of infection studies ., A control consisting of mock-infected cells was obtained by transient transfection of 293T cells with an equimolar amount of pCDNA 3 . 1 ( control empty vector ) ., Next , purified CD4+ T cells were treated with a volume of supernatant from 293T cells transfected with pCDNA 3 . 1 similar to the one used for HIV-1 infection experiments ., A total of 100×106 primary human CD4+ T cells were exposed to NL4-3 BAL-IRES-HSA and 30×106 target cells were used for mock-infected controls for each of the three donors tested ., In order to get about 5×105 CD4+ T cells productively infected with HIV-1 ( i . e . HSA+ ) , a total of 50 , 30 and 20×106 cells were used to isolate an average of 5×105 virus-infected cells at 24 , 48 and 72 h post-infection , respectively ., Following infection of primary human CD4+ T cells with the fully competent HSA-encoding virions , HIV-1-infected and uninfected bystander cell populations were isolated using a previously described protocol with slight modifications 3 ., In brief , all magnets were pre-cooled for one hour at 4°C ., Only the first HSA-negative fraction was used to obtain uninfected bystander cells because subsequent negative fractions have an increased risk of containing HIV-1-infected cells ( unpublished data ) ., Mock-infected cells were subjected to the same procedure as the uninfected bystander fraction ., A protocol was designed for RNA isolation , as neither Trizol nor silica-based column methods could yield sufficient amounts of high quality RNA from HIV-1-infected fractions ., Indeed , the positive fractions contain magnetic beads which were found to interfere with the Trizol reagent ( See Protocol S1 ) ., Flow cytometry analyses were performed with 5×105 cells that were incubated with 100 µl of wash buffer ( PBS pH 7 . 4 , BSA 1% , and EDTA 2 mM ) containing a saturating amount of a monoclonal rat anti-mouse HSA antibody ( clone M1/69 , PE-coupled , BD Biosciences , Mississauga , ON ) , anti-CD4 , anti-CD3 , anti-CD27 , anti-CD45RO ( all from BD Biosciences ) or a corresponding isotype-matched control antibody for 30 min at 4°C ., Cells were then washed , fixed with 2% paraformaldehyde for 30 min at 4°C and analyzed on a cytofluorometer ( FACSCanto , BD Biosciences ) ., Further analyses were performed using FCS Express V3 . 0 software ( De Novo Software , Los Angeles , CA ) ., A total input of 200 ng of RNA was used to prepare targets for array hybridization using the Ambion WT Expression Kit ( Applied Biosystems , Austin , TX ) ., Data was normalized using RMA at the gene and exon level with Affymetrix Power Tools – core-level probe definition were used in both cases for results presented in this manuscript ., Bioconductor package limma 52 was used to find modulated genes – an FDR of 1% and a fold change of 1 . 7 were used to filter the lists ., A minimum signal filter of 100 on the average of three replicates was also applied ., Time-wise and aggregate comparisons were done between infected , bystander and mock treated cells ., DAVID analysis was done using version 6 . 7 with standard parameters using the following categories: GOTERM_BP_FAT , GOTERM_MF_FAT , KEGG_PATHWAY , BIOCARTA , SP_PIR_KEYWORDS , UP_SEQ_FEATURE , SMART , INTERPRO , UCSC_TFBS ., Bonferroni corrected p-values<0 . 001 were considered as significant ., Bibliosphere analysis was performed using version 7 . 24 with the compiled list of 835 modulated genes identified by limma ., We settled on the presence of three co-citations or more in the same sentence in a PubMed abstract as a relationship criterion ., We exported the data to Gephi ( http://gephi . org/ ) , a powerful and interactive network visualization and exploration platform for further analysis using the Gefx library ., Nodes were arranged using a directed force algorithm ( Force2 ) , colored according to fold change between uninfected bystander and HIV-1-infected cells at 24 h and sized according to the –log10 of the p-value for the same comparison – these can be dynamically changed at will using the original Gephi file containing the graph and all associated data , available as Dataset S2 ., Alternative splicing analysis was performed with PECA-SI ., The following filters were applied: a threshold of splicing index of at least 1 . 7 fold , a significant DABG signal ( p<0 . 001 ) in at least 3 groups ( equivalent to 9 chips ) , probes contained in exonic regions and an FDR of 1% ., Data was dynamically overlaid and visualized on both Annmap ( http://annmap . picr . man . ac . uk/ ) via Bioconductor xmapcore and xmapbridge packages and Splice Center 53 ., qRT-PCR ., qRT-PCR against the Tat-spliced isoform was performed to quantify the level of enrichment of HIV-1-infected CD4+ T cells achieved in the HSA-positive fraction and their absence in the HSA-negative fractions ., TaqMan RNA-to-CT 1-Step Kits from Invitrogen was used for quantification ., The following probe and primers were used in our study: probe , TATCAAAGCAACCCACCTCC , forward primer , GAAGCATCCAGGAAGTCAGC , reverse primer , CTATTCCTTCGGGCCTGTC ., PCR was performed under standard TaqMan cycling conditions using a Rotor-gene 3000 ( Corbett Life Science , San Francisco , USA ) ., SYBR Green detec

Introduction, Results, Discussion, Materials and Methods

HIV-1 is extremely specialized since , even amongst CD4+ T lymphocytes ( its major natural reservoir in peripheral blood ) , the virus productively infects only a small proportion of cells under an activated state ., As the percentage of HIV-1-infected cells is very low , most studies have so far failed to capture the precise transcriptomic profile at the whole-genome scale of cells highly susceptible to virus infection ., Using Affymetrix Exon array technology and a reporter virus allowing the magnetic isolation of HIV-1-infected cells , we describe the host cell factors most favorable for virus establishment and replication along with an overview of virus-induced changes in host gene expression occurring exclusively in target cells productively infected with HIV-1 ., We also establish that within a population of activated CD4+ T cells , HIV-1 has no detectable effect on the transcriptome of uninfected bystander cells at early time points following infection ., The data gathered in this study provides unique insights into the biology of HIV-1-infected CD4+ T cells and identifies genes thought to play a determinant role in the interplay between the virus and its host ., Furthermore , it provides the first catalogue of alternative splicing events found in primary human CD4+ T cells productively infected with HIV-1 .

Some previous studies have monitored HIV-1-induced gene expression in various host cell targets and tissues but the discrimination between productively infected cells and uninfected bystander cells represents a technical challenge yet to be solved ., Consequently , data interpretation has always been biased towards the transcriptional response of a majority of uninfected bystander cells that were exposed to soluble factors released by virus-infected cells ., Following the design of a unique and innovative molecular tool to identify cells productively infected with HIV-1 and the description of an efficient magnetic beads-based technique to separate them from uninfected bystander cells , we undertake this challenge and perform the first comparative whole-genome transcriptomic and large-scale proteomic profiling of both HIV-1-infected and uninfected bystander CD4+ T cells ., We demonstrate herein that HIV-1- infected and uninfected bystander cells display distinctive transcriptomic signatures which might permit to identify new susceptibility and resistance factors .

medicine, genome expression analysis, infectious diseases, genome analysis tools, hiv, retrovirology and hiv immunopathogenesis, biology, genomics, viral diseases, genetics and genomics, transcriptomes

journal.pcbi.1004338

Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome

Human health is inseparably connected to the billions of microbes that live in and on us ., Current research shows that our associations with microbes are , more often than not , essential for our health 1 ., The microbes that live in and on us ( collectively our “microbiome” ) help us to digest our food , train our immune systems , and protect us from pathogens 2 , 3 ., The gut microbiome is an enormous community , consisting of hundreds of species and trillions of individual interacting bacteria 4 ., Microbial community composition often persists for years without significant change 5 ., When change comes , however , it can have unpredictable and sometimes fatal consequences ., Acute and recurring infections by Clostridium difficile have been strongly linked to changes in gut microbiota 6 ., The generally accepted paradigm is that antibiotic treatment ( or some other perturbation ) significantly disrupts the microbial community structure in the gut , which creates a void that C . difficile will subsequently fill 7–10 ., Such infections occur in roughly 600 , 000 people in the United States each year ( this number is on the rise ) , with an associated mortality rate of 2 . 3% 11 ., Each year , healthcare costs associated with C . difficile infection are in excess of $3 . 2 billion 11 ., An altered gut flora has further been identified as a causal factor in obesity , diabetes , some cancers and behavioral disorders 12-17 ., What promotes the stability of a microbial community , or causes its collapse , is poorly understood ., Until we know what promotes stability , we cannot design targeted treatments that prevent microbiome disruption , nor can we rebuild a disrupted microbiome ., Studying the system level properties and dynamics of a large community is impossible using traditional microbiology approaches ., However , network science is an emerging field which provides a powerful framework for the study of complex systems like the gut microbiome 18–23 ., Previous efforts to capture the essential dynamics of the gut have made heavy use of ordinary differential equation ( ODE ) models 24 , 25 ., Such models require the estimation of many parameters ., With so many degrees of freedom , it is possible to overfit the underlying data , and it is difficult to scale up to larger communities 26 , 27 ., Boolean dynamic models , conversely , require far less parameterization ., Such models capture the essential dynamics of a system , and scale to larger systems ., Boolean models have been successfully applied at the molecular 28 , 29 , cellular 20 , and community levels 30 ., Here we present the first Boolean dynamic model constructed from metagenomic sequence information and the first application of Boolean modeling to microbial community analysis ., We analyze the dynamic nature of the gut microbiome , focusing on the effect of clindamycin antibiotic treatment and C . difficile infection on gut microbial community structure ., We generate a microbial interaction network and dynamical model based on time-series data from metagenome data from a population of mice ., We present the results of a dynamic network analysis , including steady-state conditions , how those steady states are reached and maintained , how they relate to the health or disease status of the mice , and how targeted changes in the network can transition the community from a disease state to a healthy state ., Furthermore , knowing how microbes positively or negatively impact each other—particularly for key microbes in the community—increases the therapeutic utility of the inferred interaction network ., We produced genome-scale metabolic reconstructions of the taxa represented in this community 31 , and probe how metabolism could—and could not—contribute to the mechanistic underpinnings of the observed interactions ., We present validating experimental evidence consistent with our computational results , indicating that a member of the normal gut flora , Barnesiella , can in fact slow C . difficile growth ., Buffie et al . reported treating mice with clindamycin and tracking microbial abundance by 16S sequencing 32 ., Mice treated with clindamycin were more susceptible to C . difficile infection than controls ., The collection of 16S sequences corresponding to these experiments was analyzed by Stein et al . 24 ., First , Stein et al . aggregated the data by quantifying microbial abundance at the genus level ., Abundances of the ten most abundant genera and an “other” group were presented as operational taxonomic unit ( OTU ) counts per sample ., We use the aggregated abundances from Stein et al . as the starting point for our modeling pipeline ( Fig 1 ) ., This processed dataset consisted of nine samples and three treatment groups ( n = 3 replicates per treatment group ) ., The first treatment group ( here called “Healthy” ) received spores of C . difficile at t = 0 days , and was used to determine the susceptibility of the native microbiota to invasion ., The second treatment group ( here called “clindamycin treated” ) received a single dose of clindamycin at t = -1 days to assess the effect of the antibiotic alone , and the third treatment group ( here called “clindamycin+ C . difficile treated” ) received a single dose of clindamycin ( at t = -1 days ) and , on the following day , was inoculated with C . difficile spores ( S1A Fig ) ., Under the clindamycin+ C . difficile treatment group conditions , C . difficile could colonize the mice and produce colitis; however this was not possible under the first two treatment group conditions ., The gut bacterial genus abundance dataset included some variation in terms of time points in which genera were sampled ., That is , genus abundances were measured between 0 to 23 days; however , not all samples had measurements at all the time points ( S1A Fig ) ., Particularly , the healthy population only included time points at 0 , 2 , 6 , and 13 days and Sample 1 of clindamycin+ C . difficile treated population was missing the 9 day time point ., Missing abundance values for these 4 points were estimated using an interpolation approach ( S1B Fig ) ., For healthy samples , the 16 and 23 day time points could not be interpolated as the last experimentally identified time point for these samples is at 13 days ., The assumption of the approximated polynomial for these samples is that extrapolated data points are linear using the slope of the interpolating curve at the nearest data point ., Because genera abundances are fairly stable across time in this treatment group ( i . e . the slope of most of the genera abundances is approximately zero ) , extrapolating two time points was deemed reasonable ., A principal component analysis was completed on the interpolated data ( Fig 2A ) and shows that the interpolated time series bacterial genus abundance data clusters by experimental treatment group in the first two principal components ., Furthermore , the results of the binarization for the healthy population suggest that interpolation did not have any concerning effects on the 16 and 23 day time points ( S2 Fig ) ., Natural cubic spline interpolation was used to estimate genus abundances at missing time points in some samples ., A cubic spline is constructed of piecewise third order ( cubic ) polynomials which pass through the known data points and has continuous first and second derivatives across all points in the dataset ., Natural cubic spline is a cubic spline that has a second derivative equal to zero at the end points of the dataset 33 ., Natural splines were interpolated such that all datasets had time points at single day intervals through the 23 day time point ( S1B Fig ) ., We use a Boolean framework in which each network node is described by one of two qualitative states: ON or OFF ., We chose this framework because of its computational feasibility and capacity to be constructed with minimal and qualitative biological data 34 ., The ON ( logical 1 ) state means an above threshold abundance of a bacterial genus whereas the OFF ( logical 0 ) state means below-threshold genus absence ., The putative biological relationships among genera are expressed as mathematical equations using Boolean operators 29 , 34 ., We inferred putative Boolean regulatory functions for each node , which are able to best capture the trends in the bacterial abundances ., These rules , ( edges in the interaction network ) can be assigned a direction , representing information flow , i . e . effect from the source ( upstream ) node to the target ( downstream ) node ., Furthermore , edges can be characterized as positive ( growth promoting ) or negative ( growth suppressing ) ., An additional layer of network analysis is the dynamic model , which is used to express the behavior of a system over time by characterizing each node by a state variable ( e . g . , abundance ) and a function that describes its regulation ., Dynamic models can be categorized as continuous or discrete , according to the type of node state variable used ., Continuous models use a set of differential equations; however , the paucity of known kinetic details for inter-genus and/or inter-species interactions makes these models difficult to implement ., Genus abundance data was binarized ( converted to a presence-absence dataset ) to enable inference of Boolean relationships for modeling applications ., We adapted a previously developed approach called iterative k-means binarization with a clustering depth of 3 ( KM3 ) for this purpose 35 ., This approach was employed because binarized data is able to maintain complex oscillatory behavior in Boolean models constructed from this data , whereas other binarization approaches fail to maintain these features 35 ., Briefly , this approach uses k-means clustering with a depth of clustering d and an initial number of clusters k = 2d ., In each iteration , data for a specific genus G are clustered into k unique clusters C1G , … , CkG , then for each cluster , CnG , all the values are replaced by the mean value of CnG ., For the next iteration , the value of d is decreased and clustering is repeated ., This methodology is repeated until d = 1 ., This approach , with d = 3 ( called here as KM3 binarization ) has previously been demonstrated as a superior binarization methodology to other binarization approaches for Boolean model construction because it conserves oscillatory behavior 35 ., These analyses were performed using custom Python code based on a previously written algorithm 35 and is available in the supplemental materials ., Because KM3 binarization has a stochastic component ( the initial grouping of binarization clusters ) , we employed KM3 binarization on the entire bacterial genus abundance time series dataset 1000 times ., The average binarization for each sample ( S2 Fig ) was used to determine the most probable binarized state of each genus in each sample at each time point ( S3 Fig ) ., A principal component analysis of the most probable binarized genus abundances for each sample demonstrates that as with the continuous time series abundances ( Fig 2A ) , binarized bacterial genus abundance data cluster by experimental treatment group ( Fig 2B ) ., For inference of Boolean rules from the binarized genus abundances ( S3 Fig ) , the consensus of two of three samples for each treatment population was used as the binarized state of each genus at each time point in each sample ( Fig 2C ) ., The Best-fit extension was applied to learn Boolean rules from the binarized time series genus abundance information 36 ., For each variable ( genus ) Xi in the binarized time series genus abundance data , Best-fit identifies the set of Boolean rules with k variables ( regulators ) that explains the variable’s time pattern with the least error size ., The algorithm uses partially defined Boolean functions pdBf ( T , F ) , where the set of true ( T ) and false vectors ( F ) are defined as T = {X′ ∈ {0 , 1}k: Xi ( t + 1 ) = 1} and F = {X′ ∈ {0 , 1}k: Xi ( t + 1 ) = 0} ., Intuitively , the partial Boolean function summarizes the states of the putative regulators that correspond to a turning ON ( T ) or turning OFF ( F ) of the target variable ., The error size ε of pdBf ( T , F ) is defined as the minimum number of inconsistencies within X′ that best classifies the T and F values of the dataset ., The Best-Fit extension works by identifying smallest size X′ for Xi ., For more detailed information refer to 36 ., In line with this , we considered the most parsimonious representation of the rules with the smallest ε ., If the most parsimonious rule was self-regulation , we also considered rules with the same ε that included another regulator ., If multiple rules fit these criteria for a given Xi , it implied that they can independently represent the inferred regulatory relationships ., In cases where the alternatives had the same value of ( non-zero ) ε , we explored combinations ( such as appending them by an OR rule ) and used the combination that best described the experimentally observed final ( steady state ) outcomes ., For example , we combined the two alternative rules for Blautia with an OR relationship ., In the case of Barnesiella , we chained three rules ( Other , Lachnospiraceae_other , Lachnospiraceae ) by an OR relationship , and not Clindamycin by an AND relationship to incorporate the loss of Barnesiella in the presence of clindamycin ( Fig 2C ) ., This was also done for rules for “Lachnospiraceae” , “Lachnospiraceae_other” and “Other” and all four nodes attained the same rule ., There are six nodes with multiple inferred ( alternative ) rules: “Barnesiella” , ”Blautia” , ”Enterococcus” , ”Lachnospiraceae” , ”Lachnospiraceae_other” , and”Other” had 4 , 2 , 5 , 4 , 4 , and 4 rules , respectively ., The six other nodes had a single inferred rule ., The network in Fig 2C represents the union of all of the alternative rules produced by Best-Fit , or in other words , –it is a super-network of all alternative rules ., Any alternative networks would be a sub-network of what we show ., A strongly connected component between the nodes inhibited by clindamycin is a feature of the vast majority of these sub-networks ., We used the implementation of Best-Fit in the R package BoolNet 37 ., Dynamic analysis is performed by applying the inferred Boolean functions in succession until a steady state is reached ., Boolean models and discrete dynamic models in general focus on state transitions instead of following the system in continuous time ., Thus , time is an implicit variable in these models ., The network transitions from an initial condition ( initial state of the bacterial community ) until an attractor is reached ., An attractor can be a fixed point ( steady state ) or a set of states that repeat indefinitely ( a complex attractor ) ., The basin of attraction refers to the set of initial conditions that lead the system to a specific attractor ., For the network under consideration , the complete state space can be traversed by enumerating every possible combination of node states ( 212 ) and applying the inferred Boolean functions ( or “update rules” ) to determine paths linking those states ., The state transition network describes all possible community trajectories from initial conditions to steady states , given the observed interactions between bacteria in the community ., We made use of two update schemes to simulate network dynamics: synchronous ( deterministic ) and asynchronous ( stochastic ) ., Synchronous models are the simplest update method: all nodes are updated at multiples of a common time step based on the previous state of the system ., The synchronous model is deterministic in that the sequence of state transitions is definite for identical initial conditions of a model ., In asynchronous models , the nodes are updated individually , depending on the timing information , or lack thereof , of individual biological events ., In the general asynchronous model used here , a single node is randomly updated at each time step 38 ., The general asynchronous model is useful when there is heterogeneity in the timing of network events but when the specific timing is unknown ., Due to the heterogeneous mechanisms by which bacteria interact , we made the assumption of time heterogeneity without specifically known time relationships ., Synchronous and asynchronous Boolean models have the same fixed points , because fixed points are independent of the implementation of time ., However , the basin of attraction of each fixed point ( i . e . the initial conditions that lead to each fixed point ) may differ between synchronous and asynchronous models ( S2 Table ) ., For identification of all of the fixed points in the network ( the attractor landscape ) , the synchronous updating scheme was used ., However , for the perturbation analysis , the asynchronous updating scheme was used because it more realistically models the possible trajectories in a stochastic and/or time-heterogeneous system ., The simulations of the gut microbiome model were performed using custom Python code built on top of the BooleanNet Python library , which facilitates Boolean simulations 39 ., Our custom Python code is available in the supplemental materials ., To capture the effect of removal ( knockout ) or addition ( probiotic; forced over abundance ) of genera , modification of the states/rules to describe removal or addition states were performed ., These modifications were implemented in BooleanNet by setting the corresponding nodes to either OFF ( removal ) or ON ( addition ) and then removing the corresponding updating rules for these nodes for the simulations ., By examining many such forced perturbations , we can identify potential therapeutic strategies , many of which may not be obvious or intuitive , particularly as network complexity increases ., We used asynchronous update when simulating the effect of perturbations on the microbial communities ., In each case we performed 1000 simulations and report the percentage of simulations that achieve a certain outcome ., To generate draft metabolic network reconstructions for each of the ten genera in the paper , we first obtained genome sequences for representative species by searching the “Genomes” database of the National Center for Biotechnology Information ( NCBI ) ., Complete genomes for the first ten ( or if less than ten , all ) species within the appropriate genus were downloaded ., During the process of reconstructing genus-level metabolic reconstructions , some genera were underrepresented ( fewer than 10 species genomes ) in the NCBI Genome database , including Akkermansia , Barnesiella and Coprobacillus ( S3 Table ) ., The search result order is based on record update time , and so it is quasi-random ., Genomes were uploaded to the rapid annotations using subsystems technology ( RAST ) server for annotation 40 ., Draft metabolic network reconstructions were generated by providing the RAST annotations to the Model SEED service 41 ., Metabolic network reconstructions were downloaded in “ . xls” format ., Genus-level metabolic reconstructions were produced by taking the union of all species-level reconstructions corresponding to each genus , as has been done previously 42 ., The one exception was C . difficile , which was produced by taking the union of three strain-level reconstructions ., Subsystems were defined as the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) map with which each reaction was associated 43 , 44 ., These associations were determined based on annotations in the Model SEED database 41 ., To quantify enrichment , the complete set of unique reactions from all genus-level reconstructions was pooled , and the subsystem annotations corresponding to those reactions were counted ., To determine enrichment for a given subset of the community ( either a single genus-level reconstruction , or a set of reconstructions corresponding to a subnetwork ) , the subsystem occurrences were counted within the subset ., The probability of a reconstruction containing N total subsystem annotations , with M or more occurrences of subsystem I , was determined by taking the sum of a hypergeometric probability distribution function ( PDF ) from M to the total occurrences of subsystem I in the overall population ., Enrichment analysis was performed in Matlab 45 ., To quantify metabolic interactions , we started by utilizing the seed set detection algorithm developed by Borenstein et al . 46 , 47 ., The algorithm follows three steps: The rationale is that metabolites that feed into the network , but cannot be produced by any reactions within the network , must be obtained from the environment ., Competition metrics were generated following the process of Levy and Borenstein 46 ., For a given pair of genera , the competition score is defined as: Here SeedSeti is the set of obligatory input metabolites to the metabolic network reconstruction for genus i , and |SeedSeti| is the number of metabolites contained in SeedSeti ., The competition score indicates the fractional overlap of inputs that genus i shares with genus j , and so ranges between zero and one ., The higher the score , the more similar the metabolic inputs to the two networks , making competition more likely ., For a given pair of genera , the mutualism score is defined as: Here ¬SeedSetj is the set of metabolites that can be produced by the metabolic network for species j ( i . e . all non-seed metabolites ) ., The mutualism score indicates the fractional overlap of inputs that genus i consumes which genus j can potentially provide ., The mutualism score ranges between zero and one ., The higher the score , the more potential there is for nutrient sharing between species ., While the score does not measure “mutualism” per se ( it cannot necessarily distinguish between other interactions such as commensalism or amenalism 48 ) , for simplicity , we will refer to these scores as the competition and mutualism scores ., All metabolic reconstructions , seed sets , competition scores and mutualism scores are available in the supplemental materials ., Seed set generation was performed using custom Matlab scripts , which are available in the supplement ., 45 ., Statistical tests were performed in R 49 ., Barnesiella intestinihominis DSM 21032 and Clostridium difficile VPI 10463 were grown anaerobically in PRAS chopped meat medium ( CMB ) ( Anaerobe Systems , Morgan Hill , CA ) at 37 C . To prepare B . intestinihominis spent medium , B . intestinihominis was grown in CMB until stationary phase ( 44 hours ) ., The saturated culture was centrifuged , and the supernatant was filter sterilized ( 0 . 22 μM pore size ) ., Growth curves were obtained by inoculating batch cultures in 96-well plates and gathering optical density measurements ( 870 nm ) using a small plate reader that fits in the anaerobic chamber 50 ., Single cultures were inoculated from overnight liquid culture to a starting density of 0 . 01 ., The co-cultures were started at a 1:1 ratio , for a total starting density of 0 . 02 ., Optical density was measured every 2 minutes for 24 hours , and the resulting growth curves were analyzed in Matlab 45 ., Maximum growth rates were calculated by fitting a smooth line to each growth curve , and finding the maximum growth rate from among the instantaneous growth rates over the whole time course: log ( ODt+1 ) —log ( ODt ) / t+1-t ., The achieved bacterial density—area under the growth curve ( AUC ) —in a culture was calculated by integrating over the growth curve in each experiment using the “trapz ( ) ” function in Matlab ., It can be thought of as representing the total biomass produced over time ., The simply additive null model was calculated by fitting a Lotka-Volterra model 24 to the single cultures for both B . intestihominis and C . difficile ., The null model of co-culture ( assuming zero interaction between species ) was simulated by using the parameters from single culture , and summing the predicted OD870 values ., All scripts used to analyze the data are available at https://bitbucket . org/gutmicrobiomepaper/microbiomenetworkmodelpaper/wiki/Home ., To capture the dynamics of inter-genus interactions in the intestinal tract we employed a pipeline ( Fig, 1 ) which translates metagenomic genus abundance information into a dynamic Boolean model ., This approach involves three steps:, 1 ) discretization ( binarization ) of genus abundances ,, 2 ) learning Boolean relationships among genera , and, 3 ) translation of genus associations into a Boolean ( discrete ) dynamic model ., Boolean rules ( S1 Table ) were inferred from the time series binarized genus abundances using an implementation of the Best-fit extension 36 in the R Boolean network inference package BoolNet 37 ( see Methods ) ., A network of 12 nodes and 33 edges was inferred ( Fig 2D ) ., The inferred interaction network has a clustered structure: the cluster ( subnetwork ) containing the two Lachnospiraceae nodes and Barnesiella is strongly influenced by clindamycin whereas the other subnetwork is largely independent of the first , except for the single edge between Barnesiella and C . difficile ( Fig 2D ) ., In fact , Lachnospiraceae nodes , Barnesiella and the group of “Other” genera form a strongly connected component; that is , every node is reachable from every other node ., Most nodes of the second subnetwork are positively influenced by C . difficile , with the exception of Coprobacillus , for which no regulation by other nodes was inferred , and Akkermansia , which is inferred to be regulated only by Coprobacillus ., These latter two genera are transiently present ( around day 5 ) in the clindamycin treatment group , but they do not appear in the final states of any of the treatment groups ( see S1 Fig ) ., This network structure is consistent with published data in which the dominant Firmicutes ( Lachnospiraceae ) and Bacteroidetes ( Barnesiella ) are devastated by antibiotic administration 51 , 52 ., Furthermore , the clustered structure ( Fig 2D ) supports the established mechanism of C . difficile colitis: loss of normal gut flora , which normally suppresses opportunistic infection ( clindamycin cluster ) , and the presence of C . difficile at a minimum inoculum ( C . difficile cluster ) 10 , 53 ., The network clusters have a single route of interaction between Barnesiella and C . difficile ., The negative influence of Barnesiella on C . difficile is in agreement with recently published findings in which Barnesiella was strongly correlated with C . difficile clearance 54 ., The role of Barnesiella as an inhibitor of another pathogen ( vancomycin-resistant Enterococci ( VRE ) ) has been shown in mice 55 , which is also visible in the network model as an indirect relationship between Barnesiella and Enterococcus ( Fig 2D ) ., Related species of Bacteroidetes have been shown to play vital roles in protection from C . difficile infection in mice 56 ., Furthermore , the network structure shows that Lachnospiraceae positively interacts with Barnesiella , leading to an indirect suppression of C . difficile ., Interestingly , the two Lachnospiraceae nodes and the “Other” node form a strongly connected component , suggesting a similar role in the network , particularly in promoting growth of Barnesiella , which directly suppresses C . difficile ., In support of this finding , Lachnospiraceae has been shown to protect mice against C . difficile colonization 52 , 57 ., Therefore , the structure of the network is both a parsimonious representation of the current data set , and is supported by literature evidence ., We applied dynamic analysis using the synchronous updating scheme ( see Methods ) to determine all the possible steady states of the microbiome network model ., In a 12 node network , there are 212 possible network states ., We employed model simulations using the synchronous updating scheme to visit all possible network states and identify all fixed points of the model ., Exploration of the steady states of this network reveals 23 possible fixed point attractors ( S4 Fig ) ., Three of the identified attractors ( Fig 3A ) are in exact agreement with the experimentally identified terminal time points of binarized genus abundances ( Fig 2C ) ., These attractors make up a small subset of the entire microbiome network state space ( S2 Table ) ., The attractor landscape can be divided into six groups based on abundance patterns they share ( S4 Fig ) ., Group 1 is made up of a single attractor wherein all genera are absent ( OFF ) ., The second group attractor consists of the experimentally defined healthy state ( Attractor, 2 ) and genera in the C . difficile subnetwork which can be abundant ( ON ) independent of the clindamycin subnetwork ., The third grouping has the clindamycin treated steady state ( Attractor 7 ) and genera in the C . difficile subnetwork that can survive in the presence of the clindamycin ., Group 4 contains the clindamycin plus C . difficile steady state ( Attractor 12 ) and its subsets in which one or both of the source nodes Mollicutes and Enterobacteriaceae are absent ., Group 5 contains attractors in which clindamycin is absent and C . difficile is present ., Even if clindamycin is absent , our model suggests that C . difficile can thrive if Lachnospiraceae and Barnesiella are absent , i . e . these states represent a clindamycin-independent loss of Lachnospiraceae and Barnesiella ., Lastly , group 6 attractors have both clindamycin and C . difficile as OFF ., Blautia and Enterococcus are always abundant in these attractors ., Indeed , because of the mutual activation between Blautia and Enterococcus they always appear together ., Attractors in this group may also include the abundance ( ON state ) of the source nodes Mollicutes and Enterobacteriaceae ., We next explored the perturbation of genera in the gut microbiome network model ., We considered the clinically relevant question of which perturbations might alter the microbiome steady states produced by clindamycin or clindamycin+C ., difficile treatment after clindamycin treatment was removed ., Thus , we considered the clindamycin-treated steady state ( Attractor 7 in S3 Fig ) and the clindamycin+C ., difficile treated steady state ( Attractor 12 ) as initial conditions and assumed that clindamycin treatment was stopped ., Our simulations , employing asynchronous update ( see Methods ) , indicate that for both initial conditions , only the state of clindamycin changes after the treatment is stopped; these steady states become Attractor 1 and Attractor 19 , respectively ( S4 Fig ) ., In other words , the steady states remain identical in the absence of clindamycin ., We next explored the effect of addition ( overabundance; Fig 3B , left column ) and removal ( knockout; Fig 3B , right column ) of individual genera , simultaneously with the stopping of clindamycin treatment , on the model predicted steady states ., For the perturbation analysis , the model was initialized from the clindamycin treated steady state ( Fig 3B , top row ) or the clindamycin+C ., difficile steady state ( Fig 3B , bottom row ) ., From the clindamycin treated state , addition of Lachnospiraceae or “Other” nodes restores the healthy steady state; however , no removal restore the healthy steady state ( Fig 3B ) ., From the clindamycin+C ., difficile state , addition of Barnesiella , Lachnospiraceae , or “Other” nodes lead to a shift toward the healthy steady state ( suppression of C . difficile ) ., Species-level reconstructions from the genus Enterobacteriaceae contained the most reactions on average ( 1335 ) , while those from Mollicutes contained the least ( 485 ) ( S3 Table ) ., The Barnesiella and Enterococcus reconstructions contained the most unique reactions ( S4 Table ) and , interestingly , also displayed more overlap in reaction content between each other ( 503 reactions ) than was observed between any other pair of reconstructions ( S5 Table ) ., Lachnospiraceae and Barnes

Introduction, Methods, Results, Discussion

We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions ., We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection ., Our model recapitulates known dynamics of clindamycin antibiotic treatment and C . difficile infection and predicts therapeutic probiotic interventions to suppress C . difficile infection ., Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions ., In vitro experimental data validate a key result of our computational model , that B . intestinihominis can in fact slow C . difficile growth .

The community of bacteria that live in our intestines ( called the “gut microbiome” ) is important to normal intestinal function , and destruction of this community has a causative role in diseases including obesity , diabetes , and even neurological disorders ., Clostridum difficile is an opportunistic pathogenic bacterium that causes potentially life-threatening intestinal inflammation and diarrhea and frequently occurs after antibiotic treatment , which wipes out the normal intestinal bacterial community ., We use a mathematical model to identify how the normal bacterial community interacts and how this community changes with antibiotic treatment and C . difficile infection ., We use this model to identify bacteria that may inhibit C . difficile growth ., Our model and subsequent experiments indicate that Barnesiella intestinihominis inhibits C . difficile growth ., This result suggests that B . intestinihominis could potentially be used as a probiotic to treat or prevent C . difficile infection .

journal.pcbi.1006218

Control of neurite growth and guidance by an inhibitory cell-body signal

The development of the nervous system requires the precise wiring of billions of cells ., To achieve this considerable feat , growing axons navigate over long distances to reach their synaptic targets , and hence establish appropriate patterns of connectivity ., Dysregulation of this process contributes to developmental and neurological disorders 1 , 2 , and the inability to recapitulate early growth events hinders nerve injury repair 3 ., One major regulator of axonal growth ( trophism ) and guidance ( tropism ) is signalling by extracellular chemical cues ., A large number of these are known , with nerve growth factor ( NGF ) being perhaps the best studied 4–8 ., The effects of NGF on growth and guidance are exerted via tight control of signalling along the axon for extension and turning , and at the cell-body to coordinate synthesis and supply of raw materials ., As might be expected , neither growth nor guidance occurs at very low concentrations ., More puzzling is that they are also both inhibited at higher concentrations , exhibiting a biphasic dose response that peaks in an intermediate concentration regime 9–18 ., Such tight constraints on both growth and guidance are important since they increase the challenge for therapeutically effective interventions ., Existing theories of the biphasic effects of NGF on growth depend on mechanisms intrinsic to single cells 16 , 19–21 , or a collective effect in which growth within aggregates of cells is hindered by increased fasciculation ( the grouping of neurites into bundled fibres ) 15 ., However , these theories remain purely qualitative and lack thorough experimental tests ., Two fundamental mechanisms underlie guidance: turning and differential growth ., Turning largely occurs for steep gradients ., The decline in its sensitivity with overall concentration can therefore be explained by saturation of finite numbers of receptors 18 ., However , for shallow gradients , ( for NGF , < 1% concentration change per 10 μm ) guidance is remarkably sensitive , and depends on preferential growth towards higher concentrations 17 , 22 ., In this paradigm of chemotactic response , neurites do not exhibit biased turning , but grow more quickly or more slowly when extending up or down a gradient , respectively ., The biphasic effects of NGF on guidance by differential growth therefore inherit the puzzle of those on growth in general ., Intriguingly , however , even in the decreasing portion of the dose response curve for growth , where lower concentrations should generate more growth than higher concentrations , differential growth remains biased up the gradient 22 ., Thus , guidance involves a true detection of the gradient , yet as it is also inextricably linked to growth , this tropic response cannot be fully understood in isolation ., To address these issues we first test previous proposals for NGF growth inhibition at high concentrations , and find they do not explain the biphasic response of dorsal root ganglia ( DRG ) explants in collagen gels ., Second , inspired by a reanalysis of the extensive shallow gradient data set of ref ., 18 , we propose a novel signal transduction mechanism which resolves the apparent contradictions introduced above ., In this , the growth at the neurite tip is promoted by the local concentration of NGF , but , critically , is also inhibited by a somatically-computed signal that results from NGF-dependent signalling among the collection of cell bodies ., Anterograde transport from the soma modulates growth by supply of signalling components , whereas retrograde transport from the tip provides the soma with information about the distal concentration ., The inhibition implements the decrease in growth at high concentrations , and provides the normalisation that allows differential growth to be a suitably sensitive guidance mechanism ., The model represents a new signalling paradigm for understanding nervous system development and repair , and makes testable predictions applicable to NGF and other growth and guidance cues ., There are two main hypotheses as to how high concentrations of NGF inhibit growth ., Based on experiments on chick thoracic DRGs , ref ., 15 proposed that an NGF-dependent increase in neurite fasciculation hinders growth from aggregates of cells , and reported no effect in single cells ., Conversely , others have proposed that inhibition acts at the single-cell level , such as by TrkA receptor saturation or downregulation 16 , 19 , 21 or signalling via the low affinity receptor p75 20 ., To test these competing theories , we measured the NGF response of early postnatal rat DRG explants and dissociated cells , grown together at low density in collagen gels for 48 h ., Previous studies have quantified explant outgrowth by manual measurements of radial extension 15 , 16 , 19 , semi-automated measurement of area and density 17 , 18 , 22 , 23 , and least-squares fitting of ellipses to explant shape 24 ., However , manual measurements become impractical for large data sets , and previous semi-automated methods provide only coarse descriptions of collective outgrowth that are difficult to interpret at a lower level ., To gain a more detailed understanding of growth patterns , we instead used a Fourier decomposition that is capable , in principle , of capturing arbitrary patterns of neurite extension , and is directly related to specific features of the response ., Briefly , we fitted boundary curves to the central cell-body region of the explant and to the outer limit of neurite outgrowth , and parameterised the distance between the curves with an angular variable ., The Fourier coefficients of this radial outgrowth function quantified the average radial outgrowth a0 ( average distance between explant body and limit of neurite extension ) , outgrowth bias in orthogonal image axes a1 , b1 , and other higher-order features ( Methods ) ., The average radial outgrowth of the explants exhibited the expected biphasic response curve ( Fig 1A–1C ) ., Explant outgrowth peaked at 0 . 3nM NGF concentration ( mean 817 μm ) , and was comparatively reduced ( mean 491 μm ) at 10 nM ( p = 1 × 10−4 , Mann-Whitney U-test for difference between 0 . 3 nM and 10 nM conditions , n = 15 explants per condition , 8 animals from 4 separate experiments ) ., By contrast , recording the length of the longest neurite of each dissociated cell ( Fig 1D–1F ) , we observed no evidence of growth inhibition at high NGF concentrations ( p = 0 . 35 , Mann-Whitney U-test for difference between 0 . 3 nM and 10 nM conditions , n = 126 and n = 156 cells respectively ) ., Comparing dissociated cells in the 0 . 1 nM condition , which exhibited the highest median neurite length , with the 10 nM condition gave a similar result ( p = 0 . 2 , Mann-Whitney U-test , n = 71 cells for 0 . 1 nM ) ., The results of both comparisons were robust to the removal of outliers ( defined as values lying more than 1 . 5 times the interquartile range above the third quartile; Fig 1F ) ., To test whether the lack of observed effect in dissociated cells may be due to a growth latency caused by the dissociation procedure , we repeated the experiment with an extended period of 96 h total growth ., Consistent with the 48 h results , we observed a pronounced difference in explant outgrowth after 96 h , but no detectable difference in dissociated-cell neurite length distributions ( S1 Fig ) ., Thus , inhibition of growth at high NGF concentrations is a property of intact ganglia , and not of isolated single cells ., To assess the effect of NGF on fasciculation ( cf . ref 15 ) , we acquired higher resolution images of explants in the 0 . 3 nM and 10 nM conditions , and performed an automated image analysis to compute distributions of neurite bundle widths ( Fig 1G–1I ) ., As a positive control , we tested the ability of our method to discriminate distributions from sample patches containing mainly thick or thin bundles as judged by eye ( taken from both NGF conditions , see S2 Fig for examples ) ., The difference between control samples , corresponding to a 1 − 2 μm increase in bundle widths , was easily detected ( p = 5 × 10−30 , one-tailed Mann-Whitney U-test , n = 353 segments and n = 414 segments for thick and thin respectively ) ., Applying the automated analysis to the test conditions , we found no detectable increase in bundle widths at 10 nM compared with 0 . 3 nM ( p = 1 , one-tailed Mann-Whitney U-test , n = 1687 segments and n = 2546 segments for 10 nM and 0 . 3 nM respectively , from 8 explants each ) ., Thus , in our system , increased fasciculation does not explain the biphasic NGF response ., This suggests the correlation between fasciculation and growth inhibition observed by ref ., 15 is not a causal relationship , nor is it a general property of NGF-dependent growth regulation ., As the results of our experiments contradict previous suggestions 15 , 16 , 19–21 , we propose an alternate mechanism for neurite growth regulation ., A fundamental difference between an intact ganglion and a single dissociated cell is the central mass of neuronal cell bodies and support cells that comprise the ganglion body ., This suggests the possibility that an NGF-dependent signal within the collection of cell bodies plays an inhibitory role in ganglion outgrowth ., This may be mediated , for instance , by cell-cell interactions in which a paracrine factor is secreted from the cell bodies and inhibits neighbouring cells ( as shown for regulation of cell survival 25 ) ., Another possibility is that satellite glial cells within the ganglion , which also carry NGF receptors 26 , communicate an inhibitory signal to the neural cell bodies that they ensheathe ., By analogy with other systems 27 , 28 , we further propose that this inhibitory mechanism permits sensitive gradient detection , and may thus explain the guidance by differential growth observed by ref ., 22—there termed guidance by growth rate modulation ., We develop this hypothesis with a mathematical model , and thus build a quantitative and predictive description of inhibitory growth and guidance signalling ., To constrain the model , we first applied our explant image analysis to the NGF gradient data set of ref ., 18 , which documents the growth of DRG explants after 48 h in very shallow NGF gradients in collagen gels ., The data set comprises 3460 images of explants in which the ganglion body region had been manually segmented from the neurite region ., Gradient parameters in the experiments varied between 0 − 0 . 3% concentration change per 10 μm , and background concentrations ≈ 0 . 001 − 100 nM , making it the most extensive record of NGF growth regulation yet compiled ., An example image of an explant is shown in Fig 2A , here displaying a pronounced bias in neurite outgrowth in the direction of increasing NGF concentration ., Applying the image analysis to the data , only two Fourier coefficients , a0 and b1 , varied systematically with the gradient parameters ., We thus quantified the growth response by the average radial outgrowth a0 , and directional bias b1/a0 , which gives the fractional increase in neurite extension on the up-gradient side of the explant , relative to the average ( or fractional decrease on the down-gradient side ) ., Explants with less than 100 μm average radial outgrowth ( 125/3460; 4% ) were excluded from the analysis of directional bias ., Consistent with the results of ref ., 18 , and Fig 1C , the average radial outgrowth exhibited a biphasic NGF dependence , with a peak at 0 . 3 nM and inhibition at higher concentrations ( Fig 2B ) ., Outgrowth was biased in the direction of the gradient for background concentrations between 0 . 01 − 1 nM ( Fig 2C , S1 Table ) ., By contrast , analysing the explant-body boundary curves alone , we found that the average explant body boundary was well-approximated by a circle of radius RE = 300 μm ., We found no correlations in shape properties within or between the outgrowth and explant body regions ( S2 Table ) ., The peak directional bias was observed at ≈ 0 . 1 nM in a 0 . 3% gradient , in which neurites facing directly up the gradient extended ≈ 15% further than the average over the explant ., With these gradient parameters and the measurements in Fig 2B , this implies a ≈ 100 μm increase in growth has resulted from a maximum concentration difference of only 0 . 03 nM across the full length of the neurites ., Observed over two orders of magnitude of background concentrations , this remarkable response involves a tropic growth modulation 22 , and places a strong constraint on any proposed mechanism ., We construct a model that assumes an inhibitory NGF-dependent signal within the ganglion , integrated with known signalling components of NGF growth promotion , and that satisfies the constraints of the experimental data ., Our approach is motivated by the seminal signalling models of ref ., 28 , who demonstrated that a network architecture that combines competing activating and inhibitory pathways with upstream signal amplification is sufficient to explain perfect adaptation and high sensitivity in amoebae and neutrophils ., We find that , in a different region of parameter space , a similar network structure also embodies the minimal ingredients required to explain growth and gradient sensing in our system ., We begin by treating a neurite as a single well-mixed compartment that receives two NGF-dependent inputs ., One input is assumed to be transduced by receptors at the growth cone , and the other by the proposed inhibitory signal at the cell body ., Although in reality this system is more complex , involving , for instance , transport along an extending neurite , our first objective was to determine sufficient processes by which the two primary inputs can be integrated to explain the data ., With these simplifications , we initially construct a model that accounts for the biphasic NGF dependence of ganglion outgrowth , but is unable to simultaneously satisfy the requirements of gradient detection ., We then construct a signalling network for gradient detection that sensitively compares two concentrations , independent of their magnitude and associated signal saturation ., Finally , we couple these two modules together in a two-compartment model , in which we explicitly include transport of signalling components between growth cone and cell body ., Simulating this network in neurites extending in a gradient , we account for all competing demands of the experimental data ., We consider activating ( A ) and inhibitory ( I ) signals that interact within a cell to regulate the conversion of a substrate G to a growth promoting active form G* ( Fig 3A ) ., We construct the activating pathway as a coarse-grained representation of known NGF/TrkA receptor signalling ., The signal A represents the binding occupancy of TrkA receptors at the growth cone , which drives growth in response to the local NGF concentration c1 ., We assume saturable binding , such that the steady-state of A is given by the standard expression, A ¯ ( c 1 ) = A T c 1 c 1 + K A , ( 1 ), with AT the total number of receptors on the growth cone and KA the dissociation constant ., Consistent with measured values of ∼ 0 . 01 − 1 nM 29–31 , we fix KA = 0 . 1 nM , and use an order of magnitude estimate of AT = 1000 total receptors ., For the proposed inhibitory pathway , we do not model possible processes of secretion or cell-cell communication explicitly ., Working under the assumption that these are short-range effects between neighbouring cells , we simply model the signal I as responding to the local NGF concentration c2 at the cell body within the ganglion ., As , at some stage , this must be transduced by receptor binding , we coarse-grain this signal into a similar form to that of A ,, I ¯ ( c 2 ) = I T c 2 c 2 + K I ., ( 2 ), We assume for simplicity that I has the same maximal intensity as A , achieved straightforwardly in the model by setting IT = AT , and leave KI as a free parameter ., We return to discuss the interpretation of this signal in Discussion ., Integration of the activating and inhibitory signals is modelled as a simple push-pull reaction ., The substrate is produced in the inactivated form at a constant rate , activated in proportion to A and inactivated in proportion to I , and decays exponentially in either form ., The output of the model is the concentration of protein in the active form G* , which we assume acts linearly to control neurite extension ., We provide the governing differential equations and parameters of the model in Methods ., We compute the steady state of the network under the assumption that the input concentrations remain fixed ( ignoring for now the change in growth-cone concentration during neurite extension in a gradient ) ., Expressed in terms of the signals of Eqs ( 1 ) and ( 2 ) , the steady-state output is given by, G*¯=k0A¯k1+k2A¯+I¯ , ( 3 ), where the constants k0 , k1 and k2 are combinations of rate parameters ., When c1 = c2 , fitting of parameters ki , along with KI from Eq ( 2 ) , yields a response in good agreement with the NGF dependence of explant outgrowth ( Fig 3B ) ., Setting c2 = 0 , to remove the influence of inhibitory signalling , gives a simple model for dissociated cell growth that exhibits the saturating response of Fig 1F ., Thus , the mechanism we propose , expressed as a very simple model , quantitatively accounts for the results of our experiments ., By itself , however , this model lacks the gradient sensitivity implied by the experiments of ref ., 18 ., We illustrate this in Fig 3B , in which we plot the response of the constrained model with a 50% asymmetry in inputs ( c1 = 1 . 5c2; red line ) ., Although this is nearly double the maximum value experienced by neurites in the experiments of ref ., 18 , only a modest increase in response compared to the uniform condition is observed ., The obstruction to gradient sensitivity in Model 1 is the saturable form of the signals A and I , combined with the parameter requirements of a biphasic growth response ., Within the framework of the model , sensitive gradient sensing requires a comparison of A and I while both are in the linear regime with respect to concentration ., In this case , A/I ≈ c1/c2 , and a highly effective gradient detector can be constructed ., Indeed , a central assumption of the models of ref ., 28 is that both activating and inhibitory signals are far from saturation ., Here , however , a biphasic steady-state response ( Fig 3B ) requires that activation occurs at much lower concentrations than inhibition ., This imposes the necessary condition that KA ≪ KI , precluding a direct comparison in respective linear regimes when the difference in input concentrations is small ., Independent of the model , TrkA activation by NGF is indeed saturable 29–31 , yet remarkable gradient sensitivity was observed experimentally over two orders of magnitude of concentration ( Fig 2C ) ., Thus , both the model and experimental data point to a mechanism for gradient detection that operates with high sensitivity , despite receptor saturation ., How can the effects of signal saturation be overcome ?, A common theoretical assumption is that cells can perform the necessary computations to invert expressions such as ( 1 ) and ( 2 ) , and thus access the original input variables 32–36 ., In this way , a system that depends on the ratio of inputs could be constructed by forming the expression, K A A ¯ ( I T - I ¯ ) K I I ¯ ( A T - A ¯ ) = c 1 c 2 , ( 4 ), providing a possible means for sensitive gradient detection ., However , to the best of our knowledge , no biologically realisable implementation of this operation has been derived ., To make our hypothesis concrete , we construct a network that performs the algebraic manipulations required of Eq ( 4 ) ( motivated by ref . 37 ) , and thus present an explicit gradient sensing mechanism ., To do so requires only minor modifications of Model 1 , sharing both the input pathways and basic network structure ( Fig 3C ) ., A dual negative regulation , induced by interaction between A and I , provides the necessary processing to unpack both saturating signals simultaneously ., In this network , the activating and inhibitory pathways are integrated indirectly through downstream effector molecules X and Y . The two effectors enzymatically convert a target protein between an inactive F and active form F* ., Inhibitors ZX and ZY are produced through upstream interaction between A and I , and thus degrade the effectors in proportion to the product AI ., Intuitively , this can be understood as a form of mutual inhibition where A acts to suppress the activity of I , when I is present , and vice versa ., We provide the governing differential equations and parameters of the model in Methods ., At steady-state , and assuming that degradation of ZX and ZY is much slower than the inhibitory reactions ,, X ¯ ≈ k 3 A ¯ ( k 4 - I ¯ ) and Y ¯ ≈ k 5 I ¯ ( k 6 - A ¯ ) , ( 5 ), where the ki are combinations of rate parameters , and equality holds when the rates of inhibitor degradation go to zero ., Assuming general Michaelis-Menten kinetics for activation and inactivation of F , the steady-state output of the network F * ¯ is given by the Goldbeter-Koshland function 38 , which depends only on X ¯ and Y ¯ through the ratio X ¯ / Y ¯ ., Thus , with appropriate choice of parameters ki , by comparison with Eq ( 4 ) , the network output is a function of the concentration gradient , and independent of background concentration and associated receptor saturation ., Moreover , if the enzyme kinetics are assumed to operate in the zero-order regime , the network can be made arbitrarily sensitive to small differences in concentrations , while remaining bounded in the case that c2 = 0 ., Functionally , this is approximately equivalent to the response ,, F*¯ ( A ( c1 ) , I ( c2 ) ) ∼ ( c1/c2 ) h1+ ( c1/c2 ) h , ( 6 ), with tunable Hill coefficient h ., To perfectly extract the gradient signal through this network requires an appropriate choice of the parameters that appear in Eq ( 5 ) , such that k4 = IT , k6 = AT and k3/k5 = KA/KI ., To test the robustness of the output to changes in these values we computed the steady-state with random perturbations to parameters ., For individual pairs of inputs ( c1 , c2 ) spanning 0 . 001 − 100 nM , a 10% multiplicative , uniformly distributed noise term η ∼ U ( 0 . 9 , 1 . 1 ) was applied independently to each parameter in Eq ( 5 ) ., We performed this procedure 100 times for each pair of concentrations , and computed the average output over trials 〈 F * ¯ ( A ( c 1 ) , I ( c 2 ) ) 〉 ( Fig 3D ) , representing the average response of a collection of neurites with some cell-cell variability in intrinsic parameters ., For concentrations between 0 . 001 − 1 nM , a sharp separation persists between up-gradient ( c1 > c2 ) and down-gradient ( c1 < c2 ) conditions , with an eventual loss of discriminability at higher concentrations ., Further simulations revealed k6 to be the most sensitive parameter , as keeping this value fixed extended the sharp boundary to concentrations up to 100 nM ., We have shown how an inhibitory cell-body signal can be integrated with TrkA activation at the growth cone to produce two distinct outcomes ., The push-pull network of Model 1 reproduces the biphasic ganglion outgrowth response , whereas the dual negative regulation of Model 2 yields an adaptive sensor that is highly sensitive to small differences in input concentrations ., We now couple these motifs together to produce a model of NGF signalling that quantitatively accounts for the experiments of refs ., 18 , 22 ., For the coupled system , we model a neurite as two well-mixed compartments , representing the growth cone and cell body ., The network of Model 1 is localised to the growth cone , whereas the network of Model 2 is localised to the cell body ( Fig 4 ) ., Communication between compartments occurs via retrograde transport of activated receptors A to form a cell body population Ac , and anterograde transport of the inhibitory signal I to produce a copy at the growth cone Ig ., Similar to the signal amplification by substrate supply of ref ., 28 , when the output of the cell-body compartment F* regulates the synthesis and transport of growth cone substrate G , neurites extend preferentially in the direction of a gradient ., We tested the model against the gradient data set by simulating 48 h ganglion outgrowth in shallow exponential gradients ( Methods ) ., Fitting the parameters of the model ( Table 1 ) yielded good agreement with the data over all concentration and gradient conditions tested ., The average radial outgrowth of simulated explants follows the characteristic biphasic NGF dependence , and is independent of the gradient steepness ( Fig 5A ) ., The directional bias of simulated outgrowth also closely matches that of the experimental data , exhibiting a large asymmetry in outgrowth for background concentrations of 0 . 01 − 1 nM ( Fig 5B ) ., Thus , for the first time , we have provided a mechanistic and quantitative explanation of these two fundamental features of neurite growth control ., Modular processing , coupled by protein transport and supply , permits powerful integration of antagonistic signals and fine tuning of collective growth ., There are two key structural features of the model that contribute to the NGF response ., The first is our central hypothesis that NGF-dependent inhibition arises from signalling within the ganglion body ., The second is that detection of shallow gradients occurs via transport and comparison of signals between the growth cone and cell body , thus maximising the concentration differences being sensed ., We describe two experiments which could test these claims , and simulate the model to predict the observable response ., The role of inhibitory signalling within the ganglion can be tested by growing explants in compartmentalised chambers that separate neurite and cell-body regions , similar to the assay of ref ., 39 ., With a fixed NGF concentration at the cell bodies , the model predicts an absence of growth inhibition when high concentrations are applied to the distal neurites ( Fig 5C ) ., Moreover , due to the sensitivity of gradient detection , the model predicts a switch-like transition to this regime ., In the absence of gradient sensing ( setting the output of this component of the model F* to a constant ) , the transition is less steep , but the predicted outgrowth remains uninhibited as the distal neurite concentration is increased ( Fig 5C ) ., As NGF/TrkA retrograde transport is slow compared with receptor binding and the rate of neurite growth 40 , the necessity of this mechanism for gradient detection can be tested with temporal manipulations ., Fig 5D shows a simulation of the model in a uniform 0 . 1 nM concentration which was transiently increased to 0 . 2 nM with a 500 min pulse of NGF ., Because there is a time delay in transporting newly bound receptors from the growth cone , the concentration at the cell-body is initially perceived as higher , creating an artificial negative gradient ., The model therefore predicts , counterintuitively , that a uniform concentration increase will transiently decrease the rate of neurite outgrowth ( Fig 5D ) ., Similarly , a concentration decrease is predicted to have the opposite effect ., The predicted out of phase response is robust to the precise temporal regulation of NGF , requiring mainly that the timescale is equivalent to that of retrograde transport , or slower ( ≈400min or more ) ., What is the molecular basis of the mechanism we describe ?, As growth inhibition was observed only for explants , but not for dissociated cells , our experiments provide strong evidence against any pathway in which inhibitory effects are mediated by direct binding of NGF to cell-surface receptors ., This includes the proposal of ref ., 16 that excess NGF stabilises the population of TrkA receptors in an inactive configuration , as well as the suggestion that TrkA receptors undergo activity-dependent down regulation analogous to chemotactic receptors in leukocytes 21 ., Similarly , the lack of effect in dissociated cells precludes other possible hypotheses such as direct inhibition by the low-affinity NGF receptor p75 , or toxicity from overstimulation of downstream TrkA pathways ., This led us to propose the involvement of paracrine signalling within the ganglion by NGF-dependent secretion and subsequent binding of an inhibitory factor ., In a period of competitive survival in sympathetic neurons , NGF signalling leads to cell-body secretion of brain derived neurotrophic factor , which promotes apoptosis of neighbouring cells through the receptor p75 25 ., Although p75 is also an antagonist of NGF/TrkA growth signalling , whether it plays a general role in inhibition in high concentrations is unclear; genetic knockout of p75 had no observable effect on DRG explants in bath applications of NGF 19 , whereas outgrowth from trigeminal ganglia was no longer repelled from NGF-coated beads 20 ., However , p75 belongs to the broader tumour necrosis factor receptor superfamily , of which many members influence neurite growth in critical developmental stages 41–47 ., Shared signalling pathways among this family of receptors suggest a potential general basis for paracrine growth regulation ., In DRGs , tumour necrosis factor receptor-1 ( TNFR1 ) is localised to neuronal cell bodies , and strongly antagonises NGF/TrkA responses when stimulated by tumour necrosis factor alpha ( TNFα ) 48 ., A source of secreted TNFα in DRGs is the satellite glial cells that closely ensheathe neuronal cell bodies , and express both p75 and TrkA receptors 26 ., Although the role of NGF receptors in satellite glial cells is only beginning to be understood 26 , 49 , it is plausible that high NGF concentrations could produce the glial cell activation that elicits TNFα release , and thus a cell-body inhibitory signal through TNFR1 ., Consistent with this prediction , genetic knockout of either TNFα or TNFR1 yielded a threefold increase in embryonic DRG explant outgrowth at an NGF concentration of ≈ 2 nM 48 ., Integration of NGF and TNFα signalling could occur via the Akt pathway , analogous to DRG neurons stimulated with related neurotrophin insulin-like growth factor ( IGF ) 50 ., High concentrations of TNFα antagonise Akt activation , growth associated protein 43 ( GAP43 ) expression and neurite growth promoted by IGF , in a phosphatidylinositol 3-kinase ( PI3K ) -dependent manner 50 ., PI3K/Akt signalling is a primary pathway of retrograde TrkA activity 51 , 52 , and GAP43 activation by TrkA at the growth cone promotes cytoskeletal assembly and growth 53 , suggesting possible candidates for the substrates F and G in the model ., Thus , an interpretation of the signalling network of Fig 4 is that TrkA and TNFR1 control activation of Akt at the cell body ( F ↔ F* ) , regulating expression of GAP43 , which is then activated at the growth cone ( → G → G* ) ., A second pathway of TNFR1 signalling involves a cascade of several caspases ., Caspase- 3 , 6 and 9 , in particular , are key effectors of the axonal degeneration that accompanies NGF withdrawal 54 , 55 ., GAP43 is a substrate of caspase-3 56 , thus providing a link between TNFR1 activation at the cell body , and growth inhibition at the growth cone ( G ← G* ) ., These molecular candidates provide further means by which the proposed role of TNFα/TNFR1 signalling can be tested experimentally ., The sensitivity of explant outgrowth to shallow gradients observed experimentally by refs ., 17 , 18 , 22 , 57 , 58 demonstrates the exquisite chemosensory ability of developing neurites ., In our model , this is explained by a comparison of concentrations between growth cone and cell body , and a chemical computation that overcomes the deleterious effects of receptor saturation ., Our approach was inspired by the study of ref ., 28 ., There it was shown h

Introduction, Results, Discussion, Methods

The development of a functional nervous system requires tight control of neurite growth and guidance by extracellular chemical cues ., Neurite growth is astonishingly sensitive to shallow concentration gradients , but a widely observed feature of both growth and guidance regulation , with important consequences for development and regeneration , is that both are only elicited over the same relatively narrow range of concentrations ., Here we show that all these phenomena can be explained within one theoretical framework ., We first test long-standing explanations for the suppression of the trophic effects of nerve growth factor at high concentrations , and find they are contradicted by experiment ., Instead we propose a new hypothesis involving inhibitory signalling among the cell bodies , and then extend this hypothesis to show how both growth and guidance can be understood in terms of a common underlying signalling mechanism ., This new model for the first time unifies several key features of neurite growth regulation , quantitatively explains many aspects of experimental data , and makes new predictions about unknown details of developmental signalling .

For the brain to become wired up during development , growing nerve fibres use molecular guidance factors to navigate over long distances and find their appropriate targets ., However , the ability of nerve fibres to do this is severely limited by the loss of both growth and guidance as the concentration of guidance factors increases—a phenomenon that has never been fully explained ., We propose a mathematical model that couples growth and guidance at the level of signal transduction , and show that it can , for the first time , quantitatively explain the largest current dataset of precisely controlled measurements ., This finding impacts on understanding of both the causes of neurodevelopmental disorders , and repair after brain or spinal injury .

innate immune system, medicine and health sciences, immune physiology, cytokines, pathology and laboratory medicine, immunology, signaling networks, neurites, neuroscience, collagens, developmental biology, signal inhibition, signs and symptoms, network analysis, molecular development, neuronal dendrites, computer and information sciences, animal cells, proteins, biological tissue, immune system, biochemistry, signal transduction, cellular neuroscience, diagnostic medicine, cell biology, anatomy, fasciculations, ganglia, neurons, physiology, biology and life sciences, cellular types, cell signaling

journal.ppat.1004524

Human Cytomegalovirus Vaccine Based on the Envelope gH/gL Pentamer Complex

HCMV infection causes morbidity and mortality in vulnerable hosts following horizontal or vertical transmission 1 ., In immunosuppressed subjects , multi-system life-threatening disease can occur with primary infection , reinfection by a different HCMV strain , or after viral reactivation ., HCMV is the most common congenital infection worldwide ( 0 . 2–2 . 0% of all pregnancies ) often resulting in long-term consequences to the developing fetus including mortality 2 , 3 ., Intrauterine HCMV has a marked tropism for the developing central nervous system , and a consequence of congenital infection can be irrevocable neurological sequelae in newborns ., Despite intensive research that spans four decades , there is no licensed vaccine to prevent HCMV infection 4 , 5 ., In 1999 , the Institute of Medicine ranked HCMV infection in the highest category for vaccine preventable diseases because potential societal benefits from reduction in associated morbidity and mortality would far outweigh development costs 6 ., Development of an HCMV vaccine that efficiently confers protection against primary HCMV infection or reinfection of a woman of child-bearing years by limiting horizontal transmission is a candidate solution to significantly reduce the devastating consequences of intrauterine HCMV infection 7 ., A number of challenges have inhibited progress in HCMV vaccine development 8 ., HCMV vaccine strategies have been guided , in large part , by historical precedents of currently licensed vaccines demonstrating that induction of pathogen-specific B cell immunity protects against infection and/or disease 9–11 ., The disappointing Phase 2 trial showing that HCMV hyperimmune globulins are ineffective in the treatment of congenital HCMV infection has renewed interest in eliciting NAb by an HCMV vaccine 12–14 ., Studies using fibroblasts as a cell substrate for infection have demonstrated that the envelope glycoprotein B ( gB ) is essential for HCMV entry 15–17 ., gB elicits the majority of antibodies in immune individuals that neutralize fibroblast infection by blocking gB-mediated fusion between the virion and cell membrane 18–23 ., Importantly , pregnant women who develop high avidity anti-gB NAb during primary infection are less likely to give birth to an infant with congenital infection compared to women who are initially CMV seronegative 24–26 ., These observations formed the rationale to focus HCMV subunit vaccine design using gB 7 , 27 , 28 ., A Phase II clinical trial based on recombinant gB admixed in the adjuvant MF59 established a protection level of 50% against primary HCMV infection among women who had given birth in the preceding year 29 ., gB adjuvanted in MF59 is the sole example of a vaccine that has shown significant efficacy to protect healthy women against primary HCMV infection and seronegative solid organ transplant recipients from infection as a result of receiving a seropositive donor organ 30 ., These clinical results provide evidence that a vaccine which targets major neutralizing epitopes may have a role in protection against primary HCMV infection ., Consequently there is a strong interest to optimize vaccine strategies by identifying dominant viral targets of the host immune response that will improve protective efficacy 8 ., While prior work highlights the importance of gB-specific immune responses in limiting primary infection , recent discoveries on viral entry conclude that NAb responses that prevent HCMV infection of various non-fibroblast cell types , including endothelial , epithelial cells ( EC ) and monocytes , are qualitatively different from those blocking fibroblast infection 31–33 ., A current model proposes that HCMV entry into fibroblasts occurs by fusion at the plasma membrane and depends on envelope glycoprotein complexes composed of gB , gH/gL and/or gH/gL/gO 34–37 ., Alternatively , HCMV entry into EC is mediated by endocytosis and requires an additional glycoprotein complex consisting of UL128 , UL130 , UL131A , and gH/gL , termed the gH/gL pentamer complex ( gH/gL-PC ) 33 , 37–39 ., Several studies provide evidence that gH/gL-PC is the dominant target of NAb in sera from naturally HCMV positive individuals that prevents HCMV infection of EC 31 , 40–44 ., Significantly , the majority of NAb in HCMV hyperimmune globulins ( CMV-IVIg ) are directed against epitopes of gH/gL-PC 44 ., In contrast to NAb targeting epitopes of gB , gH/gL , or gM/gN that neutralize HCMV infection of fibroblasts or EC with moderate potency , NAb that recognize conformational epitopes of gH/gL-PC neutralize HCMV infection of EC with unusually high potency 41 ., In addition , there is correlative evidence obtained by in vitro measurements for NAb responses to gH/gL-PC which have implications for the in vivo control of viral dissemination 45 ., These data are consistent with previous studies showing that gH/gL-PC is an important target of the host response to HCMV infection , and induction of gH/gL-PC-specific NAb should be evaluated as a potential component of an HCMV vaccine trial ., We demonstrated that vaccination of rhesus macaques ( RM ) with a bacterial artificial chromosome ( BAC ) -derived MVA vector co-expressing all five rhesus cytomegalovirus ( RhCMV ) orthologs of HCMV gH/gL-PC elicits potent RhCMV-specific NAb responses 46 ., Based on this observation , we constructed a single MVA that co-expressed all five HCMV gH/gL-PC subunits derived from the clinical-like isolate , TB40/E which has both EC and macrophage tropism 47 ., This vector induced EC-specific NAb titers in mice and RM that were orders of magnitude higher than those induced by MVA expressing only gH/gL-PC subunit subsets ( gH/gL or UL128/UL130/UL131A ) , or solely gB ., In addition , MVA-gH/gL-PC elicited NAb preventing HCMV infection of fibroblasts with titers comparable to those induced by MVA expressing gH/gL or gB ., NAb generated in RM by vaccination with MVA-gH/gL-PC also inhibited infection of placental macrophages called Hofbauer cells ( HC ) 48 ., The avidity of CMV-specific antibodies durably rose after MVA-gH/gL-PC vaccination , indicating that the quality of responses is consistent with protective immunity ., These results describe a novel vaccine strategy that induces high level NAb titers that interfere with two prominent HCMV entry routes utilizing a single vaccine vector , a possible requirement to prevent clinical HCMV infection ., We used as a model our recently described BAC-derived MVA vaccine vector for RhCMV gH/gL-PC , to rapidly and efficiently construct a single MVA vector expressing all five HCMV gH/gL-PC subunits ( see Materials and Methods and Figure 1A for details ) 46 ., gH/gL-PC subunits were derived from HCMV TB40/E , a well-characterized clinical-like HCMV strain with an intact complement of gH/gL-PC genes 49 ., Two versions of gH/gL-PC vectors were constructed expressing either a full-length version of gH ( MVA-gH/gL-PC ) or a variant of gH in which the transmembrane ( TM ) and cytoplasmic domains were deleted ( MVA-gH/gL-PCΔ , Figure 1B ) ., To provide a comparative basis for evaluating the NAb responses elicited by immunization with MVA-gH/gL-PC and MVA-gH/gL-PCΔ , MVA vectors expressing full-length gH/gL ( MVA-gH/gL ) , UL128/UL130/UL131A ( MVA-UL128-131 ) , full-length gB ( MVA-gB ) , and TM-deleted gB ( MVA-gBΔ ) were also constructed ( Figure 1B and 1C ) ., gO is known to associate with gH/gL and to contribute to the endoplasmic reticulum transport of these proteins , anchoring either gH/gL or gH/gL/gO complexes within the virion envelope 50 , 51 ., We did not investigate co-expression of gH/gL and gO because gO does not promote gH/gL cell surface expression , and is therefore unlikely to aid in more effectively displaying gH/gL to the immune system 36 ., We also did not test a combination of the gH/gL-PC and gO , since gO would likely compete with UL128 , UL130 and UL131A subunits to associate with gH/gL and , thus , prevent or decrease the formation of gH/gL-PC and the induction of potent EC specific NAb responses36 , 50 , 51 ., Full-length gH or gB ORFs with an intact TM domain as well as their TM-deleted versions were inserted between the essential ORFs G1L and I8R of the MVA genome , an insertion site that is known to provide stable propagation of large and/or unstable sequences ( Figure 1B and 1C ) 52 ., Vectors to express full-length or TM-deleted gH and gB were constructed to test which complex , a membrane-tethered or a soluble one would elicit the strongest NAb responses ., Deletion of the TM domain of gB has been shown to improve the protein expression and the generation of NAb when compared to its full-length counterpart 53 , 54 ., MVA recombinants were reconstituted in baby hamster kidney ( BHK ) cells 46 , and expanded to generate virus stocks for in vitro and in vivo characterization ., HCMV proteins that were expressed from all gH/gL-PC-related vectors displayed sizes in agreement with published values ( Figure 2A ) 33 , 39 , 55 , 56 ., There was no discernible size difference between full-length gH ( MVA-gH/gL-PC and MVA-gH/gL ) and truncated gH ( MVA-gH/gL-PCΔ ) , since the size difference of the TM-deleted gH ( ΔgH ) and its full-length counterpart was only fourteen amino acids ( AA ) taking into account deletion of the TM and addition of the myc-tag ( Figure 1B ) ., Comparable levels of expression for each of the gH/gL-PC components were found from either the MVA-gH/gL-PC or MVA-gH/gL-PCΔ vectors ( Figure 2A ) ., Expression levels of UL128 , UL130 , and UL131A appeared to be slightly greater in MVA-UL128-131 than in either MVA-gH/gL-PC or MVA-gH/gL-PCΔ ., Of note , the MVA-expressed BR5 antigen 57 used as a loading control had slightly higher expression in both MVA-UL128-131 and MVA expressing the fluorescent marker Venus 58 than any of the gH expressing MVA vectors ( Figure 2A ) ., These small differences in expression levels have not been explored further , and may reflect properties of MVA and/or the protein subunits ., Full-length gB harvested from whole cells shows a 130 kDa precursor ( PC ) band , and a 55 kDa band corresponding to the processed C-terminal ( CT ) fragment ( Figure 2B ) ., The 110 kDa precursor form and a truncated C-terminal ( CT ) cleavage product ( 35 kDa ) were detectable in MVA-gBΔ infected cells ( Figure 2B ) ., In summary , these data show that MVA-BAC technology allows rapid generation of MVA expressing all five gH/gL-PC subunits with either gH or gHΔ , subunit subsets ( UL128-UL131A or gH/gL ) , gB or gBΔ proteins with sufficient stability for animal vaccination ., We first investigated if deletion of the TM domain of gH led to secretion of the gH/gL-PC subunits by performing immunoblot analysis of concentrated serum-free medium from chicken embryo fibroblasts ( CEF ) which are permissive for MVA infection ., As observed in infected BHK cells ( Figure 2A ) , comparable levels of the individual gH/gL-PC subunits were detectable in whole cell lysates of CEF cells infected with MVA expressing subsets or all 5 gH/gL-PC subunits ( Figure 2C;Cell ) ., In contrast , efficient secretion of all 5 gH/gL-PC subunits was only detectable in the medium of CEF cells infected with MVA-gH/gL-PCΔ ( without gH TM ) ; whereas none of the subunits were detected in the medium of MVA-gH/gL-PC ( with gH TM ) infected CEFs ( Figure 2C; Med ) ., In addition , gH or gL was not detectable in the medium of MVA-gH/gL ( with gH TM ) infected CEFs ., In the case of MVA-UL128-131 , only small amounts of UL130 and UL131A were observed in the medium of MVA-UL128-131 infected cells , though UL128 was undetectable ( Figure 2C ) ., These data indicate that deletion of the gH TM promotes efficient secretion of all 5 gH/gL-PC subunits , whereas maintenance of the gH TM retains the gH/gL-PC subunits or gH/gL in the cell or on the cell surface when co-expressed from MVA ., Therefore , we believe that the data implicates the gH TM as a pivotal structure for the co-localization of all five gH/gL-PC subunits on the cell surface , and in the absence of the gH TM , the complex is secreted into the medium from the MVA-infected cell ( Figure 2C ) ., Presumably , the scaffold properties of the gH protein allow assembly of the full five member complex anchored to the cell surface ., We also investigated the impact of TM deletion on secretion of the gB protein ., Protein species corresponding to the precursor and the C-terminal fragments of gB and gBΔTM in lysates of MVA-gB or MVA-gBΔ infected CEFs were similar to those found in BHK cells ( Figure 2B and D; Cell ) ., However , only the cleavage product of the gBΔTM protein was detectable in the medium from gBΔ-MVA-infected CEF ( Figure 2D , Med ) ., We hypothesize that the gB TM tethers the full-length or precursor forms of gB onto the cell surface which limits the secretion of these forms of gB from MVA-gB infected cells ., We examined if multi-protein complexes were formed by expression of gH/gL-PC from MVA-gH/gL-PC , gH/gL expressed from MVA-gH/gL or UL128-UL130-UL131A expressed from MVA-UL128-131 , and characterized their subunit composition using co-immunoprecipitation ( co-IP ) with two different antibody ( Ab ) preparations ., Using the monoclonal antibody ( mAb ) 11-1-1 to HCMV gH 59 to isolate protein complexes containing gH , all five gH/gL-PC subunits were immunoprecipitated from BHK cells infected with MVA-gH/gL-PC , and gH/gL proteins were immunoprecipitated from cells infected with MVA-gH/gL ( Figure 3A ) ., As expected , the UL128-UL131A proteins expressed from MVA-UL128-131 were not immunoprecipitated by the anti-gH mAb ., Similar to the gH antibody results , polyclonal antibodies to UL130 immunoprecipitated all five gH/gL-PC subunits from BHK cells infected with MVA-gH/gL-PC ( Figures 3A and B ) ., As expected , gH/gL protein complexes expressed from MVA-gH/gL were not immunoprecipitated by UL130 polyclonal antibodies , though UL128 , UL130 and UL131A proteins expressed from MVA-UL128-131 were immunoprecipitated by UL130 antisera ( Figure 3 ) ., In comparison to the immunoprecipitated proteins from MVA-gH/gL-PC , higher amounts of gH/gL were confirmed following IP of gH from MVA-gH/gL infected cells , and of UL128 , UL130 , and UL131A after IP of UL130 from cells infected with MVA-UL128-131 ., This difference in immunoprecipitated gH/gL or UL128-131A proteins can be explained by differing protein amounts extracted from MVA-gH/gL-PC infected cells in comparison to proteins extracted from cells infected with MVA-gH/gL or MVA-UL128-131 , respectively ( input; Figure 3A and 3B ) ., HCMV proteins were not immunoprecipitated by an IgG control Ab ( Figure 3A ) or rabbit polyclonal pre-immune serum ( Figure 3B ) , confirming the specific co-IP of individual gH/gL-PC subunits by the gH mAb or UL130 antiserum ., The major conclusions from these observations were that ( 1 ) four other gH/gL-PC subunits associate either directly or indirectly with gH or UL130 and are pulled down with anti-gH or anti-UL130 antibodies when co-expressed from MVA-gH/gL-PC , ( 2 ) gL interacts with gH when co-expressed from MVA-gH/gL , and ( 3 ) UL128-UL130-UL131A interact with each other when expressed by MVA-UL128-131 ., Together , these data indicate that all three MVA constructs can express HCMV proteins capable of forming complexes , at least when cell-associated ., An important result would be to demonstrate that the subunits that compose gH/gL-PCΔ physically associate with each other to form a secreted complex ., To determine if the complex that was released into the medium of cells infected with MVA-gH/gL-PCΔ contained all five gH/gL-PC subunits , we performed co-IP using concentrated medium harvested from MVA-gH/gL-PCΔ infected CEF ., IP was carried out either using mAb 11 . 1 . 1 to gH ( Figure 3C ) or rabbit polyclonal antiserum to UL130 ( Figure 3D ) , with a similar result that all 5 subunits were shown to associate by co-IP ., While the gH mAb was more efficient in detecting and precipitating the gH/gL-PC than the polyclonal antiserum to UL130 , the qualitative result was the same , which shows evidence that the secreted form of the gH/gL-PCΔ is maintained as an intact complex in the cell culture medium ., Next , we investigated whether the efficient transport of gH complexes ( gH/gL-PC , gH/gL-PCΔ , or gH/gL ) to the cell surface depends on the TM of the gH subunit ( Figure 4A ) ., BHK cells infected with MVA recombinants were analyzed by flow cytometry ( FC ) using the anti-gH Ab 14-4b to evaluate cell surface expression of the gH subunit with or without its TM domain ., Cells infected with MVA-gH/gL-PC showed higher expression of cell-surface gH than other infected samples and the control cells ( Figure 4A ) ., The minimal detection of cell-surface gH on MVA-gH/gL-PCΔ-infected cells ( Figure 4A ) , may reflect transit of soluble complexes through the plasma membrane , rather than actual cell-surface expression ., As expected , cell surface expression of gH was detected on MVA-gH/gL-infected cells , although at a lower density than on MVA-gH/gL-PC infected cells , consistent with previous observations that the transport of gH to the cell-surface is more efficient when all 5 gH/gL-PC subunits are co-expressed in comparison to only gH and gL 39 ., We confirmed the FC results that we obtained with the anti-gH mAb by using the same rabbit polyclonal Ab preparation specific for UL130 that was used in the co-IP experiments in Figure 3 ., Cell surface UL130 antigen was detected on MVA-gH/gL-PC infected cells , although not on cells infected either with MVA-gH/gL-PCΔ or MVA-gH/gL ( Figure 4B ) ., UL130 was also detectable on cells infected with MVA-UL128-131 with comparable density to that observed for MVA-gH/gL-PC ( Figure 4B ) ., GFP expression quantified by its fluorescence properties was used to demonstrate equal infection of cells by all MVA vectors examined in Figure 4A and B ( data not shown ) ., BAC derived MVA expressed GFP due to the vector construction 60 ., Collectively , these results indicate that gH and UL130 are transported to the cell surface when all five gH/gL-PC subunits are co-expressed from MVA when gH contains a TM ., Further proof is needed to establish if gH and UL130 are expressed on the cell surface in a complex with UL128 , UL131A , and gL ., Nonetheless , as shown by the secretion experiment in Figure 3C and D , complexes composed of all five gH/gL-PC subunits are secreted and not cell-surface associated when the gH TM is deleted ., Further work is needed to explain how UL130 is transported to the cell surface in the absence of gH/gL ., We utilized immunofluorescence ( IF ) to detect surface expression of gH on BHK cells infected with MVA-gH/gL-PC , MVA-gH/gL-PCΔ or MVA-gH/gL by staining with anti-gH HCMV mAb 14-4b ., We permeabilized MVA-infected cells to facilitate intracellular Ab penetration ., This enabled labeling of internal cytoplasmic proteins to compare with the results of Ab labeling of non-permeabilized cells ., Consistent with the FC data ( Figure 4A ) , cell surface staining of gH without permeabilization was only observed for constructs expressing TM containing glycoprotein complexes ( MVA-gH/gL-PC and MVA-gH/gL Figure S1; non-permeabilized ) ., In contrast , only a few cells infected with MVA-gH/gL-PCΔ showed staining for gH with less intensity when compared to MVA-gH/gL-PC infected cells , which may reflect TM-deleted gH protein transitioning through the plasma membrane or intracellular penetration of the gH Ab through leaky cell membranes ( Figure S1 ) ., However , when cells infected with any of the three constructs were permeabilized , the signal was brighter and emanated prominently from the cytoplasm ., The effect is most prominent in the case of MVA-gH/gL-PCΔ , but clearly visible with either MVA-gH/gL or MVA-gH/gL-PC infected cells ., GFP expression was monitored to localize areas of viral foci ., As shown in the control panel ( Figure S1 ) , most nuclei were intensely stained with an anti-Histone 3 Ab when cells were permeabilized , while only a few scattered nuclei were stained with less intensity in non-permeabilized cells ., This indicates that only permeabilization allowed intracellular Ab entry , whereas non-permeabilized cells were resistant to Ab entry ., We were unable to effectively use the UL130 polyclonal antiserum for IF studies to replicate the results we found with the anti-gH mAb; therefore we cannot conclude that UL130 is present on the cell-surface alone or as part of a protein complex using the IF approach ( data not shown ) ., Direct evidence that gH with a TM is associated with the other four gH/gL-PC subunits will require further study with Ab reagents that bind to cell-surface forms of these proteins ., Vaccination of mice using MVA recombinants was performed to evaluate which combination of HCMV antigens induced NAb that prevented in vitro HCMV infection of ARPE-19 epithelial cells and human umbilical vein endothelial cells ( HUVEC ) ., Mice were vaccinated three times ( 0 , 4 , and 8 weeks ) by the intraperitoneal ( i . p . ) route ( Figure 5A ) and the NAb titer that blocked 50% infection ( NT50 ) of HCMV strain TB40/E was evaluated on human ARPE-19 cells 49 ., Marked differences were noted in the ability of different vectors to induce NAb ., Only MVA-gH/gL-PC and MVA-gH/gL-PCΔ stimulated high NAb titers that blocked TB40/E infection of ARPE-19 cells ( peak titers >62 , 000; Figure 5B and Table S1 ) ., NAb titers in MVA-gH/gL-PC vaccinated mice were detectable after the initial vaccination , increased to maximum NT50 levels after the first boost , and only slightly declined following the second boost ( Figure 5B and Table S1 ) ., However , the kinetics of reaching peak NAb titers in MVA-gH/gL-PCΔ vaccinated mice were quite different ., NAb were undetectable following the initial vaccination , but increased to maximum NT50 levels after two boosts ( peak titers >62000; Figure 5B and Table S1 ) ., Substantially lower titers were observed with MVA-gH/gL ( peak titer\u200a=\u200a2 , 170 ) and lower yet with MVA-gBΔ ( peak titer\u200a=\u200a250 ) ., No EC NAb were stimulated with MVA-UL128-131 or MVA-gB ( Figure 5B ) ., In a repeat experiment examining MVA-gH/gL-PC , high titer NAb were stably elevated over a period of at least fifty weeks after initial vaccination ( Figure 5F ) ., In experiments conducted with HUVECs using the identical methods , we found almost identical results as documented on ARPE-19 cells ( Figure 5B-D , F ) using week seven ( data not shown ) and week sixteen ( Figure 5D ) serum ., Both gH/gL-PC expression constructs , either with or without gH TM , induce comparable peak NAb levels , but these titers are more rapidly induced with a membrane-anchored form than with a TM deleted form of the gH/gL-PC ., The impact of the TM on the kinetics of the NAb response is noteworthy as it may reflect a reduced capacity by the immune system to generate a rapid humoral response to a secreted form versus the cell surface form of the gH/gL-PC ., Ultimately , after three vaccinations , both forms of the complex support the development of equivalent levels of NAb ., Since UL128-UL131A are highly conserved proteins among different HCMV strains 61 , NAb recognizing them should neutralize different HCMV strains with comparable potency ., We performed neutralization assays on ARPE-19 cells using the TB40/E , TR 62 and VHL/e 63 , 64 strains of HCMV for infection ., The five proteins that compose the gH/gL-PC of TB40/E and TR have protein sequences which are >97% identical ( Accession # EF999921 and KF021605 ) ., Sequence information for the clinical-like VHL/e strain is unavailable ., There is sufficient sequence variability in the gH amino acid sequence to define two separate clusters of gH sequences designated as gH-group 1 and group 2 ( Figure S2 ) ., The strains that we investigated in Figure 5C are each assigned to a separate gH group ( Figure S2 ) ., Yet , serum antibodies generated against TB40/E proteins expressed from MVA neutralized group 1 ( TR ) and 2 ( TB40/E ) gH strains equivalently ( Figure 5C ) ., In fact , MVA-gH/gL-PC and MVA-gH/gL-PCΔ induced equivalent NAb titers against all three HCMV strains , reaching NT50 levels >30 , 000 ( Figure 5C ) ., Similarly , MVA-gH/gL elicited comparable NAb titers against all three strains , although all were markedly lower than those generated following immunization with either MVA-gH/gL-PC or MVA-gH/gL-PCΔ ( Figure 5C ) ., MVA-UL128-131 , MVA-gB , or MVA-gBΔ did not stimulate detectable EC-specific NAb in this experiment ., These data indicate that MVA-gH/gL-PC and MVA-gH/gL-PCΔ elicit high titer NAb , and far greater than MVA-gH/gL to prevent HCMV infection of EC with heterologous HCMV isolates with limited gH/gL-PC sequence diversity ., We discovered that an MVA expressing all five RhCMV orthologs of HCMV gH/gL-PC induced robust NAb responses that prevented RhCMV infection of rhesus EC and fibroblasts 46 ., That was the premise for investigating if the mice that were vaccinated with MVA expressing HCMV gH/gL-PC developed NAb that blocked HCMV TB40/E infection of MRC-5 fibroblasts ., Measurable NAb titers were generated following vaccination of mice with all MVA recombinants , except MVA-UL128-131 ( Figure 5E and Table S1 ) ., NAb induced by MVA-gH/gL-PC and MVA-gH/gL were comparable in titer and kinetics ( peak titers >1000; Table S1 ) ., NAb titers induced by MVA-gH/gL-PC were stable over a period of twenty weeks following the second boost ( Figure 5F ) ., In contrast , NAb titers from MVA-gH/gL-PCΔ infected mice were undetectable after the initial vaccination , but increased to levels comparable to those induced by MVA-gH/gL-PC in response to two boosts ( Figure 5E and Table S1 ) ., NAb titers of mice vaccinated with MVA-gB kept pace with MVA-gH/gL-PC vaccinated mice , but titers dropped to baseline at the conclusion of the experiment ( sixteen weeks ) ., Mice vaccinated with MVA-gBΔ developed NAb titers ( peak titers\u200a=\u200a510 ) that were lower than all other vaccine groups except MVA-UL128-131 which failed to induce any measurable NAb titer , identical to its behavior in the EC system ( Figure 5B and Table S1 ) ., Collectively , these results emphasize the biologic importance of the MVA-gH/gL-PC vaccine and highlight immunologic differences in potency of sera from mice vaccinated with different MVA constructs to neutralize HCMV infection of fibroblasts and EC ., We performed vaccinations studies in RM to investigate NAb induction by MVA-gH/gL-PC or MVA-gH/gL in an animal model that is evolutionarily closely related to humans ., RhCMV-uninfected RM were immunized three times with MVA-gH/gL-PC , MVA-gH/gL or MVA-Venus ( four RM per group ) ( Figure 6A ) ., We chose MVA-gH/gL-PC instead of MVA-gH/gL-PCΔ because the full-length version of gH/gL-PC produced higher titer NAb with faster kinetics than with TM-deleted gH in our mouse experiments ( Figure 5B and E ) ., All MVA vaccines were well-tolerated with no evidence of local injection site or systemic reaction ( data not shown ) ., Both MVA-gH/gL and MVA-gH/gL-PC stimulated EC- and fibroblast-specific NAb responses in RM , and , as observed following vaccination in mice , notably higher EC titers were elicited with MVA-gH/gL-PC than with MVA-gH/gL ( Figure 6B-F; Table S2 ) ., EC-specific NAb were detected in all four vaccinated RM at the time of the first booster immunization with MVA-gH/gL-PC ( six weeks ) , and anamnestic responses were observed after the booster immunizations at weeks six and twelve ( Figure 6B and F ) ., Memory NAb responses were stimulated after two booster immunizations with MVA-gH/gL ( Figure 6D and F ) , but peak titers ( 150–530; Table S2 ) were two orders of magnitude less than those generated with MVA-gH/gL-PC ( 29 , 960–78 , 340 , Table S2 ) ., NAb titers declined for both MVA-gH/gL-PC and MVA-gH/gL , but one distinction for MVA-gH/gL-PC was that NAb titers declined to a plateau , and remained elevated fourteen weeks after the second booster at week twelve ( Figures 6B and F , Table S2 ) ., In contrast , EC NAb titers declined to background over the same time frame in animals immunized with MVA-gH/gL ( Figures 6D and F ) ., Progressive declines in NAb titers down to the limit of detection are a common observation in RM vaccinated with DNA vaccines or viral vectors 43 , 46 , and the sustainability of EC-specific NAb after MVA-gH/gL-PC vaccination is a notable exception to this pattern ., We also investigated if serum from RM that was active on ARPE-19 cells could neutralize HCMV infection of HUVECs ( Figure 6G ) ., Similar to the case of immune mouse serum ( Figure 5D ) , NT50 titers on HUVECs were almost identical to what we measured on ARPE-19 cells , showing that neutralization extends to both epithelial and endothelial cell types with an almost identical pattern and sensitivity ., Sera from monkeys vaccinated with MVA-gH/gL-PC or MVA-gH/gL were tested for NAb that prevented HCMV TB40/E infection of MRC-5 fibroblasts ., As observed in mice , MVA-gH/gL-PC and MVA-gH/gL induced comparable fibroblast-specific NAb titers in vaccinated RM ( peak titers 190–630 and 100–310 , respectively ) ( Figures . 6C , E , and F; Table S2 ) ., Two injections were required to stimulate detectable NAb with both vectors ., As with NAb that interfered with HCMV infection of EC , comparable NAb interfering with fibroblast infection progressively declined after the first and second booster ., While the second booster stimulated NAb titers , peak titers after the second boost did not reach those observed after the first boost , and NAb titers were below or at the level of detection within six weeks of the second boost ( eighteen weeks ) ., These data confirm in RM that MVA-gH/gL-PC induces NAb that prevent HCMV infection of fibroblasts with levels comparable to those induced by MVA-gH/gL ., Moreover , these data extend our mouse results in a primate host and demonstrate that MVA expressing all five gH/gL-PC subunits induce high titer HCMV-specific NAb ., Detection of ARPE-19 specific NAb titers that exceed those measured on fibroblasts is consistent with observations made with HCMV-positive human sera 44 , 65 ., We note that NAb titers measured on fibroblasts are significantly higher using MVA constructs expressing gH/gL or gH/gL-PC than control sera ., Nevertheless , they are far lower than those measured for ARPE-19 and HUVEC cells using identical sera ., To better gauge the magnitude of the immune responses generated in vaccinated mice and RM , NAb titers were compared to a pool of HCMV seropositive sera , commercially available HCMV IgG sera , human intravenous immunoglobulin ( IVIg ) and CMV-IVIg preparations which all should be considered as arising from chronically infected individuals ., NAb titers induced by MVA-gH/gL-PC in mice and RM markedly exceeded EC-specific NAb titers of three individual HCMV seropositive samples , a pool of seven seropositive donors ( Pool HCMV+ ) , and IVIg ( Figure 7 ) ., Peak NAb titers generated in vaccinated RM and mice were similar to the titer of CMV-IVIg in sera from chronically infected humans ., These titers were likely lower than immediately after primary infection ., Together , these results indicate that MVA-gH/gL-PC boosts EC-specific NAb titers in two species to levels higher than those measured in pooled sera from chronically HCMV-positive humans and to comparable levels measured from concentrated hyperimmune sera ( CMV-IVIg ) 40 , 66 ., It is hypothesized that maternal Ab plays an important role to prevent virus transmission to the fetus67 , 68 , therefore we investigated neutralization of HCMV on human placental macrophages using sera from vaccinated RM described in Figure 6 ., Hofbauer cells ( HC ) are specialized placental macrophages which enter the venous circulation of the placenta and could act as a reservoir to transmit HCMV infection to the fetus 69–72 ., We evaluated whether MVA-gH/gL-PC induces increased NAb responses to block HCMV TB40/E infection of freshly isolated HC in comparison to the standard ARPE-19 EC cell line ., As show

Introduction, Results, Discussion, Materials and Methods

Human Cytomegalovirus ( HCMV ) utilizes two different pathways for host cell entry ., HCMV entry into fibroblasts requires glycoproteins gB and gH/gL , whereas HCMV entry into epithelial and endothelial cells ( EC ) requires an additional complex composed of gH , gL , UL128 , UL130 , and UL131A , referred to as the gH/gL-pentamer complex ( gH/gL-PC ) ., While there are no established correlates of protection against HCMV , antibodies are thought to be important in controlling infection ., Neutralizing antibodies ( NAb ) that prevent gH/gL-PC mediated entry into EC are candidates to be assessed for in vivo protective function ., However , these potent NAb are predominantly directed against conformational epitopes derived from the assembled gH/gL-PC ., To address these concerns , we constructed Modified Vaccinia Ankara ( MVA ) viruses co-expressing all five gH/gL-PC subunits ( MVA-gH/gL-PC ) , subsets of gH/gL-PC subunits ( gH/gL or UL128/UL130/UL131A ) , or the gB subunit from HCMV strain TB40/E ., We provide evidence for cell surface expression and assembly of complexes expressing full-length gH or gB , or their secretion when the corresponding transmembrane domains are deleted ., Mice or rhesus macaques ( RM ) were vaccinated three times with MVA recombinants and serum NAb titers that prevented 50% infection of human EC or fibroblasts by HCMV TB40/E were determined ., NAb responses induced by MVA-gH/gL-PC blocked HCMV infection of EC with potencies that were two orders of magnitude greater than those induced by MVA expressing gH/gL , UL128-UL131A , or gB ., In addition , MVA-gH/gL-PC induced NAb responses that were durable and efficacious to prevent HCMV infection of Hofbauer macrophages , a fetal-derived cell localized within the placenta ., NAb were also detectable in saliva of vaccinated RM and reached serum peak levels comparable to NAb titers found in HCMV hyperimmune globulins ., This vaccine based on a translational poxvirus platform co-delivers all five HCMV gH/gL-PC subunits to achieve robust humoral responses that neutralize HCMV infection of EC , placental macrophages and fibroblasts , properties of potential value in a prophylactic vaccine .

Human cytomegalovirus ( HCMV ) fetal infection during pregnancy and infection of immunocompromised patients are both clinical problems considered extremely important by the Institute of Medicine ., Limited efficacy against primary HCMV infection was found using a subunit vaccine based on glycoprotein B , an important neutralizing antibody determinant blocking HCMV entry into fibroblasts ., The HCMV field has been transformed by the discovery that a five-member ( pentamer ) protein complex is a required factor for epithelial and endothelial cell entry and indispensable for transmission as shown in non-human primates ., Targeting HCMV with antibodies specific to the pentamer may interrupt horizontal and vertical transmission ., We describe an innovative vaccine strategy to induce serum neutralizing antibodies of impressive magnitude against HCMV in two animal models ., Using an attenuated poxvirus vector system , we demonstrate that co-expression of all five pentamer components is significantly more potent to induce serum neutralizing antibodies than subunit subsets of the complex or glycoprotein B , reaching peak levels comparable to HCMV hyperimmune globulin ., A vaccine that elicits systemic and mucosal antibody responses that prevents infection of multiple cell types crucial to natural history of HCMV infection could play a role in preventing congenital HCMV infection and control of infection in immunocompromised patients .

biotechnology, infectious diseases, medicine and health sciences, model organisms, biology and life sciences, immunology, microbiology, research and analysis methods

journal.pcbi.1004967

Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex

One of the properties that sets primates apart in the animal kingdom is their extraordinary adaptation skills which are supported by efficient context-dependent learning mechanisms ., The ability to reliably encode unanticipated behavioral contexts appears to be crucial to such adaptive capabilities ., Indeed , one of the most influential theories of prefrontal cortex ( PFC ) function states that in order to link sensory information to appropriate actions , the PFC must develop the relevant contextual representations , with a high capacity for multimodality and integration 1 ., Although the remarkable representation capabilities in the activity of PFC areas have been explored in a wide variety of tasks , their origin is still unknown , as is the formidable capacity of the PFC to represent such diverse relevant situations ., Recently , Rigotti et al . 2 proposed that , rather than prewiring a network for the relevant representations needed for a task , activity in a randomly connected network could represent essentially all possible combinations of the task stimuli ., The corresponding recombination of inputs observed in the activity of single units has been termed mixed selectivity , and its non-linear components are believed to support the representation of the conjunction of several stimuli ., Although observed in early PFC studies while animals performed tasks involving multiple variables 3–6 , mixed selectivity in the PFC has only recently become a specific research focus 7 ., In the latter study , the authors demonstrated that these non-linear combinations of task variables were absent in PFC activity when monkeys made errors , emphasizing the importance of mixed selectivity in encoding behavioral context ., This context can be defined not only with the current set of stimuli directly available from the environment , but also with previous stimuli and actions that define the internal state of the agent ., Interestingly , such network representations of arbitrary combinations of current and past inputs have been the focus of several research groups studying the recently baptized reservoir computing framework ., Reservoirs are recurrent networks with fixed connections that are randomly generated according to certain parameters to obtain rich spatial ( i . e . nature of inputs ) and temporal ( i . e . previous inputs , and their order and timing ) representations composed of combinations of inputs ., A simple linear output reads the activity of the recurrent network to extract meaningful representations ., The first instantiation of reservoir computing was Dominey et al . s temporal recurrent network model 8 of cortico-striatal function in sequence processing and production ., With the PFC as the reservoir and the striatum as readout , the model provided an explanation of one of the first neurophysiological studies of mixed selectivity in PFC 4 ., Barone and Joseph 4 identified neurons in the peri-arcuate oculomotor area whose responses encoded a mixture of spatial location and sequence rank , in an oculomotor sequencing task ., In Dominey et al . 8 , the recurrent PFC reservoir modeled a prevalent feature of cortical connectivity which is a strong local recurrence , and generated a mixture of spatial and sequential rank selectivity in the reservoir neurons , as observed in primate prefrontal cortex single units 4 ., This reservoir computing paradigm was further developed independently by two teams in computational neuroscience 9 and machine learning 10 , 11 ., Maass et al . 9 developed a spiking neuron reservoir called the “liquid state machine” and demonstrated the universal computing power of this type of network , while Jaeger investigated the signal processing capacities of an analog reservoir called the “echo state network” ., Remarkably , these spatio-temporal reservoir properties have also been found in primary cortical areas of monkeys and cats 12–15 , as stimuli presented in the past influence the representation of subsequent stimuli ., Furthermore , and importantly , in vitro randomly connected recurrent networks of cortical rat neurons display spatio-temporal processing similar to a reservoir 16 ., The simplest architecture of the reservoir networks does not include feedback from output neurons to the recurrent network ., Hence , the memory of previous inputs is only supported by recurrent connections that create loops in the connectivity of the network , yielding a dynamic system that allows past inputs to reverberate and influence the processing of current inputs ., As a consequence , this classic architecture supports a fading memory of inputs ., To circumvent this temporal limitation , Pascanu and Jaeger 17 demonstrated that output units , which feed back into the recurrent network and explicitly represent relevant information act as a working memory through an input driven attractor which can indefinitely hold this information in memory ., Similarly , Maass and colleagues demonstrated the simultaneity of attracting dynamics and real-time computing in reservoirs with feedback units , thereby expanding the computational power of reservoirs 18 , 19 ., This feedback mechanism would allow the representation of task related contexts that are defined by current/recent inputs and contextual information that span longer time periods than the limited fading memory of a classic reservoir ., Pascanu and Jaeger 17 thus note the crucial distinction between attractors in autonomous systems , vs . input-driven systems ., They introduce a mechanism whereby memory states intuitively correspond to attractors in an input driven system ., Memory is implemented by neurons that are trained to lock into an on state when the remembered item appears ., This on state is reinjected into the reservoir thus creating modified attractor state ., They demonstrate this in scenarios where the memory task is to keep track of bracket nesting in a sequence of characters , and the ongoing task is to predict the next character , which varies depending on the bracketing level ., Six WM units allow up to six levels of bracketing to be represented ., The activity of these WM units feeds back into the reservoir thus creating different attractors , such that the reservoir behaves differently in the prediction task depending on which attractor it is in ., Pascanu and Jaeger 17 note that a similar switchable system was demonstrated by Sussillo and Abbott 20 via learning that operated simultaneously on reservoir and readout to yield robust submodes of reservoir dynamics ., In the present study , we present a proof of concept of the reservoir computing framework to model information representation schemes and neural dynamics properties of the PFC ., With a reservoir , we modeled a complex cognitive task initially developed for monkeys and explored neural activity representations at both the single unit and population levels ., Non-linear mixed selectivity was inherently present in the network as was a dynamic mixed-selectivity that related to temporal information ., In the memory version of the model , a feedback neuron explicitly representing behavioral context created a hybrid dynamical regime with two input driven attractor states that were induced by the behavioral context in memory allowing the system to process stimuli and perform the task in a context dependent manner ., These experiments demonstrate the spatio-temporal processing capacities of reservoir networks in both situations—with and without explicit context feedback—in the context of a cognitive task originally developed for monkeys ., In order to compare reservoir and cortical activity and dynamics , we used similar analyses on dorsal anterior cingulate cortex ( dACC ) activity from monkeys that performed the same cognitive task ., We previously demonstrated that the dACC 21 and dorsolateral prefrontal cortex 22 play complimentary roles in this task 23 ., dACC plays a greater role than DLPFC in integration of positive and negative feedback and tracking exploration vs . exploitation phases of the task ., DLPFC activity was more tightly related to monkeys’ behavior than dACC activity , displaying higher mutual information with animals’ choices than dACC ., dACC thus displays rich activity related to behavioral feedback , exploration vs . exploitation and behavior selection ., We show that both representational and dynamical features present in the reservoir are observed in this prefrontal area , further validating the reservoir computing framework as a relevant approach to understand information processing and representation in the PFC ., All procedures were carried out according to the 1986 European Community Council Directives ( 86/609/EEC ) , the French Ministère de l’Agriculture et de la Forêt , French Commission of animal experimentation , the Department of Veterinary Services ( DDSV Lyon , France ) ., At the time of the experiments authorization was granted under regional rules to the laboratory for a range of experiments , rather than for specific studies ., Specific authorization covering this study was delivered by the ‘‘Préfet de la Région Rhône Alpes” and the ‘‘Directeur départemental de la protection des populations” under Permit Number: #A690290402 , including approved protocols in NHPs ( #047 , #048 , #0198 , #0199 , #0200 ) ., All procedures complied with guidelines for animal welfare in accordance with the recommendations of the Weatherall report , ‘‘The use of non-human primates in research” ., In order to compare the neural activity in the recurrent network model with that of the behaving primate cortex , we tested both systems using a problem solving task that was originally developed by Procyk and Goldman-Rakic 24 to investigate shifts between exploration and exploitation behavior ( see Quilodran et al . , 21 for detailed description ) ., Two rhesus monkeys had to find by trial and error which among four targets presented on a touch screen was rewarded by fruit juice ( Fig 1A ) ., At the onset of a trial , monkeys fixated a central fixation point and held their hand on a lever displayed on the screen below the fixation point ( Fig 1B ) ., After a delay period of 1 . 5 seconds , 4 targets appeared on the screen ., The animals made a saccade to one target and fixated it for 0 . 5 seconds until the lever disappeared , giving the GO signal to touch the chosen ( fixated ) target ., Feedback was preceded by a 0 . 6-second delay , and followed by a 2-second delay ending at the beginning of next trial ., The search phase included the incorrect trials ( INC ) and the first rewarded trial ( COR1 ) during which animals explored the targets ., The following 3 correct trials ( COR ) allowed the monkeys to repeat the rewarded choice and constituted the repetition phase ., Occasionally ( 10% of cases ) repetition lasted for 7 or 11 trials to prevent the animals from anticipating the end of the repetition phase ., A signal to change appeared at the end of the last repetition trial to indicate to the animal that a new target was going to be rewarded ., A search phase and its following repeat phase are referred to as a problem ., In only 10% of cases the same target was rewarded in two consecutive problems ., After training , monkeys performed the task in a nearly optimal fashion ., In each search , they avoided previously explored targets that were not rewarded , and correctly repeated the rewarded choice ., Likewise , they generally avoided repeating the previously rewarded target in the subsequent problem ., The average number of trials in search was 2 . 4 ± 0 . 15 for first monkey and 2 . 65 ± 0 . 23 for second monkey ( knowing that the same target is not rewarded two problems in a row , the expected number of trials of an optimal search is ~2 . 2 ) and in repetition 3 . 14 ± 0 . 7 and 3 . 4 ± 0 . 55 for first and second monkey respectively ( the optimal-repetition trial number is above 3 , as some problems had more than 3 rewarded repetition trials ) ., We developed a recurrent neural network model using reservoir computing ( RC ) to perform the cognitive task in order to generate predictions that could then be tested with dACC monkey data ., According to the RC principle , a fixed , large , random reservoir ( recurrent neural network RNN ) is excited by input signals , and the desired output is combined from the excited reservoir signals by a trainable readout mechanism ( a simple linear regression in the most simple versions ) ., As mentioned , the RC principle has been independently discovered in cognitive neuroscience ( temporal recurrent networks , 8 , 25 ) , in computational neuroscience ( liquid state machine , 9 ) , and in machine learning ( echo state networks , 10 ) ., Models have been recently developed along the RC principle to reproduce cognitive functions like working memory 17 and language comprehension and production 26–28 ., Two versions of the model were used in order to obtain the results of this paper: the original version and a second version implementing a simple contextual memory ., The initial version was used in single neuron analyses in the first part of the results while the contextual memory version was introduced later to show the benefits of context encoding 29 ., In both versions , a recurrent network of firing rate neurons received task inputs and were fully connected to a readout layer , the output of the model ( Fig 2A ) ., Reservoir recurrent connections provide rich dynamics formed by nonlinear recombinations of inputs that evolve through time ., Readout neurons activate to represent models target choice , and feed back the choice through readout-reservoir connections ., Reservoir-readout connections are the only modifiable connections of the model ., Several parameters define the reservoir ., The principal necessary property for reservoirs is to have rich dynamics ., The essential characteristics are to have a sufficient number of non-linear neurons that are sparsely and randomly connected ., Fixed network parameters include networks size ( 1000 neurons ) , and standard values for input sparsity ( 10% ) , internal reservoir connection sparsity ( 10% ) , and spectral radios ( 0 . 9 ) ., The simulation time step is set at 25ms in order to give a reasonable granularity for comparison with the primate data ., Fixed unit level parameters include the choice of the tanh non-linearity and the time constant or leak rate of the reservoir units ., The tanh non-linearity is traditionally used in the echo state networks , but others can be used such as the Fermi sigmoid ., However , reservoirs with Fermi neurons have been shown to have significantly smaller short-term memory 30 ., The reservoir unit leak rate was optimized for performance , and was set at 375ms ( 15 network timesteps ) ., Deviations from this value resulted in degraded performance ., Neurons were simulated as leaky-integrator firing-rate units ., Inputs were integrated over time with the following equation:, x ( t+Δt ) = ( 1−Δtτ ) x ( t ) +Δtτ ( Wres⋅r ( t ) +Win⋅u ( t ) +Wfb⋅z ( t ) ), ( 1 ), where x ( t ) denotes the membrane potential vector of reservoir neurons , Δt the time step ( 25 ms ) , τ the time constant of the leaky integration ( 375 ms or 15 time steps ) , Wres the reservoir internal-weight matrix , r ( t ) the firing rate vector of the reservoir neurons , Win is the input weight matrix , u ( t ) the input neuron vector , Wfb the readout to reservoir weight matrix and z ( t ) the readout neuron vector ., At each time step the firing rate r ( t ) of reservoir neurons was computed as the hyperbolic tangent of its membrane potential x ( t ) generating a nonlinearity in the dynamics of the neuron:, r ( t ) =tanh ( x ( t ) ), The readout unit activity was defined as the weighted sum of the reservoir-neuron firing rate:, z ( t ) =Woutr ( t ), where Wout is the readout-weight matrix ., Experiments included a version of the model in which noise was added to the model to test its robustness and the effect of noise on performances and mixed selectivity ., Noise with the same properties was injected during training and testing ., Because a large proportion of noise in cortical populations has been found to be correlated among neurons 31 , noise was simulated as a random Gaussian component added to the activity of input neurons:, unoisy ( t ) =u ( t ) +N ( 0 , σ ), where N ( 0 , σ ) is a vector the size of u ( t ) of pseudo randomly generated values following a Gaussian distribution of mean 0 and standard deviation σ ., To assess the effect of noise injection into the model , values of σ ranging from 0 to 7 in increments of 0 . 5 were each used in 30 simulation instances ., We implemented an RC model where a reservoir of 1000 recurrently connected neurons was fully connected to a readout layer ., Learning took place only between the reservoir neurons and the readout units , at the level of the readout weights ., Weights between reservoir neurons ( internal weights ) and between input and reservoir neurons ( input weights ) were stochastically generated and fixed ., Input weights were generated with a uniform distribution in the interval –1 , 1 with a 0 . 1 probability of connection ., Internal weights followed a Gaussian distribution ( μ = 0 , σ = 1 ) with a 0 . 1 probability of connection between each pair of neurons ., These were scaled so that the largest absolute eigen-value of the weight matrix—commonly referred to as the spectral radius—was equal to 0 . 9 ., This ensured a dynamical regime allowing for sustained activity in the recurrent network without saturation ., Activity in the network thus developed and integrated successive stimuli inputs so that activity at each time point represented the combination of previous and current inputs ( reservoir computing principle ) ., Input neurons represented the major external features of the task ( Fig 2A ) ., They included 5 inputs , each represented by one neuron: the fixation point , the lever , the targets , the reward and the signal to change ., Each of these neurons had a 0 . 1 chance of connecting with each reservoir neuron ., Weights were generated following a uniform distribution in the interval –1 , 1 ., The model generated outputs corresponding to oculomotor saccades and arm touches to the spatial targets corresponding to the monkeys behavioral output and time course of the task events ., A first set of 4 readout neurons represented the four possible target choices for eye saccades and a second set of 4 readout neurons represented arm touches ., The highest activated neuron for each of the two sets represented the models choice and both neurons were required to represent the same target in each trial ., In the contextual memory version of the model , an additional readout neuron was trained to represent the phase ( search vs repetition ) ., In both versions , all reservoir neurons were connected to the readout neurons and constituted the only modifiable connections of the network ., The readout neurons were connected back to the reservoir neurons to feed the choice information back to the recurrent neurons with a 0 . 1 chance of connection ., These connections were generated prior to learning and remained fixed for the duration of the experiment ., Connection weights were drawn from a uniform distribution between -1 and 1 for the choice outputs and for the contextual readout neuron in the contextual memory version of the model ., We trained the model to learn a task that reproduces the major features of the actual task performed by monkeys ( Fig 2B ) ., Timing of these elements closely matched the actual monkey task in order to compare evolution of activity in dACC and reservoir neurons ., Fixation point and lever were each simulated as the activation of their corresponding input neurons ., They provided GO signals as they switched off for saccade to and fixation of a target , and for touching this target respectively ., The readout neurons corresponding to these choices were trained to activate at their respective GO signal after a reaction time of 250 ms and deactivated after a 250 ms reaction time following touch ., Following the fixation point , an input neuron represented the presence of the targets on screen and is deactivated after touch ., Activation of the arm touch neuron started before the actual touch event to allow the neuron representing arm choice to reach full activation before switching off the targets input and to simulate the preparation and movement itself ., Feedback was simulated with a reward input neuron that activated when a choice was correct ., At the end of a problem , a fifth input neuron was activated to represent the signal to change indicating the start of a new problem ., In the contextual memory version , the context neuron representing the phase was trained to activate when the signal to change input neuron was being activated , and to remain active for the duration of the search phase , until the first reward ., Each trial lasted 5550 ms ( 222 time steps ) , except for the last correct trial ( COR4 ) that ended with the presentation of the signal to change and lasted 8050 ms ( 322 time steps ) ., The task was taught to the reservoir with supervised learning using a matched set of <stimuli , desired output> pairs made of 600 problems ., Readout neurons were trained to represent choice by activating to value 1 at periods of choice while remaining silent the rest of the time , thus acting like binary neurons ., The training procedure employed a slightly modified version of the FORCE learning method developed by Sussillo and Abbott 20 ., With the FORCE method , learning of connection weights between reservoir and readout neurons is based on an on-line process of weight adjustment that allows for sampling of the readout error by the system ., Weights are corrected so that a small fraction of the readout error is fed back to the reservoir ., Readout weights are successively modified to produce the target output while sampling deviations in the reservoir activity that result from readout feedback with a slight discrepancy between actual and desired output ., Hence , the system learns to produce a stable readout even in the face of readout errors that are propagated to the reservoir ., We used the recursive least-squares algorithm in combination with the FORCE learning principle to modify readout weights , as described in Sussillo and Abbott 20:, Wout ( t ) =Wout ( t−Δt ) −e ( t ) P ( t ) r ( t ) 1+rTP ( t ) r ( t ), Where e ( t ) is the error before weights are modified and is defined as the difference between actual and desired output ., The error of readout neuron i is defined as follows:, e ( t ) =WoutTi ( t−Δt ) r ( t ) −di ( t ), where Wout is the weight vector between the reservoir neurons and the readout neurons and di ( t ) is the desired output ., P ( t ) can be assimilated to the matrix of all learning rates for each pair of reservoir and readout neurons and is modified as follows:, P ( t ) =P ( t−Δt ) −P ( t−Δt ) r ( t ) rT ( t ) P ( t−Δt ) 1+rT ( t ) P ( t−Δt ) r ( t ) with\xa0P ( 0 ) =I, where I is the identity matrix ., To allow for better convergence of the weights , we modified the feedback from the readout to the reservoir generated with the original FORCE learning method ., We blended the actual output , produced after weight modification according to the FORCE principle , with a clamped feedback i . e . a delayed version of the desired output ., The proportion of clamped feedback and actual output varied smoothly and steadily during training , starting with only clamped feedback and ending with actual output ., The signal f ( t ) was fed back to the reservoir and replaced z ( t ) in eq ( 1 ) :, f ( t ) =tLz ( t ) +L−tLc ( t ), where L is the full duration of training ( entire 600 problem block ) and c ( t ) the clamped feedback that is a 325 ms ( 13 time steps , determined through optimization ) delayed version of the desired output ., This delay greatly improved learning in our experiment ., With a delayed desired output as clamped feedback , readout neurons had to learn to activate at the onset of fixation and arm choice without the correct and expected readout activity that would have been fed back to the reservoir with FORCE-learning fast adaptation of the weights ., Likewise , when readout neurons should deactivate at the end of fixation and arm choice , the reservoir neurons still received the clamped activity resembling a readout that was not deactivated ., Similar to the FORCE learning principle , this method allowed the learning algorithm to sample a higher number of time steps with discrepancies between actual and desired readout around the activation and deactivation of the readout neurons ., In order to assess the trained models behavioral performance , a sequence of 200 problems was provided as input to the reservoir and the output choices were evaluated ., The maximally activated neurons for saccade and hand choices had to match , and thus represented the models choice ., Trials where saccade and hand choices did not match were counted as errors ., Performance was assessed on the basis of three rules: ( 1 ) do not repeat an unrewarded target choice; ( 2 ) repeat rewarded target choice once found; ( 3 ) while searching for the rewarded target , do not choose the target rewarded on the previous problem ., Performance of the model was measured according to these rules ., Trials that did not respect one of the three rules counted as an error ., Error rate was defined as the number of trials that did not respect the rules over the total number of trials ., In order to balance the length of the search period , the number of search trials was generated for each problem in advance ., Thus , no target was predefined as rewarded , rather , after a predefined number of search trials ( from 1 to 3 ) , the reward was given and the behavioral output of the model was assessed according to the above described rules ( for a similar method used to test human subjects , see Amiez , Sallet , Procyk , & Petrides 32 ) ., We are interested in the capacity of the model to perform the problem solving task ., Previous detailed analyses of monkey behavior in this task have shown that the animals produced planned and structured search behaviors 23 ., Rather than trying to reproduce trial-by-trial behavior of the monkeys , we trained the model on examples that followed the above rules , and then tested its performance and analyzed its activity ., We generated training data based on three different search behaviors , among which two were structured ., All three search behaviors used to train the model complied with the above mentioned rules ., A fourth training set was created from data from one of the monkeys trained on this task 21 ., We thus tested four training schedules: First , using a random search where the targets were explored in a different order at each problem ., Second , using an ordered search where targets were explored following the same target sequence at each problem while avoiding the previously rewarded target in the sequence ., In other words , the search always started with the same target , except if it was rewarded on the previous problem , and followed the same sequence , again , avoiding the target rewarded on the previous problem ., Third , using a circular search where targets were explored in infinite repeating circle ., As an example , lets define the repeating sequence upper-left ( UL ) , upper-right ( UR ) , lower-right ( LR ) , lower-left ( LL ) , UL , UR , LR and so on ., If for a given problem , the rewarded target is UR , the search of the next problem will start with the next target in the sequence , namely , LR and continue with LL and UL until it finds the rewarded target ., Fourth , the model was trained with the search behavior from monkey 1 who best solved the task ., In order for the model to effectively learn the task , error trials from the monkey were removed from the behavior fed to the network ., Khamassi et al . 23 provide a detailed description of the monkeys behavior with reinforcement learning models ., Reservoir neuron analyses reported here are based on the activity of networks that learned to explore targets with the circular search ., Results did not differ when the model was trained with the ordered search ., Fig 2C illustrates the activity of reservoir and readout neurons corresponding to a sequence of inputs once the task has been taught with a circular search ., Quilodran et al . recorded 546 neurons in the dorsal bank of the cingulate sulcus of two rhesus monkeys and analyzed them along with local field potential for their correlation with the behavioral shift 21 ., The present article reports on a new and separate reanalysis of this dataset to support findings obtained with modeling ., All reanalyzes of these data were based on firing rate estimates of the recorded neurons ., Subsets of this pool of neurons were selected depending on the requirements of the analyses ., The number of neurons per analysis is specified in each case in the related method description ., Mixed-selectivity analysis was performed by using the same methods for both reservoir neurons from the model ( neurons from the recurrent network ) and dACC neurons ., The analysis focused on specific 500 ms trial epochs ., Epochs used were: early fixation ( 0–500 ms from fixation onset ) , late fixation ( -500–0 ms to targets appearance ) , before touch ( -500–0 ms to target touch ) , before feedback ( -500–0 ms to feedback ) and after feedback ( 0–500 ms from feedback ) ( Fig 2B ) ., Firing rates of reservoir neurons were averaged within these periods , thus obtaining a single firing rate value for each epoch ., Average activity of dACC neurons for each epoch of each trial was estimated as the number of spikes within these epochs ., Epoch , along with phase ( search vs . repetition ) and choice ( UL , UR , LR , LL ) constitute the three factors used in single neuron analysis with 5 ( epoch ) , 2 ( phase ) , and 4 ( choice ) possible levels respectively ( 40 conditions total ) ., The dACC neuron pool for the mixed selectivity analysis was a subset of 85/546 dACC neurons selected for having at least 15 trials per condition ., All reservoir neurons were included in the analysis ., A three-way ANOVA was conducted on the activity of each neuron with factors Epoch x Phase x Choice ., A neuron was considered significant for a factor or an interaction between factors if its p-value was inferior to 0 . 05 ( corrected for multiple comparisons with false discovery rate across all neurons ) ., Interaction effects between phase and choice are considered here as an indicator of mixed selectivity which is defined by the interaction of these variables in their contribution to the firing rate of a single neuron ., Thus in this present study we use the term “mixed selectivity” to refer exclusively to its non-linear component ., Moreover , we introduce the terminology “dynamic mixed selectivity” to refer to mixed selectivity patterns that interact with epoch and correspond in our experiment to the interaction between epoch , phase and choice variables in the ANOVA analyses ., In the monkey , responses specific to the first correct choice ( COR1 ) were considered important as they mark the transition from search to repetition 21 ., Thus , reservoir neurons of the model were also analyzed for their response to the first correct choice in a problem to compare with results obtained in Quilodran et al . 21 ., For that purpose , firing rate activities of single reservoir neurons were averaged over the time window 300 ms to 800 ms after feedback onset and then pooled in incorrect ( INC ) , first correct ( COR1 ) and correct ( COR ) trials ., Pairwise t-test with false discovery rate correction over all tests was used to quantify the number of reservoir neurons that fired significantly more in COR1 trials than in INC and COR trials ( pooling tests of all neurons and all simulations , and with a threshold p-value of 0 . 05 ) ., To demonstrate the presence of the COR1 information in the activity of the rese

Introduction, Materials and Methods, Results, Discussion

Primates display a remarkable ability to adapt to novel situations ., Determining what is most pertinent in these situations is not always possible based only on the current sensory inputs , and often also depends on recent inputs and behavioral outputs that contribute to internal states ., Thus , one can ask how cortical dynamics generate representations of these complex situations ., It has been observed that mixed selectivity in cortical neurons contributes to represent diverse situations defined by a combination of the current stimuli , and that mixed selectivity is readily obtained in randomly connected recurrent networks ., In this context , these reservoir networks reproduce the highly recurrent nature of local cortical connectivity ., Recombining present and past inputs , random recurrent networks from the reservoir computing framework generate mixed selectivity which provides pre-coded representations of an essentially universal set of contexts ., These representations can then be selectively amplified through learning to solve the task at hand ., We thus explored their representational power and dynamical properties after training a reservoir to perform a complex cognitive task initially developed for monkeys ., The reservoir model inherently displayed a dynamic form of mixed selectivity , key to the representation of the behavioral context over time ., The pre-coded representation of context was amplified by training a feedback neuron to explicitly represent this context , thereby reproducing the effect of learning and allowing the model to perform more robustly ., This second version of the model demonstrates how a hybrid dynamical regime combining spatio-temporal processing of reservoirs , and input driven attracting dynamics generated by the feedback neuron , can be used to solve a complex cognitive task ., We compared reservoir activity to neural activity of dorsal anterior cingulate cortex of monkeys which revealed similar network dynamics ., We argue that reservoir computing is a pertinent framework to model local cortical dynamics and their contribution to higher cognitive function .

One of the most noteworthy properties of primate behavior is its diversity and adaptability ., Human and non-human primates can learn an astonishing variety of novel behaviors that could not have been directly anticipated by evolution ., How then can the nervous system be prewired to anticipate the ability to represent such an open class of behaviors ?, Recent developments in a branch of recurrent neural networks , referred to as reservoir computing , begins to shed light on this question ., The novelty of reservoir computing is that the recurrent connections in the network are fixed , and only the connections from these neurons to the output neurons change with learning ., The fixed recurrent connections provide the network with an inherent high dimensional dynamics that creates essentially all possible spatial and temporal combinations of the inputs which can then be selected , by learning , to perform the desired task ., This high dimensional mixture of activity inherent to reservoirs has begun to be found in the primate cortex ., Here we make direct comparisons between dynamic coding in the cortex and in reservoirs performing the same task , and contribute to the emerging evidence that cortex has significant reservoir properties .

learning, medicine and health sciences, decision making, neural networks, prefrontal cortex, brain, social sciences, vertebrates, neuroscience, learning and memory, animals, mammals, primates, cognitive psychology, animal behavior, cognition, memory, zoology, computer and information sciences, monkeys, animal cells, behavior, cellular neuroscience, psychology, cell biology, anatomy, neurons, biology and life sciences, cellular types, cognitive science, amniotes, organisms

journal.pgen.1002867

Comparative Analysis of Regulatory Elements between Escherichia coli and Klebsiella pneumoniae by Genome-Wide Transcription Start Site Profiling

Escherichia coli K-12 MG1655 and Klebsiella pneumoniae MGH78578 belong to the same enteric family of bacteria of the class gammaproteobacteria ., While E . coli K-12 MG1655 represents an extensively studied laboratory strain that is not known to be pathogenic , K . pneumoniae MGH78578 is a well-known pathogenic strain isolated from a patient with pneumonia 1 ., There have been many comparative genomics approaches used to understand the similarities of closely related species in a wide range of genera such as Escherichia , Klebsiella , Salmonella , and Listeria 2 , 3 , 4 , 5 , 6 ., These comparative genomics studies have mostly focused on comparing the gene contents , either shared or specific for each genome ., However , it is also important to investigate the similarities and differences in non-coding regulatory elements including promoter , 5′ untranslated region ( 5′ UTR ) , and small RNA ( sRNA ) , due to their influence on transcriptional and post-transcriptional processes ., The transcription start site ( TSS ) is where transcription begins and is the +1 position of the 5′ untranslated region ( 5′ UTR ) of mRNA ., The promoter , which governs the ability to initiate transcription and control the expression of genes , is directly upstream of the TSS ., The identification of promoter elements in DNA by computational methods depends on the statistical analysis of consensus sequences as overrepresented regions 7 , 8 ., Regulatory sequence elements have been studied by computational methods based on the genomic sequence of the non-coding upstream region 9 , 10 , 11 , however those sequence elements in promoters are short and not fully conserved in the sequence ., Thus , there is a high probability of finding similar sequence elements outside the promoter regions ., In the case of the TSS , the region is not overrepresented enough by any consensus sequences and is thus difficult to predict by computational efforts ., However , when the TSS is known , the DNA region most likely to contain regulatory binding sites is circumscribed , and the effectiveness of searching sequence motifs of interest is greatly enhanced 12 ., Thus , determining the precise locations of TSSs by experimental methods is necessary to accurately annotate the promoter region and the untranslated region ., Knowledge of the 5′ UTR region is important for studying the sequence and structure of the 5′ end of mRNA ( which is associated with transcription regulation , mRNA transcript stability , and translational efficiency ) because translational efficiency in bacteria is often controlled by RNA-binding proteins , noncoding regulatory RNAs , endoRNases , the 30S subunit of ribosome , and structural rearrangements within 5′ UTR 13 ., Genome-wide identification of TSSs with the aid of deep sequencing has allowed researchers to reveal a landscape of TSSs across the whole genome in many microorganisms , including E . coli 14 , 15 , 16 , H . pylori 17 , G . sulfurreducens 18 , and other species 12 , 19 ., In these studies , experimental TSS datasets were used to understand the transcription architecture , to appreciate the complexity of genomic structure , and to analyze regulatory elements for each species ., Comparison of regulatory elements , which can be addressed by experimentally determined TSSs under the same growth condition , is expected to elucidate any regulatory similarities or differences , based not only on the genomic sequence , but also on the transcriptional context of compared species as well ., Here , we carried out the genome-wide TSS profiling experiments for E . coli K-12 MG1655 and K . pneumoniae MGH78578 to accurately determine the boundaries in the regulatory regions between the promoter region and the 5′ UTR ., The upstream regulatory regions between those two closely related species were then compared to investigate whether those regions are conserved and organized in similar manners ., In addition , we used the TSS dataset to identify sRNAs in K . pneumoniae , because very little is known about them ., We then compared the K . pneumoniae sRNAs to orthologous sRNAs in E . coli , in terms of sequence conservation and their target sites ., The range of sequence conservation or diversion between non-coding regulatory elements in interspecies microorganisms could lead to insights about regulatory features that may also play similar roles in the respective species ., Primary mRNA transcripts in prokaryotes are triphosphorylated at the 5′ ends ., We isolated total RNA from E . coli and K . pneumoniae cells growing in mid-exponential phase , and enriched primary mRNAs by removing any monophosphorylated ribosomal 23S , 16S rRNA , tRNA , and any degraded mRNAs by treatment with terminator exonuclease 17 , 18 ., By using a modified 5′RACE ( rapid amplification of cDNA ends ) followed by deep sequencing as described in 18 , libraries were prepared and sequenced to determine potential TSSs for each strain of E . coli K-12 MG1655 and K . pneumoniae MGH78578 ., These TSS libraries yielded >11 . 6 million and >2 . 4 million sequence reads for E . coli and K . pneumoniae , respectively ., 15 . 70% and 19 . 60% of those sequence reads were uniquely mapped with 36 bp read length onto the E . coli and K . pneumoniae reference genomes respectively ., Unique sequence reads that perfectly matched the respective genome sequence were mapped to annotate a total of 3 , 746 and 3 , 143 TSSs for the E . coli K-12 MG1655 and K . pneumoniae MGH78578 genome , respectively ( Table S1 ) ., The average number of TSS reads of E . coli and K . pneumoniae TSSs was 107 . 8 and 78 . 5 , respectively ., The lower number of identified TSSs for K . pneumoniae could be due to a lesser number of sequence reads , and this factor was taken into account in further analysis ., To verify the quality of the TSS data , we compared our experimental E . coli TSS data with previously published E . coli TSS datasets 15 , 16 ., There is no public genome-wide TSS dataset available for K . pneumoniae , which is why only TSS data for E . coli was used for this analysis ., In RegulonDB , there are 1258 upstream sense TSSs annotated for E . coli , generated by 5′ triphosphate enrichment method ., 624 ( 49 . 6% ) TSSs out of 1258 matched exactly with TSSs of this study , and 257 ( 20 . 4% ) TSSs matched within 3 bp tolerance ., Thus , 70 . 0% of known TSSs from RegulonDB agreed with the TSSs from our study ., From the TSS dataset generated without 5′ triphosphate enrichment method , 3661 TSSs were reported for the exponential growth condition ., 1603 ( 43 . 8% ) TSSs matched exactly with TSSs of this study , and 527 ( 14 . 4% ) TSSs matched within 3 bp tolerance ., In sum , 58 . 2% of TSSs were found in TSSs of this study ., A comparison of our TSS dataset with two other datasets suggested TSS datasets generated by a similar method were in better agreement , and E . coli TSSs determined by an independent experiment were matched by TSSs used in this study ., A genome-wide TSS landscape of E . coli and K . pneumoniae was built by assigning TSSs to the nearest downstream gene including ORFs and sRNAs , but excluding TSSs located beyond 700 bp from the translation start site of the closest ORF in a strand specific manner ( Fig . 1A ) ., In E . coli , TSSs were assigned to 2654 genes , while TSSs in K . pneumoniae were assigned to 2301 genes ( 2175 genes in the main chromosome , and 126 genes in the plasmids ) ., While over 80 sRNAs have been identified and experimentally verified in E . coli , very little is known about K . pneumoniae sRNAs ., Identifying the occurrence of sRNAs and determining their boundaries in a genome-wide manner is challenging , especially for less studied organisms , because sRNAs generally have no clear-cut signatures unlike protein-coding genes , which are specified by a genetic code ., In order to overcome limitations of previous experimental approaches , and to interrogate sRNAs in a genome-wide manner , a deep-sequencing approach was applied and proved successful 20 ., Before investigating the possible presence of sRNAs in K . pneumoniae , E . coli TSS datasets were analyzed to assess how many currently annotated sRNAs in E . coli could be identified under the experimented condition , and how well TSS signals were matched with 5′ ends of those sRNAs ., In addition , TSS datasets generated with the 5′ triphosphate enrichment method in this study were compared to four other TSS datasets generated by different methods 14 , 15 , 16 in the light of using 5′ triphosphate enrichment ., Many sRNAs are subjected to post-transcriptional processing , however , which results in an accumulation of shorter products with 5′ monophosphate 21 , 22 , 23 ., Therefore , only unprocessed sRNAs or precursor transcripts of sRNAs , which have 5′ triphosphate and can be detected by this method , were analyzed ., Of 81 annotated sRNAs in E . coli , 58 ( 71 . 6% ) had corresponding TSSs , and were thus considered to be expressed during exponential growth ., Expression profiling data taken from 16 also supported the expression of those sRNAs under the experimented condition , although rprA showed no significant expression according to that data ( Figure S1 ) ., This could be because E . coli RprA transcript is subject to specific endoribonuclease cleavage 21 , resulting in the accumulation of processed shorter form , which is not long enough to be detected by the tiling array ., TSS signals were well matched with the 5′ ends of unprocessed or precursor transcripts of 58 sRNAs including rprA ( Fig . 1B , Table S2 ) ., In comparison , TSS datasets generated by deep-sequencing without 5′ triphosphate enrichment 16 presented TSS signals for 44 sRNAs ( 54 . 3% ) ., Three other TSS datasets , generated by other methods 14 , 15 , were obtained from RegulonDB database ( http://regulondb . ccg . unam . mx/ ) ., They showed TSSs assigned to 11 ( 13 . 6% ) , 6 ( 7 . 4% ) , and 0 ( 0% ) sRNAs for each method ( Fig . 1B ) ., Thus , experimental TSS generated by deep sequencing is a practical indicator that shows the occurrences of sRNAs in E . coli and determines the genomic positions of their 5′ ends ., Additionally , our TSS dataset detected the largest number of annotated sRNAs in E . coli , compared to previous methods ., We believe this result shows that the TSS dataset for K . pneumoniae , generated with the same method , can be used to detect possible sRNAs in that species and to determine the 5′ ends of those sRNA candidates ., In order to identify and confirm the occurrence of sRNAs in K . pneumoniae by experimentally determined TSSs , tentative sRNA candidates should first be predicted by computational methods ., A number of computational algorithms have been developed over the last decade for the purpose of predicting sRNAs in bacterial genomes , and primary sequence conservation in closely related species is one of the most useful data types for predicting whether a genomic sequence corresponds to an sRNA 24 ., Since a majority of E . coli annotated coding genes ( 63 . 7% ) have homologs in the K . pneumoniae genome , and conserved sRNAs are frequently identified adjacent to conserved coding genes in other organisms , we looked up the closest orthologous ORFs to annotated sRNAs of E . coli , and then searched tentative sRNA sequences in K . pneumoniae genomic sequences bound to those neighboring orthologous genes ., For example , in E . coli , micF sRNA is surrounded by ompC and rcsD , both of which are conserved coding genes between the two species ., The K . pneumoniae genomic sequence bound to ompC and rcsD orthologous ORFs was used for searching the genomic sequence of micF by sequence alignment ( Fig . 1D , detailed method described in Materials and Methods section ) ., This approach was supplemented by running Infernal algorithm 25 with sRNA models from the Rfam database 10 . 1 ( http://rfam . sanger . ac . uk/ ) ., Using this combined approach , we identified 48 tentative sRNAs in the K . pneumoniae genome , and 36 of them were expressed by associated TSSs ( Fig . 1C , Table S3 ) ., Expression of those sRNAs was also supported with expression profiling data , with the one exception being rprA ( Figure S1 ) ., rprA of K . pneumoniae showed no significant level of transcription according to the expression profiling data , however rprA had an assigned TSS with 1865 reads , which was also observed in E . coli rprA with an assigned TSS of 3012 reads ., This indicates a possibility of post-transcriptional processing of K . pneumoniae RprA transcript as is the case in E . coli ., 47 of 48 putative sRNAs were located in the main chromosome ( NC_009648 ) of K . pneumoniae , while one sRNA , rnai , was found in the plasmid ( NC_009652 ) ( Fig . 1E ) ., Of 36 small RNAs detected during the exponential phase in K . pneumoniae , 34 had orthologous sRNAs in E . coli , leaving 2 non-orthologous sRNAs , rnai , and ryhB-2 ( Figure S1 , Figure S2 and Figure S3A ) ., Their expression was supported by TSS and expression profiling ( Figure S3A ) ., ryhB-2 was so-named because another orthologous ryhB sRNA was identified in a position between orthologous ORFs yhhX and yhhY ., rnai non-coding RNA is an antisense repressor of the replication of some E . coli plasmids 26 ., While E . coli K-12 MG1655 does not have any plasmid , K . pneumoniae MGH78578 has 5 plasmids , one of which ( NC_009652 ) contains rnai sRNA ., The majority of E . coli annotated genes , 1945 ( 73 . 5% ) , were annotated with a single TSS , and the remaining 26 . 5% had multiple TSSs mainly ranging from 2 to 7 , allowing alternative transcripts ( Fig . 2A ) ., Similar to the complex organization of promoter regions and usage of multiple TSSs shown in E . coli , 534 ( 22 . 8% ) of K . pneumoniae annotated genes had multiple TSSs , leaving a large fraction of genes , 1802 ( 77 . 2% ) , which were assigned to a single TSS ( Fig . 2A ) ., In order to investigate other regulatory features shared by E . coli and K . pneumoniae , the length distribution of the 5′ UTR bounded by experimental TSS and translational start site was calculated , and possible sequence motifs were examined with the MEME motif search algorithm 27 ., The length of the 5′ UTR ranged from 0 to 700 nucleotides , with the most abundant length found to be between 25 to 35 bp for both bacterial species ( Fig . 2B ) ., For 18 genes from E . coli and 10 genes from K . pneumoniae , leaderless mRNAs with the TSSs corresponding exactly to the start codon were found ., The leaderless mRNAs encoded proteins of various functions ( Table S4 ) ., Experimentally determined TSSs in E . coli and K . pneumoniae were used to detect the Shine-Dalgarno ( SD ) sequence of the ribosome binding site ( RBS ) ., Expecting to find that motif within the boundaries of the 5′ UTR , which are defined by the TSS and translation start site of the downstream ORF , we took sequences from 5′ UTR regions in E . coli and K . pneumoniae and searched for consensus motifs ., A conserved caGGaaaa sequence motif ( lower-case characters indicate an information content <1 bit ) was found in E . coli , and an identical conserved caGGaaaa motif was also found in the 5′ UTR of K . pneumoniae ., The most dominant distance between the SD sequence motif and translational start site was 6 nucleotides in both species ., Motif logos for both species are illustrated in Fig . 2B ., Bacterial promoters usually contain specific sequences , which RNA polymerase-associated sigma factors can recognize and to which they can bind ., For example , the E . coli housekeeping sigma factor σ70 ( rpoD , b3067 ) is known to recognize −10 ( TATAAT ) and −35 ( TTGACA ) boxes 28 ., Although sequence motifs of major E . coli sigma factors have been investigated by experimental and computational approaches , less is known for K . pneumoniae sigma factors and their binding motifs ., E . coli and K . pneumoniae are closely related , and they share major sigma factors , such as rpoD , rpoS , rpoH , rpoN , and rpoE with a high level of amino acid sequence conservation over 95% , with the exception of rpoN that has 89 . 8% amino acid sequence similarity ( Table S5 ) ., Since sigma factor σ70 is housekeeping during exponential growth in E . coli and presumably in other gammaproteobacteria including K . pneumoniae as well , conservation of subregions 2 and 4 of bacterial sigma factor σ70 , which are known to recognize the −10 and −35 boxes , can give insights toward understanding the promoter structure of K . pneumoniae ., Thus , amino acid sequences of rpoD of 5 strains belonging to gammaproteobacteria and 3 strains belonging to other classes were aligned and analyzed ( Fig . 2D ) ., Notably , region 2 , which recognizes the −10 box , was perfectly conserved among species in gammaproteobacteria , and region 4 , which recognizes the −35 box , was almost conserved as well ., Since the conservation of sigma factor σ70 subregions recognizing sequence motifs in the promoter and the expression of housekeeping rpoD in E . coli and K . pneumoniae was confirmed with the TSS dataset and expression profile , it is likely that the promoter structure of those species are identical ., Thus , TSSs in E . coli and K . pneumoniae identified in this study were used to find sequence motifs of the promoter region , which includes the −10 and −35 boxes , in order to see whether two closely related bacteria share similar or identical promoter sequence motifs ., We extracted 50 bp long sequences directly upstream of the TSSs , which are long enough to cover the −10 and −35 boxes , and ran the MEME motif search algorithm ., As a result , the consensus sequence of the extended Pribnow box motif ( tgnTAtaaT ) including the −10 box was obtained , and the −35 box sequence motif ( cTTgaca ) was also found , as expected ( Fig . 2C ) ., Moreover , the most dominant distances between the −10 box and TSS and between the −10 and −35 boxes were also the same in both bacteria ., Although the sequence motif obtained herein is based on genome-wide TSS profiles generated only under exponential growth and other sigma factors having different binding sequence motifs may play a minor role in transcription regulation under the experimented condition , overrepresented sequence motifs of promoter regions in E . coli are in accordance with prior knowledge , and the two species in this study showed identical sequence motifs of the promoter ., Thus , these closely related species seem to share identical promoter structures , reflecting a high conservation of major sigma factors ., Previous studies have shown evidence of a purine ( A/G ) preference at the TSS in E . coli 29 ., Here , we investigated if the experimentally derived TSS data provide insights into any such nucleotide preference at the TSS ., Thus , nucleotide preferences from −5 to +5 sites surrounding the TSSs for E . coli and K . pneumoniae were calculated ., The current experimentally derived TSSs in both species also showed a significant dinucleotide preference at the +1 TSS and −1 site ( Fig . 2E ) ., In E . coli , 78 . 6% of the TSSs were represented by purine base ( 45 . 2% A and 33 . 4% G ) at the TSS ., Similarly , 79 . 4% of K . pneumoniae TSSs presented the purine base ( 48 . 0% A and 31 . 4% G ) at that site ., Interestingly , another nucleotide preference at the −1 site , the nucleotide before the TSS and the last nucleotide that is not transcribed , was observed in both species ., In E . coli , 80 . 2% showed the pyrimidine base ( 35 . 4% T and 44 . 8% C ) preference at the −1 site ., Likewise , in K . pneumoniae , 81 . 5% of cases also showed the pyrimidine base ( 31 . 0% T and 50 . 5% C ) at the −1 site ., Flanking regions ranging from +2 to +5 sites and −2 to −5 sites showed no significant nucleotide preference ( Fig . 2E ) ., Thus , both species showed the purine preference at the +1 TSS and the pyrimidine preference at the −1 site ., In accordance with this observation , H . pylori , which belongs to a different class of alphaproteobacteria , also showed purine preference at the TSS ( 66 . 0% A or G ) and pyrimidine preference at the −1 site ( 68 . 3% T or C ) 17 ., Similar to the dinucleotide sequence preference at +1 and −1 sites found in bacteria , transcription from the S . cerevisiae promoter 30 and the mammalian 31 promoter preferentially starts with a purine at position +1 , having a preference for pyrimidine at position −1 ., These results suggest that E . coli and K . pneumoniae share many regulatory features at the transcriptional and translational level ., They have a conserved promoter structure reflecting preserved sigma factors , use multiple TSSs that extensively increase transcriptome complexity by resulting in alternative transcripts , and show dinucleotide preference near the TSS position ., In addition to this similarity in transcriptional features , E . coli and K . pneumoniae exhibit conserved Shine-Dalgarno sequence motifs , the same distance from Shine-Dalgarno motif to translation start site , and 5′ UTR length distribution , suggesting similarity in regulatory features of translation ., While E . coli and K . pneumoniae share several regulatory features , it is still unknown whether the two species use them to regulate gene expression in the same manner ., Thus , we analyzed the usage of regulatory elements upstream of orthologous genes between two strains in order to investigate whether those conserved genes are regulated in a similar or different manner ., The orthologous genes present in E . coli and K . pneumoniae were selected by reciprocal alignments using a threshold of 50% amino acid sequence similarity and 50% alignment length between the encoded proteins , resulting in a set of 2 , 876 orthologs ( Fig . 3A , Table S5 ) ., 2962 ( 79 . 1% ) E . coli TSSs were assigned to orthologous genes defined herein , and in K . pneumoniae , 2317 ( 73 . 1% ) of TSSs were assigned to orthologous genes ., Considering 63 . 7% ( 2876 out of 4513 ) of genes in E . coli and 54 . 2% ( 2876 out of 5305 ) of genes in K . pneumoniae were orthologous , detection of over 79 . 1% of TSSs in E . coli and 73 . 1% in K . pneumoniae assigned to orthologous coding genes implies over 73% of primary transcripts were expressed from operons or transcription units having orthologous genes at the first position ., In E . coli , the average number of genes in an operon is about 1 . 5 16 , and operons containing orthologous genes in E . coli have a tendency to keep their sequential position in K . pneumoniae , suggesting possible conservation of operon structures ., This result suggests that the majority of primary transcripts were expressed from operons containing conserved orthologous genes during exponential growth in both species ., Thus , further analysis of regulatory regions upstream of orthologous genes with genome-wide TSSs covers a majority of expressed gene contents under the experimented condition ., Despite the fact that orthologous genes were used to express the majority of primary transcripts during exponential growth , regulatory regions upstream of those conserved coding genes were organized in a different manner with multiple TSSs ( Fig . 3B ) ., In order to perform a detailed investigation comparing promoter regions between two species , each TSS was used to define a promoter region ., A promoter region was defined as 50 bp long nucleotide sequences upstream of each TSS , which was long enough to include most of the regulatory elements identified , including the −1 site , −10 box , and −35 box , but not too long to exclude unnecessary sequences ., Then , the promoter region was categorized into one of four groups , based on sequence conservation of the promoter region and presence of an experimental TSS: conserved promoter region with TSS ( CPT ) , conserved promoter region with no matching TSS ( CPNT ) , orphan promoter region ( OP ) , or species-specific promoter ( SSP ) ., CPT was defined as a promoter region with a conserved sequence in both strains with a matching experimental TSS , and was used to define the promoter region and 5′ UTR , which were comparable between the two species ., CPNT was defined as a promoter region with a conserved sequence in both strains , however with experimentally determined TSSs in only one species ., Similarly , OP was defined as a promoter region with no conserved sequence between E . coli and K . pneumoniae , and with experimental TSSs in only one species ., SSP was defined as a promoter region upstream of non-orthologous genes ., ( More details described in Materials and Methods section ), If the sequence of regulatory regions upstream of orthologous coding genes is also conserved , then conserved promoter ( CPT ) should be the most frequent type of promoter region ., However , an exhaustive comparison of promoter regions resulted in only 662 conserved promoters ( CPT ) between E . coli and K . pneumoniae , which covered 17 . 7% of TSSs and corresponding promoter regions in E . coli and 21 . 1% in K . pneumoniae ., An unexpectedly small portion of conserved promoter regions with matching TSSs in two species under the exponential growth supports a different organization of regulatory regions containing multiple TSSs and their associated promoters between those two closely related species ., Interestingly , in both species , the promoter type with the largest number was the conserved promoter with no matching TSS ( CNPT ) ., In E . coli , 49 . 6% of TSSs were associated with promoters with conserved sequence , and no matching TSSs were found upstream of corresponding orthologous genes in K . pneumoniae ., Similarly , 41 . 3% of TSSs of K . pneumoniae were associated with that type of promoter ., A smaller number of TSSs was detected in K . pneumoniae versus E . coli , despite K . pneumoniae having the larger genome ., This was possibly due to fewer raw reads being obtained from the K . pneumoniae TSS library ., Thus , it is arguable that the portion of conserved promoters with matching TSSs could increase as the coverage of TSS reads goes up ., However , over 40% of promoters with conserved sequences had TSSs in one species , but had no matching TSS in the other species ., Thus , the regulatory regions upstream of orthologous genes are organized in a different manner , despite a large portion of promoters having conserved sequences between two species ., This suggests different sets of TSSs are used to express those orthologous genes ., For example , lpd had two experimental TSSs in E . coli and K . pneumoniae ( Fig . 3C ) ., Proximal TSSs were matching , and had a highly conserved promoter sequence ., Distal TSSs of lpd had conserved sequences , but were in different locations ., Moreover , promoters with no conserved sequence and TSS in one species ( OP , orphan promoter ) also support that interpretation ., Thus , while two closely related species may share identical transcriptional machineries including sigma factors and RNA polymerase , upstream regulatory regions are organized differently , so that even conserved genes can be regulated differently , and in many cases mRNA transcripts from orthologous genes can have different 5′ UTRs , which may have disparate regulatory elements in that region ., To investigate similarities and differences in 5′ UTR regions , their length and sequences were defined by 662 comparable conserved promoter regions with TSSs in both species , as shown in Fig . 3D and Fig . 3E ., The length comparison of the 5′ UTR between E . coli and K . pneumoniae showed a strong correlation ( R2 value of 0 . 877 ) , and 169 ( 25 . 5% ) 5′ UTR regions had exactly the same length ., However , in general , the K . pneumoniae 5′ UTR was longer than that of E . coli , reflecting the bigger size of the genome ( Fig . 3D ) ., For example , the 5′ UTR length of rpoS , which is one of the orthologous genes , was 566 in E . coli , while the length of the K . pneumoniae rpoS was 670 ., To investigate the sequence conservation between those comparable 5′ UTR regions , sequences of 5′ UTR regions from two species were aligned and percentage sequence identity for each 5′ UTR pair was calculated ., The sequence variation of the 5′ UTR region along with the percentage identity of corresponding promoter and ORF is shown in Fig . 3E ., Consequently , ORF sequence was found to be the most conserved element , followed by sequence of promoter regions and sequence of the 5′ UTR region as the most diverse regulatory element among them ., The averages of sequence identity of orthologous ORFs , comparable conserved promoters , and their 5′ UTR were 88 . 9% , 79 . 0% , and 66 . 0% , respectively ., In order to calculate the level of conservation of the regions surrounding translation start site of orthologous genes , sequences of 200 bp long regions around translation start sites were aligned for orthologous genes having clearly aligned translation start sites between E . coli and K . pneumoniae ( Fig . 3F ) ., In the 5′ UTR , there was a relatively more conserved regions 6 bp upstream of the translation start site ., This region was considered to be the Shine-Dalgarno sequence of the ribosome binding site because in both species the most dominant distance between the Shine-Dalgarno sequence motif and translation start site was 6 nucleotides ., In the coding region , the first codon , frequently ATG , was most conserved with slightly less conservation of the first nucleotide of the first codon ., This was because the start codon , ATG , was replaced with GTG or TTG in some orthologs ., In agreement with the wobble theory 32 , the third nucleotide of each codon was least conserved ., Interestingly , however , the second nucleotide was more conserved than the first in every codon analyzed ., This might be because conservation of the second nucleotide can contribute to preserving the same amino acids like leucine , or amino acids with a similar property ., Accordingly , codon analysis of the coding sequence between orthologous genes of the two species suggested that the majority of substitutions in the first nucleotide of the codon resulted in either keeping leucine or changing amino acids having similar properties , such as leucine/isoleucine , leucine/valine , valine/isoleucine , serine/threonine , glutamine/glutamic acid , or asparagine/aspartic acid ., In addition to species-specific gene content , E . coli and K . pneumoniae also exhibited differences in the organization of regulatory regions upstream of conserved orthologous genes ., Different usage of TSSs and their promoter regions can contribute to varied regulation of genes downstream of those promoters , resulting in transcripts with different 5′ UTR ., Moreover , both species extensively use multiple TSSs , which increase the complexity and diverse nature of regulatory regions ., Thus , E . coli and K . pneumoniae , which are closely related , have regulatory regions of orthologous genes organized in a different manner ., The investigation of regulatory features of coding genes based on genome-wide TSSs and their comparison between two closely related enterobacteria showed that the two species share almost identical regulatory features ., However , they deploy those regulatory features upstream of conserved or orthologous coding genes in a different manner , suggesting a variation of transcriptional regulation by using multiple TSSs and post-transcriptional regulation by having different 5′ UTRs , generated from a different set of TSSs ., Since small regulatory RNAs can function in post-transcriptional control of gene expression in many processes including stress responses , metabolic reactions , and pathogenesis 33 , 34 , and identification of sRNAs in K . pneumoniae resulted in 34 orthologous sRNA pairs between two species , we compared sequences of those conserved sRNAs and investigated whether they would regulate their target genes in the same manner ., This was done , similarly in previous studies 23 , 35 , 36 ., The conserved RNA-binding protein Hfq , first discovered in E . coli , is a pleiotropic regulator that modulates the stability or the translation of an increasing number

Introduction, Results, Discussion, Materials and Methods

Genome-wide transcription start site ( TSS ) profiles of the enterobacteria Escherichia coli and Klebsiella pneumoniae were experimentally determined through modified 5′ RACE followed by deep sequencing of intact primary mRNA ., This identified 3 , 746 and 3 , 143 TSSs for E . coli and K . pneumoniae , respectively ., Experimentally determined TSSs were then used to define promoter regions and 5′ UTRs upstream of coding genes ., Comparative analysis of these regulatory elements revealed the use of multiple TSSs , identical sequence motifs of promoter and Shine-Dalgarno sequence , reflecting conserved gene expression apparatuses between the two species ., In both species , over 70% of primary transcripts were expressed from operons having orthologous genes during exponential growth ., However , expressed orthologous genes in E . coli and K . pneumoniae showed a strikingly different organization of upstream regulatory regions with only 20% identical promoters with TSSs in both species ., Over 40% of promoters had TSSs identified in only one species , despite conserved promoter sequences existing in the other species ., 662 conserved promoters having TSSs in both species resulted in the same number of comparable 5′ UTR pairs , and that regulatory element was found to be the most variant region in sequence among promoter , 5′ UTR , and ORF ., In K . pneumoniae , 48 sRNAs were predicted and 36 of them were expressed during exponential growth ., Among them , 34 orthologous sRNAs between two species were analyzed in depth , and the analysis showed that many sRNAs of K . pneumoniae , including pleiotropic sRNAs such as rprA , arcZ , and sgrS , may work in the same way as in E . coli ., These results reveal a new dimension of comparative genomics such that a comparison of two genomes needs to be comprehensive over all levels of genome organization .

In order to investigate similarities and differences of closely related species , most of the comparative genomics studies focus on comparing the gene contents either shared or specific for each genome ., However , it is also important to investigate the differences in non-coding regulatory elements because they influence the transcriptional and post-transcriptional processes ., Thus , we performed a genome-wide profiling of transcription start sites ( TSSs ) in two species , E . coli K-12 MG1655 and K . pneumoniae MGH78578 ., Experimental identification of TSSs is important for precise definition of promoter regions and 5′ untranslated regions upstream of coding genes ., Comparative analysis of these regulatory elements revealed the use of multiple TSSs , identical sequence motifs of promoter and Shine-Dalgarno sequence ., However , we observed that the upstream regulatory regions of the majority of operons having orthologous genes were organized with different usage of promoters and TSSs , resulting in diverse and complex gene regulation ., We also found that the 5′ UTR is the least conserved regulatory element in sequence between the two species ., Moreover , 34 orthologous sRNAs between E . coli and K . pneumoniae were analyzed in depth ., The analysis suggested many of K . pneumoniae sRNAs might regulate the target genes as in E . coli .

escherichia coli, prokaryotic models, model organisms, gene expression, genetics, biology, genomics, comparative genomics, microbiology, genetics and genomics

journal.pcbi.1003308

Natural, Persistent Oscillations in a Spatial Multi-Strain Disease System with Application to Dengue

Mathematical models based upon the various derivatives of the classic susceptible-infected-recovered ( SIR ) framework have greatly improved our understanding of the transmission and population dynamics of many important pathogens 1 , 2 ., Common to this class of models is their propensity to exhibit damped oscillations around an approaching equilibrium where the rate of new infections equals the loss from the infectious pool due to recovery ., In reality , however , many infectious diseases will not remain in this state of equilibrium but instead exhibit persistent oscillations , ranging from seasonal increases in incidence rates to multi-annual epidemic outbreaks ., Measles and influenza are just two examples of pathogens for which incidence levels can vary by orders of magnitude within a single year 3 , 4 ., External forces are often incorporated into models to reflect a seasonal increase or decrease in the number of infectious contacts or vector densities , for example , which move the systems dynamics away from its natural equilibrium into a regime characterised by periodic or chaotic oscillations , akin to those observed in nature 5 , 6 ., For antigenically diverse pathogens , periods of high or low infection rates or the timing by which one dominant antigenic strain is replaced by another strain , are often out of sync with those dictated by the external forces , however ., Theoretical studies have therefore concentrated on biological or pathogen-intrinsic factors instead ., Immunological interactions between the constituent strains in the form of cross-immunity or cross-enhancement have been repeatedly highlighted as some of the most important determinants of the epidemiological dynamics of multi-strain pathogens ., Under this scenario , enhanced competition for susceptible hosts can offer a temporary selective advantage to a particular strain or subset of strains , causing their amplification and subsequent decline ., This process of immune-mediated selection has been proposed to underlie the population biology of a variety of important pathogens , including the influenza virus 7 , Plasmodium falciparum 8 , Vibrio cholerae 9 , dengue virus 10–12 , respiratory syncytial virus 13 and rotavirus 14 ., Whereas many deterministic multi-strain models rely on the presence of immune interactions to destabilize the system , existing natural variabilities or stochasticities in the interactions between the relevant players have also been shown sufficient to generate regular or chaotic oscillations in single-strain and ecological predator-prey systems 15–19 ., Furthermore , demographic stochasticities have been found to play an important role when relaxing the assumption of homogeneous mixing and when taking spatial ecological aspects into consideration ., In this scenario , spatial heterogeneities due to host-population structure or local ecological variations can create short-lived spatial refuges 20 and significantly affect pathogen persistence 21–23 ., The consideration of spatio-temporal variations is of particular importance for vector-borne pathogens , where the underlying drivers of the observed epidemiologies may be confounded by substantial heterogeneities in host and vector densities through space and time , as in the case of the dengue virus ( DENV ) ., DENVs population comprises four antigenically related viral groups , or serotypes ( DENV1-4 ) , that are the cause of clinically indistinguishable illnesses in humans ., Different immunological interactions in the form of antibody-dependent enhancement ( ADE ) or temporary and/or partial cross-immunity have been independently proposed as the driving forces behind the virus complex epidemiology that comprises multi-annual epidemic outbreaks and sequential replacement of dominant serotypes 10–12 , 24–27 ., Although these differential equation models qualitatively capture dengues epidemiological dynamics , they do not consider the natural variability in disease transmission across time and space and thus cannot account for observed differences in incidence and serotype distribution within endemic regions ( see e . g . 28 ) ., Meta-population and agent-based models allow a more explicit description and investigation of demographic and spatial , ecological stochasticities 29–31 , and thus provide a natural alternative to study these host-pathogen systems ., Here , using dengue as a case study , we show that heterogeneities and stochasticities underlying host-vector contacts can give rise to persistent oscillations in multi-strain pathogen systems , even in the absence of immune competition between antigenic types ., We demonstrate that viral persistence is significantly enhanced through the temporal generation of susceptibility pockets within the population , leading to highly heterogeneous distributions in disease and serotype prevalence that can explain observed geographic differences in dengue endemic regions ., Complimentary to immune interaction , host demographic factors and vector ecologies thus emerge as important drivers of dengues epidemiological dynamics ., Using a stochastic , agent-based framework we first analysed the dynamics of a host-pathogen system comprising 4 co-circulating antigenic types under the assumption of homogeneous mixing within the population ., Contrasting the predictions of deterministic multi-strain models , in which the dynamics inevitably converge towards a stable equilibrium in the absence of strong immune competition , the system exhibited sustained oscillations in the total number of infections and out-of-phase oscillations in strain prevalence , as illustrated in Figure 1A ., In agreement with previously studied stochastic single-strain systems 17 , 18 , these dynamics are driven by the amplification of stochastic effects at the individual level , which keep each strain in a transient regime rather than approaching the expected deterministic equilibrium ., At the same time , short-term stochastic differences in each strains transmission success accumulate in time and start to generate significant asymmetries in the immunity profile within the host population , which then leads to the desynchronisation between strains ., This extreme case of minimal strain interaction more resembles a system of four co-circulating but unrelated pathogens ., Not surprisingly , therefore , we found that the periods of oscillations in total incidence and strain prevalence were essentially the same , determined by the parameters relating to pathogen transmission and host demography ( Figure S1A in electronic supplementary material ) ., In the case of dengue , however , differences between the inter-epidemic period and average cycle length in strain prevalence have been well documented 32 ., We therefore extended the model to incorporate mosquito vectors and used dengue-relevant epidemiological parameters values ( see Table 1 ) to investigate the effect of stochastic amplifications on the viruss epidemiological dynamics and inter-epidemic periods ., The resulting qualitative dynamics in terms of persistent oscillations in incidence and serotype prevalence appeared invariant to the addition of mosquito vectors but showed a significant increase in average disease prevalence ( Figure 1B ) ., This increase was mainly caused by a reduction in the risk of stochastic extinction due to the inclusion of viral incubation periods as well as the increase in the basic reproductive number from in the directly transmission model to in the vector model ., Importantly , also , we started to observe a divergence between the epidemic and serotype periodicities ( figure S1B in electronic supplementary material ) and also found epidemic peaks more likely to be comprised of multiple serotypes ., Further including seasonality through annual variations in mosquito densities ( see Materials and Methods ) resulted in dengue-like epidemiological behaviour with a distinct seasonal signature , strong multi-annual periodicities in incidence and fluctuating distribution in serotype prevalence ( Figure 1C ) ., This behaviour was further accompanied by a considerable increase in peak incidence levels and more pronounced epidemic troughs , which could partly be explained by an increase in the average to but also by the strong synchronizing effect of vector seasonality on serotype dynamics ., We hypothesized that the occurrence of large epidemic outbreaks ( as seen in Figure 1C ) was partly facilitated by our assumption of homogeneous mixing , which facilitates rapid disease transmission throughout the whole population ., We thus restructured our model into a meta-population formulation by subdividing the human and mosquito populations into sets of spatially arranged communities ( see Materials and Methods ) and examined the effect of spatial segregation between hosts on the epidemiological dynamics of this multi-strain system ., Within this set-up we assumed that individuals get infected predominantly by mosquitoes of their own and surrounding communities and with a small probability , , by mosquitoes from distant communities through ( temporal ) human movements , or visits , to these communities ., We argued that because of the limited flight range of Aedes mosquitoes , human movement is more important for long-distance transmission 33 and therefore assumed to be independent of geographic distance , contrasting continuous and distance-dependent dispersal kernels often employed in spatial ecological models ( but also see 33 and 34 for alternative realisations ) ., With the addition of this spatial component the system exhibited more defined seasonal dynamics as well as a lower variability in the epidemic behaviour ( Figure 2A ) , with the overall temporal dynamics closely resembling epidemiological time series from dengue endemic regions with the characteristic multi-annual cycles in epidemic outbreaks and sequential serotype dominance ( Figure 2B showing data from Puerto Rico , and Figure S2 showing data from Thailand , Mexico and Vietnam ) ., The periodicity in serotype prevalence also increased and settled onto a 8–9 year cycle ( figure S1C in electronic supplementary material ) , which is in line with the suggested periodicity derived from epidemiological time series 32 ( also apparent from Figure 2B ) and dengues phylodynamics in Thailand 25 ., In agreement with previous studies on meta-populations , the spatial segregation between hosts enhanced global disease persistence ( compare e . g . baseline incidence in Figures 1C and 2A ) but at the same time facilitated local extinction 17 , 22 , 35 ., This created a spatially heterogeneous susceptibility landscape within the population ( Figure 3A , left panel ) upon which individual serotypes were sequentially selected and amplified , frequently exhibiting locally propagating waves ( Figure 3A , middle panel ) ., This heterogeneity in susceptibility and disease prevalence also affected the timing between heterologous infections , here referred to as heterologous exposure period , or HEP , leading to a highly variable , spatio-temporal distribution in HEP across the population ( Figures 3A , right panel ) ., We argued that these self-emergent phenomena could explain some of the spatial epidemiological differences in dengue-endemic countries , where markedly different distributions in serotype prevalence can be observed between geographically neighbouring regions or between urban and suburban districts ( Figure 3B ) ., Importantly , these differences would be masked if only aggregate data were being considered ., The spatio-temporal dynamics illustrated in Figures 2 and 3 clearly demonstrate the importance of human and vector demographic heterogeneities for the population dynamics of dengue 36–38 , which in our case are the result of stochasticities and spatial restrictions in disease transmission ., To further address the effects of spatial structuring and host mobility on our simulated epidemiologies , we quantified key epidemiological properties , such as mean prevalence ( averaged over humans and vectors ) , extinction risk and serological age-profiles in the population , in response to changes in these parameters ., Increasing spatial structuring , and thereby decreasing the size of each sub-population , reduced the variability in total annual outbreak size and local serotype co-circulation ( Figure 4A ) , here defined as the percent time where multiple serotypes are present in a given patch ., Although the overall force of infection was not affected by the increase in population structure , as evidenced by the constant average ages of primary or secondary infections ( right panel , Figure 4A ) , total infection prevalence increased as a result of a reduction in the risk of serotype extinction ., This indicates that spatial segregation between hosts greatly reduces the propensity for large-scale , population-encompassing outbreaks by restricting a pathogens access to the susceptible pool , which is also in agreement with previous studies in the context of disease transmission through complex or/and heterogeneous networks 17 , 35 , 39 ., In contrast to population structuring , increasing the probability of transmission between hosts of distant communities , , as a proxy for daily human mobility , had a more homogenizing effect and led to an increase in local viral co-circulation ( Figure 4B ) ., More frequent and brief localized outbreaks could be observed , resulting in increased epidemic variability ., However , this increase in outbreak size variability did not equate to an increase in mean infection prevalence levels because of localised extinction risk ., In other words , the heterogeneous distribution of herd-immunity 40 to individual serotypes ( as illustrated in Figures 3A–C ) within the spatially structured population counteracts the occurrence of population-wide outbreaks that are otherwise expected from the synchronizing effect of higher mobility or dispersal rates 22 , 41 ., We next analysed the degree of epidemic synchrony , or coherence , between communities under variations in host mobility ., As mentioned above , the rate at which human hosts acquire infections in geographically distant communities , , has a significant effect on viral co-circulation and hence the susceptibility/immunity landscape in the population ., This is further illustrated in Figure 5A for two different values of , showing a transition to a less variable but a more patchy distribution of susceptibility to DENV1 with an increasing rate of long-distance transmission events ., When disease transmission was predominantly local ( ) , as expected , we observed that spatial synchrony was dependent on spatial distance ( blue line in Figure 5B , and Figure S3 ) ., In contrast , as a result of a reduction in locally acquired infection with increasing , epidemic synchrony between neighbouring communities was disrupted , causing an overall low but homogeneous spatial coherence across the population ( , red line in Figure 5B ) ., These results are in general agreement with a growing body of studies on dengues epidemic , spatial scale ., For instance , cases appear to cluster at the level of households or neighborhoods 42 , whereas epidemics across larger regions present strong spatial dependence 43 and appear to follow a power-law distribution , implying that outbreaks are predominantly driven by a limited set of spatial clusters 44 ., It should be noted that migration , or dispersal , has previously been shown to increase synchronization between populations within different spatially explicit model frameworks 22 , 41 ., However , this is not necessarily the case when local demographic stochasticity is considered 19 , 34 ., For instance , within a spatially extended meta-population model , Blasius et al . demonstrated that phase-locking amongst patches is easily achieved by dispersal rates , while peak and trough abundances in each patch can remain chaotic and variably uncorrelated 34 ., The same effect is observed in the local dynamics of the patches within our framework ( see Figure S3A and S3B for examples ) ., It is thus not surprising that we only find low-to-intermediate coherence across space , even between close-range patches ( Figure 5B ) ., Although dengue-characteristic dynamics could be obtained even in the absence of immune interaction between the viruss four serotypes , temporary ( serotype-transcending ) cross-immunity and ADE have previously been proposed as important drivers of dengue epidemiology , and we therefore analysed their effects within this spatial setting ., As demonstrated in Figures 3A and 4 ( right panels ) , the time required for an individual to acquire a secondary , heterologous infection ( HEP ) was on average in the order of 4–5 years ., While this is in general agreement with a previous study from Thailand 45 , and might also explain the peak in older children in the age-profiles of dengue haemorrhagic fever ( DHF ) in endemic regions 32 , it is much higher than the reported 3–9 months period of serotype-transcending immunity following a primary infection 46 ., Consequently , and contrary to previous predictions based on continuous and homogeneous mixing models , the inclusion of temporary cross-immunity did not have a significant effect on the simulated , qualitative epidemiologies within our stochastic and spatially explicit framework ., When quantifying key epidemiological characteristics under changes to the duration of temporary immunity , we found that only once this period increased beyond 12 months there was a small , negative effect on infection prevalence and epidemic variability ( Figure 6A ) ., On the other hand , even short periods of transcending immunity had a significant effect on both serotype extinction risk and periodicity , suggesting its regulatory role on how the different viruses can explore the susceptibility ( spatial ) landscapes ., In contrast to temporary cross-immunity , immune enhancement through the process of ADE had a more noticeable and anticipatory effect ., That is , increasing the probability of transmission through the enhancement of secondary , heterologous infections led to an increase in disease prevalence along with an increase in epidemic variability , serotype co-circulation and viral extinction risk ( Figure 6B ) , which is broadly in line with previous studies 10 , 12 , 24 ., The increase in prevalence did not significantly affect the average age at which individuals experience their first infection , however , whereas the age of secondary infection showed a more dramatic reduction ., In fact , due to the combined effect of elevated serotype co-circulation and an increase in the susceptibility to secondary infections through ADE , even moderate levels of enhancement caused the HEP to go below the average time at which individuals experience their first infection ., Hence , in the presence of population structure , ADE , and especially its proposed susceptibility enhancing manifestation , may induce a signature in the epidemiological age-profiles of the population that is characterised by a longer period for first infection than the time required for heterologous exposure , which has indeed been observed in studies of clinical infections in dengue endemic areas 32 , 45 , 47 ., Finally we turned our attention to the effect of changes to disease transmission within this spatial setting ., Differences in the estimates of a pathogens transmission potential , or , can be attributed to a multitude of factors , and in the case of dengue , this has resulted in a wide spectrum of estimations , ranging in values from close to 1 to bigger than 20 ( see Table S1 for an overview ) ., To quantify the effects of changes to viral transmission , and in general , we analysed the model behaviour , in the absence of immune interactions , under variations in key parameters related to dengues basic reproductive number , whose derivation within this framework can be found in the Materials and Methods section ., Specifically , we investigated the effects of through variations in the probability of transmission per mosquito bite , using both symmetric and asymmetric transmission probabilities , viral incubation periods ( both intrinsic and extrinsic ) and mosquito vector density ., The results were mostly in accordance with those expected from increasing parameters related to in equivalent continuous multi-strain models and can be found in Figure S4 in Supplementary Material; here , we only highlight two of the more important findings ., First , assuming symmetric transmission probabilities between host and vectors we found that the viral extinction risk was not monotonously associated with changes in transmissibility and was in fact minimized for ( Figure S4A ) , in range with estimations using age-stratified indexation of sero-conversion rates 48 ., Notably , this was also the range in which the age profiles of infection where more similar to what is commonly described for endemic regions in South East Asia 32 , 45 , 49 , 50 ., Secondly , our model confirmed that changes in the extrinsic incubation period had a much more dramatic effect on dengue epidemiology than incubation periods in the human host ( Figure S4D and S4E ) , as this directly affects the duration of infectiousness in the mosquito ., Crucially , this re-emphasizes the notion that seasonally driven temperature , and its effect on viral incubation , is as important a determinant of dengue epidemiology , as is vector density 51 ., Understanding the evolutionary forces that shape the spatio-temporal patterns of pathogen populations is essential for disease control and public health planning ., Important new insights into the population dynamics of host-pathogen systems have been gained by the application of deterministic mathematical models to the study of many important infectious diseases 1 ., Nevertheless , stochastic and discrete events significantly influence the real world counterpart of such systems and their explicit incorporation can provide alternative frameworks in which to examine major determinants of the observed epidemiologies 17 , 39 , 52 , 53 ., In this context , demographic stochasticity has been suggested to be an important driver for population oscillations in single-strain epidemiological systems 5 , 6 , 17 , 18 ., Here we advanced upon previous findings by studying the dynamical behavior of dengues four antigenic types within a stochastic and spatially explicit framework ., Dengues epidemiological dynamics have been the focus of extensive theoretical research that often concentrated on the immunological interactions between its four serotypes 10–12 , 24–26 ., Protective and infection-enhancing effects of cross-reacting antibodies have been well documented both in vivo and in vitro 46 , 54 , 55 ., Less clear , however , is their contributing effect to disease transmission and general epidemiology ., For example , although a short period of 3 to 9 months of serotype-transcending immunity following a primary infection has been demonstrated by direct experiment , the average time between consecutive , heterologous infections is often found to be an order of magnitude higher 45 ., Equally , despite the reported increase in within-host viral replication through antibody-dependent enhancement of secondary , heterologous infections and observed correlations between disease severity and previous exposure , it is currently not known if and how much this increase in viral load contributes to total dengue transmission , especially when taking into consideration that severe , clinical cases may constitute only a small fraction of all dengue infections 32 , 45 , and that viraemia appears to peak earlier but also clears faster during secondary , heterologous infections 56 ., In contrast to previous model predictions , our results could not ascertain a decisive role of either temporary cross-immunity or ADE in driving the complex epidemiological dynamics of dengue ., That is , while our findings do not question the pathological or clinical significance of immune interactions per se , they suggest that the strength of within-host serotype interactions , and therefore the consequences of acquired immunity , are unlikely to be the sole drivers of the complex epidemiological dynamics of dengue ., Crucially , the results herein presented further suggest that such cross-immunological reactions , at least within biological reasonable ranges , would not cause significant spatio-temporal signatures that could allow the inference of their presence to be unambiguously resolved from studying epidemiological time series alone ., More detailed data , for example from human infectivity studies that relate infection history with clinical outcome and infection/transmission probabilities , are essential to close the gap in our understanding of the full transmission potential of dengue ., Furthermore , to better understand the importance of host demographic factors and spatial ecology highlighted in this work , a phylodynamics approach could be considered in which the spatio-temporal evolution of dengue genotypes is simulated and compared to available data from different settings across the endemicity spectrum ., Dengues recent molecular evolution is characterized by strong intra-serotype purifying selection with no clear trend for continuous antigenic change ., As DENV has evolved to replicate efficiently in both the vertebrate and arthropod hosts , it is thought to express a compromise genome in which most structural mutations are expected to be deleterious and selectively removed from the population 57 ., On the other hand , strong ecological bottlenecks and inter-serotype competition can severely hamper the emergence of viral mutants even if they express advantageous phenotypes 58 ., The cyclical replacement of dengues four serotypes is therefore not expected to be driven by the same inter-strain selective forces that have ( reportedly ) shaped the phylodynamics of antigenically rapidly evolving pathogens , such as influenza A 59 , for example ., It instead argues for a critical role of demographic and ecological stochasticities underlying both dengues epidemiology and molecular evolution ., The strong impact of host population structures and mobility highlighted in this work also corroborates the hypothesis that DENVs ( re- ) emergence and world-wide success is mainly due to current demographic and ecological trends rather than viral adaptation 39 , 57 ., To understand dengues epidemiology in the long-term , it is therefore crucial to establish how these meta-population disease dynamics correlate with evolutionary constraints and respective selective signatures ., Importantly , the discrete nature of our framework and its meta-population formulation readily allow to explore more realistic population structures , including heterogeneities in ( host and vector ) population sizes and/or connectivity between sub-populations , for example by means of complex network structures , and to simulate viral evolution in time and space within these frameworks ., Our model thus presents itself as a good starting point for a more thorough investigation of DENVs phylodynamics 60 ., Accounting for community-specific vector control and drug intervention policies is equally possible within this meta-population formulation and constitutes another important extension for future studies on the control of vector-borne diseases ., For example , candidate vaccines against dengue that are in advanced stages of clinical trials might require a prime-boost protocol running over a period of up to 12 months , which has been indicated as a potential concern due to the risk of severe disease during the time when antibody-levels are at sub-neutralizing titers 61 , 62 ., By reproducing the spatial heterogeneity in disease prevalence and serotype distribution we found the timing between consecutive , heterologous infections to be highly variable in space ., Our observations thus reassert that spatially explicit epidemiological frameworks , as the one presented here , are essential for assessing the risks and efficacies of vaccine introduction strategies against dengue 62 ., In summary , the results presented here have highlighted the importance of considering spatial segregation between individual hosts and vectors and stochasticities in disease transmission for understanding the epidemiology of dengue and other related pathogens ., Previous theoretical studies have demonstrated that immune interactions can significantly influence the population dynamics of multi-strain pathogen systems ., The inclusion of host and vector ecologies adds to this understanding and provides complimentary hypotheses about the underlying causes for the oscillatory nature in incidence and serotype distributions that commonly characterize their complex epidemiologies ., To study the stochastic dynamics of a multi-strain pathogen we used an individual-based model , realised as a discrete-time , random process with finite state-space ( Markov chain ) , is which a state refers to the hosts epidemiological profile , such as infection status and immune history ., Demographic , biological and ecological stochasticities were derived from the probabilistic nature of state transitions , e . g . in the probability that the bite of an infectious mosquito leads to an infection ., The size of the host population was kept constant with deaths being replaced by newborns ., We assumed an age-dependent risk of mortality for both humans and mosquitoes , described by the continuous Weibull distribution:where is the host age , and and are the shape and scale parameters , respectively ., Spatial structure was added by subdividing the host population into a spatially organized set of communities , forming a squared and non-wrapping lattice wherein each community had neighbors ( ) ., Individuals were assumed to mix homogeneously within each , such that each mosquito bite took place between a vector and human chosen randomly from this community ., We further assumed that mosquitoes disperse only locally , implying that each vector in community will only bite human individuals belonging to the set of communities , i . e . within and its neighboring communities ., Long distance transmission was considered through human movement by allowing mosquitoes to bite humans of randomly chosen , distant patches with probability ( the probability of human hosts temporarily ‘visiting’ these communities ) , which reduces the local transmission rate to ., This formulation differs from the ones considered in other meta-population studies , which often assume a constant ( continuous ) , and possibly distance-dependent migration or dispersal term between any two patches or communities .

Introduction, Results, Discussion, Materials and Methods

Many infectious diseases are not maintained in a state of equilibrium but exhibit significant fluctuations in prevalence over time ., For pathogens that consist of multiple antigenic types or strains , such as influenza , malaria or dengue , these fluctuations often take on the form of regular or irregular epidemic outbreaks in addition to oscillatory prevalence levels of the constituent strains ., To explain the observed temporal dynamics and structuring in pathogen populations , epidemiological multi-strain models have commonly evoked strong immune interactions between strains as the predominant driver ., Here , with specific reference to dengue , we show how spatially explicit , multi-strain systems can exhibit all of the described epidemiological dynamics even in the absence of immune competition ., Instead , amplification of natural stochastic differences in disease transmission , can give rise to persistent oscillations comprising semi-regular epidemic outbreaks and sequential dominance of dengues four serotypes ., Not only can this mechanism explain observed differences in serotype and disease distributions between neighbouring geographical areas , it also has important implications for inferring the nature and epidemiological consequences of immune mediated competition in multi-strain pathogen systems .

The population dynamics of multi-strain pathogens are often characterized by persistent and irregular fluctuations in disease incidence and strain prevalence levels over time ., Previous theoretical approaches have often evoked strong immunological interactions between individual strains , such as cross-immunity , in order to explain these complex epidemiologies; however , spatial segregation between hosts and stochastic heterogeneities in transmission success are rarely considered in these studies ., Here , with specific reference to dengue , we show that the stochasticities underlying disease transmission within a spatially explicit , agent-based model can give rise to multi-annual epidemic outbreaks and fluctuating pathogen population structures - even in the absence of immune competition ., In contrast to previous modeling studies , which have resulted in ambiguous predictions about the exact nature and strength of interactions between dengues four serotypes , our results present a parsimonious , demographic mechanism , that highlights the importance of spatial ecology for understanding and interpreting the epidemiological dynamics of dengue and other multi-strain pathogen systems .

journal.pcbi.1005135

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing

Conventional one-drug-one-gene drug discovery and drug development is a time-consuming and expensive process ., It suffers from high attrition rate and possible unexpected post-market withdrawal 1 ., It has been recognized that a drug rarely only binds to its intended target , and off-target interactions ( i . e . interactions between the drug and unintended targets ) are common 2 ., The off-target interaction may lead to adverse drug reactions ( ADRs ) 3 , as demonstrated by the deadly side effect of a Fatty Acid Amide Hydrolase ( FAAH ) inhibitor in a recent clinical trial 4 ., On the other hand , the off-target interaction may be therapeutically useful , thus providing opportunities for drug repurposing and polypharmacology 2 ., Therefore , identifying off-target interactions is an important step in drug discovery and development in order to reduce the drug attrition rate and to accelerate the drug discovery and development process , and ultimately to make safer and more affordable drugs ., Many efforts have been devoted to developing statistical machine learning methods for the prediction of unknown drug-target associations by screening large chemical and protein data sets 5 ., One of the fundamental assumptions in applying statistical machine learning methods to drug-target interaction prediction is that similar chemicals bind to similar protein targets , and vice versa ., Based on this similarity principle , both semi-supervised and supervised machine learning techniques have been applied ., The semi-supervised learning methods either build statistical models for the k nearest neighbors ( k-NN ) of the query compound with similar compounds in the database ( e . g . Parzen-Rosenblatt Window ( PRW ) 6 and Set Ensemble Analysis ( SEA ) 7 are examples ) ., Although a large number of 2D and 3D fingerprint representations of chemical structures have been developed , chemical structure similarity that is measured by Tanimoto coefficient ( TC ) or other similarity metrics of fingerprints is not continuously correlated with the binding activity ., Activity cliff exists in the chemical space , where a small modification of a chemical structure can lead to a dramatic change in binding activity 8 ., Thus , the chemical structural similarity alone is not sufficient to capture genome-wide target binding profile , as protein-chemical interaction is determined by both protein structures and chemical structures ., New deep learning techniques that can learn non-linear , hierarchical relationships may provide new solutions for representing chemical space 9–12 ., However , few work has been done to incorporate protein relationships into the deep learning framework ., It remains to be seen whether the deep learning is applicable to genome-wide target prediction ., A number of techniques such as Gaussian Interaction Profile ( GIP ) , Weighted Nearest Neighbor ( WNN ) , Regularized Least Squares ( RLS ) classifier 13 , 14 , and matrix factorization 15–17 have been developed to integrate chemical and genomic space ., Among them , Neighborhood Regularized Logistic Matrix Factorization ( NRLMF ) 17 and Kernelized Bayesian Matrix Factorization ( KBMF ) 16 are two of the most successful methods ., However , several drawbacks in these algorithms hinder their applications in genome-wide off-target predictions ., First , several algorithms with high performance such as KBMF are extremely time and memory-consuming ., Second , these algorithms depend on a supervised learning framework that requires negative cases ., While publicly available biological and/or chemical databases ( e . g . ZINC 18 , ChEMBL 19 , DrugBank 20 , PubChem 21 , and UniProt 22 ) have enabled large-scale screening of drug-target associations , the known chemical-protein associations are sparse , and the number of reported negative cases ( i . e . chemical-protein pairs not associated ) is too small to optimally train a prediction algorithm 23 ., Using randomly generated negative cases will adversely impact the performance of these algorithms , and algorithmically derived negative cases are often based on unrealistic assumptions 23 ., Finally , these algorithms have been mainly evaluated for the prediction of off-targets within the same gene family ( e . g . GPCR ) using a small benchmark with hundreds of drugs and targets ., Their performances in predicting off-target across gene families on a large scale are uncertain ., Indeed , drug cross-reactivity often occurs across fold spaces 2 ., Thus , the development of in silico prediction methods that are fast as well as accurate enough to explore the available data is urgent ., Here , we make several contributions to address the aforementioned problems ., First , we present an efficient method , REMAP , which formulates the off-target predictions as a dual-regularized One Class Collaborative Filtering ( OCCF ) problem ., Thus , negative data are not needed for the training , but can be used if available ., Secondly , REMAP is highly scalable with promising accuracy , thus can be applied to large-scale off-target predictions ., Thirdly , we introduce a new benchmark set to evaluate the performance of drug-target interactions across gene families ., Finally , we apply REMAP to repurposing existing drugs for new diseases ., We identified seven drugs that have anti-cancer activity ., Six of them are supported by experimental evidence ., The problem we try to solve here is to predict how likely it is that a chemical interacts with a target protein , using a chemical-protein association network , chemical-chemical similarity , and protein-protein similarity information ., We start by preparing a bipartite network for chemical-protein associations as a sparse n × m matrix R , where n is the number of chemicals and m is the number of proteins ., Ri , j = 1 if the ith chemical is associated with the jth protein , and Ri , j = 0 , otherwise ., The chemical-chemical similarity scores are in an n × n square matrix C , with Ci , j representing the chemical-chemical similarity score between the ith and jth chemicals ( 0 ≤ Ci , j ≤ 1 ) for total n chemicals ., The protein-protein similarity scores are in the same format for total m proteins ( 0 ≤ Ti , j ≤ 1 ) ., We consider this problem an analog of user-item preferences such that users and items represent chemicals and proteins , respectively ., Therefore , the problem is to provide an n × m matrix P in which Pi , j is the prediction score for the interaction between the ith chemical and the jth protein ., Our prediction method REMAP is based on a one-class collaborative filtering algorithm that recommends the users’ preferences to the listed items 24 ., It assumes that similar users will prefer similar items , unobserved associations are not necessarily negative , and user-item preferences can be analogous to drug-target associations ., Assuming that a fairly low number of factors ( i . e . smaller number of features than the number of total chemicals or protein targets ) may capture the characteristics determining the chemical-protein associations , two low-rank matrices , U ( chemical side ) and V ( protein side ) , were approximated such that ∑in∑jm{R− ( U⋅VT ) } is minimized where R is the matrix for known chemical-protein associations and VT is the transposition of the protein side low-rank matrix V . The two low rank matrices , Un×r and Vm×r are obtained by iteratively minimizing the objective function ,, minU , V≥0∑ ( i , j ) pwt ( R ( i , j ) +pimp−U ( i , : ) ⋅V ( j , : ) T ) 2+preg ( ‖U‖2+‖V‖2 ) +pchemtr ( UT ( DC−C ) U ) +pprottr ( VT ( DT−T ) V ), ( 1 ), All symbols used in the paper are summarized in Table 1 , and the overall process of REMAP is in Fig 1 ., Here , pwt is the penalty weight on the observed and unobserved associations which indicate the reliability of the assigned probability of true association , pimp is the imputed value ( i . e . the probability of unobserved associations as real associations ) , preg is the regularization parameter to prevent overfitting , pchem is the importance parameter for chemical-chemical similarity , pprot is the importance parameter for protein-protein similarity , and tr ( A ) is the trace of matrix A ( Table 1 ) ., In this study , we use global weight and imputation ., However , the weight and imputation values may be determined by a priori knowledge or from the prediction of other machine learning algorithms ( i . e . pwt and pimp can be matrices with the same dimension as the matrix R ) ., The raw predicted score for the ith chemical to bind the jth protein can be calculated by P ( i , j ) =UUP ( i , : ) ⋅VUP ( j , : ) T ., The raw scores were adjusted based on the ratio of observed positive and negative cases when the negative data are available ( explained in the prediction score adjustment section ) ., Also , the matrix Un×r is referred to as a low-rank drug profile since its ith row represents the ith drug’s behavior in the drug-target interaction network as well as drug-drug similarity spaces compressed to r number of features ., The REMAP code was originally written in Matlab and modified for drug-target predictions ., Chemical-chemical similarity scores are one of the required inputs of REMAP ., Although there are a number of metrics developed for chemical-chemical similarity , a recent study showed that Tanimoto coefficient-based similarity is highly efficient for fingerprint-based similarity measurement 25 ., The fingerprint of choice in this study is the Extended Connectivity Fingerprint ( ECFP ) , which has been successfully applied to chemical structure-based target prediction method , PRW 6 ., Thus , it allows for a fair comparison of REMAP with PRW ., It is interesting to compare the different fingerprints in the future study ., To calculate a similarity score between two chemicals , c1 and c2 , the Tanimoto dissimilarity coefficient dTani ( c1 , c2 ) was obtained using JChem with the Tanimoto metric for the ECFP descriptor type using the command in the Unix environment , “ChemAxon/JChem/bin/screenmd target_smi query_smi -k ECFP -g -c -M Tanimoto” 26 ., The chemical-chemical similarity score , C ( c1 , c2 ) is defined as C ( c1 , c2 ) =\xa01-dTani ( c1 , c2 ) ., Briefly , two chemicals have a higher similarity score if they have more of the same chemical moieties ( e . g . functional groups ) at more similar relative positions ., Chemical similarity scores below 0 . 5 were treated as noise and set to 0 ., Protein-protein similarity scores are also one of the required inputs for REMAP ., The similarity between two proteins was calculated based on their sequence similarity using NCBI BLAST 27 with an e-value threshold of 1 × 10−5 and its default options ( e . g . 11 for gap open penalty and 1 for its extension , BLOSUM62 for the scoring matrix , and so on ) ., Based on our 10-fold cross validation ( see below ) , e-value thresholds from 1 to 1 × 10−20 did not significantly affect the performance ( S1 Fig ) ., Therefore , we decided to use a moderately stringent threshold ( BLAST default is 1 × 10−3 ) ., A similarity score for query protein p1 to target protein p2 was calculated by the ratio of a bit score for the pair compared to the bit score of a self-query ., To be specific , for the query protein p1 to the target protein p2 , protein-protein the similarity score was defined such that T ( p1 , p2 ) = dbit ( p1 , p2 ) /dbit ( p1 , p1 ) ., For benchmark tests , ZINC data was filtered by IC50 ≤ 10 μM , which yielded 31 , 735 unique chemical-protein associations for 12 , 384 chemicals and 3 , 500 proteins ( ZINC dataset 18 ) ., Targets that are protein complexes or cell-based tests were excluded ., Proteins whose primary sequence is unavailable were also excluded ., Protein sequences were obtained from UniProt 22 , and the whole protein sequences were used to calculate protein-protein similarity scores ., To assess the predictive power of our algorithm , we performed a 10-fold cross validation on the ZINC dataset described above ., We set the parameters as follows: pwt = pimp = preg = 0 . 1 , r = 300 , pchem = 0 . 75 , pprot = 0 . 1 , and piter = 400 ., The optimized values determined by the 10-fold cross validation of benchmark are shown in S2 Fig . It is noted that the best performance is achieved when pchem = 0 . 25 and pprot = 0 . 25 ., To further evaluate REMAP , we compared its performance on the ZINC dataset with several methods: a chemical similarity-based method ( PRW 6 ) , the best performed matrix factorization methods so far ( NRLMF 17 and KBMF with twin kernels ( KBMF2K ) 16 ) , combination of WNN and GIP ( WNNGIP 14 ) , and another type of matrix factorization method ( Collaborative Matrix Factorization ( CMF ) 15 ) for different types of chemicals and proteins ., To obtain a detailed view of the performance of the methods , we divided the ZINC dataset into 3 categories with 2 subcategories for each , based on the connectivity of known chemical-protein associations and the degree of uniqueness of the chemicals ., First , all the chemicals in the dataset were classified into the chemicals having only one known target ( NT1 ) , two known targets ( NT2 ) , or three or more known targets ( NT3 ) ., Then , for the chemicals in each category , they were further divided based on either the number of known chemicals ( ligands ) the target proteins are associated with ( number of ligands in increments of 5 ) or the maximum chemical-chemical similarity score for the chemical in the dataset ( the similarity score range increment is 0 . 1 ) ., The label used in this paper for the dataset are NTaLb , or NTaMaxTcd , where ‘NT’ stands for the Number of known Target , ‘L’ for the number of known Ligand , and ‘Tc’ for the maximum ( Tanimoto coefficient-based ) chemical-chemical similarity score for the given chemical in the dataset , with NT = a , b ≤ L ≤ b +4 , and d − 0 . 1 < Tc ≤ d ., For instance , NT2L1 is the data set label for chemicals having two known targets and proteins having 1 to 5 ligands in the dataset , and NT1Tc0 . 9 is for chemicals with the most similar chemicals between 0 . 8 and 0 . 9 of similarity scores and having one known target ., Chemicals having more than three known targets are included in the NT3 class , and proteins having more than twenty-one known ligands were included in L21 ( not limited to 25 ) ., The categories of the ZINC dataset were then used to evaluate the performance of off-target prediction , and their labels mean the number of known ligands ( L ) or the maximum structural similarity ( Tc ) with their corresponding ranges ., For example , ‘L21more’ stands for the dataset for proteins having 21 or more known targets , and ‘Tc0 . 9to1 . 0’ stands for maximum structural similarity greater than 0 . 9 and up to 1 . 0 ( Tc0 . 5to0 . 6 is inclusive of 0 . 5 ) ., Note that NT1 is equivalent to chemicals without any known target when they are tested for cross validation ., Therefore , performances on NT1 datasets reflect the ability to address the cold start problem ., In other words , when one known drug-target association is intentionally hidden for the chemicals in the NT1 dataset , the tested chemicals will not have any known target in the training data , and they are less likely to be given a good recommendation of targets ., This is analogous to the new user or new item problem reviewed by Su et al . 28 ., A typical measure of prediction performance is the Receiver Operating Characteristic ( ROC ) curve by which one can assess the reliability of the positively predicted results ., However , it is difficult to apply the ROC curve on our chemical-protein association datasets since the vast majority of the chemical-protein pairs have not been tested , and thus it is unclear whether the missing entries are actually unassociated or just not yet observed ., In order to assess how reliable the positively predicted results from REMAP are , we needed to define a performance measurement that is analogous to ROC curve but not dependent on the true negatives ., Our primary measure of performance is the true positive rate ( ∑True\xa0Positives∑Condition\xa0Positives; Recall or Recovery ) at the top 1% of predictions for each chemical ., To be specific , the top 1% of predictions includes up to the 35th-ranked predicted target protein for a chemical for our datasets ( 3 , 500 possible target proteins for each chemical ) ., Thus , for instance , a TPR of 0 . 965 at the 35th cutoff rank ( top 1% ) means that 96 . 5% of the total tested positive pairs were ranked 35th or better for the tested chemicals ., In order to assess the speed of REMAP for practical uses , we measured its running time by varying the rank parameter or the size of dataset ., On the ZINC dataset ( 12 , 384 chemicals and 3 , 500 proteins ) , up to r = 2 , 000 was tested , and at fixed r = 200 , dataset sizes up to 200 , 000 chemicals and 20 , 000 proteins were tested ., The number of iterations ( piter ) was fixed to 400 ., A single node of CPU with 2 . 88 GB of memory in the City University of New York High Performance Computing Center ( CUNY HPCC ) was used for REMAP running time tests ., We also compared the running times of different matrix factorization methods with ours ., Due to the large time complexity and memory requirement for other algorithms , a multi-core node with up to 700 GB of shared memory system in CUNY HPCC was used for them on the ZINC dataset ., Chemical-protein associations were obtained from the ZINC 18 , ChEMBL 19 and DrugBank 20 databases ., To obtain reliable chemical-protein association pairs , binding assays records with IC50 information were extracted from the databases , and the cutoff IC50 value of 10 μM was used where applicable ., Two chemicals were considered the same if their InChI Keys are identical , and two proteins were considered so if their UniProt Accessions are identical ., For records with IC50 in μg/L ( found in ChEMBL ) , the full molecular weights of the compounds listed on ChEMBL were used to convert μg/L to μM ., Chemical-protein pairs were considered associated if IC50≤10 μM ( active pairs ) , unassociated if IC50>10 μM ( inactive pairs ) , ambiguous if records exist in both ranges ( ambiguous pairs ) , and unobserved otherwise ( unknown pairs ) ., A total of 198 , 712 unique chemicals and 3 , 549 unique target proteins were obtained from the combination of ChEMBL and ZINC with 228 , 725 unique chemical-protein active pairs , 76 , 643 inactive pairs , and 4 , 068 ambiguous pairs ., Of the 198 , 712 chemicals , 722 were found to be FDA-approved drugs ., Furthermore , drug-target relationships were extracted from the DrugBank and integrated into the ZINC_ChEMBL dataset above ., A total of 199 , 338 unique chemicals and 6 , 277 unique proteins were obtained from the combination of ZINC , ChEMBL , and DrugBank with 233 , 378 unique chemical-protein active pairs ., Since REMAP showed promising performances on predicting off-targets for chemicals with at least one known target , it is possible to use REMAP to suggest new purposes for some FDA approved drugs ., As the matrix product of UUP ( chemical-side low-rank matrix ) and VUP ( protein side low-rank matrix ) is the predicted drug-target interaction matrix P , the ith row of UUP contains the target interaction profile for the ith drug ., Therefore , we analyzed the drug-drug similarities based on the low-rank matrix UUP ., We ran REMAP with the data combination of three databases explained above , with the parameters used in the benchmark evaluations ., Then , we calculated drug-drug cosine similarities based on the matrix UUP ., For each row of UUP for FDA approved drugs , the cosine similarity of drug c1 and drug c2 can be calculated by , Scos , ( c1 , c2 ) =\xa0Uc1→∙Uc2→Uc1Uc2 ., To search for possibly undiscovered uses of the drugs , we focused on drugs that are found to have high cosine similarity but low Tanimoto similarity ( < 0 . 5 ) ., Markov Cluster ( MCL ) Algorithm 29 , 30 was used to cluster drugs based on their cosine similarity of a low-rank target profile ., Drug-disease associations were obtained from the Comparative Toxicogenomics Database ( CTD ) 31 ., The raw prediction score ( P ( i , j ) =\xa0UUP ( i , : ) ∙VUP ( j , : ) T ) can be adjusted to better reflect the real data as well as to statistically discriminate the positive and negative predictions ., We used the active , inactive and ambiguous pairs obtained from the ChEMBL database to adjust the score ., REMAP prediction on the ZINC_ChEMBL dataset showed a clear division between the active and inactive pairs , suggesting that predictions scored around 1 . 0 are highly likely to be positive ( Fig 2A ) ., As mentioned above , however , there is a large difference between the number of active and inactive pairs , which is not likely to reflect the ratio of the actual positive and negative chemical-protein pairs ., Greater accuracy is expected by adjusting the prediction scores to reflect such a positive/negative ratio ., To estimate the ratio , we first normalized the counts in each bin in the histogram ( Fig 2A ) and calculated the weights that minimize the sum of error , Esum ., Esum ( w1 ) = ΣiAi − {w1pi + ( 1 − w1 ) Ni}2 , where w1 and w2 are the weights on active and inactive pairs , respectively ( w1 + w2 = 1 . 0 ) , and Ai , pi and Ni are the normalized counts in ith bin of ambiguous , active and inactive pairs , respectively ., The optimum adjustment weights were approximately w1 = 0 . 16 , w2 = 0 . 84 ( Fig 2B ) ., This implies that approximately 16% of total observations are positive ., Since the ratio of negative/positive is about 5 . 25 ( w2w1\xa0=\xa05 . 25 ) , we increased the number of observations for inactive pairs in each bin by 5 . 25 times and rounded down ., The adjusted prediction score for each bin ( Bi ) was calculated using the increased negative counts ., It is noted that the prediction score adjustment was not used in the benchmark study , where no negative data were used ., Drug-drug clustered network was visualized using Cytoscape 32 ., We evaluated the performances of algorithms for chemicals having one , two , or more than three known targets with varying maximum chemical-chemical similarity ranges or with proteins having a certain number of known ligands ( dataset prepared as explained in the methods and materials section ) ., In general , the performances of both algorithms improve as the number of known ligands per protein or the maximum chemical-chemical similarity value increases ., It was noticeable that REMAP performed significantly better than PRW when there was at least one known target for a chemical whose targets are predicted ( Figs 3 and 4 ) ., REMAP showed greater than 90% recovery at the top 1% when the tested chemicals have at least one known target ., All algorithms are sensitive to the number of ligands per target ., The more ligands , the higher accuracy ., While PRW also reached reasonably high recovery for some categories ( e . g . more than 11 known ligands per proteins , or C ( c1 , c2 ) >0 . 6 of the most similar trained chemicals ) , REMAP showed that it is reliable for testing chemicals without high similarity to the trained chemicals ( Figs 3B and 4B ) ., In other words , REMAP is applicable to chemicals that are structurally distant to the chemicals already in the dataset ., Except where the target proteins have 1 to 5 known ligands , REMAP performed best among the three algorithms in all cases with at least one known target for the tested chemicals ( Figs 3 and 4 ) ., In the most of cases , the differences in the performance between REMAP and other two algorithms are statistically significant ., Therefore , in practice , REMAP can predict potential drug targets for chemicals with at least one known target as training data , even when the chemicals are structurally dissimilar to the training chemicals ., With the optimized parameters ( see below ) , ROC-like curves shows the general trend of performances of the three algorithms up to the top 10% of predictions ( S3 and S4 Figs ) ., As shown in Figs 3 and 4 , REMAP outperforms the state-of-the-art NRLFM algorithm in most of the tested cases ., As NRLMF is sensitive to the rank parameter , we carried out optimizations to determine optimal rank and iterations for NRLMF ( S5 Fig ) ., The optimal rank and iterations used in the evaluation were 100 and 300 , respectively ., Moreover , in the current implementation , REMAP is approximately 10 times faster and uses 50% less memory than NRLMF ., Consistent with the results by Liu et al . 17 , the accuracies of NRLFM are significantly higher than KBMF2K , CMF , and WNNGIP in all of ZINC benchmarks ., Overall , REMAP is one of the best-performing methods for the genome-wide off-target predictions ., To test whether the chemical-chemical similarity matrix helps prediction , we performed 10-fold cross validation on the ZINC dataset with the contents of the chemical-chemical or the protein-protein similarity matrix controlled ., In other words , about half of the non-zero chemical-chemical similarity scores were randomly chosen and removed ( set to 0 ) for the “half-filled chemical similarity” matrix , and all entries are set to 0 for the “zero-filled chemical similarity” matrix ., The predictive power of REMAP showed noticeable improvement when all available chemical-chemical similarity pairs were used , compared to the half-filled or the zero-filled similarity matrix ( Fig 5A ) ., Similarly , the contents of the protein-protein similarity matrix were controlled ( e . g . half-filled protein similarity , and zero-filled protein similarity ) while the full chemical similarity matrix was used ., Unlike the chemical-chemical similarity , the protein-protein similarity information did not necessarily improve REMAP’s predictive power ., The performance was best when a half of the protein-protein similarity information was used together with the full chemical-chemical similarity matrix ( Fig 5B ) ., This suggests that there is significant noise in the protein-protein sequence similarity matrix although the information does help prediction ., A careful examination of the BLAST-based protein-protein similarity matrix may give an insight into the design of a novel protein-protein similarity metric for drug-target binding activities ( see discussion section ) ., We also performed optimization tests for pchem and pprot on ZINC dataset ., Although the performance was slightly better when the chemical-chemical similarity importance was maximum ( Fig 6A ) , the difference was too small to conclude that it is best to fix pchem = 1 ., Instead , the prediction may rely too much on the chemical-chemical similarity scores ., Therefore , to allow flexibility on chemical-chemical similarity information , we set pchem = 0 . 75 at which the performance was almost as accurate as pchem = 1 ., On the other hand , the performance was best when the protein-protein sequence similarity importance , pprot , was 0 . 1 ( Fig 6B ) , further supporting our claim that protein-protein sequence similarity is not an optimal choice for the prediction of a drug-target interaction ., When jointly optimizing pchem and pprot , their optimal value is 0 . 25 and 0 . 25 , respectively , in the 10-fold cross validation benchmark evaluation ( S2B Fig ) ., Our result supports a recent study 25 which showed that Tanimoto coefficient is efficient for the chemical similarity calculation ., Chemical fingerprint-based chemical-protein association prediction has been studied by Koutsoukas et al 6 ., By defining bins ( target proteins ) that can contain certain chemical features based on the chemical fingerprints , Koutsoukas et al . successfully demonstrated that their algorithm , PRW , can efficiently predict unknown chemical-protein associations 6 ., While the basic idea of dissecting chemical compounds into functional groups is the same , it should be noted that PRW does not consider the information obtained from proteins , as well as interactome ., For all our tests , REMAP showed great speed without losing its accuracy ., On our benchmark dataset ( ZINC; 12 , 384 chemicals and 3 , 500 proteins ) , it took approximately 120 seconds to run 400 iterations at the rank of 200 ( r = 200 , piter = 400 ) ., The time complexity is linearly dependent on the rank ( Fig 7A ) ., The scalability of REMAP is superior when compared to KBMF2K , a state-of the art matrix factorization algorithm that is implemented in Matlab and has been extensively studied for predicting drug-target interactions 16 ., KBMF2K took more than 10 days for the same size matrix using the same computer system in the ZINC benchmark ., Moreover , REMAP was capable of higher rank factorization while KBMF2K was limited to rank 200 in our system due to the memory requirement ( over 100 GB of memory ) ., At a much higher rank ( r = 2 , 000 ) , less than one hour was required for REMAP on the same dataset ( Fig 7A ) ., Time complexity experiments on larger dataset showed that REMAP completed predictions on a dataset with 200 , 000 rows and 20 , 000 columns within 2 hours on a single core computing system with 2 . 88 GB of memory , demonstrating its ability to screen the whole human genome of approximately 20 , 000 proteins in two hours ( Fig 7B ) ., Since REMAP is scalable and shows superior accuracy based on our benchmark tests , we performed large scale prediction of drug-target interactions on the ZCD dataset ( explained in the Materials and Methods section ) ., As explained in the prediction score adjustment section , prediction scores for the active pairs were mostly located between 0 . 75 and 1 . 0 ( Fig 2A ) ., As expected , the percentage of pairs of chemicals that share common targets decreases with the decrease of the chemical structural similarity measured by the Tc of ECFP fingerprints ( C ( c1 , c2 ) ) ., The percentage of target-sharing chemical pairs drops below 50% and 0 . 5% when the Tc is between 0 . 5 and 0 . 6 , and less than 0 . 5 , respectively ( S6 Fig ) ., Thus , it is less likely that the chemical structural similarity alone can reliably detect novel binding relations between two chemicals when the Tc is less than 0 . 5 ., It is interesting to see how REMAP performs when the chemical structural similarity fails ., We analyzed the low-rank drug profile ( matrix UUP ) to check whether it represented the target-binding behavior of the drugs ., When filtered by low chemical structure similarity ( C ( c1 , c2 ) <0 . 5 ) ) , there are 899 , 871 drug-drug pairs ., Among them , the profile similarity score ( Scos , ( c1 , c2 ) ) of 91 , 888 pairs is higher than 0 . 3 ., With high profile similarity ( 0 . 99≤Scos , ( c1 , c2 ) ≤1 ) ) , a total of 1 , 327 drug-drug pairs were found of which 1 , 033 pairs shared at least one common known target ., S7 Fig shows the percentage of pairs that share the common target in different profile similarity bucket for FDA-approved drugs ., This result suggests that REMAP is able to provide a chemical-protein binding profile that cannot be captured by chemical structure similarity alone ., When Scos , ( c1 , c2 ) ≤0 . 3 , the percentage of two drugs that share a common target drops below 50% ( S7 Fig ) ., We constructed a drug-drug similarity network by filtering out drug pairs with Scos , ( c1 , c2 ) ≤0 . 3 , then applied the MCL algorithm on the drug-drug network to find clusters of similar drugs ., The largest cluster of drugs contained a total of 313 drugs , and their relationships to diseases were examined based on the known associations annotated in CTD 31 ., As a result , we found that the drugs are mostly related to mental disorders , including hyperkinesis , dystonia , catalepsy , schizophrenia and basal ganglia diseases as the mostly related diseases ., The most frequent known protein targets by the drugs were GPCRs ( S1 Table ) ., It is comparable that GPCRs were 1 , 924 times targeted while kinases were targeted only 55 times ., While it is interesting to further examine the cluster , validating all of the possible drug-target pairs in the largest cluster may be inefficient .,

Introduction, Materials and Methods, Results, Discussion

Target-based screening is one of the major approaches in drug discovery ., Besides the intended target , unexpected drug off-target interactions often occur , and many of them have not been recognized and characterized ., The off-target interactions can be responsible for either therapeutic or side effects ., Thus , identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs , and providing new opportunities for drug repurposing ., Although many computational methods have been developed to predict drug-target interactions , they are either less accurate than the one that we are proposing here or computationally too intensive , thereby limiting their capability for large-scale off-target identification ., In addition , the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals ., It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale ., Here , we are presenting a fast and accurate off-target prediction method , REMAP , which is based on a dual regularized one-class collaborative filtering algorithm , to explore continuous chemical space , protein space , and their interactome on a large scale ., When tested in a reliable , extensive , and cross-gene family benchmark , REMAP outperforms the state-of-the-art methods ., Furthermore , REMAP is highly scalable ., It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours ., Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound , we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies ., The anti-cancer activity of six of them is supported by experimental evidences ., Thus , REMAP is a valuable addition to the existing in silico toolbox for drug target identification , drug repurposing , phenotypic screening , and side effect prediction ., The software and benchmark are available at https://github . com/hansaimlim/REMAP .

High-throughput techniques have generated vast amounts of diverse omics and phenotypic data ., However , these sets of data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery , a process which has traditionally adopted a one-drug-one-gene paradigm ., Consequently , the cost of bringing a drug to market is astounding and the failure rate is daunting ., The failure of the target-based drug discovery is in large part due to the fact that a drug rarely interacts only with its intended receptor , but also generally binds to other receptors ., To rationally design potent and safe therapeutics , we need to identify all the possible cellular proteins interacting with a drug in an organism ., Existing experimental techniques are not sufficient to address this problem , and will benefit from computational modeling ., However , it is a daunting task to reliably screen millions of chemicals against hundreds of thousands of proteins ., Here , we introduce a fast and accurate method REMAP for large-scale predictions of drug-target interactions ., REMAP outperforms state-of-the-art algorithms in terms of both speed and accuracy , and has been successfully applied to drug repurposing ., Thus , REMAP may have broad applications in drug discovery .

medicine and health sciences, applied mathematics, cancer treatment, enzymology, simulation and modeling, oncology, algorithms, genomic databases, tyrosine kinase inhibitors, mathematics, forecasting, statistics (mathematics), genome analysis, pharmacology, enzyme inhibitors, research and analysis methods, genomics, mathematical and statistical techniques, biological databases, drug discovery, biochemistry, kinase inhibitors, drug-drug interactions, drug research and development, database and informatics methods, genetics, biology and life sciences, physical sciences, computational biology, drug interactions, statistical methods

journal.pntd.0005832

Larrea tridentata: A novel source for anti-parasitic agents active against Entamoeba histolytica, Giardia lamblia and Naegleria fowleri

Intestinal protozoan parasite infections , through contaminated water and food supplies , are global health problems affecting hundreds of millions of people annually ., The two most common intestinal parasites are Giardia lamblia and Entamoeba histolytica , which can lead to giardiasis or invasive amebiasis , respectively ., G . lamblia and E . histolytica have simple infection life cycles that begin with ingesting viable cysts , excystation , followed by trophozoite multiplication in the small intestine or trophozoite migration and invasion in the colon ( Fig 1A ) 1–3 ., Annually , giardiasis , has an estimated worldwide prevalence of 200 million cases 4 , and according to the World Health Organization ( WHO ) giardia infections contribute substantially to the 846 , 000 deaths annually from diarrheal disease 5 , 6 ., Once G . lamblia has excysted in the small intestines , trophozoites attach to epithelial cells and elicit aberrant signaling events that disrupt organ function including the induction of programed cell death or apoptosis 3 ., Although less prevalent than G . lamblia , E . histolytica infections lead to 50 million cases of invasive disease and up to 100 , 000 deaths , annually 7 ., Invasive amebiasis is characterized by profound intestinal tissue damage and ulceration 8 ., Recently , Ralston and colleagues determined trogocytosis as the mechanism by which E histolytica feeds on its host ., The term ‘trogocytosis’ was taken from the Greek word trogo which means to nibble 8 , 9 ., The amebae damage and consume the intestinal mucosa epithelium by nibbling away at epithelial cell membranes , triggering cell death ., Interestingly , Ralston et al . concluded that amebae feed on bacteria in the gut for nutrition but that host cell ingestion is done by the amebae to create a more spacious environment 8 ., Free-living ameba Naegleria fowleri has been described as the cause of primary amebic meningoencephalitis ( PAM ) in more than 16 countries 10 ., Until 2012 , about 310 cases have been reported globally with a fatality rate of more than 95% 11 ., According to the Centers for Disease Control and Prevention ( CDC ) , 138 cases of PAM have been reported in the U . S . A . from 1962–2015 with a 98% mortality rate ., PAM results from water containing N . fowleri entering the nasal cavity followed by migration of the amebae to the brain ( Fig 1B ) 12–17 ., Within the brain , N . fowleri causes extensive inflammation , hemorrhage , and necrosis ., The time from initial exposure to onset of illness is usually 5–7 days but may be as early as 24 h , leading to death in 3 to 7 days 18 ., Treatment for giardiasis and invasive amebiasis is largely limited to the nitroimidazole drug class ( e . g . metronidazole ) 19 ., Metronidazole , is the primary drug of choice , which requires a relatively long treatment time and high dosage to eradicate intestinal parasite infections 20 ., Moreover , metronidazole is both mutagenic and carcinogenic and its use presents other significant adverse effects 21 , 22 ., In addition , G . lamblia and E . histolytica drug resistance and treatment failures remain an increasing problem 23–26 ., Amphotericin B remains a cornerstone of therapy for PAM but is not FDA-approved for this indication and has had limited success despite its worldwide use 27 ., Treatment with amphotericin B requires a high dosage and its use is frequently associated with renal toxicity and anemia , among other adverse effects 27 ., Recently , an investigational drug , miltefosine , clinically used to treat leishmaniasis , has shown some promise in combination with other drugs as a treatment for PAM 28 ., The CDC , through an established protocol with the FDA , is now directly providing miltefosine to the clinicians as a treatment option for PAM ., However , it is still not FDA approved and has limited availability in the U . S . A . Furthermore , G . lamblia , E . histolytica and N . fowleri are listed by the United States National Institutes of Health and the Centers for Disease Control as a category B biodefense/bioterrorism pathogens due to their low infectious dose and potential for dissemination through compromised food and water supplies ., Given the prevalence and mortality caused by these protozoan pathogens , compounded by their potential bioterrorism threat , more effective antiparasitic agents is a critical unmet need to treat the current pandemic and avert future outbreaks and deaths ., Natural products have played an important role throughout history in the treatment of human disease through traditional medicines and as a source for effective pharmaceutical development 29 , 30 ., In particular , plants have been a vast source of secondary metabolites that display potent antiparasitic activity , including protozoan parasites 30–33 ., For example , G . lamblia and E . histolytica are endemic to Mexico and infections are prevalent 34 , 35 ., Moreover , nitroimidazole drugs display limited efficacy in the Mexican population 36 ., Therefore , scientists have turned to native plants used as Mexican traditional medicines for intestinal diseases in the search for novel more effective antiparasitic agents 37 , 38 ., Similarly , using our established assays 39 , 40 , we have surveyed plants used as traditional medicines from around the world and that are common to the southwestern United States and throughout Mexico ., Herein , we report the discovery of Larrea tridentata , commonly known as creosote bush or chaparral , as a novel source for antiparasitic secondary metabolites 41 ., Though the extract of L . tridentata earlier showed antiparasitic activity against Trypanosoma brucei rhodesiense , T . cruzi , Leishmania donovani and Plasmodium falciparum 42 , this is the first report to show their activity against a free-living amoeba N . fowleri and against diarrhea causing parasites E . histolytica and G . lamblia ., We have identified seven known compounds 1–7 ( Fig 2 ) with 1–6 displaying antiparasitic activity against E . histolytica , G . lamblia , and N . fowleri ., Compounds 1 and 2 showed better activity against N . fowleri than the current drug miltefosine ., In addition , we have identified two secondary metabolites , compounds 8 and 9 ( Fig 2 ) , that we isolated from the same active fractions as 1–7 that appeared to have novel structures ., Compound 9 displayed modest antiparasitic activity against G . lamblia and N . fowleri ., An examination of the literature indicated that 8 and 9 structures have been reported 43 , 44 ., Interestingly , compound 8 has not previously been isolated or structurally characterized from the creosote plant , rather , Cho and colleagues used Larreatricin 3’-hydroxylase enzyme purified from creosote and the known secondary metabolite from creosote , larreatricin , to enzymatically prepare 8 , albeit in very low yield 44 ., However , the structure of 9 was dubiously deduced from Graziela mollisima as an impure mixture with insufficient analytical data to accurately characterize the structure 43 ., Therefore , this is the first report to unambiguously characterize the novel secondary metabolites 8 and 9 from L . tridentata ., Since compounds 1 and 2 were found more active against N . fowleri than miltefosine , we selected these two compounds to investigate their ability to inhibit N . fowleri cysteine protease , an enzyme shown to play an important role in host tissue invasion by N . fowleri 45 ., 1H , 13C and 2D NMR spectra were recorded on a Bruker Avance III spectrometer ( 400 MHz for 1H NMR and 100 MHz 13C NMR ) ., Chemical shifts are recorded in ppm ( δ ) using residual solvent signal as internal reference , and coupling constants ( J ) are reported in Hz ., The following splitting abbreviations were used for NMR signals: s = singlet , d = doublet , t = triplet , q = quartet , m = multiplet , br = broad ., High-resolution mass spectra ( HRMS ) were recorded on a Bruker Q-TOF-2 Micromass spectrometer equipped with lock spray , using ESI with methanol as the carrier solvent ., Accurate mass measurements were performed using leucine enkephalin as a lock mass and the data were processed using MassLynx 4 . 1 ., Exact m/z values are reported in Daltons ., Optical rotations were measured in CH3OH on a JASCO P1010 polarimeter at 589 nm ( Na D-line ) with a path length of 1 dm and are reported with implied units of 10−1 deg cm2 g-1 ., Concentrations ( c ) are given in g/100 mL ., UV was measured in CH3OH on an Agilent 8453 UV-Visible Spectrophotometer ., Analytical and preparative HPLC were performed on a Shimadzu Prominence HPLC system equipped with LC-6AD pumps , an autosampler ( SIL-20AC ) and manual injection port ( Rheodyne , 3725i ) , a column oven ( CTO-20A , temperature set at 27°C ) , a photo diode array detector ( SPD-M20A , using a Deuterium lamp and a tungsten lamp as light sources ) and a system controller ( CBM-20A ) ., A Phenomenex Kinetex C18 reversed phase column ( 5 μm , 100 Å , 250 ✕ 4 . 6 mm ) fitted with a guard cartridge , with a flow rate of 0 . 7 mL/min was used for analytical chromatography , and a Phenomenex Kinetex C18 reversed phase column ( 5 μm , 100 Å , 250 ✕ 21 . 1 mm ) fitted with a guard cartridge with a flow rate of 5 . 0 mL/min was used for preparative chromatography ., The HPLC data were processed using LabSolutions Lite software ( version 5 . 22 ) ., The dried powdered material ( 11 . 0 g ) of L . tridentata ( Mountain Rose Herbs ) was extracted with methanol at room temperature for 72 h ., After filtration through Celite , the methanol extract was concentrated under reduced pressure to give a crude residue ( 2 . 55 g ) ., The extract residue ( 2 . 53 g ) was treated with water ( 150 mL ) and partitioned against hexane ( 150 mL × 3 ) , ethyl acetate ( 150 mL × 3 ) and n-butanol ( 150 mL × 2 ) successively to yield a hexane fraction ( 128 . 6 mg ) , an ethyl acetate fraction ( 1 . 5 g ) , a n-butanol fraction ( 411 . 7 mg ) , and a water fraction ( 504 . 2 mg ) , respectively ., The parasite active ethyl acetate fraction ( 808 . 5 mg ) was then chromatographed on a Sephadex LH-20 column eluted with 20% hexane in CH2Cl2 ( 200 mL ) , 60% CH2Cl2 in acetone ( 400 mL ) , 20% CH2Cl2 in acetone ( 200 mL ) , 20% CH2Cl2 in methanol ( 200 mL ) , and 100% methanol ( 200 mL ) ., Ten fractions were collected: fractions A ( 12 . 9 mg ) and B ( 13 . 4 mg ) from 20% hexane in CH2Cl2; fractions C ( 28 . 6 mg ) , D ( 386 . 1 mg ) , E ( 181 . 9 mg ) , and F ( 81 . 6 mg ) from 60% CH2Cl2 in acetone; fractions G ( 48 . 7 mg ) and H ( 35 . 9 mg ) from 20% CH2Cl2 in acetone; fraction I ( 52 . 6 mg ) from 20% CH2Cl2 in methanol and fraction J ( 2 . 1 mg ) from 100% methanol ., Fraction E ( 138 . 9 mg ) was chromatographed on reverse phase preparative HPLC and eluted with gradient 20–100% acetonitrile in water for 40 min to yield 1 ( 11 . 7 mg ) and 3 ( 43 . 3 mg ) as yellowish resinous solid along with sub-fraction E1 ( 11 . 0 mg ) ., Sub-fraction E1 was re-chromatographed under similar HPLC conditions to afford 2 ( 4 . 0 mg ) , 7 ( 3 . 3 mg ) , and 8 ( 1 . 8 mg ) ., Fraction D ( 386 . 1 mg ) was chromatographed on silica gel column ( 13 . 0 g ) eluted with increasing amounts of methanol in CH2Cl2 to afford seven fractions , D1 ( 0 . 6 mg ) , D2 ( 181 . 1 mg ) , D3 ( 73 . 5 mg ) , D4 ( 65 . 6 mg ) , D5 ( 5 . 6 mg ) , D6 ( 5 . 5 mg ) , D7 ( 7 . 5 mg ) ., Fraction D2 ( 133 . 0 mg ) was chromatographed on preparative HPLC and eluted with isocratic 50% acetonitrile in water to yield 2 ( 29 . 7 mg ) , 4 ( 14 . 6 mg ) , 5 ( 2 . 0 mg ) and 6 ( 4 . 5 mg ) as yellow resinous solids ., ( 7R , 7’R ) -7 , 7’-bis ( 4’ , 3 , 4-trihydroxyphenyl ) - ( 8R , 8’S ) -8 , 8’-dimethyltetrahydrofuran ( 8 ) : colorless oil; αD25–88 . 1 ( c 0 . 16 , CH3OH ) ; UV ( MeOH ) λmax ( log ε ) 211 ( 3 . 44 ) ; 236 ( 2 . 54 ) , 282 ( 1 . 64 ) ; 1H and 13C NMR data , see Table 1; HRESIMS m/z 301 . 1506 M + H+ ( calcd for C18H21O4 , 301 . 1439 ) 3-Methoxy-6 , 7 , 4’-trihydroxyflavonol ( 9 ) : Yellow solid; UV ( MeOH ) λmax ( log ε ) 211 ( 5 . 06 ) , 266 ( 4 . 90 ) , 348 ( 4 . 86 ) ; 1H and 13C NMR data , see Table 2; HRESIMS m/z 301 ., 0690 M + H+ ( calcd for C16H13O6 , 301 . 0712 ) ., Trophozoites of E . histolytica HM1: IMSS and G . lamblia WB strains were axenically maintained in TYI-S-33 medium supplemented with penicillin ( 100 U/ml ) , streptomycin ( 100 μg/ml ) 46 , 47 ., Trophozoites of N . fowleri strain KUL were axenically cultured in Nelson’s medium supplemented with 10% FBS at 37°C 45 ., All experiments were performed using trophozoites harvested during the logarithmic phase of growth ., Four solvent partitioned fractions of an aqueous methanolic extract of L . tridentata and compounds 1–9 were screened for activity against E . histolytica , G . lamblia , and N . fowleri ., For primary screening , the positive control for E . histolytica and G . lamblia was 5 μg/mL of metronidazole ( Sigma-Aldrich ) and 46 μg/mL of amphotericin B for N . fowleri ( Sigma-Aldrich ) ., Test samples were diluted to 10 mg/mL of extracts , HPLC fractions , and pure compounds in DMSO ., Finally , 0 . 5 μL of diluted sample was transferred to white , solid bottom tissue culture 96-well plates ( E&K Scientific ) followed by addition of 99 . 5 μL trophozoites ( 5 , 000 E . histolytica and G . lamblia , and 10 , 000 N . fowleri ) in TYI-S-33 medium or Nelson’s medium ., The final concentration for test compounds was 50 μg/mL and 0 . 5% DMSO , which was the negative control and compound vehicle that we have shown has no effect the growth rate of trophozoites 39 , 40 , 48 ., Assay plates were incubated for 48 h at 37°C ., E . histolytica and G . lamblia plates were kept in the GasPak EZ Anaerobe Gas Generating Pouch System ( VWR ) to maintain anaerobic condition throughout the incubation period ., Screening was performed in duplicate using the CellTiter-Glo assay ( Promega ) and luminescence was measured using an EnVision plate reader ( PerkinElmer ) 40 , 48 ., The antiparasitic activity of 1–6 and 9 were confirmed by EC50 dose response experiments , using the CellTiter-Glo assay , conducted in triplicate over a concentration range from 5-to-700 μM against trophozoites ( Table 3 ) ., Miltefosine and metronidazole , current drugs for the treatment of PAM and amebiasis and giardiasis were also tested in triplicate as positive controls for EC50 determination ( Table 3 ) ., Dose response curves including standard deviation ( SD ) calculation were processed using GraphPad Prism software 5 . 0 ., Percent inhibition relative to maximum and minimum reference signal controls was calculated using the formula: % Inhibition = ( mean of Maximum Signal Reference Control—Experimental Value ) / ( mean of Maximum Signal Reference Control—mean of Minimum Signal Reference Control ) × 100 ., The HUVEC-TERT2 cell line was purchased from Evercyte GmbH ( Vienna , Austria ) and cultured and maintained in endothelial cell basal medium ( Lonza ) as described previously 49 , 50 ., Briefly , cells were seeded into a white 384-well solid bottom plate ( Nunc , ThermoFisher ) at a density of 1000 cells/well in 39 μL of media using a Janus liquid handler ( PerkinElmer ) ., Serial dilutions using 1 μL of compound 1 and 2 at varying concentrations were dispensed into each well in triplicate ., After 48 h incubation , 40 μL of CellTiter-Glo reagent ( Promega ) was added into each well ., The contents were mixed for 2 min on a microplate shaker to induce cell lysis and further incubated at room temperature for 10 min to stabilize the luminescent signal ., Luminescence was measured with an EnVision plate reader ( PerkinElmer ) and %inhibition calculations were performed using the following formula for single-point normalization: %Inhibition = ( 1-Raw Sample Value/Mean of DMSO Signal Reference Value ) × 100 ., Dose response curves including EC50 calculations were processed using GraphPad Prism software ., To prepare the cell lysate , N . fowleri trophozoites were removed from the culture flask surface by incubating in an ice bath for 10 min , centrifuged at 300 g for 10 min , and washed twice with PBS ( pH 7 . 2 ) ., The cells were disrupted by four cycles of freeze thawing in PBS 51 ., Protein concentration was quantified by the method of Bradford ( Bio-Rad ) ., The activity of the cysteine protease present in the crude extract after incubating in presence and absence of different concentrations of compounds 1 and 2 was assayed by the liberation of the fluorescent leaving group , 7-amino-4-methyl coumarin ( AMC ) , from the peptide substrate Z–Phe–Arg–AMC ( 40 μM ) ( where Z is benzyloxycarbonyl , R&D Systems ) 45 ., The assay was performed at 25°C in an automated microtiter plate spectrofluorometer ( EnVision , PerkinElmer ) with excitation wavelength at 355 nm and emission wavelength at 460 nm 52 ., Enzyme samples were added to the reactivation buffer ( 10 mM Tris , 5 mM EDTA , 50 mM NaCl , pH 7 . 4 , 10 mM DTT ) , and preincubated for 20 min at 37°C prior to the hydrolysis of substrate ., The rate of substrate hydrolysis at ambient temperature was determined from the rate of increase of fluorescence , monitored on a continuously recording spectrofluorometer and measured as RFU/min/μg protein ., An aqueous methanolic extract of the creosote plant was partitioned against hexane , ethyl acetate and n-butanol successively to obtain four solvent partitioned fractions ., These fractions were tested for antiparasitic activity , the ethyl acetate fraction showed activity at 50 μg/mL and was selected for further study ., It was fractionated on Sephadex LH-20 and the fractions were subjected to chromatographic separation by HPLC to yield 1–9 as pure compounds ., Compound 1 was obtained as a yellow resinous mass ., The 1H , 13C , and HMQC NMR ( acetone-d6 ) indicated 9 carbon resonances and corresponding proton signals , consisting of one methyl δH 0 . 83 d ( 6 . 6 ) , four methines δH 1 . 74 m , three aromatic signals displaying an ABC splitting pattern δH 6 . 52 dd ( 7 . 9 , 1 . 8 ) ; δH 6 . 69 d ( 1 . 8 ) ; and δH 6 . 73 d ( 7 . 9 ) , and one methylene δH 2 . 21 dd ( 13 . 3 , 9 . 2 ) ; δH 2 . 70 dd ( 13 . 3 , 5 . 0 ) ., These data were identical with the known creosote secondary metabolite , nordihydroguairetic acid ( NDGA ) ( Table S1 and Fig . S1-S3 in S1 Appendix ) 53 ., Next , we identified known compound 2 as 3’-O-methylnordihydroguairetic acid ( 3’-O-methyl-NDGA ) 54 ., Although similar in structure to 1 , compound 2 is non-symmetrical , which revealed the full 19 carbon resonances and corresponding proton signals as follows: two methyls δH 0 . 82 d ( 6 . 6 ) , 0 . 83 d ( 6 . 6 ) , eight methines ( δH 1 . 74 m , 2H ) , six aromatics δH 6 . 58 dd ( 8 . 0 , 2 . 0 ) , δH 6 . 61 d ( 1 . 9 ) , δH 6 . 64 dd ( 8 . 0 , 1 . 9 ) , δH 6 . 67 d ( 2 . 0 ) , δH 6 . 77 d ( 8 . 0 ) , and δH 6 . 82 d ( 8 . 0 ) , and two methylenes δH 2 . 25 dd ( 13 . 1 , 9 . 3 ) , δH 2 . 71 dd ( 13 . 3 , 4 . 8 ) , δH 2 . 25 dd ( 13 . 1 , 9 . 4 ) , δH 2 . 68 dd ( 13 . 3 , 5 . 0 ) ., In addition , DEPT-135 and HMQC supported the presence of two methyls ( δc 16 . 6 , 16 . 4 ) , eight methines of which two aliphatic ( δc 39 . 3 , 39 . 1 ) and six aromatic ( δc 113 . 2 , 115 . 4 , 115 . 8 , 116 . 9 , 121 . 2 , 122 . 3 ) , two methylenes ( δc 40 . 0 , 39 . 2 ) and six quaternary aromatic ( δc 134 . 1 , 134 . 3 , 143 . 8 , 145 . 4 , 145 . 7 , 48 . 1 ) ( Table S2 and Fig . S4-S8 in S1 Appendix ) ., We identified compound 3 as Nor-3’-demethoxyisoguaiacin and 4–6 as analogs of 3 that have a tetrahydronaphthalene ring system 54 , 55 ., The 1H NMR ( CDCl3 ) displayed the following signals: two methyls δH 0 . 88 d ( 6 . 9 ) , 0 . 89 d ( 6 . 9 ) , nine methines including three aliphatic δH 3 . 57 d ( 6 . 2 ) , 1 . 89 m , 1 . 99 m , two aromatic singlets ( δH 6 . 60 s , δH 6 . 29 s ) resulting from an A2B2 tetra-substituted phenyl ring , four signals giving an A2B2 splitting pattern δH 6 . 86 ( 2H , d 8 . 5 ) , δH 6 . 69 ( 2H , d 8 . 5 ) due to a 1 , 4-disubstituted phenyl , and one methylene δH 2 . 83 dd ( 16 . 4 , 5 . 5 ) , δH 2 . 41 dd ( 16 . 4 , 7 . 2 ) ., The 13C NMR ( acetone-d6 ) displayed eighteen signals and HMQC supported the presence of two methyls ( δc 16 . 1 , 16 . 3 ) , three methines ( δc 50 . 8 , 41 . 8 , 30 . 1 ) , one methylene ( δc 35 . 7 ) , one A2B2 substituted phenyl ( δc 115 . 9 d , 117 . 7 d , 128 . 1 s , 130 . 7 s , 140 . 0 s , 144 . 4 s ) , and one 1 , 4-disubstituted phenyl δc 115 . 7 ( 2C , d ) , 130 . 8 ( 2C , d ) , 139 . 3 s , 156 . 3 s ( Table S3 , Fig . S9-S13 in S1 Appendix ) ., 4–6 were easily dereplicated due to varying methoxy and phenol substituents ., Specifically , compound 4 ( Nor-isoguaicin ) has a methoxy in the 3’-position , which was determined by the ABC proton splitting pattern δH 6 . 79 d ( 8 . 0 ) , δH 6 . 52 d ( 1 . 8 ) , δH 6 . 50 dd ( 8 . 0 , 1 . 8 ) from the tri-substituted phenyl ring ( Table S4 and Fig . S14-S18 in Appendix ) ., Conversely , compounds 5 ( 3’-Demethoxyisoguaiacin ) has a methoxy group in the 7 position of the tetra-substituted ring ( Table S5 and Fig . S19-S22 in S1 Appendix ) and 6 ( 6 , 3-Di-O-demethylisoguaiacin ) which contains a 3’ , 4’-dihydroxy phenyl moiety were determined by comparison with the reported chemical shifts ( Table S6 and Fig . S23-S25 in S1 Appendix ) 54 , 56 ., Finally , 7 was purified as a colorless oil and identified as 3-hydroxy-4-epi-larreatricin with 1H and 13C NMR matching the known literature structure ( Table S7 and Fig . S26-S30 in S1 Appendix ) 57 ., During the purification of 1–7 we identified lignan 8 and flavanol 9 , however , these secondary metabolites have never been isolated from the creosote plant ( 8 ) or were not structurally well characterized ( 9 ) ., Therefore , we report herein the isolation and structure elucidation from the creosote plant ., Compound 8 , was purified as a colorless oil and the molecular formula was deduced from the HRMS and 13C NMR as C18H20O4 ., The 1H NMR ( Table, 1 ) displayed signals attributable to two methyl groups δH 0 . 97 d ( 6 . 6 ) , δH 0 . 57 d ( 7 . 1 ) , and eleven methines , including: two oxygenated aliphatic protons δH 5 . 38 d ( 4 . 2 ) , δH 4 . 54 d ( 9 . 4 ) , two aliphatic protons δH 2 . 38–2 . 44 , m , 2H , four aromatic protons giving an A2B2 splitting pattern δH 7 . 17 d ( 8 . 1 ) , δH 6 . 81 d ( 7 . 8 ) , and three aromatic protons giving an ABC splitting pattern δH 6 . 91 br s , δH 6 . 81 br dd ( 7 . 2 ) , δH 6 . 72 d ( 7 . 2 ) ., The 13C NMR revealed the occurrence of eighteen carbons resonances , DEPT-90 in conjunction with HMQC supported the presence of seven aromatic methines , including A2B2 δc 128 . 0 x 2 and δc 115 . 5 x 2 and ABC splitting patterns ( δc 118 . 5 , δc 115 . 7 and δc 114 . 0 ) ., Further , we observed two oxygenated δc 86 . 2 and δc 85 . 2 and two non-oxygenated ( δc 48 . 4 and δc 44 . 0 ) methines as well as two methyl functional groups ( δc 12 . 2 and δc 9 . 7 ) ., The remaining five quaternary 13C NMR signals were indicative of aromatic chemical shifts ( δc 157 . 0 , 146 . 0 , 145 . 1 , 136 . 5 and 132 . 8 ) ., These NMR data were identical with the previously reported enzymatically synthesized ( ± ) 3-hydroxy-larreatricin 44 ., We observed HMBC correlations from aromatic H-2 ( δH 6 . 91 ) of the tri-substituted phenyl ring to C-7 ( δc 86 . 2 ) of the furan ring ., In addition , HMBC correlations from H-2’/H-6’ ( δH 7 . 17 ) of the 1 , 4-di-substituted phenyl ring to C-7’ ( δc 85 . 2 ) of furan ring proved the attachment of two phenyl rings at C-7 and C-7’ of furan ring , respectively ( Fig 3A ) ., These assignments were further confirmed by the HMBC correlations of H-7/C-2 and H-7’/ C-2’ , C-6’ ., The position of two methyls of furan ring was elucidated using HMBC cross peaks between methine H-7’ ( δH 5 . 38 ) and methyl C-9’ ( δc 9 . 7 ) and between methine H-7 ( δH 4 . 54 ) and methyl C-9 ( δc 12 . 2 ) ., Finally , the relative stereochemistry of four stereogenic centers in furan ring was assigned by the 1D nuclear Overhauser effect ( NOE ) experiment ( Fig 3B ) ., Irradiation at δH 4 . 54 ( H-7 ) gave enhanced signals at δH 6 . 92 ( H-2 ) , δH 0 . 97 ( H-9 ) and δH 0 . 57 ( H-9’ ) , indicating the spatial proximity of H-2 , H-9 and H-9’ ., In addition , irradiation at δH 5 . 38 ( H-7’ ) gave enhanced signal exclusively at δH 7 . 17 ( H-2’/H-6’ ) , the absence of correlations between H-7’ and H-7 clearly indicated the trans configuration of the 2-substituted phenyl ring ., Accordingly , the structure of 8 was established as ( 7R , 7’R ) -7 , 7’-bis ( 4’ , 3 , 4-trihydroxyphenyl ) - ( 8R , 8’S ) -8 , 8’-dimethyltetrahydrofuran ( Fig . S31-S38 in S1 Appendix ) , which is a stereoisomer of 7 ., Compound 9 was obtained as yellow solid and its molecular formula , C16H12O6 , was deduced by HRMS as well as 1H and 13C NMR analysis ., In the 1H NMR ( CDCl3 + CD3OD ) spectrum , a methoxy functionality δH 3 . 74 s was observed as well as six aromatic methines including two singlets δH 6 . 35 s , 6 . 20 , s and an A2B2 splitting pattern δH 7 . 93 d ( 8 . 6 ) , 2H; 6 . 88 d ( 8 . 6 ) , 2H resulting from a 1 , 4-disubstituted phenyl ring ., The 13C NMR ( Table, 2 ) showed sixteen carbon signals and DEPT-90 in conjunction with HMQC supported the presence of one methoxy ( δc 60 . 1 ) and six aromatic methines of which four δc 130 . 3 x 2 and 115 . 6 x 2 correlated to two doublet signals giving an A2B2 pattern ., In addition , we observed two signals that correlated with two aromatic proton singlets of the tetra-substituted phenyl ring ( δc 98 . 9 and 94 . 1 ) ., The remaining nine quaternary 13C NMR signals include a carbonyl ( δc 178 . 8 ) , six aromatic and two olefinic carbons ( δc 163 . 9 , 161 . 5 , 159 . 7 , 157 . 0 , 156 . 5 , 138 . 4 , 121 . 7 and 105 . 2 ) ., These NMR data were consistent with a flavonol ring system containing three hydroxyls and one methoxy group ., The HMBC cross peaks observed between the aromatic protons in the A-ring with H-8 ( δH 6 . 35 ) , C-7 ( δc 163 . 9 ) , C-8a ( δc 157 . 0 ) , and C-5a ( δc 105 . 2 ) ( Fig 4 ) ., Cross peaks were also observed between proton H-5 ( δH 6 . 20 ) , C-6 ( δc 161 . 5 ) , and C-5a ( δc 105 . 2 ) suggesting the attachment of two hydroxyl groups at C-7 ( δc 163 . 9 ) and C-6 ( δc 161 . 5 ) ., In addition , these cross peaks indicated an oxygen attachment to C-8a ( δc 157 . 0 ) , signifying the O-1 position of the flavonol C-ring ., The flavonol B and C ring connectivity were elucidated using HMBC correlations between protons H-2’/H6’ ( δH 7 . 93 ) and carbons C-2 ( δc 156 . 5 ) , and C-4’ ( δc 159 . 7 ) ., The phenolic substitution on ring B was indicated through H-3’/H-5’ ( δH 6 . 88 ) and carbon C-1’ ( δc 121 . 7 ) correlations ., Finally , the HMBC cross peak between methoxy protons ( δH 3 . 74 ) and C-3 ( δc 138 . 4 ) indicated that attachment at the C-3 position of the flavonol C-ring ( Fig . S23-S33 in S1 Appendix ) 58 ., Therefore , we have precisely determined compound 9 to be 3-methoxy-6 , 7 , 4’-trihydroxyflavonol ., We previously developed a high-throughput screening CellTiter-Glo ATP bioluminescence-based assay to assess antiparasitic activity 48 , and used this assay to test compounds 1–9 against the trophozoite stage of E . histolytica , G . lamblia , and N . fowleri ., Compounds 1–6 displayed dose response antiparasitic activity against all three pathogens by reducing the culture density by 50% ( EC50 ) compared to untreated trophozoite cultures ( Table 3 ) ., Compound 1 proved to be the most potent against both G . lamblia and N . fowleri ( EC50 = 36 μM ) ( Fig 5 ) ., However , 1 and 2 display similar EC50 values , and both exhibited only moderate activity against E . histolytica with EC50 values of 103 μM and 171 μM , respectively ., Both compounds 1 and 2 were found to be about 1 . 5-fold more active relative to the current standard drug miltefosine ( EC50 = 54 . 5 μM ) against N . fowleri ., Compound 3 was more active against G . lamblia ( EC50 = 49 μM ) than E . histolytica ( EC50 = 94 μM ) or N . fowleri ( EC50 = 73 μM ) , whereas compound 4 had similar activity against all three pathogens with EC50 values from 74 μM to 83 μM ., Compounds 5 and 6 had comparatively weak activity against the three pathogens ., Similarly , 9 displayed modest antiparasitic activity against G . lamblia ( EC50 = 153 μM ) and N . fowleri ( EC50 = 235 μM ) ( Table 3 ) ., Larreatricin derivatives and stereoisomers 7 and 8 displayed no antiparasitic activity ., To further assess the therapeutic potential of 1 and 2 , which displayed the most potent antiparasitic activity agains N . fowleri , we conducted a cytotoxicity study with human umbilical vein endothelial cells ( HUVEC ) , using the same CellTiter-Glo assay and time course that we used for assessing trophozoite toxicity ( Fig 5B ) ., Compounds 1 and 2 inhibit HUVEC cell viability with EC50 values of 86 μM and 59 μM , respectively ., Thus , 1 and 2 are correspondingly 2 . 4 fold and 1 . 6 fold less toxic to human cells compared to N . fowleri , which is statistically significant ( P<0 . 0001 ) ( Fig 5C ) ., NDGA was previously shown to inhibit cysteine protease in cancer 59 , and recent studies linked the involvement of cysteine protease in the pathogenesis of N . fowleri 45 ., Thus , we investigated the effects of compounds 1 and 2 on cysteine protease activity present in total crude lysate of N . fowleri over a concentration range from 1 . 875-to-30 μM ., The dose dependent effect varied between 1 and 2 , however , both inhibited the cysteine protease activity by almost 50% at 1 . 875 μM ( Fig 6 ) ., This data indicates that the activity of compounds 1 and 2 against whole cell N . fowleri may be due to the modulation of cysteine protease activity present in the trophozoites ., Because lignans 1–6 are from the same structural class of compounds we could assess notable structure activity relationships ( SAR ) ., For example , 1 and 2 displayed overall more potent activity compared to 3–6 , which may be a result of the more flexible straight chain structure that offers more conformational flexibility compared to 3–6 ., In addition , introducing a methoxy group in the 3’-position of 2 appears to be negligible with regard to SAR ., Conversely , 3 and 4 only differ by one methoxy group in the 3’ position ( i . e . compound 4 ) , which reduced the antiparasitic activity against G . lamblia by ~2 fold ., However , this functional group was dispensable when comparing the activity between E . histolytica and N . fowleri ., Similarly , introducing a phenol in the 3’ position as in 6 also results in reduced activity compared to 3 ., The most striking SAR is observed by introducing a methoxy group in the seven position such as in 5 , which results in a substantial loss of activity compared to 3: ~3 fold ( E . histolytica ) , 4-fold ( G . lamblia ) , and ~ 2 fold ( N . fowleri ) ., Although 1–6 are proposed to be biosynthesized from 7 and 8 44 and share many of the same structural features , these compounds displayed no antiparasitic activity ., To better understand this SAR we compared the calculated LogP values for 1–9 ., Compounds 7 and 8 are 10 fold more hydrophilic ( CLogP = 3 . 5 ) compared to 1–6 ( CLogP = 4 . 5 ) ., However , the flavonol 9 ( CLogP = 1 . 1 ) is 1 , 000 fold more hydrophilic compared to 7 and, 8 . Interestingly , flavonoids are known to actively diffuse through organism membranes via membrane transporters such as the ATP-binding cassette ( ABC ) transporters 60 ., Moreover , parasitic protozoa are known to express these ABC transporters and other relevant transporters utilized by flavonoids 61 , which may explain the activity of 9 compared to 7 and, 8 . Thus , it is plausible that the difference in hydrophilicity may be a physical property of 7 and 8 preventing diffusion into the parasite trophozoites , explaining their inactivity compared to 1–6 and, 9 . Compounds 1 and 2 did not display more potent activity against E . histolytica and G . lamblia compared to metronidazole , but both compounds where 1 . 5 fold more potent against N . fowleri compared to miltefosine , which is used for the treatment of

Introduction, Methods, Results, Discussion

Protozoan parasites infect and kill millions of people worldwide every year , particularly in developing countries where access to clean fresh water is limited ., Among the most common are intestinal parasites , including Giardia lamblia and Entamoeba histolytica ., These parasites wreak havoc on the epithelium lining the small intestines ( G . lamblia ) and colon ( E . histolytica ) causing giardiasis and amebiasis , respectively ., In addition , there are less common but far more deadly pathogens such as Naegleria fowleri that thrive in warm waters and infect the central nervous systems of their victims via the nasal passages ., Despite their prevalence and associated high mortality rates , there remains an unmet need to identify more effective therapeutics for people infected with these opportunistic parasites ., To address this unmet need , we have surveyed plants and traditional herbal medicines known throughout the world to identify novel antiparasitic agents with activity against G . lamblia , E . histolytica , and N . fowleri ., Herein , we report Larrea tridentata , known as creosote bush , as a novel source for secondary metabolites that display antiparasitic activity against all three pathogens ., This report also characterizes the lignan compound classes , nordihydroguairetic acid and demethoxyisoguaiacin , as novel antiparasitic lead agents to further develop more effective drug therapy options for millions of people worldwide .

Entamoeba histolytica , Giardia lamblia , and Naegleria fowleri pathogens are widespread throughout the world infecting and killing hundreds of thousands of people every year ., They are also listed as category B bioterrorism agents by the NIH and the CDC ., However , there is a serious unmet need to develop more effective therapies to treat these deadly pathogens ., Herein we describe that lignans isolated from the creosote bush , common to the southwestern U . S . A . and throughout Mexico , display relatively potent antiparasitic activity against E . histolytica , G . lamblia , and N . fowleri .

trophozoites, parasite groups, medicine and health sciences, giardia, enzymes, enzymology, parasitic diseases, parasitic protozoans, parasitology, apicomplexa, protozoans, cysteine proteases, naegleria fowleri, giardia lamblia, parasitic intestinal diseases, entamoeba histolytica, proteins, protozoan infections, primary amoebic meningoencephalitis, biochemistry, biology and life sciences, proteases, organisms

journal.pcbi.1005535

Post-transcriptional regulation across human tissues

The relative ease of measuring mRNA levels has facilitated numerous investigations of how cells regulate their gene expression across different pathological and physiological conditions 1–6 ., However , often the relevant biological processes depend on protein levels , and mRNA levels are merely proxies for protein levels 7 ., If a gene is regulated mostly transcriptionally , its mRNA level is a good proxy for its protein level ., Conversely , post-transcriptional regulation can set protein levels independently from mRNA levels , as in the cases of classical regulators of development 8 , cell division 9 , 10 and metabolism 11 , 12 ., Thus understanding the relative contributions of transcriptional and post-transcriptional regulation is essential for understanding their trade-offs and the principles of biological regulation , as well as for assessing the feasibility of using mRNA levels as proxies for protein levels ., Previous studies have considered single cell-types and conditions in studying variation in absolute mRNA and protein levels genome-wide , often employing unicellular model organisms or mammalian cell cultures 13–19 ., However , analyzing per-gene variation in relative mRNA and protein expression across different tissue-types in a multicellular organism presents a potentially different and critical problem which cannot be properly addressed by examining only genome-scale correlations between mRNA and protein levels ., 20–22 have measured protein levels across human tissues , thus providing valuable datasets for analyzing the regulatory layers shaping tissue-type-specific proteomes ., The absolute levels of proteins and mRNAs in these datasets correlate well , highlighting that highly abundant proteins have highly abundant mRNAs ., Such correlations between the absolute levels of mRNA and protein mix/conflate many sources of variation , including variability between the levels of different proteins , variability within the same protein across different conditions and cell-types , and the variability due to measurement error and technological bias ., However , these different sources of variability have very different biological interpretations and implications ., A major source of variability in protein and mRNA data arises from differences between the levels of mRNAs and proteins corresponding to different genes ., That is , the mean levels ( averaged across tissue-types ) of different proteins and mRNAs vary widely ., We refer to this source of variability as mean-level variability ., This mean-level variability reflects the fact that some proteins , such as ribosomal proteins , are highly abundant across all profiled tissues while other proteins , such as cell cycle and signaling regulators , are orders of magnitude less abundant across all profiled conditions 20 ., Another principal source of variability in protein levels , intuitively orthogonal to the mean-level variability , is the variability within a protein across different cell-types or physiological conditions and we refer to it as across-tissues variability ., The across-tissues variability is usually much smaller in magnitude , but may be the most relevant source of variability for understanding different phenotypes across cells-types and physiological conditions ., Here , we sought to separately quantify the contributions of transcriptional and post-transcriptional regulation to the mean-level variability and to the across-tissues variability across human tissues ., Our results show that much of the mean-level protein variability can be explained well by mRNA levels while across-tissues protein variability is poorly explained by mRNA levels; much of the unexplained variance is due to measurement noise but some of it is reproducible across datasets and thus likely reflects post-transcriptional regulation ., These results add to previous results in the literature 13–18 , 20 , 22 and suggest that the post-transcriptional regulation is a significant contributor to shaping tissue-type specific proteomes in human ., We start by outlining the statistical concepts underpinning the common correlational analysis and depiction 13 , 15 , 17 , 20 of estimated absolute protein and mRNA levels as displayed in Fig 1a and 1b ., The correlation between the absolute mRNA and protein levels of different genes and across different tissue-types has been used to estimate the level at which the protein levels are regulated 20 , 22 ., One measure reflecting the post-transcriptional regulation of a gene is its protein to mRNA ratio , which is sometimes referred to as a gene’s “translational efficiency” ., Since this ratio also reflects other layers of regulation , such as protein degradation and noise 18 , we will refer to it descriptively as protein-to-mRNA ( PTR ) ratio ., If the across-tissues variability of a gene is dominated by transcriptional regulation , its PTR in different tissue-types will be a gene-specific constant ., Based on this idea , 20 , 22 estimated these protein-to-mRNA ratios and suggested that the median PTR for each gene can be used to scale its tissue-specific mRNA levels and that this “scaled mRNA” predicts accurately tissue-specific protein levels ., Indeed , mRNA levels scaled by the corresponding median PTR explain large fraction of the total protein variance ( R T 2 = 0 . 70 , across all measured proteins , Fig 1a and 1b ) as previously observed 15 , 20 , 22 ., However , this high R T 2 does not indicate concordance for across-tissues variability of mRNAs and proteins ., R T 2 quantifies the fraction of the total protein variance explained by mRNA levels between genes and across tissue-types; thus , it conflates the mean-level variability with the across-tissues variability ., This conflation is shown schematically in Fig 1c for a subset of 100 genes measured across 12 tissues ., The across-tissues variability is captured by the variability within the regression fits while the mean-level variability is captured by the variability between the regression fits ., Such aggregation of distinct sources of variability , where different subgroups of the data show different trends , may lead to counter-intuitive results and incorrect conclusions , and is known as the Simpson’s or amalgamation paradox 23 ., To illustrate the Simpson’s paradox in this context , we depicted a subset of genes for which the measured mRNA and protein levels are unrelated across-tissues while the mean-level variability still spans the full dynamic range of the data , Fig 1c ., For this subset of genes , the overall ( conflated/amalgamated ) correlation is large and positive , despite the fact that all across-tissues ( within-gene ) trends are close to zero ., This counter-intuitive result is possible because the conflated correlation is dominated by the variability with larger dynamical range , in this case the mean-level variability ., This conceptual example using data from 20 demonstrates that R T 2 is not necessarily informative about the across-tissues variability , i . e . , the protein variance explained by scaled mRNA within a gene ( R P 2 ) ., Thus the conflated correlation is not generally informative about the level—transcriptional or post-transcriptional—at which across-tissues variability is regulated ., This point is also illustrated in S1 Fig with data for all quantified genes: The correlations between scaled mRNA and measured protein levels are not informative for the correlations between the corresponding relative changes in protein and mRNA levels ., To further illustrate this point with more datasets , Fig 1d displays the cumulative distributions of across-tissues mRNA-protein correlations ( RP ) for all proteins quantified across the large shotgun datasets 20 , 21 , as well as the corresponding conflated correlations between scaled mRNA and protein levels ( RT ) ., This depiction demonstrates that RT are not representative for RP ., To extend this analysis to protein levels measured by targeted MS 22 , we plotted the distributions of across-tissues mRNA-protein correlations ( RP ) for the subset of 33 genes quantified across all datasets 20–22; see dotted curves in Fig 1d ., These genes were selected to have larger variance across tissues 22 and have relatively higher across-tissues correlations , especially in the data by 21 , 22 ., Nevertheless , all datasets include low and even negative across-tissues correlations ( RP ) and very high conflated correlations between scaled mRNA and protein levels ( RT ) , Fig 1d ., These results underscore the weak connection between RP and RT even for a carefully selected and measured subset of mRNAs and proteins ., The across-tissues variability has a dynamic range of about 2 − 10 fold and is thus dwarfed by the 103 − 104 fold dynamic range of abundances across different proteins ., While across-tissues variability is smaller than mean-level variability , it is exactly the across-tissues variability that contributes to the biological identity of each tissue type and we focus the rest of our analysis to factors regulating the across-tissues protein variability ., Next , we sought to estimate the fractions of across-tissues protein variability due to transcriptional regulation and to post-transcriptional regulation ., This estimate depends crucially on noise in the mRNA and protein data , from sample collection to measurement error ., Both RNA-seq 24 , 25 and mass-spectrometry 15 , 26 have relatively large and systematic error in estimating absolute levels of mRNAs and proteins , i . e . , the ratios between different proteins/mRNAs ., These errors originate from DNA sequencing GC-biases , and variations in protein digestion and peptide ionization ., However , relative quantification of the same gene across tissue-types by both methods can be much more accurate since systematic biases are minimized when taking ratios between the intensities/counts of the same peptide/DNA-sequence measured in different tissue types 18 , 25 , 27 , 28 ., It is this relative quantification that is used in estimating across-tissues variability , and we start by estimating the reliability of the relative quantification across human tissues , Fig 2a–2d ., Reliability is defined as the fraction of the observed/empirical variance due to signal ., Thus reliability is proportional to the signal strength and decreases with the noise levels ., To estimate the within study reliability of mRNA levels , we took advantage that each mRNA dataset contains data from multiple subjects ., We split the subjects in each dataset into two subsets , each of which containing measurements for all 12 tissues from several subjects ., The levels of each mRNA were estimated from each subset by averaging across subjects and the estimates from the two subsets correlated , Fig 2a ., These correlations provide estimates for the reliability of each mRNA and their median provides a global estimate for the reliability of relative RNA measurement , not taking into account noise due to sample collection and processing ., To estimate the within study reliability of protein levels , we computed separate estimates of the relative protein levels within a dataset ., For each protein , Estimate 1 was derived from 50% of the quantified peptides and Estimate 2 from the other 50% ., Since much of the analytical noise related to protein digestion , chromatographic mobility and peptide ionization is peptide-specific , such non-overlapping sets of of peptides provide mostly , albeit not completely , independent estimates for the relative protein levels ., The correlations between the estimates for each protein ( averaging across 12 tissues ) are displayed as a distribution in Fig 2b ., In addition to the within study measurement error , protein and mRNA estimates can be affected by study-dependable variables such as sample collection and data processing ., To account for these factors , we estimated across study reliability by comparing estimates for relative protein and mRNA levels derived from independent studies , Fig 2c and 2d ., For each gene , we estimate the reliability for each protein by computing the empirical correlation between mRNA abundance reported by the ENCODE 29 and by 30 ., The correlations in Fig 2c have much broader distribution than the within-study correlations , indicating that much of the noise in mRNA estimates is study-dependent ., To estimate the across study reliability of protein levels , we compared the protein levels estimated from data published by 20 and 21 ., To quantify protein abundances , 20 used iBAQ scores and 21 used spectral counts ., To ensure uniform processing of the two datasets , we downloaded the raw data and analyzed them with maxquant using identical settings , and estimated protein abundances in each dataset using iBAQ; see Methods ., The corresponding estimates for each protein were correlated to estimate their reliability ., Again , the correlations depicted in Fig 2d have a much broader distribution compared to the within-study protein correlations in Fig 2b , indicating that , as with mRNA , the vast majority of the noise is study-dependent ., As a representative estimate of the reliability of protein levels , we use the median of the across tissue correlations from Fig 2d ., The across tissues correlations and the reliability of the measurements can be used to estimate the across tissues variability in protein levels that can be explained by mRNA levels ( i . e . , transcriptional regulation ) as shown in Fig 2e; see Methods ., As the reliabilities of the protein and the mRNA estimates decrease , the noise sensitivity of the estimated transcriptional contribution increases ., Although the average across-tissues mRNA protein correlation was only 0 . 29 ( R2 = 0 . 08 ) , the data are consistent with approximately 50% of the variance being explained by transcriptional regulation and approximately 50% coming from post-transcriptional regulation; see S2 Fig for reliability-corrected estimates for specific functional gene sets ., However , the low reliability of the data and large sampling variability precludes making such estimate reliable ., Thus , we next considered analyses that can provide estimates for the scope of post-transcriptional regulation even when the reliability of the data is low ., The low reliability of estimates across datasets limits the reliability of estimates of transcriptional and post-transcriptional regulation for individual proteins , Fig 2 ., Thus , we focused on estimating the post-transcriptional regulation for sets of functionally related genes as defined by the gene ontology ( GO ) 31 ., By considering such gene sets , we may be able to average out some of the measurement noise and see regulatory trends shared by functionally related genes ., Indeed , some of the noise contributing to the across-tissues variability of a gene is likely independent from the function of the gene; see Methods ., Conversely , genes with similar functions are likely to be regulated similarly and thus have similar tissue-type-specific PTR ratios ., Thus , we explored whether the across-tissues variability of the PTR ratios of functionally related genes reflects such tissue-type-specific and biological-function-specific post-transcriptional regulation ., Since this analysis aims to quantify across-tissues variability , we define the “relative protein to mRNA ratio” ( rPTR ) of a gene in a given tissue to be the PTR ratio in that tissue divided by the median PTR ratio of the gene across the other 11 tissues ., We evaluated the significance of rPTR variability for a gene-set in each tissue-type by comparing the corresponding gene-set rPTR distribution to the rPTR distribution for those same genes pooled across the other tissues ( Fig 3 ) ; we use the KS-test to quantify the statistical significance of differences in the rPTR distributions; see Methods ., The results indicate that the genes from many GO terms have substantially higher rPTR in some tissues than in others ., For example the ribosomal proteins of the small subunit ( 40S ) have high rPTR in kidney but low rPTR in stomach ( Fig 3a–3c ) ., While the strong functional enrichment of rPTR suggests functionally concerted post-transcriptional regulation , it can also reflect systematic dataset-specific measurement artifacts ., To investigate this possibility , we obtained two estimates for rPTR from independent datasets: Estimate 1 is based on data from 20 and 30 , and Estimate 2 is based on data from 21 and 29 ., These two estimates are reproducible ( e . g . , ρ = 0 . 7 − 0 . 8 ) for most tissues but less for others ( e . g . , ρ = 0 . 14 ) , as shown by the scatter plots between the median rPTR for GO terms in Fig 3d; S3 Fig shows the reproducibility for all tissues ., The correlations between the two rPTR estimates remain statistically significant albeit weaker ( i . e . , ρ = 0 . 1 − 0 . 4 ) when computed with all GO terms ( not only those showing significant enrichment ) as shown in S1 Table , as well as when computed between the rPTRs for all genes , S2 Table ., Given the low reliability of protein estimates across studies show in Fig 2 , we sought to increase it by deriving consensus estimates ., Indeed , by appropriately combining data from both protein studies , we can average out some of the noise thus improving the reliability of the consensus estimates; see Methods ., As expected for protein estimates with increased reliability , the consensus protein levels correlate better to mRNA levels than the corresponding protein levels estimated from a either dataset alone , Fig 4a and 4b ., We further validate our consensus estimates against 124 protein/tissue measurements from a targeted MS study 22 ., We computed the mean squared errors ( MSE ) between the protein levels estimated from the targeted study and the other three datasets using only protein/tissue measurements quantified in all datasets , facilitating fair comparison ( Fig 4c ) ., The MSE are lower for the consensus dataset than for either 20 or 21 and are consistent with a 10% error reduction relative to the 21 dataset ., In addition to increased reliability , the consensus dataset increased coverage , providing a more comprehensive quantification of protein levels across human tissues than either draft of the human proteome taken alone ( Table 1 ) ., Highly abundant proteins have highly abundant mRNAs ., This dependence is consistently observed 13–15 , 17 , 18 and dominates the explained variance in the estimates of absolute protein levels ( Fig 1 and S1 Fig ) ., This underscores the role of transcription for setting the full dynamic range of protein levels ., In stark contrast , differences in the proteomes of distinct human tissues are poorly explained by transcriptional regulation , Fig 1 ., This is due to measurement noise ( Fig, 2 ) but also to post-transcriptional regulation ., Indeed , large and partially reproducible rPTR ratios suggest that the mechanisms shaping tissue-specific proteomes involve post-transcriptional regulation , Fig 3 ., This result underscores the role of translational regulation and of protein degradation for mediating physiological functions within the range of protein levels consistent with life ., As with all analysis of empirical data , the results depend on the quality of the data and the estimates of their reliability ., This dependence on data quality is particularly strong given that some conclusions rest on the failure of across-tissues mRNA variability to predict across-tissues protein variability ., Such inference based on unaccounted for variability is substantially weaker than measuring directly and accounting for all sources of variability ., The low across study reliability suggest that the signal is strongly contaminated by noise , especially systematic biases in sample collection and handling , and thus the data cannot accurately quantify the contributions of different regulatory mechanisms , Fig 2 ., Another limitation of the data is that isoforms of mRNAs and proteins are merged together , i . e . , using razor proteins ., This latter limitation is common to all approaches quantifying proteins and mRNAs from peptides/short-sequence reads ., It stems from the limitation of existing approaches to infer and distinctly quantify isoforms and proteoforms ., The strong enrichment of rPTR ratios within gene sets ( Fig, 3 ) demonstrates a functionally concerted regulation at the post-transcriptional level ., Some of the rPTR trends can account for fundamental physiological differences between tissue types ., For example , the kidney is the most metabolically active ( energy consuming ) tissue among the 12 profiled tissues 32 and it has very high rPTR for many gene sets involved in energy production ( Fig 3a ) ., In this case , post-transcriptional regulation likely plays a functional role in meeting the high energy demands of kidneys ., Quantifying and understanding mRNA and protein covariation in single cells is an important frontier of this analysis 33 ., The rPTR patterns and the across tissue correlations in S1 Fig indicate that the relative contributions of transcriptional and post-transcriptional regulation can vary substantially depending on the tissues compared ., Thus , the level of gene regulation depends strongly on the context ., For example transcriptional regulation is contributing significantly to the dynamical responses of dendritic cells 18 and to the differences between kidney and prostate gland ( S1h Fig ) but less to the differences between kidney and liver ( S1g Fig ) ., All data , across all profiled tissues , suggest that post-transcriptional regulation contributes substantially to the across-tissues variability of protein levels ., The degree of this contribution depends on the context ., Indeed , if we only increase the levels for a set of mRNAs without any other changes , the corresponding protein levels must increase proportionally as demonstrated by gene inductions 34 ., However , the differences across cell-types are not confined only to different mRNA levels ., Rather , these differences include different RNA-binding proteins , alternative untranslated regions ( UTRs ) with known regulatory roles in protein synthesis , specialized ribosomes 35–38 , and different protein degradation rates 39–43 ., The more substantial these differences , the bigger the potential for post-transcriptional regulation ., Thus cell-type differentiation and commitment may result in much more post-transcriptional regulation than observed during perturbations preserving the cellular identity ., Consistent with this possibility , tissue-type specific proteomes may be shaped by substantial post-transcriptional regulation; in contrast , cell stimulation that preserves the cell-type , may elicit a strong transcriptional remodeling but weaker post-transcriptional remodeling ., We used RNA estimates based on RNA-seq from 29 , 30 and protein estimates based on shotgun mass-spectrometry from 20 , 21 ., These large scale datasets contained N = 6104 genes measured in each of twelve different human tissues: adrenal gland , esophagus , kidney , ovary , pancreas , prostate , salivary gland , spleen , stomach , testis , thyroid gland , and uterus ., For these genes , about 8% of the mRNA measurements and about 40% of the protein measurements are missing ., The mRNA datasets contain measurements from multiple subjects/people and the subjects were split into two subsets in estimating the within study reliability in Fig 2a ., We also used a small scale targeted dataset from 22 containing data for 33 proteins measured across 5 tissues ., The datasets were collected by different groups and measurements derived from different subjects ., Raw data from 21 , 22 were searched by MaxQuant 44 1 . 5 . 7 . 0 against a protein sequence database including all entries from a Human UniProt database from 2015 and known contaminants such as human keratins and common laboratory contaminants ., MaxQuant searches were performed with trypsin specificity allowing up to two missed cleavages , with fixed Carbamidomethyl acetylation on cysteines , and with variable modifications allowing methionine oxidation and acetylation on Protein N-termminus ., All razor peptides were used for quantifying the proteins to which they were assigned by MaxQuant ., False discovery rate ( FDR ) was set to 1% at both the protein and the peptide levels ., First , denote mit the log mRNA levels for gene i in tissue t ., Similarly , let pit denote the corresponding log protein levels ., First , we normalize the columns of the data , for both protein and mRNA , to different amounts of total protein per sample ., Any multiplicative factors on the raw scale correspond to additive constants on the log scale ., Consequently , we normalize data from each tissue-type by minimizing the absolute differences between data from the tissue and the first tissue ( arbitrarily chosen as a baseline ) ., That is , for all t > 1 , we define, p i t n = ( p i t u - μ ^ t ), with, μ ^ t = argmin μ ∑ i | p i 1 u - ( p i t u - μ ) |, Where p i t n and p i t u represent the normalized and non-normalized protein measurements respectively ., For each t , the value of μt which minimizes the absolute difference is, μ ^ t = median u ( p i 1 - p i t u ), We use the same normalization for mRNA ., This normalization , which corresponds to a location shift of the log abundances for each tissue , corrects for any multiplicative differences in the raw ( unlogged ) mRNA or protein ., We normalize these measurements by aligning the medians rather than the means , as the median is more robust to outliers ., After normalization , we define rit = pit − mit as the log PTR ratio of gene i in condition t ., If the post-transcriptional regulation for the ith gene were not tissue-specific , then the ith PTR ratio would be independent of tissue-type and can be estimated as, T ^ i = median t ( p i t - m i t ), In such a situation the log “scaled mRNA” ( or mean protein level ) can be defined as, p ¯ i t = m i t + T i, On the raw scale this amounts to scaling each mRNA by its median PTR ratio and represents and estimate of the mean protein level ., The residual difference between the log mean protein level and the measured log protein level , which we call the log rPTR ratio, r i t = p i t - p ¯ i t, consists of both tissue-specific post-transcriptional regulation and measurement noise ., For each gene , i , we compute the correlation between mRNA and protein across tissues ., Unlike the between gene correlations which are consistently large after scaling for each tissue ( Fig 1a ) , across-tissues correlations are highly variable between genes ., Although this could be in part because true mRNA/protein correlations vary significantly between genes , a huge amount of the heterogeneity can be explained by sampling variability ., There are only 10 and 12 tissues in common across datasets ( depending on which datasets are used ) and for many genes the abundances are missing , which means that the empirical estimates of across tissue correlation for each gene are very noisy ., To find a representative estimate of the across-tissues correlation we can take the median over all genes ., As an alternative , if the correlation was roughly constant between genes , we can pool information to yield a representative estimate of this across-tissues correlation ., For a gene i , we compute the Fisher transformation of the within-gene correlation ., This Fisher transformation , z i = arctanh ( r ^ i ) is approximately normally distributed:, z i ∼ N 1 2 l o g ( 1 + ρ 1 - ρ ) , 1 N i - 3, where Ni are the number of observed mRNA-protein pairs for gene i ( at most 12 ) and ρ corresponds to the population correlation ., We estimate the maximum likelihood estimate of the Fisher transformed population correlation by weighting each observation by its variance:, ω i = 1 n i - 3 W i = ω i ∑ j ω j z ^ p o p = ∑ W i z i, We then transform this estimate back to the correlation scale, ρ ^ = e 2 z ^ p o p - 1 e 2 z ^ p o p + 1, Depending on the data sets used , with this method we estimate the population across-tissues mRNA/protein correlation to be between 0 . 21 ( 20 ) and 0 . 29 ( 21 ) ., This correlation cannot be used as direct evidence for the relationship between mRNA and protein levels since both mRNA and protein datasets are unreliable due to measurement noise ., This measurement noise attenuates the true correlation ., Below we address this by directly estimating data reliability and correcting for noise ., Measurement noise attenuates estimates of correlations between mRNA and protein level 45 ., A simple way to quantify this attenuation of correlation due to measurement error is via Spearman’s correction ., Spearman’s correction is based on the fact that the variance of the measured data can be decomposed into the sum of variance of the noise and the signal ., If the noise and the signal are independent , this decomposition and the Spearman’s correction are exact 17 ., Note that it is simple to show that the empirical variance is the sum of the variance of the signal and the variance of the noise:, σ x 2 = 1 n ∑ i x ˜ i 2 = 1 n ∑ ( e ˜ i + ζ i ) 2 = = 1 n ∑ i e ˜ i 2 ︸ σ e 2 + 1 n ∑ i ζ i 2 ︸ σ ζ 2 + 2 n ∑ i e ˜ i ζ i ︸ ≈ 0, Spearman’s correction is based on estimates of the “reliability” of the measurements , which is defined as the fraction of total measured variance due to signal rather than to noise:, Reliability = σ s i g n a l 2 σ t o t a l 2 ( 1 ), = 1 − σ e r r 2 σ e r r 2 + σ s i g n a l 2 ( 2 ), If X and Y are noisy measurements of two quantities , we can compute the noise corrected correlation between them as, C o r ( X , Y ) R e l ( X ) R e l ( Y ) ( 3 ), In practice , reliabilities are not known but we can often estimate them ., In this application , for both mRNA and protein we need measurements in which all steps , from sample collection to level estimation , are repeated independently ., In order to estimate the mRNA reliabilities we use independent measurements from 30 and 29 ., For estimating protein reliabilities we use measurements from 20 and 21 ., across-tissues reliabilities are computed per gene whereas within-tissue reliabilities are computed per tissue across genes ., If two independent measurements have the same reliability , it can be estimated by computing the correlation between the two measurements 17 , 46 , 47 ., We estimated the approximate across-tissues protein reliability to be 0 . 21 and the across-tissues mRNA reliability to be 0 . 77 ., Given the estimated across-tissues mRNA/protein correlation of 0 . 29 ( calculated using data from 21 and 30 ) we estimated the noise-corrected fraction of across-tissues protein variance explained by mRNA to be approximately 50% , Fig 2 ., Note that if both mRNA or both protein datasets share biases , then the estimated reliabilities will be too small , thus deflating the inferred fraction of protein variance explained by mRNA ., Moreover , because the reliabilities are low , sampling variability is large , missing data is prevalent , and mRNA/protein correlation likely vary by gene , there is uncertainty about this estimate ., We use the two independent protein datasets to create a single consensus data set which is of arguably higher reliability than either dataset individually ., To create this dataset , we take a weighted average of the two protein abundance datasets , by tissue ., We compute the weights based on measurement reliabilities for each tissue in each of the two datasets ., Assume we have two random variables , X ∼ 1 and X ∼ 2 , corresponding to measurements on the same quantity ( e . g . two independent protein measurements ) with X ∼ i = X + ϵ i where X ∼ N ( 0 , σ X 2 ) is the signal which is independent of ϵ i ∼ N ( 0 , σ ϵ i 2 ) , the measurement error for sample i ., We have a third random variable corresponding to a different quantity ( e . g . an mRNA measurement ) , Y ∼ that is typically positively correlated with X ∼ 1 and X ∼ 2 with the same covariance σ X Y 2 ., To

Introduction, Results, Discussion, Methods

Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes , but their relative contributions remain contested ., Estimates of the factors determining protein levels in human tissues do not distinguish between, ( i ) the factors determining the variability between the abundances of different proteins , i . e . , mean-level-variability and ,, ( ii ) the factors determining the physiological variability of the same protein across different tissue types , i . e . , across-tissues variability ., We sought to estimate the contribution of transcript levels to these two orthogonal sources of variability , and found that scaled mRNA levels can account for most of the mean-level-variability but not necessarily for across-tissues variability ., The reliable quantification of the latter estimate is limited by substantial measurement noise ., However , protein-to-mRNA ratios exhibit substantial across-tissues variability that is functionally concerted and reproducible across different datasets , suggesting extensive post-transcriptional regulation ., These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types , and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes .

The identity of human tissues depends on their protein levels ., Are tissue protein levels set largely by corresponding mRNA levels or by other ( post-transcriptional ) regulatory mechanisms ?, We revisit this question based on statistical analysis of mRNA and protein levels measured across human tissues ., We find that for any one gene , its protein levels across tissues are poorly predicted by its mRNA levels , suggesting tissue-specific post-transcriptional regulation ., In contrast , the overall protein levels are well predicted by scaled mRNA levels ., We show how these speciously contradictory findings are consistent with each other and represent the two sides of Simpson’s paradox .

medicine and health sciences, tissue proteins, gene regulation, messenger rna, post-transcriptional gene regulation, genome analysis, kidneys, research and analysis methods, genomics, proteins, gene expression, gene ontologies, research assessment, biochemistry, rna, research validity, nucleic acids, anatomy, proteomes, genetics, biology and life sciences, renal system, computational biology

journal.pcbi.1001093

Natural Selection on Functional Modules, a Genome-Wide Analysis

Adaptation analysis at a large or genome scale relies on methods and concepts originally conceived for the study of single genes ( i . e . : positively selected genes , PSGs ) ., The current paradigm for large scale analysis of adaptation typically involves conducting a given test individually for all of the genes of a genome in order to find those with statistically significant deviations from neutrality ( that is , a significant increase above a threshold value of the nonsynonymous to synonymous rate ratio ω = dN/dS = 1 ) 1 ., Nominal p-values obtained in this way require the adjustment for multiple testing to derive the definitive list of PSGs ., In a second step , a conventional functional enrichment test 2 , 3 is applied to detect if functional modules are significantly enriched by PSGs ., The test ascertains the overabundance of modules of functionally related genes ( e . g . GO: gene ontology , KEGG: the Kyoto Encyclopedia of Genes and Genomes pathways , etc . ) in the resulting list of PSGs ., With variations in the methods chosen to test for positive selection and/or to search for functional enrichment , this threshold-based approach has been applied in different comparative genomic studies 4 , 5 , 6 , 7 with results falling below the initial expectation ., In fact , the few functional modules apparently under selection hardly ever reached statistical significance in single species after correcting for multiple testing ., To circumvent this statistical problem recent works have drawn their conclusions by looking for signatures of selection in related groups of species 8 , 9 , 10 ., Specifically , by modeling heterogeneous rates across sites , functional modules with significantly elevated ω values ( not necessarily containing PSGs ) were described in 12 Drosophila genomes 8 ., Categories showing significant deviations included defense response , proteolysis , DNA metabolic process , and odorant binding , among others ., In the analysis of 6 mammalian genomes 9 , chemosensory perception and defense/immunity related processes were functionally enriched after pooling together all PSGs ( 400 genes ) in primates and rodents respectively ., Finally , using the deviations from the expected branch length on gene trees , similar patterns of selection across genomes were found for a group of gamma proteobacteria 10 ., Although the strategy of pooling signatures across species has shown sufficient statistical power to describe adaptive functional differences , it fails to offer a solution for testing adaptive functional events occurring in independent lineages after speciation 8 , 9 , 10 ., The limitations of methods based on a prior threshold application have already been noticed in other omics fields such as transcriptomics 11 , and have successfully been overcome by gene-set based methods 2 , 12 ., These kinds of methods , regularly applied in the field of functional genomics 2 , 12 can be used to search for quantitative differences in evolutionary rates among functional modules of individual genomes ., The hypothesis we aim to test here is not about individual genes , but about functional modules ., Mutations occur at DNA level but selection acts on phenotypes modifying gene frequencies that finally accounts for functional properties of cells 13 ., Most mutations in genes either remain finally fixed or disappear because of their beneficial or disadvantageous effect , respectively ., This effect on the function of individual proteins can only be understood in the context of the system in which proteins are involved ( e . g . a pathway , GO functional roles , etc . ) ., If a list of genes arranged by some parameter that accounts for their evolutionary rates is examined , it is expected that genes belonging to pathways or functional classes favored or disfavored by selection will tend to appear towards the extremes ., Here we set forth the Gene-Set Selection Analysis ( GSSA ) , a gene-set based test that searches for significant evidences of the action of natural selection modeling the evolutionary rates of groups of genes in genomes ., Two different and widely accepted definitions of functional modules: GO 14 terms and KEGG 15 pathways have been used on the genomic coding sequences of five mammals and six Drosophila species ., By using this gene-set strategy we found a large number of functional modules that have significantly increased or decreased their rates of evolution with respect to the ancestral state ., We will show evidences of selection working in groups of functionally related genes , suggesting that they share a common pattern of evolution imprinted by natural selection ., In addition , all biological GO processes previously found as significantly enriched by PSGs were distinguished within the set of functions evolving at higher rates than the expected in genomes ., Finally , the relationship between GSSA results and the relative influence of PSGs during adaptive evolution is discussed ., Mammals , represented by human , chimpanzee , rat and mouse , and five Drosophila genomes were studied ., For each species , genes were ranked into four lists according to the estimation of i- synonymous ( dS ) , ii- nonsynonymous ( dN ) rates of substitution , iii- selective pressures ( ω\u200a=\u200adN/dS ) , and iv- the change of selective pressures between ( A ) ancestor and ( D ) descendant species ( ΔωD\u200a=\u200aωD−ωA ) along the phylogeny ( Figure 1 ) ., Maximum likelihood ( ML ) estimates of evolutionary variables were performed using a free-ratio branch model 16 ., As such , four lists containing 12 , 543 and 9 , 240 orthologous genes in mammals in Drosophila species were obtained for the analyses , respectively ., GSSA was conducted using a total of 1 , 394/199 and 1 , 331/116 GO/KEGG terms in mammals and Drosophila species respectively ., GSSA is performed in five different steps ( S1 to S5 in Figure 2 ) ., First , the method ranks all genes within a genome ( G ) according to one of the alternative evolutionary variables ( dS , dN , ω and Δω ) ., Second , genes are associated ( dark dots ) to different functional categories ( GO or any other functional term ) ., Note that a single gene can be associated with multiple functions ( yellow bar in Figure 2 ) ., Third , for each functional category a total of 30 partitions are established along the list of ranked values 17 , 18 ., Fourth , for each partition GSSA computes a two-tailed Fishers exact test and reports significant over or under represented functional classes comparing the upper side ( A ) and the lower side ( B ) of the list ., Finally , p-values are corrected for multiple testing ( FDR ) ., Throughout the manuscript only p-values for partitions with the highest confidence were reported after FDR ., The application of GSSA to lists of genes ranked by dS , dN , ω and the Δω values yielded a large number of functional modules ( defined by GO and KEGG annotations ) with rates that were significantly skewed toward the extremes of the lists ( Table, 1 ) in mammal and Drosophila species ., For instance , 11% of GO terms , and 15% of KEGG pathways contain genes with biased distribution of rates towards the top of the ranked list , and found statistically significant at high ω ratio ( SHω , 5% false-discovery rate , FDR ) in mammals ., Alternatively , 4 . 1% and 2 . 6% of GO terms and KEGG pathways were found with significantly high values of ω ( SHω ) in Drosophila , respectively ., Table 1 also reveals that functional modules with genes changing at significantly low ω ratios ( SLω ) , and therefore showing a distribution shifted towards the bottom of the ranked list ( see Figure 2 ) , were more frequent than modules under the significantly high ω ( SHω ) ., This observation is in agreement with the fact that purifying selection is the predominant form of selection in biological systems ., Moreover , in support of the slightly neutral character of synonymous mutations , and the effects of population size in the final outcome of selection 19 GSSA results show a higher number of significant deviations of dS in Drosophila rather than in mammals ., Only a minor proportion of functional terms changed significantly at higher or lower rates relative to estimates of the corresponding ancestral lineages ., Specifically , increased or decreased ω values on the external branches ( recorded by positive and negative values of Δω ) were observed for only half of the cases where a significant increase or decrease of ω was identified in mammals and Drosophilas ., This observation points out the conservative character of the selective constraints in functional related groups of genes during evolution ., A summary of the results of the GSSA for mammals and Drosophilas is shown in Figure 3 ( see Figures S1 to S4 for a complete description of results after GSSA in mammals and Drosophila species ) ., The figure shows that GSSA has the power to detect many functional changes in evolutionary rates within a substantial number of functional categories ., Although the rough pattern shows similar evolutionary constraints in groups of genes between the two main clusters of species , important differences were also detected within them ., For instance , functional terms associated to neurological process and sensory perception clearly contrasted between primates and rodents ( Figure 3A ) ., While most of these terms are associated to a significant relative increase in rates from the common ancestor of primates ( +Δω ) , all the changes observed in rodents were due to the relative increase of the selective constraints ( -Δω ) probably due to the effects of purifying selection from the common ancestor ., Alternatively , functional modules associated to Immunity and Defense response evolved at significantly higher rates than expected in rodents , but decreased significantly in relation to the ancestral rates in primates ., Such functional differences between primates and rodents were previously observed when pooling groups of species 9 ., Other functional modules such as Development , and Transcription/Transduction comparatively evolved at very low dN and ω ratio but experienced a higher relaxation of the ancestral constraints ( +Δω ) in primates than in rodents ., Moreover , significant differences in rates can be detected between human and chimpanzee ( Ha04360: Axon guidance , Ha04610: Antigen processes and presentation , GO0007268: synaptic transmission , among others ) , and between mouse and rat ( GO0007186: G-protein coupled receptor protein signaling pathway , and Ha04310: Wnt signaling pathway , among others ) ., In addition , most of the GO terms significantly associated to high dN and ω in Drosophilas were unevenly distributed within the two clusters of the phylogeny ( Figure 3B ) ., GO terms such as sensory perception , defense response , immune response and metabolic process , among others , presented a remarkable divergence in the monophyletic groups of D . erecta and D . yakuba but they were not observed in D . sechellia , D . melanogaster and D . simulans ., Most of GO terms from Development , Transcription and Translation ( Figure 3A and 3B ) were significantly accumulated towards the extremes of the lists corresponding to the lowest rates of substitutions , suggesting they are significantly constrained by strong purifying selection ( 5% FDR ) in both taxa ., The fact that most of the functional modules under selection ( SHω and SLω ) correlate with changes in dN , suggests that selective pressures are mainly driven by nonsynonymous rather than by synonymous substitutions during evolution ., Moreover , according to the expectation of the nearly neutral theory , a low but still considerable number of significant associations of functional modules to dS were found in Drosophila ( 19 . 5% ) and rodents ( 11 . 3% ) , while in primates ( 6 . 4% ) , where population sizes are known to be smaller , the number of significant modules was smaller 20 ., The strategy presented here lead to detect significant patterns of increments and decrements modeled by natural selection in evolutionary rates of functional groups of genes ., This pattern is consistent with the hypothesis that natural selection acts on phenotypes by the combined action of many functional related genes ., Moreover , this functionally based approach identified with statistical significance , and on individual species , all the functional modules previously found significantly enriched by positively selected genes and therefore the main targets of adaptive biological functions in species ( Table, 2 ) ( see Supplementary Table S3 for a complete list of terms ) ., Although GSSA is not a test for positive selection , it is evident that functional modules containing PSGs can be significantly detected by this method on individual species ., In the next section we will analyze the relative contribution of PSGs to the statistical differentiation of functional modules in genomes ., GSSA tests for differences in rates over functional related groups of genes ., To what extent genes under positive selection contribute to the significance of functional modules in mammals and Drosophila species after GSSA ?, To answer this question , branch-site ( the most sensitive ) test of positive selection was conducted on terminal branches of phylogenies ( Figure 1 ) ., We found 715 PSGs in mammals and 626 in Drosophila ., Figure 4A shows the distribution of the mean evolutionary rates ( dN and dS ) of functional modules providing significant and not significant results after GSSA of the w ratio ., When considering the total number of the functional modules with PSGs , 55% , 53% , and 42% of these original functional categories observed with SH , SL and NS results after GSSA ( ω values ) still remained ( Figure 4B ) ., This suggests that: 1- evolution of many of the functional modules changing at SHω ratios in the genome is not driven by a considerable accumulation of PSGs ., Functional modules such as complement and coagulation cascades in human , gonad development in chimpanzee , regulation of innate immune response in mouse , primary immunodeficiency in rat , and spermatid differentiation in D . melanogaster are examples of functional modules evolving at significantly elevated ω ratio without any PSGs; 2- molecular adaptation takes place in functional modules under strong selective constraints ( see last part of Table 2 ) ., For instance , apoptosis in human , generation of neurons in chimpanzee , tissue development in mouse , Wnt signaling pathway in rat , eye development in D . melanogaster , wing disc development in D . yakuba , and generation of neurons in D . erecta are some of the functional modules evolving at SLω ratios in the corresponding genomes that contain PSGs; and finally , 3- an important number of functional modules without significant differences in ω ratios ( grey dots in Figure 4 ) still contain genes under positive selection ., For instance , homologous recombination in humans , brain development in chimpanzee , female or male sex differentiation in mouse , regulation of mitotic cell cycle in rat , chromatin modification in D . sechellia , and oogenesis in D . melanogaster ., These results are in agreement with previous observations in Drosophila were it was emphasized that not every mutation under positive selection responds to a change in selection 21 ., Beneficial changes could occur at evolutionary equilibrium , repairing previous deleterious changes and restoring existing functions 21 ., Finally , we ask if PSGs preferentially concentrate in functional modules evolving at faster rates in different genomes ., For doing that we computed the mean number of PSGs in functional modules with SHω and SLω results ( red and blue dots in Figure 4B ) ., As expected , functional modules evolving at high ω ratio contain higher numbers of PSGs in rodents ( p≪0 . 01 ) , mammals ( p≪0 . 01 ) , and Drosophila ( p≪0 . 01 ) species ., For primates however , it was not significant ( p\u200a=\u200a0 . 47 ) , indicating that PSGs are distributed almost evenly in functional modules evolving at significantly high and low values of ω in human and chimpanzee ., To contrast these results , PSGs from previous works in mammal and Drosophila species were collected 8 , 9 ., The pattern of distribution of PSGs in functional modules was in agreement with the mentioned results: significantly skewed ( p≪0 . 01 ) towards higher numbers of PSGs in mammals , rodents , and Drosophila species , but showing no differences in primates ( p\u200a=\u200a0 . 73 ) ., In summary , PSGs are frequently observed in functional modules evolving under a wide range of evolutionary scenarios; however , they concentrate more frequently in functional groups of genes changing at elevated rates in rodents and Drosophila species ., Alternatively , PSGs were evenly distributed in functional modules changing at the extreme rates of evolution in primates ., This observation suggests that a more complex scheme than the cumulative differences of PSGs must rely on the observed adaptive differences in human and chimpanzee genomes ., The search for integrative factors taking into account the action of multiple genes other than only those which have been targeted by positive selection 22 , could provide a more accurate view for the analysis of the integrated framework underlying adaptation in complete genomes ., Evolutionary biologists recognize that natural selection works on phenotypes indirectly by changing the frequency of genes in populations 23 ., Since the revolution of molecular techniques and its use in evolutionary genetics , the statistical search for adaptation at a gene level has superseded the complexity of measuring fitness in nature 24 ., Nowadays , we look for adaptive evidences on genes and afterwards we search for over-represented functional modules among the list of PSGs found in the genomes ., Given that tests which are generally employed assume independence in both steps , the cooperative action of the network of genes underlying phenotypes 22 is systematically disregarded 25 ., The aim of the GSSA is not to test for evolutionary constraints on individual genes as has been addressed in several previous studies ., GSSA tests for significant differences in rates over functionally related groups of genes and therefore , the relative contribution of a gene is weighed among all genes of the same functional module and their values compared with the general constraints observed in a genome ., Many functional modules changing at elevated ω ratios will correspond to those previously described as functions significantly enriched by PSGs 6 , 9 simply because many of the genes within that functional module were among those contributing towards statistical significance ., In correspondence with the hypothesis that phenotypes change during evolution by the coordinated action of genes we provided evidences that natural selection changes evolutionary rates of many functional related genes in genomes ., By using this strategy we increase the statistical power to search for biological functions that significantly change in rates during evolution ., The existence of many PSGs in functional modules evolving at significant low ( or no-significant ) ω ratios does not represent false positive results in the analysis of molecular adaptation ., This observation , registered in our data and detected in previous publications , suggests that PSGs are frequently recruited in the genomes for other purposes than the classical increase of rates of functional set of genes compromised in adaptive processes such as evolutionary arm-races ., A possible explanation is that many of the PSGs in the genomes are changing in association with the constraints imposed by the architecture of the network 26 , or adjusting deleterious mutations of other genes of the network , just for the maintenance of its phenotypic function ., In this sense , adaptation will requires positive selection , but not every mutation under positive selection contributes to the adaptive dynamical process of evolution of species 21 ., Currently , with the possibility of conducting analysis at the genome level , evolutionary biology cannot disregard major aspects of systems biology approaches that consider the modular organization of genomes ., With the testing strategy used here , we increased the statistical power for the evolutionary analysis on individual genomes and suggest that PSGs could have additional roles in the genome than the adaptive evolutionary change of phenotypes ., The subset of 23 , 438 known Ensembl human protein-coding genes of the Ensembl vs56 . 37a H . sapiens was retrieved from the Ensembl-Compara database vs56 27 ., All the human ortholog transcripts were retrieved for chimpanzee vs56 . 21l , mouse vs56 . 37i , rat vs56 . 34x , and dog vs56 . 2m ., The subset of 14 , 076 known Ensembl D . melanogaster protein-coding genes of D . melanogaster was retrieved from the Ensembl Metazoa-Compara database vs4 27 ., Orthologs transcripts were retrieved from versions 56 . 13a of D . simulans , D . sechellia , D . yakuba , D . erecta , and D . ananassae ., DNA coding sequences ( CDS ) were aligned using the Muscle vs3 . 7 28 ., In mammals , the upper limit for dN and dS considered was those of the human interferon γ ( dN\u200a=\u200a3 . 06 ) and the relaxin protein 29 ( dS\u200a=\u200a6 . 39 substitutions per site per 1e9 years ) ., Assuming the human–mouse , mouse-rat and human–chimp differentiation times to be about 80 , 70 and 5 million years 30 , respectively , ortholog comparisons between primates and rodents with dS≥1 and dN≥0 . 5 , rodents with dS≥0 . 256 , dN≥0 . 122 , and primates with dS≥0 . 064 and dN≥0 . 030 substitutions/site were excluded ., To improve alignments we run TrimAl 31 with heuristic method ( -automated1 ) in Drosophila ., Alignments smaller than 100 bp were excluded ., The total number of alignments analyzed was of 12 , 453 and 9 , 240 in mammals and Drosophila respectively ., Maximum likelihood estimation of dN , dS , and ω was computed using CodeML program from PAML16 ., Evolutionary rates were computed in orthologous sequences according to the free-ratio branch model assuming independent ω ratio for each branch of the tree of mammals and Drosophila species ( see raw values of rates in Table S1 and S2 ) ., Evolutionary rates ( dN , dS ) , its ratio ( ω ) , and its difference between ancestral and descendant species ( Δω ) were ranked along all genes of genomes and further analyzed by GSSA ., External branches of Figure 1 were labeled as foreground to test for positive selection using branch-site models in Test I and Test II 32 ., Positive results of relaxation of selective constraints ( or weak signals of positive selection ) were discarded 4 ., To quantify the relative contribution of PSGs in functional modules showing SHω and SLω results in GSSA , a t-test ( from R package 33 ) with the mean number of PSGs per functional modules was computed in primates , rodents , mammals and Drosophila species ., An independent set of PSGs was collected to test the robustness of our results in mammals 9 , and Drosophila species 8 ., Gene-set selection analysis across lists of genes ranked by different evolutionary rate parameters ( dS , dN , ω and Δω ) was computed using the program Babelomics 34 ., This program implements a version of GSA 17 which can be applied to any list of ranked genes regardless of the initial experimental design 2 , 12 ., The aim of the test is to find functional classes , namely blocks of genes that share some functional property , showing a significant asymmetric distribution towards the extremes of a list of ranked genes ., This is achieved by means of a segmentation test , which consists on the sequential application of a Fishers exact test over the contingency tables formed with the two sides of different partitions ( A and B in Figure, 2 ) made on an ordered list of genes ., The two-tailed Fishers exact test finds significantly over or under represented functional classes when comparing the upper side to the lower side of the list , as defined by any partition ( in Figure 2 , four of the five partitions show significant differences ) ., Similarly to other equivalent gene-set analyses , the outcomes are those modules ( GO and KEGG ) significantly associated to high or low values of the evolutionary parameter used to rank the genes ., Previous results showed that a number between 20 and 50 partitions often gives optimal results in terms of sensitivity and results recovered 18 ., Here we applied 30 partitions along all the GSSA performed ., Given that multiple functional classes ( C ) are tested in multiple partitions ( P ) , the unadjusted p-values for a total of C×P tests were corrected by the widely accepted FDR method 35 ., Originally , 1 , 394/1 , 331 GO terms , and 199/116 KEGG pathways were analyzed in mammals and Drosophila species respectively ., The global GO directed acyclic graph was processed with Blast2GO 36 to extend the annotation at missing parental nodes , discarding GO levels out of 2 to 8 for mammals , and 2 to 12 for Drosophilas ., The final set of GO and KEGG terms used in the GSSA corresponds to those containing a minimum number of 15 genes ., To test possible biases attributed to the size of the functional category , the magnitude of change in evolutionary rate or the proportion of genes experiencing a rate change we randomized the original assignation of ENSGs to the list of ranked values and functional annotation ( see Figure S5A ) ., For each evolutionary variable and species 10 . 000 randomizations and the corresponding GSSA were performed ., The proportion of false positives ( significant results after GSSA ) was computed for each evolutionary variable and plotted along the size of functional categories ( from 20 to 1 , 400 with intervals of 20 ) ., Because this proportion never reached values higher than 0 . 5% ( FDR ) we rejected the possibility that either group size or rate distribution biased GSSA results in our data set ( see Figure S5A and S5B-C ) ., Finally , in order to validate the independence of the GSSA from the effects of alternative evolutionary constraints we simulated selective regimes ( purifying selection , positive selection and relaxation of selective constraints ) using branch-site models ., Here we addressed the possibility of a variation in the representation of significant results after GSSA ( see Supplementary Figure S6 ) ., We found that when a massive enrichment of genes under each of the evolutionary scenarios described take place in the genome , none of them bias the results of GSSA ( see Text S1 ) .

Introduction, Results, Discussion, Materials and Methods

Classically , the functional consequences of natural selection over genomes have been analyzed as the compound effects of individual genes ., The current paradigm for large-scale analysis of adaptation is based on the observed significant deviations of rates of individual genes from neutral evolutionary expectation ., This approach , which assumed independence among genes , has not been able to identify biological functions significantly enriched in positively selected genes in individual species ., Alternatively , pooling related species has enhanced the search for signatures of selection ., However , grouping signatures does not allow testing for adaptive differences between species ., Here we introduce the Gene-Set Selection Analysis ( GSSA ) , a new genome-wide approach to test for evidences of natural selection on functional modules ., GSSA is able to detect lineage specific evolutionary rate changes in a notable number of functional modules ., For example , in nine mammal and Drosophilae genomes GSSA identifies hundreds of functional modules with significant associations to high and low rates of evolution ., Many of the detected functional modules with high evolutionary rates have been previously identified as biological functions under positive selection ., Notably , GSSA identifies conserved functional modules with many positively selected genes , which questions whether they are exclusively selected for fitting genomes to environmental changes ., Our results agree with previous studies suggesting that adaptation requires positive selection , but not every mutation under positive selection contributes to the adaptive dynamical process of the evolution of species .

Characterizing genome adaptation is paramount for understanding evolutionary genomics ., Classically , the search for positively selected genes has been used to identify adaptive differences in morphology , physiology and behavior between species ., However , this approach assumed gene independence and was unable to identify sets of functions significantly enriched by positively selected genes ., To overcome such limitation , we apply an alternative test on the evolutionary rates of genes , called Gene-Set Selection Analysis ( GSSA ) , which is able to detect functional sets of genes evolving at high and low evolutionary rates in genomes ., Our analysis illustrates that by focusing on sets of genes instead of individual loci , we are able to describe a richer relationship between positive selected genes and the adaptive evolution of functions in different genomes ., For example , GSSA identified many positively selected genes within biological functions under strong evidence purifying selection in mammals and Drosophilae; or an almost equal distribution of positively selected genes in functions evolving at significantly high and low rates in primates ., Such findings show the complex correspondence between positive selection and the dynamic process of adaptive evolution in genomes .

evolutionary biology/human evolution, evolutionary biology/genomics, evolutionary biology/bioinformatics, evolutionary biology/evolutionary and comparative genetics

journal.pcbi.1003501

An Integrated Model of Multiple-Condition ChIP-Seq Data Reveals Predeterminants of Cdx2 Binding

Profiling the activity of regulatory proteins in multiple cell types is important for understanding cellular function , as a single regulator can bind to distinct sets of genomic targets depending on the cellular context in which it is expressed ., Characterizing the determinants of such binding specificity is key to understanding how a single regulator can play multiple roles during development and other dynamic cellular processes ., For example , pre-existing genomic context such as chromatin accessibility or the binding of other regulators may determine the binding of some developmental transcription factors ( TFs ) 1–3 , while other ‘pioneer’ TFs may find their binding targets independently of the established chromatin state 4 , 5 ., Here we introduce MultiGPS , an integrated machine learning approach for the analysis of condition-specific binding events from multi-condition ChIP-seq data ., MultiGPS performs binding event analysis across multiple conditions , sharing information across conditions to produce accurate joint binding estimates while simultaneously allowing for condition-specific binding events ., MultiGPS employs a flexible framework for incorporating prior information into binding event discovery , allowing models of joint binding and sequence dependence to be used ., The novel multi-experiment modeling approach of MultiGPS identifies the read enrichment associated with binding events that are bound in specific conditions , enabling principled methods of discovering differential binding 6–9 ., Most current strategies for defining consistent ChIP-seq binding event locations across multiple experiments either analyze each experiment independently or pool reads for analysis ., For example , the ENCODE2 project used standard ChIP-seq event finders on each experiment independently , and then merged event locations across experiments using a fixed-sized window to define event identity 10 , 11 ., Related methods specifically developed for multi-condition ChIP-seq analysis require that binding events be called in each condition individually as a preprocessing step , then apply statistical models to matched regions to detect differential effects 9 , 12 ., Other multi-condition approaches focus on ChIP-seq signals arising from broad regions of enrichment , such as histone modifications ., These methods instead search for larger genomic regions where coverage patterns differ across experiments 8 , 13–15 ., In contrast , MultiGPS uses a joint multi-experiment model that considers the read data from all experiments to produce accurate location estimates of punctate binding events ., Approaches that first identify binding events and then attempt to merge locations across conditions may inappropriately combine distinct binding events that happen to be located within the same window ., In genomic regions with a high density of binding events , the problem of matching sites across conditions is difficult and may lead to erroneous comparisons between binding strengths ., Furthermore , the experiment-by-experiment event calling approach fails to use the full power of the experimental data when a large fraction of binding events are shared across conditions ., An alternative method is to pool ChIP-seq reads from all experiments and then use a single event finding run to yield a consistent set of binding event locations that can be subsequently quantified in each individual experiment ., However , this pooling approach may not discover weak condition-specific binding locations that are swamped by noise from other experiments in the pooled set of reads ., Additionally , applying a single detection threshold in the pooled read set may bias the binding event calls to experiments that had higher sequencing coverage , better antibody batches , or fewer technical sources of error ., Similarly , varying experimental parameters such as the fragmentation distribution could render the pooled read dataset harder to analyze by algorithms that assume a single , consistent set of experimental properties ., MultiGPS combines the theoretical benefits of pooling and separate ChIP-seq experimental analysis by using a Bayesian prior to couple the analysis of independent experiments together ., This multi-experiment model is one aspect of a novel modeling approach that enables external sources of information to be included as priors in binding event identification ( see Methods ) ., In this work , we use the following priors , while recognizing that other directions are also possible: MultiGPS detects binding events independently in each experiment in each step of its iterative optimization , allowing it to model experiment-specific parameters such as the distribution of reads around binding events and the properties of background noise ., The iterative optimization procedure analyzes each experimental condition in turn , using binding event locations from other experiments to form an inter-experiment prior term for a single experiment optimization ., MultiGPS therefore encourages the base locations of binding events to align across experiments when appropriate , and automatically produces coherent sets of binding events that are linked across experiments without any potentially noisy windowed analysis ., To our knowledge , MultiGPS is the first ChIP-seq analysis approach that uses read data from multiple experiments in a joint and fully integrated method for identifying consistent and accurate binding event locations ., As a case study of our frameworks sensitive and accurate multi-condition analysis , we applied MultiGPS to Cdx2 binding data in three developmentally relevant cellular contexts and found that condition-specific Cdx2 binding events are predicted by preexisting chromatin state ., Surprisingly , condition-independent Cdx2 binding events that are bound in multiple contexts do not appear to be predetermined by accessibility or other chromatin signatures , and instead may be predicted on the basis of cognate motif occurrence ., Our results suggest that Cdx2 can act as a pioneer factor at a subset of sites , while also being influenced by preexisting genomic context at other sites ., Therefore , our results have consequences for understanding where TFs will bind when introduced into an established regulatory state during development , or when induced artificially during cellular programming techniques ., We find that MultiGPSs inter-experiment and motif priors encourage binding location consistency on CTCF biological replicate experiments ., The binding events that are called in both CTCF replicates should by definition be located at the same base location ., As we can see in Figure 1a , when MultiGPS is run without either prior , predicted binding events do not typically align to each other or to cognate motif instances ., Each prior alone makes a significant , though incomplete , improvement in binding event accuracy ( Figure 1b–c ) ., The inter-experiment prior is able to significantly improve the distance to the nearest motif when compared to sites identified without any positional priors ( p<5×10−5 , Mann-Whitney U test comparing binned distance to nearest motif match ) ., The motif prior significantly improves the distance to the nearest site in another experiment ( p<1×10−12 , Mann-Whitney U test comparing binned distance to the nearest event in another experiment ) ., In these two comparisons , we used information sources not considered by the prior as validation ( motif distance for the inter-experiment prior and inter-experiment distance for the motif prior ) ., The use of both priors together fully utilizes available sequence and multi-experiment information and allows almost all binding events in this example to be aligned to consistent ( typically motif-associated ) locations ( Figure 1d ) ., These comparisons are not meant as absolute performance assessments for the MultiGPS modeling approach , but instead as relative measurements of the benefit of using additional types of prior information within a single modeling framework ., MultiGPS facilitates the detection of differential binding events by accurately quantifying read count levels associated with each binding event in each analyzed experiment ., Since at present no ChIP-seq datasets exist for which absolute binding levels are known across multiple conditions , we generated simulated ChIP-seq datasets to test the relative performance of MultiGPS in defining differential binding events ., In our simulated data , the distribution of reads at binding events mirrors the properties of real ChIP-seq datasets ( see Methods ) ., A subset of binding events is chosen to be differentially enriched across conditions , and while we chose to set the absolute level of differential enrichment to be constant at all differential events ( 4-fold in Figure 2 , 8-fold in Figure S1 ) , simulated sampling noise leads to a wide array of apparent fold differences ( Figure 2a , blue dots ) ., Using the simulated data , we compared MultiGPS with other approaches for determining differential binding events ., We used MultiGPS ( without the motif prior since no sequence information was used to simulate the data ) , MultiGPS in single-condition mode ( i . e . without using either inter-experiment or motif priors ) , and the single-condition event finders MACS 18 and SISSRs 19 to predict binding events in each simulated condition ., All methods made comparable numbers of binding event predictions in each dataset ( Figure S2 ) ., For the methods other than MultiGPS , differential binding events were defined using:, a ) binding event list comparison , where differential binding events are those that are detected in one condition and no binding event is detected within 200 bp in the other condition;, b ) using the software DBChIP 9; or, c ) by counting reads that occur within the enriched regions and inputting the resulting tables into edgeR 6 ( using the same parameters as used by edgeR within MultiGPS ) ., The results illustrate the problems with defining differentially bound events using binding event list comparison ., Regardless of which event finding method was used to provide input binding events , list comparisons have poor sensitivity when predicting differentially bound events with higher mean read counts ( Figure 2b , dashed lines ) ., Such events are more likely to be detected in both conditions and hence would be treated as non-differential binding events regardless of quantitative differences in ChIP enrichment levels ., Conversely , binding event list comparisons have low specificity when predicting differentially bound events with lower mean read counts ( Figure 2c , dashed lines ) ., Low enrichment binding events may have read counts that are just above a binding event detection threshold in one condition , and just below in another , even if there is no significant quantitative difference in the underlying ChIP enrichment levels ., Such events would appear as false positive differential binding event predictions according to the binding event list comparison approach ., In contrast , approaches that test differential binding using statistical analyses of read count tables have uniformly high specificity across our test datasets ( Figure 2c , solid lines ) ., These methods also have higher sensitivity when predicting differential binding events with higher mean read counts ( Figure 2b , solid lines ) or involving greater absolute differences in binding levels ( Figure S1b , solid lines ) ., EdgeR attains the highest overall sensitivity using the read count tables generated by MultiGPS , thus illustrating the advantages of MultiGPS probabilistic approach to quantifying read enrichment at binding sites across conditions ., MultiGPS models experiment-specific parameters such as the distribution of reads around binding events and the properties of background noise ., To investigate whether these parameters yield improved quantification of binding event ChIP enrichment , we ran the complete MultiGPS model on 14 ChIP-seq experiment sets in which the ENCODE2 project has performed replicated ChIP-seq of a given protein in all three human Tier 1 cell lines ., While no gold standard exists for measuring the accuracy of ChIP-enrichment quantification , we reasoned that accurate quantification estimates should be correlated across biological replicate experiments ., For each of the 14 experiment sets , MultiGPS yields per-replicate estimates of binding enrichment for binding events discovered in any cell line ., We compared these values to those produced by the widely used approaches of counting read occurrences in a window around the binding event locations ( here we use a 400 bp window centered on the MultiGPS-defined binding event locations ) , or by using the peak heights defined by MACS 18 analyses of the same data ., Quantified read counts were compared across biological replicate pairs using Spearmans rank correlation , a nonparametric assessment of statistical dependence that makes no distributional assumptions that could artificially favor one model over another ., Note that MACS does not produce per-replicate read counts or peak heights at each event , and so to compare MultiGPS with MACS we ran MACS on each replicate separately and compared read counts and heights at only those binding events detected in both replicates by MACS and MultiGPS ., Read counts at these reproducibly detected binding events may be more highly correlated than read counts associated with the wider sets of binding events tested in the comparison between MultiGPS estimates and windowed read counts ., As shown in Figure 3 , MultiGPS improves the cross-replicate correlation of binding event quantification estimates in most tested datasets , implying that MultiGPS has reduced the effects of inter-replicate noise in comparison to the window counting approaches ., We expect that reducing the degree of over-dispersion between replicates will yield greater sensitivity in detecting significant differences between conditions ., Indeed , in all 14 tested datasets we find substantially greater numbers of statistically significant differentially enriched binding events between cell lines when we run edgeR 6 on the MultiGPS quantification table as opposed to the table of read counts produced by the window approach ( Table S1 ) ., Therefore , MultiGPS improves the quantification of binding event ChIP-enrichment and the detection of condition-specific binding events ., To demonstrate the ability of MultiGPS to analyze biologically relevant condition-specific binding events , we examined if MultiGPS improves upon the independent analysis of experiments when identifying Cdx2 events in multiple conditions ., Cdx2 is a mammalian caudal-type homeobox protein that plays a key role in regulating the development of diverse tissue types ., For example , Cdx2 is a master regulator of the intestinal lineage when expressed in endoderm 20 , and also plays a key role in defining caudal motor neuron fate when expressed in motor neuron progenitors ( pMNs ) 21 ., In addition , over-expression of Cdx2 in embryonic stem ( ES ) cells forces cells to differentiate into the trophectoderm lineage 22 , 23 ., We thus wanted to elucidate how Cdx2 performs its different regulatory functions in these three developmental contexts ., Does it bind to the same genomic targets in all cell types , or does it bind distinct targets in each context ?, If the latter , how is such specificity achieved ?, To determine the context-dependent binding activity of Cdx2 , we performed ChIP-seq analysis of Cdx2 after it was over-expressed in ES cells , endoderm , and pMNs ., We call these cell types after Doxycycline-dependent Cdx2 induction ES+Dox Cdx2 , endoderm+Dox Cdx2 , and pMN+Dox Cdx2 , respectively ., Since Cdx2 is not natively expressed in any of these three cell types , our experiments provide a useful model of how a transcription factor responds to a new cellular environment ., We found that MultiGPS outperformed an independent binding event analysis ( i . e . using independent runs of MultiGPS without the use of priors ) on the three Cdx2 conditions using a binding event list comparison approach to determine differentially bound sites ., While this is a common approach in the literature , it leads to highly misleading results ., As can be seen in Figure 4 , the binding event list comparison suggests that 95% of pMN+Dox sites are not bound in ES+Dox cells ., However , the apparent degree of differential binding is largely caused by the disparity in the total numbers of binding events predicted in each condition ( 3 , 704 in ES+Dox and 36 , 651 in pMN+Dox ) ., The difference in the total number of events is in turn caused by differences in read coverage between the conditions and the thresholds employed to determine bound events ., In addition , the binding event list comparison approach may miss differences at events when the level of ChIP enrichment varies significantly between conditions ., To perform a more principled analysis of Cdx2 differential binding , we analyzed the ChIP-seq data collection using MultiGPS ( Table 1 ) ., With the coupled MultiGPS method only 24% of all pMN+Dox Cdx2 binding events are significantly differentially enriched in pMN+Dox cells compared with ES+Dox cells ( p<10−3 ) , while 37% of all ES+Dox Cdx2 binding events are significantly differentially enriched in ES+Dox cells compared with pMN+Dox ( p<10−3 ) ., Since MultiGPS identifies a large proportion of condition-specific Cdx2 binding events without finding any evidence for a corresponding change in Cdx2s DNA-binding preference , we asked whether ES cell genomic context could predict the observed condition-specific binding of Cdx2 after induction ., To answer this question , we examined the ES genomic patterns at the locations of Cdx2 sites that are significantly enriched in ES+Dox cells according to MultiGPS ., Interestingly , we found that ES+Dox-specific Cdx2 sites are enriched for ES signatures of chromatin accessibility ( DNaseI hypersensitivity ) , enhancers ( H3K4me1 and H3K27ac ChIP-seq ) , and TF binding ( Oct4 , Sox2 , and Nanog ChIP-seq ) , but not active transcription ( H3K4me3 ChIP-seq ) ( Figure 5 ) ., Conversely , pMN+Dox-specific Cdx2 sites and endoderm+Dox-specific Cdx2 sites show no enrichment for these ES cell chromatin signatures ( Figure 5 & Figure S3 ) ., To more rigorously test the capacity of ES cell genomic context to predict ES+Dox-specific Cdx2 binding events , we trained support vector machines ( SVMs ) to classify Cdx2 binding events vs . unbound Cdx2 motif instances using the read count information from a collection of 55 ES experiments ( 2 DNaseI-seq , 13 histone modification ChIP-seq , 35 TF , co-activator and chromatin modifier ChIP-seq , and 5 Pol2 ChIP-seq experiments ) ., Cross-validation was used to generate disjoint training and test sets ( see Methods ) ., Our SVMs discriminate ES+Dox-specific Cdx2 sites from unbound sites with an area under true-positive vs . false-positive curve ( AUC ) of 0 . 95–0 . 96 , suggesting that the pre-existing genomic context in ES cells is highly predictive of future Cdx2 binding ., Conversely , our SVMs are unable to discriminate pMN+Dox-specific Cdx2 sites from unbound Cdx2 motif instances using ES genomic context ( AUC\u200a=\u200a0 . 63 , Figure 6 ) ., Our results therefore suggest that condition-specific Cdx2 binding events are more likely to be located in genomic regions that already displayed regulatory activity or accessibility before Cdx2 expression was induced ., Since condition-specific Cdx2 binding events appear highly correlated with immediately pre-existing genomic context , we reasoned that the condition-independent Cdx2 sites that are bound in multiple conditions might also display the same associations ., For example , Cdx2 sites that are bound in two conditions may represent locations that happened to have pre-existing regulatory activity or accessibility in both conditions ., Surprisingly , the Cdx2 sites bound in both ES+Dox and pMN+Dox conditions are not enriched for accessibility , enhancer chromatin marks , or TF binding in ES cells ( Figure 5 ) ., Furthermore , SVMs trained as before are unable to discriminate between these shared Cdx2 sites and unbound motif instances using ES genomic context information ( AUC\u200a=\u200a0 . 61 , Figure 6 ) ., These results suggest that the condition-independent Cdx2 sites are not determined by pre-existing genomic context , in contrast with the condition-specific sites ., Given that the condition-independent Cdx2 sites do not seem to have any distinguishing chromatin features before Cdx2 induction , we asked how Cdx2 recognizes these sites regardless of genomic context ., We hypothesized that such sites may have sequence features that enable condition-independent binding ., To test this hypothesis , we trained SVMs to discriminate condition-independent Cdx2 sites from condition-specific Cdx2 sites using only 4-mer word frequencies in 200 bp windows around the sites ., Surprisingly , even these crude sequence features were sufficient to discriminate between the two types of sites ( AUC\u200a=\u200a0 . 89–0 . 92 , Figure 7a ) , suggesting that some sites contain sequence information that enables condition-independent Cdx2 binding ., We next used the discriminative motif finders DEME and DECOD 24 , 25 to determine which sequence motifs discriminate between Cdx2 site types ., Interestingly , both tools returned the primary Cdx2 motif as being the most discriminative , even though most condition-specific and condition-independent sites contain instances of the same primary motif ., This apparent contradiction is resolved by considering features of the motif instances in each set of Cdx2 sites ., SVMs trained with just three simple primary Cdx2 motif-related metrics – the maximum motif score in the 200 bp window around sites , the number of motif instances above a threshold , and a score that integrates motif scores across the entire 200 bp window 26 – were able to discriminate between condition-independent and condition-specific sites with reasonable accuracy ( AUC\u200a=\u200a0 . 81 , Figure 7b ) ., In other words , the strength and multiplicity of motif instances are somewhat predictive of condition-independent Cdx2 binding ., Taken together , our results suggest that sequence information allows Cdx2 to act as a pioneer TF at some sites , overriding the lack of pre-existing accessibility or chromatin markers ., MultiGPS provides a principled platform for the analysis of differential protein-DNA binding across multiple experimental conditions by preferring consistent binding locations across related experiments while also modeling condition-specific experimental parameters ., Rather than treating reads from all experiments as equivalent , MultiGPS models experiment-specific read distributions around binding events ., MultiGPS can thus correctly analyze collections of related ChIP experiments that were performed according to different protocols such as mixtures of related ChIP-seq and ChIP-exo 27 experiments ., As demonstrated above , MultiGPS improves the quantification of ChIP enrichment at binding events in comparison with the typically used window-counting approaches , thus enabling more sensitive analyses of differential binding enrichment between conditions ., Since MultiGPS prefers but does not force binding events to align across experiments , it may also be used to study possible forms of differential binding activity that we did not illustrate ., For instance , it may be of interest to examine locations where the underlying read evidence overrides the MultiGPS inter-experiment prior , resulting in differing reported binding locations across experiments ., Such locations may represent shifts in binding location between conditions , which may be useful for studies of nucleosome positioning or regulators that might bind alternate nearby locations in different conditions ., We demonstrated that MultiGPS can characterize condition-specific binding and then used MultiGPS to characterize the nature of both condition-specific and condition-independent binding of Cdx2 ., Our results suggest that many condition-specific Cdx2 binding events are located in regions that had pre-existing regulatory activity , thus agreeing with hypotheses proposed to explain the observed binding of other developmental TFs 1–3 ., However , Cdx2 also appears to act as a ‘pioneer’ at a subset of sites that are bound condition-independently ., Our analysis suggests that such sites on average contain stronger and more frequent Cdx2 motif instances than condition-specific sites , thus suggesting a possible mechanism by which condition-independent sites can be bound regardless of preexisting genomic context ., These findings also accord with our recent demonstration that TF combinations can override pre-existing cellular state to synergistically bind composite motifs during motor neuron programming 28 , perhaps pointing to a deeper relationship between sequence information and ‘pioneer’ binding activity ., In our previously described GPS 16 and GEM 17 approaches to binding event detection , ChIP sequencing data are modeled as being generated by a mixture of binding events along the genome , and an Expectation Maximization ( EM ) learning scheme is used to probabilistically assign sequencing reads to binding event locations ., The assignment of reads is achieved via an empirically estimated multinomial distribution , Pr ( rn|x ) , which gives the probability of observing read rn from a binding event located at genomic coordinate x ., Conceptually , every base position is treated as a potential binding event , although the use of a sparse prior 29 has the effect of allowing only a small subset of these potential binding events to take responsibility for observed reads and survive the EM training process ., In MultiGPS , we decouple the relationship between a binding events index and its spatial ( genomic ) location ., Specifically , we introduce a vector of component locations μ where μj is the genomic location of event j ., We initialize a large number of potential events , M , such that the events are evenly spaced in 30 bp intervals along the genome ., Note , however , that the use of a sparse prior will again result in only a subset of events remaining active in the model after training ( i . e . components having mixing probability πj>0; see MAP estimation of π below ) ., In the new mixture model , the likelihood of observing the N total ChIP read locations r is given by:where Pr ( rn|μj ) is the distribution over ChIP-seq read positions conditioned on membership in a binding event at location μj ., This distribution is initialized to a strand-specific shape typical of many ChIP-seq datasets ( see Figure S5 ) , and is iteratively re-estimated during EM training using the distribution of reads observed around high-confidence binding site locations ., The above expression calculates the observed data likelihood of a mixture model by taking the product over all reads , where each read averages over each possible binding event that may have caused it ., This extension of the model allows us to apply prior knowledge directly to the positions of the binding events ( μ ) , without affecting the binding event strength estimation or the sparsity-promoting prior , which continues to act on raw expected read counts ., We introduce a Bernoulli prior over each genomic location where each element ki of the parameter k corresponds to the probability that location i is a binding event ( that is , i μ ) ., This prior assumes that there can be only one or zero binding events at a single position and that binding positions are selected independently along the genome according to this weighting ., The prior assigns likelihoods to a set of binding events on a genome of size L as follows: As in the original framework , the latent assignments of reads to binding events are represented by the vector z ., The complete-data log posterior can thus be derived as follows:Here , C is a normalization constant that does not involve any of the terms to be optimized ., It can be seen that the overall binding event sparsity-inducing negative Dirichlet prior α acts only on the mixing probabilities π , which controls the total fraction of reads assigned to each binding event , and the positional prior k acts only on the binding event locations μ ., Therefore , the E-step that calculates the relative responsibility of each binding event in generating each read is unchanged from our original framework , following standard mixture model approaches:Furthermore , the maximum a posteriori probability ( MAP ) estimation of π is also unchanged:where Nj is the effective number of reads assigned to binding event j ., The α parameter can thus be interpreted as the minimum number of ChIP-seq reads required to support a binding event remaining active in the mixture model ., We set the value of α per experiment to be the maximum number of reads that would be expected to occur ( p>10−7 ) in a window equal to the effective range of the binding distribution should the experiments reads be distributed uniformly along the mappable portion of the genome ., We can estimate μ component-wise since it only participates in sums in the log likelihood ., However , no closed form solution exists since the prior k has no parametric form ., We can determine the MAP ( integer ) value of μj by simply enumerating over all possible values of μj ., Specifically , the MAP value of μj is: ., If the maximization step results in two components sharing the same location , they are combined in the next iteration of the algorithm ., One practical use for the positional prior k is to bias the estimated binding locations towards biologically appropriate base positions ., For example , a TFs position weight matrix scores along the genome can be directly encapsulated in k in the above framework ., As described previously for our GEM approach 17 , we can estimate binding motifs from current estimates of binding locations , and reciprocally use those motifs as prior information to re-estimate binding event locations ., Note that motif priors are incorporated quite differently in GEM and MultiGPS ., In practice , MultiGPS uses MEME 30 to discover a set of over-represented motifs in the top 500 most enriched binding events ( 80 bp windows ) , chooses the motif with the highest true-positive vs . false-positive AUC for discriminating bound regions from random sequences ( if any motif AUC≥0 . 7 ) , and incorporates the genomic log-odds scores for that motif in the positional prior ., Unlike our previously described approaches , MultiGPS incorporates an additional mixture component that explicitly models noise ( i . e . reads arising from nonspecific binding and independent of any binding event ) ., Whereas binding component read distributions have approximately finite support ( and therefore only allow binding events to take responsibility for reads in their local vicinity ) , the noise component is defined as having a global distribution ., The form of the noise distribution can be defined as uniform or can be parameterized using the read density observed in a control experiment ., In the latter case , the shape of the noise distribution is defined by smoothing the control experiments read counts using a 50 bp sliding window ( adding fractional pseudocounts to 50 bp windows that contain no control reads ) ., For a more efficient and stable training process , some parameters in MultiGPS are re-estimated only periodically , including the form of the binding event read distribution , the noise component mixing probability ( πM+1 ) , and the binding motif position weight matrix ., We can therefore think of MultiGPS as an instance of a generalized EM algorithm ., Generalized EM algorithms increase the expected log likelihood in each M step without necessarily achieving a maximum in each iteration ( as in the original EM algorithm ) 31 ., Convergence to a local optimum is guaranteed with generalized EM algorithms , as it is with the EM algorithm 31 ., As with GPS and GEM

Introduction, Results, Discussion, Methods

Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions ., To reliably characterize such condition-specific regulatory binding we introduce MultiGPS , an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments ., MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery ., We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes , sequence dependence , and replicate-specific noise sources ., MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event ., MultiGPSs multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions ., We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts ., By accurately characterizing condition-specific Cdx2 binding , MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity ., Specifically , the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context , suggesting that such sites are pre-determined by cell-specific regulatory architecture ., However , MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals , suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment ., A summary of this paper appears in the proceedings of the RECOMB 2014 conference , April 2–5 .

Many proteins that regulate the activity of other genes do so by attaching to the genome at specific binding sites ., The locations that a given regulatory protein will bind , and the strength or frequency of such binding at an individual location , can vary depending on the cell type ., We can profile the locations that a protein binds in a particular cell type using an experimental method called ChIP-seq , followed by computational interpretation of the data ., However , since the experimental data are typically noisy , it is often difficult to compare the computational analyses of ChIP-seq data across multiple experiments in order to understand any differences in binding that may occur in different cell types ., In this paper , we present a new computational method named MultiGPS for simultaneously analyzing multiple related ChIP-seq experiments in an integrated manner ., By analyzing all the data together in an appropriate way , we can gain a more accurate picture of where the profiled protein is binding to the genome , and we can more easily and reliably detect differences in protein binding across cell types ., We demonstrate the MultiGPS software using a new analysis of the regulatory protein Cdx2 in three different developmental cell types .

gene regulation, molecular genetics, biology, computational biology

journal.pntd.0002378

Soil-Transmitted Helminth Infections and Nutritional Status in School-age Children from Rural Communities in Honduras

Honduras is among 30 countries in the Americas that are endemic for soil-transmitted helminth ( STH ) infections , which are caused by four species of intestinal nematodes: the common roundworm , Ascaris lumbricoides; the whipworm , Trichuris trichiura; and the hookworms , Necator americanus and Ancylostoma duodenale 1 ., The health impact of these infections is more dramatic in children , for whom STH show a particular predilection 2 partly due to their differential exposure to contaminated soil ., Health adverse effects such as anemia , growth stunting , protein-calorie malnutrition , fatigue , and poor cognitive development tend to occur and persist in populations affected by STH 3 , and all too often , helminth infections are seen as normal and unavoidable part of life in endemic populations 4 ., According to the World Health Organization ( WHO ) , two thirds of Honduran children aged 1–14 years require preventive chemotherapy ( PC ) for STH 1 ., In fact , the Preventive Chemotherapy and Transmission Control ( PCT ) databank of the WHO estimates that 2 . 6 million Honduran children ( 769 , 405 pre-school and 1 , 832 , 476 school-age children ) are at risk for STH transmission therefore requiring regular administration of anthelminthic drugs 5 ., Organized STH control activities in the country began in 1998 with the establishment of the Healthy Schools Program , a collaborative effort between the ministry of health , the ministry of education and the World Food Program 6 ., By 2001 , Honduras had started subnational control activities 1 , 7 and these soon evolved into a national program guided by the recommendations outlined in the World Health Assembly resolution 54 . 19 6 , 8 ., Although the goal of providing anthelminthic medication in a regular manner to at least 75% of all school-age children at risk has yet to be attained 1 , 5 , 6 , 7 , Honduras efforts of tackling STH transmission are , nevertheless , commendable ., An important complement to these efforts would be to undertake a complete situation analysis at subnational levels; one that would establish baseline data in terms of prevalence as well as transmission risk factors so priority areas can be identified and intervention efforts tailored to specific populations 9 ., Additionally , to effectively monitor the success of PC and other interventions , studies assessing STH-associated morbidity in infected children are needed in Honduras ., Based on scientific evidence linking STH morbidity with worm burden ( i . e . , the number of adult parasites inhabiting the intestine 10 ) , the elimination of moderate and heavy infections is the target of PC programs 1 ., In addition to worm burden , polyparasitism -the concurrent infection with multiple parasite species- has also been associated with childrens malnutrition 11 , 12 ., As well , even when recent data is scarce , some studies have reported that even light infections may impose a threat to childrens health 13 , especially if living in endemic communities with poor nutritional status 10 , 11 , 14 ., Hence , the overlap of poverty , malnutrition and STH endemicity in some populations may obscure the true effect of these helminthiases in childhood health and accordingly , more research is needed to fully appreciate the burden of these infections on people living in these areas 15 ., With this in mind , the aim of this study was to investigate the prevalence and intensity of STH infections in a sample of Honduran school-aged children and examine whether STH are negatively associated with malnutrition and anemia ., The present study was nested within a parent study entitled ‘Gender and parasitic diseases: Integrating gender analysis in epidemiological research on parasitic diseases to optimize the impact of prevention and control measures’ ( principal investigator , T . W . Gyorkos , McGill University , Canada ) and both received ethics clearance from McGill University Health Centre , Montreal , QC ( file number MUHC 10 -175 – PED Nov . 23rd 2010 ) and Brock University , St . Catharines , ON ( file number - BU 10–161 – Sanchez/Gyorkos Jan 13th 2011 ) ., In the absence of an institutional ethics board in the participating academic unit of the Honduran university , the Ethics Officer of the Masters Program in Infectious and Zoonotic Diseases ( MEIZ ) of the School of Microbiology , National Autonomous University of Honduras , reviewed the protocol and provided clearance ( file number OF-MEIZ- 001-2011 ) ., As the study population comprised minors , both parental consent and childrens assent were required prior to enrolment of children ., Parents and guardians of children in grades 3–5 were invited to an information session in which the studys objectives , benefits and risks were fully explained ., Parents and guardians who gave oral consent were presented with an information package containing a detailed lay description of the study , an invitation to participate and a consent form for their signature ., All parents or guardians consenting for their children to participate signed the informed consent form ., Children whose parents consented were invited to participate in the study during sessions held at the schools and those who expressed assent in responding to a questionnaire , providing a stool and blood sample and allowing the collection of anthropometric measurements were then enrolled in the study ., Children assents were obtained verbally and documented through a child assent form ., Also , since the study was undertaken during class time at participating schools , authorizations from schools Principals were sought in advance and only schools with such authorizations were approached for enrolment ., Laboratory reports were issued with accompanying lay interpretations and recommendations ., Also , parents of children with STH infections were offered anti-parasitic treatment for their child ., If agreed , albendazole tablets ( 400 mg ) were administered to the child by the school teacher or parent ., A “deworming tracking card” was issued for each child ., Parents and teachers were encouraged to keep track of the childrens deworming treatment in order to either avoid missing the schools annual or bi-annual treatment or prevent excessive treatments in case deworming was offered by third parties ( e . g . , international or national medical brigades , faith-based missions , etc . ) ., Both the parent study and present study were school-based , cross-sectional studies , designed as explorative and hypothesis generating studies ., For the parent study , power and sample size determination were performed utilizing the PS software ( version 3 . 0 , January 2009 , by William D . DuPont and Walton D . Plummer Jr . ) ., This was based on a two-sided chi-square test to compare STH infection between boys and girls ., Using previous studies in Peru as a reference 16 it was assumed that half of the children in this school-age group will be male and that the prevalence of any STH would be 50% in males ( a conservative estimate ) ., An estimated design effect of 2 . 7 was used with a significance level of 0 . 05 ., A total of 314 participants were therefore needed to detect a minimum risk ratio of 1 . 5 with 80% power ., The present study was bound by this sample size determination ., This study was implemented during February and March 2011 with the collaboration of the National University of Agriculture ( UNA ) located in the city Catacamas , in the municipality of the same name ( 14°51′35 . 46″; N 85°53′58 . 19″W ) in the Department of Olancho , about 210 km north-east of the capital of Honduras , Tegucigalpa ., Geographically , Catacamas is the largest municipality in the country and is nested in a fertile valley at approximately 450 m above sea level ., Catacamas municipality consists of the urban core ( Catacamas proper ) and 14 main villages which in turn are comprised of smaller 339 hamlets ., Catacamas human development index ( HDI ) value for 2009 was 0 . 675 17 , slightly over to that of Honduras ( 0 . 625 for 2011 ) 18 ., However , 60% of Catacamas population resides in rural areas , the majority lacking public services such as electricity , potable water and indoor plumbing ., As means of livelihood inhabitants engage in mixed agricultural farming , rearing animals such as cattle , pigs and poultry and growing crops such as corn , beans , coffee and vegetables ., Others work as traders or labourers while a few work in public or private service 19 ., The following nine surrounding rural communities ( most between 2–3 hour driving distance from the city ) were visited as potential study-sites: Colonia de Poncaya , Las Lomas de Poncaya , Las Parcelas , Corosito de Poncaya , El Cerro del Vigía , El Hormiguero , Santa Clara , Los Lirios and Campamento Viejo ., The combined eligible school population was 445 children ., Schools located in those communities were identified and principals contacted by UNAs personnel to inform them about the study and obtain authorization to approach the school and potentially enrol their pupils ., As well , information was obtained in regards to school enrolment and status of their deworming program , if any ., Schools which had provided deworming treatment within the last three months were not eligible for the study ., The target participants for the study were children in grades 3–5 ( aged 9–11 ) since STH infections , especially A . lumbricoides and T . trichiura tend to peak at this age 1 ., Also , at this age children are old enough to understand survey questions and provide basic information ., Using a pre-tested , 30-minute , face-to-face standardized questionnaire in Spanish , the Gender Study collected demographic and epidemiological data as well as childrens living conditions and knowledge regarding STH infections ., From these data , the present study extracted childrens general demographics ( name , date of birth , age , and sex of the child ) , STH and deworming history , self-reported living conditions ( households type of floor , water access and type and use of sanitary facilities ) , and the possession of major home appliances ., Body weight and height measurements were taken for each child to calculate anthropometric indicators ., Weights were taken using a digital electronic balance to the nearest 0 . 1 kg while children were wearing school uniforms and without shoes ., Height was taken to the nearest 0 . 1 cm using a height pole mounted on the wall ., In order to minimize intra-individual errors , all measurements were taken twice by different researchers and the average value calculated and used thereof ., Age , height and weight were then used to calculate the following indicators:, a ) height-for-age Z-score ( HAZ ) to assess stunting;, b ) weight-for-age Z-score ( WAZ ) to assess underweight; and, c ) body-mass-index-for-age Z-score ( BAZ ) to assess thinness ., Calculations were done with the WHO AnthroPlus software version 1 . 04 ( WHO , Geneva , Switzerland ) using the WHO international reference values ( available at http://www . who . int/growthref/tools/en/ ) ., Because of its inability to differentiate between relative height and body mass , WAZ is not recommended for the assessment of growth beyond childhood ( >10 years of age ) 19 ., Therefore , BAZ was used as a complement to HAZ ., These indicators are recommended by the WHO as they provide an assessment of the childs nutritional status in comparison with a healthy reference population 20 , 21 ., According to the 2007 WHO growth reference for school-aged children and adolescents , stunting , underweight and thinness are defined as <−2 standard deviations ( SD ) HAZ , WAZ and BAZ , respectively 20 ., A single fecal sample was collected from each child and samples were taken to UNAs laboratory for analysis on the same day using the Kato-Katz technique 22 with a template of 41 . 7 mg , as recommended by the WHO 23 ., Kato-Katz templates were obtained from Vestergaard-Frandsen Disease Control Textiles ( Vestergaard Frandsen SA , Aarhus , Denmark ) ., Kato–Katz slides were examined microscopically in a systematic manner within 30–60 min of preparation; helminth eggs counted for each parasite species and the number thus obtained multiplied by a factor of 24 in order to get the number of eggs per gram of feces ( epg ) ., Egg counts were utilized to classify infection intensities into light , moderate , or heavy infections as follows , respectively: for A . lumbricoides , 1–4 , 999 epg , 5 , 000 – 49 , 999 epg and ≥50 , 000 epg; for T . trichiura , 1–999 epg , 1 , 000–9 , 999 epg and ≥10 , 000 epg; and for hookworms , 1–1 , 999 epg , 2 , 000–3 , 999 epg and ≥4 , 000 epg 1 , 24 ., Haematological analyses were done using the BC – 3000Plus AutoHematology Analyzer ( Mindray Medical Instrumentation , USA ) in a private medical laboratory contracted in Catacamas ., Anemia was determined when children aged 6–11 years had hemoglobin ( Hb ) values <11 . 5 g/dL or hematocrit ( Hct ) <34% ., For children aged 12–14 years , these values were Hb <12 g/dL or Hct <36% 25 ., Total serum protein concentrations were measured by the Biuret method and children were considered within the reference values if concentrations were within 6–8 . 5 g/dL 26 ., Data were entered by a researcher into Microsoft office Excel spreadsheet 2007 ( Microsoft ) and verified for accuracy ( compared with data in questionnaires ) by a different researcher ., Data were cleaned by checking for errors and missing values ., Statistical analyses were done using IBM , SPSS Statistics ver . 20 ( IBM . Somers , NY ) ., Descriptive statistics for continuous variables and frequency ( proportions ) for categorical variables were used to describe the characteristics of the study population ., Weight and height measurements were subjected to a reliability test and the inter observer technical error of measurements was assessed using the Mueller and Martorell method 27 ., Differences in proportions for categorical variables ( e . g . , age group , sex of the child , stunting , thinness , underweight and anemia ) were calculated using Chi square test of independence ., Differences in mean values for continuous variables ( e . g . , HAZ , WAZ , BAZ , total proteins , Hb and Hct ) were assessed using the student t-test analysis ., Since STH clinical importance is generally associated with increased worm burden , infections of moderate and heavy intensity were merged into one category “moderate-to-heavy” ., This was also useful for computational reasons since those infections were in minority among studied children ., Also , to assess polyparasitism , a category termed “infection status” was created to denote conditions of non-infected , monoparasitism or polyparasitism ( co-infections with 2 or 3 STH ) ., One-way ANOVA was used to analyze differences in anthropometric mean Z-scores of the study population by infection status and by infection intensity ( negative , light and moderate-to-heavy ) of each parasite species ., A generalized estimating equations ( GEE ) approach was used to construct both multivariable linear and logistic regression models to account for possible within-school data correlation ( clustering at the school level ) ., For these models , intensity of infection was not analyzed by parasite species ., Rather , infection categories “negative-to-light” and “moderate-to-heavy” were created to denote individuals with such infections by any of the three parasites under study ., Linear regression models to test for associations between anthropometric indicators and intensity of infection categories were constructed adjusting for age , sex , socio-economic status ( SES ) and anemia ., Similar models were done to test for association between those indicators and infection status ., Using principal component analysis ( PCA ) , the SES variable was constructed from five factors: type of floor , access to tap water , having a toilet , having a television set and having a fridge ., Separate logistic regression models were constructed to assess associations between stunting and thinness odds and STH intensity of infection and infection status adjusting for age stratum ( 7–10 or >10 years of age ) , sex and SES ., Odds ratios ( OR ) were determined with 95% confidence intervals ( CI\u200a=\u200a95% ) ., Of the nine visited , seven schools were enrolled in the study: Colonia de Poncaya , Las Lomas de Poncaya , Las Parcelas , Corosito de Poncaya , El Cerro del Vigía , El Hormiguero , and Campamento Viejo ., The reasons for not including the two remaining were: recent deworming treatment ( Santa Clara n\u200a=\u200a26 ) and time-constraints to complete questionnaires and measurements ( Los Lirios n\u200a=\u200a19 ) ., ( Los Lirios children , however , were examined for STH and treatment provided if needed ) ., Thus , the number of eligible participants in grades 3 to 5 among participating schools was 400 children ., The parents of 368 ( 92% ) children provided written informed consent for their children to participate and almost all ( 357 , 97% ) children assented to be enrolled ., After enrolment , 37 participants were dropped from the study due to insufficient or no stool sample ( n\u200a=\u200a20 ) , or unreliable Kato-Katz results that could not be repeated ( n\u200a=\u200a17 ) ., Also , five children declined blood collection but they were kept in the study since their remaining data was complete ., The final study sample was 320 children aged 7–14 years ( mean 9 . 76±1 . 4 ) and 154 ( 48% ) were girls ., Demographic , household and nutritional characteristics of the study sample are shown in Table 1 ., Additionally , habitual or occasional open defecation was reported by 15 . 6% and 12 . 8% of the children , respectively ., As for STH history , 58 . 1% of the children reported having expelled ‘worms’ in the past and 85 . 9% recalled having received deworming treatment sometime in the past but not recently ., Five of the seven schools enrolled in the study had ongoing deworming programs , some starting as far back as 2007 ., Frequency of deworming was twice a year for two schools and once a year for three schools ., The last deworming treatment had been within the last 4–6 months for four schools and two years for the remaining one ., There was no statistical difference between overall infection with any STH and schools deworming schedule ( p\u200a=\u200a0 . 767 ) ., Replicate weight and height measurements showed high reliability when tested for the inter-observer technical error of measurements ., The reliability coefficient ( R ) was 0 . 962 for weight and 0 . 973 for height ., Nutritional indicators of the study population are presented in Table 1 ., The nutritional status of most children was within healthy parameters but a few cases of stunting ( n\u200a=\u200a18 , 5 . 6% ) , thinness ( n\u200a=\u200a7 , 2 . 2% ) and underweight ( n =\u200a3 , 1 . 3% ) were observed ., Of the children who were stunted , thin or underweight , girls accounted for 50% , 43% and 67% of the cases , respectively ., No child had a total protein value below the normal range and of 315 children examined , 7 ( 57% girls ) were anemic ., Overall , of 320 children , 33 ( 10 . 3% ) had at least a form of nutritional deficit ., Five of these children ( 15 . 2% ) were negative for any STH , while 28 ( 84 . 8% ) were infected with one or more STH ., Among the latter , 15 children were monoparasitized , while 13 were polyparasitized ., Results of the one-way ANOVA analysis revealed that mean values for WAZ scores were significantly lower in children with moderate-to-heavy infections by either T . trichiura ( p\u200a=\u200a0 . 020 ) or A . lumbricoides ( p\u200a=\u200a0 . 015 ) compared to children with no or light infections ., This was not observed in the case of hookworm infections , likely due to the fact that the vast majority ( 94% ) of such infections were light ., On the other hand , the scores for the other two indicators ( HAZ and BAZ ) did not differ significantly across the various infection intensities of any of the helminth species ., However , as depicted in Figure 1 , a negative trend –although not always significant- between infection intensity and the mean values of all anthropometric indicators was identified ., In other words , the heavier the intensity , the lower the HAZ , BAZ and WAZ mean values ( Figure 1 , plots A , B and C ) ., A similar trend was observed in terms of infection status: as polyparasitism increased , the mean values of all anthropometric indicators decreased ( Figure 1 , plot D ) ., As data in Table 1 show , this trend was significant in terms of WAZ scores ( p\u200a=\u200a0 . 012 ) , marginally significant for HAZ scores ( p\u200a=\u200a0 . 071 ) but not significant for BAZ scores ( p\u200a=\u200a0 . 202 ) ., Estimated coefficients ( β ) from multivariable GEE linear models are shown in Table 3 ., Compared to no or light infections , moderate-to-heavy infections with any STH were significantly correlated with a decrease in WAZ scores ( β\u200a=\u200a−0 . 34 , 95% CI\u200a=\u200a−0 . 62 to −0 . 06 , p\u200a=\u200a0 . 018 ) ., This correlation was only marginally significant for HAZ scores ( β\u200a=\u200a−0 . 20 , 95% CI\u200a=\u200a−0 . 44 to 0 . 04 , p\u200a=\u200a0 . 108 ) but not significant for BAZ ( p\u200a=\u200a0 . 622 ) ., Polyparasitism was found inversely correlated with both WAZ and HAZ scores ., For WAZ , this correlation was significant ( β\u200a=\u200a−0 . 37 , 95% CI\u200a=\u200a−0 . 66 to −0 . 08 , p\u200a=\u200a0 . 012 ) whereas for HAZ , it was only marginally significant ( β\u200a=\u200a−0 . 24 , 95% CI\u200a=\u200a−0 . 50 to 0 . 02 , p\u200a=\u200a0 . 074 ) ., However , no evidence for association between polyparasitism and BAZ scores was found ( p\u200a=\u200a0 . 446 ) ., With respect to age , there was a strong negative correlation between age and HAZ and BAZ scores ( β\u200a=\u200a−0 . 16 , 95% CI\u200a=\u200a−0 . 24 to −0 . 09 , p<0 . 001 and β\u200a=\u200a−0 . 12 , 95% CI\u200a=\u200a−0 . 21 to −0 . 03 , p\u200a=\u200a0 . 008 , respectively ) ., Conversely , WAZ scores were not correlated with age ( p\u200a=\u200a0 . 428 ) ., Multivariable GEE logistic models revealed that age of the studied population was significantly associated with stunting ., Children >10 years old were three times more likely to be stunted ( OR\u200a=\u200a3 . 31; 95% CI\u200a=\u200a1 . 23–8 . 90 , p\u200a=\u200a0 . 018 ) than younger children ., Age , on the other hand , was only marginally significantly associated with thinness ( p<0 . 15 ) ., Neither infection intensity nor infection status ( polyparasitism ) was found associated with stunting or thinness ., Finally , since only three children were underweight ( WAZ <−2SD ) no statistical model was produced for this nutritional indicator ., In a little more than a decade , moderate economic progress alongside dedicated efforts for STH control –mainly through national deworming campaigns- have contributed to the decrease of Honduras national STH prevalence 1 ., Yet , as the data from our study show , some rural communities have persistently high STH transmission and perhaps they face greater struggles in overcoming poverty and inequities 17 ., Indeed , considering that five of the seven participating schools reported some form of mass-deworming during the past year , a prevalence of 72 . 5% for any STH among these children is remarkably high ., According to the work of Hall and colleagues , a prevalence of 70% or greater carries a high probability of disease 12 ., Moreover , 36% of infected children were harbouring moderate-to-heavy infections ., The new vision for a world free of childhood morbidity due to these helminths , according to the WHO , is reducing the prevalence of STH infection of moderate and heavy intensity to ≤1% 1 ., Therefore , these data alone underscore the need for Honduras to continue and sustain its deworming program , and more importantly , to implement and monitor integrated control efforts 28 , 29 ., The predominance of T . trichiura over A . lumbricoides ( 66 . 9% versus 30 . 3% ) may indicate that the single-dose albendazole schedule currently used for deworming has been less effective for reducing trichuriasis as this parasite is less susceptible to this drug 30 , 31 ., Even though it might not be feasible to implement a different PC regimen in Honduras at the moment , it is important to be vigilant of the different patterns of transmission of individual STH species ., At the same time , it would be useful to conduct baseline studies to measure reinfection rates 32 and drug efficacy 33 , as well as to make efforts to detect potential emergence of resistance to benzimidazoles 34 , 35 , 36 ., In terms of prevalence , our study shows that children older than 10 years of age were more likely to be infected with any STH than younger children , underscoring the importance of deworming children throughout their primary school years 1 ., The fact that in our study boys were more likely than girls to harbour hookworm infection warrants further investigation as there might be gender-related factors playing an important role in exposure to hookworms , as previously suggested 37 , 38 ., Along with high prevalence , we also found a high proportion of polyparasitism with almost half of those infected ( 44% ) harbouring 2 or 3 helminths ., This finding is consistent with the epidemiological profile of endemic countries 16 , 39 , 40 , 41 and although already observed in Honduras 6 , 42 , 43 , it has not received sufficient attention in the country ., The impact of infections by multiple parasite species has been subject to some attention in the last decade 44 and studies show that concurrent infections may have additive or synergistic detrimental effects , especially in childhood 11 , 45 ., Given that regular PC interventions will eventually result in reduced infection intensity , light polyparasitic infections will become more relevant 45 ., Therefore , addressing polyparasitism in future WHO recommendations merits consideration ., In terms of nutritional status , the majority of studied children were within the WHO reference values for growth and nutrition ., This is uncommon for a Honduran rural population 46 ., In fact , the proportion of children suffering chronic undernutrition ( as measured by stunting ) identified in this study was 5 . 6% well below current national urban ( 13 . 7% ) or rural ( 32% ) figures ., On the other hand , the proportion of global undernutrition ( as measured by thinness ) was 2 . 2% , below the national urban ( 6 . 2% ) or rural ( 14 . 8% ) averages 47 and very close to the expected value ( in a healthy population , approximately 2 . 1% of individuals will fall either above or below 2SD of the HAZ , BAZ and WAZ reference values ) ., Although assessing food-security was beyond the scope of this study , a possible explanation for this finding is that Catacamas valley and surrounding areas are situated in fertile lands and food insecurity is not as dramatic as in other parts of the country 19 , 48 ., We found that the risk of stunting increased with age , a phenomenon also found in similar studies conducted in Peru in both school-age children 16 and in pre-schoolers 49 , as well as in Malaysia 50 , Colombia 51 and Guatemala 39 ., It appears , therefore , that once stunted , children continue to be so , even when more acute indicators such as WAZ and BAZ fall within healthy parameters ., Longitudinal studies could help elucidate the most favourable moment for children to prevent growth faltering that may lead to stunting ., As mentioned , we aimed to ascertain potential associations of STH infections with the childrens nutritional status ( stunting , underweight and thinness ) but our data did not support such associations ., It is recognized that studying the impact of intestinal helminths on child growth and nutrition in endemic populations is not an easy endeavour as it is difficult to control for other environmental or socio-economic factors or seasonal changes in the food supply 4 , 12 ., It is worth mentioning , however , that SES status ( as we measured it ) was not found associated with either increased odds of stunting or thinness or with a decrease in anthropometric indicators Z-scores ., Notwithstanding this lack of association in our study , when looking at the distribution of the actual Z-scores for these indicators in the multivariable analyses , we found that both moderate-to-heavy infections with any STH and polyparasitism were significantly associated with lower WAZ scores ., Additionally , at the species level , negative trends ( albeit not all significant ) were observed between STH infection and WAZ , BAZ and HAZ mean scores; namely , as intensity of infection or the number of species parasitizing increased , Z-score mean values for the three measured anthropometric indicators decreased ., A similar finding was reported by Ordoñez and Angulo ( 2002 ) in a cross-sectional study in which polyparasitized children had lower HAZ and WAZ scores 51 ., Thus , examining anthropometric Z-scores values might be useful in providing additional insight into the impact of STH on childrens nutritional status as they may reveal subtle changes missed when focusing only on end outcomes ( i . e . , stunting , thinness and underweight ) ., By the same token , Z-score values may be able to pin-point improvements in childrens nutrition after anthelminthic treatment even if significant changes in end outcomes cannot be demonstrated ., Limitations of this study arise from its cross-sectional nature as direct causal relationship between STH and nutritional status cannot be determined ., This is why large-scale prospective studies with rigorous design and the corresponding funding are necessary to investigate neglected tropical diseases including helminthiases ., Another potential limitation stems from the fact that our investigation was part of a parent study with a sample size calculation based on being able to detect an important difference in sex-specific STH prevalences instead of nutritional indicators and this may limit the precision of our results ., We trust that our findings will shed light into the design of future studies in Honduras ., In terms of our parasitological findings , the analysis of a single stool sample may have underestimated STH prevalence in our study but by the high prevalence obtained , this underestimation might be minimal ., Further , recent work suggests that Kato-Katz is reasonably accurate for A . lumbricoides and T . trichiura although less so for hookworms 52 ., Likewise , intensity of infection may have been underestimated although recent publications suggest that Kato-Katz results are fairly reliable for the three STH investigated in the present study 52 , 53 ., Malaria , other intestinal parasites or gastrointestinal infections were not determined and a role for these on childrens nutritional status cannot be ruled out ., Finally , an important limitation in identifying undernutrition factors is that this study did not entail an exhaustive investigation of underlying causes of malnutrition ( e . g . , social determinants , food security , dietary intake and expenditure , etc ) ., Future research should address this gap although cross-sectional studies might not be able to reveal concrete answers , as shown by Gray et al . ( 2006 ) 46 ., Strengths of this study are: including a design effect in the sample size estimation to take into account clustering by school , obtaining a high participation rate , and that our sample is likely representative of the communities school children as in Honduras 95% children attend primary school 47 ., Also , by utilizing laboratory protocols and anthropometric measurements recommended by the WHO , our results permit comparisons with other studies both nationally and internationally ., In conclusion , the prevalence data obtained in this study contribute with accurate and updated information to map out the situation of STH infections in Hondura

Introduction, Methods, Results, Discussion

Soil-transmitted helminth ( STH ) infections are endemic in Honduras and efforts are underway to decrease their transmission ., However , current evidence is lacking in regards to their prevalence , intensity and their impact on childrens health ., To evaluate the prevalence and intensity of STH infections and their association with nutritional status in a sample of Honduran children ., A cross-sectional study was done among school-age children residing in rural communities in Honduras , in 2011 ., Demographic data was obtained , hemoglobin and protein concentrations were determined in blood samples and STH infections investigated in single-stool samples by Kato-Katz ., Anthropometric measurements were taken to calculate height-for-age ( HAZ ) , BMI-for-age ( BAZ ) and weight-for-age ( WAZ ) to determine stunting , thinness and underweight , respectively ., Among 320 children studied ( 48% girls , aged 7–14 years , mean 9 . 76±1 . 4 ) an overall STH prevalence of 72 . 5% was found ., Children >10 years of age were generally more infected than 7–10 year-olds ( p\u200a=\u200a0 . 015 ) ., Prevalence was 30% , 67% and 16% for Ascaris , Trichuris and hookworms , respectively ., Moderate-to-heavy infections as well as polyparasitism were common among the infected children ( 36% and 44% , respectively ) ., Polyparasitism was four times more likely to occur in children attending schools with absent or annual deworming schedules than in pupils attending schools deworming twice a year ( p<0 . 001 ) ., Stunting was observed in 5 . 6% of children and it was associated with increasing age ., Also , 2 . 2% of studied children were thin , 1 . 3% underweight and 2 . 2% had anemia ., Moderate-to-heavy infections and polyparasitism were significantly associated with decreased values in WAZ and marginally associated with decreased values in HAZ ., STH infections remain a public health concern in Honduras and despite current efforts were highly prevalent in the studied community ., The role of multiparasite STH infections in undermining childrens nutritional status warrants more research .

Soil-transmitted helminth ( STH ) infections are endemic in Honduras but their impact on childrens health is not well studied ., With the purpose of determining the prevalence and intensity of STH infections and their association with nutritional status in a sample of Honduran children , a cross-sectional study was undertaken in 2011 ., School-age children were enrolled , and in addition to demographic data , blood and stool samples and anthropometric measurements were obtained to determine nutritional status and STH infection ., The overall STH prevalence among 320 studied children was 72 . 5% and almost half of the infected children harboured multiple parasites ., Polyparasitism was more likely to occur in children attending schools with absent or annual deworming schedules than in pupils attending schools deworming twice a year ., Prevalence by species was 30% , 67% and 16% for Ascaris , Trichuris and hookworms , respectively ., Infections of moderate to heavy intensity as well as multiparasite infections were significant predictors of decreased weight-for-age scores in children ages 7–10 years after controlling for key confounders ., Sustainable efforts to control STH infections in Honduras are required ., Future research providing more insight on the nutritional impact of polyparasitic STH infections in childhood is necessary .

medicine, epidemiology, biology, microbiology, parasitology

journal.pcbi.1001022

The Evolutionary Analysis of Emerging Low Frequency HIV-1 CXCR4 Using Variants through Time—An Ultra-Deep Approach

Sequencing platforms , such as the 454 Life Sciences GS-FLX pyrosequencing system , has greatly parallelized the determination of nucleotide order within genetic material , resulting in the ability to produce extremely large datasets 1 ., The vast numbers of short sequence segments produced ( termed reads ) in conjunction with intrinsic error rates associated with the sequencing platform 2 , 3 pose challenging computational problems 4 , 5 ., Importantly , these data have the potential to provide previously unprecedented insight into the extent of pathogen variation ( diversity ) that exists within a single individual ., This is particularly important in the detection of minority variants , for example , those associated with drug resistance 6–11 ., To date , software has focused on eukaryotic and prokaryotic genome-scale sequencing with its associated megabase reference genomes and vast quantities of read data 5 , 12 ., For such studies traditional fast alignment algorithms 13–15 that employ flexible k-mer matching are not capable of mapping reads to a reference sequence within a reasonable time ., Consequently new software tools have been developed that incorporate faster string matching techniques at the expense of dealing with variation 12 , 16–18 ., For highly variable genomes this limitation will result in data loss as reads with more than the specified numbers of mismatches , in relation to a template sequence , are discarded ., This loss can occur non-randomly with reads representing minority subpopulations being less likely to be mapped to the template ., For example , two distinct phenotypes of HIV-1 exist that are defined by the host coreceptor that is used during cell entry ., The coreceptors involved are chemokine ( C–C motif ) receptor 5 ( CCR5 ) and chemokine ( C-X-C ) receptor 4 ( CXCR4 ) ., The location of the viral genome that determines the phenotype is the third variable ( V3 ) loop , a highly variable region 19 located within HIVs envelope gene , env 20–22 ., The most often used genomic reference sequence for HIV-1 is HXB2 , a CXCR4-using virus ., When mapping V3 data to HXB2 , and limiting the number of mismatches allowed , reads representing CCR5 variants are more likely to be lost during mapping as a result of known amino acid changes associated with that phenotype 23 , 24 ., This may result in a misleading ratio of coreceptor use within an HIV-1 population , which can have consequences for drug treatment decisions 25 ., Thus , for rapidly evolving viruses , such as HIV-1 26–29 , a limitation on the number of mismatches tolerated during read mapping is less than optimal ., Modification of traditional k-mer matching approaches 13 is a more suitable approach and becomes scalable ( due to the smaller genome sizes ) as they allow for increased tolerance when dealing with higher levels of variation in viral 454 data 6 , 11 ., Prior to any evolutionary study reads must be accurately mapped and aligned ., Our software performs these tasks as well as subsequent tropism testing , phylogenetic tree inference and visualization ( Fig . 1 ) ., We demonstrate the softwares underlying framework in order to quantify the effects of divergence on the mapping of reads to a template sequence ., In addition to unbiased mapping of data , a reduction of divergence between reads and template is favorable for the removal of platform dependent insertions ., Characteristically with 454 data there is a high rate of insertion error associated with the chemistry involved during the pyrosequencing process 2 , 3 ., Failure to remove such insertions can result in a further loss of usable data when translations are required during downstream analysis ., We apply our software to temporally sampled 454 datasets from two HIV-1 infected individuals in order to characterize the emergence of low frequency CXCR4-using variants following treatment with an HIV entry-inhibitor drug , the CCR5 antagonist maraviroc ., As the drug will not directly impact on viruses using the CXCR4 coreceptor 30 , patients are screened for their presence prior to treatment 25 , 31 ., The aim is to distinguish a viral population that is exclusively CCR5 tropic ( R5 ) from a viral population including either dual-mixed , DM , ( R5 and exclusively CXCR4-using X4 ) , or R5 and dual-tropic viruses ( those that can use both CCR5 and CXCR4 R5X4 ) ., Note , we refer to both X4 and R5X4 tropic viruses as CXCR4-using ., This application of our software demonstrates that sequence data generated from the 454 platform – in conjunction with coreceptor prediction tests based on HIVs V3 region 23 , 24 , 32 – permits the quantification and evolutionary analysis of HIV-1 tropism present at low frequencies within a sample more effectively than could be achieved using standard population sequencing technologies 6 , 11 ., Our software will also have utility for studying the within-host diversity of other fast evolving viruses ., Samples for pyrosequencing were obtained from two HIV-1 infected males , patients D and E , both of whom were treatment naïve and enrolled in the QD arm of the A4001026 study 31 , were infected with subtype B virus and received maraviroc once daily together with zidovudine and lamivudine ., They discontinued the study due to insufficient clinical response: patient D continued to week 2 and patient E to week 24 ., Both had the M184V mutation in reverse transcriptase at failure , which confers resistance to the background therapy and , in addition , patient D had M41M/L and K70K/R ., Samples were collected over five time points: screening ( 40 days before ) , day 1 , week 2 ( day 15 ) , week 12 ( day 80 ) and week 16 ( day 107 ) for patient D and screening ( 41 days before ) , day 1 , week 8 ( day 57 ) , week 24 ( day 162 ) and week 30 ( day 211 ) for patient E . For each sample , RNA extraction and amplification from the gp120 region of env was performed ., The amplicons were subjected to nebulization to generate fragments of approximately 600 nucleotides ., These were amplified as described in Margulies et al . 1 ., and sequenced on the Genome Sequencer 20 ( GS20 , Roche Applied Sciences ) ., Standard protocols for the generation of a library of tagged single-stranded DNA molecules were used ( for details , see Margulies et al . 1 ) ., The GS20 software package was used to generate the sequence files ., This resulted in files containing between 14 , 000 and 31 , 000 reads ( Table 1 ) ., The data is available at the NCBI Sequence Read Archive ( http://www . ncbi . nlm . nih . gov/Traces/sra ) under accession number SRA023641 . 1 ., Although RNA extraction and amplification was carried out across the entire gp120 gene , the region that is required for the coreceptor prediction of HIV-1 variants is V3 23 , 24 , 32 ., Reads covering this region were identified using a k-mer matching process similar to the initial phase of the BLAST algorithm 13 ., For a single read the location of all k-mers of size five are identified across the template sequence ., If a matching region is found for the read , the frequencies of k-mer hits will be above the random level of background noise at that location ( Fig . 1 ) ., For each dataset the coordinates of the template in relation to the HXB2 reference genome are 6900 to 7305 ., This accommodates longer reads that may span the entire V3 region ., The coordinates of the V3 loop within this region are 7110 to 7217 ., Dataset specificity was generated within the templates using a pre-mapping to the HXB2 reference sequence for which multiple alignments are then generated within windows of size 70 ( using a 20 nucleotide overlap ) ., Within each of these alignments columns containing more than 50% gaps were removed ., Consensus sequences , created for each alignment , are then appended in order to form data-specific templates to which reads are then remapped ., To explore the effects of using a consensus template on k-mer mapping , for each dataset , we compared the number of reads mapped to the data-specific template for that dataset to the number of reads mapped using HXB2 – the latter being the pre-mapping stage prior to consensus template generation ., Next , we took our patient D screening dataset and introduced random variation into the consensus template in sequential steps of 2 , 4 up to 26% ( 50 repetitions for each ) ., After each introduction of random variation , the k-mer mapping was performed and the number of reads successfully mapped recorded ., It should be noted that no precise pairwise alignment to the template sequence is generated during k-mer mapping ., Instead the reads within our datasets covering the portion of the HIV-1 genome between the coordinates 6900 to 7305 are identified along with their approximate start positions ., The k-mer mapping approach implicitly allows for a higher degree of tolerance in identifying such reads when compared to approaches that limit the number of mismatches 12 , 16–18 as , although the process involves matching exact k-mers to the template at any given location , the overall frequency of k-mer hits will increase at the most likely location of the read across the template ( Fig . 1 ) ., This approach , thus , does not specify an exact limitation on the number of mismatches allowed between the read itself and the template ., Following k-mer mapping , reads are pairwise aligned to the consensus template using Smith and Waterman 33 ., Indices obtained during k-mer mapping are used to optimize the process by only aligning reads to the appropriate region ., Platform dependent insertion error , which makes up the majority of non-biological error 2 , 3 , is accounted for by maintaining reference to the dataset specific template ., Specifically , insertions relative to the template , which represents an in frame consensus sequence , are removed ., The frequency at which these insertions occurred across the V3 region was recorded ., The usage of a data-specific consensus sequence is important to ensure that insertions naturally existing within the population are not erroneously removed based on use of a divergent template sequence ., Reads spanning the V3 region of env were extracted , truncated , identical reads removed ( frequencies were stored ) and multiply aligned using Muscle 34 , packaged with the software ., Coreceptor prediction was performed using the 11/24/25 “charge rule” 23 , 32 , implemented within the software and using the PSSM web tool 24 ., Sequence logos were generated for inferred R5 and CXCR4-using sequence present at each time point using the Web Logo tool 35 ., Nucleotide sequences , annotated with coreceptor predictions , were used to infer evolutionary relationships by maximum likelihood using PhyML 36 , packaged with the software ., The HKY model of sequence evolution was used ., The resulting phylogenetic trees were visualized using an integrated version of CTree 37 ., Because bootstrapping is unreliable when performed on very short sequence alignments , the significance of the identified clusters within datasets representing the early time points was determined by comparing the ratio between the intra-cluster pairwise distance and the inter-cluster pairwise distance ( of five random clusters ) to a distribution of values obtained for 500 sets randomly assigned clusters ., A low intra-cluster pairwise distance relative to the inter-cluster pairwise distance implies a robust cluster 38 , 39 ., Additionally clustering significance was tested using the approximate likelihood ratio test 40 for branches as implemented within PhyML ., The pipeline used for processing the initial read data is available within our software ( Fig . 1 ) ., Implemented in Java the executable runs on Mac OS X , Linux and Windows ., All required external binaries are included within the package ., The input is a FASTA formatted file containing unmapped read data ., Output files are in FASTA , TXT , PDF or NEWICK format as appropriate ., A summary of the key functions incorporated into the software are:, ( i ) accurate mapping of next generation sequence data containing high amounts of variation ,, ( ii ) exportation of reads spanning user defined regions of the template ,, ( iii ) translation of reads ,, ( iv ) determination of nucleotide and/or amino acid residue frequencies ,, ( v ) generation of a consensus sequence across the entire dataset taking into account data-specific indels , thus , reducing dependency on a generic template ,, ( vi ) removal of reads based on a hamming distance from their corresponding region on the template ,, ( vii ) generation of a multiple alignment of reads spanning a particular region of the template using Muscle 34 ,, ( viii ) detection and annotation of low frequency variants ,, ( ix ) inference of phylogenetic trees using PhyML 36 ,, ( x ) tree label searching based on the annotation produced in viii and visualized using CTree 37 , and, ( xi ) management of bar coded data ., During the scaffolding process , a number of output plots are generated to summarize the data ., These include read length distributions and template coverage ., The latter is portrayed in a circular plot to allow for longer templates to be displayed optimally ., For each dataset , following k-mer mapping to the consensus template , high read coverage was observed across the V3 region ( Table 1 ) ., In each case when HXB2 was used as a template sequence fewer reads are mapped ., For patient D the mean loss of reads is 26 . 4% , while for patient E it is 36 . 5% , the difference being due to the divergence between patients D and Es data-specific templates and HXB2 ( Table 1 ) ., When random variation in sequential steps of 2 to 26% was introduced into the consensus template derived from the patient D screening dataset and k-mer mapping performed on reads from that dataset , a reduction in the number of mapped can be observed that is directly proportional to increasing divergence ( Fig . 2 ) ., Reads were pairwise aligned to the appropriate region ( identified from the k-mer mapping step ) of the data-specific consensus templates and those spanning both the start and end of the V3 region were extracted and truncated ., Between 17 and 33% of reads contain at least one insertion event across the V3 region in comparison to the consensus template ( Table 2 ) ., The vast majority of these insertions were observed to be singleton or dinucleotide insertions ( causing a frame shift ) , with a mean per site frequency of 0 . 18% ., Note , this frequency is after the sequences have been truncated ., These are lower numbers than would be expected if complete reads had been included 3 as the starts and ends of the majority of the reads have been removed by the truncation step ., During the alignment process such insertions were removed in order to maintain as many correctly translated V3 regions as possible ., For both patients a high coverage of in-frame reads across the V3 region was observed at each time point ( Tables 3 and 4 ) with many unique variants ., Those reads that could not be translated correctly were discarded , resulting in the lower numbers observed in Tables 3 and 4 than those presented in table 2 ., When both the charge rule and PSSM tests were performed on these data , CXCR4-using variants were detected prior to treatment within both patients ( Tables 3 and 4 ) ., On maraviroc treatment , for patient D , CXCR4-using virus increased to a frequency of 41% and for patient E increased to 99% at the sampling times ( Tables 3 and 4 ) ., Interestingly , despite the CXCR4-using population increasing in patient D and becoming dominant in patient E on-treatment , the reduction in viral load corresponds to an order of magnitude less CXCR4-using virus than that prior to treatment ., The inference of the evolutionary history for each dataset , revealed the majority of CXCR4-using variants formed a distinct cluster , present prior to maraviroc treatment and divergent to the main R5 populations present prior to treatment ( Fig . 3 and . 4 ) ., The inset plots confirm the significance of each CXCR4-using cluster based on the comparison of intra- and inter-pairwise distances and confirmed using the approximate likelihood ratio test ( Fig . 3 and 4 ) ., The sequence logos beside each phylogeny represents a comparison between the sequence characteristics of the R5 tropic and CXCR4-using variants ., At each time point , key differences in charge 23 , 24 , 32 can be observed at sites 11 for patient D and at site 25 for patient E . For each patient for the two time points prior to therapy when the CXCR4-using variants located within the R5 tropic clusters were investigated , they were observed to be more similar to their closely related R5 tropic counterparts than to the distinct clusters of CXCR4-using variants ( Fig . 3 and 4 , screening and Day 1 ) ., For patient D there are four such variants ( 1 . 1% of the CXCR4-using population ) , while for patient E there are ten ( 7% of the CXCR4-using population ) prior to therapy ., It is important to note , the extent of the divergence in the phylogenetic trees is mainly due to the high number of either unique or rare variants ., These variants cluster around high frequency variants within the population ( Fig . 5 ) ., For example , at screening ( Patient D ) only ten variants make up 75% of the viral population with a single variant contributing to 23% of the population ., Despite a proportion of this variation being due to sequencing error , this level of variation emerging in relatively short time periods highlights the extreme mutability of HIV ., Tables 3 and 4 show the results of the PSSM test carried out on the extracted V3 sequences ., In all cases , with the exception of patient E ( week 8 ) , PSSM confirms similar levels of CXCR4-using virus to those predicted by the charge rule ., PSSM predicts viruses to be CXCR4-using based on scores being higher than a threshold of −2 . 88 ( Fig . 6 ) , and to be R5 tropic based on scores being below a threshold value of −6 . 96 ( Fig . 6 ) ., Between these two threshold values a reliable PSSM prediction cannot be made and so composite PSSM utilizes the charge rule at sites 11 and 25 24 ., Note , we have also included site 24 in the composite prediction 23 ., We have developed freely available software for the management and downstream analysis of pathogen sequence data ., We demonstrate the utility of this software by applying it to the detection , and subsequent evolutionary analysis , of drug resistant variants within two temporally sampled patients infected with HIV-1 ., In our study of the V3 region we demonstrate that both the CXCR4-using viral populations , which emerge during maraviroc treatment , do not evolve de novo ., Instead , confirming previous studies 6 , 11 , 41 , they emerge from a pre-existing , distinct , viral subpopulation that is present prior to therapy ( Fig . 3 and 4 ) ., Note , the phylogenetic analysis was repeated excluding sites 11 , 24 and 25 ( those used in the charge rule test ) , and the same divergent CXCR4-using cluster identified ( and statistically supported ) , indicating convergent evolution is not biasing the inferences ., If the R5 variants had evolved during treatment to become CXCR4-using they would be more closely related to these R5 counterparts ., In addition , characteristic differences between R5 and X4 variants ( specifically at sites 11 , 24 or 25 in V3 ) are observed at screening and day 1 ( Fig . 3 and 4 , sequence logos ) ., We quantified the effects of HIV-1 mutability on the k-mer mapping process prior to downstream analysis ., Using the patient D screening dataset , in conjunction with the consensus template for that data within which random mutations were introduced in sequential steps , we observed that at a divergence level of 26% just over 20% of the reads originally mapped to the unaltered consensus map successfully ( Fig . 2 ) ., This demonstrates there is a direct relationship between the number of reads that are mapped successfully and the level of divergence between the data and template sequence ., The usage of an inappropriate template will , thus , very probably result in the non-random loss of data , introducing an unnecessary bias ., Indeed for each of our datasets , when mapped to HXB2 rather than the data-specific consensus templates , between 24 and 44% of reads covering the V3 region were not mapped as a result of divergence between the consensus templates and HXB2 which ranged form 14 to16 . 5% ( Table 1 ) ., When an amino acid translation step is performed , in our case for inferring reads as R5 or CXCR4-using , data loss can be further minimized by utilizing a correction procedure relative to the in-frame template sequence ., Platform-dependent insertions make up the majority of sequencing error usually resulting in an over-representation of frame shifts within the reads 2 , 3 ., Correction based on a divergent template will result in a greater probability of complete codons being removed erroneously and therefore it is optimal to use a template that is dataset specific ( Figure S1 ) ., In Tsibris et al . , 11 where no such correction was performed on temporally sampled data from two subtype B infected patients much of the data was removed ., Within one sample a platform dependent insertion within a known homopolymeric stretch resulted in the staggering removal of 85% reads ., Using a correction approach , based on an in-frame consensus template , reduces this loss greatly ( Table 5 ) ., Algorithms for the computational prediction of tropism are highly dependent on the available training datasets ., In the case of PSSM , for example , the training data used in the web tool defines the threshold cutoff values ( −2 . 88 and −6 . 96 ) used in the coreceptor prediction 24 ., When data falls between the current threshold values the PSSM web tool uses the charge rule 24 ., This can be misleading as seen for patient E week 8 ( Fig . 6 ) , the charge rule called 99 . 3% of the population as CXCR4-using based on the presence of a positive charge at site 25 , while PSSM called 39 . 1% of the population as CXCR4-using ( Table 4 ) ., For the latter only 0 . 3% of variants fall above the PSSM CXCR4-using threshold ., The remaining 38 . 8% of CXCR4-using variants is based on the charge rule and not the PSSM scores ., The variants that fall below the CCR5-using threshold ( 60 . 9% ) , despite the majority still possessing a positively charged residue at site 25 , have been called based on their PSSM scores ., The most likely explanation is that these variants are dual tropic and typing them as R5 is incorrect ., It is also important to consider how much CXCR4-using virus is acceptable in the context of combination therapy ., At present a 2% threshold has been proposed by RH 42 ., In our study both patients had greater than 2% CXCR4-using virus at screening and the CXCR4-using population was greater than 10 , 000 copies/ml ., Interestingly , although the CXCR4-using virus is clearly present during therapy , the overall CXCR4-using plasma HIV-1 RNA was reduced during the treatment phase , presumably due to the effect of the other drugs used with maraviroc ., In conclusion , our results demonstrate that , in conjunction with appropriate software , pyrosequencing data has utility for the evolutionally analysis and detection of low frequency variants within viral populations ., In our analysis we have provided a high-resolution snapshot , through temporally sampled data , of intra-patient viral diversity and evolution associated with the CCR5-antagonist maraviroc ., We have also quantified the effects of viral diversity on the initial k-mer mapping of read data in relation to the correction of platform dependent insertion error ., The features of the software used here can be applied to other drug susceptibility and resistance studies , within other genomic regions of HIV-1 or to other pathogen genomes .

Introduction, Methods, Results, Discussion

Large-scale parallel pyrosequencing produces unprecedented quantities of sequence data ., However , when generated from viral populations current mapping software is inadequate for dealing with the high levels of variation present , resulting in the potential for biased data loss ., In order to apply the 454 Life Sciences pyrosequencing system to the study of viral populations , we have developed software for the processing of highly variable sequence data ., Here we demonstrate our software by analyzing two temporally sampled HIV-1 intra-patient datasets from a clinical study of maraviroc ., This drug binds the CCR5 coreceptor , thus preventing HIV-1 infection of the cell ., The objective is to determine viral tropism ( CCR5 versus CXCR4 usage ) and track the evolution of minority CXCR4-using variants that may limit the response to a maraviroc-containing treatment regimen ., Five time points ( two prior to treatment ) were available from each patient ., We first quantify the effects of divergence on initial read k-mer mapping and demonstrate the importance of utilizing population-specific template sequences in relation to the analysis of next-generation sequence data ., Then , in conjunction with coreceptor prediction algorithms that infer HIV tropism , our software was used to quantify the viral population structure pre- and post-treatment ., In both cases , low frequency CXCR4-using variants ( 2 . 5–15% ) were detected prior to treatment ., Following phylogenetic inference , these variants were observed to exist as distinct lineages that were maintained through time ., Our analysis , thus confirms the role of pre-existing CXCR4-using virus in the emergence of maraviroc-insensitive HIV ., The software will have utility for the study of intra-host viral diversity and evolution of other fast evolving viruses , and is available from http://www . bioinf . manchester . ac . uk/segminator/ .

Due to high data volumes , error rates , and short sequence lengths , new sequencing technologies present a new challenge for computational biology ., In addition , high-depth ( or ultra-deep ) datasets , for example from pathogens , contain exceptionally large amounts of variation over short genomes or genomic regions ., Here we present software for the processing and downstream analysis of such short-read viral sequence data ., We apply the software to the analysis of two HIV-1 infected individuals who did not respond optimally to the drug maraviroc ., For each patient , pyrosequence data was available for five time points ., In both cases we detect distinct clusters of low-frequency drug-insensitive variants that were present prior to maraviroc treatment and effectively unmasked by the removal of the drug-sensitive HIV .

computer science/applications, computational biology/comparative sequence analysis, virology/mechanisms of resistance and susceptibility, including host genetics, infectious diseases/hiv infection and aids, evolutionary biology/bioinformatics

journal.pbio.0050277

Characterization of Sleep in Zebrafish and Insomnia in Hypocretin Receptor Mutants

The function of sleep is disputed , with hypotheses ranging from energy conservation to synaptic remodeling and memory consolidation , with the possibility of disparate functions across evolution 1 , 2 ., One approach to this question is to study sleep and sleep regulatory networks in organisms amendable to molecular , neuronatomical , and genetic studies 3–5 ., Using purely behavioral criteria , a sleep-like state has been demonstrated in non-mammalian species 6 , 7 ., A sleep-like state has been characterized in Drosophila melanogaster 4 , initially using behavioral criteria , and more recently through electrophysiological studies 8 ., Identification and characterization of sleep mutants is ongoing 5 ., While unquestionably a superb genetic model organism , the phylogenetic distance between Drosophila and mammals has produced notable and relevant divergences in usage of neuromodulators ., Whereas tyramine and octopamine are critically important in Drosophila , they are trace amines of unknown function in mammals and other vertebrates 9 ., Further , certain other neurotransmitter systems have not been identified in Drosophila , including hypocretin/orexin , an important sleep modulator ., Hypocretins 10 , also called orexins 11 , are neuropeptides of primary interest in the study of sleep ., Indeed , they compose the only neurochemical system known to be involved in the generation of a clear human sleep disorder phenotype , narcolepsy 12–17 ., In mammals , preprohypocretin is primarily expressed by neurons of the posterior hypothalamus , with widespread projections to the brain and spinal cord 18 ., Two closely related receptors are known , with complementary neuronatomical distribution 11 , 19 ., Of notable importance for sleep regulation , mammalian hypocretin neurons also project widely and activate monoaminergic cell groups , which are generally known to be wake-active ( adrenergic , dopaminergic , serotoninergic , and histaminergic ) 20–23 ., Further , they also project and activate cholinergic cell groups 24–26 important for the regulation of wakefulness and rapid eye movement ( REM ) sleep ., Intracerebroventricular ( icv ) injections of hypocretin are potently wake-promoting and increase locomotion in mammals 27–29 , an effect partially blocked by histaminergic 28 and dopaminergic 30 antagonists , with higher doses inducing stereotypies similar to those observed after massive dopaminergic stimulation 30 ., These experiments suggest that hypocretin is a major sleep modulator , and that much of its activity is mediated through direct stimulation of aminergic systems ., Narcolepsy can be observed in multiple mammalian species ., In humans , the disorder is primarily due to hypocretin cell loss 14 , 16 ., It is characterized by excessive daytime sleepiness , disturbed nocturnal sleep ( inability to stay asleep at night ) , cataplexy ( transient muscle paralysis triggered by emotions ) , sleep paralysis ( transient muscle paralysis during sleep–wake transitions ) , and hypnagogic hallucinations ( dream-like experiences at sleep onset ) 31 , 32 ., Many of the symptoms of narcolepsy are related to an abnormal tendency to enter REM sleep rapidly , and to express abnormal REM sleep features while awake ( e . g . , muscle paralysis ) or dreaming ( hypnagogic hallucinations ) ., Contrary to popular belief , narcolepsy is not characterized by increased total sleep time over the 24-h period 33 , 34 ., Rather , patients with narcolepsy are unable to consolidate either wakefulness or sleep 33 , 35 ., In dogs , narcolepsy has been studied for over 20 years and has been shown to display all the symptoms of human narcolepsy 36 ., It can result from mutations in the hypocretin receptor-2 ( HCRTR2 ) gene ( familial cases ) 12 or from hypocretin deficiency ( sporadic narcolepsy ) 37 ., In rats and mice that have been engineered to lack hypocretin , hypocretin cells , or HCRTR2 , narcolepsy manifests as sleep and wake fragmentation during the day and the night , sudden episodes of muscle weakness , and rapid transitions from wake to REM sleep 15 , 38 , 39 ., In contrast , hypocretin receptor-1 ( HCRTR1 ) knockout mice do not display sudden episodes of muscle weakness , but have sleep abnormalities 39 , 40 ., These comparisons highlight the similarity of the phenotype across mammalian species and a primary role for HCRTR2-mediated transmission ., The zebrafish is a powerful genetic model that has , as a vertebrate , the advantage of sharing a similar central nervous system organization with mammals 41 , 42 ., We , and others , have shown that principal actors of sleep regulation in mammals are largely conserved in zebrafish , including monoaminergic 43–46 , cholinergic 47 , and hypocretinergic ( orexin ) 48–50 cell groups ., In addition to conservation of cell groups , responses to various hypnotic drugs such as H1 histaminergic antagonists , melatonin agonists , alpha-2 adrenergic agonists , and GABAergic modulators are also conserved in the zebrafish 51–53 ., A sleep-like state was first characterized in 7- to 14-d-old zebrafish larvae by Zhdanova and colleagues , who demonstrated an elevated arousal threshold , a reduced breathing respiratory rate , and a compensatory rest rebound in response to deprivation as well as modulation by hypnotic drugs 51 , 54 ., More recent studies have also explored sleep architecture in zebrafish larvae 50 ., These findings raise the question of whether conserved neuromodulators function in the same roles to regulate sleep in fish as in mammals , or whether there are presently unappreciated divergences ., The study of fish sleep is also interesting because the fish is a cold-blooded vertebrate , and mammalian-like sleep architecture , as defined by REM sleep and non-REM sleep , typically emerged with homeothermy 55 ., To address these questions , we performed a detailed characterization of sleep in adult zebrafish and characterized sleep abnormalities in adult zebrafish lacking the sole hypocretin receptor ., We first performed a fine analysis of the characteristics and architecture of the sleep-like state in fully formed adults to develop a working definition of sleep in the zebrafish ., We used videotracking of fish illuminated by an infrared source under light and dark conditions ( adult fish sleep recording system AFSRS; Figure 1A; see also Materials and Methods ) , and found that zebrafish adults are strongly diurnal , displaying higher activity during the day ( Figure 1B and 1C ) ., Brief periods of inactivity , often associated with a drooping caudal fin , were observed , suggesting a sleep-like state ( Video S1 ) ., These periods of inactivity alternated with active periods during both the night and the day ( Figure 1B ) ., Further , the state was easily reversible by gentle tapping , acoustic stimulation , or weak electrical field stimulation ( e . g . , 2–6 V ) ., One important characteristic of sleep is increased arousal threshold ., To study this phenomenon , the reaction of fish to a weak electrical field of variable strength was studied ( Video S2 ) ., Random voltage stimuli ( 0–2 V ) were applied every 30 min through the day and night ( Figure 1D ) ., We noted that fish in an active state were more likely to respond to very low voltage stimuli than those that were inactive ( see legend to Figure 1 for details ) ., At higher voltages , all fish responded regardless of activity state ( Figure 1E ) ., Inactivity was thus associated with an increased arousal threshold to electrical stimulation , with the greatest differential response observed at 0 . 38 V . Sub-analyses performed during the day versus the night , and in individual animals , yielded similar results ( data not shown ) ., We next defined the minimum epoch of immobility distinguishing sleep from simple inactivity ., To do so , we used receiver operator curve ( ROC ) analysis 56 of the results of the electrical stimulation experiments ., In this analysis , sensitivity ( SE ) and specificity ( SP ) ( and Kappas ) for response to stimulation in inactive versus active states are plotted as the discrimination voltage threshold is varied ., A true positive is defined as a demonstration of sleep , as defined by immobility and non-response to stimulation ., SE is defined as the percent of non-response to stimulation when inactive/total number of trials when inactive ., SP is defined as the percent of response when active/total number of trials when active ., These analyses considered the percentage of responses to differing voltages ( 0 . 13 , 0 . 38 , 0 . 5 , 1 and 2 V ) as well as the consecutive number of seconds ( 0–30 s ) of inactivity ( <6 pixels/s ) preceding the stimulus ., These SE/SP pairs are plotted on ROC and qualitative ROC planes ., Diagnostic lines are drawn on the qualitative ROC plane , and the ideal test point is plotted ., This analysis indicated that the best SE/SP ratio ( point closest to ideal test point and closest to the diagnostic line ) was obtained when using 0 . 38 V and 6 s of prior inactivity ., As expected , SE increased with increasingly longer periods of inactivity , as the longer the prior period of inactivity was , the likelier true sleep ( without reactivity to electrical stimulation ) was observed ( Figure 1F ) ., In contrast , SP decreased with increasingly longer periods of inactivity , as more and more short periods of true sleep were missed and considered “active . ”, This analysis provided a working definition of zebrafish sleep: an interval of inactivity ( <6 pixels/s ) lasting at least 6 s ( Figure 1E and 1F ) ., All other periods were defined as active ( awake ) ., Using this definition , we determined that most sleep episodes occurred at the bottom or the top of the tanks ( Figure 1G ) and were remarkably consolidated during the night ( Figure 1H and 1I ) ., No sex differences were found in sleep amounts or distribution ( data not shown ) ., We next investigated whether sleep episodes are homeostatically regulated by observing sleep-deprived animals ., To do this , we first attempted sleep deprivation by tapping on the aquarium walls or using noise introduced through an underwater speaker ., As we noted rapid habituation , electrical stimulation was next attempted ., Although this procedure was also imperfect , as we found it extremely difficult to keep the fish awake in the dark for long periods of time ( Video S4 ) , it was retained as the method of choice , as it did not result in rapid habituation ., We next designed a computerized system to electrically stimulate a fish each time it displayed sleep behavior ( Figure 2A ) ., A yoked control fish was stimulated concurrently at the same voltage ( though not necessarily while resting ) in a separate chamber to control for stress ., Increased voltage from 2 to 6 V was applied to both fish if the inactive fish did not react to stimulations ( Figure 2A ) ., Animals were sleep deprived during the 6 h of the dark prior to usual light onset ( 9 a . m . ) , and released into either the usual light ( 150 lux ) or an extended period of darkness ., This procedure successfully induced sleep deprivation , although partial habituation was observed after 4 h of stimulation , i . e . , toward the end of the procedure ( data not shown ) ., Indeed , sleep-deprived animals appeared increasingly immobile and unreactive to stimulation toward the end of the procedure , in contrast to yoked control stimulated fish ( Videos S3 and S4 ) ., Further , as sleep episodes normally occur at high frequency at night , random stimulation in the control fish also induced mild partial sleep deprivation ( while controlling for stress ) ., Overall , we found that this procedure induced a 30% decrease in sleep in the sleep-deprived group versus undisturbed controls ., In contrast , a 10% decrease in sleep was observed in the yoked control group , representing a milder degree of sleep deprivation ( Figure 2B ) and providing a dose-response curve of increasing amounts of sleep deprivation ., After release into an extended period of darkness during the subjective day , sleep in undisturbed control animals was lowest ., In yoked control animals ( partially deprived ) , minor recovery sleep was observed , while a significant rebound was observed in the sleep-deprived animals , indicating homeostatic regulation of sleep ., Differences were statistically significant between sleep-deprived versus yoke control stimulated or undisturbed fish ( Figure 2B ) ., Sleep bout length was also increased in both the yoke control and sleep-deprived groups , although not significantly in the yoke control , compared to the undisturbed group ., Remarkably , a sleep rebound was not observed when sleep-deprived animals were released into light ( Figure 2B ) ., Further , when fish were exposed to 150-lux light during the last 6 h of the biological night , but not electrically stimulated , there was a dramatic suppression of sleep ( 90% decrease ) that was not followed by a rebound when animals were released into darkness ( Figure 2B ) ., A similar , nearly complete suppression of sleep was also observed when animals were kept under constant light conditions for 3 d ( Table 1 ) ., Again , no significant rebound was observed during the day or the following nights ( data not shown ) ., During longer exposure to constant 150-lux light , a progressive return of sleep was noted over a period of 1–2 wk ( Figure S1 ) ., As previously reported for activity 57 , sleep was modulated by circadian influences under constant light and dark conditions ( Table 1 ) ., Unlike most mammals , however , the direct effect of dark and , more strikingly , light was stronger than circadian influences ., Indeed , for most parameters , values varied significantly more with light exposure than with circadian timing ( Table 1 ) ., To investigate functional conservation of neurotransmitters regulating sleep in zebrafish , we next anatomically and functionally studied the hypocretin system , the only system known to cause a primary sleep disorder ( narcolepsy ) in mammals ., In conjunction with previous work on the hypocretin neuropeptide 48 , we identified a single hypocretin receptor in Tetraodon and in zebrafish ( hcrtr also known as hcrtr2 ) through bacterial artificial chromosome ( BAC ) library screening and in silico database searches ( see Materials and Methods ) ., As recently noted 50 , zebrafish Hcrtr has higher homology to mammalian HCRTR2 , the subtype of primary importance in the mediation of the narcolepsy phenotype 12 ., As in mammals 19 , we found widespread hcrtr expression in the telencephalon , hypothalamus , hypophysis , posterior tuberculum , and hindbrain ( Figure 3A and 3B ) and in selected spinal cord neurons ( Figure 3C and 3D ) of larvae at age 2 d postfertilization ( dpf ) ., Limited expression was found in thalamic and pallidal areas , reminiscent of overall mammalian HCRTR1 and HCRTR2 mRNA distribution ( cortex and hippocampus , basal forebrain , central midline thalamic areas , hypothalamus , and brainstem ) 19 , although overall neuroanatomical correspondence of structures between these species is only partially established 41 ., We next simultaneously mapped the distribution of hcrtr with that of monoaminergic cell groups ., In mammals , monoaminergic cell groups modulate wakefulness and are among the most hypocretin-receptor-rich brain regions 19 ., These are stimulated by hypocretins and are commonly assumed to mediate much of the downstream effects of hypocretin on sleep regulation 12 , 58 ., Interestingly , however , using double in situ hybridization ( ISH ) on 2-dpf larvae , we saw no significant colocalization with adrenergic ( Figure 3G and 3J ) , dopaminergic ( Figure 3E–3S ) , histaminergic ( Figure 3T ) , or serotoninergic ( Figure 3U and 3V ) neurons ., Flat mounts and close-ups confirmed these results ( Figure 3H–3J ) ., Double fluorescence ISH followed by confocal microscopy ( Figure 3K–3S ) also demonstrated an absence of colocalization in dopaminergic and adrenergic cells , in contrast to a previous report 50 ., To determine whether connectivity of the hypocretin and monoaminergic systems emerges later in development , we also performed ISH on adult zebrafish brain sections , using the adult zebrafish atlas established by Wulliman and colleagues 42 ., The embryonic distribution of hcrtr was broadly maintained in the adult brain , with prominent localization in the telencephalon , hypothalamus , posterior tuberculum , hypophysis , and brainstem cranial nuclei ., In addition to the areas of detected expression in 2-dpf embryos , notable expression was also observed in the periventricular gray zone of the optic tectum ( Figure 4A–4C ) ., As in embryos , colocalization of hcrtr with monoaminergic cells was absent except for a few anterior dopaminergic neurons ( Figure 4G ) ., Most notably , and unlike previously reported 50 , expression was absent in the large majority of diencephalic dopaminergic neurons ( Figure 4B , 4E , and 4H ) and in the locus coeruleus ( Figure 4C , 4F , and 4I ) ., In this last area , however , a few receptor-positive cells were present immediately medially to the locus coeruleus ( Figure 4I ) ., Labeling of the locus coeruleus was performed using dbh ( Figure 4I ) , th , and adra2a ( data not shown ) ., As hcrtr is not significantly colocalized with major monoaminergic neurons in embryos , we next surveyed zebrafish embryos with other neuronal markers ( acetylcholine , GABA , glycine , and glutamate markers ) that have been proposed as hypocretin targets in various sleep regulatory models 2 , 58 ., We found that Adra2a , Gad67 ( also known as Gad1 ) , ChAT , and Glyt2 ( also known as Slc6a5 ) were expressed in regions similar to where hcrtr was expressed ( Figure 5 ) ., Double ISH further confirmed that most hcrtr-positive cells were Gad67-positive GABAergic cells ( Figure 5A ) , except in the hypophysis and the ventral posterior tuberculum ., A subpopulation of hcrtr-positive GABAergic cells in the anterior hypothalamic area was also positive for Adra2a ( Figure 5B ) ., Some overlap was also observed between the cholinergic system and hcrtr-positive cells in the diencephalon and in rhombomere 2 ( Figure 5C ) ., In the spinal cord , hcrtr-positive neurons were neither primary sensory neurons nor motoneurons , but were located closer to the primary sensory neuron layer , a region that could be equivalent to lamina-II in mammals; this area is involved in the secondary processing of sensory information such as pain 59 ., Most of these neurons were glycinergic ( Figure 5D ) and GABAergic ( Figure 5E ) ., In mammals , equivalent neurons receive dense hypocretin projections and are stimulated by the peptide , with a role in the modulation of nociceptive input 59–62 ., In a second analysis , double ISH data extended in adults , with a primary focus on the hypothalamic area ., As in embryos , we found that many hcrtr-positive cells were Gad67-positive ., In the anterior hypothalamus and ventral thalamic nucleus ( Figure 6 , first two columns ) , most hcrtr-positive cells were GABAergic , starting at the diencephalic–telencephalic junction ., Further , the majority of the anterior hypothalamic GABAergic cluster was Adra2a-positive ., In the posterior diencephalon , only a small region of the central posterior thalamic nucleus was Adra2a- and Gad67-positive ( Figure 6 , last column ) ., Studies using cholinergic markers were next performed ( Figure 7 ) ., Our primary focus was on the telencephalon and the pons , where cholinergic cells equivalent to sleep regulatory neurons of the nucleus basalis and laterodorsal tegmentum/pedunculopontine nuclei have been reported ., Cholinergic staining was abundant in the diencephalon and rhombencephalon , including in many cranial nerve nuclei ., Colocalization with hcrtr was only observed in a few areas , most notably in the peripheral gray zone of the optic tectum and periventricular hypothalamus ( Figure 7B and 7C ) ., In the telencephalon , we failed to detect any cholinergic neurons ( Figure 7A ) ; this population has been found only in some fishes ., Similarly , close to the locus coeruleus , where the equivalent of the laterodorsal tegmentum/pedunculopontine cells are believed to be located , no ChAT expression was noted ( Figure 7D–7G ) ., We next screened an ethylnitrosurea-mutagenized TILLING ( for “targeting induced local lesions in genomes” ) library for hcrtr mutations ., A premature stop codon mutation ( R168 to stop ) was identified ( hcrtr168 ) that results in the predicted loss of four transmembrane domains as well as the intracellular loop 3 domain required for G-protein coupling ., The truncation is also located upstream of two mutations known to produce an inactive protein resulting in canine narcolepsy 12 ( Figure 8 ) ., Homozygous hcrtr168 animals developed normally into viable and fertile adults ., Extensive observation of larvae and adults did not yield any obvious phenotype , such as the occurrence of sudden REM-sleep-like paralysis episodes ( e . g . , cataplexy or cataplexy-like behaviors ) characteristic of mammalian narcolepsy 12 , 14 , 31 , 63 , either spontaneously or when excited by food administration or mating ., Similarly , activity monitoring did not reveal any large differences between mutant , heterozygous , and wild-type larvae of similar background ( data not shown ) ., We next studied adult wild-type and mutant fish using our AFSRS ( including comparison of heterozygous and wild-type siblings within the same family ) under typical light/dark conditions ., We found that activity of hcrtr168 mutants was slightly increased ( Figure 9A; Table 1 ) and sleep amounts were decreased by 20%–30% during the night ( Figure 9B; Table 1 ) ., Most strikingly , fine architecture analysis revealed a 60%–70% increase in the number of sleep–wake transitions , and a 60% decrease in sleep bout length during the night , indicating sleep fragmentation in hcrtr168 ( Figure 9D and 9E ) ., Heterozygous animals generally behaved as wild-type siblings , although in some measures ( e . g . , sleep time and sleep transitions ) , an intermediary phenotype was observed ( Table 1 ) ., Activity and sleep architecture were normal during the day in all genotypes ., Further , wake bout length was essentially unchanged during the day or the night ( Figure 9C; Table 1 ) ., These data indicate that the hypocretin receptor is required for proper sleep regulation in adult zebrafish under light/dark conditions ., Studies under constant light and dark indicated significantly decreased sleep amounts and significant sleep fragmentation in hcrtr168 compared to wild-type animals at all circadian time points , but these effects were masked by the stimulating effect of light ( Table 1 ) ., Similar increases in locomotion ( and decreased sleep ) were observed in all three genotypes when animals were newly moved from their usual aquaria to the recording chambers; thus , disruption of nocturnal sleep in the mutant was unlikely to be due to differential effects of stress or the food deprivation associated with our monitoring method ( Figure S2 ) ., Further , food intake satiety monitoring studies ( Figure S3 ) and studies of locomotor activation after feeding were performed , and all genotypes reacted similarly ( Figure S4 ) ., Hypocretin-1 icv injections are wake-promoting and increase locomotion in mice , rats , and dogs 27 , 28 , 30 ., In contrast , hypocretin-2 is generally inactive because of rapid catabolism 64 ., Prior to usual light–dark transition time , adult zebrafish were briefly anesthetized , and hypocretin peptides ( or saline ) were injected icv ., Animals were subsequently released in the dark while activity and sleep were measured using the AFSRS ., In controls , locomotion was high ( novel environment ) , followed by habituation and reduced activity/increased sleep in the dark ( Figure 10A and 10B ) ., In hypocretin-1-injected fish , a reduction in locomotor activity was observed ( Figure 10A and 10B ) ., This effect was dose dependent and occurred with either the human or zebrafish hypocretin-1 peptide ., Based on sleep scoring of these data , we found that both mammalian and zebrafish hypocretin-1 significantly increased total sleep time ( 23 . 0% ± 11 . 3% and 28 . 7% ± 9 . 0% above baseline sleep , respectively , p < 0 . 05 in both cases after 1 , 400 pmol ) during 9 h of continuous recording ( Figure 10 ) ., As expected , zebrafish hypocretin-2 was inactive in wild-type zebrafish ( data not shown ) ., In the TILLING hcrtr168 mutant , the sleep-promoting effects of mammalian or zebrafish hypocretin-1 were abolished ( Figure 10C ) ., Overall , these experiments indicate antagonism of the sleep-promoting effects of icv hypocretin-1 injection by hcrtr168 , confirming that the effect is mediated through Hcrtr ., Our experiments demonstrate that rest episodes in adult zebrafish represent a genuine sleep-like state , characterized by reversible periods of immobility , place preference ( bottom or surface ) , circadian regulation , and homeostatic rebound ., Interestingly , unlike in larvae 50 , 51 , 54 , sleep deprivation was difficult to achieve in adults and was associated with a sleep rebound that was only detectable when the fish were released in the dark ., Our study is unique as we studied adults and demonstrated that only 6 s of prior inactivity was sufficient to be associated with decreased arousal threshold and thus to qualify as sleep ., Other studies have been primarily performed in larval zebrafish and did not test or report on intervals shorter than 1 min 50 , 51 ., In agreement with our finding , a preliminary report in adults reported that long-term sleep deprivation using a moving partition technique or electric shock produced a sleep rebound , associated with increased arousal threshold 54 ., Unlike in most mammals , we also found that even moderate levels of light exposure have strong sleep-suppressant effects in zebrafish , and that circadian regulation has a more minor role ., The light suppressant effect was not associated with deleterious behavioral effects over a week , but sleep reappeared progressively after 8 d ( Figure S1 ) ., These results are in agreement with data from Hurd and colleagues 57 , who found that only a portion of adult fish displayed detectable circadian activity rhythms under constant light or dark conditions at 28 °C , in all cases with significantly lower amplitude than under alternating light/dark conditions ., Most strikingly , we also found that light was not only able to suppress sleep ( Figures 2 and S1 ) but that no sleep rebound was observed upon release in the dark ., In goldfish and perch , a sleep rebound in the light has been found after sleep deprivation by light , but was mild 7 ., Further , the lack of rebound after sleep deprivation by light in our experiment contrasts with the observation of homeostatic regulation after a much lower level of sleep deprivation using electrical stimulation ., Overall , whereas it is likely that homeostatic regulation of rest can be demonstrated in some circumstances ( in our case , electrical stimulation when sleeping in the dark ) , we found that wake induced by light in zebrafish was not , on a short-term basis , associated with a mounting sleep debt ., How could this unusual effect of light be explained ?, Unlike in mammals , but as reported in Drosophila , most zebrafish cells are directly photoreceptive 65 , 66 ., Further , melatonin is a strong hypnotic in this species , a property that may be related to the diurnal pattern of activity of zebrafish 51 ., The combined effect of light on various cell populations , together with its suppressive effects on melatonin production , may result in multiple redundant wake-promoting inputs into the brain ., In favor of this hypothesis , variable effects of light have been observed in other teleosts , where it acts to suppress rest and induce rest rebound in the perch and goldfish 7 , both diurnal fish , whereas it has calming effects in nocturnal fish such as the tench 6 ., In this context , the strong effects of light or melatonin may be able to overcome the more minor regulatory effects of other neural networks regulating sleep homeostasis in zebrafish ., Recent results in other species , most notably in diurnal birds , indicate that some vertebrates have sleep regulatory characteristics similar to those of zebrafish ., Birds are especially interesting as , unlike fish , it is possible to document all the electroencephalographic characteristics of mammalian sleep 6 ., Migratory sparrows , for example , are able to survive for long periods of time without sleep under selected ecological conditions and are extremely sensitive to light and dark 67 ., Similarly , sleep in pigeons is strongly suppressed by light , without electroencephalogram-defined non-REM sleep rebound in darkness 68–70 ., Like zebrafish , diurnal birds such as pigeons are also remarkably sensitive to melatonin , and do not exhibit wake rebound after melatonin-induced sleep 71 ., It may thus be that the need for homeostatic regulation of sleep has not strongly evolved in zebrafish , and that it is not as universal in vertebrates as previously believed ., Rather , in both diurnal birds and fish , the direct effect of light or melatonin may be able to bypass homeostatic regulation of sleep ., Further studies of sleep deprivation by light versus other methods in these species may reveal molecular mechanisms regulating sleep homeostasis ., Our studies have also shown significant and informative divergence in the organization of the hypocretin system in zebrafish ., We have described a small group of approximately 20 hypocretin cells in the preoptic hypothalamus of embryonic zebrafish and characterized a compact promoter driving expression in these cells 48 ., ISH and immunochemistry in adult brains indicates that approximately 40 cells are present in the adult zebrafish preoptic area ( data not shown and Kaslin et al . 49 ) , although an additional more anterior group , probably detected through antibody cross-reactivity , was found using immunochemistry with mammalian antibodies 48 , 49 ., The hcrt cluster in zebrafish is distal to the histaminergic cell group expressing histidine decarboxylase , unlike in mammals where these two cells groups are closely adjacent within the posterior hypothalamus ., Unlike mammals , the zebrafish has only one hypocretin receptor ., This result , surprising when considering the frequency of gene duplications in this species , was confirmed through in silico searches , BAC library screening , genomic Southern blot analysis , and comparisons of syntenies around HCRTR1 and HCRTR2 in mice , humans , and zebrafish ., Indeed , only a single hypocretin receptor is identifiable in current releases of other teleosts ( zebrafish , Fugu , Tetraodon , medaka , and stickleback ) ., Using ISH , we found that the expression pattern of hcrtr is in agreement with overall mammalian hypocretin receptor expression patterns ( Figures 3 and 4 ) ., Indeed , the high density of hypocretin receptor mRNA in the telencephalon , hypothalamus , posterior tuberculum , and hindbrain , but not lateral thalamic and pallidal areas , is reminiscent of overall mammalian HCRTR1 and HCRTR2 distribution and density ( in cortex , hippocampus , basal forebrain , central midline thalamic areas , and hypothalamus ) 19 , although neuroanatomical correspondence of overall structure across these species is only partially established 41 ., Similarly , as in mammals , hcrtr is expressed in glycinergic/GABAergic neurons of the spinal cord immediately ventral to sensory neurons ., In mammals , these neurons receive dense hypocretin projections and are stimulated by the peptide , with a role in the modulation of nociceptive input 59–62 ., Although the overall pattern of expression initially appeared similar to that in mammals , our in-depth analysis indicates differences in expression in relevant sleep regulatory networks ( see below

Introduction, Results, Discussion, Materials and Methods, Supporting Information

Sleep is a fundamental biological process conserved across the animal kingdom ., The study of how sleep regulatory networks are conserved is needed to better understand sleep across evolution ., We present a detailed description of a sleep state in adult zebrafish characterized by reversible periods of immobility , increased arousal threshold , and place preference ., Rest deprivation using gentle electrical stimulation is followed by a sleep rebound , indicating homeostatic regulation ., In contrast to mammals and similarly to birds , light suppresses sleep in zebrafish , with no evidence for a sleep rebound ., We also identify a null mutation in the sole receptor for the wake-promoting neuropeptide hypocretin ( orexin ) in zebrafish ., Fish lacking this receptor demonstrate short and fragmented sleep in the dark , in striking contrast to the excessive sleepiness and cataplexy of narcolepsy in mammals ., Consistent with this observation , we find that the hypocretin receptor does not colocalize with known major wake-promoting monoaminergic and cholinergic cell groups in the zebrafish ., Instead , it colocalizes with large populations of GABAergic neurons , including a subpopulation of Adra2a-positive GABAergic cells in the anterior hypothalamic area , neurons that could assume a sleep modulatory role ., Our study validates the use of zebrafish for the study of sleep and indicates molecular diversity in sleep regulatory networks across vertebrates .

Sleep disorders are common and poorly understood ., Further , how and why the brain generates sleep is the object of intense speculations ., In this study , we demonstrate that a bony fish used for genetic studies sleeps and that a molecule , hypocretin , involved in causing narcolepsy , is conserved ., In humans , narcolepsy is a sleep disorder associated with sleepiness , abnormal dreaming , and paralysis and insomnia ., We generated a mutant fish in which the hypocretin system was disrupted ., Intriguingly , this fish sleep mutant does not display sleepiness or paralysis but has a 30% reduction of its sleep time at night and a 60% decrease in sleep bout length compared with non-mutant fish ., We also studied the relationships between the hypocretin system and other sleep regulatory brain systems in zebrafish and found differences in expression patterns in the brain that may explain the differences in behavior ., Our study illustrates how a sleep regulatory system may have evolved across vertebrate phylogeny ., Zebrafish , a powerful genetic model that has the advantage of transparency to study neuronal networks in vivo , can be used to study sleep .

neurological disorders, neuroscience, genetics and genomics

Zebrafish sleep, and have the receptor for the wake-inducing molecule hypocretin. While mutation in this receptor causes narcolepsy in mammals, in fish, sleep is fragmented, demonstrating differences in sleep control in vertebrates.

journal.ppat.1002040

Human Neutrophil Clearance of Bacterial Pathogens Triggers Anti-Microbial γδ T Cell Responses in Early Infection

The cellular immune system consists of an ‘innate’ arm of phagocytes and antigen-presenting cells , and an ‘adaptive’ arm of antigen-specific lymphocytes capable of developing immunological memory ., Yet , there is increasing evidence of considerable crosstalk between the two 1 ., Innate responses directly influence the shape and outcome of adaptive T cell responses , and vice versa specialized T cell subsets feedback to innate cells 2 ., Among these interactions , the regulation of neutrophil-mediated inflammatory responses by Th17 cells has received enormous attention over the past few years 3 , and with the emergence of novel T cell subsets additional networks are being proposed so that each polarized T cell eventually pairs with an innate counter player 4–7 ., The necessity to integrate complex signals in order to mount the most effective defense is best illustrated by the existence of ‘unconventional’ T cells bridging the classical divide between innate and adaptive immunity , such as natural killer T cells , mucosal-associated invariant T cells , intestinal intraepithelial CD8αα+ T cells and dendritic epidermal γδ T cells 8–14 ., These often tissue-associated lymphocytes are characterised by restricted T cell receptor ( TCR ) repertoires that allow them to respond rapidly to a limited range of conserved structures ., Unconventional T cells readily assume a plethora of effector functions , ranging from sentinel tasks and targeted killing to engaging with keratinocytes , fibroblasts , phagocytes and antigen-presenting cells as well as other lymphocyte ., γδ T cells expressing a Vγ9/Vδ2 TCR – Vγ2/Vδ2 according to an alternative nomenclature – are only found in humans and higher primates and differ fundamentally from all other conventional and unconventional T cells 15 ., Activated Vγ9/Vδ2 T cells produce a range of cytokines , kill infected and transformed target cells , regulate survival and differentiation of monocytes and maturation of dendritic cells , provide B cell help and present antigens to CD4+ and CD8+ T cells 11 , 12 , 16 , 17 ., They expand considerably in many infections , at times to >50% of all circulating T cells within a few days 18 , and respond selectively in a non-MHC restricted manner to the microbial metabolite ( E ) -4-hydroxy-3-methyl-but-2-enyl pyrophosphate ( HMB-PP ) 19 ., HMB-PP is an intermediate of the non-mevalonate pathway of isoprenoid biosynthesis that is present in many bacteria and in malaria parasites but not in humans 17–19 ., The rapid and sensitive response of Vγ9/Vδ2 T cells to a broad range of pathogens evokes cardinal features of innate immunity ., Indeed , HMB-PP fulfills Janeways criteria for a ‘pathogen-associated molecular pattern’ in that it is, ( i ) invariant among different species;, ( ii ) a product of a pathway unique to micro-organisms; and, ( iii ) essential in microbial physiology 17 ., Yet , HMB-PP recognition is not mediated via germline-encoded pattern recognition receptors but involves the re-arranged Vγ9/Vδ2 TCR 20–22 ., Bacteria that possess the non-mevalonate pathway and hence produce HMB-PP comprise some of the most detrimental human pathogens such as the causative agents of cholera , diphtheria , plague , tuberculosis and typhoid , but also numerous commensal and opportunistic species in the mucosal flora , skin and feces 19 , 23 ., In all these micro-organisms , HMB-PP is an essential intracellular metabolite , and it is not clear whether and how it is released by invading bacteria and becomes visible to the immune system as soluble molecule ., Indeed , earlier studies with mycobacteria suggested that uptake of whole bacteria by monocytes , macrophages , or DCs may be required for the recognition by Vγ9/Vδ2 T cells 24–27 ., Neutrophils are the first immune cells infiltrating the site of infection and the main phagocytes responsible for early pathogen clearance , and growing evidence points toward a crucial role of γδ T cells in regulating neutrophil responses in mouse models of infection , hypersensitivity and autoimmunity 8 , 12 ., Yet , the interplay between γδ T cells and neutrophils has not been addressed in detail 28 , 29 ., Our present data demonstrate that Vγ9/Vδ2 T cells readily respond to neutrophils harboring phagocytosed bacteria , and that this response is strictly dependent on the ability of these bacteria to produce HMB-PP and cell-cell contact of Vγ9/Vδ2 T cells with accessory monocytes ., The majority of circulating Vγ9/Vδ2 T cells shows migration properties similar to monocytes 30 , suggesting that these two cell types are co-recruited to the site of inflammation and interact with each other at early stages of infection 17 , 31 ., Our present findings thus indicate a direct link between invading pathogens , neutrophils , monocytes and microbe-responsive γδ T cells , and suggest novel diagnostic and therapeutic approaches in acute infection ., Neutrophils are short-lived phagocytes that undergo spontaneous apoptosis in vitro unless rescued by survival signals ., We previously demonstrated that activated human Vγ9/Vδ2 T cells induce monocytes to survive and differentiate into inflammatory dendritic cells 31 ., Here , HMB-PP stimulated Vγ9/Vδ2 T cells had a similar survival effect on autologous neutrophils and readily rescued them from undergoing apoptosis ( Figure 1 ) ., This effect was selective and dependent on the number of Vγ9/Vδ2 T cells and the concentration of HMB-PP ., An increase in neutrophil survival could already be observed at ratios of only 1 γδ T cell per 100 neutrophils and at HMB-PP concentrations as low as 0 . 1–1 nM ., Activation of Vγ9/Vδ2 T cells in these cultures was confirmed by upregulation of CD69 and secretion of interferon ( IFN ) -γ ( Figure S1 in Text S1 ) ., The low γδ T cell numbers and HMB-PP concentrations needed to promote neutrophil survival in vitro are likely to have physiologic relevance ., Activated neutrophils mobilize intracellular stores of CD11b to the cell surface and shed CD62L , thus enhancing their potential to undergo firm adhesions with endothelial cells and extravasate at the site of inflammation ., In line with their anti-apoptotic effect on neutrophils , Vγ9/Vδ2 T cells induced upregulation of CD11b and loss of CD62L in surviving neutrophils in an HMB-PP dependent manner ( Figure 2A ) ., Importantly , synthetic HMB-PP alone did not have any activity on neutrophils in the absence of γδ T cells ( Figure 1 , Figure 2 and data not shown ) ., Rapid recruitment of neutrophils involves the chemotactic action of CXCL8 ( IL-8 ) produced at the site of inflammation , and increased endothelial permeability mediated by tumor necrosis factor ( TNF ) -α ., Analysis of the supernatants from the above experiments revealed that co-cultures of neutrophils and HMB-PP stimulated Vγ9/Vδ2 T cells produced considerable amounts of both CXCL8 and TNF-α , in a dose-dependent manner and at levels comparable to lipopolysaccharide ( LPS ) stimulated neutrophils ( Figure 2B ) ., Another cytokine implicated in neutrophil recruitment is IL-17 , which in a number of infection models is readily produced by murine γδ T cells 8 ., While activated Vγ9/Vδ2 T cells readily produce TNF-α , IFN-γ and granulocyte/macrophage colony-stimulating factor ( GM-CSF ) 31 , 32 , we were unable to detect IL-17 in our co-cultures indicating that under the conditions tested human γδ T cells failed to secrete relevant levels of IL-17 ( data not shown ) ., This is reminiscent of recent findings that human αβ T cells including human Th17 cells modulate neutrophils ( which lack the IL-17 receptor C chain ) in an IL-17–independent manner through a combination of TNF-α , IFN-γ and GM-CSF 33 ., In our cultures , blocking experiments demonstrated that TNF-α played a key role in the γδ T cell-mediated effect on neutrophils , as judged by a partial inhibition of neutrophil survival and a reduction of CD11b expression in the presence of soluble TNF-α receptor ( sTNFR ) , while neutralizing antibodies against GM-CSF and IFN-γ had no significant effect ( Figure 3 ) ., Taken together , these data show that Vγ9/Vδ2 T cells become activated by soluble HMB-PP in the presence of autologous neutrophils and that they provide potent stimulatory signals inducing neutrophil survival and activation ., The interaction of the two cell types leads to the rapid release of the pro-inflammatory mediators CXCL8 and TNF-α into the microenvironment , thereby potentially maintaining neutrophil influx at the site of infection ., Under physiological conditions , invading pathogens are rapidly taken up by newly recruited neutrophils ., We therefore tested whether Vγ9/Vδ2 T cells respond to neutrophils harboring phagocytosed bacteria in a similar manner as they respond to soluble HMB-PP ., In order to do this , we set up triple cultures consisting of neutrophils , monocytes and Vγ9/Vδ2 T cells , mimicking physiological conditions at the site of infection ., Human neutrophils readily took up green fluorescent protein ( GFP ) expressing Escherichia coli , Listeria innocua and Mycobacterium smegmatis , with >95% of the neutrophils being GFP+ within 30 min ( Figure 4A and data not shown ) ., Triple co-cultures of neutrophils harboring different strains of M . smegmatis with autologous Vγ9/Vδ2 T cells and monocytes led to rapid γδ T cell activation , as evidenced by upregulation of CD69 and expression of TNF-α and IFN-γ within 20 hours ( Figure 4B and data not shown ) ., Activation profiles were similar to those seen in control cultures with non-infected neutrophils in the presence of synthetic HMB-PP , demonstrating that Vγ9/Vδ2 T cells respond to bacterial degradation products released or presented by neutrophils ., For the sake of clarity and simplicity all activation data in the following sections are shown as proportion of CD69+ TNF-α+ γδ T cells in the cultures although the cells were always co-stained for IFN-γ as well ., The proportion of CD69+ IFN-γ+ and TNF-α+ IFN-γ+ γδ T cells followed essentially the same pattern throughout this study and led to the same conclusions ., In order to investigate the correlation between the ability of bacteria to produce HMB-PP and their capacity to stimulate Vγ9/Vδ2 T cells , we designed experiments to distinguish a specific γδ T cell response to HMB-PP from a possible background stimulation by the plethora of other microbial compounds acting via pattern recognition receptors ., Thus , we generated a M . smegmatis transfectant stably expressing a second copy of the gene encoding HMB-PP synthase ( gcpE ) and hence overproducing HMB-PP compared to the parental wildtype ( wt ) strain 34 ( Figure S2 in Text S1 ) ., As a second bacterial model we utilized HMB-PP producing and HMB-PP deficient strains of the non-pathogenic Gram-positive bacterium Listeria innocua 35 , 36 ( Figure S3 in Text S1 ) ., Compared with M . smegmatis wt bacteria , higher levels of Vγ9/Vδ2 T cell activation were observed when using M . smegmatis-gcpE+ ( Figure 5 ) ., Furthermore , considerable Vγ9/Vδ2 T cell activation was seen with phagocytosed L . innocua-gcpE+ , a strain in which HMB-PP artificially accumulates , but not with the naturally HMB-PP deficient L . innocua wt strain that was >100× less potent ( Figure 5 ) ., The double transfectant L . innocua-gcpE+lytB+ , in which HMB-PP becomes converted into the downstream reaction products isopentenyl pyrophosphate ( IPP ) and dimethylallyl pyrophosphate ( DMAPP ) , resulted in no detectable Vγ9/Vδ2 T cell activation ( data not shown ) ., These data demonstrate that the response of Vγ9/Vδ2 T cells to neutrophils harboring phagocytosed bacteria depends on the ability of these bacteria to produce HMB-PP and suggest that phagocytosis and subsequent degradation of bacteria in neutrophils leads to either presentation of HMB-PP on the cell surface or the release of soluble HMB-PP into the microenvironment ., Vγ9/Vδ2 T cells , monocytes and neutrophils share a responsiveness toward inflammatory chemokines and are the earliest leukocytes recruited to sites of infection ., Vγ9/Vδ2 T cell responses in vitro are greatly facilitated by contact with monocytes as ‘feeder cells’ , which most likely act by ‘presenting’ HMB-PP to Vγ9/Vδ2 T cells and by providing contact-dependent signals 17 ., In support of our previous observation of a substantial HMB-PP dependent crosstalk between Vγ9/Vδ2 T cells and monocytes leading to optimum γδ T cell activation 31 , the response of Vγ9/Vδ2 T cells to neutrophils harboring phagocytosed L . innocua-gcpE+ was largely dependent on the presence of monocytes ., Omission of monocytes from the co-cultures resulted in greatly reduced expression levels of CD69 , TNF-α and IFN-γ , compared to triple co-cultures ( Figure 6A and data not shown ) , suggesting that monocytes provide essential help for the recognition of bacteria by Vγ9/Vδ2 T cells and increase the sensitivity of the response especially at low HMB-PP concentrations ., We speculated that this accessory effect might have stemmed from contact-dependent interactions of monocytes with either neutrophils or γδ T cells and tested this hypothesis in transwell cultures where neutrophils harboring phagocytosed L . innocua-gcpE+ in the lower chamber were separated from Vγ9/Vδ2 T cells in the upper chamber ., As shown in Figure 6B , cell-cell contact between monocytes and Vγ9/Vδ2 T cells was crucial for the response to phagocytosed bacteria , while no contact was needed between Vγ9/Vδ2 T cells and neutrophils , and neither between monocytes and neutrophils ., These data indicate that upon phagocytosis of HMB-PP+ bacteria , neutrophils release soluble factors that efficiently stimulate Vγ9/Vδ2 T cells , while monocytes provide important contact-dependent accessory signals ., Since neutrophils harboring bacteria were able to stimulate Vγ9/Vδ2 T cells in a transwell system , we next examined whether cell-free culture supernatants derived from infected neutrophils stimulated Vγ9/Vδ2 T cells in a similar manner ., Indeed , Vγ9/Vδ2 T cells readily responded to supernatants from neutrophils harboring L . innocua-gcpE+ but not from neutrophils harboring L . innocua wt bacteria , as evidenced by expression of CD69 , TNF-α and IFN-γ ( Figure 6C and data not shown ) ., Importantly , short-term pre-treatment of L . innocua-gcpE+ supernatants with shrimp alkaline phosphatase abrogated the bioactivity on Vγ9/Vδ2 T cells completely ( Figure 6C ) , evoking the known sensitivity of mycobacterial HMB-PP to dephosphorylation and the relative inactivity of the dephosphorylated products 37–39 ., Control experiments confirmed that alkaline phosphatase affected the response of Vγ9/Vδ2 T cells to synthetic HMB-PP but not to the phosphatase-resistant diphosphonate analogue , HMB-PCP 40 , demonstrating that the presence of alkaline phosphatase in the cultures had no inhibitory effect on the cells ability to express CD69 , TNF-α and IFN-γ ( Figure 6D and data not shown ) ., We conclude that upon phagocytosis of HMB-PP+ bacteria neutrophils release soluble HMB-PP into the microenvironment where it becomes accessible to monocytes and Vγ9/Vδ2 T cells ., In order to assess the clinical relevance of our findings , we expanded our panel of bacteria by including clinical isolates of pathogens that are frequently associated with community- and hospital-acquired infections and pose serious threats to public health ( Table S1 in Text S1 ) ., Of note , neutrophils harboring HMB-PP+ pathogens but not neutrophils harboring HMB-PP− pathogens induced in Vγ9/Vδ2 T cells the co-expression of CD69 , TNF-α and IFN-γ ( Figure 7A and data not shown ) ., This response was largely independent of the presence of other pathogen-associated molecular patterns such as LPS as both Gram-negative ( Acinetobacter baumannii , Enterobacter cloacae , Klebsiella pneumoniae , Pseudomonas aeruginosa ) and Gram-positive bacteria ( M . smegmatis ) capable of producing HMB-PP stimulated Vγ9/Vδ2 T cells equally ., Direct addition of alkaline phosphatase to these co-cultures abrogated the HMB-PP dependent cytokine responses , confirming soluble HMB-PP as common Vγ9/Vδ2 T cell stimulator in these species ( Figure 7A , Figure S4 in Text S1 ) ., The bioactivity of culture supernatants harvested after 5 hours from neutrophils harboring the above bacteria corresponded to levels of 0 . 1–10 nM HMB-PP , as titrated against a HMB-PP standard ( data not shown ) ., Residual levels of CD69 expression after phosphatase treatment may have been due to incomplete degradation of HMB-PP and to indirect stimulation of Vγ9/Vδ2 T cells by other microbial compounds such as LPS acting on neutrophils or monocytes 41 , 42 ., In contrast to HMB-PP producing species , HMB-PP deficient Gram-negative ( Chryseobacterium indologenes ) and Gram-positive bacteria ( Enterococcus faecalis , L . innocua , Staphylococcus aureus ) did not elicit Vγ9/Vδ2 T cells responses above background as demonstrated by the complete lack of TNF-α ( Figure 7A , Figure S4 in Text S1 ) and IFN-γ ( data not shown ) ., These findings illustrate the extraordinary specificity Vγ9/Vδ2 T cells for HMB-PP , even in the abundant presence of other microbial products and despite high levels of monocyte and/or neutrophil-derived mediators such as IL-1β , IL-6 and CXCL8 that were present in our triple co-cultures regardless of the HMB-PP status of the phagocytosed bacteria ( data not shown ) ., γδ T cells expand rapidly in acute bacterial infections 18 ., We therefore tested whether phagocytosed pathogens could induce expansion of 5- ( and 6- ) carboxyfluorescein diacetate succinimidyl ester ( CFSE ) -labeled Vγ9/Vδ2 T cells ., As shown in Figure 7B , Vγ9/Vδ2 T cells proliferated considerably in the presence of supernatants derived from neutrophils harboring HMB-PP+ Enterobacter cloacae but not from neutrophils harboring HMB-PP− Chryseobacterium indologenes or Staphylococcus aureus ., Similarly to the immediate up-regulation of CD69 , TNF-α and IFN-γ , the proliferation of Vγ9/Vδ2 T cells in response to Enterobacter cloacae was HMB-PP dependent and could be abrogated by alkaline phosphatase ., Expanding Vγ9/Vδ2 T cells also up-regulated the high affinity IL-2 receptor , CD25 ( Figure 7B ) and became responsive to exogenously added IL-2 , which enhanced the proliferative response even further ( data not shown ) ., Blocking experiments demonstrated a crucial requirement of soluble and contact-dependent signals for optimum stimulation of Vγ9/Vδ2 T cells ., TNF-α was recently implicated in Vγ9/Vδ2 T cell proliferation in response to IPP and IL-2 43 , and blocking of lymphocyte function-associated antigen-1 ( LFA-1 , CD11a/CD18 ) efficiently disrupted cluster formation with monocytes 31 ., Here , both Vγ9/Vδ2 T cell proliferation and CD25 up-regulation in response to supernatants derived from neutrophils harboring HMB-PP+ Klebsiella pneumoniae ( Figure 7B ) or Enterobacter cloacae ( data not shown ) were readily inhibited by sTNFR and anti-CD11a antibodies but not by anti-IFN-γ antibodies ., Finally , addition of anti-Vγ9 antibodies completely abrogated the Vγ9/Vδ2 T cell proliferation in response to HMB-PP ( data not shown ) and Enterobacter cloacae supernatants ( Figure S7 in Text S1 ) , confirming a requirement for the TCR 44 ., Taken together , our findings demonstrate that Vγ9/Vδ2 T cells are rapidly activated by a broad range of HMB-PP producing pathogens , leading to TCR , LFA-1 and TNF-α dependent γδ T cell expansion ., We next addressed whether the dichotomy between HMB-PP+ and HMB-PP− pathogens in their potential to trigger γδ T cells in vitro is replicated under physiological conditions in vivo ., As clinical correlate for HMB-PP+ and HMB-PP− infections , we analyzed episodes of acute bacterial infections in peritoneal dialysis ( PD ) patients , in whom the peritoneal catheter affords continuous and non-invasive access to the inflammatory infiltrate ( Table S2 in Text S1 ) ., PD-associated peritonitis is characterized by a considerable influx of neutrophils and monocytes into the peritoneal cavity 45 , 46 , where the two cell types may become targets for local or infiltrating γδ T cells 31 , 47 ., Here , in a total of 24 newly recruited patients examined on the first day of acute peritonitis ( i . e . before administration of antibiotics ) , both the total number and the frequency of peritoneal Vγ9/Vδ2 T cells were elevated in HMB-PP+ infections compared to HMB-PP− infections , suggesting increased recruitment and/or proliferation in response to HMB-PP released by bacteria ( Figure 8 ) ., Moreover , local activation was evidenced by higher percentages of Vγ9/Vδ2 T cells expressing CD69 in the HMB-PP+ patient group ., In contrast , we did not see any significant differences in the numbers and frequencies of peritoneal neutrophils , monocytes/macrophages and total CD3+ T cells , regardless of the HMB-PP status of the causative pathogen ( Figure S5 in Text S1 ) ., Similarly , while the proportion of Vγ9/Vδ2 T cells within peritoneal CD3+ T cells was clearly elevated in HMB-PP+ infections , CD4+ and CD8+ T cells showed no such bias ( Figure S6 in Text S1 ) ., As Medzhitov stated recently , “inflammation is beneficial in appropriate amounts but can easily become detrimental when excessive because of its tissue-damaging potential” 48 ., PD patients constitute a particularly vulnerable group where inflammatory events can have profound consequences 49–51 ., We speculated that local activation of γδ T cells may contribute to inflammation-related damage and tested whether the occurrence of clinical complications in PD patients depends on the capacity of the causative pathogen to produce HMB-PP ., Our analysis of 26 patients treated at the University Hospital of Wales , Cardiff , UK , demonstrated that infections with HMB-PP+ bacteria were associated with worse outcomes , evidenced as higher mortality rates and higher incidences of technique failure ( i . e . , cessation of therapy due to catheter removal , transfer to hemodialysis or patient death ) , while HMB-PP− bacteria caused milder disease ( Figure 9 ) ., Of note , we were able to validate this pattern in two larger and entirely independent cohorts treated in Australia ( ANZDATA Registry ) and at the University Hospital of North Staffordshire , Stoke-on-Trent , UK ( Figure 9 ) ., In order to rule out that this pattern was not due to differences in Gram staining ( and hence endotoxin-related ) , we divided the group of HMB-PP+ bacteria further into Gram+ and Gram− species ., Our outcome analysis demonstrates that even within the Gram+ group , bacteria capable of producing HMB-PP were associated with worse outcomes compared to HMB-PP− pathogens ( Figure 9 ) , suggesting that the HMB-PP producing capacity of the causative pathogen might be of predictive value for the clinical outcome from bacterial peritonitis ( Table S3 in Text S1 ) ., In order to identify potentially useful diagnostic and prognostic biomarkers of inflammation severity and outcomes from bacterial infection , we measured a large number of immunological parameters ., These analyses identified elevated frequencies of peritoneal Vγ9/Vδ2 T cells on day 1 as possible predictor of subsequent technique failure within three months after infection ( Table 1 ) ., Similarly , expression of the activation marker HLA-DR by peritoneal Vγ9/Vδ2 T cells on the day of admission was associated with increased mortality ., No other parameters tested including the numbers and frequencies of neutrophils , monocytes or CD4+ and CD8+ T cells reached statistical significance ( data not shown ) ., Among soluble factors in peritoneal effluent , only elevated levels of TNF-α on day 1 indicated higher rates of technique failure and mortality ( Table 1 ) , while no such correlation was seen for other cytokines and chemokines , including GM-CSF , IFN-γ , IL-1β , IL-2 , IL-6 , IL-10 , IL-12p70 , IL-17 , IL-22 , CXCL8 , CXCL10 and sIL-6R ( data not shown ) ., Our findings of a rapid γδ T cell response to neutrophil-engulfed HMB-PP producing pathogens and its potential detrimental consequence in episodes of acute peritonitis may not only be of diagnostic and predictive value for affected patients , they also highlight possible new avenues of therapeutic intervention in bacterial infections ., HMB-PP is an intermediate of the non-mevalonate pathway of isoprenoid biosynthesis , in which the first enzymatic step catalyzed by 1-deoxy-d-xylulose-5-phosphate reductoisomerase ( Dxr ) can be inhibited by fosmidomycin ( Figure S8A in Text S1 ) , a natural antibiotic produced by Streptomyces lavendulae 52 , 53 ., We therefore speculated that the effect of fosmidomycin pre-treatment of bacteria may serve a dual purpose in treating acute infections: by directly inhibiting an essential pathway in a broad range of pathogens and by abrogating HMB-PP driven inflammatory responses ., Tests with selected clinical isolates of common pathogens demonstrated that the majority of HMB-PP+ bacteria ( Enterobacter cloacae , Klebsiella pneumoniae , Pseudomonas aeruginosa ) was susceptible to overnight treatment with fosmidomycin ( with the exception of Acinetobacter baumannii as expected 54 ) , with a mean inhibitory concentration ( MIC ) of 1–32 µg/ml depending on the strain ( Table S1 in Text S1 ) ., Of note , fosmidomycin also acted on multidrug-resistant strains including bacteria harboring the recently discovered ‘New Delhi’ metallo-β-lactamase 1 ( NDM-1 ) 55 , 56 ( Davey MS , Tyrrell JM et al . , submitted for publication ) ., In contrast to the efficient killing of most HMB-PP+ bacteria , the HMB-PP− bacteria Chryseobacterium indologenes , Enterococcus faecalis and Staphylococcus aureus were not affected by fosmidomycin ( Table S1 in Text S1 ) ., We next investigated the potential of short-term fosmidomycin treatment to affect γδ T cell activation by inhibiting the bacterial HMB-PP biosynthesis ., Prior exposure of bacteria to fosmidomycin for 1 hour did not affect uptake by neutrophils as demonstrated using Escherichia coli-gfp+ ( Figure S8B in Text S1 ) , and neither did it affect gross bacterial viability as confirmed by re-plating treated Enterobacter cloacae on antibiotic-free plates in order to overcome the competitive inhibition by fosmidomycin ( Figure 10A ) ., Yet , pre-incubation of Escherichia coli , Enterobacter cloacae and Klebsiella pneumoniae with fosmidomycin for only 1 hour prior to phagocytosis by neutrophils clearly abrogated their capacity to stimulate Vγ9/Vδ2 T cells ., This inhibitory effect on γδ T cell responses was evident both for activation of Vγ9/Vδ2 T cells in triple co-cultures with neutrophils harboring fosmidomycin-treated bacteria ( Figure 10A and data not shown ) as well as for activation ( Figure S8B in Text S1 ) and proliferation ( Figure 10B ) of Vγ9/Vδ2 T cells in response to cell-free supernatants from neutrophils harboring fosmidomycin-treated bacteria ., Together these results indicate that fosmidomycin not only has a direct antibacterial effect but also possesses immediate anti-inflammatory properties by inhibiting γδ T cell-driven responses ( Figure 11 ) , thus making the non-mevalonate pathway an attractive novel drug target for the treatment of acute infection ., Despite its relevance in early infection , the immediate crosstalk of γδ T cells , monocytes and neutrophils in the presence of bacterial pathogens has not been addressed in detail ., This is particularly the case in humans who possess a unique γδ T cell population uniformly targeting an invariant non-self-metabolite , HMB-PP ., Previous reports already associated the activation of Vγ9/Vδ2 T cells with the production of HMB-PP by microbes ., This link was mainly based on the observation that Vγ9/Vδ2 T cell levels are often elevated in the blood of patients infected with HMB-PP producing pathogens 18 and that bacterial extracts prepared from those species activate Vγ9/Vδ2 T cells in vitro much better than extracts prepared from HMB-PP deficient micro-organisms 19 , 35 , 57 ., Other investigators have speculated that Vγ9/Vδ2 T cells respond in vivo toward infected host cells with dysregulated isoprenoid metabolism leading to accumulation of isopentenyl pyrophosphate ( IPP ) regardless of the presence or absence of HMB-PP 58 ., Here we unequivocally demonstrate that Vγ9/Vδ2 T cells respond to live bacteria upon phagocytosis by neutrophils , that this response is strictly HMB-PP dependent , and that it is amplified by the presence of monocytes providing crucial accessory signals ., While it has remained puzzling how the immune system actually ‘sees’ an intracellular metabolite that is unlikely to be secreted or released by live micro-organisms , our findings show that biologically relevant traces of HMB-PP escape into the microenvironment after phagocytosis of extracellular bacteria by neutrophils ., These conditions are likely to occur during the acute stage of the infection when Vγ9/Vδ2 T cells and monocytes are co-recruited to the site of inflammation 17 where they encounter neutrophils engaged in clearing invading pathogens ( Figure 11 ) ., The present findings explain how HMB-PP may become released at the site of infection ., However , the molecular mechanism of HMB-PP recognition by Vγ9/Vδ2 T cells remains poorly understood ., Our observation that monocytes were required for Vγ9/Vδ2 T cell responses to phagocytosed bacteria offers important clues ., Monocytes and monocyte-derived macrophages or DCs were shown before to provide accessory help and may constitute a pivotal trigger for Vγ9/Vδ2 T cell responses to different bacterial pathogens ., In the case of direct infection of monocytic cells , HMB-PP derived from intracellular bacteria may reach the cell surface bound to a presenting molecule 24 , 25 , 27 ., In the case of extracellular bacteria , monocytes may take up or bind soluble HMB-PP released by professional phagocytes and present it to Vγ9/Vδ2 T cells ( Figure 11 ) ., The HMB-PP presenting pathway remains elusive but may involve cell surface F1-ATPase 59 , together with tight cell-cell interactions via LFA-1/ICAM-1 31 , 60 , while it is independent of MHC class I , MHC class II , β2-microglobulin or CD1 61 ., Of note , any chemical modification of the molecular structure of HMB-PP abrogates its bioactivity by several magnitudes , such that the closely related natural metabolites IPP and DMAPP are >10 , 000 times less active in vitro 39 , 40 , 62 , 63 ., This is supported by our previous 35 , 64 , 65 and present demonstration that HMB-PP deficient bacteria ( but which produce IPP and DMAPP ) fail to stimulate cytokine production by Vγ9/Vδ2 T cells ., Treatment with fosmidomycin or alkaline phosphatase abrogated the Vγ9/Vδ2 T cell responses to HMB-PP producing bacteria and emphasized the importance of HMB-PP for the induction of IFN-γ and TNF-α ., However , fosmidomycin or alkaline phosphatase treated cultures as well as cultures involving HMB-PP deficient bacteria did show residual levels of CD69 expression , in line with a role for direct or indirect sensing of microbial TLR ligands 41 , 42 , 66 that is likely to amplify the overall response ., In this respect it is intriguing that our present study identified a crucial role for TNF-α in supporting Vγ9/Vδ2 T cell proliferation , a cytokine which is readily produced not only by activated Vγ9/Vδ2 T cells themselves but also by neutrophils and monocytes exposed to microbial compounds such as LPS ., This is in stark contrast to other cytokines produced by innate immune cells such as IFN-α and IFN-β which may induce upregulation of CD69 on Vγ9/Vδ2 T cells but fail to co-stimulate Vγ9/Vδ2 T cell proliferation 32 ., Taken together , we identified an inflammatory crosstalk of Vγ9/Vδ2 T cells , neutrophils and monocytes in the presence of HMB-PP producing bacteria that can be manipulated at various check-points:, ( i ) the antibiotic fosmidomycin abrogates the microbial HMB-PP production and thus renders bacterial pathogens invisible for Vγ9/Vδ2 T cells;, ( ii ) alkaline phosphatase degrades free HMB-PP released by neutrophils into the microenvironment;, ( iii ) blocking antibodies against the TCR prevent the recognition of HMB

Introduction, Results, Discussion, Materials and Methods

Human blood Vγ9/Vδ2 T cells , monocytes and neutrophils share a responsiveness toward inflammatory chemokines and are rapidly recruited to sites of infection ., Studying their interaction in vitro and relating these findings to in vivo observations in patients may therefore provide crucial insight into inflammatory events ., Our present data demonstrate that Vγ9/Vδ2 T cells provide potent survival signals resulting in neutrophil activation and the release of the neutrophil chemoattractant CXCL8 ( IL-8 ) ., In turn , Vγ9/Vδ2 T cells readily respond to neutrophils harboring phagocytosed bacteria , as evidenced by expression of CD69 , interferon ( IFN ) -γ and tumor necrosis factor ( TNF ) -α ., This response is dependent on the ability of these bacteria to produce the microbial metabolite ( E ) -4-hydroxy-3-methyl-but-2-enyl pyrophosphate ( HMB-PP ) , requires cell-cell contact of Vγ9/Vδ2 T cells with accessory monocytes through lymphocyte function-associated antigen-1 ( LFA-1 ) , and results in a TNF-α dependent proliferation of Vγ9/Vδ2 T cells ., The antibiotic fosmidomycin , which targets the HMB-PP biosynthesis pathway , not only has a direct antibacterial effect on most HMB-PP producing bacteria but also possesses rapid anti-inflammatory properties by inhibiting γδ T cell responses in vitro ., Patients with acute peritoneal-dialysis ( PD ) -associated bacterial peritonitis – characterized by an excessive influx of neutrophils and monocytes into the peritoneal cavity – show a selective activation of local Vγ9/Vδ2 T cells by HMB-PP producing but not by HMB-PP deficient bacterial pathogens ., The γδ T cell-driven perpetuation of inflammatory responses during acute peritonitis is associated with elevated peritoneal levels of γδ T cells and TNF-α and detrimental clinical outcomes in infections caused by HMB-PP positive microorganisms ., Taken together , our findings indicate a direct link between invading pathogens , neutrophils , monocytes and microbe-responsive γδ T cells in early infection and suggest novel diagnostic and therapeutic approaches .

The immune system of all jawed vertebrates harbors three distinct lymphocyte populations – αβ T cells , γδ T cells and B cells – yet only higher primates including humans possess so-called Vγ9/Vδ2 T cells , an enigmatic γδ T cell subset that uniformly responds to the majority of bacterial pathogens ., For reasons that are not understood , this responsiveness is absent in all other animals although they too are constantly exposed to a plethora of potentially harmful micro-organisms ., We here investigated how Vγ9/Vδ2 T cells respond to live microbes by mimicking physiological conditions in acute disease ., Our experiments demonstrate that Vγ9/Vδ2 T cells recognize a small common molecule released when invading bacteria become ingested and killed by other white blood cells ., The stimulation of Vγ9/Vδ2 T cells at the site of infection amplifies the inflammatory response and has important consequences for pathogen clearance and the development of microbe-specific immunity ., However , if triggered at the wrong time or the wrong place , this rapid reaction toward bacteria may also lead to inflammation-related damage ., These findings improve our insight into the complex cellular interactions in early infection , identify novel biomarkers of diagnostic and predictive value and highlight new avenues for therapeutic intervention .

medicine, infectious diseases, clinical immunology, immunology, biology, microbiology, bacterial pathogens

journal.pgen.1005854

The Epigenomic Landscape of Prokaryotes

DNA methylation has widespread roles in the regulation of eukaryotic genomes 1–3 , but the extent to which similar processes exist in prokaryotes is unknown ., Methylated DNA is found in the genomes of bacteria and archaea in the forms of 6-methyladenosine ( m6A ) , 4-methylcytosine ( m4C ) , and 5-methylcytosine ( m5C ) 4 , and is the product of DNA methyltransferase ( MTase ) enzymes 5 ., MTases are often a component of restriction-modification ( RM ) systems 6 , but have also been implicated in DNA mismatch repair 7 and other epigenetic regulatory phenomena 8 ., While MTase genes are present in the genomes of many prokaryotes , the overall abundance and patterns of prokaryotic DNA methylation , and the functional diversity of MTases remains largely unknown ., RM systems play a central role in prokaryotic defense , and their constituent enzymes are foundational tools in modern molecular biology 6 ., RM systems comprise a restriction endonuclease ( REase ) and a MTase with the same DNA binding specificity ., The REase degrades DNA from viruses and other exogenous sources , while the cognate MTase methylates potential REase target sites in the host genome and thus protects them from cleavage ., RM systems are classified into four main types 5 , 6 , 9 , 10 ., Type I RM systems are complex , multi-subunit systems composed of separate REase and MTase subunits , and a common DNA recognition specificity ( S ) subunit 11 ., The S subunit in combination with two MTase subunits methylates DNA , while the S subunit in combination with two MTase subunits and two REase subunits results in restriction ., Type I RM systems recognize bi-partite motifs ( e . g . CAGNNNNNTCA ) , and cleave at large distances ( up to several kb ) from their binding site ., Type II RM systems comprise separate REase and MTase enzymes , which are expected to show identical DNA binding specificity 12 ., They bind short , mostly palindromic , motifs ( e . g . GATC ) , and cleave DNA within or close to the recognition site ., Exceptions are the Type IIG RM systems that are single chain polypeptides containing both DNA restriction and methylation activities , bind short non-palindromic sequences ( e . g . GCCCAG ) , and cleave DNA outside of the DNA binding site 12 ., In Type III systems the MTase alone contains a DNA binding specificity domain and forms a complex with the REase in order to restrict 13 ., They bind short non-palindromic motifs ( e . g . CGAAT ) and cut outside of the DNA binding site ., Finally , Type IV RM systems cut modified DNA and do not have a MTase component 14 ., Knowledge of the binding specificities of RM systems is critical to understanding their biological functions ., Traditional approaches to determine RM system specificities rely on patterns of DNA cleavage by REases , a strategy that limits discovery largely to Type II RM systems where the REase binds and cleaves DNA at the same location 5 ., Owing to this limitation , while the DNA binding specificities of several thousand Type II RM systems are known , typically fewer than 100 of each of the other types of RM system are known 5 ., For Type I , IIG and III systems that cut outside of the RM binding site , a more recent alternative approach is to take advantage of the identical motif specificities of methylation and restriction ., In these cases , determination of the sequences methylated by the MTase can directly reveal the recognition sequence of the accompanying REase , as recently demonstrated for individual RM systems 15–21 ., Beyond RM systems , MTases can also be involved in prokaryotic genome regulation 8 , 22 ., These enzymes are typically observed as ‘orphan’ MTases that are found encoded in prokaryotic genomes in the absence of genes encoding a cognate restriction enzyme 23 ., Examples include the Dam MTases that regulate DNA replication timing and gene expression of Gammaproteobacteria 24 and the CcrM MTases that regulate cell cycle progression of Alphaproteobacteria 19 , 25 ., While genome-wide methylation analysis of individual genomes can in principle identify regulatory MTases and provide insight into the associated regulatory DNA methylation system 17 , 18 , 20 , 21 , 26 , 27 , in the absence of systematic mapping efforts it has remained unclear how common such mechanisms are in prokaryotes ., It is unknown whether the MTases associated with RM systems can also play a regulatory role ., MTase-encoding genes are present in the majority of bacterial and archaeal genomes , suggesting that DNA methylation may be similarly abundant ., Bisulfite sequencing has enabled genome-wide surveys of 5mC methylation 28 , 29 , but a historic absence of tools for studying m6A and m4C modifications that predominate in prokaryotic DNA30 has precluded more comprehensive studies ., It has recently been demonstrated that kinetic analysis of single molecule , real-time ( SMRT ) sequencing data can directly detect many types of DNA modification 4 , 31 , 32 ., While this approach is only modestly sensitive to m5C methylation , it is capable of detecting both m6A and m4C highly with a high degree of accuracy and sensitivity ., The application of SMRT sequencing to a small number of prokaryotes enabled the identification of methylated motifs , and annotation of the respective MTases 15–21 ., In the present study , we systematically use SMRT sequencing to uncover the patterns of DNA methylation across a large panel of more than 200 diverse bacterial and archaeal genomes to provide an overview of the epigenomic landscape of prokaryotes ., In so doing we reveal the ubiquity of DNA methylation , and annotate DNA binding specificities for hundreds of MTases belonging to previously intractable types of RM systems ., Furthermore , we demonstrate that a large proportion of the ‘orphan’ MTase genes encoded in prokaryotic genomes are active under normal conditions and produce patterns of DNA methylation that are consistent with gene regulatory functions ., Our findings provide evidence for the pervasiveness and potentially diverse functions of DNA methylation in prokaryotic genomes ., To explore the locations and potential functions of DNA methylation across prokaryotes , we selected 230 organisms for study , including 217 bacterial and 13 archaeal species , spanning 19 different phyla and 37 different classes ( Fig 1A , S1 Table ) ., These organisms were selected primarily based on their phylogenetic diversity to enable a comprehensive survey of bacterial methylation systems and maximize the chances for discovery of novel systems ., For each organism , we isolated genomic DNA , and performed deep single molecule , real-time ( SMRT ) sequencing ., We obtained on average 130-fold read coverage per organism , resulting in a combined dataset size of more than 79 million single-molecule reads and 105 Gb across all sequenced genomes ., We aligned all SMRT sequences to the respective reference genomes , and used kinetic data analysis to identify the locations and probable types ( m6A , m4C , m5C ) of high-confidence base modifications in each sequenced genome ( see Methods ) ., We then identified sequence motifs that were recurrently methylated in each genome ( Methods ) ., The results of these analyses were genome-wide basepair-resolution methylation maps for each organism examined , as well as a set of modified motifs for each genome , where each motif represents the likely binding specificity of a DNA MTase ., In total we identified 858 methylated motifs , with DNA modifications detected from 215 / 230 organisms ( 93% ) , and across all sequenced phyla ( Fig 1A ) ., On average , we observed 3 methylated motifs per organism , with a maximum of 19 in Neisseria gonorrhoeae ., Among modified motifs , the predominant base modification type detected was m6A ( 75% ) , with m4C and m5C accounting for 20% and 5% , respectively ( S1 Fig ) ., The large number of m6A methylated motifs is consistent with the frequent occurrence of this modification type in the database of known MTase specificities 5 , and the ease with which this modification type is detected by SMRT sequencing ., In contrast , the low frequency of m5C methylated motifs is an underestimate of the true number of such motifs across these genomes due to the lower sensitivity of SMRT sequencing to this modification type ( S2 Fig ) 16 ., The fifteen organisms without detectable methylation are from across the sampled taxa , with no obvious shared characteristics ., In 8/15 cases , their genomes lack predicted MTase genes ( but harbor methyl-directed restriction enzymes ) , while in other cases MTases are present but were not detectably active by SMRT sequencing ( S2 Table ) ., In summary , these data reveal that DNA methylation is widespread across prokaryotes , and provide a valuable resource for exploring the specificities and functions of the MTases present in these genomes ., To identify the individual MTases responsible for each methylated motif , we performed large-scale annotation of MTase binding specificities across the studied genomes ., Using an integrative RM-system gene annotation pipeline ( Methods ) , we identified 1 , 459 candidate MTase genes across the 230 genomes , and classified them according to RM-system type ( panel A in S3 Fig ) ., We then similarly classified the 858 detected motifs according to the type of MTase system to which they likely belong ( panel B in S3 Fig ) ., Comparison of the types of methylated motifs and MTase genes within the same organism enabled us to make initial predictions of the MTase enzyme responsible for each observed methylated motif ( Fig 1B ) ., For nearly all detected methylated motifs ( 849 , 99% ) , we identified at least one candidate MTase in the same genome predicted to be capable of producing the modification ., In contrast , there were many ( 640 , 44% ) candidate MTase genes for which no potential modification activity was detected ., Of these 227 are MTases that are predicted to produce m5C modifications that are difficult to detect by SMRT sequencing ., Other cases may be MTases that are inactive due to genetic drift , mis-identified enzymes that target RNA or protein rather than DNA , or genes that are not expressed , as frequently occurs when MTases are located on prophages ., In 620 cases , we were able to unambiguously match a single candidate MTase to a motif of the same type in the same genome ( Fig 1C ) , thus generating a set of high confidence annotations of MTase specificities ( S3 Table and S4 Table ) ., The remaining unmatched motifs are due to several candidate MTases being present in the same genome , with insufficient evidence to make an unambiguous assignment ., For almost all Type I and III MTase gene predictions , a cognate REase was identified in the same genomic region , suggesting that these constitute intact RM systems , and enabling the systematic annotation of restriction specificities ( Fig 1D , S1 Text ) ., In contrast , restriction enzyme candidates could not be identified for over half ( 165/318 ) of the Type II MTases that are present ( Fig 1D ) ., This is consistent with the previous observation that Type II MTases frequently occur as orphans in bacterial genomes 23 ., While we cannot exclude the possibility that some novel REase genes were not identified due to sequence divergence , these 165 orphan Type II MTases represent a large group of MTases with likely non-RM functions ., Comparison with known RM systems 5 indicates that our systematic analysis identified 148 RM systems with previously undescribed sequence determinants , substantially expanding the repertoire of available specificities ., The discovery rate of novel enzyme specificities was particularly large for Type I , IIG , and III RM systems that have been historically difficult to study using conventional approaches ( Fig 2A ) ., For example , 92% ( 161/175 ) of annotated Type I system specificities identified in our study were novel ., In addition , among the Type I motifs that could not be matched to genes the majority were new specificities not seen previously ., As a result , our analysis increases the number of known Type I system specificities almost four-fold ( from 76 to 293 , Fig 2B ) ., Our data also reveals the extraordinary diversity of modes of DNA recognition by Type I RM systems , with variation observed in all aspects of the DNA recognition architecture ( Fig 2C ) ., We also identified a substantial number of novel recognition specificities by Type IIG and Type III MTases ., Among Type IIG RM systems annotated , 82% ( 56/68 ) were novel , while the same was true for 79% ( 47/59 ) of the Type III specificities ( Fig 2A ) ., Unmatched motifs in these categories cannot always be unambiguously attributed as being from a Type IIG or Type III enzyme because both lead to characteristic single-strand methylation ., Preliminarily we have considered short recognition sequence of 4 or 5 bases to most likely belong to the Type III family , while the longer recognition sequences of 6 or more base pairs are considered as Type IIG ., Overall , the number of observed specificities across these Types of restriction system increased 2 . 7-fold ( from 144 to 385 ) as a result of our study ., Previously , protection against Type I restriction enzymes was always found to be mediated by m6A modification 11 ., In this study , we find examples of protection by m4C ( M . Dac11109IV in Desulfobacca acetoxidans and M1 . Mma5219I in Methanohalophilus mahii , S3 Table ) ., Similar results have been obtained from other recent studies 5 , and several of these systems have now been experimentally verified ( Morgan et al . personal communication ) ., Interestingly , when this happens there are two MTase genes associated with the system , one of which appears responsible for m6A methylation and the other for m4C methylation ., In these cases the bipartite recognition sequence of the Type I S subunit has only G and C residues in one of the target recognition domains , which explains why m6A cannot be used to protect both halves ., There are many homologs elsewhere in REBASE of systems like this , but often the specificity is unknown 5 ., A similar situation has also been found for some Type III MTases where occasionally m4C is found as the protective modification both in some of the systems identified here as well as others 5 ., Type IIG systems are defined by the presence of a single target recognition domain ( TRD ) for the entire RM system ., They typically consist of a single polypeptide containing both the endonuclease domain and m6A MTase , as in the prototypical enzyme MmeI 33 ( S4A Fig ) ., Here , we identified 76 novel Type IIG-like systems , many of which were atypical in terms of gene order , presence or absence of a DNA translocase , and differences in linkage between the endonuclease and MTase domains ( S3 Table and panels B-E in S4 Fig ) ., For example , we identified several different systems in which one peptide contains an MmeI family MTase/TRD , but in which the endonuclease is encoded on a separate peptide ( AchA6III and OspHL35III , panels B and C in S4 Fig ) ., Other examples such as CalB3II ( panel D in S4 Fig ) are new examples of BREX-like systems 34 ., These systems use the specific methylation of the MTase protein to distinguish self from non-self in phage restriction , but appear to accomplish restriction without generating DNA cleavage ., Finally , we observe novel systems that are unrelated to MmeI or BREX ., For example , MexAMORF1192P is a four-protein system of two translocase proteins and separate MTase-TRD and endonuclease proteins ( panel E in S4 Fig ) ., These analyses highlight the value of SMRT-sequencing in annotating novel RM systems ., The examples we describe represent just a portion of the wide diversity of Type IIG-like systems that evolve from various permutations of endonuclease , MTase and translocase domains with a single DNA recognition module ., The preliminary annotations of Type IIG-like MTases from this study can be propagated across many orthologs and will enable their further characterization and systematic classification ., While Type II RM systems represent historically the best-studied class of RM systems , our systematic survey identified a substantial number of new Type II RM systems , some of which have unusual properties ., For example , all Type II RM systems described to date are characterized by close genomic proximity of the genes encoding the REase and the MTase , respectively 5 ., We observed one pair of adjacent MTases M1 . Csp12AI and M2 . Csp12AI in Clostridium sp ., 12 ( A ) that were very similar to the m6A-MTase M . FokI from Flavobacterium okeanokoites ., However , in Clostridium sp ., 12 ( A ) the gene encoding the corresponding FokI-like restriction enzyme was not found in the immediate vicinity of M1/M2 . Csp12AI , but at a genomic location 1 . 2 megabase pairs ( Mb ) away ., All three genes were tested for activity by cloning ., While M2 . Csp12AI could be cloned alone , it was only possible to clone the M1 . Csp12AI gene in the presence of M2 . Csp12AI ., In both cases , just as in the genome , both MTases were shown to be fully functional by PacBio sequencing of DNA ( S5 Fig ) ., To exclude the possibility that the large apparent distance resulted from an incorrect genome assembly , we confirmed by PCR that the distance between the REase gene and the two MTase genes is at least 36 kb ( S6 Fig ) ., These results indicate that , unlike all previously described Type II RM systems , there are Type II RM systems in which the REase and MTase genes are located at distant sites on the chromosome ., Our systematic survey identified 165 candidate ‘orphan’ Type II MTases ( Fig 3A , S3 Table and S4 Table , Methods ) ., These MTases are found in isolation , i . e . in the absence of corresponding restriction enzymes , but nonetheless actively methylate specific sites in the genome ., This feature raises the possibility that these MTases are involved in non-RM-functions , such as gene regulation ., Orphan MTases are widely distributed among prokaryotes with at least one example in 111 ( 48% ) organisms and 15/20 different phyla included in this study ( Fig 3B ) ., To explore the properties and potential functions of orphan MTases in more detail , we first examined the phylogenetic conservation of orphan and RM system MTases ., We determined the presence or absence of each MTase among all sequenced species related to the host organism at the genus , family or class level , and with an available reference genome sequence ( Methods ) ., We considered MTases to be conserved if present in at least 50% of species within the respective taxonomic group ( Fig 3C ) ., Overall , orphan MTases are far more likely to be evolutionarily conserved than RM system-associated MTases ., For example , the majority of orphan MTases ( 57% ) are conserved at the genus level , while the same is true for only 9% of RM system MTases ., A similar contrast between orphan and RM MTases is observed at the level of family and class ( Fig 3C ) ., These results are consistent with a greater degree of conservation of orphan MTases compared with RM MTases 23 , and suggest that orphan MTases have functional roles distinct from host protection ., We next performed protein sequence similarity-based clustering to identify candidate novel families of related orphan MTases ., We generated initial protein clusters from all 260 Type II MTases in our study ( S7 Fig and S8 Fig ) , then extracted sub-clusters of orphan MTases from taxonomically related host organisms and with identical motif recognition sequences ( Methods ) ., These analyses resulted in 19 orphan MTase families accounting for 107 / 165 orphan MTases in our study ( Fig 3D ) ., The remaining 58 MTases are ‘singletons’ with no ortholog in any other genome in our dataset ., The two most highly represented orphan MTase families in our study are the known regulatory orphan Dam MTases in Gammaproteobacteria , and CcrM MTases in Alphaproteobacteria , reflecting our large sampling of organisms from these taxa ., Of the remaining 17 candidate families , 3 are apparent homologs of Dam MTases in Cyanobacteria and two archaeal classes , respectively ., The other 12 families are novel orphan MTases of unknown function and are found in diverse prokaryotes including both bacteria and archaea ., The most highly represented orphan MTase family methylates the motif 5’-RAm6ATTY-3’ ( T indicates that the A on the complementary strand is modified ) in all six Spirochaetaceae sequenced as part of this study ., This motif and orphan MTase had previously been observed in Campylobacter jejuni 16 ., In many cases , novel orphan MTase families are widely conserved in genomes beyond those included in our study ., For example , the gene for the orphan MTase targeting 5’-TTA m6A-3’ in two Arthrobacter species in our study is present in 39 / 42 ( 93% ) of all sequenced genomes from the genus Arthrobacter ., Similarly the orphan MTase targeting 5’-m4CATG-3’ in two Haloarchaeal species in our study is present in 121 / 156 ( 78% ) of all sequenced genomes from the class Haloarchaea ( Fig 3D ) ., In summary , these analyses reveal the presence of several novel evolutionarily conserved families of orphan MTases of unknown function ., We hypothesize that some of the newly discovered orphan MTases function similarly to the known regulatory orphan MTases Dam and CcrM , i . e . that they regulate gene expression through the presence or absence of methylation in regulatory sequences ., Alternatively their function may be to regulate DNA replication , through clusters of motifs in regions of the genome associated with DNA replication control 35 ., To explore these possibilities in more detail , we searched our methylome data for signatures consistent with such functions ., It has previously been shown that a subset of target sites of the E . coli regulatory MTase Dam is completely unmethylated 36–38 ., These unmethylated sites are the consequence of the competing activities of Dam MTase and regulatory proteins , and the presence or absence of methylation at these sites has a demonstrated impact on gene expression 39 , 40 ., We therefore asked if we could recapitulate these findings for Dam MTases in our dataset , and if similar patterns are associated with novel orphan MTases ., In the E . coli data from this study , the vast majority ( 17 , 544/17 , 562 , 99 . 9% ) of 5’-G m6ATC-3’ motifs are fully methylated on both strands of the genome ., However , a distinct set of 18 5’-G m6ATC-3’ motifs is unmethylated on both strands of the genome ( Fig 4A ) ., These unmethylated sites include six GATC positions in upstream regulatory regions of agn43 genes that are known to be regulatory targets of Dam methylation 39 ., Unmethylated sites are also detected in association with the dam orphan MTase gene of Salmonella bongorii , ( Fig 4B , and S5 Table ) ., In contrast , unmethylated sites are absent from the genome of Clostridium thermocellum , a bacterium harboring a 5’-G m6ATC-3’ specific MTase that is part of an RM system ( Fig 4C ) ., These results suggest that the presence of small subsets of reproducibly unmethylated recognition motifs across the genome may be a distinctive signature of orphan MTases ., We extended this analysis to all m6A orphan and RM-system associated MTases in our dataset with sufficient SMRT sequencing coverage for confident detection of unmethylated sites ( Methods ) ., We observed widespread occurrence of unmethylated sites in association with Dam MTases across Gammaproteobacteria , as well as with the regulatory CcrM orphan MTases in Alphaproteobacteria ( consistent with recent observations of unmethylated sites in Caulobacter 21 ) ., Strikingly , we also observed unmethylated sites in association with at least one MTase for the majority ( 13/16 ) of novel orphan MTase families , as well as with over half of ‘singleton’ orphan MTases ( Fig 4D , S9 Fig and S5 Table ) ., In contrast , MTases of restriction systems are almost always associated with complete modification of their genomes , with only four apparently unmethylated sites observed across 41 RM MTases ( Fig 4D ) , and consistent with a role in protecting the genome from the cognate restriction enzyme ., On further inspection , all four apparent unmethylated RM MTase sites have modification scores at the borderline of detection , and likely represent the background false-positive rate of detection of unmethylated sites ., Overall these analyses confirm that unmethylated motifs are a common signature of novel orphan MTases , and may represent novel regulatory sites in the genome ., In known cases of gene regulation by orphan MTases , functionally relevant motif sites are located in regulatory sequences upstream of genes and are unmethylated in some or all of the population 39 , 41 ., We therefore asked whether the target motifs of the orphan MTases identified in this study are similarly associated with gene regulatory regions ( Fig 4E ) ., In general , orphan MTase motifs ( irrespective of their methylation state ) are not significantly enriched at gene regulatory regions ( defined as 100bp upstream of CDS start to 50bp downstream of CDS start , Fig 4E , grey bars ) ., However , two-thirds of orphan MTases are associated with a significant enrichment of unmethylated motifs in gene regulatory regions ( Fig 4E , black bars ) ., Furthermore , unmethylated motifs are especially enriched in the promoters of genes of related function , most notably transcriptional regulators ( Fig 4E ) ., For example , in Nocardia sp BMG111209 , unmethylated 5’-ATCGm6AT-3’ motifs are 5-fold enriched in gene regulatory regions , compared with fully methylated motifs ( 17/28 ( 61% ) , compared to 13% by chance ) ., This enrichment increases to more than 20-fold for unmethylated sites upstream of transcriptional regulators ( 7/28 ( 25% ) unmethylated motifs compared with only 1 . 2% methylated motifs , p < 0 . 01 ) ., Finally , at least in the case of dam methylases in gammaproteobacteria , unmethylated motifs overlap predicted transcription factor binding sites significantly more frequently than do methylated motifs ( S10 Fig and S5 Table ) ., Overall , these results demonstrate a substantial enrichment of unmethylated motifs in regulatory regions of the genome ., Since this enrichment is not merely a consequence of an elevated density of motifs in these regions , it may instead reflect the involvement of these sites in regulatory processes ., The patterns of novel orphan MTases ( including ‘singleton’ MTases ) resemble those of the known MTases Dam and CcrM , further supporting the possibility that they may have shared functions in the epigenetic control of gene expression ., While our analyses are generally consistent with a role for orphan DNA MTases in gene regulation , it is unclear which unmethylated sites represent targets of regulation ., Indeed , previous studies of unmethylated sites have shown that while some sites are important in regulating gene expression , others may represent inconsequential blocking of DNA methylation by tightly bound transcription factors 41 , 42 ., We therefore sought to prioritize our data to identify individual cases of putative regulation ., We first searched for unmethylated motifs at the same genomic location across multiple related organisms ., This analysis revealed 14 candidate regulatory sites across 5 different orphan MTases ( Table 1 ) ., Among conserved unmethylated sites is one upstream of the glucitol/sorbitol specific PTS system ( gut locus ) ., This site was previously identified in E . coli , and appeared to have no impact of gene regulation 41 , nevertheless the absence of methylation at this locus is strikingly well conserved across the Enterobacteria in our study ( S11 Fig ) ., We identified eight other Dam motifs at conserved locations and unmethylated in at least two Gammaproteobacteria ( Table 1 and Fig 5A ) ., We also identified conserved sites in association with three novel orphan MTases ., For example , we identify conserved unmethylated sites upstream of a PadR family transcriptional regulator in both Arthrobacter species , and show that the motif in question is extensively conserved across the Arthrobacter genus ( Fig 5B ) ., We next searched for the presence of unusual clusters of adjacent unmethylated motifs in related regions of the gene regulatory region , and identified seven potential regulatory regions across six orphan MTases ( Table 2 ) ., Among these regions are known regulatory sites upstream of the agn43 locus in E . coli 39 , supporting the validity of this approach for finding true regulatory sites ., We also identified a novel cluster of unmethylated Dam target sites upstream of a TonB-dependent receptor and putative iron uptake operon in E . coli ., In addition , we identify clusters of unmethylated sites in association with three novel orphan MTases ., These include a cluster of sites upstream of a GntR family transcriptional regulator and putative sugar utilization operon in Spirochaeta smaragdinae ( Fig 5C ) ., More unusually we observe an extended region of reduced methylation along the entire length of an RPS synthesis gene in Nocardia sp ., BMG51109 ( Fig 5D ) ., Umethylated Dam motif sites are located at predicted transcription factor binding sites ( S11 Fig ) ., In summary , both known and novel orphan MTases are associated with a signature of unmethylated sites in regulatory regions of the genome ., Many of these sites show evidence of evolutionary conservation and unmethylated sites are overall enriched near transcription start sites , both of which are hallmarks of gene regulatory sequences and support the notion that selective absence of methylation at MTase recognition sites plays a role in gene regulation ., The orphan MTases Dam and CcrM are important regulators of genome replication in Proteobacteria ., Regulation occurs through the differential recognition of fully methylated or hemi-methylated DNA by cellular machinery 35 ., While such methylation patterns can in principle be determined from SMRT sequencing 21 , it requires sampling of DNA from synchronized cells , which was not performed for our study ., Nonetheless , the availability of large numbers of novel orphan MTase specificities makes it possible for us to search for general patterns of motif distribution ( regardless of methylation state ) consistent with a role in DNA replication control ., We therefore systematically searched our methylome datasets for enriched clusters of motifs in non-coding regions of the genome ., We restricted our analyses to conserved orphan MTases , and retained only those clusters of motifs that occur at orthologous locations in multiple organisms ., As these analyses do not require methylome data , initial patterns of motif clusters were subject to expanded analyses of all publicly available genome sequences from related organisms ( Methods ) ., In total , we identified conserved clusters of motifs in non-coding regions of the genome in association with four orphan MTases ( p < 1e-5 , Methods ) ., Strikingly , all cases were located at putative origins of replication ( Fig 6 ) ., First , we observed enrichment of Dam MTase motifs at the origin of replication in Enterobacteria and other Gammaproteobacteria ( Fig 6A ) ., The presence of motif clusters correlates strongly with the presence of Dam orthologs in the genome , consistent with the known role of Dam in regulating DNA replication 17 ., We observe similar patterns of motif enrichment for orphan MTases recognizing 5’-TTAm6A-3’ in Arthrobacter , and 5’-CTCGAG-3’ in Nocardia ( Fig 6B and 6C respectively ) ., In both cases , motif clusters occur in non-coding regions between bacterial replication genes dnaA and dna polIII 43 ., Furthermore , the presence of motif clusters is again strongly correlated with the presence of the respective orphan MTase ., Finally , we observe an analogous system associated with a conserved orphan MTase recognizing 5’-m4CATG-3’ motifs in Haloarchaea ( Fig 6D ) ., In this case , m

Introduction, Results, Discussion, Methods

DNA methylation acts in concert with restriction enzymes to protect the integrity of prokaryotic genomes ., Studies in a limited number of organisms suggest that methylation also contributes to prokaryotic genome regulation , but the prevalence and properties of such non-restriction-associated methylation systems remain poorly understood ., Here , we used single molecule , real-time sequencing to map DNA modifications including m6A , m4C , and m5C across the genomes of 230 diverse bacterial and archaeal species ., We observed DNA methylation in nearly all ( 93% ) organisms examined , and identified a total of 834 distinct reproducibly methylated motifs ., This data enabled annotation of the DNA binding specificities of 620 DNA Methyltransferases ( MTases ) , doubling known specificities for previously hard to study Type I , IIG and III MTases , and revealing their extraordinary diversity ., Strikingly , 48% of organisms harbor active Type II MTases with no apparent cognate restriction enzyme ., These active ‘orphan’ MTases are present in diverse bacterial and archaeal phyla and show motif specificities and methylation patterns consistent with functions in gene regulation and DNA replication ., Our results reveal the pervasive presence of DNA methylation throughout the prokaryotic kingdoms , as well as the diversity of sequence specificities and potential functions of DNA methylation systems .

DNA methylation is a chemical modification of DNA present in many prokaryotic genomes ., The best-known role of DNA methylation is as a component of restriction-modification systems ., In these systems , restriction enzymes target foreign DNA for cleavage , while DNA methylation protects the host genome from destruction ., Studies in a handful of organisms show that DNA methylation may also act independently of restriction systems and function in genome regulation ., However , a lack of technologies has limited the study of DNA methylation to a small number of organisms , and the broader patterns and functions of DNA methylation remain unknown ., Here we use SMRT-sequencing to determine the genome wide DNA methylation patterns of more than 200 diverse bacteria and archaea ., We show that DNA methylation is pervasive and present in more than 90% of studied organisms ., Analysis of this data enabled annotation of the specific DNA binding sites of more than 600 restriction systems , revealing their extraordinary diversity ., Strikingly , we observed widespread DNA methylation in the absence of restriction systems ., Analyses of these patterns reveal that they are conserved through evolution , and likely function in genome regulation ., Thus DNA methylation may play a far wider function in prokaryotic genome biology than was previously supposed .

sequencing techniques, gene regulation, regulator genes, dna replication, genome analysis, gene types, sequence motif analysis, molecular biology techniques, epigenetics, dna, dna methylation, chromatin, research and analysis methods, sequence analysis, genomics, chromosome biology, prokaryotic cells, gene expression, biological databases, chromatin modification, dna modification, molecular biology, biochemistry, cell biology, nucleic acids, database and informatics methods, genetics, biology and life sciences, cellular types, computational biology, genomic databases

journal.pcbi.1000465

A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes

Microbial whole genome sequencing has become a routine practice in recent years , because of the rapid advances of DNA sequencing technologies 1 ., One of the first analyses that biologists attempt , once they obtain a complete genome sequence , is to reconstruct the biological pathways encoded by the organism , which is usually accomplished in silico by mapping the protein coding genes onto reference pathway collections , such as KEGG 2 or SEED 3 , based on their homology to reference genes with previously characterized functions ., For example , KAAS , the pathway annotation system based on the KEGG database 4 , first annotates K numbers ( each K number represents an ortholog group of genes , and is directly linked to an object ( a biochemical step ) in the KEGG pathway map ) , and then reconstructs pathways based on the assigned K numbers ., Similarly , the RAST server ( and MG-RAST ) first annotates FIG families and then maps the identified FIG families onto the SEED subsystems 5 , 6 ., These automatic methods are promising for the analysis of most genomes , although they may leave “holes” in the reconstructed pathways , due to either missing genes ( i . e . the genes are non-homologous to reference genes of the same specific functions , and thus cannot be identified by a homology-based method , or were simply not annotated as ORFs by annotation pipelines ) 7 , or alternative and novel pathways ( i . e . the target organism adopts variant pathways , which are different from the reference pathway , to accommodate a specific niche or lifestyle ) 8 ., After all , many bacterial genomes have fewer than 60% of their genes assigned to a proposed function 9 , 10 ., We note that pathway reconstruction is essential for understanding the biological functions that a newly sequenced genome encodes ., For instance , in a recently published report , the coupling of N2 fixation to cellulolysis was revealed within protist cells in the termite gut , based solely on the in silico pathway reconstruction of the complete genome sequence of a bacterial endosymbiont 11 ., Moreover , pathway reconstruction based on some new high throughput techniques must provide conclusions from explicitly incomplete information , which poses fresh challenges ., For example , in a typical proteomics experiment , the proteins represent a particular biological sample collected under a specific physiological condition or from a specific tissue ( e . g . from yeast cells after the heat shock ) , which are in high enough abundance to be identified by tandem mass spectrometry 12 , 13 ., Based on these data , one may ask , what biological pathways were activated ( or suppressed ) under the physiological condition ?, A similar , but more complicated case is pathway analysis of metagenomic data , to characterize the aggregate metabolic processes of microbial communities in a given environment 14 ., Metagenomic profiling data can be viewed as a sampling of the genomic sequences from many kinds of microbes living in a specific environment ., Again , the incompleteness of the data makes it difficult to reconstruct the entire pathways encoded by a metagenome ., Nevertheless , it is becoming routine to “reconstruct” pathways for proteomic 15 and metagenomic data 16 , 17 , by best similarity matches ( often derived from BLAST searches ) : a pathway is inferred to be absent or present in a dataset if highly confident homolog protein hits identify one or more of the protein functions associated with the pathway in other organisms ., In addition to the problems that arise from incomplete data , existing methods of pathway reconstruction or inference may over-estimate the number of pathways because of redundancy in the protein-pathway , at four levels ., First , different pathways may share the same biological functions ., The partition of pathways ( as the entire cellular network is partitioned into several hundreds of biological pathway entities in KEGG database ) is extremely important for understanding of biological processes , even though there is only a single large biological network within any cell and all pathways are to some extent connected 18 ., It is not surprising that many pathways defined in the pathway databases are overlapping ., Second , some proteins carry out multiple biological functions 19 , e . g . through different protein domains , active sites , or substrate specificities ., Third , neither organisms nor communities are closed boxes , and the products or intermediates of pathways may be exogenously supplied ., Finally , homology-based protein searching may map one protein to multiple homologous proteins with different biological functions ( i . e . paralogous proteins ) ., In summary , it cannot be safely concluded that a pathway is present , even if one or more proteins are mapped to it ., Even for single complete genomes , pathway reconstruction does not always give a clear picture of the biological functions in an organism , and human curation and experimental verification is often needed 20 , 21 ., We illustrate this by a rather extreme example found in the pathway analysis of the human genome ., The KEGG pathway annotation of the human genome includes the reductive carboxylate cycle , with proteins annotated to 6 steps in this pathway ( http://www . genome . jp/kegg-bin/show_organism ? menu_type=pathway_maps&org=hsa ) ( as of July 2nd , 2009 ) ., The Calvin cycle is the most common method of carbon fixation , while the reductive carboxylate cycle is an alternative carbon fixation pathway , currently found only in certain autotrophic microorganisms ., In fact , the reductive carboxylate cycle is essentially the reverse of the Krebs cycle ( citric acid or tricarboxylic acid cycle ) , the final common pathway in aerobic metabolism for the oxidation of carbohydrates , fatty acids and amino acids , so they share reactions and functional roles ., For this reason , the proteins responsible for the normal function of the Krebs cycle can be mistakenly taken as evidence for the existence of a reductive carboxylate cycle in the human genome ., Here we propose a pathway reconstruction/inference method in which we do not attempt to reconstruct entire pathways from a given set of protein sequences ( e . g . identified in a proteomics experiment , or encoded by the sequences sampled in a metagenomic project ) , but to determine the minimal set of biological pathways that must exist in the biological system to explain the input protein sequences sampled from it ., In this context , we note pathway inference might be a more suitable terminology than pathway reconstruction ., However , considering that pathway inference has been used in a different context to infer networks or pathways from gene express data 22 , and pathway reconstruction is commonly used in the field , we use both pathway inference and pathway reconstruction in this paper ., To address the issues of both incomplete data , and pathway redundancy , we formulate a parsimony version of the pathway reconstruction/inference problem , called MinPath ( Minimal set of Pathways ) , which can be roughly described as the following: given a set of reference pathways and a set of proteins ( and their predicted functions ) that can be mapped to one or more pathways , we attempt to find the minimum number of pathways that can explain all proteins ( functions ) ( see Fig . 1 ) ., Although this problem is NP-hard in general , we provide an integer programming ( IP ) framework to solve it ., We focus on analyzing complete genomes in this study because there is a relatively good understanding of the pathways that actually exist in organisms with completely sequenced genomes ( as compared to the emerging metagenomes ) , making this analysis a good test of our method ., Besides , the pathway annotations of these genomes are still far from perfection , as in the example of a carbon fixation pathway in the human genome ( as well as chickens , mosquitoes , etc ) ., We also applied MinPath to the analyses of several metagenomic datasets , to demonstrate the potential applications of MinPath in metagenome annotation ., We used MinPath to re-analyze the biological pathways of several metagenomes 17 , which were previously analyzed by a naïve mapping approach ., The results are summarized in Table 3 ., We used both the KEGG and SEED databases in this experiment ., For KEGG pathways , we did local BLAST searches , using the criteria as shown in 16 for KO family identification ., For SEED subsystems , the FIG annotations were downloaded from the MG-RAST server ( http://metagenomics . theseed . org/ ) ., For all the datasets we tested , MinPath reduced the total number of annotated pathways ( or subsystems ) significantly ( as shown in Table 3 ) ., For example , for the metagenome sampled from a coral microbial community ( Coral-Mic ) , there are in total 232 KEGG biological pathways annotated in at least one of the 7 sequencing datasets ., Based on MinPath , however , only 160 KEGG biological pathways are sufficient to explain all the functions predicted for these datasets ., These results indicate that the naïve mapping of the biological pathways from predicted functions may overestimate the biological pathways ( so the functional diversity ) of those microbial communities , and we need to be cautious when interpreting the results from such an analysis 16 , 17 ., We also show the details of pathway reconstruction for a single sequence dataset from the coral biome ( 4440319 . 3 . dna . fa ) ., The naïve mapping approach identified 224 KEGG pathways , whereas MinPath identified only 143 KEGG pathways ., The pathways eliminated by MinPath include the inositol metabolism pathway , the androgen and estrogen metabolism pathway , the caffeine metabolism pathway , etc ( see more examples at the supplementary website ) ., Obviously , comparisons of microbial communities or other biomes will be more telling if spurious pathways are eliminated , and our results suggest that as many as 40% of the 224 pathways could be wrong ., We have developed the MinPath approach to provide more conservative—but more reliable—estimations of biological pathways from a sequence dataset , and applied this approach to revisit the biological pathway reconstruction problem for genomes as well as metagenomes ., Our results show that without further post-processing of the reconstructed pathways , the naïve mapping strategy may overestimate the biological pathways that are encoded by a genome or metagenome , which could jeopardize any conclusions drawn from the constructed biological pathways ( such as the metabolic diversity/capacity of an environmental microbial or viral community , as measured by the Shannon Index ) 16 , 17 , or other downstream analysis based on constructed pathways 23 ., It was noted in 16 that most of the microbial communities in that study were approaching saturation for known pathways: more conservative estimates of pathways for each environment may allow real functional differences between the samples to be detected ., Note that MinPath is not designed to directly improve the still imperfect definition of pathways and/or functions in databases such as KEGG or SEED ., For example , as a result of how some pathways are grouped in the KEGG database , peptidoglycan biosynthesis is listed for the human genome by KEGG annotation and MinPath does not eliminate this pathway from the list of annotated pathways from human genome ., In this sense , efforts are still needed to improve the elucidation and annotation of extent biochemical pathways ., But given a database of reference pathways , we feel that MinPath provides a sensible method for inferring the pathways represented in biological sequence samples ., Pathway reconstruction has become routine in functional annotation of genomes and metagenomes , in which KEGG pathways ( or other biological pathways such as SEED subsystems ) are reconstructed based on homology ., KEGG and SEED databases collect pathways ( or subsystems ) curated by experts , each pathway/subsystem consisting of a series of functional roles ( enzymes , transporters , etc ) ., Pathway reconstruction consists of two key steps: ( 1 ) predicting the functions ( represented by protein families ) of proteins encoded by the DNA sequences , which is often achieved by similarity searching of the predicted proteins against reference proteins from previously characterized genomes; and ( 2 ) predicting the presence or absence of pathways in the query dataset , based on the identified functions associated to the pathways ., Conventional pathway reconstruction usually adopts simple criterion in this second step ( herein referred to as the naïve mapping approach ) , i . e . , a pathway is considered to be present if one or more functions in the pathway are identified in the first step ., We have shown in this paper that this approach may lead to the identification of spurious pathways and an overestimation of functional ability , which motivated us to develop a novel approach to pathway reconstruction based on the parsimony principle presented below ., We define the minimal pathway reconstruction problem as the following: given a list of functions annotated for a set of genes ( which can be an incomplete set , as we encounter in metagenomic analysis , or a nearly complete set , as in complete genome analysis ) , find the minimal set of pathways that include all given functions ( see Fig 1 ) ., Note that this formulation is different from the conventional formulation of the pathway reconstruction problem , which attempts either to reconstruct the complete pathways encoded by a given genomic dataset ( in a sense , the pathway holes should to be minimized ) , or to identify the set of pathways that have at least one associated function annotated ( i . e . , the naïve mapping approach ) ., We use integer programming to solve the minimal pathway reconstruction problem ., Linear programming ( LP ) is an algorithm for finding the maximum or minimum of a linear function of variables ( objective function ) that are subject to linear constraints 24 ., Simplex and interior point methods are widely used for solving LP problems ., The related problem of integer programming ( IP ) requires some or all of the variables to take integer ( whole number ) values ., Some of the most powerful algorithms for finding exact solutions of combinatorial optimization problems 25 are based on IP ., LP and IP have been applied to many fields in the biological sciences , such as the maximum contact map overlap problem for protein structure comparison 26 , optimal protein threading 27 , probe design for microarray experiments 28 , and the pathway variant problem 8 ., Here we transform the minimal pathway reconstruction problem to an integer programming problem: Denote the number of functions ( protein families ) that are annotated in a dataset as n ., Let the total number of putative pathways which have at least one component function annotated be p ., Denote the mapping of protein functions to the pathways as M , where Mij\u200a=\u200a1 if function i is involved in pathway j , otherwise 0 ( note one function may map to multiple pathways or subsystems ) ., Denote if a pathway j is selected in the final list or not as Pj , with Pj\u200a=\u200a1 if selected , Pj\u200a=\u200a0 otherwise ., The set of pathways with Pi\u200a=\u200a1 composes the minimal set of pathways that can explain all the functions that are annotated for a dataset ., The objective function for integer programming is , i . e . , our goal is to find the minimum number of pathways that can explain all the functions carried by at least one protein from a dataset ., We use the KO and FIG protein families defined in the KEGG database and the SEED subsystems , respectively , for this study ., Many of the mappings of KO families to KEGG pathways were done manually in the KEGG database ., These families are the basic units for pathway reconstruction ( or subsystem reconstruction in SEED ) , in which a pathway ( or a subsystem ) is composed of a list of functional roles ., We use the GLPK package ( GNU Linear Programming Kit; http://www . gnu . org/software/glpk/glpk . html ) for solving the integer-programming problem; all the other functions are implemented in Python ., The input for MinPath is a list of protein families ( e . g . , KO and FIG families ) annotated in a given dataset of genes ( from a genome , or a metagenome ) , and the output is the list of pathways reconstructed/inferred for the dataset ., Note that in some cases two pathways may share most of their functional roles ( for example , the biosynthesis and degradation pathway of the same biological molecule , such as the lysine biosynthesis and degradation pathways ) ., MinPath will keep one of these pathways , because that is sufficient to explain the functional roles identified ., We added a post-processing step here to add those pathways that have more than 50% of their functional roles identified back to the pathway pool , even when these functional roles appear in another pathway that is already predicted by MinPath ., We revisited the pathway reconstruction for the 854 genomes in the KEGG database ( as of December , 2008 ) that have at least 20 KEGG pathways annotated for each of these genomes ., For these genomes , the function ( or protein families ) annotations were downloaded from the KEGG database ( ftp://ftp . genome . jp/pub/kegg/release/current/ ) ., We also applied MinPath to reanalyze the pathways for nine biome metagenomic datasets 17 ., The FIG family annotations for the metagenomic sequences were downloaded from the MG-RAST server ( http://metagenomics . theseed . org/ ) ., We conducted the KO family annotations of the sequences based on the best blast hits with E-value cutoff of 1e-5 , a typical E-value cutoff used for KEGG pathway reconstruction in metagenomes 16 ., MinPath is available as a server and the source codes are available for downloading at MinPath webpage , http://omics . informatics . indiana . edu/MinPath/ ., Supplementary material is also available at the MinPath website .

Introduction, Results, Discussion, Materials and Methods

A common biological pathway reconstruction approach—as implemented by many automatic biological pathway services ( such as the KAAS and RAST servers ) and the functional annotation of metagenomic sequences—starts with the identification of protein functions or families ( e . g . , KO families for the KEGG database and the FIG families for the SEED database ) in the query sequences , followed by a direct mapping of the identified protein families onto pathways ., Given a predicted patchwork of individual biochemical steps , some metric must be applied in deciding what pathways actually exist in the genome or metagenome represented by the sequences ., Commonly , and straightforwardly , a complete biological pathway can be identified in a dataset if at least one of the steps associated with the pathway is found ., We report , however , that this naïve mapping approach leads to an inflated estimate of biological pathways , and thus overestimates the functional diversity of the sample from which the DNA sequences are derived ., We developed a parsimony approach , called MinPath ( Minimal set of Pathways ) , for biological pathway reconstructions using protein family predictions , which yields a more conservative , yet more faithful , estimation of the biological pathways for a query dataset ., MinPath identified far fewer pathways for the genomes collected in the KEGG database—as compared to the naïve mapping approach—eliminating some obviously spurious pathway annotations ., Results from applying MinPath to several metagenomes indicate that the common methods used for metagenome annotation may significantly overestimate the biological pathways encoded by microbial communities .

Even though there is only a single large biological network within any cell and all pathways are to some extent connected , the partition of the entire cellular network into smaller units ( e . g . , KEGG pathways ) is extremely important for understanding biological processes ., Biological pathway reconstruction , therefore , is essential for understanding the biological functions that a newly sequenced genome encodes and recently for studying the functionality of a natural environment via metagenomics ., The common practice of pathway reconstruction in metagenomics first identifies functions encoded by the metagenomic sequences and then reconstructs pathways from the annotated functions by mapping the functions to reference pathways ., To address the issues of both incomplete data ( e . g . , metagenomes , unlike individual genomes , are most likely incomplete ) and pathway redundancy ( e . g . , the same function is involved in multiple pathway units ) , we formulate a parsimony version of the pathway reconstruction/inference problem , called MinPath ( Minimal set of Pathways ) : given a set of reference pathways and a set of functions that can be mapped to one or more pathways , MinPath aims at finding a minimum number of pathways that can explain all functions ., MinPath achieves a more conservative , yet more faithful , estimation of the biological pathways encoded by genomes and metagenomes .

computational biology/metagenomics, computational biology/metabolic networks, computational biology/genomics

journal.pgen.1004369

Recent Mitochondrial DNA Mutations Increase the Risk of Developing Common Late-Onset Human Diseases

Mitochondria are the principal source of cellular adenosine triphosphate ( ATP ) generated through oxidative phosphorylation ( OXPHOS ) , which is linked to the respiratory chain ., In humans , thirteen OXPHOS proteins are synthesised from the 16 . 5 Kb mitochondrial genome ( mtDNA ) ., MtDNA has accumulated genetic variants over time , and being strictly maternally inherited , undergoes negligible intermolecular recombination ., As a consequence , ancient variants extant in the human population define haplogroups that have remained geographically or ethnically restricted 1 ., Work on European haplogroups has shown that some polymorphic mtDNA variants affect mitochondrial function 2 , 3 ., Given emerging evidence that mitochondria play a key role in several common diseases , it is likely that variation of mtDNA could alter the risk of developing different human disorders ., Early mtDNA genetic association studies were under-powered , and the vast majority have not been replicated 4 ., However , some recent large studies have found replicable associations with specific human diseases 5–11 , most notably in sporadic Parkinsons disease 12–14 ., These observations implicate mtDNA as part of the “missing heritability” of complex human disease traits ., Ultimately , mtDNA codes for a limited number of proteins that affect the same common pathway of energy production implicated in several human diseases ., It is likely , therefore , that functional genetic variation of mtDNA will have impact on more than one disease – but this has not been directly studied before ., To test this hypothesis , we analysed mtDNA SNP data from 51 , 106 subjects from the Wellcome Trust Case Control Consortium , comparing genotypes from 11 major diseases: ankylosing spondylitis ( AS , n\u200a=\u200a2 , 005 ) , ischemic stroke ( IS , n\u200a=\u200a4 , 205 ) , multiple sclerosis ( MS , n\u200a=\u200a11 , 377 ) , Parkinsons disease ( PD , n\u200a=\u200a2 , 197 ) , primary biliary cirrhosis ( PBC , n\u200a=\u200a1 , 921 ) , psoriasis ( PS , n\u200a=\u200a2 , 622 ) , schizophrenia ( SP , n\u200a=\u200a2 , 019 ) , ulcerative colitis ( UC , n\u200a=\u200a2 , 869 ) , coronary artery disease ( CAD , n\u200a=\u200a3 , 215 ) , hypertension ( HT , n\u200a=\u200a2 , 943 ) and type-2 diabetes ( T2D , n\u200a=\u200a2 , 975 ) to three independent control groups genotyped on the same platforms ( WTCCC-58C , n\u200a=\u200a2997 , WTCCC-NBS , n\u200a=\u200a2897 and WTCCC2-MetabaloChip , n\u200a=\u200a5841 ) ., After applying stringent quality control measures ( Supplementary Materials , Table S1 & S2 ) , we initially compared the two healthy control groups using PLINK v2 . 050 15 ( Supplementary Materials , Figure S1 ) , and found no significant difference in allele frequencies ., We therefore merged control groups genotyped on the same platform for all subsequent analyses as follows: WTCCC-Control-1 , WTCCC-Control-2 and WTCCC-Control-3 ( Supplementary Materials , Table S2 ) ., Cluster plots produced by principle component analysis ( PCA ) revealed no significant population stratification when comparing either: datasets from the same array or array-specific control datasets ( Supplementary Materials , Figure S4 ) ., We then compared genotyped SNPs in each disease group to platform-matched control datasets using PLINK v2 . 050 15 ( Figure 1 & Supplementary Materials , Table S3 ) ., This confirmed previously reported associations at the low-resolution haplogroup level 5 , 12 , 16 , 17 , endorsing the methodological approach ., Next we performed lexical tree building to identify new associations with phylogenetically related variants , but without basing our anlysis on any prior assumptions related to the published mtDNA haplogroup structure 18 , 19 ., This method uses fewer SNPs because individuals with missing SNP data cannot be used , but has greated power , and provides graphical summaries of the combinations of SNPs that are associated with increased or descreased risk of disease ( Supplementary Materials , Table S4 ) ., Lexical tree analysis identified significant relationships between the mtDNA tree structure and schizophrenia , primary biliary cirrhosis , multiple sclerosis ( each at p<10−6 ) , ulcerative colitis ( p<10−4 ) , and Parkinsons disease ( p\u200a=\u200a0 . 004 ) ( Table 1 and Supplementary Materials Figure S3 ) , independently confirming previous haplogroup based association associations 5 , 12 , 16 , 17 , and revealing new mtDNA clades associated with several different diseases ., The other case-control trees , and comparisons between the different control populations were not significant at the 1% level ., To determine the functional basis of the associations we imputed missing genotypes across the whole mitochondrial genome using 7 , 729 complete mtDNA sequences ., Subsequent analyses were performed on 35 , 901 European cases and 15 , 302 European controls , and captured 40 . 41% of European mtDNA population genetic variation ( Supplementary Materials , Figure S2 ) ., In keeping with our original hypothesis , specific variants with predicted functional consequences conferred either an increased risk ( Table 2a ) or decreased risk ( Table 2b ) across several different diseases ., In addtion , we identified the same allelic-specific associations for different diseases compared to different platform-specific control groups , re-inforcing these findings ., Functional variants associated with an increased risk in two or more diseases were limited to two structural genes: MTCYB ( m . 14793 , m . 15218 ) and MTCO3 ( m . 9477 , m . 9667 ) ., The only non-synonmous protien encoding variant consistently associated with a reduced risk of disease was in MTND3 ( m . 10398 ) ., We also found evidence of associations across multiple diseases within the non-coding region ( d-loop ) of mtDNA , and 16S ribosomal RNA subunit genes ( Figure 2 and Table 2 and Supplementary Materials , Table . S3 ) ., Intriguingly , the same alleles were not associated with all of the diseases we studied , and for two variants ( m . 11299 , m . 16294 ) , the same allele had opposite effects for two different diseases ( Table 2c ) ., Overall , the majority of disease-associated alleles conferred an increased risk ( 61/99 ) , and not a decreased risk ( 38/99 , P<0 . 001 ) ( Supplementary Materials , Table S3 ) ., Following stringent quality control , our initial analysis confirmed previous associations between mtDNA haplogroups and common disease in a much larger data set ., These findings were independentely supported by lexical tree based analysis at higher levels of statistical significance ., Subsequent imputation of missing genotypes captured >40% of European mtDNA population genetic variation in 35 , 901 European cases and 15 , 302 European controls ., By simultaneously analysing eleven , ostensibly unrelated , diseases we identified several imputed mtDNA variants that were associated with more than one disease ., The same associations were seen in different disease groups compared to different control groups ., This provided confirmatory independent replication of a disease association , and supports our original hypothesis that the same genetic variants of mtDNA contribute to the risk of developing several common complex diseases ., Variants increasing the risk of two or more diseases were limited to MTCYB ( m . 14793 , m . 15218 ) and MTCO3 ( m . 9477 , m . 9667 ) , encoding variants in cytochrome b ( H16R , T158A ) and subunit 3 of cytochrome c oxidase ( complex IV , V91L , N154S ) ., Functional variants of MTCYB have previosly been associated with several human phenotypes 20–22 , but the most compelling evidence of a prior disease association is the increased risk of developing blindness in subjects harboring the mtDNA mutations in MTND genes known to cause Leber hereditary optic neuropathy ( LHON ) , where they synergistically interact with a primary LHON mutation to cause a defect of OXPHOS complex I activity 23 ., On the other hand , the only non-synonmous protien encoding variant associated with a reduced risk of several diseases was m . 10398 in the MTND3 variant ( complex I , T114A ) ., m . 10398 occurs twice on the human mtDNA phylogeny ( homoplastic on haplogroups J and K ) , and has previously been associated with a reduced risk of Parkinsons disease 14 , 24 ., This variant has been shown to reduce complex I activity , cytosolic calcium levels , and the mitochondrial membrane potential 3 , 25 , 26 and thus may reduce the level of reactive oxygen species , contributing to the underlying disease mechanim of several disorders . Variants in MTCO3 are typically associated with primary mitochondrial disorders 27 , 28 , but have been also been indentified as risk factors in Alzheimers disease 29 , 30 , migrainous stroke 31 and sporadic optic neuropathy 32 ., M . 9477 and m . 9667 are non-synonmous protien encoding variants which are cladally related; present on haplogroup U sub branches ( U5 and U5a1b , respectively ) ., Cybrid studies of haplogroup U show a reduction in mtDNA copy number , resulting in a reduction in mitochondrial protein synthesis and complex IV activity 3 , 25 , impairing energy production and likely contributing to disease ., We also noted disease associations with substitutions in the non-coding region and ribosomal genes ( Table 2 and Supplementary Materials , Table S3 ) ., Although highly polymorphic at the population level ( Figure 2 ) , there is emerging evidence that both regions can have functional effects either through an effect on mtDNA replication , transcription or translation 33 , 34 , as proposed in Alzheimers disease 34 ., It is intriguing that there were more functional variants associated with an increased risk , than with a decreased risk of disease ( Table 2 and Supplementary Materials , Table S3 ) ., This suggests that deleterious , novel sub-haplogroup variants have not yet been removed from the population through natural selection , possibly including the younger d-loop variants ., This has been observed in the nuclear genome in the rapidly expanding human population 35 , 36 , implying that the modern human population is far from equilibrium ., An alternative explantion is that mtDNA alleles may escape purifying selection because the associated disease phenotype only becomes manifest after female reproductive life ., For two variants ( m . 11299 , m . 16294 ) , the same allele was associated with an increased risk of developing one disease , and a reduced risk of developing another ( Table 2 ) ., Although differences in the sample size post-QC provide one explanation , these findings raise the possibility that different mtDNA-mediated mechanisms are involved in different contexts , perhaps because some variants have a greater impact on bioenergetics , and others on the generation of reactive oxygen species ., Alternatively , it is conceivable that the relevance of specific alleles may be context-specific , only excerting a functional effect on a particular haplogroup background 37 ., Substantially larger whole mtDNA genome studies will be required to detect clade-specific epistastic interactions if they exist ., In some instances we observed multiple associations with different variants found within the same phylogenetic cluster ., For example m . 499 ( K1a ) , m . 11485 ( K1a4 ) and m . 11840 ( K1a4a1 ) are known to reside within subdivisions of the major haplogroup K , and all associated decreased risk of MS and IS ., Conversely , m . 310 ( U4a2 ) and m . 3197 ( U5 ) are distinct subclades of the U associated with increased risk of PS , MS , IS PD AS and UC ., Although reassuring from a technical perpective , this illustrates the challenge of mtDNA association studies , where variants with a close ancestral relationship inevitably co-segregate , making it difficult to determine which alleles are responsible for the disease risk ., Finally , analysis of imputed data also revealed several different mtDNA alleles asssociated with different diseases , often reaching high levels of statistical significance ( P<10−10 , Supplementary Materials , Table S3 ) ., However , these findings should only be considered preliminary and require independent replication in other populations ( where specific European haplogroup distributions can vary ) and thus do not form the major focus of this report ., In conclusion , these findings underscore the role of mitochondrial mechanisms in the pathogenesis of common diseases , and emphasise the importance of incorporating the mitochondrial genome in comprehensive genetic association studies ., Although the strict phylogenetic stucture of maternally inherited mtDNA makes it difficult to identify the precise variants responsible , higher resolution genotyping at the whole mtDNA genome level will cast further light on the genetic mechanisms , particularly if recurrent homoplasies independently associate with phenotypes across several clades ., Given the case cohort sample sizes post QC ( Supplementary Materials , Table S1 ) , the corresponding control cohorts ( Supplementary Materials , Table S1 ) , an expected MAF of 0 . 01 , an α\u200a=\u200a3 . 85×10−3 to 3 . 97×10−4 ( averaging 13-126 tests dependent upon specific dataset ) and disease prevalences of: psoriasis\u200a=\u200a2% 47 , multiple sclerosis\u200a=\u200a1% 48 , ischemic stroke\u200a=\u200a1% 49 , primary biliary cirrhosis\u200a=\u200a0 . 1% 50 , Parkinsons disease\u200a=\u200a0 . 3% 51 , ankylosing spondylitis\u200a=\u200a0 . 1% 52 , ulcerative colitis\u200a=\u200a0 . 1% 53 , schizophrenia\u200a=\u200a0 . 33% 54 , Type-2 diabetes\u200a=\u200a10% 55 , coronary artery disease\u200a=\u200a3% 56 and hypertension\u200a=\u200a30% 57; we had >80% power to detect an effect size of >1 . 2 in each cohort ( specifically , psoriasis\u200a=\u200a79 . 8% , multiple sclerosis\u200a=\u200a93 . 2% , ischemic stroke\u200a=\u200a84 . 5% , primary biliary cirrhosis\u200a=\u200a79 . 9% , Parkinsons disease\u200a=\u200a85 . 9% , ankylosing spondylitis\u200a=\u200a85 . 4% , ulcerative colitis\u200a=\u200a78 . 9% , schizophrenia\u200a=\u200a80 . 3% , Type-2 diabetes\u200a=\u200a85 . 3% , coronary artery disease\u200a=\u200a82 . 6% and hypertension\u200a=\u200a98 . 7% ) ., Power calculations were carried out using Genetic Power Calculator 58 ., Stringent quality control ( QC ) was applied to each individual cohort ( Table S1 ) 59 ., Briefly , each cohort was pruned of missing phenotypes ( defined as -9 in the pedigree/sample files ) ., Poorly performing SNPs ( genotyped\u200a=\u200a0 . 159 ) , and subsequenctly , samples were removed ( individual missingness =\u200a0 . 1 59 ) using PLINK v2 . 050 15 ., Additionally non-European mtDNA sequences ( defined with m . 8701A , m . 8540T and 10873T ) were also removed 1 , 60 , 61 ., Finally , to verify the quality of genotypes cluster plots of normalized intensity for each SNP were generated using R ( http://www . R-project . org ) and inspected ., In order to increase statistical power , WTCCC-58C and WTCCC-NBS control cohorts were merged ., Initially , we compared the two healthy control groups ( Supplementary Materials , Figure S1 ) , and found no significant difference in allele frequencies ., Briefly , each control cohort was merged with its array genotyped counterpart ( Supplementary Materials , Table S2 ) ., As with individual cohorts , MAF\u200a=\u200a0 . 00001 , implemented in PLINK v2 . 050 15 , was used to remove SNPs with missing genotpyes ( i . e . call\u200a=\u200a0 0 ) ., Poorly performing SNPs ( genotyped\u200a=\u200a0 . 159 ) , and subsequenctly , samples were removed ( individual missingness\u200a=\u200a0 . 159 ) using PLINK v2 . 050 15 ., Finally , to correct for control popualtion stratitification , variant frequency was compared between -58C and –NBS using ‘—assoc PLINK v2 . 050 ( P threshold\u200a=\u200a0 . 05 ) 15 . Variants with signifcantly different 58C/NBS frequencies were removed . This QC lead to the formation of 3 merged control cohorts: WTCCC-Control-1 , WTCCC-Control-2 and WTCCC-Control-3 . Prior to association testing QCd case cohorts were merged with corresponding QCd control cohorts ( i . e . Multiple sclerosis versus WTCCC-Control-1 ) . Differential missingness tests , which statistically compare the frequency of ‘missing’ genotype data between cases and controls were performed on each case-control comparison 59 ., Variants were removed when missingness was significantly different ( P\u200a=\u200a<10−4 ) 59 ., Allelic association was implemented in PLINK v2 . 050 15 ., Given the discovery nature of the experiment , statistical significance was defined as P<0 . 05 ., Only ancestral Europeans , determined by mitchondrial DNA genotype , were included in this study 1 , 60 , 61 ., Additionally , population structure in each cohort ( post-QC ) and combined by array type was assessed by principle component analysis ( PCA ) of mitochondrial DNA variants 62 ., Plots were made of the first two components for each array dataset ( Illumina\u200a=\u200aAS , IS , MS , NBS , PBC , PD , PS , WTCCC-58C and WTCCC-NBS , Affymetrix\u200a=\u200aSP , UC , WTCCC-58C and WTCCC-NBS and Metabalo\u200a=\u200aT2D , CAD , HT and controls previously combined WTCCC-58C and WTCCC-NBS ) and separately for the control cohorts for each platform ( Supplementary Figure S3 ) ., At this resolution , individual PCA cluster analysis showed no significant stratification differences ., All principle component scores were calculated in R using the ‘princomp’ function and plotted in R using ggplot ( R Core Team 2013 ) 63 ., Imputation was implemented in PLINK v2 . 050 15 ., Initially a reference panel was constructed ., Whole Human mtDNA genome data , n\u200a=\u200a18 , 114 sequences , were downloaded from the National Centre for Biotechnology Information Nucleotide database ( http://www . ncbi . nlm . nih . gov/nuccore/ ) , using the keyword phrase ‘Homo Organism AND gene_in_mitochondrionPROP AND 14000∶19000SLEN NOT pseudogeneAll Fields . Sequences with pathogenic mtDNA variants ( available at www . mitomap . org ) were removed ( n\u200a=\u200a458 sequences ) , non Homo sapien sequences were removed ( n\u200a=\u200a7 ) . Similar to genotype QC , non-European mtDNA sequences ( defined with m . 8701A , m . 8540T and 10873T ) were also removed ( n\u200a=\u200a7051 ) . Finally truncated mtDNA sequences ( <16 , 500 bp ) were removed ( n\u200a=\u200a663 ) leaving a final dataset of n\u200a=\u200a9 , 935 sequences . The sequence dataset was aligned using MUSCLE 64 , analysed using Haplogrep 65 , 66 and subsequently filtered to match the Major European haplogroups ( H , V , J , T , U , K , W , X , I , R and N ) leaving a final sequence aplosamples and 2 , 873 variants , representing 100% of of the genetic varation in the reference dataset . The reference panel was merged with each QCd case-control cohort in PLINK ( v2 . 050 ) , 15 invoking ‘—flip-scan to detect and correct any stranding issues . Imputation association testing was carried out using ‘—proxy-assoc’ and , in order to assess the imputation performance , ‘—proxy-drop’ . 15, Significant SNPs associations with >99% of samples imputed , number of proxy SNPS >3 , a MAF >0 . 01 and a content metric >0 . 8 were retained . 15, Given a popualtion size of 7 , 729 and total genotypic information of 2 , 873 as 100% , imputation of alleles with MAF>0 . 0 captures 40% of total mtDNA genetic variabilty ( Figure S2 ) ., Cicularised Manhattan plots were generated using code adapted from http://gettinggeneticsdone . blogspot . co . uk/2013/11/a-mitochondrial-manhattan-plot . html , solarplot . R and ggplot2 ( http://ggplot2 . org/ ) ., Lexical tree analysis was performed in R ( R Core Team 2013 ) 63 using a custom library ( snptree , publically available from http://www . staff . ncl . ac . uk/i . j . wilson/ ) ., This analysis was performed on the Illumina 610K quad array , the Affymetrix SNP6 . 0 and the MetabaloChip datasets independently ., An independent stringent QC was performed , removing in order: the SNPs with a call rate of below 95% or a MAF of below 0 . 5% , the 2% of individuals with the most missing sites , the bottom 50% of SNPs with the most missing samples at that site , and those individuals with any missing data from the remaining SNPs ., Finally , those individuals with haplotypes ( defined by all the remaining SNPs ) that were not present in controls or had a frequency of less than 5 were removed ., This left 27054 individuals on 24 SNPs for the Illumina 610K quad array , 10 , 745 individual at 15 SNPs for the Affymetrix 6 . 0 chip and 14 , 484 individuals at 5 SNPs for the MetabaloChip ., The SNPs retained and their minor allele frequencies ( MAF ) in the control populations are shown in Supplementary Materials , Table S4 ., A tree structure was contructed for haplotypes made from the retained SNPs by initially grouping all individuals at the root of a tree , and then successively considering all retained SNPs in decreasing order of their minor allele frequency ( Supplementary Materials , Figure S3 ) ., At each stage , the haplotypes at each leaf node are split with those with the wild type being put on the left branch and those with the mutant allele on the right ., This creates a tree with all leaves representing complete haplotypes and internal nodes partial haplotypes ., Test statistics were then calculated for each node on the tree ., An overall test statistic for the tree was calculated by calculating the the sum of the five largest node values that were not ancestors or descendents of each other ., The test statistic was tested for significance by 1 , 000 , 000 random permutations of the Case/Control labels .

Introduction, Results, Discussion, Materials and Methods

Mitochondrial DNA ( mtDNA ) is highly polymorphic at the population level , and specific mtDNA variants affect mitochondrial function ., With emerging evidence that mitochondrial mechanisms are central to common human diseases , it is plausible that mtDNA variants contribute to the “missing heritability” of several complex traits ., Given the central role of mtDNA genes in oxidative phosphorylation , the same genetic variants would be expected to alter the risk of developing several different disorders , but this has not been shown to date ., Here we studied 38 , 638 individuals with 11 major diseases , and 17 , 483 healthy controls ., Imputing missing variants from 7 , 729 complete mitochondrial genomes , we captured 40 . 41% of European mtDNA variation ., We show that mtDNA variants modifying the risk of developing one disease also modify the risk of developing other diseases , thus providing independent replication of a disease association in different case and control cohorts ., High-risk alleles were more common than protective alleles , indicating that mtDNA is not at equilibrium in the human population , and that recent mutations interact with nuclear loci to modify the risk of developing multiple common diseases .

There is a growing body of evidence indicating that mitochondrial dysfunction , a result of genetic variation in the mitochondrial genome , is a critical component in the aetiology of a number of complex traits ., Here , we take advantage of recent technical and methodological advances to examine the role of common mitochondrial DNA variants in several complex diseases ., By examining over 50 , 000 individuals , from 11 different diseases we show that mitochondrial DNA variants can both increase or decrease an individuals risk of disease , replicating and expanding upon several previously reported studies ., Moreover , by analysing several large disease groups in tandem , we are able to show a commonality of association , with the same mitochondrial DNA variants associated with several distinct disease phenotypes ., These shared genetic associations implicate a shared underlying functional effect , likely changing cellular energy , which manifests as distinct phenotypes ., Our study confirms the important role that mitochondrial DNA variation plays on complex traits and additionally supports the utility of a GWAS-based approach for analysing mitochondrial genetics .

medicine and health sciences

journal.pgen.1000564

A Genome-Wide Association Study of Hypertension and Blood Pressure in African Americans

Genome wide association studies ( GWAS ) on large scale population samples have been remarkably successful in uncovering novel susceptibility loci for a wide range of complex human diseases including type 2 diabetes , coronary artery disease , dyslipidemia , breast cancer , obesity-related traits , prostate cancer and Crohns disease 1 ., These notable success stories represent significant advances in the global effort to understanding the genetic basis of common human diseases ., However , this has not been the case for hypertension , a common human disease affecting over one billion people worldwide 2 and a major contributor to cerebrovascular accidents , myocardial infarction , congestive cardiac failure and chronic renal disease 3 , 4 ., The earliest published GWAS that specifically sought associations for hypertension and/or BP traits ( the Wellcome Trust Case Control Consortium ( WTCCC ) 5 and the Diabetes Genetics Initiative ( DGI ) 6 studies ) did not find any genetic variant significantly associated with hypertension at the genome wide level ., While these two studies have some limitations , these negative findings have strengthened the notion that multiple rare independent variants may account for a large fraction of BP variation 7 , a situation in which GWAS ( designed to work best in “common disease , common variant” scenarios ) would be less useful ., A further note is that these studies were conducted in European populations and it is unknown if similar studies in populations with non-European ancestry would yield different insights ., In the present study , we conducted a GWAS of BP among African Americans enrolled in the Washington DC metropolitan region of the United States ., In comparison with other population groups in the United States , African Americans suffer a disproportionate burden of hypertension and its complications ., A priori , we considered that: ( 1 ) Gene variants associated with BP variation among normotensive individuals may not be exactly the same set identified as those associated with persistently elevated blood pressure ( i . e . “hypertension” ) ; ( 2 ) Since the clinical definition of hypertension utilizes elevation of either the systolic blood pressure ( SBP ) or diastolic blood pressure ( DBP ) , those with hypertension are a heterogenous group comprising those with isolated SBP elevation , those with isolated DBP elevation and those with both ., This heterogeneity is likely to be reflected in genetic associations for each of these traits ( SBP , DBP , hypertension ) ; ( 3 ) Individual response to hypertension treatment varies greatly thereby making it a real possibility that statistical adjustment of SBP and DBP for treatment ( e . g . adding a fixed quantity to measured BP ) among treated hypertensive individuals 8 , may mask real associations in GWAS ., ( 4 ) The evidence so far from GWAS of hypertension and BP suggest that there may be few or no variants with large effects , implying that p values may be modest compared to those reported for other traits ., For these reasons , we chose to:, 1 ) conduct a case-control association study for hypertension;, 2 ) conduct an association study for SBP and DBP among normotensive individuals;, 3 ) use pathway-based analyses of the GWAS data to determine if the variants most strongly associated with BP phenotypes cluster in pathways and networks that are of biological relevance to BP regulation ., Using this strategy , we hoped to maximize the chances of discovering loci influencing hypertension susceptibility and/or normal BP control ., Ethical approval for the study was obtained from the Howard University Institutional Review Board ( IRB ) ., All subjects provided written informed consent for the collection of samples and subsequent analysis ., This study was conducted according to the principles expressed in the Declaration of Helsinki ., The subjects studied were all participants in the Howard University Family Study ( HUFS ) , a population based family study of African Americans in the Washington metropolitan area ., The major objectives of the HUFS were to:, 1 ) enroll and examine a randomly ascertained cohort of African-American families , along with a set of unrelated individuals , from the Washington DC metropolitan area to study the genetic and environmental basis of common complex diseases including hypertension , obesity and associated phenotypes;, 2 ) to characterize study participants for anthropometry ( including weight , height , waist and hip circumferences , body composition measures ) and BP; and, 3 ) evaluate the association between genetic variants and selected traits ( hypertension , BP and obesity ) ., Participants were sought through door-to-door canvassing , advertisements in local print media and at health fairs and other community gatherings ., In order to maximize the utility of this cohort for the study of multiple common traits , families were not ascertained based on any phenotype ., During a clinical examination , demographic information was collected by interview ., Weight , height , waist circumference and hip circumference were measured using standard methods as follows: Weight was measured in light clothes on an electronic scale to the nearest 0 . 1 kg , and height was measured with a stadiometer to the nearest 0 . 1 cm ., Body mass index ( BMI ) was computed as weight in kg divided by the square of the height in meters ., Waist circumference was measured to the nearest 0 . 1 cm at the narrowest part of the torso as seen from the anterior aspect ., BP was measured in the sitting position using an oscillometric device ( Omron ) ., Three BP readings were taken with a ten minute interval between readings ., The reported SBP and DBP readings were the average of the second and third readings ., Pulse pressure ( PP ) was calculated as the difference between the SBP and DBP ., Hypertension status was defined as SBP>\u200a=\u200a140 mmHg and/or DBP>\u200a=\u200a90 mmHg and/or treatment with antihypertensive medication ., In the overall cohort , the frequency of hypertension was 35% and among those that were hypertensive , 64% were on antihypertensive medication at the time of the study ., Genome-wide genotyping was performed using the Affymetrix® Genome-Wide Human SNP Array 6 . 0 9 ., DNA samples were prepared and hybridized following the manufacturers instructions ., After processing , chips were scanned and genotype calls were made using the Birdseed 2 algorithm 9 , 10 ., All samples used in the analysis achieved a chip wide call rate of ≥95% ., Individual SNPs were excluded if they had a call rate of less than 95% ( n\u200a=\u200a41 , 885 ) across all individuals , a minor allele frequency <\u200a=\u200a0 . 01 ( n\u200a=\u200a19 , 154 ) or had a Hardy-Weinberg equilibrium ( HWE ) test p of <1×10−3 ( n\u200a=\u200a6 , 317 ) ., The current analysis focused on the 808 , 465 autosomal SNPs that passed these filters ., The average call rate for this set of SNPs in these individuals was 99 . 5% ., The concordance of blind duplicates was 99 . 74% ., Focused , lower-throughput genotyping for replication was carried out using Sequenom Homogenous MassEXTEND or iPLEX Gold SBE assays at the National Human Genome Research Institute ( NHGRI ) ., Evidence for population stratification or structure was sought by conducting non parametric clustering of genotypes using the AWClust algorithm 11 ., All the subjects formed one cluster with a few outliers ., Individuals identified as outliers were removed before association analysis , which in this case resulted in the removal of 7 individuals from a sample of 1024 individuals , for a final sample size of 1017 individuals ., Further checks were conducted during the association analysis on the 1017 participants as follows: first , the genomic control ( GC ) method was used to compute the genomic inflation factor for each analysis and was determined to be 1 . 007 for hypertension , 1 . 001 for SBP and 0 . 998 for DBP , showing minimal evidence of inflation of the test statistic due to stratification ., As expected , the GC-adjusted test statistics were virtually identical to the unadjusted values ., Second , a Q-Q plot was used to visualize the distribution of the test statistic for each trait analysis and these again showed no evidence of population stratification ., Finally , principal components ( PC ) were computed using the eigenstrat method 12 ., Based on examination of the scree plot ( shown in Figure S1 ) , the first two PCs were retained and used as covariates during the association analysis in order to adjust for any potential residual population stratification ., Hypertension was analyzed as a binary trait ( cases versus controls ) using a logistic regression model under an additive model with adjustment for age , sex , BMI , and the first 2 PCs of the genotypes ., Given that treatment for hypertension alters BP values , we conducted the association analysis for SBP and DBP in two ways ., First , a normotensives-only analysis was carried out using linear regression models with age , sex , BMI , and the first 2 PCs of the genotypes as covariates ., This approach was designed to uncover any BP associated loci without the “noise” effect of treatment ., Second , an analysis of the whole dataset was carried out using the same covariates and also adjusting for the effect of treatment ., All association analyses were performed using the PLINK software package , v1 . 04 13 ., Association for the replication sample of 980 unrelated non-diabetic West Africans enrolled as part of the Africa America Diabetes ( AADM ) Study 14 , 15 was done the same way ., P-values for the discovery ( African American ) sample and the replication ( West African ) samples were combined using the Meta-Analysis Tool for genome-wide association scans , METAL ( http://www . sph . umich . edu/csg/abecasis/Metal/ ) ., The METAL algorithm calculates a z-statistic for each marker summarizing the magnitude and direction of the effect relative to the reference allele in each sample and then calculates an overall z-statistic and p value from the weighted average of the statistics ., Weights are proportional to the square-root of the sample size of each study ., SNPs that showed an association p-value less than 1e-04 for each trait were mapped to genes within 5 kB using Ensembl ( http://www . ensembl . org ) ., The resulting gene list for the hypertension phenotype and for SBP and DBP , each with corresponding Entrez IDs , were entered into MetaCore ( http://www . genego . com ) and tested for enrichment in Maps , Diseases , Gene Ontology ( GO ) processes and GeneGO processes ., MetaCore uses a hypergeometric model to determine the significance of enrichment ., The subjects comprised 1017 individuals ( 419 men , 598 women ) , including 509 cases of hypertension and 508 normotensive controls ., Hypertensive subjects were older ( mean age 54 years versus 41 years ) and heavier ( mean BMI 31 . 7 kg/m2 versus 29 . 3 kg/m2 ) than the normotensive subjects ., As expected , mean BP was higher and showed more variance among hypertensive compared to normotensive subjects ( Table 1 ) ., The distribution of association p-values ( Manhattan plot ) for the three traits is shown in Figure 1 and the QQ plots in Figure 2 ., The ten top scoring SNPs for association with hypertension are shown in Table 2 ., The SNP with the lowest p-value ( 5 . 10×10−7 ) for this trait was rs9791170 located on chromosome 5 ., This intergenic SNP is about 6 kbp upstream of the P4HA2 ( GeneID 8974 ) gene ., However , it did not show genome-wide significance ( Bonferroni-corrected p\u200a=\u200a0 . 412 ) for association with hypertension; neither did any of the other SNPs ( see Table S1 for a list of the top-scoring associations for hypertension as a binary trait ) ., In contrast to the hypertension results , the T allele of the rs5743185 SNP , an intronic SNP in the PMS1 ( GeneID 5378 ) gene , was strongly associated with SBP ( nominal p\u200a=\u200a2 . 09×10−11 , Bonferroni-corrected p\u200a=\u200a1 . 69×10−5 ) among normotensive individuals ., Other SNPs that showed significant association with SBP among normotensive individuals , each with a Bonferroni-corrected p value of ≤0 . 05 , include: rs3751664 ( a non-synonymous coding SNP in CACNA1H ( GeneID 8912 ) ) , rs11160059 ( an intronic SNP inSLC24A4 ( GeneID 123041 ) ) , rs17365948 ( an intronic SNP in YWHAZ ( GeneID 7534 ) ) , rs12279202 ( an intronic SNP in IPO7 ( GeneID 10527 ) ) and rs1687730 ( an intergenic SNP , 12 kb from AL365365 . 23 , a pseudogene ) , – Table 3 ., Repeating these analyses for the whole sample , with adjustment for treatment effects , did not change the top-scoring characteristics of these six SNPs ( as shown in Table S2 ) ., The mean effect size on SBP associated with the at-risk alleles of these six SNPs ( estimated from the linear model adjusted for age , sex , BMI and PCs among normotensive individuals only ) was ∼5–6 mmHg ., If independent , each SNP significant after Bonferroni-correction correction would be associated with ∼5% of the variance in SBP ., The full list of the top-scoring associations for SBP is shown in Table S3 ., Haplotype analysis did not show any haplotype association that reached the significance of the single locus analyses ( data not shown ) ., Two-locus interaction analyses between the SNPs that were significant or marginally so did not show any significant interactions , with the lowest p-value 0 . 115 ( between rs17315498 and rs11160059 ) ., For DBP , the A allele of rs1867226 ( an intronic SNP in PRC1 ( GeneID 9055 ) ) showed the lowest p-value ( 5 . 8×10−7 ) ., However , neither this nor any other association reached genome wide significance ( Table 3; see Table S4 for list of top-scoring SNPs for DBP ) ., Pathway analysis revealed a number of significant pathways and processes that are associated with SBP and DBP ( Table 4 ) ., Examination of each of these pathways and processes showed annotations with obvious cardiovascular implications ( for example , Development_PIP3 signaling in cardiac myocytes , Transport_Potassium transport and Development_Blood vessel morphogenesis ) and several pathways and processes that are enriched for genes involved in hypertension and/or blood pressure regulation ., As a case in point , the top scoring pathway – Development_Role of HDAC and calcium/clamodulin-dependent kinase ( CaMK ) in control of skeletal myogenesis- ( Figure 3 ) contains the calcium-gated channels CACNA1E and CACNA1H , IGF-1 , and AKT , each of which is known to play a role in mechanisms of BP regulation , hypertension and/or complications of hypertension ( including left ventricular hypertrophy ) 16–21 ., The top-scoring pathways for hypertension alone are shown in Table S5 ., A total number of 17 SNPs were carried forward for replication in the sample of 980 unrelated non-diabetic West Africans ( 366 HTN cases , 614 normotensive subjects; mean age 49 ( SD 12 ) years , mean BMI 25 . 1 ( SD 6 ) kg/m2 ) enrolled as part of the Africa America Diabetes Mellitus ( AADM ) study 14 , 15 ., These 17 SNPs comprised the top-scoring seven SNPs for SBP , the top scoring three SNPs for DBP , two SNPs that had low p-values ( p<1×10−4 ) for both SBP and DBP , as well five of the top-scoring SNPs for HTN as a dichotomous trait ., Five ( rs5743185 , rs3751664 , rs12279202 , rs11659639 and rs6543012 ) were monomorphic in the West African sample ., The results for the other twelve SNPs analyzed under an additive model and with adjustment for age , sex , BMI , ethnic group and treatment for hypertension ( adjustment for treatment for SBP and DBP only ) are shown in Table 5 ., Three SNPs ( rs1867226 , rs1550576 and rs8039294 ) were significant at a p-value of <0 . 05 among the West Africans ., The combined analysis showed that five SNPs , including rs11160059 ( SLC24A4 ) , were significantly associated with the trait and with the same direction of effect in both samples ., Two recent GWA studies 22 , 23 identified the STK39 and CDH13 genes as being significantly associated with BP ., We therefore looked for evidence for association of SNPs in these genes with SBP and DBP in the present study ., Each of these genes showed multiple SNPs associated with SBP and DBP at a p<0 . 05 ( Table 6 ) ., Of note , STK39 had many more significantly associated SNPs ( 9/136 for SBP , 33/136 for DBP ) than would be expected by chance at a nominal p value of 0 . 05 ( 7/136 ) ., All of the STK39 SBP-associated SNPs and 24 of the 33 DBP-associated SNPs were in the LD bins 1 and 2 ( chr2:168 , 699 , 002-168 , 788 , 544 ) reported in the Amish ., We also looked for in silico replication of this studys top SBP-associated SNPs in the Diabetes Genetics Initiative ( DGI ) 6 GWAS , which to our knowledge , was the first published GWAS for BP ., Out of the five genes harboring the top scoring SNPs for SBP in this study , three had variants with low p-values associated with SBP under the same additive model in the DGI ( Table S6 ) ., These were SLC24A4 ( rs7142084 , p\u200a=\u200a0 . 0017 ) , IPO7 ( rs7480643 , p\u200a=\u200a0 . 009 ) and PMS1 ( rs3791767 , p\u200a=\u200a0 . 014 ) ., While this paper was under review , two GWAS for hypertension , SBP and DBP in subjects of European descent were published 35 , 36 ., One of these studies 36 also reported finding significant hits in the CACNB2 gene for hypertension and DBP , a gene with a high-scoring variant for hypertension in the present study ( Table S1 ) and in the PMS1 gene for hypertension and SBP , which scored highly for SBP in this study ( Table 3 ) .

Introduction, Methods, Results, Discussion

The evidence for the existence of genetic susceptibility variants for the common form of hypertension ( “essential hypertension” ) remains weak and inconsistent ., We sought genetic variants underlying blood pressure ( BP ) by conducting a genome-wide association study ( GWAS ) among African Americans , a population group in the United States that is disproportionately affected by hypertension and associated complications , including stroke and kidney diseases ., Using a dense panel of over 800 , 000 SNPs in a discovery sample of 1 , 017 African Americans from the Washington , D . C . , metropolitan region , we identified multiple SNPs reaching genome-wide significance for systolic BP in or near the genes: PMS1 , SLC24A4 , YWHA7 , IPO7 , and CACANA1H ., Two of these genes , SLC24A4 ( a sodium/potassium/calcium exchanger ) and CACNA1H ( a voltage-dependent calcium channel ) , are potential candidate genes for BP regulation and the latter is a drug target for a class of calcium channel blockers ., No variant reached genome wide significance for association with diastolic BP ( top scoring SNP rs1867226 , p\u200a=\u200a5 . 8×10−7 ) or with hypertension as a binary trait ( top scoring SNP rs9791170 , p\u200a=\u200a5 . 1×10−7 ) ., We replicated some of the significant SNPs in a sample of West Africans ., Pathway analysis revealed that genes harboring top-scoring variants cluster in pathways and networks of biologic relevance to hypertension and BP regulation ., This is the first GWAS for hypertension and BP in an African American population ., The findings suggests that , in addition to or in lieu of relying solely on replicated variants of moderate-to-large effect reaching genome-wide significance , pathway and network approaches may be useful in identifying and prioritizing candidate genes/loci for further experiments .

Despite intense research , the genetic risk factors for essential hypertension and blood pressure ( BP ) regulation have not been identified with consistency ., We conducted a genome wide association scan using over 800 , 000 genetic markers in an African American sample of 1 , 017 adults in the Washington , D . C . , area of the United States ., We found evidence to suggest that genetic variants in several genes , including PMS1 , SLC24A4 , YWHA7 , IPO7 , and CACANA1H , are significantly associated with systolic BP levels ., From our previous knowledge of human physiology , two of these genes have potential roles to play in BP regulation ., The evidence for genetic variants influencing diastolic BP levels and hypertension status was weaker and inconclusive ., To our knowledge , this is the first study that has used a genome-wide association approach to study hypertension and BP in an African American population , a minority group that experiences hypertension more frequently and more severely than other population groups in the United States ., The findings will be useful to other researchers seeking to advance our understanding of the genetic factors that influence BP with the hope that these insights will eventually translate to new and better treatment options for hypertension in African Americans and other global populations .

cardiovascular disorders/hypertension, genetics and genomics/genetics of disease, genetics and genomics/complex traits

journal.pcbi.1000148

Coordinate Regulation of G Protein Signaling via Dynamic Interactions of Receptor and GAP

G protein-mediated signaling modules display a variety of dynamic input-output behaviors despite their use of a single , relatively simple biochemical mechanism ., Signal amplification , the ratio of effector proteins activated to agonist-bound receptors , can vary from unity to hundreds ., Activating ligands may bind receptors with affinities ranging from picomolar through millimolar ., GAPs , which can accelerate hydrolysis of bound GTP over 2000-fold , can accelerate both activation and deactivation in cells with variable inhibitory effect 1 ., Activation and deactivation rates upon addition and removal of agonist can thus range from ∼10 ms to minutes ., Heterotrimeric G proteins convey signals by traversing a cycle of GTP binding and hydrolysis: the GTP bound state of the Gα subunit is active and deactivation is caused by hydrolysis of bound GTP to GDP 2 ., The rates of activation and deactivation , and consequent effects on signal output at steady state , are regulated by interactions of the Gα subunit with receptors 3 , Gβγ subunits 4 , GTPase-activating proteins ( GAPs ) 1 and multiple other proteins 5 ., The net effect of these inputs depends on the identities of the individual proteins , their concentrations and their own regulatory controls ., Regulatory inputs to G protein modules are interactive , and it has been difficult to establish quantitative understanding of how they cooperate to control signal output ., While some signals , particularly G protein-gated channels , can be monitored accurately in cells in real time , it has been harder to quantitate the intermediary reactions of the GTPase cycle and protein–protein binding or dissociation ., Recently developed optical sensors are promising 6–10 but still do not provide complete or simultaneous coverage of multiple events and often do not provide absolute ( i . e . , molar ) data ., Conversely , quantitative biochemical assays using in vitro reconstituted systems have provided absolute biochemical data 11 , 12 but have not adequately described the simultaneous regulatory interactions that are so important ., Consequently , quantitative understanding of the dynamic behavior of an intact G protein module remains elusive ., Computational modeling is used frequently to clarify mechanistic thinking about complex biochemical systems , including G protein signaling ., Quantitative models can potentially combine information on individual reactions to simulate the behavior of a complex system , or use system-level data to test the validity of a proposed mechanism ., The work of Linderman and colleagues , for example , has provided consistent examples of these approaches to G protein signaling 13–16 ., The G protein-mediated yeast pheromone response has also been the focus of significant modeling efforts because of its presumed paucity of off-pathway inputs 17–19 ., In at least one case , the failure of a simple model of this pathway motivated discovery of a novel mechanism for feedback regulation and subsequent refinement of the model 17 ., However , modeling of G protein modules has often been descriptive , with parameters arbitrarily chosen for a few reactions such that model output mimics an experimental result ., Alternatively , the inner workings of the G protein module itself have been condensed into an arbitrary function of agonist concentration and receptor regulation to allow analysis of a downstream event such as Ca2+ release or protein phosphorylation or , even more distal , transcription ., A major problem in developing quantitative models of G protein modules has been accurate assignment of parameters to the many processes that are known to occur ., These include both the GTPase cycle reactions and the multiple protein-protein interactions that govern these reactions ., This problem is significant because local protein concentrations at the plasma membrane and the regulated association of these proteins are both unknown , either for resting cells or during dynamic signaling ., In this study , we have used purified proteins , heterotrimeric Gq , m1 muscarinic acetylcholine receptors and phospholipase C-β1 , reconstituted at uniform and controllable concentrations into unilamellar phospholipid vesicles , to overcome this first limitation ., We estimated formation of multi-protein complexes according to their individual activities ., The second major problem in modeling signaling through G protein modules is the difficulty in assigning correct , or even plausible , values of rate or equilibrium constants for the reactions included in the model ., Despite their apparently small size , an informative model of a single G protein module will contain multiple parameters that are not readily accessible from individual measurements ., These parameters may vary widely among different modules ( receptors , G proteins , GAPs ) , which prohibits most literature-mining approaches ., If all or most of the relevant parameters are not individually available for the module of interest , then an adequately large and diverse dataset must be produced to allow parameters to be fit to the data ., Last , even with a presumably adequate dataset , the numerical fitting process that extracts values for the parameters and subsequent validation of the fit are both central problems in modeling signaling systems ., We have adapted and extended several approaches to deal with the difficulty of fitting a model with a fairly large number of parameters using a modest amount of data ., We present a modestly complex model of signal output in a G protein model that contains many of the salient regulatory interactions that characterize G protein signaling ., We used steady-state GTPase data to support a Metropolis-Monte Carlo fitting strategy , and argue that most parameters are reasonably assigned , with statistical data to help qualify fits for individual parameters ., The resultant parameter set shows that receptor accelerates both GDP dissociation and GTP binding , and that GAPs potentiate the receptors nucleotide exchange catalyst activity ., Further , the model argues strongly that GAP activity indirectly favors continued binding of receptor to G protein throughout the GTPase cycle , thus further potentiating the receptors activity ., Such indirect stabilization of receptor-G protein binding , referred to as kinetic scaffolding to distinguish it from direct interaction , was suggested as a mechanism for how a GAP can accelerate deactivation upon removal of agonist without substantially inhibiting signaling 1 , 11 , 16 , 20 ., Model-based simulation of signal output describes how GAPs combine these mechanisms to independently control signal amplitude and kinetics ., The biochemical model of the GTPase catalytic cycle ( Figure, 1 ) includes GTP binding , hydrolysis of bound GTP and simultaneous release of inorganic phosphate ( Pi ) , and the dissociation of GDP ., At each stage of the reaction , G protein is allowed to bind agonist-liganded receptor , GAP or both ., Receptor is assumed to be agonist bound and active at all times; agonist-stimulated GTPase data were obtained in the presence of saturating carbamylcholine ( 1 mM ) ., Possible dissociation of Gβγ from Gα and protein oligomerization were not included ( see Discussion ) ., All reactions were considered to be reversible to allow imposition of path-independence constraints on closed reaction loops during the fitting process ( see below ) ., For the same reason , even presumably unlikely reaction paths were retained to create symmetry in the reaction map ., For calculation of G protein activation ( see below ) , all GTP-bound species were considered to be equally active , and fractional activation was calculated as the fraction of all species that contain bound GTP ., The kinetic model for G protein signaling ( Figure, 1 ) includes 48 parameters , first- and second-order rate constants , only a few of which have been determined directly ., We therefore fit all the parameters to a relatively large and diverse set of steady-state GTPase rates determined in a purified and reconstituted system in which protein concentrations were known and where data could be obtained over a wide dynamic range ., Data for fitting came from 8 scans of GTPase activity as the concentration of one assay component , GTP , GDP or GAP , was varied from zero to saturation in the presence or absence of saturating agonist ( Table S1; Figure 2 ) ., Data were fit simultaneously to minimize the cost function , defined as the sum of the squares of deviations between experimental data and data predicted by the model ( Materials and Methods ) ., Values for the 48 kinetic parameters were adjusted simultaneously by constrained simulated annealing to best match all available data while satisfying thermodynamic constraints ( path independence , i . e . cyclicΔG\u200a=\u200a0 , for all potential cycles; and net ΔGhydrol for GTP 21 ) ., The progress of cost minimization for a typical fitting run is shown in Figure S2 ., The cost function is initially quite high ( off-scale in Figure S2 ) and decreases rapidly ., The initial decrease is followed by relatively quick adjustments of the parameters interspersed with long metastable stages , reflecting occasional escape of the Monte Carlo search from local minima in the cost manifold ., Improvement in the fit is negligible past a few thousand iterations ., To further test the adequacy of the Monte Carlo search , it was repeated with thermodynamic constraints applied as a quantitative penalty for nonconformance in the cost function rather than as an absolute constraint ( Materials and Methods ) ( Figure S3 ) ., In this case , initial convergence was slower , but subsequent enforcement of strict thermodynamic constraints decreased the value of the cost function to a level similar to that achieved if thermodynamic constraints are applied throughout the fitting process ., Because this more ergodic search method did not lead to lower values of the cost function , it is likely that imposing path-independence constraints initially does not seriously limit the ergodicity of the fitting process ., The initial test of such a modeling process is the ability of the model to simulate experimental data using the parameter set determined by fitting ( Figure 2 ) ., Simulations based on the model and parameters derived from 41 fitting runs ( Table S2 ) approximated the experimental data well over a 105-fold range of GTPase activities and a wide variety of experimental conditions ., Values of Km for GTP , Ki for GDP and EC50 for the GAP activity of PLC-β1 were all matched closely in each experiment ., Relative increases and decreases in activity were also simulated well , as were curve shape and steepness ., The largest recurrent discrepancy between data and prediction was in the absolute value of the maximal activity ., Disagreement was negligible in some experiments , but was significant in others ., In part , this reflects real difficulty in fitting such a diverse dataset , but it also arises from variation in specific activity among the experiments ., The data were obtained using several preparations of m1AChR-Gq vesicles that varied in maximum specific activities , with standard deviation of ∼40% among 13 batches of vesicles prepared during the study ., Variation between fits and data in Figure 2 are within this margin ., The values of the rate constants obtained by fitting the steady-state rates also compare well with those few that have previously been determined directly in pre-steady-state kinetic measurements 12 ( Figure 3 ) ., For five reactions , nucleotide association and dissociation and GTP hydrolysis , agreement was within a factor of 4 ., The direct determinations were performed with different preparations of vesicles and by different investigators ., Agreement is thus even more striking ., Importantly , the pre-steady-state kinetic data were not used in the present fit ., The rate constants obtained here also compare well with predictions from data obtained in non-identical preparations ( detergent-solubilized proteins , free Gαq subunits , etc . ) 11 , 12 , 22 , 23 ., Fitting is a stochastic process that , upon repeat , converges to different minima of comparable cost in a complex manifold ., For these datasets , multiple fitting runs yielded a family of parameter sets with cost functions in the range 650–800 ( not shown ) ., The extent of variation among repeated fits reflects the size of the error on each parameter ( Figure 3 ) ., For some of the parameters , reproducibility was excellent , but for others error was large ., Error may reflect the absence of necessary data or experimental error , but an additional difficulty in fitting some parameters arises from the structure of the model ., To allow imposition of path-independence constraints , the model contains all possible interactions of proteins and nucleotides , including species that are quantitatively negligible and reactions that do not contribute detectably to flux through the GTPase cycle ., Thus , some individual rate constants cannot be fit well , and some pairs of forward and reverse rate constants that describe rapid equilibria are poorly fit because the data only constrain their ratios ., To evaluate possible sources of errors associated with some of the parameters , we repeated the fitting process with synthetic data and asked whether the fitting process could accurately return the parameters used in the synthesis ., Simulated data equivalent to the original experimental data were generated using the model and a chosen parameter set ., To simulate experimental noise , Gaussian errors ( standard deviation/mean\u200a=\u200a10% ) were convoluted with the predictions ., The parameter set returned in this process simulated the synthetic data extremely well , and did not show the significant errors in maximal velocity observed when the real data were fitted ( not shown ) ., The parameter set obtained by fitting synthetic data was then compared with the set used in its generation ( Figure 4A ) ., The histogram shows that 32 of the 48 constants were fit to within 10-fold of the generating value , with 19 within 2-fold ., Examination of the outliers indicates that they describe reactions that either are not appreciably populated or are much faster than the reaction that they precede , and therefore could not be constrained ., The fitting process is thus adequate to determine most parameters well , and those that are not well fit do not contribute appreciably to overall flux through the GTPase cycle ., To see whether rapid equilibria contribute to error in evaluating individual kinetic constants , we also compared the fitted equilibrium constants for each reaction ( i . e . , the ratios of forward and reverse rate constants ) with the values used to generate the synthetic data ( Figure 4B ) ., Deviations from the generating values were fewer and smaller , indicating that equilibrium constants were constrained by the thermodynamic relationships used to construct the model ., The quality of the fit was further assessed by thermal ensemble analysis 24 ( Text S2 ) ., The analysis consists of generating statistically equivalent fits to the data and measures the extent to which parameters are coupled ( Text S2 ) ., We found lack of generalized mixing suggesting ( 1 ) a reasonable match between the model and the underlying phenomena , ( 2 ) the absence of severe over- or under-parameterization , and ( 3 ) the availability of sufficient data for accurate determination of many of the parameters ., The parameter set shown in Figure 3 and Table S2 provides the first reasonably complete set of experimentally determined rate constants for a G protein signaling module , and thus provides insights into regulatory interactions that were not previously accessible ., While the parameters are interpretable only to within the errors of the fit , several novel observations stand out at this level ., First , examination of the rates of nucleotide binding and release indicate that the salient function of receptor is to open an otherwise inaccessible ( “closed” ) nucleotide binding site on Gq to permit GDP/GTP exchange ., In addition to accelerating GDP dissociation , receptor also markedly accelerates both GDP and GTP association ( Table 1 ) ., Receptor thus promotes GDP/GTP exchange by two distinct mechanisms ., It accelerates GDP dissociation over 104-fold and GTP association more than 103-fold ., Receptors have been thought to act by binding G protein negatively cooperatively with respect to nucleotides; i . e . , that receptor decreases affinity for GDP by increasing the dissociation rate ( Kassoc\u200a=\u200akassoc/kdiss ) ., In the case of the M1 muscarinic receptor and Gq , the decrease in affinity for GDP ( ∼3-fold ) is dwarfed by acceleration of GDP dissociation ( ∼20 , 000-fold; because GDP binding to the open site is also fast ) ., Opening and closing of the nucleotide binding site is also reflected in the remarkably slow nucleotide association rates observed in the absence of receptor ., The slow basal association rate constant for GTP , ∼500 M−1·s−1 , is particularly striking , but all GDP and GTP association rate constants are less than 104 M−1·s−1 without receptor stimulation ., Receptor increases the association rates about 104-fold to 106–107 M–1·s−1 , values that are more commonly observed for binding of small ligands to proteins ., Taken together with the slow rates of spontaneous nucleotide dissociation , the slow association rates indicate that the nucleotide binding site on Gq is essentially closed in the absence of receptor and that receptor stabilizes the open conformation regardless of whether GTP , GDP or no nucleotide is bound ( see Discussion ) ., Second , comparison of the rate constants for nucleotide exchange shows that GAP potentiates the ability of the receptor to accelerate the dissociation of bound GDP by about 20-fold ( Table 2 ) ., Thus , even though GAP has negligible effect on GDP binding by itself , its facilitation of GDP/GTP exchange helps minimize potential inhibition of signaling during stimulation by receptor ., GAPs were not previously known to modulate GDP binding 1 , 25 , but this effect was probably overlooked because GAPs do not bind tightly to GDP-bound G protein; the RGAD complex will only be formed during steady-state GTPase turnover ., GAP displays little effect on the rate of GTP dissociation because the binding of GAP and GTP to G protein is positively cooperative 1 ., The parameter set also indicates that receptor and GAP bind G protein negatively cooperatively , and that cooperativity depend on the binding of GDP or GTP ( Table 3 ) ., Receptor and GAP reciprocally decrease the affinity of Gq for each other by 25-fold when GTP is bound and by ∼120-fold when GDP is bound , but there is essentially no cooperativity displayed for binding to nucleotide-free Gq ., The most striking result of this interaction is the rapid dissociation of GAP from the receptor-Gq–GDP complex after GTP is hydrolyzed ., The t1/2 for GAP dissociation is about 300 ms , about 90-fold faster than in the absence of receptor ( Table S2 ) ., In contrast , GAP dissociation from GTP-bound Gq is slow , about 170-fold slower than hydrolysis , such that essentially every GAP binding event results in GTP hydrolysis ., In summary , GAP dissociates virtually immediately after GTP hydrolysis during receptor-mediated signaling , and is thus potentially available to accelerate hydrolysis on other G proteins ., The nucleotide-dependent , negatively cooperative binding of receptor and GAP to G protein also helps determine the reaction pathway through the GTPase cycle: what intermediate species are populated and for how long ( Figures 5 and 6; see below ) ., For example , GTP accelerates the dissociation of receptor from G protein by ∼70-fold whereas GDP has a much smaller effect ., This difference further biases receptor-promoted GDP/GTP exchange toward the forward ( activating ) direction ., Qualitatively , destabilization of receptor binding by nucleotides confirms the observation that nucleotides drive dissociation of receptor from G protein 26 ., To examine the overall regulatory behavior of the G protein module , we used the complete reaction model and average fitted parameter set to simulate signal output as the fraction Z of all G protein complexes to which GTP is bound ., Figure 5A shows a contour plot of fractional activation at steady-state as a function of varying concentrations of receptor and GAP , using typical in vitro assay conditions to allow us to compare prediction with experiment ( 300 nM GTP , 10 pM GDP , no Pi ) ., At low concentrations of active receptor , signal output is predictably low regardless of GAP concentration ., In the absence of GAP ( bottom of figure ) , increasing the concentration of receptor raises Z to about 93% activation ., At saturating concentrations of GAP ( top of figure ) , Z increases with increasing concentrations of active receptor to about 4% of maximal activation ., This limiting value reflects the ratio of the rates of GTP hydrolysis to GDP/GTP exchange when GAP and receptor are both bound to G protein throughout the catalytic cycle ., At high receptor concentration ( right side ) , increasing concentrations of GAP causes Z to fall from 85% to 12% ., These transitions are relatively smooth , although slopes are asymmetric and steeper than predicted by a Hill coefficient of 1 ., The values of Z at the corners agree with analytical calculations , which are only possible at these limits ., While the precise output obviously depends on the values of the rate constants , the overall topography of this plot had sufficient similarity among fitted parameter sets to indicate that errors in the fit do not modify the essential behavior of the model ., The most striking feature of the Z contour plot lies in the region where the concentrations of G protein , receptor and GAP are similar ., Here , Z contour lines are contorted and create an abrupt transition , a “ridge” at which activity peaks and then declines with increasing concentration of receptor ., In a few locations , increasing the concentration of receptor causes Z to decrease , and in a tiny region , increasing the concentration of GAP actually increases Z . This unintuitive topography is not idiosyncratic to the average parameter set , but appears in various shapes for all the parameter sets obtained with repeats of the fitting procedure ., To clarify the origin of this behavior , we calculated the fluxes and steady-state concentrations of intermediates at locations on either side of the ridge to determine what reactions and molecular species contribute to Z near the ridge ( Figure 5C; see Figure S5 and Figure S6 for examples ) ., To the left of the ridge , the major reaction path is RG→RGT→GT→GD→RGD→RG ., GT is the major activated species ., The receptor dissociates upon GTP binding and reassociates after hydrolysis , the mechanism referred to as collisional coupling 27 ., GAP is not significantly involved in the reaction scheme and Z is low ., Figure 5B indicates that the major active species is GT in this region ., Across the ridge , the reaction pathway becomes a comparable mixture of RG→RGT→RGD→RG and RG→RGT→RGAT→RGAD→RGD→RG ., Species RGT is the major active species ( Figure 5A ) ., Receptor remains bound throughout the GTPase cycle , and significantly , GAP is recruited to the receptor–G protein complex during the GTP-bound phase ( Table 1 ) ., Z has a higher value despite involvement of the GAP in net GTPase turnover ., The ridge thus reflects the coincidence of the peak in the concentration of GT in a region where the concentration of RGT is increasing significantly ( Figure 5B ) ., The change in pathway is governed by choice of the reaction that follows the branch-point species RGT ( Figure 5A and 5C ) ., With increasing concentration of receptor , net flux switches from RGT→GT to RGT→RGAT and RGT→RGD as the concentration of receptor crosses the ridge ., The peak in activity reflects the transient accumulation of GT as the concentration of free R increases and drives GDP/GTP exchange but before it reaches the level at which GAP is recruited ., Above the Z ridge , flux through the GTPase cycle is maintained entirely by complexes that include receptor; i . e . , where receptor remains bound throughout the catalytic cycle ., The occurrence of a ridge in Z with increasing receptor concentration , rather than a monotonic increase , is caused by the negatively cooperative binding of receptor and GAP to G protein ( described above ) ., The importance of this mechanism is indicated by the location of the ridge in the R-A plane ., It lies just to the left of the line Atot\u200a=\u200aGtot−Rtot , where the sum of the concentrations of total receptor and total GAP equals the concentration of total G protein ., This straight line appears as a curve on log–log plots ( Figure 5A ) ., Negatively cooperative binding of receptor and GAP to G protein make accumulation of RG and GA species far more likely than accumulation of RGA species and thus causes the abrupt shift of pathway and consequent peak in G protein activation ., The crest of Z is displaced from the line because the GTPase cycle is not at equilibrium under steady-state reaction condition ., We also used the model and parameter set to simulate G protein activation under typical cytoplasmic conditions—0 . 2 mM GTP , 0 . 02 mM GDP , 1 mM Pi 28 ( Figure 6 ) ., Activation of Gq responds to receptor and GAP in a pattern generally similar to that seen under laboratory assay conditions , but the higher cytoplasmic concentration of GTP allows substantial activation by receptor at high GAP concentrations ., Signal output is thus significant , Z∼0 . 25 , even in the presence of saturating GAP ., Output remains high in the presence of GAP because GTPase flux is almost entirely from the RGA–>RGAT–>RGAD–>RGA pathway over a large part of the R-A plane ( Figure S6 , Figure S7 , and Text S3 ) ., Given this pathway , high values of Z result in part from the GAPs potentiation of receptor-promoted GDP release ( Table S2 ) ., GAP exerts this effect under cytoplasmic conditions because , at 0 . 2 mM GTP , nucleotide-free G protein binds GTP quickly ( t1/2<2 ms ) and because GAP does not dissociate appreciably ., Equally important , receptor remains bound because GTP is hydrolyzed rapidly , before appreciable receptor can dissociate , and therefore catalyzes GDP/GTP exchange promptly after hydrolysis ., The principal potentiating effect of cytoplasmic GTP concentration is thus to support continued association of receptor , GAP and G protein during the GTPase cycle ., A novel and unintuitive result of this simulation is the decline and subsequent increase in Z at high receptor concentrations as the concentration of GAP is increased ., As shown in Figure 6 , Z is minimal at about 10−4 M GAP and increases at higher GAP concentrations ., This effect is not predicted for lower concentrations of GTP and is relatively small for the conditions and parameters used here ., The occurrence and extent of this behavior depends sensitively on multiple rate constants , as do the relative plateau values of Z at high and low GAP concentration ., In general , the ability of GAP to increase fractional G protein activation at high concentrations depends on its potentiation of the receptors exchange catalyst activity and its indirect stabilization of receptor binding to G protein , as discussed above ., Its mechanism is discussed in the Text S3 ., In cells , GAP activity often accelerates signal termination when agonist is removed but does not inhibit signaling significantly while agonist is present 1 ., To determine whether this behavior is accurately predicted by the present model and to study its mechanism , we simulated signal termination upon removal of a rapidly dissociating agonist by first allowing the system to reach steady state and then instantaneously setting the concentration of activated receptor to zero ( Materials and Methods ) ., We first scanned the receptor and GAP concentrations shown in Figure 6 for regions where increasing the GAP concentration causes minimal inhibition but significantly accelerates signal termination ., Quantitative search criteria were chosen to mimic published experiments ( reviewed in 1; see legend to Figure 7 ) , but their exact values are not crucial ( results not shown ) ., As shown in the inset to Figure 7 , addition of GAP can accelerate deactivation with minimal steady-state inhibition at all concentrations of active receptor ., A wide range of initial and final GAP concentrations also meet the initial criteria ., This behavior is thus robust to initial conditions ., Within this region , addition of GAP can accelerate signal termination up to 180-fold , which actually exceeds the acceleration that has been observed in cells ., Figure 7 shows the deactivation time course for a representative simulation that compares signal termination at high and low concentrations of GAP , shown as red dots in the inset ., The higher GAP concentration accelerated Gq deactivation more than 15-fold , measured as time to 50% of initial activity , but inhibited receptor-stimulated G protein activation by only 5% ., Qualitatively similar behaviors are observed over much of the area of Figure 6 , indicating that fast termination combined with minimal inhibition is a common outcome of G protein GAP activity ., Neither termination time course in Figure 7 is monoexponential , and complete deactivation is markedly delayed at the lower GAP concentration ( right inset ) ., Some GAP activity thus appears to be required for reasonably fast decay of signal output to basal levels ., Simulations with intermediate GAP concentrations ( not shown ) indicate that GAPs can also facilitate return to basal activation without accelerating signal termination to the extent shown in Figure 7 , and a variety of termination behaviors can be observed at different points on this activation surface ., While multiphasic decay of G protein signals has also been observed experimentally , we do not know whether the separate phases in Figure 7 correspond to specific cellular turn-off events ., Flux analysis of the deactivation events indicates that there is a single mechanism for accelerated signal termination by GAPs ., At low GAP concentrations , the species RGT and RGAT both contribute significantly to activity in the presence of activated receptor ., Upon removal of receptor , GT and GAT are rapidly created ., GAT is then rapidly deactivated at a rate of 8 . 6 s−1 ( p21 in Table S2 ) , the initial phase of deactivation ., The second , very slow phase is deactivation of GT ., In contrast , at higher GAP concentrations almost all G protein activity is due to RGAT ., When activated receptor is removed , the GAT that is formed hydrolyzes rapidly to cause fast deactivation ., While deactivation is not really monophasic even at fairly high GAP concentrations , slow hydrolysis of GT is not significant because there is not much of it and because the GAP that dissociates from the GAD hydrolysis product binds remaining GT to accelerate its deactivation ., In this way , GAP provides a pathway for fast signal turn-off without inhibiting signaling ., A mechanistic model of signal transduction should provide quantitative understanding of how time-dependent outputs arise from the underlying binding , conformational and chemical reactions ., This study attempts to address three unresolved mechanistic questions in G protein signaling ., First , what are the underlying dynamics of the GTPase catalytic cycle that integrate the regulatory activities of receptors and GAPs , their reversible binding to the G protein , and their control of G protein activation ?, Which effects are important and what functions do they serve ?, Next , how can a GAP accelerate signal turn-off when agonist is removed , yet not inhibit activation while agonist is present ?, Both these questions are vital to understanding how G protein-regulated effectors

Introduction, Results, Discussion, Materials and Methods

Signal output from receptor–G-protein–effector modules is a dynamic function of the nucleotide exchange activity of the receptor , the GTPase-accelerating activity of GTPase-activating proteins ( GAPs ) , and their interactions ., GAPs may inhibit steady-state signaling but may also accelerate deactivation upon removal of stimulus without significantly inhibiting output when the receptor is active ., Further , some effectors ( e . g . , phospholipase C-β ) are themselves GAPs , and it is unclear how such effectors can be stimulated by G proteins at the same time as they accelerate G protein deactivation ., The multiple combinations of protein–protein associations and interacting regulatory effects that allow such complex behaviors in this system do not permit the usual simplifying assumptions of traditional enzyme kinetics and are uniquely subject to systems-level analysis ., We developed a kinetic model for G protein signaling that permits analysis of both interactive and independent G protein binding and regulation by receptor and GAP ., We evaluated parameters of the model ( all forward and reverse rate constants ) by global least-squares fitting to a diverse set of steady-state GTPase measurements in an m1 muscarinic receptor–Gq–phospholipase C-β1 module in which GTPase activities were varied by ∼104-fold ., We provide multiple tests to validate the fitted parameter set , which is consistent with results from the few previous pre-steady-state kinetic measurements ., Results indicate that ( 1 ) GAP potentiates the GDP/GTP exchange activity of the receptor , an activity never before reported; ( 2 ) exchange activity of the receptor is biased toward replacement of GDP by GTP; ( 3 ) receptor and GAP bind G protein with negative cooperativity when G protein is bound to either GTP or GDP , promoting rapid GAP binding and dissociation; ( 4 ) GAP indirectly stabilizes the continuous binding of receptor to G protein during steady-state GTPase hydrolysis , thus further enhancing receptor activity; and ( 5 ) receptor accelerates GDP/GTP exchange primarily by opening an otherwise closed nucleotide binding site on the G protein but has minimal effect on affinity ( Kassoc\u200a=\u200akassoc/kdissoc ) of G protein for nucleotide ., Model-based simulation explains how GAP activity can accelerate deactivation >10-fold upon removal of agonist but still allow high signal output while the receptor is active ., Analysis of GTPase flux through distinct reaction pathways and consequent accumulation of specific GTPase cycle intermediates indicate that , in the presence of a GAP , the receptor remains bound to G protein throughout the GTPase cycle and that GAP binds primarily during the GTP-bound phase ., The analysis explains these behaviors and relates them to the specific regulatory phenomena described above ., The work also demonstrates the applicability of appropriately data-constrained system-level analysis to signaling networks of this scale .

Throughout the eukaryotes , G proteins convey information from receptors for diverse stimuli—neurotransmitters , hormones , light , odors , and pheromones—to intracellular regulatory proteins collectively known as effectors ., G proteins function by transiting a dynamic cycle of activation and deactivation ., Receptors accelerate activation , which allows G proteins to regulate effectors , and receptors thus increase signal output ., GTPase-activating proteins , GAPs , accelerate deactivation ., GAPs can thus attenuate signaling , but GAPs can also accelerate signal termination when stimulus is removed without inhibiting signal output while stimulus is present ., Surprisingly , some effectors are also GAPs for the G proteins that activate them , essentially turning off their activator ., We developed a mathematical model that describes control of G protein signaling by receptor and GAP and used experimental data to determine its important parameters ., We show that GAPs actually potentiate G protein activation by receptor , a previously unsuspected effect ., Further , GAPs indirectly stabilize receptor–G protein binding during stimulation , which we had previously predicted based on inconsistencies among other experimental results ., The present results elucidate how GAPs can independently control signaling kinetics and amplitude and thus clarify how effectors can both respond to G proteins and act as G protein GAPs .

biophysics/theory and simulation, biochemistry/cell signaling and trafficking structures, biochemistry/theory and simulation, computational biology/signaling networks, biophysics/cell signaling and trafficking structures, computational biology/systems biology

journal.pcbi.1003783

A Deterministic Model Predicts the Properties of Stochastic Calcium Oscillations in Airway Smooth Muscle Cells

Oscillations in cytoplasmic calcium concentration ( ) , mediated by inositol trisphosphate receptors ( ; a calcium channel that releases calcium ions ( ) from the endoplasmic or sarcoplasmic reticulum ( ER or SR ) in the presence of inositol trisphosphate ( ) ) play an important role in cellular function in many cell types ., Hence , a thorough knowledge of the behavior of the is a necessary prerequisite for an understanding of intracellular oscillations and waves ., Mathematical and computational models of the play a vital role in studies of dynamics ., However , over the past decade , two major questions about models have arisen ., Firstly , how best should the be modeled ?, Models of the have a long history , beginning with the heuristic models of 1–3 ., With the recent appearance of single-channel data from in vivo 4 , 5 , a new generation of Markov models has recently appeared 6 , 7 ., These models show that exist in different modes with different open probabilities ., Within each mode there are multiple states , some open , some closed ., Importantly , it was found 8 that time-dependent transitions between different modes are crucial for reproducing puff data from 9 ., However , it is not yet clear whether transitions between states within each mode are important , or whether all the important behaviors are captured simply by inter-mode transitions ., Secondly , why do deterministic models of the perform so well as predictive models ?, Deterministic models of the have proven to be useful predictive models in a range of cell types ., For example , -based models have been developed to study oscillations in airway smooth muscle cells ( ASMC ) 10–13 , and these models have made predictions which have been confirmed experimentally ., This shows the usefulness of such models in advancing our understanding of how intracellular oscillations and waves are initiated and controlled in ASMC ., However , these models are deterministic models which assume infinitely many per unit cell volume , an assumption that contradicts experimental findings in many cell types showing that puffs and spikes occur stochastically , and that intracellular waves and oscillations arise as an emergent property of fundamental stochastic events 9 , 14 , 15 ., Here , we answer these two fundamental modeling questions using data and models from ASMC ., Firstly , we show that a simple model of the , involving only two states with time-dependent transitions , suffices to generate correct dynamics of puffs and oscillations ., Secondly , we show that , although oscillations in ASMC are generated by a stochastic mechanism , a deterministic model can make the same qualitative predictions as the analogous stochastic model , indicating that deterministic models , that require much less computational time and complexity , can be used to make reliable predictions ., Although we work in the specific context of ASMC , our results are applicable to other cell types that exhibit similar oscillations and waves ., We have previously shown 8 that the statistics of puffs in SH-SY5Y cells can be reproduced by a Markov model of the based on the steady-state data of 5 and the time-dependent data of 4 ., In this model the can exist in 6 different states , grouped into two modes , which we call Drive and Park ( see Fig . 1 ) ., The Drive mode ( which contains 4 states; 1 open and 3 closed ) has an average open probability of around 0 . 7 , while the Park mode ( which contains the remaining two states; 1 open and 1 closed ) has an open probability close to zero ., Transitions between states within each mode are independent of and ; only the transitions between modes are ligand-dependent ., In our previous study on calcium puffs 8 , we showed that , to reproduce the experimentally observed non-exponential interspike interval ( ISI ) distribution and coefficient of variation ( CV ) of ISI smaller than 1 , the time-dependent intermodal transitions are crucial ., Lack of time dependencies in the Siekmann model leads to exponential ISI distributions and CV\u200a=\u200a1 , which is not the case for calcium spikes in ASMC ., Fig . 2A shows an example of oscillations generated by 50 nM methacholine ( MCh , an agonist that can induce the production of by binding to a G protein-coupled receptor in the cell membrane ) in ASMC ., By gathering data from 14 cells in 5 mouse lung slices , we found that the standard deviation of the interspike interval ( ISI ) is approximately a linear function of the ISI mean , with a slope clearly between 0 and 1 ( i . e . ) , indicating that the spikes are generated by an inhomogeneous Poisson process ( a slope of 1 would denote a pure Poisson process ) ( see Fig . 2B ) ., This shows the necessity of inclusion of time-dependent transitions for mode-switching ., Using a quasi-steady-state approximation , and ignoring states with very low dwell times , it is possible to construct a simplified two-state version of the full six-state model ( see Materials and Methods ) ., In the simplified model the intramodal structure is ignored , and only the intermodal transitions have an effect on behavior ., In Fig . 3 we compared the simplified model to the full six-state model ., Both models have the same distribution of interspike interval , spike amplitude and spike duration ., Moreover , by looking at a more detailed comparison between the two model results ( Figs . 4A , C and E ) and experimental data ( Figs . 4B , D and F ) , we found the 2-state model not only can reproduce the behaviour of the 6-state model , but can also qualitatively reproduce experimental data ., The average experimental ISI shows a clear decreasing trend as MCh concentration increases ( although a saturation occurs in the data for high MCh ) , a trend that is mirrored by the model results as the concentration increases ., Unfortunately , since the exact relationship between MCh concentration and concentration is uncertain , a quantitative comparison is not possible ., In both model and experimental results , the average peak and duration of the oscillations are nearly independent of agonist concentration ., The quantitative difference in spike duration between the model results and the data in Figs ., 4E and F are most likely due to choice of calcium buffering parameters ., For example , adding fast buffer ( see Materials and Methods ) increases the average spike duration to 0 . 54 s or 0 . 7 s respectively , which are close to the levels shown in the data ., Thus , the intramodal structure of the six-state model is essentially unimportant , as the model behavior ( in terms of the statistics of puffs and oscillations ) is governed almost entirely by the time dependence of the intermode transitions , particularly the time dependence of the rapid inhibition of the by high , and the slow recovery from inhibition by ., The multiple states within each mode are necessary to obtain an acceptable quantitative fit to single-channel data , but are nevertheless of limited importance for function ., Hence , even when simulating microscopic events such as puffs it is sufficient to use a simpler , faster , two-state model , rather than a more complex six-state model ., In the following , we will use the 2-state model to generate all the simulation results ., Although the data ( Fig . 2 ) show that oscillations in ASMC are generated by a stochastic process , not a deterministic one , we wish to know to what extent a deterministic model can be used to make qualitative ( and experimentally testable ) predictions ., Our simplified 2-state Markov model of the can be converted to a deterministic model ( see Materials and Methods ) ., The result is a system of ordinary differential equations ( ODEs ) with four variables , which takes into account the increased at an open pore , as well as the increased within a cluster of ; the four variables are the outside the cluster ( ) , the within the cluster ( ) , the total intracellular concentration ( ) and an gating variable ( ) ., We refer to the reduced 4D model as the deterministic model for all the results and analyses ., Note that there is no physical or geometric constraint enforcing a high local ; in this case the spatial heterogeneity arises solely from the low diffusion coefficient of ., Our use of is merely a highly simplified way of introducing spatial heterogeneity of the concentration ., Since the can only “see” ( as well as the concentration right at the mouth of an open channel , which we denote by ) , but cannot be influenced directly by ( the experimentally observed signal ) , our approach allows for the functional differentiation of the rapid local oscillatory in the cluster , from the slower signal in the cytoplasm , without the need for computationally intensive simulations of a partial differential equation model ., Quantitative accuracy is thus sacrificed for computational convenience ., Calcium oscillations in the stochastic and deterministic models are shown in Fig . 5A ., According to our previous results 8 , the average value of over the cluster of primarily regulates the termination and regeneration of individual spikes ., This can be seen in the stochastic model by projecting the solution on the phase plane ( Fig . 5B ) ., Upon an initial release from one or more , a large spike is generated by Ca2+-induced release ( via the ) during which time a decreasing gradually decreases the average open probability of the clustered ., The spike is terminated when is too small to allow further release ., This phenomenon is qualitatively reproduced by the deterministic model ( Fig . 5D ) ., In both the stochastic and deterministic models the decrease in average open probability of a cluster of caused by inhibition is the main reason for the termination of each spike ., According to Figs ., 5B and D , regeneration of each spike requires a return of back to a relatively high value ( i . e . , recovery of the from inhibition by ) ., The deterministic model sets a clear threshold for the regeneration , as can be seen in Fig . 5C , where an upstroke in occurs when the trajectory creeps beyond the sharp “knee” of the white curve ., When the trajectory reaches the knees of the white curve it is forced to jump across to the other stable branch of the critical manifold , resulting in a fast increase in followed by a relatively fast increase in ( seen by combining Figs . 5C and D ) ., In contrast , the stochastic model enlarges the contributions of individual so that the generation of each spike is also effectively driven by random release through the , which can be seen in the inset of Fig . 5B where the site of spike initiation ( blue bar ) exhibits significantly greater variation than that of spike termination ( green bar ) ., In spite of this , the essential similarities in phase plane behavior result in both deterministic and stochastic models making the same qualitative predictions in response to perturbations , such as changes in concentration ( ) , influx or efflux ., In the following , we illustrate this by investigating a number of experimentally testable predictions ., Due to the extensive importance of frequency encoding in many -dependent processes , we focus particularly on the change of oscillation frequency in response to parameter perturbations ., As a side issue we also investigate how the oscillation baseline depends on physiologically important parameters ., In many cell types a moderate increase in increases the oscillation frequency ( see Fig . 2A in 11 , Fig . 4E in 16 and Fig . 6B in 17 ) , a result that is reproduced by both model types ( Fig . 6A ) ., As increases , the stochastic model increases the probability of the initial release through the first open and of the following release , thus shortening the average ISI ., Although the oscillatory region of the deterministic model is strictly confined by bifurcations which do not apply to the stochastic model , the deterministic model can successfully replicate an increasing frequency by lowering the “knee” of the red curve in Fig . 5D and shortening the time spent from the termination point c to the initiation point a ( thus shortening the ISI ) ., Hence , although the deterministic model cannot be used to predict the exact values of at which the oscillations begin and end , as stochastic effects predominate in these regions , it can be used to predict the correct qualitative trend in oscillation frequency ., In many cell types , including ASMC , transmembrane fluxes modulate the total intracellular load ( ) on a slow time scale 16 , 18 , and thereby modulate the oscillation frequency 19 ., Experimental data can be seen in Fig . 8 in 16 and Fig . 2 in 18 ., Figs ., 6B and C show that both stochastic and deterministic models predict the same qualitative changes in oscillation frequency in response to changes in membrane fluxes ( through membrane ATPase pumps and/or influx channels such as receptor-operated channels or store-operated channels ) ., The level of sarco/endoplasmic reticulum calcium ATPase ( SERCA ) expression ( or capacity ) is important for airway remodeling in asthma 20 and ASMC oscillations 21 ., We thus investigated the predictions of the two models in response to changes in SERCA expression ( ) ., As decreases , the deterministic model exhibits a decreasing frequency , in agreement with experimental data ( see Figs . 3 and 4 in 21 ) ., The same trend is seen in the stochastic model with only 20 ( see Fig . 6D ) ., Calcium buffers have been shown to be able to change the ISI and spike duration , which in turn change the oscillation frequency 15 , 22 ., We compared the effects on the two models of varying total buffer concentration ( ) by adding one buffer with relatively fast kinetics to the models ( see Materials and Methods for details ) ., In both models the frequency decreases as increases ( see Fig . 6E ) , which is consistent with experimental data ( Fig . 2B in 18 ) ., This is not surprising , because increasing can decrease the effective rates of SR release and reuptake ., Sustained elevations of baseline during agonist-induced oscillations or transients have been observed experimentally , and are believed to be a result of an increase in influx caused by opening of membrane channels 13 , 16 ., Furthermore , there is evidence showing that decreased SERCA expression could also increase the baseline ( Fig . 4 in 21 ) ., Those phenomena are successfully reproduced by both models ( see Fig . 7 ) ., In this paper we address two current major questions in the field of modeling ., Firstly , we show that puffs and stochastic oscillations can be reproduced quantitatively by an extremely simple model , consisting only of two states ( one open , one closed ) , with time-dependent transitions between them ., This model is obtained by removing the intramodal structure of a more complex model that was determined by fitting a Markov model to single-channel data 7 ., We thus show that the internal structure of each mode is irrelevant for function and mode switching is the key mechanism for the control of calcium release ., The necessity for time-dependent mode switching is shown not only by the dynamic single-channel data of 4 ) , but also by the puff data of 9 and our ASMC data ., Secondly , we investigate the role of stochasticity of in modeling oscillations in ASMC by comparing a stochastic IP3R-based model and its associated deterministic version , for parameters such that both of the models exhibit spikes but the stochastic model cannot necessarily be replaced by a mean-field model ., We find that a four-variable deterministic model has the same predictive power as the stochastic model , in that it correctly reproduces the process of spike termination and predicts the same qualitative changes in oscillation frequency and baseline in response to a variety of perturbations that are commonly used experimentally ., The mechanism for termination of individual spikes is fundamentally a deterministic process controlled by a rapid inhibition induced by the high local in the cluster , whereas spike initiation is significantly affected by stochastic opening of ., Hence , repetitive cycling is primarily induced by the time-dependent gating variables governing transitions of the from one mode to another ., Our simplified two-state model of the is identical in structure ( although not in parameter values ) to the well-known model of 23 ., It is somewhat ironic that after 20 years of detailed studies of the and the construction of a plethora of models of varying complexity , the single-channel data have led us around full circle , back to these original formulations ., Excitability is arising via a fast activation followed by a slower inactivation , a combination often seen in physiological processes 24 ., Encoding of this fundamental combination results directly from the two-mode structure of the ., Although similar single-channel data have been used to construct three-mode models 6 , 25 , neither of these models has yet been used in detailed studies of puffs and waves , and it remains unclear whether or not they have a similar underlying structure ., In contrast to previous deterministic ODE models , our four-variable model includes a more accurate model , as well as local control of clustered by two distinct microdomains; one at the mouth of an open , the other inside a cluster of ., Neglect of either of these microdomains leads to models that either exhibit unphysiological cytoplasmic concentrations or fail to reproduce reasonable oscillations ., This underlines the importance of taking microdomains into consideration when constructing any model ., Our microdomain model is highly simplified , with the microdomain being treated simply as a well-mixed compartment ., More detailed modeling of spatially-dependent microdomains is possible , and not difficult in principle , but requires far greater computational resources ., It is undeniable that a more detailed model , incorporating the full spatial complexity – and possibly stochastic aspects as well – would make , overall , a better predictive tool ., However , our goal is to find the simplest models that can be used as predictive tools ., An important similar study is that of Shuai and Jung 26 ., They compared the use of Markov and Langevin approaches to the computation of puff amplitude distributions , compared their results with the deterministic limit , and showed that stochasticity does not qualitatively change the type of puff amplitude distribution except for when there are fewer than 10 ., Here , we significantly extend the scope of their study by exploring the effects of stochasticity on the dynamics of spikes , and we do this in the context of an model that has been fitted to single-channel data ., Although this is true in a general sense for the Li-Rinzel model , which is based on the DeYoung-Keizer model , which did take into account the opening time distributions of in lipid bilayers , neither model can reproduce the more recent data obtained from on-nuclei patch clamping ., When these recent data are taken into account one obtains a model with the same structure , but quite different parameters and behavior ., We find that , in spite of a relatively large variation in spike amplitude which is partially caused by a large variation in ISI ( Fig . 5B ) , the mechanism governing individual spike terminations is the same for both a few or infinitely many , which explains why the one-peak type of amplitude distribution is independent of the choice of number ( see Fig . 6A in 26 ) ., Another important relevant study was done by Dupont et al . 27 , who compared the regularity of stochastic oscillations in hepatocytes for different numbers of clusters ., They found that the impact of stochasticity on global oscillations ( in terms of CV ) increases as the total cluster number decreases ., Our study here extends these results , and demonstrates how well stochastic oscillations can be qualitatively described by a deterministic system , even when there is only a small number of ( which appears to be the case for ASMC , in which the wave initiation site is only in diameter ) ., Indeed , as we have shown , for the purposes of predictive modeling a simple deterministic model does as well as more complex stochastic simulations ., Ryanodine receptors ( RyR ) are another important component modulating ASMC oscillations 16 , 28 , 29 but are not included in our model ., This is because the role of RyR is not fully understood and may be species-dependent; for example , in mouse or human ASMC , RyR play very little role in -induced continuing oscillations 17 , 30 , but this appears not to be true for pigs 28 ., Our study focuses on the calcium oscillations in mouse and human ( as we did in our experiments ) where inclusion of a deterministic model of RyR should have little effect ., An understanding of the role of RyR stochasticity and how the and the RyR interact needs a reliable RyR Markov model , exclusive to ASMC , which is not currently available ., Multiple Markov models of the RyR have been developed for use in cardiac cells 31 , but these are based on single-channel data from lipid bilayers , and are adapted for the specific context of cardiac cells ., Their applicability to ASMC remains unclear ., Although we have not shown that the deterministic model for ASMC has the same predictive power as the stochastic model in all possible cases ( which would hardly be possible in the absence of an analytical proof ) the underlying similarity in phase plane structure indicates that such similarity is plausible at least ., Certainly , we have not found any counterexample to this claim ., However , whether or not this claim is true for all cell types is unclear ., Some cell types exhibit both local puffs and global spikes ( usually propagating throughout the cells in the form of traveling waves ) , showing that initiation of such spikes requires a synchronization of release from more than one cluster of 14 ., This type of spiking relies on the hierarchical organization of signal pathways , in particular the stochastic recruitment of both individual and puffs at different levels 32 , and therefore cannot be simply reproduced by deterministic models containing only a few ODEs ., However , oscillations in ASMC , as observed in lung slices , may not be of this type , as IP3R-dependent puffs have not been seen in these ASMC ., It thus appears that , in ASMC in lung slices , every “puff” initiates a wave , resulting in periodic waves with ISI that are governed by the dynamics of individual puffs ., Animal experimentations carried out were approved by the Animal Care and Use Committee of the University of Massachusetts Medical School under approval number A-836-12 ., BALB/c mice ( 7–10 weeks old , Charles River Breeding Labs , Needham , MA ) were euthanized via intraperitoneal injection of 0 . 3 ml sodium pentabarbitone ( Oak Pharmaceuticals , Lake Forest , IL ) ., After removal of the chest wall , lungs were inflated with of 1 . 8% warm agarose in sHBSS via an intratracheal catheter ., Subsequently , air ( ) was injected to push the agarose within the airways into the alveoli ., The agarose was polymerized by cooling to ., A vibratome ( VF-300 , Precisionary Instruments , San Jose , CA ) was used to make thick slices which were maintained in Dulbeccos Modified Eagles Media ( DMEM , Invitrogen , Carlsbad , CA ) at in /air ., All experiments were conducted at in a custom-made temperature-controlled Plexiglas chamber as described in 17 ., Lung slices were incubated in sHBSS containing Oregon Green 488 BAPTA-1-AM ( Invitrogen ) , a Ca2+-indicator dye , 0 . 1% Pluronic F-127 ( Invitrogen ) and sulfobromophthalein ( Sigma Aldrich , St Louis , MO ) in the dark at for 1 hour ., Subsequently , the slices were incubated in sulfobromophthalein for 30 minutes ., Slices were mounted on a cover-glass and held down with mesh ., A smaller cover-glass was placed on top of the mesh and sealed at the sides with silicone grease to facilitate solution exchange ., Slices were examined with a custom-built 2-photon scanning laser microscope with a oil immersion objective lens and images recorded at 30 images per second using Videosavant 4 . 0 software ( IO Industries , Montreal , Canada ) ., Changes in fluorescence intensity ( which represent changes in ) were analyzed in an ASMC of interest by averaging the grey value of a pixel region using custom written software ., Relative fluorescence intensity ( ) was expressed as a ratio of the fluorescence intensity at a particular time ( F ) normalized to the initial fluorescence intensity ( ) ., Inhomogeneity of cytoplasmic concentration not only exists around individual channel pores of the , where a nearly instantaneous high concentration at the pore ( denoted by ) leads to a very sharp concentration profile , but is also seen inside an cluster where the average cluster concentration ( ) is apparently higher than that of the surrounding cytoplasm ( ) 33 ., This indicates that during oscillations each is controlled by either the pore concentration ( when it is open ) or the cluster concentration ( when it is closed ) ., Neither of these local concentrations influence cell membrane fluxes or the majority of SERCAs , which we assume to be distributed outside the cluster ., The scale separation between the pore concentration and the cluster concentration allows to treat as a parameter , providing a simpler way of modeling local events ( like puffs ) that has been used in several previous studies 8 , 34 , 35 ., However , evolution of the cluster concentration and wide-field cytoplasm concentration are not always separable , so an additional differential equation for the cluster is necessary ., A schematic diagram of the model is shown in Fig . 8 ., The corresponding ODEs are ( 1 ) ( 2 ) ( 3 ) where representing total intracellular concentration , and thus SR concentration , is given by ., and are the volume ratios given in Table 1 ., is the flux through the , is a background leak out of the SR , and is the uptake of into the SR by SERCA pumps ., is the flux through plasma pump , and represents a sum of main influxes including ( receptor-operated channel ) , ( store-operated channel ) and ( leak into the cell ) ., coarsely models the diffusion flux from cluster microdomain to the cytoplasm ., Details of the fluxes are Calcium concentration at open channel pore ( ) does not explicitly appear in the equations but is used in the model introduced later ., is assumed to be proportional to SR concentration ( ) and is therefore simply modeled by where is the value corresponding to ., Alternatively , can also be assumed to be a large constant ( say greater than ) without fundamentally altering the model dynamics ., The choice of is not critical as long as it is sufficiently large to play a role in inactivating the open channels ., All the parameter values are given in Table 1 ., The model used in our ASMC calcium model is an improved version of the Siekmann model which is a 6-state Markov model derived by fitting to the stationary single channel data using Markov chain Monte Carlo ( MCMC ) 5 , 7 , 8 ., Fig . 1 has shown the structure of the model which is comprised of two modes; the drive mode , containing three closed states , , and one open state , and the park mode , containing one closed state and one open state ., The transition rates in each mode are constants ( shown in Table 2 ) , but and which connect the two modes are -/-dependent and are formulated as ( 4 ) ( 5 ) where , , and are -/-modulated gating variables ., , , and are either functions of or constants and are given later ., We assume the gating variables obey the following differential equation , ( 6 ) where is the equilibrium and is the rate at which the equilibrium is approached ., Those equilibria are functions of concentration at the cytoplasmic side of the , denoted by in the equations , equal to either or depending on the state of the channel ) ., They are assumed to be ( 7 ) ( 8 ) ( 9 ) ( 10 ) Hence , we have stationary expressions of and , ( 11 ) ( 12 ) The expressions of s , s , s and s are chosen as follows so that Eq ., 11 and Eq ., 12 capture the correct trends of experimental values of and ( see Fig . 9 ) and generate relatively smooth open probability curves ( see Fig . 10 ) , Note that the above formulas are different from the relatively complicated formulas used in 8 ., The rates , , and , are constants estimated by using dynamic single channel data 4 and given in Table 2 , whereas is not clearly revealed by experimental data ., However we have shown that it should be relatively large for high but relatively small for low for reproducing experimental puff data 8 ., By introducing two concentrations , and , and the state of the channel become highly correlated , so that we can assume is a relatively large value if the channel is open and is a relatively small value if the channel is closed ., Hence , is modeled by the logic function Values of and are chosen so that simulated oscillations in ASMC are comparable to experimental observations ., Here we reduce the 6-state model to a 2-state open/closed model ., The reduction takes the following steps: Hence , the reduced two-state model contains one “open” state and one closed state with the opening transition rate of and the closing transition rate of ., Based on the stochastic calcium model and the reduced 2-state model , we construct a deterministic model ., We need to modify three things that are used in the stochastic model but inapplicable to fast simulations of the deterministic model ., The first is the discrete number of open channels; the second is state-dependent use of and in calculating and ; the last is the logic expression of ., Details of the modifications are as follows , Based on the above changes , the full deterministic model containing 8 ODEs is presented as follows , ( 13 ) ( 14 ) ( 15 ) ( 16 ) ( 17 ) ( 18 ) ( 19 ) ( 20 ) where and are functions of the gating variables given by Eqs ., 4 and 5 ., All the fluxes are the same as those of the stochastic model except ., All the parameter values of the deterministic model are the same as those of the stochastic model and are therefore given in Tables 1 and 2 ., The full deterministic model contains 8 variables which make the model difficult to implement and analyze ., Thus , we reduce the full model to a minimal model that still captures the crucial features of the full model ., First of all , , and are sufficiently large so that we can assume they instantaneously follow their equilibrium functions ., Therefore , by taking quasi-steady state approximation to , and , we remove the three time-dependent variables from the full model ., By now , the full model has been reduced to a 5D model , ( 21 ) ( 22 ) ( 23 ) ( 24 ) ( 25 ) Second , the rate of change of approaching its equilibrium , ( calculated from Eq . 24 ) , is at least one order larger than those of , and , indicating that taking the quasi-steady state approximation to Eq ., 24 could not significantly affect the evolutions of , and ., That is , ( 26 ) We emphasize here that the theory of the quasi-steady state approximation has not yet been well established , particularly about the rigorous conditions under which such a reduction is valid ., Thus , our criterion of judging the validity of the reduction is checking whether the solutions of the reduced model are capable of qualitatively reproducing that of its original model ., For this model , we find the reduction works ., Hence , the full model is eventually reduced to a 4D model summarized as follows , ( 27 ) ( 28 ) ( 29 ) ( 30 ) where is given by Eq ., 26 ., To check the effect of calcium buffers on oscillation frequency , we introduce a stationary buffer ( no buffer diffusion ) , as mobile buffers are too complicated to be included in the current deterministic model ., Since we have two different cytoplasmic concentrations , and , two pools of buffer with the same kinetics should be considered ., Hence , the inclusion of a stati

Introduction, Results, Discussion, Materials and Methods

The inositol trisphosphate receptor ( ) is one of the most important cellular components responsible for oscillations in the cytoplasmic calcium concentration ., Over the past decade , two major questions about the have arisen ., Firstly , how best should the be modeled ?, In other words , what fundamental properties of the allow it to perform its function , and what are their quantitative properties ?, Secondly , although calcium oscillations are caused by the stochastic opening and closing of small numbers of , is it possible for a deterministic model to be a reliable predictor of calcium behavior ?, Here , we answer these two questions , using airway smooth muscle cells ( ASMC ) as a specific example ., Firstly , we show that periodic calcium waves in ASMC , as well as the statistics of calcium puffs in other cell types , can be quantitatively reproduced by a two-state model of the , and thus the behavior of the is essentially determined by its modal structure ., The structure within each mode is irrelevant for function ., Secondly , we show that , although calcium waves in ASMC are generated by a stochastic mechanism , stochasticity is not essential for a qualitative prediction of how oscillation frequency depends on model parameters , and thus deterministic models demonstrate the same level of predictive capability as do stochastic models ., We conclude that , firstly , calcium dynamics can be accurately modeled using simplified models , and , secondly , to obtain qualitative predictions of how oscillation frequency depends on parameters it is sufficient to use a deterministic model .

The inositol trisphosphate receptor ( ) is one of the most important cellular components responsible for calcium oscillations ., Over the past decade , two major questions about the have arisen ., Firstly , what fundamental properties of the allow it to perform its function ?, Secondly , although calcium oscillations are caused by the stochastic properties of small numbers of is it possible for a deterministic model to be a reliable predictor of calcium dynamics ?, Using airway smooth muscle cells as an example , we show that calcium dynamics can be accurately modeled using simplified models , and , secondly , that deterministic models are qualitatively accurate predictors of calcium dynamics ., These results are important for the study of calcium dynamics in many cell types .

biophysics, biology and life sciences, computational biology

journal.pgen.1004200

Lsd1 Restricts the Number of Germline Stem Cells by Regulating Multiple Targets in Escort Cells

Stem cells undergo self-renewing divisions in which at least one daughter retains its stem cell identity , while the second daughter may or may not differentiate , depending on intrinsic and extrinsic cues ., A balance between stem cell self-renewal and differentiation must be maintained for proper organ formation during development and tissue homeostasis in adulthood ., Stem cells often reside in microenvironments called niches , and specific mechanisms tightly regulate the size and signaling output of these structures 1 ., However , in vivo niches have often proven difficult to identify in mammalian tissues ., As a result , much of the current understanding of niches stems from the study of invertebrate models such as the germline stem cells ( GSCs ) of the Drosophila ovary ., Drosophila female GSCs reside in a well-characterized niche at the tip of a structure called a germarium ( Figure 1A ) ., Within germaria , GSCs lie immediately next to a somatic cell niche comprised of cap cells and terminal filament cells 2 ., Escort cells reside adjacent to the cap cells and line the anterior portion of the germarium ., These cells act to shepherd the germ cells during the earliest stages of their differentiation 3 , 4 , after which developing germline cysts are enveloped by follicle cells derived from a second stem cell population within the germarium ., Cap cells produce Decapentaplegic ( Dpp ) , which in turn activates a canonical Bone Morphogenic Protein ( BMP ) signal transduction pathway in GSCs 5 , 6 ., BMP pathway activation results in the transcriptional repression of bag of marbles ( bam ) 7–9 , a factor both necessary and sufficient for germ cell differentiation 10 , 11 ., Ectopic Dpp signaling outside the tip of the germarium results in an expanded GSC phenotype 5 , 9 ., Other pathways and neighboring cells likely regulate niche specific BMP signaling ., For example , a recent study provides evidence that hedgehog ( hh ) produced by the cap cells stimulates the anterior escort cells to produce niche specific signals 12 Moreover , several additional intrinsic and extrinsic mechanisms help restrict Dpp ligand production and diffusion within the niche ( reviewed in 13 , 14 ) ., One such mechanism involves the histone demethylase Lysine Specific Demethylase 1 ( Lsd1 ) ., Lsd1 uses a flavin-dependent monoamine oxidative based mechanism to remove mono- and di-methyl groups from histone H3 on lysine 4 ( H3K4me1 and H3K4me2 ) 15 ., In mammals , Lsd1 has been shown to silence a number of distinct gene sets in different cellular contexts , including Notch targets , TGFβ-1 and various loci involved in the maintenance of embryonic stem cells 16–20 ., Additional studies suggest that Lsd1 may also promote gene expression under certain circumstances 21 ., Disruption of Drosophila Lsd1 results in a male and female sterile phenotype , marked by the expansion of GSC-like cells in the germarium 22 , 23 ., These cells exhibit ectopic BMP responsiveness and fail to initiate a normal differentiation program once they leave the cap cell niche 24 ., To characterize the molecular mechanism by which Lsd1 restricts signaling outside the Drosophila female GSC niche , we used ChIP-seq to define direct binding sites of Lsd1 specifically in either escort cells or cap cells ., These experiments revealed that Lsd1 binds to over one hundred sites in escort cells and provide further insights into how Lsd1 contributes to the chromatin programming of cells inside and outside of an in vivo niche ., Escort cells send out extensions that closely contact germline cysts undergoing the early steps of differentiation 3 , 4 ., Escort cell death or genetically disrupting escort cell extensions can lead to an inappropriate expansion of GSC-like cells in the germarium 3 ., Previous results showed that Lsd1 functions within escort cells to prevent expanded BMP signaling outside of the GSC niche 24 ., This phenotype was accompanied by widespread cell death in both somatic cells and germ cells ., Therefore we considered the possibility that the expansion of BMP signaling exhibited by Lsd1 mutants may depend on changes to escort cell morphology and number ., To test this , we knocked down the expression of Lsd1 specifically in the escort cells and early follicle cells by crossing UAS-Lsd1RNAi into a c587-gal4; UAS-mCD8::GFP background and stained the resulting ovaries for GFP and the fusome marker Hts ., Fusomes are highly vesiculated organelles that appear round in GSCs and cystoblasts , and become branched as these germ cells differentiate into multi-cellular cysts 25 , 26 ., Three days after eclosion , control samples appeared normal ., These germaria typically contained two to four single cells ( GSCs and cystoblasts ) with round fusomes and escort cells that extended cytoplasmic processes between the developing cysts ( Figure 1B ) ., In contrast , the Lsd1 RNAi samples showed an expansion of GSC-like cells with round fusomes ., Escort cell extensions were clearly present in some germaria , but were missing in others ( Figure 1C , D ) ., These observations suggested that while the knockdown of Lsd1 caused changes in escort cell morphology , the presence of extra single cells with round fusomes did not absolutely depend on a complete loss of escort cell extensions ., However changes in escort cell morphology likely contributed to the phenotype over time ., In addition , expression of Lsd1RNAi also led to an increase of death within escort cells , consistent with the widespread cell death previously noted in Lsd1 null mutant germaria ( Figure 1E , F ) 24 ., Next we performed clonal analysis using the mosaic analysis with a repressible cell marker ( MARCM ) system to further analyze the Lsd1 mutant phenotype ., Clonal germaria were stained for the positive clone marker GFP and for the fusome marker Hts ., We categorized the relative position of escort cell clones along the anterior-posterior axis of the germarium ., Cells were considered anterior escort cells if they were immediately next to the cap cells , posterior escort cells if they were immediately adjacent to the follicle stem cells and middle escort cells if they were located in any position in between ., We induced control and Lsd1 mutant clones in parallel ., Control escort cell clones were never associated with an obvious robust germline tumor phenotype , although we noted one exception in which a single germarium with control escort cell clones contained six single germline cells with round fusomes ( 1 out of 143 counted ) ., By contrast , 17% ( 27/155 ) of the germaria that contained Lsd1 mutant escort cell clones displayed an expanded germline stem cell-like cell phenotype ( Figure 1G–J ) ., The relative position of Lsd1 mutant clones appeared to correlate with the appearance of a germline phenotype ., The vast majority of germaria ( 96%; n\u200a=\u200a27 ) that contained greater than 5 germline stem cell-like cells carried at least one Lsd1 mutant middle escort cell clone ., We observed one example in which a germarium with a mild expansion of GSC-like cells contained an Lsd1 mutant anterior clone and posterior clone but no middle escort cell clones ( Figure 1H ) ., Of note , most germaria that carried middle escort cell clones did not exhibit a GSC expansion phenotype ( 98/125 germaria ) ., While their appearance was rare , germaria with only anteriorly or posteriorly ( Figure 1K ) positioned escort cell clones did not display a robust GSC-like cell expansion phenotype ., Similarly , loss of Lsd1 in the terminal filament did not result in an obvious phenotype ( Figure 1L ) ., Germaria that contained Lsd1 mutant escort cell clones and exhibited an increased number of GSCs occasionally had an elongated and abnormal morphology ( Figure 1J ) ., Moreover , 22 . 1% of the germaria ( n\u200a=\u200a199 ) from Lsd1 mutant females lacked marked escort cell clones , compared to 13 . 9% of control germaria ( n\u200a=\u200a166 ) , and the average number of Lsd1 mutant clones per germarium ( 4 . 72 escort cell clones/germarium ) was lower compared to controls ( 8 . 77 escort cell clones/germarium ) , suggesting that either Lsd1 mutant escort cell progenitors exhibited reduced proliferation during development or died during the course of the experiment ., If increased death within the escort cell population accounted for all the observed phenotypes , one might predict that complete loss of all Lsd1 mutant escort cell clones within a particular germarium would result in an increased number of GSC-like cells ., However , we did not observe an expanded GSC phenotype in germaria that lacked clones from Lsd1 mutant females ., Together all the phenotypic data suggest both that escort cells require Lsd1 function to limit GSC number and that loss of Lsd1 compromises the growth and survival of escort cells , consistent with previous observations 24 and those noted above , which in turn further exacerbates the observed germ cell phenotypes ., To directly define the molecular mechanisms by which Lsd1 influences escort cell function , we elected to identify direct targets of Lsd1 regulation in these cells ., Determining whether Lsd1 targeted potential candidate genes represented a significant challenge ., For example , the size and complexity of the dpp promoter precluded our ability to assay whether Lsd1 directly targeted this gene using a PCR based chromatin immunoprecipitation ( ChIP ) approach ., To systematically define Lsd1 binding sites , we conducted ChIP experiments coupled with massive parallel sequencing ( ChIP-seq ) ., We used a number of different Hemagglutinin ( HA ) tagged transgenes , including a N-terminally tagged UASt-HA::Lsd1 transgene that exhibits high expression in the somatic cells and a N-terminally tagged UASp-HA::Lsd1 transgene that displays relatively lower levels of expression in the somatic cells ( Figure 2; S1 ) ., The UASt-HA::Lsd1 and UASp-HA::Lsd1 transgenes both fully rescued the Lsd1ΔN GSC tumor phenotype when driven by the c587-gal4 driver ., Because Lsd1 is expressed ubiquitously throughout the ovary 24 , we sought to determine whether this protein bound to distinct sites in different cell populations within the germarium ., We expressed the UASt-HA::Lsd1 and UASp-HA::Lsd1 trangenes in cap cells and terminal filament cells using hh-gal4 ( Figure 2A; S1 ) and in the escort cells and early follicle cells using the c587-gal4 driver ( Figure 2B; S1 ) ., HA-directed ChIP assays were performed on dissected ovaries and the immunoprecipitated chromatin was compared to input chromatin as a control ., Within escort cells and early follicle cells , products of the UASp-HA::Lsd1 and UASt-HA::Lsd1 trangenes bound to 207 and 191 sites respectively ( Based on the FindPeaks algorithm using a p-value threshold of 1 . 00e-3 to maximize the number of potential peaks; Table S1 , S2 ) , with 100 common sites sharing some degree of overlap ( Figure 2C ) ., Within cap cells and terminal filament cells , the UASp-HA::Lsd1 and UASt-HA::Lsd1 transgenic products associated with 98 and 167 genomic loci respectively ( Table S3 , S4 ) , with 37 overlapping loci in common between the two datasets ( Figure 2C ) ., Comparing all four datasets revealed 66 common peaks between terminal filament/cap cells and escort cells/early follicle cells ( Figure 2C , D ) ., 232 peaks appeared specific for escort cells and early follicle cells and 162 specific for cap cells and terminal filament cells ( Figure 2C , D ) ., MACs analysis 27 showed similar but broader peak calls ( Table S5 , S6 , S7 , S8 ) ., Lsd1 enrichment peaks were spread throughout the Drosophila genome ( Figure S2 ) and showed a preference for the promoter and 5′UTR regions of genes ( Figure S3 ) ., We were unable to isolate a sufficient number of cells to map H3K4 methylation across the escort cell genome ., However , comparing our data with available data from the modENCODE project revealed that Lsd1 binding peaks correlate with valleys of H3K4 methylation in embryos ( Figure S4 ) , consistent with the established biochemical activity of Lsd1 ., Strikingly , we did not observe any enrichment for Lsd1 binding near the dpp locus in escort cells ( Figure 2E ) , indicating that the repression of BMP signal transduction by Lsd1 must be through an indirect mechanism ., We examined the annotation of genes near escort cell and early follicle cell peaks , cap cell and terminal filament peaks , shared peaks and from the UASt-HA::Lsd1 data sets ( Table S9 , S10 , S11 , S12 ) ., This analysis indicated that genes near escort cell specific Lsd1 binding peaks encode products with a diverse array of functions needed for development , basic cellular processes and reproduction ( Figure S5A ) 28 , 29 ., Further analysis of this gene set did not reveal significant enrichment for components of specific pathways ., MEME analysis showed an enrichment of ACTGGAA elements within Lsd1 binding sites ( Figure S5B ) ., The significance of this enrichment remains unclear ., These results suggested that the mis-expression of a functionally diverse set of genes likely contributes to the Lsd1 mutant phenotypes ., The engrailed gene stood out as one potentially relevant target among the list of candidate genes ., engrailed encodes a homeobox transcription factor that acts as a segment polarity gene during embryogenesis 30–32 ., Previous results showed that engrailed regulates early ovarian development and that Engrailed protein expression is restricted to the terminal filament and cap cells in adult germaria 33 ., Engrailed functions within these cells to help maintain GSCs 12 ., Our ChIP-seq data revealed that Lsd1 exhibits enriched binding to a 2 kb region of the engrailed promoter in the escort cells ( Figure 3A ) ., We performed RNA RT-qPCR to look at the transcript levels of engrailed in Lsd1 mutants ., We compared bam mutants to bam Lsd1 double mutants because these samples are comparable in size and have the same basic cellular makeup ( Figure S6 ) ., This analysis revealed that engrailed transcript levels increased 6 fold in the absence of Lsd1 ( Figure 3B ) ., Next , we tested whether Engrailed protein expression expanded in the absence of Lsd1 ., In wild type germaria , cap cells and terminal filament cells express readily detectable levels of Engrailed , whereas the escort cells do not ( Figure 3C ) 12 , 33 ., In Lsd1ΔN mutants , however , we observed ectopic Engrailed protein expression in a limited number of escort cells , in addition to the terminal filament and cap cells , in 85 . 7% ( n\u200a=\u200a91 ) of the germaria examined ( Figure 3D ) ., These Engrailed expressing escort cells tended to be positioned immediately adjacent to the normal niche , although occasionally we observed Engrailed expressing escort cells several cell positions away from the cap cells ( Figure 3E ) ., We cannot rule out the possibility that other cells also mis-expressed Engrailed , but at a level below our detection threshold ., These data together suggest that Lsd1 serves to repress engrailed expression within a subpopulation of escort cells ., To test the functional relevance of ectopic Engrailed expression in escort cells , we assayed whether disruption of engrailed function , either through RNAi knockdown or loss-of-function mutations , modified the Lsd1 mutant phenotype ., Knockdown of engrailed in an Lsd1 RNAi background ( Figure 4A ) suppressed the expanded GSC-like cell Lsd1 mutant phenotype ( Figure 4B , E , F ) ., Furthermore , three independent engrailed mutations also suppressed the Lsd1 RNAi-induced phenotype , so that the number of single cells with round fusomes decreased and cyst development within the germarium proceeded normally ( Figure 4C–F ) ., In all cases , engrailed suppression of the Lsd1 RNAi phenotype resulted in the formation of morphologically normal ovarioles with maturing egg chambers ., In Drosophila , Engrailed drives the expression of hedgehog ( hh ) , which in turn leads to the expression of dpp in adjacent cells 34–37 ., Previous analysis showed an expansion of hh expression in Lsd1 mutant germaria 24 ., To determine whether the mis-expression of hh in escort cells contributed to the Lsd1 mutant phenotype , we crossed both hh-specific UAS RNAi and hh mutant lines into a c587-gal4>UAS-Lsd1RNAi background ., This analysis revealed that loss of hh function , similar to engrailed , suppressed the GSC-like expansion phenotype , resulting in the formation of germaria that exhibited normal germ cell differentiation ( Figure 4G–J ) ., To assess whether mis-expression of engrailed and hh are sufficient to expand the number of stem cell-like cells in the germarium , we expressed transgenes corresponding to both genes within escort cells and early follicle cells using the c587-gal4 driver ., Similar to the phenotype exhibited by Lsd1 mutants , ectopic expression of engrailed resulted in a stem cell-like cell expansion within 49 . 5% of germaria examined ( n\u200a=\u200a111 ) ., Many of these germline cells remained as single cells with round fusomes ( Figure 5A ) ., However , mis-expression of engrailed did not completely block cyst development and many ovarioles from c587-gal4>UAS-engrailed females contained maturing egg chambers ., In contrast to engrailed , over-expression of hh using two different transgenes did not obviously perturb early germ cell differentiation ( Figure 5G , H ) ., However , the mis-expression of these transgenes did result in follicle cell defects , consistent with previously published results 38 ., These results indicated that the hh transgene is active in these cells but that hh over-expression in the escort cells and early follicle cells is not sufficient to induce an expansion of GSC-like cells in germaria ., Loss of Lsd1 results in expanded BMP signaling within the germline 24 ., Based on the expansion of Engrailed expression in Lsd1 mutants and the similarities between the Lsd1 mutant and engrailed over-expression phenotypes , we reasoned that mis-expression of engrailed may also induce ectopic BMP signaling in the ovary ., To test this , we used a Dad-lacZ enhancer trap as a positive transcriptional reporter of dpp signal transduction 9 , 39 ., In control ovarioles , stem cells express high levels of Dad-LacZ , whereas the expression of this reporter sharply decreases in differentiating cysts ( Figure 5B ) ., Upon engrailed mis-expression in the escort cells , the number of Dad-LacZ positive germline cells increases , likely reflecting expanded Dpp signaling ( Figure 5C ) ., Next , we knocked down the expression of dpp in the presence of the engrailed transgene and found that disruption of dpp suppressed the engrailed over-expression phenotype ( Figure 5D–F ) ., Together these results suggest that mis-expression of engrailed in Lsd1 mutants drives ectopic BMP signaling , resulting in the expanded GSC-like cell tumor phenotype ., To test whether ectopic engrailed expression can specifically affect adult escort cells , we performed a temporally controlled over-expression experiment ., c587-gal4>UAS-engrailed larvae were kept at low temperature to prevent robust expression of the engrailed transgene ., Ovaries from adult females maintained at a low temperature did not display ectopic Engrailed expression or an expanded undifferentiated cell phenotype ( Figure 6A , A′ ) ., However , shifting c587-gal4>UAS-engrailed females to a higher temperature after eclosion resulted in ectopic engrailed expression in escort cells and early follicle cells , and a concomitant expansion of germline stem cell-like cells ( Figure 6B–C ) ., Thus , engrailed expression specifically in adults appears sufficient to induce ectopic BMP signaling in the anterior region of the germarium ., Lsd1 associates with the promoters of many genes besides engrailed , some of which could potentially play a role in regulating escort cell function ., To begin to characterize whether Lsd1 modulates the expression of other potential target genes , we stained c587-gal4 control and c587-gal4>UAS-Lsd1RNAi ovaries using available antibodies ., Cap cells and escort cells exhibit a shared peak of Lsd1 binding near the Rho1 gene ( Table S11 , 12 ) ., Previous results showed that loss of Rho1 results in escort cell defects and an expansion of GSC-like cells 3 ., Knocking down Lsd1 levels did not appear to result in any dramatic change in Rho1 expression within the escort cells ( compare Figure 7A and 7D ) ., Likewise , Lsd1 also exhibits enriched binding near Apc1 ( Tables S9 , S12 ) , a component of the Wnt signaling pathway ., However antibody staining showed that Apc1 protein levels did not change to an appreciable degree upon knock-down of Lsd1 ( Figure 7B , E ) ., In contrast , the product of a third potential Lsd1 target gene , Broad , exhibited increased expression in c587-gal4>UAS-Lsd1RNAi samples relative to controls ( Figure 7C , F ) ., However , loss of broad did not appear to suppress the Lsd1 mutant phenotype ( data not shown ) ., The raw gene functions in the developing gonad to regulate the morphology of somatic cells as they interact with primordial germ cells 40 , 41 ., Lsd1 exhibits enriched binding just 3′ to the raw transcription termination site ( Figure 7G ) ., Antibodies were not available to assay whether loss of Lsd1 caused a change in Raw expression levels but raw mutant and RNAi lines partially suppressed the Lsd1 phenotype ( Figure 7H–L ) ., The raw134 . 47 allele weakly modified the GSC-like cell expansion phenotype exhibited by c587-gal4>UAS-Lsd1RNAi germaria , while both rawRNAi and a single copy of raw155 . 27 more strongly suppressed the c587-gal4>UAS-Lsd1RNAi phenotype , giving rise to a reduced number of germaria that carried more than 5 single cells with round fusomes ., These genetic interactions suggest that mis-regulation of raw expression or activity also contributes to the Lsd1 mutant phenotype ., Encouraged by the finding that disruption of raw suppressed the Lsd1 mutant phenotype , we crossed a number of additional knockdown lines , corresponding to other putative Lsd1 target genes , into the c587-gal4>UAS-Lsd1RNAi background ., We counted the total number of single germ cells with round fusomes within individual germaria from the resulting ovaries ., This analysis showed that knockdown of 7 out of the 34 genes tested suppressed the c587-gal4>UAS-Lsd1RNAi phenotype to the point where fewer than 50% of the assayed germaria contained greater than 5 single cells with round fusomes ( Figure 8A ) ., This group included FK506-binding protein 1 ( FK506-bp1 ) , Glutamine:fructose-6-phosphate aminotransferase 1 ( Gfat1 ) , CG11779 , ken and barbie ( ken ) , Anaphase Promoting Complex subunit 7 ( APC7 ) , barren ( barr ) and Hepatocyte nuclear factor 4 ( Hnf4 ) ., These genes have varied functions and play roles in cell cycle regulation ( APC7 and barr ) , juvenile hormone signaling ( FK506-bp1 ) , development of the genitalia ( ken ) and lipid metabolism ( Hnf4 ) ., Lack of a clear functional link between these suppressors suggests that escort cells are particularly sensitive to perturbations in their gene expression programs ., Together these data show that disruption of Lsd1 results in a complex phenotype , marked by increased BMP signaling in the germline and disruption of normal escort cell function , which likely involves the mis-expression of several direct and potentially indirect target genes ( Figure 8B ) ., We have found Lsd1 associates with a limited number of loci within two different cell populations ., Lsd1 exhibits fairly broad peaks of binding , ranging in size from 166–262 bp based on the FindPeaks algorithm ., MACs analysis calls even wider peaks ( Supplementary Tables S5 , S6 , S7 , S8 ) ., The significance of the width of these peaks remains unclear but suggests that Lsd1 either does not associate with single sequence specific elements at these sites or exhibits a certain degree of spreading upon recruitment to a particular locus ., In Drosophila escort cells , Lsd1 binds to over 100 sites spread throughout the genome ., Lsd1 binds to fewer sites in cap cells ., While some Lsd1 binding sites overlap in cap cells and escort cells , the relatively large number of different sites suggests that Lsd1 recruitment depends on multiple and perhaps distinct cell-specific co-factors ., MEME analysis 42 , 43 reveals that many of the identified Lsd1 binding sites contain ACTGGAA elements ., GGAA sequences are often present in the core binding sites of ETS transcription factors ., The Drosophila genome encodes a number of ETS family members , none of which have been characterized in the somatic cells of the germarium ., Determining the functional relevance of these specific GGAA sites within gene promoters and identifying the transcription factors that bind to them will require additional efforts ., For technical reasons and to enable comparisons of Lsd1 binding between escort cells/early follicle cells and cap cells/terminal filament cells , we elected to express the Lsd1 HA-tagged transgenes in an otherwise wild-type background ., We acknowledge the possibility that endogenous Lsd1 may outcompete the HA-tagged transgenes for binding at specific sites in the escort cells and early follicle cells ., Therefore sites identified in this study may be an underrepresentation of the total number of sites bound by endogenous Lsd1 ., Repeating the ChIP-seq analysis using material from rescued Lsd1ΔN females that express the HA-tagged Lsd1 transgene in escort cells and early follicle cells represents important work for the future ., We found that Lsd1 mutant samples exhibit a 6-fold increase in engrailed transcript levels relative to controls ., Curiously , ectopic Engrailed protein expression was only observed in a small number of escort cells ., Perhaps additional post-transcription mechanisms regulate the translation of Engrailed , and potentially other proteins , inside and outside of the cap cell niche ., Such mechanisms would allow these cells to fine-tune their signaling output more than what could be achieved through transcriptional based mechanisms alone ., Results presented here also suggest that escort cells are not uniform in nature and perhaps have specific functions or capabilities depending on their lineage and where they reside within the germarium ., MARCM analysis shows that the loss of Lsd1 in some but not all escort cells results in a marked expansion of GSC-like cells within the germarium ., Previous studies have also suggested that specific escort cells have distinct roles in supporting GSCs 12 ., Technical considerations aside , the severity of the Lsd1 null phenotype compared to the engrailed over-expression phenotype , both in terms of the penetrance and extent of the GSC expansion phenotype and the accompanying germline and somatic cell death , suggests that engrailed is not the only biologically relevant target of Lsd1 regulation in the escort cells ., Based on expression analysis ( Figure 7 ) , Lsd1 regulates the expression of some but not all genes adjacent to its binding sites ., Our genetic analysis suggests that mis-regulation of the putative target raw ( Figure, 7 ) and several additional genes ( Figure, 8 ) also contribute to the Lsd1 mutant phenotype ., Characterizing the transcriptional profile of escort cells from wild-type and Lsd1 mutant samples , which will have to await for improvements in current cell isolation and RNA profiling techniques , will help to further resolve which genes are direct and indirect targets of Lsd1 regulation ., Such approaches may also reveal additional genes that participate in niche formation and function ., Of note , the observation that functionally diverse genes can suppress the Lsd1 mutant phenotype suggests that escort cells are acutely sensitive to changes in their gene expression programs ., While our data support a model that loss of Lsd1 initially results in mis-regulation of engrailed and other genes that , in turn , drive GSC expansion , it is clear that many of the escort cells that experience reduced Lsd1 function retract their cellular extensions and undergo cell death ., This loss of escort cells further exacerbates the GSC expansion phenotype ., Given the phenotypic complexity described here and elsewhere 3 , care should be taken when analyzing gene function within the escort cell population ., How niches maintain stem cells and adjust their signaling output to ensure tissue homeostasis remains a fundamental question in stem cell biology ., Elegant work has shown that terminal filament cells , cap cells and escort cells help to support the self-renewal of two to three germline stem cells at the tip of Drosophila germaria 5 , 44 ., The predominant signal emanating from the anterior tip of the germarium is Dpp , which acts locally to induce a canonical signal transduction cascade in GSCs , which in turn represses their differentiation 5 , 9 ., Several expression and genetic studies strongly suggest that terminal filament and cap cells , and perhaps the most anterior escort cells , are the primary source Dpp ligand 5 , 9 ., More recent work has suggested that Engrailed expression in cap cells non-autonomously promotes dpp expression in escort cells through a hedgehog dependent mechanism 12 ., Loss of Lsd1 results in ectopic expansion of hh expression in escort cells 24 and data shown here ( Figure 4 ) reveals that disruption of hh partially suppresses the Lsd1 mutant phenotype ., However , consistent with previous results 38 , over-expression of hh in escort cells does not result in an Lsd1-like mutant tumor phenotype ( Figure 5G , H ) , demonstrating that hh is not sufficient to induce ectopic BMP signaling in the germarium ., Given these observations , ectopic engrailed expression in escort cells likely targets additional genes besides hh to induce ectopic BMP signaling and promote the expansion of undifferentiated germ cells ., The finding that loss of Lsd1 or mis-expression of engrailed in adult escort cells leads to expanded Dpp signal transduction within germ cells throughout the anterior portion of the germarium indicates that subpopulations of escort cells are capable , and perhaps even poised , to express dpp under certain conditions ., Such plasticity might allow the niche to expand and contract in response to various stimuli and environmental cues ., Indeed , previous studies have shown that Jak/Stat and insulin signaling can influence the number of GSCs in the ovary 45–49 ., Moreover , ongoing dynamic regulation of signaling may be a regular feature of niches under resting homeostatic conditions ., The observation that long-term knock-down of dpp in escort cells results in a reduced number of GSCs at the tip of the germarium , but not their complete elimination , is consistent with the notion that escort cells contribute to the maintenance of GSCs in some manner 12 ., Further work , with single-cell spatial and small-scale temporal resolution , will be needed to help clarify what cells express niche signals and when ., Inappropriate and extensive expansion of niches would be predicted to upset tissue homeostasis and perhaps even result in pathological conditions ., Therefore robust but flexible mechanisms that depend on chromatin factors such as Lsd1 may be in place to precisely control the expansion and contraction of in vivo stem cell niches ., The continued study of Drosophila cap cells and escort cells will provide further insights into how chromatin programming regulates niche plasticity ., Drosophila stocks were maintained at room temperature on standard cornmeal-agar medium unless specified otherwise ., The following fly strains were used in this study: w1118 was used as a control; Lsd1ΔN was provided by N . Dyson ( Massachusetts General Hospital Cancer Center , Charlestown , MA ) ; hh-gal4 and UAS-hh lines 50 were provided by J . Jiang ( University of Texas Southwestern , Dallas , TX ) ; c587-gal4 and Dad-LacZ were provided by A . Spradling ( Carnegie Institution for Science , Baltimore , MD ) ; the UAS-engrailed::GFP transgenic line was provided by Florence Masc

Introduction, Results, Discussion, Materials and Methods

Specialized microenvironments called niches regulate tissue homeostasis by controlling the balance between stem cell self-renewal and the differentiation of stem cell daughters ., However the mechanisms that govern the formation , size and signaling of in vivo niches remain poorly understood ., Loss of the highly conserved histone demethylase Lsd1 in Drosophila escort cells results in increased BMP signaling outside the cap cell niche and an expanded germline stem cell ( GSC ) phenotype ., Here we present evidence that loss of Lsd1 also results in gradual changes in escort cell morphology and their eventual death ., To better characterize the function of Lsd1 in different cell populations within the ovary , we performed Chromatin immunoprecipitation coupled with massive parallel sequencing ( ChIP-seq ) ., This analysis shows that Lsd1 associates with a surprisingly limited number of sites in escort cells and fewer , and often , different sites in cap cells ., These findings indicate that Lsd1 exhibits highly selective binding that depends greatly on specific cellular contexts ., Lsd1 does not directly target the dpp locus in escort cells ., Instead , Lsd1 regulates engrailed expression and disruption of engrailed and its putative downstream target hedgehog suppress the Lsd1 mutant phenotype ., Interestingly , over-expression of engrailed , but not hedgehog , results in an expansion of GSC cells , marked by the expansion of BMP signaling ., Knockdown of other potential direct Lsd1 target genes , not obviously linked to BMP signaling , also partially suppresses the Lsd1 mutant phenotype ., These results suggest that Lsd1 restricts the number of GSC-like cells by regulating a diverse group of genes and provide further evidence that escort cell function must be carefully controlled during development and adulthood to ensure proper germline differentiation .

The mechanisms that govern the formation , size and signaling output of in vivo niches remain poorly understood ., Studies of Drosophila germline stem cells ( GSCs ) have suggested that chromatin programming greatly influences the behavior of these cells and their progeny ., Previous work has shown that loss of the highly conserved histone demethylase Lsd1 results in ectopic niche signaling and an expanded GSC phenotype ., To determine direct regulatory targets of Lsd1 , we employed chromatin immunoprecipitation coupled with massive parallel sequencing ( ChIP-seq ) using specific cell populations inside and outside of the GSC niche ., These experiments revealed that Lsd1 exhibits highly enriched binding to over one hundred genomic sites within a specific cell population ., Furthermore , mis-regulation of some of these direct targets contributes to the expanded stem cell phenotype observed in Lsd1 mutants ., These results provide insights into how Lsd1 directly restricts the size of the GSC microenvironment and establish a platform for understanding and exploring chromatin programming inside and outside an in vivo stem cell niche .

developmental biology, cell fate determination, model organisms, stem cells, molecular development, gene expression, genetics, epigenetics, biology, cell differentiation

journal.pntd.0006047

Ecological niche modeling and distribution of Ornithodoros hermsi associated with tick-borne relapsing fever in western North America

Tick-borne relapsing fever ( TBRF ) is a zoonosis endemic to the Americas , Africa , and Asia , and caused by spirochetes transmitted by soft ticks ( Family: Argasidae ) in the genus Ornithodoros 1 ., The disease is caused by a diversity of regionally specific bacterial species in the genus Borrelia 2 ., Although of low incidence in most endemic regions , TBRF is proposed to be a major cause of fever in Senegal , West Africa , second only to malaria 3 , 4 ., The clinical disease in humans is characterized by recurring episodes of fever ( 2–6 episodes ) with general symptoms including headache , myalgia , nausea , arthralgia , and vomiting 5 , 6 ., In North America , three species of TBRF spirochetes are present and each is vectored by a different species of Ornithodoros ., Borrelia hermsii , Borrelia turicatae , and Borrelia parkeri are transmitted by Ornithodoros hermsi , Ornithodoros turicata , and Ornithodoros parkeri , respectively ., Most human cases of TBRF in North America are caused by infection with B . hermsii 2 , 5 , 7 , which is the focus of our investigation ., In the United States , TBRF was first reported in Colorado in 1915 8 , and was considered endemic there following the collection and identification of O . hermsi as the primary vector 9 ., The geographic distribution of TBRF in western North America is broadly defined by the location of exposure for reported human cases ., Ornithodoros hermsi has been documented at elevations ranging from less than 3 , 000 feet to over 8 , 000 feet in mountainous areas of Colorado , Utah , Idaho , Washington , California , and Montana 10–15 ., Human exposures occur most often while sleeping in rustic cabins located in mid to high elevation coniferous forests occupied by tree squirrels ( Tamiasciurus spp . ) and chipmunks ( Tamias spp . ) 1 , 5 , 16–18 ., Recent work demonstrates a greater diversity of small mammal species also serve as hosts for O . hermsi and B . hermsii 14 ., The geographic range and diversity of potential hosts associated with the enzootic maintenance of B . hermsii provides a wide distribution across western North America ., However most human cases of relapsing fever have originated in a relatively small and highly focal number of locations ., For example , from 1990 to 2002 approximately 50% of all human cases in the United States were infected in just 13 counties 19 ., Endemic areas with repeated human infection are well documented and include many popular tourist destinations including the North Rim of Grand Canyon National Park ( AZ ) , Estes Park ( CO ) , and several mountain lakes including Lake Coeur D’Alene ( ID ) , Lake Tahoe and Big Bear Lake ( CA ) and Flathead Lake ( MT ) 19 ., Despite the abundance of potential hosts across the landscape , focal clustering of human cases of TBRF suggests there may be constraints other than the presence or absence of a suitable host for the tick vector ., Like other vector-borne diseases , the spatial distribution of TBRF is likely multifactorial and constrained by environmental parameters ( biotic habitat and abiotic climate conditions ) in addition to host availability and their dispersal , which affect the distribution of O . hermsi ., The spatial distribution of vector-borne zoonotic pathogens depends heavily on environmental features and of course the presence of both host and vector required for their maintenance in natural foci 20 , 21 ., The distribution of tick-borne pathogens and the effect of climate on hard ticks ( Acari: Ixodidae ) has been modeled extensively , however , the effects of climate on soft ticks ( Acari: Argasidae ) is less well understood , in part due to their cryptic nature and nidicolous lifestyle that make them difficult to find in nature ., Hard and soft ticks have vastly different life histories and feeding behaviors , and thus are exposed to different environmental pressures ., Hard ticks quest in the open environment for long periods of time to encounter and attach to a host 22 ., This life-history strategy means that hard ticks are at risk of desiccation while questing , a process that defines both their survival and phenology , and hence distribution ., In contrast , soft ticks do not quest in the habitat , and feed and detach quickly to ensure they remain in or very near to the burrow or nest of the host 23 ., Specifically , O . hermsi ticks feed quickly in all life stages ( 15–90 minutes ) , are nocturnal , and thus usually feed when the host is inactive or when people are sleeping 13 ., When these ticks drop of their host , they likely remain in the confines of a relatively stable and moderated microclimate 2 ., Soft ticks may be less affected by rapidly changing environmental conditions as compared to hard ticks , and therefore may be most influenced by extremes in environmental conditions over the course of their lifetime ., Argasid ticks also have cement in the epicuticle , which make them more resistant to desiccation at higher temperatures compared to ixodid ticks 24 ., Yet despite these morphological features that enhance survival , there is still a narrow set of environmental parameters that define the physiological threshold required for Ornithodoros survival 4 , 25 ., Additionally , Argasid ticks are long-lived and can survive for many months to years between blood meals , making them both the vector and efficient de facto reservoirs for the pathogen 13 , 26–28 ., Disease ecologists have recently adopted ecological niche modeling ( ENM ) to predict regions of occurrence and the probability of vector and pathogen shifts in their distribution ., ENM is frequently used by ecologists and disease ecologists to better understand species distributions ., One program , Maxent , consistently outperforms other ENM models 29 , 30 and was developed specifically for data with low sample-sizes of presence-only locations 31 , 32 ., Initially designed to evaluate the potential distribution of endangered and threatened species , Maxent has been used extensively to model the distribution of numerous arthropods , including soft ticks that vector important disease-causing pathogens 33–35 ., The specificity of suitable living conditions for ticks make O . hermsi and its specific spirochete B . hermsii prime candidates for ecological niche modeling ., The aim of this paper is to use Maxent modeling to describe the current distribution of O . hermsi and B . hermsii using documented occurrences of both the tick and spirochete ., Further , we apply environmental constraints that predict the effects of various greenhouse gas ( GHG ) concentration trajectories on their distribution in the year 2050 ., We used georeferenced presence points for specific locations that included three types of data:, 1 ) human TBRF cases caused by B . hermsii ,, 2 ) O . hermsi ticks and, 3 ) rodents infected with B . hermsii based on bacterial isolation or qPCR assays , or positive for anti-B ., hermsii antibodies ., Presence locations were obtained from the published literature ( when detailed locations were included ) , as well as a series of samples from this study and personal communications ( TG Schwan , NC Nieto and MB Teglas , and KL Gage ( see S1 Table ) ., These sites included several popular vacation destination lakes in Washington , Idaho , California , Montana , and British Columbia , as well as several other locations in the Cascade , Sierra Nevada , San Bernardino and Rocky Mountain ranges 18 , 36 ., Ornithodoros hermsi has been documented in many of these areas 10–15 , 37–41 ., Since B . hermsii is vector-specific , we are confident that confirmed human cases caused by B . hermsii represented areas where O . hermsi was present even if no tick specimens were collected ., We sought to identify climate variables that are conducive to the persistence of the tick vector O . hermsi and its specific pathogen B . hermsii ., Climatic variables and elevation were obtained from WorldClim 42 , a freely available and widely used dataset of global climate layers , at a spatial resolution of 30 arc-seconds ( ~1 km; http://worldclim . org ) ., These data represent an interpolation of average monthly climate data recorded at weather stations throughout the region ., We chose to eliminate correlated variables to decrease model complexity and increase the interpretability of model output 30 ., We identified highly correlated variables ( Pearson’s r ≥ ǀ0 . 75ǀ ) using the Band Collection Statistics Tool in ArcMap ( v 10 . 3 , ESRI , Redlands , California , USA ) , which calculates the Pearson’s correlation coefficient ( r ) between all pairs of climate variables and elevation ., Redundant variables were reduced to a single variable that best represented the most extreme environmental effect of cold and humidity tolerance for ticks , and only these variables were carried forward for model creation and validation ., For example , we chose minimum or maximum monthly or quarterly variables over mean or annual variables ., Extremes in environmental conditions were chosen due to the life cycle of O . hermsi , which spends most of its life off the host and sheltered in the relatively stable microclimate of the host’s nest or burrow ., Thus , these ticks are most likely affected by extreme climate events that affect the microclimate of the ticks’ immediate environment ., Climate models based on the Intergovernmental Panel on Climate Change 5th Assessment ( IPCC5 ) were also downloaded at a resolution of 30 arc-seconds ( ~1 km ) from WorldClim ( www . worldclim . org ) ., We chose three global climate models ( GCMs ) —ACCESS1-0 , HadGEM2-ES , and CCSM4—that have been shown to have better agreement with observations than older models 43 ., Two representative concentration pathways ( RCP 4 . 5 and RCP 8 . 5 ) were chosen and represent predicted GHG concentration trajectories adopted by IPCC5 and commonly used in the construction of GCMs 44 ., Two RCPs were chosen in order to represent medium gas concentrations ( RCP 4 . 5 ) and high concentration potentials ( RCP 8 . 5 ) ., Ornithodoros hermsi and B . hermsii presence data were modeled using Maxent version 3 . 3 . 3k ( https://biodiversityinformatics . amnh . org/open_source/maxent/ ) ., Maxent uses presence-only data in combination with environmental data and background pseudo-absences to predict current and future distributions of a species , based on the principle of maximum entropy 31 , 32 ., Background points were chosen ( default N = 10000 ) at random from western North America ., We limited our area of interest ( AOI ) to the area shown in Figs 1 , 4 and 5 as this encompasses the reported endemic regions of B . hermsii infection in the US ., Maxent identifies the broadest probability distribution that falls within a set of constraints to ensure that the distribution reflects information contained in the presence points and to avoid over fitting of the model 31 , 45 ., Constraints ensure that the mean of each variable used in the model is close to the mean of the variable over occurrence sites , and a regularization parameter prevents over-fitting to occurrence locations 31 ., We first developed a “full model” that included all of the uncorrelated environmental variables ( described above ) and all default Maxent settings , with the addition of 1 , 500 iterations and 10 cross-validation replicates ., We assessed contribution of each variable to the model in two ways , permutation importance and jackknife tests ., Permutation importance was determined by randomly permuting each variable among the presence and background training points and measuring the resulting decrease in training Area Under the Curve ( AUC ) of the Receiver Operating Characteristic ( ROC ) curve ., ROC curves are commonly used in clinical medicine and were designed as a general method for assessing classification performance , where within a continuous data set , an effective threshold is calculated and numbers above the threshold indicate the occurrence of an event 46 ., The AUC is a measure of model performance , independent of any chosen threshold , and in the context of our study , represents the probability that a presence point will be ranked above a randomly chosen background point 31 ., Maxent normalizes these values to percentages and a large decrease indicates that the model was heavily reliant on that variable ., Jackknife tests evaluate and compare AUC values of the model utilizing all variables , with models created using only a single variable in turn and models leaving out one variable in turn ., Examination of jackknife plots reveals which variables are contributing the most unique information to the model ., After examining model output from the “full model” we chose to simplify the model by excluding variables that were not contributing to model fit , as described above ., Using only those variables that contributed considerably to the “full model” ( ≥5 permutation importance or ≥5% contribution ) , we created a “reduced model” to predict the distribution of TBRF ., The “reduced model” included all default Maxent settings with the following modifications: 1500 iterations , 10 replicate ( cross-validation ) models , and Hinge features ., Hinge features are capable of modeling piecewise linear responses to variables and allow for simpler and more succinct approximations of the response to environmental variables ., Hinge features improve model performance and smooth the fit to the data , thus simplifying the fitted features 45 , 47 , 48 ., Model performance was assessed using the average AUCtest statistic ., Additionally , we created average response curves from the 10 model replicates for each variable to explore how the logistic probability of suitability changed as each variable was permuted ., To visualize the geographical distribution given by Maxent , we created a binary distribution surface of western North America using the 10th percentile logistic training threshold , which assumes that 10% of the presence data may be prone to errors ., This is a conservative estimate often used when presence data are collected over a long time span and derived from multiple sources 49 ., To evaluate the effect of climate change on the predicted distribution of suitability , we used the “Projection” option in Maxent ., We applied the “reduced model” to climate conditions under three GCMs and two emission scenarios and compared model consensus among GCM models under each RCP and visualized the distribution using the logistic cutoff ( described above ) ., We developed the consensus maps by reclassifying each model ( that is , all suitable pixels for the first model were given a value of 1 , all suitable pixels for the second model were given a value of 10 , and all suitable pixels for the third model were given a value of 30 ) ., We then used Raster Calculator to “add” the models together to produce a single distribution showing three categories:, 1 ) all areas predicted suitable by one model ,, 2 ) all areas predicted suitable by two models , and, 3 ) all areas predicted suitable by all models ., We incorporated 96 georeferenced locations of, 1 ) human TBRF cases infected with B . hermsii ,, 2 ) the presence of O . hermsi , and, 3 ) rodents infected or previously infected with B . hermsii ., These data were incorporated into a presence-only ENM program to predict the distribution of O . hermsi in western North America and to assess the effect of environmental variables on the given distribution ( Fig 1 ) ., In total , seven environmental predictors contributed to model fit , and their importance was conserved across training , testing , and AUC regularization gain throughout all ten replicate model runs ( Table 1 ) ., The mean AUCtest for the 10 replicate models was 0 . 95 ( s . d . = 0 . 02 ) ., The average 10th percentile logistic training threshold of 0 . 14 was used as the cutoff to create a binary map of the potential distribution ( Fig 1 ) ., Three variables contributed had high permutation importance , accounting for 79 . 6% of the variation in the model ( Table 1 ) ., Jackknife analysis of variables showed that the minimum temperature of the coldest month , the mean temperature of the wettest quarter , temperature annual range , and the amount of precipitation during the coldest quarter contained the most influential information when used alone in the model ( Fig 2 ) ., The maximum temperature of the warmest month contained the most unique information that was not captured among other predictors , followed by the minimum temperature of the coldest month ( Fig 2 ) ., The effect of changing the values of each climate variable on the predicted distribution was examined using variable response curves ., The response curves show a narrow range of high suitability for all climate variables while the response curve for elevation shows a steady increase in probability or suitability as elevations increase ( Fig 3 ) ., The highest probability of suitability is found in regions with moderate temperatures during the wettest quarter of the year ( approximately -4°C to 4°C ) as well as moderate winter temperatures ( approximately -10°C to -5°C ) ( Fig 3 ) ., The highest probabilities of suitability occur at elevations over 1 , 700 m ( Fig 3 ) ., The predicted distribution corresponds to areas endemic for TBRF and also correlates with the currently known distribution of O . hermsi ( Fig 1 ) ., The distribution encompassed known endemic mountain ranges including the Sierra Nevada and San Bernardino Mountains in California , the Cascade Range in Oregon and Washington , and the Rocky Mountains extending from British Columbia to Mexico ( Fig 1 ) ., The model also predicted suitable habitat in regions that are not considered endemic for TBRF , including the mountains of northern Baja California , Mexico ( Fig 1 ) ., We applied the environmental constraints first identified by the reduced model to climate conditions predicted to occur in 2050 using three GCMs and two GHG concentration pathways ( RCP 4 . 5 and RCP 8 . 5; Fig 4 ) ., Under each RCP scenario , the global mean surface temperature is predicted to increase from 0 . 9 to 2 . 0°C under RCP 4 . 5 and 1 . 4 to 2 . 6°C under RCP 8 . 5 50 ., Two of the most important variables , in addition to elevation , defining the distribution under the current climate were the minimum temperature of the coldest month and the maximum temperature of the warmest month ., Under different climate scenarios , the range of suitability for temperature is found at higher elevations ., However , the overall amount of area and elevation range predicted as suitable does not change dramatically under predicted climate scenarios ( Table 2; Fig 5 ) ., Overall , using future climate predictions , a greater percentage of the distribution is predicted to occur at higher elevations ( Fig 5 ) ., There are notable changes to the predicted distribution in the Cascade Mountains in Washington and Oregon , the Blue Mountains in Oregon , as well as in the Okanagan Highlands in northern Washington and southern British Columbia ( Fig 5 , Fig 6 ) ., Contraction of the distribution is also predicted to occur along some lower ranges , including the Sierra Nevada Mountains ., However , expansion is predicted to occur within the Rocky Mountains from southern Wyoming to southern New Mexico , and Utah ( Fig 6 ) ., The model presented here helps to better define the environmental niche for tick-borne relapsing fever caused by B . hermsii and its vector O . hermsi in western North America and for identifying areas of increased risk for human infection ., The prediction map created from this model—trained on existing occurrences of O . hermsi and B . hermsii—highlights areas with a high probability of tick vector occurrence based on suitable environmental conditions ., The Sierra Nevada Mountain Range in California , the Cascade Range and Blue Mountains in Oregon and Washington , the Rocky Mountains in Idaho , Utah , Montana , and Colorado , and the Kaibab Plateau in northern Arizona , are all known endemic sites for TBRF , and the distribution map produced here parallels these areas ., This overlap suggests that the model is accurate and correctly identifies regions endemic for TBRF ., This model identified geographic areas in which O . hermsi and B . hermsii have been identified previously , with the exception of the occurrence of B . hermsii and O . hermsi in the northern regions of Baja California , Mexico , a region with no known B . hermsii-caused TBRF human cases , although other species of Ornithodoros do exist 51 ( Fig 1 ) ., The probability distribution of the model also identified areas where the probability of presence is high , but no cases of relapsing fever have been reported ( Fig 1 ) ., These areas include a large portion of the Coastal Range in southern Oregon and northern California and smaller but highly suitable regions in northern Baja California , Mexico , the Laramie Mountains , Wyoming , south central Idaho , the Zuni Mountains , New Mexico , and portions of the Uinta and Wasatch Mountains in Utah ., Additionally , portions of the Monitor Range , Nevada , were predicted to have suitable habitat ., The predictive map produced from our model offers insights into areas where targeted surveillance should be prioritized ., We found that maximum temperature of the warmest month ( BIO5 ) , minimum temperature of the coldest month ( BIO6 ) , and elevation were most influential for predicting suitability ., The logistic response curves demonstrated the narrow range of predicted suitable conditions for the existence of the tick , with many of these curves having defined peaks ( Fig 3 ) ., This is consistent with previous findings that soft ticks show a strict and narrowly defined tolerance to temperature and humidity for development and activity 52 ., Logistic probability distributions indicated that O . hermsi ticks are semi-cold tolerant , with an optimum minimum temperature during the coldest month and mean temperature of the wettest quarter of approximately -7 . 5°C ., Finally , areas with high predicted probability receive between 25 and 75 mm of precipitation during the wettest month ( Fig 3 ) ., The probability of suitability also increases with increasing elevation ., The information obtained from the predictive maps of the current distribution of TBRF caused by B . hermsii was compared to those assembled from the series of future predictions in 2050 with a medium GHG concentration scenario ( RCP4 . 5 ) and a high GHG concentration scenario ( RCP8 . 5 ) ., Global climate models trained on the existing potential distribution showed a relatively stable estimate for the amount of land area that was classified as suitable for O . hermsi , and therefore B . hermsii , across western North America ., The two emissions scenarios we modeled ( RCP 4 . 5 and RCP 8 . 5 ) produced very similar predicted distributions , although the pathway of high concentrations of GHG predicted slightly less overall area ( Fig 6 ) ., There was a predicted shift in the distribution with suitable areas moving from lower elevation and presumably warmer climates , to climates at higher elevations where conditions may become more suitable ( Fig 6 ) ., There is potential important habitat gain in the Rocky Mountains of southern British Columbia , Utah , Wyoming , and Colorado and in the Wasatch Range , Utah ( Fig 5 ) ., Regions of high predicted probability in 2050 were found near Yellowstone National Park , an area encompassed by the Teton and Wind River Mountain ranges , and east in the Big Horn Mountains , Wyoming , and the western front of the Rocky Mountains , Colorado ., Climate models for the predicted probability distribution in the year 2050 showed an increase in area predicted at higher elevations ( Fig, 6 ) and much of the habitat at lower elevations is predicted to be unsuitable for the tick ( Fig 5 ) ., In 2050 , significant amounts of suitable tick habitats are lost throughout the western United States ., A predicted contraction of the suitable habitats occurs throughout the foothills of the Cascade and Sierra Nevada Ranges , and the Rocky Mountains in Montana and Idaho ., A considerable amount of O . hermsi habitat is predicted to be lost in southern California , Baja California , Mexico , central Arizona , and western New Mexico and Nevada ( Fig 5 ) ., Interestingly , the contraction of suitable habitat that we see with O . hermsi and B . hermsii parallels recent contractions of Tamias spp ., that have been documented as a result of climate change 53 ., For example , the alpine chipmunk T . alpinus is native in the high Sierra Nevada Mountains in California , and its distribution has noticeably retracted into higher elevations as a result of rising temperatures over the last century 53 , 54 ., Further , T . palmeri—endemic to the Spring Mountains in southern Nevada—has predicted constraints to lower slopes , near water sources , and within conifer forests above 2400m , and due to physiological constraints , high temperatures may force this species into higher elevations 55 ., Rubidge et al . ( 2010 ) found that one chipmunk species , T . senex , which occupies a low to mid-elevation zone , has become extremely rare in their study area in Yosemite due to a massive range collapse , which may be attributed to warming impacts on vegetation structure ., Similar patterns—and even total habitat loss—have been predicted with the red squirrel , Tamiasciurus hudsonicus , and other mammalian wildlife populations across the US National Park system 56 ., However , it is important to note that not all Tamias and Tamiascurus species are retracting to higher elevations , or even retracting at all 54 ., In the construction of this model , we did not consider any biotic factors , such as vertebrate hosts and their dispersal capability that may influence the potential distribution of the tick and pathogen ., The primary rodent hosts for O . hermsi and thus B . hermsii in North America include chipmunks ( Tamias spp . ) and tree squirrels ( Tamiasciurus spp . ) , however a wider variety of small mammal and bird species likely serve as hosts for O . hermsi 1 , 9 , 14 , 16 , 17 ) ., The geographic range of potential hosts associated with O . hermsi provides a potential distribution across much of the western United States and southern central British Columbia ., In addition to the known importance of rodents as hosts , O . hermsi has been associated with a variety of wild birds and bats , which may serve as dispersal mechanisms to access previously uninhabited areas 10 , 27 , 57–61 ., Dispersal of O . hermsi and the potential for infected hosts to disperse B . hermsii across the landscape is not well understood , however the possibility for aerial dispersal exists for both organisms 14 ., Birds are well-known dispersers of Ixodes spp ., ticks that transmit Lyme disease spirochetes and tick-borne encephalitis virus 62–71 ., Moreover , human activities should not be ruled out as potential dispersers of O . hermsi , as O . hermsi has been found in sleeping bags and bedding from a cabin 40 , 51 , 72 ., As the global climate warms , the risk of TBRF infection may decline in areas of lower elevation and eventually B . hermsii transmission may be confined to isolated mountain refugia that maintain suitable climates for the tick ., Similar studies have modeled other tick-borne pathogens such as tick-borne encephalitis in Europe , where the tick was reduced to living at higher altitudes because of sensitive climatic and other abiotic suitability ranges 73 ., Changes such as this could potentially lead to a noticeable increase of TBRF infections in humans who visit these sites because the probability of tick occurrence is greater , while the potential risk at lower elevations is reduced ., Many environmental niche models of vector-borne diseases projected onto future climates show not only a shift in species distribution , but often substantial increases in the amount of suitable habitat ., Studies of Ixodes-Lyme disease systems in North America and Europe consistently predict a continued expansion of range to higher latitudes 73 , 74 , 70 ., The range of leishmaniasis and their sand fly vectors are also predicted to expand in the face of climate change in North America and in Portugal 75 , 76 ., Similar trends have been predicted in the southern hemisphere where mosquito-borne viruses are expected to expand southward as temperatures rise 77 ., Finally , as mentioned previously , two other species of soft ticks in North America , O . parkeri and O . turicata , also serve as vectors for relapsing fever Borrelia 2 ., Modeling the potential distribution of these tick species to determine if there is any environmental overlap in their distributions with O . hermsi might offer insights for understanding this vector-pathogen specificity ., The high correlation of known presence points with areas of high predicted suitability suggest the model presented here is a good representation of the risk for human TBRF ., Donaldson et al . ( 2016 ) modeled the distribution of O . turicata using Maxent and found that regions of Arizona have a high probability of suitable habitat for this tick , which overlap with regions where O . hermsi is found ., Further , their model also shows low-probability suitable regions for O . turicata throughout New Mexico and Nevada 34 that have the potential to create further overlap between these two species ., As the climate changes , important overlaps in the distribution of these species may change the frequency of human TBRF cases as the potential for tick-host interactions increase ., Spatial models like the one created here have the potential to provide important insights into disease ecology , epidemiology , and the effects of climate change on the distribution of human vector-borne diseases ., The results of this model also provide information to researchers investigating the ecology of relapsing fever and aid health care practitioners to achieve a better understanding of where endemic foci may exist ., Ultimately , we hope to enhance the recognition of TBRF , which currently is most likely under-diagnosed ., Many of the areas with high probability of presence are recreational sites that experience high numbers of human visitation and use ., This research will help health care managers in those areas to warn visitors of the potential risks of contracting relapsing fever and what preventative measures should be undertaken to lessen the risk of infection ., Visitors to endemic areas who are made aware of the potential to contract TBRF can advise attending physicians of their history of possible exposure that may assist in the diagnosis of tick-borne relapsing fever and appropriate antibiotic therapy .

Introduction, Methods, Results, Discussion

Tick-borne relapsing fever in western North America is a zoonosis caused by the spirochete bacterium , Borrelia hermsii , which is transmitted by the bite of infected Ornithodoros hermsi ticks ., The pathogen is maintained in natural cycles involving small rodent hosts such as chipmunks and tree squirrels , as well as the tick vector ., In order for these ticks to establish sustained and viable populations , a narrow set of environmental parameters must exist , primarily moderate temperatures and moderate to high amounts of precipitation ., Maximum Entropy Species Distribution Modeling ( Maxent ) was used to predict the species distribution of O . hermsi and B . hermsii through time and space based on current climatic trends and future projected climate changes ., From this modeling process , we found that the projected current distributions of both the tick and spirochete align with known endemic foci for the disease ., Further , global climate models predict a shift in the distribution of suitable habitat for the tick vector to higher elevations ., Our predictions are useful for targeting surveillance efforts in areas of high risk in western North America , increasing the efficiency and accuracy of public health investigations and vector control efforts .

The model presented here provides valuable epidemiological information on tick-borne relapsing fever in western North America ., The inference gleaned from these models represents areas where human infection with B . hermsii is likely to occur ., The predicted distribution of O . hermsi and B . hermsii may allow health officials to decrease human disease burden by implementing targeted surveillance efforts , thus better utilizing resources ., The models we created predict the current distribution of O . hermsi and B . hermsii , as well as the predicted distribution in 2050 under medium and high greenhouse gas ( GHG ) concentration trajectories ., Understanding how the distribution of the pathogen and its vector expand or contract in response to GHG concentrations is necessary for understanding human risk of infection with this debilitating disease both now and in the future .

united states, invertebrates, medicine and health sciences, ecology and environmental sciences, ecological niches, ixodes, pathology and laboratory medicine, atmospheric science, pathogens, california, geographical locations, tropical diseases, microbiology, animals, north america, relapsing fever, bacterial diseases, neglected tropical diseases, ticks, bacteria, bacterial pathogens, climate change, infectious diseases, medical microbiology, microbial pathogens, borrelia, disease vectors, arthropoda, people and places, arachnida, eukaryota, climatology, ecology, earth sciences, climate modeling, biology and life sciences, species interactions, organisms

journal.pntd.0004957

A Unified Framework for the Infection Dynamics of Zoonotic Spillover and Spread

An important class of pathogens are those transmitted from animals to humans ( zoonosis ) ., The dangers associated with zoonotic pathogens are twofold ., Firstly , the pathogen can adapt to the new human host and acquire the ability to transmit sustainably from human-to-human without the need for continued seeding from the animal reservoir ., The pathogens involved occasionally transmit rapidly amongst its immunologically naïve new host causing devastating health impacts as demonstrated by the global SARS outbreak , the swine influenza pandemic and the recent Ebola epidemic , which probably originated from one zoonotic spillover event ., Perhaps , however , HIV-1 is the most spectacular case of a recent zoonotic emergence , originating from an endemic infection of chimpanzees in Central Africa ., Zoonotic infections are the origin of the majority of established human pathogens 1 of which influenza , measles , smallpox and diphtheria are examples 2 ., Secondly , zoonotic pathogens can spill over from animal reservoirs continually and cause a heavy burden of disease ., Human rabies from domestic dogs is an important and preventable example ., The origins of major human infectious diseases can be conceptualised as a continuous transition across different epidemiologic stages 3 ., The first stage is when a pathogen exclusively infects animals ( ‘reservoir dynamics’ ) ., The second is when the pathogen occasionally jumps to the dead-end-host human population ( ‘spillover’ ) ., This is followed by a third stage , when human-to-human transmission becomes possible but leads only to self-limiting chains of transmission ( ‘stuttering transmission’ ) ., The final stage is when a pathogen gains the ability to transmit effectively between humans and no longer requires zoonotic transmission 3 ., An additional scenario is when the pathogen infects both animals and humans in a sustainable manner ., Measuring and predicting cross-species transmission is extremely difficult ., This is because spillovers are often , but not always ( as the situation for Lassa Fever demonstrates ) , rare events driven by the complex interactions of multiple causes , including ecological factors ( e . g . presence of hosts with differing degrees of susceptibility and periodicity in their abundance ) , epidemiological and genetic factors ( e . g . a broad set of pathogen life histories and periodicity of infection prevalence ) , and anthropogenic activities ( e . g . land-use and behavioural changes affecting direct and indirect interactions with reservoir hosts ) 4 ., Particularly challenging are zoonoses with stuttering transmission , as separating the contribution of animal-to-human from human-to-human transmission is extremely difficult ., Not surprisingly , theoretical 5–7 and experimental studies able to disentangle the many complex aspects of transmission at the animal-human interface are scarce 3 , 8 ., An increasing body of research recognises the need for a new paradigm integrating biological , social and environmental sciences with mathematical modelling to explain the mechanisms and impacts of zoonotic emergence 9 , 10 ., Understanding zoonotic spillover and stuttering transmission are , therefore , two very important public health challenges ., The scientific , public health and medical communities working at the interface of animal and human pathogens are challenged with many questions , such as:, i ) if we know the pathogen abundance and prevalence in the reservoir and have some knowledge of the mechanism of contact , can we estimate the likelihood of the next spillover event ?, ii ) is there a signature in the patterns of disease occurrence that enables us to distinguish the spillover ( animal-to-human ) burden from the stuttering chain ( human-to-human ) burden ?, iii ) how is zoonotic risk driven by specific social , economic , environmental and biological factors ?, Mathematical modelling has been used before to estimate the relative contributions of zoonotic spillover and human-to-human transmission , 11 ., This approach was based on rarely available information of nosocomial and extra-nosocomial outbreaks that were known to be instances of pure human-to-human chains ., More general methods are needed ., Here , we developed a unified mathematical framework for spillover and stuttering chain processes ., These are conceptually similar mechanisms; they are both arrival processes ., The key difference is that zoonotic spillovers are assumed to arise from random and independent contacts with the reservoir with no influence of past infections ( assuming no depletion of susceptibles , i . e . the pool of people who can be infected by contact with the reservoir or humans ) ., In contrast , a stuttering chain , which arises from human-to-human transmission , is affected by the number of past human infections as each infected person can also trigger a chain of new cases ., Zoonotic spillovers are also affected by past events when depletion of susceptibles , through death or development of sterilising immunity , is important ., Mathematically , zoonotic spillovers are described by Poisson processes ( Cox processes if stochasticity in the rate of infection becomes important ) or by self-correcting ( i . e . decreasing rate of infection ) processes if depletion of susceptibles occurs , while stuttering chains are described by a combination of self-exciting ( i . e . increasing rate of infection ) , due to previous human infections , and self-correcting due to depletion of susceptibles , processes ( see Table S1 in S2 Text ) ., We tested different models by comparing their predictions with the corresponding outputs from independently-simulated epidemics generated by an agent based model ( ABM ) ., As an illustrative example , we also applied the final model to Lassa Fever ( LF ) , a zoonotic , viral haemorrhagic disease common in West Africa , for which data from Kenema Government Hospital ( KGH ) in Sierra Leone 12 are available ., LF represents an important model for this kind of study ( Fig 1 ) ., The disease reservoir is Mastomys natalensis 13 , one of the most common African rodents , but an important proportion of the burden of disease is ascribable to human-to-human transmission; this is supported by the arguments presented in 11 and by a recent case of secondary transmission of locally acquired Lassa fever in Cologne , Germany 14 ., This case study was particularly instructive , revealing important challenges in current knowledge of LF , thus informing the direction of future research ., The phenomenology of spillover events ought to be linked with disease dynamics in the reservoir and the mechanism of contact between species ., We assume that LF is caused by independent random ‘contacts’ ( mediated by contaminated food , fomites etc . ) between humans and rodents ., Thus the probability P that k events occur during a time τ ( e . g . number of admissions to hospital in one week ) can be described by a stochastic Poisson process:, P ( k ) = exp - λ τ ( λ τ ) k k !, ( 1 ), where λ is a parameter ( rate ) representing the expected number of zoonotic spillovers per time unit ., The parameter λ is expected to depend on other drivers 15 ., In the simplest scenario the human population is uniformly subjected to random and independent contacts with the reservoir ., Only a fraction of these contacts , equal to the infection prevalence of the reservoir , are a potential source of infection ., Accordingly , we assume:, λ = N H P r R ( N R ) χ R η R ( N R ) ( 2 ), where NH is the human population size , i . e . the total number of people in a suitable area A , e . g . a village; PrR ( NR ) is the prevalence of infected rodents; χR is a parameter combining two complex mechanisms: the ability of the reservoir to excrete a suitable dosage of the virus and the human response to it ., We refer to this parameter as infection-response efficiency , and we formally define it as the product of the probability that the virus is excreted from the reservoir and the probability that humans acquire infection when challenged with the virus ., ηR ( NR ) is a measure of exposure , given by the product η R ( N R ) = ξ ( N R ) A where ξ ( NR ) is the probability of a contact ( direct or mediated ) between a single member of the human population and the population of NR rodents per time unit and area unit ., Both the pathogen prevalence , PrR , and the exposure , ηR , are expected to be functions of rodent abundance , NR , although a clear evidence of correlation between LASV prevalence and M . nataliensis abundance is lacking ., The area A essentially depends on the dispersal range of the rodents and , in the presence of human-to-human transmission , on the mobility of people ., Here we assumed that the area A used is suitable for considering the system closed ( no change in the population apart from the disease induced mortality ) and for assuming uniform mixing , i . e . each person is equally in contact with each other and with the rodent population ., As in the current model the size A of the system is fixed , we consider the overall parameter ηR ( NR ) ., Here and throughout , we refer to the quantities NH , PrR ( NR ) , χR , ηR ( NR ) ( and also χN and ηR ( NH ) defined below ) as constituent factors ., The assumption that the system is closed can be relaxed ., The simplest approach would be capturing the phenomenology of births , deaths and migrations by allowing a time-dependent functional form for the human population size NH = NH ( t ) ., Alternatively , changes in the human population size can arise from implementing an appropriate population dynamics model for NH ., The approach can be further extended to incorporate explicitly-spatial effects by building an interconnected meta-population model based on homogeneous regions and allowing immigration/emigration of individuals ., Quantities such as the rodent population size , NR , and infection prevalence , Pr , are often seasonal therefore the rate λ ought to be explicitly time-dependent resulting in a non-homogeneous Poisson process ., Most importantly , all the terms in Eq ( 2 ) , i . e . rodent population , NR , infection prevalence , Pr , human population size , NH , and the infection-response efficiency , χR , are in general , stochastic ., Thus the parameter λ in Eq ( 1 ) should be replaced with a random variable leading to the so-called doubly stochastic or Cox process ., When the rate λ is a gamma-distributed variable , the Cox process is described by a negative binomial distribution ( S3 Text ) ., After some algebra based on well-known properties of the negative binomial distribution , we can present further relationships between some parameters of the negative binomial distribution ( including mean μ and variance σ2 that uniquely determine the distribution ) and the mean μλ and variance σ λ 2 of the associated gamma-distribution for the rate λ ( i . e . μ = μλ , σ 2 = σ λ 2 + μ λ , see Table S1 in S3 Text ) ., As is known , when σ λ 2 approaches zero , then the negative binomial approaches a standard Poisson distribution ., The properties shown in Table S1 in S3 Text , however , have important implications for quantifying the risk of spillovers ., To estimate the probability of a spillover , it is sufficient to know the value of the parameters μ and σ2 ., These , in turn , can be estimated from the mean and variance , μλ and σ λ 2 , in the rate λ , which , ultimately depend on the constituent factors ., Based on the hypothesis posed in Eq ( 2 ) , we show how to infer the mean and variances μλ and σ λ 2 directly from knowledge of the human population size , NH , the abundance of rodents , NR , and also the infection-response efficiency , χR ., Since we expect that NR , NH and χR are independent random variables , the mean value of the rate λ is given by the product μ λ = μ N H μ η R P r R μ χ R , where μ N H and μ χ R are the mean values associated with the size of the human population , NH , and the infection-response efficiency , χR; μ η R P r R is the mean value of the random variable arising from the product ηR ( NR ) PrR ( NR ) , i . e . the exposure to the infected reservoir only ( while ηR ( NR ) is the ‘exposure to the reservoir’ , irrespective of this being infected or not ) ., Similarly , the variance σ λ 2 can be estimated as, σ λ 2 ≈ η R ( N R ) P r R ( N R ) χ R 2 σ N H 2 + N H χ R ∂ σ ( N R ) P r R ( N R ) ∂ N R 2 σ N R 2 + N H η R ( N R ) P r R ( N R ) 2 σ χ R 2 ( 3 ), where we used the usual approximation:, σ f 2 ≈ ∂ f ∂ X 2 σ X 2 + ∂ f ∂ Y 2 σ Y 2 + 2 ∂ f ∂ X ∂ f ∂ Y cov X Y ., ( 4 ), for a function of two random variables X and Y where NR , NH and χR are independent ., Of course , if σ N H 2 ≈ σ N R 2 ≈ σ χ R 2 ≈ 0 then the spillover process is well approximated by a standard Poisson process ., In some situations the explicit dependency of the quantity ηR ( NR ) PrR ( NR ) on the abundance of the reservoir is known or can be crudely estimated ., Then , the mean and variance μλ and σ λ 2 can be evaluated directly from the NR as shown for a range of relevant situations in Table S1 in S4 Text ( see also Davis et al . 15 ) ., The model above was derived with the assumption that the number of susceptibles is constant ., In a small population , however , the depletion of susceptibles is expected to be an important effect that can result in a self-constraining epidemic ., Following model Eq ( 1 ) , we replaced the ( fixed ) size of the human population NH with the ( variable ) number of susceptibles , SH ., Thus , the probability of observing k cases at any time tj during the interval ( j − 1 ) τ , jτ ( with tj ∈ ( j − 1 ) τ , jτ ) is the piecewise function defined on discrete intervals:, P ˜ ( k , t j ) = exp − λ ˜ j − 1 τ ( λ ˜ j − 1 τ ) k k !, with\u2009rate λ ˜ j = S H ( t j ) η R ( N R ) P r R ( N R ) χ R ( 5 ), where the time-dependent terms at time tj are estimated at the end of the previous interval ( j − 1 ) τ , jτ ., Underlying this choice is the assumption that the time step τ is comparable to the transition time from the susceptible to non-susceptible category , and λj can be considered constant during this time interval ., To estimate SH ( tj ) , we considered the case of an initially susceptible population ., For simplicity we assumed no external immigration and that the size of the human population at the initial time is SH ( 0 ) = NH ., As soon as spillover events start , part of the human population becomes infected; some with resulting life-time immunity and others die ., As we consider a closed human population , the number of susceptibles is:, S H ( t j ) = N H - C H ( t j ) if N H > C H ( t j ) 0 otherwise ( 6 ), where CH ( jτ ) represents the cumulative number of people who had been infected at any past time during the interval 0 , jτ , irrespective of if they recovered or died ., This corresponds to:, C H ( t j ) = C H ( t j - 1 ) + E P ˜ ( k , t j ) ( 7 ), where E P ˜ ( k , t j ) is the expected number of spillover events during the time-interval ( j − 1 ) τ , jτ , as E P ˜ ( k , t j ) = λ ˜ j - 1 τ , thus we have, C H ( t j ) = C H ( t j - 1 ) + S H ( t j - 1 ) η R ( N R ) P r R ( N R ) χ R τ ( 8 ) The probability P ˜ ( k , t j ) at time tj in Eq ( 5 ) can be iteratively calculated by replacing the susceptible and cumulative infected , SH and CH , with their explicit expressions given in Eqs ( 6 ) and ( 8 ) estimated at the previous time tj−1 ., Of course , if the depletion of susceptibles is negligible then S ( tj ) ≈ NH and the model collapses to a standard Poisson process or Cox-process if we allow for stochasticity in the rate ., Eq ( 5 ) is a particular case of a class of models known Hawkes point processes ( see 16 and references therein ) ., We refer to these processes as ‘zoonotic spillover with depletion of susceptibles’ ( in mathematical parlance ‘Self-Correcting Poisson’ ) ., Hawkes point processes introduced above suggest a natural extension of the current model to include human-to-human transmission ., In this context each infection event at time tj , represented by IH ( tj ) , has a certain probability of generating new events ., Accordingly , the probability of observing k cases at any time tj ∈ ( j − 1 ) τ , jτ is the piecewise function:, P ^ ( k , t j ) = exp − λ ^ j − 1 τ ( λ ^ j − 1 τ ) k k !, λ ^ j = S H ( t j ) η R ( N R ) P r R ( N R ) χ R ︷ zoonosis + S H ( t j ) η H ( N H ) P r H ( N H , t j ) χ H ︷ human - to - human P r H ( N H , t j ) = I H ( t j ) S H ( t j ) + I H ( t j ) + R H ( t j ), ( 9 ), where ηH ( NH ) is the probability that a single person is in contact with any other member of the human population per time unit; χH is the human analogue of the reservoir infection-response efficiency , i . e . the product of the probability that the virus is excreted from a person and the probability that a person acquires infection when exposed to the virus; PrH ( NH ) is the infection prevalence in the human population , which is the proportion of infected members IH ( tj ) in relation to the total size of the current population , i . e . for an SIR-type of model SH ( tj ) + IH ( tj ) + RH ( tj ) where RH ( tj ) is the number of recovered individuals ., SH ( tj ) is given by Eq ( 6 ) with, C H ( t j ) = C H ( t j - 1 ) + E P ^ ( k , t j ) ( 10 ), where E P ^ ( k , t j ) is the expected number of spillover events during the time-interval ( j − 1 ) τ , jτ , as E P ^ ( k , t i ) = λ ^ i - 1 τ , thus we have, C H ( t j ) = C H ( t j − 1 ) + I H z o o n + I H h − h I H z o o n = N H − C H ( t j − 1 ) η R ( N R ) P r R ( N R ) χ R τ ︷ zoonosis + I H h − h = N H − C H ( t j − 1 ) η H ( N H ) I H ( t j − 1 ) S H ( t j − 1 ) + I H ( t j − 1 ) + R H ( t j − 1 ) χ H τ ︷ human - to - human until N H ≥ C H ( t j − 1 ) ( 11 ), C H z o o n ( t j ) = ∑ j I H z o o n ( t j ) represents the cumulative number of infections up to time tj due to zoonotic spillover and C H h - h ( t j ) = ∑ j I H h - h ( t j ) represents the cumulative number of infections up to time tj arising from human-to-human transmission ., The model requires the further condition:, I H ( t j ) = C H ( t j ) − ∑ i R H ( t j ) + D H ( t j ) R H ( t j ) = R H t j − 1 + γ r I H t j − 1 τ D H ( t j ) = D H t j − 1 + γ d I H t j − 1 τ, ( 12 ), where DH ( tj ) is the disease induced mortality , γr and γd are the recovery and mortality rates respectively ., The model Eqs ( 9 ) –12 can be interpreted as an immigration-birth process 16 where the immigrants , i . e . zoonotic spillovers , arrive according to a Poisson process with rate λ ^ ( t j ) ., Each immigrant produces ‘offspring’ , which by analogy is really new infections from human-to-human transmission leading to a stuttering chain , according to a rate which is dependent on past events ., The model is a mixture of a self-exciting process ( new cases generate subsequent cases ( offspring ) ) and a self-correcting process ( due to depletion of susceptibles ) ., We refer to this type of processes as ‘zoonotic spillover with human-to-human transmission’ ( in mathematical terms ‘Poisson with Feedback’ ) ., We also considered the case when the rate λ ^ ( t j ) is drawn from a gamma-distribution , i . e . ‘zoonotic spillover with human-to-human transmission when random effect in the rate are important’ ( mathematically ‘Poisson-Gamma Mixture with Feedback’ , Table S1 in S2 Text ) ., The probability P ^ ( k , t j ) at time tj in Eq ( 9 ) can be iteratively calculated by replacing the susceptible and infected , SH and IH , with their explicit expressions given in Eqs ( 6 ) , ( 10 ) –12 estimated at the previous time tj−1 ., The contribution of human-to-human transmission at any time tj , Q ( tj ) , can be readily calculated by comparing the cumulative number of infections due to zoonotic transmission terms to those due to human-to-human transmission in Eq ( 11 ) , for example by studying the quantity:, Q ( t j ) = C H h - h ( t j ) C H ( t j ) ( 13 ) To simplify the notation , we use the symbols ζ = PrR ( NR ) χR ηR and κ = χH ηH for the overall unknown parameters , and refer to these as ‘zoonotic exposure’ ( which incorporates the host infection prevalence ) and ‘effective human exposure’ respectively ., We also define the forces of infection from animal or human source as ΛR = NHumans PrR ( NR ) χR ηR and ΛH = NHumans PrH ( NH ) χH ηH respectively ., Variation in the population size NH was also considered by discussing how the analytical solutions for the cumulative number of infections scales with the population size and by analysing predictions for NH = 1000 and NH = 2000 ( S7 and S10 Texts , for the value of the parameters used in the numerics see Table 1 and Table S1 in S6 Text ) ., We considered a set of NH agents ., Each agent being in one of four possible categories: susceptible , infected , recovered or dead ., At any time step , susceptible agents can transit to the infected category , while infected agents can either recover or die ., This is essentially a Bernoulli trial , e . g . a random process with exactly two possible outcomes ., The transition from the susceptible to the infected status is therefore mimicked by simulating , at any time tj , a number of Bernoulli trials ( SH ( tj ) or NH if we assume no depletion of susceptibles ) with probability given by the appropriate rate divided by the number of trials ., For instance , if we considered spillover and human-to-human transmission with depletion of susceptibles , the probability is λ ^ ( t j ) τ / S H ( t j ) ., This choice ensures that , at any time tj , if the number SH ( tj ) is large , then the corresponding set of Bernoulli trials are well approximated by a Poisson process with rate λ ^ ( t j ) τ ., Similarly , infected agents die or recover by simulating IH ( tj ) statistically independent Bernoulli trials with probabilities γdτ/IH ( tj ) and γrτ/IH ( tj ) respectively ., The mean time between two spillover events and the probability of observing k spillovers during a certain time τ are suitable measures for the risk of cross-species transmission that naturally arise from the present mathematical framework ., Based on the findings above , the risk of a spillover event can be represented by a discrete probability distribution , which can be generally described by a negative binomial distribution ., This is fully identified by the mean and variance , empirically inferred or calculated from the mean and variance associated with the rate of infection λ as displayed in Table S1 in S3 text ., In some situations , we know how the exposure to the reservoir and its infection prevalence depend on the abundance of the reservoir NR ., For example , it is reasonable to expect the exposure ηR ( NR ) is proportional to the reservoir abundance NR ., The dependence of the infection prevalence on NR can also be inferred for many regimes at the endemic equilibrium , e . g . frequency and density dependent Susceptible Infected Removed ( SIR ) , Susceptible Exposed Infected Removed ( SEIR ) , etc . models ( see Table S1 in S4 Text ) ., For these cases , calculation of the mean and variance μλ and σ λ 2 is straightforward ., In Table S1 in S4 Text , we consider four illustrative scenarios ., In many situations the mean risk of spillover increases with the size of the human population NH ., The associated variance , however , increases with the square of NH ., The dependency on the reservoir abundance NR is in general more complicated ., For instance , in scenario 1 the variance in the risk of spillover , σ λ 2 , increases with the square of the NR ., In contrast , in scenario 2 the variance σ λ 2 is not affected by the abundance NR , while in scenario 4 it reaches an asymptotic value for large NR ., For pure zoonotic spillovers , there is no human-to-human transmission , therefore the rate of infection is not affected by the number of humans already infected ., In some cases , variation in the number of susceptibles can be ignored , for example when the impact of immunity and/or mortality is negligible compared to the total population ., In this case , every spillover event is independent of previous spillover events ., Furthermore , the rate of infection is itself a stochastic quantity as random differences are expected from village to village and from time to time ., If these stochastic differences are small , then the rate of infection can be well approximated by its mean value and the distribution of zoonotic spillover described by the well-known Poisson distribution ., These stochastic fluctuations , however , can be important; in this case the distribution of zoonotic spillovers is better described by the so-called negative binomial distribution ( Eq ( S2 ) in S3 Text , which arises from simple Poisson processes after incorporating stochasticity in the rate of infection given that the distribution of the rates can be well approximated by a gamma-distribution ) ., In this case , the variance of the number of zoonotic spillover events is always larger that their mean value , which is over-dispersion ., Accordingly , we ran the ABM to generate zoonotic infections by simulating NH random experiments ( Bernoulli trials ) with transition probability proportional to the force of infection from an animal source ( i . e . ΛRτ/NH , see Table S1 in S5 Text ) ., Fig 2 shows the cumulative number of zoonotic infections generated by the ABM compared with the corresponding theoretical model ( expressed by Eq ( 1 ) or Eq ( S2 ) in S3 Text , when random effects in the rate of infection become important ) ., As expected , the profile for the cumulative number of occurrences averaged over the multiple stochastic realisations is linearly increasing with time with the slope given by the mean rate of infection ., When the rate of infection is also stochastic , e . g . because the outbreaks occurred in different regions with different eco-epidemiological and socio-economic factors , larger deviations from the average profile are observed ., This is the typical situation when the available data are aggregated at the national level without distinguishing the specific local factors ., In many situations , the contribution of net immigration , births and deaths ( other than infection-induced ) to the human population size is negligible , at least for short time-scales ., Still , once a spillover occurs , the infected individual might either recover or die , but will never transit back to the susceptible category ., Thus we considered the situation when the total number of individuals is fixed , but the number of susceptibles is decreasing due to the accumulation of spillover events resulting in immunity and/or mortality ., As the number of infected increase , the pool of susceptibles decreases reducing the rate of new infections; in other words the process is ‘self-correcting’ ( Eqs ( 5 ) – ( 8 ) or their generalization when random effects in the rate of infection become important ) ., Accordingly , we ran the ABM to generate zoonotic infections by simulating a number of Bernoulli trials , with number of trials being equal to the time-varying number of susceptibles , and transition probability proportional to the force of infection from animal an source ( i . e . ΛRτ/SH , see Table S1 in S5 Text . Note the force of infection is time-dependent as the number of susceptibles is changing ) ., Fig 3a shows the cumulative number of zoonotic infections generated by the ABM compared with the theoretical model ( Eq ( 5 ) , see also the analytical solution in S7 Text , for the particular case when the mortality and recovery rates are zero ) ., As expected , a key effect of incorporating depletion of susceptibles in the model is that the average cumulative number of occurrences always results in a concave ( i . e . downward ) function , provided that there is no birth/immigration of new susceptibles and no temporal variation of the exposure ., This is because the rate at which spillover events occur decreases with time and the average size of the jumps in the sample path becomes smaller and smaller ., Over time , the profile asymptotically approaches the size of the human population NH ( here set to NH = 1000 unless stated otherwise ) ., This is more pronounced for high values of the zoonotic force of infection ΛR ., The ability of a pathogen to transmit between people enables the generation of chains of infection ., Fig 3b shows the cumulative number of infections due to only the human-to-human route of transmission ., The infections are generated by the ABM by simulating NH Bernoulli trials with transition probability proportional to the force of infection from human source ( i . e . ΛHτ/NH Table S1 in S5 Text ) ., The predictions are compared with the theoretical model ( Eq ( 9 ) ) with the conditions of no zoonotic spillover and no mortality or recovery ., A human infection triggers new infections that , in turn , generate other new infections ., In other words the process is ‘self-exciting’ and the cumulative number of infections increases exponentially with rate equal to the effective human exposure ( κ , Eq ( S10 ) in S7 Text ) ., The presence of zoonotic spillover events leads to a qualitatively similar behaviour , resulting in convex ( i . e . upward ) average profiles for the cumulative number of infections with no upper bound ( S8 Text ) ., This because the rate of infection increases as the number of infections increase ., In general , both effects , self-correction due to depletion of susceptibles and self-excitation due to the impact of past infections on new chains of human-to-human transmission , are expected to play a role ., The combined effects lead to an average profile for the cumulative number of occurrences that is initially convex until the depletion of susceptibles dominates the dynamics ., This can be seen in Fig 3c which shows the cumulative number of infections for the combined ‘zoonotic and human-to-human’ model ., The infections were generated by the ABM by simulating Bernoulli trials ( with the number of trials equal to the time-varying number of susceptibles ) , with transition probability proportional to the force of infection from either animal or human source ( i . e . ΛRτ/SH or by ΛHτ/SH , Table S1 in S5 Text ) ., The predictions are compared with the corresponding theoretical model ( Eq ( 9 ) ) ., As expected , the cumulative number of infections increases as an S-shape function asymptotically approaching the human population size NH ( exactly as a logistic function if there is no mortality or recovery , Eq ( S10 ) , in S7 Text ) ., Knowing the zoonotic exposure and effective human exposure , we can estimate the relative contributions of zoonotic spillover and human-to-human transmission , ( more precisely , by substituting the values of the two exposures ζ and κ in Eq ( 13 ) ) ., In general these exposures are not known , but can be estimated via common statistical techniques , such as Markov Chain Monte Carlo ( MCMC ) ., To validate the methodology , we ran the ABM for the combined zoonotic and human-to-human model as described in the above section and counted the number of infections arising from zoonotic transmission and those from human-to-human transmission ., All the ABM-simulated infections ( with no distinction of the route of transmission ) were used as input into MCMC estimation 20 , 21 of the zoonotic exposure rate and effective human exposure rate , which are otherwise unknown ( i . e . the parameters ζ and κ ) ., The MCMC-inferred parameters were used to calculate the cumulative number of infections due to zoonotic spillover and those due to human-to-human transmission ( i . e . C H z o o n and C H h - h , according to Eq ( 10 ) ) and compared with the corresponding cumulative number of infections generated from the ABM ( Fig 4 ) ., There is a small discrepancy between the MCMC-inferred parameters and the ones imposed in the ABM ( the medians of the two estimated parameters were respectively 0 . 055 and 0 . 008 vs 0 . 05 and 0 . 01 ) ., This is expected as the ABM simulates Bernoulli trials rather than Poisson processes and the discrepancy decreases with the number of simulated trials ., The predictio

Introduction, Materials and Methods, Results, Discussion

A considerable amount of disease is transmitted from animals to humans and many of these zoonoses are neglected tropical diseases ., As outbreaks of SARS , avian influenza and Ebola have demonstrated , however , zoonotic diseases are serious threats to global public health and are not just problems confined to remote regions ., There are two fundamental , and poorly studied , stages of zoonotic disease emergence: ‘spillover’ , i . e . transmission of pathogens from animals to humans , and ‘stuttering transmission’ , i . e . when limited human-to-human infections occur , leading to self-limiting chains of transmission ., We developed a transparent , theoretical framework , based on a generalization of Poisson processes with memory of past human infections , that unifies these stages ., Once we have quantified pathogen dynamics in the reservoir , with some knowledge of the mechanism of contact , the approach provides a tool to estimate the likelihood of spillover events ., Comparisons with independent agent-based models demonstrates the ability of the framework to correctly estimate the relative contributions of human-to-human vs animal transmission ., As an illustrative example , we applied our model to Lassa fever , a rodent-borne , viral haemorrhagic disease common in West Africa , for which data on human outbreaks were available ., The approach developed here is general and applicable to a range of zoonoses ., This kind of methodology is of crucial importance for the scientific , medical and public health communities working at the interface between animal and human diseases to assess the risk associated with the disease and to plan intervention and appropriate control measures ., The Lassa case study revealed important knowledge gaps , and opportunities , arising from limited knowledge of the temporal patterns in reporting , abundance of and infection prevalence in , the host reservoir .

Many dangerous diseases emerge via spillover from animals , with limited human-to-human infection ( stuttering-transmission ) often being the first stage of human disease spread ., Understanding the conditions ( biological , environmental and socio-economic factors ) that regulate spillover and disease spread is key to its mitigation ., Here we are interested in questions such as: If we have quantified pathogen dynamics in the reservoir , with some knowledge of the mechanism of contact , can we estimate the likelihood of spillover events ?, Can we tease apart how much the disease is transmitted by animals and how much by humans ?, We developed a unified mathematical framework , based on Poisson processes with memory of past events , to understand the dynamics of spillover and stuttering-transmission ., This framework , which can be applied across the disease transmission spectrum , allows the teasing apart of the disease burden attributed to animal-human and human-human transmission ., Using this model , we can infer human disease risk based on knowledge of infection patterns in the animal reservoir host and the contact mechanisms required for transmission to humans .

medicine and health sciences, zoonotic pathogens, pathology and laboratory medicine, pathogens, tropical diseases, vertebrates, animals, mammals, neglected tropical diseases, infectious disease control, lassa fever, public and occupational health, infectious diseases, zoonoses, pathogenesis, rodents, host-pathogen interactions, biology and life sciences, viral diseases, amniotes, organisms

journal.pcbi.1000592

Trade-off between Positive and Negative Design of Protein Stability: From Lattice Models to Real Proteins

Protein stabilization can be achieved via two different strategies:, ( i ) ‘positive design’ in which the native state is stabilized; and, ( ii ) ‘negative design’ in which non-native states are destabilized 1–3 ., Positive design can be achieved by introducing favorable pairwise interactions between residues that are in contact in the native state whereas negative design can be achieved by introducing unfavorable pairwise interactions between residues that are in contact in non-native conformations of the protein ., The factors that favor employing one strategy over the other ( or some combination of both strategies ) are not known ., For example , it is possible that certain features of a proteins native structure such as its secondary structure content or contact-order 4 bias the choice of which particular strategy is employed ., Here , we explore this question with respect to lattice models of proteins and then show that the principles that we have discovered also apply to real proteins ., Although lattice models of proteins ignore many important details , they have been used successfully for elucidating general principles of protein folding and stability 5–8 and addressing evolutionary questions 9–11 ., In particular , such models have the advantage that in certain cases all the possible conformations in the ensemble can be enumerated and , therefore , preferential design strategies for certain protein conformations may be identified ., The stability , dynamics and function of proteins are determined by both short- and long-range pairwise interactions ., Long-range interactions are manifested , for example , in the energetic coupling between distant ligand-binding sites in allosteric proteins owing to conformational changes that are propagated from one site to another ., The strength of both direct ( short-range ) and indirect ( long-range ) pairwise interactions can be analysed experimentally using the double-mutant cycle ( DMC ) method 12 ., Recently , we introduced a computational version of DMCs for analysis of pairwise interactions in lattice models of proteins 13 ., Computational DMC analysis can be easily employed in an exhaustive manner to determine the strength of interaction between all possible residue pairs in a lattice model in contrast with experimental DMC analysis that must be restricted to a relatively small number of residue pairs owing to the prohibitive amount of work involved ., Using this computational DMC approach , we previously discovered that the strength of both short- ( i . e . between residues in contact in the native state ) and long-range ( i . e . between residues not in contact in the native state ) pairwise interactions changes in a linear fashion with increasing ‘contact-frequency’ , a term defined for each pair of residues in a sequence that corresponds to the fraction of states in the conformational ensemble of the sequence in which that pair of residues are in contact 13 , 14 ., In other words , a pair of residues that are in contact in many conformations available to the chain has a high contact-frequency whereas a pair of residues that are rarely in contact has a low contact-frequency ., A protein fold can , therefore , be characterized by the average contact frequency of all the residue pairs in contact in that fold ., Here , we show for lattice models that positive design is favored when this average ‘contact frequency’ is low whereas negative design is favored when this average ‘contact frequency’ is high ., A mathematical derivation of this result indicates that it is general and , thus , likely to hold also for real proteins ., Negative design in the lattice models is also found to be associated with a higher incidence , on average , of correlated mutations , i . e . mutations at one site that tend to be accompanied by other mutations at a second site ., Correlated mutations are assumed to be due to selective pressure to maintain protein structure or function and have , therefore , been used for prediction of 3D protein structure 15 , 16 , allosteric pathways 17 , 18 and protein-protein interactions 19 , 20 and in protein design 21 ., Correlated mutations in real proteins , however , also reflect common ancestry 22–24 whereas in our lattice models this concern is obviated ., Here , by applying correlated mutation analysis we show that proteins likely to have a higher average ‘contact-frequency’ , such as disordered proteins , also have a higher incidence of correlated mutations ., These results strengthen our conclusion that ‘contact-frequency’ is an important factor in determining the design strategy of real proteins ., This paper is organized as follows ., First , we show that the effects on stability of positive and negative design in lattice models are both linearly dependent , but with opposite sign , on the contact-frequency and that there is a strong trade-off between them ., We then provide a general ( not lattice specific ) mathematical derivation supporting these claims ., An analysis of correlated mutations in sequences selected for stability of a lattice fold that follows next shows that the density of correlated mutations increases with increasing contact-frequency ., Finally , we show that a similar trend is likely to exist in real proteins by analyzing correlated mutations in proteins that fold with difficulty and are suspected to have higher contact-frequencies ., Sets of 25 residue-long sequences that share a particular native state were generated with and without selection for native state stability ., The native states of the sets that were formed ( termed SBSS ) correspond to each of the 1081 compact folds on a 5×5 lattice ., The average perturbation energy ( ΔΔGper ) was then calculated for each pair of positions i and j in an alignment and the difference , D ( i , j ) , in the average perturbation energies for that pair of positions in the alignments with and without selection was determined ., The average value of D ( i , j ) was then calculated for all pairs of positions in contact in a particular native conformation , <D ( i , j ) >short , and for all pairs that form long-range interactions in that conformation , <D ( i , j ) >long ., Two positions are defined as forming a long-range interaction in a particular conformation if there is no path formed by residues in contact in that conformation that connects them ( Figure S1 ) ., For example , if residue A is in contact with residue B and residue B is in contact with residue C then we do not consider residues A and C to be involved in a long-range interaction ., Hence , the number of long-range interactions varies slightly between folds since the paths that connect residues in contact depend on the specific conformation ., In the case of a compact conformation on a 5×5 lattice , the number of long-range interactions is 1083 whereas the number of pairs in contact is always 16 ., It is important to note that all pairs of positions with a contact-frequency of zero ( in the case of a square lattice , for example , residues at positions with the same parity cannot be in contact ) are not considered in this analysis since their ΔΔGper equals zero by definition ., The values of <D ( i , j ) >short of different folds were found to be correlated with their respective average contact-frequencies ( ) ., It can be seen in Figure 1 that the value of <D ( i , j ) >short decreases when the corresponding value of for that fold increases ( r\u200a=\u200a−0 . 608; P-value<0 . 0001 ) ., Smaller values of <D ( i , j ) >short reflect a smaller contribution of pairs in contact to the gain in stability upon selection ., Surprisingly , we discovered that some native states have zero or even negative <D ( i , j ) >short values ., Such values are found when the value of is large ., This observation indicates that positive design is almost a negligible factor when stabilizing native states with a very high average contact-frequency ., We also examined whether a correlation exists between the contribution of negative design to stability and the average contact-frequency ., The correlation between <D ( i , j ) >long and is shown in Figure 2 and found to be significant ( r\u200a=\u200a0 . 639; P-value<0 . 0001 ) ., It may also be seen in Figure 2 that negative design is hardly used for stabilizing native states with a very low average contact-frequency ., In Figure 3 , <D ( i , j ) >short for each fold is plotted against the corresponding value of <D ( i , j ) >long ., The correlation observed is almost perfect ( r\u200a=\u200a−0 . 96 , P-value<0 . 0001 ) , thereby revealing the strong trade-off between the two strategies of positive and negative design ., The results shown in Figures 1–3 for a specific lattice model prompted us to examine whether a general derivation can be obtained for the linear dependence of the contributions of positive and negative design to protein stability on contact-frequency ., The starting point for the following such derivation is the previously derived 13 linear relationship between the perturbation energy and Boltzmann-weighted contact-frequency: ( 1 ) where Ec is the energy of a contact that was removed ( see Table 1 in 13 ) , ( Z is the ensemble of all possible conformations and E ( C ) is the energy of a conformation ) , ( E ( N ) is the energy of the native state ) , λ is the contact energy of the residue types found at positions i and j ( see Table 1 in 13 ) , T is the temperature and k is the Boltzmann constant ., The Boltzmann-weighted contact-frequency , BWCF ( i , j ) , is calculated by multiplying each occurrence of a contact by the Boltzmann weight of the conformation in which it occurs ., Eq ., ( 1 ) can be written in a simplified and approximate form , as follows: ( 2 ) where and is the contact-frequency that is not Boltzmann-weighted ., Given an alignment of sequences with the same native fold , one may express the average perturbation energy for a pair of positions i and j in the alignment , , as follows: ( 3 ) The difference , D ( i , j ) , in the average perturbation energies with and without selection was determined for every relevant pair of positions in the alignments ., Inspection of Eq ., ( 3 ) shows that D ( i , j ) for positions i and j in the alignment is equal to: ( 4 ) where Δ designates the differences in these terms with and without selection ., The average of D ( i , j ) over all the pairs of positions i and j that form direct short-range native-state contacts , <D ( i , j ) >short , can therefore be written using Eq ., ( 3 ) , as follows: ( 5 ) where is the average contact-frequency of the short-range native-state contacts , is the average of Δ<Ec> over all the pairs of positions i and j that form direct short-range native-state contacts and is assumed to be the same for all these pairs ., Eq ., ( 5 ) describes a linear relationship with a negative slope between <D ( i , j ) >short , which is a measure of the impact of positive design on stability , and as observed in Figure 1 for the lattice model ., An expression similar to Eq ., ( 5 ) for the case of long-range interactions can be written , as follows: ( 6 ) Eq ., 6 is similar in form to Eq ., ( 5 ) except that Ec\u200a=\u200a0 as it is for the case of long-range interactions ., Given that the sum of all the contact-frequencies is equal to some constant , α , we can write: ( 7 ) where corresponds to the sum of contact-frequencies of all the residue pairs that do not form short- or long-range interactions as defined above ., Eqs ., ( 6 ) and ( 7 ) can be combined to yield: ( 8 ) where ., Eq ., ( 8 ) describes a linear relationship with a positive slope between <D ( i , j ) >long , which is a measure of the impact of negative design on stability , and as shown in Figure 2 ., Given the simplifying assumptions we made that is the same for all the relevant residue pairs and that the contact-frequency is not Boltzmann-weighted , it is not surprising that the correlations shown in Figures 1 and 2 for a specific model are noisy ., The above derivations do show , however , that these correlations are general and not specific for particular lattice models and , thus , likely to hold for real proteins ., The different SBSS corresponding to the 1081 different 5×5 lattice folds were subjected to correlated mutation analysis in order to determine whether there is a connection between this phenomenon and the stabilization strategy ., The correlated mutation analysis was able to identify all the 16 pairs of positions that are in contact in all the 1081 different folds except for some rare cases in which one or two contacts were not detected ., In the case of the long-range interactions , the strength of the correlated mutations signal for a given fold was found to depend on the average contact frequency of its contacts ., The different folds were divided into three equal-sized classes corresponding to different ranges of values of and the distributions of densities of correlated mutations ( see Methods ) at positions involved in long-range interactions were plotted for each class ( Figure 4 ) ., Although the distributions are overlapping , a clear trend is observed that the average density of long-range correlated mutations increases with increasing ., The correlation coefficient between the density of correlated mutations at positions involved in long-range interactions and is 0 . 626 with a P-value<0 . 00001 ( not shown ) ., Hence , correlated mutations at positions involved in long-range interactions appear to be associated with negative design that is also found when is high ., The apparent connection between employing negative design and prevalence of correlated mutations at positions involved in long-range interactions enables us to expand our analysis to real protein data ., Given that the calculation of the contact-frequency parameter for a large number of real proteins is impractical owing to the huge size of their conformational spaces , we decided to look into groups of proteins for which there is good reason to assume that their average contact-frequency is high ., We analysed two sets of proteins that are likely to have a high average contact-frequency of their contacts ., The first set contains intrinsically unstructured proteins ( IUP ) that populate many conformations and are , therefore , likely to have relatively high values of contact-frequency since individual contacts probably stabilize many different conformations ., The second set is based on the GroEL-interacting proteins found by Hartl and co-workers 25 ., These proteins were divided into classes I to III with increased dependency on the GroE chaperonin system for folding correctly 25 ., A possible reason that these proteins have low folding propensities is their relatively high contact-frequency ., In addition , we generated a third set of proteins as a control ( see Methods ) for the other sets ., Given that structural information is not available for disordered regions of proteins and , also , for many of the other sequences , we could not distinguish here between correlated mutations at positions involved in short-range contacts and those involved in long-range interactions ., However , this does not affect our conclusions as the fraction of correlated mutations at positions in contact is lower than 20% 24 and is likely to be approximately equal in all the sets ., Hence , the variation in the densities stems mostly from the long-range correlations ., The distributions of correlated mutation densities calculated using the tree-based method 24 are shown in Figure 5 for the sequence alignments based on the set of disordered proteins , the three classes of GroEL-dependent proteins and the set of other randomly chosen control proteins ( equal in number to that of the IUP-based set ) ., The averages and standard deviations of all the sets are given in Table 1 ., It can be seen that the average density of correlated mutations is lowest in the case of the control set of proteins , it is higher in the case of the three classes of GroEL-dependent substrates ( and increases from class I to III ) and is highest for the IUP-based set ., This trend is observed only when comparing the average correlated mutation densities of the sets but it is important to note that correlated mutation analysis of real proteins is much noisier than that of lattice model proteins due to the larger alphabet size , errors in sequence alignment and evolutionary background and , therefore , these observations are significant ., It should also be noted that fewer correlations were obtained in the case of real proteins as compared with lattice models as the former were detected using the tree-based method that was developed to filter out evolutionary noise and is more stringent ., A key observation in this study ( Figure 3 ) is that the balance between the contributions of positive and negative design to the stability of different lattice folds varies despite the fact that all the sequences were subjected to the same selection pressure ., It is important to note that mutations that affect short-range interactions tend to have much larger effects on stability than those that affect long-range interactions 13 , suggesting that positive design should be much more common than negative design ., However , we find that some folds underwent stabilization by using only negative design ., This unexpected result indicates that positive design has limited ability to stabilize certain folds and that negative design compensates for that in cases of such folds ( Figure 3 ) ., Our results show that folds that can be stabilized by both positive and negative design are distinguished from those that are stabilized mostly by negative design in their average contact-frequency ., Folds with low contact-frequency can be stabilized by both positive and negative design whereas those with high contact-frequency can be stabilized mostly by negative design ., These results suggest that contact-frequency determines the stabilization potential of different folds and that certain folds are , therefore , more likely to emerge under difficult folding conditions such as extreme temperatures ., The analysis in this paper is based on the premise that stabilization of short-range contacts reflects positive design whereas stabilization of long-range interactions reflects negative design ., In lattice models , this assumption is correct since the energy of any native state is determined only by its contacts and , therefore , any stabilization due to long-range interactions must stem from destabilization of non-native states ( i . e . negative design ) ., In the case of real proteins , however , this assumption is not necessarily correct since long-range ( e . g . electrostatic ) interactions can also stabilize the native state ., However , the correlated mutation results that we obtained for both the lattice models and real proteins showed the same trend and , therefore , we assume that the correlated mutations that are mostly between distant positions reflect negative design ., It is interesting that two different mechanisms for thermostabilization have also been revealed by comparing mesophilic proteins with their thermophilic homologs 26 ., One mechanism termed “structure-based” is reflected in structure compactness and appears in proteins that originated in extreme environments ., The second mechanism termed “sequence-based” is reflected in a bias of the amino acid composition toward more charged residues and is found in proteins that originated as mesophiles but later had to adapt to higher temperatures ., Hence , both the findings here and the work of Berezovsky et al . 26 indicate that certain structural ( e . g . topological ) features of proteins dictate their stabilization potential and that tinkering with sequence can compensate for the lack of such structural features ., Thermostability has been attributed previously to amino acid composition 27–29 but by having all the lattice model sequences in our work share the same composition we were able to identify a purely structural basis for stabilization ., In conclusion , in this study we subjected lattice model proteins to selection for stability and showed that the balance between positive and negative design strategies differs for each fold and depends on the average ‘contact-frequency’ of that fold ., The use of negative design is found to increase with increasing values of the average ‘contact-frequency’ of the respective fold ., Our results , therefore , indicate that each fold has its own stabilization potential that limits its ability to adapt to extreme conditions ., We also showed that negative design in lattice models can be identified by correlated mutation analysis and is reflected in higher values of correlated mutation densities ., This trend was also found in correlated mutation analysis of real proteins when comparing intrinsically unfolded proteins and chaperonin-dependent protein substrates to other control proteins ., Thus , we conclude that stabilization of real proteins with high values of average contact-frequency tends to rely more on negative design and is reflected in higher densities of correlated mutations .

Introduction, Results, Discussion

Two different strategies for stabilizing proteins are, ( i ) positive design in which the native state is stabilized and, ( ii ) negative design in which competing non-native conformations are destabilized ., Here , the circumstances under which one strategy might be favored over the other are explored in the case of lattice models of proteins and then generalized and discussed with regard to real proteins ., The balance between positive and negative design of proteins is found to be determined by their average “contact-frequency” , a property that corresponds to the fraction of states in the conformational ensemble of the sequence in which a pair of residues is in contact ., Lattice model proteins with a high average contact-frequency are found to use negative design more than model proteins with a low average contact-frequency ., A mathematical derivation of this result indicates that it is general and likely to hold also for real proteins ., Comparison of the results of correlated mutation analysis for real proteins with typical contact-frequencies to those of proteins likely to have high contact-frequencies ( such as disordered proteins and proteins that are dependent on chaperonins for their folding ) indicates that the latter tend to have stronger interactions between residues that are not in contact in their native conformation ., Hence , our work indicates that negative design is employed when insufficient stabilization is achieved via positive design owing to high contact-frequencies .

Most proteins are functional only in their native states ., The stability of the native state of proteins is , therefore , of paramount importance both in vivo and for many biotechnological applications in vitro ., Protein stability is determined by the difference between the free energies of the native and non-native states ., It follows that protein stabilization can be achieved via two different strategies:, ( i ) ‘positive design’ by introducing favorable interactions between residues in contact in the native state; and, ( ii ) ‘negative design’ by introducing unfavorable interactions between residues in contact in the non-native states ., Here , we ask when is one strategy favored over the other ., We show that ‘positive design’ is favored when interactions that stabilize the native state are rarely found in the non-native states whereas ‘negative design’ is favored when the interactions that stabilize the native state are also common in the non-native states ., We also show that correlated mutations , i . e . mutations at one site that compensate for effects of mutations at other sites , tend to be associated with ‘negative design’ ., Analysis of protein sequence data shows that a higher incidence of correlated mutations is found in protein families with native states that are not stable or difficult to reach .

computational biology/macromolecular sequence analysis, biophysics/theory and simulation

journal.pcbi.1000030

The Evolution of Robust Development and Homeostasis in Artificial Organisms

During development , a mature multicellular animal is generated from a single cell through proliferation , apoptosis and cell rearrangement 1 ., Upon reaching adulthood , animals are then able to maintain their form by finely balancing the rates of cell division and cell death ., It is important that this unfolding developmental programme be reproducible ., In addition , in the real world development and homeostasis must also be robust 2 to mutation 3 , to noise internal to the system 4 , 5 and to environmental perturbation 6 ., This has been verified in experiments , where animals have been shown to recover from profound defects , that include a disruption of normal patterning 3 , 7 , 8 and severe wounds 9 ., Surprisingly , however , several experiments suggest that this capacity to tolerate and to recover from perturbations does not correlate with the expected likelihood of encountering environmental damage ., For example , well-protected embryos have been shown to repair wounds better than their mature adult counterparts , without mounting an inflammatory response 6 , 10 , 11 ., Moreover , in several instances , wound-healing has been shown to recapitulate morphogenesis 12–14 ., It therefore remains an open question how organisms are able to survive these different types of stresses ., To help address this question , our aim here was to explore the role of evolution in the generation of morphogenetic robustness , and to study the generic features of homeostasis in complex evolved systems ., A large number of experimental approaches have been used to identify the molecular and cellular processes underlying development and homeostasis in different model biological systems 1 , 15 , 16 ., However , given the length of time over which evolution shapes tissues and organisms , an experimental analysis of the evolution of development and homeostasis in a multicellular animal remains , for the moment , out of reach ( although see 17 ) ., To approach this problem , and to begin exploring the generic systems features associated with the evolution of multicellular animal development , we therefore chose to take an artificial life approach 18; using cellular-automata based digital organisms as subjects for an evolutionary analysis ., Although abstractions , such systems usefully recapitulate many aspects of real development 19 ., Moreover , this follows a long tradition of research in which cellular automata-based digital organisms are used as experimentally tractable model systems in which to study a variety of problems in evolution and development development 20 , 21 ., In particular they have been used to the scalability and wound-healing abilities of developmental systems ., Miller , for example , showed that evolved cellular automata capable of growing a complex pattern ( the French Flag ) were robust to a wide variety of environmental perturbations 22 , a result that was confirmed in cellular automata guided by a different rule set 23 ., Inspired by this work , here we use a developmental CA that we have previously shown to be evolvable 24 , 25 to study the role of evolution in the generation of robust developmental patterning and homeostasis ., As model systems for this study of morphological development and homeostasis we chose 3D cellular automata ( CA ) , whose development is guided by a linear rule-set or ‘genome’ 24 , 26 ., This consists of 100 rules or ‘genes’ ( see Methods ) , each of which is defined by four integers ( Figure 1A and Table S1 ) ., One integer specifies an action , to divide ( to generate two daughter cells ) , to move , to die , or to oppose one of these actions ., In the case of cell movement or cell cloning events , this integer also dictates the direction in which the action is implemented ., The other three integers determine the conditions under which the associated action will be triggered , based upon developmental time , a cell division count , or the number/position of neighbouring cells that each cell directly contacts ., This yields ( Table S1 ) a total of 36 , 842 possible rules or genes , making each organism effectively unique ( 1 in 10036 , 842 ) ., The set of 100 genes that defines each organism constitutes a deterministic developmental programme , which guides the action of every cell in the CA-based organism at each time step , calculated on a majority-win basis 25 ., Because cells have their own distinct history and environment , individual cells in each organism follow their own developmental path , despite their having identical genomes ., Taking togther , the result is an evolvable CA capable of generating complex three-dimensional forms ( see Figure 1C , Methods , 25 , 26 ) , a pre-requisite for this analysis ., With these rules guiding digital organism development in place , a genetic algorithm was then used to select for organisms that exhibit homeostasis ( Figure 1B and 1C ) 24 , 25 ., In each case , development was initiated from a single cell ., Organisms were allowed 50 time-steps in which to grow , after which we selected for individuals that are best able to maintain their form over a period of 100 time-steps 25 , 27 ., This homeostatic phase was operationally defined as a minimal change in organismal shape between time-steps 50 , 100 and 150 , as measured using two simple algorithms , a ‘2-point correlation’ 28 and a lineal path function 29 ., From an initial set of 1000 organisms with random genomes , a tournament process was used to select the fittest 26 ., These organisms were subjected to a round of mutation and recombination to generate the next generation of 1000 ., This process of mutation , development and selection was then reiterated for 30 generations , and the process of evolution repeated 36 times to generate a zoo of evolved CAs , which we hoped would include individuals that exhibit distinct developmental phases of growth followed by homeostasis ., Such behaviour could of course arise by chance ., To assess the likelihood of this occurring , we used the GA to analyse the fitness of a control set of 1000 individuals generated at random ( Figure S1A ) ., Although one of these had a modest degree of fitness , the 999 other individuals had negligible fitness scores ., Taking this further , we generated movies to visualise the development of each successful individual identified in generation 1 of each run ., It was clear from this analysis that there was not a single individual out of a random population of 36 , 000 that exhibited the desired behaviour-growth followed by homeostasis ( as defined in the Methods section ) ., By contrast , the fittest individual in 7 of the 36 ( 19% ) evolutionary runs carried out over 30 generations exhibited clearly defined phases of growth and homeostasis ., This analysis confirms that the GA is evolving the CA and verifies that , in the absence of evolution , the likelihood of a homeostatic individual arising by chance is very small ., Within this set of evolved homeostatic organisms , two general types of behaviour were used to maintain form during the period from time-step 50 to 150: stasis and dynamic equilibrium ., These differences in strategy were clearly visible when cells in each organism were colour-coded according to age ( compare Figures 2A , 2B , and 2C ) ., In organism #11 , the early period of growth was followed by a period of stasis as the organism takes on a fixed form ( Figure 2A ) and the cells age rather than die ., This resembles morphogenesis in Ecdysoa , such as D . melanogaster and C . elegans , where the majority of cells terminally differentiate upon the completion of development ., In contrast , ‘dynamic’ organisms such as #17 achieve homeostasis by maintaining an active balance of cell birth and death ( Figure 2B ) ., The remaining 4 homeostatic digital organisms exhibited behaviour somewhere between these two extremes , as exemplified by organism #18 ( Figure 2C and Video S3 ) ., These were all asymmetric , with a relatively stable domain and a spatially distinct domain characterised by active cell turnover ., To identify the rule-based mechanisms involved in maintaining homeostasis , digital organisms of each type were subjected to a systematic genetic loss of function analysis ( Figure 2D , 2E , and 2F ) ., Each gene was removed in turn , and a 2-point correlation and a lineal path function were used once more to measure resultant changes in form ., This identified a small number of genes that play a disproportionately large role in the maintenance of homeostasis in each case ., In organisms that exhibit balanced cell birth and death , the critical genes involved were all found to have an identical function in promoting cell death during a fixed period of time ( genes #14 and #94 in organism #17 and gene #80 in organism #18 , Figure 2D , 2E , and 2F ) ., We also noted these genes were over-represented in the genomes of homeostatic organisms compared to random ( data not shown ) ., The loss of any one of these genes compromised programmed cell death , causing disorganised growth like that seen in cancer ( Videos S7 and S8 ) ., A similar overgrowth phenotype is seen when the critical gene #85 is mutated in organism #11 ( data not shown ) ., Thus , deregulated growth appears to be a major source of vulnerability in homeostatic digital organisms , as it is in real organisms 30 ., In spite of this , most individual genes play a minor role in the maintenance of homeostasis ( Figure 2D , 2E , and 2F ) , reflecting a significant level of functional redundancy in evolved organisms 2 , 31 ., Having tested the response of evolved organisms to genetic perturbation , we next tested their ability to maintain morphological homeostasis in the face of an environmental challenge ., For this analysis , each organism was subjected to a systematic series of ‘gun-shot’ wounds in which ∼5% or more of the total cell population was removed at time-step 100 ( Figure 3 ) ., As before , a 2-point correlation and a lineal path function were used to quantify any ensuing repair response ., Remarkably , organisms of each type ( dynamic , static or asymmetric ) were able to heal wounds encompassing hundreds of cells within ∼30 time-steps ( Figure 3A , 3B , and 3C and Videos S4 , S5 , and S6 ) ., This wound-healing response was most striking in the case of organism #11 ( Figure 3A and Video S4 ) , which during normal development undergoes a period of steady growth , followed by a period of stasis in which cell division and cell death rates fall to zero ., Upon wounding , however , this organism mounted an effective repair-response; closing wounds to achieve a good restoration of the organisms original static form ., Although normally static , this organism is therefore poised ready to respond appropriately to a variety of environmental insults and , as such , has achieved a relatively sophisticated form of homeostasis ., In order to study the evolution of homeostasis , however , we focused our attention on organism #18 ., This organism was chosen as a subject for further analysis because its layered architecture serves as a useful point of reference for the systematic analysis of regional cellular behaviour and for the generation of equivalent wounds amongst morphological variants ., Importantly , these features are a pre-requisite for the quantitative analysis of the evolution of homeostasis and wound-healing ., We began the analysis of homeostasis in organism #18 by developing computational methods to visualise the distribution of cell divisions within its different layers ( moving from bottom to top ) ( Figure 4A ) ., During the homeostatic phase , a graded pattern of cell birth was observed along this axis ( from bottom to top ) , with most cell divisions occurring in the central portion and fewer divisions occurring within its stable base or within its dynamic upper layer ., In addition , a net flux of cells was observed moving from the bottom to the top of this organism ( Figure 4B ) ., Next , we used systematic wounding as a tool to test whether the observed cell behaviour translates into functional differences between different layers in this organism ( Figure 4C , 4D , and Figure 5 ) ., Following the removal of several upper planes , lost cells were rapidly replenished from below ., As a result , this organism was able to rapidly recover from surface wounds that eliminate the majority of its total cell mass ( Figure 4C ) ., Similar healing was observed after the removal of a central cell layer ( Figure 5 ) ., By contrast , the elimination of cells from the stable niche at the base of the organism resulted in a progressive loss of tissue ( Figure 4D ) and , ultimately , to the organisms disappearance or ‘death’ ( data not shown ) ., These data confirm the impression gained by visual inspection ( Figure 2C ) , that a directional flux of cells resulting from cell divisions with the stable ‘stem cell niche’ at the base of this organism drives the turnover of this organisms upper layers , enabling it to regenerate its form following surface damage ., We then systematically removed each of this organisms rules in turn to identify the genes involved in the wound-healing response ., To do this , 100 single mutant gene variants of organism #18 were generated , each of which was wounded by the removal of a central plane of cells at time-step 100 ( Figure 4A ) ., In each case , the extent of recovery over the next 50 time-steps was assessed as a quantitative measure of the effect of each gene on the wound-healing response ., It was clear from the spread of phenotypic scores that many genes play a minor role in this process ., However , a few genes stood out as having a relatively important role in wound-repair , whilst playing little role during normal development ( Figure 4C ) ., We focused our attention on one of these , gene #67 ( corresponding to: if ( west in interval 7-7 ) then clone in dir ( −1 , 0 , 0 ) ) ., By visualizing gene activity in time and space during a wound-healing response ( Video S11 ) , we were able to show that gene #67 is induced at the wound-margin immediately after wounding ( Figure 6A and 6D ) ., Once activated , gene #67 then promotes local cell proliferation in one direction ( clone in direction ( −1 , 0 , 0 ) ) along the axis of the plane ., In this way , gene #67 promotes helps restore the organisms original form following wounding ., This is the case , even though it plays no role in the normal development of organism #18 in the absence of perturbation ( Figure 2F and Figure 7 ) ., To explore the role of evolution in the establishment of this repair response , we developed a BLAST-like algorithm 32 which compares genomes in each generation , and then uses homology to trace the lineages of individual organisms back in evolutionary time ., This approach identified an ancestor of organism #18 at generation 3 that was similar to its descendent at generation 30 in both its morphology and behaviour ( Figure 6B ) ., Interestingly , this ancestral organism was found to carry a copy of gene #67 ( Table S2 ) , which plays a small , but measurable role in its development ( Figure 7 and data not shown ) ., Taking advantage of this conservation of form during the evolution of organism #18 , we were able to generate equivalent wounds carry out a comparative analysis to determine the effect of evolution on the repair response ., When compared to its descendent , the ancestral form of organism #18 failed to mount a rapid recovery when faced with a series of systematic wounds ( compare Figures 4C and 4E , 6A and 6B , Figures 8 and 9 , and Videos S13 and S14 ) ., Moreover , by looking at its descendents , this capacity to heal a wound was found to gradually improve with evolutionary time ( Figure 8 ) ., These data show that , in this case , reiterative rounds of mutation , recombination and selection have led to a steady increase in homeostatic robustness , as measured by the ability to withstand environmental perturbation ., This is the case even though wound-healing itself did not form part of the selection criteria used in the genetic algorithm ., We then carried out a similar analysis to establish how evolution affects the robustness of organisms in the face of mutation ( Figure 7 ) ., To accurately assess the effects of genetic perturbation , we calculated the impact of removing of each of the 100 genes in turn on organismal form at generation 3 and generation 30 ( measured using 2 point correlation and a lineal path function ) ., This revealed that the evolved organism #18 at generation 30 is 8-fold more robust to systematic gene deletions than its ancestor , ( the average difference in form was 0 . 74 ( std dev . 2 . 99 ) for generation 3 , and 0 . 09 ( std dev . 0 . 12 ) for generation 30 ) ., Moreover , a similar result was obtained when we discarded the 4 genes with the highest impact from this analysis ( 0 . 1381 ( std dev . 0 . 3886 ) for generation 3 compared with 0 . 0437 ( std dev . 0 . 0195 ) for generation 30 ) ., This trend is exemplified by the behaviour of gene #67 , which plays a visible role in the control of homeostasis in the ancestral organism , but which is phenotypically silent in generation 30 , in the absence of perturbation ( Figure 2F ) ., These data show that the increase in fitness seen during the evolution of this organism ( Figure S1B and S1C ) is accompanied by an increase in its robustness in response to both genetic and environmental perturbation ., Our analysis using artificial multicellular organisms has revealed several important general features of homeostatic systems that contribute to wound-healing and tissue regeneration ., First , it shows that a robust wound-healing response arises as an indirect consequence of morphological evolution ., This feature of evolved developmental systems has been observed before , firstly by Miller 22 , 33 , who used CA to model a growth of a pattern , in this case the French Flag as conceived by Lewis Wolpert 1 ., This CA used cell-cell interactions and the diffusion of morphogen-like information to construct and pattern a French Flag of defined size ., In doing so , Miller showed that this model has the remarkable property of being robust in the face of environmental perturbations , as a by-product of evolution Miller , 2003 #54; Liu , 2005 #60 ., More recently , these findings have been taken further by Federici and Downing 23 , who approached the problem using a different developmental model based on a neural network that included the ability of cells to emit and detect chemicals , yielding similar results ., Building on this work , in this study we have used a simple GA ( that excludes the use of morphogens to induce action at a distance ) , to evolve digital 3D multicellular organisms that exhibit a growth phase followed by homeostasis , without defining a specific form they should adopt ., This helps to generalise the results of Miller , Federici and Downing and identifies a variety of mechanisms that can undelie morphological homeostasis ., In addition , by focusing on one organism , we were able to undertake a detailed mechanistic analysis to reveal important features of these evolved developmental CA systems that contribute to their morphological robustness ., We began this mechanistic analysis of the wound-repair response by identifying the key genes involved ., One of the most important proved to be gene #67 ., Upon wounding , this gene was found to be activated at the wound margin , where it promotes cell division , helping to restore the organisms original form ( Figure 4 and Figure 9 ) ., Interestingly , although this gene is dispensable for normal development of organism #18 at generation 30 , earlier in the evolutionary process , at genearation 3 , gene #67 was found to facilitate normal homeostatic development ., Thus , gene #67 follows the overall trend in which individual genes exhibit an increasing functional redundancy ( a reduced impact on development ) during the course of evolution ( Figure 7 ) ., This increasing genetic redundancy is a common feature of evolved systems 2 , 31 , and suggests a link between evolution , functional redundancy and system robustness ., Although this may seem puzzling , genes like gene #67 , which appear largely phenotypically silent , can be selected for during evolution if they promote phenotypic stability in the face of the genetic noise that necessarily accompanies rounds of mutation , recombination and selection ., In doing so , they help to ensure that incremental changes in the genetic makeup of the evolving organism do not translate into catastrophic changes in form ., Based upon this analysis , we hypothesise that many of the genes identified as important for wound-healing in real embryos 9 , 13 may have evolved in a similar way to buffer developmental patterning from genetic noise ., This study also reveals an important role for tissue dynamics in the ability of an organism to withstand environmental perturbation ., Although all the homeostatic organisms tested displayed a remarkable capacity to heal a wound , organism #18 was unique in being able to recover from profound surface wounds that removed the majority of its total cells , which was related to its capacity for self-renewal ., In this organism , cells born in a stable niche at its base establish a directional flow of cells towards the organisms upper surface ., This protects the organism from damage , whilst leaving it vulnerable to wounds that affect the stable niche , and to mutations that deregulate cell death ( e . g . loss of gene #80 ) ., Given the general nature of our model , it is not surprising to find similar systems properties in real tissues constructed using an equivalent architecture ., This is evident in surface epithelial tissues , such as the human skin or gut , which continue functioning throughout the lifespan of the organism in the face of continual damage from the outside ., Their ability to maintain a constant form and function over time relies on a small population of stem cells embedded within a ‘niche’ at the tissue base 34–36 ., At each division , these stem cells self-renew to generate a daughter stem cell that remains within the niche , together with a second daughter cell that divides multiple times to generate an overlying population of transit amplifying cells ., These rapidly dividing cells then differentiate as they move up through the tissue , giving the stratified tissue its dynamic form; as seen in organism #18 ( Figure 4A and 4B ) ., Because of this , like organism #18 , stratified epithelia are relatively robust to surface damage , but are vulnerable to the loss of the stem cell niche and to genetic defects in the stem cell compartment; defects , which are linked to aging and cancer , respectively 37 ., This appears to be a simple , evolutionary accessible form of homeostasis , since this type of globally polarised cell behaviour was independently evolved in 4 out of 7 homeostatic organisms ., In conclusion , by studying cellular automata-based organisms we have been able to follow the evolution of generic systems features that contribute to their capacity to maintain their form and to recover from wounding , something that is impossible in real organisms ., In this way we have shown that wound-healing can arise as an indirect consequence of evolution itself and is most effective in organisms that have a dynamic , self-renewing stratified tissue organisation , like that seen in the human skin and gut ., CA have frequently been used in biomedical research to model a variety of biological processes 20 ., The CA employed here differs from many other models 38 , 39 in that it the rule-set been designed to be evolvable 25 ., Cellular Automata ( CA ) develop in a simple cubic 3D lattice with 50×50×50 sites or voxels , each of which can be in one of two states; either occupied by a cell or empty ., Cell behaviour is determined by a set of 100 rules ( the rule-set or genome ) that is inherited by all cells in a given CA ., This has the following structure:Rules are defined by a string of 4 integer numbers , which specify the conditions under which a rule is active , together with the action it implements ., Rules are contingent either on internal factors , i . e . the number of divisions that a cell has undergone or the total number of time-steps since the beginning of the simulation , or on environmental conditions ., This is defined as occupancy of the 26 adjacent sites in a cells local 3D neighborhood ( 9 below , 9 above and 8 in the plane ) ., Once the designated precondition is satisfied , the rule is activated ., The initial state of the system in each simulation is a single occupied cell in an empty lattice ., At each time step , the current state of each cell is evaluated in order to determine which action , if any , it will perform ., A cell then implements one of three actions: to move to a neighbouring location , to divide ( creating a copy in a neighbouring location ) , or to die ( see Figure 1A ) ; or it implements one of the equivalent anti-actions ., Because at any one time a large number of rules may have their preconditions satisfied , a conflict resolution system is used to decide the course of action a given cell will take: Because more than one rule may be applicable in any cell at any given time , actions are complex decisions ., As a result , mutations do not carry the same weight as they do in conventional CA , so that small changes in the genome translate into comparatively small changes in the phenotype ., Crucially , this makes the CA model highly evolvable , as compared to other CA models ., In addition , this system ensures that the behaviour of each cell is unique and CA behaviour rich , as a vast set of possible 3D forms is explored ., Genetic algorithms ( GA ) are a class of optimisation algorithms that use ideas inspired by Darwinian evolution ( survival of the fittest and inheritance with variation ) to evolve a population of potential solutions to a problem ., In this study a GA was used to search for CA that display homoeostasis ., Figure 1C shows the development of an evolved individual ., Starting from a single cell , the rules direct the growth of an organism during the first 50 time-steps ., Successful individuals like the one illustrated are then capable of maintaining this shape for another 100 more time-steps ., The GA contains populations of rule-sets that are encoded by strings of integer numbers ( Figure 1A ) ., In this instance , the starting population of 1000 individuals ( each with 100 rules that consist of 4 numbers ) is created at random 40 ., At each generation , the best 50 rulesets are selected based on their ability to maintain their form and are transmitted unchanged to the next generation ., The remaining 950 slots in the next generation are filled using a tournament selection , in which three randomly chosen individuals in the population are pitted against one another ., Winners are then subjected to a round of recombination and mutation , in which a two-point crossover operator exchanges a portion of 2 chosen genomes at a random position ., Mutations lead to the replacement of rules with another chosen at random , with a probability of 0 . 05/rule ., The fitness function measures the ability of an individual to:, ( a ) to minimise changes in form that occur between time-steps 50 , 100 and 150, ( b ) to grow into a 3D form in which cells are connected to the same body ,, ( c ) to grow to a 3D form with a particular surface to volume ratio ( in this instance , between 0 . 5 and 0 . 8 ) , and to select against individuals that cross the boundary of the 50×50×50 lattice ., These criteria select for large , compact homeostatic organisms , and select against infinite columns ., The better the individual is able to fulfil these criteria the higher the fitness value ., For, ( a ) , a two point correlation and a lineal path function are used to compare organismal form at the different time-steps ., Rintoul and Torquato 41 have found that the combination of two point correlation and lineal path function can characterise faithfully a large range of spatial patterns ., The lineal path function L ( x , x+r ) is defined as the probability of finding a line segment with end points at x and x+r that lies entirely in the body of the individual ., The lineal path function contains connectedness information along a lineal path and hence reflects some long range information about object form ., Cule et al . 27 have found that the probability of finding strings of different sizes that fall entirely within the body provides an efficient measure of the lineal path ., To obtain the lineal path function: The two point correlation of a phase in a digitised medium can be interpreted as the probability of finding two points in the same state at different distances ., A two point correlation function takes the form given by equation ( 1 ) : ( 1 ) where d is the correlation distance , Ns is the total number of occupied cells in the CA , and nd is the number of alive cells separated by distance d from cell i ., The different criteria mentioned to compute the adequacy of an individual ( volume to surface ratio , degree of connectedness of cells and degree of homoeostasis at time steps 50 , 100 and 150 ) are combined into a single fitness value using a method called Sum of Weighted Global Ratios ( SWGR ) 42 ., Using this scheme the three criteria are normalised using the maximum and minimum values found during the evolution ., The three normalised values are then added together to obtain a measure of the fitness of the individual ., The Blast analysis 32 allows the comparison of the genomes of two different individuals and produces as a result the percentage of genes in common as well as the locations in which there are differences ., The comparison takes places by aligning the two genomes as used in the genetic algorithm and counting all the places in which there is a match and all those in which there is not ., In order to analyse the contribution of each gene in the maintenance of homeostasis we produced 100 single gene deletion mutants ., The effect of each mutation on development was then measured using the two point correlation and lineal path functions to compare the form of the wildtype and of each mutant ., The wounding tool removes all the cells of the digital organism that happen to fall within a given radius of the wound axis ( X , Y or Z ) ., In most cases , we applied a systematic series of ‘gun-shot’ wounds at timestep 100 by shifting the wound in increments in X , Y or Z . The recovery was assessed by measuring the morphological differences between the organism immediately prior to wounding and during the course of its recovery ., A gene could be considered active in two difference senses ,, i ) if the preconditions under which the rule can act are met ,, ii ) if the specified gene action is executed ., We used the second definition to track gene activity during organism development ., For each timestep , we then measure the number of cells that execute a give rule as a proportion of the total number of cells .

Introduction, Results, Discussion, Methods

During embryogenesis , multicellular animals are shaped via cell proliferation , cell rearrangement , and apoptosis ., At the end of development , tissue architecture is then maintained through balanced rates of cell proliferation and loss ., Here , we take an in silico approach to look for generic systems features of morphogenesis in multicellular animals that arise as a consequence of the evolution of development ., Using artificial evolution , we evolved cellular automata-based digital organisms that have distinct embryonic and homeostatic phases of development ., Although these evolved organisms use a variety of strategies to maintain their form over time , organisms of different types were all found to rapidly recover from environmental damage in the form of wounds ., This regenerative response was most robust in an organism with a stratified tissue-like architecture ., An evolutionary analysis revealed that evolution itself contributed to the ability of this organism to maintain its form in the face of genetic and environmental perturbation , confirming the results of previous studies ., In addition , the exceptional robustness of this organism to surface injury was found to result from an upward flux of cells , driven in part by cell divisions with a stable niche at the tissue base ., Given the general nature of the model , our results lead us to suggest that many of the robust systems properties observed in real organisms , including scar-free wound-healing in well-protected embryos and the layered tissue architecture of regenerating epithelial tissues , may be by-products of the evolution of morphogenesis , rather than the direct result of selection .

During development , multicellular animals are shaped by cell proliferation , cell rearrangement , and cell death to generate an adult whose form is maintained over time ., Disruption of this finely balanced state can have devastating consequences , including aging , psoriasis , and cancer ., Typically , however , development is robust , so that animals achieve the same final form even when challenged by environmental damage such as wounding ., To see how morphogenetic robustness arises , we have taken an in silico approach to evolve digital organisms that exhibit distinct phases of growth and homeostasis ., During the homeostasis period , organisms were found to use a variety of strategies to maintain their form ., Remarkably , however , all recovered from severe wounds , despite having evolved in the absence of selection pressure to do so ., This ability to regenerate was most striking in an organism with a tissue-like architecture , where it was enhanced by a directional flux of cells that drives tissue turnover ., This identifies a stratified architecture , like that seen in human skin and gut , as an evolutionarily accessible and robust form of tissue organisation , and suggests that wound-healing may be a general feature of evolved morphogenetic systems ., Both may therefore contribute to homeostasis , wound-healing , and regeneration in real animals .

developmental biology, developmental biology/stem cells, developmental biology/developmental evolution, evolutionary biology/bioinformatics, computational biology, evolutionary biology, evolutionary biology/developmental evolution

journal.pcbi.1006723

A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex

Vocal communication requires an auditory system that can reliably classify signals ., For example , in human speech , phonemes produced by different speakers and in different contexts vary broadly in acoustic structure ., Despite this , they are perceived as discrete , invariant categories 1 , even when boundaries between phonemes are exceptionally sharp , as with the approximately 10 ms difference in voice-onset time that separates the English phonemes /d/ and /t/ 2 ., Categorization of vocal signals is not limited to human speech perception; many other species that communicate vocally show similar abilities 3–6 ., As in other sensory systems , categorical responses to auditory objects emerge in higher-order areas of the cortex 7–12 ., Neurons in these areas are characterized by a high degree of selectivity , or sparseness , in their responses to exemplars from different categories 13 , 14 , as well as tolerance , or invariance , in their responses to exemplars of the same category 15 ., Similar properties are observed in the responses of secondary auditory areas in humans to phonemes 16–18 ., However , in spite of a substantial body of theoretical work 19–21 , the circuit and cellular mechanisms underlying the emergence of categorical responses remains poorly understood ., Songbirds communicate with acoustically complex vocalizations , which requires them to perform many of the same kinds of auditory discrimination tasks as humans 22 ., In the avian auditory system , the caudal mesopallium ( CM ) is a cortical-level area that contains a population of neurons highly selective for particular song elements , yet tolerant of low-level acoustic differences between renditions 8 , 10 ., In contrast , the neurons in Field L , immediately upstream of CM , show low selectivity and tolerance 23 ., In the classical model of selectivity , complex feature representations emerge through feedforward synaptic connections that pool sparsely from upstream sources 24 ., This model does not match experimental evidence from CM , however , which shows that selective neurons receive a more distributed pool of inputs than the sparse model would predict 25 ., A distributed scheme of selectivity could arise from nonlinear dynamics within the neurons themselves , but the mechanisms of this have not been explored ., In many of the auditory areas in the hindbrain and midbrain , nonlinear neural dynamics profoundly affect how a variety of low-level acoustic features are encoded 26–28 ., For example , neurons that express a low-threshold potassium current ( I K L T ) produce highly phasic responses , responding to rapid increases in excitation but not to slow or steady-state depolarizations 29 , 30 ., These dynamics are critical to temporal precision in sound localization circuits 28 , 31 and can enhance signal detection in noisy conditions 32 ., Could intrinsic dynamics also contribute to sensory processing at cortical levels ?, In CM , the putatively excitatory neurons exhibit diverse intrinsic firing patterns 33 ., About 30% exclusively produce phasic responses that depend on a low-threshold potassium current , whereas the remainder produce mostly tonic responses ., The functional significance of this diversity remains unclear ., The goal of the present study is to understand how I K L T and the phasic dynamics it produces could contribute to the emergence of selectivity and tolerance in CM ., To investigate how intrinsic dynamics could interact with sensory-driven inputs , we developed an auditory response model that combines a spectrotemporal receptive field ( RF ) with a biophysical spike generation mechanism ., In this linear-dynamical cascade model , the RF component approximates the integration over multiple spectral channels and time lags performed by the circuits upstream of the neuron and by the neuron’s dendritic tree ., This part of the model is identical to the linear filter component of the classic linear-nonlinear Poisson ( LNP ) model 34 , 35 ., However , instead of using the output of the filter as the conditional intensity of a probabilistic spiking process , we feed it into a single-compartment , biophysical model that produces spikes through Hodgkin-Huxley dynamics ., Using this approach , which allows us to integrate our knowledge about intracellular properties in CM into a model for high-level coding properties , we found that I K L T enhances selectivity and robustness to noise ., Fig 1A represents the operation of the linear-dynamical cascade model ., The spectrogram of a zebra finch song stimulus was convolved with the RF to produce a driving current Istim ( t ) , which was injected into a single-compartment dynamical neuron model containing several voltage-dependent sodium and potassium currents ., The mathematical descriptions of these currents were derived from an intracellular study of the excitatory neurons in the caudal mesopallium ( CM ) of zebra finches 33 , and the RFs are drawn from parameterized features of zebra finch field L neurons 36 ., A key feature of this model is that it can produce phasic or tonic responses depending on the conductance of a low-threshold potassium current ( g K L T ) , which is experimentally known to be present in some CM neurons ., Variability in the responses of the model was generated with the addition of the Inoise ( t ) current ., This current adds random noise with a 1/f2 power spectrum ( red noise ) , which approximates the statistics of spontaneous synaptic noise in vivo 37 , at a signal-to-noise ratio ( SNR ) of four ., As seen in Fig 1B , the model’s intrinsic dynamics affected its response to zebra finch song ., When g K L T was high ( phasic model ) , responses were more precise and more reliable than when g K L T was absent ( tonic model ) ., This effect was consistent across RFs with different spectral and temporal parameters and across multiple stimuli ( Fig 2 ) ., It is important to note that although I K L T is an outward current with an overall hyperpolarizing influence , increasing it did not simply reduce excitability ., In some cases , the phasic model responded at a much lower rate and to only a handful of notes across the stimulus set ( Fig 2B ) ., In other cases , the firing rates of the phasic and tonic models were nearly the same ( Fig 2A and 2C ) , and the effects of manipulating g K L T were primarily on trial-to-trial variability ., Overall , the average difference in firing rate between tonic and phasic models was significant but small and highly variable ( 0 . 36 ± 0 . 36 Hz; t59 = 7 . 74; p < 0 . 001 ) ., To investigate how phasic excitability affects functional response properties , in this study we focused on rate-based theories of sensory coding ., The fundamental idea of rate coding is that neurons convey information about sensory inputs by modulating their average firing rate over relatively long intervals ., Following previous studies 10 , 38 , we defined these intervals by dividing the responses into segments corresponding to song syllables ( as in Fig 1 ) , which are well-defined units of zebra finch song that convey information about individual identity 39 ., We then calculated metrics based on how the average rates within those intervals were distributed across syllables and trials ( Fig 3A ) ., We first examined whether phasic dynamics made neurons more selective ., Selectivity , also known as lifetime sparseness , is a well-established rate-coding metric defined as the tendency of a neuron to respond strongly to only a small subset of stimuli 13 ., A selective neuron has a skewed , heavy-tailed response distribution in which a few stimuli account for the strongest responses , and the remainder produce only weak excitation ., In contrast , a less selective neuron has a small-tailed , Gaussian response distribution , with most of the responses concentrated around the mean ., For the examples shown in Fig 3A , the models with phasic dynamics tended to be significantly more selective than the corresponding models with tonic dynamics ., This effect was not uniform , but appeared to be stronger in the models with higher temporal-modulation frequencies in their RFs ., The effects highlighted in these examples were also seen in a larger set of simulations with 60 different RFs , which were matched to the distribution of spectral and temporal parameters seen in the zebra finch auditory cortex 36 ., Each RF was combined with phasic and tonic dynamics for a total of 120 models ., Across this population , phasic models were consistently more selective than tonic models with the same RFs ( Fig 3B and 3C ) , and the effects of intrinsic dynamics were larger for models with higher temporal modulation ( Fig 3D ) ., We also observed that phasic models were more reliable across trials , indicating that they were less affected by the noise current Inoise ., To quantify this effect in the context of rate coding , we calculated the mutual information ( MI ) between the response rate and syllable identity ., MI is defined as the difference between the response ( total ) entropy , which represents how much information the neuron can carry based on its range of firing rates , and noise ( or conditional ) entropy , which represents how much information is lost due to the variability of a neuron’s response to the same stimulus ., A neuron that responds more similarly across trials will have a low noise entropy , bringing its MI closer to its total entropy ., Fig 4 illustrates how intrinsic dynamics affected the distribution of response rates across trials for the same nine example RFs shown previously ., The tonic models tended to have a higher total entropy across these examples , although in several cases , the phasic models are slightly higher ., However , for all of these examples , the response rates of the phasic models cluster more closely around the mean response , resulting in lower noise entropy compared to the tonic models ., Across all 60 RF pairs , the total entropy was slightly higher for tonic neurons ( Fig 5A and 5B ) ., That advantage was more than canceled out by a much larger increase in noise entropy ( Fig 5C and 5D ) ., Every phasic simulation had lower noise entropy than its tonic pair ., The net effect was that phasic models had higher MI than the corresponding tonic models ( Fig 5E and 5F ) ., In the data described above , the noise current had a 1/f2 spectral distribution ( red noise ) , which is thought to approximate how spontaneous synaptic currents are dominated by low-frequency fluctuations 37 ., The total amplitude of the noise was set so that the inter-trial variations in firing patterns resembled what has been reported in extracellular recordings 23 , 36 , 40 ., To test whether the results were robust to these assumptions , we varied the amplitude and spectral distribution of the noise current ., As would be expected , selectivity and mutual information decreased overall as signal-to-noise ratio ( SNR ) decreased ( Fig 6A–6D ) ., However , at each SNR value tested , the phasic models consistently had lower noise entropy and higher MI and selectivity ., We also found that the same pattern of results was seen when the noise current had a 1/f distribution , which is less dominated by low-frequency fluctuations but is also considered to be biologically valid 41 ., In contrast , when the noise had a flat frequency distribution ( white noise ) , the effects of intrinsic dynamics were not significant ( Fig 6E–6H ) ., As a further test of parameter sensitivity , we compared results across a wide range of values for g K L T , which determines the magnitude of the low-threshold potassium current that causes phasic firing ., As shown previously 33 , 42 , increasing g K L T over a narrow range leads to a dramatic change in firing patterns: once the low-threshold current is strong enough to counteract voltage-gated sodium currents , it becomes nearly impossible for the neuron to fire more than one action potential in response to a step current ., This may explain the bimodal distribution of firing properties in CM 33 ., The phasic and tonic dynamical models examined here represent these two modes , but it is likely that the distribution of g K L T varies more broadly ., Interestingly , we did not see any evidence of a switch-like change in encoding properties as we varied g K L T over the range encompassed by the two exemplar models ( Fig 6I–6L ) ., Increasing g K L T instead produced nearly linear increases in selectivity and mutual information , and linear decreases in total and noise entropy ., How are selectivity and MI related ?, In these simulations , total entropy and selectivity were negatively correlated ( Fig 7A ) ., This effect is unsurprising given that selective neurons respond similarly to a large proportion of stimuli ., They encode more information about a few stimuli at the expense of encoding less information about the entire stimulus set ., However , when we consider coding efficiency , which quantifies how much of this potential bandwidth is actually used instead of being lost to noise ( Fig 7C ) , two trends emerge ., First , tonic models have lower coding efficiency than phasic models , consistent with the observation that they have higher noise entropy and lower MI ., Second , only phasic neurons are able to achieve both high selectivity and high coding efficiency ., As an independent test of the validity of this model , we applied the same selectivity and MI analyses to a public corpus of recordings from zebra finch CM 43 ., The relationships between selectivity and MI predicted by the model were largely borne out in the experimental data ., We observed a similar tradeoff between selectivity and total entropy ( Fig 7B ) , and an even stronger positive correlation between selectivity and coding efficiency ( Fig 7D ) ., The average coding efficiency was lower in the experimental data , which likely reflects additional sources of variability in vivo ., Selectivity was somewhat higher in vivo , perhaps because of additional nonlinearities in the actual CM receptive fields ., Interestingly , the cluster of models with high coding efficiency and low selectivity seen in the simulated data is not present in the experimental data ., Why are models with phasic dynamics consistently more reliable and selective than models with tonic dynamics ?, Low-threshold potassium currents counteract the regenerative sodium current produced during spike initiation ., As a result , cells expressing these currents only spike in response to a rapid increase in excitation ., These dynamics enable phasic neurons in the auditory hindbrain and midbrain to detect coherent excitation with high temporal precision , even in noisy conditions 32 , 44 , 45 ., Consistent with this idea , we observed in our sensory model that moments of high concordance between the RF and the stimulus created peaks in the driving current Istim ( t ) ., The phasic models spiked almost exclusively at these moments ( Fig 8A ) ., In contrast , the tonic models were more sensitive to the absolute level of V ( t ) and increased their firing rate as the integral of Istim ( t ) became more positive ( Fig 8B ) ., Based on this observation and the positive correlation between selectivity and temporal modulation frequency ( Fig 3D ) , we hypothesized that the effect of phasic excitability on selectivity would be stronger when the RF produced a driving current with sparse peaks of excitation ., In the spectral domain , this corresponds to convolutions that have more power at higher frequencies and RFs that have higher temporal modulation frequencies ., To test this effect specifically , we simulated another set of data with eight RFs , holding all RF parameters constant except temporal modulation ( Ωt ) , which we varied between 10 and 80 Hz ., At low values of Ωt , convolutions of the RF and a stimulus produced mostly slow modulations , and the response of phasic and tonic models were similar , as the example in Fig 9A shows ., As the modulations in the convolution became faster and large deflections became sparser ( Fig 9A , 80 Hz ) , the response of the phasic model became more selective , while the tonic model , responding to the shape of the slow modulation still present in the signal , showed no inclination toward selectivity ., Fig 9B presents the full set of simulations , showing a strong interaction between the temporal modulation of the RF and selectivity ., Phasic models showed a strong increase in selectivity as Ωt of the RF increased , but tonic models were unaffected ., This study investigated how intrinsic dynamics could contribute to the emergence of selectivity and tolerance in CM , a cortical-level auditory area ., We used a novel linear-dynamical cascade model that combines a spectrotemporal receptive field with a biophysical description of intrinsic membrane dynamics ., We found that a low-threshold potassium current ( I K L T ) strongly affected how the model neurons encoded information about song stimuli in firing rates ., Phasic models ( with high I K L T ) were more selective and more tolerant of noise than models with identical RFs and tonic dynamics ., Furthermore , a population of phasic and tonic models reproduced the distribution of selectivity and coding efficiency seen in vivo ( Fig 7 ) ., These results suggest that a diversity of intrinsic intracellular properties contributes to the higher-order functional properties seen in CM ., The model we developed for this analysis is a special case of the linear-nonlinear ( LN ) cascade model used in many studies of sensory coding 46–48 ., In the standard LN model , the nonlinearity is a history-independent function that transforms the output of the linear stage into an instantaneous spiking probability ., More recent LN models incorporate history dependence through a linear kernel convolved with past spike times , as in the generalized linear model 35 , 49 , or through non-biological , dynamical state variables , as in the spike response and generalized integrate-and-fire models 50–52 ., The present study required a more biologically realistic representation of the dynamics so that we could manipulate a specific current of interest , I K L T . We therefore used a conductance-based biophysical model to generate spikes ., This model lacks many of the morphological and physiological properties of CM neurons and omits any circuit-level organization , but it is capable of reproducing the tonic and phasic firing patterns observed in CM slices 33 ., For the linear stage of the model , we used simplified STRFs from a previous study of Field L 36 , which is the primary source of ascending auditory input to CM 53 ., The reason we did not use STRFs from CM is that linear RF estimates from this area have poor predictive power 40 and are therefore unlikely to be representative of the true synaptic tuning ., As seen in another secondary auditory area , the caudomedial nidopallium 54 , RFs in CM may involve nonlinear combinations of multiple feature vectors ., These nonlinearities may reflect local excitatory and inhibitory circuitry , and as methods for estimating parameters in these more complex models improve 55 , it will be important to also consider the contributions of intrinsic dynamics ., Here , we chose to use a simplified , parametric RF model so that we could systematically investigate how different RF features interacted with nonlinear dynamics ., The clear effects seen here with simplified RFs argue that intrinsic dynamics have the potential to facilitate categorical responses even in the absence of complex circuitry ., Our main finding is that increased I K L T caused responses to conspecific song to become sparser , more precise , and more reliable ( Fig 2 ) ., We focused here on quantifying these effects using several well-established rate-based encoding metrics 10 , 38 , though the effects on temporal precision are worthy of future consideration ., Selectivity , which is also called activity fraction or lifetime sparseness 13 , 14 , was strongly enhanced by I K L T ( Fig 3 ) ., Syllables that produced strong responses remained strong , but weak responses became weaker , causing response rate distributions to become more heavy-tailed ., This led to a reduction in the total entropy of the distributions ( Fig 5 ) , which reflects the fact that a more selective neuron responds similarly to all the weak stimuli and therefore encodes little information about the majority of the stimulus set ., At the same time , the phasic models lost much less of their bandwidth to noise , which led to a net increase in mutual information and coding efficiency ., Thus , I K L T can increase both the sparseness of the neural code and its reliability ., The effects of I K L T were not simply a product of reduced excitability ., Rather , they arose from complex interactions between the RF , the stimulus , and the nonlinear dynamics of the membrane ., Phasic dynamics resulted in larger increases in selectivity when RFs were tuned to rapid temporal modulations ., Because zebra finch song has strong , broadband rhythmic structure , these RFs produced convolutions with brief peaks of excitation ( Figs 8 and 9 ) 36 ., Phasic dynamics also had stronger effects for realistic noise currents that were dominated by low-frequency fluctuations ( Fig 6E–6H ) 37 , 41 ., Together , these results indicate that I K L T enables neurons to reliably pick out brief moments of strong excitation against a background of low-frequency noise ., This is consistent with extensive theoretical and experimental studies of I K L T in the auditory hindbrain and midbrain 29 , 56–59 ., Although these results provide strong support for the possibility that intrinsic dynamics are relevant to neural encoding in zebra finch CM , they do not rule out the contribution of synaptic mechanisms ., Indeed , strong feedforward inhibition sharpens selectivity and increases temporal precision in cortical-level rodent and avian auditory areas 60–63 in a manner similar to the effects produced by I K L T in this study ., Further work is needed to determine if the cell-intrinsic effects of I K L T are replicating the effects of feedforward inhibition , albeit in a more compact way , or if there are more consequential differences between these mechanisms ., This study shows that phasic dynamics can enhance selectivity and tolerance for conspecific song , which raises the question of why CM contains both tonic and phasic neurons ., The functional significance of diverse intrinsic dynamics has been examined in a variety of brain areas ., In the olfactory bulb , diverse dynamics in mitral cells help to decorrelate responses across neurons 64; in the visual thalamus , bursting in neurons that project to the cortex enhances signal-to-noise ratios 65; and in the electrosensory lateral line lobe , bursting dynamics provide a parallel information stream for low-frequency events 66 ., In CM , some neurons are strongly selective and others are not 8 , 10 , 67 , and a similar pattern is also seen in mammalian secondary auditory areas 15 , 68 ., Although we can only speculate at this point as to the behavioral significance , our data indicate that diversity in the intrinsic dynamics of CM neurons may contribute substantially to the functional diversity ., The models in this study sampled from a distribution of RFs with a broad range of spectral and temporal statistics , but only when the population included both phasic and tonic dynamics did we observe the broad , correlated distribution of selectivity and coding efficiency seen in zebra finch CM ( Fig 7 ) ., Interestingly , the model response properties were more diverse than seen in vivo , suggesting that I K L T may be selectively expressed in a subset of neurons , such as the ones tuned to rapid temporal modulations ., Animals from a broad range of species are able to perform invariant auditory object recognition in challenging conditions 69–72 ., In many sensory pathways , there is a hierarchical increase in selectivity and tolerance for natural objects that is thought to underlie this remarkable ability ., However , the circuit-level implementation of this computation remains largely theoretical 73 ., Although many models have followed Hubel and Wiesel 24 in focusing on how excitatory and inhibitory connectivity enables neurons to aggregate inputs tuned to simpler features , the nonlinear mechanisms of spike generation , which determine how these inputs are summed , are also thought to be important 74 ., The present study supports this idea by showing how a single voltage-gated current , I K L T , has the potential to dramatically shift how information about stimulus identity is encoded ., As we become increasingly aware of the diversity of cell types in the brain 75–77 and the activity-dependent mechanisms that can modulate intrinsic electrophysiological properties 78–80 , it is important to account for intrinsic dynamics in models of sensory processing ., Thirty male zebra finches provided song recordings that were used as stimuli in the simulation experiments ., All animal use was performed in accordance with the Institutional Animal Care and Use Committee of the University of Virginia ., Adult zebra finches were obtained from the University of Virginia breeding colony ., During recording , zebra finches were housed in a soundproof auditory isolation box ( Eckel Industries , Cambridge , MA ) with ad libitum food and water and were kept on a 16:8h light:dark schedule ., A mirror was added to the box to stimulate singing ., Recordings were made with an Audio-Technica Pro 70 microphone , digitized with a Focusrite Scarlett 2i2 at 44 . 1 kHz , and stored to disk using custom C++ software ( https://github . com/melizalab/jill; version 2 . 1 . 4 ) ., A typical recording session lasted 2–3 days ., A single representative song was selected from each bird’s recorded corpus and was high-pass filtered at 500Hz with a 4th-order Butterworth filter ., Analyses based on extracellular data were performed on the publicly available dataset from Theunissen et al . 43 available at http://crcns . org/data-sets/aa/aa-2 ., Neural recordings were collected from adult male zebra finches as described in Gill et al . 82 ., Only responses from CM neurons presented with conspecific song were used for these analyses ( n = 37 ) ., Selectivity and MI analyses were performed as described above , except that 10 response bins were used for MI analysis instead of 15 , due to the smaller stimulus set .

Introduction, Results, Discussion, Materials and methods

Birdsong is a complex vocal communication signal , and like humans , birds need to discriminate between similar sequences of sound with different meanings ., The caudal mesopallium ( CM ) is a cortical-level auditory area implicated in song discrimination ., CM neurons respond sparsely to conspecific song and are tolerant of production variability ., Intracellular recordings in CM have identified a diversity of intrinsic membrane dynamics , which could contribute to the emergence of these higher-order functional properties ., We investigated this hypothesis using a novel linear-dynamical cascade model that incorporated detailed biophysical dynamics to simulate auditory responses to birdsong ., Neuron models that included a low-threshold potassium current present in a subset of CM neurons showed increased selectivity and coding efficiency relative to models without this current ., These results demonstrate the impact of intrinsic dynamics on sensory coding and the importance of including the biophysical characteristics of neural populations in simulation studies .

Maintaining a stable mental representation of an object is an important task for sensory systems , requiring both recognizing the features required for identification and ignoring incidental changes in its presentation ., The prevailing explanation for these processes emphasizes precise sets of connections between neurons that capture only the essential features of an object ., However , the intrinsic dynamics of the neurons themselves , which determine how these inputs are transformed into spiking outputs , may also contribute to the neural computations underlying object recognition ., To understand how intrinsic dynamics contribute to sensory coding , we constructed a computational model capable of simulating a neural response to an auditory stimulus using a detailed description of different intrinsic dynamics in a higher-order avian auditory area ., The results of our simulation showed that intrinsic dynamics can have a profound effect on processes underlying object recognition ., These findings challenge the view that patterns of connectivity alone account for the emergence of stable object representations and encourage greater consideration of the functional implications of the diversity of neurons in the brain .

syllables, information entropy, linguistics, engineering and technology, signal processing, vertebrates, social sciences, neuroscience, animals, animal models, computational neuroscience, experimental organism systems, convolution, thermodynamics, coding mechanisms, research and analysis methods, zebra finch, computer and information sciences, entropy, birds, animal cells, animal studies, mathematical functions, mathematical and statistical techniques, grammar, physics, cellular neuroscience, eukaryota, cell biology, signal to noise ratio, neurons, phonology, biology and life sciences, cellular types, physical sciences, computational biology, information theory, amniotes, organisms

journal.pgen.1001141

Genome-Wide Double-Stranded RNA Sequencing Reveals the Functional Significance of Base-Paired RNAs in Arabidopsis

Recent discoveries reveal that RNAs perform a variety of tasks—ranging from the regulation of gene expression ( e . g . small RNAs ( smRNAs ) , and riboswitches ) to catalytic activities ( e . g . group I self-splicing introns ) —and indicate that this functionality is intimately linked to their three-dimensional structure 1–5 ., Correct secondary structure is also central to the proper regulation and maturation of RNA molecules 2 , 3 , 6 , 7 ., RNAs fold into their three-dimensional structures through specific base-pairing interactions ( double-stranded RNA ( dsRNA ) ) that are encoded within their sequence 2 , 3 , 6 , 7 ., These interactions can either be within ( intra-molecular ) or between ( inter-molecular ( heteroduplex ) ) RNA molecules ., Although it is clear that secondary structure is abundantly important for the functionality and regulation of RNAs , comprehensive base-pairing interaction data are completely lacking for the majority of these molecules 3 ., The recent discovery that RNA silencing pathways play a significant role in gene regulation has brought attention to a vast evolutionarily conserved post-transcriptional regulatory network dependent on self and foreign base-paired RNAs ( dsRNAs ) 8–10 ., In RNA silencing , production of heteroduplex dsRNA or self-complementary fold-back structures gives rise to smRNAs through the activity of DICER or DICER-LIKE ( DCL ) RNase III-type ribonucleases 9–12 ., In eukaryotes , smRNAs consist of microRNAs ( miRNAs ) and several classes of endogenous small interfering RNAs ( siRNAs ) , which are differentiated from one another by their distinct biogenesis pathways and the classes of genomic loci from which they arise 8 ., These smRNAs are the sequence-specific effectors of RNA silencing , and direct the negative regulation or control of genes , repetitive sequences , viruses , and mobile elements through inter-molecular base-pairing interactions 13 , 14 ., Overall , base-paired RNAs are at the core of both the biogenesis and function of all eukaryotic small silencing RNAs , emphasizing the importance of base-paired RNA in regulating gene expression ., In plants and several other organisms , there are numerous classes of endogenous and exogenous siRNAs that are processed from long dsRNA molecules synthesized by an RNA-dependent RNA polymerase ( RDR ) 8–10 , 15 ., The first RDR to be functionally identified as an RNA silencing pathway component in Arabidopsis thaliana , was RDR6 16 , 17 ., RDR6 was initially uncovered due to its ability to utilize aberrant RNAs produced by transgenes as substrates for dsRNA synthesis 16–18 ., These dsRNA molecules are subsequently converted by DCL4 into siRNAs that silence the transgenes 19–23 ., More recently , RDR6 has been demonstrated to function in the biogenesis of endogenous smRNA populations 8 , 20 , 24–26 ., One example is trans-acting siRNAs ( tasiRNAs ) , which are processed from regions of non-coding RNAs known as TRANS-ACTING siRNA ( TAS ) transcripts 20 , 25–27 ., Biogenesis of tasiRNAs is initiated by siRNA or miRNA-mediated cleavage of the TAS transcript 20 , 25–27 ., The cleaved TAS transcript is then converted by RDR6 to dsRNA 20 , 25–27 , which is subsequently cleaved by DCL4 into phased 21 nucleotide ( nt ) siRNAs 20–23 , 28 ., Here , we describe a novel , genome-wide , high-throughput sequencing-based method , which we term dsRNA-seq , that can specifically interrogate base-paired ( dsRNA ) RNA molecules , and use this approach to identify and characterize ∼200 novel , smRNA-producing substrates of the dsRNA-synthesizing enzyme RDR6 ., Additionally , we find that mRNAs encoding proteins with functions in nucleic acid-based processes have a tendency to be highly structured ., Making use of a seven-way comparative genomic approach , we demonstrate that the dsRNA-seq methodology can identify functionally conserved portions of UTRs ( 3′ and 5′ ) , introns , transposable elements , as well as novel , structured RNA molecules throughout the Arabidopsis genome ., Finally , we exploit the ability of dsRNA-seq to capture intra-molecular base-pairing interactions to produce mRNA secondary structural models on a genome-wide scale ., To obtain a transcriptome-wide view of base-paired RNA ( dsRNA ) in unopened flower buds of Arabidopsis thaliana Col-0 ecotype ( hereafter referred to as wild-type Col-0 ) , we married classical nuclease-based structure mapping techniques 29 , 30 with high-throughput sequencing technology ( see Figure S1A , and Materials and Methods for details ) ., We characterized the dsRNA component of the Arabidopsis transcriptome after one round of ribosomal RNA ( rRNA ) -depletion , and obtained 15 , 499 , 789 raw reads representing 4 , 802 , 974 non-redundant ( NR ) sequences with an average clone-abundance of 3 . 2 ( Accession #: GSE23439 ) ., ( The size distributions for this dataset can be seen in Figure S3A . ), As expected , we found that the majority of our dsRNA sequencing reads corresponded to highly structured classes of RNA molecules ( e . g . , rRNA , tRNA , snoRNA , snRNA , etc . ) , smRNA-producing loci ( e . g . , miRNAs ) , and repetitive elements ( e . g . , transposons ) ( Figure 1A ) ., We also found a large proportion of dsRNAs that correspond to protein-coding transcripts , which likely represent the self-complementary , base-pairing regions that form the secondary structure of mRNA molecules ( Figure 1A ) ., It is noteworthy that dsRNA-seq data mapped to all portions of protein-coding mRNAs , including introns , exons , and both ( 3′ and 5′ ) UTRs ., Therefore , the dsRNA-seq methodology can identify base-paired regions within both mature and preprocessed mRNA molecules ., ( For this reason , we refer to protein-coding mRNAs within this manuscript as pre-mRNA . ), Overall , our dsRNA-seq approach is robustly biased towards classes of RNA molecules that are highly base-paired in nature , which strongly suggests that this approach is interrogating the desired component of the transcriptome with a stringently estimated false discovery rate ( FDR ) of ≤0 . 067 ( see Text S1 ) ., The strand-specific nature of dsRNA-seq affords the opportunity to distinguish between intra-molecular fold-back dsRNAs ( 16 . 6% of total identified dsRNAs; example tRNA in Figure 1C ) and inter-molecular heteroduplex molecules ( 83 . 4% of total identified dsRNAs; example in Figure 1D ) ., To determine the strand bias for the different classes of RNAs captured by dsRNA-seq , we interrogated the ratio of sense versus anti-sense sequence reads ., As indicated by the Log-odds ( Lods ) values of sense to antisense reads , the majority of RNA classes were strongly enriched for sense-strand reads , especially for the non-coding RNA classes ( rRNA , tRNA , snoRNA , etc . ) ( Figure 1B ) ., Specifically , functional RNAs ( tRNA , miRNA , snoRNA , snRNA , and rRNA ) were between 100–1000 fold enriched for the sense compared to the antisense-strand ( Figure 1B ) ., Conversely , we identified a strong anti-sense bias in our dsRNA-seq data for transposable element-derived sequences ( Figure 1B ) ., This may reflect an amplification of the antisense transposon sequence by an RDR to initiate production of siRNAs and subsequent RNA silencing of these mobile elements ., For protein coding regions ( exons ) and 5′ UTRs of mRNAs , there was a significant sense-strand bias ( ∼16-fold ) , which was diluted for introns or 3′ UTRs of these RNA molecules ., We suspect that the existence of many overlapping genes and non-coding RNAs ( tRNAs , snRNAs , and snoRNAs ) on the strand opposite to introns or 3′ UTRs is the confounding factor ., This hypothesis is consistent with the stronger sense-strand bias in coding regions of mRNAs ( Figure 1B ) , which have an extremely low probability of overlapping with expressed elements on the opposite strand ., Additionally , there are numerous instances of 3′ end overlapping transcripts , as well as snRNA , snoRNA , and tRNA loci encoded within the introns and UTRs of protein coding mRNAs throughout the Arabidopsis genome ., Taken together , these results suggest that by using dsRNA-seq we have identified the majority of base-paired RNA molecules ( Figure S1B and S1C ) , which encompass a surprisingly large portion of the Arabidopsis genome ( ∼14 . 4% ( 17 . 3 Mb ) ) ., As described above , dsRNA-seq captured both intra- and inter-molecular base-pairing interactions ( Figure 1B–1D ) ., In fact , we found that regions of tRNAs predicted to form intra-molecularly base-paired stems corresponded to higher levels of dsRNA-seq reads than the unpaired anti-codon loop and the amino acid acceptor stem as expected ( Figure 1C ) ., Furthermore , we observed dsRNAs that corresponded to both the Watson and Crick strands of the genome for a known substrate of the intermolecular dsRNA-synthesizing RDR6 ( Figure 1D ) ., Taken together , these results suggest that dsRNA-seq can be used to differentiate intra- from inter-molecular base-pairing interactions ., An ideal test to both validate and determine the utility of dsRNA-seq is to identify all known and novel substrates of Arabidopsis RDR6 ., Accordingly , we sequenced the full complement of base-paired RNA ( using dsRNA-seq ) and smRNA ( using smRNA-seq ) molecules from unopened flower buds of wild-type Col-0 and rdr6-11 mutant ( referred to hereafter as rdr6 ) plants ., For wild-type Col-0 , we obtained the dsRNA-seq data described above , as well as 17 , 340 , 638 raw sequence reads representing 8 , 575 , 097 non-redundant smRNA sequences ( the size distributions for this smRNA dataset can be seen in Figure S3B ) ., Additionally , we generated a total of 18 , 345 , 980 and 18 , 850 , 891 raw sequence reads representing 9 , 725 , 315 and 9 , 860 , 471 non-redundant dsRNA and smRNA sequences for rdr6 mutant plants , respectively ( the size distributions for these rdr6 datasets can be seen in Figure S3C and S3D , respectively ) ., To identify potential RDR6 substrates , we used a sliding-window analysis to select 1 kilobase ( kb ) regions of the genome that produced ≥2-fold more dsRNA in wild-type Col-0 than in rdr6 mutant plants with a p-value <0 . 001 ( see Text S1 ) ., Using this approach , we identified 7 , 144 regions where dsRNAs are significantly depleted in rdr6 mutant compared to wild-type Col-0 plants ( Figure 2A , positive Lods-ratio values ) ., Within these molecules , we identified 7 of 8 previously characterized TAS transcripts ( Figure 2A , Figure S2A and S2B , blue diamonds ) , while the eighth was represented by a single read in both ( Col-0 and rdr6 ) dsRNA-seq libraries ., Additionally , we found that the majority of RDR6-dependent dsRNAs are transposable elements ( mostly MuDRs and Helitrons ) , mRNAs , intergenic RNAs ( mostly centromeric tandem repeats ) , or tRNAs ( Figure 2A and 2B ( green bars ) , and Figure S2B ) ., Taken together , these results suggest that RDR6 utilizes specific classes of repetitive elements , numerous categories of functional RNAs ( e . g . tRNAs , snRNAs , snoRNAs , etc . ) , mRNAs , and intergenic transcripts as templates for dsRNA synthesis ., Our sliding window approach also identified 7 , 584 dsRNAs that are significantly stabilized in rdr6 mutant compared to wild-type Col-0 plants ( Figure 2A , negative Lods-ratio values ) ., The vast majority ( >80% ) of the molecules stabilized in rdr6 mutant plants are TEs ( Figure 2B , yellow bars ) , most of which ( ∼95% ) are pericentrometric Gypsy-like transposons ( Figure 2A and 2B ( yellow bars ) , and S2B ) ., We also found a number of these dsRNAs correspond to mRNAs ( ∼15% ) and intergenic transcripts ( ∼4% ) ( Figure 2B , yellow bars ) ., Overall , the identification of dsRNA molecules that are stabilized in rdr6 mutant plants suggests a potential model where RDR6 antagonizes the action of other RDRs at some targets , especially at Gypsy-like transposons ., The consequence of dsRNA synthesis by RDR6 is often the subsequent formation of siRNAs 19 ., Therefore , to identify those RDR6 dsRNA substrates that produce smRNAs , we identified regions that produce ≥2-fold more smRNAs in wild-type Col-0 than in rdr6 mutant plants ., These sources of smRNA were then compared with the regions of the genome that produce more dsRNA in wild-type Col-0 than in rdr6 mutant plants , which identified 218 regions that met both criteria ( Figure 2C and Figure 3A–3D; Table S1 ) ., These common regions include ∼50% ( 27 total ) of the previously identified smRNA-producing RDR6 substrates , the majority of which were not known to be expressed in Arabidopsis unopened flower buds ( Figure 2C and Figure S2C; Tables S1 and S2 ) 31–34 ., The other 6 , 926 regions where dsRNAs , but not smRNAs , are significantly depleted in rdr6 mutant compared to wild-type Col-0 plants consist of mostly MuDR and Helitron transposable elements ., These results suggest that the double-stranded MuDRs and Helitrons produced by RDR6 may only constitute an insignificant subset of the smRNA-producing population of these transposons ., Conversely , RDR6 synthesized MuDR and Helitron dsRNAs may simply not be processed into smRNAs ., Our analysis also revealed that the majority of highly confident smRNA-producing RDR6 substrates are mRNAs with a variety of biological functions ( Figure 2D and 2E ) and , surprisingly , tRNAs ( Figure 2D ) ., As expected , the identified RDR6 substrates tend to produce 21 nt smRNAs ( Figure 2F ) ., It is noteworthy that RDR6-targeted mRNAs mostly encode proteins that function in nucleic acid-based biological functions ( e . g . translation , RNA processing , etc . ) and regulation of gene expression ( Figure 2E ) ., Taken together , these results suggest that an RDR6-dependent RNA silencing pathway regulates multiple stages of gene expression through siRNA production in Arabidopsis ., The identification of tRNAs as RDR6 substrates is intriguing because it was recently suggested that the mammalian telomerase reverse transcriptase catalytic subunit ( Tert ) functions as a smRNA-producing RDR that can also use tRNAs as substrates 15 ., Taken together , these results suggest that plant RDR6 and animal Tert are functional orthologs that can use tRNAs as substrates for production of dsRNA precursors of smRNAs ., Therefore , studies of RDR6 may be informative for gaining insight into the function of mammalian RDRs , and vice versa ., In order to validate and expand our characterization of new smRNA-producing RDR6 substrates , we turned to a quantitative reverse transcription polymerase chain reaction ( qRT-PCR ) approach ., For these loci , RDR6 is required to produce a dsRNA precursor of siRNAs ( see Figure 3A–3D ) ., Therefore , if RDR6 is not active ( rdr6 mutant plants ) , then the single-stranded transcripts may be stabilized ., To test this hypothesis , we designed qRT-PCR primers to 14 ( four known , 10 novel ) identified smRNA-producing RDR6 substrates ., We found that all fourteen tested loci , including the 10 newly identified RDR6 substrates ( e . g . At1g20370 ( Figure 3B ) , the intergenic region just upstream of At2g41490 ( Figure 3C ) , and At3g19890 ( Figure 3D ) ) , had higher transcript levels in rdr6 mutant compared to wild-type Col-0 plants ( Figure 3E ) ., These results suggest that most , if not all of the 218 loci we identified using a combination of dsRNA-seq and smRNA-seq methodologies are true smRNA-producing RDR6 substrates; approximately 200 of these loci are novel ( Tables S1 and S2 ) ., Most previously identified endogenous RDR6 substrates produce phased 21 nt siRNAs 20–23 , 28 ., We found that 51 of the RDR6 substrates identified in this study also produce phased smRNAs ( Table S2 and Figure S2D ) ., This group includes 22 of the RDR6 substrates that have been previously reported 31–34 , as well as the newly identified substrates , At1g20370 ( Figure 3B and 3E ) , the intergenic region just upstream of At2g41490 ( Figure 3C and 3E ) , and At5g02370 ( Figure 3E; Tables S1 and S2 ) ., However , we found that >75% of all endogenous smRNA-producing RDR6 substrates ( 167 ) do not produce siRNAs with any recognizable phasing , including the newly identified At3g19890 ( Figure 3D and 3E; Tables S1 and S2 ) ., These results suggest that there are multiple mechanisms by which transcripts become susceptible to RDR6-mediated silencing ., In summary , our results suggest that the combination of dsRNA-seq and smRNA-seq is a highly sensitive method for identifying transcripts subject to RDR6-dependent silencing , and is likely to be useful for characterizing the substrates of other eukaryotic RDRs - such as mammalian Tert 15 - that have not been demonstrated to produce phased siRNAs ., We next identified regions of the Arabidopsis genome that are significantly enriched for base-paired RNA using the dsRNA-seq data for wild-type Col-0 ., For this purpose , we used a geometric distribution-based approach to identify unusually long dsRNA molecules ( dsRNA ‘hotspots’ ) based on the average size of dsRNAs computed for each chromosome independently ., This analysis revealed 9 , 719 dsRNA ‘hotspots’ of varying lengths scattered along the entire length of all Arabidopsis chromosomes ( Figure 4A and Figure S4A; Tables S3 and S4 ) ., In fact , we have identified the vast majority of highly base-paired RNA molecules in the Arabidopsis transcriptome ( Figure S9 ) ., For example , the highly repetitive , transposon-rich pericentromeric regions of the Arabidopsis genome were found to be a rich source of dsRNA ( Figure 4A and 4B , and Figure S4A ) ., This is not surprising because cis transcriptional silencing of transposons and repetitive elements in the pericentromeric regions of Arabidopsis chromosomes is mediated by RDR2-dependent siRNAs 35–38 ., These findings not only substantiate that dsRNA-seq interrogates the desired portion of the transcriptome , but also suggest that , as expected , Arabidopsis transposons and repetitive elements are highly enriched in dsRNA on a genome-wide scale ., A classification of Arabidopsis dsRNA ‘hotspots’ revealed that transposons and protein-coding mRNAs are the two most highly base-paired classes of RNA molecules ( Figure 4B ) ., In fact , we identified 1949 protein-coding mRNAs that contained dsRNA ‘hotspots’ ( Figure 4B ) , so we interrogated over-represented molecular functions for these genes using Gene Ontology ( GO ) analysis ., Ribulose-bisphosphate carboxylase was the most significantly over-represented protein in this analysis ., However , the most highly over-represented group of genes were those involved in nucleic acid biology ( e . g . , translation , nucleic acid binding , etc . ) ( Figure 4C ) ., Interestingly , genes involved in nucleic acid metabolism are also over-represented in dsRNA ‘hotspot’-containing transcripts of Drosophila melanogaster and Caenorhabditis elegans ( Q . Z . and B . D . G . , unpublished data ) ., Thus , a propensity to form complex secondary structure ( self base-pairing ) may be a general feature of eukaryotic transcripts that encode proteins involved in processes involving nucleic acids ., This may point to a feedback regulatory mechanism that is dependent on an interaction between the proteins encoded by these transcripts and highly structured RNA intermediates ., The biogenesis of all functional small silencing RNAs ( e . g . miRNAs and siRNAs ) requires a dsRNA intermediate ., Therefore , we determined the propensity of highly base-paired regions ( dsRNA ‘hotspots’ ) to be processed into smRNAs ( Figure 4D ) using corresponding smRNA-seq data ( Figure 2C; see Figure S8 for smRNA data analysis ) ., We found that the highly base-paired regions within 9 of 10 interrogated RNA categories were extremely likely to be processed into smRNAs , the exception being pre-mRNA molecules ( Figure 4D ) ., Although these results were expected for transposable elements and miRNAs - which are known to be smRNA biogenesis substrates - it was surprising that functional RNAs ( e . g . rRNA , tRNA , snRNA , etc . ) also have a high likelihood of being processed into smRNAs since intramolecular base-pairing interactions are intrinsic to their function ., The evidence that highly base-paired regions of RNA molecules are frequently processed into smRNA , suggests that this process may be important for regulating the abundance of functional RNAs in Arabidopsis cells ., Our finding that any highly base-paired molecule can be processed into smRNAs , may provide an explanation for the restriction of the miRNA biogenesis machinery to specific sites within the plant nucleus ( dicing bodies ) 39 , 40 ., An intriguing hypothesis is that the sequestration of proteins involved in miRNA biogenesis and their MIRNA substrates to dicing bodies provides specificity to miRNA biogenesis , while protecting other structured RNAs ( e . g . rRNA ) from these proteins ., Our findings suggest further studies of smRNA sources in eukaryotes will reveal additional siRNA-mediated regulatory pathways , as demonstrated , for example , by the analysis of tRNA-derived RNA fragments ( tRFs ) in human cells 41 ., Regulation and maturation of eukaryotic pre-mRNA molecules is intimately linked to the proper formation of secondary structure 2 , 3 , 6 , 7 , which suggests that base-paired regions of these molecules are likely to be functionally conserved ., To test this hypothesis , we employed a seven-way comparative genomics approach that determines an average conservation score ( consScore ) for all bases of dsRNA ‘hotspots’ and all other sequences ( ‘flanking regions’ ) within the four structural moieties ( exons , introns , and both UTRs ) of every mRNA ., The consScores for dsRNA ‘hotspots’ and ‘flanking regions’ were then compared to determine if base-pairing mediates evolutionary conservation of mRNAs ., Using this approach , we found that dsRNA ‘hotspots’ in exons are significantly less evolutionarily conserved than ‘flanking regions’ ( Figure 5A ) , which suggests that intra- and/or intermolecular base-pairing interactions are disfavored in the protein-coding regions of plant mRNAs ., Our comparative genomic analysis of pre-mRNA data also demonstrated that dsRNA ‘hotspots’ are significantly more conserved than ‘flanking regions’ in 3′ UTRs ( p\u200a=\u200a0 . 0012 ) and introns ( p\u200a=\u200a1 . 73e–58 ) ( Figure 5A ) , and that highly base-paired regions within 5′ UTRs ( p\u200a= . 072 ) were more evolutionarily conserved than ‘flanking regions’ , but far less significantly than in 3′ UTRs and introns ., This analysis suggests the ability to base-pair is functionally important , and has been selected during plant evolution ., Just as selection for protein function maintains exonic sequences , base-pairing interactions may be important for conserving functionally important moieties in non-coding regions of mRNAs ., These functions may include, 1 ) providing appropriate structure for post-transcriptional and/or translational regulation ,, 2 ) maintaining mRNA stability ,, 3 ) providing cis-element sites for RNA binding proteins , and/or, 4 ) forming the processed precursors of non-coding RNAs ., Similar results have been obtained for Drosophila melanogaster and Caenorhabditis elegans ( Q . Z . and B . D . G . , unpublished data ) , suggesting that the ability to base pair is a critical feature of UTRs and introns in both plants and animals ., An mRNA secondary structure prediction methodology ( see below ) was used to obtain a folded model of two highly conserved intronic dsRNAs ( see Figure S5A and S5B for alignments ) , and suggested that these regions are almost entirely base-paired , and fold into unique , stable secondary structures ( Figure 5C and 5D ) ., Taken together , our results reveal that dsRNA-seq identifies functionally conserved regions of 5′ and 3′ UTRs and introns transcriptome-wide , and thus provides the critical first step towards understanding how such structural moieties affect the maturation and stability of transcripts in eukaryotic organisms ., We also noticed that a number of our dsRNA ‘hotspots’ are located in transposons and portions of the genome that do not contain any known genes ., Comparative analysis revealed that dsRNA ‘hotspots’ in intergenic regions ( p\u200a=\u200a7 . 3e–5 ) and transposons ( p\u200a=\u200a9 . 1e–16 ) are significantly more conserved than their flanking regions ( Figure 5B ) ., In the case of transposons , this finding was quite surprising because the majority of these repetitive elements are selectively neutral , especially for ancestral repeats ( ARs ) 42 , 43 ., However , our findings demonstrate that the highly antisense-prone transposable element dsRNA ‘hotspots’ ( Figure S4C and S4D ) have been undergoing a significant purifying selection compared to their ‘flanking regions’ , suggesting that these portions of TEs are not selectively neutral , but have important functions in plant cells ., An intriguing hypothesis is that a class of smRNAs that are integral to initiate and/or maintain the transcriptional silencing of transposable elements are processed from these conserved highly-base paired regions ., Overall , these results reveal functionally conserved portions of transposons , as well as novel , structured RNAs that have not been previously identified ., We identified a total of 1602 novel transcripts , ∼60% of which are unannotated transposable elements and/or simple repeats ( Figure 6J; Tables S5 and S6 ) ., The other >700 transcripts represent newly identified RNAs ., To determine the function of these 1602 transcripts we looked for the presence of these sequences in our flower bud smRNA dataset ( see Figure S8 for smRNA analysis ) ., 1437 ( 89 . 7% ) of the novel RNAs overlapped regions of the genome that produce significant quantities of smRNAs ( smRNA ‘hotspots’ , Figure S8 ) ( Figure 6 and Figure S6; Tables S5 and S6 ) ., Specifically , >98% of the unannotated transposable elements and/or simple repeats and ∼79% of the entirely novel RNAs produced smRNAs , respectively ( Figure 6J ) ., Most smRNAs from these transcripts were 24 nt in length ( Figure 6K and 6L ) ., In Arabidopsis , this size class is highly correlated with DNA methylation and heterochromatin formation 44 , suggesting that these loci produce 24 nt smRNAs that direct transcriptional silencing ., To validate our sequencing data and further interrogate the newly identified transcription units , we characterized several of these RNAs by reverse transcription ( RT ) polymerase chain reaction ( RT-PCR ) in five different Arabidopsis tissues ( leaf blade , leaf petiole , cauline leaves , stem , and unopened flower buds ) ., We selected four loci that do ( see Figure 6A and 6C; Figure S6A , S6C , and S6E; Table S5 ) and seven RNAs that do not ( Figure 6B and 6D; Figure S6B , S6D , S6F , S6G , S6K , S6L , and S6M; Table S5 ) produce statistically significant amounts of smRNAs ( 11 total transcripts ) ., As expected , all 11 of these RNAs are expressed in flower buds , the tissue used for the initial analysis of base-paired RNAs ., Eight of these transcription units are expressed in all five tissues , and three are expressed only in unopened flower buds ( Figure 6E–6I; Figure S6H , S6I , S6J , S6N , S6O , and S6P ) ., Two of these latter transcripts are also the source of smRNAs ( Figure 6A and Figure S6A; Table S5 ) ., Overall , our findings reveal a large collection of novel , structured RNAs in Arabidopsis flower buds , many of which have evolutionarily conserved functions in land plants ( Figure 5B , intergenic ) ., In principle , dsRNA-seq data should reveal the pairing status of all sequences within expressed mRNA molecules ( Figure 1 ) ., If this is true , this approach can be used to generate and/or validate secondary structural predictions on a genome-wide scale ., To test this hypothesis , we employed a novel methodology that produces structural models using sequence data obtained with a dsRNA-seq approach ., For this analysis , we used sequence data obtained from samples that were processed using two rRNA-depletion steps ( 2X Ribominus approach ( see Text S1; Figure S7 ) ) ., We used this dataset because - although incredibly similar to the normal dsRNA-seq approach ( see Text S1 ) - it is enriched for sense-strand mRNA sequences ( Figure 7A and 7B , Figure S4D , and Figure S7 ) , increasing the likelihood of generating useful secondary structure models ., This mRNA secondary structure analysis revealed base-pairing differences between the structural models produced by the RNAfold program of the Vienna package ( http://www . tbi . univie . ac . at/~ivo/RNA/ ) with and without dsRNA-seq constraints ., Many regions that were predicted not to base-pair , but to form large loops and open regions by non-constrained RNAfold were more highly paired when constrained , and vice versa ( see Figure 7C and 7D , http://tesla . pcbi . upenn . edu/annoj_at9/ ) ., To test the ability of our structural modeling approach to predict highly base-paired regions , we characterized significantly paired regions of mRNAs ( as determined by our methodology ) ( Figure 7C and 7D , see yellow regions ) by reverse transcription ( RT ) polymerase chain reaction ( RT-PCR ) after digestion with a single-stranded or double-stranded RNase ., We expected that the selected mRNA regions would be sufficiently intact for RT-PCR amplification after treatment with the single-stranded , but not the double-stranded RNase ., As predicted , the regions of mRNA molecules determined to be highly base-paired were amplified following treatment with the ssRNase ( Figure 7E ) ., Conversely , we could not amplify these same regions after treatment with the dsRNase , which implies that they were completely degraded by this enzyme ., These results demonstrated that dsRNA-seq reliably identifies base-paired portions of mRNAs ., We also found that the models of secondary structure produced using dsRNA-seq data as constraints are predicted to be stable ( Figure 7C , 7D , and 7F–7H , negative G values ) ., In total , these results suggest that the constrained secondary structure models are accurate representations of folded RNAs in solution , providing valuable insight into the pairing status of RNA molecules genome-wide ., Finally , we used our mRNA secondary structure prediction methodology to produce folded models for the novel intergenic transcripts identified by the RNA-seq approach ( Figure 6 and Figure S6 ) ., These structural models indicated that the new RNAs are highly base-paired , and are folded into a diverse array of stable ( negative G values ) secondary structures ( Figure 7F–7H ) ., Further evidence that these models are likely to be correct is provided by the observation that we obtained no dsRNA-reads for regions that are predicted to contain large loops by both dsRNA-seq data , as well as the RNAfold program of the Vienna package ( http://www . tbi . univie . ac . at/~ivo/RNA/ ) ., We believe that these transcriptome-wide mRNA secondary structure models and corresponding web-based viewer ( http://tesla . pcbi . upenn . edu/annoj_at9/ ) will be useful tools for elucidating the function of RNA folding in regulating gene expression and protein translation ., We describe in this report novel methodologies that produce a comprehensive genomic view of intra- and intermolecular base-paired RNAs at unprecedented resolution ., We take advantage of the data from these approaches , which capture intra-molecular base-pairing interactions , to generate models of mRNA secondary structure in solution on a genome-wide scale ( Figure 7 ) ., Although our methodology reveals the pairing status of RNA molecules in the absence of cellular proteins , previous studies have demonstrated that structural information obtained for RNAs in solution accurately reflects their structure in ribonucleoprotein complexes 3 , 45 ., Furthermore , our identification of conserved functional RNA domains using dsRNA-seq strongly suggests that RNA molecules are correctly folded into their secondary structure in solution ( Figure 5 ) ., Overall , our results suggest we have produced highly informative models of mRNA secondary structure on a genome-wide

Introduction, Results/Discussion, Materials and Methods

The functional structure of all biologically active molecules is dependent on intra- and inter-molecular interactions ., This is especially evident for RNA molecules whose functionality , maturation , and regulation require formation of correct secondary structure through encoded base-pairing interactions ., Unfortunately , intra- and inter-molecular base-pairing information is lacking for most RNAs ., Here , we marry classical nuclease-based structure mapping techniques with high-throughput sequencing technology to interrogate all base-paired RNA in Arabidopsis thaliana and identify ∼200 new small ( sm ) RNA–producing substrates of RNA–DEPENDENT RNA POLYMERASE6 ., Our comprehensive analysis of paired RNAs reveals conserved functionality within introns and both 5′ and 3′ untranslated regions ( UTRs ) of mRNAs , as well as a novel population of functional RNAs , many of which are the precursors of smRNAs ., Finally , we identify intra-molecular base-pairing interactions to produce a genome-wide collection of RNA secondary structure models ., Although our methodology reveals the pairing status of RNA molecules in the absence of cellular proteins , previous studies have demonstrated that structural information obtained for RNAs in solution accurately reflects their structure in ribonucleoprotein complexes ., Furthermore , our identification of RNA–DEPENDENT RNA POLYMERASE6 substrates and conserved functional RNA domains within introns and both 5′ and 3′ untranslated regions ( UTRs ) of mRNAs using this approach strongly suggests that RNA molecules are correctly folded into their secondary structure in solution ., Overall , our findings highlight the importance of base-paired RNAs in eukaryotes and present an approach that should be widely applicable for the analysis of this key structural feature of RNA .

At the heart of RNA functionality , maturation , and regulation is the formation of intricate secondary structures that are dependent on specific nucleotide base-pairing interactions encoded within their sequences ., These interactions can either be within ( intra-molecular ) or between ( inter-molecular ( heteroduplex ) ) RNA molecules ., Although it is clear that secondary structure is abundantly important for the functionality and regulation of RNAs , comprehensive base-pairing interaction data are completely lacking for the majority of these molecules ., To address this , we have developed a new approach for studying the base-pairing interactions of RNA molecules by marrying classical nuclease-based structure mapping techniques with high-throughput sequencing technology ., We have used this approach to identify known and novel substrates of the base-paired RNA producing enzyme RNA–DEPENDENT RNA POLYMERASE6 , reveal conserved functionality within introns and both 5′ and 3′ untranslated regions ( UTRs ) of mRNAs , uncover a novel population of functional RNAs , and produce a genome-wide collection of RNA secondary structure models by identifying the base-pairing interactions within each RNA molecule ., Our findings demonstrate that our methodology should be widely applicable for the identification and analysis of base-paired RNAs in all biological organisms .

plant biology/plant genomes and evolution, evolutionary biology/plant genomes and evolution, computational biology/comparative sequence analysis, computational biology/molecular genetics, evolutionary biology/genomics, molecular biology/bioinformatics, evolutionary biology/bioinformatics, computational biology/genomics, evolutionary biology/plant genetics and gene expression, plant biology/plant genetics and gene expression, computational biology/systems biology

journal.pcbi.1000764

A Critical Quantity for Noise Attenuation in Feedback Systems

It has been identified that feedback loops play important roles in a variety of biological processes , such as calcium signaling 1 , 2 , p53 regulation 3 , galactose regulation 4 , cell cycle 5–8 , and budding yeast polarization 9–13 ., Although the detailed regulation of feedback loops may vary in different systems , the overall functions of feedback loop modules may be similar ., For example , positive feedback loops are mainly used for promoting bi-stable switches and amplifying signals ., One example is the cell cycle system 5–8 in which the mitotic regulator CDK1 activates Cdc25 , which in turn activates CDK1 , forming a positive feedback loop ., Conversely , Wee1 and CDK1 inactivate each other , forming a double-negative feedback loop , equivalent to a positive feedback loop ., The overall positive feedback regulation gives rise to a bi-stable switch that toggles between the inter-phase state and the mitotic-phase state ., Another example is the system of yeast mating 9–15 , in which multi-stage positive feedback loops enable the localization of signaling molecules at the plasma membrane by amplifying signals to initiate cell polarization and mating ., While most studies of feedback loops have been concerned with their roles in signal amplification , switch ( or switch-like ) responses 16–20 , and oscillations 21 ( See 22 , 23 for the latest review . ) , recently , another important aspect of feedback loops has drawn more and more attention: modulating ( accelerating or delaying ) timing of signal responses 22 , 24 , 25 ., Intuitively , positive feedback could amplify signals inducing an expeditious activation , or delay an activation by setting a higher threshold such that the system is activated only when the response accumulates beyond that threshold 22 , 25 ., Because characteristics of noises ( e . g . , the temporal frequency of a noise ) in a biological process are closely related to timing of a signaling system , feedbacks clearly play a critical role in noise attenuation 26–29 ., Thus , one of the central questions on noise analysis is how the architecture of a feedback circuit affects its noise property ., Some studies suggested that positive feedbacks tended to amplify noise and negative feedbacks typically attenuated noise 30–32; on the other hand , some other studies demonstrated that the positive feedbacks could attenuate noises and there were no strong correlations between the sign of feedbacks ( negative or positive ) and the noise attenuation properties 28 , 33 ., In their novel work 34 , Brandman et al . linked the effect of positive feedback loops on noise attenuation to the time scales of the feedback loops ., They studied a canonical feedback module consisting of three components , i . e . , an output and two positive feedback loops , and ., The output is turned on by the two positive feedback loops and , which are stimulated by an external ( or upstream ) stimulus and are also facilitated by ( Figure 1A ) ., The output becomes active ( or stays inactive ) as the pulse stimulus is high ( or low ) ., Through numerical simulations , Brandman et al . 34 showed that , if one of the positive feedback loops ( e . g . , loop ) was slow and the other one was fast ( termed as dual-time loops ) , the system could lead to distinct active output even in the presence of noise in the stimulus ( at the high state ) ., Following this work , Zhang et al . 35 studied dual-time loops in producing a bi-stable response with a constant input ( unlike a pulse input in 34 ) ., They concluded that dual-time loops were the most robust design among all combinations in producing bi-stable output for a slightly different system in which the stimulus could activate or without the participation of ., Kim et al . 36 considered systems coupled with negative and positive feedback loops ., By assuming all the positive feedback loops have the same time scale but different time delays , they obtained a system that was capable of performing fast activation , fast deactivation , and noise attenuation ., What remains unclear are the sufficient and necessary conditions for a feedback system to achieve noise attenuation ., Are two , or at least two , positive feedback loops ( as used in 34–36 ) required for controlling noise amplification in the input ?, Is a fast loop necessary for a positive feedback loop system to achieve noise attenuation ?, Are there any intrinsic quantities that connect the dynamic property of a system in absence of noises with the systems capability of noise suppression ?, If such quantities exist , how do positive feedbacks or negative feedbacks affect them ?, In this work , we find that the capability of noise suppression in a system strongly depends on a quantity that measures the difference between the deactivation and activation times relative to the input noise frequency ., Specifically , this quantity , termed as the “signed activation time” , has an inverse relationship with the noise amplification rate , with larger signed activation time leading to better noise attenuation ., In addition , the signed activation time , representing one of the essential temporal characteristics of the system in absence of noises , may be controlled by either negative or positive feedbacks ., We explore the properties of the quantity through both analytic approach ( including linear stability analysis , multiple time scale analysis , and Fluctuation Dissipation Theorem ) and numerical simulations ., We first consider the same modules as in 34 , and find that , for example , an additional positive feedback loop could drastically increase the signed activation time by speeding up the activation time while still keeping the deactivation time slow , as consistent with the previous observation 34 that dual-time-loop systems suppress noises better than single-loop systems ., We next add a negative feedback loop to the positive-feedback-only system and show that a negative feedback loop usually slows down both activation and deactivation processes , leading to better or worse noise attenuation depending on which process ( between activation and deactivation ) is more significantly affected ., Finally , we study the signed activation time and its relations to the noise amplification rate in different systems involving various feedbacks ( e . g . , positive , negative , and feedforward ) , including a yeast cell polarization model 14 , 37 , a polymyxin B resistance model in enteric bacteria 38 , and four connector-mediated models 39 ., All simulations confirm that the capability of noise attenuation in those systems improves as the signed activation time increases ., A simple model with one positive feedback loop may have two components with one upstream stimulus ( inside the red dashed box in Figure 1A ) ., In this system , the output is activated by , and is triggered by a stimulus and regulated by ., The stimulus drives the output of the system with a high ( or low ) stimulus that corresponds to an active ( or inactive ) state of ., Many biological circuits have positive feedback regulations of this nature 1 , 2 , 19 , 40 ., For example , is a kinase to phosphorylate to , and once is activated , it catalyzes a conversion from an inactive form to an active form 5 ., Neglecting the mechanistic details , while keeping the essential interactions , we model the dynamics of the above module by the following system of ordinary differential equations ( Text S1 ) : ( 1 ) where and represent normalized concentrations of and , respectively ., The normalized stimulus , as a function of time , , usually varies ( continuously ) between two states , i . e . , an inactive state in which ( or the “off” state ) and an active state in which ( or the “on” state ) ., The parameters , , , and are kinetic constants , and indicates the time scale for loop ., Once the output of the system reaches the “on” state driven by the stimulus , how does system ( 1 ) respond to temporal noises in the input signal ?, What are the strategies for effectively maintaining the system in the “on” state even with noises presented by the stimulus ?, We find that the time scales , denoted by and ( Figure 2 ) , for the system to switch from the “on” state to the “off” state and from “off” to “on” respectively in the absence of noises in the signal , play a critical role ., Specifically , when is significantly larger than the time scale of the noise , i . e . , , where is the frequency of the noise , the output of the system remains in the “on” state ( Figures 3A–3B ) ., Intuitively , when the system in the stable “on” state receives a noisy signal with an instantaneous value possibly near , it needs time to react and detour to the “off” state ., In the case of , before the system settles down to the “off” state , a noisy signal with an instantaneous value near shows up , forcing the system to synchronize with the new value of the input signal ., If , the output recovers fast from the drift towards the inactive state , and is more likely to maintain around the “on” state ., The above intuition suggests that the noise attenuation at the “on” state depends positively on and negatively on ., Thus , the quantity , i . e . , the signed activation time , could be a good indicator of a systems ability of attenuating noise ., To investigate how noise level in the solution depends on the signed activation time , we study the noise amplification rate , defined as the relative ratio of the coefficients of variation of the output ( ) and the noise ( ) 28:First , we perform numerical simulations on system ( 1 ) ( Methods ) to study the relationship between and by varying the activation and deactivation time scales while fixing ., This is achieved by changing the kinetic parameteres , and individually in the system , and is found decreasing in ( Figure 3C ) ., Next , we hold constant , corresponding to no changes in all parameters , and vary the noise frequency ., The trend of remains the same ( Figure 3D ) ., We also consider the dependence of on and individually ( Figure S1 ) ., In the single loop case , it turns out that is always decreasing in ( Figures S1A–S1C ) , but it might be increasing in ( Figure S1D ) ., Similar results are also obtained for positive-positive-loop systems ( Figure S2 ) ., Both suggest that neither deactivation nor activation alone can fully characterize the noise amplification rate , and the noise amplification rate is more likely determined by the difference between the deactivation and activation time scales ., Next , we further explore this system through the following two analytical approaches ., In the previous section , we have demonstrated that the noise amplification rate depends negatively on the signed activation time ., Thus , if a system is persistent to noise at the “on” state , it should have a large signed activation time ., In this section , by studying the dynamics of the noise-free system , we show that a small is necessary for a slow deactivation , but not sufficient ., With a fixed small , larger or could lead to slower deactivation and faster activation ., In many biological processes , such as cell cycle 5 , 6 , often two positive feedback loops and activate the output simultaneously ( Figure 1A ) ., Similar to system ( 1 ) , the corresponding equations take the form: ( 13 ) Through direct numerical simulations , we find that the noise amplification rate decreases in the signed activation time ( Figures 4A–4B ) , following the same principle as in the single-positive-loop system ( 1 ) ., The activation time scale decreases in and , while the deactivation time scale increases in and ( Figures 4C–4D , Table 1 ) ., We also find that an additional feedback loop can lead to a faster activation ( red and black versus blue in Figures 4C–4D , bottom ) and a slower ( or similar ) deactivation ( red and black versus blue in Figures 4C–4D , top ) , compared to a single-positive-loop system , and a positive-positive-loop system can achieve similar activation and deactivation rates with larger ranges of kinetic parameters than a single-positive-loop system ( Table 2 ) ., Consequently , noise attenuation can be better achieved in the positive-positive-loop system ( Figures 4E–4F ) ., Below are details of the mathematical analysis for the roles of the additional positive feedback ., In this section , we study how an additional negative feedback loop affects noise attenuation in a system ., One of the simplest ways to introduce negative feedback to the single-positive-loop system ( 1 ) is to let deactivate ( Figure 1B ) 21 ., In this case , the model becomes ( 20 ) Our analytical results show that the additional negative feedback loop leads to slower deactivation and slower ( or slightly faster ) activation compared to its single-positive-loop counterpart ( red and black versus blue in Figures 5C–5D ) ., Moreover , the deactivation time scale increases in and , and the activation time scale decreases in and ( Figures 5C–5D , Table 1 ) , similar to the single-positive-loop ( Figures 3E–3F ) and positive-positive-loop systems ( Figures 4C–4D ) ., Numerical simulations reinforce these findings and demonstrate that the noise amplification rate of negative-positive-loop systems decreases in the signed activation time , following the same principle as their single-positive-loop counterparts ( Figures 5A–5B ) ., Below , we provide detailed analysis to show how the deactivation and activation time scales depend on various kinetic parameters , compared to the single-positive-loop case ., In our analytical studies , we assume for simplicity ., However , and are varied independently in numerical simulations ., Unlike the simple models in the previous section , a yeast cell polarization signaling pathway model that we study next ( Figure 6A ) consists of more than three components and multiple feedback regulations 37 , 48 ., Polarization in yeast cells ( a or cells ) is activated by pheromone gradients 48 ., The pheromone ( L ) binds to the receptor ( R ) and becomes activated ( RL ) ., The activated receptor facilitates the conversion of the heterotrimeric G-protein ( G ) into an activated -subunit ( G ) and a free G dimmer 49 ., G is then deactivated to an inactive -subunit ( Gd ) , which in turn binds to G and forms the heterotrimeric G-protein ., The free G recruits cytoplasmic Cdc24 to the membrane , forming the membrane-bounded Cdc24 ( Cdc24m ) , an activator of Cdc42 ., Accumulation of the activated Cdc42 ( Cdc42a ) at the projection site is a key feature of polarization , and thus is regarded as the output of the proposed system ., The activated Cdc42 participates in other polarization processes , forming positive or negative feedback loops ., For example , the activated Cdc42 sequesters the scaffold protein Bem1 to the membrane , which then recruits Cdc24 to the membrane 50 ., This forms a positive feedback loop ., Other functions of Cdc42 include the activation of Cla4 ( Cla4a ) , an inhibitor of Cdc24 , resulting in a negative feedback loop 51 ., Following the model proposed in 37 but ignoring the spatial effect , we have the following system of equations: ( 25 ) Here , denotes the concentration of the corresponding protein; L is the input signal , and Cdc42a is the output; the concentrations of G , Gd , the inactive form of Cdc42 , the cytoplasmic Cdc24 , and the cytoplasmic Bem1 are derived through conservation relations:Here , is the volume of the cell; is the surface area of the cell; , and are the total numbers of molecules per cell of the corresponding proteins ., The two Hill functions and are defined asThese two functions represent two different ways of bringing Cdc24 to the membrane ., One is by the free G ( function ) , and the other is through Bem1 ., The Bem1 recruitment is known to be facilitated by Gs binding to Ste20 52 , and the influence from G is modeled by the function ., Kinetic parameters take the same values as in 37 , and see also the caption of Figure 6 ., Starting from zero Cdc42a , giving high ( LnM ) or low ( LnM ) constant inputs , the output reaches active and inactive states , respectively , which are clearly distinguished ( Figure 6B ) ., Inputs with small amplitude ( Figure 6C ) can be detected by the system ( Figure 6D ) ., On the other hand , the output is robust to noise when it is around the active state ( Figures 6E–6F ) ., To study how the noise amplification rate depends on the relative time scales , we vary ten parameters systematically in their -fold ranges ., All of them show the same decreasing trend of the noise amplification rate as a function of the signed activation time ( Figure 6G ) ., This suggests that the negative relation between the noise amplification rate and the signed activation time , derived from the simple models , could also apply to models of complex interactions and combinations of positive and negative feedback loops ., Such negative relationship may be a generic principle on noise suppression for input-output systems with feedback loops ., Our theoretical and numerical studies have demonstrated that it is not the sign of the feedback that determines the degree of noise attenuation ., In searching for a general framework for a relation between feedback and noise attenuation , we have identified a critical quantity , termed as the “signed activation time” ., Its relation with the systems ability of noise attenuation has been explored , and we have revealed that the noise amplification rate decreases in the signed activation time ., These results are concluded through employing multiple time scale analysis , Fluctuation Dissipation Theorem , and linear stability analysis , combined with numerical simulations , in three feedback modules ( Figure 1 ) : single-positive-loop , positive-positive-loop , and positive-negative-loop systems ., To test the generality of the conclusion , we have explored models ( Figure 1A ) with saturation effect , i . e . , modeling feedback loops by Hill functions ( Text S1 , Section 6 , Figures S5 , S6 ) , a yeast cell polarization model consisting of multiple intermediate components ( Figure 6 ) , a polymyxin B resistance model in enteric bacteria ( Figure 7 ) , and four connector mediated models ( Table 3 , Figure 8 ) ., In all cases , the noise amplification rate has been confirmed to be a decreasing function in the signed activation time ., To analyze the roles of multiple positive and negative feedback loops in our toy models , we have found that:, 1 ) an additional positive feedback loop could drastically reduce the activation time scale , improving performance in noise attenuation;, 2 ) the time scales in positive-positive-loop feedback systems are more robust to rate constant variations ( e . g . due to variability of organisms or variation of environments ) ; and, 3 ) adding a negative feedback loop usually sustains both deactivation and activation processes , and thus its overall effect on the signed activation time could be either negative or positive ., To obtain slow deactivation and fast activation , we have identified two key parameters , , the association constant of to , and , the association constant of to ( Figure 1A ) , that tightly control the deactivation and the activation time scales ( Tables 1 and 2 ) ., Interestingly , under appropriate conditions , even the simplest single positive feedback loop system could display slow deactivation and fast activation , which were not observed in previous works 34–36 ., The idea of connecting noise attenuation with the time scales of signal responses was mentioned in other works , for example , 53 , in which only the activation time scale was considered ., However , we have shown that in our models neither the deactivation time scale nor activation time scale alone predict correctly the trend of the noise amplification rate ( comparing Figure 3C to Figures S1C–S1D , for example ) and the noise amplification rate is an interplay between the two time scales ., Our proposed quantity , the signed activation time , provides a more consistent relation linking to the noise attenuation rate ., Direct approaches for analyzing noise may be applied to feedback systems , such as the energy landscape method 4 , 35 , 54–56 and the methods used for noise attenuation or amplification in signaling cascades 28 , 57–60 and covalent modification cycles 53 ., To characterize signaling time scales , we have studied the magnitude of eigenvalues and their corresponding eigenvectors of the Jacobian matrices at each distinct state of the signal ., Questions concerning how the magnitude of signal output and signal duration depend on properties of pathway components ( e . g . , the effect of cascades ) were explored from a system control point of view in other works 61–63 ., Our study features a novel approach using multiple time scale asymptotic expansion 41 ., Different from the one-time-scale expansion , this approach provides an explicit relation between the solutions and the two separated time scales , suggesting that the single-positive-loop system can function as a low-pass filter and explaining why the relative size of noise time scale and a systems intrinsic time scales is important to noise attenuation ., This approach may be applied to other biological systems with time scale separations ., Our findings suggest that the negative relationship between the noise amplification rate and the signed activation time could be a general principle for many biological systems regardless of specific regulations or feedback loops ., Notice that the deactivation and activation time scales are widely defined and could be measured without detailed knowledge of a systems internal structure ., Thus , the underline system could be treated as a black box and its ability of noise attenuation could be estimated based on the signed activation time ., In general , if a system prefers to better attenuate noise at the “on” state , the system should have a large signed activation time ., We would like to point out that the studies done here mainly focus on time scale changes within a fixed system , although comparisons across different systems are likely to be consistent with our result ( e . g . the four connector-mediated models ) ., However , we might not expect two drastically different systems with equal signed activation time to exhibit the same noise amplification rate , which is likely to depend on other factors in the system as well ., We hope that the present work can shed some light on general principles of noise attenuation , in particular , their connections with timing of a system in the absence of noises ., All simulations are performed using Mathematica 6 . 0 . 0 ., To compute the noise amplification rate , we useto approximate , whereWe useto approximate , whereThe noise is generated by dividing the time interval into sub-intervals of length , and on each sub-interval the signal takes a random number from a uniform distribution in ., See Figure 3A for a typical noisy signal ., See Text S1 .

Introduction, Results, Discussion, Methods

Feedback modules , which appear ubiquitously in biological regulations , are often subject to disturbances from the input , leading to fluctuations in the output ., Thus , the question becomes how a feedback system can produce a faithful response with a noisy input ., We employed multiple time scale analysis , Fluctuation Dissipation Theorem , linear stability , and numerical simulations to investigate a module with one positive feedback loop driven by an external stimulus , and we obtained a critical quantity in noise attenuation , termed as “signed activation time” ., We then studied the signed activation time for a system of two positive feedback loops , a system of one positive feedback loop and one negative feedback loop , and six other existing biological models consisting of multiple components along with positive and negative feedback loops ., An inverse relationship is found between the noise amplification rate and the signed activation time , defined as the difference between the deactivation and activation time scales of the noise-free system , normalized by the frequency of noises presented in the input ., Thus , the combination of fast activation and slow deactivation provides the best noise attenuation , and it can be attained in a single positive feedback loop system ., An additional positive feedback loop often leads to a marked decrease in activation time , decrease or slight increase of deactivation time and allows larger kinetic rate variations for slow deactivation and fast activation ., On the other hand , a negative feedback loop may increase the activation and deactivation times ., The negative relationship between the noise amplification rate and the signed activation time also holds for the six other biological models with multiple components and feedback loops ., This principle may be applicable to other feedback systems .

Many biological systems use feedback loops to regulate dynamic interactions among different genes and proteins ., Here , we ask how interlinked feedback loops control the timing of signal transductions and responses and , consequently , attenuate noise ., Drawing on simple modeling along with both analytical insights and computational assessments , we have identified a key quantity , termed as the “signed activation time” , that dictates a systems ability of attenuating noise ., This quantity combining the speed of deactivation and activation in signal responses , relative to the input noise frequency , is determined by the property of feedback systems when noises are absent ., In general , such quantity could be measured experimentally through the output response time of a signaling system driven by pulse stimulus ., This principle for noise attenuation in feedback loops may also be applicable to other biological systems involving more complex regulations .

computational biology/signaling networks, mathematics, computational biology/systems biology, cell biology/cell signaling

journal.pcbi.1004160

Antenna Mechanism of Length Control of Actin Cables

Eukaryotic cells have a complex cytoskeleton that includes vast arrays of microtubules and actin filaments , which governs the internal positioning and movement of cellular substructures such as vesicles and organelles , and dynamic changes in cell polarity , shape , and movement ., Many of these processes require the length of the cytoskeletal structures to be tightly controlled ., For example , during cell division , the microtubule-based mitotic spindle maintains a remarkably constant size despite undergoing highly dynamic turnover 1–4 ., Another example of cellular structures whose lengths are regulated are cilia , which are used for motility and sensation 5–8 ., These microtubule-based structures maintain a precise length even though their tubulin building blocks are constantly turning over ., Recent studies have begun to address how the length of these microtubule-based structures is maintained 5 , 7–12 ., However , there has been far less attention paid to how the size and length of actin-based structures is determined ., The key question that we address here is the mechanism by which the length of actin cables in budding yeast ( Saccharomyces cerevisiae ) is controlled ., Actin is one of the major elements of the cytoskeleton in all eukaryotic cells ., It is a protein that polymerizes to form helical two-stranded filaments ., The actin cables found in budding yeast cells are estimated to consist of 2–4 filaments bundled in parallel by actin crosslinking proteins ., These structures are polymerized by formins13–16 , and serve as tracks for the rapid , directed transport of organelles and vesicles through the mother cell and toward the bud tip ., Observations in yeast have shown that during budding , one set of cables is polymerized at the bud neck by the formin Bnr1 , which is anchored to a physical scaffold at the bud neck17 ., Bnr1-polymerized actin cables grow into the mother cell , extending toward the rear of the cell , and line the cell cortex 18 , 19 ., As rapidly as the cables grow from the bud neck , they are dismantled at the other end; cables rarely grow past the back of the mother cell , suggesting that their length is regulated 20 ., In this paper , we explore theoretically a mechanism of cable length control that acts on the polymerization machinery , formins , which is supported by recent molecular and cellular observations ., Actin cables polymerized by Bnr1 in a yeast cell grow rapidly ( ~1 μm/s , or ~370 actin subunits/s ) ., Like other formins , Bnr1 remains tightly associated with the fast-growing end of the actin filament 14 , 21 , and thus physically tethers the growing end of the cable to the bud neck while the other end of the cable is disassembled in the cytosol by other cellular factors 22 ., The balance of these two antagonistic processes ( assembly and disassembly ) leads to a steady state cable length ., Still , in order to obtain a peaked distribution of cable lengths at steady state , one or both of the rates for assembly and disassembly have to be length dependent ., In particular , if the two rates are length independent , and the rate of disassembly ( d ) is greater than the rate of assembly ( r ) , then the steady state is characterized by an exponential distribution of lengths ., This distribution has a characteristic length given by, 1logdr ,, which is typically small , unless the two rates are almost identical ., Therefore , in the absence of a mechanism that leads to a fine balancing of the two rates , the characteristic length is expected to be only a few monomers ., Mechanisms for length dependent depolymerisation have been proposed for microtubule- and actin-based structures ., Kinesin motors such as Kip3 and KIF19A move along microtubules and when they reach the end of the track promote dissociation of tubulin subunits , leading to a length-dependent depolymerisation rate 6 , 9 , 10 , 23–25 ., In the case of actin , cofilin severs filaments thereby reducing their length in a length-dependent manner ., Recently theoretical and experimental studies have shown that this activity alone leads to a peaked distribution of filament lengths in steady state 26–29 ., Here we consider an alternative mechanism , in which actin filament length is controlled by negative feedback , which is provided by myosin-motor transport , leading to a length-dependent polymerization rate ., Type-V myosin motors move on cables towards the bud neck and then the bud tip at ~ 3 μm/s , transporting vesicles and other essential cargo destined for the growing bud 19 , 30 ., Recent experiments have shown that Smy1 is a passenger protein of the myosin motor , and is transported to the bud neck where it pauses briefly and is thought to interact with Bnr1 , which is anchored there 20 ., Further experiments have shown that Smy1 directly binds to Bnr1 and inhibits its actin polymerization activity ., As such , when the SMY1 gene is deleted from cells , a number of the cables grow abnormally long 4 ., Here we propose that the active transport of Smy1 along a cable sets up a negative feedback cue to the formin , making the effective cable growth rate length dependent ., The length dependence derives from the fact that the rate at which this negative cue is delivered to the formins is proportional to the number of myosin motors bound to and walking on a cable , which serves as an antenna for myosin binding ., The goal of this paper is to mathematically explore this antenna mechanism of actin-cable length regulation , and to propose experimental tests of the basic tenets of this model ., In particular , we make quantitative predictions for how modulating the strength of the Smy1-formin interaction and the concentration of Smy1 in cells affect the cable-length distribution ., The antenna model of actin cable length regulation is based on the idea that a motor delivering an inhibitory cue for polymerization leads to a length dependent growth rate ., Smy1 molecules are rapidly transported by myosinV along cables to the barbed ends of the actin filaments in a cable , where they transiently bind to and inhibit the formin ( Bnr1 ) ., The cable thus acts as a landing pad for myosin+Smy1 inhibitory complexes ., Long cables on average encounter more myosin+Smy1 complexes and thereby deliver inhibitory cues at a higher frequency to the formins ., This sets up a length dependent negative feedback loop regulating cable elongation rates , and ultimately narrows the distribution of cable lengths in the cell ., This antenna model for actin filament length control is related conceptually to the antenna model for a recently-described microtubule length control mechanism , but with a key difference being that in the latter model kinesin motors themselves move directionally on the antenna and upon reaching its end modulate the rate of microtubule disassembly 23 , 24 , whereas in our model the motors carry inhibitors , which upon reaching the end modulate the rate of the actin polymerization engine ., Here we model the actin cable as a single polymer which grows by the addition of subunits at the formin bound end , and shrinks by subunit removal at the opposite end ( Fig 1 ) ., Since cables polymerized by Bnr1 in yeast are thought to be comprised of multiple parallel actin filaments bundled together , our model should be taken as an effective description of the assembly and disassembly of this composite structure ., In our single-filament model the cable does not grow when Smy1 is inhibiting the formin; subunits are added by the formin at a rate r when the formin is free of Smy1 ., koff is the rate at which Smy1 molecules detach from the formin , thereby allowing the formin to return to the free/uninhibited state ., The rate at which the formin switches from the uninhibited state to the Smy1-bound/inhibited state ( kon ) is equal to the rate of arrival of Smy1 particles to the formin ., At steady state , this rate is equal to the rate at which Smy1 proteins diffusing in the cytoplasm are captured by the myosin-carried vesicles ( Fig 1A ) ; this assumes that there are no traffic-jams encountered by the myosin motors , which is consistent with our cell experiments and discussed in more detail in the Methods section ., According to Smoluchowski , the rate of Smy1 capture is proportional to the Smy1 concentration , and most importantly for our model , the length of the cable , i . e . , kon ( l ) = wl ., This myosin-dependent delivery of the formin inhibitor Smy1 leads to a length dependent average rate of assembly , which together with a constant disassembly of the cable , which we take to occur by the removal of subunits from the end of the cable at rate d , produces a peaked steady-state distribution of cable lengths ., The average time the cable spends in the on state , when the formin is active and the cable is growing at rate r , is, 1kon ( l ) ,, while the average time the cable spends in the off state is, 1koff ., Since we assume that the rate of growth in the off state is zero ( note that all our conclusions are independent of this assumption as long as the rate of polymerization when Smy1 is bound to formin is smaller than when the formin is free of Smy1 ) , the average rate of polymerization is, r¯ ( l ) =r ( koffkoff+kon ( l ) ) ,, ( 1 ), where the factor appearing in parenthesis is the fraction of time that the cable spends in the on state ., From this calculation we conclude that the average rate of polymerization is length dependent and decreases as the length of a cable increases , since kon ( l ) = wl ., Furthermore , the average rate of polymerization depends on the concentration of Smy1 ( i . e . , w is proportional to Smy1 ) and its binding affinity to the formin ( koff is proportional to the dissociation constant ) , both of which are parameters that can be tuned in experiments ., From the expression for the average rate of polymerization we can compute the steady-state average cable length by equating it with the disassembly rate d:, ⟨l⟩=koffw ( rd−1 ) ., ( 2 ), The key prediction of this equation is that increasing the Smy1 concentration ( i . e . , increase in, w ) reduces the average cable length , whereas weakening the formin-binding affinity of Smy1 ( i . e . , increase in koff ) increases the average cable length ., We explore these predictions more thoroughly in the next section ., We estimate all four parameters ( r , d , w , koff ) appearing in Eq 1 from in vivo experiments on wild-type yeast cells ( see Methods ) and study the changes to the cable-length distribution by varying the Smy1 concentration, ( w ) and its affinity to formins ( koff ) ., In order to describe the dynamics of an individual cable we mathematically model the antenna mechanism using the master equation formalism ., The key quantity to compute is the probability , P ( l , t ) , that the cable has length l ( measured here in units of actin subunits ) at time t ., The master equation describes the evolution of P ( l , t ) in time , by taking into account all the possible changes of the cable state that can occur in a small time interval Δt ( Fig 1B ) ., For a given cable length , we distinguish between two states depending on whether the formin at its end is inhibited by Smy1 ( the off state ) or free ( the on state ) ., Therefore we can write P ( l , t ) = Poff ( l , t ) + Pon ( l , t ) , where the probabilities for cable length in the off and on states satisfy the following master equations ( for l > 0 and w , koff , d non-zero ) We use these equations to compute the steady-state distribution of cable lengths P ( l ) = Pon ( l ) + Poff ( l ) , where Pon ( l ) and Poff ( l ) are solutions to Eq 2 when the left-hand sides of these equations are set to zero ., The variation of the length distribution with the parameters of the model then provides a stringent set of predictions of the antenna model that can be tested experimentally ., The steady state distribution of cable lengths can be computed exactly using the method of detailed balance in the fast switching regime , i . e . , when the rates for switching between the on and the off states ( kon ( l ) and koff ) are much greater than the rates of assembly/disassembly ., In this limit , the cable can be assumed to have a polymerization rate that is length dependent ( see Eq 1 ) and a disassembly rate d ., Using the detailed balance condition, Plr¯ ( l ) =P ( l+1 ) d, , we obtain, P ( l ) = ( rd ) l ( koffw ) l−1 ( Γ ( koffw+l ) Γ ( l−1 ) ) ( ekoffrdwkoffr ( koff−w ) ( koffrdw ) − ( koffw ) ( Γkoff−ww−Γ−1+koffw , koffrdw ) dw2 ) −1, ( 4 ), where Γ ( x ) is the Gamma function ., When the rates of switching are comparable to the rates of assembly and disassembly , as is the case for actin cables in budding yeast cells , we are no longer able to obtain an analytic form of the steady state distribution and we resort to numerical simulations of the master equation , Eq 2 ., We start with a cable of zero length growing from the formin , which acts as a nucleation site ., We use the Gillespie algorithm ( see Methods ) 31 , 32 to follow the stochastic trajectory in time of the cable length as it polymerizes and depolymerises , while also switching between the off and on states depending on whether Smy1 is bound to the formin or not ., After some time we observe the cable reaching a steady state , when the length distributions no longer changes with time; see Fig 2 ., For parameter values corresponding to the fast switching regime we find excellent agreement between the stochastic simulations and Eq 3 ( see S1 Fig ) ., In the slow switching regime , which describes the dynamics of yeast actin cables ( see Methods for parameter estimates ) , we rely solely on the stochastic simulations to obtain steady state distributions of cable lengths for different values of the model parameters ., In Fig 3 we explore the effect of the rate parameters koff and w on the steady state distribution of cable lengths ., As explained earlier , the first is proportional to the dissociation constant that measures the binding affinity of Smy1 to formins , while the second rate is proportional to the Smy1 concentration ( see Methods for parameter estimates ) ., The results of our simulations for the dependence of the mean cable length on these two parameters are in excellent agreement with Eq 1 ., Also , in the parameter range explored we observe a difference in the dependence of the width of the steady state length distribution on koff and w ., Changing the binding affinity of Smy1 to formins has little effect on the width of the length distribution while the Smy1 concentration has a large effect ., In Fig 4 we show in more detail how the variance and the square of the coefficient of variation ( CV2 =, variancemean2 ), change as a function of koff and w ., We see that the square of the coefficient of variation , a standard measure of noise described by a probability distribution , in both cases decreases with increasing average cable length ., Fig 3 and Fig 4 also provide a quantitative assessment of how sensitive the length distributions are with respect to the model parameters , in particular the two parameters related to the Smy1 concentration, ( w ) and its affinity to formins ( koff ) ., All the plots shown in Fig 3 and Fig 4 constitute specific predictions of the antenna model , which can be readily tested by in vitro experiments ., While more difficult , experiments in vivo in which these two parameters are varied and the change of cable length distribution is measured , are also possible ., The key feature of the antenna model proposed here is the switching of the cable between two states , one in which the formin is active and the cable is growing , and the other in which the formin is inactive ( by virtue of Smy1 being bound to it ) and the cable is therefore shrinking ., The balance of the two states leads to the average cable length given in Eq 1 ., The same average length can be achieved either by large koff and w , or by small koff and w , as long as the average rate of polymerization , Eq 1 , is the same ., In other words , the same mean length can be achieved either by having a small concentration of Smy1 proteins present in solution but they stay bound to the formin for a longer time , or in the alternate case where a large number of Smy1 proteins are in solution , but they associate with formin for a shorter time ., The width of the length distribution , on the other hand , will not be the same in these two extremes ., When the switching rates are slow , we expect that the formins will spend long periods of time in the active and the inactive state leading to large fluctuations in the cable length , when compared to the situation when the switching is fast ., This leads to the possibility that by tuning the concentration of Smy1 and its binding affinity to formins one is able to control the mean and the width of the length distribution independently ., These properties distinguish the antenna mechanism discussed here from most other models of length control described previously ., Interestingly , and roughly related to our findings , different versions of the antenna model of microtubule length control , which lead to the same mean microtubule length , have been reported to predict dramatically different steady-state fluctuations 33 ., In order to test our expectations about how the variance and the mean of the cable length distribution can be controlled separately , we computed the distributions for different values of the rates koff and w while keeping their ratio the same; in accordance with Eq 1 this guarantees that the mean length is fixed ., We also kept the rate of assembly r and the rate of disassembly d fixed as we do not expect these to change when tuning the concentration of Smy1 and its binding affinity to the formin ., Using a Gillespie simulation of the master equation ( Eq 2 ) we obtained length distributions for the slow and fast switching cases; see Fig 5 ., As expected , we observe more noise ( larger width for the same mean ) in the slow switching situation , which could be realized experimentally by having a small concentration of Smy1 mutants with a large binding affinity for formins ., The decrease in the square of coefficient of variation of the length distribution with decreasing binding affinity of Smy1 is shown in the inset to Fig 5 ., An alternative length control mechanism to the antenna mechanism , discussed above , is the finite supply of actin monomers in a cell 34 ., As the cables grow , the free actin concentration decreases , leading to a decrease in the polymerization rate of actin filaments that make up the cables ., When the polymerization rate equals the disassembly rate , steady state is reached ., However , below we make estimates that suggest that the finite monomer pool cannot be the only source of length regulation in vivo , and this is supported by the observation that some of the cables overgrow in cells when SMY1 is deleted 20 ., In the presence of a finite monomer pool , the average polymerization rate can be estimated as r′ ( N – Nc D ⟨n⟩ ) , where N is the total number of actin molecules in the cell ( in both filamentous and monomeric forms ) , Nc is the number of cables and r′ is the assembly rate of free monomer; note than in the absence of cables , when all of the actin molecules are in monomeric form , r′ = r/N ., Here , for the purposes of an estimate , we assume a simple geometry for the cables , where each cable has an average length ⟨n⟩ , and consists of D actin filaments in parallel bundled together ., In steady state , the average polymerization rate is equal to the depolymerisation rate d , which leads to an average cable length ⟨n⟩ = ( N – d/r′ ) /NcD ., The total number of actin molecules in the mother-cell ( which contains the cables of interest ) can be estimated by considering the concentration of actin in the cell’s cytoplasm , which we have measured by quantitative western blotting , and multiplying it by the known volume of a yeast mother-cell ,, N=10μM×4π32 . 5μm3=3×105, actin proteins ., Observations in vivo suggest that the number of cables is roughly 10 and they have a thickness of about D = 4 filaments ., Furthermore , if we take into account the in vivo rates of cable assembly and disassembly ,, r=3701s , r′=1 . 2×10-31s , d=451s, , we estimate an average cable length of ⟨n⟩ = 18 μm ( using the conversion 1 μm = 370 monomers ) ., ( This estimate doesn’t take into account actin patches as there are relatively few of these structures in the mother cell . ), The estimated average cable length is more than a factor of three longer than what is observed in wild-type yeast cells , suggesting the presence of additional length-control mechanisms ., Interestingly enough in mutant cells lacking Smy1 , we observe some cables whose length is roughly twice that seen in wild type cells; this observation is consistent with the idea that the finite monomer pool limits cable length in the absence of the Smy1-dependent antenna mechanism ., Another process that can control cable length is actin severing , in which proteins like cofilin bind to the sides of filaments and induce breaks ., This leads to the breaking off of polymer fragments , which are rapidly capped and depolymerized since they no longer have formins at their ends 27 , 29 ., Since filaments within a cable provide binding sites for cofilin , the longer the cable , the higher the rate of cofilin binding ., This may lead to a length-dependent severing rate , sl , where s is the severing rate per micron of cable per second ., Since cables are anchored at the bud-neck , when a cable gets severed ( by the severing of constitutive filaments ) , approximately and on average half of the subunits are lost , i . e . , they are no longer part of the cable attached to the bud-neck ., Therefore , assuming that severing can occur at any position along the cable that cofilin binds to , the depolymerisation rate ( i . e . , rate of subunit loss ) becomes length dependent ,, dl=sl×l2=sl22, ., To obtain the steady state filament length we set this depolymerisation rate equal to the polymerization rate , which leads to the formula, l=2r/s, ., Taking our estimated value for the polymerization rate , r = 1μm/s , and the maximum in vitro measured severing rate ( at 10 nM cofilin ) s = 10−3μm-1 s-1 35 , the estimate of the steady state cable length is ⟨l⟩ = 45μm , more than five times the length observed in vivo ., We expect this estimate to be in fact a lower bound on the average length obtained by the severing mechanism , since the optimized severing rate used above is actually decreased at both lower and higher concentrations of cofilin 35 ., Therefore this estimate suggests that severing cannot be the only mechanism of length control ., It should be noted that in our consideration of the effects of severing on cable length control we only consider severing by cofilin ., However , in cells there are a number of other co-factors that work with cofilin ( e . g . , coronin , Srv2/CAP , Aip1 ) and are likely to increase the rate of severing to further reduce cable length 36–38 ., Hard numbers for the contributions of these co-factors to severing are not yet available , but once they are , they can be worked into this model ., Another key factor is the presence of Tropomyosin proteins coating the cables ., Tropomyosin is essential for cable formation 39 , 40 , and is thought to protect cables at least temporarily from cofilin-mediated severing ., Thus , Tropomyosin may direct cofilin-mediated severing to the older ends of the cables , which is consistent with the model of dissociation that we have adopted for the antenna mechanism ., The above estimates suggest that cable lengths in vivo cannot be controlled by the finite actin monomer pool and severing alone , and requires additional length-dependent feedback mechanisms ., This is consistent with our cell experiments in which we observe striking changes in cable lengths upon deletion of SMY120 ., This raises the intriguing possibility that cells have evolved multiple mechanisms of cable-length control , including several other potential ones besides Smy1 ., For example , the specific conformation that F-actin adopts in different nucleotide states is likely to affect severing along cables , and therefore any protein that decorates cables and alters the nucleotide state and/or conformation of F-actin could be part of an additional length control mechanism 41 ., In addition , the ends of overgrown cables colliding with the cell cortex might change the mechanical stress of a cable leading to a change in its assembly or disassembly rate ., Further , the mechanical strain on filaments induced by myosin action can affect severing by cofilin 42 and therefore alter the disassembly rate ., In this paper we focused on cables assembled by only one of the two budding yeast formins , Bnr1 , which is stably anchored to the bud neck 17 ., However , the other budding yeast formin , Bni1 , is highly distinct in its cellular dynamics ., Bni1 molecules appear to be transiently recruited to the bud tip to assemble cables , then released , similar to the formin For3 in fission yeast17 , 43 , 44 ., A recent study of For3 discussed how this formin might control cable length in fission yeast 43 , 44 ., Their model considered the transient association of For3 with the cell tip leading to the assembly of actin filaments by the formin ., For3 and the newly polymerized actin filaments are then released from the cell tip and carried passively into the cell interior by the retrograde flow of actin filaments in the cable ., Upon release from the cell cortex , the actin filaments in cables can disassemble , increasing the amount of free actin which , in turn , increases For3 dissociation from the cell tip ., This coupling between actin monomer levels and For3 attachment leads to a steady state at realistic values of rate constants and actin and For3p concentrations 43 ., Whether or not a similar length control mechanism is employed for Bni1 generated cables in budding yeast is an intriguing open question ., In our model we assume for simplicity that myosin motors transporting Smy1 to the anchored formins do not fall off the cables ., Furthermore , it is assumed that the rate of delivery of Smy1 by myosin is greater than the polymerization rate of the cable ., Both conditions are necessary for every Smy1 molecule captured by the actin-cable ‘antenna’ to be delivered to the formins ., Here we address the experimental evidence for these two assumptions ., In wild type cells , Smy1-GFP was directly observed to be trafficked by the myosin motor and delivered , uninterrupted , to the formin 20 ., Smy1 is on vesicles , which have multiple myosin motors attached to them , which may explain why processivity does not seem to be an issue in vivo , and validates the assumption in our model that delivery of Smy1 is uninterrupted ., Also , in a wild type cell , the observed anterograde transport rate of vesicles toward the bud neck is 3 μm/s 20 , which , given a retrograde elongation rate of cables of 0 . 5–1 μm/s 19 , suggests a myosin motor speed of about 3 . 5–4 μm/s ., These observations are consistent with the assumption that the rate of transport of Smy1 toward the formin is much greater than the rate of cable elongation ., Further , this predicts that the antenna mechanism would not be effective for controlling cable length if the myosin speed was less than 1 μm/s since in that case Smy1 will not be delivered to the formin ., This qualitative prediction can be tested using myosin mutants 45 with reduced in vivo transport speeds ., Another interesting point to consider is the wide range of cable elongation rates reported in the literature , ranging between a few tenths of a micron per second to several microns per second 18 , 19 ., The antenna model provides a possible explanation for this observation ., Namely , the model predicts that the cable extension rate decreases with the cable length ( Eq 1 ) ., Therefore , it is possible that the range of reported cable elongation rates is due to the variability of the lengths of cables whose extension rate was measured ., In conclusion , the antenna model involving formins , Smy1 and myosin motors , is a novel molecular mechanism for length control of actin cables , which we have proposed based on experimental evidence in living cells ., While in cells it is almost certain that multiple mechanisms contribute to cable length control , in vivo observations as well as theoretical estimates indicate that the antenna mechanism is an important factor in controlling the length of these actin structures ., Here we have explored this model theoretically , and made a number of predictions that can be tested in vivo , and in vitro using a reconstituted system consisting of purified actin , formin , myosin and Smy1 ., In particular , we compute the effect of changing the concentration of Smy1 and its binding affinity to formin on the distributions of cable lengths ., Therefore quantitative measurements of this distribution in an in vitro reconstituted system of length control would serve as a stringent test of the antenna mechanism ., An interesting qualitative prediction of the model is that the variability and the mean of the actin cable length can be tuned independently by simultaneously tuning these two control parameters ( Smy1 concentration , and Smy1 affinity to formin ) ., Whether such differential control is something that is used by cells to tune the length of actin cables is an interesting open question ., The antenna mechanism is specified by four parameters , which can be estimated based on published experiments ., In fact , there are two published studies that measured rates of cable growth ., In an earlier study , Pon and colleagues measured the rate to be ~ 0 . 3–0 . 6 μm/s 18 ., In a later study , Wedlich-Soldner and colleagues used improved methods for imaging and quantifying cable growth rates ( employing TIRF microscopy in vivo ) and reported rates of ~ 1μm/s 19 ., The value r = 1μm/s ( for the polymerization rate when the formin is free of Smy1 ) we have adopted is based on the observed maximum rate of cable growth in vivo 19; in making this estimate we assume that the maximum growth rate corresponds to small cables for which the attenuation of growth by Smy1 is not significant and therefore the average polymerization rate is much greater than the depolymerisation rate and is roughly equal to the observed growth rate of the cable ., This value for the growth rate has also been independently confirmed by TIRF microscopy in our own lab ( Julian Eskin and B . G . , unpublished data ) ., In cell experiments GFP labelled Smy1 proteins are seen to pause at the bud neck for about a second in wild type cells 20 and so we estimate koff = 1/s for the rate of Smy1 falling off of the formins ., The myosin-aided delivery rate of Smy1 to the formin , leads to a length dependent on rate kon ( l ) = wl ., We estimate the value of the parameter w using the observed number of myosin+Smy1 complexes on the cable ., If we model the actin cable as a polymer with l subunits , at every subunit we can consider all the processes by which the myosin+Smy1 complexes arrive and depart the particular subunit ., In steady state the number of complexes arriving and departing need to balance ., In particular , myosin+Smy1 can either reach the xth subunit ( 1 < x < l ) diffusively from the cell cytosol with a rate, kon0, ( which is proportional to the concentration of Smy1 proteins ) , or by translocating from the x – 1 subunit , with a rate v . We assume that the motors do not fall off the polymer and therefore the only way that they leave the xth subunit is by translocating to subunit x + 1 ., At steady state , the number of complexes arriving and departing the xth subunit are equal and therefore the steady state number is, Nx=xkon0v, 24 ., Using this quantity we can compute the total number of motors ( myosin+Smy1 complexes ) on the polymer ( or cable ) by summing over all subunits: The

Introduction, Results, Discussion, Methods

Actin cables are linear cytoskeletal structures that serve as tracks for myosin-based intracellular transport of vesicles and organelles in both yeast and mammalian cells ., In a yeast cell undergoing budding , cables are in constant dynamic turnover yet some cables grow from the bud neck toward the back of the mother cell until their length roughly equals the diameter of the mother cell ., This raises the question: how is the length of these cables controlled ?, Here we describe a novel molecular mechanism for cable length control inspired by recent experimental observations in cells ., This “antenna mechanism” involves three key proteins: formins , which polymerize actin , Smy1 proteins , which bind formins and inhibit actin polymerization , and myosin motors , which deliver Smy1 to formins , leading to a length-dependent actin polymerization rate ., We compute the probability distribution of cable lengths as a function of several experimentally tuneable parameters such as the formin-binding affinity of Smy1 and the concentration of myosin motors delivering Smy1 ., These results provide testable predictions of the antenna mechanism of actin-cable length control .

Based on published cell experiments , we propose a novel mechanism of length control of actin cables in budding yeast cells ., The key feature of this “antenna mechanism” is negative feedback of the cable length on the activity of formins , which are proteins that attach to the growing ends of actin filaments and catalyse their polymerization ., We recently showed that the protein Smy1 is critical for maintaining proper cable length in yeast cells ., Smy1 proteins are delivered to the formins by directed motion of myosin motors toward the growing end , and they transiently inhibit actin cable polymerization when bound to the formins ., This provides negative feedback resulting in an average rate of cable assembly that diminishes with cable length ., Here we incorporate this antenna mechanism into a physical model of cable polymerization and provide experimentally testable predictions for the dependence of the length distribution of cables on the concentration of Smy1 , and on mutations that affect its affinity to formins .

journal.pgen.1003482

Genome-Wide Association Studies Identify Two Novel BMP15 Mutations Responsible for an Atypical Hyperprolificacy Phenotype in Sheep

There is strong evidence supporting the concept that oocyte plays a central role in follicle growth and developmental regulation 1 , 2 , 3 ., It has been established that ovary-derived transforming growth factor-ß ( TGFß ) family members play an integral role during folliculogenesis ., Indeed , the two paracrine factors Bone Morphogenetic Protein 15 ( BMP15 ) and Growth and Differentiation Factor 9 ( GDF9 ) stimulate follicle growth 4 , 5 , promote granulosa cell proliferation 6 , 7 , influence cell-survival signaling 8 , 9 and modulate other growth factors and hormones 10 , 11 , 12 ., The identification of BMP15 , GDF9 and Bone Morphogenetic Protein Receptor 1B ( BMPR1B ) gene mutations ( Table 1 ) as the causal mechanism underlying either the highly prolific or infertile phenotypes of several sheep breeds in a dosage-sensitive manner highlighted the crucial role these genes play in ovarian function ., The TGFß family member BMP15 was the first gene to be associated with prolificacy ., All the known BMP15 mutations ( named FecXI , H , B , G , L or R ) produce the same phenotype i . e . heterozygous ewes are highly prolific whereas homozygous females are infertile due to a blockade of follicular development at the primary stage 13 , 14 , 15 , 16 , 17 ., A second member of the BMP pathway , BMPR1B , was later found to be associated with prolificacy ., The genetic variant ( FecBB ) segregating in the Booroola Merinos breed presents an additive effect on ovulation rate ( OR ) and a partially dominant effect on litter size ( LS ) 18 , 19 , 20 ., For GDF9 ( known as FecG ) , also member of the TGFß family , the two first described mutations present a phenotypic inheritance pattern similar to all BMP15 variants 14 , 21 ., A third mutation in GDF9 ( FecGE ) associated with prolificacy was identified recently in the Brazilian Santa Inês strain 22 ., Interestingly , Silva et al . showed for the first time a novel phenotype since FecGE homozygous ewes are not sterile but exhibit a significant increase , compared to non mutated individuals , in their OR ( 2 . 22±0 . 12 vs . 1 . 22±0 . 11 ) and LS ( 1 . 78 vs . 1 . 13 ) 22 ., Similarly to sheep , a large number of mutations in the GDF9 and BMP15 genes have been described in women with fertility disorders ., The identification of GDF9 missense and nonsense mutations in premature ovarian failure ( POF ) patients 23 , 24 , 25 but also in mothers of dizygotic twins 26 , 27 , 28 suggests that altered GDF9 function is involved in ovarian dysfunction and polyovulatory phenotypes ., Indeed , 3 missense variants GDF9P103S , GDF9P374L and GDF9R454C were significantly associated with an increased OR in women 27 ., Critical roles of BMP15 in female fertility have also been demonstrated in women ., The first BMP15 mutation ( BMP15Y235C ) responsible for hypergonadotropic ovarian failure , due to ovarian dysgenesis , was detected in two sisters who inherited the variant from their father 29 ., A number of other mutations and rare deletions in those 2 genes have been described in women with POF 24 , 29 , 30 , 31 , 32 ., Recently , Hanevik et al . 33 confirmed results previously observed by Moron et al . 34 showing that a BMP15 variant ( BMP15−9G ) was associated with ovarian hyperstimulation syndrome ( OHSS ) , similar to effects observed in ewes with heterozygous BMP15 mutations ., Therefore , GDF9 and BMP15 are likely to alter OR and LS in women as well as in ewes ., Interestingly , BMP15 regulates ovulation rate and female fertility in a species-specific manner , being crucial in humans and sheep and largely trivial in mice since loss-of-function of BMP15 results only in subfertility 35 ., Although numerous mutations improving reproduction traits have been discovered in various sheep strains , the genetic variants have still to be identified for some other prolific sheep breeds ., The French Grivette and the Polish Olkuska breeds present good maternal characteristics and abnormally high LS and OR , respectively ., Moreover , both populations exhibit a very high variability of mean prolificacy among related females , suggesting the existence of an autosomal major gene for prolificacy segregating the same way as the FecBB mutation ( reviewed in 36 ) ., The FecBB or FecXI mutations do not account for the OR phenotype in Olkuska ewes 37 ., This work aims at identifying genetic variants affecting LS and OR phenotypes in the French Grivette and Polish Olkuska sheep populations , respectively ., A GWAS based on a case/control design followed by fine-mapping genetic analyses was performed ., We identified 2 novel non-conservative mutations in the BMP15 gene associated with an increase of LS and OR ., Following the nomenclature used for previous fecundity genes , these 2 mutations were named FecXGr in the Grivette population and FecXO in the Olkuska population ., Noteworthy , homozygous ewes of both strains are highly prolific but not sterile as known so far ., Our results bring new insights into the key role played by the BMP15 protein in ovarian function ., To identify loci associated with highly prolific phenotype in the French Grivette and the Polish Olkuska sheep breeds , a GWAS comparing allele frequencies between highly prolific ewes ( cases ) versus normally prolific ewes ( controls ) was conducted as outlined in Materials and Methods and Table S1 ., Genotype data from the Illumina OvineSNP50 Genotyping Beadchip was obtained for 39 ewes ( 28 cases vs . 11 controls ) and 63 ewes ( 29 cases vs . 34 controls ) from the Grivette and the Olkuska populations , respectively ., No significant genome-wide association after Bonferroni correction was found in the French Grivette breed although an association was suggested for a cluster of markers located on the X chromosome ( Figure 1A ) ., Indeed , 5 OARX SNPs , in a region close to BMP15 gene , present evidence of association at chromosome-wide level as shown on Figure 1C and in Table, 2 . For the Polish Olkuska breed , 2 and 4 markers located on OARX close to the BMP15 gene are significantly associated with OR at genome-wide and chromosome-wide levels , respectively ( Figure 1B , 1D and Table 2 ) ., To further characterize the X chromosome locus , we selected 87 markers spanning the 10 Mb region ( from 45 Mb to 55 Mb according to the OARv2 . 0 assembly available on ( http://www . livestockgenomics . csiro . au/sheep/ website ) and we determined the most likely linkage phase for each individual ., After haplotype clusterization for each population , we identified a specific segment that was more frequent in highly prolific ewes than in control individuals as shown on Figure 2A and 2B ., In the French Grivette population , a significant association was found for the 1 . 3 Mb ( 48240883 bp–49526964 bp ) haplotype containing 20 markers ( Frcases\u200a=\u200a0 . 95 vs . Frcontrols\u200a=\u200a0 . 54 , pcorrected\u200a=\u200a9 . 90E−03 ) ( Figure 2A ) ., Similarly , a different haplotype but located in the same region ( 48240883 bp–49381143 bp ) was shown significantly associated with the OR phenotype in the Polish Olkuska breed ( Frcases\u200a=\u200a0 . 76 vs . Frcontrols\u200a=\u200a0 . 27 , corrected\u200a=\u200a9 . 90E−04 ) ( Figure 2B ) ., The very close location of the identified region to the BMP15 gene ( 48140251 bp–48146740 bp ) and the crucial role of BMP15 in ovarian function prompted us to consider BMP15 as a positional and functional candidate gene ., The BMP15 coding sequence was obtained for all the individuals genotyped in GWAS for both sheep breeds ., In the French Grivette population , 3 polymorphisms were identified: the already known c . 28–30delCTT and c . 747C>T and a new c . 950C>T leading to the ΔL10 deletion , the P249P synonymous substitution and a non-conservative ( T317I ) substitution ( Figure S1A ) , respectively as shown in Table, 3 . The distribution of the BMP15T317I mutation strongly suggested its association with increased litter size since 26 out of the 28 highly prolific ewes were homozygous for the T mutated allele ( Table 3 ) ., To validate this hypothesis , the BMP15T317I genotype was added to the GWAS and a significant chromosome-wide association was found ( punadjusted\u200a=\u200a5 . 98E−06 , pGenome-wide corrected\u200a=\u200a2 . 83E−01 and pChromosome-wide corrected\u200a=\u200a7 . 61E−03 ) ( Figure S2A ) ., Preliminary genetic results were confirmed via the genotyping of 360 additional ewes randomly chosen in 9 flocks and then litter size means between the 3 genotypes at BMP15T317I ( C/C vs . C/T vs . T/T ) were compared as shown on Figure 3A ., Litter size of individuals with the T/T genotype ( n\u200a=\u200a119 , LS\u200a=\u200a2 . 50±0 . 65 ) was significantly higher than litter sizes of ewes with the C/C ( n\u200a=\u200a85 , LS\u200a=\u200a1 . 83±0 . 41 ) or the C/T ( n\u200a=\u200a195 , LS\u200a=\u200a1 . 93±0 . 42 ) genotypes ( p<1E−03 ) ., However , there was no difference between LS from C/T and C/C genotypes although a trend was observed ( p\u200a=\u200a0 . 06 ) ., These results were confirmed on the ovulation rate measured for a few ewes ( n\u200a=\u200a27 ) ( Figure 3C ) ., Additionally , 6 kb of the 5′ regulatory region of the BMP15 gene were sequenced in 2 extreme ewes ( homozygous C/C , LS\u200a=\u200a1 . 17 and homozygous T/T , LS\u200a=\u200a4 . 33 ) and no polymorphism was identified , therefore reinforcing the role of the BMP15T317I substitution ., A similar strategy was used for the Polish Olkuska breed , and 6 polymorphisms were identified including 5 new ones ., Among them , c . 231C>T and c . 330C>T led to synonymous substitutions ( A77A and L110L ) , and c . 301G>C , c . 404T>G and c . 1009A>C cause P101A , V135G and N337H non-conservative substitutions ( Figure S1B ) , respectively ( Table 3 ) ., Only the BMP15N337H mutation was shown to be more frequent in cases than in controls ( 0 . 81 vs . 0 . 26 ) ( Table, 3 ) and was significantly associated to OR at a genome-wide level ( punadjusted\u200a=\u200a2 . 55E−08 , pGenome-wide corrected\u200a=\u200a1 . 18E−03 ) ( Figure S2B ) ., Analysis of the genotypic distribution of the BMP15N337H polymorphism in additional Olkuska ewes confirmed the key role of the mutation on OR ., Indeed , the 3 groups of genotype showed statistically different ovulation rate means ( A/A , OR\u200a=\u200a1 . 52±0 . 26 vs . A/C , OR\u200a=\u200a2 . 02±0 . 47 , p<5E−02; A/A , OR\u200a=\u200a1 . 52±0 . 26 vs . C/C , OR\u200a=\u200a3 . 28±0 . 85 , p<1E−03 and A/C , OR\u200a=\u200a2 . 02±0 . 47 vs . C/C , OR\u200a=\u200a3 . 28±0 . 85 , p<1E−03 ) as shown on Figure 3B ., These results were validated on the litter size phenotype ( Figure 3D ) ., Polymorphisms were identified in the 6 kb 5′ regulatory region of the BMP15 gene of 4 extreme animals but none of them seemed to be associated with the high prolificacy emphasizing the interest of the BMP15N337H substitution ( Table S2 ) ., Altogether , this data suggests that the non-conservative BMP15T317I and BMP15N337H mutations , called FecXGr and FecXO , respectively , are causal mutations ., Both mutations are associated with increased litter size and ovulation rate in the French Grivette and Polish Olkuska sheep populations ., Interestingly , homozygous ewes are highly prolific and not sterile , a phenotype discordant from all BMP15 mutations described so far in sheep ., Both FecXGr and FecXO mutations are closely located into two very well conserved domains of the sheep , cow , pig , human and mouse BMP15 proteins ( Figure 4 ) ., Impacts of BMP15T317I and BMP15N337H variants on the intrinsic properties of the BMP15 protein were estimated by comparing hydrophobicity , polarity and molecular weight between mutated and wild type BMP15 proteins ., FecXGr , which corresponds to a substitution of a threonine to an isoleucine , clearly affected the hydrophobicity of the protein while FecXO altered the polarity and the molecular weight of the protein by replacing an asparagine to a histidine as shown on Figure S3 ., To evaluate the effects and then the causality of these two naturally occurring mutations on the BMP15 signaling activity , each mutated allele was introduced by targeted mutagenesis in the human BMP15 expressing plasmid ., The functional assay was realized in vitro using a human granulosa-derived COV434 cell line stably expressing a BMP-responsive element luciferase reporter construct , as previously described 32 ., The quantification of the luciferase activity after transient transfection was compared between plasmids expressing the wild-type allele , the mutated allele and a mix of both , mimicking the heterozygous status ( Figure 5 ) ., As positive controls , COV434 cells expressing the luciferase reporter gene showed a significant activation of the BMP signaling pathway when stimulated with 100 ng of the exogenous recombinant human BMP15 ( p<1E−03 ) , or when transiently transfected with the wild-type human BMP15 expressing construct ( p<1E−03 ) ., In comparison to the wild-type , the BMP15T317I mutation totally abolished the BMP15 signaling activity ., When the BMP15T317I plasmid was cotransfected with an equal amount of the wild-type form , the impaired luciferase activity was partially rescued ., Concerning the BMP15N337H mutation , it altered also the BMP15 signaling by half-reducing its activity ( p<1E−02 ) and this was totally rescued by the BMP15T317I/WT cotransfection ., This data shows that the two novel mutations drastically affect the signaling pathway of BMP15 and thus , supports their causal role into the highly prolific phenotype observed in the French Grivette and Polish Olkuska sheep populations ., Segregation of major genes increasing LS and OR was suspected in the French Grivette and the Polish Olkuska sheep flocks , respectively ., In the Olkuska breed , a previous study had rejected the hypothesis of the segregation of 2 already known mutations , FecXI and FecBB 37 ., Due to an atypical highly prolific phenotype with no sterile individual and no presence of streak ovaries in both breeds , a priori excluding the FecX gene , a GWAS based on a case/control design was performed to identify a causal locus in each population ., A significant association was found for a cluster of SNPs located on the X chromosome close to the BMP15 gene suggesting its possible involvement ., We identified 2 novel non-conservative mutations in the BMP15 gene called FecXGr and FecXO for the Grivette and the Olkuska variants , respectively ., However , due to the specific genetic background of each breed as well as the difference of measured traits ( OR vs LS ) , at this stage , the effect of the mutations appears to be slightly different for Olkuska and Grivette ., Noteworthy , FecXGr and FecXO led to highly prolific ewes at the homozygous status in contradiction with other BMP15 variants described so far ., Indeed , all the FecX variants ( 6 different mutated alleles ) were associated with an increased LS/OR in heterozygous animals but sterility in homozygous animals 13 , 14 , 15 , 16 ., Then , it was unexpected to identify mutations in the BMP15 gene exclusively responsible for a high prolificacy phenotype ., Nevertheless , an identical phenotypic effect has been recently shown for a new GDF9 allele called FecGE in the Brazilian Santa Ines sheep breed ., Silva et al . , 22 described for the first time , a polymorphism in the GDF9 gene that increases the ovulation rate ( 82% ) and prolificacy ( 58% ) in fertile homozygous ewes ., Therefore , our two novel mutations ( FecXGr and FecXO ) as well as FecGE are associated with a prolific phenotype in homozygous ewes ., Both FecXGr and FecXO mutations are closely located into two very well conserved domains of the sheep , cow , pig , human and mouse BMP15 proteins ., The BMP15 protein belonging to the TGFβ family is translated as a pre-proprotein which consists of a signal peptide , a large proregion and a mature peptide ( reviewed in 39 ) ., In sheep , 2 ( FecXR 16 , 17 and FecXG 14 ) and 6 ( FecXH 14 , FecXI 13 , FecXL 15 , FecXB 14 , FecXGr and FecXO ) BMP15 mutations have been found in the proregion and the mature protein , respectively ., The effect of sheep variants seems tightly related to the kind of mutations described ., Indeed , 3 out of the 8 mutations identified so far are aminoacids deletion ( FecXR 16 , 17 ) or premature stop codon ( FecXG and FecXH ) 14 in the BMP15 sequence impairing consequently the production of the BMP15 active form ., Our mutations are located in the mature BMP15 protein , on both sides of the FecXL mutation ., A putative misfolding inhibiting the maturation/production of the BMP15 protein was suspected in the case of the FecXL mutation which leads to a substitution of a cysteine with a tyrosine in one of the six conserved cysteines involved in the characteristic folding of the TGFβ factors 15 ., The identified FecXGr and FecXO mutations convert threonine to isoleucine and asparagine to histidine in the French Grivette and the Polish Olkuska sheep populations , respectively ., These two mutations clearly affect the intrinsic properties of the BMP15 protein since they correspond to substitutions of polar aminoacids by nonpolar and basic aminoacids suspected to modify consequently its three dimensional structure ., Functionally , both FecXGr and FecXO mutations clearly impaired the BMP15 signaling as shown by our in vitro test in COV434 cells ., Indeed , BMP15T317I induced a complete inhibition of BMP15 in vitro activity whereas the BMP15N337H only partially disturbed BMP15 signaling ., The partial loss-of-function observed for the BMP15N337H mutation fits well with the hyperprolific phenotype in homozygous Olkuska sheep following the dosage-sensitive hypothesis of BMP15 action on OR 13 , 40 ., In vitro effect of FecXGr on the BMP15 pathway is similar to the FecXL variant 32 although prolificacy phenotypes of homozygous ewes are opposite i . e . FecXL/FecXL animals are sterile whereas FecXGr/FecXGr individuals are highly prolific ., Such functional discrepancy has been already observed when comparing FecXL with other known non-conservative substitutions in the BMP15 mature peptide such as FecXI and FecXB ., Indeed , functional analyses of FecXI and FecXB mutations indicated that neither V299D nor S367I substitutions are able to alter the production , processing , homodimerization , or biological activity of the BMP15 protein 41 while homozygous animals are sterile ., The underlying mechanism occurs through the heterodimerization of BMP15 with the closely related oocyte-derived GDF9 protein 41 , 42 , 43 and their cooperative effect on granulosa cells 44 , 45 , 46 ., Although the FecXGr mutation totally impaired the BMP15 direct signaling , it would still maintain a biological activity through its interaction with GDF9 suggesting a normal folding of the BMP15 protein in contrast to the FecXL variant 15 ., Furthermore , we hypothesized that the BMP15T317I null mutation doesnt prevent the heterodimerization process with GDF9 compared to the FecXI variant for which the heterodimer signaling pathway has been implicated 41 , 42 , 43 ., To better understand molecular effects of both FecXGr and FecXO mutations , independent actions of BMP15 and GDF9 as well as their synergic role remains to be determined ., Based on the current knowledge , the mono ovulation quota seems to be tightly controlled by the triple action of, i ) BMP15 homodimer and its signaling pathway through BMPR1B , BMPR2 and SMAD1/5/8 47 ,, ii ) BMP15/GDF9 heterodimer signaling also through SMAD2/3 46 and, iii ) GDF9 homodimer signaling through TBR1 , BMPR2 and SMAD2/3 48 , 49 ( Figure 6A ) ., However , the integration of these 3 pathways under a threshold activity precociously blocks the folliculogenesis and leads to sterility ., This could be illustrated by the FecXL , H , G , R effect directly abolishing the BMP15 production 13 , 14 , 15 , 16 , 17 thus impairing at least BMP15 homodimer and BMP15/GDF9 heterodimer signaling ., Moreover , when the FecXI or FecXB mutants are coexpressed with normal GDF9 , the secretion of both BMP15 and GDF9 is significantly reduced 42 ., Although effects of FecGH and FecGT variants have never been tested in vitro , it is strongly thought that GDF9 loss-of-function mutations might alter both GDF9 homodimer and BMP15/GDF9 heterodimer pathways leading to infertile animals ., When the activity of the combined action of the 3 pathways is in between the sterility threshold and the normal level , i . e . heterozygous status , OR and LS are increased through modulation of granulosa cells proliferation and gonadotropin sensitivity 38 , 40 ., It is likely that Fecundity mutations resulting in hyperprolificacy without sterility might affect either totally the BMP15 , GDF9 homodimers signaling pathways or partially the homodimer and heterodimer signaling pathways ( Figure 6B and 6C ) ., Our FecXGr and FecXO as well as FecGE mutations support this hypothesis ., Indeed , the FecXO mutation would mainly have an effect on the BMP15/GDF9 heterodimer signaling via the heterodimerization process whereas the FecXGr mutation clearly impairs the BMP15 homodimer pathway through the BMP15 homodimerization or the binding to its receptor ( Figure 6D ) ., This assumption is close to the Booroola ( FecBB ) homozygous hyperprolific phenotype where a loss-of-function mutation altered the BMP15 receptor BMPR1B 50 and then suggested that only the BMP15 homodimer signaling pathway was affected ., Concerning the FecXGr mutation for which the abolishment of the Smad 1/5/8 pathway was observed , it is also possible that another BMP15 signaling pathway , the TAK1 pathway , remains functional maintaining a biological BMP15 activity 46 ., This assumption would explain why homozygous FecXGr/FecXGr ewes are not sterile ., The BMP15 protein has also been described in women to play critical roles in female fertility ., Indeed , a large number of mutations in the BMP15 gene have been identified in women with POF 24 , 29 , 30 , 31 and in patients with OHSS 33 , 34 but none are in common between women and sheep ., Interestingly , all the human BMP15 mutations described so far and involved in the POF syndrome were found at a heterozygous status ., Therefore , heterozygous carriers exhibit an ovarian phenotype 29 similar to sterile homozygous FecX ewes which present an early folliculogenesis arrest , ovarian dysgenesis and streak ovaries 13 , 15 ., In contrary , heterozygous and homozygous ewes carrying FecXGr or FecXO mutations show an increase of LS and OR with normal and functional ovaries ., Our data suggest that these two variants are novel kind of BMP15 mutations responsible for a new prolificacy phenotype as it was already shown in the case of the FecGE allele 22 ., Noteworthy , BMP15 was recently involved in women suffering of OHSS since a BMP15 variant ( BMP15−9G ) was associated with this disorder 33 , 34 , similarly to reported effects of heterozygous BMP15 mutations in sheep ., Although the connection between the BMP15−9G SNP and OHSS was mainly attributable to the considerably high number of heterozygous patients , a few women cases BMP15−9G/BMP15−9G homozygous were also identified and contribute to the significant signal obtained 33 ., Therefore , the atypical phenotypic effect ( high prolificacy ) observed in ewes carrying FecXGr or FecXO mutations seems somewhat comparable to the OHSS in women at both heterozygous and homozygous status ., Further studies need to be conducted to characterize the physiological ovarian phenotype of the highly prolific Grivette and Olkuska individuals ., In summary , our results bring new insights into the key role played by the BMP15 protein in ovarian function ., The BMP15 gene is almost the only gene identified to date whose mutations in mammals as sheep and human result in impaired early folliculogenesis and POF as well as excessive ovulation rate and OHSS ., Although the BMP15 biological effects might be specie-specific , parallels between phenotypes in women and sheep carrying BMP15 mutations emphasize the importance of the sheep model to further determine the involvement of BMP15 in ovarian physiology and pathophysiology in women ., The French Grivette population is a local breed mainly located in Rhône and Loire French departments ., Ewes present naturally good maternal characteristics and a high prolificacy with more than 2 newborns per dam and per year ., The very high variability of LS among related ewes suggested the segregation of a major gene affecting this trait ., To test this assumption , 29 highly prolific ewes ( LS≥2 . 7 ) considered as cases and 11 normally prolific ewes ( LS≤1 . 8 ) considered as controls were selected to perform a genome-wide association study ( GWAS ) using a case/control design ., The Polish Olkuska population is a native Polish long-wool breed , traditionally kept in the southern region of the country , near Olkusz and Cracow ., The Olkuska is an endangered prolific breed which in 90thies comprised only about 100 ewes dispatched in a few flocks , two of them belonging to Cracow and Warsaw Agricultural Universities that ensured the survival of this breed ( reviewed in 36 ) ., At present , due to a successful conservation program , the population increased to over 800 ewes ., The main feature of the breed is its exceptional prolificacy , well over 200% , with also a very large variability of LS and OR for related animals ., A wide range of mean individual OR ( from 1 . 33 on 9 records to 6 . 20 on 15 records ) led to assume the segregation of a putative major gene increasing OR ., 29 cases ewes with high repeated OR ( μ≥3 . 00 ) and 35 controls ewes with normal OR ( μ≤2 . 00 ) were chosen to conduct a GWAS ., Ovulation rates have been determined by laparoscopy ., The laparoscopy was performed using a 5 mm diameter Vega S endoscope by experienced operators ., The description of the study design is presented in Table S1 ., To confirm the effect of mutations on LS and OR in both Grivette and Olkuska populations , additional individuals have been genotyped and phenotyped ., In total , 360 Grivette ewes coming from 9 flocks for which LS had been measured at least twice have been analyzed ., In addition , laparoscopies performed at least 3 times have been obtained on 27 Grivette ewes in order to validate the effect of the mutation on OR ., For the Olkuska population , the effect of the mutation on OR has been estimated from 103 ewes checked by laparoscopy ( n>3 ) and LS data ( n>2 ) have been retrieved for 137 animals ., Jugular venous blood samples were collected by venipuncture ., Genomic DNA was extracted from blood samples following a salt-based DNA extraction as described in 15 ., For the Grivette population , all procedures were approved by the “Direction Départementale des Services Vétérinaires de Haute-Garonne” ( approval number C31-429-01 ) for the agricultural and scientific research agency INRA ( French National Institute for Agricultural Research ) , and conducted in accordance with the Guide for the Care and Use of Agricultural Animals in Research and Teaching ., For the Olkuska population , all procedures were performed with permission of the animal welfare commission from the University ., Genotyping for the whole genome scan was performed on the Illumina OvineSNP50 Genotyping Beadchip according to the manufacturers protocol ( http://www . illumina . com ) ., Individuals with a Call Rate <0 . 98 were excluded ., SNPs were excluded if they showed a Call Freq <0 . 98 , a minor allele frequency ( MAF ) <0 . 01 in cases or controls or significant deviation from Hardy-Weinberg equilibrium ( HWE ) in the controls ( p<1E−06 ) ., Non polymorphic SNPs and markers with no position on the OARv2 . 0 map ( http://www . livestockgenomics . csiro . au/sheep/ ) were also eliminated ., Finally , an initial design of 54241 SNPs available on the Illumina OvineSNP50 Genotyping Beadchip and 104 individuals ( 40 and 64 ewes of the French Grivette and Polish Olkuska protocols respectively ) was reduced to final datasets of 47290 SNPs analysed in 39 individuals for the French Grivette population and 46451 SNPs analysed in 63 individuals for the Polish Olkuska population for further analyses as showed in Table S1 ., Genotyping of both FecXGr and FecXO mutations was performed by allele-specific amplification using the KASPar SNP genotyping system and followed by fluorescence detection on a ABI7900HT ., KASPar assays were carried out in 5 µL reactions according to the KBioscience published conditions ( http://www . kbioscience . co . uk/ ) ., The primers used in this study are listed in Table S3 ., The most likely linkage phase for each individual was determined using a coalescent theory approach under a Bayesian population genetic model implemented in the PHASE program 51 , 52 ., Long-range PCR amplifications were performed using the Long PCR Enzyme Mix provided by Fermentas ( http://www . fermentas . de ) and internal primers were used for the sequencing reaction realized via the BigDye Terminator v3 . 1 Cycle Sequencing Kit ( http://www . appliedbiosystems . com ) ., The primers used in this study are listed in Table S3 ., BMP15 gene ovine variants ( FecXGr/oBMP15T317I and FecXO/oBMP15N337H ) were introduced by site-directed mutagenesis into the pCShBMP15wt vector , containing a full-length human BMP15 wild-type cDNA leading to the hBMP15T316I and hBMP15N336H , respectively ., For the sake of simplicity , the numbering of ovine BMP15 was kept all along the manuscript ., As previously described 32 , mutagenesis reaction for each variant was performed using the QuickChange Site-Directed Mutagenesis kit ( Stratagene ) and specific couples of primers ( Table S3 ) ., A COV434 human granulosa cells line stably expressing the BMP responsive element ( BRE ) - luciferase reporter was transiently transfected with BMP15 expressing vectors as already described by Rossetti et al . 32 ., Briefly , COV434 cells were transfected in triplicate with 500 ng/well of pCShBMP15 vector expressing the wild-type or the mutant each alone or in equal combination ( 250 ng each ) by using Fugene HD ( Roche Applied Sciences , Indianapolis , IN ) ., Cells were also transfected with the pCS2 empty vector and treated or not by 100 ng/ml of rhBMP-15 ( R&D Systems , Minneapolis , MN ) , as positive or negative control , respectively ., Forty hours after transfection , cells were lysed in Passive Lysis Buffer and assayed for luciferase activity using the Dual Luciferase reporter Assay kit ( Promega ) ., Luminescence in relative light units ( RLU ) was measured in a Fluoroskan Ascent instrument ( Labsystems , Oy , Finland ) ., Single-marker association analyses were conducted using a Fishers exact test and a Bonferroni correction has been applied to check for significance levels ., The chromosome-wide and genome-wide values have been established as mentioned by Balding et al . 53 ., Briefly , Bonferroni correction was applied to both the genome-wide and chromosome-wide analyses ., The p-values generated were evaluated according to an adjusted significance threshold generated by dividing the 0 . 05 threshold by the total number of tests ( number of SNPs considered ) performed in each case ( whole genome or whole chromosome ) ., Statistical analyses were done using the PLINK software 54 ., Haplotypic association analysis specifying all haplotypes in sliding windows of a fixed number of SNPs ( n\u200a=\u200a20 ) was also performed for X chromosome locus using chi-squared and logistic regression methods similar to other recent approaches 55 , 56 ., Empirical significance levels were calculated using the maximum statistic permutation approach ( max ( T ) , n\u200a=\u200a1000 ) ., For genotype effect on OR or LS and reporter luciferase assays , differences between means were analyzed by one-way ANOVA followed by Neuman-Keuls post-hoc test to compare between conditions ., p<5E−02 was considered statistically significant ., All the results were presented as means±SD .

Introduction, Results, Discussion, Materials and Methods

Some sheep breeds are naturally prolific , and they are very informative for the studies of reproductive genetics and physiology ., Major genes increasing litter size ( LS ) and ovulation rate ( OR ) were suspected in the French Grivette and the Polish Olkuska sheep populations , respectively ., To identify genetic variants responsible for the highly prolific phenotype in these two breeds , genome-wide association studies ( GWAS ) followed by complementary genetic and functional analyses were performed ., Highly prolific ewes ( cases ) and normal prolific ewes ( controls ) from each breed were genotyped using the Illumina OvineSNP50 Genotyping Beadchip ., In both populations , an X chromosome region , close to the BMP15 gene , harbored clusters of markers with suggestive evidence of association at significance levels between 1E−05 and 1E−07 ., The BMP15 candidate gene was then sequenced , and two novel non-conservative mutations called FecXGr and FecXO were identified in the Grivette and Olkuska breeds , respectively ., The two mutations were associated with the highly prolific phenotype ( pFecXGr\u200a=\u200a5 . 98E−06 and pFecXO\u200a=\u200a2 . 55E−08 ) ., Homozygous ewes for the mutated allele showed a significantly increased prolificacy ( FecXGr/FecXGr , LS\u200a=\u200a2 . 50±0 . 65 versus FecX+/FecXGr , LS\u200a=\u200a1 . 93±0 . 42 , p<1E−03 and FecXO/FecXO , OR\u200a=\u200a3 . 28±0 . 85 versus FecX+/FecXO , OR\u200a=\u200a2 . 02±0 . 47 , p<1E−03 ) ., Both mutations are located in very well conserved motifs of the protein and altered the BMP15 signaling activity in vitro using a BMP-responsive luciferase test in COV434 granulosa cells ., Thus , we have identified two novel mutations in the BMP15 gene associated with increased LS and OR ., Notably , homozygous FecXGr/FecXGr Grivette and homozygous FecXO/FecXO Olkuska ewes are hyperprolific in striking contrast with the sterility exhibited by all other known homozygous BMP15 mutations ., Our results bring new insights into the key role played by the BMP15 protein in ovarian function and could contribute to a better understanding of the pathogenesis of women′s fertility disorders .

Although causes altering reproductive function and leading to several fertility syndromes in women are various , a clear association between mutations in some members of the TGFβ family ( BMP15 and GDF9 ) and abnormal ovarian phenotype has established the importance of these factors for normal female fertility ., Some sheep breeds are naturally prolific since they carry major genes affecting ovulation rate and litter size ., These species are therefore unique informative models for the study of reproductive genetics and physiology ., Through a GWAS conducted in two sheep breeds using highly prolific ewes as cases and normal prolific ewes as controls , we identified an X chromosome locus close to the BMP15 gene in both breeds ., Two novel non-conservative BMP15 mutations , one in each population , located in very well conserved domains of the protein were associated with the phenotype at heterozygous and homozygous status ., Moreover , we showed that both mutations altered the BMP15 signaling activity , suggesting a novel kind of BMP15 variant responsible for an atypical high prolificacy , in contrast to all other BMP15 variants described so far ., Our findings suggest an additional role of the BMP15 protein in folliculogenesis and could contribute to a better understanding of the pathogenesis of women′s fertility disorders .

genome-wide association studies, medicine, mutation, haplotypes, animal genetics, reproductive system, genetic mutation, genetic polymorphism, genetics, population genetics, biology, anatomy and physiology

journal.pntd.0001771

Seroprevalence of Chagas Infection in the Donor Population

Chagas Disease is a common and economically devastating disease of Latin America , with an estimated infected population of over 7 million and over 100 million at risk 1 ., Despite the significant number of immigrants from Chagas-endemic regions , prevalence data outside of its countries of origin remains limited 2–5 ., Estimates of prevalence in non native areas are challenging given the asymptomatic nature of chronic Chagas Disease , the lack of familiarity of local physicians with this imported disease 6 , and the often undocumented immigration status of some infected patients ., As a result , no large scale seroprevalence studies of immigrant populations have been done ., Instead , many studies have followed a model first seen in Chagas endemic populations where the seroprevalence of Chagas infection in blood donors was used as proxy for overall population prevalence ., However , donor seroprevalence of Chagas infection has been reported only from a limited set of populations , and epidemiologic associations of the donors are often lacking ., We therefore retrospectively calculated Chagas infection seroprevalence and individual epidemiologic characteristics of infected patients in the greater New York blood donor population ., The study was approved by the Institutional Review Board of both the New York Blood Center and Weill Cornell Medical Center ., All data were analyzed anonymously ., The New York Blood Centers database of the New York metropolitan area donor population was used to calculate the prevalence of Chagas infection in the general donor population ., Chagas positivity was defined as a positive enzyme-linked immunosorbent assay ( ELISA ) screen ( using the T . cruzi test system from Ortho Clinical Diagnostics in Raritan , NJ ) with subsequent radioimmunoprecipitation assay ( RIPA ) confirmation ( from Quest Diagnostics in Madison , NJ ) ., The data set covered April 2007 to March 2010 ., Screening started in April 2007 so 2007 was adjusted to match the March to March 12 month period of other years by assuming the average monthly number of Chagas positive cases in 2007 continued for one more month ., Collected variables included Sex , Racial/Ethnic Background , and Home Zip code , which were originally collected on the Blood Centers standard intake questionnaire given to individual donors ., Seventy Trypanosoma cruzi positive unique donors were identified from among 876 , 614 donors over a 3 year period , giving an adjusted prevalence of 0 . 0083% , with 0 . 0080% in 2007 , 0 . 0073% in 2008 , and 0 . 0097% in 2009 ., When filtered only for self-described “Hispanic/Latino” donors , there were 52 Chagas positive donors in that 3 year period ( from a sample of 105 , 122 self-described Hispanic donors ) with an adjusted prevalence of 0 . 052% , with 0 . 055% in 2007 , 0 . 047% in 2008 , and 0 . 053% in 2009 ., The remaining 18 Chagas positive donors described themselves as either “Black” ( 1 ) or selected no Racial/Ethnic Background ( 17 ) ., Age range was 17 to 76 ( median 43 ) and there were slightly more Females ( 54% ) ., When mapped by zip code , the Chagas positive donor contact addresses showed a geographical concentration in one New York metropolitan area , with one notable city in that area seeing a cluster of Chagas positivity ., Figure 1 shows one such concentration in Eastern Long Island , mapped on to 2000 Census data ., We found a persistent and possibly increasing population of patients with Chagas infection in the New York City Blood Donor population ., Intriguingly , Chagas positivity appears to cluster in a limited set of geographic locations of that population ., This study expands what was previously known about Chagas prevalence outside its endemic regions , particularly in the United States ., Previous studies have described the prevalence of Chagas infection in the donor population of Spain ( 0 . 62% ) 4 , Mexico ( 0 . 75% ) 7 , citing two examples , but the only detailed published U . S . data is from a sample set from 1994–1998 , showing a 0 . 19% prevalence in Los Angeles and 0 . 08% in Miami 5 ., The CDC has published more recent data in 2007 but with no detailed description of donor characteristics 8 ., We also found geographic clustering of the donor population in areas with high Foreign Born Hispanic immigrant populations ., For example , Eastern Long Island is unique in its large ( 50 k+ ) population of native Salvadorans 9 , which may be mirrored by the geographic clustering of the positive donors in that area ( please see Figure 1 ) ., Future efforts at identification of Trypanosoma cruzi infected populations may benefit from this donor-population derived “map” of areas of probable increased population Chagas prevalence ., This has already been seen in Europe , where two studies , one In Spain and other in Switzerland , targeted high risk immigrant populations with direct screening ( not during blood donation ) and found a much higher seroprevalence than previously expected ., They both confirmed , for example , that the Bolivian immigrant population is at particularly high risk for Chagas infection and merits focused outreach 10–11 ., Additionally , while neither study looked at the economics of such screening , other studies indicate that even broader screening may make economic sense 12 ., This study has several limitations ., The prevalence in the Hispanic/Latino group may be underestimated due to lack of race self-identification among many donors , as 24% of Chagas positive donors did not indicate race and therefore could not be included in the “Hispanic/Latino” only results despite most studies indicating there are very few non-Hispanics with Chagas ., Thus , the Hispanic/Latino prevalence could be as high as 0 . 067% over all three years if all the Chagas positive patients were in fact Latino ., In addition , the donors country of origin was not included in the questionnaire , and the Hispanic/Latino population in the study database was not segregated by place of birth ., The Hispanic/Latino population in New York City includes Dominicans and Puerto Ricans ( the largest Foreign born and the largest non Foreign born Hispanic groups in New York City , respectively 9 ) , groups not at high risk of Chagas positivity ., Otherwise our data may have better mirrored the overall trend of increasing positivity , as seen in earlier , larger studies 4 ., This increase would be consistent with the rise in immigration in the last decade of particular populations ( i . e . rural Mexicans ) with known higher Chagas positivity 13 ., Also of note , blood donor populations do not necessarily mirror society as a whole 14 ., However , this has been an accepted practice even in areas of highest Chagas seroprevalence given the difficulty of getting blood samples for the population most likely to be exposed to T . cruzi 15 ., Finally it is important to note that no clinical follow up was available ( Blood Center protocol is limited to referring them to an infectious disease physician ) , and thus we were unable to ascertain if any of the seropositive Donors were symptomatic ., These results indicate further analysis and outreach is warranted ., Chagas Disease is an infection with both asymptomatic latency and debilitating sequelae in a substantial minority of infected patients ., Identification , monitoring , and possible treatment of infected persons are best done through targeted identification and testing of at risk population groups ., Diagnosis of Chagas infection in blood donors captures only a segment of the population infected with imported Chagas Disease ., Characterization of high prevalence communities through blood donor seroprevalence suggests that follow up larger scale community-focused screenings of foreign-born populations could be both lifesaving and cost effective .

Introduction, Methods, Results, Discussion

We retrospectively calculated the prevalence and epidemiologic characteristics of Chagas infection in the New York blood donor population over three years utilizing the New York Blood Centers database of the New York metropolitan area donor population ., Seventy Trypanosoma cruzi positive donors were identified from among 876 , 614 donors over a 3-year period , giving an adjusted prevalence of 0 . 0083% , with 0 . 0080% in 2007 , 0 . 0073% in 2008 , and 0 . 0097% in 2009 ., When filtered only for self-described “Hispanic/Latino” donors , there were 52 Chagas positive donors in that 3-year period ( among 105 , 122 self-described Hispanic donors ) with an adjusted prevalence of 0 . 052% , with 0 . 055% in 2007 , 0 . 047% in 2008 , and 0 . 053% in 2009 ., In conclusion , we found a persistent population of patients with Chagas infection in the New York metropolitan area donor population ., There was geographic localization of cases which aligned with Latin American immigration clusters .

Chagas Disease is a common and economically devastating disease of Latin America , with millions infected and many more at risk of infection ., The hallmark of Chagas Disease is a long asymptomatic latent period ( after an often tiny bug bite ) followed by potentially fatal cardiac or gastrointestinal sequelae ., Despite the significant number of immigrants from Chagas-endemic regions , prevalence data outside of its countries of origin remains limited ., Our study looks at Trypanosoma cruzi infection in one group , blood donors in the New York metropolitan area , as this was a non invasive way to sample a sometimes difficult-to-reach population ., We found that Chagas infection is in fact present , particularly in the Hispanic donors , at a consistent level over the three years we studied ., We then compared the blood donor locations to a map of foreign born Hispanics in eastern Long Island in New York and found overlapping concentrations ., This may mean that there is an opportunity for large scale community-focused screenings of foreign-born populations that could be both lifesaving and cost effective .

medicine, disease mapping, infectious diseases, public health and epidemiology, chagas disease, epidemiology, infectious disease epidemiology, neglected tropical diseases, infectious disease control, transfusion medicine, parasitic diseases, hematology

journal.pgen.1005642

Spindle-F Is the Central Mediator of Ik2 Kinase-Dependent Dendrite Pruning in Drosophila Sensory Neurons

The precise assembly of neural circuits is crucial for the nervous system to function properly ., The developing nervous systems often start with a primitive prototype , characterized by exuberant branches and excessive connections ., Thus , further remodeling is required to refine the developing nervous systems to maturity ., Neuronal pruning , one such remodeling mechanism , is a highly regulated self-destruct process that eliminates excessive neuronal branches in the absence of cell death ., Pruning is widely observed in the nervous systems of both vertebrates and invertebrates 1 , 2 , that not only ensures precise wiring during development , but also allows for adjustment of neuronal connections in response to injury and disease ., Various studies have shown that defects in developmental pruning affect the function of the nervous systems in C . elegans 3 and Drosophila 4 ., Moreover , a progressive loss of neurites far ahead of cell death is commonly observed in many neurodegenerative disorders 5 , 6 ., Thus , any dysregulation of pruning activity even at the level of individual neurons would bring catastrophic consequences to the nervous systems ., Although the primary triggers for developmental pruning and pruning that ensues upon neuronal injury and disease are diverse , the downstream machinery that eliminates neuronal processes shared some common features ., For example , microtubule disruption is the earliest cellular event observed in all types of pruning 2 , 7 , 8 , and the ubiquitin-proteasome system is required in all circumstances 7–10 ., During Drosophila metamorphosis , substantial neuronal remodeling takes place in both the central and peripheral nervous systems 11–14 ., Most of the larval peripheral neurons die during metamorphosis , whereas few , including some class IV dendritic arborization ( C4da ) neurons , survive and undergo large-scale dendrite pruning 13 , 14 ., Dendrite pruning of the dorsal C4da neuron ddaC starts with severing of the proximal dendrites at 4–6 h APF ( after puparium formation ) 15 ., Subsequently these disconnected dendrites become fragmented and eventually eliminated by the surrounding epidermal cells 16 by 16–18 h APF ., In contrast to the central brain mushroom body ( MB ) γ neurons where both larval dendrites and axons are pruned during development , the peripheral C4da neurons specifically prune their dendrites keeping the axons intact 14 ., The molecular basis for how the pruning activity is confined to the dendrites of C4da neurons remains unknown ., We reasoned that molecular differences between dendrites and axons should be considered for such differential pruning activity in C4da neurons ., It is known that microtubule polarity is different in the dendrites and axons of neurons 17 , including in the Drosophila sensory neurons 18 ., For example , C4da neurons have polarized microtubules in their proximal dendrites predominantly with microtubule minus end pointing away from the cell body , but have an opposite polarity in their axons 18 , 19 ., This difference in microtubule polarity is essential for maintaining the proper function and compartmental identities of dendrites and axons , and might be an important determinant for spatially restricting pruning activity in the dendritic compartments of C4da neurons ., Based on this assumption , some molecules are required to connect the pruning activity with the distinctive microtubule polarity of the dendrites in C4da neurons during dendrite pruning ., Previous studies have shown that dendrite pruning in C4da neurons is initiated by the steroid hormone ecdysone and its heterodimeric receptors , ecdysone receptor B1 ( EcR-B1 ) and Ultraspiricle ( Usp ) 13 , 14 ., Through transcriptional regulation of sox14 , ecdysone signaling activates the Sox14 target gene mical , which encodes a cytoskeletal regulator , to regulate dendrite pruning 20 ., A few other molecules mediating specific cellular activities have been shown to participate in dendrite pruning of C4da neurons , such as the ubiquitin-proteasome system 14 , caspases 21 , 22 , matrix metalloproteases 14 , microtubule severing proteins 15 and mediators of dendritic calcium transients 23 ., Our previous studies identified Ik2 kinase , a homologue of vertebrate IKK- ε ιν Drosophila , that plays an essential role in dendrite pruning of pupal neurons , and further demonstrated that Ik2 is sufficient to induce precocious dendrite severing in larval neurons 15 ., To our knowledge , Ik2 is the only known molecule sufficient to induce premature dendrite severing in larvae , reflecting a central role of Ik2 kinase in dendrite pruning ., Therefore , in this study we aimed to elucidate the mechanism by which Ik2 kinase signaling is transduced and regulated in Drosophila sensory neurons during dendrite pruning ., As Ik2 is essential for dendrite pruning , to elucidate the mechanism of Ik2 kinase signaling , we searched for candidate molecules that mediate Ik2 signals during dendrite pruning ., Several lines of evidence suggested that Spn-F , a coil-coiled protein , is a good candidate ., Firstly , spn-F mutant flies showed defects in developing oocytes and bristles 24 , similar to the phenotypes observed in ik2 mutants 25 ., Secondly , Spn-F physically interacts with Ik2 26 ., It implied that ik2 and spn-F may act in the same pathway during oogenesis and bristle morphogenesis , and raised the possibility that a similar pathway might also be involved in dendrite pruning of C4da neurons ., Here , we demonstrate Spn-F playing a key role in linking Ik2 kinase to microtubule motor dynein complex for dendrite pruning ., Spn-F acts downstream of Ik2 kinase in the same pathway for dendrite pruning ., We show that Spn-F displays a punctate pattern in larval neurons and these Spn-F puncta become dispersed in pupal cells ., The redistribution of Spn-F from puncta is essential for dendrite pruning , and depends on the activity of Ik2 kinase and the function of microtubule motor dynein complex ., Our data also demonstrate that Spn-F not only links Ik2 to dynein motor complex , but also mediates the formation of Ik2/Spn-F/dynein complex , that is critical for Spn-F punctum disassembly and dendrite pruning ., To examine the role of spn-F in dendrite pruning , we expressed spn-F double-strand RNAs ( dsRNAs ) under the control of class IV-specific ppk-GAL4 27 to reduce endogenous Spn-F expression ., By 18 h APF wild-type neurons have pruned their dendrites ( Fig 1A ) , however the primary dendrites remained connected to the cell body of C4da neurons with spn-F RNAi ( RNA interference ) ( Fig 1B ) ., We observed a similar phenotype in spn-F loss-of-function mutants ( spn-F2 , 24 ) ( Fig 1C ) ., Dendrite severing was likewise suppressed in neurons of spn-F2/Df mutants ( Fig 1D ) , which carry the spn-F2 allele and a deficiency uncovering the entire spn-F gene locus ., To confirm that these pruning defects were due to loss of spn-F from C4da neurons , we expressed the full-length spn-F-GFP directed by ppk-GAL4 in spn-F2 mutants and found that the impaired dendrite pruning was rescued ( Fig 1E and 1F ) ., Moreover , we also examined the dendritic morphology of larval C4da neurons of spn-F and ik2 mutants , and found that dendrites develop normally in both mutant neurons ( S1 Fig ) 15 , suggesting that the pruning defects observed in both mutants are not secondary to abnormal dendrite development ., Taken together , these results indicated that spn-F is required for dendrite pruning in C4da neurons ., Since ik2 and spn-F mutant neurons displayed the same phenotype , we hypothesized that both genes function in the same pathway during dendrite pruning ., To test this hypothesis , we performed genetic analyses between ik2 and spn-F ., It was known that ik2 over-expression in larval C4da neurons causes cell death 15 ., To avoid excessive apoptosis , we employed ppk-GAL4 coupled with its temperature-sensitive inhibitor GAL80ts to achieve spatial and temporal ik2 expression in larval neurons by shifting temperature ., Consistent with previous studies 15 , no abnormality was detected in larval neurons under permissive temperature 25°C ( Fig 2A ) ., After shifting to non-permissive temperature 29°C , ik2 overexpression not only triggered precocious dendrite severing ( Fig 2B ) , but also caused apoptosis ( Fig 2C ) in wild-type larval neurons ., Interestingly , we found that both precocious dendrite severing and apoptosis caused by ik2 overexpression were significantly suppressed in neurons of spn-F mutants ( Fig 2D ) , suggesting that spn-F functions downstream ( or in parallel ) of ik2 in dendrite pruning ., Additionally , we found no significant difference between the dendrite pruning defects observed in spn-F2 mutants and that in spn-F2 mutants with ik2 RNAi ( Fig 2E ) , indicating that both ik2 and spn-F act in the same pathway of dendrite pruning ., Together , our findings suggested that spn-F acts downstream of ik2 in the same pathway during dendrite pruning ., Given that the spn-F-GFP transgene could rescue observed defects in spn-F mutants , we concluded that this transgene could functionally substitute for the endogenous spn-F gene ., Therefore , studying the role of Spn-F-GFP in C4da neurons should help us to uncover the molecular function of endogenous Spn-F in dendrite pruning ., First , we examined the distribution of Spn-F-GFP in larval and pupal neurons ., The Spn-F-GFP proteins displayed a punctate pattern in the soma , dendrites and axons of larval C4da neurons ( Fig 3A and S2A Fig ) ., However , those punctate Spn-F-GFP proteins were redistributed in the soma of pupal neurons at 5 h APF ( Fig 3B ) ., Since both Ik2 kinase and ecdysone are required for dendrite pruning 15 , we asked whether Ik2 kinase activity and ecdysone signaling regulate the dispersion of Spn-F-GFP puncta in pupal neurons during pruning ., To test this possibility , we examined the Spn-F-GFP distribution in mutant pupal neurons with expression of ik2-RNAi , kinase-dead mutant ik2-G250D 28 or dominantly negative ecdysone receptor ( EcR-DN ) , and found that the Spn-F-GFP puncta remained intact in all three mutant neurons at 5 h APF ( Fig 3C–3E ) ., These results indicated that there is a negative correlation between Spn-F puncta and dendrite pruning , and both Ik2 kinase and ecdysone signaling are required to redistribute Spn-F from puncta in neurons during dendrite pruning ., Moreover , we also found that redistribution of Spn-F from puncta was also observed in the larval C4da neurons with Ik2 , but not with Ik2-G250D , overexpression ( S2F–S2I Fig ) ., To gain mechanistic insight into the redistribution of Spn-F from puncta , we performed live-cell imaging to monitor Spn-F-GFP distribution in the same C4da neurons from larvae to pupae ( S1 and S2 Movies ) ., The live imaging and signal profiling of Spn-F-GFP showed high punctate and low dispersed signals in the cytosol of larval neurons ( S2B and S2E Fig ) ., We quantified the numbers and the sizes of Spn-F puncta in single live ddaC neurons , and found both numbers and sizes of Spn-F puncta decrease along the pupation time ( Fig 3F ) ., In contrast , the dispersed Spn-F-GFP signals in the cytosol of pupal neurons increased as the punctate signals decreased ( S2C–S2E Fig ) ., We quantified the averaged fluorescent intensity of dispersed cytosolic Spn-F-GFP signals in the soma , and measured the fold changes of these signals in the same neurons at various time points ., In the cytosol of wild-type C4da neurons , the dispersed cytosolic GFP signals started to increase soon after pupation ( Fig 3G ) , peaked at 1 h APF , and decreased to a steady state after 2 h APF ., We noticed that these dispersed cytosolic signals measured in pupal neurons were all higher than those in larval cells ( Fig 3G ) ., Furthermore , this prompt increase of dispersed Spn-F-GFP signals in wild-type neurons was not observed in the mutant neurons with ik2-RNAi , ik2-G250D , or EcR-DN expression ( Fig 3G ) , indicating that Ik2 kinase and ecdysone signaling promote the increase of dispersed Spn-F in early pupal neurons ., Next , we assessed Ik2 kinase activities in C4da neurons by staining with antibodies against phosphorylated Ik2 ( P-Ik2 ) on serine 175 , whose phosphorylation is essential for Ik2 activation 29 ., Consistent with the observation of maximum dispersed Spn-F-GFP signals in the cell body of pupal neurons at 1 h APF ( Fig 3G ) , strong P-Ik2 signals were detected in C4da neurons at the same stage ( Fig 3I and 3L ) , whereas no specific signals were found in larval cells ( Fig 3H and 3L ) or in mutant neurons expressing Ik2-G250D ( Fig 3J and 3L ) ., These results indicated that Ik2 kinase activity is upregulated in early pupal neurons , but downregulated in larval neurons ., Since transiently increasing Ik2 expression caused precocious dendrite severing of larval neurons ( Fig 2B ) 15 , we concluded that elevation of Ik2 activity in pupal neurons initiates dendrite severing , and suppression of Ik2 activity in larval neurons prevents premature severing ., We also examined P-Ik2 signals in mutant neurons with disrupted ecdysone signaling and failed to detect activated Ik2 signals ( Fig 3K and 3L ) , indicating that Ik2 kinase acts downstream of ecdysone signaling in dendrite pruning ., Taken together , these data demonstrate that Ik2 kinase is activated promptly in early pupal neurons and regulates Spn-F distribution in C4da neurons during dendrite pruning ., The inverse correlation between Spn-F puncta and dendrite pruning suggests that redistribution of Spn-F from puncta in C4da neurons might be a critical event for dendrite pruning ., If this is true , a mutant Spn-F that remains punctate and disperse-resistant in pupal neurons should have pruning defects ., To generate such a mutant Spn-F , understanding the regulatory mechanism of Spn-F distribution is prerequisite ., The detection of a band shifting of Spn-F-GFP , which is only expressed in C4da neurons , specifically in the larval lysates with Ik2 expression , but not with kinase dead Ik2-G250D expression or without Ik2 expression ( S3 Fig ) , indicated that Spn-F could be phosphorylated by Ik2 kinase in C4da neurons ., Since our study revealed that Ik2 kinase activity regulates Spn-F distribution , and Dubin-Bar et al . also shown that Ik2 phosphorylates Spn-F in kinase assays 26 , we further characterized how the phosphorylation of Spn-F by Ik2 kinase regulates Spn-F distribution and dendrite pruning ., First , to identify the residues of Spn-F phosphorylated by Ik2 kinase , we carried out mass spectrometry analyses ., Given that Ik2-dependent redistribution of Spn-F-GFP in neurons could also be observed in Drosophila S2 cells , we reasoned that the mechanism underlying the reduction of Spn-F puncta is conserved in both cell types ., Thus , we purified, 1 ) Spn-F proteins from S2 cells with Ik2 expression referred to as the group of high Ik2 activity , and, 2 ) Spn-F from S2 cells alone or, 3 ) S2 cells with kinase-dead Ik2-K41A expression 30 referred as the group of low kinase activity for mass spectrometry analyses ., The results revealed five serine residues ( S53 , S85 , S264 , S270 and S349 ) of Spn-F that are phosphorylated by Ik2 kinase specifically ( S1 and S2 Tables and Fig 4A ) , and three other serine residues ( S172 , S202 and S325 ) showing increased level of phosphorylation in the group of high Ik2 activity ( Fig 4A and S4 Fig ) ., To determine the function of phospho-Spn-F , we substituted all eight serine residues with alanine to generate the phospho-deficient Spn-F-8A , and with aspartic acid to make the phospho-mimetic Spn-F-8D ( Fig 4A ) ., We observed evident gel mobility shift of Spn-F in the cell lysates of S2 cells co-expressing Ik2 , but not Ik2-G250D ( Fig 4B ) ., In contrast , we detected no mobility shift of Spn-F-8A in any case ( Fig 4B ) ., This confirmed that the eight serine residues identified by mass spectrometry are the main sites of Spn-F phosphorylated by Ik2 kinase ., Both Spn-F-8A and Spn-F-8D retained the interaction with Ik2 as the control Spn-F did ( S5 Fig ) , consistent with previous reports that Ik2-dependent phosphorylation of Spn-F does not affect the interaction between Ik2 and Spn-F 26 ., The presence of Spn-F puncta in cells suggested that Spn-F might form oligomers through self-association ., To test this possibility , we performed co-immunoprecipitation ( co-IP ) in S2 cells and found that Spn-F proteins could interact with themselves ( Fig 4J ) ., Moreover , we observed reduced self-association of Spn-F in the S2 cells with Ik2 co-expression , but not in either the control S2 cells alone or the S2 cells with Ik2-G250D expression ( Fig 4J ) ., These results indicated that the phosphorylation of Spn-F by Ik2 kinase decreases Spn-F self-association ., As expected , the self-association of Spn-F-8D , but not Spn-F-8A , was strongly reduced in both the control and the Ik2-expressing cells ( Fig 4J ) ., These data suggested a mechanism that Ik2 phosphorylates Spn-F to decrease Spn-F self-association and promotes the redistribution of Spn-F from punctate to dispersed in cytosol ., Next , we examined the distribution of Spn-F-8A-GFP and -8D-GFP proteins in larval and pupal neurons respectively ., The puncta formed by SpnF-8A-GFP were comparable to those formed by Spn-F-GFP in larval neurons ( Fig 4C and 4D ) ., However , the puncta formed by SpnF-8D-GFP were generally smaller than those by Spn-F-GFP in larval cells ( Fig 4E ) ., Moreover , the dispersed GFP signals in the cytosol of larval neurons with SpnF-8D-GFP expression were higher than those with Spn-F-GFP expression ( Fig 4E ) ., Notably , SpnF-8A-GFP puncta were resistant to disperse in neurons at 5 h APF ( Fig 4G ) , while both Spn-F-GFP and SpnF-8D-GFP puncta were redistributed ( Fig 4F and 4H ) ., These findings confirmed that phosphorylation of Spn-F by Ik2 kinase promotes the dissociation of Spn-F oligomers and facilitates Spn-F redistribution in early pupal neurons ., Finally , to determine the role of Spn-F redistribution in dendrite pruning , we examined whether SpnF-8A , whose puncta are resistant to redistribution in pupal neurons , could rescue dendrite-pruning phenotypes in spn-F mutant neurons ., Compared to the pruning defects rescued by wild-type Spn-F , we found Spn-F-8A is incapable of fully rescue the pruning defects in spn-F mutants ( Fig 4I , p<0 . 005 ) ., This result confirmed that Spn-F redistribution in pupal C4da neurons is required for dendrite pruning ., Moreover , SpnF-8D rescued the pruning phenotypes in neurons of spn-F mutants as efficiently as wild-type Spn-F did ( Fig 4I ) ., However , SpnF-8D cannot rescue the pruning defects in mutant neurons with ik2-G250D overexpression ( Fig 4K ) , suggesting that other unidentified factors are required for dendrite pruning ., Together , these results demonstrated that phosphorylation of Spn-F by Ik2 kinase , Spn-F redistribution and unidentified factors are critical for dendrite pruning in C4da neurons ., Since Spn-F redistribution from puncta is critical for dendrite pruning in C4da neurons , to further study the mechanism underlying Ik2-dependent Spn-F redistribution , we searched for other candidates that involve in Spn-F redistribution during dendrite pruning ., Given that Spn-F interacts with a subunit of cytoplasmic dynein complex 24 , Cut up ( Ctp ) , the Drosophila homologue of dynein light chain 1 31 , 32 , and dynein is a microtubule-based motor protein , we questioned whether cytoplasmic dynein is involved in dendrite pruning ., To address this question , we began to examine the roles of microtubules and cytoplasmic dyneins in Ik2-dependent redistribution of Spn-F in S2 cells with disrupted microtubule cytoskeletons or impaired dynein function ., The redistribution of Spn-F from punctate to dispersed in the cytosol of S2 cells is also Ik2-dependent ( Fig 5A and 5B ) ., We found that Ik2-dependent Spn-F redistribution was suppressed in cells treated with microtubule-disrupting chemical , colchicine ( Fig 5C ) ., The similar observation was also obtained from S2 cells treated with cytoplasmic dynein specific inhibitor , ciliobrevin D 33 ( Fig 5D ) ., Notably , Spn-F puncta formed normally in S2 cells treated with either colchicine or ciliobrevin D ( S6 Fig ) , suggesting that the intact microtubule cytoskeletons and functional dynein complexes are not required for the formation of Spn-F puncta ., These results indicated that Ik2-dependent redistribution of Spn-F requires both functional dynein and intact microtubule cytoskeletons , and further suggested that Spn-F redistribution is caused by the movement of cytoplasmic dynein on microtubules in cells ., Next , to verify whether the function of cytoplasmic dynein complexes is required to redistribute Spn-F in neurons for dendrite pruning , we examined the distribution of Spn-F-GFP in mutant C4da neurons with impaired dynein function ., Considering the interaction between Spn-F and Ctp proteins , we first examined the Spn-F-GFP distribution in ctp mutant neurons ., Since ctp is an essential gene for embryonic development 32 , we used a hypomorphic ctp mutant allele ctpG0153 , which had few mutant larvae developing to pupae , for analyses ., The Spn-F-GFP puncta formed normally in larval neurons of ctpG0153 mutants ( Fig 5F , compared to Fig 5E ) , but failed to disperse in mutant pupal neurons at 5 h APF ( Fig 5I , compared to Fig 5H ) ., We next examined the role of dynein heavy chain ( Dhc ) , which encodes the only motor subunit in dynein complex , in the redistribution of Spn-F in C4da neurons ., Among seven Dhc genes in fly genomes 34 , Dhc64C ( thereafter referred to as Dhc ) is the only one exhibiting ubiquitous expression , including the nervous systems , throughout development 35 ., We employed Dhc RNAi lines to knockdown endogenous Dhc ., The thick axons found in larval neurons with Dhc dsRNAs expression ( Fig 5G’ , compared to Fig 5E’ ) were consistent with reported phenotypes in mutant neurons with impaired dynein function 36 , and validated the knockdown efficiency ., The Spn-F-GFP puncta formed normally in larval neurons of Dhc-RNAi mutants ( Fig 5G ) , but remained intact in mutant neurons at 5 h APF ( Fig 5J ) ., These findings demonstrated the requirement of functional dynein complex to redistribute Spn-F in C4da neurons during dendrite pruning ., Finally , to verify the roles of dynein complexes in dendrite pruning , we examined whether mutant C4da neurons with impaired dynein function show pruning defects ., At 16 h APF , the primary dendrites remained connected to the soma of C4da neurons in Dhc RNAi mutants ( Fig 5L , compared to Fig 5K ) , suggesting a critical role of dynein motor in dendrite pruning ., We also verified the dendrite pruning defects in neurons of trans-heterozygous Dhc mutants with hypomorphic alleles , Dhc6-6 and Dhc6-10 ( Fig 5Q ) 37 ., In order to confirm that the phenotypes observed in hypomorphic Dhc mutants were not due to other unidentified mutations on the same chromosome , we examined C4da neurons of Dhc/Df mutants ( deficiency uncovers the entire Dhc64C gene ) ., The defective dendrite pruning was evident in C4da neurons of Dhc/Df mutants; 61% ( n = 90 ) of ddaC neurons in Dhc6-6/Df mutants ( Fig 5M and 5Q ) and 20% ( n = 100 ) of cells in Dhc6-10/Df mutants ( Fig 5Q ) showed phenotypes at 16 h APF ., Since dynein has been shown to play an essential role in larval dendrite development 36 , to verify the pruning defects in Dhc mutants were not secondary to the early dendrite development defects , we examined the dendritic morphology of C4da neurons in Dhc mutant larvae , and observed normal dendrites of most larval C4da neurons in Dhc mutants ( S7 Fig ) ., These findings demonstrated that Dhc plays a critical role in dendrite pruning of C4da neurons ., Furthermore , at 16 h APF , about 22 . 5% of C4da neurons ( n = 80 ) in hypomorphic ctpG0153 mutants exhibited severe dendrite clearance phenotypes , which is characterized by long disconnected dendrites surrounding the soma ( Fig 5N ) ., We hypothesized that ctpG0153 mutant neurons might have delayed dendrite severing , which resulted in deferred clearance ., To test this hypothesis , we examined the dendrites of C4da neurons in wild-type and ctpG0153 mutant pupae at 12 h APF ., All dendrites of wild-type neurons ( n = 38 ) were severed at 12 h APF ( Fig 5O ) ; however , about 26 . 8% of neurons ( n = 56 ) in ctpG0153 mutants still retained their primary dendrites attached to the soma at the same stage ( Fig 5P ) ., These results indicated that dendrite severing was delayed in hypomorphic ctp mutant neurons , and confirmed that ctp plays a role in dendrite pruning ., Finally , we asked whether SpnF-8D could rescue the dendrite pruning defects observed in Dhc mutants ., We found no significant difference between the pruning defects of C4da neurons in Dhc6-6/Df mutants and that in Dhc mutants with SpnF-8D overexpression at 16 h APF ( Fig 5R ) , indicating that SpnF-8D cannot rescue the dendrite pruning defects in Dhc mutants ., Taken together , our data demonstrates that cytoplasmic dynein motor complex is required for Spn-F redistribution in C4da neurons and for dendrite pruning ., Since Spn-F interacts with both Ik2 and Ctp 24 , 26 , we hypothesized that Ik2 , Spn-F and Ctp might form ternary complexes , which could be transported by cytoplasmic dynein ., To test this hypothesis , we performed co-IP and antibody staining in S2 cells ., The formation of Ik2/Spn-F/Ctp ternary complexes could be detected by co-IP only from the lysates of S2 cells with Spn-F co-expression , but not from that without Spn-F co-expression ( Fig 6A ) , suggesting the central role of Spn-F in the ternary complex formation ., As Ik2 kinase causes Spn-F redistribution , the even distribution of all three molecules observed in S2 cells ( S8 Fig ) did not provide convincing evidence to support the colocalization ., To visualize the colocalization of these three molecules in S2 cells , we used Ik2-G250D for antibody staining based on the following reasons ., First , the dependence of Spn-F to form ternary complex was also observed between Ik2-G250D and Ctp proteins ( Fig 6A ) ., Second , since Ik2 kinase activity does not affect Ik2/Spn-F interaction 26 , kinase-dead Ik2-G250D still possesses normal interaction with Spn-F and remains as puncta in S2 cells ., The immunofluorescence staining ( Fig 6B and 6C ) and quantification ( Fig 6D ) showed that the colocalization of Ik2-G250D , Spn-F and Ctp molecules were observed only in S2 cells with Spn-F co-expression , but not in cells without Spn-F co-expression , supporting the formation of Ik2/Spn-F/Ctp ternary complex in cells ., To test whether Spn-F phosphorylation by Ik2 kinase may affect the formation of Ik2/Spn-F/Ctp complexes , we performed co-IP and colocalization experiments with SpnF-8A and SpnF-8D in S2 cells ., Our results showed that SpnF-8A and SpnF-8D have similar abilities as wild-type Spn-F in mediating the formation of Ik2/Spn-F/Ctp complexes in S2 cells ( S9 Fig ) , suggesting that Spn-F phosphorylation have no or minor effects on Ik2/Spn-F/Ctp complex formation ., These data demonstrated that Spn-F acts as a central mediator to link Ik2 kinase to dynein motor complex via Spn-F/Ctp interaction in cells ., As Ik2 , Spn-F and Ctp are crucial for dendrite pruning , and Spn-F plays a central role in the formation of Ik2/Spn-F/Ctp ternary complex in cells , we asked whether this ternary complex is required for the Spn-F redistribution and for dendrite pruning in C4da neurons ., To approach this question , we set out to map the Ik2- and Ctp-interacting domains of Spn-F , and determined the role of Ik2/Spn-F/Ctp ternary complex in neurons for dendrite pruning ., Spn-F protein contains three coiled-coil domains 38 , namely CC1 , CC2 and CC3 , and an Spn-F conserved domain ( SCD ) at its C terminus ( Fig 4A ) , which is evolutionarily conserved among different insect species ( S10A Fig ) ., To identify the Ik2- and Ctp-interacting domains of Spn-F , we generated a series of Spn-F deletion mutants ( S10B Fig ) and examined their interactions with Ik2 and Ctp in S2 cells by co-IP ., The results showed that removal of Spn-F CC3 domain completely abolished the interaction between Ik2 and Spn-F ( Fig 7A ) , but remained weak interaction between Spn-F and Ctp ( Fig 7B ) , indicating that both Ik2/Spn-F and Spn-F/Ctp interactions are disrupted in SpnF-ΔCC3 ., Moreover , the co-IP experiments revealed normal interaction between Ik2 and SpnF-ΔSCD ( Fig 7A ) , but weak interaction between SpnF-ΔSCD and Ctp ( Fig 7B ) , demonstrating that SpnF-ΔSCD keeps a normal interaction with Ik2 , but a defective interaction with Ctp ., Both the SpnF-ΔCC3 and -ΔSCD puncta formed normally in larval neurons ( Fig 7D and 7E ) , comparable to the wild-type Spn-F puncta ( Fig 7C ) ; however , both Spn-F deletion mutants remained punctate in neurons at 5 h APF ( Fig 7G and 7H , compared to Fig 7F ) , indicating that both Spn-F CC3 and SCD domains are crucial for Spn-F redistribution in pupal neurons ., Finally , to determine the role of Ik2/Spn-F/Ctp ternary complex in C4da neurons for dendrite pruning , we performed rescue experiments with SpnF-ΔCC3-GFP and -ΔSCD-GFP in spn-F mutant neurons ., SpnF-ΔCC3-GFP failed to rescue the dendrite pruning defects of ddaC neurons in spn-F mutants at 16 h APF ( Fig 7I ) , demonstrating that the Spn-F CC3 domain , which mediates both Ik2/Spn-F and Spn-F/Ctp interactions , is essential for dendrite pruning in C4da neurons ., In contrast to SpnF-ΔCC3-GFP , SpnF-ΔSCD-GFP only partially rescued the dendrite pruning defects in spn-F mutants at 16 h APF ( Fig 7I ) , suggesting the role of Spn-F/Ctp interaction in facilitating dendrite pruning ., Collectively , our data demonstrated that the Ik2/Spn-F/Ctp complex is critical for Spn-F redistribution and for dendrite pruning in C4da neurons ., In addition to apoptosis , neurons have a second self-destruct program in their axons for axonal pruning during development and in response to neuronal injury and disorders 39 ., Here , we propose a third self-destruct program , which is mediated by Ik2 kinase activity in Drosophila sensory neurons , specific for dendrite pruning ., Ik2 is essential for dendrite severing in pupal C4da neurons 15 , and currently is the only known molecule sufficient to cause precocious dendrite severing in larval cells 15 , indicating that Ik2 activation must be regulated temporally ., For temporal regulation , ecdysone signaling plays a key role in dendrite pruning 13 , 14 ., Our studies showing that no Ik2 activation was detected in pupal C4da neurons with impaired ecdysone signaling and thus placed Ik2 kinase downstream of ecdysone signaling ., Microarray studies have identified ik2 as one of the ecdysone/EcR up-regulated genes in brain MB γ neurons during axon pruning 40 ., This suggests one possible mechanism where ecdysone/EcR regulates Ik2 activation through increasing ik2 expression in C4da neurons ., Although Ik2 kinase activity is crucial for oogenesis and bristle morphogenesis 25 , 29 , the activation mechanisms of Ik2 kinase in both processes remain unknown ., Since pruning activity is considered as a self-destruct program , how to regulate this activity spatially in subcellular compartments within individual neurons is an intriguing issue to investigate ., In this study , we identified Spn-F and cytoplasmic dynein complex as critical regulators of Ik2-mediated dendrite pruning activity in C4da neurons ., It is known that endogenous Spn-F exhibits a punctate pattern in nurse cells 24 , consistent with our observation of punctate Spn-F-GFP in larval C4da neurons ., The formation of Spn-F puncta in cells is through self-association , and does not depend on the integrity of microtubule network or the function of cytoplasmic dynein ( S6 Fig ) ., Since Ik2 could form oligomers in cells 29 , the interaction between Ik2 and Spn-F might also play a role in Spn-F puncta formation ., Indeed , we observed that SpnF-ΔCC3-GFP has normal interac

Introduction, Results, Discussion, Materials and Methods

During development , certain Drosophila sensory neurons undergo dendrite pruning that selectively eliminates their dendrites but leaves the axons intact ., How these neurons regulate pruning activity in the dendrites remains unknown ., Here , we identify a coiled-coil protein Spindle-F ( Spn-F ) that is required for dendrite pruning in Drosophila sensory neurons ., Spn-F acts downstream of IKK-related kinase Ik2 in the same pathway for dendrite pruning ., Spn-F exhibits a punctate pattern in larval neurons , whereas these Spn-F puncta become redistributed in pupal neurons , a step that is essential for dendrite pruning ., The redistribution of Spn-F from puncta in pupal neurons requires the phosphorylation of Spn-F by Ik2 kinase to decrease Spn-F self-association , and depends on the function of microtubule motor dynein complex ., Spn-F is a key component to link Ik2 kinase to dynein motor complex , and the formation of Ik2/Spn-F/dynein complex is critical for Spn-F redistribution and for dendrite pruning ., Our findings reveal a novel regulatory mechanism for dendrite pruning achieved by temporal activation of Ik2 kinase and dynein-mediated redistribution of Ik2/Spn-F complex in neurons .

In Drosophila , the nervous systems undergo extensive neuronal remodeling during metamorphosis , as many larval neurons die and adult neurons are generated while some larval neurons survive and prune their branches ., Pruning that removes specific parts of neuronal branches without causing cell death is a self-destruct process , thus requiring precise regulation to prevent undesired damage to the nervous systems ., Certain Drosophila sensory neurons that undergo dendrite pruning , specifically eliminating the dendrites but leaving the axons intact , provide us an opportunity to study the mechanism of how pruning activity is regulated in the dendrites ., We reasoned that the distinctive microtubule polarity in dendrites and axons might be involved and factors are required to regulate the pruning activity in the dendrites through their interaction with microtubules ., Here , we identified Spindle-F that mediates Ik2-dependent pruning activity in the dendrites by linking Ik2 to the microtubule motor dynein complex ., We showed that elevation of Ik2 activity during dendrite pruning promotes Ik2/Spindle-F/dynein complex moving along the microtubules ., We also showed that the formation and redistribution of Ik2/Spindle-F/dynein complex are essential for dendrite pruning ., Our study reveals a connection between the polarized microtubules of dendrites and the pruning activity through Spindle-F for dendrite pruning .